bluuebunny/crossref_metadata_2025 huggingface dataset:
The effect of surface structure on hydrophobicity and corrosion resistance of the MgAlCe-LDH film prepared on the micro-arc oxidation coating of magnesium alloy
Authors: Wang Jia , Li Junming , Zhao Ziyuan , Liu Lintao
Published: September 2024

To improve the corrosion resistance of the AZ31B magnesium alloy, a micro-arc oxidation coating (MAO) was fabricated on the magnesium alloy, and a MgAlCe-doped layered double hydroxide (LDH) film was prepared on the surface of the MAO coating using the hydrothermal method. The LDH film was composed of LDH sheets, which can not only cover the pores of the MAO coating but also form a rough surface structure. With the increase of hydrothermal temperature from 80 to 110 °C, the average diameter of LDH sheets decreased from 2.5 to 0.75 μm; the average distance between the LDH sheets decreased from 3 μm to 0.39 μm; and the coverage rate increased from 56.5 % to 99.8 %. The further increase of hydrothermal temperature led to an inverse tendency of these structural parameters. The hydrophobicity increases with the increase of roughness factor. LDH sheets with small sizes and distances have a large roughness factor and excellent hydrophobicity. The corrosion resistance increases with the increase of the LDH coverage rate. The sample with an LDH coverage rate of 99.8 % had excellent corrosion resistance, with Ecorr of −0.311 V, Icorr of 2.967 × 10−9 A·cm−2, and the

Z

0.01
H
z

of 9.76 × 105 Ω·cm2.
Effect of natural hybrid fillers reinforced vinyl ester composites on mechanical and physical properties
Authors: Babu N B Karthik , V Vignesh , Arun Balasubramanian K , S Kamatchi Devi , Khan Anish , Hashem Mohamed , Fouad Hassan
Published: September 2024

Lately, green filler has become an imperative material in polymer composites due to its usage in engineering applications and increased environmental awareness. Herein, we introduce two novel green fillers namely Rice Husk (RH) and Tamarind Seed powder (TS) and using those fillers, hybrid vinyl ester composites were made in different combinations (0, 5, 10, 15, 20, 25, and 30 wt%) through compression moulding. The mechanical properties of prepared green fillers vinyl ester composites are assessed using flexural, impact, tensile and water absorption (hot, cold and salt water as the medium) tests. The fractured surface morphology has been analysed by SEM analysis. The test results revealed that the hybrid filler reinforcement has properties alteration capability and some properties of the vinyl ester composite were enhanced. In addition, it was revealed that the addition of natural fillers beyond a certain level (i.e. 15 wt%) leads to agglomeration of fillers and subsequent reduction in mechanical properties. For instance, the hybrid filler-reinforced samples showed higher elongation, flexural and impact strength than the pure vinyl ester.
Simulating adiabatic quantum computing with parameterized quantum circuits
Authors: Kolotouros Ioannis , Petrongonas Ioannis , Prokop Miloš , Wallden Petros
Published: September 2024

Adiabatic quantum computing is a universal model for quantum computing whose implementation using a gate-based quantum computer requires depths that are unreachable in the early fault-tolerant era. To mitigate the limitations of near-term devices, a number of hybrid approaches have been pursued in which a parameterized quantum circuit prepares and measures quantum states and a classical optimization algorithm minimizes an objective function that encompasses the solution to the problem of interest. In this work, we propose a different approach starting by analyzing how a small perturbation of a Hamiltonian affects the parameters that minimize the energy within a family of parameterized quantum states. We derive a set of equations that allow us to compute the new minimum by solving a constrained linear system of equations that is obtained from measuring a series of observables on the unperturbed system. We then propose a discrete version of adiabatic quantum computing that can be implemented in a near-term device while at the same time is insensitive to the initialization of the parameters and to other limitations hindered in the optimization part of variational quantum algorithms. We compare our proposed algorithm with the variational quantum eigensolver on two classical optimization problems, namely MaxCut and number partitioning, and on a quantum-spin configuration problem, the transverse-field ising chain model, and confirm that our approach demonstrates superior performance.
Parametric optimization of weld bead of aluminium 6061 fabricated through wire arc additive manufacturing
Authors: Lekkala Nalini , Prasad Kondapalli Siva
Published: September 2024

Additive fabrication with wire arc shown tremendous advancements in recent years. The current study investigates the effect of welding conditions on the width of an additively produced layer by using Gas Metal Arc Welding - Cold Metal Transfer welding. Further, the Taguchi method was used to optimize the effect of weld settings on layer width which was additively deposited. The wire spool is made of Aluminium 6061, which has a wide range of industrial applications. Flaws, porosity, waviness,and weld bead's discontinuity on a surface could be minimized by the assortment and optimization of parameters or else, strange forms would emerge during creation of slender wall construction utilising Wire Arc Additive manufacturing. In Taguchi, an L9 Orthogonal array was utilised to investigate the input parameters namely wire feed speed, weld speed and voltage. The main plots identify the best parameter combination, while the analysis of variance determines the significant parameters. For the response variable, a linear regression approach is used to create a numerical model. The minimum width of bead of 2.39 mm is achieved at weld velocity of 0.9 m/min with wire feed speed of 6 m/min and at voltage of 12 V. The maximum width of the bead was 6.40 mm, achieved at weld velocity of 0.6 m/min with wire feed speed of 8 m/min at a voltage of 16 V. After optimising the parameters, confirmation tests are run, and the outcomes are compared to the initial outcome.
The effects of tool rotational speed and post-weld heat treatment of friction stir welded AZ31-B magnesium alloy
Authors: Kumaresh D , S Venkatesan
Published: September 2024

