Miracle-dz/UniFAR-FTUs huggingface dataset:
Gear fault diagnosis using discrete wavelet transform and deep neural networks

Automatic fault diagnosis is an inseparable part of today's electromechanical systems. Advanced signal processing and machine learning techniques are required to address variabilities and uncertainties associated with the monitoring signals. In this paper, deep neural networks are employed to diagnose five classes of gearbox faults applied to three common monitoring signals, i.e. vibration, acoustic and torque. Discrete wavelet transform is used to provide the initial features as the inputs of the network. A test-rig based on a 250W three-phase squirrel cage induction machine shaft connected to a single stage helical gear is built for validation of the proposed method. The experimental results indicate accurate fault diagnosis in various conditions such as different modalities, signal variabilities, and load conditions.
Households electricity consumption analysis with data mining techniques

Smart Grid improves the electricity grid infrastructure by introducing new powerful communication system between consumer and supplier. Implementation of smart meters increases the availability of detail level of consumer electricity load profile data. To improve and efficient planning and development of this new power system, a primary challenge is to analyze the electricity consumption data. To analyze the energy consumption or achieve our objective we choose the best analytic process is data mining techniques including exploratory data analysis and preprocessing, frequent patterns mining and associations, classification /characterization,clustering and outlier deduction. In this paper, we use these techniques and apply on two different public available datasets. Explain and evaluate which techniques is use full for the better understanding of electricity load profile consumption data.
The effects of the 5-HT2A receptor antagonist sarpogrelate hydrochloride on chronic hypoxic pulmonary hypertension in rats

ABSTRACT Purpose of the Study: This study investigated whether sarpogrelate hydrochloride (SPG), a 5-HT2A receptor antagonist, alleviates chronic hypoxic pulmonary hypertension (CH-PH) in rats by stimulating apoptosis and inhibiting proliferation in pulmonary artery smooth muscle cells (PASMCs). Materials and Methods: Forty male Sprague-Dawley rats were pretreated with SPG (50 mg/kg/day by oral gavage) or saline vehicle and then subjected to chronic hypoxia (CH) (hypobaric chamber set to 380 mmHg, 10% oxygen) or normoxia for 14 days. Mean pulmonary artery pressure (PAP) and right ventricular hypertrophy (RVH) were measured. Hypertensive pulmonary vascular remodeling was assayed by light microscopy. Terminal deoxynucletidyl transferase dUTP nick end ligase (TUNEL) assays, western blotting, and real-time polymerase chain reaction were used to assess apoptosis, proliferation and underlying signaling pathways in PASMCs from lung tissue and isolated pulmonary artery rings. Results: CH increased mean PAP and RVH. CH increased the percentage of muscularized arteries in the peripheral pulmonary vasculature and medial wall thickness in small muscular arteries. CH increased pulmonary protein and mRNA levels of the B-cell lymphoma protein 2 (Bcl-2), pyruvate dehydrogenase kinase (PDK), phosphorylation of extracellular signal-regulated kinases 1 and 2 (pERK1/2), cyclin D1, proliferating cell nuclear antigen (PCNA) and decreased protein and mRNA levels of Bcl-2-associated X protein (BAX), cleaved caspase-3. Pretreatment with SPG, which has been shown previously to inhibit ERK1/2 phosphorylation and PDK, countered all of these effects. Isolated pulmonary artery rings incubated with 5-HT increased pERK1/2, PDK, and Bcl-2 expression, and decreased Bax expression. Conclusion: Administration of SPG ameliorated the development of CH-PH by stimulating apoptosis in and inhibiting proliferation of PASMCs.
NK-CD11c+ Cell Crosstalk in Diabetes Enhances IL-6-Mediated Inflammation during Mycobacterium tuberculosis Infection

