bluuebunny/crossref_metadata_2025_split huggingface dataset:
Advancing marker-gene-based methods for prokaryote-mediated multifunctional redundancy: exploring random and nonrandom extinctions in a watershed
Authors: Cheng Wan-Hsuan , Miki Takeshi , Ido Motohiro , Yoneya Kinuyo , Matsui Kazuaki , Yokokawa Taichi , Yamanaka Hiroki , Nakano Shin-ichi 
Published: March 2024

AbstractMultifunctional redundancy, the extent of loss in multiple ecosystem functions with decreasing biodiversity, stands as a crucial index for evaluating ecosystem resilience to environmental changes. We aimed to refine a marker-gene-based methodology for quantifying multifunctional redundancy in prokaryotic communities. Using PICRUSt2, we predicted KEGG orthologs (KOs) for each Amplicon Sequence Variant (ASV), assessed community-wide KO richness, and validated predictions against experimentally quantified phenotypic multifunctionality. Additionally, we introduced a refined regression on ASV richness–KO richness curves, providing a reliable estimate of the power-law exponent within computational time constraints, serving as the multifunctional redundancy index. Incorporating various non-random extinction scenarios alongside a random one allowed us to quantify estimate variations between scenarios, providing conservative estimates of multifunctional redundancy. Applied to Lake Biwa and four of its inlet rivers, the refined methodology unveiled spatio-temporal variations in multifunctional redundancy. Our analysis demonstrated lower redundancy in Lake Biwa compared to rivers, aiding in prioritizing conservation targets and inferring distinct community assembly processes. Future directions include a deeper exploration of KO composition information for detailed multifunctionality quantification and the refinement of extinction scenarios. This study demonstrates the promising integration of bioinformatic functional prediction and modeling biodiversity loss, offering a valuable tool for effective ecosystem management.
A Hybrid Diffusion Model for Stable, Affinity-Driven, Receptor-Aware Peptide Generation
Authors: Vishva Saravanan R , Choudhuri Soham , Ghosh Bhaswar 
Published: March 2024

ABSTRACTThe convergence of biotechnology and artificial intelligence has the potential to transform drug development, especially in the field of therapeutic peptide design. Peptides are short chains of amino acids with diverse therapeutic applications that offer several advantages over small molecular drugs, such as targeted therapy and minimal side effects. However, limited oral bioavailability and enzymatic degradation have limited their effectiveness. With advances in deep learning techniques, innovative approaches to peptide design have become possible. In this work, we demonstrate HYDRA: a hybrid deep learning approach that leverages the distribution modeling capabilities of a diffusion model and combines it with a binding affinity maximization algorithm that can be used forde novodesign of peptide binders given target receptors. As an application, we have used our approach to design therapeutic peptides targeting proteins expressed by Plasmodium falciparum Erythrocyte Membrane Protein 1 (PfEMP1) genes. The ability of our model to generate peptides conditioned on the target receptor’s binding sites makes it a promising approach for developing effective therapies for malaria and other diseases.
LoCS-Net: Localizing Convolutional Spiking Neural Network for Fast Visual Place Recognition
Authors: Akcal M. Ugur , Raikov Ivan Georgiev , Gribkova Ekaterina , Choudhuri Anwesa , Soltesz Ivan , Gillette Rhanor , Chowdhary Girish 
Published: March 2024

