bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structural diversity and stress regulation of the plant immunity-associated CALMODULIN-BINDING PROTEIN 60 (CBP60) family of transcription factors inSolanum lycopersicum(tomato)
Authors: Shivnauth Vanessa , Pretheepkumar Sonya , Marchetta Eric , Amani Keaun , Castroverde Christian Danve M. 
Published: December 2022

AbstractCellular signalling generates calcium (Ca2+) ions, which are ubiquitous secondary messengers decoded by calcium-dependent protein kinases, calcineurins, calreticulin, calmodulins (CAMs) and CAM-binding proteins. Previous studies in the model plantArabidopsis thalianahave shown the critical roles of the CAM-BINDING PROTEIN 60 (CBP60) protein family in plant growth, stress responses and immunity. Certain CBP60 factors can regulate plant immune responses, like pattern-triggered immunity, effector-triggered immunity, and synthesis of major plant immune-activating metabolites salicylic acid (SA) and N-hydroxypipecolic acid (NHP). Although homologous CBP60 sequences have been identified in the plant kingdom, their function and regulation in most species remain unclear. In this paper, we specifically characterized 11 members of the CBP60 family in the agriculturally important crop tomato (Solanum lycopersicum). Protein sequence analyses revealed that three CBP60 homologs have the closest amino acid identity toArabidopsisCBP60g and SARD1, master transcription factors involved in plant immunity. Strikingly, AlphaFold deep learning-assisted prediction of protein structures highlighted close structural similarity between these tomato andArabidopsisCBP60 homologs. Conserved domain analyses revealed that they possess CAM-binding domains and DNA-binding domains, reflecting their potential involvement in linking Ca2+signalling and transcriptional regulation in tomato plants. In terms of their gene expression profiles under biotic (Pseudomonas syringaepv.tomatoDC3000 pathogen infection) and/or abiotic stress (warming temperatures), five tomatoCBP60genes were pathogen-responsive and temperature-sensitive, reminiscent ofArabidopsis CBP60gandSARD1. Overall, we present a genome-wide identification of the CBP60 gene/protein family in tomato plants, and we provide evidence on their regulation and potential function as Ca2+-sensing transcriptional regulators.
Structural pre-training improves physical accuracy of antibody structure prediction using deep learning
Authors: Kończak Jarosław , Janusz Bartosz , Młokosiewicz Jakub , Satława Tadeusz , Wróbel Sonia , Dudzic Paweł , Krawczyk Konrad 
Published: December 2022

AbstractProtein folding problem obtained a practical solution recently, owing to advances in deep learning. There are classes of proteins though, such as antibodies, that are structurally unique, where the general solution still lacks. In particular, the prediction of the CDR-H3 loop, which is an instrumental part of an antibody in its antigen recognition abilities, remains a challenge. Antibody-specific deep learning frameworks were proposed to tackle this problem noting great progress, both on accuracy and speed fronts. Oftentimes though, the original networks produce physically implausible bond geometries that then need to undergo a time-consuming energy minimization process. Here we hypothesized that pre-training the network on a large, augmented set of models with correct physical geometries, rather than a small set of real antibody X-ray structures, would allow the network to learn better bond geometries. We show that fine-tuning such a pre-trained network on a task of shape prediction on real X-ray structures improves the number of correct peptide bond distances. We further demonstrate that pre-training allows the network to produce physically plausible shapes on an artificial set of CDR-H3s, showing the ability to generalize to the vast antibody sequence space. We hope that our strategy will benefit the development of deep learning antibody models that rapidly generate physically plausible geometries, without the burden of time-consuming energy minimization.
Heating tolerance of ectotherms is explained by temperature’s non-linear influence on biological rates
Authors: Kong Jacinta D. , Arnoldi Jean-Francois , Jackson Andrew L. , Bates Amanda E. , Morley Simon A. , Smith James A. , Payne Nicholas L. 
Published: December 2022

