Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Identity Mapping in Addiction Recovery (SIM-AR): extension and application of a visual method

Abstract Background: The Social Identity approach offers a unifying framework for understanding recovery from addiction as a process of identity change, associated with change in social network composition. This paper introduces Social Identity Mapping in Addiction Recovery (SIM-AR) — a visual method for capturing social group memberships, extended to integrate the substance use ‘status’ of group members as an indicator of group substance use norms. The aim here is to test theory-derived predictions focused on the relationship between changes in social identity and network composition reflected in groups' substance use norms in early recovery. Method: 155 therapeutic community (TC) residents in Victoria, Australia, completed the SIM-AR plus measures of substance-using and recovery identities and substance use shortly after admission, and 65% (N = 101) again 6 months later. Results: As predicted, substance use severity at follow up was associated with changes in both social identity and network composition. Furthermore, reduced strength of substance-using identity was associated with a decrease in the proportion of groups with heavy substance use norms, while increased strength of ‘recovery’ identity was associated with an increased proportion of non-using groups. Conclusion: SIM-AR proved useful in testing predictions about social identity and network changes in a residential treatment context. It captured key social identity constructs in recovery using a visual technique with value to both research and applied contexts. Findings highlight the clinical importance of assessing a person’s group-based relationships in treatment and early recovery, especially the influence of social group norms in relation to substance use.
The Primacy Effect in Amnestic Mild Cognitive Impairment: Associations with Hippocampal Functional Connectivity

Background: The “primacy effect,” i.e., increased memory recall for the first items of a series compared to the following items, is reduced in amnestic mild cognitive impairment (aMCI). Memory task-fMRI studies demonstrated that primacy recall is associated with higher activation of the hippocampus and temporo-parietal and frontal cortical regions in healthy subjects. Functional magnetic resonance imaging (fMRI) at resting state revealed that hippocampus functional connectivity (FC) with neocortical brain areas, including regions of the default mode network (DMN), is altered in aMCI. The present study aimed to investigate whether resting state fMRI FC between the hippocampus and cortical brain regions, especially the DMN, is associated with primacy recall performance in aMCI. Methods: A number of 87 aMCI patients underwent resting state fMRI and verbal episodic memory assessment. FC between the left or right hippocampus, respectively, and all other voxels in gray matter was mapped voxel-wise and used in whole-brain regression analyses, testing whether FC values predicted delayed primacy recall score. The delayed primacy score was defined as the number of the first four words recalled on the California Verbal Learning Test. Additionally, a partial least squares (PLS) analysis was performed, using DMN regions as seeds to identify the association of their functional interactions with delayed primacy recall. Results: Voxel-based analyses indicated that delayed primacy recall was mainly (positively) associated with higher FC between the left and right hippocampus. Additionally, significant associations were found for higher FC between the left hippocampus and bilateral temporal cortex, frontal cortical regions, and for higher FC between the right hippocampus and right temporal cortex, right frontal cortical regions, left medial frontal cortex and right amygdala (p < 0.01, uncorr.). PLS analysis revealed positive associations of delayed primacy recall with FC between regions of the DMN, including the left and right hippocampus, as well as middle cingulate cortex and thalamus (p < 0.04). In conclusion, in the light of decreased hippocampus function in aMCI, inter-hemispheric hippocampus FC and hippocampal FC with brain regions predominantly included in the DMN may contribute to residual primacy recall in aMCI.
Functional association networks as priors for gene regulatory network inference

Motivation: Gene regulatory network (GRN) inference reveals the influences genes have on one another in cellular regulatory systems. If the experimental data are inadequate for reliable inference of the network, informative priors have been shown to improve the accuracy of inferences. Results: This study explores the potential of undirected, confidence-weighted networks, such as those in functional association databases, as a prior source for GRN inference. Such networks often erroneously indicate symmetric interaction between genes and may contain mostly correlation-based interaction information. Despite these drawbacks, our testing on synthetic datasets indicates that even noisy priors reflect some causal information that can improve GRN inference accuracy. Our analysis on yeast data indicates that using the functional association databases FunCoup and STRING as priors can give a small improvement in GRN inference accuracy with biological data. Contact: matthew.studham@scilifelab.se Supplementary information: Supplementary data are available at Bioinformatics online.
A lower limb exoskeleton research platform to investigate human-robot interaction

