Miracle-dz/UniFAR-FTUs huggingface dataset:
Endothelial Lipase Is Involved in Cold-Induced High-Density Lipoprotein Turnover and Reverse Cholesterol Transport in Mice

The physiologic activation of thermogenic brown and white adipose tissues (BAT/WAT) by cold exposure triggers heat production by adaptive thermogenesis, a process known to ameliorate hyperlipidemia and protect from atherosclerosis. Mechanistically, it has been shown that thermogenic activation increases lipoprotein lipase (LPL)-dependent hydrolysis of triglyceride-rich lipoproteins (TRL) and accelerates the generation of cholesterol-enriched remnants and high-density lipoprotein (HDL), which promotes cholesterol flux from the periphery to the liver. HDL is also subjected to hydrolysis by endothelial lipase (EL) (encoded by LIPG). Genome-wide association studies have identified various variants of EL that are associated with altered HDL cholesterol levels. However, a potential role of EL in BAT-mediated HDL metabolism has not been investigated so far. In the present study, we show that in mice, cold-stimulated activation of thermogenic adipocytes induced expression of Lipg in BAT and inguinal WAT but that loss of Lipg did not affect gene expression of thermogenic markers. Furthermore, in both wild type (WT) and Lipg-deficient mice, activation of thermogenesis resulted in a decline of HDL cholesterol levels. However, cold-induced remodeling of the HDL lipid composition was different between WT and Lipg-deficient mice. Notably, radioactive tracer studies with double-labeled HDL indicated that cold-induced hepatic HDL cholesterol clearance was lower in Lipg-deficient mice. Moreover, this reduced clearance was associated with impaired macrophage-to-feces cholesterol transport. Overall, these data indicate that EL is a determinant of HDL lipid composition, cholesterol flux, and HDL turnover in conditions of high thermogenic activity.
Sol-Gel Thin Film Processing for Integrated Waveguide Sensors

Integrated opto-chemical sensors present great advantages in comparison to the current lab equipment. They bring portability, reduced costs, facilitate in-situ measurements, as well as largely reduced sample volumes. In this quest, standard processing protocols over established materials, such as silicon nitride, silicon, silicon dioxide, titanium oxide, and even a wide variety of polymers have so far been the key toward on-chip devices. However, if very specific materials in terms of composition and tailored properties are required, the deposition via a solution represents a viable alternative. In this review, we highlight the role of sol-gel chemistry and top-down processing of sol-gel thin film layers in the design of waveguide-based optical sensors. In particular, we stress the advantages of porous sol-gel based materials as a new approach to increase sensitivity and selectivity, first when used as claddings, and, more recently, as waveguides with enhanced light–analyte interaction. We finally discuss the future perspectives of such devices to increase specificity in complex matrices, which is of utmost importance for bio-sensing.
Dysbiosis From a Microbial and Host Perspective Relative to Oral Health and Disease

The significance of microbiology and immunology with regard to caries and periodontal disease gained substantial clinical or research consideration in the mid 1960's. This enhanced emphasis related to several simple but elegant experiments illustrating the relevance of bacteria to oral infections. Since that point, the understanding of oral diseases has become increasingly sophisticated and many of the original hypotheses related to disease causality have either been abandoned or amplified. The COVID pandemic has reminded us of the importance of history relative to infectious diseases and in the words of Churchill “those who fail to learn from history are condemned to repeat it. ” This review is designed to present an overview of broad general directions of research over the last 60 years in oral microbiology and immunology, reviewing significant contributions, indicating emerging foci of interest, and proposing future directions based on technical advances and new understandings. Our goal is to review this rich history (standard microbiology and immunology) and point to potential directions in the future (omics) that can lead to a better understanding of disease. Over the years, research scientists have moved from a position of downplaying the role of bacteria in oral disease to one implicating bacteria as true pathogens that cause disease. More recently it has been proposed that bacteria form the ecological first line of defense against “foreign” invaders and also serve to train the immune system as an acquired host defensive stimulus. While early immunological research was focused on immunological exposure as a modulator of disease, the “hygiene hypothesis,” and now the “old friends hypothesis” suggest that the immune response could be trained by bacteria for long-term health. Advanced “omics” technologies are currently being used to address changes that occur in the host and the microbiome in oral disease. The “omics” methodologies have shaped the detection of quantifiable biomarkers to define human physiology and pathologies. In summary, this review will emphasize the role that commensals and pathobionts play in their interaction with the immune status of the host, with a prediction that current “omic” technologies will allow researchers to better understand disease in the future.
Differential Impact of Education on Gray Matter Volume According to Sex in Cognitively Normal Older Adults: Whole Brain Surface-Based Morphometry

