Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional polymorphisms of NOS3 and GUCY1A3 affect both nitric oxide formation and association with hypertensive disorders of pregnancy

Impaired nitric oxide (NO) formation may be associated with endothelial dysfunction and increased cardiovascular disease risk in preeclampsia (PE). Functional single-nucleotide polymorphisms (SNPs) of nitric oxide synthase 3 (NOS3) (rs3918226) and guanylate cyclase 1, soluble, alpha 3 (GUCY1A3) (rs7692387) increase susceptibility to the adverse consequences due to inadequate generation of NO by the endothelium. However, no previous study has examined whether these SNPs affect NO formation in healthy pregnancy and in gestational hypertension (GH) and PE. Here, we compared the alleles and genotypes of NOS3 (rs3918226) and GUCY1A3 (rs7692387) SNPs in normotensive pregnant women (NP, n = 153), in GH (n = 96) and PE (n = 163), and examined whether these SNPs affect plasma nitrite concentrations (a marker of NO formation) in these groups. We further examined whether the interaction among SNP genotypes is associated with GH and PE. Genotypes were determined using TaqMan allele discrimination assays, and plasma nitrite concentrations were determined by an ozone-based chemiluminescence assay. Multifactor dimensionality reduction was used to examine the interactions among SNP genotypes. Regarding NOS3 rs3918226, the CT genotype (p = 0.046) and T allele (p = 0.020) were more frequent in NP than in GH, and GH patients carrying the CT+TT genotypes showed lower nitrite concentrations than NP carrying the CT+TT genotypes (p < 0.05). Regarding GUCY1A3 rs7692387, the GA genotype (p = 0.013) and A allele (p = 0.016) were more frequent in PE than in NP, and NP women carrying the GG genotype showed higher nitrite concentrations than GH or PE patients carrying the GG genotype (p < 0.05). However, we found no significant interactions among genotypes for these functional SNPs to be associated with GH or PE. Our novel findings suggest that NOS3 rs3918226 and GUCY1A3 rs7692387 may affect NO formation and association with hypertensive disorders of pregnancy.
Communication-Constrained Distributed Learning: TSI-Aided Asynchronous Optimization with Stale Gradient

Distributed machine learning including federated learning has attracted considerable attention due to its potential of scaling the computational resources, reducing the training time, and helping protect the user privacy. As one of key enablers of distributed learning, asynchronous optimization allows multiple workers to process data simultaneously without paying a cost of synchronization delay. However, given limited communication bandwidth, asynchronous optimization can be hampered by gradient staleness, which severely hinders the learning process. In this paper, we present a communication-constrained distributed learning scheme, in which asynchronous stochastic gradients generated by parallel workers are transmitted over a shared medium or link. Our aim is to minimize the average training time by striking the optimal tradeoff between the number of parallel workers and their gradient staleness. To this end, a queueing theoretic model is formulated, which allows us to find the optimal number of workers participating in the asynchronous optimization. Furthermore, we also leverage the packet arrival time at the parameter server, also referred to as Timing Side Information (TSI), to compress the staleness information for the stalenessaware Asynchronous Stochastic Gradients Descent (Asyn-SGD). Numerical results demonstrate the substantial reduction of training time owing to both the worker selection and TSI-aided compression of staleness information.
A Practical Concatenated Coding Scheme for Noisy Shuffling Channels with Coset-based Indexing

Noisy shuffling channels capture the main characteristics of DNA storage systems where distinct segments of data are received out of order, after being corrupted by substitution errors. For realistic schemes with short-length segments, practical indexing and channel coding strategies are required to restore the order and combat the channel noise. In this paper, we develop a finite-length concatenated coding scheme that employs Reed-Solomon (RS) codes as outer codes and polar codes as inner codes, and utilizes an implicit indexing method based on cosets of the polar code. We propose a matched decoding method along with a metric for detecting the index that successfully restores the order, and correct channel errors at the receiver. Residual errors that are not corrected by the matched decoder are then corrected by the outer RS code. We derive analytical approximations for the frame error rate of the proposed scheme, and also evaluate its performance through simulations to demonstrate that the proposed implicit indexing method outperforms explicit indexing.
Observing Complexity in Teachers’ Choices: Preparing Preservice Teachers for Work-Integrated Learning

