bioRxiv The study introduces VRPTR, a three-dimensional encoder-decoder combining a compressed Transformer bottleneck, variational latent sampling, and multiscale skip connections, trained on 360 healthy adults from the WU-Minn Human Connectome Project and evaluated on 40 held-out participants for the story-versus-math language contrast, achieving mean voxel-map Pearson r=0.642 and Dice AUC=0.519, exceeding compact volumetric BrainSurfCNN-like and SWIFUN-like comparators by Δr=0.0376 and 0.0335 respectively, while raw 95% intervals covered only 4.7% of observed values and five-fold calibration within the held-out cohort raised coverage to 94.8%.
The study introduces VRPTR, a three-dimensional encoder-decoder combining a compressed Transformer bottleneck, variational latent sampling, and multiscale skip connections, trained on 360 healthy adults from the WU-Minn Human Connectome Project and evaluated on 40 held-out participants for the story-versus-math language contrast, achieving mean voxel-map Pearson r=0.642 and Dice AUC=0.519, exceeding compact volumetric BrainSurfCNN-like and SWIFUN-like comparators by Δr=0.0376 and 0.0335 respectively, while raw 95% intervals covered only 4.7% of observed values and five-fold calibration within the held-out cohort raised coverage to 94.8%.
The study introduces VRPTR, a three-dimensional encoder-decoder combining a compressed Transformer bottleneck, variational latent sampling, and multiscale skip connections, trained on 360 healthy adults from the WU-Minn Human Connectome Project and evaluated on 40 held-out participants for the story-versus-math language contrast, achieving mean voxel-map Pearson r=0.642 and Dice AUC=0.519, exceeding compact volumetric BrainSurfCNN-like and SWIFUN-like comparators by Δr=0.0376 and 0.0335 respectively, while raw 95% intervals covered only 4.7% of observed values and five-fold calibration within the held-out cohort raised coverage to 94.8%.
The study introduces VRPTR, a three-dimensional encoder-decoder combining a compressed Transformer bottleneck, variational latent sampling, and multiscale skip connections, trained on 360 healthy adults from the WU-Minn Human Connectome Project and evaluated on 40 held-out participants for the story-versus-math language contrast, achieving mean voxel-map Pearson r=0.642 and Dice AUC=0.519, exceeding compact volumetric BrainSurfCNN-like and SWIFUN-like comparators by Δr=0.0376 and 0.0335 respectively, while raw 95% intervals covered only 4.7% of observed values and five-fold calibration within the held-out cohort raised coverage to 94.8%.
bioRxiv The authors tested a dynamic amplitude-modulated (dAM) envelope-following response (EFR) that sweeps the full modulation spectrum in a single brief stimulus, found selective deficits at fast modulation rates without threshold elevation in Mongolian gerbils with histologically verified cochlear neural degeneration (CND), trained a machine-learning classifier that distinguished young from middle-aged animals with high accuracy and whose most informative feature (power near 400-500 Hz) tracked synapse counts, and applied the gerbil-trained classifier without retraining to 56 human listeners, where it separated age groups above chance.
The authors tested a dynamic amplitude-modulated (dAM) envelope-following response (EFR) that sweeps the full modulation spectrum in a single brief stimulus, found selective deficits at fast modulation rates without threshold elevation in Mongolian gerbils with histologically verified cochlear neural degeneration (CND), trained a machine-learning classifier that distinguished young from middle-aged animals with high accuracy and whose most informative feature (power near 400-500 Hz) tracked synapse counts, and applied the gerbil-trained classifier without retraining to 56 human listeners, where it separated age groups above chance.
The authors tested a dynamic amplitude-modulated (dAM) envelope-following response (EFR) that sweeps the full modulation spectrum in a single brief stimulus, found selective deficits at fast modulation rates without threshold elevation in Mongolian gerbils with histologically verified cochlear neural degeneration (CND), trained a machine-learning classifier that distinguished young from middle-aged animals with high accuracy and whose most informative feature (power near 400-500 Hz) tracked synapse counts, and applied the gerbil-trained classifier without retraining to 56 human listeners, where it separated age groups above chance.
The authors tested a dynamic amplitude-modulated (dAM) envelope-following response (EFR) that sweeps the full modulation spectrum in a single brief stimulus, found selective deficits at fast modulation rates without threshold elevation in Mongolian gerbils with histologically verified cochlear neural degeneration (CND), trained a machine-learning classifier that distinguished young from middle-aged animals with high accuracy and whose most informative feature (power near 400-500 Hz) tracked synapse counts, and applied the gerbil-trained classifier without retraining to 56 human listeners, where it separated age groups above chance.
GEO Knowledge Hub This OEMC project use case proposes an open workflow that leverages other remote sensing data such as LST and NIRv with a semi-empirical approach combining data-driven methods and physical constraints to enhance the spatial resolution of Sentinel-5P TROPOMI-based SIF estimates from about 5 km to about 1 km, produces a gridded dataset at 0.05 degrees with 8-daily frequency for 2018-2025, ports the tool to the Copernicus Data Space Ecosystem via the OpenEO framework for on-demand downscaling by users, and attempts to match satellite grid cells to eddy covariance flux site GPP ground measurements to assess the effect of spatial heterogeneity.
This OEMC project use case proposes an open workflow that leverages other remote sensing data such as LST and NIRv with a semi-empirical approach combining data-driven methods and physical constraints to enhance the spatial resolution of Sentinel-5P TROPOMI-based SIF estimates from about 5 km to about 1 km, produces a gridded dataset at 0.05 degrees with 8-daily frequency for 2018-2025, ports the tool to the Copernicus Data Space Ecosystem via the OpenEO framework for on-demand downscaling by users, and attempts to match satellite grid cells to eddy covariance flux site GPP ground measurements to assess the effect of spatial heterogeneity.
