ethicalabs/Research-Intent-Collab huggingface dataset:
sj-pdf-2-pmj-10.1177_02692163211019321 – Supplemental material for Improved quality of care and reduced healthcare costs at the end-of-life among older people with dementia who received palliative home care: A nationwide propensity score-matched decedent cohort study

Supplemental material, sj-pdf-2-pmj-10.1177_02692163211019321 for Improved quality of care and reduced healthcare costs at the end-of-life among older people with dementia who received palliative home care: A nationwide propensity score-matched decedent cohort study by Rose Miranda, Tinne Smets, Robrecht De Schreye, Kristof Faes, Nele Van Den Noortgate, Joachim Cohen and Lieve Van den Block in Palliative Medicine
Parameter-free prediction of phase transition in PbTiO3 through combination of quantum mechanics and statistical mechanics

Thermodynamics of ferroelectric materials and their ferroelectric to paraelectric (FE-PE) transitions including those in PbTiO3 is commonly described by the phenomenological Landau theory and more recently by effective Hamiltonian and various potentials, all with model parameters fitted to experimental or theoretical data. Here we show that the zentropy theory, which considers the total entropy of a system as a weighted sum of entropies of configurations that the system may experience and the statistical entropy among the configurations, can predict the FE-PE transition without fitting parameters. For PbTiO3, the configurations are identified as the FE configurations with 90- or 180-degree domain walls in addition to the ground state of the FE configuration without domain wall. With the domain wall energies predicted from first-principles calculations based on the density functional theory in the literature as the only inputs, the FE-PE transition for PbTiO3 is predicted showing remarkable agreement with experiments, unveiling the microscopic fundamentals of the transition.
Keynote Presentation: Room-Temperature Li-S, Li-Se and Na-S Batteries: Electrochemistry and Materials

Abstract not Available.
Voyager and Pioneer spacecraft measurements of cosmic ray intensities in the outer heliosphere: Toward a new paradigm for understanding the global modulation process: 2. Maximum solar modulation (1990–1991)

In this paper we present interplanetary radial intensity profiles of five particle energies/species of cosmic rays measured by the IMP, Voyager 1, Voyager 2, and Pioneer 10 spacecraft at the time of maximum solar modulation in 1990–1991. These radial profiles are then compared with those measured in 1987 at a period of minimum modulation in order to examine the radial dependence of the 11‐year solar modulation effects. It is found that the radial intensity profiles observed in 1990–1991 can be described to first order by simple reduction factors of the radial profiles observed in 1987. These reduction factors range from ∼2.5 at the highest rigidities to ∼10 at the lowest rigidities measured here. These reduction factors describe the magnitude of the 11‐year modulation and imply that the intensities at the estimated location of the termination shock (TS), here taken to be at 88 AU, are reduced by a corresponding factor to those at the Earth at each energy. Therefore these TS intensities range from only 0.4 to 0.05 of the estimated IS intensities at the highest to lowest rigidities, respectively. This implies that most of the overall (11‐year and residual) modulation at this time and at these rigidities is occurring beyond the TS. Some of this 11‐year modulation (∼50% or less) occurs within this location. This part of the modulation is usually described in the present modulation models by invoking changing heliospheric current sheet tilt and changes in the drift and diffusion coefficients. However, overall, the 11‐year solar modulation, at least at the rigidities examined here, is most properly described as an outer heliospheric modulation phenomenon occurring mainly at or beyond the TS with a secondary (weaker) modulation occurring inside the TS. This is similar to the situation described by Webber and Lockwood [this issue], where the dominant modulation at times of minimum (residual) solar modulation also appears to occur in the outer heliosphere near to or beyond the TS. This large outer heliosphere modulation remains largely unexplained in present modulation theories. A new paradigm in solar modulation theory is required to more fully account for the large fraction of the 11‐year and/or residual solar modulation that is occurring in the outer heliosphere near to or beyond the TS.
Randomized, controlled clinical trials in sepsis: Has methodological quality improved over time?