Although preservice teachers are familiar with classroom life, they are largely unfamiliar with teachers’ intentions and reasoning. Those completing a Postgraduate Certificate in Education (PGCE) have just one year to acquire new insights into teaching. This article investigates whether their school-based learning can be enhanced by preparing them to analyse the classroom practices of diverse teachers. A module used to augment work-integrated learning, Teacher Choices in Action, has introduced more than 70,000 South African preservice teachers to key choices that all teachers make. They see how diverse teachers enact these choices through guided studies of recorded lessons. We use Legitimation Code Theory to compare lesson observation reports written by 83 PGCE preservice teachers at the start and end of completing this module. Initially, most participants gave superficial descriptions of classroom activities with basic explanations of what teachers do and why. Afterwards, their reports contained more complex interpretations of teaching, with more connections between their lesson observations and insights from their coursework. Guided lesson study potentially empowers them to interpret prevalent teaching practices and consider pedagogic choices for their lessons more thoughtfully and systematically, regardless of their educational backgrounds. It may also address some concerns about vastly different mentoring that preservice teachers receive during the practicum.
This paper is included

As the weakest link in cybersecurity, humans have become the main target of attackers who take advantage of sophisticated web-based social engineering techniques. These attackers leverage low-tier ad networks to inject social engineering components onto web pages to lure users into websites that the attackers control for further exploitation. Most of these exploitations are Web-based Social Engineering Attacks (WSEAs), such as reward and lottery scams. Although researchers have proposed systems and tools to detect some WSEAs, these approaches are very tailored to specific scam techniques (i.e., tech support scams, survey scams) only. They were not designed to be effective against a broad set of attack techniques. With the ever-increasing diversity and sophistication of WSEAs that any user can encounter, there is an urgent need for new and more effective in-browser systems that can accurately detect generic WSEAs. To address this need, we propose T RIDENT , a novel defense system that aims to detect and block generic WSEAs in real-time. T RIDENT stops WSEAs by detecting Social Engineering Ads (SE-ads), the entry point of general web social engineering attacks distributed by low-tier ad networks at scale. Our extensive evaluation shows that T RIDENT can detect SE-ads with an accuracy of 92.63% and a false positive rate of 2.57% and is robust against evasion attempts. We also evaluated T RIDENT against the state-of-the-art ad-blocking tools. The results show that T RIDENT outperforms these tools with a 10% increase in accuracy. Additionally, T RIDENT only incurs 2.13% runtime overhead as a median rate, which is small enough to deploy in production.
Web Application-Based WebAssembly Container Platform for Extreme Edge Computing

IoT devices are now used in many different fields and applications, and they generate a large amount of traffic when sending sensor data to the cloud for analysis. By offloading these computing tasks to edge computing, the network traffic on the core network and latency can be reduced. However, the resources at the edge are limited, making it difficult to respond to the rapidly expanding number of IoT devices. To solve the problem, extreme edge computing, which utilizes the computing resources of end devices for task offloading, is emerging. The conventional task execution methods in edge computing are unsuitable for extreme edge computing because of the nonuniformity of end devices and connectivity. To meet the requirements of extreme edge computing, we propose a novel WebAssembly container system that is portable, lightweight, and manageable. Our proposed system has two types of container engines working as native applications and Web applications. This enables the execution of general-purpose computing tasks on various devices with different CPU architectures and OSs. We evaluate our prototype implementation working on these devices to confirm the benefits and effects of our proposed system for extreme edge computing.
Key genes involved in nonalcoholic steatohepatitis improvement after bariatric surgery

