bluuebunny/crossref_metadata_2025 huggingface dataset:
Search for protein kinase(s) related to cell growth or viability maintenance in the presence of ethanol in budding and fission yeasts
Authors: Ushiyama Yuto , Nishida Ikuhisa , Tomiyama Saki , Tanaka Hitomi , Kume Kazunori , Hirata Dai
Published: April 2024

Alcohol fermentation comprises two phases: phase 1, alcohol fermentation occurs while yeast cells proliferate; phase 2, growth stops and alcohol fermentation continues. We categorized genes related to proliferation in low ethanol (phase 1) and viability in high ethanol (phase 2) as Alcohol Growth Ability (AGA) and Alcohol Viability (ALV), respectively. Although genes required for phase 1 are examined in budding yeast, those for phase 2 are unknown. We set conditions for ALV screening, searched for protein kinases (PKs) related to ALV in budding yeast, and expanded two screenings to fission yeast. Bub1 kinase was important for proliferation in low ethanol but not for viability in high ethanol, suggesting that the important PKs differ between the two phases. It was indeed the case. Further, 3 common PKs were identified as AGA in both yeasts, suggesting that the important cellular mechanism in phase 1 is conserved in both yeasts, at least partially.
From tradition to innovation: conventional and deep learning frameworks in genome annotation
Authors: Chen Zhaojia , Ain Noor ul , Zhao Qian , Zhang Xingtan
Published: April 2024

Following the milestone success of the Human Genome Project, the ‘Encyclopedia of DNA Elements (ENCODE)’ initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the emergence of numerous novel technologies, accompanied by the provision of vast amounts of whole-genome sequences, high-throughput data such as ChIP-Seq and RNA-Seq. Extracting biologically meaningful information from this massive dataset has become a critical aspect of many recent studies, particularly in annotating and predicting the functions of unknown genes. The core idea behind genome annotation is to identify genes and various functional elements within the genome sequence and infer their biological functions. Traditional wet-lab experimental methods still rely on extensive efforts for functional verification. However, early bioinformatics algorithms and software primarily employed shallow learning techniques; thus, the ability to characterize data and features learning was limited. With the widespread adoption of RNA-Seq technology, scientists from the biological community began to harness the potential of machine learning and deep learning approaches for gene structure prediction and functional annotation. In this context, we reviewed both conventional methods and contemporary deep learning frameworks, and highlighted novel perspectives on the challenges arising during annotation underscoring the dynamic nature of this evolving scientific landscape.
Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes
Authors: Ma Guoxuan , Kang Jian , Yu Tianwei
Published: April 2024

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.
GLDM: hit molecule generation with constrained graph latent diffusion model
Authors: Wang Conghao , Ong Hiok Hian , Chiba Shunsuke , Rajapakse Jagath C
Published: April 2024

Discovering hit molecules with desired biological activity in a directed manner is a promising but profound task in computer-aided drug discovery. Inspired by recent generative AI approaches, particularly Diffusion Models (DM), we propose Graph Latent Diffusion Model (GLDM)—a latent DM that preserves both the effectiveness of autoencoders of compressing complex chemical data and the DM’s capabilities of generating novel molecules. Specifically, we first develop an autoencoder to encode the molecular data into low-dimensional latent representations and then train the DM on the latent space to generate molecules inducing targeted biological activity defined by gene expression profiles. Manipulating DM in the latent space rather than the input space avoids complicated operations to map molecule decomposition and reconstruction to diffusion processes, and thus improves training efficiency. Experiments show that GLDM not only achieves outstanding performances on molecular generation benchmarks, but also generates samples with optimal chemical properties and potentials to induce desired biological activity.
PUTransGCN: identification of piRNA–disease associations based on attention encoding graph convolutional network and positive unlabelled learning
Authors: Chen Qiuhao , Zhang Liyuan , Liu Yaojia , Qin Zhonghao , Zhao Tianyi
Published: April 2024

Piwi-interacting RNAs (piRNAs) play a crucial role in various biological processes and are implicated in disease. Consequently, there is an escalating demand for computational tools to predict piRNA–disease interactions. Although there have been computational methods proposed for the detection of piRNA–disease associations, the problem of imbalanced and sparse dataset has brought great challenges to capture the complex relationships between piRNAs and diseases. In response to this necessity, we have developed a novel computational architecture, denoted as PUTransGCN, which uses heterogeneous graph convolutional networks to uncover potential piRNA–disease associations. Additionally, the attention mechanism was used to adjust the weight parameters of aggregation heterogeneous node features automatically. For tackling the imbalanced dataset problem, the combined positive unlabelled learning (PUL) method comprising PU bagging, two-step and spy technique was applied to select reliable negative associations. The features of piRNAs and diseases were derived from three distinct biological sources by PUTransGCN, including information on piRNA sequences, semantic terms related to diseases and the existing network of piRNA–disease associations. In the experiment, PUTransGCN performs in 5-fold cross-validation with an AUC of 0.93 and 0.95 on two datasets, respectively, which outperforms the other six state-of-the-art models. We compared three different PUL methods, and the results of the ablation experiment indicate that the combined PUL method yields the best results. The PUTransGCN could serve as a valuable piRNA–disease prediction tool for upcoming studies in the biomedical field. The code for PUTransGCN is available at https://github.com/chenqiuhao/PUTransGCN
A new paradigm for applying deep learning to protein–ligand interaction prediction
Authors: Wang Zechen , Wang Sheng , Li Yangyang , Guo Jingjing , Wei Yanjie , Mu Yuguang , Zheng Liangzhen , Li Weifeng
Published: April 2024

