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Deep learning links lung shape differences and COVID-19 severity – HealthITAnalytics.com

June 24, 2024 -A research team from Emory AI.Health used deep learning to determine that COVID-19 patients experience significant lung damage and deformities associated with the diseases severity, according to a study published in the Journal of Computers in Medicine and Biology.

The researchers emphasized that severe cases of COVID-19 can result in extensive lung disease and deformities, but the impact of disease severity on lung structure has not been well-studied to date. Studying the diseases effects on internal organs is critical to informing care strategies for COVID-19 patients.

COVID-19 can cause serious complications such as pneumonia, severe lung damage, and blood infections, which can lead to lasting lung problems like scarring and chronic breathing issues. While some people recover fully, others may suffer permanent lung damage, explained first author of the study Amogh Hiremath, AI scientist at Picture Health, in a news release. Understanding how COVID-19 affects the lungs during its early onset can help us better understand and treat the disease.

To investigate the relationship between COVID-19 severity and lung structure, the research team turned to deep learning.

Chest computed tomography (CT) scans were gathered from a cohort of 3,230 patients. These participants were then split into three groups based on COVID-19 presence and severity: healthy, mild-COVID-19 and severe COVID-19.

This information was used to explore lung shape differences among the groups with baseline CT imaging. The researchers fed this data to a 3D residual convolutional network to segment and analyze each image.

In doing so, the deep learning approach allowed the team to build a map of lung shape changes. This analysis revealed that lung deformations were prominent in those infected with COVID-19, regardless of severity.

Across both mild and severe cases, differences along the mediastinal surfaces of the lungs were observed, and significant differences in the basal surfaces were found when the healthy and severe COVID-19 cohorts were compared.

The researchers indicated that these deformities are likely to impair lung function, leading to potential adverse outcomes in overall health, quality of life and mortality for affected patients. The team further noted that their findings could help shed light on the lingering effects of COVID-19 on lung function, especially as health systems and patients grapple with long COVID.

Although the acute phase of COVID-19 has largely been mitigated, the persistence and impact of long COVID remains a concern. Our AI analysis identified specific areas of lung damage that could have enduring consequences, said Anant Madabhushi, PhD, executive director of Emory AI.Health and principal investigator of the study. While we have not yet examined long COVID patients explicitly, its crucial to investigate whether these individuals exhibit residual lung deformation, which could provide valuable insights into the long-term effects of this disease.

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Assessing the risk of HCC with machine learning – Drug Target Review

A novel screening tool may increase the five-year survival rate of hepatocellular carcinoma patients to 90 percent.

Researchers at the University Pittsburgh School of Medicine have developed a serum-fusion-gene machine-learning (ML) model. Due to its enhanced accuracy in early diagnosis of hepatocellular carcinoma (HCC), the most common form of liver cancer, this screening tool could increase the five-year survival rate of HCC patients from 20 percent to 90 percent.

The most common screening test searches for the HCC biomarker, serum alpha-fetal protein. However, it is not always accurate, and up to 60 percent of liver cancers are diagnosed at advanced stages, meaning a poor survival rates for patients. Lead investigator Dr Jian-Hua Luo, Department of Pathology, High Throughput Genome Center, and Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, commented:What we need is a cost-effective, accurate, and convenient test to screen early-stage liver cancer in human populations. We wanted to explore if a machine-learning approach could be used to increase the accuracy of screening for HCC based on the status of the fusion genes.

The team analysed nine fusion transcripts in serum samples from 61 patients with HCC and 75 patients with non-HCC conditions using real-time quantitative reverse transcription PCR (RT-PCR). In HCC patients, seven of the nine fusions were often found. Then, based on the serum fusion-gene levels to predict HCC in the training cohort, ML models were generated.

An accuracy of 83 percent to 91 percent in predicting the occurrence of HCC was produced from a four fusion gene logistic regression model. When combined with serum alpha-fetal protein, the two-fusion gene plus alpha-fetal protein logistic regression model produced 95 percent accuracy for all the cohorts. Additionally, quantification of fusion gene transcripts in the serum samples accurately evaluated the impact of the treatment and could monitor for the recurrence of the cancer.

Dr Luo explained:The fusion gene machine-learning model significantly improves the early detection rate of HCC over the serum alpha-fetal protein alone. It may serve as an important tool in screening for HCC and in monitoring the impact of HCC treatment. This test will find patients who are likely to have HCC.

The study was published in The American Journal of Pathology.

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RAIN: machine learning-based identification for HIV-1 bNAbs – Nature.com

Ethics statement

The research complies with all relevant ethical regulations and informed consent was obtained by all study participants (n=25, 16 females and 9 males). Study protocols were approved by the Ethikkomission beider Basel (EKBB; Basel, Switzerland; reference number 342/10), the Ifakara Health Institute Institutional Review Board (Reference number IHI/IRB/No. 24-2010), and the National Institute for Medical Research (NIMR; Dar es Salaam, United Republic of Tanzania; reference number NIMR/HQ/R.8a/Vol.IX/1162).

Serum samples from HIV-1-infected individuals were incubated with Protein G Sepharose (GE Life Sciences) 4C for 1h. IgGs were eluted from chromatography columns using 0.1M glycine (pH=2.9) into 0.1M Tris (pH=8.0)70. Samples were run through Zeba Spin Desalting Columns 7K MWCO (Thermo Scientific, 89882) Concentrations of purified IgGs were determined by UV/Vis spectroscopy (A280) on a Nanodrop 2000 and samples were stored at 80C.

The CD19+ cell fraction was enriched from PBMCs by positive selection with CD19 magnetic microbeads (Miltenyi Biotech) and subsequently stained on ice for 30min with the following fluorochrome-labeled mouse monoclonal antibodies: CD20-PE-Cy7 (dilution 1:50, clone L27, catalog no. 335793, BD Biosciences) and F(ab)2-Goat anti-Human IgG Fc secondary antibody, APC (dilution 1:100, RRID: AB_2337695, Jackson ImmunoResearch). Cells were sorted to over 98% purity on a FACS Aria III (BD) using the following gating strategy: circulating memory B cells were sorted as CD20+ IgG+ cells. FACS-sorted cells were collected in 6l FCS in Eppendorf tubes that were pre-coated overnight with 2% BSA.

The 5 single-cell VDJ libraries were generated using Chromium Next GEM Single Cell V(D)J Reagent kit v.1, 1.1 or v.2 (10X Genomics) according to the manufacturers protocol. Paired heavy and light-chain BCR libraries were prepared from the sorted B-cell populations. Briefly, up to 20,000 memory B cells per well of 10X chip were loaded in the 10X Genomics Chromium Controller to generate single-cell gel beads in emulsion. After reverse transcription, gel beads in the emulsion were disrupted. Barcoded complementary DNA was isolated and used for the preparation of BCR libraries. All the steps were followed as per the manufacturers instructions in the user guide recommended for 10X Genomics kit v.1, 1.1, or 2. The purified libraries from each time point were pooled separately and sequenced on the NextSeq550 (Illumina) as per the instructions provided in 10X Genomics user guide for the read length and depth.

Memory B cells were targeted for single-cell targeted RNA-seq and BCR-Seq analysis using the BD Rhapsody Single-Cell Analysis System71 (BD Biosciences). Briefly, the single-cell suspension was loaded into a BD Rhapsody cartridge with >200,000 microwells, and single-cell capture was achieved by random distribution and gravity precipitation. Next, the bead library was loaded into the microwell cartridge to saturation so that the bead was paired with a cell in a microwell. The cells were lysed in a microwell cartridge to hybridize mRNA molecules onto barcoded capture oligos on the beads. These beads were then retrieved from the microwell cartridge into a single tube for subsequent cDNA synthesis, exonuclease I digestion, and multiplex-PCRbased library construction. Sequencing was performed on NovaSeq paired-end mode.

Single-cell suspensions with 1105 cells/mL in PBS were prepared. Then, the suspensions were loaded onto microfluidic devices, and scRNA-seq libraries were constructed according to the Singleron GEXSCOPE protocol in the GEXSCOPE Single-Cell RNA Library Kit (Singleron Biotechnologies)72. Individual libraries were diluted to 4nM and pooled for sequencing. Pools were sequenced on an Illumina HiSeq X with 150bp paired-end reads.

