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UNLABELLED: Small sample sizes and loss of sequencing reads during the microbiome data preprocessing can limit the statistical power of differentiating fresh produce phenotypes and prevent the detection of important bacterial species associated with produce contamination or quality reduction. Here, we explored a machine learning-based k-mer hash analysis strategy to identify DNA signatures predictive of produce safety (PS) and produce quality (PQ) and compared it against the amplicon sequence variant (ASV) strategy that uses a typical denoising step and ASV-based taxonomy strategy. Random forest-based classifiers for PS and PQ using 7-mer hash data sets had significantly higher classification accuracy than those using the ASV data sets. We also demonstrated that the proposed combination of integrating multiple data sets and leveraging a 7-mer hash strategy leads to better classification performance for PS and PQ compared to the ASV method but presents lower PS classification accuracy compared to the feature-selected ASV-based taxonomy strategy. Due to the current limitation of generating taxonomy using the 7-mer hash strategy, the ASV-based taxonomy strategy with remarkably less computing time and memory usage is more efficient for PS and PQ classification and applicable for important taxa identification. Results generated from this study lay the foundation for future studies that wish and need to incorporate and/or compare different microbiome sequencing data sets for the application of machine learning in the area of microbial safety and quality of food. IMPORTANCE: Identification of generalizable indicators for produce safety (PS) and produce quality (PQ) improves the detection of produce contamination and quality decline. However, effective sequencing read loss during microbiome data preprocessing and the limited sample size of individual studies restrain statistical power to identify important features contributing to differentiating PS and PQ phenotypes. We applied machine learning-based models using individual and integrated k-mer hash and amplicon sequence variant (ASV) data sets for PS and PQ classification and evaluated their classification performance and found that random forest (RF)-based models using integrated 7-mer hash data sets achieved significantly higher PS and PQ classification accuracy. Due to the limitation of taxonomic analysis for the 7-mer hash, we also developed RF-based models using feature-selected ASV-based taxonomic data sets, which performed better PS classification than those using the integrated 7-mer hash data set. The RF feature selection method identified 480 PS indicators and 263 PQ indicators with a positive contribution to the PS and PQ classification.

OBJECTIVES: The Salivary Gland Committee of the American Academy of Otolaryngology-Head and Neck Surgery seeks to standardize terminology and technique for ultrasonograpy used in the evaluation and treatment of salivary gland disorders. METHODS: Development of expert opinion obtained through interaction with international practitioners representing multiple specialties. This committee work includes a comprehensive literature review with presentation of case examples to propose a standardized protocol for the language used in ultrasound salivary gland assessment. RESULTS: A multiple segment proposal is initiated with this focus on the submandibular gland. We provide a concise rationale for recommended descriptive language highlighted by a more extensive supplement that includes an extensive literature review with additional case examples. CONCLUSION: Recommendations are provided to improve consistency both in performing and reporting submandibular gland ultrasound.

Dominant X-linked diseases are uncommon due to female X chromosome inactivation (XCI). While random XCI usually protects females against X-linked mutations, Rett syndrome (RTT) is a female neurodevelopmental disorder caused by heterozygous MECP2 mutation. After 6-18 months of typical neurodevelopment, RTT girls undergo a poorly understood regression. We performed longitudinal snRNA-seq on cerebral cortex in a construct-relevant Mecp2e1 mutant mouse model of RTT, revealing transcriptional effects of cell type, mosaicism, and sex on progressive disease phenotypes. Across cell types, we observed sex differences in the number of differentially expressed genes (DEGs) with 6x more DEGs in mutant females than males. Unlike males, female DEGs emerged prior to symptoms, were enriched for homeostatic gene pathways in distinct cell types over time and correlated with disease phenotypes and human RTT cortical cell transcriptomes. Non-cell-autonomous effects were prominent and dynamic across disease progression of Mecp2e1 mutant females, indicating that wild-type-expressing cells normalize transcriptional homeostasis. These results advance our understanding of RTT progression and treatment.

