Miracle-dz/UniFAR-FTUs huggingface dataset:
Medicinal Chemical Biology

activity genetic proﬁling of resistant mutants that emerged following prolonged incubation of the lead compound with replicon cells A biotinylated derivative was used to afﬁnity isolate NS5A, and streptavidin-mediated enrichment was competed by the presence of the parent compound conﬁrm the speciﬁcity of target engagement. This work led to the development of the drug daclatasvir, an NS5A inhibitor for the treatment of HCV To further validate
Interpretable AI to Understand Early Effective and Ineffective Programming Behaviours from CS1 Learners

Building predictive models to estimate the learner performance in the beginning of CS1 courses is essential in education to allow early interventions. However, the educational literature notes the lack of studies on early learner behaviours that can be effective or ineffective, that is, programming behaviours that potentially lead to success or failure, respectively. Hence, beyond the prediction, it is crucial to explain what leads the predictive model to make the decisions (e.g., why a given student s is classified as `passed'), which would allow a better understanding of which early programming behaviours are to be encouraged and triggered. In this work in progress, we use a state-of-the-art unified approach to interpret black-box model predictions, which uses SHapley Additive exPlanations (SHAP) method. SHAP method can be used to explain linearly a complex model (e.g. DL or XGboost) in instance level. In our context of CS1 performance prediction, this method gets the predictive model and the features values for a given student as input and the possibility of explanation of which feature values are increasing or decreasing the learner chances of passing as output. That is, using SHAP we can identify early effective and ineffective behaviours in student-level granularity. More than that, using this local explanation as building blocks, we can also extract global data insight and give a summarisation of the model. A video explaining this work can be found at the following link: https://youtu.be/pd6Ma6uInHo
Anatomical Details of Aetosauria (Archosauria: Pseudosuchia) as Revealed by an Articulated Posterior Skeleton from the Upper Triassic Ischigualasto Formation, San Juan Province, Argentina

Abstract. Aetosaurs are an early-diverging clade of “crocodile-line” archosaurs whose oldest records come from Argentina and Brazil. Articulated skeletons of aetosaurs are rare, but offer insight into their paleobiology. We describe here an incomplete, articulated posterior skeleton of an aetosaur from the Upper Triassic Ischigualasto Formation of San Juan Province, Argentina. It resembles Aetosauroides, the only aetosaur genus currently recognized from the Ischigualasto Formation, but lacks apomorphies that would allow us to assign it to that taxon, appears to be more robust than the holotype of A. scagliai, and preserves a different tail morphology. We identify the specimen as a basal aetosaur in part because the few exposed dorsal and lateral osteoderms have a typical ornamentation of radially distributed pits, grooves, and ridges emanating from a “center of ossification”. Although the specimen is incomplete and exposed primarily in ventral view from the sacrum posteriorly, it preserves many anatomical features not often preserved in aetosaurs, including extensive appendicular armor and a well-preserved caudal ventral carapace. The latter apparently consists of only two columns of ventral osteoderms, and preserves a large cloacal vent proximally. Posteriorly, the ventral paramedian osteoderms fuse to form a single element, something that has not previously been demonstrated in aetosaurs. The arrangement of osteoderms around the vent is distinct from that seen in A. scagliai. The ventral caudal osteoderms differ from many other aetosaurs in that they do not transition from wider than long to longer than wide, indicating that the specimen had a relatively abbreviated tail.
Exposure to a mixture of 23 chemicals associated with unconventional oil and gas operations alters immune response to challenge in adult mice

Abstract The prevalence of unconventional oil and gas (UOG) operations raises concerns regarding the potential for adverse health outcomes following exposure to water tainted by mixtures of UOG associated chemicals. The potential effects that exposure to complex chemical mixtures has on the immune system have yet to be fully evaluated. In this study, effects on the immune system of adult mice exposed to a mixture of 23 chemicals that have been associated with water near active UOG operations were investigated. Female and male mice were exposed to the mixture via their drinking water for at least 8 weeks. At the end of the exposure, cellularity of primary and secondary immune organs, as well as an immune system function, were assessed using three different models of disease, i.e. house dust mite (HDM)-induced allergic airway disease, influenza A virus infection, and experimental autoimmune encephalomyelitis (EAE). The results indicated exposures resulted in different impacts on T-cell populations in each disease model. Furthermore, the consequences of exposure differed between female and male mice. Notably, exposure to the chemical mixture significantly increased EAE disease severity in females, but not in male, mice. These findings indicated that direct exposure to this mixture leads to multiple alterations in T-cell subsets and that these alterations differ between sexes. This suggested to us that direct exposure to UOG-associated chemicals may alter the adult immune system, leading to dysregulation in immune cellularity and function.
Projection Alignment Correction for In-Vehicle Projector-Camera System