Expi293 cells (Thermo Fisher Cat No. A14527) were diluted to a final volume of 0.5L at a concentration of 2.5106cells mL1 in Expi293 media73. Heavy-chain and light-chain plasmids were complexed with Polyethyleneimine (Thermo Fisher) and added to the cells. On day five, cells were cleared from cell culture media by centrifugation at 10,000g for 30min, and the supernatant was subsequently passed through a 0.45-m filter. The supernatant containing the recombinant antibody was purified with the HiTrap Protein A HP column (Cytiva, 17040301) on the kta pure system (Cytiva). The resin was washed with 75mL of phosphate-buffered saline (PBS). A total of 25mL of 0.1M glycine pH 2.9 were used to elute the antibody from the protein A resin. The acidic pH of the eluted antibody solution was increased to ~7 by the addition of 1M Tris pH 8.0. The antibody solution was buffer exchanged to PBS by the HiPrep 26/10 Desalting column (GE Healthcare) or Size Exclusion Chromatography Superdex 16/600 HiLoad (Cytiva), filtered, snap-frozen in liquid nitrogen, and stored at 80C.

For the Fab production, the heavy chain was engineered with a two amino acids glycine serine linker followed by a six-histidine tag and stop codon. Light and mutated heavy chains were transfected as described in the previous section. Cell supernatant was harvested five days post- transfection and purified by IMAC chromatography (HisTrap excel, Cytiva) using the elution buffer 25mM Tris pH 7.4, 150mM NaCl, 500mM imidazole. The eluat was buffer exchanged to 25mM Tris pH 7.4, 150mM NaCl, 0.085mM n-dodecyl -D-maltoside (DDM) on a HiPrep 26/10 Desalting column (GE Healthcare), followed by Size Exclusion Chromatography on a Superdex 16/600 HiLoad column (Cytiva)74. The sample was concentrated using an Amicon filter 10kDa cutoff, snap-frozen, and stored at 80C until further use.

BG505 DS-SOSIP trimer75 production and purification were performed as previously described48. Briefly, prefusion-stabilized Env trimer derived from the clade A BG505 strain was stably transfected in CHO-DG44 cells and expressed in ActiCHO P medium with ActiCHO Feed A and B as feed (Cytiva). Cell supernatant was collected by filtration through a Clarisolve 20MS depth filter followed by a Millistak + F0HC filter (Millipore Sigma) at 60 LMH. Tangential Flow Filtration was used to concentrate and buffer exchange clarified supernatant in 20mM MES, 25mM NaCl, pH 6.5. The trimer was then purified by ion exchange chromatography as described48. Fractions containing the BG505 DS-SOSIP protein were pooled, sterile-filtered, snap-frozen, and stored at 80C.

Neutralization assays with IgGs against the 12-strain global virus panel, were performed in 96-well plates as previously described44,76,77. Briefly, 293T-derived HIV-1 Env-pseudotyped virus stocks were generated by cotransfection of an Env expression plasmid and a pSG3Env backbone. Animal sera were heat-inactivated at 56C for 1h and assessed at 8-point fourfold dilutions starting at 1:20 dilutions. Monoclonal antibodies were tested at 8-point fivefold dilutions starting at 50g/ml or 500g/ml. Virus stocks and antibodies (or sera) were mixed in a total volume of 50L and incubated at 37C for 1h. TZM-bl cells (20l, 0.5 million/ml) were then added to the mixture and incubated at 37C. Cells were fed with 130L cDMEM on day 2, lysed, and assessed for luciferase activity (RLU) on day 3. A nonlinear regression curve was fitted using the 5-parameter hill slope equation. The 50% and 80% inhibitory dilutions (ID50 and ID80) were determined for sera and the 50% and 80% inhibitory concentrations (IC50 and IC80) were determined for mAbs. All samples were tested in duplicates.

The biolayer interferometry experiments using SOSIP were performed as follows. All experiments were performed in reaction buffer (TBS pH 7.4+0.01% (w/v) BSA+0.002% (v/v) Tween 20) at 30C using an Octet K2 instrument (ForteBio). Protein A (Fortebio) biosensor probes were first equilibrated in reaction buffer for 600s. IgGs were diluted to 5g/ml in reaction buffer and immobilized onto the protein A probes for 300s, followed by a wash for 300s in reaction buffer. The binding of SOSIP trimers to the IgGs was then measured at various concentrations for 500s, followed by dissociation for 300s in reaction buffer. Analysis was performed using the Octet software with bivalent analyte fitting for antibody binding and 1.1 analyte fitting for the interaction with Fabs. Association and dissociation curves are visualized by GraphPad Prism version 9.0.

The samples were adsorbed to a glow-discharged carbon-coated copper grid 400 mesh (EMS, Hatfield, PA, USA), washed with deionized water, and stained with a 1% uranyl acetate solution for 20s. Observations were made using an F20 electron microscope (Thermo Fisher, Hillsboro, USA) operated at 200kV73. Digital images were collected using a direct detector camera Falcon III (Thermo Fisher, Hillsboro, USA) 40984098 pixels. Automatic data collection was performed using the EPU software (Thermo Fisher, Hillsboro, USA) at a nominal magnification of x62,000, corresponding to a pixel size of 1.65 using a defocus range from 1m to 2.5m. Image preprocessing, two-dimensional classification, and three-dimensional processing was done using the CryoSPARC software (Version 4.4)78.

BG505 DS-SOSIP trimers complexes were prepared using a stock solution of 5mg/ml trimer incubated with a threefold molar excess of bNAb4251 for 10min. To prevent aggregation and interaction of the trimer complexes with the air-water interface during vitrification, the samples were incubated in 25mM Tris pH 7.4, 150mM NaCl, 0.085mM DDM. Samples were applied to plasma-cleaned QUANTIFOIL holey carbon grids (EMS, R2/2 Cu 300 mesh). The grid was plunge frozen using a Vitrobot MarkIV (Thermo Fisher, Hillsboro, USA)with humidity and temperature control.

Grids were screened for particle presence and ice quality on a TFS Glacios microscope (200kV), and the best grids were transferred to a TFS Titan Krios G4. Cryo-EM data were collected using a TFS Titan Krios G4 transmission electron microscope, equipped with a Cold-FEG on a Falcon IV detector in electron counting mode. Falcon IV gain references were collected just before data collection. Data were collected using TFS EPU v2.12.1 utilizing the aberration-free image shift protocol, recording four micrographs per ice hole. Movies were recorded at a magnification of 165,000, corresponding to the 0.73 pixel size at the specimen level, with defocus values ranging from 0.9 to 2.4m. Exposures were obtained with 39.89e2 total dose, resulting in an exposure time of ~2.75s per movie. In total, 15,163 micrographs in EER format were collected.

Data processing was performed with cryoSPARC (Version 4.4) including Motion correction and CTF determination78. Particle picking and extraction (extraction box size 350 pixels2) were carried out using cryoSPARC Version 4.478. Next, several rounds of reference-free 2D classification were performed to remove artifacts and selected particles were used for ab initio reconstruction and hetero-refinement. After hetero-refinement, 72497 particles contributed to an initial 3D reconstruction of 3.8 resolution (Fourier-shell coefficient (FSC) 0.143) with C1 symmetry. A model of a SOSIP trimer (PDB ID 4TVP)79 or AlphaFold2 (ColabFold implementation) models of the 4251 Fab were fitted into the cryo-EM maps with UCSF ChimeraX (Version 1.5). These docked models were extended and rebuilt manually with refinement using Coot (Version 0.9.8.8) and Phenix (Version 1.21)80,81. Figures were prepared in UCSF ChimeraX, and Pymol (Version 4.6)82. The numbering of Fab4251 is based on the Kabat numbering of immunoglobulin models83. Buried surface area measurements were calculated within ChimeraX and PISA84.

