Miracle-dz/UniFAR-FTUs huggingface dataset:
Proof of Concept for a Mindfulness-Informed Intervention for Eating Disorder Symptoms, Self-Efficacy, and Emotion Regulation among Bariatric Surgery Candidates

Abstract Up to 64% of patients seeking bariatric (weight-loss) surgery report eating disorder (ED) symptoms (addictive-like eating, binge eating, emotional eating, grazing) that can interfere with post-surgical weight loss. This prospective proof-of-concept study aimed to evaluate the impact of a pre-surgical mindfulness-informed intervention (MII) on ED symptoms and potential mechanisms-of-action to inform optimization of the intervention. Surgery-seeking adults attended four, 2-hour, MII sessions held weekly. Participants completed validated questionnaires assessing ED symptoms, eating self-efficacy, emotion regulation, and mindful eating pre-MII, post-MII, and at a 12-week follow-up. The MII consisted of mindfulness training, with cognitive, behavioral, and psychoeducational components. Fifty-six patients (M = 47.41 years old, 89.3% female) participated. Improvements in addictive-like eating, binge eating, emotional eating, and grazing were observed from pre- to post-MII. ED symptom treatment gains were either maintained or improved further at 12-week follow-up. Eating self-efficacy and emotion regulation improved from pre-MII to follow-up. Scores on the mindful eating questionnaire deteriorated from pre-MII to follow-up. In mediation analyses, there was a combined indirect effect of emotion regulation, eating self-efficacy, and mindful eating on grazing and binge eating, and an indirect effect of emotion regulation on emotional eating and addictive-like eating. Participation in the MII was associated with improvements in ED symptoms and some mechanisms-of-action, establishing proof-of-concept for the intervention. Future work to establish the MII’s efficacy in a randomized controlled trial is warranted.
Solid-Phase Synthesis and In-Silico Analysis of Iron-Binding Catecholato Chelators

Siderophores are iron-complexing compounds synthesized by bacteria and fungi. They are low molecular weight compounds (500-1500 Daltons) possessing high affinity for iron(III). Since 1970 a large number of siderophores have been characterized, the majority using hydroxamate or catecholate as functional groups. The biosynthesis of siderophores is typically regulated by the iron levels of the environment where the organism is located. Because of their exclusive affinity and specificity for iron(III), natural siderophores and their synthetic derivatives have been exploited in the treatment of human iron-overload diseases, as both diagnostic and therapeutic agents. Here, solid-phase approach for the preparation of hexadentate, peptide-based tricatecholato containing peptides is described. The versatility of the synthetic method allows for the design of a common scaffolding structure whereby diverse ligands can be conjugated. With so many possibilities, a computational approach has been developed which will facilitate the identification of those peptides which are capable of providing a high affinity iron(III) binding site. This study reports an integrated computational/synthetic approach towards a rational development of peptide-based siderophores.
Apnea test for the diagnosis of brain death in a patient undergoing extracorporeal membrane oxygenation

Extracorporeal membrane oxygenation is used as extracirculatory support for the care of patients with severe and reversible cardiac and/or respiratory failure. Neurological complications may be related to the procedure. Given the unfavorable neurological evolution and the need to perform a brain death protocol, the performance of an apnea test in this context remains a challenge. We report the use of an apnea test for the diagnosis of brain death post-cardiac surgery in a patient receiving venoarterial extracorporeal membrane oxygenation.
Anti‐Interleukin‐6 Therapy Decreases Hip Synovitis and Bone Resorption and Increases Bone Formation Following Ischemic Osteonecrosis of the Femoral Head