Designing the underlying mechanical structure of lower limb exoskeletons for assistance and rehabilitation is a demanding task that requires a good understanding of the interaction that takes place between the exoskeleton and the human user wearing it. Often the effects of a given mechanical design on the user are not straightforward or intuitive. One obstacle for research is that existing rehabilitation systems do not offer the flexibility that is necessary to investigate different designs and ideas. In this paper we present a passive experimental lower limb exoskeleton that is specifically built to evaluate exoskeleton design-elements and different characteristics. These are namely a joint misalignment compensation mechanism, a three DOF hip joint design, the lack of mechanical transparency as well as the placement of the interfacing cuffs. The motivation and mechanical design of the system is presented along with the results of pilot trials to validate the system as a suitable experimental platform for our investigations.
The stellar mass spectrum in warm and dusty gas: deviations from Salpeter in the Galactic centre and in circumnuclear starburst regions

Understanding the origin of stellar masses is a key problem in astrophysics. In the solar neighbourhood, the mass distribution of stars follows a seemingly universal pattern. In the centre of the Milky Way, however, there are indications for strong deviations and the same may be true for the nuclei of distant starburst galaxies. Here we present the first numerical hydrodynamical calculations of stars formed in a molecular region with chemical and thermodynamic properties similar to those of warm and dusty circumnuclear starburst regions. The resulting initial mass function is top-heavy with a peak at similar to 15 M-circle dot, a sharp turn-down below similar to 7 M-circle dot and a power-law decline at high masses. We find a natural explanation for our results in terms of the temperature dependence of the Jeans mass, with collapse occurring at a temperature of similar to 100 K and an H-2 density of a few x10(5) cm(-3), and discuss possible implications for galaxy formation and evolution.
Developing a Framework to Generate Evidence of Health Outcomes From Social Media Use in Chronic Disease Management

Background While there is an abundance of evidence-based practice (EBP) recommendations guiding management of various chronic diseases, evidence suggesting best practice for using social media to improve health outcomes is inadequate. The variety of social media platforms, multiple potential uses, inconsistent definitions, and paucity of rigorous studies, make it difficult to measure health outcomes reliably in chronic disease management. Most published investigations report on an earlier generation of online tools, which are not as user-centered, participatory, engaging, or collaborative, and thus may work differently for health self-management. Objective The challenge to establish a sound evidence base for social media use in chronic disease starts with the need to define criteria and methods to generate and evaluate evidence. The authors’ key objective is to develop a framework for research and practice that addresses this challenge. Methods This paper forms part of a larger research project that presents a conceptual framework of how evidence of health outcomes can be generated from social media use, allowing social media to be utilized in chronic disease management more effectively. Using mixed methods incorporating a qualitative literature review, a survey and a pilot intervention, the research closely examines the therapeutic affordances of social media, people with chronic pain (PWCP) as a subset of chronic disease management, valid outcome measurement of patient-reported (health) outcomes (PRO), the individual needs of people living with chronic disease, and finally translation of the combined results to improve evidence-based decision making about social media use in this context. Results Extensive review highlights various affordances of social media that may prove valuable to understanding social media’s effect on individual health outcomes. However, without standardized PRO instruments, we are unable to definitively investigate these effects. The proposed framework that we offer outlines how therapeutic affordances of social media coupled with valid and reliable PRO measurement may be used to generate evidence of improvements in health outcomes, as well as guide evidence-based decision making in the future about social media use as part of chronic disease self-management. Conclusions The results will (1) inform a framework for conducting research into health outcomes from social media use in chronic disease, as well as support translating the findings into evidence of improved health outcomes, and (2) inform a set of recommendations for evidence-based decision making about social media use as part of chronic disease self-management. These outcomes will fill a gap in the knowledge and resources available to individuals managing a chronic disease, their clinicians and other researchers in chronic disease and the field of medicine 2.0.
A complementary systems account of word learning: neural and behavioural evidence