For all antigenic sites, paired bNAb sequences were collected from the CATNAP database32 as of January 1, 2022 as nucleotide and amino acid sequences. First, the 249 heavy-chain and 240 light-chain nucleotide sequences were annotated with Igblastn36. Sequences were then processed and analyzed using the Immcantation Framework (http://immcantation.org) with MakeDB.py from Change-O v1.2.0 (with the options --extended partial). Next, bNAbs were filtered by a dedicated Java script to keep only sequences with an annotated CDR3 and paired sequences (VH+VK/L). Each paired antibody was associated with its targeting Env antigenic site, information provided by the database CATNAP text file (abs.txt as of January 1, 2022). The 27 CATNAP antibodies with only the protein sequences available were annotated with IgBlastp followed by MakeDB.py from Change-O v1.2.0 (with the options igblast-aa --extended). In parallel, using the fasta protein sequences, ANARCI85 was used to identify the junction region. As for nucleotide sequences, paired and annotated CDR3 bNAbs were filtered in. In total, 255 bNAbs sequences were collected. Repartition of the antigenic site is as follows: 54 bNAbs target the CD4bs, 21 MPER, 98 V1V2, 56 V3, and 26 interface.

For the training and evaluation of the machine-learning models, paired BCR repertoires of ten healthy donors were collected. The repertoires were obtained from various sources (Supplementary Data Files1) and sequenced using 10X genomics technology. Annotation and processing of the sequences were done as previously described39 and resulted in the generation of a customized AIRR format table containing 14,962 paired BCRs. For HIV-1 immune donors three different sequencing technologies were employed: 10X genomics (D1, D2, G3, and G4), Singleron (S4), and BD Rhapsody (B3). Single-cell sequencing of selected HIV-1 immune donors using Singleron technology was processed using celescope v1.14.1 (https://github.com/singleron-RD/CeleScope) with flv_CR mode utilizing cellranger v7.0.1. BD rhapsody single-cell sequencing was first processed using BD Rhapsody Targeted mRNA Analysis Pipeline (version 1.11) and then, using a custom script, the generated VDJ_Dominant_Contigs.csv file was converted into cellranger-like output files, namely filtered_contig_annotations.csv and filtered_contig.fasta. Lastly, the 10X Genomics single-cell sequencing was processed with cellranger v7.0.1. The cellranger output files of the different HIV-1 repertoires enabled us to annotate and process them as described earlier, resulting in a table of paired BCRs with AIRR characteristics. The six different experiments resulted in 2152 BCRs for D1, 6195 BCRs for D2, 4008 BCRs for B3, 3794 BCRs for G3, 3112 BCRs for S4, and 4799 BCRs for G4.

All mAbs and bNAbs VDJ protein sequences were initially aligned using ANARCI with IMGT format. Subsequently, employing a custom R script, two similarity matrices were generated: one encompassing the entire VDJ sequence (VH) and the other focusing solely on the CDRH3 region. For each pair of sequences, a Levenshtein distance was computed, yielding a similarity score ranging from 0 to 1 (higher score representing lower Levenshtein distance). Heatmaps were constructed with the pheatmap R package, to visualize the following comparisons: all five antigenic site categories of bNAbs and the comparison bNAbs versus mAbs (mAbs sequences were downsampled to 500 sequences). Sequences were ranked based on their V and J genes.

Using a custom script, AIRR characteristics were converted into our features of interest. The mutation frequency was calculated using the difference of residues between the protein sequence of the BCR and its germline sequence in the FWR1+CDR1+FWR2+CDR2+FWR3 regions (VH gene). The framework mutation frequency was calculated similarly but using only FWR1+FWR2+FWR3. The hydrophobicity of the CDRH3 sequences was computed using a customized score, with aromatic residues having the highest value (1 for W, 0.75 for Y, and 0.5 for F). Residues A, L, I, M, P, and V were set to 0.1, while the rest of the resides were set to zero. The values of all residues were summed up for each CDRH3. In addition, the length of the CDRH3, CDRL3, VH, and VL/K genes were considered as features. Two extra features were added to be used by the anomaly detection algorithm: VH1+CDRL3 length of five residues with a zero or one value designed for the bNAbs targeting the CD4bs and VH1-69+VK3-20+GW motif in the CDRH3 with a zero or one value for the bNAbs targeting MPER.

Three ML-based approaches were trained on the features table generated using BCRs obtained from healthy donors and bNAbs datasets, using Python v3.8.16 and scikit-learn v1.0.2. These algorithms were: Anomaly Detection (AD), Decision Tree (DT), and Random Forest (RF). For each antigenic site, the dataset was partitioned into training, validation, and test sets with a 60:20:20 ratio, setting random.seed to 1 for all models. For the AD model, bNAbs data were removed from the training set, since this algorithm only trains with non-anomaly data. For this model, the features with discrete values were first normalized using the preprocessing.normalize method (axis=0) from the scikit-learn library. Features exhibiting significantly different values from the normal distribution were selected for each antigenic site, which included the frequency of mutations in the V genes and in the frameworks. For CD4bs, we added the combined feature VH1+CDR3L with a length of five residues. For MPER, we included the combined feature VH1-69, VK3-20, and the GW motif in CDRH3. In addition, CDRH3 hydrophobicity was added for MPER, V1V2, and V3. Lastly, CDRH3 length was incorporated for V1V2 and V3. Using the validation test, a multivariate normal random variable was calculated with the mutivariate_normal function from the scipy package v1.8.0 and used for setting the optimal Epsilon parameter () minimizing the false positive numbers. The Epsilon value was set to 619.55 for CD4bs, 231501.41 for MPER, 866803.64 for V1V2, 845445.99 for V3, and 24.36 for interface. Those threshold values were used on the test set to predict a BCR as an anomaly (bNAb) or not. For DT and RF models, V genes (for heavy and light chains) were one-hot encoded as a preprocessing step, resulting in a total of 122 features in the features table. Hyperparameter tuning was conducted using the validation dataset, minimizing the number of false positives. DT models were trained with a balanced class weight, the Entropy criterion for measuring the quality of splits, and the cost complexity pruning parameter alpha of zero. RF models were trained with 100 estimators, a balanced class weight, the Entropy criterion for measuring the quality of splits, maximum samples were set to 1.0, maximum depth of tree of none, maximum features of 11 (122), and bootstrapping to build trees. Matplot library v3.6.2 was used to generate ROC plots from performance results and to generate the Venn diagrams showing the intersection of the number of true positives or false positives between the three models. The Super Learner Ensembles algorithm was implemented using the ML-Ensemble (mlens) v0.2.3 library. For each antigenic site, the dataset was partitioned into train and test sets with a 75:25 ratio. The Super Learner was created with the precision score as scorer parameter, a k-fold cross-validation of tenfolds, and the option shuffle set to true. The following classifiers were used as based models in the Super Learner algorithm: DecisionTreeClassifier, SVC (Support Vector Classification), KNeighborsClassifier, AdaBoostClassifier, BaggingClassifier, RandomForestClassifier, and ExtraTreesClassifier. A LogisticRegression was used as the meta-model, with the solver parameter set to lbfgs.

Flow cytometric data were acquired using BD FACSDiva (v.9.0) software. Flow cytometric data were analyzed using FlowJo (v.10.7.1). Statistics were conducted using R Statistical Software (v4.2.1) and ggstatsplot package86. The Complex Heatmap package was used for visualization87. No statistical methods were used to predetermine the sample size. The experiments were not randomized, and investigators were not blinded to allocation during experiments and outcome assessment.

Further information on research design is available in theNature Portfolio Reporting Summary linked to this article.

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Transfer learned deep feature based crack detection using support vector machine: a comparative study | Scientific … – Nature.com

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Temporal dynamics of user activities: deep learning strategies and mathematical modeling for long-term and short-term … – Nature.com

Our framework has two main axes: classifying the users activities and constructing his dynamic profile. The following subsections clarify each axis.

Weighted-based user profile is a representation in which the user profile is represented by a keyword or a set of keywords that is directly provided by the system or automatically extracted from web pages or documents. Keywords are associated with numerical weights to represent the user's interests in different topics or categories.

In our previous research15, we considered a user u inside the social media group , with a static profile (P_{u}) and discussing N topics. We used a weighted-based user profile to present the dynamic profile of the user. (D_{u} (t)), which reflects the position (x_{u})(m-dimensions) of the user inside the topic sphere such that (x_{u} (t_{i} ) = (d_{u}^{{c_{1} }} (t_{i} ),d_{u}^{{c_{2} }} (t_{i} ),...,d_{u}^{{c_{m} }} (t_{i} ))). (d_{u}^{{c_{j} }} (t_{i} )) is the distance between the user and the jth topic after the ith iteration is a representation in which the user profile is represented by a keyword or a set of keywords that is directly provided by the system or automatically extracted from web pages or documents. Keywords are associated with numerical weights representing the user's interests in different topics or categories.

