bluuebunny/crossref_metadata_2025_split huggingface dataset:
Variational graph encoders: a surprisingly effective generalist algorithm for holistic computer-aided drug design
Authors: Lam Yuen In Hilbert , Pincket Robbe , Han Hao , Ong Xing Er , Wang Zechen , Li Weifeng , Hinks Jamie , Zheng Liangzhen , Wei Yanjie , Mu Yuguang 
Published: January 2023

1.AbstractWhile there has been significant progress in molecular property prediction in computer-aided drug design, there is a critical need to have fast and accurate models. Many of the currently available methods are mostly specialists in predicting specific properties, leading to the use of many models side-by-side that lead to impossibly high computational overheads for the common researcher. Henceforth, the authors propose a single, generalist unified model exploiting graph convolutional variational encoders that can simultaneously predict multiple properties such as absorption, distribution, metabolism, excretion and toxicity (ADMET), target-specific docking score prediction and drug-drug interactions. Considerably, the use of this method allows for state-of-the-art virtual screening with an acceleration advantage of up to two orders of magnitude. The minimisation of a graph variational encoder’s latent space also allows for accelerated development of specific drugs for targets with Pareto optimality principles considered, and has the added advantage of explainability.
Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA
Authors: Szulc Natalia A. , Mackiewicz Zuzanna , Bujnicki Janusz M. , Stefaniak Filip 
Published: January 2023

ABSTRACTRibonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA—small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion-and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes.Key PointsStructural Interaction fingerprints (SIFts), combined with machine learning, were successfully used to develop activity models for ligands binding to RNA.SIFt-based models outperformed the classic, general-purpose scoring functions in virtual screening.Explainable Artificial Intelligence allowed us to understand the decision-making process and decipher molecular recognition processes in the analysis of RNA—ligand binding activity models.We provide a benchmark dataset based on ligands with known or putative binding activity toward six RNA targets. It can be readily used by the scientific community to test new algorithms of virtual screening on RNA—ligand complexes.
Description ofProtosticta armageddonensis sp. nov. (Odonata: Zygoptera: Platystictidae) from the Western Ghats of India
Authors: Payra Arajush , Chandran Reji , Deshpande Ameya , Koparde Pankaj 
Published: January 2023

AbstractA new species ofProtostictaSelys, 1885 (Odonata: Zygoptera: Platystictidae) is described based on two male specimens collected from Kerala, at the southern end of the Western Ghats in India. We comparedP. armageddonensissp. nov. with the three closely similarProtostictaspecies recently described from the Western Ghats, namelyP.anamalaicaSadasivan, Nair & Samuel, 2022,P. cyanofemoraJoshi, Subramanian, Babu & Kunte, 2020, andP. monticolaEmiliyamma & Palot, 2016, to provide comprehensive differential diagnosis. The new species is distinguished from its congeners by a combination of characters, including the structure of prothorax, caudal appendages, genital ligula, and markings on the 8th abdominal segment. A revised key ofProtostictaspp. of the Western Ghats, based on mature male specimens is provided.
Phosphorylation of PSD-95 at Serine 73 in dCA1 is required for extinction of contextual fear
Authors: Ziółkowska Magdalena , Borczyk Malgorzata , Cały Anna , Nalberczak-Skóra Maria , Nowacka Agata , Śliwińska Małgorzata Alicja , Łukasiewicz Kacper , Skonieczna Edyta , Tomaszewski Kamil F. , Wójtowicz Tomasz , Włodarczyk Jakub , Bernaś Tytus , Salamian Ahmad , Radwanska Kasia 
Published: January 2023

ABSTRACTThe updating of contextual memories is essential for survival in a changing environment. Accumulating data indicate that the dorsal CA1 area (dCA1) contributes to this process. However, the cellular and molecular mechanisms of contextual fear memory updating remain poorly understood. Postsynaptic density protein 95 (PSD-95) regulates the structure and function of glutamatergic synapses. Here, using dCA1-targeted genetic manipulationsin vivo, combined withex vivo3D electron microscopy and electrophysiology, we identify a novel, synaptic mechanism that is induced during attenuation of contextual fear memories and involves phosphorylation of PSD-95 at Serine 73 in dCA1. Our data provide the proof that PSD-95-dependent synaptic plasticity in dCA1 is required for updating of contextual fear memory.
Oviposition behaviour is not affected by ultraviolet light in a butterfly with sexually-dimorphic expression of a UV-sensitive opsin
Authors: Malo Jose Borrero , Wright Daniel Shane , Bacquet Caroline Nicole , Merrill Richard M. 
Published: January 2023