In this study, we propose a projection registration method for the projections from a continuously moving vehicle for driver vision assistance during night driving. Accordingly, we employ a context-aware projection technique with adaptive pixel mapping generation. Because vehicle movements lead to misalignment of the projection latency, a co-axial projector-camera con-ﬁguration or high frame rate processing cannot solve this problem. However, adaptive pixel mapping corrects pixel mapping according to the vehicle speed and achieves a misalignment-free dynamic projection mapping. The e ﬀ ectiveness of the proposed method was evaluated through experiments using a moving projector-camera system mounted on a motorized linear stage
Efficacy of Transcutaneous Electrical Nerve Stimulation on Salivary Flow Rates in Normal Healthy Adults According to Gender and Age Groups

Introduction: Salivary gland hypofunction might be associated with various local and systemic conditions and is managed with a plethora of therapeutic options with associated side effects. Transcutaneous electrical nerve stimulation (TENS) is one such option with no known systemic side effects for dealing with this crippling condition. The present study was planned with a similar intent of assessing impact of TENS on salivary flow rates in normal healthy adults according to gender and age groups. Materials and Methods: The present study was designed as a cross-sectional study on 130 healthy adults wherein unstimulated and stimulated saliva was collected for 5 min in graduated test tubes fitted with a funnel while mean salivary flow rates were calculated. The data were analyzed using SPSS version 17.0 (SPSS Inc., Chicago, IL, USA). Results: In the present study, differences between mean unstimulated and stimulated salivary flow rates with TENS were found to be statistically significant for both genders (P < 0.001). Furthermore, in relation to age groups included, maximum increase in salivary flow rate was seen in 20–29 years of age group, though significant results were seen in all three age groups included namely 20–29 years, 30–39 years, and 40–49 years (P < 0.001). Conclusions: Based on results from the present study, it could be concluded that TENS comes out to be a safer, nonpharmacological therapeutic option for treating patients with xerostomia.
Visual Barcodes for Multiplexing Live Microscopy-Based Assays

While multiplexing samples using DNA barcoding revolutionized the pace of biomedical discovery, multiplexing of live imaging-based applications has been limited by the number of fluorescent proteins that can be deconvoluted using common microscopy equipment. To address this limitation we developed visual barcodes that discriminate the clonal identity of single cells by targeting different fluorescent proteins to specific subcellular locations. We demonstrate that deconvolution of these barcodes is highly accurate and robust to many cellular perturbations. We then used visual barcodes to generate ‘Signalome’ cell-lines by multiplexing live reporters to monitor the simultaneous activity in 12 branches of signaling, in live cells, at single cell resolution, over time. Using the ‘Signalome’ we identified two distinct clusters of signaling pathways that balance growth and proliferation, emphasizing the importance of growth homeostasis as a central organizing principle in cancer signaling. The ability to multiplex samples in live imaging applications, both in vitro and in vivo may allow better high-content characterization of complex biological system
How close are we to self-provisioning? A look at the livelihood strategies of rural households in the Southern Andean region of Colombia

Production for self-provisioning contributes to food security in rural territories; however, studies have indicated that this capacity is limited. We analyze the impact of livelihood strategies on self-provisioning conditions in 162 rural households in the department of Huila, Colombia. Different topics were examined: a. source of the food; b. composition of home gardens; c. push and pull factors towards self-provisioning; d. capacity of rural households to access food in the face of COVID-19. Results indicated that rural households are 75% dependent on grocery stores for access their food. Only 51% of households have a home garden, with limited species diversity. Capacity for self-provisioning varies according to the livelihood strategies and capital endowment of the household; those with a greater diversity of agricultural activities have better capacity to provide for self-provisioning. We conclude that resilience of rural households is minimal to face atypical events such as the pandemic generated by COVID-19.
We’re all in this together: celebrity influencer disclosures about COVID-19

ABSTRACT The current study examined social media content from celebrity influencers during the early stages of the novel coronavirus pandemic. Given the inchoate governmental and media response to the crisis, this paper argues for the importance of celebrity influencer voices. This study utilized a constant comparative method to analyze Instagram posts from 20 celebrity influencers over a two-month period. Findings suggest celebrities demonstrated and normalized the practice of “staying home,” whereby the public used social distancing to mitigate the virus spread. Celebrity influencers also demonstrated “we’re all in this together” by using their platforms to call for action.
In-Situ Defect Detection of Metal Additive Manufacturing: An Integrated Framework

