Miracle-dz/UniFAR-FTUs huggingface dataset:
Neuroprotective effects of Canagliflozin: Lessons from aged genetically diverse UM‐HET3 mice

The aging brain is characterized by a progressive increase in neuroinflammation and central insulin resistance, which contribute to neurodegenerative diseases and cognitive impairment. Recently, the Interventions Testing Program (ITP) demonstrated that the anti-diabetes drug, Canagliflozin (Cana), a sodium-glucose transporter 2 inhibitor (SGLT2i), led to lower fasting glucose and improved glucose tolerance in both sexes, but extended median lifespan by 14% only in male mice. Here we show that Cana treatment significantly improved central insulin sensitivity in the hypothalamus and the hippocampus of 30-month-old male mice. Remarkably, Cana-treated mice showed significant reductions in age-associated hypothalamic gliosis with a decrease in inflammatory cytokine production by microglia in both sexes. In the hippocampus, Cana reduced microgliosis and astrogliosis in males, but not in female mice. The decrease in microgliosis was partially dependent on mTOR signaling, as evidenced by reduced phosphorylation of S6 kinase in microglia of Cana-treated aged male, but not female mice. Remarkably, Cana treatment improved exploratory and locomotor activity of 30-month-old male but not female mice. Taken together, our findings demonstrate sex-specific neuroprotective effects of Cana treatment, suggesting its application for the potential treatment of neurodegenerative diseases.
Adipose Stromal/Stem Cell-Derived Extracellular Vesicles: Potential Next-Generation Anti-Obesity Agents

Over the last decade, several compounds have been identified for the treatment of obesity. However, due to the complexity of the disease, many pharmacological interventions have raised concerns about their efficacy and safety. Therefore, it is important to discover new factors involved in the induction/progression of obesity. Adipose stromal/stem cells (ASCs), which are mostly isolated from subcutaneous adipose tissue, are the primary cells contributing to the expansion of fat mass. Like other cells, ASCs release nanoparticles known as extracellular vesicles (EVs), which are being actively studied for their potential applications in a variety of diseases. Here, we focused on the importance of the contribution of ASC-derived EVs in the regulation of metabolic processes. In addition, we outlined the advantages/disadvantages of the use of EVs as potential next-generation anti-obesity agents.
FASR: An Efficient Feature-Aware Deduplication Method in Distributed Storage Systems

Deduplication technology can obtain higher space utilization by keeping only one duplicate. But in a distributed storage system, the overall deduplication ratio will be limited due to redundancy elimination across nodes. The traditional deduplication methods usually utilize data similarity and data locality to improve the deduplication ratio. However, higher system overhead is caused by frequent similarity calculations. To deal with this problem, this paper proposes a new Feature-Aware Stateful Routing method(FASR), aiming to reduce the system overhead and keep a high deduplication ratio in the distributed environment. Firstly, we design a feature-aware nodes selection strategy to choose similar nodes by extracting data feature and data distribution characteristics. This strategy will save the similarity calculation with the nodes that are not similar to the data. Then, we present a stateful routing algorithm to determine the target node using super-chunk and handprint technology. Meanwhile, the algorithm maintain load balance of the entire distributed system. Finally, the data is deduplicated locally based on similarity index and fingerprint cache. Extensive experiments demonstrate that FASR can reduce system overhead by around 30% at most and also effectively obtain a higher deduplication ratio.
Molecular Insight into Genetic Structure and Diversity of Putative Hybrid Swarms of Pinus sylvestris × P. mugo in Slovakia

