Public Availability Research Articles

CAD-ASTRA: a versatile and efficient mesh projector for X-ray tomography with the ASTRA-toolbox.

Accurate and fast simulation of X-ray projection data from mesh models has many applications in academia and industry, ranging from 3D X-ray computed tomography (XCT) reconstruction algorithms to radiograph-based object inspection and quality control. While software tools for the simulation of X-ray projection data from mesh models are available, they lack either performance, public availability, flexibility to implement non-standard scanning geometries, or easy integration with existing 3D XCT software. In this paper, we propose CAD-ASTRA, a highly versatile toolbox for fast simulation of X-ray projection data from mesh models. While fully functional as standalone software, it is also compatible with the ASTRA toolbox, an open-source toolbox for flexible tomographic reconstruction. CAD-ASTRA provides three specialized GPU projectors based on state-of-the-art algorithms for 3D rendering, implemented using the NVIDIA CUDA Toolkit and the OptiX engine. First, it enables X-ray phase contrast simulations by modeling refraction through ray tracing. Second, it allows the back-propagation of projective errors to mesh vertices, enabling immediate application in mesh reconstruction, deep learning, and other optimization routines. Finally, CAD-ASTRA allows simulation of polychromatic X-ray projections from heterogeneous objects with a source of finite focal spot size. Use cases on a CAD-based inspection task, a phase contrast experiment, a combined mesh-volumetric data projection, and a mesh reconstruction demonstrate the wide applicability of CAD-ASTRA.

Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment

Remotely sensed imagery has increased dramatically in quantity and public availability. However, automated, large-scale analysis of such imagery is hindered by a lack of the annotations necessary to train and test machine learning algorithms. In this study, we address this shortcoming with respect to above-ground storage tanks (ASTs) that are used in a wide variety of industries. We annotated available high-resolution, remotely sensed imagery to develop an original, publicly available multi-class dataset of ASTs. This dataset includes geospatial coordinates, border vertices, diameters, and orthorectified imagery for over 130,000 ASTs from five labeled classes (external floating roof tanks, closed roof tanks, spherical pressure tanks, sedimentation tanks, and water towers) across the contiguous United States. This dataset can be used directly or to train machine learning algorithms for large-scale risk and hazard assessment, production and capacity estimation, and infrastructure evaluation.

States of Australia’s agri-environment: visual extractions from degraded landscapes

Under perpetual growth-geared economic, governance and sociocultural systems, continued increases in agricultural output are necessary to feed an ever-expanding human population, as well as neoliberal appetites for financial profitability. These translate to intensifying and expanding agri landscapes. Yet agribusiness industries routinely seek to obfuscate visual tangibility of their production practices and impacts from public view. A gap exists in public availability of high-resolution imagery depicting Australian agriculture’s industrial scales, degraded lands and landscape impacts. This visualisation endeavour is situated between the blurry and pixelated resolution of freely available agricultural satellite imagery, and agri- marketings’ frequently glossy, colour-saturated and romanticised depictions from on-ground photography and video. Positioned between these two purviews, the drone-eye-view provides sharp and incisive imagery contributing new insights into agri- landscapes and practices. This visual essay draws from drone imagery captured during continental-scale field research spanning all Australian states and territories, all commodities and major industries, and all scales of commercial farming. As Australian agribusiness is landscape and resource extractive in its geometric pattern arrays, transmuted images are extricated from aerial stills and videos into compositional strips. Together with the poetic and interpretative text, the work reveals industrial and degraded landscapes at nested scales, links foods with their production contexts, and is suggestive of the state of Australia’s agricultural environment.

Availability of Access, Watch, and Reserve groups of essential antibiotics: a cross-sectional survey.

