ethicalabs/Research-Intent-Collab huggingface dataset:
The Very Red Afterglow of GRB 000418: Further Evidence for Dust Extinction in a Gamma‐Ray Burst Host Galaxy

We report near-infrared and optical follow-up observations of the afterglow of the GRB 000418 start- ing 2.5 days after the occurrence of the burst and extending over nearly 7 weeks. GRB 000418 represents the second case for which the afterglow was initially identi—ed by observations in the near-infrared. During the —rst 10 days its R-band afterglow was well characterized by a single power-law decay with a slope of 0.86. However, at later times the temporal evolution of the afterglow —attens with respect to a simple power-law decay. Attributing this to an underlying host galaxy, we —nd its magnitude to be R \ 23.9 and an intrinsic afterglow decay slope of 1.22. The afterglow was very red with R(K B 4 mag. The observations can be explained by an adiabatic, spherical —reball solution and a heavy reddening due to dust extinction in the host galaxy. This supports the picture that (long) bursts are associated with events in star-forming regions. Subject heading: gamma rays: bursts
CBP60-DB: An AlphaFold-predicted plant kingdom-wide database of the CALMODULIN-BINDING PROTEIN 60 (CBP60) protein family with a novel structural clustering algorithm

Abstract                                    Molecular genetic analyses in the model species                   Arabidopsis thaliana                   have demonstrated the major roles of different CAM-BINDING PROTEIN 60 (CBP60) proteins in growth, stress signaling, and immune responses. Prominently, CBP60g and SARD1 are paralogous CBP60 transcription factors that regulate numerous components of the immune system, such as cell surface and intracellular immune receptors, MAP kinases, WRKY transcription factors, and biosynthetic enzymes for immunity-activating metabolites salicylic acid (SA) and                   N                   -hydroxypipecolic acid (NHP). However, their function, regulation and diversification in most species remain unclear. Here we have created CBP60-DB, a structural and bioinformatic database that comprehensively characterized 1052                   CBP60                   gene homologs (encoding 2376 unique transcripts and 1996 unique proteins) across 62 phylogenetically diverse genomes in the plant kingdom. We have employed deep learning-predicted structural analyses using AlphaFold2 and then generated dedicated web pages for all plant CBP60 proteins. Importantly, we have generated a novel clustering visualization algorithm to interrogate kingdom-wide structural similarities for more efficient inference of conserved functions across various plant taxa. Because well-characterized CBP60 proteins in                   Arabidopsis                   are known to be transcription factors with putative calmodulin-binding domains, we have integrated external bioinformatic resources to analyze protein domains and motifs. Collectively, we present a plant kingdom-wide identification of this important protein family in a user-friendly AlphaFold-anchored database, representing a novel and significant resource for the broader plant biology community.
Draft Chapter – Individual and Organizational Science Communicators: Who Engages with the Swiss Public?

Individual and Organizational Science Communicators: Who Engages with the Swiss Public?" is an Excerpt from of the Upcoming Expert Group Report “Science in Public: The State of Science Communication and Public Engagement with Science in Switzerland”, to be published in 2021 by the Expert Group “Communicating Sciences and Arts in Times of Digital Media” of the Swiss Academies of Arts and Sciences. Readers are Invited to Provide Feedback as Outlined on the First Pages of the Preprint.
Citation and credit: The role of researchers, journals, and repositories to ensure data, software and samples are linked to publications with proper attribution.

Citation and credit: The role of researchers, journals, and repositories to ensure data, software and samples are linked to publications with proper attribution. EGU ESSI3.3, 27 April 2021 Recording: https://youtu.be/k81sWdKpse8
A framework for cost-constrained genome rearrangement under Double Cut and Join