The genetic structures of the four putative hybrid swarms of Pinus sylvestris × P. mugo in Slovakia were analyzed in terms of individual admixture proportions calculated via inter-primer binding site (iPBS) marker loci. This work aimed to reevaluate the hybrid swarms’ differentiation status as postulated in the previous studies at both population and genomic levels. The study confirmed intermediate divergence of each of the swarms examined. Based on 80-loci hybrid index scores, we have revealed the presence of introgressive and intermediate hybrids with frequencies corresponding to differentiation estimates. Surprisingly, irrespective of individual phenotypes, the most frequent intermediates were found in Sucha Hora (29.5%) and Obsivanka (28.6%) populations, which resemble rather pure P. mugo and were previously considered as bimodal hybrid zones with a negligible amount of hybrids. The remaining hybrid zone population Zuberec seems to be highly introgressed to P. sylvestris, while Tisovnica is clearly inclined to P. mugo. The results and different methodologies are discussed.
Hsp78-interaction proteome highlights chaperone role during protein aggregation in mitochondria under heat stress

Proteins of the Hsp100 chaperone family support protein homeostasis, the maintenance of protein activity under stress, by refolding aggregated proteins or targeting them for degradation. Hsp78, the ClpB-type mitochondrial member of the Hsp100 family, can be found in lower eukaryotes like yeast. Although Hsp78 has been shown to contribute to protection against elevated temperatures in yeast, the biochemical mechanisms underlying this mitochondria-specific thermotolerance are still largely unclear. To identify endogenous chaperone substrate proteins, we generated an Hsp78-ATPase mutant with a stabilized substrate binding behaviour. We used two SILAC-based quantitative mass spectrometry approaches to analyze the role of Hsp78 during heat stress-induced mitochondrial protein aggregation and disaggregation processes on a proteomic level. In the first setup, Hsp78-interacting polypeptides were identified to reveal the endogenous substrate spectrum of the chaperone. Our analysis revealed that Hsp78 is interacting with a wide variety of proteins related to metabolic functions including energy production and protein synthesis, as well as other chaperones, thus maintaining crucial functions for mitochondrial stress resistance. We compared these interaction data with a second experimental setup that focussed on the on overall aggregation and disaggregation processes in mitochondria under heat stress on a proteomic level. This revealed specific aggregation-prone protein populations and demonstrated the direct quantitative impact of Hsp78 on stress-dependent protein solubility under different conditions. We conclude that Hsp78 together with its cofactors represents a recovery system that protects major mitochondrial metabolic functions during heat stress as well restores protein biogenesis capacity after return to normal conditions.
Optimizing Patient Information Material for a New Psycho-Oncological Care Program Using a Participatory Health Research Approach in Germany

High-quality patient information material (PIM) is essential for patients´ informed decision-making, and its quality may influence a care program’s acceptance. In the new psycho-oncological care program, isPO, the initial PIM was developed top-down and required optimization. In this paper, we report on the process and experiences of optimizing PIM’s quality bottom-up by applying a Participatory Health Research (PHR) approach. Cancer-patient representatives of the national peer-support group contributed as co-researchers as part of the optimization team. A mixed-methods design was chosen. First, the quality of the initially utilized PIM was assessed with the newly designed user-friendly instrument UPIM-Check. Next, three Participatory Action Research loops were conducted, including cancers survivors and isPO service providers. The initial isPO PIM’s were assed to be of low quality, limited usability and incomplete. Bottom-up generated optimization suggestions led to the improvement of two initially used PIMs (leaflet, patient information folder) and the design of two new PIMs (poster, study information overview). The optimized PIM facilitates isPO service providers’ care provision and helps newly diagnosed cancer patients in understanding and accepting the new program. PIM optimization benefited from applying PHR. The patient representatives’ contribution and active patient engagement were central for quality assessment and designing needs-driven, mature and complete PIM.
Biological Markers of Undernutrition and Risk of Complications in Children Hospitalized at the Teaching Hospital of Brazzaville

