Miracle-dz/UniFAR-FTUs huggingface dataset:
Sleep-Deep-Net learns sleep wake scoring from the end-user and completes each record in their style

Sleep-wake scoring is a time-consuming, tedious but essential component of clinical and pre-clinical sleep research. Sleep scoring is even more laborious and challenging in rodents due to the smaller EEG amplitude differences between states and the rapid state transitions which necessitate scoring in shorter epochs. Although many automated rodent sleep scoring methods exist, they do not perform as well when scoring new data sets, especially those which involve changes in the EEG/EMG profile. Thus, manual scoring by expert scorers remains the gold-standard. Here we take a different approach to this problem by using a neural network to accelerate the scoring of expert scorers. Sleep-Deep-Net (SDN) creates a bespoke deep convolution neural network model for individual electroencephalographic or local-field-potential records via transfer learning of GoogleNet, by learning from a small subset of manual scores of each EEG/LFP record as provided by the end-user. SDN then automates scoring of the remainder of the EEG/LFP record. A novel REM scoring correction procedure further enhanced accuracy. SDN reliably scores EEG and LFP data and retains sleep-wake architecture in wild-type mice, in sleep induced by the hypnotic zolpidem, in a mouse model of Alzheimer’s disease and in a genetic knock-down study, when compared to manual scoring. SDN reduced manual scoring time to 1/12. Since SDN uses transfer learning on each independent recording, it is not biased by previously scored existing data sets. Thus, we find SDN performs well when used on signals altered by a drug, disease model or genetic modification. STATEMENT OF SIGNIFICANCE Sleep medicine is often critically advanced by translational research based on in vivo electrophysiologic mouse data. A necessary but time-consuming step in this field is scoring epochs of recordings into wakefulness, non-rapid-eye-movement sleep and non-rapid-eye-movement sleep. Despite efforts to automate this, manual scoring remains the gold-standard since automatic methods poorly handle data that is not similar enough to data used during development. Here, we describe a novel automated sleep scoring method that involves retraining a deep-convolution-neural-net capable of computer vision to score sleep-wake patterns after learning from a small set of manual scores within a record. This avoids biasing the model to expect data to be the same as its training set from previous records.
Decoding the Epigenetics and Chromatin Loop Dynamics of Androgen Receptor-Mediated Transcription

Androgen receptor (AR)-mediated transcription plays a critical role in normal prostate development and prostate cancer growth. AR drives gene expression by binding to thousands of cis-regulatory elements (CRE) that loop to hundreds of target promoters. With multiple CREs interacting with a single promoter, it remains unclear how individual AR bound CREs contribute to gene expression. To characterize the involvement of these CREs, we investigated the AR-driven epigenetic and chromosomal chromatin looping changes. We collected a kinetic multiomic dataset comprised of steady-state mRNA, chromatin accessibility, transcription factor binding, histone modifications, chromatin looping, and nascent RNA. Using an integrated regulatory network, we found that AR binding induces sequential changes in the epigenetic features at CREs, independent of gene expression. Further, we showed that binding of AR does not result in a substantial rewiring of chromatin loops, but instead increases the contact frequency of pre-existing loops to target promoters. Our results show that gene expression strongly correlates to the changes in contact frequency. We then proposed and experimentally validated an unbalanced multi-enhancer model where the impact on gene expression of AR-bound enhancers is heterogeneous, and is proportional to their contact frequency with target gene promoters. Overall, these findings provide new insight into AR-mediated gene expression upon acute androgen simulation and develop a mechanistic framework to investigate nuclear receptor mediated perturbations.
OmniClustifyXMBD: Uncover putative cell states within multiple single-cell omics datasets