Abstract Multimodal single-cell assays profile multiple sets of features in the same cells and are widely used for identifying and mapping cell states between chromatin and mRNA and linking regulatory elements to target genes. However, the high dimensionality of input features and shallow sequencing depth compared to unimodal assays pose challenges in data analysis. Here we present scPair, a multimodal single-cell data framework that overcomes these challenges by employing an implicit feature selection approach. scPair uses dual encoder-decoder structures trained on paired data to align cell states across modalities and predict features from one modality to another. We demonstrate that scPair outperforms existing methods in accuracy and execution time, and facilitates downstream tasks such as trajectory inference. We further show scPair can augment smaller multimodal datasets with larger unimodal atlases to increase statistical power to identify groups of transcription factors active during different stages of neural differentiation.

Abstract Dominant X-linked diseases are uncommon due to female X chromosome inactivation (XCI). While random XCI usually protects females against X-linked mutations, Rett syndrome (RTT) is a female neurodevelopmental disorder caused by heterozygous MECP2 mutation. After 6-18 months of typical neurodevelopment, RTT girls undergo a poorly understood regression. We performed longitudinal snRNA-seq on cerebral cortex in a construct-relevant Mecp2e1 mutant mouse model of RTT, revealing transcriptional effects of cell type, mosaicism, and sex on progressive disease phenotypes. Across cell types, we observed sex differences in the number of differentially expressed genes (DEGs) with 6x more DEGs in mutant females than males. Unlike males, female DEGs emerged prior to symptoms, were enriched for homeostatic gene pathways in distinct cell types over time and correlated with disease phenotypes and human RTT cortical cell transcriptomes. Non-cell-autonomous effects were prominent and dynamic across disease progression of Mecp2e1 mutant females, indicating that wild-type-expressing cells normalize transcriptional homeostasis. These results advance our understanding of RTT progression and treatment.

BACKGROUND: The obesity epidemic brought a need for accessible methods to monitor body composition, as excess adiposity has been associated with cardiovascular disease, metabolic disorders, and some cancers. Recent 3-dimensional optical (3DO) imaging advancements have provided opportunities for assessing body composition. However, the accuracy and precision of an overall 3DO body composition model in specific subgroups are unknown. OBJECTIVES: This study aimed to evaluate 3DOs accuracy and precision by subgroups of age, body mass index, and ethnicity. METHODS: A cross-sectional analysis was performed using data from the Shape Up! Adults study. Each participant received duplicate 3DO and dual-energy X-ray absorptiometry (DXA) scans. 3DO meshes were digitally registered and reposed using Meshcapade. Principal component analysis was performed on 3DO meshes. The resulting principal components estimated DXA whole-body and regional body composition using stepwise forward linear regression with 5-fold cross-validation. Duplicate 3DO and DXA scans were used for test-retest precision. Students t tests were performed between 3DO and DXA by subgroup to determine significant differences. RESULTS: Six hundred thirty-four participants (females = 346) had completed the study at the time of the analysis. 3DO total fat mass in the entire sample achieved R2 of 0.94 with root mean squared error (RMSE) of 2.91 kg compared to DXA in females and similarly in males. 3DO total fat mass achieved a % coefficient of variation (RMSE) of 1.76% (0.44 kg), whereas DXA was 0.98% (0.24 kg) in females and similarly in males. There were no mean differences for total fat, fat-free, percent fat, or visceral adipose tissue by age group (P > 0.068). However, there were mean differences for underweight, Asian, and Black females as well as Native Hawaiian or other Pacific Islanders (P < 0.038). CONCLUSIONS: A single 3DO body composition model produced accurate and precise body composition estimates that can be used on diverse populations. However, adjustments to specific subgroups may be warranted to improve the accuracy in those that had significant differences. This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults).

