ethicalabs/Research-Intent-Collab huggingface dataset:
Patient-reported outcome measures for assessing health-related quality of life in people with type 2 diabetes:A systematic review

Patient-Reported Outcome Measures (PROMs) are important tools to assess outcomes relevant to patients, with Health-Related Quality Of Life (HRQOL) as an important construct to be measured. Many different HRQOL PROMs are used in the type 2 diabetes field, however a complete overview of these PROMs is currently lacking. We therefore aimed to systematically describe and classify the content of all PROMs that have specifically been developed or validated to measure (aspects of) HRQOL in people with type 2 diabetes. A literature search was performed in PubMed and EMBASE until 31 December 2021. Studies on the development or validation of a PROM measuring HRQOL, or aspects of HRQOL, in people with type 2 diabetes were included. Title and abstract and full-text screening were conducted by two independent researchers and data extraction was performed independently by one of the researchers. Data were extracted on language in which the PROM was developed, target population, construct(s) being measured, names of (sub)scales and number of items per (sub)scale. In addition, all PROMs and subscales were classified according to specific aspects of HRQOL based on the Wilson &amp; Cleary model (symptom status, functional status, general health perceptions) to aid researchers in PROM selection. In total 220 studies were identified that developed or validated PROMs that measure (aspects of) HRQOL in people with type 2 diabetes. Of the 116 unique HRQOL PROMs, 91 (of the subscales) measured symptom status, 60 measured functional status and 26 measured general health perceptions. In addition, 16 of the PROMs (subscales) measured global quality of life. 61 of the 116 PROMs (subscales) also include characteristics of the individual (e.g. aspects of personality, coping) or environment (e.g. social or financial support) and patient-reported experience measures (PREMs, e.g. measure of a patient's perception of their personal experience of the healthcare they have received, e.g. treatment satisfaction), which are not part of the HRQOL construct. Only 9 of the 116 PROMs measure all aspects of HRQOL based on the Wilson &amp; Cleary model. Finally, 8 of the 116 PROMs stating to measure HRQOL, measured no HRQOL construct. In conclusion, a large number of PROMs are available for people with type 2 diabetes, which intend to measure (aspects of) HRQOL. These PROMs measure a large variety of (sub)constructs, which are not all HRQOL constructs, with a small amount of PROMs not measuring HRQOL at all. There is a need for consensus on which aspects of HRQOL should be measured in people with type 2 diabetes and which PROMs to use in research and daily practice. PROSPERO: CRD42017071012. COMET database: http://www.comet-initiative.org/studies/details/956.
HEALTH TECHNOLOGY ASSESSMENTS REPORTING COST-EFFECTIVENESS OF TRANSCATHETER AORTIC VALVE IMPLANTATION

Objectives: Made available since 2002, transcatheter aortic valve implantation (TAVI) is a minimally invasive new intervention which can provide significant survival improvement to patients with aortic stenosis. However, TAVI is expensive and currently not reimbursed by many governments. Some governments and institutions have been conducting health technology assessments (HTAs) to inform their reimbursement decisions. The aim of the present study is to review HTAs that have relied on a cost-effectiveness analysis to inform reimbursement decisions of TAVI.Methods: A systematic literature review was conducted among published literature as well as reports released by HTA agencies. Predetermined inclusion and exclusion criteria, following the Preferred Reporting System for Systematic Reviews and Meta-Analysis guidelines, were used to select relevant HTAs. The selected papers were assessed against the Consolidated Health Economic Evaluation Reporting Standards.Results: HTAs on TAVI from three countries were available for this review: Canada, Belgium, and the United Kingdom. All three HTAs used the Placement of Aortic Transcatheter Valve (PARTNER) trial data with Markov models to estimate the incremental cost effectiveness ratio. The three HTAs recommended conditional reimbursement for TAVI for otherwise inoperable patients. The HTAs did not use clear methods to estimate the health-related utility which ultimately affected their cost-effectiveness results. The UK HTA showed the best value for money (US$20,416 per quality-adjusted life-year).Conclusion: All studies found TAVI to be more costly and less effective for high-risk patients suitable for surgery, whereas TAVI was consistently found to be cost effective for otherwise inoperable patients.
Comprehensive Evaluation of AlphaFold-Multimer, AlphaFold3 and ColabFold, and Scoring Functions in Predicting Protein-Peptide Complex Structures