AbstractThe capacity of ectotherms to adjust their thermal tolerance limits through evolution or acclimation seems relatively modest and highly variable, and we lack satisfying explanations for both findings given a limited understanding of what ultimately determines an organism’s thermal tolerance. Here, we test if the amount of heating an ectotherm tolerates throughout a heating event until organismal failure scales with temperature’s non-linear influence on biological rates. To account for the non-linear influence of temperature on biological rates on heating tolerance, we rescaled the duration of heating events of 316 ectothermic taxa acclimated to different temperatures and describe the biological rate-corrected heating duration. This rescaling reveals that the capacity of an organism to resist a heating event is in fact remarkably constant across any acclimation temperature, enabling high-precision estimates of how organismal thermal tolerance limits vary under different thermal regimes. We also find that faster heating consistently reduces biological rate-corrected heating durations, which helps further explain why thermal tolerance limits seem so variable on absolute temperature scales. Existing paradigms are that heating tolerances and thermal tolerance limits reflect incomplete metabolic compensatory responses, are constrained by evolutionary conservatism, or index failure of systems such as membrane function; our data provide a different perspective and show that an organism’s thermal tolerance emerges from the interaction between the non-linear thermal dependence of biological rates and heating durations, which is an approximately-fixed property of a species.
Evaluating the Clinical Utility of Artificial Intelligence Assistance and its Explanation on Glioma Grading Task
Authors: Jin Weina , Fatehi Mostafa , Guo Ru , Hamarneh Ghassan 
Published: December 2022

AbstractBackgroundAs a fast-advancing technology, artificial intelligence (AI) has considerable potential to assist physicians in various clinical tasks from disease identification to lesion segmentation. Despite much research, AI has not yet been applied to neurooncological imaging in a clinically meaningful way. To bridge the clinical implementation gap of AI in neuro-oncological settings, we conducted a clinical user-based evaluation, analogous to the phase II clinical trial, to evaluate the utility of AI for diagnostic predictions and the value of AI explanations on the glioma grading task.MethodUsing the publicly-available BraTS dataset, we trained an AI model of 88.0% accuracy on the glioma grading task. We selected the SmoothGrad explainable AI Weina Jin and Mostafa Fatehi are co-first authors.algorithm based on the computational evaluation regarding explanation truthfulness among a candidate of 16 commonly-used algorithms. SmoothGrad could explain the AI model’s prediction using a heatmap overlaid on the MRI to highlight important regions for AI prediction. The evaluation is an online survey wherein the AI prediction and explanation are embedded. Each of the 35 neurosurgeon participants read 25 brain MRI scans of patients with gliomas, and gave their judgment on the glioma grading without and with the assistance of AI’s prediction and explanation.ResultCompared to the average accuracy of 82.5±8.7% when physicians perform the task alone, physicians’ task performance increased to 87.7±7.3% with statistical significance (p-value = 0.002) when assisted by AI prediction, and remained at almost the same level of 88.5±7.0% (p-value = 0.35) with the additional AI explanation assistance.ConclusionThe evaluation shows the clinical utility of AI to assist physicians on the glioma grading task. It also reveals the limitations of applying existing AI explanation techniques in clinical settings.Key pointsPhase II evaluation with 35 neurosurgeons on the clinical utility of AI and its explanationAI prediction assistance improved physicians’ performance on the glioma grading taskAdditional AI explanation assistance did not yield a performance boostImportance of the studyThis study is the first phase II AI clinical evaluation in neuro-oncology. Evaluating AI is a prerequisite for its clinical deployment. The four phases of AI clinical evaluation are analogous to the four phases of clinical trials. Prior works that apply AI in neurooncology utilize phase I algorithmic evaluation, which do not reflect how AI can be used in clinical settings to support physician decision making.To bridge the research gap, we conducted the first clinical evaluation to assess the joint neurosurgeon-AI task performance. The evaluation also includes AI explanation as an indispensable feature for AI clinical deployment. Results from quantitative and qualitative data analysis are presented for a detailed examination of the clinical utility of AI and its explanation.
Towards the integrative theory of Alzheimer’s disease: linking molecular mechanisms of neurotoxicity, beta-amyloid biomarkers, and the diagnosis
Authors: Molkov Yaroslav I. , Zaretskaia Maria V. , Zaretsky Dmitry V. ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: December 2022

