Miracle-dz/UniFAR-FTUs huggingface dataset:
The Self-Concept Is Represented in the Medial Prefrontal Cortex in Terms of Self-Importance

Knowledge about one's personality, the self-concept, shapes human experience. Social cognitive neuroscience has made strides addressing the question of where and how the self is represented in the brain. The answer, however, remains elusive. We conducted two functional magnetic resonance imaging experiments (the second preregistered) with human male and female participants employing a self-reference task with a broad range of attributes and carrying out a searchlight representational similarity analysis (RSA). The importance of attributes to self-identity was represented in the medial prefrontal cortex (mPFC), whereas mPFC activation was unrelated both to self-descriptiveness of attributes (experiments 1 and 2) and importance of attributes to a friend's self-identity (experiment 2). Our research provides a comprehensive answer to the abovementioned question: The self-concept is conceptualized in terms of self-importance and represented in the mPFC. SIGNIFICANCE STATEMENT The self-concept comprises beliefs about who one is as an individual (e.g., personality traits, physical characteristics, desires, likes/dislikes, and social roles). Despite researchers' efforts in the last two decades to understand where and how the self-concept is stored in the brain, the question remains elusive. Using a neuroimaging technique, we found that a brain region called medial prefrontal cortex (mPFC) shows differential but systematic activation patterns depending on the importance of presented word stimuli to a participant's self-concept. Our findings suggest that one's sense of the self is supported by neural populations in the mPFC, each of which is differently sensitive to distinct levels of the personal importance of incoming information.
Associations of internalized stigma with psychosocial functioning and quality of life in people with schizophrenia

Background: Schizophrenia is associated with poor functional recovery. Internalized stigma is one of the factors related to the functioning of individuals with schizophrenia. We aimed to investigate whether internalized stigma was associated with subjective and objective recovery-related outcomes after controlling for neurocognition and other important confounders in individuals with schizophrenia. Method: We assessed the socio demographic background, psychopathology, neurocognition, internalized stigma, psychosocial functioning, and quality of life of 86 patients who had schizophrenia. Correlation analyses and multiple linear regression were used to investigate the association of internalized stigma and other variables with recovery-related outcomes. Results: We found that the negative symptom scores of the Positive and Negative Syndrome Scale but not internalized stigma was associated with psychosocial functioning as measured by the Personal and Social Performance global score. In contrast, internalized stigma was associated with the Psychological, Social relationships, and Environment scores of the World Health Organization Quality of Life Scale Brief Version (WHOQOL-BREF). Depression was also associated with the Physical health, Psychological, and Social relationships sores of the WHOQOL-BREF. Conclusions: While internalized stigma was associated with several domains of quality of life, it was not associated with clinician-rated psychosocial functioning. The effects of internalized stigma on the subjective and objective recovery-related outcomes of individuals with schizophrenia might be divergent.
M-Ionic: prediction of metal-ion-binding sites from sequence using residue embeddings

Motivation Understanding metal-protein interaction can provide structural and functional insights into cellular processes. As the number of protein sequences increases, developing fast yet precise computational approaches to predict and annotate metal binding sites becomes imperative. Quick and resource-efficient pre-trained protein language model (PLM) embeddings have successfully predicted binding sites from protein sequences despite not using structural or evolutionary features (multiple sequence alignments). Using residue-level embeddings from the PLMs, we have developed a sequence-based method (M-Ionic) to identify metal-binding proteins and predict residues involved in metal-binding. Results On independent validation of recent proteins, M-Ionic reports an area under the curve (AUROC) of 0.83 (recall=84.6%) in distinguishing metal-binding from non-binding proteins compared to AUROC of 0.74 (recall =61.8%) of the next best method. In addition to comparable performance to the state-of-the-art method for identifying metal-binding residues (Ca2+, Mg2+, Mn2+, Zn2+), M-Ionic provides binding probabilities for six additional ions (i.e., Cu2+, Po43-, So42-, Fe2+, Fe3+, Co2+). We show that the PLM embedding of a single residue contains sufficient information about its neighbours to predict its binding properties. Availability and Implementation M-Ionic can be used on your protein of interest using a Google Colab Notebook (https://bit.ly/40FrRbK). GitHub repository (https://github.com/TeamSundar/m-ionic) contains all code and data. Contact arne@bioinfo.se Supplementary information Supplementary data are available at Bioinformatics online.
Inter-user comparison for quantification of superparamagnetic iron oxides with magnetic particle imaging across two institutions highlights a need for standardized approaches