Abstract                 Determining the three-dimensional structures of protein-peptide complexes is crucial for elucidating biological processes and designing peptide-based drugs. Protein-peptide docking has become essential for predicting complex structures. AlphaFold-Multimer, ColabFold and AlphaFold3 provided groundbreaking tools to enhance the protein-peptide docking accuracy. This study evaluates these three tools for predicting protein-peptide complex structures using Template-Based (TB) and Template-Free (TF) methods. AlphaFold-Multimer excels in TB predictions and performs moderately in TF scenarios in the prediction pool, but TF outperforms TB in the first-ranked models. ColabFold demonstrates versatility in both TB and TF settings. AlphaFold3 generates high-quality structures for more proteins, but the medium accuracy is not as good as AlphaFold-Multimer using a large model pool. We also assessed the performance of various scoring functions in ranking predicted protein-peptide complex structures. While the scoring function built in AlphaFold demonstrates the best performance, some other scoring functions, e.g., FoldX-Stability and HADDOCK-mdscore, provide complementary values. The findings suggest the potential for enhancing scoring functions targeting AlphaFold-based predictions by combining multiple scoring functions or using a consensus approach from many prediction models.
Qualitative Content Analysis of Corporate Social Responsibility Reports with Emphasis on Social Stakeholders

Having a mutual relationship and considering the stakeholder’s interests, require recognizing, measuring, and reporting of the entity’s social responsibility performance. Companies in the capital market of Iran have understood the importance of this issue in the last decade and they report some topics in their reports in a scattered manner without a structured framework based on the information needs of stakeholders about social responsibility performance. In this paper, the most important themes about different social stakeholders in social responsibility reports of Tehran Stock Exchange using qualitative content analysis were extracted. 1490 pages of reports, related to documents of 58 companies as sample research, in periods between 2013 to 2018 in a single file for per company after removing duplicate and irrelevant disclosures, were analyzed with content analysis method using MAXQDA 2018 software. Finally, 1967 codes included 18 categories and 62 subcategories were extracted about social stakeholders included employees, society, customers, and product/ service based on most codes respectively. Examining the difference between the codes of two groups included big and small companies, which were grouped by three indicators showed that, the type of disclosure differs significantly between the groups. This difference was also significant in the different industries. Furthermore, it was determined between the documents of two groups by similarity analysis that reporting structure of big companies with an emphasis on themes about social stakeholders is very similar. Similarity analysis in sample industries showed that cement and automobile industry are most similar to each other compared to other industries.
Distributed Stream Processing for Real-Time Healthcare-Motivated Analytics in Multi-Cloud: A Semantics-Aligned Benchmark of Kafka-Centric Pipelines with Flink and Spark Structured Streaming

