bluuebunny/crossref_metadata_2025_split huggingface dataset:
Antibiotic utilisation and the impact of antimicrobial resistance action plan on prescribing among older adults in New Zealand between 2005 and 2019
Authors: Chinzowu Tichawona , Chyou Te-yuan , Roy Sandipan , Jamieson Hamish , Nishtala Prasad S 
Published: February 2023

AbstractPurposeThe objectives of this study were to examine the overall utilisation of systematic antibiotics and the impact of the launch of the antimicrobial resistance action plan in older adults (65 years or more) living in New Zealand between 2005 and 2019, using data from a national database (Pharmaceutical collections), stratified by antibiotic class, patient age, and sex.MethodsPopulation-level systemic antibiotic dispensing data for older adults in New Zealand was analysed using repeated cross-sectional analysis between 01/01/2005 and 31/12/2019. Data were extracted on the prescribed systemic antibiotics using a unique identifier for each case. Antibiotic utilisation was measured in DDD/TOPD values. In addition, an interrupted time series (ITS) analysis using the autoregressive integrated moving average (ARIMA) models was performed to determine the impact of the antimicrobial resistance action plan.ResultsMost of the antibiotic classes included in this study showed a significant overall decrease in utilisation ranging from 38.64% for sulphonamides to 80.64% for fluoroquinolones compared to 2005. Systemic antibiotic utilisation decreased by 49.6% from the predicted usage between January 2018 and December 2019 following the launch of the antimicrobial resistance action plan. ARIMA model supported the reduction in utilisation with a step change of -0.2206 and slope change of -0.0029.ConclusionsThe ITS analysis has demonstrated that the intervention may have hugely impacted the antibiotic utilisation rate among older adults. Further studies are required to determine whether the reduced consumption rates of antibiotics are associated with reduced rates of antibiotic-associated adverse drug events such as acute kidney injury and haematological abnormalities.
Gymnosiphon fonensis(Burmanniaceae) a new Critically Endangered species from Simandou, Republic of Guinea, W. Africa
Authors: Cheek Martin , Tchiengue Barthelemy , Thiam Aminata , Molmou Denise , Dore Tokpa Seny , Magassouba Sekou 
Published: February 2023

ABSTRACTA new species ofGymnosiphonBlume (Burmanniaceae),G.fonensisCheek is formally described from the Forêt Classee de Pic de Fon, Simandou Range, Guinee-Forestière, Republic of Guinea (Guinee-Conakry) in West Africa. The new species was formerly confused with and resemblesG. bekensisLetouzey of central Africa in the broad flat outer tepal lobes, perianth tube >10 mm long, and (sub)sessile flower. It differs e.g. in that the length of the corolla tube, (13-)14-18 mm, exceeds the corolla diameter (10-11 mm) (vs length of the corolla tube (12 mm) < the corolla diameter (12-15 mm)), the anthers inserted c. 4 mm deep in the corolla tube (vs inserted at the corolla mouth) and the rhizome lacks scale-leaves (vs scale leaves present).Gymnosiphon fonensisis the first known species of its genus and family in which secondary pollen presentation has been recorded. The species is known from five sites, all with threats, in a single threat-based location, accordingly it is assessed as Critically Endangered (CR B1ab(iii)) using the IUCN 2012 standard, making it the most threatened species ofGymnosiphonin continental Africa. The new species is illustrated by colour photos and line-drawings and is mapped. An identification key is provided to the ten species of the genus now known from Africa-Madagascar.
The effect of ascertainment on penetrance estimates for rare variants: implications for establishing pathogenicity and for genetic counselling
Authors: Paterson Andrew D. , Seok Sang-Cheol , Vieland Veronica J. 
Published: February 2023

