bluuebunny/crossref_metadata_2025_split huggingface dataset:
Robust coexistence in ecological competitive communities
Authors: Lechón-Alonso Pablo , Kundu Srilena , Lemos-Costa Paula , Capitán José A. , Allesina Stefano 
Published: July 2024

AbstractThe notions of intraspecific competition and population self-regulation play a central role in ecological theory, leading to foundational concepts such as limiting similarity and niche differentiation[1–6]. They also are crucial for coexistence: for example, May[7] showed that ecological dynamics can be stabilized by imposing sufficiently strong intraspecific competition, and that for large systems with random interactions the transition from instability to stability is sharp[7–9], and achieved at a critical value of intraspecific competition.Here we consider the existence of an equilibrium and its stability as the strength of intraspecific competitionαis increased. We show that an ecological system can gain or lose a coexistence equilibrium as intraspecific competition becomes more prevalent, but that for any competitive system there exists a critical valueαFafter which the existence of an equilibrium is guaranteed, in analogy to stability.Having derived results on general systems, we turn to ecological systems with random interactions, and compute the probability that a system would display a coexistence equilibrium, given the level of intraspecific interaction, the size of the systemn, and the statistics on the distribution of interactions—thereby extending, generalizing and refining results that have been accumulating for decades[10–16]. We show that the transition to feasibility is smooth, contrary to what is found for stability, and that the existence of an equilibrium almost invariably guarantees its stability[10, 11, 14] This has strong consequences for the dynamics of such large ecological systems: coexistence via limit cycles or chaos[17, 18] is precluded, and dynamics always converge to an equilibrium. The surviving competitors coexist extremely robustly; the ecological community will return to equilibrium after any perturbation, can be assembled from the ground-up, and is resistant to invasions[19, 20].
First-male sperm precedence in polyandrousSpodoptera frugiperdaallows sterile males induce population suppression
Authors: Sun Hao , Bu Ling-Ao , Zhang Xin-Yue , Zhang Zhi-Ruo , Zhu Ling-Yi , Su Shao-Cong , Guo Di , Hu Gao , Gao Cong-Fen , Palli Subba Reddy , Champer Jackson , Wu Shun-Fan 
Published: July 2024

AbstractMales respond to intense sperm competition by adapting reproductive strategies to promote fertilization success, which is critical for population reproduction. Thus, investigating the patterns and mechanisms of sperm competition is crucial for the development and application of pest population management techniques. In this study, we analyzed the sperm precedence pattern of a major pest, the fall armyworm, and used this pattern to manage the pest population. First, we found that females had a post-mating response and did not gain direct benefit through multiple mating. Next, in a double mating experiment, we used a molecular marker created by CRISPR/Cas9 to determine that most females use only the sperm of the first male to produce offspring. To further explore the role of fertilizing sperm in sperm competition, we constructed a sterile male line with eupyrene sperm defect by knocking out theB2tgene. Interestingly, two round mating assays showed that first mating withB2t-null males inhibited sperm fertilization from a second wild-type male. In other words, prior mating withB2t-null males significantly reduced the fertility and fecundity of females. Based on this finding, we continued to explore whether sperm-deficient sterile males could be used in the management of FAW populations. Cage experiments and mathematical modeling analyses showed that the release of excessB2t-null males induced population suppression. Our study expands our knowledge of sperm competition patterns in lepidopteran. In addition, our study provides a paradigm to develop and apply genetic control methods based on sperm competition outcome in polyandrous pests.SignificanceSperm competition is essential for maintaining population reproduction. Understanding patterns and mechanisms of sperm competition facilitates the development of appropriate pest genetic control methods. Here, we describe that a globally major pest, the fall armyworm displays the first-male sperm precedence pattern. Interestingly, first mating withB2t-null males, which produces non-functional eupyrene sperm, significantly reduces the fertility and fecundity of females. That means that the ejaculate of the first male, even if its eupyrene sperm are defective, can inhibit sperm fertilization from a second wild-type male. Based on this, the release of excessB2t-null males significantly suppresses FAW populations. These results suggest that future development of genetic control techniques based on targeting nucleated sperm can effectively control FAW populations.
Identification of Southeast AsianAnophelesmosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach
Authors: Chaumeau Victor , Sawasdichai Sunisa , Min Thu Zar Ma Ma Moe , Kularbkeeree Thithiwarada , Jaruwan Naw , Gloria Naw , Lee Naw Yu , Trackoolchengkaew Muesuwa , Phanaphadungtham Monticha , Rongthong Patcharamai , Inta Aritsara , Watthanaworawit Wanitda , Nosten François 
Published: July 2024

AbstractMatrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is proposed for mosquito species identification. The absence of public repositories for sharing mass spectra and of open-source data analysis pipelines for fingerprint matching to mosquito species limits widespread use of this technology. The objective of this study was to develop an open-source data analysis pipeline forAnophelesspecies identification with MALDI-TOF MS. Malaria mosquitos were captured in 33 villages in Karen (Kayin) state in Myanmar. 359 specimens were identified with DNA barcodes and assigned to 21 sensu stricto species and 5 sibling species pairs or complexes. 3584 mass spectra of the head of these specimens identified with DNA barcoding were acquired and the similarity between mass spectra was quantified using a cross-correlation approach adapted from the published literature. A simulation experiment was carried out to evaluate the performance of species identification with MALDI-TOF MS at varying thresholds of cross-correlation index for the algorithm to output an identification result and with varying numbers of technical replicates for the tested specimens, considering PCR identification results as the reference. With one spot and a threshold value of −14 for the cross-correlation index on the log scale, the sensitivity was 0.99 (95%CrI: 0.98 to 1.00), the predictive positive value was 0.99 (95%CrI: 0.98 to 0.99) and the accuracy was 0.98 (95%CrI: 0.97 to 0.99). It was not possible to directly estimate the sensitivity and negative predictive value because there was no true negative in the assessment. In conclusion, the modified cross-correlation approach can be used for matching mass spectral fingerprints to predefined taxa and MALDI-TOF MS is a valuable tool for rapid, accurate and affordable identification of malaria mosquitos.
Self-Logical Consistent GPT-4 Enables Human-Level Classification of Patient Feedback
Authors: Loi Zeno , Morquin David , Derzko Xavier , Corbier Xavier , Gauthier Sylvie , Taourel Patrice , Prin-Lombardo Emilie , Mercier Grégoire , Yauy Kévin 
Published: July 2024

AbstractPatient satisfaction feedback is crucial for hospital service quality, but human-led reviews are time-consuming and traditional natural language processing remains ineffective. Large Language Models (LLM) offer potential, but their tendency to generate illogical thoughts limits their use in healthcare. Here we describe Self-Logical Consistency Assessment (SLCA), a method ensuring a reproducible LLM classification explained by a logically-structured chain of thought. In an analysis targeting extrinsic faithfulness hallucinations, SLCA mitigated the 16% GPT-4 hallucination rate, leaving only three residual cases across 12,600 classifications from 100 diverse patient feedbacks. In a benchmark designed to evaluate classification accuracy, SLCA applied to GPT-4 outperformed best algorithms, with a 88% precision rate and a 71% recall rate across 49,140 classifications from 1,170 sampled patient feedbacks. This method provides a reliable, scalable solution for improving hospital services and shows potential for accurate, explainable text classifications without fine-tuning.
Influence of salinity on the thermal tolerance of aquatic organisms
Authors: Farias Luan , Beszteri Bánk , Burfeid Castellanos Andrea M. , Doliwa Annemie , Enss Julian , Feld Christian K. , Grabner Daniel , Lampert Kathrin P. , Mayombo Serge , Prati Sebastian , Schürings Christian , Smollich Esther , Schäfer Ralf B. , Sures Bernd , Yen Le T.T. 
Published: July 2024

ABSTRACTAquatic organisms are challenged by changes in the external environment, such as temperature and salinity fluctuations. The response of an organism to temperature changes can be modified by salinity, thus pointing at the potential interaction of both variables. In the present study, we tested this assumption for freshwater, brackish, and marine organisms, including algae, macrophytes, heterotrophic protists, parasites, invertebrates, and fish. We reviewed the existing body of literature on potential interactions between temperature and salinity and performed a meta-analysis that compared the thermal tolerance (characterized by the temperature optima, lower and upper temperature limits, and thermal breadths). The final database includes 90 relevant publications (algae: 15; heterotrophic protists: 1; invertebrates: 43; and fish: 31). Relevant publications for microphytes and parasites were not available. Overall, our results show that decreasing salinity significantly increased the lower temperature limits and decreased the upper temperature limits irrespective of the organism groups. These findings mainly reflect the response to salinity changes in brackish and marine systems that dominate our database. Although the number of studies on freshwater species was limited, they showed negative, although statistically nonsignificant, effects of an increased salinity on the thermal tolerance of these species (i.e. increased lower limits and decreased upper limits). In addition, our meta-analysis shows nonsignificant differences in the responsiveness of thermal tolerance to salinity changes among different groups of organisms, but the sensitivity of thermal tolerance to salinity changes generally followed the order: algae > invertebrates > fish. Facing the impact of climate change, our findings point at adverse effects of salinity changes on the temperature tolerance of aquatic organisms. Further studies that investigate the thermal performance of freshwater species at various salinity gradients are required to broaden the evidence for interactions between salinity and temperature tolerance. This also applies to the influence of parasitic infections, which have been found to modulate the temperature tolerance of aquatic invertebrates and fish.
TOR Inhibition Enhances Autophagic Flux and Immune Response in Tomato Plants Against PSTVd Infection
Authors: Silva-Valencia Samanta , Prol Francisco Vázquez , Rodrigo Ismael , Lisón Purificación , Belda-Palazón Borja 
Published: July 2024

