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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]

Background Surveillance of SARS-CoV-2 circulation is mainly based on real-time reverse transcription–polymerase chain reaction, which requires laboratory facilities and cold chain for sample transportation. This is difficult to achieve in remote rural areas of resource-limited settings. The use of dried blood spots shipped at room temperature has shown good efficiency for the detection of arboviral RNA. Using a similar approach, we conducted a study at 3 provincial hospitals in Laos to compare the detection of SARS-CoV-2 from neat and dried spot samples. Methods Between January 2022 and March 2023, patients with respiratory symptoms were recruited. Nasopharyngeal/oropharyngeal swabs in virus transport medium (VTM), dry swabs, saliva, and dried saliva spotted on filter paper were collected. All samples were tested by SARS-CoV-2 real-time reverse transcription–polymerase chain reaction. Results In total, 479 participants were included. The VTM samples tested positive for 288 (60.1%). High positive percent agreements were observed for dry swab (84.8%; 95% CI, 80.2%–88.8%) and saliva (89.2%; 95% CI, 85.1%–92.6%) as compared with VTM. There was a loss of sensitivity when saliva was dried on filter paper (73.6%; 95% CI, 68.1%–78.6%) as compared with saliva. SARS-CoV-2 variant (Delta or Omicron) had no significant impact on the performance of the different sample types. Conclusions Our findings suggest that dry swabs could be a good alternative for sample collection and permit easy shipping at ambient temperature for subsequent viral SARS-CoV-2 RNA purification and molecular investigation. This is a useful tool to consider for a rapid implementation of large-scale surveillance of SARS-CoV-2 in remote areas, which could be extrapolated to other respiratory targets during routine surveillance or in the case of a novel emerging pandemic. [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 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.

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]

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]

This is a correction to: Genetics of Bacteria: a call for papers, G3 Genes|Genomes|Genetics, Volume 14, Issue 7, July 2024, jkae133, https://doi.org/10.1093/g3journal/jkae133This article has been corrected to update the deadline for submissions of papers on the Genetics of Bacteria to December 2, 2024. [Extracted from the article]

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]

A correction is presented to the article "Genetics of Bacteria: a call for papers" published in a previous issue of the periodical.

Bone is a dynamic mineralized tissue that undergoes continuous turnover throughout life. While the general mechanism of bone mineral metabolism is documented, the role of underlying collagen structures in regulating osteoblastic mineral deposition and osteoclastic mineral resorption remains an active research area, partly due to the lack of biomaterial platforms supporting accurate and analytical investigation. The recently introduced osteoid-inspired demineralized bone paper (DBP), prepared by 20-μm thin sectioning of demineralized bovine compact bone, holds promise in addressing this challenge as it preserves the intrinsic bony collagen structure and retains semi-transparency. Here, we report on the impact of collagen structures on modulating osteoblast and osteoclast-driven bone mineral metabolism using vertical and transversal DBPs that exhibit a uniaxially aligned and a concentric ring collagen structure, respectively. Translucent DBP reveals these collagen structures and facilitates longitudinal tracking of mineral deposition and resorption under brightfield microscopy for at least 3 wk. Genetically labeled primary osteogenic cells allow fluorescent monitoring of these cellular processes. Osteoblasts adhere and proliferate following the underlying collagen structures of DBPs. Osteoblastic mineral deposition is significantly higher in vertical DBP than in transversal DBP. Spatiotemporal analysis reveals notably more osteoblast adhesion and faster mineral deposition in vascular regions than in bone regions. Subsequent osteoclastic resorption follows these mineralized collagen structures, directing distinct trench and pit-type resorption patterns. In vertical DBP, trench-type resorption occurs at an 80% frequency, whereas transversal DBP shows 35% trench-type and 65% pit-type resorption. Our studies substantiate the importance of collagen structures in regulating mineral metabolism by osteogenic cells. DBP is expected to serve as an enabling biomaterial platform for studying various aspects of cellular and extracellular bone remodeling biology. Lay Summary: While the mechanisms of bone mineral metabolism are known, the role of underlying collagen structure in directing osteogenic cellular processes remains an active research area due to the difficulty of monitoring these processes inside the bone cavity. In this study, we demonstrate the significance of collagen structure in modulating osteoblastic mineral deposition and osteoclastic mineral resorption using demineralized bone paper (DBP) created by thin slicing of demineralized bovine bone matrix. DBP preserves the natural collagen structure and retains semi-transparency for microscopic imaging, displaying distinct patterns based on vertical and transversal sectioning directions. Osteoblast adhesion and mineral deposition occur following the collagen structure, with vertical DBP supporting higher mineral deposition compared with transversal DBP. Osteoclast mineral resorption also follows these collagen patterns, with vertical DBP exhibiting higher trench-type resorption, while transversal DBP shows more pit-type resorption. This study underscores the importance of collagen structure in directing bone metabolism. We envision that DBP as a valuable tool for studying bone mineral metabolism and related processes. Graphical Abstract [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]

