bluuebunny/crossref_metadata_2025 huggingface dataset:
Convolutional-LSTM Approach for Temporal Catch Hotspots (CATCH): An AI-Driven Model for Spatiotemporal Forecasting of Fisheries Catch Probability Densities
Authors: Agmata Altair , Guðmundsson Svanur
Published: December 2024

Efficient fisheries management is crucial for sustaining both marine ecosystems and the economies that heavily depend on them, such as Iceland. Current fishing practices involve decisions informed by a combination of personal experience, current data on environmental and oceanographic conditions, reports from other captains, and target species within the constraints of the fishing quota. However, the intricate spatiotemporal dynamics of fish behaviour make it difficult to predict fish stock distributions. Despite technological breakthroughs in fishing vessel data collection, much of the decision-making still relies heavily on subjective judgment, highlighting the need for more robust, data-driven predictive methods. This paper presents CATCH, a convolutional long short-term memory neural network model that forecasts fish stock probability densities over time and space in Icelandic waters. The framework represents the first utilization of large-scale Icelandic fishing fleet data integrating multidimensional inputs like depth, bottom temperature, and catch data to produce accurate, multivariate forecasts. The model demonstrates high accuracy, low error metrics, and strong structural similarity to observed data, generalizing well across key species such as Atlantic cod, haddock, saithe, golden redfish, and Greenland halibut. Its promising results suggest deep learning models have the potential to optimize fisheries operations, enhance sustainability, and support data-driven decision-making.
A rapid approach for linear epitope vaccine profiling reveals unexpected epitope tag immunogenicity
Authors: Browne-Cole Kirsten , Hanning Kyrin R. , Beijerling Kevin , Rousseau Meghan , Loh Jacelyn , Kelton William
Published: December 2024

Antibody epitope profiling is essential for assessing the robustness of vaccine-induced immune responses, particularly while in development. Despite advancements in computational tools, high throughput experimental epitope validation remains an important step. Here, we describe a readily accessible method for rapid linear epitope profiling using phage-displayed oligo pools in combination with Nanopore deep sequencing. We applied this approach to TeeVax3, a Group A Streptococcus vaccine candidate, to investigate the antibody response generated in a pre-clinical rabbit model and assess antigen immunogenicity. Surprisingly, we found a strong bias in antibody binding response towards the N-terminal epitope tag used for purification. These tags are widely reported to have low immunogenicity and are frequently left uncleaved in pre-clinical studies. We further confirmed that the observed immune response against the epitope tag dominated even the conformational binding response and, using synthetic peptides, narrowed the epitope down to a set of 10 residues inclusive of the Histidine residues. Our findings highlight the importance of epitope-tag removal in pre-clinical studies and demonstrate the utility of rapid nanopore sequencing for early-stage vaccine evaluation.
Oligomerization of PRMT1 Regulated RNA-Binding Protein Cascade Promotes Pancreatic Ductal Adenocarcinoma
Authors: Ru Yanxia , Zhou Xinyi , Wang Xijiao , Sun Wenxin , He Yaohui , Hu Guosheng , Li Wenjuan , Hu Die , Jiang Meizhi , Su Zhiming , Niu Fengfeng , Chen Gang , Zeng Jinzhang , Sui Sen-Fang , Liu Wen , Li Yaowang , Chen Siming
Published: December 2024

PRMT1 is the predominant type I protein arginine methyltransferase responsible for generating monomethylarginine and asymmetric dimethylarginine (MMA and ADMA) in various protein substrates. It regulates numerous biological processes, including RNA metabolism, mRNA splicing, DNA damage repair, and chromatin dynamics. The crystal structure of rat PRMT1 has been previously reported as a homodimer; however, higher-order oligomeric species of human PRMT1 have been observed in vivo, and their structural basis remains elusive. In this study, we present the cryo-EM structure of human PRMT1 in its oligomeric form, revealing novel interfaces crucial for PRMT1 self-assembly and normal function. PRMT1 shows a strong preference for intrinsically disordered RGG/RG motifs, which are commonly found in RNA-binding proteins (RBPs), highlighting its critical role in regulating RNA metabolism. In vitro studies indicate that disrupting PRMT1 oligomerization impairs its binding affinity for RGG motif substrates, thereby reducing arginine methylation levels on these substrates. This finding emphasizes the essential role of the oligomeric state of PRMT1 in its function with RGG motif-containing RBPs. In vivo, the loss of PRMT1 oligomerization leads to decreased global ADMA levels in pancreatic ductal adenocarcinoma (PDAC) cells and inhibits PDAC tumor growth. Collectively, we propose that PRMT1 oligomerization, rather than mere dimerization, is sufficient for PRMT1-driven PDAC tumor growth, offering novel insights into the potential therapeutic targeting of PRMT1 oligomeric forms in PDAC.

Graphical abstract
Forecasting Dengue across Brazil with LSTM Neural Networks and SHAP-Driven Lagged Climate and Spatial Effects
Authors: Chen Xiang , Moraga Paula
Published: December 2024

Background

Dengue fever is a mosquito-borne viral disease that poses significant health risks and socioeconomic challenges in Brazil, necessitating accurate forecasting across its 27 federal states. With the country’s diverse climate and geographical spread, effective dengue prediction requires models that can account for both climate variations and spatial dynamics. This study addresses these needs by using Long Short-Term Memory (LSTM) neural networks enhanced with SHapley Additive exPlanations (SHAP) integrating optimal lagged climate variables and spatial influence from neighboring states.