This research article examines the effect of increasing the tool rotational speed and post-weld heat treatment behavior of friction stir-welded AZ31-B magnesium alloy. The variable tool rotational speeds of 1000 rpm, 1200 rpm, 1400 rpm, and 1600 rpm with fixed tool traverse speeds of 30 mm min−1 were chosen based on the trial welding method. Results reveal that the as-welded tensile strength values of 133 MPa, 209 MPa, 215 MPa, and 213 MPa for the corresponding tool rotation speeds and post-weld heat-treated tensile strength values of 140 MPa, 213 MPa, 223 MPa, and 227 MPa significantly increased joint strength when compared to the as-welded process. The measured grain size values are reduced when the tool rotational speed increases due to material dynamic recrystallization. Al-Mg brittle intermetallic compounds significantly reduced the mechanical strength during the increase of the tool’s rotational speed. Fractography analysis reveals all welded samples fractured at the center of the stir zone with a ductile fracture angle of 45°.
Synthesis of chlorotoxin functionalized metallic nanoparticles and their in vitro evaluation of cytotoxic effects in nervous system cancer cell lines
Authors: Boltman Taahirah , Sibuyi Nicole Remaliah Samantha , Ekpo Okobi , Meyer Mervin
Published: September 2024

The treatment of glioblastoma (GB) and neuroblastoma (NB) remains a challenge, as current chemotherapies are plagued with systemic toxicity, drug resistance, and inadequate blood–brain barrier (BBB) penetration. Therefore, novel therapeutic strategies with high specificity and the capacity to bypass the BBB are required. Chlorotoxin (CTX) selectively targets gliomas and neuroectodermal tumors, hence the use of CTX-targeted nanoparticles (NPs) represents a promising therapeutic approach for nervous system (NS) cancers. Bimetallic NPs composed of two metals such as gold-platinum NPs (AuPtNPs) exhibit enhanced anticancer properties compared to single-metal NPs, however their application in studying NS tumors has been relatively limited. CTX-functionalized monometallic gold NPs (CTX-AuNPs) and bimetallic gold-platinum NPs (CTX-AuPtNPs) were synthesized in this study. The NPs were characterized by Ultraviolet-Visible Spectroscopy (UV–vis), Dynamic Light Scattering (DLS), Transmission Electron Microscopy (TEM) and Fourier Transform Infra-Red Spectroscopy (FTIR). Cytotoxicity of NPs was investigated in cancer (U87 and SH-SY5Y) and non-cancer (KMST-6) cells using the water-soluble tetrazolium (WST)-1 assay. The CTX-AuNPs and CTX-AuPtNPs had a core size of ∼5 nm. The CTX-AuPtNPs showed significant anticancer activity in U87 cells possibly due to the synergistic effects of combined metals. Findings obtained from this study demonstrated that CTX can be used to target NS cancers and that bimetallic NPs could be effective in their treatment. More studies are required to investigate the mechanisms of NPs toxicity, and further explore the hyperthermia treatment of NS cancer using the CTX-AuPtNPs.
Launaea cornuta (wild lettuce) leaf extract: phytochemical analysis and synthesis of silver-zinc oxide nanocomposite
Authors: Makauki Elizabeth , Rwiza Mwemezi , Machunda Revocatus , Basu Onita D
Published: September 2024

Access to quality drinking water is an essential human right and a fundamental aspect of human dignity, yet a challenge to many in developing countries. Over 2 billion people worldwide lack access to quality drinking water due to microbial contamination, among other factors. Silver-doped zinc oxide impregnated activated carbon nanocomposites, Ag-ZnO-AC NCs, a strong antimicrobial agent have been used at point-of-use to treat water treatment. Green synthesis of Ag-ZnO-AC NCs has played a vital role since it leads to the acquisition of non-toxic nanocomposites compared to chemical synthesis. In this study, Ag-ZnO-AC NCs were green-synthesized using Launaea cornuta leaf extract as a source of reducing and capping agents in place of synthetic chemicals. Antioxidants from Launaea cornuta (Wild Lettuce) leaves were extracted using 0, 50, and 100% EtOH solvents with different volumes and extraction circles. The highest phenolic (11044 ± 63 μg) and antioxidant (44112 ± 894 μg) contents were extracted using 50% EtOH and 20 ml of extract solvent with p < 0.05. The SEM and TEM images of the synthesized Ag-ZnO-AC NCs show the formation of highly porous AC with sheet-like structures and successful Ag-ZnO NCs impregnation within the pores and on the surface of the AC. Based on the inhibition zone, the antimicrobial activity of the Ag-ZnO AC NCs had significant results with 14.00 ± 0.37 for E. coli and 17.33 ± 0.36 mm for S. aureus, p < 0.05. These results confirm the significance of Launaea cornuta (Wild Lettuce) as a source of antioxidants that can be used as reducing and capping agents to synthesize Ag-ZnO-AC NCs.
From Colonialism to Self-determination: A Historical Study of Language Testing and Institutional Policy
Authors: Zakaria Azrifah , Aryadoust Vahid
Published: September 2024

