ethicalabs/Research-Intent-Collab huggingface dataset:
A benchmark of the somatic mutation landscape using single-cell and single-molecule whole-genome sequencing

Abstract                                    Accurate detection of somatic mutations in noncancerous cells is critical for studying somatic mosaicism, a process implicated in aging and multiple chronic diseases. However, single-cell and single-molecule DNA sequencing platforms differ in their error profiles, coverage biases, and sensitivity to specific mutation types, complicating cross-platform comparisons. Here, we present                   in vitro                   and                   in silico                   benchmarks to quantify true-positive and false-positive rates in single-cell whole-genome sequencing using Single-Cell Multiple Displacement Amplification, and in single-molecule sequencing using Nanorate Sequencing (NS) and whole-genome NS (WGNS). Using standard cell lines, we show that all three methods detect single-nucleotide variants (sSNVs) and small insertions and deletions (sINDELs) with high accuracy, but differ in genomic coverage and susceptibility to artifacts. Method-specific biases influence mutational signatures and hotspot detection. Applying results of the benchmark to IMR-90 fibroblasts, we estimate higher                   in vitro                   mutation rates using NS than expected from                   in vivo                   data, consistent with potential replication stress and culture-associated DNA damage. Overall, our study highlights the substantial impact of sequencing platform-specific biases on somatic mutation detection and interpretation, and lays the foundation for standardized, cross-platform-comparable analyses of somatic mosaicism in normal human tissues.
Recognition and Protection of Indigenous Peoples’ Rights to Tongkonan Land in Tana Toraja Regency, South Sulawesi Province, Indonesia

This study aims to analyze the position of Tong konan land as part of the Tana Toraja Indigenous Peoples and analyze the Tongkonan land management system. The research method used is empirical juridical sourced from primary data and secondary data by being analyzed qualitatively descriptively. The results of this study show that the status of Tongkonan land ownership in Toraja is not comprehensive due to the need for an approach based on local wisdom values so that Toraja customary law communities can carry out all economic, social, and cultural activities on Tongkonan land and there is also a new legal relationship between customary law communities and Tongkonan land and The loss of legal relations between the two sides caused by the loss of communal value of the tongkonan land . The status of tongkonan land is bound by customary law so that it is subject to applicable customary law, among others, the land is prohibited from being transferred to other parties outside the Tongkonan community (Toraja indigenous people)
Briskchain: Decentralized Function Composition for High-Performance Serverless Computing

Serverless computing allows developers to create workflows for complex tasks through the composition of serverless functions. Current serverless workflow engines rely on master-side patterns which hinder the interaction between serverless functions, causing performance issues. In this paper, a decentralized worker-side pattern is proposed that provides enhanced performance by allowing each workflow function to schedule itself from the current node to the next without having to interact with the master controller. It treats the serverless workflow as a whole unit and uses a locality strategy to optimize performance. A new high-performance workflow engine called BriskChain has been implemented to demonstrate the effectiveness of this approach. Experiments on a real-world application show that BriskChain is able to achieve 2.5 times better performance than a state-of-the-art serverless workflow engine.
Contextualized Diffusion Models for Text-Guided Image and Video Generation

ICLR 2024. Project: https://github.com/YangLing0818/ContextDiff
The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) contribution to CMIP6: investigation of sea-level and ocean climate change in response to CO₂ forcing

The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) aims to investigate the spread in simulations of sea-level and ocean climate change in response to CO2 forcing by atmosphere–ocean general circulation models (AOGCMs). It is particularly motivated by the uncertainties in projections of ocean heat uptake, global-mean sea-level rise due to thermal expansion and the geographical patterns of sea-level change due to ocean density and circulation change. FAFMIP has three tier-1 experiments, in which prescribed surface flux perturbations of momentum, heat and freshwater respectively are applied to the ocean in separate AOGCM simulations. All other conditions are as in the pre-industrial control. The prescribed fields are typical of pattern and magnitude of changes in these fluxes projected by AOGCMs for doubled CO2 concentration. Five groups have tested the experimental design with existing AOGCMs. Their results show diversity in the pattern and magnitude of changes, with some common qualitative features. Heat and water flux perturbation cause the dipole in sea-level change in the North Atlantic, while momentum and heat flux perturbation cause the gradient across the Antarctic Circumpolar Current. The Atlantic meridional overturning circulation (AMOC) declines in response to the heat flux perturbation, and there is a strong positive feedback on this effect due to the consequent cooling of sea-surface temperature in the North Atlantic, which enhances the local heat input to the ocean. The momentum and water flux perturbations do not substantially affect the AMOC. Heat is taken up largely as a passive tracer in the Southern Ocean, which is the region of greatest heat input, while the weakening of the AMOC causes redistribution of heat towards lower latitudes. Future analysis of these and other phenomena with the wider range of CMIP6 FAFMIP AOGCMs will benefit from new diagnostics of temperature and salinity tendencies, which will enable investigation of the model spread in behaviour in terms of physical processes as formulated in the models.
Agricultural monitoring by means of wireless sensor network for large fields within the Internet of Things (IoT) paradigm