AbstractViroids are small, non-coding RNA pathogens known for their ability to cause severe plant diseases. Despite their simple structure, viroids like Potato Spindle Tuber Viroid (PSTVd) can interfere plant cellular processes, including both transcriptional and post-transcriptional mechanisms, thereby impacting plant growth and yield. In this study, we have investigated the role of the Target Of Rapamycin (TOR) signalling pathway in modulating viroid pathogenesis in tomato plants infected with PSTVd. Our findings reveal that PSTVd infection induces the accumulation of the selective autophagy receptor NBR1, potentially inhibiting autophagic flux. Pharmacological inhibition of TOR with AZD8055 mitigated PSTVd symptomatology by reducing viroid accumulation. Furthermore, TOR inhibition promoted the recovery of autophagic flux through NBR1 and primed the plant defence response, as evidenced by enhanced expression of both the defence-related genePR1bandS5H, a gene involved in the salicylic acid catabolism. These results suggest a novel role for TOR in regulating viroid-induced pathogenesis and highlight the potential of TOR inhibitors as tools for enhancing plant resistance against viroid infections.
Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning algorithms into the laboratory information system
Authors: Angeloni Miriam , Rizzi Davide , Schoen Simon , Caputo Alessandro , Merolla Francesco , Hartmann Arndt , Ferrazzi Fulvia , Fraggetta Filippo 
Published: July 2024

AbstractDigital pathology (DP) has revolutionized cancer diagnostics, allowing the development of deep-learning (DL) models supporting pathologists in their daily work and contributing to the improvement of patient care. However, the clinical adoption of such models remains challenging. Here we describe a proof-of-concept framework that, leveraging open-source DP software and Health Level 7 (HL7) standards, allows the integration of DL models in the clinical workflow.Development and testing of the workflow were carried out in a fully digitized Italian pathology department. A Python-based server-client architecture was implemented to interconnect the anatomic pathology laboratory information system (AP-LIS) with an external artificial intelligence decision support system (AI-DSS) containing 16 pre-trained DL models through HL7 messaging. Open-source toolboxes for DL model deployment, including WSInfer and WSInfer-MIL, were used to run DL model inference. Visualization of model predictions as colored heatmaps was performed in QuPath.As soon as a new slide is scanned, DL model inference is automatically run on the basis of the slide’s tissue type and staining. In addition, pathologists can initiate the analysis on-demand by selecting a specific DL model from the virtual slides tray. In both cases the AP-LIS transmits an HL7 message to the AI-DSS, which processes the message, runs DL model inference, and creates the appropriate type of colored heatmap on the basis of the employed classification model. The AI-DSS transmits model inference results to the AP-LIS, where pathologists can visualize the output in QuPath and/or directly from the virtual slides tray. The developed framework supports multiple DL toolboxes and it is thus suitable for a broad range of applications. In addition, this integration workflow is a key step to enable the future widespread adoption of DL models in pathology diagnostics.
Appearance of green tea compounds in plasma following acute green tea consumption is modulated by the gut microbiome in mice
Authors: Sterrett John D. , Quinn Kevin D. , Doenges Katrina A. , Nusbacher Nichole M. , Levens Cassandra L. , Armstrong Mike L. , Reisdorph Richard M. , Smith Harry , Saba Laura M. , Kuhn Kristine A. , Lozupone Catherine A. , Reisdorph Nichole A. 
Published: July 2024

