bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterization and Organization of Telomeric-Linked Helicase (TLH) Gene Families inFusarium oxysporum
Authors: Salimi Sahar , Abdi M. Foad , Rahnama Mostafa 
Published: March 2024

ABSTRACTTelomere-linked RecQ helicase (TLH) genes have been reported in several fungi and a choanoflagellate in the regions adjacent to the terminal telomere repeats. In this study, we explore the Telomere-linked RecQ helicase (TLH) genes in four strains ofFusarium oxysporum, offering new insights into their genomic structure, functional motifs, and impact on chromosomal ends. We conducted a comprehensive analysis, comparing the TLH genes ofF. oxysporumwith those previously identified in other organisms and uncovering significant similarities. Through comparative genomics, we identified conserved protein motifs across these genes, including a TLH domain, C2H2, and RecQ helicase motifs. Our phylogenetic analysis positions theF. oxysporumTLH genes in a cluster with other known TLHs, suggesting a shared evolutionary origin. Mutation analysis revealed a relatively low level of deleterious mutations in TLH gene paralogs, with a notable proportion of full-size structures maintained across strains. Analysis of subtelomeric sequences indicates that a region with almost identical sequences flanks the majority of chromosome ends, termed TLH-containing region (TLHcr), across these strains. The presence of TLHcrs at chromosome ends, either as single entities or in arrays, underscores their potential role in telomere function and genome stability. Our findings provide a detailed examination of TLH genes in four strains ofF. oxysporum, laying the groundwork for future studies on their biological significance and evolutionary history in fungal genomes.
Changes in Walking Function and Neural Control following Pelvic Cancer Surgery with Reconstruction
Authors: Li Geng , Ao Di , Vega Marleny M. , Zandiyeh Payam , Chang Shuo-Hsiu , Penny A.N. , Lewis Valerae O. , Fregly Benjamin J. 
Published: March 2024

AbstractSurgical planning and custom prosthesis design for pelvic cancer patients is challenging due to the unique clinical characteristics of each patient and the significant amount of pelvic bone and hip musculature often removed. Limb-sparing internal hemipelvectomy surgery with custom prosthesis reconstruction has become a viable option for this patient population. However, little is known about how post-surgery walking function and neural control change from pre-surgery conditions. This case study combined comprehensive human movement data collection with personalized neuromusculoskeletal computer modeling to provide a thorough assessment of pre-to post-surgery changes in walking function and neural control for a single pelvic sarcoma patient who received internal hemipelvectomy surgery with custom prosthesis reconstruction. Extensive walking data (video motion capture, ground reaction, and EMG) were collected from the patient before surgery and after plateau in recovery after surgery. Pre- and post-surgery personalized neuromusculoskeletal computer models of the patient were then constructed using the patient’s pre- and post-surgery walking data. These models were used to calculate the patient’s pre- and post-surgery joint motions, joint moments, and muscle synergies. The calculated muscle synergies were described by time-invariant synergy vectors and time-varying synergy activations, were consistent with the patient’s experimental EMG data, and produced the patient’s experimental joint moments found via inverse dynamics. The patient’s post-surgery walking function was marked by a slower self-selected walking speed coupled with several compensatory mechanisms necessitated by lost or impaired hip muscle function, while the patient’s post-surgery neural control demonstrated a dramatic change in coordination strategy (as evidenced by modified synergy vectors) with little change in recruitment timing (as evidenced by conserved synergy activations). Furthermore, the patient’s post-surgery muscle activations were fitted accurately using the patient’s pre-surgery synergy activations but poorly using the patient’s pre-surgery synergy vectors. These results provide valuable information about which aspects of post-surgery walking function could potentially be improved through modifications to surgical decisions, custom prosthesis design, or rehabilitation protocol, as well as how computational simulations could be formulated to predict post-surgery walking function reliably given a patient’s pre-surgery walking data and the planned surgical decisions and custom prosthesis design.
Can AI-generated clinical vignettes in Japanese be used medically and linguistically?
Authors: Yanagita Yasutaka , Yokokawa Daiki , Uchida Shun , Li Yu , Uehara Takanori , Ikusaka Masatomi 
Published: March 2024

