bluuebunny/crossref_metadata_2025_split huggingface dataset:
Two TAL effectors ofXanthomonas citripv.malvacearumtargetGhSWEET15as the susceptibility genes for bacterial blight of cotton
Authors: Ali Shah Syed Mashab , Haq Fazal , Huang Kunxuan , Wang Qi , Liu Linlin , Li Ying , Wang Yong , Khan Asaf , Yang Ruihuan , Khojasteh Moein , Xu Xiameng , Xu Zhengyin , Chen Gongyou 
Published: August 2024

AbstractBacterial Blight of Cotton (BBC) caused byXanthomonas citripv.malvacearum(Xcm) is an important and destructive disease affecting cotton plants. Transcription activator-like effectors (TALEs) released by the pathogen regulate cotton resistance to the susceptibility. In this study, we sequenced the whole genome ofXcmXss-V2-18 and identified eighttalgenes; seven on the plasmids and one on the chromosome. Deletion and complementation experiments of Xss-V2-18talgenes demonstrated that Tal1b is required for full virulence on cotton. Transcriptome profiling coupled with TALE-binding element prediction revealed that Tal1b targetsGhSWEET15A04/D04andGhSWEET15D02simultaneously. Expression analysis confirmed the independent inducibility ofGhSWEET15A04/D04andGhSWEET15D02by Tal1b, whereasGhSWEET15A04/D04is additionally targeted by Tal1. Moreover, GUS (β-glucuronidase) andXa10-mediated HR (hypersensitive response) assays indicated that the EBEs are required for the direct and specific activation of the candidate targets by Tal1 and Ta1b. These findings may advance our understanding of the dynamics between TALEs and EBEs, and decipher a simple and effective DNA-binding mechanism that could lead to the development of more efficient methods for gene editing and transgenic research.
Local Mean Gradient Pattern (LMGP): A novel approach made for the classification of brain CT scan images
Authors: Singh Kavya , Koundal Anil Kumar , Kaur Navjeet 
Published: August 2024

ABSTRACTObjectiveThe visual descriptor methods like Local Binary Pattern (LBP) capture anatomical structures in captured images along with their disparities, which can be exploited by suitable methods for the diagnosis of medical anomalies. We developed a Local Mean Gradient Pattern (LMGP), based partly on LBP, a feature extraction algorithm for the classification of Computed Tomography (CT) images of the brain into normal, ischemic, or hemorrhage categories.MethodsThe ASID dataset comprises of 397 acute ischemic stroke CT scan images and the Kaggle dataset consists of 200 CT scan images, out of which, 100 images were of the abnormal brain type (hemorrhage), and the remaining 100 images which don’t show any abnormalities were categorized as normal cases. The results of LMGP were compared with eight other feature extraction methods using linear, sigmoid, polynomial, and RBF kernels of the SVM classifier.ResultsThe effectiveness of our methodology was evaluated using recall, logarithmic loss, accuracy (ACC), confusion matrix and area under curve (ROC) metrics. The LMGP performed best when RBF-SVM was used for the classification and gave an accuracy of 93% and 95% in the case of five-fold and ten-fold cross-validation respectively. The ten-fold cross-validation gave a precision score of 0.9410, a recall score of 0.9521, an F1 score of 0.9458, and a negative log loss of -0.1645.ConclusionLMGP combines a unique and robust approach of CT scan image feature extraction by combining local information along with gradient change in pixels of the image. The present results of the study suggest the improved performance of the LMGP method over other methods compared in this study effectively.
Mixture-of-Experts Approach for Enhanced Drug-Target Interaction Prediction and Confidence Assessment
Authors: Lu Yijingxiu , Lee Sangseon , Kang Soosung , Kim Sun 
Published: August 2024