Clustering plays a pivotal role in characterizing cell states in single-cell omics data. Nonetheless, there is a noticeable gap in clustering algorithms tailored for unveiling putative cell states across datasets containing samples with diverse phenotypes. To bridge this gap, we implement an innovative method termed OmniClustifyXMBD, which integrates adaptive signal isolation with cell clustering. The adaptive signal isolation effectively disentangles gene expression variations linked to distinct factors within individual cells. This separation restores cells to their inherent states, free from external influences. Concurrently, a clustering algorithm built upon a deep variational Gaussian mixture model is devised to identify these putative cell states. Experiments showcase the effectiveness of OmniClustifyXMBD in identifying putative cell states while minimizing the influence of various undesired variations, including batch effects and random inter-sample differences. Moreover, OmniClustifyXMBD demonstrates robustness in its results across different clustering parameters.
Transcriptomic analysis of melanoma cells reveals an association of α-synuclein with regulation of the inflammatory response

The Parkinson’s disease protein, alpha-synuclein (α-syn/SNCA), is highly expressed in neurons and melanomas. The goal of this study was to reveal the mechanism(s) of α-syn’s involvement in melanoma pathogenesis. To decipher the genes and pathways affected by α-syn, we conducted an RNA sequencing analysis of human SK-MEL-28 cells and several SK-MEL-28 SNCA-KO clones. We identified 1098 significantly up-regulated genes and 660 significantly down-regulated genes. Several of the upregulated genes are related to the immune system, i.e., the inflammatory response and the matrisome. We validated five upregulated genes (IL-1ý, SAA1, IGFBP5, CXCL8, and CXCL10) by RT-qPCR and detected IGFBP5 and IL-1ý in spent media of control and SNCA-KO cells. The levels of each of these secreted proteins were significantly higher in the spent media of the SNCA-KO clones than control cells. We suggest that the loss of α-syn expression unleashes chemokine/cytokine secretion, which could help melanoma cells evade the immune system.
Intestinal LKB1 loss drives a pre-malignant program along the serrated cancer pathway

Peutz-Jeghers syndrome (PJS) is a familial disorder caused by heterozygous inactivating Liver Kinase B1 (LKB1/STK11) mutations that induce hamartomatous polyp formation in the gastrointestinal tract. Additionally, PJS patients carry an elevated risk of developing malignancies in multiple tissues, including the intestine. How LKB1 deficiency alters the hierarchical organization and phenotypical landscape of epithelial tissues to mediate an increased cancer risk remains poorly understood. Here, we investigate these issues using small intestinal organoids that carry heterozygous or homozygous Lkb1 mutations. We show that Lkb1 loss causes allele dosage-dependent activation of transcriptional programs for tissue repair, particularly within stem and transit amplifying cell lineages. Furthermore, enhanced EGFR ligand expression promotes niche-independent growth of Lkb1-deficient organoids. Strikingly, we uncover that mono-allelic loss of Lkb1 is sufficient to shift the epithelium into the serrated premalignant program for colorectal carcinogenesis, which is strongly amplified upon loss-of-heterozygosity (LOH) or activating Kras mutations. We conclude that heterozygous mutations in LKB1 predispose the intestinal epithelium to uncontrolled growth along the serrated pathway, providing an explanation for the increased cancer risk in PJS patients.
EryDB: a transcriptomic profile database for erythropoiesis and erythroid-related diseases

Erythropoiesis is a finely regulated and complex process that involves multiple transformations from hematopoietic stem cells to mature red blood cells at hematopoietic sites from the embryonic to the adult stages. Investigations into its molecular mechanisms have generated a wealth of expression data, including bulk and single-cell RNA sequencing data. A comprehensively integrated and well-curated erythropoiesis-specific database will greatly facilitate the mining of gene expression data and enable large-scale research of erythropoiesis and erythroid related diseases. Here, we present EryDB, an open-access and comprehensive database dedicated to the collection, integration, analysis, and visualization of transcriptomic data for erythropoiesis and erythroid-related diseases. Currently, the database includes expertly curated quality-assured data of 3,803 samples and 1,187,119 single cells derived from 107 public studies of three species (Homo sapiens, Mus musculus, and Danio rerio), nine tissue types, and five diseases. EryDB provides users with the ability to not only browse the molecular features of erythropoiesis between tissues and species, but also perform computational analyses of single-cell and bulk RNA sequencing data, thus serving as a convenient platform for customized queries and analyses. EryDB v1.0 is freely accessible at https://ngdc.cncb. ac.cn/EryDB/home.
Identification of fecal contamination sources of groundwater in rural areas of Vhembe District Municipality, Limpopo Province, South Africa