Legg‐Calvé‐Perthes disease (LCPD) is a juvenile form of ischemic femoral head osteonecrosis, which produces chronic hip synovitis, permanent femoral head deformity, and premature osteoarthritis. Currently, there is no medical therapy for LCPD. Interleukin‐6 (IL‐6) is significantly elevated in the synovial fluid of patients with LCPD. We hypothesize that IL‐6 elevation promotes chronic hip synovitis and impairs bone healing after ischemic osteonecrosis. We set out to test if anti‐IL‐6 therapy using tocilizumab can decrease hip synovitis and improve bone healing in the piglet model of LCPD. Fourteen piglets were surgically induced with ischemic osteonecrosis and assigned to two groups: the no treatment group (n = 7) and the tocilizumab group (15 to 20 mg/kg, biweekly intravenous injection, n = 7). All animals were euthanized 8 weeks after the induction of osteonecrosis. Hip synovium and femoral heads were assessed for hip synovitis and bone healing using histology, micro‐CT, and histomorphometry. The mean hip synovitis score and the number of synovial macrophages and vessels were significantly lower in the tocilizumab group compared with the no treatment group (p < .0001, p = .01, and p < .01, respectively). Micro‐CT analysis of the femoral heads showed a significantly higher bone volume in the tocilizumab group compared with the no treatment group (p = .02). The histologic assessment revealed a significantly lower number of osteoclasts per bone surface (p < .001) in the tocilizumab group compared with the no treatment group. Moreover, fluorochrome labeling showed a significantly higher percent of mineralizing bone surface (p < .01), bone formation rate per bone surface (p < .01), and mineral apposition rate (p = .04) in the tocilizumab group. Taken together, tocilizumab therapy decreased hip synovitis and osteoclastic bone resorption and increased new bone formation after ischemic osteonecrosis. This study provides preclinical evidence that tocilizumab decreases synovitis and improves bone healing in a large animal model of LCPD. © 2020 American Society for Bone and Mineral Research (ASBMR).
Interventions to Improve Health among Reproductive-Age Women of Low Health Literacy: A Systematic Review

Background: Limited or low health literacy (HL) has been associated with poor health outcomes, including inadequate self-caring and preventive behaviors. A few studies have systematically summarized the effect of interventions to improve reproductive health and care in women with insufficient HL. The main objective of the study was to investigate health care promotion interventions and examine their effectiveness on women with inadequate HL through a systematic review of randomized controlled trials (RCT). Methods: RCTs and quasi-experimental studies that assessed HL interventions to improve reproductive health of women with low HL were included. The study protocol was registered with PROSPERO (CRD42020137059). Results: Of the 292 records initially identified, a total of 6 articles were included for review. Five different HL screening tools were used. Four different interventions were included: educational intervention, communication skills, a multimedia interactive tool, and text adaptation to enhance reading comprehension. Not enough research practice has been conducted on the influence of interventions on HL, and thus, it is difficult to implement evidence-based interventions. Conclusions: Interventions aiming to benefit and improve HL should consider the complex web of intersectional determinants that end up shaping the opportunities of women to make optimal decisions regarding their health and care, and which may require attention to much more than clinical or service delivery factors.
Comparison of Machine Learning Algorithms for Predicting Crime Hotspots

Crime prediction is of great significance to the formulation of policing strategies and the implementation of crime prevention and control. Machine learning is the current mainstream prediction method. However, few studies have systematically compared different machine learning methods for crime prediction. This paper takes the historical data of public property crime from 2015 to 2018 from a section of a large coastal city in the southeast of China as research data to assess the predictive power between several machine learning algorithms. Results based on the historical crime data alone suggest that the LSTM model outperformed KNN, random forest, support vector machine, naive Bayes, and convolutional neural networks. In addition, the built environment data of points of interests (POIs) and urban road network density are input into LSTM model as covariates. It is found that the model with built environment covariates has better prediction effect compared with the original model that is based on historical crime data alone. Therefore, future crime prediction should take advantage of both historical crime data and covariates associated with criminological theories. Not all machine learning algorithms are equally effective in crime prediction.
Language technology into high schools for revitalization of endangered languages

Language technology is the missing piece of the puzzle that will bring us closer to a complete revitalization of endangered languages. Almost every digital product uses and is dependent on language; language technology is not anymore an option but the key enabler and solution to boosting future growth. Technical issues are hard but the lesser problems on the building of the corpus of endangered languages, centuries of oppression managed to dent the pride and sense of belonging which is reflected in a lack of awareness of the loss of the own language. In order to reach a revitalization based on language technology, powered by artificial intelligence, to be successful, it is necessary to show a value proposition from the very beginning, from the creation of the corpus. In that direction, we propose the inclusion of the fundamentals of linguistics into high schools with the twofold goal of building corpus from the grassroots and providing highly valued skills to teenagers.
Docking and QSAR of Aminothioureas at the SARS-CoV-2 S-Protein–Human ACE2 Receptor Interface