Purpose Magnetic particle imaging (MPI) is being explored in biological contexts that require accurate and reproducible quantification of superparamagnetic iron oxide nanoparticles (SPIONs). While many groups have focused on improving imager and SPION design to improve resolution and sensitivity, few have focused on improving quantification and reproducibility of MPI. The aim of this study was to compare MPI quantification results by two different systems and the accuracy of SPION quantification performed by multiple users at two institutions. Procedures Six users (3 from each institute) imaged a known amount of Vivotrax+ (10 μg Fe), diluted in a small (10 μL) or large (500 μL) volume. These samples were imaged with or without calibration standards in the field of view, to create a total of 72 images (6 users x triplicate samples x 2 sample volumes x 2 calibration methods). These images were analyzed by the respective user with two region of interest (ROI) selection methods. Image intensities, Vivotrax+ quantification, and ROI selection was compared across users, within and across institutions. Results MPI imagers at two different institutes produce significantly different signal intensities, that differ by over 3 times for the same concentration of Vivotrax+. Overall quantification yielded measurements that were within ± 20% from ground truth, however SPION quantification values obtained at each laboratory were significantly different. Results suggest that the use of different imagers had a stronger influence on SPION quantification compared to differences arising from user error. Lastly, calibration conducted from samples in the imaging field of view gave the same quantification results as separately imaged samples. Conclusions This study highlights that there are many factors that contribute to the accuracy and reproducibility of MPI quantification, including variation between MPI imagers and users, despite pre-defined experimental set up, image acquisition parameters, and ROI selection analysis.
Effects of VCD-induced ovarian failure on single muscle fiber contractility in a mouse model of menopause

Objective Menopause is associated with impairments in muscle contractile function. The temporal and mechanistic basis of this dysfunction are not known. Using a mouse model of menopause we identified how gradual ovarian failure affects single muscle fiber contractility. Study design Mice were injected with VCD over 15 days and ovarian failure developed over 120 days. Mice were then sacrificed and slow-type soleus (SOL) and fast-type extensor digitorum longus (EDL) muscles were dissected and chemically permeabilized for mechanical testing. Main outcome measures Muscle fiber contractility was assessed via: force, rate of force redevelopment, instantaneous stiffness, and calcium sensitivity across three relative force levels (pCa10,pCa50,pCa90). Results Peak force and cross-sectional area (CSA) of the SOL were ∼33% and ∼24% greater in the VCD group as compared with controls (P<0.05), respectively, with no differences in force produced by the EDL fibers across groups (P>0.05). Upon normalizing force to CSA there were no differences across groups (P>0.05). Rate of force development was ∼33% faster for SOL in the VCD group compared to control. Ca2+ sensitivity did not differ between groups for either muscle at pCa50 (P>0.05). In the VCD group, Ca2+ sensitivity was higher for EDL, but lower for SOL at pCa10 and pCa90 (P<0.05), respectively. Conclusions In our mouse model of menopause, alterations to muscle contractility were much less evident as compared with ovariectomized models. This divergence across models highlights the importance of better approximating the natural trajectory of menopause during and after the transitional phase of ovarian failure on neuromuscular function.
A computational morphology for Spanish sign language nominal inflection

This paper presents a finite-state computational model for Spanish Sign Language nominal morphology with particular attention to the treatment of morphological alternations. A computational morphology consists of a lexicon, a rewrite rules component relating lexical representations to surface forms, and a morphotactic component. All these components are compiled into finite-state transducers. Nominal morphology in Spanish Sign Language deals with bound morphemes through inflection, introflection and suppletion. The model and its implementation deal with plurals, numeral incorporation and irregularity and can be extended to other word classes as well to other sign languages. The implemented technology allows the application of the morphology either to the analysis or to the generation of signs.
Walking speed estimation based on the use of a single inertial measurement unit and an artificial neural network

This work addresses the estimation of the walking speed by using an artificial neural network (ANN) and a single Inertial Measurement Unit (IMU) placed in the lumbar region, at L3 level. In this context, the main contribution resides in the new features proposed for accomplishing the task, which confer simplicity and effectiveness to the procedure. The ANN application was validated through a database comprising IMU signals recorded from the execution of gait tests on 23 subjects with ages between 20 to 50 years old. In this work, eleven
Risks and Moderators of Mental Health and Illness

Technology-based interventions have been suggested to prevent depression and loneliness. However, it is not yet wellestablished if internet use is a protective factor for older adults, allowing them to combat depression and loneliness. Addressing this neglected area of research, this paper examines whether frequent internet use moderated the relationship between depression and age. Using 2016 General Social Survey (n = 869), a series of ordinary least squares regression models were employed. The findings revealed that older adults who frequently used the internet were more depressed than those who did not. This may imply that internet use by older individuals may not be an adequate source of social engagement and may even heighten existing levels of depression. Other interventions may need to be developed to compensate for the loss of physical contact and reduce the level of depression.
Comparative Phylogenomics and Phylotranscriptomics Provide Insights into the Genetic Complexity of Nitrogen Fixing Root Nodule Symbiosis