Method

An LSTM-based model was developed to forecast dengue cases across Brazil’s 27 federal states, incorporating a comprehensive set of climate and spatial variables. SHAP was used to identify and select the most important lagged climate predictors. Additionally, lagged dengue cases from neighboring states were included to capture spatial dependencies. Model performance was evaluated using MAE, MAPE, and CRPS, with comparisons to baseline models.

Results

The LSTM-Climate-Spatial model consistently demonstrated superior performance, effectively integrating temporal, climatic, and spatial information to capture the complex dynamics of dengue transmission. SHAP-enhanced variable selection improved accuracy by focusing on key drivers such as temperature, precipitation and humidity. The inclusion of spatial effects further strengthened forecasts in highly connected states showcasing the model’s adaptability and robustness.

Conclusion

This study presents a scalable and robust framework for dengue forecasting across Brazil, effectively integrating temporal, climatic, and spatial information into an LSTM-based model. By integrating diverse data sources, the framework captures key transmission drivers, demonstrating the potential of LSTM neural networks for robust predictions. These findings provide valuable insights to enhance public health strategies and outbreak preparedness in Brazil.
Evaporation and pathogenesis of levitated bacteria-laden surrogate respiratory fluid droplets: At different relative humidity and evaporation stages
Authors: Agharkar Amey Nitin , Hajra Dipasree , Dewangan Kush Kumar , Roy Durbar , Chakravortty Dipshikha , Basu Saptarshi
Published: December 2024

Hypothesis

Aerosols are the principal cause of airborne infections and respiratory diseases. Droplets ejected from the host can evaporate and form a precipitate in the air (aerosol mode), or evaporate for some time, and fall on the ground (mixed mode) or directly fall on the ground and evaporate as sessile mode. Different evaporation modes, stages of evaporation and the relative humidity (RH) conditions affect the survival and infectivity of the bacteria in the precipitate.

Experiments

We have investigated three droplet diameter reduction ratio-based stages of evaporation of a bacteria-laden levitated droplet at two different RH settings and evaporation modes (aerosol and mixed) mimicking real-life scenarios. The low RH condition mimics evaporation in arid regions. e.g., Delhi and the high RH conditions imitate cold areas like London. The study analyses the mass transport, micro-characterizes the samples, and investigates the survival and infectivity of bacteria in the sample.

Findings

The bacteria survive more in the high RH condition than in the low RH condition for all diameter reduction ratio-based stages and modes of evaporation. For the aerosol mode, at a fixed RH condition, the evaporation time plays a vital role as the bacteria in early-stage partially dried samples are more viable than the full precipitate. The evaporation rate, and the generation of reactive oxygen species (ROS) cause a remarkable difference in the viability and infectivity of the bacterial samples. Therefore, our findings report that the evaporation history of an infected droplet is an indispensable factor in determining bacterial viability and subsequent infectivity.
Deriving Mendelian Randomization-based Causal Networks of Brain Imaging Phenotypes and Bipolar Disorder
Authors: O'Connell Shane , Brown Brielin C. , Cannon Dara M. , O Broin Pilib , Knowles David A. , Parker Nadine , Alnaes Dag , Westlye Lars T. , Banerjee Saikat , Nabulsi Leila , Corley Emma , Andreassen Ole A. , Mullins Niamh
Published: December 2024

Neuroanatomical variation in individuals with bipolar disorder (BD) has been previously described in observational studies. However, the causal dynamics of these relationships remain unexplored. We performed Mendelian Randomization of 297 structural and functional neuroimaging phenotypes from the UK BioBank and BD using genome-wide association study summary statistics. We found 28 significant causal relationship pairs after multiple testing corrections containing BD as a term, 27 of which described neuroimaging phenotype effects on BD. We applied an inverse sparse regression algorithm to estimate the direct effect of phenotypes conditional on all other causal effects, finding that white matter tract phenotypes have larger absolute effects on BD than vice versa. We found that white matter phenotypes have significantly larger out-degrees than non-white matter tract phenotypes, and that the effect of neuroimaging variation on BD is larger than vice versa. Our results provide support for the hypothesis that neuroanatomical variation, specifically in white matter tracts such as the superior and inferior longitudinal fasciculi, is a cause rather than a consequence of BD.
What are you talking about? Representation of the Topic of Speech in the Human Brain
Authors: Boncz Adam , Tóth Brigitta , Winkler István
Published: December 2024

A sentence outside context provides very restricted information to the listener. Real understanding requires one to be able to place it within the sequence of sentences forming a coherent “story” and just as importantly, within one’s general knowledge of the “topic”. Here we demonstrate the existence of functional brain networks robustly sensitive to the topic of ca. 6-minute-long coherent newspaper articles. Main network hubs are located in parietal and temporal brain regions. These networks changed very slowly during the course of the article, consistent with their involvement in representing the topic rather than the story aspect of the context. Because even the meaning of words can depend on the topic (see e.g., the word “shuttle” in transportation and weaving) the topic-sensitive network must also be intimately linked with the mental lexicon underlying language comprehension.