Healthcare providers increasingly rely on Kafka-centric streaming architectures for sub-second alerting and continuous analytics over heterogeneous clinical and device events, yet practitioners lack a semantics-aligned benchmark to compare Apache Flink and Spark Structured Streaming in multi-cloud, lakehouse-oriented deployments. We present a benchmark of Kafka-based pipelines using Flink 1.18 and Spark Structured Streaming 3.5 on AWS and Azure, with outputs persisted via a neutral Databricks Delta ingestion job that decouples engine performance from lakehouse commit behavior. Our healthcare-motivated workload suite models stateless FHIR-like validation, stateful event-time window analytics with large patient-level state, and enrichment with deduplication under controlled late arrivals, all under a formal semantic-alignment protocol. Across colocated deployments at 50 k events/s, Flink achieves 3.1x lower p99 alert latency (74 +/- 3.1 ms vs. 231 +/- 8.4 ms) under exactly-once guarantees. RocksDB incremental checkpoints reduce checkpoint duration by 2.6x and recovery time by 2.9x versus Spark’s HDFSBackedStateStore; switching Spark to RocksDBStateStoreProvider narrows the checkpoint gap to 1.4x while leaving Spark’s p99 latency unchanged, confirming the micro-batch trigger as the latency bottleneck. Directional cross-cloud experiments reveal 78–112 ms additional p99 latency, with AWS-to-Azure consistently 18–24 ms slower than Azure-to-AWS. Persistence latency to Delta (SLA-B) is dominated by the ingestion job’s commit interval and is engine-invariant, reframing engine selection for lakehouse architectures.
Data is power: Towards additional guidance on profiling and automated decision-making in the GDPR

In contrast to automated decision-making, profiling is a relatively novel concept in European data protection law. It is now explicitly defined in Article 4(4) of the EU General Data Protection Regulation (GDPR), and refers to the automated processing of data (personal and not) to derive, infer, predict or evaluate information about an individual (or group), in particular to analyse or predict an individual’s identity, their attributes, interests or behaviour. Through profiling, highly sensitive details can be inferred or predicted from seemingly uninteresting data, leading to detailed and comprehensive profiles that may or may not be accurate or fair. Increasingly, profiles are being used to make or inform consequential decisions, from credit scoring, to hiring, policing and national security. Ever since the approval of the regulation in 2016, debates have focussed on the GDPR’s potential to limit or offer protection against increasingly sophisticated means of processing data, in particular with regard to profiling and automated decision-making. While the GDPR offers new rights and protection, their scope and limits are open to debate, partly due to the clumsy syntax of the relevant articles and the lack of authoritative guidance concerning their interpretation. The European Data Protection Board that will replace the Working Party on the Protection of Individuals with regard to the Processing of Personal Data is specifically tasked with publishing ‘guidelines, recommendations and best practices’ on the GDPR. In October 2017, the Working Party 29 has published draft guidance on profiling and automated decisionmaking. In this article we propose our suggestions to contribute to the development of guidelines which provide the strongest protections for data subjects.
Effects of juvenile situational experience on behavioral development of Brandt's vole (<i>Lasiopodomys brandtii</i>): From cognition to psychology

AbstractJuvenile situational experiences that animals are exposed to different environmental conditions have the potential to shape the developmental trajectory of individuals, influencing their personality traits, anxiety level, cognition, and memory. However, empirical evidence regarding these effects remains limited. In this study on male Brandt's voles (Lasiopodomys brandtii) undergoing weaning process, we examined the consequences of juvenile situational experiences on their behavioral development, involving daily exploration of newly introduced objects with a diverse array of materials, shapes, sizes, and colors. The results showed that (1) the situational experience improved spatial cognition and memory, as evidenced by significantly higher scores in both the recognition index during test period (RIB) and the discrimination index during test period (DI) compared to the control group; (2) no significant differences were observed in autonomic activity between the two groups, including total distance traveled, resting time, mean speed, maximum speed, and duration of slow and fast movements across the entire arena; (3) situational experience reduced anxiety levels, indicated by increased exploration of the center area, more transitions between zones, shorter latency to enter the center zone, prolonged resting time in the center area, and greater total time spent and distance moved in the center area; and (4) the situational experience group exhibited significantly higher individual personality scores compared to the control group. In conclusion, our results imply that juvenile situational experience exerts significant positive effects on spatial cognition and memory, anxious behavior, and personality development in L. brandtii. These insights offer novel perspectives on the long‐term impacts of juvenile situational experiences on adulthood, with potential applications in addressing mental health and behavioral anomalies. Additionally, our study provides practical implications for animal management and environmental enrichment strategies.
High-speed photonic neuromorphic computing using recurrent optical spectrum slicing neural networks

