bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impaired embryo sac cellularization byPMEIgene mutation affects gamete specification and twin plants in Arabidopsis
Authors: Sharma Isha , Malathi Pinninti , Srinivasan Ramamurthy , Bhat Shripad Ramachandra , Sreenivasulu Yelam 
Published: October 2023

AbstractArabidopsislines with loss-of-function mutations in the gene encodingEmbryo sacPectinMethylEsteraseInhibitor(Atepmei) were found to have short silique and high seed sterility. Examination of tissue-cleared mature ovules (FG7-stage) revealed irregularly positioned nuclei within the embryo sacs. Instead of horse-shoe-shaped ovules, defective globular ovules without proper micropylar and chalazal ends were found. Embryo sac cell-type-specific GFP marker expression studies confirmed gamete and accessory cell identity alterations. Egg cell-specific marker (DD45) expression analysis confirmed the presence of multiple egg cells in the mutant embryo sacs, possibly due to defect in embryo sac cellularization. These supernumerary egg cells were functional as evident from the production of twin embryos when supernumerary sperm cells were provided. The results of Ruthenium red and tannic acid-ferric chloride staining ofAtepmeimutant developing ovules, conferred its interaction with the specific PME in proper cell wall formation and maintenance around embryo sac nuclei which also coincide with its fate as a specific gamete. This is the first report implicating role of cell wall in gamete cell fate determination by altering cell-cell communication. Our analysis of the twin-embryo phenotype ofepmeimutants also sheds light on the boundary conditions for double fertilization in plant reproduction.
Integrative analysis of the methylome and transcriptome of tomato fruit (Solanum lycopersicumL.) induced by postharvest handling
Authors: Zhou Jiaqi , Zhou Sitian , Chen Bixuan , Sangsoy Kamonwan , Luengwilai Kietsuda , Albornoz Karin , Beckles Diane M. 
Published: October 2023

AbstractTomato fruit ripening is triggered by the demethylation of key genes, which alters their transcriptional levels thereby initiating and propagating a cascade of physiological events. What is unknown, is how these processes are altered when fruit are ripened using postharvest practices to extend shelf-life, as these practices often reduce fruit quality. To address this, postharvest handling-induced changes in the fruit DNA methylome and transcriptome, and how they correlated with ripening speed, and ripening indicators such as ethylene, ABA, and carotenoids, were assessed. This study comprehensively connected changes in physiological events with dynamic molecular changes. Ripening fruit that reached ‘Turning’ (T) after storage under dark at 20°C, 12.5°C, or 5°C chilling (followed by 20°C rewarming), were compared to fresh-harvest fruit ‘FHT’. Fruit stored at 12.5°C, had the biggest epigenetic marks and alterations in gene expression, exceeding changes induced by postharvest chilling. Fruit physiological and chronological age were uncoupled at 12.5°C, as the time-to-ripening was longest. Fruit ripening at 12.5°C was not climacteric; there was no respiratory or ethylene burst, rather, fruit were high in ABA. Clear differentiation between postharvest-ripened and ‘FHT’ was evident in the methylome and transcriptome. Higher expression of photosynthetic genes and chlorophyll levels in ‘FHT’ fruit, pointing to light as influencing the molecular changes in fruit ripening. Finally, correlative analyses of the -omics data putatively identified genes regulated by DNA methylation. Collectively these data improve our interpretation of how tomato fruit ripening patterns are altered by postharvest practices, and long-term are expected to help improve fruit quality.
Do Domain-Specific Protein Language Models Outperform General Models on Immunology-Related Tasks?
Authors: Deutschmann Nicolas , Pelissier Aurelien , Weber Anna , Gao Shuaijun , Bogojeska Jasmina , Martínez María Rodríguez 
Published: October 2023