AbstractNext-generation sequencing has led to an explosion of genetic findings for many rare diseases. However, most of the variants identified are very rare and were identified in small pedigrees, which creates challenges in terms of penetrance estimation and translation into genetic counselling in the setting of cascade testing. We use simulations to show that for a rare (dominant) disorder where a variant is identified in a small number of small pedigrees, the penetrance estimate can both have large uncertainty and be drastically inflated, due to underlying ascertainment bias. We have developed PenEst, an app that allows users to investigate the phenomenon across ranges of parameter settings. We also illustrate robust ascertainment corrections via the LOD score, and recommend a LOD-based approach to assessing pathogenicity of rare variants in the presence of reduced penetrance.
A Simple Way to Incorporate Target Structural Information in Molecular Generative Models
Authors: Zhang Wenyi , Zhang Kaiyue , Huang Jing 
Published: February 2023

ABSTRACTDeep learning generative models are now being applied in various fields including drug discovery. In this work, we propose a novel approach to include target 3D structural information in molecular generative models for structure-based drug design. The method combines a message-passing neural network model that predicts docking scores with a generative neural network model as its reward function to navigate the chemical space searching for molecules that bind favorably with a specific target. A key feature of the method is the construction of target-specific molecular sets for training, designed to overcome potential transferability issues of surrogate docking models through a two-round training process. Consequently, this enables accurate guided exploration of the chemical space without reliance on the collection of prior knowledge about active and inactive compounds for the specific target. Tests on eight target proteins showed a 100-fold increase in hit generation compared to conventional docking calculations, and the ability to generate molecules similar to approved drugs or known active ligands for specific targets without prior knowledge. This method provides a general and highly efficient solution for structure-based molecular generation.
Biodiversity is enhanced by sequential resource utilization and environmental fluctuations via emergent temporal niches
Authors: Bloxham Blox , Lee Hyunseok , Gore Jeff 
Published: February 2023

AbstractHow natural communities maintain their remarkable biodiversity and which species survive in complex communities are central questions in ecology. Resource competition models successfully explain many phenomena but typically predict only as many species as resources can coexist. Here, we demonstrate that sequential resource utilization, or diauxie, with periodic growth cycles can support many more species than resources. We explore how communities modify their own environments by sequentially depleting resources to form sequences of temporal niches, or intermediately depleted environments. Biodiversity is enhanced when community-driven or environmental fluctuations modulate the resource depletion order and produce different temporal niches on each growth cycle. Community-driven fluctuations under constant environmental conditions are rare, but exploring them illuminates the temporal niche structure that emerges from sequential resource utilization. With environmental fluctuations, we find most communities have more stably coexisting species than resources with survivors accurately predicted by the same temporal niche structure and each following a distinct optimal strategy. Our results thus present a new niche-based approach to understanding highly diverse fluctuating communities.
An integrated bioinformatics approach to identify candidate biomarkers and the evaluation of drugs for pheochromocytoma
Authors: Javali S Prashanth , Jeyakanthan J , Chen Chun-Jung , Arumugam Mohanapriya 
Published: February 2023

ABSTRACTObjectivesIdentification of the most potential druggable targets and drugs for treating pheochromocytoma by understanding the molecular mechanisms in the progression of pheochromocytoma and discovering additional biomarkers for early diagnosis.MethodsThe differential gene expression in microarray data was analyzed using CRAN packages such as Geoquery, Affy, Limma, Tidyverse, GOchord, etc., and functional and pathway enrichment was performed through the David database, Further PPI network is constructed by retrieving the Protein partners were retrieved from the STRING database. The top 3 ranked gene products from cytohubba were docked with the ligand molecules, which are all in phase III trials. The complex with the highest binding affinity was simulated using molecular dynamics simulations for 100ns, PCA and FEL analysis were performed to determine the stability of the complex at each time interval additionally miRNA and TRFs were predicted for the prominent genes.ResultsAs a result of functional enrichment, most of the 97 differentially expressed genes (DEGs) are involved in cancer-causing pathways. Pheochromocytomas were associated with CDC42, VEGFA, PIK3R1, ITGAV, and LAMB-1, among prominent genes among the ten hub genes. Simulated results verified the effectiveness of targeting VEGFA with Lenvatinib.ConclusionsWe found that differentially expressed genes in the interactome and hub genes with high correlations could serve as biomarkers, along with their miRNAs and TRFs. By targeting VEGFA as a drug target, Lenvatinib displays a high affinity for VEGFA, as it interacts with other genes through homodimerization, activation, phosphorylation, and dephosphorylation. It also appears to interact with the VEGFA protein via hydrogen bonds. In conclusion, this study suggests that Lenvatinib might be a promising anticancer agent that could treat pheochromocytoma, and VEGF-A, CDC42, PIK3R1, as well as several predicted miRNAs, such as miR-224 and miR-221, might be biomarkers for the early diagnosis.
Sea Star Wasting Syndrome Reaches the High Antarctic: Two Recent Outbreaks in McMurdo Sound
Authors: Moran Amy , McLachlan Rowan H. , Thurber Andrew R. 
Published: February 2023