Protein–ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein–ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel’s generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein–ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein–ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.
Computational model for drug research
Authors: Chen Xing , Huang Li
Published: April 2024

This special issue focuses on computational model for drug research regarding drug bioactivity prediction, drug-related interaction prediction, modelling for immunotherapy and modelling for treatment of a specific disease, as conveyed by the following six research and four review articles. Notably, these 10 papers described a wide variety of in-depth drug research from the computational perspective and may represent a snapshot of the wide research landscape.
Life history of an evolutionary distinct aquatic caecilian amphibian (genus Potomotyphlus) in eastern Amazonia, Brazil
Authors: Araujo Elisia Clara Menezes , dos Santos-Costa Maria Cristina , Maciel Adriano Oliveira
Published: April 2024

Basic ecological data for caecilians are relatively lacking. This study presents the first comprehensive ecological investigation of the Amazonian aquatic caecilian Potomotyphlus kaupii, utilizing a large sample size. We collected population data over a 14-month period, and also used data of museum specimens, both from Belém, State of Pará, Brazil. Our analysis focused on sex ratio, sexual size dimorphism (SSD), female fecundity, and ecological observations. No significant differences in adult sex ratio were found. SSD was found in some characters such as circumference at midbody and body mass with females being larger than males, while males had a wider and longer cloacal disc. We provide the first determination of the approximate size at which males diverge from females in the terminal portion of the body, demonstrating an allometric growth pattern of the cloacal region. Female size showed a positive correlation with the number of ovarian eggs, indicating an important reproductive strategy. Furthermore, a novel behaviour in P. kaupii was documented. We expand the understanding of Gymnophiona biology, in particular of a fully aquatic species, and provide support for future ecological and conservation research on caecilians.
Topological disruption of low- and high-order functional networks in presbycusis
Authors: Xu Yixi , Li Xiangxiang , Yan Qi , Zhang Yao , Shang Song’an , Xing Chunhua , Wu Yuanqing , Guan Bing , Chen Yu-Chen
Published: April 2024

Prior efforts have manifested that functional connectivity (FC) network disruptions are concerned with cognitive disorder in presbycusis. The present research was designed to investigate the topological reorganization and classification performance of low-order functional connectivity (LOFC) and high-order functional connectivity (HOFC) networks in patients with presbycusis. Resting-state functional magnetic resonance imaging (Rs-fMRI) data were obtained in 60 patients with presbycusis and 50 matched healthy control subjects (HCs). LOFC and HOFC networks were then constructed, and the topological metrics obtained from the constructed networks were compared to evaluate topological differences in global, nodal network metrics, modularity and rich-club organization between patients with presbycusis and HCs. The use of HOFC profiles boosted presbycusis classification accuracy, sensitivity and specificity compared to that using LOFC profiles. The brain networks in both patients with presbycusis and HCs exhibited small-world properties within the given threshold range, and striking differences between groups in topological metrics were discovered in the constructed networks (LOFC and HOFC). NBS analysis identified a subnetwork involving 26 nodes and 23 signally altered internodal connections in patients with presbycusis in comparison to HCs in HOFC networks. This study highlighted the topological differences between LOFC and HOFC networks in patients with presbycusis, suggesting that HOFC profiles may help to further identify brain network abnormalities in presbycusis.
Towards building a trustworthy pipeline integrating Neuroscience Gateway and Open Science Chain
Authors: Sivagnanam S , Yeu S , Lin K , Sakai S , Garzon F , Yoshimoto K , Prantzalos K , Upadhyaya D P , Majumdar A , Sahoo S S , Lytton W W
Published: April 2024

When the scientific dataset evolves or is reused in workflows creating derived datasets, the integrity of the dataset with its metadata information, including provenance, needs to be securely preserved while providing assurances that they are not accidentally or maliciously altered during the process. Providing a secure method to efficiently share and verify the data as well as metadata is essential for the reuse of the scientific data. The National Science Foundation (NSF) funded Open Science Chain (OSC) utilizes consortium blockchain to provide a cyberinfrastructure solution to maintain integrity of the provenance metadata for published datasets and provides a way to perform independent verification of the dataset while promoting reuse and reproducibility. The NSF- and National Institutes of Health (NIH)-funded Neuroscience Gateway (NSG) provides a freely available web portal that allows neuroscience researchers to execute computational data analysis pipeline on high performance computing resources. Combined, the OSC and NSG platforms form an efficient, integrated framework to automatically and securely preserve and verify the integrity of the artifacts used in research workflows while using the NSG platform. This paper presents the results of the first study that integrates OSC–NSG frameworks to track the provenance of neurophysiological signal data analysis to study brain network dynamics using the Neuro-Integrative Connectivity tool, which is deployed in the NSG platform.