AbstractAnimal vision is important for mediating multiple complex behaviours. InHeliconiusbutterflies, vision guides fundamental behaviours such as oviposition, foraging and mate choice. Colour vision inHeliconiusinvolves ultraviolet (UV), blue and long-wavelength sensitive photoreceptors (opsins). Additionally,Heliconiuspossess a duplicated UV opsin, and its expression varies widely within the genus. InHeliconius erato, opsin expression is sexually dimorphic; only females express both UV-sensitive opsins, enabling UV wavelength discrimination. However, the ecological pressures that have driven these sex-specific differences in visual perception remain unresolved.Heliconiusfemales invest heavily in finding hostplants to lay their eggs, a behaviour heavily reliant on visual cues. We tested whether UV vision is used for oviposition inH. eratoandHeliconius himerafemales by manipulating the availability of UV in behavioural experiments under naturalistic conditions. We found that UV did not influence the number of oviposition attempts or the number of eggs laid. In addition, their hostplant,Passiflora punctata, does not reflect UV wavelengths, and models ofH. eratofemale vision suggest only minimal stimulation of the UV opsins. Overall, these findings suggest that UV wavelengths do not directly affect the ability ofHeliconiusfemales to find suitable oviposition sites. Alternatively, UV discrimination could be used in the context of foraging or mate choice, but this remains to be tested.
Newer Surveillance Data Extends our Understanding of the Niche ofRickettsia montanensis(Rickettsiales: Rickettsiaceae) Infection of the American Dog Tick (Acari: Ixodidae) in the United States
Authors: Lippi Catherine A. , Gaff Holly D. , Nadolny Robyn M. , Ryan Sadie J. 
Published: January 2023

AbstractBackgroundUnderstanding the geographic distribution ofRickettsia montanensisinfections inDermacentor variabilisis important for tick-borne disease management in the United States, as both a tick-borne agent of interest and a potential confounder in surveillance of other rickettsial diseases. Two previous studies modeled niche suitability forD. variabiliswith and withoutR. montanensis, from 2002-2012, indicating that theD. variabilisniche overestimates the infected niche. This study updates these, adding data since 2012.MethodsNewer surveillance and testing data were used to update Species Distribution Models (SDMs) ofD. variabilis, andR. montanensisinfectedD. variabilis, in the United States. Using random forest (RF) models, found to perform best in previous work, we updated the SDMs and compared them with prior results. Warren’s I niche overlap metric was used to compare between predicted suitability for all ticks and ‘pathogen positive niche’ models across datasets.ResultsWarren’s I indicated <2% change in predicted niche, and there was no change in order of importance of environmental predictors, forD. variabilisorR. montanensispositive niche. The updatedD. variabilisniche model overpredicted suitability compared to the updatedR. montanensispositive niche in key peripheral parts of the range, but slightly underpredicted through the northern and midwestern parts of the range. This reinforces previous findings of a more constrained pathogen-positive niche than predicted byD. variabilisrecords alone.ConclusionsThe consistency of predicted niche suitability forD. variabilisin the United States, with the addition of nearly a decade of new data, corroborates this is a species with generalist habitat requirements. Yet a slight shift in updated niche distribution, even of low suitability, included more southern areas, pointing to a need for continued and extended monitoring and surveillance. This further underscores the importance of revisiting vector and vector-borne disease distribution maps.
SCS: cell segmentation for high-resolution spatial transcriptomics
Authors: Chen Hao , Li Dongshunyi , Bar-Joseph Ziv 
Published: January 2023

AbstractSpatial transcriptomics promises to greatly improve our understanding of tissue organization and cell-cell interactions. While most current platforms for spatial transcriptomics only offer multi-cellular resolution, with 10-15 cells per spot, recent technologies provide a much denser spot placement leading to sub-cellular resolution. A key challenge for these newer methods is cell segmentation and the assignment of spots to cells. Traditional image-based segmentation methods are limited and do not make full use of the information profiled by spatial transcrip-tomics. Here we present SCS, which combines imaging data with sequencing data to improve cell segmentation accuracy. SCS assigns spots to cells by adaptively learning the position of each spot relative to the center of its cell using a transformer neural network. SCS was tested on two new sub-cellular spatial transcriptomics technologies and outperformed traditional image-based segmentation methods. SCS achieved better accuracy, identified more cells, and provided more realistic cell size estimation. Sub-cellular analysis of RNAs using SCS spots assignments provides information on RNA localization and further supports the segmentation results.
Fast, accurate ranking of engineered proteins by receptor binding propensity using structure modeling
Authors: Ding Xiaozhe , Chen Xinhong , Sullivan Erin E. , Shay Timothy F. , Gradinaru Viviana 
Published: January 2023