AbstractSea star wasting syndrome (SSWS) can cause widespread mortality in starfish populations as well as long-lasting changes to benthic community structure and dynamics. SSWS symptoms have been documented in numerous species and locations around the world, but to date there is only one record of SSWS from the Antarctic and this outbreak was associated with volcanically-driven high temperature anomalies. Here we report outbreaks of SSWS-like symptoms that affected ∼30% of individuals ofOdontaster validusat two different sites in McMurdo Sound, Antarctica in 2019 and 2022. Unlike many SSWS events in other parts of the world, these outbreaks were not associated with anomalously warm temperatures. Instead, we suggest they may have been triggered by high nutrient input events on a local scale. Although the exact cause of these outbreaks is not known, these findings are of great concern because of the keystone role ofO. validusand the slow recovery rate of Antarctic benthic ecosystems to environmental stressors.
Audit to Forget: A Unified Method to Revoke Patients’ Private Data in Intelligent Healthcare
Authors: Zhou Juexiao , Li Haoyang , Liao Xingyu , Zhang Bin , He Wenjia , Li Zhongxiao , Zhou Longxi , Gao Xin 
Published: February 2023

AbstractRevoking personal private data is one of the basic human rights, which has already been sheltered by several privacy-preserving laws in many countries. However, with the development of data science, machine learning and deep learning techniques, this right is usually neglected or violated as more and more patients’ data are being collected and used for model training, especially in intelligent healthcare, thus making intelligent healthcare a sector where technology must meet the law, regulations, and privacy principles to ensure that the innovation is for the common good. In order to secure patients’ right to be forgotten, we proposed a novel solution by using auditing to guide the forgetting process, where auditing means determining whether a dataset has been used to train the model and forgetting requires the information of a query dataset to be forgotten from the target model. We unified these two tasks by introducing a new approach called knowledge purification. To implement our solution, we developed AFS, a unified open-source software, which is able to evaluate and revoke patients’ private data from pre-trained deep learning models. We demonstrated the generality of AFS by applying it to four tasks on different datasets with various data sizes and architectures of deep learning networks. The software is publicly available athttps://github.com/JoshuaChou2018/AFS.
High-quality genome assemblies provide clues on the evolutionary advantage of blue peafowl over green peafowl
Authors: Chakraborty Abhisek , Mondal Samuel , Mahajan Shruti , Sharma Vineet K. 
Published: February 2023

ABSTRACTAn intriguing example of differential adaptability is the case of two Asian peafowl species,Pavo cristatus(blue peafowl) andPavo muticus(green peafowl), where the former has a “Least Concern” conservation status and the latter is an “Endangered” species. To understand the genetic basis of this differential adaptability of the two peafowl species, a comparative analysis of these species is much needed to gain the genomic and evolutionary insights. Thus, we constructed a high-quality genome assembly of blue peafowl with an N50 value of 84.81 Mb (pseudochromosome-level assembly), and a high-confidence coding gene set to perform the genomic and evolutionary analyses of blue and green peafowls with 49 other avian species. The analyses revealed adaptive evolution of genes related to neuronal development, immunity, and skeletal muscle development in these peafowl species. Major genes related to axon guidance showed adaptive evolution in peafowl species. However, blue peafowl showed the presence of 42% more coding genes compared to the green peafowl along with a higher number of unique gene clusters, segmental duplicated genes and expanded gene families, and comparatively higher evolution in neuronal and developmental pathways. Blue peafowl also showed longer branch length compared to green peafowl in the species phylogenetic tree. These genomic insights obtained from the high-quality genome assembly ofP. cristatusconstructed in this study provide new clues on the superior adaptability of the blue peafowl over green peafowl despite having a recent species divergence time.
Structome: Exploring the structural neighbourhood of proteins
Authors: Malik Ashar J. , Verma Chandra S. , Poole Anthony M. , Allison Jane R. 
Published: February 2023

