Miracle-dz/UniFAR-FTUs huggingface dataset:
Maternal Hyperglycemia Induces Autonomic Dysfunction and Heart Failure in Older Adult Offspring

Abstract Aims Offspring exposed to an adverse fetal environment, such as gestational diabetes, may manifest increased susceptibility to several chronic diseases later in life. In the present study, the cardiovascular function of three different ages of offspring from diabetic rats was evaluated. Methods and results Diabetes mellitus was induced in pregnant rats by a single dose of streptozotocin (50 mg/kg). The offspring from diabetic (OD) and control rats (OC) were evaluated at three different ages: 6, 12 or 18 months. In the corresponding OC groups, fasting glycemia, baseline mean arterial pressure, and sympathetic tonus increased in the OD rats at 12 (OD12) and 18 (OD18) months of age, while cardiac hypertrophy was observed in all OD groups. Cardiac function evaluation in vivo showed low left ventricular systolic pressure and+dP/dt in the OD18 rats, suggesting a systolic dysfunction. OD12 and OD18 groups showed high left ventricle end-diastolic pressure, suggesting a diastolic dysfunction. OD groups showed an age-related impairment of both baroreflex-mediated tachycardia and baroreflex-mediated bradycardia in OD12 and OD18 rats. In isolated hearts from OD18 rats, both inotropic and tachycardiac responses to increasing isoproterenol were significantly reduced compared to the corresponding OC group. Conclusion These results suggest that gestational diabetes triggers the onset of hyperglycemia hypertension with impaired baroreflex sensitivity and heart failure in older age of offspring, representing important risk factors for death. Therefore, ensuring optimal glycemic control in diabetic pregnancy is important and serves as a key to preventing cardiovascular disease in the offspring in their older age.
GAMMA: Graspability-Aware Mobile MAnipulation Policy Learning based on Online Grasping Pose Fusion

Mobile manipulation constitutes a fundamental task for robotic assistants and garners significant attention within the robotics community. A critical challenge inherent in mobile manipulation is the effective observation of the target while approaching it for grasping. In this work, we propose a graspability-aware mobile manipulation approach powered by an online grasping pose fusion framework that enables a temporally consistent grasping observation. Specifically, the predicted grasping poses are online organized to eliminate the redundant, outlier grasping poses, which can be encoded as a grasping pose observation state for reinforcement learning. Moreover, on-the-fly fusing the grasping poses enables a direct assessment of graspability, encompassing both the quantity and quality of grasping poses.
Management Tools for Prescribed Burning for Tallgrass Prairie Restoration at the Lewisville Lake Environmental Learning Area

The Lewisville Lake Environmental Learning Area (LLELA) is a wildlife management area with tallgrass prairie, an endangered ecosystem. Essential ecosystem processes, especially fire, are part of restoration. To support fire management efforts at LLELA and surrounding areas, this project evaluated and developed tools for fire restoration. The four primary prairie grasses respond favorably to burning. Fuel loads and fuel models vary by scale and survey method. One- and 10-hour fuel moisture can be predicted using a statistical model; 100- and 1,000-hour fuel moisture cannot. Historic weather data suggests that burning can occur when it is most effective. The production of ozone precursors produced by burning is comparable to those emitted every six minutes by regional automobiles.
Exciton-Polariton Condensates: A Fourier Neural Operator Approach

Advancements in semiconductor fabrication over the past decade have catalyzed extensive research into all-optical devices driven by exciton-polariton condensates. Preliminary validations of such devices, including transistors, have shown encouraging results even under ambient conditions. A significant challenge still remains for large scale application however: the lack of a robust solver that can be used to simulate complex nonlinear systems which require an extended period of time to stabilize. Addressing this need, we propose the application of a machine-learning-based Fourier Neural Operator approach to find the solution to the Gross-Pitaevskii equations coupled with extra exciton rate equations. This work marks the first direct application of Neural Operators to an exciton-polariton condensate system. Our findings show that the proposed method can predict final-state solutions to a high degree of accuracy almost 1000 times faster than CUDA-based GPU solvers. Moreover, this paves the way for potential all-optical chip design workflows by integrating experimental data.
LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints

Integrating first-order logic constraints (FOLCs) with neural networks is a crucial but challenging problem since it involves modeling intricate correlations to satisfy the constraints. This paper proposes a novel neural layer, LogicMP, whose layers perform mean-field variational inference over an MLN. It can be plugged into any off-the-shelf neural network to encode FOLCs while retaining modularity and efficiency. By exploiting the structure and symmetries in MLNs, we theoretically demonstrate that our well-designed, efficient mean-field iterations effectively mitigate the difficulty of MLN inference, reducing the inference from sequential calculation to a series of parallel tensor operations. Empirical results in three kinds of tasks over graphs, images, and text show that LogicMP outperforms advanced competitors in both performance and efficiency.
Predictors of stroke-associated pneumonia and the predictive value of neutrophil percentage-to-albumin ratio

ABSTRACT Background Early recognition of stroke-associated pneumonia (SAP) is critical to reducing morbidity and mortality associated with SAP. This study investigated the predictors of SAP, and the predictive value of the neutrophil percentage-to-albumin ratio (NPAR) for SAP. Methods This retrospective cohort study was conducted among stroke patients admitted to Jordan University Hospital from January 2015 to May 2021. Multivariable logistic regression was used to identify independent predictors for SAP. The predictive performance was assessed using C-statistics, described as the area under the receiver-operating characteristic curve (AUC, ROC) with a 95% confidence interval. Results Four hundred and six patients were included in the analysis, and the prevalence of SAP was 19.7%. Multivariable logistic analysis showed that males (Adjusted Odds Ratio (AOR): 5.74; 95% Confidence Interval (95%CI): 2.04–1 6.1)], dysphagia (AOR: 5.29; 95% CI: 1.80–15.5), hemiparesis (AOR: 3.27; 95% CI: 1.13–9.47), lower GCS score (AOR: 0.73; 95% CI: 0.58–0.91), higher levels of neutrophil-lymphocyte ratio (NLR) (AOR: 1.15; 95% CI: 1.07–1.24), monocyte-lymphocyte ratio (MLR) (AOR: 1.49; 95% CI: 1.13–1.96), and neutrophil percentage to albumin ratio (NPAR) (AOR: 1.53; 95% CI: 1.33–1.76) were independent predictors of SAP. The NPAR demonstrated a significantly higher AUC than both the NLR (0.939 versus 0.865, Z = 3.169, p = 0.002) and MLR (0.939 versus 0.842, Z = 3.940, p < 0.001). The AUCs of the NLR and MLR were comparable (0.865 versus 0.842, Z = 1.274, p = 0.203). Conclusion Male gender, dysphagia and hemiparesis were the strongest predictors of SAP, and NPAR has an excellent performance in predicting SAP which was better than high NLR and MLR.
The strengthening relationship between Arctic Oscillation and ENSO after the mid‐1990s

The Arctic Oscillation (AO) and ENSO have dominant influences on the extratropical and tropical climate variabilities in the Northern Hemisphere, respectively. In this paper, we document a strengthening of the AO–ENSO relationship in January after the mid‐1990s, when the interannual variability of the previous September Arctic sea ice cover (ASIC) had consistently increased, by analyzing the AO, ENSO, and associated atmosphere–ocean variability. Our analysis further suggests that the larger ASIC interannual variability may account for the larger scale wave train across the Pacific basin, and the AO is strongly coupled to the circulation in the North Pacific. The strengthened connection between the −AO and Aleutian Low (AL) may contribute to the identified closer relationship between the −AO and ENSO.
Decoding Nucleosome-Depleted Regions: Insights from Epigenetic Marks, Nucleosome Size, and Thermodynamic Modelling