AbstractDeciphering the antigen recognition capabilities by T cell and B cell receptors (antibodies) is essential for advancing our understanding of adaptive immune system responses. In recent years, the development of protein language models (PLMs) has facilitated the development of bioinformatic pipelines where complex amino acid sequences are transformed into vectorized embeddings, which are then applied to a range of downstream analytical tasks. With their success, we have witnessed the emergence of domain-specific PLMs tailored to specific proteins, such as immune receptors. Domain-specific models are often assumed to possess enhanced representation capabilities for targeted applications, however, this assumption has not been thoroughly evaluated. In this manuscript, we assess the efficacy of both generalist and domain-specific transformer-based embeddings in characterizing B and T cell receptors. Specifically, we assess the accuracy of models that leverage these embeddings to predict antigen specificity and elucidate the evolutionary changes that B cells undergo during an immune response. We demonstrate that the prevailing notion of domain-specific models outperforming general models requires a more nuanced examination. We also observe remarkable differences between generalist and domain-specific PLMs, not only in terms of performance but also in the manner they encode information. Finally, we observe that the choice of the size and the embedding layer in PLMs are essential model hyperparameters in different tasks. Overall, our analyzes reveal the promising potential of PLMs in modeling protein function while providing insights into their information-handling capabilities. We also discuss the crucial factors that should be taken into account when selecting a PLM tailored to a particular task.
AnophelesModel: An R package to interface mosquito bionomics, human exposure and intervention effects with models of malaria intervention impact
Authors: Golumbeanu Monica , Briët Olivier , Champagne Clara , Lemant Jeanne , Winkel Munir , Zogo Barnabas , Gerhards Maximilian , Sinka Marianne , Chitnis Nakul , Penny Melissa , Pothin Emilie , Smith Tom 
Published: October 2023

AbstractIn recent decades, field and semi-field studies of malaria transmission have gathered geographic-specific information about mosquito ecology, behaviour and their sensitivity to interventions. Mathematical models of malaria transmission can incorporate such data to infer the likely impact of vector control interventions and hence guide malaria control strategies in various geographies. To facilitate this process and make model predictions of intervention impact available for different geographical regions, we developed AnophelesModel. AnophelesModel is an online, open-access, R package that directly allows incorporating generated entomological data for adjustment of models to assess intervention scenarios according to species and location-specific characteristics. In addition, it includes a previously published, comprehensive, curated database of field entomological data from over 50Anophelesspecies, field data on mosquito and human behaviour, and on estimates of vector control effectiveness. Using the input data, the package parameterizes a discrete-time, state transition model of the mosquito oviposition cycle and infers species-specific impacts of various interventions on vectorial capacity. In addition, it offers formatted outputs ready to use in downstream analyses and by other models of malaria transmission for accurate representation of the vector-specific components. Using AnophelesModel, we show how the key implications for intervention impact change for various vectors and locations. The package facilitates quantitative comparisons of likely intervention impacts in different geographical settings varying in vector compositions, and can thus guide towards more robust and efficient malaria control recommendations. The AnophelesModel R package is available under a GPL-3.0 license athttps://github.com/SwissTPH/AnophelesModel.
Targeted Acidosis Mediated Delivery of Antigenic MHC-Binding Peptides
Authors: Kelly Joey J. , Ankrom Emily , Thévenin Damien , Pires Marcos M. 
Published: October 2023