The study of genome rearrangement has many flavours, but they all are somehow tied to edit distances on variations of a multi-graph called the breakpoint graph. We study a weighted 2-break distance on Eulerian 2-edge-colored multi-graphs, which generalizes weighted versions of several Double Cut and Join problems, including those on genomes with unequal gene content. We affirm the connection between cycle decompositions and edit scenarios first discovered with the Sorting By Reversals problem. Using this we show that the problem of finding a parsimonious scenario of minimum cost on an Eulerian 2-edge-colored multi-graph - with a general cost function for 2-breaks - can be solved by decomposing the problem into independent instances on simple alternating cycles. For breakpoint graphs, and a more constrained cost function, based on coloring the vertices, we give a polynomial-time algorithm for finding a parsimonious 2-break scenario of minimum cost, while showing that finding a non-parsimonious 2-break scenario of minimum cost is NP-Hard.
Additional file 2 of Key factors in developing effective digital health promotion tools for cancer prevention and health behavior change in adolescence through a multi-country survey

Supplementary Material 2.
Barriers to Clinical Guideline Implementation in Mental Health: A Scoping Review of Computable Knowledge, Interoperability, and AI-Enabled Decision Support

Abstract                                    Background:                   Clinical practice guidelines support evidence-based mental health care, yet their implementation remains inconsistent across clinical settings. Advances in digital health technologies have created new opportunities to support guideline use, but translation into routine practice remains limited.                   Objective:                   This scoping review identified and synthesised barriers to clinical guideline implementation in mental health and examined implications for the design of computable and governable decision-support systems.                   Methods:                   A structured scoping review was conducted using systematic search, screening, and narrative synthesis across studies on guideline implementation, digital health technologies, and AI in mental health.                   Results:                   46 studies were included. Barriers spanned clinical, technical, and governance domains, including organisational constraints, knowledge representation limitations, interoperability challenges, and AI-related issues. Barriers were frequently interconnected, limiting consistent guideline translation into practice.                   Conclusion:                   Current approaches lack a unified, computable, interoperable, and governable framework for mental health guideline implementation. This review proposes the                   Guideline-as-Guardrail model                   , in which formalised guidelines act as executable constraints within decision-support and AI systems while preserving clinical judgement.
Assessing vaccine efficacy for the prevention of acute otitis media by pneumococcal vaccination in children: A methodological overview of statistical practice in randomized controlled clinical trials

Acute otitis media (AOM) is the most common bacterial infectious disease among children. Vaccination is proposed to prevent otitis and several clinical trials were performed to assess the efficacy of pneumococcal vaccines. The way vaccine efficacy is analysed varies among trials. However, the clinical meaning of an estimate of vaccine effect and its statistical test depends on the applied statistical method. We aim to bring the meaning and validity of statistical trial results to the attention of researchers. We consider all methodological approaches for analysing vaccine efficacy applied in pneumococcal vaccination trials included in a recent Cochrane Review. We demonstrate how different methods address different scientific questions on the effect of vaccination, how they can complement each other and why some methods can produce misleading results.
Quantum Computing Error Mitigation via the Scalar Temporal Field Ontology: Temporal Dressing, Coherence Stabilization, and τ-Noise Control

This paper extends the Scalar Temporal Field Ontology (STFO) to quantum information processing, showing that fluctuations of the temporal scalar field τ(xμ) can passively mitigate qubit error rates by altering phase accumulation and suppressing environmental noise.   Qubits couple to τ via L_int = −γτ Jμ ∂μ τ, producing:(1) spectral filtering of dephasing channels through τ-exchange,(2) exponential suppression of effective phase noise Γ_eff = Γ_0 exp[−γτ² Var(Δτ)/2], and(3) direction-dependent coherence enhancement aligned with ∇τ.  Integrating out τ-fluctuations yields an effective noise kernel Kτ(ω) = γτ² ω² / (ω² + q² + mτ²), suppressing high-frequency noise while stabilizing low-frequency drift through gradient alignment. This leads to longer effective T₂ times without requiring active error correction.   We derive the associated kernel, coherence scaling laws, qubit-gradient angle dependence, and propose diagnostic sequences including CPMG, Ramsey drift scans, and noise spectroscopy around ω ~ mτ. Applications to superconducting qubits, trapped ions, and neutral-atom platforms are discussed.   This work suggests STFO provides a universal, passive error-mitigation layer for quantum hardware.
A National Institute for Health and Care Excellence (NICE) - New guidance on the use of surrogate endpoints in cost-effectiveness analysis for use in Health Technology Assessment (HTA)