Elucidating the global and local rules that govern genome-wide nucleosome organisation and chromatin architecture remains a critical challenge. Thermodynamic modelling based on DNA elastic properties predicts the presence of sequence-encoded nucleosome-inhibiting energy barriers (NIEBs) along vertebrate genomes. They delineate in vivo nucleosome-depleted regions (NDRs) flanked by 2-3 well positioned nucleosomes. Here, we compared mouse NIEBs to NDRs observed at CTCF binding sites and active TSSs to reveal specific chromatin organizations. We uncover in MNase-seq chromatin profiles the presence of particles of subnucleosomal length specifically positioned at the border of NIEBs with an enrichment of H3.3 and its modification H3.3 S31Ph, whereas the positioning of nucleosomes bearing H3K27ac appears insensitive to NIEBs. Surprisingly, post-translational modifications affect the size distribution of nucleosomes as seen by MNase digestion and so likely their breathing capability. We implemented an extension of our thermodynamic model allowing for variable particle size and suggest that subnucleomes at NIEB borders would result from the recruitment of chromatin remodellers at NIEBs. Our findings provide new insights into the mechanisms by which the DNA sequence and epigenetic marks shape the nucleosome positioning and breathing.
PATCH

Recent advancements in deep learning have shown that multimodal inference can be particularly useful in tasks like autonomous driving, human health, and production line monitoring. However, deploying state-of-the-art multimodal models in distributed IoT systems poses unique challenges since the sensor data from low-cost edge devices can get corrupted, lost, or delayed before reaching the cloud. These problems are magnified in the presence of asymmetric data generation rates from different sensor modalities, wireless network dynamics, or unpredictable sensor behavior, leading to either increased latency or degradation in inference accuracy, which could affect the normal operation of the system with severe consequences like human injury or car accident. In this paper, we propose PATCH, a framework of speculative inference to adapt to these complex scenarios. PATCH serves as a plug-in module in the existing multimodal models, and it enables speculative inference of these off-the-shelf deep learning models. PATCH consists of 1) a Masked-AutoEncoder-based cross-modality imputation module to impute missing data using partially-available sensor data, 2) a lightweight feature pair ranking module that effectively limits the searching space for the optimal imputation configuration with low computation overhead, and 3) a data alignment module that aligns multimodal heterogeneous data streams without using accurate timestamp or external synchronization mechanisms. We implement PATCH in nine popular multimodal models using five public datasets and one self-collected dataset. The experimental results show that PATCH achieves up to 13% mean accuracy improvement over the state-of-art method while only using 10% of training data and reducing the training overhead by 73% compared to the original cost of retraining the model.
PulmoListener

Prior work has shown the utility of acoustic analysis in controlled settings for assessing chronic obstructive pulmonary disease (COPD) --- one of the most common respiratory diseases that impacts millions of people worldwide. However, such assessments require active user input and may not represent the true characteristics of a patient's voice. We propose PulmoListener, an end-to-end speech processing pipeline that identifies segments of the patient's speech from smartwatch audio collected during daily living and analyzes them to classify COPD symptom severity. To evaluate our approach, we conducted a study with 8 COPD patients over 164 ± 92 days on average. We found that PulmoListener achieved an average sensitivity of 0.79 ± 0.03 and a specificity of 0.83 ± 0.05 per patient when classifying their symptom severity on the same day. PulmoListener can also predict the severity level up to 4 days in advance with an average sensitivity of 0.75 ± 0.02 and a specificity of 0.74 ± 0.07. The results of our study demonstrate the feasibility of leveraging natural speech for monitoring COPD in real-world settings, offering a promising solution for disease management and even diagnosis.
CrowdQ

Hospital Emergency Departments (EDs) are essential for providing emergency medical services, yet often overwhelmed due to increasing healthcare demand. Current methods for monitoring ED queue states, such as manual monitoring, video surveillance, and front-desk registration are inefficient, invasive, and delayed to provide real-time updates. To address these challenges, this paper proposes a novel framework, CrowdQ, which harnesses spatiotemporal crowdsensing data for real-time ED demand sensing, queue state modeling, and prediction. By utilizing vehicle trajectory and urban geographic environment data, CrowdQ can accurately estimate emergency visits from noisy traffic flows. Furthermore, it employs queueing theory to model the complex emergency service process with medical service data, effectively considering spatiotemporal dependencies and event context impact on ED queue states. Experiments conducted on large-scale crowdsensing urban traffic datasets and hospital information system datasets from Xiamen City demonstrate the framework's effectiveness. It achieves an F1 score of 0.93 in ED demand identification, effectively models the ED queue state of key hospitals, and reduces the error in queue state prediction by 18.5%-71.3% compared to baseline methods. CrowdQ, therefore, offers valuable alternatives for public emergency treatment information disclosure and maximized medical resource allocation.
Identification of Genetic Suppressors for a Berardinelli-Seip Congenital Generalized Lipodystrophy Type 2 (BSCL2) Pathogenic Variant in C. elegans