This study examines the development of institutional policies on language testing, taking the case of a former British colony, Singapore. Using historical narrative inquiry to analyse the Ministry of Education’s archived documents, we investigate how institutions form part of the assessment ecosystem that continues to function in the present day. We identified two main themes in the development of English language testing in Singapore. The first theme From Free Enterprise to Governance describes the trajectory of language testing from the colonial era to independence, with a fragmented assessment landscape gradually moving towards centralized planning. The second theme Individual Agency and Institutional Change illustrates how the needs of the individual interact with the institution. Responses by the ministry and examination board provide important insights into the processes of test development from a micro perspective and how consequences for the individual intersect with macro issues. We highlight the necessity of taking a historical perspective to provide a more holistic picture of how we currently practice language testing.
The Study of the Safety and Effectiveness of Motiva SmoothSilk Silicone Gel-Filled Breast Implants in Patients Undergoing Primary and Revisional Breast Augmentation: Three-Year Clinical Data
Authors: Glicksman Caroline , Wolfe Andrew , McGuire Patricia
Published: September 2024

Background

Silicone breast implant design has evolved over the last 50 years. Regulatory bodies including the FDA require data to support the modifications designed to improve the safety, efficacy, longevity, and biocompatibility of breast implants.

Objectives

The authors reviewed the 3-year data on the safety and effectiveness of Motiva (Establishment Labs Holdings, Inc., Alajuela, Costa Rica) SmoothSilk silicone gel-filled breast implants submitted to the FDA. The current submitted data include the primary breast augmentation and revisional augmentation cohorts.

Methods

The Motiva IDE is a prospective, single-arm, multicenter, 10-year pivotal study in which data are collected on breast augmentation, reconstruction, and revisional surgery. Three-year data were submitted to the FDA on adverse events, reoperations, patient and physician satisfaction, connective tissue diseases, and quality of life validated instruments. A subset of the patients underwent annual magnetic resonance imaging (MRI) at years 1, 2, and 3 to screen for implant rupture.

Results

A total of 451 patients were implanted in the primary augmentation cohort and 109 patients in the revisional augmentation cohort. There were 218 patients enrolled in the MRI cohort. Reported rates for reoperation for any reason were 6.1% in the primary augmentation cohort (92.4% follow-up) and 25.8% in the revisional augmentation cohort (88.7% follow-up).

Discussion

Motiva implants were first introduced in 2010. The 3-year Motiva data suggests that the leading cause of revisional surgery has shifted from capsular contracture and rupture to more subjective indications for reoperation such as malposition and size change.

Conclusions

Three-year data from the primary augmentation and revisional augmentation cohorts submitted to the FDA demonstrate the safety and efficacy of the Motiva implants. There were low complication rates for implant-related complications and high surgeon and patient satisfaction.

Level of Evidence: 2
nsDCC: dual-level contrastive clustering with nonuniform sampling for scRNA-seq data analysis
Authors: Wang Linjie , Li Wei , Zhou Fanghui , Yu Kun , Feng Chaolu , Zhao Dazhe
Published: September 2024

Dimensionality reduction and clustering are crucial tasks in single-cell RNA sequencing (scRNA-seq) data analysis, treated independently in the current process, hindering their mutual benefits. The latest methods jointly optimize these tasks through deep clustering. However, contrastive learning, with powerful representation capability, can bridge the gap that common deep clustering methods face, which requires pre-defined cluster centers. Therefore, a dual-level contrastive clustering method with nonuniform sampling (nsDCC) is proposed for scRNA-seq data analysis. Dual-level contrastive clustering, which combines instance-level contrast and cluster-level contrast, jointly optimizes dimensionality reduction and clustering. Multi-positive contrastive learning and unit matrix constraint are introduced in instance- and cluster-level contrast, respectively. Furthermore, the attention mechanism is introduced to capture inter-cellular information, which is beneficial for clustering. The nsDCC focuses on important samples at category boundaries and in minority categories by the proposed nearest boundary sparsest density weight assignment algorithm, making it capable of capturing comprehensive characteristics against imbalanced datasets. Experimental results show that nsDCC outperforms the six other state-of-the-art methods on both real and simulated scRNA-seq data, validating its performance on dimensionality reduction and clustering of scRNA-seq data, especially for imbalanced data. Simulation experiments demonstrate that nsDCC is insensitive to “dropout events” in scRNA-seq. Finally, cluster differential expressed gene analysis confirms the meaningfulness of results from nsDCC. In summary, nsDCC is a new way of analyzing and understanding scRNA-seq data.
Meeting the challenge of genomic analysis: a collaboratively developed workshop for pangenomics and topological data analysis
Authors: Contreras-Peruyero Haydeé , Guerrero-Flores Shaday , Zirión-Martínez Claudia , Mejía-Ponce Paulina M , Navarro-Miranda Marisol , Lovaco-Flores J Abel , Ibarra-Rodríguez José M , Pashkov Anton , Licona-Cassani Cuauhtémoc , Sélem-Mojica Nelly
Published: September 2024