Lower-middle income countries face drastic challenges in Access to essential medicines. Data regarding Pakistan is scarce with no comprehensive study in this regard. The objectives of the study are to document and compare public and private sector availability of all essential antibiotics as well as to conduct a comparison among the AWaRe groups. The study analyzed 103 essential antibiotics comprising 51 Access, 29 Watch, 6 Reserve, and 17 anti-tuberculosis drugs from 15th August to 10th September 2020 in Lahore, Pakistan. It included on-spot physical availability and availability trend surveys. The survey sites included five public tertiary care hospitals with one as anchor and four randomly selected. Their hospital pharmacies and one randomly selected private retail pharmacy from the vicinity each hospital comprised the ten sampling sites. Percentage availability for each antibiotic was categorized as high (>80%), fairly high (50-80%), low (30-<50%), very low (<30->0%), and not available (0%). The mean percentage on-spot availability was 23.76% ± 5.19 (14-25%) for public facilities and 59.20% ± 4.45 (54-66%) for private sector retail pharmacies. The overall percentage of available essential antibiotics varied significantly (p** < 0.001) in public and private sector sampling sites. Except for the Watch group, all other groups showed the mode of 0% availability. A significant difference (p**** < 0.00001) was seen in percentage availability by Access, Watch, Reserve, and anti-TB-all groups of essential antibiotics. The availability trend survey revealed a list of 18 medicines as 'as never been available', and five medicines were 'not available for 5 years or more than 5 years.' Fourteen medicines as 'never been heard.' Non-availability of essential medicines is a significant public health challenge at public-sector facilities in Pakistan. It was observed that a number of essential antibiotics were not available in both public and private sectors. A number of corrective strategies are required. This includes the engagement of stakeholder and government bodies. This can help to improve supply chain barriers.

A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Dataset for Automatic Region-based Coronary Artery Disease Diagnostics Using X-Ray Angiography Images

X-ray coronary angiography is the most common tool for the diagnosis and treatment of coronary artery disease. It involves the injection of contrast agents into coronary vessels using a catheter to highlight the coronary vessel structure. Typically, multiple 2D X-ray projections are recorded from different angles to improve visualization. Recent advances in the development of deep-learning-based tools promise significant improvement in diagnosing and treating coronary artery disease. However, the limited public availability of annotated X-ray coronary angiography image datasets presents a challenge for objective assessment and comparison of existing tools and the development of novel methods. To address this challenge, we introduce a novel ARCADE dataset with 2 objectives: coronary vessel classification and stenosis detection. Each objective contains 1500 expert-labeled X-ray coronary angiography images representing: i) coronary artery segments; and ii) the locations of stenotic plaques. These datasets will serve as a benchmark for developing new methods and assessing existing approaches for the automated diagnosis and risk assessment of coronary artery disease.

The Advanced BRain Imaging on ageing and Memory (ABRIM) data collection: Study design, data processing, and rationale.

To understand the neurocognitive mechanisms that underlie heterogeneity in cognitive ageing, recent scientific efforts have led to a growing public availability of imaging cohort data. The Advanced BRain Imaging on ageing and Memory (ABRIM) project aims to add to these existing datasets by taking an adult lifespan approach to provide a cross-sectional, normative database with a particular focus on connectivity, myelinization and iron content of the brain in concurrence with cognitive functioning, mechanisms of reserve, and sleep-wake rhythms. ABRIM freely shares MRI and behavioural data from 295 participants between 18-80 years, stratified by age decade and sex (median age 52, IQR 36-66, 53.20% females). The ABRIM MRI collection consists of both the raw and pre-processed structural and functional MRI data to facilitate data usage among both expert and non-expert users. The ABRIM behavioural collection includes measures of cognitive functioning (i.e., global cognition, processing speed, executive functions, and memory), proxy measures of cognitive reserve (e.g., educational attainment, verbal intelligence, and occupational complexity), and various self-reported questionnaires (e.g., on depressive symptoms, pain, and the use of memory strategies in daily life and during a memory task). In a sub-sample (n = 120), we recorded sleep-wake rhythms using an actigraphy device (Actiwatch 2, Philips Respironics) for a period of 7 consecutive days. Here, we provide an in-depth description of our study protocol, pre-processing pipelines, and data availability. ABRIM provides a cross-sectional database on healthy participants throughout the adult lifespan, including numerous parameters relevant to improve our understanding of cognitive ageing. Therefore, ABRIM enables researchers to model the advanced imaging parameters and cognitive topologies as a function of age, identify the normal range of values of such parameters, and to further investigate the diverse mechanisms of reserve and resilience.