Groundwater is a valuable source of drinking water worldwide, recognized as an improved drinking water source. However, on‐site sanitation systems may put groundwater at risk of fecal contamination. In the present study, two approaches were used to ascertain the sources of fecal contamination in groundwater used by communities of the Vhembe District Municipality. Overall, 87.5% of boreholes (n = 70) in the wet and 72.5% in the dry season were contaminated with Escherichia coli, and septic tank (n = 18) wastewaters displayed up to 104 cfu/mL E. coli. Host‐specific Bacteroidales quantitative polymerase chain reaction (qPCR) assays established the presence of human (BacHum and HF183) and animal (Cytb, BacCan, and Pig‐2Bac) genetic markers in groundwater from 15.7% of boreholes (wet) and 10% of boreholes (dry). No strong associations were founded between culturable E. coli counts and the presence/absence of marker genes for all the markers except for Cytb marker, which showed a weak significant correlation (r = 0.217; p = <0.01) between E. coli and the Cytb marker under dry seasonal conditions. Human markers and Cytb were present in the household septic tank wastewater samples. Significant differences in marker genes distribution in wastewater were observed using the Chi‐squared test: HF183 (p = <0.001) and BacHum (p = <0.001). Overall, no association was recorded between markers in groundwater and in wastewater for 18 households' septic tanks. A combined culturable E. coli and host‐specific Bacteroidales qPCR assays remain an appropriate approach for the identification of fecal contamination of groundwater.
Exogenous mitochondrial transfer increases energy expenditure and attenuates adiposity gains in mice with diet-induced obesity

Obesity is associated with chronic multi-system bioenergetic stress that may be improved by increasing the number of healthy mitochondria available across organ systems. However, treatments capable of increasing mitochondrial content are generally limited to endurance exercise training paradigms, which are not always sustainable long-term, let alone feasible for many patients with obesity. Recent studies have shown that local transfer of exogenous mitochondria from healthy donor tissues can improve bioenergetic outcomes and alleviate the effects of tissue injury in recipients with organ specific disease. Thus, the aim of this project was to determine the feasibility of systemic mitochondrial transfer for improving energy balance regulation in the setting of diet-induced obesity. We found that transplantation of mitochondria from lean mice into mice with diet-induced obesity attenuated adiposity gains by increasing energy expenditure and promoting the mobilization and oxidation of lipids. Additionally, mice that received exogenous mitochondria demonstrated improved glucose uptake, greater insulin responsiveness, and complete reversal of hepatic steatosis. These changes were, in part, driven by adaptations occurring in white adipose tissue. Together, these findings are proof-of-principle that mitochondrial transplantation is an effective therapeutic strategy for limiting the deleterious metabolic effects of diet-induced obesity in mice.
Associations between adolescent alcohol use and neurocognitive functioning in young adulthood

Abstract This study examined the associations between excessive alcohol intake during adolescence and neurocognitive functioning in young adulthood and whether these relations varied by sex. Participants were working-class Chilean adolescents (N = 692; Mage 16.0 years; 54.5% female) who provided frequency of past 30-day bingeing and past-year intoxication. Neurocognitive measures were completed in young adulthood (Mage 21.2 years). Illicit substance users were excluded a priori and other substance use was controlled. When males and females were considered simultaneously, no main effects of intoxication or bingeing were found. However, several sex-specific effects emerged for intoxication, such that more frequent intoxication was associated with poorer visual memory, attention, processing speed, response inhibition, and cognitive flexibility in females, while frequent intoxication related to better attention and processing speed in males. In general, effect sizes were small. No relations emerged for verbal memory, working memory, or spatial learning. Possible factors that contribute to divergent sex effects are discussed.
The triple oxygen isotope composition of phytoliths as a proxy of continental atmospheric 1 humidity : insights from climate chamber and climate transect calibrations 2