The wind turbine design process requires performing thousands of simulations for a wide range of inflow and control conditions, which necessitates computationally efficient yet time-accurate models, especially when considering wind farm settings. To this end, FAST.Farm is a dynamic-wake-meandering-based mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. This work is an extension of a study that addressed constructing a diurnal cycle evolution based on experimental data (Quon, 2024). Here, this inflow is used to validate the turbine structural and wake-meandering response between experimental data, FAST.Farm simulation results, and high-fidelity large-eddy simulation results from the coupled Simulator fOr Wind Farm Applications (SOWFA)–OpenFAST tool. The validation occurs within the nocturnal stable boundary layer when corresponding meteorological and turbine data are available. To this end, we compared the load results from FAST.Farm and SOWFA–OpenFAST to multi-turbine measurements from a subset of a full-scale wind farm. Computational predictions of blade-root and tower-base bending loads are compared to 10 min statistics of strain gauge measurements during 3.5 h of the evolving stable boundary layer, generally with good agreement. This time period coincided with an active wake-steering campaign of an upstream turbine, resulting in time-varying yaw positions of all turbines. Wake meandering was also compared between the computational solutions, generally with excellent agreement. Simulations were based on a high-fidelity precursor constructed from inflow measurements and using state-of-the-art mesoscale-to-microscale coupling.

Abstract Background Prenatal hypoxia, a common pregnancy complication, leads to impaired cardiovascular outcomes in the adult offspring. It results in impaired vasodilation in coronary and mesenteric arteries of the adult offspring, due to reduced nitric oxide (NO). Thromboxane A2 (TxA2) is a potent vasoconstrictor increased in cardiovascular diseases, but its role in the impact of prenatal hypoxia is unknown. To prevent the risk of cardiovascular disease by prenatal hypoxia, we have tested a maternal treatment using a nanoparticle-encapsulated mitochondrial antioxidant (nMitoQ). We hypothesized that prenatal hypoxia enhances vascular TxA2 responses in the adult offspring, due to decreased NO modulation, and that this might be prevented by maternal nMitoQ treatment. Methods Pregnant Sprague–Dawley rats received a single intravenous injection (100 µL) of vehicle (saline) or nMitoQ (125 µmol/L) on gestational day (GD)15 and were exposed to normoxia (21% O2) or hypoxia (11% O2) from GD15 to GD21 (term = 22 days). Coronary and mesenteric arteries were isolated from the 4-month-old female and male offspring, and vasoconstriction responses to U46619 (TxA2 analog) were evaluated using wire myography. In mesenteric arteries, L-NAME (pan-NO synthase (NOS) inhibitor) was used to assess NO modulation. Mesenteric artery endothelial (e)NOS, and TxA2 receptor expression, superoxide, and 3-nitrotyrosine levels were assessed by immunofluorescence. Results Prenatal hypoxia resulted in increased U46619 responsiveness in coronary and mesenteric arteries of the female offspring, and to a lesser extent in the male offspring, which was prevented by nMitoQ. In females, there was a reduced impact of L-NAME in mesenteric arteries of the prenatal hypoxia saline-treated females, and reduced 3-nitrotyrosine levels. In males, L-NAME increased U46619 responses in mesenteric artery to a similar extent, but TxA2 receptor expression was increased by prenatal hypoxia. There were no changes in eNOS or superoxide levels. Conclusions Prenatal hypoxia increased TxA2 vasoconstrictor capacity in the adult offspring in a sex-specific manner, via reduced NO modulation in females and increased TP expression in males. Maternal placental antioxidant treatment prevented the impact of prenatal hypoxia. These findings increase our understanding of how complicated pregnancies can lead to a sex difference in the programming of cardiovascular disease in the adult offspring. Graphical Abstract