Motivation

As genomics data analysis becomes increasingly intricate, researchers face the challenge of mastering various software tools. The rise of Pangenomics analysis, which examines the complete set of genes in a group of genomes, is particularly transformative in understanding genetic diversity. Our interdisciplinary team of biologists and mathematicians developed a short Pangenomics Workshop covering Bash, Python scripting, Pangenome, and Topological Data Analysis. These skills provide deeper insights into genetic variations and their implications in Evolutionary Biology. The workshop uses a Conda environment for reproducibility and accessibility. Developed in The Carpentries Incubator infrastructure, the workshop aims to equip researchers with essential skills for Pangenomics research. By emphasizing the role of a community of practice, this work underscores its significance in empowering multidisciplinary professionals to collaboratively develop training that adheres to best practices.

Results

Our workshop delivers tangible outcomes by enhancing the skill sets of Computational Biology professionals. Participants gain hands-on experience using real data from the first described pangenome. We share our paths toward creating an open-source, multidisciplinary, and public resource where learners can develop expertise in Pangenomic Analysis. This initiative goes beyond advancing individual capabilities, aligning with the broader mission of addressing educational needs in Computational Biology.

Availability and implementation

https://carpentries-incubator.github.io/pangenomics-workshop/
Identifying nucleotide-binding leucine-rich repeat receptor and pathogen effector pairing using transfer-learning and bilinear attention network
Authors: Qiao Baixue , Wang Shuda , Hou Mingjun , Chen Haodi , Zhou Zhengwenyang , Xie Xueying , Pang Shaozi , Yang Chunxue , Yang Fenglong , Zou Quan , Sun Shanwen
Published: September 2024

Motivation

Nucleotide-binding leucine-rich repeat (NLR) family is a class of immune receptors capable of detecting and defending against pathogen invasion. They have been widely used in crop breeding. Notably, the correspondence between NLRs and effectors (CNE) determines the applicability and effectiveness of NLRs. Unfortunately, CNE data is very scarce. In fact, we’ve found a substantial 91 291 NLRs confirmed via wet experiments and bioinformatics methods but only 387 CNEs are recognized, which greatly restricts the potential application of NLRs.

Results

We propose a deep learning algorithm called ProNEP to identify NLR-effector pairs in a high-throughput manner. Specifically, we conceptualized the CNE prediction task as a protein–protein interaction (PPI) prediction task. Then, ProNEP predicts the interaction between NLRs and effectors by combining the transfer learning with a bilinear attention network. ProNEP achieves superior performance against state-of-the-art models designed for PPI predictions. Based on ProNEP, we conduct extensive identification of potential CNEs for 91 291 NLRs. With the rapid accumulation of genomic data, we expect that this tool will be widely used to predict CNEs in new species, advancing biology, immunology, and breeding.

Availability and implementation

The ProNEP is available at http://nerrd.cn/#/prediction. The project code is available at https://github.com/QiaoYJYJ/ProNEP.
Discussion on “LEAP: the latent exchangeability prior for borrowing information from historical data” by Ethan M. Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, H. Amy Xia, and Joseph G. Ibrahim
Authors: Scott Darren , Lewin Alex
Published: September 2024

In the following discussion, we describe the various assumptions of exchangeability that have been made in the context of Bayesian borrowing and related models. In this context, we are able to highlight the difficulty of dynamic Bayesian borrowing under the assumption of individuals in the historical data being exchangeable with the current data and thus the strengths and novel features of the latent exchangeability prior. As borrowing methods are popular within clinical trials to augment the control arm, some potential challenges are identified with the application of the approach in this setting.
Use of Linked Administrative Adult Social Care Data for Research: A Scoping Review of Existing UK Studies
Authors: Lugg-Widger Fiona , Sydenham Mia , Oatley Rebecca , Scourfield Jonathan
Published: September 2024

Administrative data provide unique coverage of populations using care and the use of new datasets and advancing digital technologies only contribute to this growing area. There are ongoing limitations in the linkage of administrative datasets, which for adult social care have been exacerbated by inconsistent coding and varied IT systems. The aim of this review was to provide an overview of all studies to date that have used linked adult social care administrative data in the UK. This scoping review included primary quantitative research using individual-level adult social care service or workforce data when linked with at least one other data source. The search strategy was developed systematically and two authors independently screened and reviewed full-texts for inclusion. Fourteen articles were included—a mix of national and local projects. Definitions for the adult social care population ranged, with only four including all ages. One included unpaid carers and one included the domiciliary care workforce. Where described, linkage of datasets was slow, with challenges in the governance and linkage process.