Database URL: https://www.opensciencechain.org.
Field estimation of fallen deadwood volume under different management approaches in two European protected forested areas
Authors: Rousseau Mélody , Adiningrat Devara P , Skidmore Andrew K , Siegenthaler Andjin , Wang Tiejun , Abdullah Haidi
Published: April 2024

Fallen deadwood is essential for biodiversity and nutrient cycling in forest ecosystems. In modern forest management, there is growing interest in developing accurate and efficient methods for field estimation of deadwood volume due to its many benefits (e.g. carbon storage, habitat creation, erosion control). The most common methods for deadwood inventories are fixed-area sampling (FAS) and line-intersect sampling (LIS) methods. While the estimations of deadwood volume by LIS generally show results comparable to FAS estimations, active management (e.g. production forestry clearcutting, logging, and thinning activities) can impair LIS accuracy by changing local deadwood patterns. Yet, the comparison of LIS and FAS methods has typically focused on production forests where deadwood is limited and deadwood volumes are comparably low. In this study, we assessed fallen deadwood volume in two large national parks—one being a more actively managed landscape (including, e.g., selective thinning for maintaining cultural–historical values and enhancing recreational opportunities) with overall lower levels of fallen deadwood, and the other having a strict non-intervention approach with higher levels of deadwood. No significant differences between average FAS and LIS estimations of deadwood volumes were detected. Additional experimentations using simulated data under varied stand conditions confirmed these results. Although line-intersect sampling showed a slight overestimation and some variability at the individual plot level, it remains an efficient, time-saving field sampling method providing comparable results to the more laborious fixed-area sampling. Line-intersect sampling may be especially suitable for rapid field inventories where relative changes in deadwood volume rather than absolute deadwood volumes are of large interest. Due to its practicality, flexibility, and relative accuracy, line-intersect sampling may gain wider use in natural resource management to inform national park managers, foresters, and ecologists.
UNC-16 interacts with LRK-1 and WDFY-3 to regulate the termination of axon growth
Authors: Drozd Cody J , Chowdhury Tamjid A , Quinn Christopher C
Published: April 2024

In humans, MAPK8IP3 (also known as JIP3) is a neurodevelopmental disorder-associated gene. In Caenorhabditis elegans, the UNC-16 ortholog of the MAPK8IP3 protein can regulate the termination of axon growth. However, its role in this process is not well understood. Here, we report that UNC-16 promotes axon termination through a process that includes the LRK-1 (LRRK-1/LRRK-2) kinase and the WDFY-3 (WDFY3/Alfy) selective autophagy protein. Genetic analysis suggests that UNC-16 promotes axon termination through an interaction between its RH1 domain and the dynein complex. Loss of unc-16 function causes accumulation of late endosomes specifically in the distal axon. Moreover, we observe synergistic interactions between loss of unc-16 function and disruptors of endolysosomal function, indicating that the endolysosomal system promotes axon termination. We also find that the axon termination defects caused by loss of UNC-16 function require the function of a genetic pathway that includes lrk-1 and wdfy-3, 2 genes that have been implicated in autophagy. These observations suggest a model where UNC-16 promotes axon termination by interacting with the endolysosomal system to regulate a pathway that includes LRK-1 and WDFY-3.
N-glycoproteomic analyses of human intestinal enteroids, varying in histo-blood group geno- and phenotypes, reveal a wide repertoire of fucosylated glycoproteins
Authors: Nilsson Jonas , Rimkute Inga , Sihlbom Carina , Tenge Victoria R , Lin Shih-Ching , Atmar Robert L , Estes Mary K , Larson Göran
Published: April 2024