Our model is based on the following assumptions about the connection between the user and topics:

The topics the user is interested in represent 100% of his mind.

The total similarity between the user and each topic depends on the users static profile (sim_{u}^{{c_{j} }} left( {t_{0} } right)), the user's activities (A_sim_{u}^{{c_{j} }} left( t right)), and the user's following list (F_sim_{u}^{{c_{j} }} left( t right)).

The user's interests found in his static profile are used to calculate the initial similarity between the user and each topic (c_{j}).

Users activities like posts P, shares S, or likes L have different significance weights.

The similarities between the user and the topic increased as the distance between the user and the topic decreased.

The distance between the user and each topic changed after each activity.

Consider bloggers who use social media to display their daily activities and aren't interested in wars or disasters. One day, a catastrophe occurred in their country, so they used their social accounts to express their feelings and to support the victims, etc. Their user profiles should reflect the unusual reaction to the crisis as a short-term interest and the entertainment and other elder interests as long-term ones.

In this paper, we will introduce how to use our model to accommodate the short-term and long-term profiles.

(Temporal user profile) The temporal profile (D_{u} (time)) of user u is the position (x_{u}) of the user inside the topic sphere based on specific timespans.

$$ x_{u} (time) = (d_{u}^{{c_{1} }} (time),d_{u}^{{c_{2} }} (time),...,d_{u}^{{c_{m} }} (time)), $$

(1)

where (d_{u}^{{c_{j} }} (time)) is the distance between the user and the jth topic category at the end of a given period. For the long-term profile, the beginning point of the user is the creation of the profile till the current moment. Accordingly, the initial values will be determined as mentioned in the 3rd point by using the users static profile. On the other hand, the beginning of the user in the short-term profile is the start of the specified period. Hence, the start values of (d_{u}^{{c_{j } }}) will be the users dynamic profile at the beginning of the time span. Using the temporal-based profile, we can explore how the user profile evolves over time; for example, we could investigate if there are any variations between the users profile generated on weekends compared to his profile on weekdays, etc.

In order to measure the difference between the two profiles, we apply the Manhattan distance (also known as L1-distance) in vector representation:

$$ L_{1} left( {x_{u} left( {time_{y} } right),x_{u} left( {time_{z} } right)} right) = mathop sum limits_{i} left| {,d_{u}^{{c_{i} }} left( {time_{y} } right) - d_{u}^{{c_{i} }} left( {time_{z} } right)} right|,,,,,,L_{1} in left[ {0..2} right] $$

(2)

The higher the L1 value, the larger the disparity between the two profiles, and vice versa. Manhattan distance provides an overall measure of similarity or dissimilarity between the two profiles. As it calculates the distance between two points by summing the absolute differences in their coordinates, it is more robust to outliers and variations in individual dimensions (i.e., it does not specify which interests contribute more or less to the overall distance). To analyze the user's behavior and detect if there is any unexpected change in it, we will calculate the squared differences to obtain more detailed information about the differences between each corresponding distance in the two profiles.

$$ squared,difference,for , d_{u}^{{c_{i} }} , = left( {d_{u}^{{c_{i} }} (time_{y} ) - d_{u}^{{c_{i} }} (time_{z} )} right)^{2} $$

(3)

The squared difference is used to calculate the squared value of the difference between the corresponding coordinates of two points in a multidimensional space. It is useful when assessing the magnitude of change within specific categories, as it amplifies differences between values. The squared distance may be sensitive to outliers and can overemphasize large differences, so it's typically utilized at the category level rather than for overall profile changes. By setting specific thresholds or criteria, we can define significant differences in user behavior or discover unusual changes in user interests. For example, we might consider elements with squared differences above a certain threshold to reflect a significant change. Criteria such as when a user becomes interested in a topic for the first time and for how long he was interested in it could be an indicator of whether it is a temporary change or if it will be a lasting one.

Classifying the activities of a user is a key task in creating his dynamic profile. Since deep learning models have consistently proven their effectiveness in resolving numerous text classification challenges, we used them to classify text into specific topics. Figure1 shows an overview of the proposed models.

The architecture of proposed topic-classification models.

We applied the models to two sets of tweets; the first one is the tweet data set collected by16, which consists of 22,424 manually labeled tweets divided into 11 topic categories (C1) business/finance, (C2) crisis [disaster/war], (C3) entertainment, (C4) politics, (C5) health/medical, (C6) law/crime, (C7) weather, (C8) life/society, (C9) sports, (C10) technology/internet, and (C11) others distributed as shown in Table 2. We observed that the dataset is imbalanced as there is a substantial disparity in the number of tweets between different classes, which could affect the performance of classifiers.

In order to handle this problem, we modified the dataset in a way that each class contains 3500 tweets. For classes with tweets less than 3500, we collected relevant tweets using Twitter API to reach the specified number; on the other hand, classes with tweets more than 3500 are deducted by randomly removing redundant tweets. The final dataset consists of 35,000 tweets distributed equally between 10 categories by eliminating the others class C11.

Preprocessing steps are applied to ensure that the tweets are clean and suitable for the classification process. We lowercase all tweets to eliminate case-related variations. Special characters except ($ and %), punctuations, URLs, mentions, and hashtags are removed. After that, we applied tweet tokenization by the tokenizer in the NLTK package.

After the tokenization, the tweets text is represented as vectors (numerical values) using an embedding model. Word embeddings are a type of distributed representation in an n-dimensional space designed to capture the semantic meanings of words. We used two distributed pre-trained word embedding models, GloVe17 and FastText18, to capture the semantic meaning of words in a sequence of text. Glove focuses on capturing global co-occurrence statistics of words in large text corpora, aiming to represent words based on their contextual relationships. In our model, we used GloVe embeddings that are trained on a large corpus with 300d vectors. FastText is an algorithm developed by Facebook that treats each word as a combination of n-gram characters, allowing it to represent out-of-vocabulary words and morphological variations effectively. FastText offers more flexibility and robustness in handling a wide range of languages and text types. We used FastText and GloVe separately and compared the results to study which one has a better impact on achieving higher classification accuracy.

Embedding vectors produced by embedding models are fed into the deep-learning classification model. We applied two kinds of classification models in this paper:

Recurrent Neural Networks (RNNs): These are a type of neural network designed for processing sequential data. They have a unique ability to maintain an internal memory or hidden state that allows them to capture dependencies over time. However, traditional RNNs suffer from vanishing gradient problems during training, making it challenging to capture long-term dependencies effectively. To solve these issues, several modifications and variants of RNNs have been developed. Long Short-Term Memory (LSTM) networks19. introduce sophisticated gating mechanisms to control the flow of information, enabling them to capture long-range dependencies. Bidirectional LSTM (Bi-LSTM)20 processes data in both forward and backward directions, enhancing context understanding. Gated Recurrent Unit (GRU)21 is another variant of RNNs that is known for its efficiency and simplicity. They are effective at capturing sequential patterns and have been widely employed in various natural language processing tasks, text classification, and time series prediction, offering a balance between computational efficiency and modeling capability.

BERT Model: BERT22 is a transformer-based model that could be fine-tuned to solve a wide range of real-world NLP tasks. Fine-tuning BERT to classify text typically involves feeding labeled data to BERT and updating its parameters through backpropagation. This process allows BERT to leverage its pre-trained knowledge of language and semantics to excel in the classification task, often achieving state-of-the-art results with relatively little training data. In our experiments, we used a compact version of BERT called DistilBERT23 that is designed to be smaller and faster while maintaining much of BERT's language understanding capabilities. It achieves this by employing knowledge distillation techniques during training, where it learns from a larger pre-trained BERT model. The key distinctions lie in the reduced size and efficiency of DistilBERT, making it more suitable for applications with limited computational resources or a need for faster inference.

The first layer of the DistilBERT model involves the initial preprocessing and transformation of raw tweet text data into a structured format that can be fed into the DistilBERT model for further processing and classification. It encompasses tokenization, padding, truncation, the addition of special tokens to create input tensors, and creating attention masks. DistilBERT takes the tokenized tweet text as input and generates contextualized embeddings for each token in the text. These embeddings capture semantic and contextual information.