ABSTRACTIn recent years, numerous deep learning models have been developed for drug-target interaction (DTI) prediction. These DTI models specialize in handling data with distinct distributions and features, often yielding inconsistent predictions when applied to unseen data points. This inconsistency poses a challenge for researchers aiming to utilize these models in downstream drug development tasks. Particularly in screening potential active compounds, providing a ranked list of candidates that likely interact with the target protein can guide scientists in prioritizing their experimental efforts. However, achieving this is difficult as each current DTI model can provide a different list based on its learned feature space. To address these issues, we propose EnsDTI, a Mixture-of-Experts architecture designed to enhance the performance of existing DTI models for more reliable drug-target interaction predictions. We integrate an inductive conformal predictor to provide confidence scores for each prediction, enabling EnsDTI to offer a reliable list of candidates for a specific target. Empirical evaluations on four benchmark datasets demonstrate that EnsDTI not only improves DTI prediction performance with an average accuracy improvement of 2.7% compared to the best performing baseline, but also offers a reliable ranked list of candidate drugs with the highest confidence, showcasing its potential for ranking potential active compounds in future applications.CCS CONCEPTS•Applied computing→Bioinformatics; •Computing methodologies→Artificial intelligence.
Predicting chemotherapy-induced thrombotoxicity by NARX neural networks and transfer learning
Authors: Steinacker Marie , Kheifetz Yuri , Scholz Markus 
Published: August 2024

AbstractBackgroundThrombocytopenia is a common side effect of cytotoxic chemotherapies, which is often dose-limiting. Predicting an individual’s risk is of high clinical importance, as otherwise, a small subgroup of patients limits dosages for the overall population for safety reasons.MethodsWe aim to predict individual platelet dynamics using non-linear auto-regressive networks with exogenous inputs (NARX). We consider different architectures of the NARX networks, namely feed-forward networks (FNN) and gated recurrent units (GRU). To cope with the relative sparsity of individual patient data, we employ transfer learning (TL) approaches based on a semi-mechanistic model of hematotoxicity. We use a large data set of patients with high-grade non-Hodgkin’s lymphoma to learn the respective models on an individual scale and to compare prediction performances with that of the semi-mechanistic model.ResultsOf the examined network models, the NARX with GRU architecture performs best. In comparison to the semi-mechanistic model, the network model can result in a substantial improvement of prediction accuracy for patients with irregular dynamics, given well-spaced measurements. TL improves individual prediction performances.ConclusionNARX networks can be utilized to predict an individual’s thrombotoxic response to cytotoxic chemotherapy treatment. For reasonable model learning, we recommend at least three well-spaced measurements per cycle: at baseline, during the nadir phase and during the recovery phase. We aim at generalizing our approach to other treatment scenarios and blood lineages in the future.
The Impact of Frailty Syndrome on Skeletal Muscle Histology: Preventive Effects of Exercise
Authors: Ji Fujue , Lee Hae Sung , Lee Haesung , Kim Jong-Hee 
Published: August 2024

AbstractAging-induced frailty syndrome significantly impairs skeletal muscle health, yet its impact on muscle histology remains unclear. This study investigates the histological alterations in muscle associated with frailty syndrome and evaluates the preventive effects of exercise. Mice were divided into groups based on age and condition, including an exercised group. Evaluated variables include body weight, lean mass ratio, myofiber size and number, extracellular matrix (ECM) content, and myosin heavy chain isoforms. Findings indicate that frailty syndrome increases body weight and ECM content, while reducing myofiber size and number, highlighting its negative impact on skeletal muscle histology. Notably, exercise effectively mitigated these adverse changes, suggesting its potential role in preventing skeletal muscle dysfunction associated with frailty syndrome.
Cysteine depletion triggers adipose tissue thermogenesis and weight-loss
Authors: Lee Aileen H. , Orliaguet Lucie , Youm Yun-Hee , Maeda Rae , Dlugos Tamara , Lei Yuanjiu , Coman Daniel , Shchukina Irina , Andhey Sairam , Smith Steven R. , Ravussin Eric , Stadler Krisztian , Hyder Fahmeed , Artyomov Maxim N. , Sugiura Yuki , Dixit Vishwa Deep 
Published: August 2024