AbstractDeep learning-based methods for protein structure prediction have achieved unprecedented accuracy. However, the power of these tools to guide the engineering of protein-based therapeutics remains limited due to a gap between the ability to predict the structures of candidate proteins and the ability to assess which of those proteins are most likely to bind to a target receptor. Here we bridge this gap by introducing Automated Pairwise Peptide-Receptor AnalysIs for Screening Engineered proteins (APPRAISE), a method for predicting the receptor binding propensity of engineered proteins. After generating models of engineered proteins competing for binding to a target using an established structure-prediction tool such as AlphaFold-Multimer or ESM-Fold, APPRAISE performs a rapid (under 1 CPU second per model) scoring analysis that takes into account biophysical and geometrical constraints. As a proof-of-concept, we demonstrate that APPRAISE can accurately classify receptor-dependent vs. receptor-independent adeno-associated viral vectors and diverse classes of engineered proteins such as miniproteins targeting the SARS-CoV-2 spike, nanobodies targeting a G-protein-coupled receptor, and peptides that specifically bind to transferrin receptor or PD-L1. APPRAISE is accessible through a web-based notebook interface using Google Colaboratory (https://tiny.cc/APPRAISE). With its accuracy, interpretability, and generalizability, APPRAISE promises to expand the utility of protein structure prediction and accelerate protein engineering for biomedical applications.
Immature leaves are the dominant volatile sensing organs of maize
Authors: Wang Lei , Jäggi Simon , Walthert Mario , Waterman Jamie M. , Cofer Tristan M. , Erb Matthias 
Published: January 2023

AbstractPlants perceive herbivory induced volatiles and respond to them by upregulating their defenses. So far, the organs responsible for volatile perception remain poorly described. Here, we show that responsiveness to the herbivory induced green leaf volatile (Z)-3-hexenyl acetate (HAC) in terms of volatile emission, transcriptional regulation and defense hormone activation is largely constrained to younger maize leaves. Older leaves are much less sensitive to HAC. In a given leaf, responsiveness to HAC is high at immature developmental stages and drops off rapidly during maturation. Responsiveness to the non-volatile elicitor ZmPep3 shows an opposite pattern, demonstrating that hyposmia is not driven by defective canonical defense signaling. Neither stomatal conductance nor leaf cuticle composition explain the unresponsiveness of older leaves to HAC, suggesting perception mechanisms upstream of canonical defense signaling as driving factors. Finally, we show that hyposmia in older leaves is not restricted to HAC, and extends to the full blend of herbivory induced volatiles. In conclusion, our work identifies immature maize leaves as dominant stress volatile sensing organs. The tight spatiotemporal control of volatile perception may facilitate within-plant defense signaling to protect young leaves, and may allow plants with complex architectures to explore the dynamic odor landscapes at the outer periphery of their shoots.
Galectin-9 has non-apoptotic cytotoxic activity towards Acute Myeloid Leukemia independent of cytarabine resistance
Authors: Choukrani Ghizlane , Visser Nienke , Avtenyuk Natasha Ustyanovska , Olthuis Mirjam , Marsman Glenn , Ammatuna Emanuele , Lourens Harm Jan , Niki Toshiro , Huls Gerwin , Bremer Edwin , Wiersma Valerie R. 
Published: January 2023

AbstractAcute myeloid leukemia (AML) is a malignancy still associated with poor survival rates, among others due to frequent occurrence of therapy-resistant relapse after standard-of-care treatment with cytarabine (AraC). AraC triggers apoptotic cell death, a type of cell death to which AML cells often become resistant. Therefore, therapeutic options that trigger an alternate type of cell death are of particular interest. We previously identified that the glycan-binding protein Galectin-9 (Gal-9) has tumor-selective and non-apoptotic cytotoxicity towards various types of cancer, which depended on autophagy inhibition. Thus, Gal-9 could be of therapeutic interest for (AraC-resistant) AML. In the current study, treatment with Gal-9 was cytotoxic for AML cells, including for CD34+patient-derived AML stem cells, but not for healthy cord blood-derived CD34+stem cells. This Gal-9-mediated cytotoxicity did not rely on apoptosis but negatively associated with autophagic flux. Importantly, both AraC-sensitive and -resistant AML cell lines as well as AML patient samples were sensitive to single agent treatment with Gal-9. Additionally, Gal-9 potentiated the cytotoxic effect of DNA demethylase inhibitor Azacytidine (Aza), a drug that is clinically used for patients that are not eligible for intensive AraC treatment. Thus, Gal-9 is a potential therapeutic agent for the treatment of AML, including AraC resistant AML, by inducing caspase-independent cell death.
High Throughput Sequencing technologies complemented by grower’s perception highlight the impact of tomato virome in diversified vegetable farms
Authors: Temple Coline , Blouin Arnaud G. , Tindale Sophie , Steyer Stephan , Marechal Kevin , Massart Sebastien 
Published: January 2023