Significance Statement

When we listen to someone talking, our brain continuously integrates the speech stream with our general knowledge associated with the topic. While this is a central process of comprehension, it is still unclear how the brain maintains the relevant representations. Here we demonstrate the existence of a cortical network, which is sensitive to the topic of the speech just heard. This network may form the neural substrate for “topic representation” in the brain. Understanding how such representations are maintained and accessed would let us reveal the computations enabling rapid linguistic integration, a key process in human communication.
Supersymmetry on the lattice: Geometry, topology, and flat bands
Authors: Roychowdhury Krishanu , Attig Jan , Trebst Simon , Lawler Michael J.
Published: December 2024

In quantum mechanics, supersymmetry (SUSY) posits an equivalence between two elementary degrees of freedom, bosons, and fermions defined by local rules. Here we apply it to find connections between bosonic and fermionic lattice models in the realm of condensed-matter physics and uncover a novel fivefold way topology it demands in these systems. At the single-particle level, our connections pair a bosonic and fermionic lattice model, either describing the hopping of number-conserving particles or local couplings between fermion parity-conserving particles. The pair are isospectral except for zero modes, such as flat bands, quadratic band touchings, and nexus points, whose existence is undergirded by the Witten index of the SUSY theory. We develop a unifying framework to formulate these SUSY connections in terms of general lattice graph correspondences. Notably, in this framework, the supercharge operator that generates SUSY is Hermitian and can itself be interpreted as a hopping Hamiltonian on a bipartite lattice, a feature that enables the discovery of materials or model lattices hosting the SUSY partners. To illustrate the power of SUSY, we present 16 use cases of SUSY, that span topics including frustrated magnets, Kitaev spin liquids, and topological superconductors, the majority of which turn out to provide insights into the discovery and design of flat bands and topological materials.

Published by the American Physical Society
2024
Information interference driven by environmental activity
Authors: Nicoletti Giorgio , Busiello Daniel Maria
Published: December 2024

Real-world systems are shaped by both their complex internal interactions and the changes in their noisy environments. In this work, we study how a shared active bath affects the statistical dependencies between two interacting Brownian particles by evaluating their mutual information. We decompose the mutual information into three terms: information stemming from the internal interactions between the particles; information induced by the shared bath, which encodes environmental changes; a term describing information interference that quantifies how the combined presence of both internal interactions and environment either masks (destructive interference) or boosts (constructive interference) information. By studying exactly the case of linear interactions, we find that the sign of information interference depends solely on that of the internal coupling. However, when internal interactions are described by a nonlinear activation function, we show that both constructive and destructive interference appear depending on the interplay between the timescale of the active environment, the internal interactions, and the environmental coupling. Finally, we show that our results generalize to hierarchical systems where asymmetric couplings to the environment mimic the scenario where the active bath is only partially accessible to one particle. This setting allows us to quantify how this asymmetry drives information interference. Our work underscores how information and functional relationships in complex multiscale systems are fundamentally shaped by the environmental context.

Published by the American Physical Society
2024
Exploring Quantum Materials with Resonant Inelastic X-Ray Scattering
Authors: Mitrano M. , Johnston S. , Kim Young-June , Dean M. P. M.
Published: December 2024

Understanding quantum materials—solids in which interactions among constituent electrons yield a great variety of novel emergent quantum phenomena—is a forefront challenge in modern condensed matter physics. This goal has driven the invention and refinement of several experimental methods, which can spectroscopically determine the elementary excitations and correlation functions that determine material properties. Here we focus on the future experimental and theoretical trends of resonant inelastic x-ray scattering (RIXS), which is a remarkably versatile and rapidly growing technique for probing different charge, lattice, spin, and orbital excitations in quantum materials. We provide a forward-looking introduction to RIXS and outline how this technique is poised to deepen our insight into the nature of quantum materials and of their emergent electronic phenomena.

Published by the American Physical Society
2024
Magnetoresistance Oscillations in Vertical Junctions of 2D Antiferromagnetic Semiconductor
CrPS4
Authors: Shi Pengyuan , Wang Xiaoyu , Zhang Lihao , Song Wenqin , Yang Kunlin , Wang Shuxi , Zhang Ruisheng , Zhang Liangliang , Taniguchi Takashi , Watanabe Kenji , Yang Sen , Zhang Lei , Wang Lei , Shi Wu , Pan Jie , Wang Zhe
Published: December 2024

Magnetoresistance (MR) oscillations serve as a hallmark of intrinsic quantum behavior, traditionally observed only in conducting systems. Here we report the discovery of MR oscillations in an insulating system, the vertical junctions of CrPS4 which is a two-dimensional A-type antiferromagnetic semiconductor. Systematic investigations of MR peaks under varying conditions, including electrode materials, magnetic field direction, temperature, voltage bias, and layer number, elucidate a correlation between MR oscillations and spin-canted states in CrPS4. Experimental data and analysis point out the important role of the in-gap electronic states in generating MR oscillations, and we propose that spin selected interlayer hopping of localized defect states may be responsible for it. Our findings not only illuminate the unusual electronic transport in CrPS4 but also underscore the potential of van der Waals magnets for exploring interesting phenomena.