AbstractCytotoxic T lymphocytes are the primary effector immune cells responsible for protection against cancer, as they target peptide neoantigens presented through the major histocompatibility complex (MHC) on cancer cells, leading to cell death. Targeting peptide-MHC (pMHC) complexes offers a promising strategy for immunotherapy due to its specificity and effectiveness against cancer. In this work, we exploit the acidic tumor micro-environment to selectively deliver antigenic peptides to cancer cells using pH(low) insertion peptides (pHLIP). We demonstrated that the delivery of MHC binding peptides directly to the cytoplasm of melanoma cells resulted in the presentation of antigenic peptides on MHC, and subsequent activation of T cells. This work highlights the potential of pHLIP as a vehicle for targeted delivery of antigenic peptides and their presentationviaMHC-bound complexes on cancer cell surfaces for activation of T cells with implications for enhancing anti-cancer immunotherapy.
The Synaptic Architecture of Layer 5 Thick Tufted Excitatory Neurons in the Visual Cortex of Mice
Authors: Bodor Agnes L. , Schneider-Mizell Casey M , Zhang Chi , Elabbady Leila , Mallen Alex , Bergeson Andi , Brittain Derrick , Buchanan JoAnn , Bumbarger Daniel J. , Dalley Rachel , Gamlin Clare , Joyce Emily , Kapner Daniel , Kinn Sam , Mahalingam Gayathri , Seshamani Sharmishtaa , Suckow Shelby , Takeno Marc , Torres Russel , Yin Wenjing , Bae J. Alexander , Castro Manuel A. , Dorkenwald Sven , Halageri Akhilesh , Jia Zhen , Jordan Chris , Kemnitz Nico , Lee Kisuk , Li Kai , Lu Ran , Macrina Thomas , Mitchell Eric , Mondal Shanka Subhra , Mu Shang , Nehoran Barak , Popovych Sergiy , Silversmith William , Turner Nicholas L. , Yu Szi-chieh , Wong William , Wu Jingpeng , Celii Brendan , Campagnola Luke , Seeman Stephanie C , Jarsky Tim , Ren Naixin , Arkhipov Anton ,   The MICrONS Consortium, Reimer Jacob , Seung H Sebastian , Reid R. Clay , Collman Forrest , Maçarico da Costa Nuno 
Published: October 2023

SummaryThe neocortex is one of the most critical structures that makes us human, and it is involved in a variety of cognitive functions from perception to sensory integration and motor control. Composed of repeated modules, or microcircuits, the neocortex relies on distinct cell types as its fundamental building blocks. Despite significant progress in characterizing these cell types1–5, an understanding of the complete synaptic partners associated with individual excitatory cell types remain elusive.Here, we investigate the connectivity of arguably the most well recognized and studied excitatory neuron in the neocortex: the thick tufted layer 5 pyramidal cell6–10also known as extra telencephalic (ET)11neurons. Although the synaptic interactions of ET neurons have been extensively explored, a comprehensive characterization of their local connectivity remains lacking. To address this knowledge gap, we leveraged a 1 mm3electron microscopic (EM) dataset.We found that ET neurons primarily establish connections with inhibitory cells in their immediate vicinity. However, when they extend their axons to other cortical regions, they tend to connect more with excitatory cells. We also find that the inhibitory cells targeted by ET neurons are a specific group of cell types, and they preferentially inhibit ET cells. Finally, we observed that the most common excitatory targets of ET neurons are layer 5 IT neurons and layer 6 pyramidal cells, whereas synapses with other ET neurons are not as common.These findings challenge current views of the connectivity of ET neurons and suggest a circuit design that involves local competition among ET neurons and collaboration with other types of excitatory cells. Our results also highlight a specific circuit pattern where a subclass of excitatory cells forms a network with specific inhibitory cell types, offering a framework for exploring the connectivity of other types of excitatory cells.
Highly pathogenic Avian Influenza A (H5N1) Clade 2.3.4.4b in Wild Birds, Ecuador
Authors: Carrazco-Montalvo Andrés , Luje Lucia , Rodríguez-Pólit Cristina , Ampuño Antonio , Patiño Leandro , Gutiérrez-Pallo Diana , Alava Daniel , Alarcón-Vallejo Damaris , Arguello Natali , Echeverría-Garcés Gabriela , De La Torre David 
Published: October 2023

