Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychological Impacts of COVID-19 in Dental Patients are Moderated and Mediated by Hospital-Infection-Control-Policy and Satisfaction-with-Life: A Prospective Observational Dental-COVID Study

Introduction COVID-19 pandemic has imposed nation-wide lock-downs which severely impacted day-to-day lifestyle and caused anxiety, stress and fear among patients taking medical care including dental treatments. These psychological behaviors have also been observed during the strategic relaxation of social restrictions (Unlock). However, potential effect of these psychological behavior in endodontic cases have not been probed. Here, it is of great interest to explore the magnitude and buffering effect of two important psychological resources: satisfaction-with-life, and confidence in hospital-infection-control-policy in relation with COVID-19 risk perception on psychological impact due to fear for COVID-19. Methods Patients visiting Dental Clinic & Research Center for endodontic procedures were randomly asked to fill survey questionnaire, and were later enrolled as per the study criteria. The study carried out in two phases: from 15 Sept 2020 to 15 Dec 2020 (restrained confinement; Unlock 1.0); and from 16 Dec 2020 to 12 Feb 2021 (mild confinement; Unlock 2.0) with total sample size of 136. We used data collection tools such as fear-of-COVID-19 scale (FCV-19), perceived-stress-scale (PSS), modified-dental-anxiety-scale (MDAS), satisfaction-with-life scale (SWLS), COVID-19 risk perception, and confidence in hospital-infection-control-policy for COVID-19. Results A double moderation and dual moderated mediation structured model were used to establish the correlation of various parameters using SPSS (version 25.0) software suite. Confidence in hospital-infection-control-policy and SWLS were negatively correlated with FCV-19, MDAS, and PSS. Risk perception of COVID-19 was found to positively associated with FCV-19, MDAS, and PSS. Discussion Patient’s confidence in hospital-infection-control-policy for COVID-19 and SWLS acted as independent moderator for FCV-19 and mental distress. FCV-19 and risk perception were found to be lower during mild confinement (Unlock 2.0), and were also the positive predictors of PSS; and negative predictors of SWLS. The higher SWLS correlated very well with lower COVID-19 risk perception, concerning PSS and MDAS.
Surface State Treatment of Carbon Dots Using Sulphur Dioxide Isotherm

Low toxicity carbon dots are combating the disadvantages of quantum dots. The carbon dots find their applications in many fields due to their versatile nature. Four different types of carbon dots are present, according to the way of manufacturing and application the type is chosen. The water-soluble characteristics of carbon dots help them be involved in biomedicine applications. The optical properties of the carbon dots find applications as drug delivery, biosensors, LED, etc. The properties like fluorescence, photoluminescence, and phosphorescence are found in the carbon dots. The carbon dots occupy the tiny spot that exhibits different optical properties on excitation. The carbon dots excitation is mainly due to surface states. The characterization of surface states is very complex. The surface states contain the core structure of carbon and oxygen functional groups on the surfaces. The anions and cations formed from functional groups on excitation will recombine themselves. The functional groups are usually carboxyl and hydroxyl groups. The Π-collaborative network of the electronic structure contains many quantization levels which help the carbon dots to produce different wavelengths adapting to different applications. Due to the interference of the structure of the carbon dots, the entire property will vary. Doping of heteroatom methods is employed to enhance the fluorescence, and photoluminescence property carried out. They used N, S, P, and B heteroatoms singly and in combination to doping carbon dots. Here, the paper proposes the sulphur dioxide adsorption technique to enhance the optical properties of the carbon dots. The proposed method shows 8.5% efficiency in relative fluorescence intensity and 8% efficiency in terms of photoluminescence intensity.
Graphene Oxide-Reinforced Alginate Hydrogel for Controlled Release of Local Anesthetics: Synthesis, Characterization, and Release Studies

In pain relief, lidocaine has gained more attention as a local anesthetic. However, there are several side effects that limit the use of local anesthetics. Therefore, it is hypothesized that a hydrogel system with facile design can be used for prolonged release of lidocaine. In this study, we developed a formulation comprises of sodium alginate (SA) and graphene oxide (GO) to prolong the release of lidocaine. The gelation was induced by physically crosslinking the alginate with Ca2+ ions. The formation of blank SA and GO-reinforced SA hydrogels was investigated with different concentration of Ca2+ ions. The controlled release of lidocaine hydrochloride (LH) on both hydrogel systems was studied in PBS solution. The GO-reinforced SA hydrogels exhibited more sustained release than SA hydrogels without GO. In vitro biocompatibility test in L929 fibroblast cells confirmed the non-toxic property of hydrogels. Furthermore, to prove the in-situ gelation and biodegradability of hydrogels the hydrogels were injected on mice model and confirmed the stable gel formation. The hydrogels implanted onto the subcutaneous tissue of hydrogels retained over one week. These results indicate that LH-loaded GO-reinforced SA hydrogel can be a potential biomaterial for controlled release of local anesthetics.
The Association of Waminoa with Reef Corals in Singapore and Its Impact on Putative Immune- and Stress-Response Genes