Background: The effect of educational status on brain structural measurements depends on demographic and clinical factors in cognitively healthy older adults. Objectives: The current study aimed to evaluate the impact of interaction between years of education and sex on gray matter volume and to investigate whether cortical volume has a differential impact on cognitive function according to sex. Methods: One hundred twenty-one subjects between 60 and 85 years old were included in this study. Gray matter volume was evaluated by whole brain surface-based morphometry. Multiple regression analysis was used to analyze the effects of sex-cortical volume interactions on cognitive functions. Results: There was a significant interaction between years of education and sex on the cortical volume of the left inferior temporal gyrus after adjusting for age, APOE ε4 allele prevalence, and total intracranial volume. In addition, we found a significant impact of the interaction between adjusted left inferior temporal volume and sex on CERAD-K total scores. Conclusion: These findings have significant implications for the understanding of how sex could affect the role of cognitive reserve for cortical atrophy in cognitively intact older adults.
AI-Based Damping of Electromechanical Oscillations by Using Grid-Connected Converter

The proliferation of grid-connected converter interfaced energy sources in Smart Grids, enhance sustainability and efficiency as well as minimizing power losses and costs. However, concerns arise regarding the stability and reliability of future smart grids due to this wide integration of power electronic devices, which are recognized to affect the dynamic response of the system, especially during disturbances. For instance, apart from the lower damping of existing electromechanical modes, new low-frequency oscillations begin to appear. Yet, the ability of grid-connected converters to provide grid support functionalities can alleviate the aforementioned challenges. Relevant studies show that these functionalities can be enhanced even further, if information regarding the oscillation characteristics are available. Traditional methods for extracting modal information are very well suited for monitoring purposes, however, they pose certain limitations when considered for control applications. Therefore, this paper proposes a multi-band intelligent power oscillation damper (MiPOD) that exploits 1) the inherent characteristics of grid-connected converters to damp multiple power oscillations and 2) the modeling capabilities of Artificial Intelligence (AI) for predicting the frequency of electromechanical oscillations in the system, as operating conditions change. Essentially, the MiPOD integrates the AI model in the control loop of the converter to attenuate multiple modes of oscillation. The proposed controller is validated for different disturbances and randomly generated operating points in the two area system. Specifically, in this case the AI model is a Random Forest ensemble regressor that is developed for tracking two electromechanical modes. As it is shown, the MiPOD can improve the overall performance of the system under various contingency scenarios with only 6% of the corresponding total nominal capacity of synchronous generators. In addition, the monitoring and damping abilities of the MiPOD are demonstrated for a vast range of operating points just by tuning two parameters; the predicted oscillation frequencies of the local and inter-area mode.
Alpha-2-Macroglobulin as a New Promising Biomarker Improving the Diagnostic Sensitivity of Bovine Paratuberculosis

Johne's disease (JD) is a chronic granulomatous enteritis of ruminants caused by Mycobacterium avium subsp. paratuberculosis (MAP), which induces persistent diarrhea and cachexia. JD causes huge economic losses to the dairy industry due to reduced milk production and premature culling. Infected animals excrete MAP via feces during the prolonged subclinical stage without exhibiting any clinical signs. Therefore, accurate detection of subclinical stage animals is crucial for successful eradication of JD in the herd. In the current study, we analyzed serum samples of MAP-infected and non-infected cattle to identify potential biomarker candidates. First, we identified 12 differentially expressed serum proteins in subclinical and clinical shedder groups compared to the healthy control group. Second, we conducted ELISA for three selected biomarkers (alpha-2-macroglobulin (A2M), alpha-1-beta glycoprotein, and transthyretin) and compared their diagnostic performance with that of two commercial ELISA diagnostic kits. Serum A2M levels were significantly higher in the MAP-exposed, subclinical shedder, subclinical non-shedder, and clinical shedder groups than in the healthy control group, suggesting its possible use as a diagnostic biomarker for MAP infection. Furthermore, A2M demonstrated a sensitivity of 90.4%, and a specificity of 100% while the two commercial ELISA kits demonstrated a sensitivity of 67.83 and 73.04% and a specificity of 100%, respectively. In conclusion, our results suggest that measuring A2M by ELISA can be used as a diagnostic tool to detect MAP infection, considerably improving the detection rate of subclinical shedders and MAP-exposed animals that are undetectable using current diagnostic tools.
Active Vision for Robot Manipulators Using the Free Energy Principle

Occlusions, restricted field of view and limited resolution all constrain a robot's ability to sense its environment from a single observation. In these cases, the robot first needs to actively query multiple observations and accumulate information before it can complete a task. In this paper, we cast this problem of active vision as active inference, which states that an intelligent agent maintains a generative model of its environment and acts in order to minimize its surprise, or expected free energy according to this model. We apply this to an object-reaching task for a 7-DOF robotic manipulator with an in-hand camera to scan the workspace. A novel generative model using deep neural networks is proposed that is able to fuse multiple views into an abstract representation and is trained from data by minimizing variational free energy. We validate our approach experimentally for a reaching task in simulation in which a robotic agent starts without any knowledge about its workspace. Each step, the next view pose is chosen by evaluating the expected free energy. We find that by minimizing the expected free energy, exploratory behavior emerges when the target object to reach is not in view, and the end effector is moved to the correct reach position once the target is located. Similar to an owl scavenging for prey, the robot naturally prefers higher ground for exploring, approaching its target once located.
Effects of a Teaching Personal and Social Responsibility Model Intervention in Competitive Youth Sport