NICE collaborated with international HTA agencies from the US, Canada, Australia, the Netherlands, and Columbia to produce best practice recommendations for using surrogate endpoints in health economic models to guide HTA decisions. Surrogate endpoints are biomarkers or other intermediate outcomes that predict a treatment effect on a final clinical outcome and these outcomes are increasingly being used in licensing. The new guidance provides clarity and support for technology developers when using...
An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes

Many applications involve reasoning about time durations before a critical event happens--also called time-to-event outcomes. When will a customer cancel a subscription, a coma patient wake up, or a convicted criminal reoffend? Time-to-event outcomes have been studied extensively within the field of survival analysis primarily by the statistical, medical, and reliability engineering communities, with textbooks already available in the 1970s and '80s. This monograph aims to provide a reasonably self-contained modern introduction to survival analysis. We focus on predicting time-to-event outcomes at the individual data point level with the help of neural networks. Our goal is to provide the reader with a working understanding of precisely what the basic time-to-event prediction problem is, how it differs from standard regression and classification, and how key "design patterns" have been used time after time to derive new time-to-event prediction models, from classical methods like the Cox proportional hazards model to modern deep learning approaches such as deep kernel Kaplan-Meier estimators and neural ordinary differential equation models. We further delve into two extensions of the basic time-to-event prediction setup: predicting which of several critical events will happen first along with the time until this earliest event happens (the competing risks setting), and predicting time-to-event outcomes given a time series that grows in length over time (the dynamic setting). We conclude with a discussion of a variety of topics such as fairness, causal reasoning, interpretability, and statistical guarantees. Our monograph comes with an accompanying code repository that implements every model and evaluation metric that we cover in detail.
Integrating traditional and local ecological knowledge into forest biodiversity conservation in the Pacific Northwest

Abstract   The potential for traditional and local ecological knowledge to contribute to biodiversity conservation has been widely recognized, but the actual application of this knowledge to biodiversity conservation is not easy. This paper synthesizes literature about traditional and local ecological knowledge and forest management in the Pacific Northwest to evaluate what is needed to accomplish this goal. We address three topics: (1) views and values people have relating to biodiversity; (2) the resource use and management practices of local forest users, and their effects on biodiversity; (3) models for integrating traditional and local ecological knowledge into biodiversity conservation on public and private lands. We focus on the ecological knowledge of forest users belonging to three groups who inhabit the region: American Indians, family forest owners, and commercial nontimber forest product harvesters.  We argue that integrating traditional and local ecological knowledge into forest biodiversity conservation is most likely to be successful if the knowledge holders are directly engaged as active participants in these efforts. Although several promising models exist for how to integrate traditional and local ecological knowledge into forest management, a number of social, economic, and policy constraints have prevented this knowledge from flourishing and being applied. These constraints should be addressed alongside any strategy for knowledge integration. Also needed is more information about how different groups of forest practitioners are currently implementing traditional and local ecological knowledge in forest use and management, and what the ecological outcomes are with regard to biodiversity.
Introducing Variety in Risk Management

We review the recently introduced concept of variety of a financial portfolio and we sketch its importance for risk control purposes. The empirical behaviour of variety, correlation, exceedance correlation and asymmetry of the probability density function of daily returns is discussed. The results obtained are compared with the ones of a one-factor model showing strengths and limitations of this model.
A New Measure to Assess Psychopathic Personality in Children: The Child Problematic Traits Inventory