Maintaining the metabolic homeostasis of fatty acids is crucial for human health. Excess fatty acids are stored in lipid droplets (LDs), the primary energy reservoir that helps regulate fat and lipid homeostasis in nearly all cell types. Seipin (BSCL2), a conserved endoplasmic reticulum protein, plays a critical role in LD biogenesis and regulating LD morphology. Pathogenic variants of seipin are associated with multiple human genetic diseases, including Berardinelli-Seip Congenital Generalized Lipodystrophy Type 2 (BSCL2). However, the cellular and molecular mechanisms by which dysfunctional seipin leads to these diseases remain unclear. To model BSCL2 disease, we generated an orthologous BSCL2 pathogenic variant seip-1(A185P) using CRISPR/Cas9 genome editing in Caenorhabditis elegans. This variant led to severe developmental and cellular defects, including embryonic lethality, impaired eggshell formation, and abnormally enlarged LDs. We set out to identify genetic determinants that could suppress these defective phenotypes in the seip-1(A185P) mutant background. To this end, we conducted an unbiased chemical mutagenesis screen to identify genetic suppressors that restore embryonic viability in the seip-1(A185P) mutant background. A total of five suppressor lines were isolated and recovered from the screen. The defective phenotypes of seip-1(A185P), including embryonic lethality and impaired eggshell formation, were significantly suppressed in each suppressor line. Two of the five suppressor lines also alleviated the enlarged LDs in the oocytes. We then mapped a suppressor candidate gene, R05D3.2 (renamed as lmbr-1), which is an ortholog of human LMBR1 (limb development membrane protein 1). The CRISPR/Cas9 edited lmbr-1 suppressor alleles, lmbr-1(Ser647Phe) and lmbr-1(Pro314Leu), both significantly suppressed embryonic lethality and defective eggshell formation in the seip-1(A185P) background. The newly identified suppressor lines offer valuable insights into potential genetic interactors and pathways that may regulate seipin in the lipodystrophy model.
Acetylcholine signaling in the medial prefrontal cortex mediates the ability to learn an active avoidance response following learned helplessness training

Increased brain levels of acetylcholine (ACh) are observed in subsets of patients with depression and increasing ACh levels chronically can precipitate stress-related behaviors in humans and animals. Conversely, optimal ACh levels are required for cognition and memory. We hypothesize that ACh signaling is important for encoding both appetitive and stress-relevant memories, but that excessive increases in ACh result in a negative encoding bias in which memory formation of a stressful event is aberrantly strengthened, potentially contributing to the excessive focus on negative experience that could lead to depressive symptoms. The medial prefrontal cortex (mPFC) is critical to control the limbic system to filter exteroceptive cues and stress-related circuits. We therefore evaluated the role of ACh signaling in the mPFC in a learned helplessness task in which mice were exposed to repeated inescapable stressors followed by an active avoidance task. Using fiber photometry with a genetically-encoded ACh sensor, we found that ACh levels in the mPFC during exposure to inescapable stressors were positively correlated with later escape deficits in an active avoidance test in males, but not females. Consistent with these measurements, we found that both pharmacologically- and chemogenetically-induced increases in mPFC ACh levels resulted in escape deficits in both male and female mice, whereas chemogenetic inhibition of ACh neurons projecting to the mPFC improved escape performance in males, but impaired escape performance in females. These results highlight the adaptive role of ACh release in stress response, but also support the idea that sustained elevated ACh levels contribute to maladaptive behaviors. Furthermore, mPFC ACh signaling may contribute to depressive symptomology differentially in males and females.
Gene expression changes in Drosophila melanogaster females associated with the rescue of the bag of marbles (bam) hypomorph fertility defect by Wolbachia pipientis

