Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidence that implementation intentions reduce self‐harm in the community

Abstract Objectives Implementation intentions are ‘IF‐THEN’ plans that encourage goal‐intended behaviour. This study was designed to test whether an intervention encouraging the formation of implementation intentions can reduce self‐harm in the community. Design A randomized controlled design was used. Methods At pre‐intervention, outcome variables (self‐harm in both specified and unspecified critical situations and suicidality) and potential moderators of implementation intentions (goal intention, mental imagery, and exposure to self‐harm) were measured using self‐report questionnaires. The participants (N = 469, aged 18–66 years, 86.4% female, 6.8% male and 6.7% other) were then randomized to either an experimental (implementation intention) or control task. At three‐months post‐intervention, self‐report questionnaires were used again to measure the outcome variables. Results There were no overall differences between the conditions at post‐intervention. However, goal intention and mental imagery, but not exposure to self‐harm, moderated the effects of condition on self‐harm in specified critical situations. At high (mean + 1SD) levels of both goal intention and mental imagery, the experimental condition reported self‐harming less frequently in the situations specified in their implementation intentions. Conclusions Implementation intentions therefore represent a useful intervention for reducing self‐harm in specified critical situations for people in the community who wish to avoid self‐harm and those who frequently experience self‐harm and suicide related mental imagery.
Development of severe alcohol related liver disease over four decades in Iceland: impact of increased access and use of alcohol

Abstract Objective Limited data exist on the association between per capita alcohol consumption and incidence of alcohol related liver disease (ARLD). The aims were to analyse this relationship and assess prevalence of ARLD in Iceland and among patients treated for alcohol use disorder (AUD) and its impact on outcomes. Methods A retrospective study on all patients diagnosed with severe ARLD: alcohol related cirrhosis (ARC) and alcohol related hepatitis (ARH) in Iceland 1984–2020. Medical records were scrutinized for clinical features, severity of ARLD, proportion undergoing treatment for AUD, data on abstinence and long-term outcomes. Results A total of 314 patients, males 76%, median age 56 years, fulfilled the predetermined criteria for ARLD. Median MELD was 17, 73% with Child-Pugh B/C and 70/314 (22%) who had ARH. Incidence of ARLD increased from 0.77 cases per 100 000 inhabitants annually 1984–2000 to 6.1 per 100 000 in 2016–2020. Per capita alcohol consumption increased from 4.3 Liters to 7.5 L in in the same time periods. Overall 220/314 (70%) with ARLD had undergone treatment for AUD. Of all individuals who had AUD treatment during the study period (n = 21.845), 1% were diagnosed with ARLD. Patients who underwent treatment for AUD after the ARLD diagnosis had better prognosis than those who had treatment prior to ARLD diagnosis (hazard ratio 2.5 [95% CI 1.3–5.0]). Conclusions The incidence of ARLD increased 8-fold during the study period coinciding with 74% increase in per capita alcohol consumption. Patients with prior diagnosis of AUD had worse prognosis that needs special attention.
ChatGPT-A double-edged sword for healthcare education? Implications for assessments of dental students.

INTRODUCTION Open-source generative artificial intelligence (AI) applications are fast-transforming access to information and allow students to prepare assignments and offer quite accurate responses to a wide range of exam questions which are routinely used in assessments of students across the board including undergraduate dental students. This study aims to evaluate the performance of Chat Generative Pre-trained Transformer (ChatGPT), a generative AI-based application, on a wide range of assessments used in contemporary healthcare education and discusses the implications for undergraduate dental education. MATERIALS AND METHODS This was an exploratory study investigating the accuracy of ChatGPT to attempt a range of recognised assessments in healthcare education curricula. A total of 50 independent items encompassing 50 different learning outcomes (n = 10 per item) were developed by the research team. These included 10 separate items based on each of the five commonly used question formats including multiple-choice questions (MCQs); short-answer questions (SAQs); short essay questions (SEQs); single true/false questions; and fill in the blanks items. Chat GPT was used to attempt each of these 50 questions. In addition, ChatGPT was used to generate reflective reports based on multisource feedback; research methodology; and critical appraisal of the literature. RESULTS ChatGPT application provided accurate responses to majority of knowledge-based assessments based on MCQs, SAQs, SEQs, true/false and fill in the blanks items. However, it was only able to answer text-based questions and did not allow processing of questions based on images. Responses generated to written assignments were also satisfactory apart from those for critical appraisal of literature. Word count was the key limitation observed in outputs generated by the free version of ChatGPT. CONCLUSION Notwithstanding their current limitations, generative AI-based applications have the potential to revolutionise virtual learning. Instead of treating it as a threat, healthcare educators need to adapt teaching and assessments in medical and dental education to the benefits of the learners while mitigating against dishonest use of AI-based technology.
Comportement anisotrope du grès des Vosges : élasto-plasticité, localisation, rupture

