Search

We measure the broad impact of galaxy structure on galaxy formation by examining the ongoing star formation and integrated star formation history as revealed through the stellar masses of galaxies at z < 7 based on JWST CEERS data from the Extended Groth Strip (EGS). Using the morphological catalog of 3965 visually classified JWST galaxies from Ferreira et al. (2023), we investigate the evolution of stars, and when they form, as a function of morphological type as well as galaxies classified as passive and starburst through spectral energy distributions. Although disc galaxies dominate the structures of galaxies at z < 7, we find that these discs are in general either 'passive', or on the main sequence of star formation, and do not contain a large population of starburst galaxies. We also find no significant correlation between morphological type and the star formation rate or colours of galaxies at z < 7. In fact, we find that the morphologically classified 'spheroids' tend to be blue and are not found to be predominately passive systems at z > 1.5. We also find that the stellar mass function for disc galaxies does not evolve significantly during this time, whereas other galaxy types, such as the peculiar population, evolve dramatically, declining at lower redshifts. This indicates that massive peculiars are more common at higher redshifts. We further find that up to z ∼ 7, the specific star formation rate (sSFR) does not vary with visual morphology, but strongly depends on stellar mass and internal galaxy mass density. This demonstrates that at early epochs galaxy assembly is a mass-driven, rather than a morphologically driven process. Quenching of star formation is therefore a mass-dominated process throughout the universe's history, likely due to the presence of supermassive black holes. [ABSTRACT FROM AUTHOR]

We present the analysis of the stellar population and star formation history of 181 MIRI selected galaxies at z  = 0 − 3.5 in the massive galaxy cluster field SMACS J0723.3–7327, commonly referred to as SMACS0723, using the JWST Mid-Infrared Instrument (MIRI). We combine the data with the JWST Near Infrared Camera (NIRCam) catalogue, in conjunction with the Hubble Space Telescope (HST) WFC3/IR and ACS imaging. We find that the MIRI bands capture PAH features and dust emission, significantly enhancing the accuracy of photometric redshift and measurements of the physical properties of these galaxies. The median photo- z 's of galaxies with MIRI data are found to have a small 0.1 per cent difference from spectroscopic redshifts and reducing the error by 20 per cent. With MIRI data included in SED fits, we find that the measured stellar masses are unchanged, while the star formation rate is slightly lower by 0.1 dex. We also fit the median SED of active galactic nuclei (AGNs) and star-forming galaxies (SFG) separately. MIRI data provides tighter constraints on the AGN contribution, reducing the typical AGN contributions by ∼15 per cent. In addition, we also compare the median SED obtained with and without MIRI, and we find that including MIRI data yields steeper optical and UV slopes, indicating bluer colours, lower dust attenuation, and younger stellar populations. In the future, MIRI/MRS will enhance our understanding by providing more detailed spectral information and allowing for the study of specific emission features and diagnostics associated with AGN. [ABSTRACT FROM AUTHOR]

Objective The primary aim of this paper is to review evidence and clinical implications related to lifestyle activities associated with promoting brain and cognitive health. Our review targets four key lifestyle factors: physical activity and exercise, social engagement, cognitively stimulating activity, and consuming Mediterranean-style diets. Method We conducted a critical review of the lifestyle factor literature in the four domains listed earlier. We contextualize this literature review by translating findings, when possible, into evidence-based recommendations to consider when providing neuropsychological services. Results There is significant current evidence supporting the role of physical activity and exercise, social engagement, cognitively stimulating activity, and consuming Mediterranean-style diets on positive brain and cognitive health outcomes. While some null findings are present in all four areas reviewed, the weight of the evidence supports the notion that engaging in these activities may promote brain and cognitive functioning. Conclusions Clinical neuropsychologists can have confidence in recommending engagement in physical activity, social activity, and cognitively stimulating activity, and adhering to a Mediterranean-style diet to promote brain and cognitive health. We discuss limitations in existing lifestyle factor research and future directions to enhance the existing evidence base, including additional research with historically underrepresented groups and individuals with neurological conditions. [ABSTRACT FROM AUTHOR]

In this work, we update and develop algorithms for KMTNet tender-love care (TLC) photometry in order to create a new, mostly automated, TLC pipeline. We then start a project to systematically apply the new TLC pipeline to the historic KMTNet microlensing events, and search for buried planetary signals. We report the discovery of such a planet candidate in the microlensing event MOA-2019-BLG-421/KMT-2019-BLG-2991. The anomalous signal can be explained by either a planet around the lens star or the orbital motion of the source star. For the planetary interpretation, despite many degenerate solutions, the planet is most likely to be a Jovian planet orbiting an M or K dwarf, which is a typical microlensing planet. The discovery proves that the project can indeed increase the sensitivity of historic events and find previously undiscovered signals. [ABSTRACT FROM AUTHOR]

The Genetics Society of America is calling for submissions of papers on the Genetics of Bacteria for their journals GENETICS and G3: Genes|Genomes|Genetics. The series aims to highlight recent genetic and genomic work on various aspects of bacterial genetics, including genetic diversity, genome preservation, mutagenesis, gene function and regulation, bacterial defense systems, and the genetic basis of medically important traits. The series will be launched in mid-2025, and authors are invited to submit manuscripts by September 30. The papers will undergo peer review and will be published online shortly after acceptance. [Extracted from the article]

The Genetics Society of America is calling for submissions of papers on biobank-scale genomic data analyses. Biobanks, which are large collections of biological, medical, and genetic data on the same individuals, have revolutionized human genomics research. However, there is substantial heterogeneity between biobanks worldwide, creating both challenges and opportunities for data analysis. The series will focus on various topics, including population structures, selection bias, measurement errors, clinical observations, linkage with external data sources, meta-analysis, computational analysis methods, family-based analyses, ethical and privacy-related issues, and technological considerations. Manuscripts can be submitted until May 31, 2024, and the series will be an ongoing resource in this area. [Extracted from the article]

The article calls for papers on genomic data analyses in biobanks by highlighting the opportunities and challenges stemming from the heterogeneity between biobanks worldwide. Topics include population structures, selection bias effects, clinical observations, ethical considerations, and technological advancements in data generation for omics studies.