Waminoa spp. are acoel flatworms mainly found as ectosymbionts on scleractinian corals. Although Waminoa could potentially represent a threat to their hosts, not enough information is available yet regarding their ecology and effect on the coral. Here, the Waminoa sp. –coral association was analyzed in Singapore reefs to determine the prevalence, host range, and preference, as well as the flatworm abundance on the coral surface. Moreover, the impact of Waminoa sp. on the expression of putative immune- and stress-response genes (C-type lectin, C3, Hsp70 and Actin) was examined in the coral Lobophyllia radians. The association prevalence was high (10.4%), especially in sites with lower sedimentation and turbidity. Waminoa sp. showed a wide host range, being found on 17 coral genera, many of which are new association records. However, only few coral genera, mostly characterized by massive or laminar morphologies appeared to be preferred hosts. Waminoa sp. individuals displayed variable patterns of coral surface coverage and an unequal distribution among different host taxa, possibly related to the different coral growth forms. A down-regulation of the expression of all the analyzed genes was recorded in L. radians portions colonized by Waminoa individuals compared to those without. This indicated that Waminoa sp. could affect components of the immune system and the cellular homeostasis of the coral, also inhibiting its growth. Therefore, Waminoa sp. could represent a potential further threat for coral communities already subjected to multiple stressors.
It's a Whole New Ball Game: The Mitchell Report, Performance Enhancing Drugs, and Professional Sports

This dissertation investigates the findings of a congressional investigation into the use of performance enhancing drugs (PEDs) in Major League Baseball, known as the Mitchell Report. It analyzes the primary arguments presented in the report, the argument for integrity, role models, and apology specifically, through the lens of governmentality and moral regulation. It argues that the report represents a distinct mode of governance that seeks to condemn PED use in a moralizing way. This mode of governance is characterized by its emergence from a variety of locations as opposed to the relatively simple use of the state and its legal apparatus. Importantly, one of those locations includes the individual subject who is urged to self-govern without the need of external threat or recourse. The dissertation also suggests that this mode of governance is inextricable linked to rhetoric and communication.
Packet-Level Open-World App Fingerprinting on Wireless Traffic

—Mobile apps have profoundly reshaped modern lifestyles in different aspects. Several concerns are naturally raised about the privacy risk of mobile apps. Despite the prevalence of encrypted communication, app fingerprinting (AF) attacks still pose a serious threat to users’ online privacy. However, existing AF attacks are usually hampered by four challenging issues, namely i) hidden destination, ii) invisible boundary, iii) app multiplexing, and iv) open-world recognition, when they are applied to wireless traffic. None of existing AF attacks can address all these challenges. In this paper, we advance a novel AF attack, dubbed P ACKET P RINT , to recognize user activities associated with the app of interest from encrypted wireless traffic and tackle the above challenges by proposing two novel models, i.e., sequential XGBoost and hierarchical bag-of-words model. We conduct extensive experiments to evaluate the proposed attack in a series of challenging scenarios, including i) open-world setting, ii) packet loss and network congestion, iii) simultaneous use of different apps, and iv) cross-dataset recognition. The experimental results show that P ACKET P RINT can accurately recognize user activities associated with the apps of interest. It achieves the average F1-score 0 . 884 for open-world app recognition and the average F1-score 0 . 959 for in-app user action recognition.
Exploring the Relationship between Urban Youth Sentiment and the Built Environment Using Machine Learning and Weibo Comments

As the relationship between the built environment and the sense of human experience becomes increasingly important, emotional geography has begun to focus on sentiments in space and time and improving the quality of urban construction from the perspective of public emotion and mental health. While youth is a powerful force in urban construction, there are no studies on the relationship between urban youth sentiments and the built environment. With the development of the Internet, social media has provided a large source of data for the metrics of youth sentiment. Based on data from more than 10,000 geolocated Sina Weibo comments posted over one week (from 19 to 25 July 2021) in Shanghai and using a machine learning algorithm for attention mechanism, this study calculates the sentiment label and sentiment intensity of each comment. Ten elements in five aspects were selected to assess the built environment at different scales and also to explore the correlations between built environment elements and sentiment intensity at different scales. The study finds that the overall sentiment of Shanghai youth tends to be negative. Sentiment intensity is significantly associated with most built environment elements at smaller scales. Urban youth have a higher proportion of both happy and sad sentiments, within which sad sentiments are more closely related to the built environment and are significantly related to all built environment elements. This study uses a deep learning algorithm to improve the accuracy of sentiment classification and confirms that the built environment has a great impact on sentiment. This research can help cities develop built environment optimization measures and policies to create positive emotional environments and enhance the well-being of urban youth.
Visual Quality Control in Heat Shrink Tubing