Plant root nodule symbiosis (RNS) with mutualistic nitrogen-fixing bacteria is restricted to a single clade of angiosperms, the Nitrogen-Fixing Nodulation Clade (NFNC), and is best understood in the legume family. It is widely accepted that nodulation originated through the assembly of modules recruited from existing functions, such as mycorrhizal symbiosis, polar growth, and lateral root development. Because nodulating species are scattered within the NFNC, the number of times nodulation has evolved or has been lost has been a matter of considerable speculation. This interesting evolutionary question has practical implications concerning the ease with which nodulation might be engineered in non-nodulating crop plants. Nodulating species share many commonalities, due either to divergence from a common ancestor over 100 million years ago or to convergence or deep homology following independent origins over that same time period. In either case, comparative analyses of diverse nodulation syndromes can provide insights into constraints on nodulation—what must be acquired or cannot be lost for a functional symbiosis—and what the latitude is for variation in the symbiosis. However, much remains to be learned about nodulation, especially outside of legumes. Here we present new information across the spectrum of nodulating groups. We find no evidence for convergence at the level of amino acid residues or gene family expansion across the NFNC. Our phylogenomic analyses further emphasize the uniqueness of the transcription factor, NIN, as a master regulator of nodulation, and identify key mutations affecting its function across the NFNC. We find that nodulation genes are over-represented among orthologous gene groups (OGs) present in the NFNC common ancestor, but that lineage-specific OGs play major roles in nodulation. We identified over 900,000 conserved noncoding elements (CNEs), of which over 300,000 were unique to NFNC species. A significant proportion of these are associated with nodulation-related genes and thus are candidates for transcriptional regulators.
A Community Divisive Algorithm Based on Local Weak Edges

It is indicated that community structure is a common property in complex network. Detecting community structure can give a significant insight to structural and functional properties of complex network. In this paper, we propose an divisive algorithm to detect hierarchical community structure in complex network. By regarding nodes and communities as random variables, the algorithm uses correlation coefficient to calculate similarity of nodes, and then finds and removes set of local weak edges in complex network until the reasonable hierarchical communities are revealed. Experimental results in real-world and artificial networks demonstrate that the proposed algorithm is more accurate than existing mechanisms.
A Model of the Internal Feedback System Involved in Late Quaternary Climatic Variations

Abstract Because of the small net rates of energy flow involved in very long-term changes in ice mass (10−1 W m−2) it will be impossible to proceed in a purely deductive manner to develop a theory for these changes. An inductive approach will be necessary-perhaps beg formulated in terms of multi-component stochastic-dynamical systems of equations governing the variables and feedbacks thought to be relevant from qualitative physical reasoning (e.g., “conceptual models”). The output of such models should be required to conform as closely as possible to all lines of observational evidence on climatic change, have a predictive quality in the search for new observational evidence, and satisfy the general conservation laws and all the results of physical measurement of the fast response (high energy flux) processes that generally lead to diagnostic relationships. A prototype of such an inductive model is described. This model is formulated as a nonlinear dynamical system governing three components: continental .
A Removability Result for Holomorphic Functions of Several Complex Variables

Suppose that I© is a domain of C n , n â‰¥1 , E âS‚I© closed inÂ I© , the Hausdorff measure H 2 n -1 (E) = 0 , andÂ AE’ is holomorphic inÂ I©\ E . It is a classical result of Besicovitch that if n =1 and AE’ is bounded, thenÂ AE’ has a unique holomorphic extension to I© . Using an important result of Federer, Shiffman extended Besicovitchâ€™s result to the general case of arbitrary number of several complex variables, that is, forÂ n â‰¥1 . Now we give a related result, replacing the boundedness condition ofÂ AE’ by certain integrability conditions of AE’ and of âˆ‚ 2 AE’/âˆ‚I– 2 j , j= 1,2,K , n.
Spherical harmonics based HRTF datasets: Implementation and evaluation for real-time auralization

HRTF filters used in most binaural synthesis application stem from a discrete set of either measured or simulated far-field data. While this data format allows fast filter generation times and is fairly straightforward to use, correct filter representation is only possible for the measured points while an interpolation is needed between the points. As these interpolated filters are not a physically correct representation of the HRTF, this approach is not suited for the auralization of very small head movements that humans tend to do to improve localization [1]. For the auralization of sources in the vicinity of the listener the near-field HRTF is important [2]. With discrete HRTF data sets, multiple measurements for different distances have to be combined to account for this.
Piperitone (p-Menth-1-En-3-One): A New Repellent for Tea Shot Hole Borer (Coleoptera: Curculionidae) in Florida Avocado Groves