L'objet de ce travail est l'analyse experimentale et theorique du comportement mecanique anisotrope d'un gres des Vosges, dans les domaines des deformations elasto-plastiques, de la localisation des deformations et de la rupture. Un interet particulier porte sur l'influence de l'orthotropie de revolution initiale du materiau sur l'orientation des bandes de cisaillement apparaissant pour des etats de compressions sous pressions de confinement. Un programme experimental a ete developpe pour la caracterisation mecanique du comportement de la roche sous compressions isotropes, compressions orientees sous pressions de confinement et tractions simples orientees. Des methodes experimentales specifiques pour les mesures de toutes les composantes du tenseur des deformations elasto-plastiques d'une roche poreuse sous pressions de confinement ont ete concues et developpees dans le souci permanent d'obtenir des donnees experimentales fiables. Les essais realises ont permis, d'une part, d'obtenir un ensemble de resultats realistes et coherents sur les caracteristiques elasto-plastiques orthotropes de revolution de la roche, et d'autre part, de mettre en evidence l'orientation privilegiee des bandes de cisaillement par rapport aux structures internes orientees du materiau. Une methode de detection du seuil de localisation des deformations a egalement ete developpee, Parallelement, l'orientation privilegiee des bandes de cisaillement apparaissant dans des roches orthotropes de revolution soumises a des compressions triaxiales de revolution a ete modelisee. Cette modelisation a ete realisee par l'introduction des structures materielles dans l'analyse de Rice de la localisation des deformations elasto-plastiques, grâce aux formes generales invariantes des lois de comportement anisotrope.
Aspects of the ecology of the common raven in Harney Basin, Oregon

Common Ravens (Corvus corax L.) have been implicated as significant predators on the eggs of waterfowl and shorebirds on Malheur National Wildlife Refuge. Malheur Refuge, located in southeastern Oregon on the northern edge of the Great Basin, is one of the largest waterfowl refuges in the United States and is an important breeding area for waterfowl. In order to provide fundamental information on which a sound raven management plan could be based, research was conducted from 1975-1977 on aspects of population density, brood phenology, nesting success, seasonal use of the study area, roosting behavior, and food habits of ravens on and near the refuge. Nesting density was determined to be one pair per approximately 25 km2 . Most nests occurred in rimrocks, but trees and abandoned human structures were also used. Investigation of 87 nests revealed that the incubation period was 21 + 1 days. Incubation began with the laying of the first egg; hatching was asynchronous. Nesting period was 41 + 3 days. An original method of age-classing ravens is described. A total of 266 ravens was marked with patagial tags. Observation of marked individuals as far as 480 km from the study area suggests considerable mobility in the population. Population numbers vary seasonally, peaking in the winter. The Harney Basin is the location of an exceptionally large winter roost for ravens. Analysis of food remains, collected from 34 nests, indicates that ravens have varied diets and that there are significant differences in the diets of ravens nesting in different habitats. A correlation exists between the proportion of the diet that is avian material and the proximity of the raven nest to waterfowl production areas. 2 Based on these findings, suggestions are offered for a management plan for Common Ravens on Malheur National Wildlife Refuge.
The role of zinc on nutritional status, sarcopenia, and frailty in older adults: a scoping review.

BACKGROUND Zinc (Zn) deficiency, malnutrition, sarcopenia, and frailty are prevalent among older adults and are prominent factors contributing to disability and mortality. OBJECTIVE This scoping review was conducted to aid understanding of the extent and types of research addressing the role of Zn in nutritional status, sarcopenia, and frailty, among older individuals. METHOD A systematic search was performed in August 2022 of 3 electronic databases (PubMed, Web of Science, and ProQuest) using predefined search terms. The review was conducted referring to the Arksey and O'Malley framework and PRISMA-ScR. RESULTS The search retrieved 16 018 records, and a total of 49 studies were included in this review after the screening. Of those, 30 were based on dietary Zn intake, 18 on tissue Zn levels, and 1 on both. Most studies were based on cross-sectional data from community-dwelling older adults. Studies addressing the associations between Zn status and individual anthropometric and sarcopenia-related variables reported inconsistent results. However, most studies reported inverse associations between malnutrition, frailty, and Zn status. CONCLUSION There was more consistent evidence of the relationship of Zn status with malnutrition, sarcopenia, and frailty rather than with individual nutritional parameters. Validated screening and assessment tools and criteria and prospective studies are required to elucidate the relationship of Zn with sarcopenia and frailty in the older population.
The genetic basis of variation in immune defense against Lysinibacillus fusiformis infection in Drosophila melanogaster