AbstractDietary interventions such as caloric restriction (CR)1and methionine restriction2that prolong lifespan induce the ‘browning’ of white adipose tissue (WAT), an adaptive metabolic response that increases heat production to maintain health3,4. However, how diet influences adipose browning and metabolic health is unclear. Here, we identified that weight-loss induced by CR in humans5reduces cysteine concentration in WAT suggesting depletion of this amino-acid may be involved in metabolic benefits of CR. To investigate the role of cysteine on organismal metabolism, we created a cysteine-deficiency mouse model in which dietary cysteine was eliminated and cystathionine γ-lyase (CTH)6, the enzyme that synthesizes cysteine was conditionally deleted. Using this animal model, we found that systemic cysteine-depletion causes drastic weight-loss with increased fat utilization and browning of adipose tissue. The restoration of dietary cysteine in cysteine-deficient mice rescued weight loss together with reversal of adipose browning and increased food-intake in an on-demand fashion. Mechanistically, cysteine deficiency induced browning and weight loss is dependent on sympathetic nervous system derived noradrenaline signaling via β3-adrenergic-receptors and does not require UCP1. Therapeutically, in high-fat diet fed obese mice, one week of cysteine-deficiency caused 30% weight-loss and reversed inflammation. These findings thus establish that cysteine is essential for organismal metabolism as removal of cysteine in the host triggers adipose browning and rapid weight loss.
Iota oscillations (25-35 Hz) during wake and REM sleep in children and young adults
Authors: Snipes Sophia 
Published: August 2024

1ABSTRACTHigh-frequency brain oscillations in humans are currently categorized into beta (13-30 Hz) and gamma (>30 Hz). Here, I introduce a new class of oscillations between 25 and 35 Hz, which I propose to call “iota.” Iota oscillations have low amplitudes but can still be measured with surface electroencephalography (EEG). Within an individual, iota has a narrow spectral bandwidth of 2-4 Hz, thus distinguishing it from broadband beta and gamma. Iota oscillations occur as sustained bursts during both wakefulness and REM sleep but do not appear during NREM sleep. They are only found in a minority of individuals, more in children than in adults. Overall, iota oscillations are challenging to detect but could serve as a marker of both brain development and states of vigilance.
Accelerated Limbal Epithelial Differentiation of Human Induced Pluripotent Stem Cells Using a Defined Keratinocyte Serum-Free Medium
Authors: Roshandel Danial , Alvarez-Palomo Belén , Zaw Khine , Zhang Dana , Edel Michael J , Wong Evan N , Wiffen Steven , Chen Fred K , Morgan William , McLenachan Samuel 
Published: August 2024

AbstractPurposeTreatment of bilateral limbal stem cell deficiency (LSCD) is challenging due to the limited autologous stem cell sources. This study aimed to differentiate human induced pluripotent stem cells (hiPSCs) into limbal epithelial stem cells (LESCs) using a defined keratinocyte serum-free medium (DKSFM).MethodsA fully characterized hiPSC line was committed to ectodermal differentiation using Essential 6 (E6) medium supplemented with 10 µM Y-27632 (Day 1), 10 µM SB-505124 plus 50 ng/ml bFGF (Day 2) and 25 ng/ml BMP-4 (Days 3 and 4). Differentiation was continued in DKSFM for an additional 21 days. Quantitative PCR (qPCR) and/or immunocytochemistry (ICC) for pluripotency, proliferation, LESC, and corneal epithelial markers were performed on samples collected at days 5, 10, 15, and 25 (D5 to D25) and compared with undifferentiated hiPSCs (UD).ResultsqPCR revealed a significant decrease in the expression ofOCT4andNANOGand a significant increase inABCG2andTP63following ectodermal induction (i.e., D5), compared with UD (P < 0.05). The expression levels ofKi67,ABCG2,TP63, andCK14were significantly higher at D10, compared with D5 and D25 (P < 0.05). The ratio of p63α-positive cells was 71% and 56% in D10 and D15 cells, respectively (P < 0.05).DiscussionOur method resulted in a limited but rapid differentiation of hiPSCs into LESC-like cells. The LESC-like cells appeared as early as 5 days following ectodermal induction and their population peaked after 10 days. Upon further optimization and validation, DKSFM can be used for rapid limbal epithelial differentiation of hiPSCs.
Tokenized and Continuous Embedding Compressions of Protein Sequence and Structure
Authors: Lu Amy X. , Yan Wilson , Yang Kevin K. , Gligorijevic Vladimir , Cho Kyunghyun , Abbeel Pieter , Bonneau Richard , Frey Nathan 
Published: August 2024