Introduction: Undernutrition is a condition frequently encountered in pe-diatrics. It leads to an increased morbidity and mortality regardless of the underlying condition or other risk factors such as age. In countries with limited resources such as ours, the diagnosis of undernutrition is often limited to the clinical presentation, and the contribution of biology is not often taken into account. Objectives: To establish the relationship between anthropometric parameters and biological markers in the diagnosis and classification of undernutrition and to assess the risk of infectious complications during undernutrition in children. Materials and Methods: A cross-sectional study conducted in Brazzaville among undernourished children aged 1 - 59 months between October 2018 and April acute (58.8%) and 35 chronic (41.2%). CRP and orosomucoid were statistically higher in severe acute undernutrition (p < 0.001). Serum albumin levels varied between severe and non-severe undernutrition only in the acute form (p < 0.001); transthyretin was decreased to a lesser degree (p < 0.05) in both forms of undernutrition. All children with severe undernutrition were at risk of infectious complications (p < 0.05). PINI had a positive predictive value for the detection of severe undernutrition of 87%. Conclusion: Anthropometric parameters have a front-line advantage for assessing and classifying undernutrition. However, biological markers of undernutrition with PINI should be systematized in the diagnosis and management of undernutrition.
A Funnel Fukunaga–Koontz Transform for Robust Indoor–Outdoor Detection Using Channel-State Information in 5G IoT Context

The massive machine-type communication will be at the core of ambient connectivity, requiring for energy-efficient systems. Earlier studies highlighted the efficiency of positioning approaches based on channel-state information (CSI) in different environments. Many works limited the solution assessment to a single room in a fully indoor testbed. This article extends the application of CSI for indoor–outdoor detection on an unprecedented large area and considers mMTC-oriented long-term evolution and fifth-generation Internet of Things in the sub-GHz frequency band. Hinged on a novel long-term evolution protocol dedicated for machine-type communications, the results focus on a unique packet exchange with a single access point to save battery life and simplify deployment. The study evaluates different input features and investigates the target positioning accuracy for multiple unsupervised and supervised dimensionality reduction methods. We present a new dimension reduction scheme consisting of an unsupervised funnel on top of a supervised dimension reduction approach. Results show that the introduced Funnel Fukunaga–Koontz transform outperforms other dimension reduction approaches, regardless of the input features and the number of locations.
Total and Phosphorylated Cerebrospinal Fluid Tau in the Differential Diagnosis of Sporadic Creutzfeldt-Jakob Disease and Rapidly Progressive Alzheimer’s Disease

Background: CSF total-tau (t-tau) became a standard cerebrospinal fluid biomarker in Alzheimer’s disease (AD). In parallel, extremely elevated levels were observed in Creutzfeldt-Jakob disease (CJD). Therefore, tau is also considered as an alternative CJD biomarker, potentially complicating the interpretation of results. We investigated CSF t-tau and the t-tau/phosphorylated tau181 ratio in the differential diagnosis of sCJD and rapidly-progressive AD (rpAD). In addition, high t-tau concentrations and associated tau-ratios were explored in an unselected laboratory cohort. Methods: Retrospective analyses included n = 310 patients with CJD (n = 205), non-rpAD (n = 65), and rpAD (n = 40). The diagnostic accuracies of biomarkers were calculated and compared. Differential diagnoses were evaluated in patients from a neurochemistry laboratory with CSF t-tau >1250 pg/mL (n = 199 out of 7036). Results: CSF t-tau showed an AUC of 0.942 in the discrimination of sCJD from AD and 0.918 in the discrimination from rpAD. The tau ratio showed significantly higher AUCs (p < 0.001) of 0.992 versus non-rpAD and 0.990 versus rpAD. In the neurochemistry cohort, prion diseases accounted for only 25% of very high CSF t-tau values. High tau-ratios were observed in CJD, but also in non-neurodegenerative diseases. Conclusions: CSF t-tau is a reliable biomarker for sCJD, but false positive results may occur, especially in rpAD and acute encephalopathies. The t-tau/p-tau ratio may improve the diagnostic accuracy in centers where specific biomarkers are not available.
Drag reduction configuration analysis of the grooves on amphibious vehicles walking mechanism

