Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontology-based validation and identification of regulatory phenotypes

Motivation Function annotations of gene products, and phenotype annotations of genotypes, provide valuable information about molecular mechanisms that can be utilized by computational methods to identify functional and phenotypic relatedness, improve our understanding of disease and pathobiology, and lead to discovery of drug targets. Identifying functions and phenotypes commonly requires experiments which are time-consuming and expensive to carry out; creating the annotations additionally requires a curator to make an assertion based on reported evidence. Support to validate the mutual consistency of functional and phenotype annotations as well as a computational method to predict phenotypes from function annotations, would greatly improve the utility of function annotations. Results We developed a novel ontology-based method to validate the mutual consistency of function and phenotype annotations. We apply our method to mouse and human annotations, and identify several inconsistencies that can be resolved to improve overall annotation quality. Our method can also be applied to the rule-based prediction of phenotypes from functions. We show that the predicted phenotypes can be utilized for identification of protein-protein interactions and gene-disease associations. Based on experimental functional annotations, we predict phenotypes for 1,986 genes in mouse and 7,301 genes in human for which no experimental phenotypes have yet been determined. Availability https://github.com/bio-ontology-research-group/phenogocon Contact robert.hoehndorf@kaust.edu. sa
Automated generation of convolutional neural network training data using video sources

One of the challenges of using techniques such as convolutional neural networks and deep learning for automated object recognition in images and video is to be able to generate sufficient quantities of labeled training image data in a cost-effective way. It is generally preferred to tag hundreds of thousands of frames for each category or label, and a human being tagging images frame by frame might expect to spend hundreds of hours creating such a training set. One alternative is to use video as a source of training images. A human tagger notes the start and stop time in each clip for the appearance of objects of interest. The video is broken down into component frames using software such as ffmpeg. The frames that fall within the time intervals for objects of interest are labeled as “targets,” and the remaining frames are labeled as “non-targets. ” This separation of categories can be automated. The time required by a human viewer using this method would be around ten hours, at least 1–2 orders of magnitude lower than a human tagger labeling frame by frame. The false alarm rate and target detection rate can by optimized by providing the system unambiguous training examples.
Resource Scalability as Preventive and Remedial Measures for Cloud Service Violation

Cloud-based applications are heavily consumed and resourced due to the increasing demand and needs of the service users. The issue of overly provisioning the resources results in a waste of cloud resources provided by the service facilitator and also costing more to the service user. Thus, it is very crucial to create a balance trade-off between specified Quality of Service (QoS) terms which are stated in Service Level Agreement (SLA) with scalable resource allocation. Vertical scalability is utilized for effective and improved resource usage. Our proposed approach presents scalable resource allocation using vertical scalability to ensure predefined QoS metrics such as response time and throughput is fulfilled. QoS violation is checked and detected using fuzzy if-then rules to determine the quality status. Once violation or probable violation is detected, system evaluation is performed to identify possible erroneous behavior which then leads to the execution of optimized resource allocation activities. We presented case scenarios to demonstrate the feasibility of the proposed approach through simulation.
Impacts of Indoxyl Sulfate and p-Cresol Sulfate on Chronic Kidney Disease and Mitigating Effects of AST-120

Uremic toxins, such as indoxyl sulfate (IS) and p-cresol, or p-cresyl sulfate (PCS), are markedly accumulated in the organs of chronic kidney disease (CKD) patients. These toxins can induce inflammatory reactions and enhance oxidative stress, prompting glomerular sclerosis and interstitial fibrosis, to aggravate the decline of renal function. Consequently, uremic toxins play an important role in the worsening of renal and cardiovascular functions. Furthermore, they destroy the quantity and quality of bone. Oral sorbent AST-120 reduces serum levels of uremic toxins in CKD patients by adsorbing the precursors of IS and PCS generated by amino acid metabolism in the intestine. Accordingly, AST-120 decreases the serum IS levels and reduces the production of reactive oxygen species by endothelial cells, to impede the subsequent oxidative stress. This slows the progression of cardiovascular and renal diseases and improves bone metabolism in CKD patients. Although large-scale studies showed no obvious benefits from adding AST-120 to the standard therapy for CKD patients, subsequent sporadic studies may support its use. This article summarizes the mechanisms of the uremic toxins, IS, and PCS, and discusses the multiple effects of AST-120 in CKD patients.
An Approach to Handle Big Data Warehouse Evolution