Human noroviruses, globally the main cause of viral gastroenteritis, show strain specific affinity for histo-blood group antigens (HBGA) and can successfully be propagated ex vivo in human intestinal enteroids (HIEs). HIEs established from jejunal stem cells of individuals with different ABO, Lewis and secretor geno- and phenotypes, show varying susceptibility to such infections. Using bottom-up glycoproteomic approaches we have defined and compared the N-linked glycans of glycoproteins of seven jejunal HIEs. Membrane proteins were extracted, trypsin digested, and glycopeptides enriched by hydrophilic interaction liquid chromatography and analyzed by nanoLC-MS/MS. The Byonic software was used for glycopeptide identification followed by hands-on verifications and interpretations. Glycan structures and attachment sites were identified from MS2 spectra obtained by higher-energy collision dissociation through analysis of diagnostic saccharide oxonium ions (B-ions), stepwise glycosidic fragmentation of the glycans (Y-ions), and peptide sequence ions (b- and y-ions). Altogether 694 unique glycopeptides from 93 glycoproteins were identified. The N-glycans encompassed pauci- and oligomannose, hybrid- and complex-type structures. Notably, polyfucosylated HBGA-containing glycopeptides of the four glycoproteins tetraspanin-8, carcinoembryonic antigen-related cell adhesion molecule 5, sucrose-isomaltase and aminopeptidase N were especially prominent and were characterized in detail and related to donor ABO, Lewis and secretor types of each HIE. Virtually no sialylated N-glycans were identified for these glycoproteins suggesting that terminal sialylation was infrequent compared to fucosylation and HBGA biosynthesis. This approach gives unique site-specific information on the structural complexity of N-linked glycans of glycoproteins of human HIEs and provides a platform for future studies on the role of host glycoproteins in gastrointestinal infectious diseases.
NOD2 and Crohn’s Disease Clinical Practice: From Epidemiology to Diagnosis and Therapy, Rewired
Authors: Kayali Stefano , Fantasia Stefano , Gaiani Federica , Cavallaro Lucas Giovanni , de’Angelis Gian Luigi , Laghi Luigi
Published: April 2024

Crohn’s disease (CD) is a chronic inflammatory bowel disease with a multifactorial pathogenesis involving environmental and genetic factors. Since the late 20th century, the discovery of the first susceptibility gene (NOD2, previously referred to as CARD15) for CD has paved the way for further investigations into the correlations between clinical features and genetics, and its potential impact on clinical practice has fueled the research in the last 2 decades. Recent therapeutic advancements involving novel biologic drugs and small molecules have shifted inflammatory bowel disease management from a disease-centered to a patient-centric approach. To date, the role of NOD2 has not been fully understood yet. Recent data suggest that its clinical impact may be greater than currently recognized. This review overviews the most common NOD2 variants’ role in real-life clinical practice. These genetic variants increase the risk of developing the disease and can aid in tailoring diagnosis and treatment. They are associated with the stricturing phenotype and ileal involvement and increase the risk of steroid refractoriness. In the meantime, limited and inconclusive evidence exists regarding their predictive role in response to azathioprine, biologic drugs, and small molecules. Eventually, their role in increasing the risk for surgery is evident, especially in those with the L1007fs variant. If further trials will support the initial evidence reported so far, NOD2 genetic variants will emerge as possible candidates for developing precision medicine in CD.
Divisional patents: a system prone to abuse
Authors: Pantelidis Konstantinos
Published: April 2024

Divisional patents are derivatives to an already active patent or pending application used to separate part of the parent-patent’s subject-matter and create a new autonomous patent. They are extensively used in the pharmaceuticals sector to protect blockbuster medicines with multiple overarching patent rights, thus creating complex patent portfolios. These strategies have attracted the attention of competition enforcers and judicial bodies as potential abuses of dominant position because they aim to prevent and delay generic market entry for the patent holder to maintain their monopolistic prices. However, scrutinizing these strategies has proven challenging because the current rules are prone to exploitation, there is no unified approach to evaluating them, and the core of the issue is the much-debated interplay between IP and competition law. It will be shown that European case law varies across jurisdictions, with none of the current solutions fully addressing the issues. In the search for a functional, effective system, it is essential to understand the rationale behind patent rights, the unique features of the pharmaceuticals sector, and the arguments for and against the use of competition law to scrutinize patent strategies. On this basis, the article proposes a system which combines competition law enforcement with patent law amendments.
Sow serenity: automatic long-term measurement of lying behavior in crates and free-farrowing pens using 3D accelerometers
Authors: Knoll Maximilian , Gygax Lorenz , Hillmann Edna
Published: April 2024