Assessment of the gender gap in urology industry payments: An Open Payments Program data analysis.

The Open Payments Program (OPP), established in 2013 under the Sunshine Act, mandated medical device and pharmaceutical manufacturers to submit records of financial incentives given to physicians for public availability. The study aims to characterize the gap in real general and real research payments between man and woman urologists. The study sample included all urologists in the United States who received at least one general or research payment in the OPP database from 2015 to 2021. Recipients were identified using the National Provider Identifier and National Downloadable File datasets. Payments were analyzed by geography, year, payment type, and years since graduation. Multivariable analysis on odds of being in above the median in terms of money received was done with gender as a covariate. This analysis was also completed for all academic urologists. There was a total of 15,980 urologists; 13.6% were woman, and 86.4% were man. Compared to man urologists, woman urologists were less likely to be in the top half of total payments received (odds ratio [OR] 0.62) when adjusted for other variables. When looking at academic urologists, 18.1% were woman and 81.9% were man. However, woman academic urologists were even less likely to be in the top 50% of payments received (OR 0.55). This study is the first to characterize the difference in industry payments between man and woman urologists. The results should be utilized to educate physicians and industry, in order to achieve equitable engagement and funding for woman urologists.

Beneficial Ownership – Demand for Transparency, Threat to Privacy

The basic idea behind establishing the register of beneficial owners is to increase the transparency and accessibility of data on beneficial ownership of companies and other legal entities with the aim of ensuring the public availability of data on domestic and foreign natural and legal persons. However, the possibility of the data being accessible to the general public instead of to persons or organizations that can demonstrate a legitimate interest raised the issue of violating the principles of respect for private or family life and the protection of personal data. Consequently, this raises the question of drawing the line between contributing to the common good and fighting against money laundering and terrorist financing, on the one hand, and protecting personal data, with the possibility of their misuse, on the other. A balance as well as a response to the possibility of setting soft limits of legitimate interest that would result in the achievement of all set goals was sought in the practice of the European Court of Justice. One of the legislative solutions regarding the extent of access to data on beneficial owners for the entire public, along with the establishment of different types of registers in order to prevent money laundering and terrorist financing, is described using the example of Croatia.

Persistent effects of colonial land tenure institutions: Village-level evidence from India

This paper estimates the causal impact of land revenue institutions on long run rural development using a Spatial Regression Discontinuity framework on a new village level data set from colonial India. An early 19th century historical quirk meant that villages in close geographical proximity were assigned to different property rights systems — some falling under landlords and others under the government. Villages that were assigned to landlords in the colonial era have a higher poverty rate and lower consumption per capita in 2012. Village census data from 1961 to 2011 shows that historically rooted characteristics in landlord villages prevented them from accessing Green Revolution technologies. Analysis demonstrates that non-landlord, cultivator villages secured preferential access to public investment in the early decades. Despite some convergence in public goods availability, lower private wealth and investment in landlord villages causes continuing spatial inequalities.

Genetic basis and repeatability for desiccation resistance in Drosophila melanogaster (Diptera: Drosophilidae).