IntroductionThis is a prospective, rigorous inquiry into the systemic immune effects of standard adjuvant chemoradiotherapy, for WHO grade 4, glioblastoma. The purpose is to identify peripheral immunologic effects never yet reported in key immune populations, including myeloid-derived suppressor cells, which are critical to the immune suppressive environment of glioblastoma. We hypothesize that harmful immune-supportive white blood cells, myeloid derived suppressor cells, expand in response to conventionally fractionated radiotherapy with concurrent temozolomide, essentially promoting systemic immunity similar what is seen in chronic diseases like diabetes and heart disease.Methods16 patients were enrolled in a single-institution, observational, immune surveillance study where peripheral blood was collected and interrogated by flow cytometry and RNAseq. Tumor tissue from baseline assessment was analyzed with spatial proteomics to link peripheral blood findings to baseline tissue characteristics.ResultsWe identified an increase in myeloid-derived suppressor cells during the final week of a six-week treatment of chemoradiotherapy in peripheral blood of patients that were not alive at two years after diagnosis compared to those who were living. This was also associated with a decrease in CD8+ T lymphocytes that produced IFNγ, the potent anti-tumor cytokine.DiscussionThese data suggest that, as in chronic inflammatory disease, systemic immunity is impaired following delivery of adjuvant chemoradiotherapy. Finally, baseline investigation of myeloid cells within tumor tissue did not differ between survival groups, indicating immune surveillance of peripheral blood during adjuvant therapy may be a critical missing link to educate our understanding of the immune effects of standard of care therapy for glioblastoma.

After resolution of infection, T cells differentiate into long-lived memory cells that recirculate through secondary lymphoid organs or establish residence in tissues. In contrast to CD8+ tissue-resident memory T cells (TRM), the developmental origins and transcriptional regulation of CD4+ TRM remain largely undefined. Here, we investigated the phenotypic, functional, and transcriptional profiles of CD4+ TRM in the small intestine (SI) responding to acute viral infection, revealing a shared gene expression program and chromatin accessibility profile with circulating TH1 and the progressive acquisition of a mature TRM program. Single-cell RNA sequencing identified heterogeneity among established CD4+ TRM, which were predominantly located in the lamina propria, and revealed a population of cells that coexpressed both effector- and memory-associated genes, including the transcriptional regulators Blimp1, Id2, and Bcl6. TH1-associated Blimp1 and Id2 and TFH-associated Bcl6 were required for early TRM formation and development of a mature TRM population in the SI. These results demonstrate a developmental relationship between TH1 effector cells and the establishment of early TRM, as well as highlighted differences in CD4+ versus CD8+ TRM populations, providing insights into the mechanisms underlying the origins, differentiation, and persistence of CD4+ TRM in response to viral infection.

Although the importance of genome organization for transcriptional regulation of cell-fate decisions and function is clear, the changes in chromatin architecture and how these impact effector and memory CD8+ T cell differentiation remain unknown. Using Hi-C, we studied how genome configuration is integrated with CD8+ T cell differentiation during infection and investigated the role of CTCF, a key chromatin remodeler, in modulating CD8+ T cell fates through CTCF knockdown approaches and perturbation of specific CTCF-binding sites. We observed subset-specific changes in chromatin organization and CTCF binding and revealed that weak-affinity CTCF binding promotes terminal differentiation of CD8+ T cells through the regulation of transcriptional programs. Further, patients with de novo CTCF mutations had reduced expression of the terminal-effector genes in peripheral blood lymphocytes. Therefore, in addition to establishing genome architecture, CTCF regulates effector CD8+ T cell heterogeneity through altering interactions that regulate the transcription factor landscape and transcriptome.

Collective cell behavior contributes to all stages of cancer progression. Understanding how collective behavior emerges through cell-cell interactions and decision-making will advance our understanding of cancer biology and provide new therapeutic approaches. Here, we summarize an interdisciplinary discussion on multicellular behavior in cancer, draw lessons from other scientific disciplines, and identify future directions.