AbstractVisual place recognition (VPR) is the ability to recognize locations in a physical environment based only on visual inputs. It is a challenging task due to perceptual aliasing, viewpoint and appearance variations and complexity of dynamic scenes. Despite promising demonstrations, many state-of-the-art VPR approaches based on artificial neural networks (ANNs) suffer from computational inefficiency. Spiking neural networks (SNNs), on the other hand, implemented on neuromorphic hardware, are reported to have remarkable potential towards more efficient solutions computationally, compared to ANNs. However, the training of the state-of-the-art (SOTA) SNNs for the VPR task is often intractable on large and diverse datasets. To address this, we develop an end-to-end convolutional SNN model for VPR, that leverages back-propagation for tractable training. Rate-based approximations of leaky integrate-and-fire (LIF) neurons are employed during training to enable back-propagation, and the approximation units are replaced with spiking LIF neurons during inference. The proposed method outperforms the SOTA ANNs and SNNs by achieving 78.2% precision at 100% recall on the challenging Nordland dataset, compared with 53% SOTA performance, and exhibits competitive performance on the Oxford RobotCar dataset while being easier to train and faster in both training and inference when compared to other ANN and SNN-based methods.
Decoding strawberry volatile cultivar diversity through comparative transcriptome analysis
Authors: Passa Kondylia , Tsormpatsidis Evangelos , Ganopoulos Ioannis , Bazakos Christos , Papasotiropoulos Vasileios 
Published: March 2024

AbstractThis study presents a comparative transcriptomic analysis of three commercial strawberry cultivars: ‘Rociera’, ‘Calderon’, and ‘Victory’, aimed at uncovering the molecular basis of their distinct flavor and aroma profiles. Through RNA sequencing, we analyzed the transcriptomic landscape of these varieties, uncovering a notable array of differentially expressed genes (DEGs) between them. Specifically, ‘Rociera’ showed a higher count of DEGs compared to ‘Victory’, suggesting significant differences in gene expression related to flavor and aroma between those cultivars. Our further investigations revealed pivotal metabolic pathways— such as those involving furanones, amino acids, fatty acids, and terpenoids—participating in generating volatile organic compounds in strawberries. These pathways exhibited variety-specific expression patterns, underlining the genetic determinants of each variety’s unique sensory characteristics. Gene Ontology (GO) enrichment analysis underscored important pathways like phototropism, sugar-mediated signaling, and terpenoid biosynthesis, highlighting the genetic intricacy that influences strawberry flavor and aroma. Additionally, our study identified 31,677 single nucleotide polymorphisms (SNPs) with a significant impact on genes associated with volatile compound biosynthesis. Notably, SNPs linked to key enzymes such as lipoxygenase 6, omega-6 fatty acid desaturase (FaFAD1), and phenylalanine ammonia-lyase 1 (PAL) were discovered, shedding light on the genetic variations that underlie flavor differences. Therefore, this research advances our comprehension of the genetic elements that impact strawberry fruit flavor and aroma, offering invaluable insights for molecular breeding endeavors focused on enhancing strawberry flavor.
BMP2 binds non-specifically to PEG-passivated biomaterials and induces substantial signaling
Authors: Le Pennec Jean , Guibert Amaury , Vivès Romain R. , Migliorini Elisa 
Published: March 2024

AbstractBiomaterials are widely employed across diverse biomedical applications and represent an attractive strategy to explore physiologically how extracellular matrix components influence the cellular response. In this study, we aimed to use previously developed biomimetic streptavidin platforms to investigate the role of glycosaminoglycans (GAGs) in bone morphogenetic protein 2 (BMP2) signaling. However, we observed that the interpretation of our findings was skewed due to the GAG-unrelated, non-specific adsorption of BMP2 on components of our biomaterials. Non-specific adsorption of proteins is a recurrent and challenging issue for biomaterial studies. Despite the initial incorporation of anti-fouling poly(ethylene glycol) (PEG) chains within our biomaterials, the residual non-specific BMP2 adsorption still triggered BMP2 signaling within the same range as our conditions of interest. To tackle this issue, we explored various options to prevent BMP2 non-specific adsorption. Specifically, we tested alternative constructions of our biomaterials on gold or glass substrate using distinct PEG-based linkers. We identified the aggregation of BMP2 at neutral pH as a potential cause of non-specific adsorption and thus determined specific buffer conditions to prevent it. We also investigated the induced BMP2 signaling over different culture periods. Nevertheless, none of these options resulted in a viable suitable solution to reduce the non-specific BMP2 signaling.Next, we studied the effect of various blocking strategies. We identified a blocking condition involving a combination of bovine serum albumin and trehalose that successfully reduced the unspecific attachment of BMP2 and the non-specific signaling. Furthermore, the effect of this blocking step was improved when using gold platforms instead of glass, particularly with Chinese hamster ovary (CHO) cells that seemed less responsive to non-specifically bound BMP2 than C2C12 cells.
Impact of genomic background and developmental state on signaling pathways and response to therapy in glioblastoma patient-derived cells
Authors: Berezovsky Artem , Nuga Oluwademilade , Datta Indrani , Bergman Kimberly , Sabedot Thais , Gurdziel Katherine , Irtenkauf Susan , Hasselbach Laura , Meng Yuling , Mueller Claudius , Petricoin Emanuel F. , Brown Stephen , Purandare Neeraja , Aras Sidhesh , Mikkelsen Tom , Poisson Laila , Noushmehr Houtan , Ruden Douglas , deCarvalho Ana C. 
Published: March 2024