This review demonstrates that administrative data can be a rich source of information for the adult social care sector and linkage can expand its utility and impact.
Organisational Leadership and Service Quality of Child and Adolescent Social Workers: A Hierarchical Linear Modelling Approach
Authors: Zhang Huan , Lin Junwei , Xu Shuge , Wang Yean
Published: September 2024

Enhancing the quality of services provided by child and adolescent social workers is crucial for promoting the healthy development of children and adolescents. However, limited research has integrated both organisational and individual factors into the examination of the influencing factors of social work service quality. This study uses the hierarchical linear model to study how organisational leadership can improve the service quality of child and adolescent social workers through the mediating influence of professional identity. The hypotheses were examined using data from 2,248 child and adolescent social workers in 157 social service organisations collected during the China Social Work Longitudinal Study in 2019. The findings reveal two critical insights: (i) there is a significant correlation between organisational leadership in social service organisations and the service quality provided by child and adolescent social workers and (ii) professional identity appears to play a fully mediating role in this relationship. These findings encourage the boards of social service organisations to implement appropriate measures for reinforcing internal governance, exporting effective leadership and nurturing the professional identity of child and adolescent social workers, thereby elevating the quality of their services.
The role of neuronal variability in cognitive modulation via prefrontal direct current stimulation
Authors: Hogeveen Jeremy
Published: September 2024

This scientific commentary refers to ‘Direct current stimulation modulates prefrontal cell activity and behaviour without inducing seizure-like firing’ by Fehring et al. (https://doi.org/10.1093/brain/awae273).
Glutamine promotes the proliferation of intestinal stem cells via inhibition of TP53-induced glycolysis and apoptosis regulator promoter methylation in burned mice
Authors: Zhang Panyang , Wu Dan , Zha Xule , Su Sen , Zhang Yajuan , Wei Yan , Xia Lin , Fan Shijun , Peng Xi
Published: September 2024

Background

Intestinal stem cells (ISCs) play a pivotal role in maintaining intestinal homeostasis and facilitating the restoration of intestinal mucosal barrier integrity. Glutamine (Gln) is a crucial energy substrate in the intestine, promoting the proliferation of ISCs and mitigating damage to the intestinal mucosal barrier after burn injury. However, the underlying mechanism has not yet been fully elucidated. The objective of this study was to explore the mechanism by which Gln facilitates the proliferation of ISCs.

Methods

A mouse burn model was established to investigate the impact of Gln on intestinal function. Subsequently, crypts were isolated, and changes in TP53-induced glycolysis and apoptosis regulator (TIGAR) expression were assessed using real-time quantitative polymerase chain reaction (RT-qPCR), western blotting, immunohistochemistry, and immunofluorescence. The effects of TIGAR on cell proliferation were validated through CCK-8, EdU, and clonogenicity assays. Furthermore, the effect of TIGAR on Yes-associated protein (YAP) nuclear translocation and ferroptosis was examined by western blotting and immunofluorescence staining. Finally, dot blot analysis and methylation-specific PCR were performed to evaluate the effect of Gln on TIGAR promoter methylation.

Results

The mRNA and protein levels of TIGAR decreased after burn injury, and supplementation with Gln increased the expression of TIGAR. TIGAR accelerates the nuclear translocation of YAP, thereby increasing the proliferation of ISCs. Concurrently, TIGAR promotes the synthesis of nicotinamide adenine dinucleotide phosphate (NADPH) and glutathione to suppress ferroptosis in ISCs. Subsequent investigations demonstrated that Gln inhibits TIGAR promoter methylation by increasing the expression of the demethylase ten-eleven translocation. This change increased TIGAR transcription, increased NADPH synthesis, and reduced oxidative stress, thereby facilitating the restoration of intestinal mucosal barrier integrity post-burn injury.

Conclusions

Our data confirmed the inhibitory effect of Gln on TIGAR promoter methylation, which facilitates YAP translocation into the nucleus and suppresses ferroptosis, ultimately promoting the proliferation of ISCs.
The Rise in the Participation of Asia Pacific States in the Proceedings of the International Court of Justice (2010-2023)
Authors: de la Rasilla Ignacio
Published: September 2024

Recent State practice shows a unprecedented rise in participation by Asia Pacific States in proceedings before the International Court of Justice. Section II provides a holistic view with consideration of these recent developments in the light of sub-regional variation regarding the ICJ in the Asia Pacific region providing four detailed tables and three maps to that effect. Section III follows on by providing a thematically ordered analytical survey of all eighteen recent contentious cases brought by, or involving, Asia Pacific States before the Court since 2010. Section IV examines a rising pattern in participation by Asia Pacific States, including China, in ICJ advisory proceedings over the last twenty-five years, culminating with their all-time record participation in the Obligations of States in Respect of Climate Change. Section V illustrates the epistemological value of the regional and sub-regional lens in complementing country-specific lenses when studying the engagement of sovereign States with the ICJ in the light of recent State practice from the Asia Pacific region. The conclusion considers the potential for, and limitations of, future engagement by Asia Pacific States with the ICJ.
CPMKG: a condition-based knowledge graph for precision medicine
Authors: Yang Jiaxin , Zhuang Xinhao , Li Zhenqi , Xiong Gang , Xu Ping , Ling Yunchao , Zhang Guoqing
Published: September 2024