BackgroundDecades of research have transformed hemophilia from severely limiting children's lives to a manageable disorder compatible with a full, active life, for many in high-income countries. The direction of future research will determine whether exciting developments truly advance health equity for all people with hemophilia (PWH). National Hemophilia Foundation (NHF) and American Thrombosis and Hemostasis Network conducted extensive inclusive all-stakeholder consultations to identify the priorities of people with inherited bleeding disorders and those who care for them.Research design and methodsWorking group (WG) 1 of the NHF State of the Science Research Summit distilled the community-identified priorities for hemophilia A and B into concrete research questions and scored their feasibility, impact, and risk.ResultsWG1 defined 63 top priority research questions concerning arthropathy/pain/bone health, inhibitors, diagnostics, gene therapy, the pediatric to adult transition of care, disparities faced by the community, and cardiovascular disease. This research has the potential to empower PWH to thrive despite lifelong comorbidities and achieve new standards of wellbeing, including psychosocial.ConclusionsCollaborative research and care delivery will be key to capitalizing on current and horizon treatments and harnessing technical advances to improve diagnostics and testing, to advance health equity for all PWH.

Abstract Background Spatially explicit simulation models of animal movements through the atmosphere necessarily require a representation of the spatial and temporal variation of atmospheric conditions. In particular, for movements of soaring birds that rely extensively on vertical updrafts to avoid flapping flight, accurate and reliable estimation of the vertical component of wind is critical. The interaction between wind and complex terrain shapes both the horizontal and vertical wind fields, highlighting the need to model the coupling between local terrain features and atmospheric conditions at scales relevant to animal movement. Methods In this work, we propose a new empirical model for estimating the orographic updraft field. The model is developed using computational fluid dynamics simulations of canonical atmospheric conditions over moderately complex terrain. To isolate buoyancy and thermal effects, and focus on terrain-induced effects, we use only simulations of a neutrally stratified atmosphere to develop the model. The model, which we name Engineering Vertical Velocity Estimator (EVVE), is simple to implement and is a function of the underlying terrain elevation map, the desired height above ground level (AGL), and wind conditions at a reference height (80 m). We validate the model with data from the Alaiz mountain (Spain) field campaign. Results Compared to observations, the proposed improved model estimates the updrafts at 120 m AGL with a mean error of 0.11 m/s ( $$\sigma =0.28$$ σ = 0.28 m/s), compared to 0.85 m/s ( $$\sigma =0.58$$ σ = 0.58 m/s) for its baseline. For typical land-based wind turbine hub heights of 80 m AGL, the proposed model has a mean error of 0.04 m/s ( $$\sigma =0.25$$ σ = 0.25 m/s), compared to baseline 0.54 m/s ( $$\sigma =0.45$$ σ = 0.45 m/s) estimations. We illustrate an application of the model in movement ecology by comparing simulated tracks and presence maps of golden eagles (Aquila chrysaetos) moving across two distinct landscapes. The tracks and presence maps are obtained using a simple heuristic-based movement model, with the updraft field given by the proposed model and a wind vector-based estimation approach that is currently in wide use in movement ecology studies of raptors and other soaring birds. Conclusions We highlight that movement model results can be sensitive to the underlying orographic updraft model, especially in studies of fine-scale movements in regions of complex topography. We suggest adopting the proposed model rather than the wind vector estimation method for studies of soaring bird movements.

High-fidelity flow modeling with data assimilation enables accurate representation of the wind farm operating environment under realistic, nonstationary atmospheric conditions. Capturing the temporal evolution of the turbulent atmospheric boundary layer is critical to understanding the behavior of wind turbines under operating conditions with simultaneously varying inflow and control inputs. This paper has three parts: the identification of a case study during a field evaluation of wake steering; the development of a tailored mesoscale-to-microscale coupling strategy that resolved local flow conditions within a large-eddy simulation (LES), using observations that did not completely capture the wind and temperature fields throughout the simulation domain; and the application of this coupling strategy to validate high-fidelity aeroelastic predictions of turbine performance and wake interactions with and without wake steering. The case study spans 4.5 h after midnight local time, during which wake steering was toggled on and off five times, achieving yaw offset angles ranging from 0 to 17°. To resolve nonstationary nighttime conditions that exhibited shear instabilities, the turbulence field was evolved starting from the diurnal cycle of the previous day. These background conditions were then used to drive wind farm simulations with two different models: an LES with actuator disk turbines and a steady-state engineering wake model. Subsequent analysis identified two representative periods during which the up- and downstream turbines were most nearly aligned with the mean wind direction and had observed yaw offsets of 0 and 15°. Both periods corresponded to partial waking on the downstream turbine, which had errors in the LES-predicted power of 4 % and 6 %, with and without wake steering. The LES was also able to capture conditions during which an upstream turbine wake induced a speedup at a downstream turbine and increased power production by up to 13 %.