AbstractBackgroundCreating clinical vignettes requires considerable effort. Recent developments in generative artificial intelligence (AI) for natural language processing have been remarkable and may allow for the easy and immediate creation of diverse clinical vignettes.ObjectiveIn this study, we evaluated the medical accuracy and grammatical correctness of AI-generated clinical vignettes in Japanese and verified their usefulness.MethodsClinical vignettes in Japanese were created using the generative AI model GPT-4-0613. The input prompts for the clinical vignettes specified the following seven elements: 1) age, 2) sex, 3) chief complaint and time course since onset, 4) physical findings, 5) examination results, 6) diagnosis, and 7) treatment course. The list of diseases integrated into the vignettes was based on 202 cases considered in the management of diseases and symptoms in Japan’s Primary Care Physicians Training Program. The clinical vignettes were evaluated for medical and Japanese-language accuracy by three physicians using a five-point scale. A total score of 13 points or above was defined as “sufficiently beneficial and immediately usable with minor revisions,” a score between 10 and 12 points was defined as “partly insufficient and in need of modifications,” and a score of 9 points or below was defined as “insufficient.”ResultsRegarding medical accuracy, of the 202 clinical vignettes, 118 scored 13 points or above, 78 scored between 10 and 12 points, and 6 scored 9 points or below. Regarding Japanese-language accuracy, 142 vignettes scored 13 points or above, 56 scored between 10 and 12 points, and 4 scored 9 points or below. Overall, 97% (196/202) of vignettes available with some modifications.ConclusionsOverall, 97% of the clinical vignettes proved practically useful, based on confirmation and revision by Japanese medical physicians. Given the significant effort required by physicians to create vignettes without AI assistance, the use of GPT is expected to greatly optimize this process.
Establishing the Automatic Identification of Clinical Trial Cohorts from Electronic Health Records by Matching Normalized Eligibility Criteria and Patient Clinical Characteristics
Authors: Lee K. , Mai Y. , Liu Z. , Raja K. , Jun T. , Ma M. , Wang T. , Ai L. , Calay E. , Oh W. , Schadt E. , Wang X. 
Published: March 2024

AbstractThe use of electronic health records (EHRs) holds the potential to enhance clinical trial activities. However, the identification of eligible patients within EHRs presents considerable challenges. We aimed to develop a pipeline for phenotyping eligibility criteria, enabling the identification of patients from EHRs with clinical characteristics that match those criteria. We utilized clinical trial eligibility criteria and patient EHRs from the Mount Sinai Database. The criteria and EHR data were normalized using national standard terminologies and in-house databases, facilitating computability and queryability. The pipeline employed rule-based pattern recognition and manual annotation. Our pipeline normalized 367 out of 640 unique eligibility criteria attributes, covering various medical conditions including non-small cell lung cancer, small cell lung cancer, prostate cancer, breast cancer, multiple myeloma, ulcerative colitis, Crohn’s disease, non-alcoholic steatohepatitis, and sickle cell anemia. 174 were encoded with standard terminologies and 193 were normalized using the in-house reference tables. The agreement between automated and manual normalization was high (Cohen’s Kappa = 0.82), and patient matching demonstrated a 0.94 F1 score. Our system has proven effective on EHRs from multiple institutions, showing broad applicability and promising improved clinical trial processes, leading to better patient selection, and enhanced clinical research outcomes.
Identification of Novel Gene variants for Autism Spectrum Disorder in an Indian Patient using Whole Exome Sequencing
Authors: Yadav Prashasti , Roychowdhury Saileyee , Mukherjee Nilanjan , Mukherjee Reema , Roy Sudipta Kumar , Bhattacharjee Soumen , Das Parimal 
Published: March 2024

AbstractBackgroundAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by persistent deficits in social communication and interaction, along with restricted and repetitive behaviour patterns, interests or activities. Its prevalence has risen over the past few years, being four times more common in boys than girls. The cause of ASD is unclear, its etiology involves genetic, environmental, and gene-environment interactions. While past studies highlighted clinical genetic risks, genetic complexity of ASD, with variants of diverse frequencies, type, and inheritance patterns, requires further exploration for better management of disease. Researches have shown that the whole exome sequencing can be used to identify genetic variants associated with genetically heterogeneous conditions. The purpose of this study is to identify genetic variants by employing whole exome sequencing in an Indian ASD patient.MethodsA female patient of age within 0-5 years, having characteristic features like hyperactivity and language impairment, was investigated and diagnosed using DSM-5 criteria. Peripheral blood sample collection was done followed by DNA extraction and whole exome sequencing. Variants analysis, identification and annotation were done using bioinformatics tools and databases. Identified pathogenic variants were reconfirmed by Sanger sequencing.Results and conclusionOur study uncover four genetic variations, comprising three missense variations inKIF1A(c.3839C>T),SETD5(c.314A>C),MAPK81P3(c.2849C>T), and one-stop gain variation inERMARD(c.1523G>A). TheERMARDstop gain variation, predicted to induce nonsense-mediated decay, alter normal protein function through truncation and classified as likely pathogenic based on the ACMG guidelines and current available scientific evidence. In conclusion, we identified a likely pathogenic variant inERMARDalong with three missense variants inKIF1A, SETD5andMAPK81P3respectively. These findings suggest the potential contribution ofERMARDmutations to ASD susceptibility, emphasizing the need for further validation through functional studies.
Multiplex single-cell analysis of serotonergic neuron function in planarians reveals widespread effects in diverse cell types
Authors: Emili Elena , Rodríguez-Fernández Dianalí , Pérez-Posada Alberto , García-Castro Helena , Solana Jordi 
Published: March 2024

AbstractNeurons function by interacting with each other and with other cell types, often exerting organism-wide regulation. Serotonergic neurons play a systemic role in processes such as appetite, sleep and motor control. Functional studies in the planarianSchmidtea mediterraneahave shown that impairment of serotonergic neurons results in systemic effects. Studying neurons and the tissues they interact with is challenging using either bulk or single-cell analysis techniques. While bulk methods merge the information from all cell types, single-cell methods show promise in overcoming this limitation. However, current single-cell approaches encounter other challenges including stress of cell dissociation, high cost, multiplexing capacity, batch effects, replication and statistical analysis. Here we used ACME and SPLiT-seq to generate a multiplex single-cell analysis of serotonergic neuron function in planarians by inhibitingpitxandlhx1/5-1, two transcription factors expressed in them. We recovered single-cell transcriptomic profiles of 47,292 cells from knockdown and control animals, including biological and technical replicates. Our results show that epidermal, muscular and the recently described parenchymal cell types are affected the most by serotonergic neuron impairment. By computationally dissecting each cell type, we elucidated gene expression changes in each, including changes in epidermis cilia genes and myofiber genes in muscle. Interestingly, parenchymal cells downregulate genes involved in neurotransmitter recycling, suggesting a glial-like function of these recently described enigmatic cell types. Our results will allow disentangling the complexity of serotonergic neuron inhibition by studying the downstream effectors and the affected tissues, and offer new data on the function of parenchymal cells in planarians. Ultimately, our results pave the way for dissecting complex phenotypes through multiplex single-cell transcriptomics.
Learning-Based Estimation of Fitness Landscape Ruggedness for Directed Evolution
Authors: Towers Sebastian , James Jessica , Steel Harrison , Kempf Idris 
Published: March 2024

AbstractDirected evolution is a method for engineering biological systems or components, such as proteins, wherein desired traits are optimised through iterative rounds of mutagenesis and selection of fit variants. The process of protein directed evolution can be envisaged as navigation over high-dimensional landscapes with numerous local maxima, mapping every possible variant of a protein to its fitness. The performance of any strategy in navigating such a landscape is dependent on several parameters, including its ruggedness. However, this information is generally unavailable at the outset of an experiment, and cannot be computed using analytical methods. Here we propose a learning-based method for estimating landscape ruggedness from a mutating population, using only population average performance data. This method uses a short period of exploration at the beginning of an experiment to predict the ruggedness, subsequently guiding the choice of high-performing parameters for directed evolution control. We then simulate this approach on two real-world protein fitness landscapes, demonstrating an improvement upon the performance of standard strategies, particularly on rugged landscapes. In addition to improving the overall outcomes of directed evolution, this method has the advantage of being readily deployable in laboratory settings, even in configurations that exclusively capture average population measures. Given the rapidly expanding application space of engineered proteins, the products of improved directed evolution are relevant in medicine, agriculture and manufacturing.
A novel tomato inter-specific (Solanum lycopersicumvar.cerasiformeandS. pimpinellifolium) MAGIC population facilitates trait association and candidate gene discovery in untapped exotic germplasm
Authors: Arrones Andrea , Antar Oussama , Pereira-Dias Leandro , Solana Andrea , Ferrante Paola , Aprea Giuseppe , Plazas Mariola , Prohens Jaime , Díez María José , Giuliano Giovanni , Gramazio Pietro , Vilanova Santiago 
Published: March 2024