17 Continental atmospheric relative humidity (RH) is a key climate-parameter. Combined with 18 atmospheric temperature, it allows us to estimate the concentration of atmospheric water vapor 19 which is one of the main components of the global water cycle and the most important gas 20 contributing to the natural greenhouse effect. However, there is a lack of proxies suitable for 21 reconstructing, in a quantitative way, past changes of continental atmospheric humidity. This 22 reduces the possibility to make model-data comparisons necessary for the implementation of 23 climate models. Over the past 10 years, analytical developments have enabled a few laboratories 24 to reach sufficient precision for measuring the triple oxygen isotopes, expressed by the 17O-excess 25 (17O-excess = ln (d17O + 1) – 0.528 x ln (d18O + 1)), in water, water vapor and minerals. The 17O26 excess represents an alternative to deuterium-excess for investigating relative humidity conditions 27 that prevail during water evaporation. Phytoliths are micrometric amorphous silica particles that 28 form continuously in living plants. Phytolith morphological assemblages from soils and sediments 29 are commonly used as past vegetation and hydrous stress indicators. In the present study, we 30 examine whether changes in atmospheric RH imprint the 17O-excess of phytoliths in a measurable 31 way and whether this imprint offers a potential for reconstructing past RH. For that purpose, we 32 first monitored the 17O-excess evolution of soil water, grass leaf water and grass phytoliths in 33 response to changes in RH (from 40 to 100 %) in a growth chamber experiment where transpiration 34 reached a steady state. Decreasing RH from 80 to 40% decreases the 17O-excess of phytoliths by 35 4.1 per meg / % as a result of kinetic fractionation of the leaf water subject to evaporation. In order 36 to model with accuracy the triple oxygen isotope fractionation in play in plant water and in 37 phytoliths we recommend direct and continuous measurements of the triple isotope composition 38 of water vapor. Then, we measured the 17O-excess of 57 phytolith assemblages collected from top 39
Detection of genetically modified organisms using highly multiplexed amplicon sequencing

The circulation of products based on genetically modified (GM) organisms is highly regulated by some governments having implemented strict rules on the breeding, planting, marketing, labelling, and trading of such products. To ensure compliance, accurate detection methods for GM events are necessary, along with assurance that GM material falls within relevant threshold levels. The increasing complexity and potential of undocumented GM are a growing challenge for genetic screening. In this study, we developed and assessed a highly multiplexed amplicon sequencing assay for the detection of GM events based on a microfluidics platform and next-generation sequencing (NGS). To probe GM events comprehensively, we designed a total of 230 new amplicons to cover flanking, promoter, junction and coding sequences of GM sequences. In addition, we designed and implemented parallel amplification of ribosomal and chloroplast markers to define crop species identity from potentially mixed samples. Using reference GM material of 11 crop species and multiple amplicons, we successfully detected the presence of 10 known modifications per GM event. We also find that reported flanking sequences of GM events may not be all useful for diagnostic. We assessed the assay’s potential to detect GM events in mixed samples as well as in highly diluted DNA. Finally, we performed a prospective search of potentially undocumented GM events in plant material. Our microfluidics-based amplicon GM detection approach fills important gaps in detecting potentially undocumented and complex GM events by recovering a wide range of specific amplicon sequences for evaluation. Integrating highly parallel amplicon assays in GM screening efforts should be an effective complement to aid post-market monitoring and regulatory compliance efforts.
An integrated approach to identify protein complex based on best neighbor and modularity increment