To solve the problems of low logging interpretation accuracy and fluid identification in shale and calcium-bearing formations in the study area, this paper uses the effective medium theory and electric double layer theory, combined with artificial core technology, to carry out the first quantitative study of the influence of calcareous on rock resistivity, and establishes a conductive model for the evaluation of the oil and gas properties of shale and calcium-bearing formations. First, considering the salt exclusion effect of clay pore water and its dispersion distribution, an improved Berg model was established. Compared with the original Berg model, the improved Berg model achieved a higher interpretation accuracy. Second, artificial cores with different calcium and clay content were designed and prepared using artificial core technology. A quantitative formula describing the relationship between the cementation exponent m and the calcium and clay content was established and combined with the improved Berg model to build an effective medium HB resistivity model suitable for shale and calcium-bearing formations. Finally, the hierarchical decomposition crossplot approach was used to identify the calcareous cemented rock layers, then the reservoir parameters were calculated. The new model is applied to the quantitative evaluation of reservoir logging, and the comparison with the oil test results indicates that the new model established in this paper can be applied to the quantitative evaluation of reservoir logging in this area, and the corresponding evaluation techniques provided a good reference for logging interpretation and evaluation in other corresponding areas. [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]

Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models. [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]

The article provides information about the 23rd European Conference on Computational Biology (ECCB2024) held in Turku, Finland. The conference focuses on data and algorithms for health and science and attracts scientists and industry professionals from various disciplines. It showcases cutting-edge developments in computational biology, including systems biology, artificial intelligence, single-cell and spatial technologies, and data integration. The conference features keynote lectures, scientific debates, poster presentations, workshops, and tutorials. The ECCB2024 proceedings include 24 papers selected through a peer-review process, and the conference promotes gender equity and adheres to a code of conduct to ensure a safe and respectful environment for all attendees. [Extracted from the article]

Claims in a recent paper by Taniguchi for the normal-ordered linked-cluster expansion of the modified Marumori boson mapping are based on a flawed argument. Several papers not cited by the author have explained this weakness in detail and concluded that, in order to construct a meaningful boson expansion theory, the unphysical components must be removed from the fermion space before mapping it onto the boson space. [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]

Gender-responsive monitoring and evaluation (M&E) for health and health systems interventions and programs is vital to improve health, health systems, and gender equality outcomes. It can be used to identify and address gender disparities in program participation, outcomes and benefits, as well as ensure that programs are designed and implemented in a way that is inclusive and accessible for all. While gender-responsive M&E is most effective when interventions and programs intentionally integrate a gender lens, it is relevant for all health systems programs and interventions. Within the literature, gender-responsive M&E is defined in different and diverse ways, making it difficult to operationalize. This is compounded by the complexity and multi-faceted nature of gender. Within this methodological musing, we present our evolving approach to gender-responsive M&E which we are operationalizing within the Monitoring for Gender and Equity project. We define gender-responsive M&E as intentionally integrating the needs, rights, preferences of, and power relations among, women and girls, men and boys, and gender minority individuals, as well as across social, political, economic, and health systems in M&E processes. This is done through the integration of different types of gender data and indicators, including: sex- or gender-specific, sex- or gender-disaggregated, sex- or gender-specific/disaggregated which incorporate needs, rights and preferences, and gender power relations and systems indicators. Examples of each of these are included within the paper. Active approaches can also enhance the gender-responsiveness of any M&E activities, including incorporating an intersectional lens and tailoring the types of data and indicators included and processes used to the specific context. Incorporating gender into the programmatic cycle, including M&E, can lead to more fit-for-purpose, effective and equitable programs and interventions. The framework presented in this paper provides an outline of how to do this, enabling the uptake of gender-responsive M&E. [ABSTRACT FROM AUTHOR]