A significant proportion of patients who suffer from atrial fibrillation (AF) and are in need of thromboembolic protection are not treated with oral anticoagulation or discontinue this treatment shortly after its initiation. This undertreatment has not improved sufficiently despite the availability of direct oral anticoagulants which are associated with less major bleeding than vitamin K antagonists. Multiple reasons account for this, including bleeding events or ischaemic strokes whilst on anticoagulation, a serious risk of bleeding events, poor treatment compliance despite best educational attempts, or aversion to drug therapy. An alternative interventional therapy, which is not associated with long-term bleeding and is as effective as vitamin K anticoagulation, was introduced over 20 years ago. Because of significant improvements in procedural safety over the years, left atrial appendage closure, predominantly achieved using a catheter-based, device implantation approach, is increasingly favoured for the prevention of thromboembolic events in patients who cannot achieve effective anticoagulation. This management strategy is well known to the interventional cardiologist/electrophysiologist but is not more widely appreciated within cardiology or internal medicine. This article introduces the devices and briefly explains the implantation technique. The indications and device follow-up are more comprehensively described. Almost all physicians who care for adult patients will have many with AF. This practical guide, written within guideline/guidance boundaries, is aimed at those non-implanting physicians who may need to refer patients for consideration of this new therapy, which is becoming increasingly popular. [ABSTRACT FROM AUTHOR]

Background The National Institutes of Health (NIH) mobilized more than $4 billion in extramural funding for the COVID-19 pandemic. Assessing the research output from this effort is crucial to understanding how the scientific community leveraged federal funding and responded to this public health crisis. Methods NIH-funded COVID-19 grants awarded between January 2020 and December 2021 were identified from NIH Research Portfolio Online Reporting Tools Expenditures and Results using the "COVID-19 Response" filter. PubMed identifications of publications under these grants were collected and the NIH iCite tool was used to determine citation counts and focus (eg, clinical, animal). iCite and the NIH's LitCOVID database were used to identify publications directly related to COVID-19. Publication titles and Medical Subject Heading terms were used as inputs to a machine learning–based model built to identify common topics/themes within the publications. Results and Conclusions We evaluated 2401 grants that resulted in 14 654 publications. The majority of these papers were published in peer-reviewed journals, though 483 were published to preprint servers. In total, 2764 (19%) papers were directly related to COVID-19 and generated 252 029 citations. These papers were mostly clinically focused (62%), followed by cell/molecular (32%), and animal focused (6%). Roughly 60% of preprint publications were cell/molecular-focused, compared with 26% of nonpreprint publications. The machine learning–based model identified the top 3 research topics to be clinical trials and outcomes research (8.5% of papers), coronavirus-related heart and lung damage (7.3%), and COVID-19 transmission/epidemiology (7.2%). This study provides key insights regarding how researchers leveraged federal funding to study the COVID-19 pandemic during its initial phase. [ABSTRACT FROM AUTHOR]

Background Following years of sustained pressure on the UK health service, there is recognition amongst health professionals and stakeholders that current models of healthcare are likely to be inadequate going forward. Therefore, a fundamental review of existing social models of healthcare is needed to ascertain current thinking in this area, and whether there is a need to change perspective on current thinking. Method Through a systematic research review, this paper seeks to address how previous literature has conceptualized a social model of healthcare and, how implementation of the models has been evaluated. Analysis and data were extracted from 222 publications and explored the country of origin, methodological approach, and the health and social care contexts which they were set. Results The publications predominantly drawn from the USA, UK, Australia, Canada and Europe identified five themes namely: the lack of a clear and unified definition of a social model of health and wellbeing; the need to understand context; the need for cultural change; improved integration and collaboration towards a holistic and person-centred approach; measuring and evaluating the performance of a social model of health. Conclusion The review identified a need for a clear definition of a social model of health and wellbeing. Furthermore, consideration is needed on how a model integrates with current models and whether it will act as a descriptive framework or, will be developed into an operational model. The review highlights the importance of engagement with users and partner organizations in the co-creation of a model of healthcare. [ABSTRACT FROM AUTHOR]

Background The Patient Cloud ePRO app was adopted by the National Cancer Institute National Clinical Trials Network (NCTN) to facilitate capturing electronic patient-reported (ePRO) outcome data, but use has been low. The study objectives were to test whether a patient-targeted ePRO educational resource (ePRO-E) would increase ePRO intent (number of users) and improve data quality (high quality: ≥80% of the required surveys submitted) within an ongoing NCTN study. Methods The ePRO-E intervention, a patient-targeted educational resource (written material and 6-minute animated YouTube video), was designed to address ePRO barriers. ePRO intent and data quality were compared between 2 groups (N  =   69): a historical control group and a prospectively recruited intervention group exposed to ePRO-E. Covariates included technology attitudes, age, sex, education, socioeconomic status, and comorbidity. Results Intervention group ePRO intent (78.8%) was statistically significantly higher than historical control group intent (47.1%) (P  =  .03). Patients choosing ePRO versus paper surveys had more positive and higher technology attitudes scores (P  =  .03). The odds of choosing ePRO were 4.7 times higher (95% Confidence Interval [CI] = 1.2 to 17.8) (P  =  .02) among intervention group patients and 5.2 times higher (95% CI = 1.3 to 21.6) (P = .02) among patients with high technology attitudes scores, after controlling for covariates. However, the 80% submission rate (percentage submitting ≥80% of required surveys) in the ePRO group (30.6%) was statistically significantly lower than in the paper group (57.9%) (P  =  .05). Conclusions ePRO-E exposure increased ePRO intent. High technology attitudes scores were associated with ePRO selection. Since the ePRO survey submission rate was low, additional strategies are needed to promote high-quality data submission. [ABSTRACT FROM AUTHOR]