Accelerometers are useful in analyzing lying behavior in farm animals. The effect of the farrowing system on sow lying behavior has been studied around parturition, but not long-term. In a natural environment, sows increase activity 14 d post parturition, which we expected to be also evident in housed sows when they can move freely. The objective of this study was (1) to validate the methodology to automatically measure sow lying bouts and duration with accelerometers and (2) to apply it to crated and free-farrowing sows 24-h pre-parturition until weaning. We used videos with manual behavior coding as the gold standard for validation and calculated the agreement with an intraclass correlation coefficient (ICC), which was 0.30 (95% CI: −0.10 to 0.64) for the number of lying bouts. When transitional sitting bouts were excluded from the video dataset, the ICC for lying bouts increased to 0.86 (95% CI: 0.40 to 0.95). For lying duration, the ICC was 0.93 (95% CI: 0.26 to 0.98). We evaluated the effects of housing, day relative to parturition, and time of day on lying using the accelerometer data and linear mixed models. In crated sows, the number of lying bouts increased toward parturition, peaking at about five bouts per 6 h, and decreased to almost zero bouts after parturition. Then, it increased again (P = 0.001). In free-farrowing sows, the number of lying bouts gradually decreased from a high level towards parturition and was lowest after parturition. It remained constant, as in the crated sows, until day 15, when the number of bouts increased to eight bouts on day 20 (P = 0.001). Sows in both systems were lying almost all of the time between 18:00 and 00:00 hours and on all days (P = 0.001). The crated sows showed a very similar pattern in the other three-quarters of the day with a reduced lying time before parturition, a peak after parturition, reduced lying time from days 5 to 20, and an increase again towards weaning (P = 0.001). Free-farrowing sows had a similar pattern to the crated sows from 00:00 to 06:00 hours, but without the reduction in lying time from days 5 to 20. They showed an increase in lying time toward parturition, which remained constant with a final decrease toward weaning, especially during the day (P = 0.001). This study proves the accuracy of accelerometer-based sow lying behavior classification and shows that free-farrowing systems benefit lactating sows around parturition but also towards weaning in the nest-leaving phase by facilitating activity.
Data dimensionality reduction for an optimal switching mode classification applied to a step-down power converter
Authors: Fernandez-Serantes Luis-Alfonso , Casteleiro-Roca José-Luis , Berger Hubert , Simić Dragan , Calvo-Rolle José-Luis
Published: April 2024

A dimensional reduction algorithm is applied to an intelligent classification model with the purpose of improving the efficiency and accuracy. The proposed classification model, used to distinguish the operating mode: Hard- and Soft-Switching, is presented and an analysis of the synchronized rectified step-down converter is done. With the aim of improving the accuracy and reducing the computational cost of the model, three different methods for dimensional reduction are applied to the input dataset of the model: self-organizing maps, principal component analysis and correlation matrix. The obtained results show how the number of variable is highly reduced and the performance of the classification model is boosted: the results manifest an improve in the accuracy and efficiency of the classification.
Scanning efficacy of p-Chips implanted in the wing and leg of the Big Brown Bat (Eptesicus fuscus)
Authors: Seheult Shane D I , Panchal Raj , Borisenko Alex V , Bennett Patrick J , Faure Paul A
Published: April 2024

Individual marking techniques are critical for studying animals, especially in the wild. Current marking methods for bats (Order Chiroptera) have practical limitations and some can cause morbidity. We tested the p-Chip (p-Chip Corp.)—a miniaturized, laser light-activated microtransponder—as a prospective marking technique in a captive research colony of Big Brown Bats (Eptesicus fuscus). We assessed long-term readability and postimplantation effects of p-Chips injected subcutaneously above the second metacarpal (wing; n = 30) and the tibia (leg; n = 13 in both locations). Following implantation (Day 0), p-Chips were scanned with a hand-held ID reader (wand) on postimplantation days (PIDs) 1, 8, 15, 22, 32, 60, 74, 81, 88, 95, and over 1 year later (PID 464). For each trial, we recorded: (1) animal handling time; (2) scan time; (3) number of wand flashes; (4) p-Chip visibility; and (5) overall condition of the bat. Average scan times for p-Chips implanted in both the wing and leg increased over the duration of the study; however, the number of wand flashes decreased, suggesting that efficacy of p-Chip recording increased with user experience. Importantly, over 464 days both the visibility and readability of p-Chips in the wing remained high and superior to tags in the leg, establishing the second metacarpal as the preferred implantation site. Observed morbidity and mortality in captive bats with p-Chips was similar to baseline values for bats without these tags. Because scan efficiency on PID 464 was comparable with earlier days, this indicates that p-Chips implanted in the wing may be suitable as a long-term marking method. Our provisional results suggest that p-Chips are viable for extended field testing to see if they are suitable as an effective alternative to traditional methods to mark bats.
First record of a structural infestation by the bird flea Dasypsyllus gallinulae perpinnatus (Siphonaptera: Ceratophyllidae) in the San Francisco Bay Area, California, USA
Authors: Roth Tara , Crews Arielle
Published: April 2024

Herein we present the first-known case report of a structural infestation by the bird flea (Dasypsyllus gallinulae perpinnatus) (Dale 1878) in the United States. In March of 2023, the San Mateo County Mosquito and Vector Control District was contacted by a resident in South San Francisco, CA who reported the presence of fleas inside their condominium. The resident had 2 dogs who were on oral flea medication and only 1 inhabitant out of 4 reported receiving flea bites. The front walkway, backyard, and garage were flagged and a small passerine nest was removed from a fire alarm bell in the front walkway. A total of 31 fleas (13 males, 18 females) were collected by flagging from the front entryway underneath the nest. One female flea was collected from the backyard, and 20 (9 males, 11 females) were collected by the resident from inside the house. A total of 387 fleas (163 males, 224 females) were collected from the nest of a small passerine. All life stages (egg, larvae, pupae, and adult) were observed within the nest. Additional parasites in the nest included bird lice and larval western black-legged ticks (Ixodes pacificus, Cooley and Kohls 1943). Research should be conducted into whether bird fleas are capable of transmitting avian pathogens to humans and this information should be communicated to healthcare providers as part of a One Health approach. Additionally, resources to aid in species-level flea identification should be made accessible to pest control operators as this will aid the development of targeted treatments as part of an integrated pest management plan.
Bayesian model selection via mean-field variational approximation
Authors: Zhang Yangfan , Yang Yun
Published: April 2024