Published by the American Physical Society
2024
Examining Saudi Arabia’s housing tenure and price trends: a comparative study with global context
Authors: Dano Umar Lawal
Published: December 2024

Purpose

This paper aims to analyze and compare housing tenure and model housing price index (HPI) in Saudi Arabia with selected Organization for Economic Cooperation Development (OECD) countries.

Design/methodology/approach

The research uses quantitative data from the Saudi 2022 Statistical Census and OECD sources. Analytical methods include polynomial regression modeling for housing price trends and analysis of variance (ANOVA) to explore the relationship between housing variables, alongside descriptive and inferential statistics.

Findings

The polynomial regression analysis reveals distinct HPI trends across the studied countries, indicating stability and growth. Countries like Australia, France and the US are projected to see substantial HPI increases by 2026, reaching values around 175, signaling strong market recovery and growth. Greece’s trajectory is marked by fluctuations, recovering modestly post-2020, while Saudi Arabia’s market shows stability with a slight increase forecasted to 92.8 by 2026. The ANOVA analysis for Saudi Arabia highlights significant regional differences in housing tenure, with economic conditions and housing types significantly impacting tenure patterns.

Originality/value

This study fills a void in research by offering a comparative analysis of housing tenure and HPI, shedding light on how economic and demographic factors influence housing trends. The findings are crucial for policymakers to develop targeted strategies that address affordability and stability, catering to diverse demographic needs.
Industry 4.0 adoption challenges in lean-agile-resilient-green agri-food supply chain
Authors: Mahajan Pramod Sanjay , Raut Rakesh , Cheikhrouhou Naoufel , Yadav Vinay Surendra , Ghoshal Sudishna
Published: December 2024

Purpose

By incorporating I4.0 technologies, the agri-food supply chain (AFSC) can become leaner, faster, more robust and greener. However, many challenges must be overcome to fully realise I4.0 in this context. Therefore, this paper aims to identify the challenges that hinder the adoption of I4.0 technologies on the development of the Lean, Agile, Resilient and Green (LARG) AFSC.

Design/methodology/approach

The approach adopted was to identify challenges addressed in the literature with expert opinion and Total Interpretive Structural Modelling (TISM) for adaptation. In addition, a Weighted Influence Non-linear Gauge Systems (WINGS) methodology has been developed that uses expert opinion to generate a power and influence matrix.

Findings

The results show that lack of commitment and understanding of top management (X12), lack of long term vision (X17) and lack of incentives and government support (15) are the most important challenges.

Research limitations/implications

This study does not explore the effectiveness of the concluded challenges of I4.0 and their strategy to overcome them. Also, the authors relied on a limited sample size for this study, which might not cover the detailed challenges within LARG AFSC. Finally, this study lacks in future advancement of I4.0, which may further affect the challenges.

Practical implications

By mentioning the key challenges, this study empowers LARG AFSC organisations to build a targeted strategy for smoother I4.0 implementation.

Originality/value

Industry 4.0 challenges remain unexplored in LARG AFSC. This improved awareness equips managers to navigate better the potential issues and complexity that may arise when adopting I4.0 in the LARG AFSC.
Lean production in family farming: application of its principles in the production of vegetables
Authors: Cuer Laiane , Scalco Andréa Rossi , Satolo Eduardo Guilherme
Published: December 2024

Purpose

The losses of vegetables in the world are significant and are distributed throughout the production chain. In the face of this problem, the purpose of this study is to applied the principles of the Lean Production to reduce waste in the production process on a family farm.

Design/methodology/approach

A case study was conducted on a family farm producing organic vegetables, with the focus of study being on lettuce. Data collection procedures involved interviews, observations and filming of the lettuce production process. The instruments used to identify the sources of waste were the value flow mapping, vertical flowchart, Ishikawa diagram, consumer voice and spaghetti diagram.

Findings

The main sources of waste observed were the movement in the planting stage and excess processing in the fertilization stage (excess fertilization). Improvements such as adaptation and even exclusion of activities related to the production process and organization of the site were carried out and contributed to a 45% reduction in the time of the activities cycle that did not add value but which were necessary.

Practical implications

The study here presented came to innovate and contribute to the reduction of waste related to agriculture since the lean production principles were applied to reduce waste in the productive process of lettuce in a family farm. The positive results of the application of lean production prove to be replicable to other properties and cultures, given that the conduction took place in a structured way in the five lean principles and their tools. It is up to the guidance and dissemination of this knowledge to producers.

Social implications

The study here presented can estimate that lean principles, when applied in agriculture, can contribute to the sustainability of food production systems.

Originality/value

Lean Production is a model developed for application in industrial sectors, and its application in agriculture is incipient in the international literature. This paper contributes to reducing waste in a sector that has significant loss rates by adapting the lean production model to agriculture.
Human intelligence versus artificial intelligence in classifying economics research articles: exploratory evidence
Authors: Heikkilä Jussi T.S.
Published: December 2024

Purpose

We compare human intelligence to artificial intelligence (AI) in the choice of appropriate Journal of Economic Literature (JEL) codes for research papers in economics.

Design/methodology/approach

We compare the JEL code choices related to articles published in the recent issues of the Journal of Economic Literature and the American Economic Review and compare these to the original JEL code choices of the authors in earlier working paper versions and JEL codes recommended by various generative AI systems (OpenAI’s ChatGPT, Microsoft’s Copilot, Google’s Gemini) based on the abstracts of the articles.

