Miracle-dz/UniFAR-FTUs huggingface dataset:
Image prediction of disease progression by style-based manifold extrapolation

Disease-modifying management aims to prevent deterioration and progression of the disease, not just relieve symptoms. Unfortunately, the development of necessary therapies is often hampered by the failure to recognize the presymptomatic disease and limited understanding of disease development. We present a generic solution for this problem by a methodology that allows the prediction of progression risk and morphology in individuals using a latent extrapolation optimization approach. To this end, we combined a regularized generative adversarial network (GAN) and a latent nearest neighbor algorithm for joint optimization to generate plausible images of future time points. We evaluated our method on osteoarthritis (OA) data from a multi-center longitudinal study (the Osteoarthritis Initiative, OAI). With presymptomatic baseline data, our model is generative and significantly outperforms the end-to-end learning model in discriminating the progressive cohort. Two experiments were performed with seven experienced radiologists. When no synthetic follow-up radiographs were provided, our model performed better than all seven radiologists. In cases where the synthetic follow-ups generated by our model were available, the specificity and sensitivity of all readers in discriminating progressors increased from $72.3\%$ to $88.6\%$ and from $42.1\%$ to $51.6\%$, respectively. Our results open up a new possibility of using model-based morphology and risk prediction to make predictions about future disease occurrence, as demonstrated in the example of OA.
Dissociative identity state-dependent working memory in dissociative identity disorder: a controlled functional magnetic resonance imaging study

Background Memory function is at the core of the psychopathology of dissociative identity disorder (DID), but little is known about its psychobiological correlates. Aims This study aims to investigate whether memory function in DID differs between dissociative identity states Method Behavioural data and neural activation patterns were assessed in 92 sessions during an n-back working memory task. Participants were people with genuine diagnosed DID (n = 14), DID-simulating controls (n = 16) and a paired control group (post-traumatic stress disorder (n = 16), healthy controls (n = 16)). Both DID groups participated as authentic or simulated neutral and trauma-related identity states. Reaction times and errors of omission were analysed with repeated measures ANOVA. Working memory neural activation (main working memory and linear load) was investigated for effects of identity state, participant group and their interaction. Results Identity state-dependent behavioural performance and neural activation was found. DID simulators made fewer errors of omission than those with genuine DID. Regarding the prefrontal parietal network, main working memory in the left frontal pole and ventrolateral prefrontal cortex (Brodmann area 44) was activated in all three simulated neutral states, and in trauma-related identity states of DID simulators, but not those with genuine DID or post-traumatic stress disorder; for linear load, trauma-related identity states of those with genuine DID did not engage the parietal regions. Conclusions Behavioural performance and neural activation patterns related to working memory in DID are dependent on the dissociative identities involved. The narrowed consciousness of trauma-related identity states, with a proneness to re-experiencing traumatising events, may relate to poorer working memory functioning.
The Appropriate Work Environment for Older Employees: The Case of Slovenia

Abstract Background: The increase in population life expectancy in developed economies is also reflected in the aging of the workforce; therefore, enterprises should create an appropriate work environment for all employees, emphasizing the older ones. Objectives: The major objectives of this paper are to establish the impact of appropriate working conditions and training programs for older employees on their work motivation, as well as to establish the latter’s impact on the older employees’ work engagement in Slovenian medium-sized and large enterprises. Methods/Approach: Structural equation modeling explored the links between four constructs – appropriate working conditions, training programs, work motivation, and work engagement. Results: In Slovenian enterprises, appropriate working conditions and training programs for older employees positively impact their work motivation. Moreover, the work motivation of older employees positively impacts their work engagement. Conclusions: These findings can be useful for employers or managers for developing targeted employee motivation and employee engagement programs to leverage the talents and dedication of older employees.
Capital Market Returns and Inflation Nexus in Croatia: Wavelet Coherence Analysis

Abstract Background: Hedging against inflation assumes instruments such as gold, stocks, fixed income securities, and real estate. There still exists a lack of appropriate strategy to hedge against inflation. Objectives: This paper examines the possibilities for hedging against inflation in Croatia offered by the Zagreb Stock Exchange indices. Methods/Approach: Based on monthly data from January 2000 to September 2019 and using a wavelet coherence approach, this paper brings the results across time and frequency domains. Results: Empirical results suggest that inflation was a leading variable in a statistically significant positive correlation between the inflation rate and Crobex returns in 2007-2011. The relationship between Crobex10, Crobis, and Crobistr returns on one side, and the inflation rate on the other side has statistically significant correlations only in specific and different periods, in which respective returns are a leading variable. Conclusions: The results imply that hedging against inflation is rather problematic under current Croatian capital markets conditions. Zagreb Stock Exchange indices could serve as a hedge against inflation for some periods but not during the whole observation period.
The Effects of Electronic Surveillance on Job Tension, Task Performance and Organizational Trust