This article considers Bayesian model selection via mean-field (MF) variational approximation. Towards this goal, we study the non-asymptotic properties of MF inference that allows latent variables and model misspecification. Concretely, we show a Bernstein–von Mises (BvM) theorem for the variational distribution from MF under possible model misspecification, which implies the distributional convergence of MF variational approximation to a normal distribution centring at the maximal likelihood estimator. Motivated by the BvM theorem, we propose a model selection criterion using the evidence lower bound (ELBO), and demonstrate that the model selected by ELBO tends to asymptotically agree with the one selected by the commonly used Bayesian information criterion (BIC) as the sample size tends to infinity. Compared to BIC, ELBO tends to incur smaller approximation error to the log-marginal likelihood (a.k.a. model evidence) due to a better dimension dependence and full incorporation of the prior information. Moreover, we show the geometric convergence of the coordinate ascent variational inference algorithm, which provides a practical guidance on how many iterations one typically needs to run when approximating the ELBO. These findings demonstrate that variational inference is capable of providing a computationally efficient alternative to conventional approaches in tasks beyond obtaining point estimates.
Deciphering molecular events behind Systemin-induced resistance to Botrytis cinerea in tomato plants
Authors: Pastor-Fernández Julia , Sanmartín Neus , Manresa-Grao Maria , Cassan Cédric , Pétriacq Pierre , Gibon Yves , Gamir Jordi , Romero-Rodriguez Beatriz , Castillo Araceli G , Cerezo Miguel , Flors Victor , Sánchez-Bel Paloma
Published: April 2024

Plant defence peptides are paramount endogenous danger signals secreted after a challenge, intensifying the plant immune response. The peptidic hormone Systemin (Sys) was shown to participate in resistance in several plant pathosystems, although the mechanisms behind Sys-induced resistance when exogenously applied remain elusive. We performed proteomic, metabolomic, and enzymatic studies to decipher the Sys-induced changes in tomato plants in either the absence or the presence of Botrytis cinerea infection. Sys treatments triggered direct proteomic rearrangement mostly involved in carbon metabolism and photosynthesis. However, the final induction of defence proteins required concurrent challenge, triggering priming of pathogen-targeted proteins. Conversely, at the metabolomic level, Sys-treated plants showed an alternative behaviour following a general priming profile. Of the primed metabolites, the flavonoids rutin and isorhamnetin and two alkaloids correlated with the proteins 4-coumarate-CoA-ligase and chalcone-flavanone-isomerase triggered by Sys treatment. In addition, proteomic and enzymatic analyses revealed that Sys conditioned the primary metabolism towards the production of available sugars that could be fuelling the priming of callose deposition in Sys-treated plants; furthermore, PR1 appeared as a key element in Sys-induced resistance. Collectively, the direct induction of proteins and priming of specific secondary metabolites in Sys-treated plants indicated that post-translational protein regulation is an additional component of priming against necrotrophic fungi.
When you think you should transfuse…don’t!
Authors: Gammon Richard R , Alvarez Harold , Masias Camila , Benitez Nancy , Resto Claribel
Published: April 2024

A 48-year-old female presented to the emergency department with severe fatigue. Admission laboratory test results were hemoglobin 6.6 g/dL, platelet count 287,000/μL, and white blood cell count 25,200/μL. Lactate dehydrogenase was elevated at 898 U/L, haptoglobin was markedly decreased (< 31 mg/dL), indirect bilirubin was elevated (5.3 mg/dL), and the absolute reticulocyte count was low at 0.0050/μL. A sample was sent to the immunohematology reference laboratory. The direct antiglobulin test immunoglobulin G was negative; C3 was 1+. All cells were reactive at immediate spin phase, indirect antiglobulin testing (IAT) with polyethylene glycol, with low ionic strength saline, neat, prewarm, and in the solid phase. All cells were nonreactive at IAT-ficin. Additional testing included a cold antibody titer that was 1:4096 and thermal amplitude studies demonstrating reactivity of 2+ at 37°C. These results were consistent with a clinically significant anti-Pr and cold agglutinin disease (CAD). Although rituximab is effective in autoimmune hemolytic anemia, this may take weeks. The patient was treated with pegcetacoplan, a pegylated peptide that targets C3 inhibiting hemolysis. The patient was discharged on day 29 with a hemoglobin of 8 g/dL. This is a report of one of the first patients successfully treated with pegcetacoplan for CAD.
Uncommon pathogen misidentification of Herbaspirillum huttiense as Burkholderia cepacia in bacteremia: a case report
Authors: Wang Qun , Cai Xinjian , Zhang Li
Published: April 2024