AbstractProtein structures carry signal of common ancestry and can therefore aid in reconstructing their evolutionary histories. To expedite the structure-informed inference process, a web server, Structome, has been developed, that allows users to rapidly identify protein structures similar to a query protein and to assemble datasets useful for structure-based phylogenetics. Structome was created by clustering∼94% of the structures in RCSB PDB using 90% sequence identity and representing each cluster by a centroid structure. Structure similarity between centroid proteins was calculated, and annotations from PDB, SCOP and CATH were integrated. To illustrate utility, an H3 histone was used as a query, and results show that the protein structures returned by Structome span both sequence and structural diversity of the histone fold. Additionally, the pre-computed nexus-formated distance matrix, provided by Structome, enables analysis of evolutionary relationships between proteins not identifiable using searches based on sequence similarity alone. Our results demonstrate that, beginning with a single structure, Structome can be used to rapidly generate a dataset of structural neighbours and allows deep evolutionary history of proteins to be studied. Structome is available at:https://structome.bii.a-star.edu.sg
Citizenship status and career self-efficacy: An intersectional study of biomedical trainees in the United States
Authors: Chatterjee Deepshikha , Nogueira Ana T. , Wefes Inge , Chalkley Roger , Varvayanis Susi Sturzenegger , Fuhrmann Cynthia N. , Varadarajan Janani , Jacob Gabrielle A. , Gaines Christiann H. , Hubbard Nisan M. , Chaudhary Sunita , Layton Rebekah L. 
Published: February 2023

AbstractThis study examines the intersectional role of citizenship and gender with career self-efficacy amongst 10,803 doctoral and postdoctoral trainees in US universities. These biomedical trainees completed surveys administered by 17 US institutions that participated in the National Institutes of Health Broadening Experiences in Scientific Training (NIH BEST) Programs. Findings indicate that career self-efficacy of non-citizen trainees is significantly lower than that of US citizen trainees. While lower career efficacy was observed in women compared with men, it was even lower for non-citizen female trainees. Results suggest that specific career interests may be related to career self-efficacy. Relative to US citizen trainees, both male and female non-citizen trainees showed higher interest in pursuing a career as an academic research investigator. In comparison with non-citizen female trainees and citizen trainees of all genders, non-citizen male trainees expressed the highest interest in research-intensive (and especially principal investigator) careers. The authors discuss potential causes for these results and offer recommendations for increasing trainee career self-efficacy which can be incorporated into graduate and postdoctoral training.
System analysis of differentially expressed miRNAs in hexaploid wheat display tissue-specific regulatory role during Fe deficiency response
Authors: Sharma Shivani , Singh Dalwinder , Joon Riya , Shukla Vishnu , Singh Ajit Pal , Singh Palvinder , Mantri Shrikant , Pandey Ajay K 
Published: February 2023