In this study, we developed a mouse model of type 2 diabetes mellitus (T2DM) using streptozotocin and nicotinamide and identified factors that increase susceptibility of T2DM mice to infection by Mycobacterium tuberculosis (Mtb). All Mtb-infected T2DM mice and 40% of uninfected T2DM mice died within 10 months, whereas all control mice survived. In Mtb-infected mice, T2DM increased the bacterial burden and pro- and anti-inflammatory cytokine and chemokine production in the lungs relative to those in uninfected T2DM mice and infected control mice. Levels of IL-6 also increased. Anti-IL-6 monoclonal antibody treatment of Mtb-infected acute- and chronic-T2DM mice increased survival (to 100%) and reduced pro- and anti-inflammatory cytokine expression. CD11c+ cells were the major source of IL-6 in Mtb-infected T2DM mice. Pulmonary natural killer (NK) cells in Mtb-infected T2DM mice further increased IL-6 production by autologous CD11c+ cells through their activating receptors. Anti-NK1.1 antibody treatment of Mtb-infected acute-T2DM mice increased survival and reduced pro- and anti-inflammatory cytokine expression. Furthermore, IL-6 increased inflammatory cytokine production by T lymphocytes in pulmonary tuberculosis patients with T2DM. Overall, the results suggest that NK-CD11c+ cell interactions increase IL-6 production, which in turn drives the pathological immune response and mortality associated with Mtb infection in diabetic mice.
Safety and Efficacy of Lysergic Acid Diethylamide-Assisted Psychotherapy for Anxiety Associated With Life-threatening Diseases

Abstract A double-blind, randomized, active placebo-controlled pilot study was conducted to examine safety and efficacy of lysergic acid diethylamide (LSD)-assisted psychotherapy in 12 patients with anxiety associated with life-threatening diseases. Treatment included drug-free psychotherapy sessions supplemented by two LSD-assisted psychotherapy sessions 2 to 3 weeks apart. The participants received either 200 &mgr;g of LSD (n = 8) or 20 &mgr;g of LSD with an open-label crossover to 200 &mgr;g of LSD after the initial blinded treatment was unmasked (n = 4). At the 2-month follow-up, positive trends were found via the State-Trait Anxiety Inventory (STAI) in reductions in trait anxiety (p = 0.033) with an effect size of 1.1, and state anxiety was significantly reduced (p = 0.021) with an effect size of 1.2, with no acute or chronic adverse effects persisting beyond 1 day after treatment or treatment-related serious adverse events. STAI reductions were sustained for 12 months. These results indicate that when administered safely in a methodologically rigorous medically supervised psychotherapeutic setting, LSD can reduce anxiety, suggesting that larger controlled studies are warranted.
Low subjective social status in the police is linked to health-relevant changes in diurnal salivary alpha-amylase activity in Swiss police officers

Abstract The objective of this study was to assess basal autonomic nervous system (ANS) activity as a pathway linking subjective social status to health in a high-demand work environment. It was hypothesized that officers with a lower status experienced more chronic stress (higher basal ANS activity) and that chronic stress was related to more health problems. Fifty-six male and female Swiss police officers self-reported on subjective social status (country, community, friends, police) and their health (depression, post-traumatic stress, physical symptoms) and collected 12 saliva samples over two days for basal α-amylase activation (sAA) assessment. Multilevel regression analyses revealed that subjective social status in the police and physical symptoms explained a significant part of the variance in diurnal sAA activity patterns. The current findings support the idea that more narrowly defined subjective social status may be more closely linked to biological stress mechanisms. Additionally, sAA activity was specifically related to physical, but not mental health problems. These results suggest that subjective social status referencing one’s work environment may be a promising early indicator of health-relevant changes in stress-related physiological systems.
Association Between Platelet Count and Postoperative Blood Loss in Patients Undergoing Cardiac Surgery With Cardiopulmonary Bypass and Fresh Frozen Plasma Administration Guided by Thromboelastometry.