Metal Additive Manufacturing (AM) is a pillar of the Industry 4.0, with many attractive advantages compared to traditional subtractive fabrication technologies. However, there are many quality issues that can be an obstacle for mass production. The in-situ camera-based monitoring and detection of defects, taking advantage of the layer-by-layer nature of the build, can be an effective solution to this problem. In this context, the use of Computer Vision and Machine Learning algorithms have a very important role. Nonetheless, they are up to this date limited by the scarcity of data for the training, as well as by the difficulty of accessing and integrating the AM process data throughout the fabrication. To tackle this problem, this article proposes a system for in-situ monitoring that analyses images from an off-axis camera mounted on top of the machine to detect the arising defects in real-time, with automated generation of synthetic images based on Generative Adversarial Network (GAN) for dataset augmentation purposes. The computing functionalities are embedded into a holistic distributed AM platform allowing the collection, integration and storage of data at all stages of the AM pipeline.
A Tabletop for the Natural Inspection of Decorative Surfaces

Designs for decorative surfaces, such as ﬂooring, must cover several square meters to avoid visible repeats. While the use of desktop systems is feasible to support the designer, it is challenging for a non-domain expert to get the right impression of the appearances of surfaces due to limited display sizes and a potentially unnatural interaction with digital designs. At the same time, large-format editing of structure and gloss is becoming increasingly important. Advances in the printing industry allow for more faithful reproduction of such surface details. Unfortunately, existing systems for visualizing surface designs cannot adequately account for gloss, especially for non-domain experts. Here, the complex interaction of light sources and the camera position must be controlled using software controls. As a result, only small parts of the data set can be properly inspected at a time. Also, real-world lighting is not considered here. This work presents a system for the processing and realistic visualization of large decorative surface designs. To this end, we present a tabletop solution that is coupled to a live 360 ◦ video feed and a spatial tracking system. This allows for reproducing natural view-dependent effects like real-world reﬂections, live image-based lighting, and the interaction with the design using virtual light sources employing natural interaction techniques that allow for a more accurate inspection even for non-domain experts.
Advances in Clickbait and Fake News Detection Using New Language-independent Strategies

—Online publishers rely on different techniques to trap web visitors, clickbait being one such technique. Besides being a bad habit, clickbait is also a strong indicator for fake news spreading. Its presence in online media leads to an overall bad browsing experience for the web consumer. Recently, big players on the Internet scene, such as search engines and social networks, have turned their attention towards this negative phenomenon that is increasingly present in our everyday browsing experience. The research community has also joined in this effort, a broad band of detection techniques being developed. These techniques are usually based on intelligent classifiers, for which feature selection is of great importance. The work presented in this paper brings our own contributions to the field of clickbait detection. We present a new language-independent strategy for clickbait detection that takes into consideration only features that are general enough to be independent of any particular language. The methods presented in this paper could be applied to web content written in different languages. In addition, we present the results of a complex experiment that we performed to evaluate our proposed method and we compare our results with the most relevant results previously obtained in the field.
Binary Label Learning for Semi-Supervised Feature Selection

Semi-supervised feature selection methods jointly exploit the labelled and unlabelled samples when selecting the features. Under the semi-supervised learning scenario, the number of labelled data significantly impacts the feature selection performance. In this paper, we introduce the label learning with binary hashing to the research field of feature selection and propose a novel Semi-supervised Feature Selection with Binary Label Learning (SFS-BLL) model. Specifically, we learn the binary hash codes as the pseudo labels by specially imposing binary hash constraints on the spectral embedding process to increase the number of labels. Meanwhile, we propose a self-weighted sparse regression module which exploits the learned labels and given manual labels together with importance differentiation to guide the feature selection process. Finally, we develop an effective discrete optimization method based on the Alternating Direction Method of Multipliers (ADMM) to iteratively optimize the binary labels and the feature selection matrix. Extensive experiments on widely tested benchmarks demonstrate the superiority of the proposed method from various aspects.
A Multidimensional Culturally Adapted Representation of Emotions for Affective Computational Simulation and Recognition

One of the main challenges in affective computing is the development of models to represent the information that is inherent to emotions. It is necessary to consider that the terms used by humans to name emotions depend on the culture and language used. This article presents an experiment-based method to represent and adapt emotion terms to different cultural environments. We propose using circular boxplots to analyze the distribution of emotions in the Pleasure-Arousal space. From the results of this analysis, we define a new cross-cultural representation model of emotions in which each emotion term is assigned to an area in the Pleasure-Arousal space. An emotion is represented by a vector in which the direction indicates the type, and the module indicates the intensity of the emotion. We propose two methods based on fuzzy logic to represent and express emotions: the emotion representation process in which the term associated with the recognized emotion is defuzzified and projected as a vector in the Pleasure-Arousal space; and the emotion expression process in which a fuzzification of the vector is produced, generating a fuzzy emotion term that is adapted to the culture and language in which the emotion will be used.
Investor overreaction and global financial crisis: A case of Pakistan stock exchange