Findings

There are significant discrepancies and often limited overlap between authors’ choices of JEL codes, editors’ choices as well as the choices by contemporary widely used AI systems. However, the observations suggest that generative AI can augment human intelligence in the micro-task of choosing the JEL codes and, thus, save researchers time.

Research limitations/implications

Rapid development of AI systems makes the findings quickly obsolete.

Practical implications

AI systems may economize on classification costs and (semi-)automate the choice of JEL codes by recommending the most appropriate ones. Future studies may apply the presented approach to analyze whether the JEL code choices between authors, editors and AI systems converge and become more consistent as humans increasingly interact with AI systems.

Originality/value

We assume that the choice of JEL codes is a micro-task in which boundedly rational decision-makers rather satisfice than optimize. This exploratory experiment is among the first to compare human intelligence and generative AI in choosing and justifying the choice of optimal JEL codes.
Assemblies of leaf and root mycobiomes in a temperate grassland: Dispersal limitation overpowers selection
Authors: Bai Ren , Hu Hang‐Wei , Ge An‐Hui , Zhou Meng , Sheng Jun , Yuan Guangyuan , Zhang Wen‐Hao , Bai Wenming
Published: December 2024

The emergence of β‐diversity of plant‐associated fungi across diverse coexisting host plant species in natural habitats is intricately linked to specific community assembly processes. Despite this, the relative contributions of various assembly processes to the observed β‐diversity patterns, as well as the influence of plant traits on these contributions, are still poorly understood.

Here, we investigated the leaf/root‐associated fungal communities across nine coexisting dominant herbaceous perennials in a temperate grassland that had undergone a 17‐year mowing treatment. We elucidated the β‐diversity components and community assembly processes of these fungal communities. Furthermore, we explored relationships between leaf/root functional trait variations and fungal community assemblies. We tested the following hypotheses: (1) both species turnover and nestedness are important components of the fungal β‐diversity, with selection predominating in the fungal community assemblies; (2) mowing enhances the contributions of nestedness/selection; (3) plant trait variations significantly affect the fungal community assembly processes.

Unexpectedly, our findings demonstrated a predominance of leaf/root fungal species turnover among coexisting plant hosts, contrasting with nestedness. Moreover, dispersal limitation emerged as the primary factor shaping fungal community assemblies, rather than selection processes. Although mowing significantly inhibited plant growth, its effects on the overall patterns of fungal assemblages were limited. We further observed that higher degrees of plant trait variations were primarily linked to stronger dispersal limitation, with a relatively weaker influence on heterogeneous selection. Additionally, the impact of plant traits on the selection process of root‐associated fungi was more pronounced compared to that of leaf‐associated fungi.

Synthesis. Our study reveals that the β‐diversity of fungi associated with coexisting plants in natural grasslands is primarily attributed to fungal species replacement rather than gain‐and‐loss dynamics among these plants. Concurrently, this observed pattern is largely governed by dispersal limitation as opposed to selection. We propose that the primary mechanism through which plant hosts and their traits influence the structures of associated fungal communities is by limiting fungal dispersal, while niche differentiation among fungal taxa plays a secondary role. These findings offer a mechanistic insight into the assemblies of plant mycobiomes and further elucidate the plant‐mycobiome relationships within complex plant communities.
Case Report: A Young Woman Diagnosed With Overlap Syndrome of Systemic Sclerosis and Ankylosing Spondylitis
Authors: Li Chen , Jin Di , Gong Yu‐Fang , Liu Hong‐Xu , Guan Yi‐Wei , Li Sheng‐Guang
Published: December 2024

This case report describes a rare occurrence of a 25‐year‐old female diagnosed with both systemic sclerosis (SSc) and ankylosing spondylitis (AS), two distinct autoimmune diseases. The patient presented with a combination of symptoms, including progressive skin tightening, lumbosacral pain, and Raynaud's phenomenon, which complicated the diagnosis. Despite the challenges posed by the coexistence of SSc and AS, a multidisciplinary treatment approach involving corticosteroids, immunosuppressants, and supportive therapies led to significant clinical improvement. Over the course of 3 weeks, the patient's Rodnan Skin Score improved from 18 to 11, and her pain and overall disease activity were markedly reduced. Although skin sclerosis showed substantial improvement, pulmonary involvement remains a concern that requires long‐term monitoring. This case highlights the complexities in diagnosing and managing overlap syndromes, emphasizing the need for personalized treatment strategies and further investigation into the underlying genetic and immunological mechanisms.
Intraspecific competition and performance of Anagrus virlai parasitizing the corn leafhopper
Authors: Hill Jorge G. , Coll‐Aráoz María V. , Luft‐Albarracin Erica , Fernández Patricia C. , Virla Eduardo G.
Published: December 2024