BACKGROUND Coagulopathy after cardiopulmonary bypass (CPB) is caused by multiple factors, including reduced coagulation factors and a low platelet count.Methods and Results:In this study, we undertook a post hoc analysis to identify factors associated with increased postoperative blood loss in 97 patients undergoing cardiac surgery with CPB, with fresh frozen plasma administered according to a ROTEM-guided algorithm. We identified 24 patients for the top quartile of postoperative blood loss, >528 mL and defined as having excessive blood loss. Using Spearman's rank correlation test and multivariable linear regression, we reanalyzed the participants' demographic, surgical and anesthetic variables, laboratory test results, blood loss, and transfusion data. Univariate analysis indicated that patients who experienced higher postoperative blood loss received a significantly higher heparin dose, had a higher requirement for fresh frozen plasma transfusion during surgery, and had a significantly lower hematocrit and platelet count at the end of surgery compared with patients without excessive blood loss. Multivariate analysis showed that platelet count at the end of surgery (odds ratio 0.780, 95% confidence interval 0.629-0.967; P=0.024) was an independent factor for excessive blood loss. CONCLUSIONS Low platelet count at the end of surgery was associated with excessive postoperative bleeding during cardiac surgery with CPB.
Evaluation of electrophoretic protein extraction and database‐driven protein identification from marine sediments

Intact proteins comprise a major component of organic carbon and nitrogen produced globally and are likely an important fraction of organic matter in sediments and soils. Extracting the protein component from sediments and soils for mass spectral characterization and identification represents a substantial challenge given the range of products and functionalities present in the complex matrix. Multiple forms of gel electrophoresis were evaluated as a means of enhancing recovery of sedimentary protein before proteomic characterization and compared with a direct enzymatic digestion of proteins in sediments. Resulting tryptic peptides were analyzed using shotgun proteomics and tandem mass spectra were evaluated with SEQUEST. Multiple databases were then evaluated to examine the ability to confidently identify proteins from environmental samples. Following evaluation of electrophoretic extraction of proteins from sediments, the recovery of an experimentally added standard protein (BSA) from older (>1 ky) sediments was optimized. Protein extraction from sediments via direct electrophoresis of a slurry mixture and the specified extraction buffer resulted in the greatest number of confident protein identifications and highest sequence coverage of the BSA standard. Searching tandem mass spectral data against larger databases with a higher diversity of proteomes did not yield a greater number of, or more confidence in, protein identifications. Regardless of the protein database used, identified peptides correlated to proteins with the same function across taxa. This suggests that while determining taxonomic‐level information remains a challenge in samples with unknown mixed species, it is possible to confidently assign the function of the identified protein.
What’s next for chronobiology and drug discovery

Chronobiology is a study of biological rhythms, and the predominant rhythms are those of daily, or circadian, dimension. The circadian rhythms of tissue and organismal functions are driven by a circadian clock system consisting of cell-autonomous, selfsustainable molecular oscillators. In both the core loop and the secondary, stabilization loop of the oscillator, positive (CLOCK/ NPAS2, BMAL1, RORs) and negative (PERIODs, CRYPTOCHROMEs, REV-ERBs) factors form transcription-translation negative feedback (Figure 1) [2]. Several other feedback loops have been shown to superimpose on this basic framework, playing important roles to regulate the core oscillator. The cellular oscillators throughout the body are synchronized by a master pacemaker, the suprachiasmatic nuclei (SCN) in the hypothalamus, in response to photic signals throughout the day. The circadian clock, through both systemic signals originated from the SCN and local control by tissue oscillators, drives downstream gene expression. While the circadian transcriptome is highly tissuespecific, overall half of the genes in mammalian genomes display circadian expression in at least one tissue [3], underscoring a prevalent clock control of physiology. Consistently, circadian dysregulation has been broadly implicated in various diseases and aging. Studies from shift workers and mouse circadian disruption models have revealed pervasive pathophysiological consequences of clock dysregulation [4]. In mice, both genetic and environmental means have been employed to disrupt the clock and, in some cases, downstream effectors that connect dysfunctional clocks and disease phenotypes have been identified. A complementary line of evidence comes from circadian characterization in diseases and aging conditions where dysregulated, often damped, circadian gene expression and physiological rhythms are routinely observed [1,5]. Finally, human genetic studies, particularly of familial sleep disorders [6], have established clinical relevance of clock genes in human disease. Collectively, studies thus far revealed an important role of robust rhythms for health and fitness.
Degradation of Internalized αvβ5 Integrin Is Controlled by uPAR Bound uPA: Effect on β1 Integrin Activity and α-SMA Stress Fiber Assembly