AbstractWe developed a novel eight-way tomato multi-parental advanced generation inter-cross (MAGIC) population to improve the accessibility of the genetic resources of tomato relatives to geneticists and breeders. The inter-specific MAGIC population (ToMAGIC) was obtained by inter-crossing four accessions each ofSolanum lycopersicumvar.cerasiforme(SLC) andS. pimpinellifolium(SP), which respectively are the weedy relative and the ancestor of cultivated tomato. The eight exotic ToMAGIC founders were selected based on a representation of the genetic diversity and geographical distribution of the two taxa. The resulting MAGIC population comprises 354 lines which were genotyped using a new 12k tomato Single Primer Enrichment Technology (SPET) panel and yielded 6,488 high-quality SNPs. The genotyping data revealed a high degree of homozygosity (average 93.69%), an absence of genetic structure, and a balanced representation (11.62% to 14.16%) of the founder genomes. To evaluate the potential of the ToMAGIC population for tomato genetics and breeding, a proof-of-concept was conducted by phenotyping it for fruit size, plant pigmentation, leaf morphology, and earliness traits. Genome-wide association studies (GWAS) identified strong associations for the studied traits, pinpointing both previously identified and novel candidate genes near or within the linkage disequilibrium blocks. Domesticated alleles for fruit size were recessive and were found, at low frequencies, in wild/ancestral populations. Our findings demonstrate that the newly developed ToMAGIC population is a valuable resource for genetic research in tomato, offering significant potential for identifying new genes that govern key traits in tomato breeding. ToMAGIC lines displaying a pyramiding of traits of interest could have direct applicability for integration into breeding pipelines providing untapped variation for tomato breeding.
3D Transcranial ultrasound localization microscopy reveals major arteries in the sheep brain
Authors: Coudert Antoine , Denis Louise , Chavignon Arthur , Bodard Sylvain , Naveau Mikael , Sistiaga Palma Pro , Saulnier Romaric , Orset Cyrille , Vivien Denis , Chappard Christine , Couture Olivier 
Published: March 2024

AbstractObjectiveStroke, a leading cause of mortality and disability globally, demands swift and accurate diagnosis for effective treatment. Although MRI and CT scans serve as conventional methods, their accessibility remains a challenge, prompting exploration into alternative, portable, and non-ionizing imaging solutions like ultrasound with reduced costs. While Ultrasound Localization Microscopy (ULM) displays potential in high-resolution vessel imaging, its 2D constraints limit its emergency utility.Materials and MethodsThis study delves into the feasibility of 3D ULM with multiplexed probe for transcranial vessel imaging in sheep brains, emulating human skull characteristics. Three sheep underwent 3D ULM imaging, compared with angiographic MRI, while skull characterization was conducted in vivo using ultrashort bone MRI sequences and ex vivo via micro CT.Results and conclusionsThe study showcased 3D ULM’s ability to highlight vessels, down to the Circle of Willis, yet within a confined 3D field-of-view. Future enhancements in signal, aberration correction, and human trials hold promise for a portable, volumetric, transcranial ultrasound angiography system.Summary statement3D Ultrasound localization microscopy, using a low-frequency matrix probe, enables transcranial reconstruction of the main vessels in sheep brains.
A Root Mean Square Deviation Estimation Algorithm (REA) and its use for improved RNA Structure Prediction
Authors: Kalampaliki Agoritsa , Dimopoulos Alexandros C. , Reczko Martin 
Published: March 2024