AbstractHighly pathogenic avian influenza A(H5N1) virus clade 2.3.4.4b was detected in four wild birds of two species,Fregata magnificensandSula nebouxii, on the Ecuadorian Coast. This report highlights the importance of intersectoral collaboration and timely genotyping for monitoring this zoonotic pathogen, especially in regions with a rich biodiversity.
Amyloidogenic regions in beta-strands II and III modulate the aggregation and toxicity of SOD1 in living cells
Authors: McAlary Luke , Nan Jeremy R , Shyu Clay , Sher Mine , Plotkin Steven S. , Cashman Neil R. 
Published: October 2023

AbstractMutations in the protein superoxide dismutase-1 (SOD1) promote its misfolding and aggregation, ultimately causing familial forms of the debilitating neurodegenerative disease amyotrophic lateral sclerosis (ALS). Currently, over 220 (mostly missense) ALS-causing mutations in the SOD1 protein have been identified throughout the primary sequence, indicating that common structural features responsible for aggregation and toxicity may be present. Here, we usedin silicotools to predict amyloidogenic regions in the ALS-associated SOD1-G85R mutant, finding 7 regions spread throughout the protein structure. We found that the introduction of proline residues into β-strands II (I18P) or III (I35P) reduced the aggregation propensity and toxicity of SOD1-G85R in living cells, significantly more so than proline mutations in other amyloidogenic regions. The I18P and I35P mutations also reduced the capability of SOD1-G85R to template onto previously formed non-proline mutant SOD1 aggregates as measured by fluorescence recovery after photobleaching. Finally, we found that, while the I18P and I35P mutants are less structurally stable than SOD1-G85R, the proline mutants are less aggregation-prone during proteasome inhibition, and less toxic overall. Our research highlights the importance of a previously underappreciated SOD1 amyloidogenic region in β-strand II (15QGIINF20) to the aggregation and toxicity of SOD1 in ALS mutants, and suggests that β-strands II and III may be good targets for the development of SOD1-associated ALS therapies.
SELECTIVELY MANIPULATING SOFTNESS PERCEPTION OF MATERIALS THROUGH SOUND SYMBOLISM
Authors: Nalbantoğlu H. , Hazır B. M. , Dövencioğlu D. N. 
Published: October 2023

AbstractCross-modal interactions between auditory and haptic perception manifest themselves in language, such as sound symbolic words: crunch, splash, and creak. Several studies have shown strong associations between sound symbolic words, shapes (e.g., Bouba/Kiki effect), and materials. Here, we identified these material associations in Turkish sound symbolic words and then tested for their effect on softness perception. First, we used a rating task in a semantic differentiation method to extract the perceived softness dimensions from words and materials. We then tested whether Turkish onomatopoeic words can be used to manipulate the perceived softness of everyday materials such as honey, silk, or sand across different dimensions of softness. In the first preliminary study, we used 40 material videos and 29 adjectives in a rating task with a semantic differentiation method to extract the main softness dimensions. A principal component analysis revealed 7 softness components, including Deformability, Viscosity, Surface Softness, and Granularity, in line with the literature. The second preliminary study used 47 Turkish onomatopoeic words and 31 adjectives in the same rating task. Again, the findings aligned with the literature, revealing dimensions such as Fluidity, Granularity, and Surface Softness. However, no factors related to Deformability were found due to the absence of sound symbolic words in this category. Next, we paired the onomatopoeic words and material videos based on their associations with each softness dimension. We conducted a new rating task, synchronously presenting material videos and spoken onomatopoeic words. We hypothesized that congruent word-video pairs would produce significantly higher ratings for dimension-related adjectives, while incongruent word-video pairs would decrease these ratings, and the ratings of unrelated adjectives would remain the same. Our results revealed that onomatopoeic words selectively alter the perceived material qualities, providing evidence and insight into the cross-modality of perceived softness.
Dynamic off-resonance correction improves functional image analysis in fMRI of awake behaving non-human primates
Authors: Shahdloo Mo , Khalighinejad Nima , Priestley Luke , Rushworth Matthew , Chiew Mark 
Published: October 2023