Dehydration is a stress factor for organisms inhabiting natural habitats where water is scarce. Thus, it may be expected that species facing arid environments will develop mechanisms that maximize resistance to desiccation. Insects are excellent models for studying the effects of dehydration as well as the mechanisms and processes that prevent water loss since the effect of desiccation is greater due to the higher area/volume ratio than larger animals. Even though physiological and behavioral mechanisms to cope with desiccation are being understood, the genetic basis underlying the mechanisms related to variation in desiccation resistance and the context-dependent effect remain unsolved. Here we analyze the genetic bases of desiccation resistance in Drosophila melanogaster and identify candidate genes that underlie trait variation. Our quantitative genetic analysis of desiccation resistance revealed sexual dimorphism and extensive genetic variation. The phenotype-genotype association analyses (GWAS) identified 71 candidate genes responsible for total phenotypic variation in desiccation resistance. Half of these candidate genes were sex-specific suggesting that the genetic architecture underlying this adaptive trait differs between males and females. Moreover, the public availability of desiccation data analyzed on the same lines but in a different lab allows us to investigate the reliability and repeatability of results obtained in independent screens. Our survey indicates a pervasive micro-environment lab-dependent effect since we did not detect overlap in the sets of genes affecting desiccation resistance identified between labs.

Electronic Shooting Simulator Family "STrIzh": Implementation Levels and Free Software Structure

Development of rifle electronic simulators (i.e. for hand weapons and not using ammunition) is an important task, since the production of any type of small arms according to regulatory documents also requires the production of a simulator to instill aiming and firing skills. A family of electronic shooting simulators "STrIzh" of four levels of implementation: initial, basic, professional and special is described. Structural diagrams of different configurations are given, functional purpose and capabilities of each level of simulators are shown. The initial level allows independent assemble the simulator from publicly available elements (laptop, webcam, weapon layouts, IR LEDs), which can contribute to widespread use both in schools and at home, but is low in accuracy and manufacturabili (requires daily calibration). The basic level also allows independent assemble the simulator, and less publicly available elements (laptop, projector, mockup weapons, laser emitters, FHD camera), which allows to be used both in schools and in DOSAAF structures, and has acceptable accuracy and manufacturability (a weekly calibration is enough, but assembling a mock-up weapon with laser emitters requires adjustments). It is recommended to limit the special level to virtual reality simulators, including a helmet with a smartphone and a mockup of a weapon with its smartphone which also contributes to its public availability and widespread use. The simulator software algorithm should fully support all implementation levels with different configurations and include a multimedia shooting training system. Mathematical models of external ballistics of thrown equipment for Kalashnikov assault rifle, Makarov pistol, hand-held anti-tank grenade launcher 7 products are described in detail, taking into account changes in atmospheric factors (temperature, air pressure, wind force) and dispersion of various types of ammunition. The above review of rifle simulators and their experience revealed the main trends of improvement – use of virtual reality and training not only direct shooting skills, but also training in the eligibility of the use of weapons, safe handling of them and even tactical interaction in the group.

Chronic disease outcome metadata from German observational studies – public availability and FAIR principles

Metadata from epidemiological studies, including chronic disease outcome metadata (CDOM), are important to be findable to allow interpretability and reusability. We propose a comprehensive metadata schema and used it to assess public availability and findability of CDOM from German population-based observational studies participating in the consortium National Research Data Infrastructure for Personal Health Data (NFDI4Health). Additionally, principal investigators from the included studies completed a checklist evaluating consistency with FAIR principles (Findability, Accessibility, Interoperability, Reusability) within their studies. Overall, six of sixteen studies had complete publicly available CDOM. The most frequent CDOM source was scientific publications and the most frequently missing metadata were availability of codes of the International Classification of Diseases, Tenth Revision (ICD-10). Principal investigators’ main perceived barriers for consistency with FAIR principles were limited human and financial resources. Our results reveal that CDOM from German population-based studies have incomplete availability and limited findability. There is a need to make CDOM publicly available in searchable platforms or metadata catalogues to improve their FAIRness, which requires human and financial resources.