ABSTRACTA major gap in amyloid-centric theories of Alzheimer’s disease (AD) is that even though amyloid fibrilsper seare not toxicin vitro, the diagnosis of AD clearly correlates with the density of beta-amyloid (Aβ) deposits. Based on our proposed amyloid degradation toxicity hypothesis, we developed a mathematical model explaining this discrepancy. It suggests that cytotoxicity depends on the cellular uptake of soluble Aβ rather than on the presence of amyloid aggregates. The dynamics of soluble Aβ in the cerebrospinal fluid (CSF) and the density of Aβ deposits is described using a system of differential equations. In the model, cytotoxic damage is proportional to the cellular uptake of Aβ, while the probability of an AD diagnosis is defined by the Aβ cytotoxicity accumulated over the duration of the disease. After uptake, Aβ is concentrated intralysosomally, promoting the formation of fibrillation seeds inside cells. These seeds cannot be digested and are either accumulated intracellularly or exocytosed. Aβ starts aggregating on the extracellular seeds and, therefore, its concentration decreases in the interstitial fluid. The dependence of both Aβ toxicity and aggregation on the same process – cellular uptake of Aβ – explains the correlation between AD diagnosis and the density of amyloid aggregates in the brain.We tested the model using clinical data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), which included records of beta-amyloid concentration in the cerebrospinal fluid (CSF-Aβ42) and the density of beta-amyloid deposits measured using positron emission tomography (PET). The model predicts the probability of AD diagnosis as a function of CSF-Aβ42 and PET and fits the experimental data at the 95% confidence level.Our study shows that existing clinical data allows for the inference of kinetic parameters describing beta-amyloid turnover and disease progression. Each combination of CSF-Aβ42 and PET values can be used to calculate the individual’s cellular uptake rate, the effective disease duration, and the accumulated toxicity. We show that natural limitations on these parameters explain the characteristic distribution of the clinical dataset for these two biomarkers in the population.The resulting mathematical model interprets the positive correlation between the density of Aβ deposits and the probability of an AD diagnosis without assuming any cytotoxicity of the aggregated beta-amyloid. Finally, to the best of our knowledge, this model is the first to mechanistically explain the negative correlation between the concentration of Aβ42 in the CSF and the probability of an AD diagnosis.
Functional characterization of cytochrome P450s associated with pyrethroid resistance in the olive fruit flyBactrocera oleae
Authors: Kampouraki Anastasia , Tsakireli Dimitra , Koidou Venetia , Stavrakaki Marianna , Kaili Stavroula , Livadaras Yannis , Grigoraki Linda , Ioannidis Panagiotis , Roditakis Emmanouil , Vontas John 
Published: December 2022

AbstractResistance to pyrethroid insecticides has evolved inBactrocera oleaepopulations in Greece, threatening the efficacy of control interventions based on this insecticide class. Here we report the collection of populations from Crete with resistance levels reaching up to 331-folds, compared to susceptible laboratory strains and show that pyrethroid resistance is substantially suppressed by the PBO synergist, suggesting the involvement of detoxification enzymes. To identify specific candidate genes implicated in resistance, we performed comparative transcriptomic analysis, between the pyrethroid resistant populations from Crete and the susceptible laboratory strains, using both whole bodies and Malpighian tubules. Several genes were found differentially transcribed between resistant and susceptible flies in each comparison, with P450s being among the most highly over-expressed detoxification genes in pyrethroid resistant populations. Four of the over-expressed P450s (Cyp6A61, Cyp6G6, Cyp4P6andCyp6G28) were recombinantly expressed inEscherichia coliandin vitrometabolism assays revealed that CYP6A61 is capable of metabolizing alpha-cypermethrin, while CYP6G6, CYP4P6 and CYP6G28 are capable of metabolizing deltamethrin. No metabolism of neonicotinoid insecticides was recorded. We further silencedCYP6G6 in vivo, via RNAi, which led to a small, but significant increase in deltamethrin toxicity. The study provides valuable information towards the development of molecular diagnostics and evidence-based insecticide resistance management strategies.
Telomerase Reverse Transcriptase Enzyme (hTERT) expression in bronchial mucosa inversely correlates with the severity of airway obstruction in patients with chronic airway obstructive diseases
Authors: Moermans C. , Medard L. , Graff S. , Njock MS. , Bougard N. , Schleich F. , Guiot J. , Corhay J-L. , Louis R. 
Published: December 2022