AbstractGlioblastoma (GBM) tumors represents diverse genomic epigenomic, and transcriptional landscapes, with significant intratumoral heterogeneity that challenges standard of care treatments involving radiation (RT) and the DNA-alkylating agent temozolomide (TMZ). In this study, we employed targeted proteomics to assess the response of a genomically-diverse panel of GBM patient-derived cancer stem cells (CSCs) to astrocytic differentiation, growth factor withdrawal and traditional high fetal bovine serum culture. Our findings revealed a complex crosstalk and co-activation of key oncogenic signaling in CSCs and diverse patterns of response to these external stimuli. Using RNA sequencing and DNA methylation, we observed common adaptations in response to astrocytic differentiation of CSCs across genomically distinct models, including BMP-Smad pathway activation, reduced cholesterol biosynthesis, and upregulation of extracellular matrix components. Notably, we observed that these differentiated CSC progenies retained a subset of stemness genes and the activation of cell survival pathways. We also examined the impact of differentiation state and genomic background on GBM cell sensitivity and transcriptional response to TMZ and RT. Differentiation of CSCs increased resistance to TMZ but not to RT. While transcriptional responses to these treatments were predominantly regulated by p53 in wild-type p53 GBM cells, its transcriptional activity was modulated by the differentiation status and treatment modality. Both mutant and wild-type p53 models exhibited significant activation of a DNA-damage associated interferon response in CSCs and differentiated cells, suggesting this pathway may play a wider role in GBM response to TMZ and RT. Our integrative analysis of the impact of GBM cell developmental states, in the context of genomic and molecular diversity of patient-derived models, provides valuable insights for pre-clinical studies aimed at optimizing treatment strategies.
Septo-hypothalamic regulation of binge-like alcohol consumption by the nociceptin system
Authors: Haun Harold , Hernandez Raul , Yan Luzi , Flanigan Meghan , Hon Olivia , Lee Sophia , Méndez Hernán , Roland Alison , Taxier Lisa , Kash Thomas 
Published: March 2024

ABSTRACTHigh intensity alcohol drinking during binge episodes overwhelmingly contributes to the socioeconomic burden created by Alcohol Use Disorders (AUD). Novel interventions are needed to add to the current therapeutic toolkit and nociceptin receptor (NOP) antagonists have shown promise in reducing heavy drinking days in patients with an AUD. However, an endogenous locus of nociceptin peptide and discrete sites of NOP action underlying this effect remains understudied. Here we show that the lateral septum (LS), a region contributing to binge drinking, is enriched in neurons expressing mRNA coding for the nociceptin peptide (Pnoc). Pnoc-expressing neurons of the LS (LSPnoc) are tuned to stimuli associated with negative valence and display increased excitability during withdrawal from binge-like alcohol drinking. LSPnocactivation was found to have aversive qualities and also potentiates binge-like drinking behavior, suggesting a convergence of circuitry that promotes aversion and drives alcohol consumption. Viral mediated tracing and functional assessment of LSPnocprojection fields revealed GABAergic synapses locally within the LS, and downstream within the lateral hypothalamus (LH) and supramammillary nucleus (SuM). Genetic deletion of NOP from the LS attenuated binge-like alcohol intake in male mice while NOP deletion from the LH and SuM decrease alcohol intake in females. Together, these findings are the first to demonstrate an endogenous population of nociceptin-expressing neurons that contributes to alcohol consumption and identifies sex-dependent modulation of alcohol drinking by NOP.
TargetingLeishmania infantumMannosyl-oligosaccharide glucosidase with natural products: pH-dependent inhibition explored through computer-aided drug design
Authors: Goyzueta-Mamani Luis Daniel , Barazorda-Ccahuana Haruna Luz , Candia-Puma Mayron Antonio , Galdino Alexsandro Sobreira , Machado-de-Avila Ricardo Andrez , Giunchetti Rodolfo Cordeiro , Medina-Franco José L. , Florin-Christensen Mónica , Ferraz Coelho Eduardo Antonio , Chávez-Fumagalli Miguel Angel 
Published: March 2024