Background: Children from racial and ethnic minority groups have higher prevalence of perforated appendicitis, and the COVID-19 pandemic worsened racial and ethnic health-related disparities. We hypothesized that the incidence of perforated appendicitis worsened for children from racial and ethnic minorities during the COVID-19 pandemic. Methods: We performed a retrospective cohort study of the Pediatric Health Information System for children ages 2-18y undergoing appendectomy pre-pandemic (3/19/2019–3/18/2020) and intra-pandemic (3/19/2020–3/30/2021). The primary outcome was presentation with perforated appendicitis. Multivariable logistic regression with mixed effects estimated the likelihood of presentation with perforated appendicitis. Covariates included race, ethnicity, pandemic status, Child Opportunity Index, gender, insurance, age, and hospital region. Results: Overall, 33,727 children underwent appendectomy: 16,048 (47.6 %) were Non-Hispanic White, 12,709 (37.7 %) were Hispanic, 2261 (6.7 %) were Non-Hispanic Black, 960 (2.8 %) were Asian, and 1749 (5.2 %) Other. Overall perforated appendicitis rates were unchanged during the pandemic (37.4 % intra-pandemic, 36.4 % pre-pandemic, p = 0.06). Hispanic children were more likely to present with perforated appendicitis intra-pandemic versus pre-pandemic (OR 1.18, 95%CI: 1.07, 1.13). Hispanic children had higher odds of perforated appendicitis versus Non-Hispanic White children pre-pandemic (OR 1.10, 95%CI: 1.00, 1.20) which increased intra-pandemic (OR 1.19, 95%CI: 1.09, 1.30). Publicly-insured children had increased odds of perforated appendicitis intra-pandemic versus pre-pandemic (OR 1.14, 95%CI: 1.03, 1.25), and had increased odds of perforated appendicitis versus privately-insured children (intra-pandemic OR 1.26, 95%CI: 1.16, 1.36; pre-pandemic OR 1.12, 95%CI: 1.04, 1.22). Conclusions: During the COVID-19 pandemic, Hispanic and publicly-insured children were more likely to present with perforated appendicitis, suggesting that the pandemic exacerbated existing disparities in healthcare for children with appendicitis. Key message: We found that Hispanic children and children with public insurance were more likely to present with perforated appendicitis during the COVID-19 pandemic. Public health efforts aimed at ameliorating racial and ethnic disparities created during the COVID-19 pandemic should consider increasing healthcare access for Hispanic children to address bias, racism, and systemic barriers that may prevent families from seeking care.

Multi-modal single cell RNA assays capture RNA content as well as other data modalities, such as spatial cell position or the electrophysiological properties of cells. Compared to dedicated scRNA-seq assays however, they may unintentionally capture RNA from multiple adjacent cells, exhibit lower RNA sequencing depth compared to scRNA-seq, or lack genome-wide RNA measurements. We present scProjection, a method for mapping individual multi-modal RNA measurements to deeply sequenced scRNA-seq atlases to extract cell type-specific, single cell gene expression profiles. We demonstrate several use cases of scProjection, including identifying spatial motifs from spatial transcriptome assays, distinguishing RNA contributions from neighboring cells in both spatial and multi-modal single cell assays, and imputing expression measurements of un-measured genes from gene markers. scProjection therefore combines the advantages of both multi-modal and scRNA-seq assays to yield precise multi-modal measurements of single cells.