Personalized medicine tailors treatments and dosages based on a patient’s unique characteristics, particularly its genetic profile. Over the decades, stratified research and clinical trials have uncovered crucial drug-related information—such as dosage, effectiveness, and side effects—affecting specific individuals with particular genetic backgrounds. This genetic-specific knowledge, characterized by complex multirelationships and conditions, cannot be adequately represented or stored in conventional knowledge systems. To address these challenges, we developed CPMKG, a condition-based platform that enables comprehensive knowledge representation. Through information extraction and meticulous curation, we compiled 307 614 knowledge entries, encompassing thousands of drugs, diseases, phenotypes (complications/side effects), genes, and genomic variations across four key categories: drug side effects, drug sensitivity, drug mechanisms, and drug indications. CPMKG facilitates drug-centric exploration and enables condition-based multiknowledge inference, accelerating knowledge discovery through three pivotal applications. To enhance user experience, we seamlessly integrated a sophisticated large language model that provides textual interpretations for each subgraph, bridging the gap between structured graphs and language expressions. With its comprehensive knowledge graph and user-centric applications, CPMKG serves as a valuable resource for clinical research, offering drug information tailored to personalized genetic profiles, syndromes, and phenotypes.

Database URL: https://www.biosino.org/cpmkg/
Widespread occurrence of dissolved oxygen anomalies, aerobic microbes, and oxygen-producing metabolic pathways in apparently anoxic environments
Authors: Ruff S Emil , Schwab Laura , Vidal Emeline , Hemingway Jordon D , Kraft Beate , Murali Ranjani
Published: September 2024

Nearly all molecular oxygen (O2) on Earth is produced via oxygenic photosynthesis by plants or photosynthetically active microorganisms. Light-independent O2 production, which occurs both abiotically, e.g. through water radiolysis, or biotically, e.g. through the dismutation of nitric oxide or chlorite, has been thought to be negligible to the Earth system. However, recent work indicates that O2 is produced and consumed in dark and apparently anoxic environments at a much larger scale than assumed. Studies have shown that isotopically light O2 can accumulate in old groundwaters, that strictly aerobic microorganisms are present in many apparently anoxic habitats, and that microbes and metabolisms that can produce O2 without light are widespread and abundant in diverse ecosystems. Analysis of published metagenomic data reveals that the enzyme putatively capable of nitric oxide dismutation forms four major phylogenetic clusters and occurs in at least 16 bacterial phyla, most notably the Bacteroidota. Similarly, a re-analysis of published isotopic signatures of dissolved O2 in groundwater suggests in situ production in up to half of the studied environments. Geochemical and microbiological data support the conclusion that “dark oxygen production" is an important and widespread yet overlooked process in apparently anoxic environments with far-reaching implications for subsurface biogeochemistry and ecology.
Modeling Substitution Rate Evolution across Lineages and Relaxing the Molecular Clock
Authors: Mello Beatriz , Schrago Carlos G
Published: September 2024

Relaxing the molecular clock using models of how substitution rates change across lineages has become essential for addressing evolutionary problems. The diversity of rate evolution models and their implementations are substantial, and studies have demonstrated their impact on divergence time estimates can be as significant as that of calibration information. In this review, we trace the development of rate evolution models from the proposal of the molecular clock concept to the development of sophisticated Bayesian and non-Bayesian methods that handle rate variation in phylogenies. We discuss the various approaches to modeling rate evolution, provide a comprehensive list of available software, and examine the challenges and advancements of the prevalent Bayesian framework, contrasting them to faster non-Bayesian methods. Lastly, we offer insights into potential advancements in the field in the era of big data.
Examining the Effects of Environment, Geography, and Elevation on Patterns of DNA Methylation Across Populations of Two Widespread Bumble Bee Species
Authors: Heraghty Sam D , Rahman Sarthok Rasique , Verble Kelton M , Lozier Jeffrey D
Published: September 2024

Understanding the myriad avenues through which spatial and environmental factors shape evolution is a major focus in biological research. From a molecular perspective, much work has been focused on genomic sequence variation; however, recently there has been increased interest in how epigenetic variation may be shaped by different variables across the landscape. DNA methylation has been of particular interest given that it is dynamic and can alter gene expression, potentially offering a path for a rapid response to environmental change. We utilized whole genome enzymatic methyl sequencing to evaluate the distribution of CpG methylation across the genome and to analyze patterns of spatial and environmental association in the methylomes of two broadly distributed montane bumble bees (Bombus vancouverensis Cresson and Bombus vosnesenskii Radoszkowski) across elevational gradients in the western US. Methylation patterns in both species are similar at the genomic scale with ∼1% of CpGs being methylated and most methylation being found in exons. At the landscape scale, neither species exhibited strong spatial or population structuring in patterns of methylation, although some weak relationships between methylation and distance or environmental variables were detected. Differential methylation analysis suggests a stronger environment association in B. vancouverensis given the larger number of differentially methylated CpG's compared to B. vosnesenskii. We also observed only a handful of genes with both differentially methylated CpGs and previously detected environmentally associated outlier SNPs. Overall results reveal a weak but present pattern in variation in methylation over the landscape in both species.
Identifying Clinically Meaningful Muscle Power Enhancements and Their Functional Correlates in Hospitalized Older Patients
Authors: Cadore Eduardo L , Izquierdo Mikel , Martínez-Velilla Nicolás , Blanco-Rambo Eduarda , Zambom-Ferraresi Fabricio , Sáez de Asteasu Mikel L
Published: September 2024

Background

This study aimed to determine the threshold of muscle power and strength enhancements that lead to functional gains after exercise intervention in an acute care unit.