ABSTRACTRationaleAsthma and Chronic Obstructive Pulmonary Disease (COPD) are the two main chronic airway inflammatory diseases related to aging. Cellular aging is prevented by telomeres and their shortening induces cellular senescence. Human Telomerase Reverse Transcriptase Enzyme (hTERT) can act directly on DNA and prevent telomere shortening. The goal of this study was to assess hTERT expression in the bronchial mucosa of patients suffering from chronic airway obstructive diseases and to relate the hTERT expression to demographics and lung function characteristics.MethodsWe collected bronchial biopsies from 38 patients suffering from chronic airway diseases including 21 severe asthmatics and 17 COPD who underwent bronchoscopy in routine practice. hTERT expression was assessed by immunochemistry and co-immunostaining was performed to identify hTERT positive cells within the different immune cell populations.ResultshTERT expression in airway mucosa was essentially found in structural cells. Among leukocytes, lymphocytes were the principal cells expressing hTERT. On the whole cohort, hTERT expression score was not different between men and women and not influenced by the age nor by the smoking history as reflected by the pack years. Total airway hTERT positive staining score positively correlated with post-bronchodilation (post-BD) FEV1% predicted and FEV1/FVC. (r=0.38, p<0.05 for both). hTERT+ lymphocytes were also positively correlated to post-BD FEV1/FVC (r=0.37, p<0.05). Consequently, hTERT expression was lower in those patients with fixed airway obstruction (post-BD FEV1/FVC < 70%). There was no difference between asthmatics and COPD.ConclusionsIn patients with chronic airway diseases, total and lymphocyte hTERT expressions inversely correlate with the degree of airway obstruction and are reduced in patients with fixed airway obstruction, which supports a role of hTERT in airway remodeling.
Selection bias as an explanation for the observed protective association of childhood adiposity with breast cancer
Authors: Schooling C M , Fei K , Zhao J V 
Published: December 2022

AbstractObjectiveRecalled childhood adiposity is inversely associated with breast cancer observationally, including in Mendelian randomization (MR) studies, questioning its role. Breast cancer studies recruited in adulthood only include survivors of childhood adiposity and breast cancer. We assessed recalled childhood adiposity on participant reported sibling and maternal breast cancer to ensure ascertainment of non-survivors using MR.Study Design and SettingWe obtained independent strong genetic predictors of recalled childhood adiposity for women and their associations with participant reported own, sibling and maternal breast cancer from UK Biobank genome wide association studies (GWAS). We obtained MR inverse variance weighting estimates.ResultsChildhood adiposity in women was inversely associated with own breast cancer (odds ratio (OR) 0.66, 95% confidence interval (CI) 0.52 to 0.84) but unrelated to participant reported sibling (OR 0.85, 95% CI 0.60 to 1.20) or maternal breast cancer (OR 0.84, 95% CI 0.67 to 1.05 respectively).ConclusionWeaker inverse associations of recalled childhood adiposity with breast cancer with more comprehensive ascertainment of cases before recruitment suggests the inverse association of recalled childhood adiposity with breast cancer is due to selection bias arising from preferential selection of survivors. Greater consideration of left truncation in public health relevant causal inferences is warranted.HighlightsRecalled childhood adiposity is inversely associated with breast cancer.Studies of childhood exposures recruited in adulthood are open to left truncation.Participant reports about family members include deaths before recruitment.We tested childhood adiposity on sibling breast cancer using Mendelian randomization.Childhood adiposity with sibling breast cancer was null, suggesting left truncation.
Therapeutic administration of mouse mast cell protease 6 improves functional recovery after traumatic spinal cord injury in mice by promoting remyelination and reducing glial scar formation
Authors: Vangansewinkel Tim , Lemmens Stefanie , Tiane Assia , Geurts Nathalie , Dooley Dearbhaile , Vanmierlo Tim , Pejler Gunnar , Hendrix Sven 
Published: December 2022