Amphibious vehicles are usually equipped with walking mechanisms and grooves which greatly affect the resistance characteristics of sailing. For purpose of improving the sailing speed and reduce energy consumption, the additional resistance at the groove is studied by numerical calculation. A Computational Fluid Dynamics method based on Reynolds-averaged Navier-Stokes solver was used to simulate the resistance performance of the amphibious vehicle, which was verified by towing test. Based on the ideal model without grooves, the size and trend of additional resistance on grooves were compared and studied. Combined with the analysis of flow field and sailing attitude, the cause of additional resistance is explained. Four different groove configurations were compared and analyzed, respectively. The results show that the curved surface type HSAV-3 has better water resistance performance at high speed, which depends on its better flow diversion and lifting effect.
A Review of NLIDB with Deep Learning: Findings, Challenges and Open Issues

Relational databases are storage for a massive amount of data. Knowledge of structured query language is a prior requirement to access that data. That is not possible for all non-technical personals, leading to the need for a system that translates text to SQL query itself rather than the user. Text to SQL task is also crucial because of its economic and industrial value. Natural Language Interface to Database (NLIDB) is the system that supports the text-to-SQL task. Developing the NLIDB system is a long-standing problem. Previously they were built based on domain-specific ontologies via pipelining methods. Recently a rising variety of Deep learning ideas and techniques brought this area to the attention again. Now end to end Deep learning models is being proposed for the task. Some publicly available datasets are being used for experimentation of the contributions, making the comparison process convenient. In this paper, we review the current work, summarize the research trends, and highlight challenging issues of NLIDB with Deep learning models. We discussed the importance of datasets, prediction model approaches and open challenges. In addition, methods and techniques are also summarized, along with their influence on the overall structure and performance of NLIDB systems. This paper can help future researchers start having prior knowledge of findings and challenges in NLIDB with Deep learning approaches.
AFTERSHOCK DECAY RATES IN THE ZAGROS AND PROBABILISTIC SEISMIC AFTERSHOCK HAZARD ANALYSIS OF THE 2013 APRIL 9 SHONBE (BUSHEHR) EARTHQUAKE

The main goal of this article is to study the Zagros aftershock decay rates. For this propose, the Iranian earthquake catalogue has been collected and homogenized between 2002 to 2014. Eight prominent earthquakes in the Zagros region were selected for aftershock decay rate study. Completeness magnitude and its variation was determined for each event. In order to investigate the behavior of aftershocks in the Zagros seismotectonic province, the Omori law parameters were calculated for selected events. Then, probabilistic aftershock hazard assessment (PAHA) based on aftershock parameters (a, b, P, K) of the 2013 April 9 Shonbe earthquake, has been estimated in temporal duration of 14, 30 and 60 days. In order to evaluate the variation of peak ground acceleration with time in the Zagros region, present attenuation relations and NGA formula has been applied. For calculating the PGA variations with time in 33% probability, we used logic tree for weighing different equation.
A Convolutional Neural Network-Based Method for Discriminating Shadowed Targets in Frequency-Modulated Continuous-Wave Radar Systems

The radar shadow effect prevents reliable target discrimination when a target lies in the shadow region of another target. In this paper, we address this issue in the case of Frequency-Modulated Continuous-Wave (FMCW) radars, which are low-cost and small-sized devices with an increasing number of applications. We propose a novel method based on Convolutional Neural Networks that take as input the spectrograms obtained after a Short-Time Fourier Transform (STFT) analysis of the radar-received signal. The method discerns whether a target is or is not in the shadow region of another target. The proposed method achieves test accuracy of 92% with a standard deviation of 2.86%.
Pain detection from facial landmarks using spatial-temporal deep neural network

Managing pain is a fundamental care need, of which inadequate management may have deleterious short- and long-term consequences to patients. In adult patients, pain is commonly assessed by an individual reporting on the numerical rating scale. However, self-reporting of pain may not be possible for patients with cognitive dysfunction, or those who are burdened by medical conditions e.g., stroke, degenerative diseases, damaged vocal cords, critically ill patients etc. Similarly, Pre-verbal children, and those with profound cognitive impairment are unable to self-report and describe their pain level, making pain assessment more challenging. Difficulty in self reporting in pain assessment is a significant clinical problem that affects clinical decision. Therefore, automatic pain detection is important for patient care. In this paper, we propose a Spatial-temporal Deep Neural Network (DNN) to detect the pain level of a patient from the facial landmarks. The proposed system firstly detect face from a frame, and then extract facial landmarks from face detected, after that a series of frames containing facial landmarks are fed into the 3D convolutional networks for high level spatial and temporal feature extraction, and finally classify into 3 categories: no pain, mild pain and serve pain. The proposed system achieved 97% accuracy after being trained and tested using UNBC-McMaster Pain Archive Database.
Brushless DC motor drive with power factor regulation using Landsman converter