AbstractThe 3D structure of RNA is crucial for biotechnological applications and to comprehend its biological function. Recent developments using AlphaFold-inspired deep neural networks improved the prediction of 3D structure from RNA sequence, but evaluation of the accuracy of these predictions is still necessary. We present the RMSD Estimation Algorithm (REA), a feed-forward neural network to predict the root-mean-square deviation (RMSD) of a 3D RNA structure from its experimentally determined counterpart using its Molprobity [1] stereochemical validation features. It is trained on structures predicted by the DeepFoldRNA [2] and trRosettaRNA [3] transformer-based deep neural networks on a set of 182 models of RNA structures with pseudoknots. We compare REA with ARES [4], a deep learning algorithm that predicts the RMSD by extracting geometric patterns with equivariant convolution, assessing the prediction accuracy on RNAs with and without pseudoknots. REA outperformed ARES on both test sets with smaller absolute difference between the true and the predicted RMSD. Using a combination of REA and a Support Vector Regression (SVR) trained on the same data as REA, we can select RNA structures predicted with DeepFoldRNA, trRosettaRNA and Rhofold [5] to achieve a significantly higher prediction accuracy than any of the prediction methods used alone. This was shown on a validation set with 261 novel RNA chains extracted from the Nonredundant 3D Structure Dataset [5] and a test set with 55 novel RNA chains from RNA-Puzzles [5]. Our selection based prediction method can easily incorporate additional prediction algorithms.
Cisplatin reprogramming of protein phosphorylation
Authors: Qi Luyu , Zhou Yang , Hou Yinzhu , Luo Qun , Xu Yan , Liu Xingkai , Yu Wanchen , Xin Bobo , Huang Yaolong , Han Juanjuan , Li Xiangjun , Zhang Yanyan , Jia Feifei , Fang Tiantian , Wang Shijun , Sadler Peter J. , Zhao Yao , Wang Fuyi 
Published: March 2024

ABSTRACTCisplatin is a DNA-targeting chemotherapeutic. We have mapped cisplatin damage to specific genes in human lung cancer cells. Surprisingly, cisplatin targets the majority of protein kinase genes and protein phosphatase genes in the human genome. This suggests that cisplatin can reprogram protein phosphorylation genome-wide. We have profiled the protein expression and phosphorylation in human testicular cancer cells subjected to cisplatin treatment, since testicular cancer is curable by cisplatin. We demonstrate that most downregulated proteins are encoded by cisplatin damaged genes. These proteins include a series of protein kinases and protein phosphatases, leading to significant changes in the phosphorylation level of >600 proteins in testicular cancer cells. Hence, reprograming of protein phosphorylation is proteome-wide. Importantly, the reprogrammed protein phosphorylation activates G2/M DNA-damage checkpoint regulation and ATM signalling pathways, causing cell cycle arrest at the G2/M phase. These findings suggest that specific protein phosphorylation pathways are potential new targets for platinum drug design.
LogoMotif: a comprehensive database of transcription factor binding site profiles in Actinobacteria
Authors: Augustijn Hannah E. , Karapliafis Dimitris , Joosten Kristy , Rigali Sébastien , van Wezel Gilles P. , Medema Marnix H. 
Published: March 2024

AbstractActinobacteria undergo a complex multicellular life cycle and produce a wide range of specialized metabolites, including the majority of the antibiotics. These biological processes are controlled by intricate regulatory pathways, and to better understand how they are controlled we need to augment our insights into the transcription factor binding sites. Here, we present LogoMotif (https://logomotif.bioinformatics.nl), an open-source database for characterized and predicted transcription factor binding sites in Actinobacteria, along with their cognate position weight matrices and hidden Markov models. Genome-wide predictions of binding site locations inStreptomycesmodel organisms are supplied and visualized in interactive regulatory networks. In the web interface, users can freely access, download and investigate the underlying data. With this curated collection of actinobacterial regulatory interactions, LogoMotif serves as a basis for binding site predictions, thus providing users with clues on how to elicit the expression of genes of interest and guide genome mining efforts.HighlightsActinobacterial regulatory networks are key for compound discovery, including antibiotics.Contains ∼400 validated and ∼12,100 predicted interactions, presented in interactive networks.Serves as foundation for regulatory predictions in the gene cluster detection tool, antiSMASH.LogoMotif’s data and algorithms provide knowledge on expression and functional inference of genes.LogoMotif aids in the discovery of novel chemistry within Actinobacteria and beyond.Graphical abstract