The aim of this study was to implement the teaching personal and social responsibility (TPSR) model in a competitive context analyzing the differences between the intervention and the control group on personal and social responsibility, prosocial behaviors, and self-efficacy in youth soccer players. Participants were 34 youth soccer players between the ages of 14 and 16 years old (15.18 ± 0.72) divided into two different soccer teams of 17 members, corresponding to the control and intervention groups. The implementation of the TPSR model took place during 9 months, including initial and ongoing coach training (3 months), program implementation (three sessions per week lasting 90 min during 6 months), and a series of expert-led seminars for athletes (one session per week lasting 90 min during 4 months). The questionnaires used to collect data were the Personal and Social Responsibility Questionnaire, Prosocial Behavior Scale, and two Children’s Self-efficacy Scales. Results indicated that the TPSR intervention group obtained an increase in post-test levels of personal and social responsibility, prosocial behavior, and self-efficacy due to the application of the TPSR model compared with control group that used a conventional sport teaching methodology. The conclusion is that the TPSR model has the potential to be adapted and implemented with flexibility in youth sport competition contexts in order to improve personal and social responsibility, prosocial behavior, and self-efficacy.
Prognostic Relevance of Expression of EMP1, CASP1, and NLRP3 Genes in Pediatric B-Lineage Acute Lymphoblastic Leukemia

Glucocorticoid (GC), such as prednisolone, is an essential component of multidrug chemotherapy regimen for pediatric acute lymphoblastic leukemia (ALL). Resistance to GC in leukemia cells is associated with disease progression and poor prognosis. Despite the extensive use of GC for many years, molecular mechanisms underlying its resistance in ALL have not been fully uncovered. Recent studies have shown a potential role of EMP1, CASP1, and NLRP3 genes in prednisolone response. In this study on 148 pediatric B-ALL patients, we studied these three genes to assess their association with prednisolone response measured by day 8 blast count after 7 days of induction therapy with prednisolone. Intriguingly, ALL samples exhibited higher expression of EMP1 along with a low expression of CASP1 and NLRP3 compared to disease free normal bone marrow collected from patients with solid tumors. Among the three analyzed genes, only EMP1 was found to be overexpressed in prednisolone poor responders (p=0.015). Further, a comparison of gene expression between cytogenetic subtypes revealed higher expression of EMP1 in BCR-ABL subtype. Expression of EMP1 in multiple gene expression datasets was used for gene set enrichment analysis, which revealed TNF-α, IL-2-STAT5 signaling, inflammatory responses and hypoxia as the major positively associated pathways and E2F targets as negatively associated pathways. Interestingly, the clinical remission rate was higher in CASP1 high patients (p=0.048). In univariate survival analysis, higher EMP1 expression was associated with poor prognostic measures while higher expression of NLRP3 and CASP1 was associated with better prognostic measures in our data. Further, multivariate analysis revealed an independent association of high CASP1 and NLRP3 with a better prognosis. This study strengthens the available evidence that mRNA expression of EMP1, CASP1, and NLRP3 may serve as potential biomarkers for risk stratification of pediatric B-ALL patients.
White Matter Correlates of Theory of Mind in Patients With First-Episode Psychosis

Deficits in theory of mind (ToM) are considered as a distinctive feature of schizophrenia. Functional magnetic resonance imaging (fMRI) studies have suggested that aberrant activity among the regions comprising the mentalizing network is related to observed ToM deficits. However, the white matter structures underlying the ToM functional network in schizophrenia remain unclear. To investigate the relationship between white matter integrity and ToM impairment, 35 patients with first-episode psychosis (FEP) and 29 matched healthy controls (HCs) underwent diffusion tensor imaging (DTI). Using tract-based spatial statistics (TBSS), fractional anisotropy (FA) values of the two regions of interest (ROI)–the cingulum and superior longitudinal fasciculus (SLF)–were acquired, and correlational analysis with ToM task scores was performed. Among the patients with FEP, ToM strange story scores were positively correlated with the FA values of the left cingulum and left SLF. There was no significant correlation between FA and ToM task scores in HCs. These results suggest that the left cingulum and SLF constitute a possible neural basis for ToM deficits in schizophrenia. Our study is the first to demonstrate the white matter connectivity underlying the mentalizing network, as well as its relation to ToM ability in patients with FEP.
Microfluidic Flow Injection Immunoassay System for Algal Toxins Determination: A Case of Study