The model variant used for classification is DistilBERT-base-uncased. This variant is based on the DistilBERT architecture and is case-insensitive (lowercase). It is a smaller and more efficient version of the original BERT model. DistilBERT models typically consist of 6 layers of transformer encoder blocks, 768 hidden dimensions, and 12 attention heads in each multi-head self-attention mechanism. The vocabulary size of DistilBERT is typically 30,000. This means that the model can tokenize and work with a vocabulary of 30,000 unique sub-word pieces.

The performance metrics used to evaluate our models are accuracy, precision, recall, and F1-score. Accuracy measures the overall correctness of the model's predictions by calculating the ratio of correctly classified instances to the total number of instances.

$$ Accuracy = frac{Number;of;corrected;topic;predictions}{{Total;number;of;predictions}} $$

(3)

Precision evaluates the model's ability to make accurate positive predictions within each class, indicating the fraction of correctly predicted positive instances among all instances predicted as positive.

$$ Precision = frac{{Number,of;correct;predictions;of;the;topic left( {TP} right)}}{{Total;number;of;instances;predicted;as;that;topic left( {TP + FP} right)}} $$

(4)

Recall, on the other hand, gauges the model's ability to capture all positive instances within each class, measuring the fraction of correctly predicted positive instances among all actual positive instances.

$$ Recall = frac{{Number;of;correct;predictions;of;the;topic left( {TP} right)}}{{Total;number;of;instances;actually,in;that;topic left( {TP + FN} right)}} $$

(5)

The F1-score is a balanced measure that combines precision and recall, providing a single value that reflects the model's overall performance across all classes.

$$ F1 - Score = 2 times frac{{left( {precision times recall} right)}}{{left( {precision + recall} right)}} $$

(6)

Weighted average (WA) and macro average (MA) are two approaches for aggregating precision, recall, and F1-score metrics. Weighted average takes into account the class imbalance by assigning weights based on class proportions, giving more importance to the majority classes. This is useful when optimizing the model's performance with respect to class distribution. In contrast, macro average treats all classes equally, providing an unbiased assessment of the model's ability to perform across all classes, regardless of size or imbalance.

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Machine-learning-guided recognition of and cells from label-free infrared micrographs of living human islets of … – Nature.com

From image collection to dataset creation

The whole machine learning workflow is schematically represented in Fig.1. In brief, it starts with an algorithms training which consists of three main phases, namely: (i) live-islet autofluorescence intensity imaging by exciting at 740nm and collecting in the 420460-nm range, which is dominated by NAD(P)H and lipofuscin signals; (ii) NAD(P)H auto-fluorescence lifetime imaging at the same focal plane in live islets at both low (2.2mM) and high glucose (16.7mM), with subtraction of the lipofuscin intrinsic signal, to produce metabolic data in terms of balance between free and protein-bound NAD(P)H; (iii) islet fixation and immunostaining using antibodies against glucagon and insulin to identify single and cells and then extract single-cell information from both intensity and lifetime data through spatial matching of immunofluorescence and live-islet acquisitions (Fig.1a). At this point, we curate the manual processing of experimental data to extract a set of numerical features (Fig.1b) and store them in a feature matrix. Each row of the matrix is associated with an outcome (cell identity, obtained by immunofluorescence) denoted as either or , and this is described in the target vector. At this point, the majority of the dataset is used to train a model that captures the relationship between numerical features and cell type (Fig.1c). The rest of dataset is used during the testing phase, where the performance of the model is evaluated by predicting cell type using the data portion withheld from the training phase. Upon successful completion of the testing phase, the model becomes capable of inferring cell type (i.e. the target vector) from newly collected data of sole autofluorescence and lifetime imaging, eliminating the need of performing immunostaining for cell type recognition.

General workflow. (a) Using a fluorescence microscope equipped with a FLIM (Fluorescence Lifetime Imaging Microscopy) module, data were collected from 15 Human Langerhans islets in three types of images: autofluorescence intensity (cartoon in grayscale), FLIM images, typically visualized as a phasor plot (blue cloud), and immunofluorescence images (red and green cartoon, where red represents cells, and green represents cells). (b) Single-cell data were obtained through manual segmentation of the acquired images, which resulted in one image per each segmented cell. For each cell, a number of parameters were calculated and included in a dataset then used to train a Machine Learning algorithm. (c) After a testing phase, the model can be employed to determine cell identity from new images without the need for additional immunostaining.

In more detail, to build the input dataset we performed label-free multi-photon imaging of human islets (Fig.2a), which provided two distinct types of data: islets autofluorescence intensity (Fig.2a, top panel) and lifetime (Fig.2a, center panel) micrographs. The autofluorescence signal was elicited at 740nm through multiphoton excitation and collected in the 420460-nm optical window. Each islet was measured twice: first, at 2-mM glucose concentration, which maintains a starvation condition, and then after 510min exposure to 16-mM glucose concentration, which stimulates insulin secretion from cells. Following multiphoton imaging of live islets, these were fixed and prepared for immunofluorescence (Fig.2a, bottom panel). This step involves tissue fixation, followed by permeabilization and, ultimately, incubation with anti-glucagon (red signal) and anti-insulin (green signal) antibodies to identify and cells, respectively. After image acquisition, manual segmentation (Fig.2b) was carried out to extract single-cell information: 151 features were extracted (Fig.2c) and used to construct what is referred to as the feature matrix. Each row of the matrix is associated with an outcome, specifically cell identity, denoted as either or , and this is described in the target vector. At the end, the feature matrix contains data from N=1932 cells, with each cell associated with N=151 features. In contrast, the target vector exclusively contains immunofluorescence-derived information on cell identity.

From image collection to dataset creation. (a) Human Langerhans islets' autofluorescence and lifetime are measured using label-free fluorescence microscopy, giving an autofluorescence image (top) and a phasor plot (center) of the islet as result of the live-cell imaging step. In the following step, fixation and permeabilization are performed. Then, islets are incubated with antibodies (green: anti-insulin, red: anti-glucagon), leading to the corresponding immunofluorescence image (bottom) of the islet. (b) Already obtained autofluorescence images are manually segmented by outlining Regions Of Interest (ROIs), obtaining single-cell data. Likewise the entire islet, each single cell has an associated autofluorescence image, a phasor plot, and cell identity information obtained from immunofluorescence. (c) Single-cell images are used to extract 151 features per cell, which are organized in a feature matrix. In this matrix, each row represents a single cell and each column corresponds to a specific feature. The target vector contains information about cell identity, which are derived from the immunofluorescence images.

Most of the numerical entries of the feature matrix (thecomplete list is reported in Supplementary Material)are derived from either autofluorescence intensity and lifetime data through the utilization of descriptive statistics parameters including, for instance, minimum and maximum values, trends, range of most common values, and data dispersion (Fig.3). Notably, in the optical window used for NAD(P)H detection, human islets also contain marked autofluorescence originating from lipofuscin-enriched granules20,21. These granules, byproducts of lysosomal digestion, are primarily composed of lipids and proteins, and directly correlate with age of donor19,22. Since and cells are known to possess different amounts of lipofuscin19, we decided to include a parametrization of lipofuscin granules by estimating their area normalized by the cell area. Cell morphology is instead described by three key parameters: cell area, perimeter, and circularity. Circularity quantifies how closely the cell shape resembles a perfect circle, with a value of 1 indicating a perfect circle. For what concerns autofluorescence lifetime data, the Fourier transformation converts the lifetime decay measured in each pixel of the image into a data point in the phasor plot, characterized by three parameters: the g and s coordinates, which describe the time constant of autofluorescence decay, and the frequency of observation of each specific set of g, s coordinates. Phasor clusters were quantitatively analyzed by extracting both the cluster barycenter and its standard deviation. In addition, by combining phasor-FLIM data acquired at two glucose concentrations, additional information about cell metabolism could be obtained: in fact, the shift in NAD(P)H lifetime upon glucose stimulation can be used as a descriptor of the average metabolic balance between glycolysis and oxidative phosphorylation in and cells. Finally, infrared-imaging-derived features were supplemented by adding donor-related clinical parameters (Table S1) such as age, body mass index (BMI), and the insulin stimulatory index (SI), this latter intended as the overall insulin secretion efficiency of donor-derived islets measured by a standard ELISA assay.