The current study on the synthesis problems of four-bar mechanism trajectories primarily relies on establishing a numerical atlas based on trajectory characteristics and employing neural networks to synthesize mechanism parameters. However, this approach has several shortcomings, including a vast database, inefficient retrieval, and challenges in maintaining accuracy. This paper presents a method for synthesizing a trajectory-generation mechanism that combines the extreme gradient boosting (XGBoost) algorithm with a genetic algorithm (GA). The purpose is to synthesize, based on a particular trajectory, the dimensions and installation position parameters of a four-bar mechanism. The paper classifies the trajectories according to their shape features and geometric center placements, thereby improving the accuracy of the XGBoost model for synthesizing mechanisms. The XGBoost algorithm is employed to synthesize the basic dimensional parameters for the mechanism, with the relative slopes of trajectories as input features. The synthesized basic dimensional parameters are turned into parameters for the actual mechanism by researching the scaling, translation, and rotation relationships between mechanisms and the trajectories they generate. The accuracy of the generated trajectories from the synthesized mechanism can be improved by applying GA to optimize the mechanism parameters. Five comparative examples are provided in this research for the different scenarios of given trajectory curves and trajectory points. The effectiveness and accuracy of the proposed approach in this study are validated in comparison to existing research methods by comparing errors between the generated trajectories and the given trajectories. [ABSTRACT FROM AUTHOR]

Background During the first epidemic wave, COVID-19 surveillance focused on quantifying the magnitude and the escalation of a growing global health crisis. The scientific community first assessed risk through basic indicators, such as the number of cases or rates of new cases and deaths, and later began using other direct impact indicators to conduct more detailed analyses. We aimed at synthesizing the scientific community's contribution to assessing the direct impact of the COVID-19 pandemic on population health through indicators reported in research papers. Methods We conducted a rapid scoping review to identify and describe health indicators included in articles published between January 2020 and June 2021, using one strategy to search PubMed, EMBASE and WHO COVID-19 databases. Sixteen experts from European public health institutions screened papers and retrieved indicator characteristics. We also asked in an online survey how the health indicators were added to and used in policy documents in Europe. Results After reviewing 3891 records, we selected a final sample of 67 articles and 233 indicators. We identified 52 (22.3%) morbidity indicators from 33 articles, 105 severity indicators (45.1%, 27 articles) and 68 mortality indicators (29.2%, 51). Respondents from 22 countries completed 31 questionnaires, and the majority reported morbidity indicators (29, 93.5%), followed by mortality indicators (26, 83.9%). Conclusions The indicators collated here might be useful to assess the impact of future pandemics. Therefore, their measurement should be standardized to allow for comparisons between settings, countries and different populations. [ABSTRACT FROM AUTHOR]

Twin Support Vector Machine (TSVM) is a powerful machine learning method that is usually used to solve binary classification problems. But although the classification speed and performance of TSVM is better than that of primitive support vector machine, TSVM still faces the problem of difficult parameter selection; therefore, to overcome the problem of parameter selection of TSVM, this paper proposes a Chaotic Mapping Dung Beetle Optimization Algorithm-based Twin Support Vector Machine (CMDBO-TSVM) for automatic parameter selection. Due to the uncertainty of the random initialization population of the original Dung Beetle Optimization Algorithm, this paper additionally adds chaotic mapping initialization to improve the Dung Beetle Optimization Algorithm. Experiments on the dataset through this paper show that the classification accuracy of the CMDBO-TSVM has a better performance. [ABSTRACT FROM AUTHOR]

Space layout design is a critical aspect of architectural design, influencing functionality and aesthetics. The inherent combinatorial nature of layout design poses challenges for traditional planning approaches; thus, it demands the exploration of novel methods. This paper presents a novel framework that leverages the potential of deep reinforcement learning (RL) algorithms to optimize space layouts. RL has demonstrated remarkable success in addressing complex decision-making problems, yet its application in the design process remains relatively unexplored. We argue that RL is particularly well-suited for the design process due to its ability to accommodate offline tasks and seamless integration with existing computer-aided design software, effectively acting as a simulator for design exploration. Framing space layout design as an RL problem and employing RL methods allows for the automated exploration of the expansive design space, thereby enhancing the discovery of innovative solutions. This paper also elucidates the synergy between the design process and the RL problem, which opens new avenues for exploring the potential of RL algorithms in design. We aim to foster experimentation and collaboration within the RL and architecture communities. To facilitate our research, we have developed SpaceLayoutGym , an environment specifically designed for space layout design tasks. SpaceLayoutGym serves as a customizable environment that encapsulates the essential elements of the layout design process within an RL framework. To showcase the effectiveness of SpaceLayoutGym and the capabilities of RL as an artificial space layout designer, we employ the Proximal Policy Optimization (PPO) algorithm to train the RL agent in selected design scenarios with both geometrical constraints and topological objectives. The study further extends to contrast the effectiveness of PPO agents with that of genetic algorithms, and also includes a comparative analysis with existing layouts. Our results demonstrate the potential of RL to optimize space layouts, offering a promising direction for the future of artificial intelligence-aided design. [ABSTRACT FROM AUTHOR]

Arthroscopic iliopsoas fractional lengthening (IFL) is a surgical option for the treatment of internal snapping hip syndrome (ISHS) after failing conservative management. Systematic review. A search of PubMed central, National Library of Medicine (MEDLINE) and Scopus databases were performed by two individuals from the date of inception to April 2023. Inclusion criteria were ISHS treated with arthroscopy. Sample size, patient-reported outcomes and complications were recorded for 24 selected papers. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed and registered on PROSPERO database for systematic reviews (CRD42023427466). Thirteen retrospective case series, ten retrospective comparative studies, and one randomized control trial from 2005 to 2022 were reported on 1021 patients who received an iliopsoas fractional lengthening. The extracted data included patient satisfaction, visual analogue scale, the modified Harris hip score and additional outcome measures. All 24 papers reported statistically significant improvements in post-operative patient-reported outcome measures after primary hip arthroscopy and iliopsoas fractional lengthening. However, none of the comparative studies found a statistical benefit in performing IFL. Existing studies lack conclusive evidence on the benefits of Iliopsoas Fractional Lengthening (IFL), especially for competitive athletes, individuals with Femoroacetabular Impingement (FAI), and borderline hip dysplasia. Some research suggests IFL may be a safe addition to hip arthroscopy for Internal Snapping Hip Syndrome, but more comprehensive investigations are needed. Future studies should distinguish between concurrent procedures and develop methods to determine if the psoas muscle is the source of pain, instead of solely attributing it to the joint. [ABSTRACT FROM AUTHOR]