In this paper we present a novel theory of the cognitive and neural processes by which adults learn new spoken words. This proposal builds on neurocomputational accounts of lexical processing and spoken word recognition and complementary learning systems (CLS) models of memory. We review evidence from behavioural studies of word learning that, consistent with the CLS account, show two stages of lexical acquisition: rapid initial familiarization followed by slow lexical consolidation. These stages map broadly onto two systems involved in different aspects of word learning: (i) rapid, initial acquisition supported by medial temporal and hippocampal learning, (ii) slower neocortical learning achieved by offline consolidation of previously acquired information. We review behavioural and neuroscientific evidence consistent with this account, including a meta-analysis of PET and functional Magnetic Resonance Imaging (fMRI) studies that contrast responses to spoken words and pseudowords. From this meta-analysis we derive predictions for the location and direction of cortical response changes following familiarization with pseudowords. This allows us to assess evidence for learning-induced changes that convert pseudoword responses into real word responses. Results provide unique support for the CLS account since hippocampal responses change during initial learning, whereas cortical responses to pseudowords only become word-like if overnight consolidation follows initial learning.
A comparison of machine learning algorithms for proactive hard disk drive failure detection

Failures or unexpected events are inevitable in critical and complex systems. Proactive failure detection is an approach that aims to detect such events in advance so that preventative or recovery measures can be planned, thus improving system availability. Machine learning techniques have been successfully applied to learn patterns from available datasets and to classify or predict to which class a new instance of data belongs. In this paper, we evaluate and compare the performance of 21 machine learning algorithms by using them for proactive hard disk drive failure detection. For this comparison, we use WEKA as an experimentation platform and benchmark publicly available datasets of hard disk drives that are used to predict imminent failures before the actual failures occur. The results show that different algorithms are suitable for different applications based on the desired prediction quality and the tolerated training and prediction time.
Texture Analysis Using Modified Discrete Radon Transform

In this paper, we address the problem of the rotation-invariant texture analysis. For this purpose, we first present a modified version of the discrete Radon transform whose performance, including accuracy and processing time, is significantly better than the conventional transform in direction estimation and categorization of textural images. We then utilize this transform with a rotated version of Gabor filters to propose a new scheme for texture classification. Experimental results on a set of images from the Brodatz album indicate that the proposed scheme outperforms previous works.
The 15-20 μm Spitzer Spectra of Interstellar Emission Features in NGC 7023

We present 15-20 μm long-slit spectra of NGC 7023 from the Infrared Spectrograph (IRS) on Spitzer. We observe recently discovered interstellar emission features at 15.9, 16.4, 17.0, 17.4, 17.8, and 18.9 μm, throughout the reflection nebula. The 16.4 μm emission feature peaks near the photodissociation front northwest of the star, as the aromatic emission features (AEFs) at 3.3, 6.2, and 11.3 μm do. The 16.4 μm emission feature is thus likely related to the AEFs and radiates by nonequilibrium emission. The new 18.9 μm emission feature, by contrast, decreases monotonically with stellar distance. We consider candidate species for the 18.9 μm feature, including polycyclic aromatic hydrocarbons, fullerenes, and diamonds. We describe future laboratory and observational research needed to identify the 18.9 μm feature carrier.
Stemming the Qur’an