This OEMC project use case proposes an open workflow that leverages other remote sensing data such as LST and NIRv with a semi-empirical approach combining data-driven methods and physical constraints to enhance the spatial resolution of Sentinel-5P TROPOMI-based SIF estimates from about 5 km to about 1 km, produces a gridded dataset at 0.05 degrees with 8-daily frequency for 2018-2025, ports the tool to the Copernicus Data Space Ecosystem via the OpenEO framework for on-demand downscaling by users, and attempts to match satellite grid cells to eddy covariance flux site GPP ground measurements to assess the effect of spatial heterogeneity.
This OEMC project use case proposes an open workflow that leverages other remote sensing data such as LST and NIRv with a semi-empirical approach combining data-driven methods and physical constraints to enhance the spatial resolution of Sentinel-5P TROPOMI-based SIF estimates from about 5 km to about 1 km, produces a gridded dataset at 0.05 degrees with 8-daily frequency for 2018-2025, ports the tool to the Copernicus Data Space Ecosystem via the OpenEO framework for on-demand downscaling by users, and attempts to match satellite grid cells to eddy covariance flux site GPP ground measurements to assess the effect of spatial heterogeneity.
Natural Sciences and Applied Technology The work presents a fast two-dimensional direction-of-arrival (DOA) estimation approach for low-elevation targets of very-high-frequency array radar: it uses the azimuth and pitch angle uncoupling properties of a uniform planar array to turn the 2D angle estimation problem into two 1D DOA estimation problems, retrieves target information in the azimuth and elevation dimensions with digital beamforming, and then estimates azimuth and pitch angles using the alternating direction method of multipliers, thereby reducing complexity and eliminating the need for eigenvalue decomposition during operation.
The work presents a fast two-dimensional direction-of-arrival (DOA) estimation approach for low-elevation targets of very-high-frequency array radar: it uses the azimuth and pitch angle uncoupling properties of a uniform planar array to turn the 2D angle estimation problem into two 1D DOA estimation problems, retrieves target information in the azimuth and elevation dimensions with digital beamforming, and then estimates azimuth and pitch angles using the alternating direction method of multipliers, thereby reducing complexity and eliminating the need for eigenvalue decomposition during operation.
The work presents a fast two-dimensional direction-of-arrival (DOA) estimation approach for low-elevation targets of very-high-frequency array radar: it uses the azimuth and pitch angle uncoupling properties of a uniform planar array to turn the 2D angle estimation problem into two 1D DOA estimation problems, retrieves target information in the azimuth and elevation dimensions with digital beamforming, and then estimates azimuth and pitch angles using the alternating direction method of multipliers, thereby reducing complexity and eliminating the need for eigenvalue decomposition during operation.
The work presents a fast two-dimensional direction-of-arrival (DOA) estimation approach for low-elevation targets of very-high-frequency array radar: it uses the azimuth and pitch angle uncoupling properties of a uniform planar array to turn the 2D angle estimation problem into two 1D DOA estimation problems, retrieves target information in the azimuth and elevation dimensions with digital beamforming, and then estimates azimuth and pitch angles using the alternating direction method of multipliers, thereby reducing complexity and eliminating the need for eigenvalue decomposition during operation.
Claude 产品博客 Anthropic announced that Claude for Government is generally available to federal and state agencies, delivering coding and agentic work capabilities comparable to its commercial customers through a FedRAMP High authorized environment, alongside governance controls such as department-level budget allocation, SCIM seat tiering, audit logs, and two-person approval, while the Claude Code command-line interface and Claude for Microsoft 365 enter early access in the same environment.
Anthropic announced that Claude for Government is generally available to federal and state agencies, delivering coding and agentic work capabilities comparable to its commercial customers through a FedRAMP High authorized environment, alongside governance controls such as department-level budget allocation, SCIM seat tiering, audit logs, and two-person approval, while the Claude Code command-line interface and Claude for Microsoft 365 enter early access in the same environment.
Anthropic announced that Claude for Government is generally available to federal and state agencies, delivering coding and agentic work capabilities comparable to its commercial customers through a FedRAMP High authorized environment, alongside governance controls such as department-level budget allocation, SCIM seat tiering, audit logs, and two-person approval, while the Claude Code command-line interface and Claude for Microsoft 365 enter early access in the same environment.
Anthropic announced that Claude for Government is generally available to federal and state agencies, delivering coding and agentic work capabilities comparable to its commercial customers through a FedRAMP High authorized environment, alongside governance controls such as department-level budget allocation, SCIM seat tiering, audit logs, and two-person approval, while the Claude Code command-line interface and Claude for Microsoft 365 enter early access in the same environment.
发表出处待核验 Combining two multimodal qMRI-PET datasets (n = 74, spanning Alzheimer's disease, subjective cognitive decline, and healthy controls), this study used [18F]UCB-H PET distribution volume VT from Logan graphical analysis as the synaptic-density reference, applied ComBat harmonization, and compared classical machine learning (SVR, PLS, Elastic Net, Random Forests) with deep learning (U-Net, ResUNet++, Pix2Pix-like conditional GANs) for predicting PET-like synaptic density images from qMRI maps such as R1, R2*, MTsat, and PD; Elastic Net was best among classical models (R² = 0.50, RMSE = 0.448, MAE = 0.331), deep learning improved accuracy with 3D U-Net most consistent, and gray-matter z-scored evaluation showed strong agreement with reference PET (MSE 0.1294 ± 0.0778, SSIM 0.9832 ± 0.
Combining two multimodal qMRI-PET datasets (n = 74, spanning Alzheimer's disease, subjective cognitive decline, and healthy controls), this study used [18F]UCB-H PET distribution volume VT from Logan graphical analysis as the synaptic-density reference, applied ComBat harmonization, and compared classical machine learning (SVR, PLS, Elastic Net, Random Forests) with deep learning (U-Net, ResUNet++, Pix2Pix-like conditional GANs) for predicting PET-like synaptic density images from qMRI maps such as R1, R2*, MTsat, and PD; Elastic Net was best among classical models (R² = 0.50, RMSE = 0.448, MAE = 0.331), deep learning improved accuracy with 3D U-Net most consistent, and gray-matter z-scored evaluation showed strong agreement with reference PET (MSE 0.1294 ± 0.0778, SSIM 0.9832 ± 0.