Abstract Background: In contemporary work models, employees use the Internet and electronic devices more than ever. This phenomenon has also changed the way of monitoring employees and generated a new form called ‘electronic surveillance’. Objectives: The central purpose of this paper is to reveal the effects of electronic surveillance on job tension, task performance, and employees’ organizational trust. Methods/Approach: Survey research was applied as a quantitative method to collect data. Surveys were generated as Likert-type scales, and they were distributed by hand because the use of the in-person survey technique was employed throughout the study. The research sample was created using the purposive sampling technique, and it included 228 participants from fifteen different branches of one of the biggest private banks in Turkey. Results: Electronic surveillance in the workplace has turned out to have positive effects on job tension and task performance, whereas it harms organizational trust. Conclusions: When the degree of electronic surveillance increases, the job tension level of employees tends to increase as well. Additionally, task performance increases when electronic surveillance increases. But this is not the case with organizational trust since electronic surveillance affects it negatively.
Experiences From Developing Software for Large X-Ray Crystallography-Driven Protein-Ligand Studies

The throughput of macromolecular X-ray crystallography experiments has surged over the last decade. This remarkable gain in efficiency has been facilitated by increases in the availability of high-intensity X-ray beams, (ultra)fast detectors and high degrees of automation. These developments have in turn spurred the development of several dedicated centers for crystal-based fragment screening which enable the preparation and collection of hundreds of single-crystal diffraction datasets per day. Crystal structures of target proteins in complex with small-molecule ligands are of immense importance for structure-based drug design (SBDD) and their rapid turnover is a prerequisite for accelerated development cycles. While the experimental part of the process is well defined and has by now been established at several synchrotron sites, it is noticeable that software and algorithmic aspects have received far less attention, as well as the implications of new methodologies on established paradigms for structure determination, analysis, and visualization. We will review three key areas of development of large-scale protein-ligand studies. First, we will look into new software developments for batch data processing, followed by a discussion of the methodological changes in the analysis, modeling, refinement and deposition of structures for SBDD, and the changes in mindset that these new methods require, both on the side of depositors and users of macromolecular models. Finally, we will highlight key new developments for the presentation and analysis of the collections of structures that these experiments produce, and provide an outlook for future developments.
Development and Validation of a Risk Assessment Tool for Gaming Disorder in China: The Gaming Hazard Assessment Scale

Despite the growing research interest in gaming disorder, risk screening tools developed specifically for the Chinese population are still lacking. This study aimed to construct a screening tool to evaluate the risk of gaming disorder (GD) development, by assessing the severity of GD symptoms among Chinese gamers, based on clinical expert interviews, structured interviews with GD patients, a background literature review, and IGD/GD criteria proposed by the DSM-5 and ICD-11. It introduced the Gaming Hazard Assessment Scale—a multidimensional GD risk screening tool—and evaluated the dimension structure, reliability, and validity of the scale among 959 Chinese gamers. A three-level structure, consisting of 18 items scored from 0 to 54, ultimately indicated satisfactory reliability, good validity, and acceptable model fit. The scale will help large-scale initial screening and early identification of patients with a high risk of GD. Further evaluation of the Gaming Hazard Assessment Scale in clinical settings is highly recommended.
Understanding App Inventor forums

MIT App Inventor is a visual blocks language that allows users to create mobile applications for Android. App Inventor users have the option of posting in a public support forum to discuss anything from specific programming issues to education issues. In order to leverage the information on the forums to improve App Inventor, we must first understand what is being discussed. In this thesis, we used unsupervised machine learning methods to automate discovery of discussion topics. First, we transformed posts into feature vectors using a bag-of-words model. Next, we clustered posts using k-means clustering and evaluated our results both quantitatively, by calculating the average silhouette of the posts, and qualitatively, by simply looking at the clusters of posts. Finally, we used LDA topic modeling to determine the topics being discussed and compared the extracted topic words to a manual evaluation of each cluster. Using this technique, we were able to uncover common problems with App Inventor that users encountered. We hope to use this information to improve users’ experience with App Inventor. Thesis Supervisor: Hal Abelson Title: Class of 1922 Professor
Designing Expandable-Structure Robots for Human-Robot Interaction