AbstractNeuromorphic computing using photonic hardware is a promising route towards ultrafast processing while maintaining low power consumption. Here we present and numerically evaluate a hardware concept for realizing photonic recurrent neural networks and reservoir computing architectures. Our method, called Recurrent Optical Spectrum Slicing Neural Networks (ROSS-NNs), uses simple optical filters placed in a loop, where each filter processes a specific spectral slice of the incoming optical signal. The synaptic weights in our scheme are equivalent to the filters’ central frequencies and bandwidths. Numerical application to high baud rate optical signal equalization (&gt;100 Gbaud) reveals that ROSS-NN extends optical signal transmission reach to &gt; 60 km, more than four times that of two state-of-the-art digital equalizers. Furthermore, ROSS-NN relaxes complexity, requiring less than 100 multiplications/bit in the digital domain, offering tenfold reduction in power consumption with respect to these digital counterparts. ROSS-NNs hold promise for efficient photonic hardware accelerators tailored for processing high-bandwidth (&gt;100 GHz) optical signals in optical communication and high-speed imaging applications.
A framework of partial error correction for intermediate-scale quantum computers

14+9 pages, 7+2 figures, comments welcome
Identifying characteristics for a cost-effective psoriatic arthritis biomarker test: a development-focused health technology assessment

Abstract          Objectives      This study aimed to evaluate the required test characteristics that a psoriatic arthritis (PsA) biomarker test would need to achieve to be considered cost-effective.              Methods      We adapted an existing Markov model to compare a hypothetical biomarker with current practice. The model followed a patient cohort aged 45 years with moderate psoriasis (PsO) in which PsA was prevalent but unrecognized over a 40-year time horizon. Patients were assumed to be routinely seen at a dermatology clinic. In the current practice arm, patients with PsA were clinically detected. In the biomarker arm, a hypothetical test was assumed to be administered at baseline. Patients who screened positive would accept a combination of conventional disease-modifying antirheumatic drugs and targeted treatment to slow disease progression. Progression was modeled as linear changes in Health Assessment Questionnaire (HAQ) scores. We varied the sensitivity, specificity, and biomarker price based on current development progress. Scenario analyses considered alternative patient cohorts with mild and severe PsO separately.              Results      The base case showed that a biomarker test with 70 percent sensitivity, 80 percent specificity, and a price of US$500 would be cost-effective (incremental cost-effectiveness ratio US$47,566 per quality-adjusted life-year [QALY]). Three-way analyses showed that a test with 80 percent specificity could be cost-effective at a US$50,000 per QALY threshold with a sensitivity as low as 66 percent at US$500. Only a near-perfect test would be cost-effective at a US$1,000 price point. Results were sensitive to HAQ progression under treatment, therapy costs, and the patient population.              Conclusion      This study supports the continued product development of candidate PsA biomarkers.
Application of Natural Language Processing to Learn Insights on the Clinician’s Lived Experience of Electronic Health Records

We interviewed six clinicians to learn about their lived experience using electronic health records (EHR, Allscripts users) using a semi-structured interview guide in an academic medical center in New York City from October to November 2016. Each participant interview lasted approximately one to two hours. We applied a clustering algorithm to the interview transcript to detect topics, applying natural language processing (NLP). We visualized eight themes using network diagrams (Louvain modularity 0.70). Novel findings include the need for a concise and organized display and data entry page, the user controlling functions for orders, medications, radiology reports, and missing signals of indentation or filtering functions in the order page and lab results. Application of topic modeling to qualitative interview data provides far-reaching research insights into the clinicians’ lived experience of EHR and future optimal EHR design to address human-computer interaction issues in an acute care setting.
Phylogenetic Systematics and Evolutionary Biology