Combining two multimodal qMRI-PET datasets (n = 74, spanning Alzheimer's disease, subjective cognitive decline, and healthy controls), this study used [18F]UCB-H PET distribution volume VT from Logan graphical analysis as the synaptic-density reference, applied ComBat harmonization, and compared classical machine learning (SVR, PLS, Elastic Net, Random Forests) with deep learning (U-Net, ResUNet++, Pix2Pix-like conditional GANs) for predicting PET-like synaptic density images from qMRI maps such as R1, R2*, MTsat, and PD; Elastic Net was best among classical models (R² = 0.50, RMSE = 0.448, MAE = 0.331), deep learning improved accuracy with 3D U-Net most consistent, and gray-matter z-scored evaluation showed strong agreement with reference PET (MSE 0.1294 ± 0.0778, SSIM 0.9832 ± 0.
Combining two multimodal qMRI-PET datasets (n = 74, spanning Alzheimer's disease, subjective cognitive decline, and healthy controls), this study used [18F]UCB-H PET distribution volume VT from Logan graphical analysis as the synaptic-density reference, applied ComBat harmonization, and compared classical machine learning (SVR, PLS, Elastic Net, Random Forests) with deep learning (U-Net, ResUNet++, Pix2Pix-like conditional GANs) for predicting PET-like synaptic density images from qMRI maps such as R1, R2*, MTsat, and PD; Elastic Net was best among classical models (R² = 0.50, RMSE = 0.448, MAE = 0.331), deep learning improved accuracy with 3D U-Net most consistent, and gray-matter z-scored evaluation showed strong agreement with reference PET (MSE 0.1294 ± 0.0778, SSIM 0.9832 ± 0.
Journal of Applied Health Sciences and Medicine Using UV-Vis spectroscopy under physiological conditions (pH 7.4, 37 °C, 0.1 mM), this study tracked the interaction of the anticancer drug 5-fluorouracil (5-FU) with the anionic micelle SDS and the cationic micelle TBAB, finding that both form molecular complexes treatable as reversible first-order equilibria: TBAB gave k* = 17 × 10⁻³ min⁻¹, t1/2 = 40.76 min and Keq = 17.72, whereas SDS gave k* = 7.60 × 10⁻³ min⁻¹, t1/2 = 91.20 min and Keq = 12.38, with SDS involving relaxation equilibrium processes because both reactants carry negative charge, and both complexes showed negative ΔG⁰ (SDS −6486.62 J/mol, TBAB −7409.98 J/mol), indicating spontaneous binding driven by van der Waals forces or hydrogen bonding.
Using UV-Vis spectroscopy under physiological conditions (pH 7.4, 37 °C, 0.1 mM), this study tracked the interaction of the anticancer drug 5-fluorouracil (5-FU) with the anionic micelle SDS and the cationic micelle TBAB, finding that both form molecular complexes treatable as reversible first-order equilibria: TBAB gave k* = 17 × 10⁻³ min⁻¹, t1/2 = 40.76 min and Keq = 17.72, whereas SDS gave k* = 7.60 × 10⁻³ min⁻¹, t1/2 = 91.20 min and Keq = 12.38, with SDS involving relaxation equilibrium processes because both reactants carry negative charge, and both complexes showed negative ΔG⁰ (SDS −6486.62 J/mol, TBAB −7409.98 J/mol), indicating spontaneous binding driven by van der Waals forces or hydrogen bonding.
Using UV-Vis spectroscopy under physiological conditions (pH 7.4, 37 °C, 0.1 mM), this study tracked the interaction of the anticancer drug 5-fluorouracil (5-FU) with the anionic micelle SDS and the cationic micelle TBAB, finding that both form molecular complexes treatable as reversible first-order equilibria: TBAB gave k* = 17 × 10⁻³ min⁻¹, t1/2 = 40.76 min and Keq = 17.72, whereas SDS gave k* = 7.60 × 10⁻³ min⁻¹, t1/2 = 91.20 min and Keq = 12.38, with SDS involving relaxation equilibrium processes because both reactants carry negative charge, and both complexes showed negative ΔG⁰ (SDS −6486.62 J/mol, TBAB −7409.98 J/mol), indicating spontaneous binding driven by van der Waals forces or hydrogen bonding.
Using UV-Vis spectroscopy under physiological conditions (pH 7.4, 37 °C, 0.1 mM), this study tracked the interaction of the anticancer drug 5-fluorouracil (5-FU) with the anionic micelle SDS and the cationic micelle TBAB, finding that both form molecular complexes treatable as reversible first-order equilibria: TBAB gave k* = 17 × 10⁻³ min⁻¹, t1/2 = 40.76 min and Keq = 17.72, whereas SDS gave k* = 7.60 × 10⁻³ min⁻¹, t1/2 = 91.20 min and Keq = 12.38, with SDS involving relaxation equilibrium processes because both reactants carry negative charge, and both complexes showed negative ΔG⁰ (SDS −6486.62 J/mol, TBAB −7409.98 J/mol), indicating spontaneous binding driven by van der Waals forces or hydrogen bonding.
Natural Sciences and Applied Technology The work proposes FedTrust-GNN, a decentralized user-modeling framework that combines differentially private federated learning with secure multi-party computation, a permissioned blockchain using PBFT consensus, and a heterogeneous graph attention network (HGAT) that infers dynamic trust scores, with trust-weighted robust aggregation (TWRA, combining norm clipping and coordinate-wise median aggregation) providing Byzantine fault tolerance; on Federated EMNIST, Stack Overflow, and synthetic datasets with 10,000-100,000 participants it reports 94.2% accuracy (within 1.3% of centralized models), a reduction of label-flipping attack success from 34% to 6.1% (an 82% reduction), a 41% improvement in convergence stability, and blockchain performance of 1,200 TPS with 2.3-second finality.
