Miracle-dz/UniFAR-FTUs huggingface dataset:
Grafting with rootstocks promotes phenolic compound accumulation in grape berry skin during development based on integrative multi-omics analysis

Abstract In viticulture, grafting has been practiced widely and influences grape development as well as berry and wine quality. However, there is limited understanding of the effects of rootstocks on grape phenolic compounds, which are located primarily in the berry skin and contribute to certain sensory attributes of wine. In this study, scion–rootstock interactions were investigated at the green-berry stage and the veraison stage when grapevines were hetero-grafted with three commonly used rootstock genotypes (5BB, 101-14MG, and SO4). Physiological investigations showed that hetero-grafts, especially CS/5BB, contained higher concentrations of total proanthocyanidins (PAs) and various PA components in berry skins compared with the auto-grafted grapevines. Further metabolomics analysis identified 105 differentially accumulated flavonoid compounds, the majority of which, including anthocyanins, PAs, and flavonols, were significantly increased in the berry skins of hetero-grafted grapevines compared with auto-grafted controls. In addition, transcriptomic analysis of the same samples identified several thousand differentially expressed genes between hetero-grafted and auto-grafted vines. The three rootstocks not only increased the transcript levels of stilbene, anthocyanin, PA, and flavonol synthesis genes but also affected the expression of numerous transcription factor genes. Taken together, our results suggest that hetero-grafting can promote phenolic compound accumulation in grape berry skin during development. These findings provide new insights for improving the application value of grafting by enhancing the accumulation of nutritious phenolic components in grape.
Evaluation of Antioxidant Activities and Tyrosinase Inhibitory Effects of Ginkgo biloba Tea Extract

Ginkgo biloba L. (Ginkgoaceae) is one of the best-selling products, popular in nutritional properties and health benefits. In the present study, the total phenolic compounds and flavonoid content of the ethanolic extract from G. biloba tea were also evaluated. Furthermore, the antioxidant activity was determined using DPPH assay and tyrosinase inhibitory activity was also determined with L-DOPA as a substrate. The extract showed the total phenolic compound and flavonoid content were 14.13 mg GE g−1 extract and 71.33 mg rutin equivalence g−1 DW, respectively. Taking into account the results of the DPPH, the antioxidant property at the concentration of 500 µg ml−1 was 95.29% that is similar to that of the BHT, ascorbic acid, and gallic acid used as positive controls. The inhibitory capacity of the sample against tyrosinase is lower than that of positive controls at all concentrations. The results of inhibition in terms of IC50 confirm the inhibition patterns. On the other hand, the statistical similarity of the anti-DOPA auto-oxidation (IC50) of G. biloba leaf extract and kojic acid was found (456.27 and 418.5 µg ml−1) but was lower than that of ascorbic acid (IC50 989.61 µg ml−1). A relationship was observed between the potential of antioxidant activity, tyrosinase inhibition, and anti-DOPA auto-oxidation with concentration levels of the extracts. The results of phytochemical analysis revealed the presence of tannins, flavonoids, terpenoids, and reducing sugars.
A GAN-Based Data Injection Attack Method on Data-Driven Strategies in Power Systems

With the expansion of system scale and data size in power systems, data-driven methods are gradually becoming widely used. However, compared with traditional methods, data-driven methods face more threats in data security and algorithm security. This paper proposes a black box data injection attack method against data-driven strategies in power systems. To obtain a stealthy attack scheme with the greatest impact, an attack vector generator based on deep convolutional neural networks (DCNN) is designed. The generator includes a node selector, a filter, an encoder, and a decoder. The node selector is used to select the most likely successful attack scheme for the filter, the encoder is used to extract sample features, and the decoder is used to generate disturbances based on the features. The proposed generator is trained with an improved generative adversarial network (GAN) and can generate minimal disturbances in real time based on measurement data from the power grid. The effectiveness of the proposed method is demonstrated with an attack experiment on an online transient stability application.
Automatic Asymmetric Weight Distribution Detection and Correction Utilizing Electrical Muscle Stimulation

Postural control is a constant re-establishment process for the maintenance of balance and stability. Asymmetric weight distribution (AWD), characterized by uneven leg loading, leads to increased instability, injury, and progressive deterioration of posture and gait. Postural self-correction is automatically affected by the human body in response to visual, vestibular, and proprioceptive sensory information. However, simultaneous cognitive loads can increase the demand for extra resources and require balance monitoring and correction techniques. We address these issues with a novel physiological feedback system that utilizes load sensors for AWD detection, and electrical muscle stimulation (EMS) for automatic correction and restoration of balance by affecting a counter-weight shift. In a user study involving 36 participants, we compare our automatic approach against two alternative feedback systems (Audio and Vibro-tactile). We find that our automatic approach delivered faster correction and outperformed alternative feedback mechanisms and perceived to be interesting, comfortable and a potential commercial product.
Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review

Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual’s quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.
Disinfection Procedures and Their Effect on the Microorganism Colonization of Dental Impression Materials: A Systematic Review and Meta-Analysis of In Vitro Studies

Dental impressions are contaminated with potentially pathogenic microorganisms when they come into contact with patient blood, saliva, and plaque. Numerous disinfectants are used; however, no sole disinfectant can be designated as universal for all the impression materials. Thus, the aim of this study is to systemically review the literature to evaluate the effect of the existing disinfection procedures on the bacterial colonization of dental impression materials. This systematic review and meta-analysis was conducted according to the PRISMA statement. PubMed (MEDLINE), Web of Science, Scopus, EMBASE, and SciELO databases were screened up to April 2021. Eligibility criteria included in vitro studies reporting the antibacterial activity of disinfectant solutions in dental impression materials. The meta-analysis was performed using Review Manager (version 5.3.5). A global comparison was performed with the standardized mean difference based on random-effect models at a significance level of α = 0.05. A total of seven studies were included in the meta-analysis. The included studies described the effect of disinfection processes with chlorhexidine gluconate, alcohol, sodium hypochlorite, glutaraldehyde, and hydrogen peroxide in alginate, polyvinyl siloxane, and polyether impression materials. The meta-analyses showed that the use of chlorhexidine, alcohol, glutaraldehyde, and sodium hypochlorite reduced the colony-forming units by a milliliter (CFU/mL) in alginate (p < 0.001). On the other hand, glutaraldehyde, sodium hypochlorite, and alcohol reduced the CFU/mL in polyvinyl siloxane (p < 0.001). Finally, alcohol and glutaraldehyde reduced the CFU/mL in polyether material (p < 0.001). High heterogenicity was observed for the alginate and polyvinyl siloxane materials (I2 = 74%; I2 = 90%). Based on these in vitro studies, the disinfection of impression materials with several disinfection agents reduces the CFU/mL count.
Special Feature Type 2 Translational Research for CKD

Strategies to effectively treat people with CKDhave been identified by conventional clinical research. Despite this evidence, awareness, screening, detection, diagnosis, risk factor control, treatment, and outcomes remain substandard. Translating clinical evidence into actionable measures that reduce the burden of CKD is a pressing need. Expansion from a “bench-to-bedside” paradigm (conventional type 1 translation) to research that encompasses “clinic and community” is the core concept of type 2 translation. Specifically, this is the discipline of identifying factors and using strategies that lead to adoption, maintenance, and sustainability of science-based interventions in practice. This review identifies key elements of type 2 translational research and highlights the current scope of this type of research for CKD. For type 2 translation to achieve the goals of providing high-quality care and better health outcomes, key facilitators (e.g., theory-based frameworks, adaptable interventions, and inclusion of sustainability and evaluationmetrics) and essential elements (e.g., multidisciplinary team care, health information technology, and stakeholder engagement) must be integrated. The National Institute of Diabetes and Digestive and Kidney Diseases recently funded five proposals that aim to improve outcomes for people with CKD, focusing on diverse components of the healthcare continuum: patient safety and transitions; delivery of highquality, evidence-based CKD care; and elimination of disparities. The need for type 2 translational research in CKD is urgent because of preventable human suffering and unsustainable costs of providing care. Focus on the theory, framework, and approaches we have suggested may help us meet that challenge. Clin J Am Soc Nephrol 8: 1829–1838, 2013. doi: 10.2215/CJN.00130113
Vernacular as a renewable resource: toward region-specific architecture in Saudi Arabia, a case from KFUPM

ABSTRACT In an increasingly industrialized and globalized world, it is believed that future architects will be in a better position to solve architectural and urban problems if they have been exposed to education about vernacular and region-specific architecture. Research that explicitly examines how to utilize vernacular knowledge and skills in contemporary architectural education and practice is rare. Lack of research that addresses and investigates vernacular traditions as a renewable resource with potential for continuity and viability is one of the main deficiencies in teaching vernacular architecture and the current vernacular discourse in general. Enhancing students’ ability to produce and develop region-sensitive architecture is one of the main goals of the architecture program at the King Fahd University for Petroleum Minerals (KFUPM). Issues such as the detailed analysis and investigation of vernacular traditions and values, and their use and integration in contemporary architectural practice, among others, are emphasized in the program. The paper reports on the author's experience in teaching three courses, at KFUPM, in which students are introduced to various analytical concepts and approaches that enable them to better understand vernacular traditions and integrate vernacular knowledge in the design process. A compressive pedagogical approach that combines theoretical teaching with practical exercises was developed and used in teaching the three courses. The aim of pedagogical approach is to enhance students’ capacity to develop their individualized methods and decision-making abilities relevant to utilizing vernacular knowledge in practicing and creating contemporary region-specific architecture. Results show that the implemented pedagogical approach is successful in enhancing students’ understanding of vernacular traditions and values, their ability to integrate vernacular knowledge in their practice of design, their ability to argue and create region-specific architecture, and in general promoting a culture of region-sensitive architecture among students.
Reproducibility report of From goals, waypoints & paths to longterm human trajectory forecasting for ML Reproducibility Challenge 2021