Motivation In the modern era of genomic research, the scientific community is witnessing an explosive growth in the volume of published findings. While this abundance of data offers invaluable insights, it also places a pressing responsibility on genetic professionals and researchers to stay informed about the latest findings and their clinical significance. Genomic variant interpretation is currently facing a challenge in identifying the most up-to-date and relevant scientific papers, while also extracting meaningful information to accelerate the process from clinical assessment to reporting. Computer-aided literature search and summarization can play a pivotal role in this context. By synthesizing complex genomic findings into concise, interpretable summaries, this approach facilitates the translation of extensive genomic datasets into clinically relevant insights. Results To bridge this gap, we present VarChat (varchat.engenome.com), an innovative tool based on generative AI, developed to find and summarize the fragmented scientific literature associated with genomic variants into brief yet informative texts. VarChat provides users with a concise description of specific genetic variants, detailing their impact on related proteins and possible effects on human health. In addition, VarChat offers direct links to related scientific trustable sources, and encourages deeper research. Availability and implementation varchat.engenome.com. [ABSTRACT FROM AUTHOR]

Automated tape placement (ATP) is an important automated process adopted for the fabrication of large composite components. Trajectory planning is the key link of ATP, which directly affects the precision and efficiency of the layup process and the quality of final products. Presently, most existing trajectory optimization methods for ATP focus on smooth surfaces. Nevertheless, as commercial CAD/CAM software generally uses NURBS (Non-uniform Rational B-Splines) for modelling, the difficulty of finding the solution and the low efficiency associated with the calculation process are inevitable. The discrete methods provide alternatives for designing layup trajectories, whereas their accuracy is seldom analysed. Furthermore, a path optimization algorithm for eliminating gap problems while preventing wrinkles on discrete models is rarely reported. In this paper, the adjustment of layup trajectories for ATP is considered on triangular meshes. First, the triangular mesh is reconstructed as a Nagata patch to recover the original geometry with good accuracy. Then, a numerical method for tracing desired paths on the Nagata patch set is provided and the computation efficiency is validated. Next, two optimization methodologies are proposed to improve the layup of composite tapes while avoiding wrinkles. Finally, the presented two strategies are examined on a discrete hyperbolic surface and a discrete freeform surface and some of the results are delivered. [ABSTRACT FROM AUTHOR]

This editorial describes the selection process and the collection of articles selected for presentation for the Proceedings Track of the ISMB 2024 conference. [ABSTRACT FROM AUTHOR]

Biostatistics plays a pivotal role in developing, interpreting and drawing conclusions from clinical, biological and epidemiological data. However, the improper application of statistical methods can lead to erroneous conclusions and misinterpretations. This paper provides a comprehensive examination of the most frequent mistakes encountered in the biostatistical analysis process. We identified and elucidated 10 common errors in biostatistical analysis. These include using the wrong metric to describe data, misinterpreting P -values, misinterpreting the 95% confidence interval, misinterpreting the hazard ratio as an index of prognostic accuracy, ignoring the sample size calculation, misinterpreting analysis by strata in randomized clinical trials, confusing correlation and causation, misunderstanding confounders and mediators, inadequately codifying variables during the data collection, and bias arising when group membership is attributed on the basis of future exposure in retrospective studies. We discuss the implications of these errors and propose some practical strategies to mitigate their impact. By raising awareness of these pitfalls, this paper aims to enhance the rigor and reproducibility of biostatistical analyses, thereby fostering more robust and reliable biomedical research findings. [ABSTRACT FROM AUTHOR]

This paper introduces and investigates a class of noncommutative spacetimes that I will call "T-Minkowski," whose quantum Poincaré group of isometries exhibits unique and physically motivated characteristics. Notably, the coordinates on the Lorentz subgroup remain commutative, while the deformation is confined to the translations (hence the T in the name), which act like an integrable set of vector fields on the Lorentz group. This is similar to Majid's bicrossproduct construction, although my approach allows the description of spacetimes with commutators that include a constant matrix as well as terms that are linear in the coordinates (the resulting structure is that of a centrally extended Lie algebra). Moreover, I require that one can define a covariant braided tensor product representation of the quantum Poincaré group, describing the algebra of N-points. This also implies that a 4D bicovariant differential calculus exists on the noncommutative spacetime. The resulting models can all be described in terms of a numerical triangular R-matrix through RTT relations (as well as RXX, RXY, and RXdX relations for the homogeneous spacetime, the braiding, and the differential calculus). The R-matrices I find are in one-to-one correspondence with the triangular r-matrices on the Poincaré group without quadratic terms in the Lorentz generators. These have been classified, up to automorphisms, by Zakrzewski, and amount to 16 inequivalent models. This paper is the first of a series, focusing on the identification of all the quantum Poincaré groups that are allowed by my assumptions, as well as the associated quantum homogeneous spacetimes, differential calculi, and braiding constructions. [ABSTRACT FROM AUTHOR]