This study presents a novel configuration of power factor regulation (PFR)-based Landsman converter feeding a brushless DC motor (BLDCM) drive for low-power (400 W) white goods applications. The speed control of the drive is achieved through adjusting the DC bus voltage of voltage source inverter (VSI) feeding to a BLDCM. Moreover, low-frequency switching signals are used for electronic commutation of BLDCM, which reduces the switching power losses of six solid-state switches of VSI. This Landsman converter-based front-end power factor corrector operating in discontinuous inductor current mode is used to control DC bus voltage and PFR is achieved inherently. The DC bus voltage of the drive is controlled by using a single DC voltage sensor. For evaluating the performance of proposed drive, a prototype is developed in the laboratory. The performance of the BLDCM is also analysed for its operation at varying AC mains voltage (90-265 V). Experiential results for power quality indices are found within the limits of power quality standard IEC 61000-3-2.
Blood Cell Classification using Deep Learning CNN Model

Deep Learning has already shown power in many application fields and is accepted by more and more people as a better approach than the traditional machine learning models. In particular, the implementation of deep learning algorithms, especially Convolutional Neural Networks (CNN), brings huge benefits to the medical field, where a huge number of images are to be processed and analyzed. This paper aims to develop a deep learning model to address the blood cell classification problem, which is one of the most challenging problems in blood diagnosis. A CNN-based framework is built to automatically classify the blood cell images into subtypes of the cells. Experiments are conducted on a dataset of 13k images of blood cells with their subtypes, and the results show that our proposed model provides better results in terms of evaluation parameters.
Life Cycle Flow (LCF) Application to Evaluate the Real Estate Investment in Residential Buildings with Tax Benefit Incentives in Cases of Positive Externalities

Investing in the main home is an important form of investment for families and represented one of the main family assets in Italy in the second half of the 20th century. The evaluation of the convenience of the house purchase needs to consider that technologies in construction have undergone a rapid change in recent times that proposes an approach to sustainable building technologies, such as dry construction systems that can reduce energy consumption over time. Moreover, these construction systems are encouraged with tax deductions from the state, that is, financing with public spending via a tax benefit, which recognizes the role of externalities of investments in sustainable construction. The article would apply Life Cycle Flow (LCF) model to a residential building; LCF is based on the Life Cycle Cost (LCC) model and adapted to assess a real estate investment, such as a residential building for private use, taking into account the effects of a tax benefit. The model quantifies the cost of satisfying consumers’ housing needs in the long run. The model takes into account the absorption of financial resources at all stages of property investment. The proposed LCF approach quantifies an average discounted cash outflow per year and an average discounted cash outflow per year per surface unit in square meters; thus, it is possible to compare project alternatives and choose the alternative that minimizes the absorption of financial resources in the long run. In this article, the LCF model is applied to three project alternatives and highlights the energy savings in the long run for consumption choices and the importance of tax benefits for the reduction of the cash outflow for a family in long-term housing. Given the results of the research, the proposed LCF model can be applied on a larger scale, in particular, to quantify social welfare generated by tax benefits financed with public spending, in terms of economic activation and assessing environmental externalities.
Continuous Gender Identity and Economics

Note: Scores are from the first-order gender identity questions and are standardized to have a mean of zero and a standard deviation of one. For our single item CGI question in Panel (a), the unidimensional scale ranges from “very masculine” to “very feminine” on a 7-point scale. For the Magliozzi scales in Panels (b) and (c), the scales range from “not at all” to “very” masculine and feminine, respectively. 0 10 20 30 40 50