Myofibroblasts (Mfs) that persist in a healing wound promote extracellular matrix (ECM) accumulation and excessive tissue contraction. Increased levels of integrin αvβ5 promote the Mf phenotype and other fibrotic markers. Previously we reported that maintaining uPA (urokinase plasminogen activator) bound to its cell-surface receptor, uPAR prevented TGFβ-induced Mf differentiation. We now demonstrate that uPA/uPAR controls integrin β5 protein levels and in turn, the Mf phenotype. When cell-surface uPA was increased, integrin β5 levels were reduced (61%). In contrast, when uPA/uPAR was silenced, integrin β5 total and cell-surface levels were increased (2–4 fold). Integrin β5 accumulation resulted from a significant decrease in β5 ubiquitination leading to a decrease in the degradation rate of internalized β5. uPA-silencing also induced α-SMA stress fiber organization in cells that were seeded on collagen, increased cell area (1.7 fold), and increased integrin β1 binding to the collagen matrix, with reduced activation of β1. Elevated cell-surface integrin β5 was necessary for these changes after uPA-silencing since blocking αvβ5 function reversed these effects. Our data support a novel mechanism by which downregulation of uPA/uPAR results in increased integrin αvβ5 cell-surface protein levels that regulate the activity of β1 integrins, promoting characteristics of the persistent Mf.
Emotional modelling and classification of a large-scale collection of scene images in a cluster environment

The development of network technology and the popularization of image capturing devices have led to a rapid increase in the number of digital images available, and it is becoming increasingly difficult to identify a desired image from among the massive number of possible images. Images usually contain rich semantic information, and people usually understand images at a high semantic level. Therefore, achieving the ability to use advanced technology to identify the emotional semantics contained in images to enable emotional semantic image classification remains an urgent issue in various industries. To this end, this study proposes an improved OCC emotion model that integrates personality and mood factors for emotional modelling to describe the emotional semantic information contained in an image. The proposed classification system integrates the k-Nearest Neighbour (KNN) algorithm with the Support Vector Machine (SVM) algorithm. The MapReduce parallel programming model was used to adapt the KNN-SVM algorithm for parallel implementation in the Hadoop cluster environment, thereby achieving emotional semantic understanding for the classification of a massive collection of images. For training and testing, 70,000 scene images were randomly selected from the SUN Database. The experimental results indicate that users with different personalities show overall consistency in their emotional understanding of the same image. For a training sample size of 50,000, the classification accuracies for different emotional categories targeted at users with different personalities were approximately 95%, and the training time was only 1/5 of that required for the corresponding algorithm with a single-node architecture. Furthermore, the speedup of the system also showed a linearly increasing tendency. Thus, the experiments achieved a good classification effect and can lay a foundation for classification in terms of additional types of emotional image semantics, thereby demonstrating the practical significance of the proposed model.
Physiological and behavioural response of two dairy cows’ genotypes during summertime in the central region of Chile

ABSTRACT: Heat stress has been recognised as a serious problem in dairy farms. The study goal was to assess the effects of climatic conditions on physiological and behavioural responses of dairy cows in Central Chile. Data of tympanic temperature (TT), panting score, respiration rate (RR), and shade utilization of cows from two genotypes, Holstein (H) and Holstein x Montbeliarde (HM), were collected twice per day (AM/PM) during three periods of the summer season in Central Chile. Moreover, three thermal comfort indices: Comprehensive climate index (CCI), temperature humidity-index (THI), and adjusted THI were estimated using meteorological data. The hour of each day was classified as “Normal” or “Stressful” based on CCI threshold of 25 °C. Statistical analysis included ANOVA, repeated measures analysis and Chi square test (a=0.05). There was an interaction of genotype x CCI condition x period (P=0.0026) with the highest TT of both genotypes under a stressful condition within each period. In addition, interactions of genotype x hour (P<0.0001) and genotype x CCI condition (P<0.0002) were also observed. The HM cows showed greater TT than H cows in both CCI conditions. The RR was higher during the afternoon and a greater proportion of cows used shade at “Mild” and “Moderate” CCI categories (P<0.001). Both genotypes showed some degree of heat stress, but cool nights and shade seem to be enough to allow to cows’ cope with the challenging diurnal conditions observed in the summer season. A study of these effects on milk production is necessary to confirm or discard the previous.
Does Functional Connectivity Provide a Marker for Cognitive Rehabilitation Effects in Alzheimer’s Disease? An Interventional Study