Motivation Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This manual verification ensures the accuracy of the GO annotations based on a comprehensive review of the literature used as evidence, Gene Ontology (GO) terms, and annotated genes in GOA records. While automatic approaches for the detection of semantic inconsistencies in GOA have been developed, they operate within predetermined contexts, lacking the ability to leverage broader evidence, especially relevant domain-specific background knowledge. This paper investigates various types of background knowledge that could improve the detection of prevalent inconsistencies in GOA. In addition, the paper proposes several approaches to integrate background knowledge into the automatic GOA inconsistency detection process. Results We have extended a previously developed GOA inconsistency dataset with several kinds of GOA-related background knowledge, including GeneRIF statements, biological concepts mentioned within evidence texts, GO hierarchy and existing GO annotations of the specific gene. We have proposed several effective approaches to integrate background knowledge as part of the automatic GOA inconsistency detection process. The proposed approaches can improve automatic detection of self-consistency and several of the most prevalent types of inconsistencies.   This is the first study to explore the advantages of utilizing background knowledge and to propose a practical approach to incorporate knowledge in automatic GOA inconsistency detection. We establish a new benchmark for performance on this task. Our methods may be applicable to various tasks that involve incorporating biological background knowledge. Availability and implementation https://github.com/jiyuc/de-inconsistency. [ABSTRACT FROM AUTHOR]

The health-care system and particularly renal replacement therapy has a significant carbon footprint adding to global warming and extreme weather conditions. Improving sustainability has become the focus of national and international working groups. Many reviews underline the need for improvement of sustainability in nephrology, in particular dialysis, and provide recommendations on how to reduce waste, energy, and water consumption. However, how to implement these recommendations, and where to start, is not always clear. This paper summarizes discussions within the 'working group on sustainable nephrology' of the Swiss Society of Nephrology. We do not provide a detailed review of the topic but instead present a practical 10-point action plan to help health-care workers in nephrology make a start and improve the carbon footprint of their dialysis centres. We emphasize the importance of ongoing research, cooperation, and dialogue, and welcome additional ideas from the wider renal community. [ABSTRACT FROM AUTHOR]

As an emerging geophysical exploration technology in recent years, airborne electromagnetic exploration has the advantages of adapting to diverse terrains, wide coverage, and providing a large amount of electromagnetic data, and can be applied to the rapid collection of large amounts of data. Scenarios are often used in fields such as deep geological structures, mineral resource exploration, and environmental engineering research. However, traditional airborne electromagnetic data inversion technology usually takes a long time to process a large amount of airborne electromagnetic data, and it is difficult to remove the noise in the later signals. Therefore, this paper proposes a multi-task learning network structure based on Transformer. By constraining the two network branches of imaging and denoising, a sub-network with simultaneous denoising and imaging is established to process aeronautical electromagnetic data. The noise test set is introduced for testing. This model achieved a 582.61% signal-to-noise ratio improvement in smooth Gaussian noise denoising, and a 129.69% and 112.74% signal-to-noise ratio improvement in non-smooth Gaussian noise and random impulse noise denoising, respectively. The method proposed in this article overcomes the shortcomings of traditional inversion imaging such as slow speed and low resolution, and at the same time eliminates the influence of noise in airborne electromagnetic data. This is of great significance for the application of deep learning in the field of geophysical exploration. [ABSTRACT FROM AUTHOR]

Hydraulic tortuosity is a crucial parameter affecting the movement of fluid in porous media. Currently, researchers have used different methods to construct porous media models and studied the variation of hydraulic tortuosity with porosity. In this paper, we use Monte Carlo random particle, quartet structure generation set (QSGS), and CT-scan reconstruction to construct porous media models with different porosity. The finite element method is used to simulate the fluid passing through the models. The effectiveness of the QSGS algorithm in constructing porous media is verified. The hydraulic tortuosity of the three types of model is computed using the streamline length ratio method, and its variation law with porosity is explored. The results show that the change law of the three models is consistent. The law of power function change is satisfied between the two for all models, which means the increase in porosity causes a decrease in tortuosity. Different models are constructed to explore the effect of tortuosity on permeability. An increase in tortuosity results in a smaller permeability when other conditions are equal. This paper aims to provide effective methods for constructing porous media models and a reference for studying hydraulic tortuosity. [ABSTRACT FROM AUTHOR]

Plateau–plateau transition in the integer quantum Hall effect is a phase transition between metal and insulator. The behavior describing how the width Δ B of the transition changes with temperature and frequency of radiations imposed has been explored extensively. It decreases with the decrease of temperature and frequency, but saturates at a critical temperature or frequency. We have recently discussed the effect of axion dark matter on the saturation. The axion generates radiations under a strong magnetic field in the experiment of the quantum Hall effect. The radiations play a similar role to that of radiations imposed externally. In this paper we discuss in detail how the width behaves in relation to temperature and frequency under the effect of axion dark matter. We show that the axion effect can be observable in low temperatures roughly below 100 mK. According to our detailed analysis of the saturation, we find that the critical frequency of saturation observed in the previous experiment strongly suggests axion mass ma = (0.95∼0.99) × 10−5 eV. [ABSTRACT FROM AUTHOR]

Background and Objectives The United Nations has projected a 218% increase in older people in Sub-Saharan Africa (SSA) between 2019 and 2050, underscoring the need to explore changes that would occur over this time. Longitudinal studies are ideal for studying and proffering solutions to these changes. This review aims to understand the breadth and use of longitudinal studies on aging in the SSA regions, proffering recommendations in preparation for the projected aging population. Research Design and Methods This paper is the third of a four-part series paper of a previous systematic mapping review of aging studies in SSA. We updated the search (between 2021 and 2023) and screened the titles/abstracts and full-text articles by a pair of independent reviewers. Data were extracted using a standardized data-charting form, identifying longitudinal studies in SSA. Results We identified 193 studies leveraging 24 longitudinal study data sets conducted at 28 unique sites. The World Health Organization's Study on Global AGEing and Adult Health (WHO-SAGE) (n  = 59, 30.5%) and Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa (HAALSI) (n  = 51, 26.4%) were the most used longitudinal data sets. Four studies used more than one longitudinal study data set. Eighteen of the longitudinal study data sets were used only in 1–4 studies. Most (n  = 150, 77.7%) of the studies used a cross-sectional analytical approach. Discussion and Implications Longitudinal studies on aging are sparingly being utilized in SSA. Most analyses conducted across the longitudinal data set were cross-sectional, which hindered the understanding of aging changes that occurred over time that could better inform aging policy and interventions. We call for funding bodies, such as WHO-SAGE, to develop funding competitions that focus on conducting longitudinal analyses, such as structural equation modeling, highlighting changes occurring among the aging population in SSA. [ABSTRACT FROM AUTHOR]