AbstractTraumatic spinal cord injury (SCI) most often leads to permanent paralysis due to the inability of axons to regenerate in the adult mammalian central nervous system (CNS). In the past, we have shown that mast cells (MCs) improve the functional outcome after SCI by suppressing scar tissue formation at the lesion site via mouse mast cell protease 6 (mMCP6). In this study, we investigated whether recombinant mMCP6 can be used therapeutically to improve the functional outcome after SCI. Therefore, we applied mMCP6 locally via an intrathecal catheter in the subacute phase after a spinal cord hemisection injury in mice. Our findings showed that hind limb motor function was significantly improved in mice that received recombinant mMCP6 compared to the vehicle-treated group. In contrast to our previous findings in mMCP6 knockout mice, the lesion size and expression levels of the scar components fibronectin, laminin, and axon-growth-inhibitory chondroitin sulfate proteoglycans were not affected by the treatment with recombinant mMCP6. Surprisingly, no difference in infiltration of CD4+ T cells and reactivity of Iba-1+ microglia/macrophages at the lesion site was observed between the mMCP6-treated mice and control mice. Additionally, local protein levels of the pro- and anti-inflammatory mediators IL-1β, IL-2, IL-4, IL-6, IL-10, TNF-α, IFNγ, and MCP-1 were comparable between the two treatment groups, indicating that locally applied mMCP6 did not affect inflammatory processes after injury. However, the increase in locomotor performance in mMCP6-treated mice was accompanied by reduced demyelination and astrogliosis in the perilesional area after SCI. Consistently, we found that TNF-a/IL-1B-astrocyte activation was decreased, and that oligodendrocyte precursor cell (OPC) differentiation was increased after recombinant mMCP6 treatmentin vitro. Mechanistically, this suggests effects of mMCP6 on reducing astrogliosis and improving (re)myelination in the spinal cord after injury. In conclusion, these data show for the first time that recombinant mMCP6 is therapeutically active in enhancing recovery after SCI.
Theta oscillations optimize a speed-precision trade-off in phase coding neurons
Authors: Amil Adrián F. , Albesa-González Albert , Verschure Paul F.M.J. 
Published: December 2022

AbstractLow-frequency oscillations shape how neurons sample their synaptic inputs, regulating information exchange across networks. In the hippocampus, theta-band oscillations (3–8 Hz) reorganize cortical input signals temporally, resulting in a phase code. However, the reason hippocampal oscillations are limited to low frequencies like the theta band remains unclear. Here, we derive a theoretical framework for neuronal phase coding to show that realistic noise levels create a trade-off between sampling speed (controlled by oscillation frequency) and encoding precision in hippocampal neurons. This speed-precision trade-off produces a maximum in information rate within the theta band of~1–2 bits/s. Additionally, we demonstrate that our framework explains other key hippocampal properties, such as the preservation of theta along the dorsoventral axis despite various physiological gradients, and the modulation of theta frequency and amplitude by the animal’s running speed. Extending our analysis to extra-hippocampal areas, we propose that theta oscillations may also support efficient encoding of stimuli in visual cortex and olfactory bulb. More broadly, we lay the groundwork for rigorously studying how system constraints determine optimal sampling frequency regimes for phase coding neurons in biological and artificial brains.Author SummaryThe rodent hippocampus exhibits prominent oscillations in the theta band (3–8 Hz) during exploration, enabling individual neurons to rhythmically sample and represent sensory signals from the cortex. However, the reason behind the specific frequency of this hippocampal rhythm has remained unclear. In this study, we developed a biologically-based theoretical framework to demonstrate that neurons using oscillations to efficiently sample noisy signals encounter a trade-off between their sampling speed (i.e., oscillation frequency) and their coding precision (i.e., reliability of encoding). Notably, our findings reveal that this trade-off is optimized precisely within the theta band, while also providing insights into other fundamental features. In conclusion, we offer an explanation grounded in efficient coding for why hippocampal oscillations are confined to the theta band and establish a foundation for exploring how the properties of individual neurons determine optimal sampling frequencies in specific neural circuits.