AbstractExisting protein machine learning representations typically model either the sequence or structure distribution, with the other modality implicit. The latent space of sequence-to-structure prediction models such as ESMFold represents thejoint distributionof sequence and structure; however, we find these embeddings to exhibit massive activations, whereby some channels have values 3000× higher than others, regardless of the input. Further, on continuous compression schemes, ESMFold embeddings can be reduced by a factor of 128× along the channel and 8× along the length, while retaining structure information at <2Å scale accuracy, and performing competitively on protein function and localization benchmarks. On discrete compression schemes, we construct a tokenized all-atom structure vocabulary that retains high reconstruction accuracy, thus introducing atokenized representation of all-atom structure that can be obtained from sequence alone. We term this series of embeddings as CHEAP (Compressed Hourglass Embedding Adaptations of Proteins) embeddings, obtained via the HPCT (Hourglass Protein Compression Transformer) architecture. CHEAP is a compact representation of both protein structure and sequence, sheds light on information content asymmetries between sequence and structure, democratizes representations captured by large models, and is designed to have flexible downstream applications such as generation, search, and prediction.
Molecular Glue-Design-Evaluator (MOLDE): An Advanced Method for In-Silico Molecular Glue Design
Authors: Ben Geoffrey A S , Agrawal Deepak , Kulkarni Nagaraj M , Manonmani G 
Published: August 2024

AbstractProtein function modulation using small molecule binding is an important therapeutic strategy for many diseases. However, many proteins remain undruggable due to lack of suitable binding pockets for small molecule binding. Proximity induced protein degradation using molecular glues has recently been identified as an important strategy to target undruggable proteins. Molecular glues were discovered serendipitously and as such currently lack an established approach for in-silico driven rationale design. In this work, we aim to establish an in-silico method for designing molecular glues. To achieve this, we leverage known molecular glue-mediated ternary complexes and derive a rationale for in-silico design of molecular glues. Establishing an in-silico rationale for molecular glue design would significantly contribute to the literature and accelerate the discovery of molecular glues for targeting previously undruggable proteins. Our work presented here and named as Molecular Glue-Designer-Evaluator (MOLDE) contributes to the growing literature of in-silico approaches to drug design in-silico literature.
Molecular Identification and Antimicrobial Potential of Endophytic Fungi Against Some Grapevine Pathogens
Authors: Nashat Lava H. , Haleem Raed A. , Ali Shayma H. 
Published: August 2024

AbstractIn the Duhok province of the Kurdistan region, the plant speciesVitis viniferahas been explored as a habitat for diverse endophytic microorganisms across various ecological environments. During the period from 2021 to 2022, a total of 600 samples were collected from four distinct locations: Bagera, Besfke, Barebhar, and Atrush. From these samples, twelve endophytic fungal species were isolated, includingAspergillus flavus, Botryosphaeria dothidea, Fusarium oxysporum, Fusarium ruscicol, Fusarium venenatum, Chaetomium globosum, Clonostachys rosea, Mucor racemosus, Penicillium glabrum, Aspergillus terreus, Aspergillus ustus, andAspergillus niger. These fungi are recognized for their potential as biocontrol agents against grapevine trunk diseases and grape rotting fungi, which pose significant risks to grapevine health and productivity. Based on ITS and SSU sequencing, molecular identification confirmed these fungi’s presence with sequence identities ranging from 99% to 100%. Phylogenetic analysis revealed that these endophytes could be categorized into five main clusters (A, B, C, D, and E), showing high intra-group similarity. Utilizing the Dual Culture method, the endophytePaecilomyces maximusdemonstrated a 70.83% inhibition rate againstIlyonectria destructans. In the Food Poisoning method,A. flavusandP. maximusemerged as the most effective inhibitors ofIlyonectria destructans, whereasA. terreus, M. racemosus, andP. maximusachieved complete inhibition (100%) ofBotrytis cinerea. Additionally,M. racemosuswas identified as the most effective biocontrol agent againstNeoscytalidium dimidiatum.
Persistence of respiratory, enteric, and fecal indicator viruses in fecal sludge from on-site sanitation in Dakar, Senegal
Authors: Mendoza Grijalva Lorelay , Seck Alsane , Roldan-Hernandez Laura , Graham Katherine E. , Boehm Alexandria B. , Tarpeh William A. 
Published: August 2024