AbstractVisceral Leishmaniasis (VL) is a serious public health issue, documented in more than ninety countries, where an estimated 500,000 new cases emerge each year. Regardless of novel methodologies, advancements, and experimental interventions, therapeutic limitations, and drug resistance are still challenging. For this reason, based on previous research, we screened natural products (NP) from Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBEDB), Mexican Compound Database of Natural Products (BIOFACQUIM), and Peruvian Natural Products Database (PeruNPDB) databases, in addition to structural analogs of Miglitol and Acarbose, which have been suggested as treatments for VL and have shown encouraging action against parasite’s N-glycan biosynthesis. Using computer-aided drug design (CADD) approaches, the inhibitory effect of these NP candidates was evaluated by inhibiting the Mannosyl-oligosaccharide Glucosidase Protein (MOGS) fromLeishmania infantum, an enzyme essential for the protein glycosylation process, at various pH to mimic the parasite’s changing environment. Also, computational analysis was used to evaluate the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profile, while molecular dynamic simulations were used to gather information on the interactions between these ligands and the protein target. Our findings indicated that Ocotillone and Subsessiline have potential antileishmanial effects at pH 5 and 7, respectively, due to their high binding affinity to MOGS and interactions in the active center. Furthermore, these compounds were non-toxic and had the potential to be administered orally. This research indicates the promising anti-leishmanial activity of Ocotillone and Subsessiline, suggesting further validation throughin vitroandin vivoexperiments.
CSF proteomic profiling with amyloid/tau positivity identifies distinctive sex-different alteration of multiple proteins involved in Alzheimer’s disease
Authors: Do Anh N. , Ali Muhammad , Timsina Jigyasha , Wang Lihua , Western Daniel , Liu Menghan , Sanford Jessie , Rosende-Roca Matitee , Boada Merce , Puerta Raquel , Wilson Ted , Ruiz Agustin , Pastor Pau ,   the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Wyss-Coray Tony , Cruchaga Carlos , Sung Yun Ju 
Published: March 2024

AbstractIn Alzheimer’s disease (AD), the most common cause of dementia, females have higher prevalence and faster progression, but sex-specific molecular findings in AD are limited. Here, we comprehensively examined and validated 7,006 aptamers targeting 6,162 proteins in cerebral spinal fluid (CSF) from 2,077 amyloid/tau positive cases and controls to identify sex-specific proteomic signatures of AD. In discovery (N=1,766), we identified 330 male-specific and 121 female-specific proteomic alternations in CSF (FDR <0.05). These sex-specific proteins strongly predicted amyloid/tau positivity (AUC=0.98 in males; 0.99 in females), significantly higher than those with age, sex, and APOE-ε4 (AUC=0.85). The identified sex-specific proteins were well validated (r≥0.5) in the Stanford study (N=108) and Emory study (N=148).Biological follow-up of these proteins led to sex differences in cell-type specificity, pathways, interaction networks, and drug targets. Male-specific proteins, enriched in astrocytes and oligodendrocytes, were involved in postsynaptic and axon-genesis. The male network exhibited direct connections among 152 proteins and highlighted PTEN, NOTCH1, FYN, and MAPK8 as hubs. Drug target suggested melatonin (used for sleep-wake cycle regulation), nabumetone (used for pain), daunorubicin, and verteporfin for treating AD males. In contrast, female-specific proteins, enriched in neurons, were involved in phosphoserine residue binding including cytokine activities. The female network exhibits strong connections among 51 proteins and highlighted JUN and 14-3-3 proteins (YWHAG and YWHAZ) as hubs. Drug target suggested biperiden (for muscle control of Parkinson’s disease), nimodipine (for cerebral vasospasm), quinostatin and ethaverine for treating AD females. Together, our findings provide mechanistic understanding of sex differences for AD risk and insights into clinically translatable interventions.
Location of ryanodine receptor type 2 mutation predicts age of onset of sudden death in catecholaminergic polymorphic ventricular tachycardia – A systematic review and meta-analysis of case-based literature
Authors: Beqaj Halil , Sittenfeld Leah , Chang Alexander , Miotto Marco , Dridi Haikel , Willson Gloria , Jorge Carolyn Martinez , Li Jaan Altosaar , Reiken Steven , Liu Yang , Dai Zonglin , Marks Andrew R. 
Published: March 2024