AbstractUse of functional MRI in awake non-human primate (NHPs) has recently increased. Scanning animals while awake makes data collection possible in the absence of anaesthetic modulation and with an extended range of possible experimental designs. Robust awake NHP imaging however is challenging due to the strong artifacts caused by time-varying off-resonance changes introduced by the animal’s body motion. Recently, an image reconstruction technique has been proposed to estimate these off-resonance changes using the navigator data that is typically collected during fMRI scans to correct the data and compensate for the changes. In this study, we sought to thoroughly investigate the effect of this correction on the brain activation estimates using extended awake NHP data. Our results show significant improvements in image fidelity using our proposed correction strategy, as well as greatly enhanced and more reliable activation estimates in GLM analyses.
Open-source LED lamp for the LI-6800 photosynthesis system
Authors: Vélez Ramírez Aarón I. , Moreno Juan de Dios , Pérez-Guerrero Uriel G. , Juarez Antonio M. , Castillo-Arriaga Hector , Vázquez-Medrano Josefina , Hernández-Morales Ilane 
Published: October 2023

ABSTRACTPremiseControlling light flux density during carbon dioxide assimilation measurements is essential in photosynthesis research. Commercial lamps are expensive and are based on monochromatic light emitting diodes (LEDs), which deviate significantly in their spectral distribution compared to sunlight.Methods and ResultsUsing a LED emitting white light with a color temperature similar to sunlight, we developed a cost-effective lamp compatible with the LI-6800 photosynthesis measuring system. When coupled with customized software, the lamp can be controlled via the LI-6800 console by a user or Python scripts. Testing and calibration show that the lamp meets the quality needed to estimate photosynthesis parameters.ConclusionsThe lamp can be built using a basic electronics lab and a 3D printer. Calibration instructions are supplied and only require equipment commonly available at plant science laboratories. The lamp is a cost-effective alternative to perform photosynthesis research coupled with the popular LI-6800 photosynthesis measuring system.
BayesKAT: Bayesian Optimal Kernel-based Test for genetic association studies reveals joint genetic effects in complex diseases
Authors: Das Adhikari Sikta , Cui Yuehua , Wang Jianrong 
Published: October 2023

ABSTRACTGWAS methods have identified individual SNPs significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power, or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT(https://github.com/wangjr03/BayesKAT), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules, and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases.
De novogeneration of T-cell receptors with desired epitope-binding property by leveraging a pre-trained large language model
Authors: Yang Jiannan , He Bing , Zhao Yu , Jiang Feng , Wang Zhonghuang , Guo Yixin , Xu Zhimeng , Yuan Bo , Song Jiangning , Zhang Qingpeng , Yao Jianhua 
Published: October 2023

ABSTRACTGenerating T-cell receptors (TCRs) with desired epitope-binding properties is a fundamental step in the development of immunotherapies, yet heavily relies on laborious and expensive wet experiments. Recent advancements in generative artificial intelligence have demonstrated promising power in protein design and engineering. In this regard, we propose a large language model, termed Epitope-Receptor-Transformer (ERTransformer), for thede novogeneration of TCRs with the desired epitope-binding property. ERTransformer is built on EpitopeBERT and ReceptorBERT, which are trained using 1.9 million epitope sequences and 33.1 million TCR sequences, respectively. To demonstrate the model capability, we generate 1000 TCRs for each of the five epitopes with known natural TCRs. The artificial TCRs exhibit low sequence identity (average Bit-score 27.64 with a standard deviation of 1.50) but high biological function similarity (average BLOSUM62 score 32.32 with a standard deviation of 12.01) to natural TCRs. Furthermore, the artificial TCRs are not very structurally identical to natural ones (average RMSD 2.84 Å with a standard deviation of 1.21 Å) but exhibit a comparable binding affinity towards the corresponding epitopes. Our work highlights the tremendous potential of applying ERTransformer to generate novel TCRs with desired epitope-binding ability.