Abstract Recently, the investor overreaction catches the attention of the academicians and practitioners. The topic comes under the limelight of academicians and policymakers. This study, therefore, addresses investor overreaction and its relationship with the global financial crisis for the period of 2004–15. The study used stratified random sampling technique; equal allocation method of Krejcie and Morgan. By adopting the methodology of Debondt and Thaler, the study finds that there is highly significant overreaction (distinct reversal) in the stock market in the global-financial crisis period, which may attribute to the aggressive behavior of individual investors in the market. Overreaction has also been shown in the line graph shaded area which opposes the efficient market hypothesis and verifies the presence of a weak form of efficiency. Econometric tests are applied for robustness check that confirms weak form of efficiency in the PSX. The study has implications for both the investors and policymakers. This study is quintessential for those investors who have the aptitude to look introspectively and to evaluate their behavioral biases. Further, investors would learn to transmute behaviors and to build portfolios which will help them to stick to their long-term investment strategies and hence achieve their investment goals.
Magnetic Field Prediction of the Saturated Surface-Mounted Permanent Magnet Synchronous Machine With Rotor Eccentricity

This article presents an analytical method to predict the magnetic field of eccentric surface-mounted permanent magnet synchronous machine (SPMSM) by applying the hybrid magnetic circuit and subdomain method. Taking stator saturation into account, the surface current is appended to the stator coil conductor, which can generate a magnetomotive force (MMF) to compensate the MMF drop across the stator core. The rotor yoke saturation effect is modeled by increasing the air-gap length. To solve the problem resulted from rotor eccentricity in analytical analysis, an equivalent transformation method is given. The transformation principle is that the eccentric machine can be equivalent to a concentric one with redistributed PM remanence and armature current density. On this basis, the subdomain method is adopted to predict the magnetic field of the reestablished machine. The proposed analytical method is implemented and evaluated on the 6p/36s and 8p/12s SPMSM and exhibits good agreement with the FEM and experiment results.
Understanding climate smart agriculture and the resilience of smallholder farmers in Umguza district, Zimbabwe

Abstract Climate smart agriculture has enhanced the viability and sustainability of smallholder farming in Umguza District of Zimbabwe. This study documents how smallholder farmers in Umguza District have responded to the adverse effects of climate change by venturing into small grains production, conservation farming and small livestock production. This paper discusses the vulnerability of smallholder farmers in Umguza District to climate change induced risks. The vulnerability profile is followed by an exposition of the resilience strategies used by the farmers to mitigate the effects of climate change. Additionally, the paper considers how the government institutions and development agencies can mainstream the adoption of climate smart farming technologies. The study used qualitative and quantitative approaches as methods of data collection, data were gathered through five key informants, and 30 in-depth semi structured questionnaires. This study suggests that government institutions and development practitioners should intensify resilience-building programmes, conduct climate awareness programmes and avail funding to improve the acceptance of CSA practices among smallholder farmers in Umguza and the rest of Zimbabwe.
Hybrid-Microgrid Planning, Sizing and Optimization for an Industrial Demand in Pakistan

Industrial sector is of great significance for the economic growth of every country. The energy crisis in Pakistan has become the prime stumbling block in the economic development of the country. There are many industrial processes that need uninterrupted supply; even a trivial outage can cost millions of dollars. The main cause of "load shedding" in Pakistan is that it produces a major portion of its energy from fossil fuels, whose price and demand is constantly increasing. Most of the customers at industrial and commercial level use Diesel Generator (DiG) as a reliable alternative source of electrical power when grid supply is unavailable. The use of DiG during loading shedding hours would increase the Cost of Energy (COE) per kWh and also enhance environmental emissions. Pakistan has a wide range of renewable power sources like bioenergy, wind, solar, hydel, geothermal etc. The distinct emphasis on the implementation of an industrial microgrid in Faisalabad, Pakistan has been specified in this paper. The prospective benefits of the microgrid fall into three major kinds: cost reduction, fuel saving, and improved environmental emissions. The optimized objective of this work is to maximize these benefits. Moreover while designing the hybrid microgrid system it encounters many design challenges like sizing of the components, system feasibility, COE, system reliability etc. This study contributes to the ongoing studies about hybrid microgrid system and draws attention to the optimal design and sizing considering several techno-economic factors including Net Present Cost (NPC), COE, supply reliability, capacity shortage constraint, battery state of charge (SOC), dispatch strategy, PV power generation and PV array tracking systems. Different cases are studied; microgrid sizing, techno-economic exploration, sensitivity analysis and environmental effects are addressed using (Hybrid Optimization Model for Electric Renewables) HOMER. The results show that COE and environmental emissions have been significantly reduced for the proposed system.