The role of the emergency medical service (EMS) is changing globally as ambulance crews respond to a shifting burden of disease, as well as societal stressors such as violence and inequality. New ways of thinking about how to provide emergency care are required to shift EMS from a role primarily focused on clinical care and transporting patients to hospital. In this paper, we present the experience of the Philippi Project (PP), an innovative community-based model of care developed by front line ambulance crews in a low-income neighbourhood in Cape Town, South Africa. Our insights were developed through observational, interview and document review work, within an overall embedded research approach. Our analysis draws on the everyday health systems resilience (EHSR) framework, which sees resilience as an emergent process that may be stimulated through response to stress and shock. Responses take the form of absorptive, adaptive or transformative strategies and are underpinned by system capacities (cognitive, behavioural and contextual). We consider the PP as a potentially transformative resilience strategy, defined as a new way of working that offered the promise of long-term health system gains. We found that the PP's initial development was supported by a range of system capacity attributes (such as the intentional development of relationships, a sense of collective purpose and creating spaces for constructive sense-making). However, the PP was hard to sustain over time because emergent ways of working were undermined both by other capacity attributes rooted in pre-existing organizational routines and two contextual shocks (Coronavirus and a violent incident). The paper adds a new empirical contribution to the still-small EHSR literature. In addition, the PP experience offers globally relevant lessons for developing community-based models of EMS care. It demonstrates that front line staff can develop creative solutions to their stressful daily realities, but only if space is created and protected. [ABSTRACT FROM AUTHOR]

SUMMARY: Before inverting Moho topography, the traditional Parker–Oldenburg method requires the determination of two important hyperparameters, the average Moho depth and Moho density contrast. The selection of these two hyperparameters will directly affect the inversion results. In this paper, a new method for estimating hyperparameters is proposed which is used to improve the Parker–Oldenburg method. The new method is improved by using simulated annealing to accurately estimate the average Moho depth and Moho density contrast based on the relationship between Moho depths and corresponding gravity anomalies at seismic control points. Synthetic tests show that compared to the improved Bott's method and the trial and error method, our method reduces the error in Moho density contrast and average Moho depth by 0.83 and 1.81 per cent, respectively. In addition, compared with the trial and error method, our method greatly improves the computational efficiency. In a practical example, we apply this method to invert the Moho topography in the northern South China Sea. The inversion results show that the Moho topography in the northern South China Sea ranges from 8.2 to 33 km. The root mean squared error between our Moho topography and the seismic validation points is 0.94 km. Compared with the CRUST 1.0 model, our Moho topography is more accurate. [ABSTRACT FROM AUTHOR]

Predicting the strength of promoters and guiding their directed evolution is a crucial task in synthetic biology. This approach significantly reduces the experimental costs in conventional promoter engineering. Previous studies employing machine learning or deep learning methods have shown some success in this task, but their outcomes were not satisfactory enough, primarily due to the neglect of evolutionary information. In this paper, we introduce the Chaos-Attention net for Promoter Evolution (CAPE) to address the limitations of existing methods. We comprehensively extract evolutionary information within promoters using merged chaos game representation and process the overall information with modified DenseNet and Transformer structures. Our model achieves state-of-the-art results on two kinds of distinct tasks related to prokaryotic promoter strength prediction. The incorporation of evolutionary information enhances the model's accuracy, with transfer learning further extending its adaptability. Furthermore, experimental results confirm CAPE's efficacy in simulating in silico directed evolution of promoters, marking a significant advancement in predictive modeling for prokaryotic promoter strength. Our paper also presents a user-friendly website for the practical implementation of in silico directed evolution on promoters. The source code implemented in this study and the instructions on accessing the website can be found in our GitHub repository https://github.com/BobYHY/CAPE. [ABSTRACT FROM AUTHOR]