Background Nonalcoholic steatohepatitis (NASH) is the advanced stage of nonalcoholic fatty liver disease (NAFLD), one of the most prevalent chronic liver diseases. The effectiveness of bariatric surgery in treating NASH and preventing or even reversing liver fibrosis has been demonstrated in numerous clinical studies, but the underlying mechanisms and crucial variables remain unknown. Methods Using the GSE135251 dataset, we examined the gene expression levels of NASH and healthy livers. Then, the differentially expressed genes (DEGs) of patients with NASH, at baseline and one year after bariatric surgery, were identified in GSE83452. We overlapped the hub genes performed by protein-protein interaction (PPI) networks and DEGs with different expression trends in both datasets to obtain key genes. Genomic enrichment analysis (GSEA) and genomic variation analysis (GSVA) were performed to search for signaling pathways of key genes. Meanwhile, key molecules that regulate the key genes are found through the construction of the ceRNA network. NASH mice were induced by a high-fat diet (HFD) and underwent sleeve gastrectomy (SG). We then cross-linked the DEGs in clinical and animal samples using quantitative polymerase chain reaction (qPCR) and validated the key genes. Results Seven key genes (FASN, SCD, CD68, HMGCS1, SQLE, CXCL10, IGF1) with different expression trends in GSE135251 and GSE83452 were obtained with the top 30 hub genes selected by PPI. The expression of seven key genes in mice after SG was validated by qPCR. Combined with the qPCR results from NASH mice, the four genes FASN, SCD, HMGCS1, and CXCL10 are consistent with the biological analysis. The GSEA results showed that the ‘cholesterol homeostasis’ pathway was enriched in the FASN, SCD, HMGCS1, and SQLE high-expression groups. The high-expression groups of CD68 and CXCL10 were extremely enriched in inflammation-related pathways. The construction of the ceRNA network obtained microRNAs and ceRNAs that can regulate seven key genes expression. Conclusion In summary, this study contributes to our understanding of the mechanisms by which bariatric surgery improves NASH, and to the development of potential biomarkers for the treatment of NASH.
Utero-ovarian transposition before pelvic radiation in a patient with rectal cancer: a case report and systemic literature review

Objective To describe a case of utero-ovarian transposition (UOT) before pelvic radiation in a patient with rectal cancer and provide a systematic literature review on all reported cases of UOT. Methods We performed a prospective collection and revision of clinical, intraoperative, and postoperative data from a patient who underwent UOT. In addition, a systematic review of the literature available to date on all cases of UOT was realized, and 14 patients from 10 articles were included. Results We reported the case of a 28-year-old nulligravida patient who was diagnosed with a low-grade rectal adenocarcinoma and underwent neoadjuvant chemoradiotherapy, followed by transanal total mesorectal excision (TaTME). Before starting neoadjuvant oncological therapies, the patient underwent laparoscopic UOT. The intervention was performed without complications, and the patient received neoadjuvant oncological treatments as planned. TaTME and uterus repositioning were completed six weeks after the end of radiotherapy. No complications were observed during the first 9 postoperative months. Adequate utero-ovarian perfusion was assessed by Doppler ultrasound, cervicovaginal anastomosis appeared to have healed correctly, and the patient experienced menstrual bleeding. Data from the literature review of all reported cases of UOT were presented and discussed. Conclusions UOT represents a valuable option to preserve fertility in patients requiring pelvic radiotherapy. This study provides additional evidence on the feasibility and safety of performing UOT.
Modeling retroreflectivity degradation of pavement markings across the US with advanced machine learning algorithms