A belowground bud bank is a collection of asexual propagules produced by the underground storage organs of geophytes. Renewal through belowground bud banks is the main reproductive strategy of geophytes. The belowground bud bank density reflects the potential renewal capacity of geophyte communities. However, the effects of different perturbation regimes and habitats on the belowground bud bank density of geophytes are not comprehensively understood. Moreover, whether different types of belowground bud banks respond differently to perturbations is still unclear. For this meta-analysis, relevant papers on the effects of environmental perturbations on the belowground bud bank density of geophytes were systematically collected. The cumulative effect size of different perturbation regimes and habitats on belowground bud banks among different bud types was analyzed. Overall, the effect of environmental perturbations on belowground bud bank density was small, which may result from opposite or fluctuating responses of bud banks to different perturbations. Drought negatively affected bud bank density. Environmental perturbations decreased rhizome bud density but increased tiller bud density. In wetlands, perturbations decreased belowground bud bank density. However, no significant effect was found for other habitat types. In general, belowground bud banks of geophytes are highly resistant and resilient. Changes in belowground bud bank density depend on the type of perturbations, the habitats in which plants are distributed and the type of bud banks. [ABSTRACT FROM AUTHOR]

In the context of increasing data scale, contemporary optimization algorithms struggle with cost and complexity in addressing the feature selection (FS) problem. This paper introduces a Harris hawks optimization (HHO) variant, enhanced with a multi-strategy augmentation (CXSHHO), for FS. The CXSHHO incorporates a communication and collaboration strategy (CC) into the baseline HHO, facilitating better information exchange among individuals, thereby expediting algorithmic convergence. Additionally, a directional crossover (DX) component refines the algorithm's ability to thoroughly explore the feature space. Furthermore, the soft-rime strategy (SR) broadens population diversity, enabling stochastic exploration of an extensive decision space and reducing the risk of local optima entrapment. The CXSHHO's global optimization efficacy is demonstrated through experiments on 30 functions from CEC2017, where it outperforms 15 established algorithms. Moreover, the paper presents a novel FS method based on CXSHHO, validated across 18 varied datasets from UCI. The results confirm CXSHHO's effectiveness in identifying subsets of features conducive to classification tasks. [ABSTRACT FROM AUTHOR]

Background Previous meta-analyses have indicated that aspirin could affect breast cancer outcomes, particularly when taken post-diagnostically. However, several recent studies appear to show little to no association between aspirin use and breast cancer mortality, all-cause mortality, or recurrence. Aims This study aims to conduct an updated systematic review and meta-analysis on the associations of pre-diagnostic and post-diagnostic aspirin use with the aforementioned breast cancer outcomes. It also looks, through subgroup analyses and meta-regressions, at a range of variables that could explain the associations between aspirin use and breast cancer outcomes. Results In total, 24 papers and 149 860 patients with breast cancer were included. Pre-diagnostic aspirin use was not associated with breast-cancer-specific mortality (HR 0.98, 95% CI, 0.80-1.20, P =.84) or recurrence (HR 0.94, 95% CI, 0.88-1.02, P =.13). Pre-diagnostic aspirin was associated with non-significantly higher all-cause mortality (HR 1.27, 95% CI, 0.95-1.72, P =.11). Post-diagnostic aspirin was not significantly associated with all-cause mortality (HR 0.87, 95% CI, 0.71-1.07, P =.18) or recurrence (HR 0.89, 95% CI, 0.67-1.16, P =.38). Post-diagnostic aspirin use was significantly associated with lower breast-cancer-specific mortality (HR 0.79, 95% CI, 0.64-0.98, P =.032). Conclusions The only significant association of aspirin with breast cancer outcomes is lower breast-cancer-specific mortality in patients who used aspirin post-diagnostically. However, factors such as selection bias and high inter-study heterogeneity mean that this result should not be treated as conclusive, and more substantial evidence such as that provided by RCTs is needed before any decisions on new clinical uses for aspirin should be made. [ABSTRACT FROM AUTHOR]

The Worldvolume Hybrid Monte Carlo method (WV-HMC method) is a reliable and versatile algorithm towards solving the sign problem. Like the tempered Lefschetz thimble method, this method removes the ergodicity problem inherent in algorithms based on Lefschetz thimbles. In addition to this advantage, the WV-HMC method significantly reduces the computational cost because one need not compute the Jacobian of deformation in generating configurations. A crucial step in this method is the RATTLE algorithm, where the Newton method is used at each molecular dynamics step to project a transported configuration onto a submanifold (worldvolume) in the complex space. In this paper, we simplify the RATTLE algorithm by employing a simplified Newton method (the fixed-point method) along with iterative solvers for orthogonal decompositions of vectors, and show that this algorithm further reduces the computational cost. We also apply this algorithm to the HMC algorithm for the generalized thimble method (GT-HMC method). We perform a numerical test for the convergence of the simplified RATTLE algorithm, and show that the convergence depends on the system size only weakly. The application of this simplified algorithm to various models will be reported in subsequent papers. [ABSTRACT FROM AUTHOR]