AbstractStudies have suggested that phytochemicals in green tea have systemic anti-inflammatory and neuroprotective effects. However, the mechanisms behind these effects are poorly understood, possibly due to differential metabolism of phytochemicals resulting from variation in gut microbiome composition. To unravel this complex relationship, our team utilized a novel combined microbiome analysis and metabolomics approach applied to low complexity microbiome (LCM) and human colonized (HU) gnotobiotic mice treated with an acute dose of powdered matcha green tea. A total of 20 LCM mice received 10 distinct human fecal slurries for an n=2 mice per human gut microbiome; 9 LCM mice remained un-colonized with human slurries throughout the experiment. We performed untargeted metabolomics on green tea and plasma to identify green tea compounds that were found in plasma of LCM and HU mice that had consumed green tea. 16S ribosomal RNA gene sequencing was performed on feces of all mice at study end to assess microbiome composition. We found multiple green tea compounds in plasma associated with microbiome presence and diversity (including acetylagmatine, lactiflorin, and aspartic acid negatively associated with diversity). Additionally, we detected strong associations between bioactive green tea compounds in plasma and specific gut bacteria, including associations between spiramycin andGemmiger, and between wildforlide andAnaerorhabdus. Additionally, some of the physiologically relevant green tea compounds are likely derived from plant-associated microbes, highlighting the importance of considering foods and food products as meta-organisms. Overall, we describe a novel workflow for discovering relationships between individual food compounds and composition of the gut microbiome.ImportanceFoods contain thousands of unique and biologically important compounds beyond the macro- and micro-nutrients listed on nutrition facts labels. In mammals, many of these compounds are metabolized by the community of microbes in the colon. These microbes may impact the thousands of biologically important compounds we consume; therefore, understanding microbial metabolism of food compounds will be important for understanding how foods impact health. We used metabolomics to track green tea compounds in plasma of mice with and without complex microbiomes. From this, we can start to recognize certain groups of green tea-derived compounds that are impacted by mammalian microbiomes. This research presents a novel technique for understanding microbial metabolism of food-derived compounds in the gut, which can be applied to other foods.
MYC Binding Near Transcriptional End Sites Regulates Basal Gene Expression, Read-Through Transcription and Intragenic Contacts
Authors: Wang Huabo , Ma Bingwei , Stevens Taylor , Knapp Jessica , Lu Jie , Prochownik Edward V. 
Published: July 2024

SummaryThe MYC oncoprotein regulates numerous genes involved in cellular processes such as cell cycle and mitochondrial and ribosomal structure and function. This requires heterodimerization with its partner, MAX, and binding to specific promoter and enhancer elements. Here, we show that MYC and MAX also bind near transcriptional end sites (TESs) of over one-sixth of all annotated genes. These interactions are dose-dependent, evolutionarily conserved, stabilize the normally short-lived MYC protein and regulate expression both in concert with and independent of MYC’s binding elsewhere. MYC’s TES binding occurs in association with other transcription factors, alters the chromatin landscape, increases nuclease susceptibility and can alter transcriptional read-through, particularly in response to certain stresses. MYC-bound TESs can directly contact promoters and may fine-tune gene expression in response to both physiologic and pathologic stimuli. Collectively, these findings support a previously unrecognized role for MYC in regulating transcription and its read-through via direct intragenic contacts between TESs and promoters.
Influence of truthful and misleading instructions on statistical learning across the autism spectrum
Authors: Angeletos Chrysaitis Nikitas , Seriès Peggy 
Published: July 2024

AbstractBayesian studies of perception have documented how the brain learns the statistics of a new environment and uses them to interpret sensory information. Impairments in this process have been hypothesised to be central to autism spectrum disorders. However, very few such studies have differentiated between implicit and explicit learning. We manipulated the instructions given before a cue-stimulus association task to investigate their effects on statistical learning. The task was conducted online, in 335 participants with varying autistic traits. In the implicit condition, where no information was provided, participants acquired weak prior beliefs about the task regularities. Conversely, explicit information about the presence of regularities resulted in strong priors, correctly reflecting the task’s statistics, regardless of the information’s veracity. Contrary to our hypothesis, autistic traits did not correlate with the influence of priors in any condition.Author SummaryPerception is greatly influenced by the brain’s prior knowledge of the environment, through a process calledBayesian inference. Recent theories of psychiatric disorders and particularly autism view them as impairments in this process. A crucial aspect of this process is how individuals form their knowledge of the environment. However, previous studies have not differentiated between learning that occurs when participants are aware of what they are learning and learning that happens implicitly. In the present study, we conducted an experiment with four conditions, each varying in terms of what participants were trying to learn and whether they were aware of the general form of the regularities. Our findings revealed that participants form much stronger beliefs about the regularities when they are informed about their presence. Additionally, we discovered that participants with strong autistic traits did not differ in their beliefs about the regularities in any of the conditions.