In order to overcome the limitations of global modularity and the deficiency of local modularity, we introduce a hybrid modularity measure LGQ (Local-Global Quantification) which adopts a suitable modularity adjustable parameter to control the balance of global detecting capability and local search capability in Protein-Protein Interaction (PPI) network. On the other hand, a new protein complex mining algorithm called BN-LGQ has been proposed, which integrates the definitions of best neighbor node and the modularity increment. And by comparison with other known algorithms, the experimental results show BN-LGQ performs a better accuracy on predicting protein complexes and has a higher match with the reference protein complexes. Moreover, it can identify protein complexes with better biological significance in PPI network.
Cross-lingual sentiment classification in low-resource Bengali language

Sentiment analysis research in low-resource languages such as Bengali is still unexplored due to the scarcity of annotated data and the lack of text processing tools. Therefore, in this work, we focus on generating resources and showing the applicability of the cross-lingual sentiment analysis approach in Bengali. For benchmarking, we created and annotated a comprehensive corpus of around 12000 Bengali reviews. To address the lack of standard text-processing tools in Bengali, we leverage resources from English utilizing machine translation. We determine the performance of supervised machine learning (ML) classifiers in machine-translated English corpus and compare it with the original Bengali corpus. Besides, we examine sentiment preservation in the machine-translated corpus utilizing Cohen’s Kappa and Gwet’s AC1. To circumvent the laborious data labeling process, we explore lexicon-based methods and study the applicability of utilizing cross-domain labeled data from the resource-rich language. We find that supervised ML classifiers show comparable performances in Bengali and machine-translated English corpus. By utilizing labeled data, they achieve 15%-20% higher F1 scores compared to both lexicon-based and transfer learning-based methods. Besides, we observe that machine translation does not alter the sentiment polarity of the review for most of the cases. Our experimental results demonstrate that the machine translation based cross-lingual approach can be an effective way for sentiment classification in Bengali.
U N I V E R S I T Y OF C A L I F O R N I A

Theoretically neptunium in the plus 5 oxidation state mieht be expected to form the pentachloride (NpCi5), oxychlorides (NpOCl3 and Np02Cl), and hydrated oxychlorides. By direct evaporation of Np(V) from dilute hydrochloric acid solutio,1s, compounds hav . ng Cl/Np ratios varying between 1.4 and 2.8 have been prepared. The -prcparF:~tions are believed to be mixtures of the two oxychlorides. Treatment of the above materials rrith carbon tetrachloride and with chlorine at various tem? eratures between 110 and 600° C has resulted in the preparation of several a:>parently new compounds containing Np(V) and chlo:rine. Of these, a red sublimate might be NpC15 in view of thG similarity of its x-ray diffraction ;Jattern ''lith those of CbC1 5 and TaCl5. Variou~ yellon, era3r, and green residues might be oxychlorides. Neptunium(V) oxychloride does not seem to stable at 400° C in a vacuum of 10-L} nun or at 600° C under 760 mm pressure of chlor:! ne. A new Np(V) absorption spectrum peak has been located at 10930 A in the near infrared. The molar extinction coefficient is about 21 for 0. 014 . M
Modelling of Calcium Carbonate Precipitation in Natural Karst Environments Under Hydrodynamic and Chemical Kinetic Control

Rimstone dams are barriers composed mainly of calcium carbonate deposited from solution in ground and surface waters. These structures form a subclass of travertine formations which include flowstone and stalactites, and often appear in close proximity to these features. The initial formation of rimstone dams requires some degree of cave slope, a semi-continuous flow of water, and the preexistence of irregularities in the cave floor. These dams develop at heights from a few millimeters to several meters within free-surface streams, and create a self-propagating dam and pool structure which grows upward. The genesis and evolution of rimstone dams is theorized to be the result of hydrodynamic and chemical-kinetic control. The purpose of this paper is to develop a model, the scope of which encompasses both hydrodynamics and the reactive transport, which is qualitatively consistent with observed and experimentally derived results, and method for analyzing the mechanism governing the formation of these unique rimstone features.