Nowadays, the need to use smart devices with connectivity to information clouds is more common, both for control and for receiving information through sensors, for which there are a variety of technical solutions depending on the type of application of the use case, This could be some of the selection criteria, costs, coverage area, amount of data to be transmitted, etc. The present work consists of a hybrid sensor network solution for agriculture, where they communicate with LoRa technology and this information is sent to an information cloud through Sigfox technology, in this way by only paying a subscription of a device. you can have many low power sensors working simultaneously. A data protection system has been added to the LoRa communication of these sensors, to prevent any intruder from deciphering and interfering with the communication of the sensors, thus making a more robust and reliable system. In addition, a protocol for linking and light data transmission is proposed, which supports the reduction of energy consumption, which is self-sustaining thanks to the inclusion of an energy harvesting system by solar energy, thus making the system work for consecutive years of energy autonomy. The coverage and operation tests have been carried out in urban and rural environments, guaranteeing correct operation at a maximum of 8 km in places without intermediate obstacles between the concentrator and the sensor, 1.5 km in rural environments with many obstacles and 500 m in urban environments. . The data collected is stored in the cloud on the Google Cloud platform, from where it can be consulted anywhere in the world by the final user.
Reconstructing the inflationary power spectrum from cosmic microwave background radiation data

The cosmic microwave background radiation (CMBR) holds information about almost all the fundamental cosmological parameters, and by performing a likelihood analysis of high precision CMBR fluctuation data, these parameters can be inferred. However, this analysis relies on assumptions about the initial power spectrum, which is usually taken to be a featureless power law, $P(k)\ensuremath{\propto}{k}^{{n}_{s}\ensuremath{-}1}.$ Many inflationary models predict power spectra with non-power-law features. We discuss the possibility for detecting such features by describing the power spectrum as bins in k space. This method for power spectrum reconstruction is demonstrated in practice by performing likelihood optimization on synthetic spectra, and the difficulties arising from reconstructing smooth features using discontinuous bins are discussed in detail.
Visualization Based on Concept Maps: An Efficient Way to Knowledge Sharing and Knowledge Discovery in E-science Environment

Knowledge visualization is a fascinating study field that has received more attention. In e-Science environment, people pay more attention to the scientific visualization and data visualization. Visualization about information exchange and knowledge sharing is rare to be mentioned. If the concept maps, a visualization tool, are used to help members in e-Science environment to describe their structure of knowledge in communication, it can facilitate thinking together and the collective wisdom, it also can promote the knowledge sharing and the creation of the new knowledge. In this paper, we discuss the application of concept maps and we promote an application model in e-Science environment.
Psychological intervention in women victims of childhood sexual abuse: a randomized controlled clinical trial comparing EMDR psychotherapy and trauma-focused cognitive behavioral therapy

IntroductionChildhood sexual abuse persists as a painful societal reality, necessitating responses from institutions and healthcare professionals to prevent and address its severe long-term consequences in victims. This study implements an intervention comprising two psychotherapeutic approaches recommended by the WHO and international clinical guidelines for addressing short-, medium-, and long-term posttraumatic symptomatology: Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) and Eye Movement Desensitization and Reprocessing (EMDR). Both approaches are adapted from group formats for implementation in small online groups via Zoom.MethodsThe impact of both therapeutic approaches on trauma improvement was assessed in a sample of 19 women who were victims of childhood sexual abuse through a Randomized Clinical Trial comparing EMDR Psychotherapy and Trauma-Focused Cognitive Behavioral Therapy after a baseline period. Intra and inter comparison were made using statistics appropriate to the sample.ResultsBoth therapeutic approaches significantly reduced symptomatology across various evaluated variables, suggesting their efficacy in improving the quality of life for these individuals. Following CBT-FT treatment, patients exhibited enhanced emotional regulation, reduced reexperiencing, and avoidance. The EMDR group, utilizing the G-TEP group protocol, significantly improved dissociation, along with other crucial clinical variables and the perception of quality of life.DiscussionAlthough the limitations of this study must be taken into account due to the size of the sample and the lack of long-term follow-up, the results align with existing scientific literature, underscoring the benefits of trauma-focused psychological treatments. The online group format appears promising for enhancing the accessibility of psychological treatment for these women. Furthermore, the differential outcomes of each treatment support recent research advocating for the inclusion of both approaches for individuals with trauma-related symptomatology.Ethics and disseminationThe study has been approved by the Ethics Committee of the Valencian International University (VIU) (Valencia, Spain) (Ref. CEID2021_07). The results will be submitted for publication in peer-reviewed journals and disseminated to the scientific community.Clinical trial registrationhttps://clinicaltrials.gov/ct2/show/NCT04813224, identifier NCT04813224.
Analysis of Patient Safety Incident reporting system as an indicator of quality nursing in critical care units in KwaZulu-Natal, South Africa