In this contribution a machine vision inspection system is presented which is designed as a length measuring sensor. It is devel- oped to be applied to a range of heat shrink tubes, varying in length, diameter and color. The challenges of this task were the precision and accuracy demands as well as the real-time applicability of the entire ap- proach since it should be realized in regular industrial line production. In production, heat shrink tubes are cut to specific sizes from a continuous tube. A multi-measurement strategy has been developed, which measures each individual tube segment several times with sub pixel accuracy while be- ing in the visual field. The developed approach allows for a contact-free and fully automatic control of 100% of produced heat shrink tubes ac- cording to the given requirements with a measuring precision of 0.1mm. Depending on the color, length and diameter of the tubes considered, a true positive rate of 99.99% to 100% has been reached at a true negative rate of > 99.7.
Blockade of ALDH in Cisplatin-Resistant Ovarian Cancer Stem Cells In Vitro Synergistically Enhances Chemotherapy-Induced Cell Death

Epithelial ovarian cancer (EOC) is the leading cause of gynecological cancer-related death. The high mortality and morbidity associated with EOC are mostly due to late diagnosis and chemotherapy drug resistance. Currently, the standard first-line chemotherapy regimen is systemic administration of platinum-based chemotherapy combined with a taxane. A major problem besides cisplatin resistance (occurring in nearly one-third of patients) is the greater toxicity of the drug combinations. A synergistic treatment with drug supporting activity could maximize the cytotoxic effects of chemotherapeutic agents on tumor cells while decreasing the dosage of each drug to potentially reduce toxicity. The ALDH-blocking agent Disulfiram (DSF), a clinically approved drug used for alcoholism treatment, has displayed promising anti-cancer activity. We previously described that blocking ALDH activity enhances the induction of apoptosis, especially in ovarian cancer stem cells treated with chemotherapeutic agents. In this study, we further investigated the synergistic effect of DSF in combination with cytotoxic chemotherapeutic drugs. The concentration of each chemotherapeutic agent could be significantly reduced with sustained efficacy on tumor cell apoptosis in cell lines in vitro (Dose-Reduction Index at IC50 from 1 to 50). Moreover, the potential chemo-sensitizing effects of DSF on ALDH-associated cisplatin-resistant ovarian cancer stem cells were also investigated and shown that in contrast to its high resistance to cisplatin, the cisplatin-resistant cells remain very sensitive to DSF-induced cytotoxicity (apoptosis and necrosis: cisplatin-resistant cells vs. parental cells: 60.4% vs. 20.5%). In combination with DSF and cisplatin, relatively more apoptosis and necrosis were induced in cisplatin-resistant cells than in their parental cells (apoptosis and necrosis: cisplatin-resistant cells vs. parental cells: 81.5% vs. 50.1%). A transcriptome analysis identified that ALDH was mainly enriched in the cancer-associated fibroblasts and showed that ALDH plays roles in responding to oxidative stress, metabolisms, and energy transition in the ALDH-associated cisplatin-resistant ovarian cancer stem cells. In conclusion, our data demonstrate a key role of ALDH-associated cisplatin-resistant cancer stem cells and identifies DSF as a potential adjuvant for a rational protocol design by computational quantitative assessment in vitro on ovarian cancer cell lines. Our work contributes to resolving the ALDH-associated cisplatin resistance and provides a resource for the development of novel chemotherapeutic regimens.
Rail Tracking and Detection with Drone in Gazebo Environment

Rail transport is considered one of the safest modes of transport. Along with the development of technology, significant developments have been observed in railway transportation over the years. With the increasing railway line, the demand for railway transportation is increasing day by day. The number of passengers using railway transportation has also increased in this context. With this intensity, the damage to the railway line increases. In ensuring the safety of railway transport, methods based on deep learning have become important to ensure railway safety. In order for the railway line to provide healthy service, the monitoring of the railway line should be done regularly. Traditional rail monitoring services require different vehicles. Today, besides conventional vehicles, drones are used for the monitoring of the railway line. Experimenting with drones in the real environment can be difficult and costly. It is always more advantageous to run the codes that will run on the drone first in a simulation environment to save time. In this study, a drone-based system that autonomously tracks and detects railway tracks is proposed as an alternative to traditional methods. The proposed method detects the rails with the semantic segmentation method and follows the rails with its front camera. The proposed method was developed in the Gazebo environment. The general purpose of the study is to record the rail images with the drone camera that follows the railway autonomously. In this way, drone experiments to be carried out in the real railway environment will be completed in a shorter time.
An Emergency Event Detection Ensemble Model Based on Big Data