Overview of calculated features. In total, 151 features (in italic) have been extracted from phasor, autofluorescence, clinical, and experimental data. These features describe or summarize (in bold): Phasor plot characteristics, Cell metabolism, Cell morphology Lipofuscin content, Donor-related demographic and clinical data, Experimental conditions. In addition, various descriptive statistics parameters are used as general-purpose descriptors to summarize both autofluorescence and phasor relevant characteristics.

To facilitate the exploratory data analysis we employed the Principal Component Analysis (PCA)23 as a dimensionality reduction algorithm. We first chose the optimal number of components to avoid information loss and plot the explained variance with respect to the number of components (Fig.4a). The explained variance decreases rapidly even with few components, thus we reduced the dimensionality of the dataset from 151 to 2, making the entire dataset amenable to visualization in a 2D Cartesian plot and enabling us to observe the impact of specific features through color mapping. The PCA outcome is represented as a 19322 matrix in order to visualize only single-cell data.

Explorative data analysis with PCA and K-means clustering. The dataset dimensionality has been reduced from 151 to 2 using PCA to allow graphical representation. (a) A graphical representation of explained variance respect to the number of principal components gives an idea on how much components/dimensions are needed to retain enough information after dimensionality reduction. The explained variance drops rapidly, meaning that two components are enough to visualize the data without significant information loss. (b) The bidimensional PCA scatterplot (bottom, right) appears separated on the basis of cell type, despite mildly clustered. This suggests classification is possible using complex algorithms, maybe using a supervised approach or neural networks, as confirmed their distribution using kernel density estimation plots on the first principal component (top) and second principal component (botton, left). (c) Using experimental glucose concentration as colormap, it becomes evident that glucose concentration does not significantly affect cell classification. This implies that glucose concentration has low classification power, implying that the classification model will be able to classify cells independently of this experimental condition. (d) The elbow method allowed to choose a suitable number of clusters to have good performance by computing the WCSS (Within-Cluster Sum of Squares, i.e. sum of squared distances of all points from the centroid they belong) indicates for each iteration. The elbow (red dot) indicates the optimal number of clusters, which is 10. (e) The Gini impurity index has been computed for all clusters to assess within-cluster heterogeneity. The ideal case would be having only one class per cluster, which would result in Gini=0. However, the average Gini among all clusters is 0.37.

For instance, if data are color-coded according to cell type, and cells show mild segregation (Fig.4b, bottom right), as confirmed by kernel density estimation (KDE) plot on both the first principal component (Fig.4b, top) and second principal one (Fig.4b, bottom left), suggesting that classification might be reached, but using sophisticated supervised algorithms. If cells are color-coded by means of the glucose concentration used in the experiment (Fig.4c), it becomes challenging to accurately distinguish between and cells. This implies that glucose concentration may not possess strong classification power, thus the algorithm might be able to classify cells independently of the experimental glucose concentration used. To support this hypothesis more quantitatively the need of a Supervised Learning approach, we conducted a clustering analysis using the widely-employed k-means algorithm. First, we selected the proper amount of clusters using the elbow method. This consists in performing k-means iteratively by progressively increasing the number of clusters and calculating, for each iteration, the WCSS (Within-Cluster Sum of Squares), which represents a quantitative evaluation of how much data points are tight-bound to the cluster centroid. The optimal number of clusters should ideally match the number of classes of the classification problem (i.e. 2), but this would perform poorly here, as demonstrated by the elbow-test results (Fig.4d). The best score is reached for the highest number of clusters, but this in turn is a sign of data overfitting: the suitable number of clusters chosen was 10 (Fig.4d, red dot). For the chosen number of clusters, we assessed the performance of k-means by quantifying data heterogeneity within each cluster using the Gini impurity index (Fig.4e), exploiting the labels on the data obtained by immunofluorescence. The ideal scenario would be Gini=0, which indicates that the cluster only contains one class. Other way round, if Gini=1 (worst case), it means that data within the cluster is entirely diverse. The average Gini coefficient across all clusters is 0.37, which confirms our hypothesis about the supervised approach. To give the reader a more synthetic view of the results, we calculated the ROC_AUC (i.e. area under a ROC curve) of a two-component K-Means on PCA data, obtaining 0.60, thus reinforcing our conclusions: the explorative data analysis using PCA showed mild clustering of and cells, prompting us to use supervised classification algorithms.

Before training the model, we cleaned the dataset by manually reviewing cells, and we discarded those for which cell identity could not be confidently determined to prevent the introduction of noise into the training phase (Fig.5a). The following step involved data-preprocessing operations to favor model performance and stability: these included numerical encoding categorical features, features scaling, handling of missing values and outliers. A critical point in data preprocessing was that of addressing dataset imbalance, i.e. the unequal number of and cells in the training set. Neglecting cells from the most abundant class (i.e. cells) could lead to a biased model due to the high biological heterogeneity of Langerhans islets (Table S2)24, considering that several algorithms are built on the hypothesis of balanced classes as inputs). To address this, we employed the Synthetic Minority Oversampling Technique (SMOTE)25. This algorithm leverages existing data to generate synthetic data entries, rebalancing the : ratio of the whole dataset from 2:1 to 1:1, thus improving model training. At this point, the dataset was divided into the training and test sets (Fig. S1) to prevent overestimation of model performance during testing. Model performance and stability were further enhanced by implementing both Cross Validation and hyperparameters tuning procedures. Repeated stratified fivefold Cross Validation (with 3 repetitions) was applied, and Grid Search was chosen for cross-validation and hyperparameters tuning. The area under a ROC curve (ROC_AUC) was selected as the optimization metric, given its appropriateness for machine-learning problems based on imbalanced classes, as in this case. Four different algorithms were trained and tested (Fig.5b) using 970 cells for training (a mix of real data and synthetic data generated by SMOTE) and 216 cells (real data) for testing. Training and testing performances were then compared based on various metrics, including precision, recall, and F1 score, in addition to the area under the ROC curve. Regarding the two cell types under study (Fig.5c), cells generally exhibited scores exceeding 0.80, while cells exhibited slightly lower overall performances ranging from 0.60 to 0.70. This discrepancy may be linked to the degree of cell-type-specific information embedded in the extracted biological features. For instance, it was recently demonstrated and confirmed that cells have a significantly higher lipofuscin content compared to cells (i.e., twofold)22 and display a distinct metabolic shift toward oxidative phosphorylation upon glucose stimulation26, which is not as clearly observed in cells19. In this scenario, the extracted features convey the proper amount of information to explain the behavior of cells with confidence, while it takes more effort to take decisions on cells. All the tested algorithms showed high performance, but unsatisfactory precision or recall on -cell classification, with the exception of XGBoost. XGBoost displayed high performance and classification stability (i.e. all the computed scores were quite similar within the same class), and was thus selected for a further optimization step.

Supervised-learning results from four different models. (a) After creating the feature matrix and target vector, data undergo several preprocessing steps to enhance the performance and stability of classification. The process starts with manual cleaning, where only cells with clearly defined identities are retained in the dataset, excluding over a thousand cells, resulting in a cleaned dataset with 861 cells. Preprocessing includes encoding categorical features, handling missing values, handling outliers, and scaling the data. The dataset is then rebalanced using SMOTE (Synthetic Minority Oversampling Technique), and it is split into training and test sets. The training set, after SMOTE, comprises 970 cells and 151 features. Before training, cross-validation and hyperparameter tuning are performed to obtain a stable and high score. The model is tested on the testing data, which can be considered as new, unseen data. The original data is cleaned to improve algorithm performance. (b) Four different algorithms are tested and compared: multivariate logistic regression, boosted decision tree (XGBoost), Support Vector Machine for classification, and K-Nearest Neighbor for binary classification. Each algorithm is optimized using the most common hyperparameter range and Grid Search as the optimization algorithm. (c) Evaluation of precision, recall, F1 score, and the area under an ROC curve reveals that XGBoost is the most promising algorithm in terms of classification performance and stability. XGBoost is further optimized with Optuna, allowing for the selection of a wider hyperparameters range to improve its performance.