Motivation The rapid increase of bio-medical literature makes it harder and harder for scientists to keep pace with the discoveries on which they build their studies. Therefore, computational tools have become more widespread, among which network analysis plays a crucial role in several life-science contexts. Nevertheless, building correct and complete networks about some user-defined biomedical topics on top of the available literature is still challenging. Results We introduce NetMe 2.0, a web-based platform that automatically extracts relevant biomedical entities and their relations from a set of input texts—i.e. in the form of full-text or abstract of PubMed Central's papers, free texts, or PDFs uploaded by users—and models them as a BioMedical Knowledge Graph (BKG). NetMe 2.0 also implements an innovative Retrieval Augmented Generation module (Graph-RAG) that works on top of the relationships modeled by the BKG and allows the distilling of well-formed sentences that explain their content. The experimental results show that NetMe 2.0 can infer comprehensive and reliable biological networks with significant Precision–Recall metrics when compared to state-of-the-art approaches. Availability and implementation https://netme.click/. [ABSTRACT FROM AUTHOR]

In a recent paper [N. Sato, Prog. Theor. Exp. Phys. 2021, 6, 063A01 (2021)] we introduced a generalization of Hamiltonian mechanics to three-dimensional phase spaces in terms of closed 3-forms. This correction addresses an error in the proof of theorem 3, which concerns the existence of a coordinate change transforming a closed 3-form into a constant form. Indeed, invertibility of a 3-form is not sufficient to ensure the existence of a solution Xt to eq. (77) when n > 3. The theorem can be corrected by restricting the class of 3-forms to those that are relevant to generalized Hamiltonian mechanics. Although the new theorem requires a stronger hypothesis, the formulation of dynamical systems with 2 invariants in terms of closed 3-forms, which is the key contribution of the paper, holds. [ABSTRACT FROM AUTHOR]

The discrete cosine transform is a commonly used technique in the field of signal processing that employs cosine basis functions for signal analysis. Traditionally, the regression coefficients of the cosine basis functions are solely based on frequency information. This paper extends the regression coefficients associated with the cosine basis functions to take into account both frequency and time information, not just frequency information alone. This modification results in an ill-posed linear system, which requires regularization to prevent overfitting. To address this, this paper uses shaping regularization, a technique used to stabilize ill-posed problems. By doing so, the absolute values of these extended coefficients, now exhibiting variations in both frequency and time domains, are defined as the time–frequency distribution of that input signal. The numerical experiments conducted to validate this approach demonstrate that the proposed method yields a commendable time–frequency resolution. Consequently, it proves valuable for interpreting seismic data, showcasing its potential for applications in this field. [ABSTRACT FROM AUTHOR]

An accurate pure qP-wave equation in transverse isotropic (TI) media and its efficient and stable implementation are valuable for seismic imaging and inversion. Owing to the complexity of the qP-wave phase velocity expression in anisotropic media, it is difficult to construct such a pure qP-wave equation. In this paper, we combine the Taylor expansion and scalar operator methods to formulate an efficient and stable pure qP-wave equation in TI media. First, the Taylor expansion method is used to convert the square-root term into a fractional term in the qP-wave phase velocity expression. We further improve the approximation accuracy of the resulting equation by a correction technique. Then, the scalar operator is applied to scalarize the equivalent form of the fractional term in the approximated dispersion equation, deriving a simple and easy-to-implement pure qP-wave equation. We use the optical flow method to compute the direction of wave propagation, which improves the calculation accuracy of the scalar operators. Numerical experiments with representative models demonstrate that the new method has higher accuracy and better adaptability to models with strong anisotropy, complex structure, and rapid variation of the tilt angle than previous methods. [ABSTRACT FROM AUTHOR]

The quality factor (Q) is a parameter reflecting the physical properties of reservoirs. Accurate estimation of the quality factor plays an important role in improving the resolution of seismic data. Spectral-ratio method is a widely used traditional method based on the linear least-squares fitting to extract the quality factor, but is sensitive to noise. This is the main reason preventing this method from being widely used. Some supervised deep-learning methods are proposed to extract Q in which the construction of training labels is a key link. The proposed method is based on the spectral-ratio method to create training labels, avoiding errors in generating them. In contrast to the least-squares method, this paper proposes to use a nonlinear regression algorithm based on a fully connected network to fit the spectral logarithmic ratio and frequency. Meanwhile, the empirical equation is applied to constrain prediction results. The proposed method can effectively overcome the influence of noise and improve the accuracy of prediction results. Tests on the synthesized data of vertical seismic profile and common middle profile show that the proposed method has better generalization ability than the spectral-ratio method. Applying the method to the field vertical seismic profile data successfully extracts the quality factor, which can provide effective information for dividing stratigraphic layers. [ABSTRACT FROM AUTHOR]

Accurately predicting the external morphology and internal structure of fractured-vuggy reservoirs is of significant importance for the exploration and development of carbonate oil and gas reservoirs. Conventional seismic prediction methods suffer from serious non-uniqueness and low efficiency, while recent advances in deep learning exhibit strong feature learning capabilities and high generalization. Therefore, this paper proposes an intelligent prediction technique for fault-controlled fracture-vuggy reservoirs based on deep learning methods. The approach involves constructing 3D seismic geological models that conform to the geological characteristics of the study area, simulating seismic wavefield propagation, and combining the interpretation results of fractured-vuggy reservoirs. Training sample datasets are separately established for strike-slip faults, karst caves, and fault-controlled fractured-vuggy reservoir outlines, which are then input into the U-Net model in batches for training. This leads to the creation of a deep learning network model for fault-controlled fractured-vuggy reservoirs. The trained network model is applied to the intelligent identification of fault, karst cave, and fault-controlled fracture-vuggy reservoir outlines using actual seismic data from the Shunbei area. A comparison with traditional methods is conducted, and the experimental results demonstrate that the proposed deep learning approach shows excellent performance in the identification and prediction of fault-controlled fractured-vuggy reservoirs. [ABSTRACT FROM AUTHOR]