To systematically evaluate the methodological quality of randomized clinical trials and to determine whether randomized clinical trials of sepsis improved in methodological quality over time.Computerized MEDLINE search of articles published in any language from 1966 to 1998 combined with a manual search of bibliographies of published articles and communication with known experts in the field.All randomized clinical trials of sepsis, severe sepsis, and septic shock performed in adults and published as full articles.Abstracts of all retrieved records were reviewed and the inclusion criteria were applied. All selected articles were classified into (a) trials designed to detect differences in mortality as the primary end point, or (b) trials focusing on surrogate outcome measures (i.e., physiological or biochemical parameters). All retrieved trials were then graded for methodological quality using an objective grading scheme developed specifically for this study. The data selection and extraction process was carried out independently by two of the authors; any disagreement was resolved by discussion.Seventy-four randomized clinical trials involving septic patients qualified for inclusion in this study (40 reporting mortality outcomes, 34 reporting other surrogate outcomes). Trials reporting mortality as the primary outcome had significantly higher quality scores compared with trials reporting surrogate outcome measures (29.6 +/- 1.0 vs. 24.3 +/- 0.8, p =.0006). From 1976 to 1998, trial methodology improved significantly over time (an average of 0.36 points per year, p =.021). Mortality outcome trials improved an average of 0.58 points per year (p =.0011) whereas surrogate outcome trials did not demonstrate an improvement in methodological quality over time (p =.249).The methodological limitations identified in this article can help to target further improvement in trial design to enhance the validity of findings from future randomized clinical trials of sepsis.
'Our old pastor thinks the mobile phone is a source of evil.' Capturing contested and conflicting insights on digital wellbeing and digital detoxing in an age of rapid mobile connectivity

في حين أن أفريقيا تعتبر إلى حد كبير دولة منفصلة رقميًا، فقد أظهرت الدراسات الحديثة أن أرقام الاتصال آخذة في الارتفاع. في هذه الورقة، نقوم بتنظير الرفاهية الرقمية في سياق يتميز بعدد سريع النمو من مستخدمي البيانات المتنقلة على الرغم من انخفاض معدل انتشار الإنترنت تاريخيًا. وهو يركز على بحث إثنوغرافي مستمر حول مستخدمي الهواتف المحمولة وعدم المساواة الرقمية في أفريقيا، مع التركيز على نتائج دراسة استكشافية تضم 10 مقابلات متعمقة مع المستخدمين الكبار من الشباب (أكثر من 4–5 ساعات في اليوم) والسعي إلى فهم الاستراتيجيات التي يستخدمونها في تحقيق الرفاهية الرقمية. تُظهر النتائج كيف يعاني الشباب الذين تم أخذ عينات منهم (18–30 عامًا) من الحقائق اليومية للمشاركة الرقمية بما في ذلك الإدمان والصراع بين الأجيال في استخدام التكنولوجيا. تكشف النتائج أيضًا عن الطرق التي يُنظر من خلالها إلى الاتصال الإلكتروني على أنه أداة للحرية وكذلك شكل خفي من العبودية الرقمية المحتملة.
Management of Relief Supply Chain & Humanitarian Aids Logistics through Supply Chain Resilience Case Study : South West Asia Tsunami (2004)

Humanitarian logistics and relief supply chain management is a relatively new area of investigation which is typically associated with unexpected disasters that require immediate actions and responses. It can be defined as “the process of planning, implementing, and controlling the efficient, cost-effective flow and storage of goods and materials, as well as related information, from point of origin to point of consumption for the purpose of meeting the end beneficiary‟s requirements” (Thomas, 2005).In both man-made and natural disaster relief processes, humanitarian logistics and relief supply chain operations have been hampered by a lack of information and coordination between actors. Especially in sudden-onset disasters, humanitarian logistics and relief supply chain teams have to be deployed in situations with destabilized infrastructure and with very limited knowledge about the situation at hand (Beamon 2004, Long and Wood 1995, Tomasini and Van Wassenhove 2004).This lack directly affects effective performance in terms of validity and reliability enhancement in which an adapted resiliency management in relief supply chain strategies could offer a solution to cover the problem.The purpose of study ahead is to underline the beneficial advantages offered by using resiliency methods in humanitarian logistics and relief supply chain operations, and enriching the existing benefits that relief chain management teams through the humanitarian logistics techniques have brought to satisfy the survival needs.In fact, the end result of the research will be in both fields of humanitarian logistics and relief supply chain management, and the use of resiliency theories to overcome on barriers and difficulties during relief and aid operations.
Structured integration of rapid antimicrobial susceptibility testing and antimicrobial resistance detection into an antimicrobial stewardship programme for bacteraemia

Abstract                                        Background                     Bloodstream infections are associated with substantial morbidity and mortality. Rapid diagnostic tests (RDTs) have been shown to improve clinical outcomes when combined with antimicrobial stewardship (AMS) programmes. The lack of systematic reporting of rapid antimicrobial susceptibility testing (AST) can cause delays in communication to prescribers and in targeted antimicrobial treatment.                                                           Objectives                     To evaluate the impact of a structured intervention in integrating systematic RAST and AMR detection within an existing AMS programme. The main outcomes were time to report (from Gram to AST report) (TTR), time from Gram to AMS recommendation (TAMS) and time to change antimicrobial prescription (TCAP) following this recommendation.                                                           Methods                     We performed a contemporaneous comparative study evaluating episodes of bacteraemia managed with rapid AST (RAST) and/or antimicrobial resistance mechanisms (AMR) detection integrated into AMS workflows versus episodes managed without RAST/AMR during the same implementation period.                                                           Results                     Episodes managed with RAST/AMR had significantly shorter TTR, TAMS and TCAP compared with episodes without RAST/AMR (median absolute reduction in hours of 31, 37 and 24, respectively). There was no significant reduction in duration of antimicrobial therapy, hospital length of stay (LOS) or mortality in the RAST/AMR group. Compliance with AMS recommendations was high (90%).                                                           Conclusions                     As reported previously, RAST or AMR detection in combination with AMS was an effective strategy for targeting antimicrobial therapy. This intervention demonstrates that structured implementation of RAST/AMR detection, when embedded within the AMS workflow, can substantially shorten time to targeted therapy even in settings with pre-existing AMS infrastructure.
Analyzing complex functional brain networks: fusing statistics and network science to understand the brain