One of the purposes of Big Data systems is to support analysis of data gathered from heterogeneous data sources. Since data warehouses have been used for several decades to achieve the same goal, they could be leveraged also to provide analysis of data stored in Big Data systems. The problem of adapting data warehouse data and schemata to changes in these requirements as well as data sources has been studied by many researchers worldwide. However, innovative methods must be developed also to support evolution of data warehouses that are used to analyze data stored in Big Data systems. In this paper, we propose a data warehouse architecture that allows to perform different kinds of analytical tasks, including OLAP-like analysis, on big data loaded from multiple heterogeneous data sources with different latency and is capable of processing changes in data sources as well as evolving analysis requirements. The operation of the architecture is highly based on the metadata that are outlined in the paper.
Referential Gaze Makes a Difference in Spoken Language Comprehension: Human Speaker vs. Virtual Agent Listener Gaze

An interlocutor's referential gaze is of great importance in face-to-face communication as it facilitates spoken language comprehension. People are also able to exploit virtual agent gaze in interactions. Our study addressed effects of human speaker gaze vs. virtual listener gaze on reaction times, accuracy and eye movements. We manipulated: (1) whether the human speaker - uttering the sentence - was visible, (2) whether the agent listener was present and (3) whether the template following each video matched the scene. Participants saw videos in which a static scene depicting three characters was visible on a screen. We recorded participants ' eye movements as they listened to German SVO sentences describing an interaction between two of these three characters. After each trial a template schemat-ically depicting three characters and their interaction appeared on screen. Participants veriﬁed congruence between sentence and template. Participants solved the matching task very well across all conditions. They responded faster to matches than mismatches between sentence and template. Participants were slower when the agent was present. Eye movement results suggest that during the NP2 region participants tended to look at the NP2 referent to a greater extent when the speaker was present compared to the other conditions.
Level of Satisfaction on Service Quality Dimensions Based on SERVQUAL Model Among Patients Attending 1 Malaysia Clinic in Kota Bharu, Malaysia.

Objectives Implemented in 2010, 1 Malaysia Clinic (1MC) is the latest innovation in public primary healthcare services in Malaysia to serve the urban population. This study aimed to assess the level of satisfaction and its associated factors using the SERVQUAL (SERV-service, QUAL-quality) instrument. We also sought to compare the difference of mean score between expectation and perception of SERVQUAL dimension among patients attending 1MC in Kota Bharu district, Malaysia. Methods This cross-sectional study included all nine 1MCs in the Kota Bharu district, Malaysia, and used the validated SERVQUAL questionnaire from Ministry of Health, Malaysia. The primary data was collected in January 2017. The sample size was proportionate to the average daily patient attendance for each clinic, and a simple random sampling method was applied to reduced selection bias. Satisfaction level was determined by the mean gap score of expectation and perception of the SERVQUAL dimension. Results A total of 386 patients were involved in this study (184 male and 202 female). Most were from the low socioeconomic group. Less than half of the patients were satisfied with the clinic services. The tangible dimension (service quality (SQ) gap: -0.25) was the most critical dimension among all five SQ dimensions assessed. The factors included low education level (adjusted odds ratio (adj. OR) 1.87; 95% confidence interval (CI): 1.06, 2.67; p = 0.024), monthly household income less than RM 3000 (adj. OR 2.97; 95% CI: 1.72, 5.51; p < 0.001), and frequency 3 3 visits (adj. OR 2.23; 95% CI: 1.44, 3.45; p < 0.001) had significant association with high level of satisfaction. Conclusion 1MC has achieved its objective to increase the accessibility of health services among the low-income population in urban settings. However, most patients were not satisfied with certain parts of the services. Thus, proper evaluation of the service gap is required for improvement of the service.
ANALYSIS OF METHODS INCREASING THE QUALITY OF THE OPERATION OPTICAL SIGNAL TRANSFER SYSTEMS IN TELECOMMUNICATION SYSTEMS