The optimization of XGBoost was performed by using Optuna27 that, contrary to Grid Search, does not evaluate all possible hyperparameter combinations but efficiently explores the hyperparameter space through sampling and pruning algorithms. For feature selection, we leveraged the embedded method of the XGBoost algorithm, which provides an importance score for each feature ranging from 0 to 1, based on their significance within the classification task. After an initial XGBoost training using all features, these were sorted from the most relevant to the least, and a new training phase initiated with a restricted number of features and setting different cutoff thresholds (Fig.6a). This process was aimed at enhancing model performance and, potentially, at reducing computational cost. A detailed view of all computed scores can be found in Table S3. The model with the highest performance achieved a ROC_AUC of 0.86 by using the top 116 features out of 151, thus indicating that the majority of the features are essential for optimal classification (Fig.6b). This is likely due to the high biological heterogeneity of Langerhans islet cells, both within and across donors. As mentioned earlier, Rouiller and co-workers showed that and cells disaggregated from rat islets can be separated using fluorescence-activated cell sorting (FACS). This separation relied on their intrinsic autofluorescence (mostly due to flavoproteins elicited at 488nm) and the characteristic size of the cells18. This observation prompts us to consider the significance of delving deeper into the analysis of intrinsic signals (e.g. by building a more complex algorithm as deep learning, at expense of interpretability) or by extracting more information-rich features to achieve similar or higher model performances based on standard imaging. However, a classification algorithm is needed to not underperform - or - cells classification, as evidenced by the K-Means analysis in the Explorative Data Analysis. In order to make a direct comparison with XGBoost, we applied the same pre-processing to the dataset as in the algorithm training phase, then we applied the 2-component k-Means, obtaining ROC_AUC=0.72, much lower than XGBoost (Fig.6b). Coming back to model interpretability, XGBoost has an embedded method which allows to extract and identify the most important features able to explain the classification power. By plotting the nine most important features (Fig.6c) we can observe that 6 out of 9 features are related to static autofluorescence, and the first three are able to explain more than 60% of the classification power, suggesting that most of classificatory information is encoded in the autofluorescence intensity. Indeed, by color-mapping the PCA plot (Fig. S2) for the most important feature (i.e. intensity_all_whisker_high), it can be seen that it follows the cell-type distribution shown in KDE plots. This observation is also corroborated by previous ones on the higher lipofuscin content of cells22 and their increased fluorescence intensity due to oxidative metabolism28,29,30 as compared to cells. To ensure model stability, we conducted additional assessments. First, we increased the number of folds from 5 to 10, implementing tenfold repeated stratified cross-validation. All training and Optuna-optimization steps were repeated and the same evaluation scores calculated (Table S4a), showing ROC_AUC=0.86, which is comparable to the fivefold cross-validation results (Fig.6b) together with the other metrics. Additionally, we performed the Salzberg test31, a method that involves shuffling the labels in the target vector of the training dataset, allowing the algorithm to learn from noise. This test showed a ROC_AUC=0.53, which is a 33% decrease for both training and testing (Table S4b), confirming that the model optimized during the standard training procedure was not influenced by overfitting. Furthermore, we attempted to classify data that had been excluded from training during the dataset cleaning procedure. The resulting ROC_AUC was 0.64, and all computed metrics displayed lower performance (Table S4c), thus validating the effectiveness of the cleaning procedure.

XGBoost Optimization with Optuna, Feature Importance, and Model Stability Assessment. XGBoost performances have been further improved via feature selection and larger hyperparameters tuning using Optuna. (a) We selected the optimal number of features by training XGBoost with Optuna, selecting a subset of the most important features, discovering that almost all features are needed for optimal performance. (b) The best model has been obtained for 116 features; it shows a ROC_AUC=0.86, and precision comparable with FACS on dissociated cells made by other researchers. (c) By plotting the 9 best features, we can observe that more than 50% of classification power comes almost entirely from autofluorescence images.

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Solventum Launches AI Denial Prevention Tool to Boost Health System Revenue – AiThority

Solventum [formerly3M Health Care] announced a new artificial intelligence (AI)-driven payment integrity and revenue cycle solution, Solventum Revenue Integrity System. In collaboration withSift Healthcare, this solution is designed to help health systems not only reduce potential denials but prevent them and ensure timely and accurate payer reimbursement.

Read:AI In Marketing: Why GenAI Should Be in All 2024 Marketing Plans?

With tight margins and reduced resources, claim denials from payers are a persistent challenge across the healthcare industry, leading to significant revenue loss and increased administrative costs. Healthcare organizations cited claim denials as thegreatest revenue cycle management challenge, with more than half of respondents (58%) giving it a top rank, followed by specific payer challenges (44%), staffing (41%) and cost of collections (26%).

Targeting clinical documentation integrity (CDI), coding and utilization review workflows, Solventum aims to transform denials prevention by integrating machine learning-based interventions, coding and prebill validation into the front end of the healthcare revenue cycle.

As healthcare systems continue to face increased write-offs due to the growing denials burden, the current reactionary approach of relying on c***** data to reverse engineer denials isnt working, saidGarri Garrison, president, Health Information Systems, Solventum. Our new solution, developed in collaboration with Sift, will help equip health systems to move from a reactionary stance to a proactive strategy, ultimately preventing denials in the clinical workflow.

With this technology, healthcare providers are empowered to optimize payment outcomes by predicting reimbursement at every step of the patients clinical journey. Functioning as an integrated artificial intelligence (AI) agent within clinical workflows, Solventum Revenue Integrity provides health systems with near real time insights into reimbursement likelihoods and o**** strategic, compliant and actionable recommendations for intervention.

Read:AI in Content Creation: Top 25 AI Tools

Our objective in collaborating with Solventum is to reshape how healthcare systems handle denials prevention and, more broadly, support a timely and accurate payer reimbursement outcome, saidJustin Nicols, founder and CEO of Sift Healthcare. This solution facilitates near real time intelligence and guides data-driven decisions, truly empowering providers to optimize payment outcomes.

The Solventum Revenue Integrity solution complements Solventums existing suite of healthcare information system software that supports the entire journey,

Read:10 AI In Manufacturing Trends To Look Out For In 2024

[To share your insights with us as part of editorial or sponsored content, please write topsen@martechseries.com]

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Rags to Riches: 3 Machine Learning Stocks That Could Make Early Investors Rich – InvestorPlace

Find out which machine-learning stocks to get into now before the industry explodes

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Unless you have been hiding under a rock for the past year, you are probably aware that artificial intelligence (AI) and machine-learning stocks have been leading the markets. Machine learning technology is advancing at a rapid pace. Many believe that AI will eventually replace a majority of mundane and monotonous human tasks. For corporations looking to cut costs, that is music to their ears.

Although AI has dominated the headlines recently, we are still in the early innings of what could be a trillion-dollar market in just a few years. Being an early investor in these companies can lead to exponential gains in the future. As with any emerging technology, these can be volatile stocks to hold but those patient enough should be rewarded in the long run. Here are three machine-learning stocks that could make early investors rich!

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It probably comes as no surprise that we start this article off with Nvidia (NASDAQ:NVDA). Wall Street analysts have a street-high price target of $150, which implies about 15% upside from the current price.

Nvidia is the global leader in GPU chip production. These chips are used to power machine-learning infrastructure and some of the most powerful computers in the world. Nvidia just underwent a 10-for-1 stock split earlier this month. While it does not intrinsically change anything about the stock, the lower share price does make this an attractive time to load up on Nvidia stock. After a parabolic run we could see a pullback at some point but holding this stock for the next few decades should pay off in spades.

One argument from the bears against Nvidia is how expensive it is. If you compare it to its peers, Nvidia only trades at 54x forward earnings which is cheaper than both Advanced Micro Devices (NASDAQ:AMD) and Broadcom (NASDAQ:AVGO). If there is one machine-learning stock to buy early and own forever, it is Nvidia.

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Palantir Technologies (NYSE:PLTR) has been a controversial stock since it went public in 2020. Dont believe us? The current one-year price target range from Wall Street analysts ranges between $9 to $35. Palantir is trading slightly above its average price target of $21.45 after gaining more than 40% so far in 2024.

The main question that always comes up with Palantir is about what the company does as a business. Palantir sells data analytics software platforms to enterprises and governments around the world. This platform can instantly organize and analyze large sets of data while using machine learning to complete these tasks more efficiently for the user. In late 2023, Palantir was named the worlds top vendor for AI and machine learning software.

One problem with Palantirs stock is how expensive it trades compared to the companys growth rate. Shares trade at 19.4x forward sales and 71x forward earnings. These multiples put Palantir well ahead of other similarly sized software. You will often pay a premium for industry leaders and Palantir is on track to being the top machine-learning software provider in the world.