Disparities in access to healthcare services in a regional neonatal transport network

IntroductionThe disproportionate distribution of healthcare resources in the U.S. leads to an annual estimated 68,000 critically-ill newborns transported to regional centers for intensive care. As described in the Systems Engineering Initiative for Patient Safety (SEIPS) 3.0 framework, patients with limited access to healthcare experience multiple healthcare settings in their journey to specialty care. Little is known about community resources for transported neonatal patients in the Pacific Northwest region. MethodsTransport data from January 2014–December 2019 identified referral zip codes for transfers to a regional neonatal intensive care unit (NICU). Using the SEIPS 3.0 framework to explore the journey of families experiencing neonatal complications, we linked referral zip codes to data on healthcare and resource access including: Health Transportation Shortage Index (HTSI); US Census Small Area Income and Poverty Estimates; public transportation availability; Health Professional Shortage Area (HPSA) and number of Federally Qualified Health Centers. Results1834 neonatal transports from 75 zip codes were analyzed. Forty percent of patients were transferred from a HPSA, one-quarter from areas with limited public transit availability (28%) and household internet access (24%). Almost all infants transferred from a high HTSI area (96%) were transported by air. ConclusionMany patients were referred from communities with high poverty and limited services. Improving patient outcomes requires population and policy level recommendations and care coordination between healthcare organizations and community services. Considering the patient family perspective within the SEIPS 3.0 framework will ensure patient centered care and an inclusive experience for families transitioning through healthcare settings.

From news deserts to news resilience: Analysis of media in depopulated areas

This article delves into the established concept of news deserts within the broader field of Communication Geography, aiming to propose a comprehensive methodological framework for exploring the absence of local media. This approach considers crucial variables including geography, demography, public services, and infrastructure, all contributing to the configuration of the media landscape. By focusing on the depopulated region of Castile-La Mancha in Spain, we examine the relationship between the presence (or lack) of media outlets and factors such as population density, average per capita income, business activity, public service availability, accessibility, and other pertinent elements. Our analysis reveals that the media structure mirrors demographic and concentration patterns, primarily favoring central areas while leaving peripheral areas virtually isolated. This is despite the fact that the majority of the region has public services such as libraries, health centers, and telecommunications infrastructure. Income and business activity do not seem to correlate with a greater media presence. These findings underscore the necessity for sub-national studies with a specific focus on rural environments, accounting for territorial diversity, in order to better understand what occurs happens in areas considered to be news deserts.

Reliability and validity of a widely-available AI tool for assessment of stress based on speech

Cigna’s online stress management toolkit includes an AI-based tool that purports to evaluate a person’s psychological stress level based on analysis of their speech, the Cigna StressWaves Test (CSWT). In this study, we evaluate the claim that the CSWT is a “clinical grade” tool via an independent validation. The results suggest that the CSWT is not repeatable and has poor convergent validity; the public availability of the CSWT despite insufficient validation data highlights concerns regarding premature deployment of digital health tools for stress and anxiety management.

The crowding out of conventional electricity generation by renewable energy sources: implications from Greek, Hungarian, and Romanian electricity markets

To achieve ambitious energy-climate targets, all EU member states have introduced policies to support the market introduction of renewable energy sources (RES) generation. Motivated to close the gap of the merit order effect (MOE) in less mature Central and South East European electricity markets, we empirically confirm economic theory predictions that in the short run, an increase in RES generation reduces electricity prices. The merit order effect is initially econometrically confirmed and quantified. Different econometric model specifications are estimated to differentiate the MOE caused by wind and solar generation and to differentiate the MOE on high-load and low-load days. In addition, we simulate the adjustment of the realised day-ahead electricity prices to the no-RES generation scenario. Modern statistical methods are applied to bridge the gap in the limited public data availability to solve simulation models used in the power system or agent-based simulations. A family of data mining algorithms is applied for the merit order estimation used in the dynamic adaptation of the generation mix to the omitted RES generation. The estimated energy imbalance caused by the excluded RES generation is therefore compensated by the additional conventional generation dispatch according to the estimated power plant merit order. The estimated supply curves for each generation technology assist the reasoning behind the established MOE in econometric models. Based on our findings, policymakers should prioritise policies that facilitate the integration of RES into their electricity markets, which would in turn accelerate energy transition. With increasingly growing shares of renewables in the system, the governments need to rethink the support scheme, where the emphasis should be placed on efficiently integrating renewables in the power system by taking into account temporal and spatial dimensions.

Vaccine hesitancy and related factors among South African adults in 2021: unpacking uncertainty versus unwillingness.