Methods for improving the performance optical transmission systems in telecommunication systems are analyzed. Fiber-optic communication lines (FOCL) and fiber-optic transmission systems (FOTS) using fiber-optic cable (FOC), receiving and transmitting optical modules (ROM and TOM) based on WDM and DWDM (Wavelength Division Multiplexing & Dense WDM) technologies were studied. A method is proposed for calculating the quality of the operation optical signal transmission systems, taking into account the characteristics FOCL, and relationships are obtained that establish an analytical relationship between the length of the regeneration section and the transmission rate.
Stochastic Geometry Analysis of Downlink Data Rate Over Fast Fading Channels

Performance analysis of cellular networks using stochastic geometry has received extensive studies. Different from existing works that are mainly applicable for slow fading channels, in the letter, we consider fast fading channels and analyze the statistics of downlink achievable rate by first averaging out small-scale channels and then only treating large-scale channels as random variables. We first derive an approximated cumulative distribution function (CDF) of downlink achievable rate, based on which two approximated CDFs with simpler expressions are then developed. Simulations validate the accuracy of the derived analytical results, and an application example of the obtained results for video on demand service is demonstrated.
Modified Huangqi Chifeng decoction inhibits excessive autophagy to protect against Doxorubicin-induced nephrotic syndrome in rats via the PI3K/mTOR signaling pathway

The aim of the present study was to investigate whether modified Huangqi Chifeng decoction (MHCD) could be an effective treatment against Doxorubicin-induced nephrosis in rats and whether it regulates autophagy via the phosphoinositide-3 kinase/mammalian target of rapamycin (PI3K/mTOR) signaling pathway. A total of 40 male Sprague-Dawley rats were randomly divided into blank, model, telmisartan and MHCD groups. The rat model of nephrosis was induced by intragastric administration of Doxorubicin for 8 weeks. Rats were housed in metabolic cages and urine was collected once every 2 weeks to measure 24-h protein levels. Blood samples were obtained from the abdominal aorta and levels of albumin (ALB), total cholesterol (TCH), triacylglyceride (TG) and serum creatinine (Scr) were assessed. Renal pathological changes were examined using hematoxylin-eosin, Masson's trichome and periodic acid-Schiff staining. Podocytes and autophagosomes were observed using an electron microscope. The expression and distribution of microtubule-associated proteins 1A/1B light chain 3B (LC3), LC3-I, LC3-II, beclin-1, PI3K and mTOR were determined using immunohistochemistry and western blotting. At weeks 6 and 8, 24-h proteinuria significantly decreased in the MHCD group compared with the model group (P<0.05). Compared with the model group, the MHCD group exhibited significantly reduced levels of TG, TCH and Scr, as well as significantly increased ALB levels (P<0.05). MHCD was demonstrated to prevent glomerular and podocyte injury. The number of autophagosomes was significantly decreased and the expression of beclin-1, LC3, LC3-I and LC3-II was inhibited following MHCD treatment compared with the model group (P<0.05). MHCD treatment significantly increased the expression of PI3K and mTOR in Doxorubicin nephrotic rats compared with the model group (P<0.05). In conclusion, MHCD was demonstrated to ameliorate proteinuria and protect against glomerular and podocyte injury by inhibiting excessive autophagy via the PI3K/mTOR signaling pathway.
A 2.2- $\mu$ W Cognitive Always-On Wake-Up Circuit for Event-Driven Duty-Cycling of IoT Sensor Nodes