The following paper is a reproducibility report for From Goals, Waypoints & Paths To Long Term Human Trajectory 2 Forecasting [9]. The basic code was made available by the author at this https url . We have verified all claims and 3 results from the experiments mentioned in the paper to support the claims. The central claim of YNet is that it sets 4 state-of-the-art short and long-term prediction standards by a multi-modal network employing both segmentation 5 matrices and past trajectory heat-maps together. 6
Terror Activities Detection by Using Data Mining on Social Networking Sites

—This paper represents application of data mining techniques to analyze fraud. The impact of the fraud on organizations is becoming increasing costly. The problem of fraud detection is concern with not only capturing the fraudulent activities, but also capturing them as quickly as possible. Now a days there are many terror attacks happened by using social network, such a terrorist attacks are hazardous for peoples, organizations and countries. Terrorist are using internet to spread terror and form terrorist group . This paper is to represents the data mining methods to detection of terror activities on social networking sites.
Reward Shaping for Deep Reinforcement Learning in VizDoom

Reward shaping helps reinforcement learning agents to succeed in challenging tasks when environmental rewards are either sparse or delayed. In this work we propose an approach which combines both information from the game screen and additional information about in-game events to produce an estimation of novelty of the visited states and used behaviors. We use this estimation to motivate the agent to seeking novel experiences and show that our method helps in accelerating learning and reaching better and secures more robust strategies in complex VizDoom scenarios
Get To The Point! Problem-Based Curated Data Views To Augment Care For Critically Ill Patients

Electronic health records in critical care medicine offer unprecedented opportunities for clinical reasoning and decision making. Paradoxically, these data-rich environments have also resulted in clinical decision support systems (CDSSs) that fit poorly into clinical contexts, and increase health workers cognitive load. In this paper, we introduce a novel approach to designing CDSSs that are embedded in clinical workflows, by presenting problem-based curated data views tailored for problem-driven discovery, team communication, and situational awareness. We describe the design and evaluation of one such CDSS, In-Sight, that embodies our approach and addresses the clinical problem of monitoring critically ill pediatric patients. Our work is the result of a co-design process, further informed by empirical data collected through formal usability testing, focus groups, and a simulation study with domain experts. We discuss the potential and limitations of our approach, and share lessons learned in our iterative co-design process.
Serological Responses to the First Three Doses of SARS-CoV-2 Vaccination in Inflammatory Bowel Disease: A Prospective Cohort Study

Background Individuals with inflammatory bowel disease (IBD) who are immunocompromised may have a reduced serological response to the SARSCoV2 vaccine. We investigated serological responses following 1st, 2nd, and 3rd doses of SARS-CoV-2 vaccination in those with IBD. Methods A prospective cohort study of persons with IBD (n = 496) assessed serological response 1-8 weeks after 1st dose vaccination, 1-8 weeks after 2nd dose, 8 or more weeks after 2nd dose, and at least 1 week after 3rd dose. Seroconversion and geometric mean titer (GMT) with 95% confidence intervals (CI) were assessed for antibodies to the SARS-CoV-2 spike protein. Multivariable linear regression models assessed the adjusted fold change (FC) in antibody levels. Results Seroconversion and GMT increased from post-1st dose to 1-8 weeks post-2nd dose (81.6%, 1814 AU/mL vs. 98.7%, 9229 AU/mL, p<0.001), decreased after 8 weeks post-2nd dose (94.9%, 3002 AU/mL, p<0.001), and rebounded post-3rd dose (99.6%, 14639 AU/mL, p<0.001). Prednisone was the only IBD-related medication associated with diminished antibody response after 3rd-dose vaccination (FC: 0.07 [95% CI: 0.02, 0.20]). Antibody levels steadily decline following the 2nd (FC: 0.92 [95% CI: 0.90, 0.94] per week) and 3rd dose (FC: 0.88 [95% CI: 0.84, 0.92] per week) of the SARS-CoV-2 vaccine. Conclusion A three-dose regimen of vaccination to SARS-CoV-2 yields a robust antibody response for those with IBD across all classes of IBD therapies except for prednisone. The decaying antibody levels following the 3rd dose of the vaccine should be monitored in future studies.
Wind Time Series Modeling for Power Turbine Forecasting