Herbaspirillum huttiense is an opportunistic pathogen associated with rare cases of bacteremia. In this case report, H huttiense was isolated from blood samples collected from an intravenous catheter (incubated for 20.8 hours) and a peripheral vein (incubated for 14.16 hours) of a lung adenocarcinoma patient. Positive blood culture bottles were subjected to smear preparation, and Gram staining and microscopic examination revealed the presence of gram-negative rods in both aerobic bottles. We used the VITEK MS automatic microbial mass spectrometry system, VITEK 2 Compact automatic microbial analysis system, and high-throughput nucleic acid sequencing for accurate identification of the isolate. It is noteworthy that although the VITEK 2 Compact identified the isolate as Burkholderia cepacia, confirmation through VITEK MS mass spectrometry and 16S ribosomal DNA (rDNA) sequencing identified it as H huttiense. Subsequently, antimicrobial susceptibility testing was performed using the broth microdilution method, following the guidelines for nonfermenting gram-negative bacilli provided by the Clinical and Laboratory Standards Institute. This case highlights the possibility of misidentification of H huttiense as B cepacia by VITEK 2 Compact in certain situations, emphasizing the importance of considering uncommon pathogens, such as H huttiense, in the context of bacteremia in cancer patients.
Interferons dominate damage and activity in juvenile scleroderma
Authors: Kose Hulya , Simsek Abdurrahman , Kizmaz Muhammed Ali , Bozkurt Tugce , Ozturk Ferdi , Cekic Sukru , Budak Ferah , Sarıcaoglu Hayriye , Kilic Sara Sebnem
Published: April 2024

Objectives

Juvenile scleroderma is a heterogeneous group of diseases associated with sclerotic skin lesions, grouped as juvenile systemic sclerosis and juvenile localized scleroderma. This study aims to measure the cytokine and chemokine levels involved in interferon (IFN) signalling in patients with juvenile scleroderma and determine their correlation with disease severity.

Methods

Twenty-nine juvenile localized scleroderma, five juvenile systemic sclerosis, and nine healthy controls were included in the study. Cytokines and chemokines involved in IFN gene signalling (IL-1, IL-6, IL-8, IP-10, MCP1, TNF-α, CXCL-11, IFN-α, IFN-β, IFN-γ) and IFN-stimulated genes (ISGs), including IFI27, IFI44, ISIG15, IFIT1, OAS1, RSAD2, were measured by ELISA and RT-PCR method, respectively.

Results

A significant increase in IFN-α, IFN-β, IFN-γ, TNF-α, IL-1, IL-6 IL-8, IP-10, and MCP1 levels was observed in patients with juvenile systemic sclerosis compared with the healthy control group. Furthermore, IFN-α and IP-10 were elevated in both juvenile localized scleroderma and juvenile systemic sclerosis compared to the healthy control group. IFN-γ and IFN-α positively correlated with LoSAI and LoSDI levels, respectively. According to PGA-A analysis, IFN-β, IFN-γ, TNF-α, IL-8, IP10, MCP1, and CXCL11 were significantly higher in active disease than in the inactive state in both groups.

Conclusion

The results suggest that IFN signalling may be impaired in patients with juvenile scleroderma. Significant changes were observed in cytokines and genes related to IFN signalling, which may have a crucial role in monitoring disease activity. In addition, we have gained important insights into the possibility of using IFN-α and IFN-γ as biomarkers for monitoring juvenile scleroderma activity and damage.
Predictors of disease course in systemic juvenile idiopathic arthritis
Authors: Kaplan Melike Mehveş , Ekici Tekin Zahide , Çelikel Elif , Güngörer Vildan , Karagöl Cüneyt , Öner Nimet , Polat Merve Cansu , Öztürk Didem , Özçelik Emine , Işıklar Ekici Mehveş , Çelikel Acar Banu
Published: April 2024

Objectives

The aim of this study was to identify potential predictors of the disease course of systemic juvenile idiopathic arthritis (sJIA) at the time of diagnosis.

Methods

This retrospective observational study was conducted in patients diagnosed with sJIA in our hospital between April 2009 and October 2023. The relationship between the disease course of sJIA patients and demographic, clinical, laboratory findings and complications was analysed.

Results

Of the 51 patients diagnosed with sJIA, 26 (51%) patients had monocyclic, 7 (13.7%) polycyclic, and 18 (35.2%) persistent disease course. The presence of arthritis, polyarticular involvement, and hip involvement at the time of diagnosis were associated with persistent disease course (P = .009, P = .003, P = .003). Serositis and higher white blood cell and neutrophil counts at the time of diagnosis were associated with a monocyclic disease course (P = .034, .002, .008). However, no significant correlation was found between macrophage activation syndrome and disease course (P = 1).