With the exponential growth of digital data, there is a pressing need for innovative storage media and techniques. DNA molecules, due to their stability, storage capacity, and density, offer a promising solution for information storage. However, DNA storage also faces numerous challenges, such as complex biochemical constraints and encoding efficiency. This paper presents Explorer , a high-efficiency DNA coding algorithm based on the De Bruijn graph, which leverages its capability to characterize local sequences. Explorer enables coding under various biochemical constraints, such as homopolymers, GC content, and undesired motifs. This paper also introduces Codeformer , a fast decoding algorithm based on the transformer architecture, to further enhance decoding efficiency. Numerical experiments indicate that, compared with other advanced algorithms, Explorer not only achieves stable encoding and decoding under various biochemical constraints but also increases the encoding efficiency and bit rate by ¿10%. Additionally, Codeformer demonstrates the ability to efficiently decode large quantities of DNA sequences. Under different parameter settings, its decoding efficiency exceeds that of traditional algorithms by more than two-fold. When Codeformer is combined with Reed–Solomon code, its decoding accuracy exceeds 99%, making it a good choice for high-speed decoding applications. These advancements are expected to contribute to the development of DNA-based data storage systems and the broader exploration of DNA as a novel information storage medium. [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]

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]

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]

SUMMARY: Atmospheric pressure changes on Earth's surface can deform the solid Earth. Sorrells derived analytical formulae for displacement in a homogeneous, elastic half-space, generated by a moving surface pressure source with speed $c$. Ben-Menahem and Singh derived formulae when an atmospheric P wave impinges on Earth's surface. For a P wave with an incident angle close to the grazing angle, which essentially meant a slow apparent velocity $c_a$ in comparison to P- ($\alpha ^{\prime }$) and S-wave velocities ($\beta ^{\prime }$) in the Earth ($c_a \ll \beta ^{\prime } \lt \alpha ^{\prime }$), they showed that their formulae for solid-Earth deformations become identical with Sorrells' formulae if $c_a$ is replaced by $c$. But this agreement was only for the asymptotic cases ($c_a \ll \beta ^{\prime }$). The first point of this paper is that the agreement of the two solutions extends to non-asymptotic cases, or when $c_a /\beta ^{\prime }$ is not small. The second point is that the angle of incidence in Ben-Menahem and Singh's problem does not have to be the grazing angle. As long as the incident angle exceeds the critical angle of refraction from the P wave in the atmosphere to the S wave in the solid Earth, the formulae for Ben-Menahem and Singh's solution become identical to Sorrell's formulae. The third point is that this solution has two different domains depending on the speed $c$ (or $c_a$) on the surface. When $c/\beta ^{\prime }$ is small, deformations consist of the evanescent waves. When $c$ approaches Rayleigh-wave phase velocity, the driven oscillation in the solid Earth turns into a free oscillation due to resonance and dominates the wavefield. The non-asymptotic analytical solutions may be useful for the initial modelling of seismic deformations by fast-moving sources, such as those generated by shock waves from meteoroids and volcanic eruptions because the condition $c / \beta ^{\prime } \ll 1$ may be violated for such fast-moving sources. [ABSTRACT FROM AUTHOR]

This paper reviews the nature of two quasiparticles, the Majorana fermion and vison, emergent in the Kitaev model, mainly from the theoretical point of view. We demonstrate how the fractionalization of a quantum spin into these two quasiparticles occurs and clarify their properties in the presence and absence of magnetic fields. In addition to discussions on the itinerant nature inherent to Majorana fermions, the quantum dynamics of visons induced by an applied magnetic field is revealed. Fractional quasiparticles manifest themselves in temperature evolutions of thermodynamic quantities, excitation spectra, and transport properties. The formulations for them and calculation results are shown in detail. Based on the results, we present how the signatures of fractional quasiparticles appear in physical quantities. The paper also mentions the recent developments of the experimental and theoretical works of Kitaev-related systems and presents outlooks of studies on these systems. [ABSTRACT FROM AUTHOR]