Retroreflectivity is the primary metric that controls the visibility of pavement markings during nighttime and in adverse weather conditions. Maintaining the minimum level of retroreflectivity as specified by Federal Highway Administration (FHWA) is crucial to ensure safety for motorists. The key objective of this study was to develop robust retroreflectivity prediction models that can be used by transportation agencies to reliably predict the retroreflec-tivity of their pavement markings utilizing the initially measured retroreflectivity and other key project conditions. A total of 49,632 transverse skip retroreflectivity measurements of seven types of marking materials were retrieved from the eight most recent test decks covered under the National Transportation Product Evaluation Program (NTPEP). Decision Tree (DT) and Artificial Neural Network (ANN) algorithms were considered for developing performance prediction models to estimate retroreflectivity at different prediction horizons for up to three years. The models were trained with randomly selected 80% data points and tested with the remaining 20% data points. Sequential ANN models exhibited better performance with the testing data than the sequential DT models. The training and testing R 2 ranges of the sequential ANN models were from 0.76 to 0.96 and 0.55 to 0.94, respectively, which were significantly higher than the R 2 range (0.14 to 0.75) from the regression models proposed in past studies. Initial or predicted retroreflectivity, snowfall, and traffic were found to be the most important inputs to model predictions.
Perceptions on Regional Beneﬁt of Airport Development and Operation

: Airport development and operation has a signiﬁcant impact on local economy. However, the impact is assessed differently from different perspectives. In this paper, the authors investigated how the public think on the beneﬁt of airport development and operation from a regional perspective. More speciﬁcally, the paper focuses on what the main factors are in public perceptions on the regional beneﬁt of airport development and operation. The authors analyzed a survey collected in South Korea using structural equation models. The authors assigned ﬁve categories of perception factors and examined these factor relationships regarding the ripple effects caused by airport development and operation on each region. The ﬁve factors were “increased use in air transportation”, “increased exchange with other regions”, “regional economic beneﬁt”, “regional development”, and “dis-beneﬁt to the region”. The model was veriﬁed with structural equations and path analysis results for each factor. It was revealed that people perceive the increased use in air transportation and the increased exchange with other regions as bringing about the most signiﬁcant ripple effects, followed by regional development and regional economic beneﬁt in this order, according to the order of path groups. The results of this paper are expected to provide stakeholders’ perspectives and insights when planning airport development and operation.
Title: Math on cortex – Underlying delta synchrony during naturalistic math demonstrations in math experts and novices

: Neuroimaging studies show that expertise in math shapes brain functions. To understand the brain processes behind complex math tasks and manipulation of large abstract concepts, we need to study the brain with naturalistic stimuli. Such stimuli are, for example, long math demonstrations that evoke simultaneous and overlapping cognitive and metacognitive processes. In our EEG study, we compared math experts and novices while they actively processed long math demonstrations in sitting and standing. Experts had an enhanced delta (0.5-4 Hz) phase synchrony over the centro-parieto-occipital electrodes compared to novices. Internal concentration and engagement may play a role in such enhanced delta synchrony.
Exploratory Geochemical Studies to Determine the Mineralization Zones Around the Zarshuran Gold Mine

The study area is located in the northern part of Takab, West Azarbaijan Province, northwest of Iran. Since geochemical explorations, especially the drainage sediments, play a very important role in finding promising areas and providing conditions for more detailed exploration. Detection and separation of geochemical anomalies from the field is one of the most important and fundamental issues in geochemical exploration. In this study, using the classical statistcs methods, anomalous values were determined. Other methods used in this study is the concentration-area (C-A) fractal method. This method is a very successful method for separating anomalies from the field due to simultaneous consideration of the frequency and spatial variations of geochemical data. Multivariate statistical analysis including principal component analysis (PCA) was used to obtain anomaly values related to determine the anomaly values. Favorable results for the methods used for this region was presented.
Thermo-Hydraulic Performance of an Enhanced Geothermal System Under Different Spatiotemporal Heterogeneous Conditions