In this paper, we survey the emerging design space of expandable structures in robotics, with a focus on how such structures may improve human-robot interactions. We detail various implementation considerations for researchers seeking to integrate such structures in their own work and describe how expandable structures may lead to novel forms of interaction for a variety of different robots and applications, including structures that enable robots to alter their form to augment or gain entirely new capabilities, such as enhancing manipulation or navigation, structures that improve robot safety, structures that enable new forms of communication, and structures for robot swarms that enable the swarm to change shape both individually and collectively. To illustrate how these considerations may be operationalized, we also present three case studies from our own research in expandable structure robots, sharing our design process and our findings regarding how such structures enable robots to produce novel behaviors that may capture human attention, convey information, mimic emotion, and provide new types of dynamic affordances.
Transcriptomic and Drug Discovery Analyses Reveal Natural Compounds Targeting the KDM4 Subfamily as Promising Adjuvant Treatments in Cancer

KDM4 proteins are a subfamily of histone demethylases that target the trimethylation of lysines 9 and 36 of histone H3, which are associated with transcriptional repression and elongation respectively. Their deregulation in cancer may lead to chromatin structure alteration and transcriptional defects that could promote malignancy. Despite that KDM4 proteins are promising drug targets in cancer therapy, only a few drugs have been described as inhibitors of these enzymes, while studies on natural compounds as possible inhibitors are still needed. Natural compounds are a major source of biologically active substances and many are known to target epigenetic processes such as DNA methylation and histone deacetylation, making them a rich source for the discovery of new histone demethylase inhibitors. Here, using transcriptomic analyses we determined that the KDM4 family is deregulated and associated with a poor prognosis in multiple neoplastic tissues. Also, by molecular docking and molecular dynamics approaches, we screened the COCONUT database to search for inhibitors of natural origin compared to FDA-approved drugs and DrugBank databases. We found that molecules from natural products presented the best scores in the FRED docking analysis. Molecules with sugars, aromatic rings, and the presence of OH or O- groups favor the interaction with the active site of KDM4 subfamily proteins. Finally, we integrated a protein-protein interaction network to correlate data from transcriptomic analysis and docking screenings to propose FDA-approved drugs that could be used as multitarget therapies or in combination with the potential natural inhibitors of KDM4 enzymes. This study highlights the relevance of the KDM4 family in cancer and proposes natural compounds that could be used as potential therapies.
A Model of Unified Perception and Cognition

This article discusses an approach to add perception functionality to a general-purpose intelligent system, NARS. Differently from other AI approaches toward perception, our design is based on the following major opinions: (1) Perception primarily depends on the perceiver, and subjective experience is only partially and gradually transformed into objective (intersubjective) descriptions of the environment; (2) Perception is basically a process initiated by the perceiver itself to achieve its goals, and passive receiving of signals only plays a supplementary role; (3) Perception is fundamentally unified with cognition, and the difference between them is mostly quantitative, not qualitative. The directly relevant aspects of NARS are described to show the implications of these opinions in system design, and they are compared with the other approaches. Based on the research results of cognitive science, it is argued that the Narsian approach better fits the need of perception in Artificial General Intelligence (AGI).
Roles and new Insights of Macrophages in the Tumor Microenvironment of Thyroid Cancer

Although most thyroid cancers have a good and predictable prognosis, the anaplastic, medullary, and refractory thyroid cancers still prone to recurrence and metastasis, resulting in poor prognosis. Although a number of newly developed targeted therapies have begun to be indicated for the above types of thyroid cancer in recent years, their ability to improve overall survival remain hindered by low efficacy. As the largest component of immune cells in tumor microenvironment, tumor-associated macrophages play a key role in the invasion and metastasis of thyroid cancer. There is much evidence that the immune system, tumor microenvironment and cancer stem cell interactions may revolutionize traditional therapeutic directions. Tumor-associated macrophages have been extensively studied in a variety of tumors, however, research on the relationship between thyroid cancer and macrophages is still insufficient. In this review, we summarize the functions of tumor-associated macrophages in different types of thyroid cancer, their cytokines or chemokines effect on thyroid cancer and the mechanisms that promote tumor proliferation and migration. In addition, we discuss the mechanisms by which tumor-associated macrophages maintain the stemness of thyroid cancer and potential strategies for targeting tumor-associated macrophages to treat thyroid cancer.
Evaluation of Wound Healing and Antibacterial Potential of Greyia radlkoferi Szyszyl. Ethanolic Leaf Extract