Background: Acute appendicitis (AA) is the most common abdominal surgical emergency in children and adolescents. In the year immediately following the declaration of the coronavirus disease 2019 (COVID-19) pandemic by the World Health Organization (WHO), there was a precipitous decline in emergency department (ED) visits especially for surgical conditions and infectious diseases. Fear of exposure to severe acute respiratory coronavirus 2 infection resulted in delay in presentation and time to surgery, and a shift toward more conservative management.Objective: Our goal was to compare the incidence and severity of AA before and during the COVID-19 pandemic.Methods: Patients aged 2–18 years admitted with the diagnosis of AA to Flushing Hospital Medical Center or Jamaica Hospital Medical Center in Queens, New York, were selected for chart review. Data extracted from electronic health records included demographics, clinical findings, imaging studies, and operative and pathological findings. We calculated the Alvarado score (AS) for incidence and the American Association for the Surgery of Trauma (AAST) grade for severity. We compared patients admitted between March 1, 2018–February 29, 2020 (pre-pandemic) to patients admitted between March 1, 2020–February 28, 2021 (pandemic). We then compared pre-pandemic and pandemic groups to determine differences in pediatric AA incidence and severity.Results: Of 239 patients diagnosed with AA, 184 (77%) were in the pre-pandemic group and 55 (23%) in the pandemic group. Incidence (number per year) of AA declined by 40%. The pandemic group had significantly greater overall AS of ≥7, indicating increased likelihood to require surgery, (P = 0.04) and higher AAST grade demonstrating increased severity (P = 0.02).Conclusion: There was a decline in the number of AA cases seen in our pediatric EDs and admitted during the first year of the pandemic. Clinicians need to be aware of increased severity of AA at time of presentation during public health emergencies such as a pandemic, possibly due to modified patient behavior.

Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that is interpretable by design, thereby enhancing downstream analysis tasks. Through interpretation, siVAE also identifies gene modules and hubs without explicit gene network inference. We use siVAE to identify gene modules whose connectivity is associated with diverse phenotypes such as iPSC neuronal differentiation efficiency and dementia, showcasing the wide applicability of interpretable generative models for genomic data analysis.

BackgroundThis Phase 1/2 study is based on the hypothesis that immune checkpoint inhibitors are more effective when given earlier in the course of the disease for advanced soft tissue sarcoma.MethodsPhase I endpoints-maximum tolerated dose in previously treated patients; Phase II endpoints-best response, progression free survival and overall survival and incidence of adverse events in previously untreated patients; Phase I treatments-escalating doses of trabectedin (1.0, 1.2, 1.5 mg/m2) as continuous intravenous infusion over 24 h every 3 weeks, 1 mg/kg of ipilimumab given intravenously every 12 weeks, and 3 mg/kg of nivolumab given intravenously every 2 weeks; Phase II treatments-maximum tolerated dose of trabectedin and defined doses of ipilimumab and nivolumab.ResultsPhase I (n = 9)-the maximum tolerated dose of trabectedin was 1.2 mg/m2; Phase II (n = 79)-6 complete responses, 14 partial responses, 49 stable disease, 25.3% best response rate, 87.3% disease control rate; median progression-free survival, 6.7 months (CI 95%: 4.4-7.9), median overall survival, 24.6 months (CI 95%: 17.0-.); Grade 3/4 therapy-related adverse events (n = 92)-increased ALT (25%), fatigue (8.7%), increased AST (8.7%), decreased neutrophil count (5.4%) and anemia (4.6%).ConclusionSAINT is a safe and effective first-line treatment for advanced soft tissue sarcoma.