The purpose of this study is to evaluate the effect of permeability heterogeneity in a hydraulic fracture of an Enhanced Geothermal System (EGS). It is common practice that a single homogeneous permeability value is assumed for the hydraulic fracture. However, in reality, the permeability distribution in the HF is more likely to be fully spatially heterogeneous. Also, during EGS operation (injection/production) geomechanical stresses in the reservoir induce permeability changes that alter the initial permeability distribution in the HF. Therefore, the heterogeneous permeability distribution in the HF becomes more dynamic, and also as a function of time. The thermo-hydraulic behavior of the EGS will be investigated under different types of heterogeneity distributions of the hydraulic fracture permeability, as a function of space and time compared to a homogeneous HF permeability. This numerical simulation study is conducted with the TOUGH+RealGasBrine V1.5 code. The cooling front propagation (in the HF and into the rock matrix), optimal production rates, and produced heat flow (MW th ) are the main criterion for the thermo-hydraulic evaluation of the performance of the EGS. The results showed a significant effect of these changes on the overall performance of the EGS. The exact heterogeneity distribution of the HF permeability/conductivity will play a big role on the injected fluid trajectory, thus the cooling front propagation rate. Pressure build-up concerns may become an issue at some point for a given permeability distribution, especially if low permeability “damaged” zones are considered.
AIC HRONO L ENS : Advancing Explainability for Time Series AI Forecasting in Mobile Networks

—Next-generation mobile networks will increasingly rely on the ability to forecast traffic patterns for resource management. Usually, this translates into forecasting diverse objectives like traffic load, bandwidth, or channel spectrum utilization, measured over time. Among the other techniques, Long-Short Term Memory (LSTM) proved very successful for this task. Unfortunately, the inherent complexity of these models makes them hard to interpret and, thus, hampers their deployment in production networks. To make the problem worsen, EXplainable Artificial Intelligence (XAI) techniques, which are primarily conceived for computer vision and natural language processing, fail to provide useful insights: they are blind to the temporal characteristics of the input and only work well with highly rich semantic data like images or text. In this paper, we take the research on XAI for time series forecasting one step further proposing AIC HRONO L ENS , a new tool that links legacy XAI explanations with the temporal properties of the input. In such a way, AIC HRONO L ENS makes it possible to dive deep into the model behavior and spot, among other aspects, the hidden cause of errors. Extensive evaluations with real-world
A Secure Backup System Using Multi-Cloud And Fog Computing

For disaster recovery, data backup is crucial. Modern cloud-based services provide a secure infrastructure. While the data is hosted on a single cloud, data privacy is not guaranteed. Using Multi-Cloud technology is a different approach. Although employing many clouds to store lesser amounts of data can increase data privacy, doing so comes at the expense of the edge device managing various accounts and managing connectivity with numerous clouds. Due to these shortcomings, this technology was hardly used. Using cutting-edge Multi-Cloud and encryption techniques, we propose Drop Store in this paper as a user-friendly, highly secure, and dependable backup system. Drop Store uses a locally hosted device called "the Droplet," which is entirely maintained by the user, to offer an abstraction layer for the end-user to hide any system complexity. As a result, the user does not rely on any unreliable parties. Utilizing fog computing technology, this was accomplished. The combination of Multi-Cloud and Fog Computing principles gives Drop Store its distinctiveness. The system implementation is online and open-source.
Effect of Total Productive Maintenance Practices on Manufacturing Performance: An Empirical Analysis of Ethiopian Soft Drinks Manufacturing Company

The objective of this study is to investigate the effect of total productive maintenance (TPM) practices on the manufacturing performance of Ethiopian Soft Drinks Manufacturing Company. A self-administered survey questionnaire (seven-point Likert scale) is used for primary data collection. Correlation and regression analysis was performed using SPSS software to identify the effect of the independent variables (TPM practices: autonomous maintenance, planned maintenance, education and training) on the dependent variable (manufacturing performance). The results show that two of the TPM practices (planned maintenance, education and training)have a positive and significant relationship with manufacturing performance and significantly improve manufacturing performance in terms of cost, quality, and delivery.