The work proposes FedTrust-GNN, a decentralized user-modeling framework that combines differentially private federated learning with secure multi-party computation, a permissioned blockchain using PBFT consensus, and a heterogeneous graph attention network (HGAT) that infers dynamic trust scores, with trust-weighted robust aggregation (TWRA, combining norm clipping and coordinate-wise median aggregation) providing Byzantine fault tolerance; on Federated EMNIST, Stack Overflow, and synthetic datasets with 10,000-100,000 participants it reports 94.2% accuracy (within 1.3% of centralized models), a reduction of label-flipping attack success from 34% to 6.1% (an 82% reduction), a 41% improvement in convergence stability, and blockchain performance of 1,200 TPS with 2.3-second finality.
The work proposes FedTrust-GNN, a decentralized user-modeling framework that combines differentially private federated learning with secure multi-party computation, a permissioned blockchain using PBFT consensus, and a heterogeneous graph attention network (HGAT) that infers dynamic trust scores, with trust-weighted robust aggregation (TWRA, combining norm clipping and coordinate-wise median aggregation) providing Byzantine fault tolerance; on Federated EMNIST, Stack Overflow, and synthetic datasets with 10,000-100,000 participants it reports 94.2% accuracy (within 1.3% of centralized models), a reduction of label-flipping attack success from 34% to 6.1% (an 82% reduction), a 41% improvement in convergence stability, and blockchain performance of 1,200 TPS with 2.3-second finality.
The work proposes FedTrust-GNN, a decentralized user-modeling framework that combines differentially private federated learning with secure multi-party computation, a permissioned blockchain using PBFT consensus, and a heterogeneous graph attention network (HGAT) that infers dynamic trust scores, with trust-weighted robust aggregation (TWRA, combining norm clipping and coordinate-wise median aggregation) providing Byzantine fault tolerance; on Federated EMNIST, Stack Overflow, and synthetic datasets with 10,000-100,000 participants it reports 94.2% accuracy (within 1.3% of centralized models), a reduction of label-flipping attack success from 34% to 6.1% (an 82% reduction), a 41% improvement in convergence stability, and blockchain performance of 1,200 TPS with 2.3-second finality.
The FASEB Journal By crossing Aldh1l1-Cre with CD9-tGFP reporter mice, the authors generated an astrocyte-specific EV reporter mouse in which 13.2% ± 1.6% of brain-isolated EVs were CD9-tGFP positive and 89.3% ± 2.2% of primary astrocyte-derived EVs were positive; CD9-tGFP signal was detected in astrocytic processes, capillaries, and neurons in cortex, hippocampus, and cerebellum, STED and AI-assisted proximity analysis showed EV cargo enrichment at neuronal mitochondria in vitro, and isolated mitochondria showed 3-fold higher CD9-tGFP puncta density on synaptic versus non-synaptic mitochondria in vivo.
By crossing Aldh1l1-Cre with CD9-tGFP reporter mice, the authors generated an astrocyte-specific EV reporter mouse in which 13.2% ± 1.6% of brain-isolated EVs were CD9-tGFP positive and 89.3% ± 2.2% of primary astrocyte-derived EVs were positive; CD9-tGFP signal was detected in astrocytic processes, capillaries, and neurons in cortex, hippocampus, and cerebellum, STED and AI-assisted proximity analysis showed EV cargo enrichment at neuronal mitochondria in vitro, and isolated mitochondria showed 3-fold higher CD9-tGFP puncta density on synaptic versus non-synaptic mitochondria in vivo.
By crossing Aldh1l1-Cre with CD9-tGFP reporter mice, the authors generated an astrocyte-specific EV reporter mouse in which 13.2% ± 1.6% of brain-isolated EVs were CD9-tGFP positive and 89.3% ± 2.2% of primary astrocyte-derived EVs were positive; CD9-tGFP signal was detected in astrocytic processes, capillaries, and neurons in cortex, hippocampus, and cerebellum, STED and AI-assisted proximity analysis showed EV cargo enrichment at neuronal mitochondria in vitro, and isolated mitochondria showed 3-fold higher CD9-tGFP puncta density on synaptic versus non-synaptic mitochondria in vivo.
By crossing Aldh1l1-Cre with CD9-tGFP reporter mice, the authors generated an astrocyte-specific EV reporter mouse in which 13.2% ± 1.6% of brain-isolated EVs were CD9-tGFP positive and 89.3% ± 2.2% of primary astrocyte-derived EVs were positive; CD9-tGFP signal was detected in astrocytic processes, capillaries, and neurons in cortex, hippocampus, and cerebellum, STED and AI-assisted proximity analysis showed EV cargo enrichment at neuronal mitochondria in vitro, and isolated mitochondria showed 3-fold higher CD9-tGFP puncta density on synaptic versus non-synaptic mitochondria in vivo.
medRxiv The study had 1,003 participants with depression read vignettes describing psychotherapy options with different levels of AI involvement (a human therapist without AI, assistive AI, collaborative AI, and fully autonomous AI) and rate them; participants consistently evaluated human therapists more favorably, reporting greater likelihood of seeking treatment, less hesitancy, and greater treatment acceptability, and compared with a human therapist they were willing to pay 31.6% less for therapists using assistive or collaborative AI and 57.
The study had 1,003 participants with depression read vignettes describing psychotherapy options with different levels of AI involvement (a human therapist without AI, assistive AI, collaborative AI, and fully autonomous AI) and rate them; participants consistently evaluated human therapists more favorably, reporting greater likelihood of seeking treatment, less hesitancy, and greater treatment acceptability, and compared with a human therapist they were willing to pay 31.6% less for therapists using assistive or collaborative AI and 57.
The study had 1,003 participants with depression read vignettes describing psychotherapy options with different levels of AI involvement (a human therapist without AI, assistive AI, collaborative AI, and fully autonomous AI) and rate them; participants consistently evaluated human therapists more favorably, reporting greater likelihood of seeking treatment, less hesitancy, and greater treatment acceptability, and compared with a human therapist they were willing to pay 31.6% less for therapists using assistive or collaborative AI and 57.