Angiogenesis is an essential mechanism in both physiological and pathological functions, such as wound healing and cancer metastasis. Several growth factors mediate angiogenesis, including vascular endothelial growth factor (VEGF) and platelet derived growth factor (PDGF). This study evaluated the potential wound healing activity of Greyia radlkoferi Szyszyl (GR) and its effect on growth factors regulating angiogenesis. The ethanolic leaf extract of GR was evaluated for antibacterial activity against wound associated bacteria; Staphylococcus aureus and Pseudomonas aeruginosa. It exhibited antibacterial activity against two strains of S. aureus (ATCC 25293 and ATCC 6538) displaying a minimum inhibitory concentration (MIC) at 250 and 500 μg/ml, respectively. The antioxidant activity of the extract was investigated for nitric oxide (NO) scavenging activity and showed a fifty percent inhibitory concentration (IC50) of 1266.5 ± 243.95 μg/ml. The extract was further investigated to determine its effect on the proliferation and modulation of growth factors secreted by human keratinocytes (HaCaT). Its effect on wound closure was evaluated using the scratch assay, where non-toxic concentrations were tested, as determined by the antiproliferative assay against HaCat cells (IC50 > 400 μg/ml). Results showed that the extract significantly inhibited wound closure, with a percentage closure of 60.15 ± 1.41% (p < 0.05) and 49.52 ± 1.43% (p < 0.01) at a concentration of 50 and 100 μg/ml, respectively, when compared to the 0.25% Dimethyl sulfoxide vehicle control (65.86 ± 1.12%). Quantification of secreted growth factors from cell-free supernatant, collected from the scratch assay, revealed that the extract significantly decreased the concentration of platelet-derived growth factor (PDGF-AA) at both 50 (p < 0.05) and 100 μg/ml (p < 0.001) (443.08 ± 77.36 and 178.98 ± 36.60 pg/ml) when compared to the 0.25% DMSO vehicle control (538.33 ± 12.64 pg/ml). Therefore, whilst the extract showed antibacterial activity against wound associated bacteria, it did not induce wound healing but rather showed a significant inhibition of wound closure, which was confirmed by the inhibition of PDGF-AA, a major growth factor involved in angiogenesis. Therefore, the GR extract, should be considered for further investigation of anti-angiogenic and anti-metastatic properties against cancer cells.
The Absence of Caspase-8 in the Dopaminergic System Leads to Mild Autism-like Behavior

In the last decade, new non-apoptotic roles have been ascribed to apoptotic caspases. This family of proteins plays an important role in the sculpting of the brain in the early stages of development by eliminating excessive and nonfunctional synapses and extra cells. Consequently, impairments in this process can underlie many neurological and mental illnesses. This view is particularly relevant to dopamine because it plays a pleiotropic role in motor control, motivation, and reward processing. In this study, we analyze the effects of the elimination of caspase-8 (CASP8) on the development of catecholaminergic neurons using neurochemical, ultrastructural, and behavioral tests. To do this, we selectively delete the CASP8 gene in cells that express tyrosine hydroxylase with the help of recombination through the Cre-loxP system. Our results show that the number of dopaminergic neurons increases in the substantia nigra. In the striatum, the basal extracellular level of dopamine and potassium-evoked dopamine release decreased significantly in mice lacking CASP8, clearly showing the low dopamine functioning in tissues innervated by this neurotransmitter. This view is supported by electron microscopy analysis of striatal synapses. Interestingly, behavioral analysis demonstrates that mice lacking CASP8 show changes reminiscent of autism spectrum disorders (ASD). Our research reactivates the possible role of dopamine transmission in the pathogenesis of ASD and provides a mild model of autism.
NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations

Many drugs can be metabolized by human microbes; the drug metabolites would significantly alter pharmacological effects and result in low therapeutic efficacy for patients. Hence, it is crucial to identify potential drug–microbe associations (DMAs) before the drug administrations. Nevertheless, traditional DMA determination cannot be applied in a wide range due to the tremendous number of microbe species, high costs, and the fact that it is time-consuming. Thus, predicting possible DMAs in computer technology is an essential topic. Inspired by other issues addressed by deep learning, we designed a deep learning-based model named Nearest Neighbor Attention Network (NNAN). The proposed model consists of four components, namely, a similarity network constructor, a nearest-neighbor aggregator, a feature attention block, and a predictor. In brief, the similarity block contains a microbe similarity network and a drug similarity network. The nearest-neighbor aggregator generates the embedding representations of drug–microbe pairs by integrating drug neighbors and microbe neighbors of each drug–microbe pair in the network. The feature attention block evaluates the importance of each dimension of drug–microbe pair embedding by a set of ordinary multi-layer neural networks. The predictor is an ordinary fully-connected deep neural network that functions as a binary classifier to distinguish potential DMAs among unlabeled drug–microbe pairs. Several experiments on two benchmark databases are performed to evaluate the performance of NNAN. First, the comparison with state-of-the-art baseline approaches demonstrates the superiority of NNAN under cross-validation in terms of predicting performance. Moreover, the interpretability inspection reveals that a drug tends to associate with a microbe if it finds its top-l most similar neighbors that associate with the microbe.