Methods

A total of 302 older patients (intervention: 169, control: 133) from 2 randomized clinical trials were included (mean age: 86.7 years). We measured maximal strength (1RM) and muscle power via a velocity transducer during leg press exercise at 30% and 60% of 1RM. A multicomponent exercise program, including power training, balance, and gait exercises performed over 3 to 6 consecutive days, served as the intervention. We used an anchor-based method to correlate muscle function increases with the Short Physical Performance Battery (SPPB) and gait velocity (GVT) to define clinically meaningful improvements.

Results

In the intervention group, marked differences were found in maximal power at 30% of 1RM between SPPB responders and nonresponders (relative 83.5% vs 34.8%; absolute 33.0 vs 12.8 W; p < .05) and at 60% of 1RM (relative 61.1% vs 22.4%; p < .05). GVT responders demonstrated significantly greater improvements in both relative and absolute maximal power than nonresponders at both 30% and 60% of 1RM (p < .05), as well as greater absolute 1RM gains (21.2 vs 15.2 kg, p < .05). Clinically meaningful improvements for muscle power based on SPPB and GVT ranged from 30.2% to 48.7%, whereas for 1RM, it was 8.2% based on GVT.

Conclusions

Muscle power gains were most notable in patients with improvements in the SPPB and GVT, highlighting the critical role of muscle power in functional recovery in these patients.
Uncertainty quantification in electrical resistivity tomography inversion: hybridizing block-wise bootstrapping with geostatistics
Authors: Khabaz Zahra Tafaghod , Ghanati Reza , Bérubé Charles L
Published: September 2024

Electrical resistivity tomography inversion often encounters uncertainty stemming from two primary sources: epistemic uncertainty, arising from imperfect underlying physics and improper initial approximation of model parameters, and aleatory variability in observations due to measurement errors. Despite the widespread application of electrical resistivity tomography in imaging, the resistivity distribution of subsurface structures for various hydro-geophysical and engineering purposes, the assessment of uncertainty is seldom addressed within the inverted resistivity tomograms. To explore the combined impact of epistemic and aleatory uncertainty on resistivity models, we initially perturb the observed data using non-parametric block-wise bootstrap resampling with an optimal choice of the block size, generating different realizations of the field data. Subsequently, a geostatistical method is applied to stochastically generate a set of initial models for each bootstrapped data set from the previous step. Finally, we employ a globally convergent homotopic continuation method on each bootstrapped data set and initial model realization to explore the posterior resistivity models. Uncertainty information about the inversion results is provided through posterior statistical analysis. Our algorithm’s simplicity enables easy integration with existing gradient-based inversion methods, requiring only minor modifications. We demonstrate the versatility of our approach through its application to various synthetic and real electrical resistivity tomography experiments. The results reveal that this approach for quantifying uncertainty is straightforward to implement and computationally efficient.
Addressing magnetometer inconsistencies in drone-borne total field gradiometer surveys: case study from Rånbogen, Arctic Norway
Authors: Petersen Jacob Thejll , Døssing Arne
Published: September 2024

Inconsistencies between (lightweight optical pumped) magnetometers in total field gradiometer surveys can introduce instrument-dependent responses, complicating geological interpretation. Here, we present an equivalent source-based processing method that combines equivalent sources with kernel regression to eliminate these instrument-dependent responses. We evaluate the method using a drone-borne triangular gradiometer survey from Rånbogen, Arctic Norway. The Rånbogen survey exhibits instrument-dependent responses in the measured total field differences, likely resulting from differences in the magnetometer’s heading error and the complexities from the induced field from the system itself. A synthetic gradiometer survey was generated to test the method, where different nonlinear synthetic heading errors were added to the synthetic magnetometer readings. The equivalent source-based processing method effectively removed these heading errors while constructing an equivalent source model that fits the actual underlying synthetic model. Furthermore, applying the method to the Rånbogen survey removes the data’s correlation to its orientation, thereby minimizing the effect of the instrument-dependent response. The method condenses the gradiometer data to a single equivalent source surface where the derivatives of the total field can be calculated to facilitate interpretation. The results suggest that the equivalent source-based method removes the inconsistencies between the magnetometers in a total field gradiometer survey, allowing for drone-borne gradiometer systems with multiple lightweight magnetometers.
The Genome Architecture of the Copepod Eurytemora carolleeae — the Highly Invasive Atlantic Clade of the Eurytemora affinis Species Complex
Authors: Du Zhenyong , Gelembiuk Gregory , Moss Wynne , Tritt Andrew , Lee Carol Eunmi
Published: September 2024