Conclusions

sJIA patients with polyarthritis and hip involvement at disease onset may develop a persistent course. Although macrophage activation syndrome is an important complication of sJIA, its effect on the course of the disease was not found in this study.
Secondary cerebro-cerebellar and intra-cerebellar dysfunction in cerebellar mutism syndrome
Authors: McAfee Samuel S , Robinson Giles , Gajjar Amar , Phillips Nicholas S , Zhang Silu , Zou Stinnett Ping , Sitaram Ranganatha , Raches Darcy , Conklin Heather M , Khan Raja B , Scoggins Matthew A
Published: April 2024

Background

Cerebellar mutism syndrome (CMS) is characterized by deficits of speech, movement, and affect that can occur following tumor removal from the posterior fossa. The role of cerebro-cerebellar tract injuries in the etiology of CMS remains unclear, with recent studies suggesting that cerebro-cerebellar dysfunction may be related to chronic, rather than transient, symptomatology.

Methods

We measured functional connectivity between the cerebellar cortex and functional nodes throughout the brain using fMRI acquired after tumor removal but prior to adjuvant therapy in a cohort of 70 patients diagnosed with medulloblastoma. Surgical lesions were mapped to the infratentorial anatomy, and connectivity with cerebral cortex was tested for statistical dependence on extent of cerebellar outflow pathway injury.

Results

CMS diagnosis was associated with an increase in connectivity between the right cerebellar and left cerebral hemisphere, maximally between cerebellum and ventromedial prefrontal cortex (VM-PFC). Connectivity dependence on cerebellar outflow was significant for some speech nodes but not for VM-PFC, suggesting altered input to the cerebellum. Connectivity between posterior regions of cerebellar cortex and ipsilateral dentate nuclei was abnormal in CMS participants, maximally within the right cerebellar hemisphere.

Conclusions

The functional abnormalities we identified are notably upstream of where causal surgical injury is thought to occur, indicating a secondary phenomenon. The VM-PFC is involved in several functions that may be relevant to the symptomatology of CMS, including emotional control and motor learning. We hypothesize that these abnormalities may reflect maladaptive learning within the cerebellum consequent to disordered motor and limbic function by the periaqueductal gray and other critical midbrain targets.
The Domain of the Right? Explaining National Parliamentary Preferences on EU-Related Self-Empowerment
Authors: Borońska-Hryniewiecka Karolina , Sacriste Guillaume
Published: April 2024

In the academic debate about the deficits of representative democracy in the European Union, the views of members of parliaments about their EU-oriented roles remain largely unknown. Against this background, we exploit a novel dataset from an author-designed survey conducted in seven national parliaments to unravel MPs’ preferences with regard to their EU-oriented empowerment. Our findings allow us to identify the dominant cognitive schemas mobilised among parliamentarians which attribute particular legitimacy-related meanings to proposed institutional reforms. They point to a stronger explanatory power of party ideological position over national constitutional orientations, with right-wing parties being more supportive towards parliamentary empowerment than their centre and centre-left counterparts, and mainstream parties being more sceptical of it than radical groups on both sides of the spectrum.
Direct and indirect responses of the Arabidopsis transcriptome to an induced increase in trehalose 6-phosphate
Authors: Avidan Omri , Martins Marina C M , Feil Regina , Lohse Marc , Giorgi Federico M , Schlereth Armin , Lunn John E , Stitt Mark
Published: April 2024

Trehalose 6-phosphate (Tre6P) is an essential signal metabolite that regulates the level of sucrose, linking growth and development to the metabolic status. We hypothesized that Tre6P plays a role in mediating the regulation of gene expression by sucrose. To test this, we performed transcriptomic profiling on Arabidopsis (Arabidopsis thaliana) plants that expressed a bacterial TREHALOSE 6-PHOSPHATE SYNTHASE (TPS) under the control of an ethanol-inducible promoter. Induction led to a 4-fold rise in Tre6P levels, a concomitant decrease in sucrose, significant changes (FDR ≤ 0.05) of over 13,000 transcripts, and 2-fold or larger changes of over 5,000 transcripts. Comparison with nine published responses to sugar availability allowed some of these changes to be linked to the rise in Tre6P, while others were probably due to lower sucrose or other indirect effects. Changes linked to Tre6P included repression of photosynthesis-related gene expression and induction of many growth-related processes including ribosome biogenesis. About 500 starvation-related genes are known to be induced by SUCROSE-NON-FERMENTING-1-RELATED KINASE 1 (SnRK1). They were largely repressed by Tre6P in a manner consistent with SnRK1 inhibition by Tre6P. SnRK1 also represses many genes that are involved in biosynthesis and growth. These responded to Tre6P in a more complex manner, pointing toward Tre6P interacting with other C-signaling pathways. Additionally, elevated Tre6P modified the expression of genes encoding regulatory subunits of the SnRK1 complex and TPS class II and FCS-LIKE ZINC FINGER proteins that are thought to modulate SnRK1 function and genes involved in circadian, TARGET OF RAPAMYCIN, light, abscisic acid, and other hormone signaling.