This paper addresses the problem of predicting the average wind speed at different prediction horizons ranging from 6-hours to 1-day based on wind velocity recorded at a point. The problem is relevant in several application fields and recently appears of particular interest for operators of electrical wind turbine plants and/or for optimisation of conventional power plants. Exogenous inputs are not taken into account in this preliminary work, so that the problem is set as pure time series identification and carried out by considering NAR (Non-linear Auto Regressive) models. Therefore model performance are solely related to the degree of autocorrelation of the considered time series and to some extent on the kind of non-linear approximation basis function taken into account. Different data set were considered for training and validation purposes in order to assess the model generalization capabilities. The role of model order was evaluated on the space of representative performance indices. Results show that while the forecasting performances are remarkable for the 6 and 12 hours prediction horizons, they look no so good for the 1-day prediction horizon. Work is still in progress in order to overcome these shortcomings. 
An Overview on the Landscape of R Packages for Open Source Scorecard Modelling

The credit scoring industry has a long tradition of using statistical models for loan default probability prediction. Since this time methodology has strongly evolved, and most of the current research is dedicated to modern machine learning algorithms which contrasts with common practice in the finance industry where traditional regression models still denote the gold standard. In addition, strong emphasis is put on a preliminary binning of variables. Reasons for this may be not only the regulatory requirement of model comprehensiveness but also the possibility to integrate analysts’ expert knowledge in the modelling process. Although several commercial software companies offer specific solutions for modelling credit scorecards, open-source frameworks for this purpose have been missing for a long time. In recent years, this has changed, and today several R packages for credit scorecard modelling are available. This brings the potential to bridge the gap between academic research and industrial practice. The aim of this paper is to give a structured overview of these packages. It may guide users to select the appropriate functions for the desired purpose. Furthermore, this paper will hopefully contribute to future development activities.
Multi-View Architecture Description and Enforcement

In software development, implementation strays from the intended architecture in what is known as architectural drift as programmers introduce communication pathways between components that shouldn’t be interacting. In our approach, we have embedded an architecture description language (ADL) into a programming language that restricts resource access with capabilities and generates connections via metaprogramming. Not only does this ensure that the sys-tem semantics reflect its architectural principles, but this also facilitates editing compatible with rapid software evolution.
The factors affecting treatment-seeking behaviors of dysphonic teachers

This study investigated the underlying factors affecting voice therapy seeking behaviors in Hong Kong school teachers with voice problems. The Theory of Planned Behavior (TPB), a widely used social psychology model for assessing factors influencing an individual‟s intention and behavior, was adopted in the present study. A questionnaire based on the TPB was developed to investigate the factors affecting treatment-seeking behaviors in teachers. The factors investigated included dysphonic teachers‟ perception of attitudes, social influence and control over seeking for regular voice therapy. One hundred and forty nine teachers who reported voice problems but were non-treatment seeking participated in the study. The results suggested that perceived social influence and control over seeking for regular voice therapy were significant predictors of intention to seek help from speech pathologists. Implication for therapeutic directions and further research were discussed.
Two 3.9 ppm/°C curvature-corrected band gap voltage references with fast and low-power start ups

ABSTRACT High-precision devices are among the important applications as well as demands on ultra-low temperature coefficient (TC) band gap voltage references (BGRs). This paper presents two ultra-low TC BGRs with fast and low-power start up. The proposed circuits used temperature from a compensation block, which consisted of one resistor and one PMOS transistor. The voltage references utilised the peak current source to prepare a temperature-independent reference output voltage. They produced an output voltage of 1.135 V, which exhibited a temperature coefficient of only 3.9 ppm/°C when temperature varied from −40°C to 50°C at a supply voltage of 1.8 V. The start-up current was limited to 51.91 nA for the proposed BGR2 and gained a low power feature. In addition, the start-up circuit had a total power consumption of only 0.0004% in the proposed BGR 2. The simulations were fully done with 0.18 µm CMOS technology.
Online Appendix for “Going Negative at the Zero Lower Bound”

This set of online appendices complement the paper “Going Negative at the Zero Lower Bound: The Effects of Negative Nominal Interest Rates”. Apenddix A provide additional results and proofs related to banks. Appendix B provides additional empirical results. Appendix C provides the equilibrium equations and steady state of the full DSGE model. Finally, Appendix D provides a couple of additional figures.