Understanding the development of psychopathic personality from childhood to adulthood is crucial for understanding the development and stability of severe and long-lasting conduct problems and criminal behavior. This paper describes the development of a new teacher rated instrument to assess psychopathic personality from age three to 12, the Child Problematic Traits Inventory (CPTI). The reliability and validity of the CPTI was tested in a Swedish general population sample of 2,056 3- to 5-year-olds (mean age = 3.86; SD = .86; 53 % boys). The CPTI items loaded distinctively on three theoretically proposed factors: a Grandiose-Deceitful Factor, a Callous-Unemotional factor, and an Impulsive-Need for Stimulation factor. The three CPTI factors showed reliability in internal consistency and external validity, in terms of expected correlations with theoretically relevant constructs (e.g., fearlessness). The interaction between the three CPTI factors was a stronger predictor of concurrent conduct problems than any of the three individual CPTI factors, showing that it is important to assess all three factors of the psychopathic personality construct in early childhood. In conclusion, the CPTI seems to reliably and validly assess a constellation of traits that is similar to psychopathic personality as manifested in adolescence and adulthood.
Multiple Peaks in the Angular Power Spectrum of the Cosmic Microwave Background: Significance and Consequences for Cosmology

15 pages, 6 figures, submitted to Ap.J
Table 1 from Blood-Based Genomic Alteration Signature for Predicting Progression-Free Survival in &lt;i&gt;De Novo&lt;/i&gt; Metastatic Hormone-Sensitive Prostate Cancer: A Real-World Study

&lt;p&gt;Clinical characteristics at diagnosis with mHSPC.&lt;/p&gt;
Physics and Chemistry on the Surface of Cosmic Dust Grains: A Laboratory View

Dust grains play a central role in the physics and chemistry of cosmic environments. They influence the optical and thermal properties of the medium due to their interaction with stellar radiation; provide surfaces for the chemical reactions that are responsible for the synthesis of a significant fraction of key astronomical molecules; and they are building blocks of pebbles, comets, asteroids, planetesimals, and planets. In this paper, we review experimental studies of physical and chemical processes, such as adsorption, desorption, diffusion, and reactions forming molecules, on the surface of reliable cosmic dust grain analogues as related to processes in diffuse, translucent, and dense interstellar clouds, protostellar envelopes, planet-forming disks, and planetary atmospheres. The information that such experiments reveal should be flexible enough to be used in many different environments. In addition, we provide a forward look discussing new ideas, experimental approaches, and research directions.
COVID-19 and Youth Mental Health Disparities: Intersectional Trends in Depression, Anxiety and Suicide Risk-Related Diagnoses

Mental health disparities were prevalent among racially and ethnically minoritized youth prior to the COVID-19 pandemic. As complete datasets from 2022 become available, we can estimate the extent to which the pandemic further magnified existing inequities. Our objective was to quantify disparities in trajectories of depression, anxiety, and suicide risk-related diagnoses in youth before and after the start of the COVID-19 pandemic, using an intersectional lens of race, ethnicity and gender.Using electronic medical record data from one mid-Atlantic health care system (2015-2022), we evaluated changes in annual rates of depression, anxiety and suicide risk-related diagnoses in 29,117 youths, aged 8-20 years, using graphical analysis, comparison of adjusted mean differences (AMD) and adjusted mixed multilevel logistic regression.Almost all racial and gender subgroups had significantly higher rates of depression and anxiety after the start of COVID-19 compared to the years prior, with the greatest changes observed in Hispanic and Asian females. Suicide risk-related diagnoses significantly increased among all female subgroups, with the largest increase among Asian females (AMD 4.8, 95% CI 0.2-9.3) and Black females (AMD 4.6, 95% CI 2.2-6.9).Rates of depression, anxiety, and suicidal thoughts and/or behaviors in young people continued to increase in the post-pandemic period. Many pre-existing disparities between subgroups, especially females, significantly widened, highlighting the importance of using an intersectional lens. Urgent action is warranted, including universal screening of pediatric patients for suicide risk, broadening effective treatment and support options in minoritized patients, and increasing support services to patients and families.
Lightweight authentication and key management of wireless sensor networks for Internet of things