We report an always-on event-driven asynchronous wake-up circuit with trainable pattern recognition capabilities to duty-cycle power-constrained Internet-of-Things (IoT) sensor nodes. The wake-up circuit is based on a level-crossing analog-to-digital converter (LC-ADC) employed as a feature-extraction block with automatic activity-sampling rate scaling behavior. A novel asynchronous digital logic classifier for sequential pattern recognition is presented. It is driven by the LC-ADC activity and trained to minimize classification errors due to falsely detected events. As proof-of-concept, a prototype of the wake-up circuit is fabricated in 130nm CMOS technology within 0.054 mm2 of active area, covering up to 2.6 kHz of input signal bandwidth. The prototype has been first validated by interfacing it with a commercial accelerometer to classify hand gestures in real-time, reaching 81% of accuracy with only 2.2 $\mu \text{W}$ at 1-V supply. To highlight the flexibility of the design, a second application, detecting pathologic electrocardiogram beats is also discussed.
Loneliness and Social Engagement in Older Adults: A Bivariate Dual Change Score Analysis

Few longitudinal studies have explored the impact of loneliness on social engagement. We investigated whether loneliness would result in decreased social engagement over time among older adults and also whether the converse, that low levels of social engagement would predict increases in loneliness, held. Additionally, we explored potential mechanisms (specifically, memory and depressive symptomatology as mediators) in the bidirectional relationship(s) between loneliness and social engagement. Data from 4,714 adults over 50 years of age, participating in Waves 3, 4, and 5 of the English Longitudinal Study of Ageing (between 2006 and 2011), were analyzed using bivariate dual change scores within structural equation models. Higher levels of loneliness were inversely associated with social engagement over time, and high levels of social engagement were inversely associated with loneliness over time. To address the 2nd aim, we used structural equation modeling to evaluate potential mediators of the bidirectional relationships between loneliness and changes in social engagement. Depressive symptomatology, semantic memory, and episodic memory were found to partially mediate the relationship between loneliness measured at baseline and social engagement 4 years later. In addition, these variables also partially mediated the relationship between social engagement at baseline and loneliness 4 years later. A comparison of the 2 models revealed that the model proposing a pathway from loneliness to social engagement (as mediated by depressive symptoms and memory) provided a better fit to the data. Implications for theories of loneliness are discussed.
Identification of Antiglycative Compounds in Japanese Red Water Pepper (Red Leaf Variant of the Persicaria hydropiper Sprout)

Glycation, the nonenzymatic reaction between proteins and excess blood sugar, is implicated in multiple disorders and occurs via the formation and accumulation of advanced glycation end products (AGEs). In our previous studies, we demonstrated that the red-leaf variant of the Persicaria hydropiper sprout (Japanese red water pepper, Benitade) is one of the potent plants that inhibit formation of AGEs. In this study, we aimed to identify antiglycative compounds in Benitade. Benitade extracts were prepared with hot water, then fractionated by using high-performance liquid chromatography (HPLC). The antiglycative efficacy of each fraction was evaluated by measuring the formation of fluorescent AGEs (Ex 370 nm/Em 440 nm). Two fractions, which contained peaks at 26.4 min and 31.8 min, showed potent antiglycative efficacy. When we hydrolyzed these peaks, they shifted to 32.5 and 41.4 min, which are the same retention times as cyanidin and quercetin, respectively. Based on thin-layer chromatography, both compounds contained galactose. Finally, ultrahigh-performance liquid chromatography/quadrupole-time of flight mass spectrometry (UHPLC-QqTOF-MS) analyses were performed to determine the structure of those compounds. Overall, we identified two glycosides, cyanidin 3-O-galactoside (idaein) and quercetin 3-O-galactoside (hyperin), as representative antiglycative compounds in Benitade.
Developing a Linked Data-Based Weekly Report Management System