AbstractAs wastewater-based epidemiology (WBE) broadens its focus to include prevalent diseases with significant global health impact, existing surveillance systems concentrate on sewer-based infrastructure, which excludes the 2.7 billion people using non-sewered systems. To address this gap, our study explores the potential of fecal sludge treatment plants (FSTPs) for WBE, emphasizing the stability of virus RNA targets within pooled fecal sludge. We screened fecal sludge from a centralized treatment facility in Dakar, Senegal for SARS-CoV-2, human norovirus (HuNoV), and microbial source trackers (MSTs) pepper mild mottle virus (PMMoV) and tomato brown rugose fruit virus (ToBRFV). Decay kinetics of genomic RNA markers from these viruses were examined at 4 °C, 15 °C, and 30 °C over 70 days. Results indicate high persistence of viral targets in fecal sludge (T90 value of 3.3 months for exogenous SARS-CoV-2 N1 and N2, 6.2 months for ToBRFV), with all targets detected throughout the 70-day experiment under various temperatures with limited decay (<1 log10 reduction). This study addresses a crucial gap in understanding virus persistence in on-site sanitation systems by providing essential decay rate constants for effective target detection. Our results indicate that sampling at centralized facilities treating fecal sludge from on-site sanitation could facilitate localized pathogen surveillance in low-income settings.HighlightsInvestigation of the persistence of SARS-CoV-2, HuNoV, PMMoV, and ToBRFV genomic RNA in pooled fecal sludge derived from on-site sanitation systems.Novel microbial source tracker (MST), ToBRFV, exhibited comparable abundance to PMMoV, a well-established MST, in fecal sludge.No significant decay observed for HuNoV and PMMoV over 70 days at all temperature conditions (4, 15, and 30 °C).SARS-CoV-2 N1 and N2 showed T90values of 3.3 months at 30 °C.Fecal sludge treatment plants offer a centralized sampling location for wastewater-based epidemiology, providing a strategic approach for monitoring public health.Graphical Abstract
The transcriptome of CD14+CD163-HLA-DRlowmonocytes predicts mortality in Idiopathic Pulmonary Fibrosis
Authors: Karampitsakos Theodoros , Tourki Bochra , Jia Minxue , Perrot Carole Y. , Visinescu Bogdan , Zhao Amy , Unterman Avraham , Tzouvelekis Argyris , Bandyopadhyay Debabrata , Juan-Guardela Brenda M. , Prasse Antje , Noth Imre , Liggett Stephen , Kaminski Naftali , Benos Panayiotis V. , Herazo-Maya Jose D. 
Published: August 2024

AbstractRationaleThe association between immune-cell-specific transcriptomic profiles and Idiopathic Pulmonary Fibrosis (IPF) mortality is unknown.ObjectivesTo determine immune-cell-specific transcriptomic profiles associated with IPF mortality.MethodsWe profiled peripheral blood mononuclear cells (PBMC) in 18 participants [University of South Florida: IPF, COVID-19, post-COVID-19 Interstitial Lung Disease (Post-COVID-19 ILD), controls] by single-cell RNA sequencing (scRNA-seq) and identified 16 immune-cell-specific transcriptomic profiles. The Scoring Algorithm of Molecular Subphenotypes (SAMS) was used to calculate Up-scores based on these 16 gene profiles. Their association with outcomes was investigated in peripheral blood, Bronchoalveolar Lavage (BAL) and lung tissue of N=416 IPF patients from six cohorts. Findings were validated in an independent IPF, PBMC scRNA-seq dataset (N=38).Measurements and main resultsCox-regression models demonstrated that 230 genes from CD14+CD163-HLA-DRlowcirculating monocytes predicted IPF mortality [Pittsburgh (p=0.02), Chicago (p=0.003)]. PBMC proportions of CD14+CD163-HLA-DRlowmonocytes were higher in progressive versus stable IPF (Yale, 0.13±0.05 versus 0.09±0.05, p=0.034). Receiving operating characteristic identified a 230 gene, Up-score >41.84 (Pittsburgh) predictive of mortality in Chicago (HR: 6.58, 95%CI: 2.15-20.13, p=0.001) and in pooled analysis of BAL cohorts (HR: 2.20, 95%CI: 1.44-3.37, p=0.0003). High-risk patients had decreased expression of the T-cell co-stimulatory genesCD28,ICOS,ITKandLCK(Pittsburgh and Chicago, p<0.01). 230 gene-up-scores negatively correlated with Forced Vital Capacity (FVC) in IPF lung tissues (LGRC, rho=-0.2, p=0.02). Results were replicated using a subset of 13 genes from the 230-gene signature (pooled PBMC cohorts - HR: 5.34, 95%CI: 2.83-10.06, p<0.0001).ConclusionsThe transcriptome of CD14+CD163-HLA-DRlowmonocytes is associated with increased IPF mortality.