The question of the relationship between phylogenetic systematics and evolutionary biology is addressed from the perspective of the problematic foundation of the concepts, principles and rules of the former on the hypotheses formulated by the latter concerning evolutionary processes. The question of the nature and role of the evolutionary hypotheses used by cladistics is raised at each stage of the method: formation of basic taxa, formulation of primary homology hypotheses, testing of these hypotheses with the construction of cladograms and derivation of classifications. The analysis of the choices made by cladists from the 1950s to the 1980s leads to the characterization of three roles played by the theory of evolution in cladistics: those of founding theory, auxiliary theory and background theory.
Decoherence Impediments in Quantum Computing and Fundamental Challenges of Quantum Error Correction

This review synthesizes current understanding of decoherence pathways across leading hardware platforms and explains why several experimentally observed noise features—drift, burst events, coherent components, correlated and non-Markovian structure, and leakage—can be disproportionately damaging for QEC. Decoherence remains the dominant impediment to scalable quantum computing because it is not a single error mechanism but a system-level phenomenon arising from materials defects, electromagnetic loss, control electronics, measurement backaction, and the broader environment in which a processor operates. Quantum error correction (QEC) is designed to algorithmically suppress physical noise, yet its practical success depends on how closely real devices satisfy the assumptions under which fault tolerance is proved: approximate locality, weak temporal correlations, sufficiently stochastic error statistics, low leakage, and reliable syndrome extraction. We then evaluate fundamental and engineering challenges that shape the viability of QEC at scale: syndrome measurement fidelity, correlated error suppression, decoding latency and classical co-processing, architectural constraints (connectivity, crosstalk, calibration overhead), and the resource cost of implementing a universal fault-tolerant gate set. Recent demonstrations of below-threshold behaviour and “break-even” regimes show that QEC is transitioning from theory to practice, but also clarify what remains unresolved: maintaining stable noise below fault-tolerance targets over long times, scaling to many logical qubits with low correlated-error rates, and integrating hardware-aware codes and decoders. We conclude with research priorities that treat decoherence and QEC as part of a co-designed stack spanning device physics, control, architecture, and algorithms.
Addressing food and nutrition security from a human rights-based perspective: A mixed-methods study of NGOs in post-Soviet Armenia and Georgia

Abstract   Increasing numbers of non-governmental organizations (NGOs) declare a move from a needs- to a human rights-based approach (HRBA) in addressing food and nutrition security. Little work, however, has been done to reveal how HRBA to food is realized at a country level. Using a mixed methods approach, we demonstrate main challenges for NGOs’ adoption of HRBA in Armenia and Georgia. A combination of NGO operational and state level factors hamper rights-based food and nutrition work of NGOs. NGOs’ willingness and capacity to engage with HRBA to food and confront the state is influenced by the history of post-Soviet transition, views on the state versus market, and isolation from transnational actors propagating the right to adequate food.
Towards sustainable fisheries and aquaculture management in the African Great Lakes

AbstractInland fisheries play important roles in food and economic security in the riparian countries surrounding the Great Lakes of Africa. However, the lakes are being systematically degraded by anthropogenic pressures, in combination with the huge population growth prevalent in the region. This paper summarises the outcomes of an international conference to develop a ‘Strategy for Conservation and Sustainable Development of the African Great Lakes Region in a Changing Climate’, held in Uganda in 2017, with particular attention on the potential for fisheries and aquaculture. The paper highlights options for addressing the problems facing the aquatic resources and specifically the importance of effective management of the fisheries and ecosystems of the Great Lakes to achieving the UN Sustainable Development Goals to ensure food and nutritional security and sustainable livelihoods. There is need to improve fisheries assessment and determine the value of the fisheries (and aquaculture). Fisheries legislation, regulation and enforcement need revision and co‐management mechanisms require rethinking. There is considerable potential for aquaculture, and especially cage culture, to increase fish production, but there is an urgent need to address technical, social, environmental and input requirements. It is also imperative that best practices guidelines are developed that will support cage culture production practices.