The genetic causes of phenotypic variation often differ depending on the population examined, particularly if the populations were founded by relatively small numbers of genotypes. Similarly, the genetic causes of phenotypic variation among similar traits (resistance to different xenobiotic compounds or pathogens) may also be completely different or only partially overlapping. Differences in genetic causes for variation in the same trait among populations suggests context dependence for how selection acts on those traits. Similarities in the genetic causes of variation for different traits, on the other hand, suggests pleiotropy which would also influence how natural selection shapes variation in a trait. We characterized immune defense against a natural Drosophila pathogen, the Gram-positive bacterium Lysinibacillus fusiformis, in three different populations and found almost no overlap in the genetic architecture of variation in survival post infection. However, when comparing our results to a similar experiment with the fungal pathogen, B. bassiana, we found a convincing shared QTL peak for both pathogens. This peak contains the Bomanin cluster of Drosophila immune effectors. Loss of function mutants and RNAi knockdown experiments confirms a role of some of these genes in immune defense against both pathogens. This suggests that natural selection may act on the entire cluster of Bomanin genes (and the linked region under the QTL) or specific peptides for specific pathogens.
Intersections of randomly translated sets

Let $\Xi_n=\{\xi_1,\dots,\xi_n\}$ be a sample of $n$ independent points distributed on a regular closed element $K$ of the extended convex ring in $\mathbb{R}^d$ according to a probability measure $\mu$ on $K$, admitting a density function. We consider random sets generated from the intersection of the translations of $K$ by elements of $\Xi_n$, as $X_n=\bigcap_{i=1}^n (K-\xi_i)$. The aim of this work is to show that scaled $X_n$ as $n\to\infty$ converges in distribution to the zero cell of a Poisson hyperplane tessellation whose distributionis is determined by the curvature measure of $K$ and the behaviour of the density of $\mu$ near the boundary of $K$.
On non-abelian dp-minimal groups

Let $G$ be a dp-minimal group; we prove some consequences of several different hypotheses on $G$. First, if $G$ is torsion-free, then it is abelian. Second, if $G$ admits a distal f-generic type, then it is virtually nilpotent; we prove this by equipping the quotient of $G$ by its FC-center in this case with a valued group structure. Finally, if $G$ has the uniform chain condition, for example if $G$ is stable, then $G$ is virtually solvable.
Composite Bulges. III. A Study of Nuclear Star Clusters in Nearby Spiral Galaxies

We present photometric and morphological analyses of nuclear star clusters (NSCs)—very dense, massive star clusters present in the central regions of most galaxies—in a sample of 33 massive disk galaxies within 20 Mpc, part of the “Composite Bulges Survey. ” We use data from the Hubble Space Telescope including optical (F475W and F814W) and near-IR (F160W) images from the Wide Field Camera 3. We fit the images in 2D to take into account the full complexity of the inner regions of these galaxies (including the contributions of nuclear disks and bars), isolating the NSC and bulge components. We derive NSC radii and magnitudes in all three bands, which we then use to estimate NSC masses. Our sample significantly expands the sample of massive late-type galaxies with measured NSC properties. We clearly identify NSCs in nearly 80% of our galaxies, putting a lower limit on the nucleation fraction in these galaxies that is higher than previous estimates. We find that the NSCs in our massive disk galaxies are consistent with previous NSC mass–NSC radius and galaxy mass–NSC mass relations. However, we also find a large spread in NSC masses, with a handful of galaxies hosting very low-mass, compact clusters. Our NSCs are aligned in PA with their host galaxy disks but are less flattened. They show no correlations with bar or bulge properties. Finally, we find the ratio of NSC to BH mass in our massive disk galaxy sample spans a factor of ∼300.
Scaling invariance of spatial autocorrelation in urban built-up area

City is proved to be a scale-free phenomenon, and spatial autocorrelation is often employed to analyze spatial redundancy of cities. Unfortunately, spatial analysis results deviated practical requirement in many cases due to fractal nature of cities. This paper is devoted to revealing the internal relationship between the scale dependence of Moran's I and fractal scaling. Mathematical reasoning and empirical analysis are employed to derive and test the model on the scale dependence of spatial autocorrelation. The data extraction way for fractal dimension estimation is box-counting method, and parameter estimation relies on the least squares regression. In light of the locality postulate of spatial correlation and the idea of multifractals, a power law model on Moran's I changing with measurement scale is derived from the principle of recursive subdivision of space. The power exponent is proved to be a function of fractal dimension. This suggests that the numerical relationship between Moran's I and fractal dimension can be established through the scaling process of granularity. An empirical analysis is made to testify the theoretical model. It can be concluded that spatial autocorrelation of urban built-up area has no characteristic scale in many cases, and urban spatial analysis need new thinking.
Synthetic Augmentation with Large-scale Unconditional Pre-training