The study had 1,003 participants with depression read vignettes describing psychotherapy options with different levels of AI involvement (a human therapist without AI, assistive AI, collaborative AI, and fully autonomous AI) and rate them; participants consistently evaluated human therapists more favorably, reporting greater likelihood of seeking treatment, less hesitancy, and greater treatment acceptability, and compared with a human therapist they were willing to pay 31.6% less for therapists using assistive or collaborative AI and 57.
medRxiv The work presents a weakly-supervised, detection-free multiple instance learning framework that uses dual-branch gated attention pooling to make slide-level predictions on whole-slide cervical cytology images, treating each slide as a bag of local instance patches so that single-cell bounding boxes or pixel-level annotations are not required; evaluated on internal multi-center cohorts (SIPaKMeD, Herlev, and CRIC) and on the unannotated, out-of-distribution Mendeley LBC validation cohort processed via an unsupervised marker-controlled watershed pipeline, it reports that the lightweight MobileNetV2 backbone optimizes in-distribution multi-center accuracy (90.96% accuracy, 0.9800 ROC-AUC) while the higher-capacity Xception provides better out-of-distribution robustness under domain shift (80.
The work presents a weakly-supervised, detection-free multiple instance learning framework that uses dual-branch gated attention pooling to make slide-level predictions on whole-slide cervical cytology images, treating each slide as a bag of local instance patches so that single-cell bounding boxes or pixel-level annotations are not required; evaluated on internal multi-center cohorts (SIPaKMeD, Herlev, and CRIC) and on the unannotated, out-of-distribution Mendeley LBC validation cohort processed via an unsupervised marker-controlled watershed pipeline, it reports that the lightweight MobileNetV2 backbone optimizes in-distribution multi-center accuracy (90.96% accuracy, 0.9800 ROC-AUC) while the higher-capacity Xception provides better out-of-distribution robustness under domain shift (80.
The work presents a weakly-supervised, detection-free multiple instance learning framework that uses dual-branch gated attention pooling to make slide-level predictions on whole-slide cervical cytology images, treating each slide as a bag of local instance patches so that single-cell bounding boxes or pixel-level annotations are not required; evaluated on internal multi-center cohorts (SIPaKMeD, Herlev, and CRIC) and on the unannotated, out-of-distribution Mendeley LBC validation cohort processed via an unsupervised marker-controlled watershed pipeline, it reports that the lightweight MobileNetV2 backbone optimizes in-distribution multi-center accuracy (90.96% accuracy, 0.9800 ROC-AUC) while the higher-capacity Xception provides better out-of-distribution robustness under domain shift (80.
The work presents a weakly-supervised, detection-free multiple instance learning framework that uses dual-branch gated attention pooling to make slide-level predictions on whole-slide cervical cytology images, treating each slide as a bag of local instance patches so that single-cell bounding boxes or pixel-level annotations are not required; evaluated on internal multi-center cohorts (SIPaKMeD, Herlev, and CRIC) and on the unannotated, out-of-distribution Mendeley LBC validation cohort processed via an unsupervised marker-controlled watershed pipeline, it reports that the lightweight MobileNetV2 backbone optimizes in-distribution multi-center accuracy (90.96% accuracy, 0.9800 ROC-AUC) while the higher-capacity Xception provides better out-of-distribution robustness under domain shift (80.
bioRxiv Using a curated 396-node human cell-lineage tree spanning the zygote to terminal somatic identities, the study tested whether organ-level standard metabolic rate (SMR) is better predicted by developmental time (lineage depth) or by terminal fate identity (anatomical compartment), finding that lineage depth explains essentially none of the variance in a cell's metabolic tier (r = 0.11, R2 approximately 1.2%) whereas compartment identity explains roughly 75%, and that the mean mitochondrial volume fraction of an organ's constituent terminal cell types tracks literature-derived organ SMR with r = 0.90 across five canonical reference-man organ groups, motivating a five-layer computable framework and a metabolic commitment-horizon model.
Using a curated 396-node human cell-lineage tree spanning the zygote to terminal somatic identities, the study tested whether organ-level standard metabolic rate (SMR) is better predicted by developmental time (lineage depth) or by terminal fate identity (anatomical compartment), finding that lineage depth explains essentially none of the variance in a cell's metabolic tier (r = 0.11, R2 approximately 1.2%) whereas compartment identity explains roughly 75%, and that the mean mitochondrial volume fraction of an organ's constituent terminal cell types tracks literature-derived organ SMR with r = 0.90 across five canonical reference-man organ groups, motivating a five-layer computable framework and a metabolic commitment-horizon model.
Using a curated 396-node human cell-lineage tree spanning the zygote to terminal somatic identities, the study tested whether organ-level standard metabolic rate (SMR) is better predicted by developmental time (lineage depth) or by terminal fate identity (anatomical compartment), finding that lineage depth explains essentially none of the variance in a cell's metabolic tier (r = 0.11, R2 approximately 1.2%) whereas compartment identity explains roughly 75%, and that the mean mitochondrial volume fraction of an organ's constituent terminal cell types tracks literature-derived organ SMR with r = 0.90 across five canonical reference-man organ groups, motivating a five-layer computable framework and a metabolic commitment-horizon model.
Using a curated 396-node human cell-lineage tree spanning the zygote to terminal somatic identities, the study tested whether organ-level standard metabolic rate (SMR) is better predicted by developmental time (lineage depth) or by terminal fate identity (anatomical compartment), finding that lineage depth explains essentially none of the variance in a cell's metabolic tier (r = 0.11, R2 approximately 1.2%) whereas compartment identity explains roughly 75%, and that the mean mitochondrial volume fraction of an organ's constituent terminal cell types tracks literature-derived organ SMR with r = 0.90 across five canonical reference-man organ groups, motivating a five-layer computable framework and a metabolic commitment-horizon model.
bioRxiv The study proposes Forest-Weighted S-map (FORWS) and Forest-Weighted Causal Inference (FORWC), which replace the Euclidean metric with adaptive "forest weights" derived from random forest ensembles, and reports comparable or improved forecasting skill relative to conventional tools, substantial resilience to dynamic process noise, mitigation of the curse of dimensionality, and multimodal directed causal inference between high-dimensional acoustic vectors and scalar temperature monitored in a honeybee hive.