Background: Physical activity (PA) promotion in healthcare systems is one of the priority areas highlighted by the World Health Organization, which recognizes that progress has been slow largely due to a lack of awareness and investment while requiring a system-based approach. Community pharmacies are one of the health structures that are more easily accessible to populations, thus constituting an ideal venue for developing health promotion activities. This research aimed to describe PA-enabling interventions developed in community pharmacies by pharmacists. Methods: An electronic search was performed in PubMed, Scopus, Web of Science, Cochrane and reference lists of the different papers until June 2023. Studies were eligible if performed in community pharmacies by pharmacists, focused on interventions aimed at increasing PA levels and if at least one PA-specific outcome was measured at two different time points. Results: We identified 7076 publications in the initial search, plus 31 records identified through backward citation tracking from relevant studies. After an initial screening, 236 were selected for full-text analysis. Of the 29 selected papers, 10 presented a low risk of bias for the measurement of PA levels. PA outcomes were generally self-reported outcomes where the change in terms of the percentage of individuals considered active or who increased PA because of the intervention. Conclusion: Several interventions to improve PA through community pharmacies were found although with a high level of heterogeneity and with only few with a low risk of bias. More targeted research that aims to capture PA levels and support the training of healthcare professionals is needed. [ABSTRACT FROM AUTHOR]

The identification of historical military remains by Unrecovered War Casualties—Army (UWC-A) currently relies on Y-chromosome Short Tandem Repeat (Y-STR) testing when maternal relatives are not available, or when a mitochondrial DNA match does not provide sufficient certainty of identification. However, common Y-STR profiles (using Yfiler™) between sets of remains or families often prevent identification. To resolve these cases, an investigation of additional Y-DNA markers is needed for their potential inclusion into the DNA identification strategy. The number of genetic transmissions between missing soldiers and their living relatives needs to be considered to avoid false exclusions between paternal relatives. Analysis of 236 World War I/II (WWI/II) era pairs of relatives identified up to seven genetic transmissions between WWII soldiers and their living relatives, and nine for WWI. Previous Y-STR meta-analyses were published approximately 10 years ago when rapidly mutating markers were relatively new. This paper reports a contemporary literature review and meta-analysis of 35 studies (which includes 23 studies not previously used in meta-analysis) and 23 commonly used Y-STR's mutation rates to inform the inclusion of additional loci to UWC-A's DNA identification strategy. Meta-analysis found mutation data for a given Y-STR locus could be pooled between studies and that the mutation rates were significantly different between some loci (at P  < 0.05). Based on this meta-analysis, we have identified two additional markers from PowerPlex® Y23 for potential inclusion in UWC-A's identification strategy. Further avenues for potential experimental exploration are discussed. Key points From 236 UWC-A pairs of relatives, we observed up to nine genetic transmissions between WWI soldiers and their living relatives, and seven for WWII. MedCalc® software for meta-analysis utilizing the Freeman–Tukey transformation was run, which analysed 35 published studies and 23 commonly used loci. Previous Y-STR mutation rate meta-analyses are now 10 years old; this paper includes 23 studies that were not included in previous meta-analyses. Through meta-analysis, we identify two markers from PowerPlex® Y23 for potential inclusion in UWC-A's historical remains identification strategy (alongside Yfiler™). We discuss potential next steps for experimental exploration of additional Y-DNA markers. [ABSTRACT FROM AUTHOR]

The article discusses the history and development of the journal Social Cognitive & Affective Neuroscience (SCAN). The author, Matthew D. Lieberman, reflects on the early years of the journal and the challenges faced in establishing a dedicated platform for social and affective neuroscience research. Lieberman announces that he will be stepping down as the Editor-in-Chief in January 2025, marking the end of his tenure. He also outlines some upcoming changes for the journal, including the acceptance of non-neuroscience papers and the publication of a "Tools of the Trade" piece to support the growth of social and affective neuroscience in academic institutions. [Extracted from the article]

Although the excitation-time imaging condition offers a lower memory consumption and higher computational efficiency compared to cross-correlation imaging condition, it has not been widely used in industrial applications because of the accuracy problem of traveltime calculation and the influence of low-wave-number noise. In this paper, we introduce the physics-informed neural network (PINN) algorithm to achieve a high-precision traveltime calculation of the source forward wavefield. Subsequently, we introduce a technique for high-precision wavefield decomposition of the reverse-time wavefield via the optical flow vector, enabling us to realize a correlation-weighted stacking imaging of each wavefield. Model experiments and real data processing show that the proposed traveltime calculation algorithm based on PINN offers high accuracy and good applicability in the excitation-time reverse-time migration imaging of complex models, and correlation-weighted stacking imaging based on optical flow vector-based wavefield separation can significantly suppress the noise with low wave-number and achieve high-precision imaging of complex models. [ABSTRACT FROM AUTHOR]

Least-squares reverse-time migration (LSRTM) has become an advanced technique for complex structures imaging of the subsurface, as it can provide a higher resolution and more balanced amplitude migrated image than conventional reverse-time migration (RTM). However, the intrinsic attenuation of the subsurface introduces amplitude attenuation and phase dispersion of the seismic wavefield, which leads to an inexact inverted image kinematically and dynamically. Moreover, the imperfect geometry, limited bandwidth of seismic data, and inappropriate modeling kernel etc. would inevitably introduce two side-effects in the migrated image, resulting in degradation of LSRTM imaging potential. To alleviate these issues, we present a data-domain sparsity constraint viscoacoustic LSRTM algorithm in this paper. In particular, we use the decoupled constant Q fractional Laplacian viscoacoustic wave equation as the modeling kernel to describe the attenuation effects of the subsurface, while a model constraint constructed in the misfit function via the L1-2 norm is carried out to clear the migrated artifacts and boost the imaging resolution. Thanks to the excellent performance in sparsity, the drawbacks of unconstrained LSRTM can be effectively mitigated by the L1-2 norm-based regularization. In this paper, we adopt the alternating direction of multipliers method to iteratively address the constrained L1-2 minimization problem by implementing a proximal operator, and three synthetic examples are used to evaluate the effectiveness and practicability of the proposed strategy. Migration results prove that the proposed scheme can effectively compensate for the attenuation effects, improve the resolution, and suppress the migration artifacts of inverted images even in the complex imaging situations. [ABSTRACT FROM AUTHOR]