New students assigned to a laboratory learn lessons from their predecessors by referring to previous reports. Our laboratory has accumulated weekly reports over more than twenty years in our weekly report management system, however, students have difficulty with searching for valuable weekly reports from the report system. We aim to extend the existing weekly report management system to help users find valuable past weekly reports. We introduce the idea of linked data to the report management system. We designed the Weekly Report Network to represent weekly reports relevant to submissions. Moreover, we implemented a publication-based search function, a visualization function, and a compilation function on the existing weekly report system to enable reuse of the accumulated weekly reports for twenty years. First, the publication-based search function enables users to find weekly reports relevant publications. The works contain a paper registration, a paper submission, a camera-ready submission, and a presentation. Second, the visualization function illustrates an overview of research activities based on weekly reports for a year. For example, the system makes a graph to show time spent on research activities described in weekly reports. Finally, the compilation function makes an annual report by concatenating and editing weekly reports for the year. This paper explains the Weekly Report Network and the implementation of the extended weekly report management system.
Improved identification of secondary hypertension: use of a systematic protocol.

Background The accurate identification and diagnosis of secondary hypertension is critical, especially while atherosclerotic cardiovascular heart disease continues to be the leading cause of death in the industrialized world. Nevertheless, despite the existence of diagnostic tools, there are significant variations of the estimated prevalence of secondary hypertension, due to multiple etiologies and suboptimal recognition. This study demonstrates the results of using a systematic and protocolled approach to improve recognition of the presence of secondary hypertension. In the future, this questionnaire can be a quick and effective tool to unveil secondary hypertension in a broad array of clinical settings. Methods A total of 28,633 consecutive patients from January 1, 2007 to January 1, 2017 were diagnosed as having primary or secondary hypertension, utilizing the International Code of Diseases. Patients were located at the Center of Hypertension, Institute of Cardiology at Austral University Hospital, Buenos Aires, Argentina and were then further classified as having TRH, or non-resistant hypertension, to which a systematic protocol was employed in search for secondary hypertension. The confirmation of secondary hypertension was subsequently confirmed by diagnostic laboratory and imaging techniques in a hospital setting. Results A final population of 12,284 patients with treatment resistant hypertension (TRH) and non-treatment resistant hypertension (NTRH) were included in this study, where an etiology of secondary hypertension was identified in 50.9% and 36% of patients in each treatment group, respectively. Physicians used confirmatory laboratory testing and imaging of patients who were identified as having a cause for their secondary hypertension, with no significant differences in sex, age and body mass index (BMI) among study groups. Conclusions These results illustrate the prevalence and distribution of the causes of secondary hypertension using a systematic, protocolled approach, which revealed a higher percentage of secondary hypertension than previously reported. This tool may be used by healthcare providers to ensure the appropriate recognition of secondary causes of hypertension in a wider range of patients with high blood pressure beyond resistant hypertension, changing the diagnostic paradigm of this condition.
Digital Multiplierless Realization of Coupled Wilson Neuron Model

The human brain is composed of 1011 neurons with a switching speed of about 1 ms. Studying spiking neural networks, including the modeling, simulation, and implementation of the biological neuron models, helps us to learn about the brain and the related diseases, or to design more efficient bio-mimic processors and smarter robots. Such applications have made this part of neuromorphic research works very popular. In this paper, the Wilson neuron model has been implemented as an approximation of the Hodgkin–Huxley biological model that is adjusted for the efficient digital realization on the platforms. Results show that the proposed model can adequately reproduce neuron dynamical behaviors. The hardware implementation on the field-programmable gate array (FPGA) shows that our modifications on the Wilson original model imitate the biological behavior of neurons, besides using feasibility, targeting a low cost and high efficiency. The modifications raised a 15% speed-up compared with the original model. The mean normalized root-mean-square error, root-mean-square error, and the mean absolute error parameters are 6.43, 0.44, and 0.31, respectively.