Intraspecific competition and avoidance of superparasitism are critical for biological control. In this study, we conducted behavioural and biological trials to assess intraspecific competition in the egg parasitoid Anagrus virlai, targeting the corn leafhopper Dalbulus maidis. Firstly, we investigated whether A. virlai can distinguish between unparasitized and parasitized host eggs using long‐range cues in an olfactometer experiment. Secondly, we evaluated whether A. virlai parasitism and emergence under varying host densities are affected by the presence of multiple conspecific wasps foraging within the same host patch. In the olfactometer test, A. virlai did not differentiate between corn leaves with parasitized and unparasitized eggs. Regarding the parasitoid's performance, the number and percentage of parasitized eggs were influenced by the density of females foraging simultaneously in the same host patch. Significant increases in collective parasitism were observed when two or more parasitoids were present in each arena. Competitive interaction did not affect wasp emergence, and A. virlai proved to be a solitary species. Moreover, the negative relationship between per‐capita parasitism and the number of conspecific wasps per arena demonstrated mutual interference among parasitoids. These findings highlight the impact of competitive interactions on A. virlai performance and suggest a potential aggregated response of the parasitoid to high‐density patches of hosts.
Mechanisms underlying neurocognitive dysfunction following critical illness: a systematic review
Authors: Andonovic Mark , Morrison Holly , Allingham William , Adam Robert , Shaw Martin , Quasim Tara , McPeake Joanne , Quinn Terence
Published: December 2024

Introduction

Cognitive impairment is a significant healthcare problem globally and its prevalence is projected to affect over 150 million people worldwide. Survivors of critical illness are impacted frequently by long‐term neurocognitive dysfunction regardless of presenting illness, but the mechanisms are poorly understood. The goal of this review was to synthesise the existing evidence regarding potential mechanisms underlying neurocognitive dysfunction following critical illness in order to guide potential avenues for future research.

Methods

We performed a systematic search of the literature for studies published between 1 January 1974 and 15 July 2023. We included publications involving adult patients with critical illness due to any aetiology that assessed for cognitive impairment following recovery from illness, and explored or investigated potential underlying causative mechanisms. The quality and risk of bias of the individual studies was assessed using the Newcastle‐Ottawa scale.

Results

Of the 7658 reviewed references, 37 studies comprising 4344 patients were selected for inclusion. Most studies were single centre with sample sizes of < 100 patients. The proportion of patients with long‐term cognitive impairment ranged from 13% to 100%. A wide variety of theoretical mechanisms were explored, with biomarkers and neuroimaging utilised most frequently. Many studies reported associations between investigated mechanisms and reduced cognition; several of these mechanisms have been implicated in other forms of long‐term neurodegenerative conditions. Increased levels of inflammatory cytokines during acute illness and white matter hyperintensities on neuroimaging following recovery were the associations reported most commonly.

Discussion

The underlying pathophysiology of neurocognitive decline after critical illness is not yet understood fully. The mechanisms implicated in other neurodegenerative conditions suggest that this may represent an accelerated version of the same processes. Large scale studies are required to further elucidate the cause of this significant problem for survivors of critical illness.
Establishment of cancer cell radiosensitivity database linked to multi‐layer omics data
Authors: Oike Takahiro , Kambe Ryosuke , Darwis Narisa Dewi Maulany , Shibata Atsushi , Ohno Tatsuya
Published: December 2024

Personalized radiotherapy based on the intrinsic sensitivity of individual tumors is anticipated, however, it has yet to be realized. To explore cancer radiosensitivity, analysis in combination with omics data is important. The Cancer Cell Line Encyclopedia (CCLE) provides multi‐layer omics data for hundreds of cancer cell lines. However, the radiosensitivity counterpart is lacking. To address this issue, we aimed to establish a database of radiosensitivity, as assessed by the gold standard clonogenic assays, for the CCLE cell lines by collecting data from the literature. A deep learning‐based screen of 33,284 papers identified 926 relevant studies, from which SF2 (survival fraction after 2 Gy irradiation) data were extracted. The median SF2 (mSF2) was calculated for each cell line, generating an mSF2 database comprising 285 cell lines from 28 cancer types. The mSF2 showed a normal distribution among higher and lower cancer‐type hierarchies, demonstrating a large variation across and within cancer types. In selected cell lines, mSF2 correlated significantly with the single‐institution SF2 obtained using standardized experimental protocols or with integral survival, a radiosensitivity index that correlates with clonogenic survival. Notably, the mSF2 for blood cancer cell lines was significantly lower than that for solid cancer cell lines, which is in line with the empirical knowledge that blood cancers are radiosensitive. Furthermore, the CCLE‐derived protein levels of NFE2L2 and SQSTM1, which are involved in antioxidant damage responses that confer radioresistance, correlated significantly with mSF2. These results suggest the robustness and potential utility of the mSF2 database, linked to multi‐layer omics data, for exploring cancer radiosensitivity.
Design, Synthesis, and Biological Evaluation of New Benzimidazole‐1,2,4‐Triazole Derivatives as Potential Anticancer Agents
Authors: Kaya‐Sezginer Ecem , Oz Bedir Beyza Ecem , Terzi Emine , Ozdemir Sanci Tuba , Maryam Zahra , Acar Çevik Ulviye
Published: December 2024