Complex functional brain network analyses have exploded over the last eight years, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrated system that produces complex behaviors. While the field of statistics has been integral in advancing activation analyses and some connectivity analyses in functional neuroimaging research, it has yet to play a commensurate role in complex network analyses. Fusing novel statistical methods with network-based functional neuroimage analysis will engender powerful analytical tools that will aid in our understanding of normal brain function as well as alterations due to various brain disorders. Here we survey widely used statistical and network science tools for analyzing fMRI network data and discuss the challenges faced in filling some of the remaining methodological gaps. When applied and interpreted correctly, the fusion of network scientific and statistical methods has a chance to revolutionize the understanding of brain function.
Dissolution study of active pharmaceutical ingredients using molecular dynamics simulations with classical force fields

Abstract   The CHARMM, general Amber and OPLS force fields are evaluated for their suitability in simulating the molecular dynamics of the dissolution of the hydrophobic, small-molecule active pharmaceutical ingredients aspirin, ibuprofen, and paracetamol in aqueous media. The force fields are evaluated by comparison with quantum chemical simulations or experimental references on the basis of the following capabilities: accurately representing intra- and intermolecular interactions, appropriately reproducing crystal lattice parameters, adequately describing thermodynamic properties, and the qualitative description of the dissolution behavior. To make this approach easily accessible for evaluating the dissolution properties of novel drug candidates in the early stage of drug development, the force field parameter files are generated using online resources such as the SWISS PARAM servers, and the software packages ACPYPE and Maestro. All force fields are found to reproduce the intermolecular interactions with a reasonable degree of accuracy, with the general Amber and CHARMM force fields showing the best agreement with quantum mechanical calculations. A stable crystal bulk structure is obtained for all model substances, except for ibuprofen, where the reproductions of the lattice parameters and observed crystal stability are considerably poor for all force fields. The heat of solution used to evaluate the solid-to-solution phase transitions is found to be in qualitative agreement with the experimental data for all combinations tested, with the results being quantitatively optimum for the general Amber and CHARMM force fields. For aspirin and paracetamol, stable crystal–water interfaces were obtained. The (100), (110), (011) and (001) interfaces of aspirin or paracetamol and water were simulated for each force field for 30 ns. Although generally expected as a rare event, in some of the simulations, dissolution is observed at 310 K and ambient pressure conditions.
Critical factors influencing data use and utilization in health systems: a focus on data and interoperability standards for health information exchange (HIE) in Uganda’s health care system

Abstract                Limited use and utilization of health data in Uganda’s health care system is influenced by various factors including: absence of standardized data formats, lack of data governance policies, unskilled data management human resources, limited data use culture, poor data quality, complacency, limited political will and lack of interoperability. Worse still, the existing data interoperability standards, primarily designed for developed world health systems, may not be suitable for Uganda due to differences in health information maturity levels. This paper examines the critical factors affecting data use and utilization in Uganda, specifically focusing on interoperability and data standards.                A cross-sectional design was used in this study in selected health facilities with electronic systems in Uganda. Purposive sampling was used to select sites and participants based on predetermined criteria. The study included 28 health center IVs, referral hospitals, government entities and eHealth stakeholders.                The findings highlight key factors including limited data collection and management systems, poor data quality, inadequate data analysis capacity, absence of data exchange standards, limited technology access, inadequate funding and deficiencies in data sharing and dissemination.                These factors, if addressed through data and interoperability standards, can play a pivotal role in promoting efficient and effective health care delivery and outcomes in Uganda.
Mitigating Discrimination and Privacy Threats in Algorithmic Pricing through Personal Data Protection Law in Indonesia