In natural language, a stem is the morphological base of a word to which affixes can be attached to form derivatives. Stemming is a process of assigning morphological variants of words to equivalence classes such that each class corresponds to a single stem. Different stemmers have been developed for a wide range of languages and for a variety of purposes. Arabic, a highly inflected language with complex orthography, requires good stemming for effective text analysis. Preliminary investigation indicates that existing approaches to Arabic stemming fail to provide effective and accurate equivalence classes when applied to a text like the Qur'an written in Classical Arabic. Therefore, I propose a new stemming approach based on a light stemming technique that uses a transliterated version of the Qur'an in western script.
Towards High-Throughput, Multi-Criteria Protein Structure Comparison and Analysis

Protein Structure Comparison (PSC) is an essential component of biomedical research as it impacts on, e.g., drug design, molecular docking, protein folding and structure prediction algorithms as well as being essential to the assessment of these predictions. Each of these applications, as well as many others where molecular comparison plays an important role, requires a different notion of similarity that naturally lead to the Multi-Criteria Protein Structure Comparison (MCPSC) problem. ProCKSI (www.procksi.org), provides algorithmic solutions for the MC-PSC problem by means of an enhanced structural comparison that relies on the principled application of information fusion to similarity assessments derived from multiple comparison methods. Current MC-PSC works well for moderately sized data sets and it is time consuming as it provides public service to multiple users. Many of the structural bioinformatics applications mentioned above would benefit from the ability to perform, for a dedicated user, thousands or tens of thousands of comparisons through multiple methods in realtime, a capacity beyond our current technology. In this paper we take a key step into that direction by means of a highthroughput distributed re-implementation of ProCKSI for very large data sets. The core of the proposed framework lies in the design of an innovative distributed algorithm that runs on each compute node in a cluster/grid environment to perform structure comparison of a given subset of input structures using some of the most popular PSC methods (e.g. USM, MaxCMO, Fast, DaliLite, CE and TMalign). We follow this with a procedure of distributed consensus building. Thus the new algorithms proposed here achieve ProCKSI’s similarity assessment quality but with a fraction of the time required by it. Our results show that the proposed distributed method can be used efficiently to compare a) a particular protein against a very large protein structures data set (target-against-all comparison), b) a particular very large scale dataset against itself or against another very large scale dataset (all-against-all comparison). We conclude the paper by enumerating some of the outstanding challenges for real-time MC-PSC.
Studies on Hepatitis B Vaccination and Factors Associated with the Vaccine Response

BACKGROUND: Hepatitis B vaccine has been shown to be highly efficient in preventing hepatitis B. However, 5%-10% of individuals fail to develop protective levels (>or=10 mIU/mL) of antibodies to hepatitis B surface antigen (anti-HBs) and are considered to be nonresponders. METHODS: A total of 48 nonresponders and 20 subjects naive to the HBV vaccine received a double dose of combined hepatitis A and B vaccine (Twinrix) at 0, 1, and 6 months. The levels of anti-HBs and antibodies to hepatitis A virus (anti-HAV) were determined before vaccination and 1 month after each dose. RESULTS: Among 44 nonresponders, protective anti-HBs levels were found in 26 (59%) after the first dose and in 42 (95%) after the third dose. Among the control subjects, the corresponding figures were 10% and 100%, respectively. All subjects seroconverted to anti-HAV. The titers of both anti-HBs and anti-HAV were lower in the previously nonresponsive subjects (P< .01). CONCLUSION: Revaccination of nonresponders to the standard hepatitis B vaccine regimen with a double dose of the combined hepatitis A and B vaccine was highly effective. This is most likely explained by the increased dose, a positive bystander effect conferred by the hepatitis A vaccine, or both.
Logistic model for determining factors associated with HIV infection among blood donor candidates at the Fundação HEMOPE