New series of benzimidazole‐1,2,4‐triazole derivatives were designed, synthesized, and characterized using 1H‐NMR, 13C‐NMR, and HRMS. These compounds were evaluated for anticancer activity toward HTB‐9 bladder and HT‐29 colorectal cancer cell lines. Compounds 7h and 7ı were found to be the most active against HTB‐9 cell line, with IC50 6.27 and 6.44 μM, respectively, comparable to positive control cisplatin (IC50 = 11.40 μM). Additionally, in HT‐29 cell line, compounds 7a and 7ı exhibited the lowest IC50 values (20.37 and 22.71 μM, respectively), which was higher than those of cisplatin (19.79 μM). All active compounds induced apoptosis and caspase 3/7 activity and reduced the migration ability in both cell lines. Particularly, HT‐29 cells treated with compound 7ı exerted a higher apoptotic index than cisplatin‐treated cells. Furthermore, compounds 7h and 7ı led to G1 cell cycle arrest of HTB‐9, and compounds 7a and 7ı against HT‐29 induced S and G1 cell cycle arrest, respectively. In conclusion, the antiproliferative effect of active compounds is associated with the induction of apoptosis through caspase 3/7 activation and cell cycle arrest at different phases in HTB‐9 and HT‐29 cell lines.
A Chemical Reprogramming Approach Efficiently Producing Human Retinal Pigment Epithelium Cells for Retinal Disease Therapies
Authors: Zhang Ke , Wang Yanqiu , An Qi , Ji Hengjing , Wu Defu , Li Xuri , Suo Lingge , Zhang Chun , Dong Xuran
Published: December 2024

Human induced pluripotent stem cells (hiPSCs) represent a promising cell source for generating functional cells suitable for clinical therapeutic applications, particularly in the context of autologous cell therapies. However, the production of hiPSCs through genetic manipulation, especially involving oncogenes, may raise safety concerns. Furthermore, the complexity and high costs associated with hiPSCs generation have hindered their broad clinical use. In this study, we utilised a recently developed chemical reprogramming method in conjunction with a guided differentiation protocol, introducing a chemically defined strategy for generating functional human retinal pigment epithelium (RPE) cells from adipose tissue, bypassing conventional hiPSCs generation challenges. By utilising small molecule‐based chemical cocktails, we reprogrammed somatic adipose cells into human chemically induced pluripotent stem cells (hCiPSCs) in a safer and more streamlined manner, entirely free from gene manipulation. Subsequent differentiation of hCiPSCs into functional RPE cells demonstrated their capability for secretion and phagocytosis, emphasising their vital role in maintaining retinal homeostasis and underscoring their therapeutic potential. Our findings highlight the transformative potential of hCiPSCs as a safer, more efficient option for personalised cell therapies, with applications extending beyond ocular disease to a wide range of medical conditions.
Predictive Analysis of Global Terrorist Attacks Using Lexical Patterns Across Multiple Datasets
Authors: Atoum Mohammed Salem , Alarood Ala Abdulsalam , Alsolmi Eesa Abdullah , Obeidat Areej , Alazab Moutaz
Published: December 2024

Worldwide terrorist activities continue to pose a significant threat to global security and stability. The unpredictable nature of these acts necessitates advanced analytical approaches to enhance prevention and response strategies. This study examines undetectable word extensions across multiple datasets, using terrorism‐related datasets as a case study. This research aims to overcome constraints in current predictive models associated with terrorist attack prediction. While many studies have used the GTD for predicting global terrorist attacks, this study expands beyond GTD by evaluating a corpus of terrorism incidents to enhance predictive analysis through lexical usage. The study employs several machine learning algorithms including Decision Tree (DT), Bootstrap Aggregating (BA), Random Forest (RF), Extra Trees (ET) and XGBoost (XG) algorithms for evaluation. Our approach integrates multiple datasets to reduce dependence on GTD alone. Findings indicate that RF performs best on the GTD database, with 90.20% accuracy in predicting worldwide terrorist attacks. DT achieves 90.40% accuracy when applied to the TF–IDF dataset. XG demonstrates superior performance across various aggregation settings and feature sets, achieving 95.77% accuracy in forecasting worldwide terrorist acts. XG's consistent and effective performance across various contexts highlights its versatility. Its high adaptability and robust performance position it as the preferred algorithm for conducting predictive research on global terrorist acts using the available datasets. Our research findings underscore the importance of incorporating diverse datasets to enhance understanding of terrorist activities and improve predictive capabilities.
Crystallization Pathways of Iron Formations: Insights From Magnetic Properties and High‐Resolution Imaging of the 2.7 Ga Carajás Formation, Brazil
Authors: Teixeira Livia , Carlut Julie , Rego Eric Siciliano , Trindade Ricardo I.F. , Philippot Pascal
Published: December 2024