Emergency events arise when a serious, unexpected, and often dangerous threat affects normal life. Hence, knowing what is occurring during and after emergency events is critical to mitigate the effect of the incident on humans’ life, on the environment and our infrastructures, as well as the inherent financial consequences. Social network utilization in emergency event detection models can play an important role as information is shared and users’ status is updated once an emergency event occurs. Besides, big data proved its significance as a tool to assist and alleviate emergency events by processing an enormous amount of data over a short time interval. This paper shows that it is necessary to have an appropriate emergency event detection ensemble model (EEDEM) to respond quickly once such unfortunate events occur. Furthermore, it integrates Snapchat maps to propose a novel method to pinpoint the exact location of an emergency event. Moreover, merging social networks and big data can accelerate the emergency event detection system: social network data, such as those from Twitter and Snapchat, allow us to manage, monitor, analyze and detect emergency events. The main objective of this paper is to propose a novel and efficient big data-based EEDEM to pinpoint the exact location of emergency events by employing the collected data from social networks, such as “Twitter” and “Snapchat”, while integrating big data (BD) and machine learning (ML). Furthermore, this paper evaluates the performance of five ML base models and the proposed ensemble approach to detect emergency events. Results show that the proposed ensemble approach achieved a very high accuracy of 99.87% which outperform the other base models. Moreover, the proposed base models yields a high level of accuracy: 99.72%, 99.70% for LSTM and decision tree, respectively, with an acceptable training time.
Computer vision techniques for fire monitoring using aerial images

The paper presents an approach to the use of aerial images for fire monitoring. It shows techniques to segment the fire on visual images and to geo-locate the fire front. Color processing is used for fire segmentation. To geo-locate the fire base, a planar surface is assumed. The homography between the image plane and the real plane can be computed from several points correspondences. This homography is later used to geo-locate the fire base. Several artificial landmarks are used to obtain an initial relation. The homography is updated as the camera moves by tracking several points over the sequence of images. The points are tracked by using a feature matching algorithm. The explained procedure has been applied to visual images of controlled field fires taken by a camera placed on a helicopter.
PregTox: A Resource of Knowledge about Drug Fetal Toxicity

Background It is of vital importance to determine the safety of drugs. Pregnant women, as a special group, need to evaluate the effects of drugs on pregnant women as well as the fetus. The use of drugs during pregnancy may be subject to fetal toxicity, thus affecting the development of the fetus or even leading to stillbirth. The U.S. Food and Drug Administration (FDA) issued a toxicity rating for drugs used during pregnancy in 1979. These toxicity ratings are denoted by the letters A, B, C, D, and X. However, the query of drug pregnancy category has yet to be well established as electronic service. Results Here, we presented PregTox, a publicly accessible resource for pregnancy category information of 1114 drugs. The PregTox database also included chemical structures, important physico-chemical properties, protein targets, and relevant signaling pathways. An advantage of the database is multiple search options which allow systematic analyses. In a case study, we demonstrated that a set of chemical descriptors could effectively discriminate high-risk drugs from others (area under ROC curve reached 0.81). Conclusions PregTox can serve as a unique drug safety data source for drug development and pharmacological research.
Identification of the molecular characteristics of Bacillus anthracis (1982-2020) isolates in East Indonesia using multilocus variable-number tandem repeat analysis

Background and Aim: Anthrax is one of the endemic strategic diseases in East Indonesia, particularly in the provinces of South Sulawesi, West Sulawesi, Gorontalo, East Nusa Tenggara, and West Nusa Tenggara. Anthrax is an important disease due to its zoonotic and economic impact on the livestock industry. This study aimed to identify the molecular characteristics of Bacillus anthracis in East Indonesia using multilocus variable-number tandem repeat (VNTR) analysis (MLVA). Materials and Methods: Isolates were obtained from an investigation of anthrax outbreaks in five provinces of East Indonesia from 1982 to 2020. Conventional polymerase chain reaction for B. anthracis was used to identify MLVA-8. Deoxyribonucleic acid sequencing analysis was based on MLVA-8 primers for VNTR identification of the phylogenetic relationship among 24 isolates of B. anthracis obtained from 17 distinct districts/cities in East Indonesia. Tandem Repeats Finder was used for VNTR identification, and Molecular Evolutionary Genetics Analysis X was used to construct phylogenetic analysis. Results: In this study, 24 isolates were classified as genotype or lineage A. There were four subgroups of B. anthracis circulating in East Indonesia based on eight molecular marker loci sequence results. Conclusion: The findings of this study show that MLVA-8 typing might be useful as a subtyping tool for the epidemiological investigation of identical genotypes and low genetic diversity of B. anthracis. No other lineage of B. anthracis was circulating in East Indonesia. Other molecular methods are needed, such as extended MLVA, whole-genome sequencing, and canonical single-nucleotide polymorphism, for a more precise study of B. anthracis genetic diversity.