Wolbachia pipientis are maternally transmitted endosymbiotic bacteria commonly found in arthropods and nematodes. These bacteria manipulate reproduction of the host to increase their transmission using mechanisms, such as cytoplasmic incompatibility, that favor infected female offspring. Interestingly, W. pipientis infection partially rescues female fertility in flies containing hypomorph mutations of bag of marbles (bam) in D. melanogaster, which functions in germline stem cell (GSC) maintenance and differentiation. Here we evaluated the effect of W. pipientis variant infection on host gene expression using RNA-seq. RNA-seq analysis using the W. pipientis infected relative to uninfected bam hypomorph genotype had differential expression for many of bam’s genetic and physical interactors. The highest magnitude of differential expression of bam’s interactors and other GSC genes involved in cystoblast differentiation occurred prior to peak fertility rescue. Our analysis showed that ubiquitination and histone lysine methylation could be important mechanisms involved in the bam hypomorph fertility rescue by W. pipientis.
A conserved role of hnRNPL in regulating alternative splicing of transcriptional regulators necessary for B cell activation

The multifunctional RNA-binding protein hnRNPL has been implicated in antibody class switching but its broader function in B cells is unknown. Here, we show that hnRNPL is essential for B cell activation, and thereby germinal center and antibody responses. Upon activation, hnRNPL-deficient B cells show proliferation defects and increased apoptosis. Comparative analysis of RNA-seq data from activated B cells and another 8 hnRNPL-depleted cell types reveals a common function in the MYC and E2F transcriptional programs required for proliferation, likely borne out of alternative splicing changes affecting multiple transcription regulators. Notably, while individual gene expression changes were cell type specific, several alternative splicing events affecting histone modifiers like, KDM6A, NSD2, and SIRT1, were conserved across cell types, which could contribute to gene expression changes and other phenotypes upon hnRNPL loss. In line with reduced SIRT1, hnRNPL-deficient B cells had dysfunctional mitochondria and ROS overproduction, which could contribute to defects in B cell activation. Thus, hnRNPL is essential for the resting-to-activated B cell transition by regulating transcriptional programs and metabolism, most likely through the alternative splicing of several histone modifiers.
Geology and geochronology of the Saddleback Greenstone Belt in the Archaean Yilgarn Block, southwestern Australia

The Saddleback Group is a volcanogenic greenstone sequence developed in the Western Gneiss Terrain of the Archaean Yilgarn Block. It consists of three major units; the Hotham Formation composed mainly of metasediments, the Wells Formation consisting of metamorphosed felsic volcanics and the Marradong Formation which is made up of metabasalts. The sequence has been metamorphosed to greenschist facies and intruded by adamellite. The sequence is poorly exposed, due to an extensive capping of bauxitic laterite which is also host to a major lateritic gold deposit (the Boddington deposit). The Saddleback Greenstone Belt contrasts with the rest of the Western Gneiss Terrain, which is composed mainly of linear, high grade, polyphase metamorphic belts enclosed by complex migmatite and intruded by late‐stage granitoids. Zircon U‐Pb results indicate that felsic volcanism in the Saddleback Greenstone belt extended from ca. 2670 to 2650 Ma ago. This was immediately followed by greenschist facies metamorphism, deformat.
Utilization of spectral bin microphysics and bulk parameterization schemes to simulate the cloud structure and precipitation in a mesoscale rain event

[1] Sea breeze convection in Florida on 27 July 1991, accompanied by squall line formation, was simulated using MM5 with various microphysical schemes, including the Hebrew University spectral (bin) microphysics (SBM) and three recently developed bulk model parameterizations. The bulk schemes are the Seifert full two-moment scheme (FTMS), the Reisner-Thompson two-moment ice scheme (TMIS), and the Thompson two-moment ice scheme. The results were evaluated using observed rainfall and radar reflectivity, including radar derived contour frequency with altitude diagrams (CFAD). The SBM simulated quite well the time evolution of average and maximum rainfall amounts. A comparison of a CFAD derived from observations and CFADs derived from model calculated radar reflectivity suggests that the SBM simulates the three-dimensional structure of squall line convection and stratiform mixed phase cloud more realistically than the bulk parameterization schemes. However, the Thompson scheme shows a qualitative improvement over the other bulk parameterization schemes in the simulation of the three-dimensional structure of the squall line as indicated by comparison of its CFAD with the observed. All of the new bulk models simulate precipitation better than the earlier bulk parameterization schemes, but each still produces too much precipitation during too short periods of time and underestimates the area covered by stratiform clouds.