Patient Safety Incidents occur frequently in critical care units, contribute to patient harm, compromise quality of patient care and increase healthcare costs. It is essential that Patient Safety Incidents in critical care units are continually measured to plan for quality improvement interventions.To analyse Patient Safety Incident reporting system, including the evidence of types, frequencies, and patient outcomes of reported incidents in critical care units.The study was conducted in the critical care units of ten hospitals of eThekwini district, in KwaZulu-Natal, South Africa.A quantitative approach using a descriptive cross sectional survey was adopted to collect data from the registered nurses working in critical care units of randomly selected hospitals. Self-administered questionnaires were distributed to 270 registered nurses of which 224 (83%) returned completed questionnaires. A descriptive statistical analysis was initially conducted, then the Pearson Chi-square test was performed between the participating hospitals.One thousand and seventeen (n = 1017) incidents in ten hospitals were self-reported. Of these incidents, 18% (n = 70) were insignificant, 35% (n = 90) minor, 25% (n = 75) moderate, 12% (n = 32) major and 10% (n = 26) catastrophic. Patient Safety Incidents were classified into six categories: (a) Hospital-related incidents (42% [n = 416]); (b) Patient care-related incidents (30% [n = 310]); (c) (Death 12% [n = 124]); (d) Medication-related incidents, (7% [n = 75]); (e) Blood product-related incidents (5% [n = 51]) and (f) Procedure-related incidents (4% [n = 41]).This study's findings indicating 1017 Patient Safety Incidents of predominantly serious nature, (47% considering moderate, major and catastrophic) are a cause for concern.
Quality of Health Insurance Coverage and Access to Care for Children in Low-Income Families

An increasing diversity of children's health coverage options under the US Patient Protection and Affordable Care Act, together with uncertainty regarding reauthorization of the Children's Health Insurance Program (CHIP) beyond 2017, merits renewed attention on the quality of these options for children.To compare health care access, quality, and cost outcomes by insurance type (Medicaid, CHIP, private, and uninsured) for children in households with low to moderate incomes.A repeated cross-sectional analysis was conducted using data from the 2003, 2007, and 2011-2012 US National Surveys of Children's Health, comprising 80,655 children 17 years or younger, weighted to 67 million children nationally, with household incomes between 100% and 300% of the federal poverty level. Multivariable logistic regression models compared caregiver-reported outcomes across insurance types. Analysis was conducted between July 14, 2014, and May 6, 2015.Insurance type was ascertained using a caregiver-reported measure of insurance status and each household's poverty status (percentage of the federal poverty level).Caregiver-reported outcomes related to access to primary and specialty care, unmet needs, out-of-pocket costs, care coordination, and satisfaction with care.Among the 80,655 children, 51,123 (57.3%) had private insurance, 11,853 (13.6%) had Medicaid, 9554 (18.4%) had CHIP, and 8125 (10.8%) were uninsured. In a multivariable logistic regression model (with results reported as adjusted probabilities [95% CIs]), children insured by Medicaid and CHIP were significantly more likely to receive a preventive medical (Medicaid, 88% [86%-89%]; P < .01; CHIP, 88% [87%-89%]; P < .01) and dental (Medicaid, 80% [78%-81%]; P < .01; CHIP, 77% [76%-79%]; P < .01) visits than were privately insured children (medical, 83% [82%-84%]; dental, 73% [72%-74%]). Children with all insurance types experienced challenges in access to specialty care, with caregivers of children insured by CHIP reporting the highest rates of difficulty accessing specialty care (28% [24%-32%]), problems obtaining a referral (23% [18%-29%]), and frustration obtaining health care services (26% [23%-28%]). These challenges were also magnified for privately insured children with special health care needs, whose caregivers reported significantly greater problems accessing specialty care (29% [26%-33%]) and frustration obtaining health care services (36% [32%-41%]) than did caregivers of children insured by Medicaid, and a lower likelihood of insurance always meeting the child's needs (63% [60%-67%]) than children insured by Medicaid or CHIP. Caregivers of privately insured children were also significantly more likely to experience out-of-pocket costs (77% [75%-78%]) than were caregivers of children insured by Medicaid (26% [23%-28%]; P < .01) or CHIP (38% [35%-40%]; P < .01).This examination of caregiver experiences across insurance types revealed important differences that can help guide future policymaking regarding coverage for families with low to moderate incomes.
A sonic One Health framework: characterizing health of the environment, animals, and humans using a soundscape perspective