Background Cost of illness studies are important tools to summarise the burden of disease for individuals, the healthcare system and society. The lack of standardised methods for reporting costs for cystic fibrosis (CF) makes it difficult to quantify the total socioeconomic burden. In this study, we aimed to comprehensively report the socioeconomic burden of CF in Canada.Methods The total cost of CF in Canada was calculated by triangulating information from three sources (Canadian CF Registry, customised Burden of Disease survey and publicly available information). A prevalence-based, bottom-up, human capital approach was applied, and costs were categorised into four perspectives (ie, healthcare system, individual/caregiver, variable (ie, medicines) and society) and three domains (ie, direct, indirect and intangible). All costs were converted into 2021 Canadian dollars (CAD) and adjusted for inflation. The cost of cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapies was excluded.Results The total socioeconomic burden of CF in Canada in 2021 across the four perspectives was $C414 million. Direct costs accounted for two-thirds of the total costs, with medications comprising half of all direct costs. Out-of-pocket costs to individuals and caregivers represented 18.7% of all direct costs. Indirect costs representing absenteeism accounted for one-third of the total cost.Conclusion This comprehensive cost of illness study for CF represents a community-oriented approach describing the socioeconomic burden of living with CF and serves as a benchmark for future studies.

Summary: Transforming growth factor β (TGF-β) represents a well-established signal required for tissue-resident memory T cell (TRM) formation at intestinal surfaces, regulating the expression of a large collection of genes coordinately promoting intestinal TRM differentiation. The functional contribution from each TGF-β-controlled transcription factor is not entirely known. Here, we find that TGF-β-induced T-bet downregulation and Hic1 induction represent two critical events during intestinal TRM differentiation. Importantly, T-bet deficiency significantly rescues intestinal TRM formation in the absence of the TGF-β receptor. Hic1 induction further strengthens TRM maturation in the absence of TGF-β and T-bet. Our results reveal that provision of certain TGF-β-induced molecular events can partially replace TGF-β signaling to promote the establishment of intestinal TRMs, which allows the functional dissection of TGF-β-induced transcriptional targets and molecular mechanisms for TRM differentiation.

Abstract Background Body shape, an intuitive health indicator, is deterministically driven by body composition. We developed and validated a deep learning model that generates accurate dual-energy X-ray absorptiometry (DXA) scans from three-dimensional optical body scans (3DO), enabling compositional analysis of the whole body and specified subregions. Previous works on generative medical imaging models lack quantitative validation and only report quality metrics. Methods Our model was self-supervised pretrained on two large clinical DXA datasets and fine-tuned using the Shape Up! Adults study dataset. Model-predicted scans from a holdout test set were evaluated using clinical commercial DXA software for compositional accuracy. Results Predicted DXA scans achieve R 2 of 0.73, 0.89, and 0.99 and RMSEs of 5.32, 6.56, and 4.15 kg for total fat mass (FM), fat-free mass (FFM), and total mass, respectively. Custom subregion analysis results in R 2s of 0.70–0.89 for left and right thigh composition. We demonstrate the ability of models to produce quantitatively accurate visualizations of soft tissue and bone, confirming a strong relationship between body shape and composition. Conclusions This work highlights the potential of generative models in medical imaging and reinforces the importance of quantitative validation for assessing their clinical utility.

OBJECTIVE: This study examined whether body shape and composition obtained by three-dimensional optical (3DO) scanning improved the prediction of metabolic syndrome (MetS) prevalence compared with BMI and demographics. METHODS: A diverse ambulatory adult population underwent whole-body 3DO scanning, blood tests, manual anthropometrics, and blood pressure assessment in the Shape Up! Adults study. MetS prevalence was evaluated based on 2005 National Cholesterol Education Program criteria, and prediction of MetS involved logistic regression to assess (1) BMI, (2) demographics-adjusted BMI, (3) 85 3DO anthropometry and body composition measures, and (4) BMI + 3DO + demographics models. Receiver operating characteristic area under the curve (AUC) values were generated for each predictive model. RESULTS: A total of 501 participants (280 female) were recruited, with 87 meeting the criteria for MetS. Compared with the BMI model (AUC = 0.819), inclusion of age, sex, and race increased the AUC to 0.861, and inclusion of 3DO measures further increased the AUC to 0.917. The overall integrated discrimination improvement between the 3DO + demographics and the BMI model was 0.290 (p