The study proposes Forest-Weighted S-map (FORWS) and Forest-Weighted Causal Inference (FORWC), which replace the Euclidean metric with adaptive "forest weights" derived from random forest ensembles, and reports comparable or improved forecasting skill relative to conventional tools, substantial resilience to dynamic process noise, mitigation of the curse of dimensionality, and multimodal directed causal inference between high-dimensional acoustic vectors and scalar temperature monitored in a honeybee hive.
The study proposes Forest-Weighted S-map (FORWS) and Forest-Weighted Causal Inference (FORWC), which replace the Euclidean metric with adaptive "forest weights" derived from random forest ensembles, and reports comparable or improved forecasting skill relative to conventional tools, substantial resilience to dynamic process noise, mitigation of the curse of dimensionality, and multimodal directed causal inference between high-dimensional acoustic vectors and scalar temperature monitored in a honeybee hive.
The study proposes Forest-Weighted S-map (FORWS) and Forest-Weighted Causal Inference (FORWC), which replace the Euclidean metric with adaptive "forest weights" derived from random forest ensembles, and reports comparable or improved forecasting skill relative to conventional tools, substantial resilience to dynamic process noise, mitigation of the curse of dimensionality, and multimodal directed causal inference between high-dimensional acoustic vectors and scalar temperature monitored in a honeybee hive.
The FASEB Journal Integrating plasma metabolomics, Mendelian randomization, and machine learning, this study identified phenylalanine as causally associated with pancreatic cancer among 55 plasma metabolites (IVW OR = 1.641, 95% CI 1.052–2.562, p = 0.029), derived eight related differentially expressed genes, built a random forest diagnostic model from 113 combinations of 12 algorithms (training AUC 0.994; validation AUCs 0.918, 0.983, 0.923), used SHAP to rank SLC6A14 as the top feature, and combined single-cell sequencing, simulated gene knockout, molecular docking, and molecular dynamics to suggest genistein binds SLC6A14 stably, with RT-qPCR confirming high expression of the five model genes in a BxPC-3 versus HPDE6-C7 cell pair.
Integrating plasma metabolomics, Mendelian randomization, and machine learning, this study identified phenylalanine as causally associated with pancreatic cancer among 55 plasma metabolites (IVW OR = 1.641, 95% CI 1.052–2.562, p = 0.029), derived eight related differentially expressed genes, built a random forest diagnostic model from 113 combinations of 12 algorithms (training AUC 0.994; validation AUCs 0.918, 0.983, 0.923), used SHAP to rank SLC6A14 as the top feature, and combined single-cell sequencing, simulated gene knockout, molecular docking, and molecular dynamics to suggest genistein binds SLC6A14 stably, with RT-qPCR confirming high expression of the five model genes in a BxPC-3 versus HPDE6-C7 cell pair.
Integrating plasma metabolomics, Mendelian randomization, and machine learning, this study identified phenylalanine as causally associated with pancreatic cancer among 55 plasma metabolites (IVW OR = 1.641, 95% CI 1.052–2.562, p = 0.029), derived eight related differentially expressed genes, built a random forest diagnostic model from 113 combinations of 12 algorithms (training AUC 0.994; validation AUCs 0.918, 0.983, 0.923), used SHAP to rank SLC6A14 as the top feature, and combined single-cell sequencing, simulated gene knockout, molecular docking, and molecular dynamics to suggest genistein binds SLC6A14 stably, with RT-qPCR confirming high expression of the five model genes in a BxPC-3 versus HPDE6-C7 cell pair.
Integrating plasma metabolomics, Mendelian randomization, and machine learning, this study identified phenylalanine as causally associated with pancreatic cancer among 55 plasma metabolites (IVW OR = 1.641, 95% CI 1.052–2.562, p = 0.029), derived eight related differentially expressed genes, built a random forest diagnostic model from 113 combinations of 12 algorithms (training AUC 0.994; validation AUCs 0.918, 0.983, 0.923), used SHAP to rank SLC6A14 as the top feature, and combined single-cell sequencing, simulated gene knockout, molecular docking, and molecular dynamics to suggest genistein binds SLC6A14 stably, with RT-qPCR confirming high expression of the five model genes in a BxPC-3 versus HPDE6-C7 cell pair.
Rapid Communications in Mass Spectrometry Using PTR-ToF-MS headspace volatile fingerprints plus HPLC- and amino-acid-analysis-quantified non-volatile metabolites, this study built a two-stream Transformer that encodes each modality separately and fuses them for four-class grading of Baimudan white tea, reaching 95.8% accuracy (23/24) and a 0.958 macro-F1 on an independent prediction set drawn from 120 samples (30 per grade; 96 training, 24 prediction), with a single Special-grade sample misclassified as Grade I, mean cross-validated accuracy of 0.979±0.026 within the training set, and SHAP/attention analyses linking high grades to floral/sweet volatile ions plus higher amino acids and soluble sugars and lower grades to greener/woody volatile ions and kaempferol-related markers.
Using PTR-ToF-MS headspace volatile fingerprints plus HPLC- and amino-acid-analysis-quantified non-volatile metabolites, this study built a two-stream Transformer that encodes each modality separately and fuses them for four-class grading of Baimudan white tea, reaching 95.8% accuracy (23/24) and a 0.958 macro-F1 on an independent prediction set drawn from 120 samples (30 per grade; 96 training, 24 prediction), with a single Special-grade sample misclassified as Grade I, mean cross-validated accuracy of 0.979±0.026 within the training set, and SHAP/attention analyses linking high grades to floral/sweet volatile ions plus higher amino acids and soluble sugars and lower grades to greener/woody volatile ions and kaempferol-related markers.
Using PTR-ToF-MS headspace volatile fingerprints plus HPLC- and amino-acid-analysis-quantified non-volatile metabolites, this study built a two-stream Transformer that encodes each modality separately and fuses them for four-class grading of Baimudan white tea, reaching 95.8% accuracy (23/24) and a 0.958 macro-F1 on an independent prediction set drawn from 120 samples (30 per grade; 96 training, 24 prediction), with a single Special-grade sample misclassified as Grade I, mean cross-validated accuracy of 0.979±0.026 within the training set, and SHAP/attention analyses linking high grades to floral/sweet volatile ions plus higher amino acids and soluble sugars and lower grades to greener/woody volatile ions and kaempferol-related markers.