Abstract                We present a sonic One Health framework with environmental, animal and human health integration centering on understanding positive and negative effects from the sounds of nature or the sounds from society. Our framework includes a sonic One Health system that describes sound and health; a sonic One Health network that describes specialists needed in our sonic One Health system; a human sonic effects model that reviews how sounds effect the auditory, neurological, cardiovascular, hormonal, and immunological systems; and an integrated set of sonic One Health remediation pathways. We review the literature in noise and health, environmental psychology as it relates to restorative effects of being in nature, and approaches to monitoring sound and perception. We also present three brief case studies, describe future directions for research across eight themes, discuss novel and challenging aspects of our framework, and describe the role that AI may play in our framework.
Epidemiologic characteristics of infectious disease-related public health emergencies in Guangdong province, 2014 – 2023: a surveillance data analysis

ObjectiveTo analyze the epidemiological characteristics of infectious disease-related public health emergencies in Guangdong Province from 2014 to 2023, for the development of effective infectious disease prevention and control strategies and rational allocation of health resources. MethodsData on infectious disease public health emergencies reported in Guangdong province from 2014 to 2023 were obtained from the Public Health Emergency Management Information System of the Chinese Center for Disease Control and Prevention. Descriptive data analysis was performed using WPS 2019 and R 4.0.3. ResultsExcluding COVID-19 outbreaks, a total of 3 783 infectious disease-related public health emergencies were reported in Guangdong province from 2014 to 2023, of which 2 369 (62.62%) occurred in schools. The top five infectious diseases causing these public health emergencies were influenza (accounting for 18.82% of all reported emergencies), other infectious diarrheal diseases (17.42%), dengue fever (17.23%), varicella (17.00%), and hand, foot, and mouth disease (6.93%). The highest proportion of events (61.80%) were reported in the Pearl River Delta region. Among the events reported in the the Eastern Wing region, 66.09% were caused by vector-borne and zoonotic diseases and the proportion was significantly different from those in other regions. Among the four major categories of infectious diseases causing these events, vector-borne and zoonotic diseases had the highest proportion (33.10%). ConclusionsThere were regional differences in the epidemiological characteristics of infectious disease-related public health emergencies in Guangdong Province from 2014 to 2023, suggesting that region- and disease-specific measures should be developed for the prevention and control of infectious disease-related public health emergencies in the province.
The Practice and Ethics of Participatory Visual Methods for Community Engagement in Public Health and Health Science

This handbook provides guidelines on the practice and ethics of participatory visual methods (PVM) with emphasis on their use in low and middle income countries (LMIC) for community and public engagement in health and health science. The guidelines are drawn from the hands-on experience of the authors and the insights they have gained as PVM practitioners working with these methodologies in LMIC for 10 years or more. The handbook has been developed for use by engagement practitioners who are relatively new to the field of PVM and want to learn more about what they are and how to work with them. It also aims to support health science researchers who wish to include visual methods when engaging local communities and wider publics in their work. The handbook does not explore the theory behind PVM or provide an in-depth review of the literature. For each method that is described the pioneering work in the field is cited, along with other suggested reading, should you wish to learn more. The content of this handbook is also available as an online course on The Global Health Network site:https://globalhealthtrainingcentre.tghn.org/practice-and-ethics-participatory-visualmethods-community-engagement-public-health-and-health-science/.
Variational Quantum Circuit-Based Quantum Machine Learning Approach for Predicting Corrosion Inhibition Efficiency of Expired Pharmaceuticals

This study examines the potential of quantum machine learning (QML) to predict the corrosion inhibition capacity of expired pharmaceutical compounds. The investigation employs a QSPR model, using features generated from density functional theory (DFT) calculations as input. At the same time, corrosion inhibition efficiency (CIE) values obtained from experimental data serve as the target output. The VQC model demonstrates varied performance across evaluation metrics, especially with encoding and ansatz design. The model achieves fine scores in evaluation metrics, with root mean square error (RMSE) of 6.15, mean absolute error (MAE) of 5.63, and mean absolute deviation (MAD) of 5.50. The research underscores the significance of larger datasets for enhancing predictive accuracy and points to QML's potential in exploring anti-corrosion materials. Although there are some limitations, this study provides a foundational framework for using QML to predict anti-corrosive properties.