Objective To determine factors associated with HIV infection in blood donor candidates in Recife, Brazil. Methods A transversal study was performed of 106,203 blood donor candidates found eligible by the routine clinical screening process in the blood bank in Recife of the Fundação de Hematologia e Hemoterapia de Pernambuco (HEMOPE) in the period from January 1998 to November 2003. Additional indirect immune and western blot confirmation tests for HIV infection were performed and the candidates were classified as HIV positive or negative. The Chi-squared test and stepwise multiple logistic regression were conducted to examine any association between HIV infection and age, gender, place of residence, schooling, number of donations and serological tests for core hepatitis B antigen virus (anti-HBc), the hepatitis C antivirus (anti-HCV), human T-type antivirus lymph cells (anti-HTLV 1 and 2), serological tests for syphilis (VDRL) and the hepatitis B virus surface antigen (HBsAg). Results In the observed sample, 0.204% of blood donor candidates were found to be HIV positive. Among the studied variables, the age, education level, residency, donation type, and serologic status for anti-HBc and VDRL tests were found to be associated with HIV infection. Conclusions The younger, spontaneous donor candidates living in the Recife metropolitan area with a low level of education and positive for anti-HBc and VDRL have an higher risk of HIV infection than the other candidates. Data such as these are useful to understand the dynamics of infection and to guide healthcare policies.
Sensing volume of open-ended coaxial probes for dielectric characterization of breast tissue at microwave frequencies

To achieve accurate measurements, the tissue sample should be homogeneous within a volume large enough so that the measured reflection coefficient is identical to that of a sample filling the entire half-space. Thus, the question arises of the appropriate size of the tissue sample. Sensing volume guidelines have been previously investigated in terms of somewhat arbitrarily chosen constraints on the relative errors in the measured reflection coefficient of tissue-equivalent liquids. In this paper we report sensing volume guidelines that have been developed by first choosing acceptable levels of error in the permittivity and then deriving the appropriate constraints on the errors in the measured reflection coefficient.
Sequence-Based Characterization of Tn5801-Like Genomic Islands in Tetracycline-Resistant Staphylococcus pseudintermedius and Other Gram-positive Bacteria from Humans and Animals

Antibiotic resistance in pathogens is often associated with mobile genetic elements, such as genomic islands (GI) including integrative and conjugative elements (ICEs). These can transfer resistance genes within and between bacteria from humans and/or animals. The aim of this study was to investigate whether Tn5801-like GIs carrying the tetracycline resistance gene, tet(M), are common in Staphylococcus pseudintermedius from pets, and to do an overall sequences-based characterization of Tn5801-like GIs detected in Gram-positive bacteria from humans and animals. A total of 27 tetracycline-resistant S. pseudintermedius isolates from Danish pets (1998–2005) were screened for tet(M) by PCR. Selected isolates (13) were screened for GI- or ICE-specific genes (intTn5801 or xisTn916) and their tet(M) gene was sequenced (Sanger-method). Long-range PCR mappings and whole-genome-sequencing (Illumina) were performed for selected S. pseudintermedius-isolates (seven and three isolates, respectively) as well as for human S. aureus isolates (seven and one isolates, respectively) and one porcine Enterococcus faecium isolate known to carry Tn5801-like GIs. All 27 S. pseudintermedius were positive for tet(M). Out of 13 selected isolates, seven contained Tn5801-like GIs and six contained Tn916-like ICEs. Two different Tn5801-like GI types were detected among S. pseudintermedius (Tn5801 and GI6287) - both showed high similarity compared to GenBank sequences from human pathogens. Two distinct Tn5801-like GI types were detected among the porcine E. faecium and human S. aureus isolates (Tn6014 and GI6288). Tn5801-like GIs were detected in GenBank-sequences from Gram-positive bacteria of human, animal or food origin worldwide. Known Tn5801-like GIs were divided into seven types. The results showed that Tn5801-like GIs appear to be relatively common in tetracycline-resistant S. pseudintermedius in Denmark. Almost identical Tn5801-like GIs were identified in different Gram-positive species of pet and human origin, suggesting that horizontal transfer of these elements has occurred between S. pseudintermedius from pets and human pathogens, including S. aureus.