Using PTR-ToF-MS headspace volatile fingerprints plus HPLC- and amino-acid-analysis-quantified non-volatile metabolites, this study built a two-stream Transformer that encodes each modality separately and fuses them for four-class grading of Baimudan white tea, reaching 95.8% accuracy (23/24) and a 0.958 macro-F1 on an independent prediction set drawn from 120 samples (30 per grade; 96 training, 24 prediction), with a single Special-grade sample misclassified as Grade I, mean cross-validated accuracy of 0.979±0.026 within the training set, and SHAP/attention analyses linking high grades to floral/sweet volatile ions plus higher amino acids and soluble sugars and lower grades to greener/woody volatile ions and kaempferol-related markers.
Acta Scientiae This survey reviews AI, machine learning, deep learning, and generative adversarial network (GAN) approaches to autism spectrum disorder (ASD) screening, focusing on multimodal learning across behavioral, genetic, environmental, neuroimaging, physiological, and clinical data and on the role of GANs in synthetic-data generation and augmentation, concluding that multimodal AI may represent ASD-related characteristics more comprehensively than single-modality approaches while facing challenges of limited and heterogeneous datasets, class imbalance, multimodal integration, GAN training instability, synthetic-data quality, privacy and security, interpretability, generalizability, and limited external clinical validation.
This survey reviews AI, machine learning, deep learning, and generative adversarial network (GAN) approaches to autism spectrum disorder (ASD) screening, focusing on multimodal learning across behavioral, genetic, environmental, neuroimaging, physiological, and clinical data and on the role of GANs in synthetic-data generation and augmentation, concluding that multimodal AI may represent ASD-related characteristics more comprehensively than single-modality approaches while facing challenges of limited and heterogeneous datasets, class imbalance, multimodal integration, GAN training instability, synthetic-data quality, privacy and security, interpretability, generalizability, and limited external clinical validation.
This survey reviews AI, machine learning, deep learning, and generative adversarial network (GAN) approaches to autism spectrum disorder (ASD) screening, focusing on multimodal learning across behavioral, genetic, environmental, neuroimaging, physiological, and clinical data and on the role of GANs in synthetic-data generation and augmentation, concluding that multimodal AI may represent ASD-related characteristics more comprehensively than single-modality approaches while facing challenges of limited and heterogeneous datasets, class imbalance, multimodal integration, GAN training instability, synthetic-data quality, privacy and security, interpretability, generalizability, and limited external clinical validation.
This survey reviews AI, machine learning, deep learning, and generative adversarial network (GAN) approaches to autism spectrum disorder (ASD) screening, focusing on multimodal learning across behavioral, genetic, environmental, neuroimaging, physiological, and clinical data and on the role of GANs in synthetic-data generation and augmentation, concluding that multimodal AI may represent ASD-related characteristics more comprehensively than single-modality approaches while facing challenges of limited and heterogeneous datasets, class imbalance, multimodal integration, GAN training instability, synthetic-data quality, privacy and security, interpretability, generalizability, and limited external clinical validation.
Journal of Computer Science and Technology Addressing the lack of a systematic survey and in-depth analysis of the latest methodologies in high-level synthesis for approximate computing (AHLS), this survey summarizes recent technologies in the field with particular focus on error estimation, approximation techniques, and design space exploration (DSE), and analyzes current research gaps, aiming to give researchers, engineers, and scholars a theoretical and practical framework for AHLS.
Addressing the lack of a systematic survey and in-depth analysis of the latest methodologies in high-level synthesis for approximate computing (AHLS), this survey summarizes recent technologies in the field with particular focus on error estimation, approximation techniques, and design space exploration (DSE), and analyzes current research gaps, aiming to give researchers, engineers, and scholars a theoretical and practical framework for AHLS.
Addressing the lack of a systematic survey and in-depth analysis of the latest methodologies in high-level synthesis for approximate computing (AHLS), this survey summarizes recent technologies in the field with particular focus on error estimation, approximation techniques, and design space exploration (DSE), and analyzes current research gaps, aiming to give researchers, engineers, and scholars a theoretical and practical framework for AHLS.
Addressing the lack of a systematic survey and in-depth analysis of the latest methodologies in high-level synthesis for approximate computing (AHLS), this survey summarizes recent technologies in the field with particular focus on error estimation, approximation techniques, and design space exploration (DSE), and analyzes current research gaps, aiming to give researchers, engineers, and scholars a theoretical and practical framework for AHLS.
bioRxiv This study maps the in-context learning operating regime of Evo 2, a nucleotide-level foundation genomic language model, across five binary classification tasks spanning biological and artificial sequences, finding robust performance on shorter natural sequences (F1=0.902 for miRNA, 0.785 for Toxins), degradation with sequence length and collapse at kilobase scale, no benefit from model scaling (the 7B model systematically outperforms the 40B variant), poor prediction of accuracy by perplexity, and mechanistic interpretability via logit-lens and Jacobian Scope suggesting a prediction-generalisation trade-off and that models might track prompt structure rather than signal-carrying content.
This study maps the in-context learning operating regime of Evo 2, a nucleotide-level foundation genomic language model, across five binary classification tasks spanning biological and artificial sequences, finding robust performance on shorter natural sequences (F1=0.902 for miRNA, 0.785 for Toxins), degradation with sequence length and collapse at kilobase scale, no benefit from model scaling (the 7B model systematically outperforms the 40B variant), poor prediction of accuracy by perplexity, and mechanistic interpretability via logit-lens and Jacobian Scope suggesting a prediction-generalisation trade-off and that models might track prompt structure rather than signal-carrying content.
This study maps the in-context learning operating regime of Evo 2, a nucleotide-level foundation genomic language model, across five binary classification tasks spanning biological and artificial sequences, finding robust performance on shorter natural sequences (F1=0.902 for miRNA, 0.785 for Toxins), degradation with sequence length and collapse at kilobase scale, no benefit from model scaling (the 7B model systematically outperforms the 40B variant), poor prediction of accuracy by perplexity, and mechanistic interpretability via logit-lens and Jacobian Scope suggesting a prediction-generalisation trade-off and that models might track prompt structure rather than signal-carrying content.