Docking of over 160 aminothiourea derivatives at the SARS-CoV-2 S-protein–human ACE2 receptor interface, whose structure became available recently, has been evaluated for its complex stabilizing potency and subsequently subjected to quantitative structure–activity relationship (QSAR) analysis. The structural variety of the studied compounds, that include 3 different forms of the N–N–C(S)–N skeleton and combinations of 13 different substituents alongside the extensive length of the interface, resulted in the failure of the QSAR analysis, since different molecules were binding to different parts of the interface. Subsequently, absorption, distribution, metabolism, and excretion (ADME) analysis on all studied compounds, followed by a toxicity analysis using statistical models for selected compounds, was carried out to evaluate their potential use as lead compounds for drug design. Combined, these studies highlighted two molecules among the studied compounds, i.e., 5-(pyrrol-2-yl)-2-(2-methoxyphenylamino)-1,3,4-thiadiazole and 1-(cyclopentanoyl)-4-(3-iodophenyl)-thiosemicarbazide, as the best candidates for the development of future drugs.
Reactive hyperemia correlates with the presence of sepsis and glycocalyx degradation in the intensive care unit: a prospective cohort study

Objective To investigate whether reactive hyperemia measured by peripheral arterial tonometry correlates with markers of endothelial dysfunction and may be used to identify sepsis in critical illness. Methods A prospective study was performed using a cohort of critically ill patients. Endothelial dysfunction was assessed on admission by quantifying reactive hyperemia-peripheral arterial tonometry and plasma levels of endothelin-1, soluble E-selectin, endocan and syndecan-1. Septic patients were compared to patients without evidence of infection. Results Fifty-eight septic patients were compared to 28 controls. The natural logarithm of reactive hyperemia-peripheral arterial tonometry was negatively correlated with cardiovascular comorbidities, disease severity and plasma levels of soluble E-selectin (p = 0.024) and syndecan-1 (p < 0.001). The natural logarithm of reactive hyperemia-peripheral arterial tonometry was lower in septic patients than in controls (0.53 ± 0.48 versus 0.69 ± 0.42, respectively). When adjusted for age, the multivariable model predicted that each 0.1-unit decrease in natural logarithm of reactive hyperemia-peripheral arterial tonometry increased the odds for infection by 14.6%. m. Conclusion Reactive hyperemia-peripheral arterial tonometry is closely related to soluble E-selectin and syndecan-1, suggesting an association between endothelial activation, glycocalyx degradation and vascular reactivity. Reactive hyperemia-peripheral arterial tonometry appears to be compromised in critically ill patients, especially those with sepsis.
Decentralized Principal Component Analysis by Integrating Lagrange Programming Neural Networks With Alternating Direction Method of Multipliers

Conventional centralized methods use entire data to estimate the projection matrix of dimensionality reduction problem, which are not suitable for the network environment where the sensitive or private data are stored or there is no fusion center. In this paper, we develop a decentralized principal component analysis (DPCA) method to deal with the distributed data without sharing or collecting them together. The main contributions of this paper are as follows: i) The proposed DPCA method only needs the projection vector information communications among neighboring nodes other than the communications of the distributed data; ii) The decentralized projection vector determination problem is replaced by a set of subproblems with consensus constraints and the excellent processing capability of alternating direction method of multipliers (ADMM) is used to obtain the consistent projection vectors; iii) Especially, the integrating Lagrange programming neural networks (LPNN) is introduced to solve the projection vectors determination problem with the complex unitary and orthogonal constraints, and iv) the converge analysis of the proposed optimization problem is provided to ensure that the obtained projection vectors of the distributed method converge to those of the centralized one. Some simulations and experiments are given to show that the proposed algorithm is an alternative decentralized principal component analysis approach, and is suitable for the network environment.
Performance of mmWave Ray Tracing Outdoor Channel Model Exploiting Antenna Directionality