AbstractThe number of small-scale diversified vegetable growers in industrialized countries has risen sharply over the last ten years. The risks associated with plant viruses in these systems have been barely studied in Europe, yet dramatic virus emergence events, such as tomato brown fruit rugose virus, sometimes occur. We developed a methodology that aimed to understand better the implications related to viruses for tomato production in Belgian’s vegetable farms by comparing growers’ perception of the presence of viral symptoms (visual inspection) with non targeting detection of nearly all viruses present in the plants by high throughput sequencing technologies (HTS). Virus presence and impact were interpreted considering the farm’s typology and cultural practices, the grower’s professional profiles, and visual inspection of plant-viral-like symptoms. Overall, The data indicated that most growers have limited understanding of tomato viruses and are not concerned about them. Field observations were correlated to this perception as the prevalence of symptomatic plants was usually lower than 1%. However, important and potentially emergent viruses, mainly transmitted by insects, were detected in several farms. Noteworthy, the presence of these viruses was correlated with the number of plant species grown per site (diversity) but not with a higher awareness of the growers regarding plant viral diseases or a higher number of symptomatic plants. In addition, both HTS and perception analysis underlined the rising incidence and importance of an emergent virus: Physostegia chlorotic mottle virus. Overall, the original methodology developed here, combining social science with HTS technologies, could be applied to other crops in other systems to identify emergent risks associated with plant viruses and can highlight the communication needed toward growers to mitigate epidemics.
Grapevine scion gene expression is driven by rootstock and environment interaction
Authors: Harris Zachary N , Pratt Julia E , Kovacs Laszlo G , Klein Laura L , Kwasniewski Misha T. , Londo Jason P , Wu Angela , Miller Allison J 
Published: January 2023

AbstractBACKGROUNDGrafting is a horticultural practice used widely across woody perennial crop species to fuse together the root and shoot system of two distinct genotypes, the rootstock and the scion, combining beneficial traits from both. In grapevine, grafting is used in nearly 80% of all commercial vines to optimize fruit quality, regulate vine vigor, and enhance biotic and abiotic stress-tolerance. Rootstocks have been shown to modulate elemental composition, metabolomic profiles, and the shape of leaves in the scion, among other traits. However, it is currently unclear how rootstock genotypes influence shoot system gene expression as previous work has reported complex and often contradictory findings.RESULTSIn the present study, we examine the influence of grafting on scion gene expression in leaves and reproductive tissues of grapevines growing under field conditions for three years. We show that the influence from the rootstock genotype is highly tissue and time dependent, manifesting only in leaves, primarily during a single year of our three-year study. Further, the degree of rootstock influence on scion gene expression is driven by interactions with the local environment.CONCLUSIONSOur results demonstrate that the role of rootstock genotype in modulating scion gene expression is not a consistent, unchanging effect, but rather an effect that varies over time in relation to local environmental conditions.
Automated quantitative trait locus analysis (AutoQTL)
Authors: Freda Philip J. , Ghosh Attri , Zhang Elizabeth , Luo Tianhao , Chitre Apurva , Polesskaya Oksana , St. Pierre Celine L. , Gao Jianjun , Martin Connor D. , Chen Hao , Garcia-Martinez Angel G. , Wang Tengfei , Han Wenyan , Ishiwari Keita , Meyer Paul , Lamparelli Alexander , King Christopher P. , Palmer Abraham A. , Li Ruowang , Moore Jason H. 
Published: January 2023

AbstractBackgroundQuantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complex decisions related to analysis of complex traits and generate diverse solutions to describe relationships that exist in genetic data.ResultsUsing a dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat,Rattus norvegicus, AutoQTL captures the phenotypic variance explained under a standard additive model while also providing evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions from simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL.ConclusionsThis proof-of-concept illustrates that automated machine learning techniques can be applied to genetic data and has the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection strategies.