A novel flow injection microfluidic immunoassay system for continuous monitoring of saxitoxin, a lethal biotoxin, in seawater samples is presented in this article. The system consists of a preimmobilized G protein immunoaffinity column connected in line with a lab-on-chip setup. The detection of saxitoxin in seawater was carried out in two steps: an offline incubation step (competition reaction) performed between the analyte of interest (saxitoxin or Ag, as standard or seawater sample) and a tracer (an enzyme-conjugated antigen or Ag*) toward a specific polyclonal antibody. Then, the mixture was injected through a “loop” of a few μL using a six-way injection valve into a bioreactor, in line with the valve. The bioreactor consisted of a small glass column, manually filled with resin upon which G protein has been immobilized. When the mixture flowed through the bioreactor, all the antibody-antigen complex, formed during the competition step, is retained by the G protein. The tracer molecules that do not interact with the capture antibody and protein G are eluted out of the column, collected, and mixed with an enzymatic substrate directly within the microfluidic chip, via the use of two peristaltic pumps. When Ag* was present, a color change (absorbance variation, ΔAbs) of the solution is detected at a fixed wavelength (655 nm) by an optical chip docking system and registered by a computer. The amount of saxitoxin, present in the sample (or standard), that generates the variation of the intensity of the color, will be directly proportional to the concentration of the analyte in the analyzed solution. Indeed, the absorbance response increased proportionally to the enzymatic product and to the concentration of saxitoxin in the range of 3.5 × 10–7–2 × 10–5 ng ml−1 with a detection limit of 1 × 10–7 ng ml−1 (RSD% 15, S N−1 equal to 3). The immunoanalytical system has been characterized, optimized, and tested with seawater samples. This analytical approach, combined with the transportable and small-sized instrumentation, allows for easy in situ monitoring of marine water contaminations.
Intel Realizes $25 Billion by Applying Advanced Analytics from Product Architecture Design Through Supply Chain Planning

Due to its scale, the complexity of its products and manufacturing processes, and the capital-intensive nature of the semiconductor business, efficient product architecture design integrated with supply chain planning is critical to Intel’s success. In response to an exponential increase in complexities, Intel has used advanced analytics to develop an innovative capability that spans product architecture design through supply chain planning with the dual goals of maximizing revenue and minimizing costs. Our approach integrates the generation and optimization of product design alternatives using genetic algorithms and device physics simulation with large-scale supply chain planning using problem decomposition and mixed-integer programming. This corporate-wide capability is fast and effective, enabling analysis of many more business scenarios in much less time than previous solutions, while providing superior results, including faster response time to customers. Implementation of this capability over the majority of Intel’s product portfolio has increased annual revenue by an average of $1.9 billion and reduced annual costs by $1.5 billion, for a total benefit of $25.4 billion since 2009, while also contributing to Intel’s sustainability efforts.
On the Accelerated Convergence of the Decentralized Event-triggered Algorithm for Convex Optimization

This article considers a problem of solving the optimal solution of the sum of locally convex cost functions over an undirected network. Each local convex cost function in the network is accessed only by each unit. To be able to reduce the amount of computation and get the desired result in an accelerated way, we put forward a fresh accelerated decentralized event-triggered algorithm, named as A-DETA, for the optimization problem. A-DETA combines gradient tracking and two momentum accelerated terms, adopts nonuniform step-sizes and emphasizes that each unit interacts with neighboring units independently only at the sampling time triggered by the event. On the premise of assuming the smoothness and strong convexity of the cost function, it is proved that A-DETA can obtain the exact optimal solution linearly in the event of sufficiently small positive step-size and momentum coefficient. Moreover, an explicit linear convergence speed is definitely shown. Finally, extensive simulation example validates the usability of A-DETA.
Evaluation of Selected Stacked Ensemble Models for the Optimal Multi-class Cyber-Attacks Detection

The significant rise in the frequency and sophistication of cyber-attacks and their diversity necessitated various researchers to develop strong and effective approaches to address recurring cyber threat challenges. This study evaluated the performance of three selected meta-learning models for optimal multi-class detection of cyber-attacks using the University of New South Wales 2015 Network benchmark (UNSW-NB15) Intrusion Dataset. The results of this study show and confirm the ability of the three base models; Naive Bayes, C4.5 Decision Tree, and K-Nearest Neighbor for solving multi-class problems. It further affirms the knack of the duo of feature selection techniques and stacked ensemble learning to optimize ML models' performances. The stacking of the predictions of the information gain base models with Model Decision Tree meta-algorithm recorded the most improved and optimal cyber-attacks detection accuracy and Mattew's correlation Coefficient than the stacking with the Multiple Model Trees (MMT) and Multi Response Linear regression (MLR) Meta algorithms.