AbstractBackgroundIron (Fe) is an essential mineral element, and its deficiency in soil largely affects crop productivity. In plants, the molecular mechanisms underlying the genetic regulation of Fe deficiency responses have yet to be well understood. Specifically, microRNA (miRNA) mediated regulation of Fe deficiency response and its regulatory network is largely elusive. In the current work, we utilized a whole genome transcriptomic approach to identify the Fe deficiency-responsive miRNAs to understand the molecular mechanisms of Fe deficiency response in wheat seedlings. The study also identifies nine novel miRNAs putatively involved in Fe deficiency response. Further, the identified miRNAs showed tissue preferences relating them to differential mechanisms against Fe deficiency.ResultsIn the present study, we performed small RNA-targeted whole genome transcriptome analysis to identify the involvement of sRNAs in Fe deficiency response. The analysis identified 105 differentially expressed miRNAs corresponding to Fe deficiency response, among them, 9 miRNAs were found to be novel in this study. Interestingly, tissue-specific regulation of Fe deficiency response also participates through miRNA-mediated regulation. We identified 17 shoot specific miRNAs and 18 root-specific miRNAs with altered expression. We validated the tissue specificity of thesemiRNAsby stem-loop quantitative RT-PCR. Further, an attempt was made to predict their targets to speculate their participation in Fe deficiency response. This miRNA target prediction analysis suggested a few major targets of the identified miRNAs, such as multicopper oxidases, E3 ubiquitin ligases, GRAS family, and WRKY transcription factors previously known to play key roles in Fe homeostasis. Our analysis of selected miRNAs also confirmed a temporal regulation of the response.ConclusionThe first information generated here will classify the repository of wheatmiRNAs(with few novel miRNAs) for their role in Fe deficiency response. Our work provides insights into miRNA-mediated regulatory pathways during Fe deficiency.
Merizo: a rapid and accurate domain segmentation method using invariant point attention
Authors: Lau Andy M. , Kandathil Shaun M. , Jones David T. 
Published: February 2023

AbstractThe AlphaFold Protein Structure Database (AFDB), containing predictions for over 200 million proteins, has been met with enthusiasm over its potential in enriching structural biological research and beyond. Currently, access to the information within the AFDB is precluded by an urgent need of tools that allow the efficient traversal, discovery and documentation of its contents. Identifying the regions that correspond to domains in the AFDB is a non-trivial endeavour and doing so will aid our understanding of protein structure and function, while facilitating drug discovery and comparative genomics. Here, we describe a deep learning method for accurate domain segmentation called Merizo, which learns to cluster residues into domains in a bottom-up manner. Merizo is trained on CATH domains and fine-tuned on a subset of AFDB models via self-distillation, enabling it to be applicable to both experimental and AFDB models. As proof of concept, we apply Merizo to models of the human proteome, and identify 40,818 putative domains that can be matched to CATH representative domains. Merizo is available athttps://github.com/psipred/Merizo.
Detecting and validating influential organisms for rice growth: An ecological network approach
Authors: Ushio Masayuki , Saito Hiroki , Tojo Motoaki , Nagano Atsushi J. 
Published: February 2023

AbstractHow to achieve sustainable food production while reducing environmental impacts is a major concern in agricultural science, and advanced breeding techniques are promising for achieving such goals. However, rice is usually grown under field conditions and influenced by surrounding ecological community members. How ecological communities influence the rice performance in the field has been underexplored despite the potential of ecological communities to establish an environment-friendly agricultural system. In the present study, we demonstrate an ecological-network-based approach to detect potentially influential, previously overlooked organisms for rice (Oryza sativa). First, we established small experimental rice plots, and measured rice growth and monitored ecological community dynamics intensively and extensively using quantitative environmental DNA metabarcoding in 2017 in Japan. We detected more than 1000 species (including microbes and macrobes such as insects) in the rice plots, and nonlinear time series analysis detected 52 potentially influential organisms with lower-level taxonomic information. The results of the time series analysis were validated under field conditions in 2019 by field manipulation experiments. In 2019, we focused on two species,Globisporangium nunnandChironomus kiiensis, whose abundance was manipulated in artificial rice plots. The responses of rice, namely, the growth rate and gene expression patterns, were measured before and after the manipulation. We confirmed that, especially in theG. nunn-added treatment, rice growth rate and gene expression pattern were changed. In the present study, we demonstrated that intensive monitoring of an agricultural system and the application of nonlinear time series analysis were helpful to identify influential organisms under field conditions. Although the effects of the manipulations were relatively small, the research framework presented here has future potential to harness the ecological complexity and utilize it in agriculture. Our proof-of-concept study would be an important basis for the further development of field-basis system management.