Background: Cognitive rehabilitation (CR) is a cognitive intervention for patients with Alzheimer’s disease (AD) that aims to maintain everyday competences. The analysis of functional connectivity (FC) in resting-state functional MRI has been used to investigate the effects of cognitive interventions. Objectives: We evaluated the effect of CR on the default mode network FC in a group of patients with mild AD, compared to an active control group. Methods: We performed a three-month interventional study including 16 patients with a diagnosis of AD. The intervention group (IG) consisted of eight patients, performing twelve sessions of CR. The active control group (CG) performed a standardized cognitive training. We used a seed region placed in the posterior cingulate cortex (PCC) for FC analysis, comparing scans acquired before and after the intervention. Effects were thresholded at a significance of p < 0.001 (uncorrected) and a minimal cluster size of 50 voxels. Results: The interaction of group by time showed a higher increase of PCC connectivity in IG compared to CG in the bilateral cerebellar cortex. CG revealed widespread, smaller clusters of higher FC increase compared with IG. Across all participants, an increase in quality of life was associated with connectivity increase over time in the bilateral precuneus. Conclusions: CR showed an effect on the FC of the DMN in the IG. These effects need further study in larger samples to confirm if FC analysis may suit as a surrogate marker for the effect of cognitive interventions in AD.
Optimized Training for Multi-Antenna Wireless Energy Transfer in Frequency-Selective Channel

This paper studies the training design problem for multiple-input single-output (MISO) wireless energy transfer (WET) systems in frequency-selective channels, where the frequency-diversity and energy-beamforming gains can be both reaped to maximize the transferred energy by efficiently learning the channel state information (CSI) at the energy transmitter (ET). By exploiting channel reciprocity, a new two-phase channel training scheme is proposed to achieve the diversity and beamforming gains, respectively. In the first phase, pilot signals are sent from the energy receiver (ER) over a selected subset of the available frequency sub-bands, through which the ET determines a certain number of “strongest” sub-bands with largest antenna sum-power gains and sends their ordered indices to the ER. In the second phase, the selected sub-bands are further trained by the ER, so that the ET obtains a refined estimate of the corresponding MISO channels to implement energy beamforming for WET. A training design problem is formulated and optimally solved, which takes into account the channel training overhead by maximizing the net harvested energy at the ER, defined as the average harvested energy offset by that consumed in the two-phase training. Moreover, asymptotic analysis is obtained for systems with a large number of antennas or a large number of sub-bands to gain useful insights on the optimal training design. Lastly, numerical results are provided to corroborate our analysis and show the effectiveness of the proposed scheme by optimally balancing the diversity and beamforming gains achieved in MISO WET systems with limited-energy training.
Remote monitoring and communication system with a doll-like robot for the elderly

In this paper, we propose a remote monitoring and communication system for elderly care. We aim to detect emergencies concerning elderly people living alone and send alerts to his/her family from a remote location. For this reason, the system has two modules: the monitoring module and the video chat module. When the monitoring module of the system detects any unusual events concerning the elderly, the system sends an alert message to their family's smart phone. Then, their family can monitor the current situation via the video chat module. The elderly and his/her family member can also easily use the video chat function for day-to-day communication without requiring any complex actions for the users. For the video chat display at the elderly side, we use a television set, which all elderly would be expected to have in their rooms. This gives our system a familiar appearance so the elderly can use it without feeling aversion towards the system. The monitoring system for detecting emergencies is a doll-like robot. Besides monitoring, the robot can also initiate simple conversations autonomously in normal situations, which can improve the elderly person's sense of familiarity towards the system.