Banded iron formations (BIFs) are chemical sedimentary rocks commonly utilized for exploring the chemistry and redox state of the Precambrian ocean. Despite their significance, many aspects regarding the crystallization pathways of iron oxides in BIFs remain loosely constrained. In this study, we combine magnetic properties characterization with high‐resolution optical and electron imaging of finely laminated BIFs from the 2.7 Ga Carajás Formation, Brazil, to investigate their nature and potential for preserving ancient environmental conditions. Our findings reveal that magnetite, in the form of large 0.1–0.5 mm crystals, is the main iron oxide, with an overall averaged saturation magnetization (Ms) of 25 Am2/kg (corresponding to ~27 wt% of magnetite) over the studied 230 m of the sequence. Nevertheless, the non‐negligible contribution of minerals with higher coercivity suggests variable proportions of hematite along the core. Additionally, we observe non‐uniform behavior in magnetite grains, with distinct populations identified through low‐temperature measurements of the Verwey transition. Petrographic observations indicate that the original sediment was an Fe–Si mud consisting of a ferrihydrite–silica mixture formed in the water column. This assemblage was rapidly transformed into nano‐scale hematite embedded in silica as indicated by a honeycomb structure composed of Si‐spherules distributed in a microscale hematite matrix. Textural relationships show that the nucleation of magnetite started during or soon after the formation of hematite, as indicated by the preservation of the Si‐spherules within magnetite cores. Further magnetite overgrowth stages are characterized by inclusion‐free rims, associated with continuous Si supply during the evolving diagenetic or early metamorphic stages. These findings, combined with existing literature, suggest that ferrihydrite precipitated alongside Si and organic material, later crystallizing as hematite on the seafloor. Anaerobic respiration by Fe(III)‐reducing microorganisms likely contributed to early magnetite formation in a fluid‐saturated, unconsolidated sediment. Subsequent low‐grade metamorphism and Si mobilization led to palisade quartz precipitation and a second stage of magnetite growth likely formed at the expense of matrix hematite through thermochemical Fe(III) reduction. Low‐temperature magnetic analyses revealed that the two generations of magnetite core and rim are associated with specific stoichiometry.
TavWA1 is critical for wheat growth by modulating cell morphology and arrangement
Authors: Chang Guowei , Li Yue , Peng Lei , Shen Chuncai , Lu Yipeng , Teng Wan , Liu Yangyang , Wang Yingchun , Zhu Weiqi , Liu Cuimin , He Xue , Tong Yiping , Zhao Xueqiang
Published: December 2024

Plant growth is determined by the production of cells and initiation of new organs. Exploring genes that control cell number and cell size is of great significance for understanding plant growth regulation. In this study, we characterized two wheat mutants, ah and dl, with abnormal growth. The ah mutant is a naturally occurring variant characterized by severe dwarfism, increased tiller number, and reduced grain length, while the dl mutant is derived from an ethyl methane sulfonate (EMS)‐mutagenized population and exhibits smaller grain size and slightly reduced plant height. Cytological analyses revealed abnormal cell number, cell morphology and arrangement in the stems and leaves of the ah mutant, along with reduced cell length in the grains of the dl mutant. Map‐based cloning identified that both mutants carry mutations in the same gene TavWA1‐7D, which encodes a protein with a von Willebrand factor A (vWA) domain. The ah mutant harbors a 174‐bp insertion in the 1,402‐bp coding sequence (CDS) of TavWA1‐7D, causing premature termination of protein translation, while the dl mutant contains a Glu420Lys substitution. Mimicking the TavWA1‐7Dah through clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR‐associated nuclease 9‐mediated genome editing leads to a severe dwarfism phenotype. The C‐terminus of the protein is crucial for its correct subcellular localization and interaction, supporting its critical role for TavWA1‐7D function. Proteomic analysis showed that the dwarf phenotype of the ah mutant is associated with impaired photosynthesis, ribosome function, and nucleosome formation. Additionally, TavWA1‐7D interacts with an E3 ligase, TaVIP1‐3B, the expression levels of which are elevated in both mutants. Overexpression and knockout studies of TaVIP1‐3B demonstrated its negative regulatory role in cell length and grain size. Together, our findings suggest that TavWA1‐7D plays a vital role in regulating wheat growth and yield‐related traits, with the dl mutant's short grain phenotype being associated with TaVIP1‐3B expression levels.
Structures of PSI–FCPI from Thalassiosira pseudonana grown under high light provide evidence for convergent evolution and light‐adaptive strategies in diatom FCPIs
Authors: Feng Yue , Li Zhenhua , Yang Yang , Shen Lili , Li Xiaoyi , Liu Xueyang , Zhang Xiaofei , Zhang Jinyang , Ren Fei , Wang Yuan , Liu Cheng , Han Guangye , Wang Xuchu , Kuang Tingyun , Shen Jian‐Ren , Wang Wenda
Published: December 2024

Diatoms rely on fucoxanthin chlorophyll a/c‐binding proteins (FCPs) for light harvesting and energy quenching under marine environments. Here we report two cryo‐electron microscopic structures of photosystem I (PSI) with either 13 or five fucoxanthin chlorophyll a/c‐binding protein Is (FCPIs) at 2.78 and 3.20 Å resolutions from Thalassiosira pseudonana grown under high light (HL) conditions. Among them, five FCPIs are stably associated with the PSI core, these include Lhcr3, RedCAP, Lhcq8, Lhcf10, and FCP3. The eight additional Lhcr‐type FCPIs are loosely associated with the PSI core and detached under the present purification conditions. The pigments of this centric diatom showed a higher proportion of chlorophylls a, diadinoxanthins, and diatoxanthins; some of the chlorophyll as and diadinoxanthins occupy the locations of fucoxanthins found in the huge PSI–FCPI from another centric diatom Chaetoceros gracilis grown under low‐light conditions. These additional chlorophyll as may form more energy transfer pathways and additional diadinoxanthins may form more energy dissipation sites relying on the diadinoxanthin–diatoxanthin cycle. These results reveal the assembly mechanism of FCPIs and corresponding light‐adaptive strategies of T. pseudonana PSI–FCPI, as well as the convergent evolution of the diatom PSI–FCPI structures.