The tea shot hole borer, Euwallacea perbrevis, has been recently established in Florida, USA, where it vectors fungal pathogens that cause Fusarium dieback in avocado. Pest monitoring uses a two-component lure containing quercivorol and α-copaene. Incorporation of a repellent into IPM programs may reduce the incidence of dieback in avocado groves, particularly if combined with lures in a push–pull system. This study evaluated piperitone and α-farnesene as potential repellents for E. perbrevis, comparing their efficacy to that of verbenone. Replicate 12-week field tests were conducted in commercial avocado groves. Each test compared beetle captures in traps baited with two-component lures versus captures in traps containing lures plus repellent. To complement field trials, Super-Q collections followed by GC analyses were performed to quantify emissions from repellent dispensers field-aged for 12 weeks. Electroantennography (EAG) was also used to measure beetle olfactory response to each repellent. Results indicated that α-farnesene was ineffective; however, piperitone and verbenone were comparable in repellency, achieving 50–70% reduction in captures, with longevity of 10–12 weeks. EAG responses to piperitone and verbenone were equivalent, and significantly greater than response to α-farnesene. Since piperitone is less expensive than verbenone, this study identifies a potential new E. perbrevis repellent.
Six Weeks of at Home BTrackS Target Tracking Training Induces Sustained Dynamic Balance Improvement in Healthy Young Adults

Purpose The Balance Tracking System’s Target Tracking Training protocol requires an individual to keep an onscreen dot within a moving target circle via leaning movements that are sensed by a Balance Tracking System’s balance plate. The present study sought to determine for the first time if short duration at-home training using Balance Tracking System’s Target Tracking Training could improve dynamic balance. Methods Fifteen healthy young adults (mean age = 22.4 years) performed Balance Tracking System’s Target Tracking Training for six weeks at home, with an average of five, three-minute sessions per week. The first three weeks of Balance Tracking System’s Target Tracking Training were completed on the firm surface of a Balance Tracking Systems Balance Plate, while the final three weeks were performed on a foam cushion on top of the plate. This was followed by a three-week retention period where participants performed no training. Dynamic balance changes were assessed at multiple timepoints with the Balance Tracking System’s Limits of Stability protocol. Results Participants significantly improved Balance Tracking System’s Target Tracking Training from the first to last day of training in both three-week periods (p<0.01). This increase was mirrored by improved Balance Tracking System’s Limits of Stability results. Specifically, Balance Tracking System’s Limits of Stability area (ie dynamic balance) increased significantly from Baseline to the end of the first three weeks of training (p<0.001), and again after the second three weeks (p<0.01). These gains were maintained following the retention period. Conclusion The present findings support use of short duration Balance Tracking System’s Target Tracking Training to improve dynamic balance at home. This increase in dynamic balance could ultimately be used a practical means of improving athletic performance.
Alzheimers Disease Diagnosis using Machine Learning: A Review

Alzheimers Disease AD is an acute neuro disease that degenerates the brain cells and thus leads to memory loss progressively. It is a fatal brain disease that mostly affects the elderly. It steers the decline of cognitive and biological functions of the brain and shrinks the brain successively, which in turn is known as Atrophy. For an accurate diagnosis of Alzheimers disease, cutting edge methods like machine learning are essential. Recently, machine learning has gained a lot of attention and popularity in the medical industry. As the illness progresses, those with Alzheimers have a far more difficult time doing even the most basic tasks, and in the worst case, their brain completely stops functioning. A persons likelihood of having early-stage Alzheimers disease may be determined using the ML method. In this analysis, papers on Alzheimers disease diagnosis based on deep learning techniques and reinforcement learning between 2008 and 2023 found in google scholar were studied. Sixty relevant papers obtained after the search was considered for this study. These papers were analysed based on the biomarkers of AD and the machine-learning techniques used. The analysis shows that deep learning methods have an immense ability to extract features and classify AD with good accuracy. The DRL methods have not been used much in the field of image processing. The comparison results of deep learning and reinforcement learning illustrate that the scope of Deep Reinforcement Learning DRL in dementia detection needs to be explored.
Entrepreneurial Behaviour and Organisational Propensity to Innovate in a Public-Sector Context

The importance of innovation, in both private and public entrepreneurial fields, is the basis of all companies’ strategic choices. This study examines entrepreneurship and innovation, as well as their dynamic interface in value creation, in the public sector. It explores entrepreneurial determinants for public-sector innovation, as collected from managers and employees involved in the water supply and sewage industries in Ukraine. The data, related to a sample of firms, were obtained from a twofold self-administered survey. Adopting an ordered logistic regression model to analyse the data obtained from a survey, it is found that the entrepreneurial determinants of self-awareness, knowledge-enabling and entrepreneurial orientation positively correlate with fostering innovation process. The findings reveal that entrepreneurial leadership and intrapreneurial self-efficacy are mediating determinants. Finally, the results demonstrate that intrapreneurial self-efficacy has more potential than entrepreneurial leadership to stimulate innovation at the individual level, which has both theoretical and practical implications.