Motivation Reconstructing evolutionary histories of biological entities, such as genes, cells, organisms, populations, and species, from phenotypic and molecular sequencing data is central to many biological, palaeontological, and biomedical disciplines. Typically, due to uncertainties and incompleteness in data, the true evolutionary history (phylogeny) is challenging to estimate. Statistical modelling approaches address this problem by introducing and studying probability distributions over all possible evolutionary histories, but can also introduce uncertainties due to misspecification. In practice, computational methods are deployed to learn those distributions typically by sampling them. This approach, however, is fundamentally challenging as it requires designing and implementing various statistical methods over a space of phylogenetic trees (or treespace). Although the problem of developing statistics over a treespace has received substantial attention in the literature and numerous breakthroughs have been made, it remains largely unsolved. The challenge of solving this problem is 2-fold: a treespace has nontrivial often counter-intuitive geometry implying that much of classical Euclidean statistics does not immediately apply; many parametrizations of treespace with promising statistical properties are computationally hard, so they cannot be used in data analyses. As a result, there is no single conventional method for estimating even the most fundamental statistics over any treespace, such as mean and variance, and various heuristics are used in practice. Despite the existence of numerous tree summary methods to approximate means of probability distributions over a treespace based on its geometry, and the theoretical promise of this idea, none of the attempts resulted in a practical method for summarizing tree samples. Results In this paper, we present a tree summary method along with useful properties of our chosen treespace while focusing on its impact on phylogenetic analyses of real datasets. We perform an extensive benchmark study and demonstrate that our method outperforms currently most popular methods with respect to a number of important 'quality' statistics. Further, we apply our method to three empirical datasets ranging from cancer evolution to linguistics and find novel insights into corresponding evolutionary problems in all of them. We hence conclude that this treespace is a promising candidate to serve as a foundation for developing statistics over phylogenetic trees analytically, as well as new computational tools for evolutionary data analyses. Availability and implementation An implementation is available at https://github.com/bioDS/Centroid-Code. [ABSTRACT FROM AUTHOR]

Motivation Software is vital for the advancement of biology and medicine. Impact evaluations of scientific software have primarily emphasized traditional citation metrics of associated papers, despite these metrics inadequately capturing the dynamic picture of impact and despite challenges with improper citation. Results To understand how software developers evaluate their tools, we conducted a survey of participants in the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We found that although developers realize the value of more extensive metric collection, they find a lack of funding and time hindering. We also investigated software among this community for how often infrastructure that supports more nontraditional metrics were implemented and how this impacted rates of papers describing usage of the software. We found that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seemed to be associated with increased mention rates. Analysing more diverse metrics can enable developers to better understand user engagement, justify continued funding, identify novel use cases, pinpoint improvement areas, and ultimately amplify their software's impact. Challenges are associated, including distorted or misleading metrics, as well as ethical and security concerns. More attention to nuances involved in capturing impact across the spectrum of biomedical software is needed. For funders and developers, we outline guidance based on experience from our community. By considering how we evaluate software, we can empower developers to create tools that more effectively accelerate biological and medical research progress. Availability and implementation More information about the analysis, as well as access to data and code is available at https://github.com/fhdsl/ITCR_Metrics_manuscript_website. [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]

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]

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]

The contradiction between increasing traffic and the relatively poor roundabout infrastructure is getting stronger. The control and optimization of the macroscopic traffic flow needs to be improved to resolve congestion and safety problems at roundabouts and the connected road network. In order to better understand the gaps and trends in this field, we have systematically reviewed the main research and developments in traffic phenomena, driving behaviour, autonomous vehicles (AVs), intelligent connected vehicles and real vehicle trajectory data sets at roundabouts. The study is based on 388 papers about roundabouts, selected through a comprehensive literature search. The review demonstrates that based on a microscopic perspective, sensing, prediction, decision-making, planning and control aspects of AVs and intelligent connected vehicles can be designed and optimized to fundamentally and significantly improve traffic capacity and driving safety at roundabouts. However, the generation mechanism of traffic conflicts among traffic participants at roundabouts is complex, which is a tremendous challenge for the systematic design of AVs. Therefore, based on naturalistic driving data and machine learning theory, it is an important research direction to build driver models by learning and imitating human driver decision-making and driving behaviours. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) powered drug development has received remarkable attention in recent years. It addresses the limitations of traditional experimental methods that are costly and time-consuming. While there have been many surveys attempting to summarize related research, they only focus on general AI or specific aspects such as natural language processing and graph neural network. Considering the rapid advance on computer vision, using the molecular image to enable AI appears to be a more intuitive and effective approach since each chemical substance has a unique visual representation. In this paper, we provide the first survey on image-based molecular representation for drug development. The survey proposes a taxonomy based on the learning paradigms in computer vision and reviews a large number of corresponding papers, highlighting the contributions of molecular visual representation in drug development. Besides, we discuss the applications, limitations and future directions in the field. We hope this survey could offer valuable insight into the use of image-based molecular representation learning in the context of drug development. [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]