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Snowflake (NYSE:SNOW) is another stock that has had a controversial start to its public tenure. The stock debuted on Wall Street around the same time that Palantir did but the two stocks have gone in opposite directions since then. Analysts remain bullish on the stock with an average price target of $209.97 and a street-high target of $600!

Throughout the years, Snowflake and Palantir have often been compared to each other. Both companies produce software platforms that provide data analytics for their users. Snowflake offers plenty of different machine-learning tools including its Snowpark ML platform. The company has partnered with Nvidia to provide users with generative AI in the data cloud. Snowflakes enterprise-grade large language models (LLM) known as Arctic also use Nvidias powerful AI software to provide optimized performance.

All of that is to say that Snowflake is emerging as a machine-learning powerhouse at a time when its stock has struggled. It is trading at historically low multiples, including at 13.9x sales compared to its five-year average multiple of 42.9x. Snowflake is also trading at 47x free cash flow compared to its five-year average of 906x. The stock still isnt cheap, but years from now this dip will be looked at as the opportunity of a lifetime.

On the date of publication, Ian Hartana and Vayun Chugh did not hold (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

Chandler Capital is the work of Ian Hartana and Vayun Chugh. Ian Hartana and Vayun Chugh are both self-taught investors whose work has been featured in Seeking Alpha. Their research primarily revolves around GARP stocks with a long-term investment perspective encompassing diverse sectors such as technology, energy, and healthcare.

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The AI Playbook: 6 steps for launching predictive AI projects – MIT Sloan News

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Companies are hankering for predictive analytics that promise to boost sales, cut costs, prevent fraud, and streamline operations.

Yet most organizations are failing to achieve their desired outcomes. An MIT Sloan Management Review and Boston Consulting Group study found that just 10% of companies garnered significant financial benefit from their AI investments. And in asurvey by Rexer Analytics, just 22% of data scientists said that their new initiatives are usually deployed and operationalized across the enterprise.

Many predictive machine learning projects fail because they focus too much on technology alone as opposed to advancing technology as a strategic business project, according to Eric Siegel, a consultant and former professor at Columbia University and the University of Virginia.

In his new book, The AI Playbook: Mastering the Rare Art of Machine Learning Deployment, Siegel makes the case that organizations are not seeing value from AI because they lack an effective business paradigm for running machine learning projects. Because most machine learning projects are highly technical, they often fall under the domain of experienced data science professionals. The result is a disconnect between the data experts preparing data and developing and operating AI models and the business stakeholders in charge of running large-scale operations who stand to benefit from predictive insights.

By focusing so much on the modeling science rather than its deployment, its like being more excited about rocket science than the actual launch of the rocket, Siegel said in a recent MIT Sloan Management Review webinar. Thats where we are today.

To boost success, Siegel said, businesses need a standardized playbook for machine learning projects that is accessible to business professionals and can help them participate in the life cycle of predictive analytics projects.

Otherwise, both sides point to the other and say, Running and managing this business-level process is not my job, he explains. It rests in no-mans-land, and that is the last remaining ingredient before we get more wide-scale success and deployment.

To bridge the divide, Siegel advocates for something he calls BizML, a set of business practices for running predictive machine learning projects.

He outlined six steps to foster collaboration among business and technical stakeholders throughout all phases of machine learning deployment:

Establish the deployment goal. To derive any real value from machine learning, businesses need a defined value proposition that details how the technology will impact operations. Data scientists cant do this in a vacuum. Its important that business stakeholders who are intimately familiar with the pain points and opportunities are technologically savvy enough to participate in realistic goal setting.

Establish the prediction goal. While modeling and predication involve complex mathematics, business goals need to be kept in mind. Business users need to have a semitechnical understanding of the technology so they can share their specific domain knowledge while also defining what the machine learning model is intended to predict for each use case.

Establish the right metrics. Determine the salient benchmarks to track during both model training and model deployment. In addition, identify what performance levels must be achieved for the machine learning project to be considered a success. Typically, most machine learning projects are grounded by technical metrics such as precision, recall, or accuracy. Organizations need to shift their focus to business metrics like profit, ROI, savings, and customer acquisition, Siegel said.

Prepare the data.Define what the training data should look like, and ensure that data is in the desired format. This critical step is nonnegotiable because it is the linchpin to experiencing high-value results, Siegel said.

Train the model.Next, the prepared data is used to train and generate a predictive model. Data experts lead the charge here, but there is always room for additional business input.

Deploy the model. Use the model to render predictive scores and in turn, use those scores to improve business operations. Its also important to maintain the models through ongoing monitoring and periodic refreshing.

While the last three steps are more technical than the first three, all of them require deep collaboration between technology and business stakeholders. Building bridges to connect the two camps requires investment and engagement in the right change management practices to ensure adequate understanding of machine learning across stakeholders throughout the business.

Change management challenges arent new in general, but when it comes to machine learning projects, the need to shrewdly manage change is often overlooked, Siegel said. Machine learning delivers a rocket, but those in charge must still command its launch.

Watch the webinar: How to Succeed with Predictive AI

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QC Ware Announces 3rd Q2B Tokyo Conference, Focusing on the Roadmap to Quantum Value in Asia and Beyond – PR Newswire

QC Ware to bring Q2B conferenceto Tokyo & Asia for a third time, connecting the global and Asian quantum computing ecosystems and bringing together quantum industry experts across computing, telecommunications, sensing, finance, automotive and more.

PALO ALTO, Calif. and TOKYO, June 24, 2024 /PRNewswire/ -- QC Ware, a leading provider of industry-disrupting quantum and quantum-inspired machine learning and chemistry simulation solutions, today announced the third edition of its Q2B Tokyo Conference, to take place on July 24-25, 2024.

The conference, which will be held at the Grand Hyatt Tokyo, will dive deep into all major quantum technologies and themes: computing, sensing, communications/security, error correction, quantum AI, HPC integration and more. Attendees can expect to see featured keynotes, industry case studies and discussions led by experts at the forefront of quantum R&D from some of the world's leading businesses and institutions across government, academia, and Fortune 100 companies.

"Our team is always super excited to be back in Tokyo for Q2B, especially given the strength of the Japanese quantum ecosystem and its willingness to attract the attention of an already captivated international community," saidQC Ware CEO Matt Johnson. "The quantum computing ecosystem in Asia, and specifically in Japan, is incredibly vibrant and exciting, and the work undertaken to directly and indirectly create that environment cannot be overstated."

Through keynotes, business seminars, breakout sessions, technical workshops, and panel discussions, attendees at Q2B Tokyo will be able to learn about the latest hardware breakthroughs and applications in optimization, chemistry simulations, pharmaceutical and materials discovery, error correction and machine learning. Additionally, the conference will feature several panels and sessions from real-world practitioners, end users and experts across industries. Notable speakers include:

Q2B is dedicated to promoting and growing the quantum technologies ecosystem. One exciting component of the conference is the start-up track, which includes a pitch competition and a "New Faces" panel. The pitch competition, organized by QAI Ventures, will offer the winner a golden ticket to the QAI Ventures speed dating event. This can lead to participation in the early-stage quantum tech startup QAI Accelerator and an investment up to CHF 200k. The New Faces panel will showcase new and exciting companies in their Q2B debut.

Finally, leaders charting the future of quantum technology adoption will deliver sessions on recent advancements in the field, including:

There will be plenty to see on the exhibit floor with a number of vendors showcasing their latest advancements in quantum technologies, including: Keysight, Quantinuum, QuEra Computing, Quemix, PsiQuantum, Q-CTRL, Strangeworks, Qedma, Classiq, C12, Pasqal, Oxford Quantum Circuits, AWS, Equal1, Norma, Carrousel Digital, and more.

Register to attend Q2B 2024 Tokyo here.

About QC Ware

QC Wareis a leading software and services company at the forefront of quantum and classical computing. Our team includes some of the industry's top experts in machine learning and chemistry applications for near-term quantum computers and high-performance classical systems. We deliver real enterprise value with cutting-edge computational technology, providing innovative solutions that drive business success. QC Ware is headquartered in Palo Alto, California, and supports its European customers through its subsidiary in Paris and customers in Asia through its business development office in Tokyo, Japan. QC Ware also organizes Q2B, a global series of conferences for industry, practitioner, and academic quantum computing communities.

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