This research is motivated by the legal ambiguity in Article 34 paragraph (2) letter (a) of Law Number 27 Year 2022 on Personal Data Protection (PDP Law), especially related to the lack of clarity in interpreting the element “automatically” stipulated in the article towards algorithmic pricing. Basically, the article has an important role in preventing problems arising from algorithmic pricing in the form of privacy threats and price discrimination. The purpose of this research is to analyze whether the characteristics of algorithmic pricing can be categorized as an automatic decision under PDP Law and to analyze the recommendation of the appropriate regulatory substance related to automatic decisions in PDP Law against algorithmic pricing. The research method used is juridical-normative by using legislative approach and comparative approach. The result of this study found that algorithmic pricing cannot be categorized as an automatic decision under PDP Law. Therefore, Indonesia can establish an automated decision guideline issued by the Ministry of Digital and Information by referring to the Guidelines on Automated Individual Decision-Making and Profiling for the Purposes of Regulation 2016/679 issued by the Article 29 Working Party in the European Union.
Leveraging natural language processing to identify social determinants of health for patients with Alzheimer’s disease and related dementias from electronic medical records

AbstractBackgroundSocial determinants of health (SDoH) have a significant impact on the well‐being of individuals with Alzheimer’s disease and related dementias (ADRD). ADRD patients from socioeconomically disadvantaged groups are generally subject to higher risks of adverse health events than those from advantaged groups. However, information related to SDoH is not collected systematically in most medical institutions. Unstructured electronic medical records (EMRs), including free‐text clinical narratives and nurse and social worker notes, may include salient information that can be used to identify individuals who may need assistance. In this study, we sought to develop a rule‐based, natural language processing (NLP) algorithm that identifies from patients’ unstructured EMRs their housing instability, transportation problems, food and medication insecurity, solitariness, and financial exploitation.MethodWe randomly selected 1,000 medical notes from Michigan Medicine between 2016 and 2019 and partitioned them into 750 notes for training and 250 notes for testing. Training notes were preprocessed by a prototype algorithm and annotated by registered nurses. We then trained the algorithm by resolving all human and machine disagreements. The post‐training NLP algorithm was evaluated on the testing notes. The most promising medical notes that found information about SDoH were social worker notes.ResultThe NLP algorithm achieved 85.3% of note‐wise accuracy. Determinant‐wise accuracy scores were 88.6% (housing), 91.1% (transportation), 86.7% (food), 92.0% (medication), 87.4% (solitariness) and 95.3% (financial exploitation). Determinant‐wise F1‐scores were 90.6% (housing), 86.9% (transportation), 91.3% (food), 95.7% (medication), 82.3% (solitariness) and 93.8% (financial exploitation).ConclusionOur NLP algorithm is a stand‐alone module that can run on real‐time EMRs and extract information about SDoH with a high level of accuracy. Findings can be used by clinicians, care managers, and social workers to pragmatically and efficiently reach out to ADRD patients who may need assistance with food, transportation, or housing. In doing this, adverse health events may be reduced among this vulnerable patient population. Future efforts will focus on making this algorithm generalizable for use at other institutions.
Contextualized and Generalized Sentence Representations by Contrastive Self-Supervised Learning: A Case Study on Discourse Relation Analysis

We propose a method to learn contextualized and generalized sentence representations using contrastive self-supervised learning. In the proposed method, a model is given a text consisting of multiple sentences. One sentence is randomly selected as a target sentence. The model is trained to maximize the similarity between the representation of the target sentence with its context and that of the masked target sentence with the same context. Simultaneously, the model minimizes the similarity between the latter representation and the representation of a random sentence with the same context. We apply our method to discourse relation analysis in English and Japanese and show that it outperforms strong baseline methods based on BERT, XLNet, and RoBERTa.
Adaptive Importance Sampling with Forward-Backward Stochastic Differential Equations

We describe an adaptive importance sampling algorithm for rare events that is based on a dual stochastic control formulation of a path sampling problem. Specifically, we focus on path functionals that have the form of cumulate generating functions, which appear relevant in the context of, e.g.~molecular dynamics, and we discuss the construction of an optimal (i.e. minimum variance) change of measure by solving a stochastic control problem. We show that the associated semi-linear dynamic programming equations admit an equivalent formulation as a system of uncoupled forward-backward stochastic differential equations that can be solved efficiently by a least squares Monte Carlo algorithm. We illustrate the approach with a suitable numerical example and discuss the extension of the algorithm to high-dimensional systems.
Building national cyber resilience and protecting critical information infrastructure

ABSTRACTThe digital transformation poses a challenge to all societies and economies, with Information and Communication Technologies forming a substrate for critical services and economic growth. National cyber policy-makers are faced with many priorities, including protecting critical infrastructure, reducing cybercrime, raising awareness, and following national security and foreign policy interests. The protection of critical services and infrastructure, fight against cybercrime, cyber incident response and recovery form a basis of nations’ cyber resilience in the digital era. In the light of recent cyber security regulations passed in the US and the European Union, the article offers an overview of how to organise and provide leadership for an efficient national cyber effort. It also discusses several methods from a public policy perspective of how to provide better cyber security for critical services and infrastructures.