The millimeter (mmWave) 5G new radio (NR) aims to provide orders of magnitude greater capacity through increased bandwidth and beamforming assisted narrow beam transmissions. The assumption of directional antennas with narrow radiating beams will reduce the number of contributing multipaths causing the channel to be sparser and simpler than the omnidirectional microwave channel. In this paper, we used a custom ray tracing model to study the behavior of directional mmWave urban micro (UMi) street canyon (SC) links. The proposed channel model harnesses the higher attenuation in high frequency mmWaves and the spatial filtering of directional antennas to reduce ray tracing complexity. The potential of this low complexity model has been evaluated through comprehensive simulation for both line of sight and non-line of sight mmWave communications at ranges up to 200m with different transmitter and receiver geometries. The impact of crossroad gaps on channel performance in a real urban SC 5G outdoor network design is also formulated. Several interesting insights have been derived. The strength of the proposed model constitutes path loss predictions at varying positions, frequency, and street orientation with/ without sidewall discontinuities.
Drosophila R8 photoreceptor cell subtype specification requires hibris

Cell differentiation and cell fate determination in sensory systems are essential for stimulus discrimination and coding of environmental stimuli. Color vision is based on the differential color sensitivity of retinal photoreceptors, however the developmental programs that control photoreceptor cell differentiation and specify color sensitivity are poorly understood. In Drosophila melanogaster, there is evidence that the color sensitivity of different photoreceptors in the compound eye is regulated by inductive signals between cells, but the exact nature of these signals and how they are propagated remains unknown. We conducted a genetic screen to identify additional regulators of this process and identified a novel mutation in the hibris gene, which encodes an irre cell recognition module protein (IRM). These immunoglobulin super family cell adhesion molecules include human KIRREL and nephrin (NPHS1). hibris is expressed dynamically in the developing Drosophila melanogaster eye and loss-of-function mutations give rise to a diverse range of mutant phenotypes including disruption of the specification of R8 photoreceptor cell diversity. We demonstrate that hibris is required within the retina, and that hibris over-expression is sufficient to disrupt normal photoreceptor cell patterning. These findings suggest an additional layer of complexity in the signaling process that produces paired expression of opsin genes in adjacent R7 and R8 photoreceptor cells.
Nitric oxide (NO) signalling in plant nanobiology: current status and perspectives.

Plant nanobiology as a novel research field provides scientific basis for the agricultural use of nanoparticles (NPs). Plants respond to the presence of nanomaterials by synthesizing signal molecules, such as the multifunctional gaseous nitric oxide (NO). Several reports have described the effects of different nanomaterials (primarily chitosan NPs, metal oxide NPs and carbon nanotubes) on endogenous NO synthesis and signalling in different plant species. Other works have demonstrated the ameliorating effect of exogenous NO donor (primarily sodium nitroprusside) treatments on NP-induced stress. NO-releasing NPs are more preferred alternatives to chemical NO donors and evaluating their effects on plants has recently begun. The accumulated literature data clearly indicate that endogenous NO production in the presence of nanomaterials or NO levels increased by exogenous treatments (NO-releasing NPs or chemical NO donors) exerts growth-promoting and stress-ameliorating effects in plants. Furthermore, a NP-based nanosensor for NO detection in plants has been developed, providing a new and excellent perspective for basic research and also for the evaluation of plants' health status in agriculture.
DNAmoreDB, a database of DNAzymes