Amidst widespread public health recommendations and availability of COVID-19 vaccinations, half of South African adults are vaccinated against COVID-19. This study investigated the socio-behavioral determinants of vaccine hesitancy in South Africa, where vaccine hesitancy was separated into unwilling ness and uncertainty to take a COVID-19 vaccine. Data was collected from a large-scale public survey during June-October 2021 that included online and telephonic surveys. Vaccination hesitancy was based on the question "When available, would you take the COVID 19 vaccine?," with responses categorized into those who were willing, unwilling, and uncertain about taking a COVID-19 vaccine. Multinomial regression examined the association between socio-behavioral variables and vaccine hesitancy. Overall, 73.8% reported they would definitely or probably take the vaccine, 16.4% were uncertain and 9.9% reported they probably or definitely would not (n = 16,988). Younger age, White and Colored population groups, no influenza vaccination history, previous vaccination refusal, knowing someone who experienced a serious vaccination side-effect, misperceptions about vaccine benefits, cultural or religious discouragement from taking a COVID-19 vaccination, lack of governmental confidence, concerns about side-effects, perceived lack of safety information, and lack of trust in the pharmaceutical industry and in the information from health care providers were all associated with higher odds of being uncertain and unwilling to take a COVID-19 vaccination. Strengths of association for unwillingness and uncertainty varied by the explanatory variables. Concern about effectiveness due to fast development was associated with uncertainty to take the vaccine but not with unwillingness. Concerns about side-effects had stronger associations with uncertainty than with unwillingness, while previous vaccine refusal, misperceptions of the protective benefits of vaccines, White population group, religious/cultural discouragement, and lack of trust in the pharmaceutical industry and health care providers' information had stronger associations with unwillingness than uncertainty. The determinants of COVID-19 vaccine hesitancy should be addressed in interventions to improve vaccine uptake. Public health interventions and health communication can be prioritized and tailored to the different forms of vaccination hesitancy.

Capacity Assessment Spatialization to Disaster Risk Reduction in Semarang Coastal Area

Global warming be the one of threats to human civilization that has to be main considerations in environmental management. In addition, global warming is one of key driven factor of tidal flooding that affect many aspects in coastal area society. This study proposes to model capacity preparedness index of Semarang Metropolitan Coastal Area. The analysis divided by two substances, physical capacity and economy capacity. The examination and modelling of capacity preparedness level consider public health facilities availability, road network accessibility, and economy activity. The research uses quantitative methods and maximized the utilization of secondary data to capacity assessment. The findings show that capacity preparedness index of urban area is higher than its peri-urban. In total, Semarang Metropolitan Coastal Area have low-capacity preparedness level. There are only two sub-district which has high level capacity, Semarang Tengah and Semarang Utara. Otherwhile, there are three medium level capacity sub-districts and four low level capacity sub-districts. Those findings show that geospatial analysis can help people and government on how to understanding their capacity in dealing with disaster which cannot be predicted precisely.

Using Machine Learning Techniques to Analyze Stress Levels in Electroencephalogram Data

Research in mental health has proposed the application of technologies that allow the analysis of substantial amounts of data and the provisioning of mechanisms to monitor and diagnose the health status of individuals with the objective of improving their quality of life. Mental stress has been widely addressed in this type of research as it is widely acknowledged as a condition that leads to negative impacts on modern life. Several studies have been directed to the generation of datasets with data from physiological signals obtained through sensors and the public availability of such datasets for further research. One of the main approaches for the processing and analysis of these datasets has been the use of Machine Learning techniques. This study explores the use of Machine Learning techniques to analyze a dataset consisting of mental stress level classification data from electroencephalograms. Different Machine Learning models were compared. The Multilayer Perceptron model presented the best performance with an accuracy rate of 98.99% in the predictions. The results demonstrate the potential of Machine Learning techniques as aiding tools to health monitoring and diagnosis. Key Words: machine learning, deep learning, mental stress, electroencephalogram