AbstractBackgroundCatecholaminergic polymorphic ventricular tachycardia (CPVT) is a rare inherited arrhythmia caused by mutations in the ryanodine receptor type 2 (RyR2). Diagnosis of CPVT often occurs after a major cardiac event, thus posing a severe threat to the patient’s health.MethodsPublication databases, including PubMed, Scopus, and Embase, were searched for articles on patients with RyR2-CPVT mutations and their associated clinical presentation. Articles were reviewed by two independent reviewers and mutations were analyzed for demographic information, mutation distribution, and therapeutics. The human RyR2 cryo-EM structure was used to model CPVT mutations and predict the diagnosis and outcomes of CPVT patients.FindingsWe present a database of 1008 CPVT patients from 227 papers. Data analyses revealed that patients most often experienced exercise-induced syncope in their early teenage years but the diagnosis of CPVT took a decade. Mutations located near key regulatory sites in the channel were associated with earlier onset of CPVT symptoms including sudden cardiac death.InterpretationThe present study provides a road map for predicting clinical outcomes based on the location of RyR2 mutations in CPVT patients. The study was partially limited by the inconsistency in the depth of information provided in each article, but nevertheless is an important contribution to the understanding of the clinical and molecular basis of CPVT and suggests the need for early diagnosis and creative approaches to disease management.FundingThe work was supported by grant NIH R01HL145473, P01 HL164319 R25HL156002, T32 HL120826.
FABEL: Forecasting Animal Behavioral Events with Deep Learning-Based Computer Vision
Authors: Catto Adam , O’Connor Richard , Braunscheidel Kevin M. , Kenny Paul J. , Shen Li 
Published: March 2024

AbstractBehavioral neuroscience aims to provide a connection between neural phenomena and emergent organism-level behaviors. This requires perturbing the nervous system and observing behavioral outcomes, and comparing observed post-perturbation behavior with predicted counterfactual behavior and therefore accurate behavioral forecasts. In this study we present FABEL, a deep learning method for forecasting future animal behaviors and locomotion trajectories from historical locomotion alone. We train an offline pose estimation network to predict animal body-part locations in behavioral video; then sequences of pose vectors are input to deep learning time-series forecasting models. Specifically, we train an LSTM network that predicts a future food interaction event in a specified time window, and a Temporal Fusion Transformer that predicts future trajectories of animal body-parts, which are then converted into probabilistic label forecasts. Importantly, accurate prediction of food interaction provides a basis for neurobehavioral intervention in the context of compulsive eating. We show promising results on forecasting tasks between 100 milliseconds and 5 seconds timescales. Because the model takes only behavioral video as input, it can be adapted to any behavioral task and does not require specific physiological readouts. Simultaneously, these deep learning models may serve as extensible modules that can accommodate diverse signals, such as in-vivo fluorescence imaging and electrophysiology, which may improve behavior forecasts and elucidate invervention targets for desired behavioral change.