Tiivistelmä Esineiden internet (IoT) on viime aikoina yleistynyt konsepti älykkäiden objektien (smart objects) liittämiseksi internetiin käyttämällä erilaisia verkko- ja kommunikaatioteknologioita. Olennaisimpia esineiden internetin pohjalla toimivia teknologioita ovat langattomat sensoriverkot (WSN), jotka ovat esineiden internetin perusrakennuspalikoita. Esineiden internetiin kytketyt langattomat sensoriverkot mahdollistavat laajan joukon erilaisia sovelluksia, kuten älykodit, etäterveydenhuollon, älykkäät kaupungit sekä älykkäät teollisuuden sovellukset. Langattomien sensoriverkkojen ja esineiden internetin yhdistäminen tuo mukanaan myös tietoturvaan liittyviä haasteita, sillä laskentateholtaan yleensä heikot anturit ja toimilaitteet eivät kykene kovin vaativiin tietoturvaoperaatioihin, joihin lukeutuvat mm. tietoturva-avaimen muodostus ja käyttäjäntunnistus. Tässä väitöskirjassa pyritään vastaamaan haasteeseen käyttämällä kevyitä avaimenmuodostus- ja käyttäjäntunnistusratkaisuja esineiden internetiin kytketyissä resurssirajoitetuissa sensoriverkoissa. Väitöstutkimuksessa keskitytään aluksi implisiittisten sertifikaattien käyttöön tietoturvallisten end-to-end-kommunikaatiokanavien alustamisessa resurssirajoitettujen sensori- ja muiden IoT-laitteiden välillä. Implisiittisiä sertifikaatteja käytetään käyttäjäntunnistuksessa sekä avaimenmuodostuksessa. Kehitettyjen ratkaisujen soveltuvuus tarkoitukseen osoitetaan suorituskykymittauksilla sekä vertaamalla niiden tietoturvaomi- naisuuksia. Seuraavaksi väitöskirjassa esitellään kaksi kevyttä ryhmäavaimenmuodostus- protokollaa tietoturvalliseen ryhmäkommunikaatioon resurssirajoitettujen IoT-laitteiden välillä. Lopuksi väitöskirjassa tarkastellaan lupaavia lähestymistapoja olemassa olevien tietoturvaprotokollien räätäläintiin IoT-laitteiden ja -verkkojen ominaisuuksille sopiviksi. Erityistä huomiota kiinnitetään Host Identity -protokollan (HIP) eri versioiden käyttöön dynaamisten ja tietoturvallisten end-to-end-yhteyksien luomiseen toisilleen ennestään tuntemattomien erityyppisten IoT-laitteiden välillä, joiden laitteistoresurssiprofiilit voivat olla hyvin erilaiset. Väitöskirjan keskeinen tulos on väitöskirjatyössä kehitetty Colla- borative HIP (CHIP) -protokolla, joka on resurssitehokas avaimenmuodostusteknologia resurssirajoitetuille IoT-laitteille. Kehitetyn teknologian soveltuvuutta tarkoitukseensa demonstroidaan prototyyppitoteutuksella tehtyjen suorituskykymittausten avulla.
Federated Neural Collaborative Filtering for privacy-preserving recommender systems

In this thesis a number of models for recommender systems are explored, all using collaborative filtering to produce their recommendations. Extra focus is put on two models: Matrix Factorization, which is a linear model and Multi-Layer Perceptron, which is a non-linear model. With an additional purpose of training the models without collecting any sensitive data from the users, both models were implemented with a learning technique that does not require the server's knowledge of the users' data, called federated learning. The federated version of Matrix Factorization is already well-researched, and has proven not to protect the users' data at all; the data is derivable from the information that the users communicate to the server that is necessary for the learning of the model. However, on the federated Multi-Layer Perceptron model, no research could be found. In this thesis, such a model is therefore designed and presented. Arguments are put forth in support of the privacy preservability of the model, along with a proof of the user data not being analytically derivable for the central server.    In addition, new ways to further put the protection of the users' data on the test are discussed. All models are evaluated on two different data sets. The first data set contains data on ratings of movies and is called MovieLens 1M. The second is a data set that consists of anonymized fund transactions, provided by the Swedish bank SEB for this thesis. Test results suggest that the federated versions of the models can achieve similar recommendation performance as their non-federated counterparts.