Abstract Deoxyribozymes, DNA enzymes or simply DNAzymes are single-stranded oligo-deoxyribonucleotide molecules that, like proteins and ribozymes, possess the ability to perform catalysis. Although DNAzymes have not yet been found in living organisms, they have been isolated in the laboratory through in vitro selection. The selected DNAzyme sequences have the ability to catalyze a broad range of chemical reactions, utilizing DNA, RNA, peptides or small organic compounds as substrates. DNAmoreDB is a comprehensive database resource for DNAzymes that collects and organizes the following types of information: sequences, conditions of the selection procedure, catalyzed reactions, kinetic parameters, substrates, cofactors, structural information whenever available, and literature references. Currently, DNAmoreDB contains information about DNAzymes that catalyze 20 different reactions. We included a submission form for new data, a REST-based API system that allows users to retrieve the database contents in a machine-readable format, and keyword and BLASTN search features. The database is publicly available at https://www.genesilico. pl/DNAmoreDB/.
Deep brain stimulation: a review of the open neural engineering challenges

Objective. Deep brain stimulation (DBS) is an established and valid therapy for a variety of pathological conditions ranging from motor to cognitive disorders. Still, much of the DBS-related mechanism of action is far from being understood, and there are several side effects of DBS whose origin is unclear. In the last years DBS limitations have been tackled by a variety of approaches, including adaptive deep brain stimulation (aDBS), a technique that relies on using chronically implanted electrodes on ‘sensing mode’ to detect the neural markers of specific motor symptoms and to deliver on-demand or modulate the stimulation parameters accordingly. Here we will review the state of the art of the several approaches to improve DBS and summarize the main challenges toward the development of an effective aDBS therapy. Approach. We discuss models of basal ganglia disorders pathogenesis, hardware and software improvements for conventional DBS, and candidate neural and non-neural features and related control strategies for aDBS. Main results. We identify then the main operative challenges toward optimal DBS such as (i) accurate target localization, (ii) increased spatial resolution of stimulation, (iii) development of in silico tests for DBS, (iv) identification of specific motor symptoms biomarkers, in particular (v) assessing how LFP oscillations relate to behavioral disfunctions, and (vi) clarify how stimulation affects the cortico-basal-ganglia-thalamic network to (vii) design optimal stimulation patterns. Significance. This roadmap will lead neural engineers novel to the field toward the most relevant open issues of DBS, while the in-depth readers might find a careful comparison of advantages and drawbacks of the most recent attempts to improve DBS-related neuromodulatory strategies.
Serum Neurofilament Light Chain Is Associated with Incident Lacunes in Progressive Cerebral Small Vessel Disease

Background and Purpose Serum neurofilament light (NfL)-chain is a circulating marker for neuroaxonal injury and is also associated with severity of cerebral small vessel disease (SVD) cross-sectionally. Here we explored the association of serum-NfL with imaging and cognitive measures in SVD longitudinally. Methods From 503 subjects with SVD, baseline and follow-up magnetic resonance imaging (MRI) was available for 264 participants (follow-up 8.7±0.2 years). Baseline serum-NfL was measured by an ultrasensitive single-molecule-assay. SVD-MRI-markers including white matter hyperintensity (WMH)-volume, mean diffusivity (MD), lacunes, and microbleeds were assessed at both timepoints. Cognitive testing was performed in 336 participants, including SVD-related domains as well as global cognition and memory. Associations with NfL were assessed using linear regression analyses and analysis of covariance (ANCOVA). Results Serum-NfL was associated with baseline WMH-volume, MD-values and presence of lacunes and microbleeds. SVD-related MRI- and cognitive measures showed progression during follow-up. NfL-levels were associated with future MRI-markers of SVD, including WMH, MD and lacunes. For the latter, this association was independent of baseline lacunes. Furthermore, NfL was associated with incident lacunes during follow-up (P=0.040). NfL-levels were associated with future SVD-related cognitive impairment (processing speed: β=–0.159; 95% confidence interval [CI], –0.242 to –0.068; P=0.001; executive function β=–0.095; 95% CI, –0.170 to –0.007; P=0.033), adjusted for age, sex, education, and depression. Dementia-risk increased with higher NfL-levels (hazard ratio, 5.0; 95% CI, 2.6 to 9.4; P<0.001), however not after adjusting for age. Conclusions Longitudinally, serum-NfL is associated with markers of SVD, especially with incident lacunes, and future cognitive impairment affecting various domains. NfL may potentially serve as an additional marker for disease monitoring and outcome in SVD, potentially capturing both vascular and neurodegenerative processes in the elderly.