Copepods are among the most abundant organisms on the planet and play critical functions in aquatic ecosystems. Among copepods, populations of the Eurytemora affinis species complex are numerically dominant in many coastal habitats and serve as food sources for major fisheries. Intriguingly, certain populations possess the unusual capacity to invade novel salinities on rapid time scales. Despite their ecological importance, high-quality genomic resources have been absent for calanoid copepods, limiting our ability to comprehensively dissect the genome architecture underlying the highly invasive and adaptive capacity of certain populations. Here, we present the first chromosome-level genome of a calanoid copepod, from the Atlantic clade (Eurytemora carolleeae) of the E. affinis species complex. This genome was assembled using high-coverage PacBio long-read and Hi-C sequences of an inbred line, generated through 30 generations of full-sib mating. This genome, consisting of 529.3 Mb (contig N50 = 4.2 Mb, scaffold N50 = 140.6 Mb), was anchored onto four chromosomes. Genome annotation predicted 20,262 protein-coding genes, of which ion transport-related gene families were substantially expanded based on comparative analyses of 12 additional arthropod genomes. Also, we found genome-wide signatures of historical gene body methylation of the ion transport-related genes and the significant clustering of these genes on each chromosome. This genome represents one of the most contiguous copepod genomes to date and is among the highest quality marine invertebrate genomes. As such, this genome provides an invaluable resource to help yield fundamental insights into the ability of this copepod to adapt to rapidly changing environments.
Assessing the feasibility and likelihood of policy options to lower specialty drug costs
Authors: Taylor Erin A , Khodyakov Dmitry , Predmore Zachary , Buttorff Christine , Kim Alice
Published: September 2024

Specialty drugs are high-cost medications often used to treat complex chronic conditions. Even with insurance coverage, patients may face very high out-of-pocket costs, which in turn may restrict access. While the Inflation Reduction Act of 2022 included policies designed to reduce specialty drug costs, relatively few policies have been enacted during the past decade. In 2022-2023, we conducted a scoping literature review to identify a range of policy options and selected a set of 9 that have been regularly discussed or recently considered to present to an expert stakeholder panel to seek consensus on (1) the feasibility of implementing each policy and (2) its likely impact on drug costs. Experts rated only 1 policy highly on both feasibility and impact: grouping originator biologics and biosimilars under the same Medicare Part B reimbursement code. They rated 3 policies focused on setting payment limits as likely to have positive (downward) impact on costs but of uncertain feasibility. They considered 4 policies as uncertain on both criteria. Experts rated capping monthly out-of-pocket costs as feasible but unlikely to reduce specialty drug costs. Based on these results, we offer 4 recommendations to policymakers considering ways to reduce specialty drug costs.
Venous obstruction of the upper extremity caused by subclavian vein valve hypertrophy
Authors: Liao Jianyu , Wu Zhoupeng
Published: September 2024

This case report detailed a rare case of upper limb venous outflow obstruction due to primary subclavian vein valve hypertrophy in a 26-year-old male. Misdiagnosed initially, the condition was identified through advanced Doppler ultrasound and Computed Tomography Venography (CTV) Endovascular treatment using Wallstent stents effectively resolved the obstruction, preserving collateral venous circulations. This case highlights the importance of combined colour Doppler ultrasonography for diagnosing unusual venous obstructions and demonstrates the potential of endovascular treatments in managing rare venous conditions.
A novel approach to estimate building electric power consumption based on machine learning method: toward net-zero energy, low carbon and smart buildings
Authors: Alotaibi Badr Saad , Abuhussain Mohammed Awad , Dodo Yakubu Aminu , Al-Tamimi Nedhal , Maghrabi Ammar , Ojobo Henry , Naibi Ahmad Usman , Benti Natei Ermias
Published: September 2024

The modern era has witnessed a surge in energy consumption and its dependence on fossil fuels, which are harmful to the environment, prompting researchers to examine techniques for regulating energy usage in buildings, specifically with regard to residential electricity consumption. The pursuit of net-zero energy consumption and low carbon emission buildings is a significant undertaking that nations across the globe are actively endeavoring to accomplish. In order to accomplish this goal, the structure in question must efficiently oversee its overall energy usage while concurrently capitalizing on sustainable energy sources. The precise estimation of future electricity usage in buildings is an essential element in the process of energy efficiency planning and optimization. The present study introduces a soft computing methodology and data decomposition as approaches for evaluating the energy usage of residential structures. An innovative machine learning approach is introduced for the purpose of estimating the initial cost required to construct a green structure that consumes no net energy. By utilizing wavelet decomposition, it is possible to determine how to transform the structure into one that is intelligent and energy efficient. Following wavelet parallel converter analysis, the data were processed with an estimator model based on an ideal neural network. The results indicate that the mean estimation errors for recurrent neural network, Autoregressive fractionally integrated moving average (ARFIMA), and gene expression programming (GEP) were reduced by 72%, 65%, and 77%, respectively, using this method. Conversely, when the proposed methodology is applied to the smart management of building energy consumption, the examined structures experience an average reduction of 8% in energy consumption. Moreover, the outcomes of CO2 gas emissions demonstrate that the suggested model possesses the capability to accurately forecast CO2 emissions. The study highlights the necessity of employing innovative techniques such as machine learning to decrease building energy usage and CO2 emissions. The discovery of these results can assist policymakers and stakeholders in the energy sector in advancing the adoption of smart building technologies.