This study maps the in-context learning operating regime of Evo 2, a nucleotide-level foundation genomic language model, across five binary classification tasks spanning biological and artificial sequences, finding robust performance on shorter natural sequences (F1=0.902 for miRNA, 0.785 for Toxins), degradation with sequence length and collapse at kilobase scale, no benefit from model scaling (the 7B model systematically outperforms the 40B variant), poor prediction of accuracy by perplexity, and mechanistic interpretability via logit-lens and Jacobian Scope suggesting a prediction-generalisation trade-off and that models might track prompt structure rather than signal-carrying content.
Statistica Sinica The authors propose a Delaunay-weighted two-sample test: under a low-dimensional manifold assumption they define a Delaunay weight from the Delaunay triangulation that captures both geodesic distance and relative direction, use the average within-group Delaunay weight as the test statistic with a permutation p-value, prove asymptotic normality under the null and consistency under the alternative, and show in simulations substantially higher power than k-NN, k-MST, kernel, e-distance, covariance, and regression tests when the two distributions differ in the principal directions of their covariance matrices, while detecting a treatment-group difference with p=0.011 in a mice protein expression dataset.
The authors propose a Delaunay-weighted two-sample test: under a low-dimensional manifold assumption they define a Delaunay weight from the Delaunay triangulation that captures both geodesic distance and relative direction, use the average within-group Delaunay weight as the test statistic with a permutation p-value, prove asymptotic normality under the null and consistency under the alternative, and show in simulations substantially higher power than k-NN, k-MST, kernel, e-distance, covariance, and regression tests when the two distributions differ in the principal directions of their covariance matrices, while detecting a treatment-group difference with p=0.011 in a mice protein expression dataset.
The authors propose a Delaunay-weighted two-sample test: under a low-dimensional manifold assumption they define a Delaunay weight from the Delaunay triangulation that captures both geodesic distance and relative direction, use the average within-group Delaunay weight as the test statistic with a permutation p-value, prove asymptotic normality under the null and consistency under the alternative, and show in simulations substantially higher power than k-NN, k-MST, kernel, e-distance, covariance, and regression tests when the two distributions differ in the principal directions of their covariance matrices, while detecting a treatment-group difference with p=0.011 in a mice protein expression dataset.
The authors propose a Delaunay-weighted two-sample test: under a low-dimensional manifold assumption they define a Delaunay weight from the Delaunay triangulation that captures both geodesic distance and relative direction, use the average within-group Delaunay weight as the test statistic with a permutation p-value, prove asymptotic normality under the null and consistency under the alternative, and show in simulations substantially higher power than k-NN, k-MST, kernel, e-distance, covariance, and regression tests when the two distributions differ in the principal directions of their covariance matrices, while detecting a treatment-group difference with p=0.011 in a mice protein expression dataset.
Natural Sciences and Applied Technology The work proposes Stability-Constrained Information-Theoretic Feature Selection (SCITFS), which integrates conditional entropy, normalized mutual information maximization, and a stability term penalizing feature-ranking variance across bootstrap samples into a single formally defined objective, implemented via a greedy forward-selection strategy with proven monotonicity guarantees at O(B n^2 m d^4 + k n^2); across eight benchmark datasets and five classifiers (SVM, Random Forest, k-NN, XGBoost, Logistic Regression), SCITFS outperforms Information Gain, Mutual Information, mRMR, ReliefF, Fisher Score, and JMI, achieving a 3.7% average accuracy improvement on SVM and 4.2% on Random Forest with 92.3% feature reduction while maintaining performance; Friedman test (χ² = 127.4, p < 0.
The work proposes Stability-Constrained Information-Theoretic Feature Selection (SCITFS), which integrates conditional entropy, normalized mutual information maximization, and a stability term penalizing feature-ranking variance across bootstrap samples into a single formally defined objective, implemented via a greedy forward-selection strategy with proven monotonicity guarantees at O(B n^2 m d^4 + k n^2); across eight benchmark datasets and five classifiers (SVM, Random Forest, k-NN, XGBoost, Logistic Regression), SCITFS outperforms Information Gain, Mutual Information, mRMR, ReliefF, Fisher Score, and JMI, achieving a 3.7% average accuracy improvement on SVM and 4.2% on Random Forest with 92.3% feature reduction while maintaining performance; Friedman test (χ² = 127.4, p < 0.
The work proposes Stability-Constrained Information-Theoretic Feature Selection (SCITFS), which integrates conditional entropy, normalized mutual information maximization, and a stability term penalizing feature-ranking variance across bootstrap samples into a single formally defined objective, implemented via a greedy forward-selection strategy with proven monotonicity guarantees at O(B n^2 m d^4 + k n^2); across eight benchmark datasets and five classifiers (SVM, Random Forest, k-NN, XGBoost, Logistic Regression), SCITFS outperforms Information Gain, Mutual Information, mRMR, ReliefF, Fisher Score, and JMI, achieving a 3.7% average accuracy improvement on SVM and 4.2% on Random Forest with 92.3% feature reduction while maintaining performance; Friedman test (χ² = 127.4, p < 0.
The work proposes Stability-Constrained Information-Theoretic Feature Selection (SCITFS), which integrates conditional entropy, normalized mutual information maximization, and a stability term penalizing feature-ranking variance across bootstrap samples into a single formally defined objective, implemented via a greedy forward-selection strategy with proven monotonicity guarantees at O(B n^2 m d^4 + k n^2); across eight benchmark datasets and five classifiers (SVM, Random Forest, k-NN, XGBoost, Logistic Regression), SCITFS outperforms Information Gain, Mutual Information, mRMR, ReliefF, Fisher Score, and JMI, achieving a 3.7% average accuracy improvement on SVM and 4.2% on Random Forest with 92.3% feature reduction while maintaining performance; Friedman test (χ² = 127.4, p < 0.