Deep learning based medical image recognition systems often require a substantial amount of training data with expert annotations, which can be expensive and time-consuming to obtain. Recently, synthetic augmentation techniques have been proposed to mitigate the issue by generating realistic images conditioned on class labels. However, the effectiveness of these methods heavily depends on the representation capability of the trained generative model, which cannot be guaranteed without sufficient labeled training data. To further reduce the dependency on annotated data, we propose a synthetic augmentation method called HistoDiffusion, which can be pre-trained on large-scale unlabeled datasets and later applied to a small-scale labeled dataset for augmented training. In particular, we train a latent diffusion model (LDM) on diverse unlabeled datasets to learn common features and generate realistic images without conditional inputs. Then, we fine-tune the model with classifier guidance in latent space on an unseen labeled dataset so that the model can synthesize images of specific categories. Additionally, we adopt a selective mechanism to only add synthetic samples with high confidence of matching to target labels. We evaluate our proposed method by pre-training on three histopathology datasets and testing on a histopathology dataset of colorectal cancer (CRC) excluded from the pre-training datasets. With HistoDiffusion augmentation, the classification accuracy of a backbone classifier is remarkably improved by 6.4% using a small set of the original labels. Our code is available at https://github.com/karenyyy/HistoDiffAug.
Molecular docking via quantum approximate optimization algorithm

Molecular docking plays a pivotal role in drug discovery and precision medicine, enabling us to understand protein functions and advance novel therapeutics. Here, we introduce a potential alternative solution to this problem, the digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA), which utilizes counterdiabatic driving and QAOA on a quantum computer. Our method was applied to analyze diverse biological systems, including the SARS-CoV-2 Mpro complex with PM-2-020B, the DPP-4 complex with piperidine fused imidazopyridine 34, and the HIV-1 gp120 complex with JP-III-048. The DC-QAOA exhibits superior performance, providing more accurate and biologically relevant docking results, especially for larger molecular docking problems. Moreover, QAOA-based algorithms demonstrate enhanced hardware compatibility in the noisy intermediate-scale quantum era, indicating their potential for efficient implementation under practical docking scenarios. Our findings underscore quantum computing's potential in drug discovery and offer valuable insights for optimizing protein-ligand docking processes.
Extended mean-field control problems with multi-dimensional singular controls

We consider extended mean-field control problems with multi-dimensional singular controls. A key challenge when analysing singular controls are jump costs. When controls are one-dimensional, jump costs are most naturally computed by linear interpolation. When the controls are multi-dimensional the situation is more complex, especially when the model parameters depend on an additional mean-field interaction term, in which case one needs to"jointly"and"consistently"interpolate jumps both on a distributional and a pathwise level. This is achieved by introducing the novel concept of two-layer parametrisations of stochastic processes. Two-layer parametrisations allow us to equivalently rewrite rewards in terms of continuous functions of parametrisations of the control process and to derive an explicit representation of rewards in terms of minimal jump costs. From this we derive a DPP for extended mean-field control problems with multi-dimensional singular controls. Under the additional assumption that the value function is continuous we characterise the value function as the minimal super-solution to a certain quasi-variational inequality in the Wasserstein space.
Improving Requirements Completeness: Automated Assistance through Large Language Models

Natural language (NL) is arguably the most prevalent medium for expressing systems and software requirements. Detecting incompleteness in NL requirements is a major challenge. One approach to identify incompleteness is to compare requirements with external sources. Given the rise of large language models (LLMs), an interesting question arises: Are LLMs useful external sources of knowledge for detecting potential incompleteness in NL requirements? This article explores this question by utilizing BERT. Specifically, we employ BERT's masked language model (MLM) to generate contextualized predictions for filling masked slots in requirements. To simulate incompleteness, we withhold content from the requirements and assess BERT's ability to predict terminology that is present in the withheld content but absent in the disclosed content. BERT can produce multiple predictions per mask. Our first contribution is determining the optimal number of predictions per mask, striking a balance between effectively identifying omissions in requirements and mitigating noise present in the predictions. Our second contribution involves designing a machine learning-based filter to post-process BERT's predictions and further reduce noise. We conduct an empirical evaluation using 40 requirements specifications from the PURE dataset. Our findings indicate that: (1) BERT's predictions effectively highlight terminology that is missing from requirements, (2) BERT outperforms simpler baselines in identifying relevant yet missing terminology, and (3) our filter significantly reduces noise in the predictions, enhancing BERT's effectiveness as a tool for completeness checking of requirements.