Miracle-dz/UniFAR-FTUs huggingface dataset:
Dissecting the roles of the Tuberin protein in the subcellular localization of the G2/M Cyclin, Cyclin B1

Tuberin is a major component of the protein regulatory complex known as the Tuberous Sclerosis Complex and plays a crucial role in cell cycle progression and protein synthesis. Mutations in the Tuberin gene, TSC2, lead to the formation of benign tumors in many organ systems and causes the Tuberous Sclerosis Complex disorder. Genotypes ranging from point mutations to large deletions in the TSC2 gene have been clinically characterized with a wide range of phenotypes from skin tumors to large brain tumors. Our current work investigates the molecular mechanisms behind Tuberin and its ability to regulate the cell cycle through its binding to the G2/M cyclin, Cyclin B1. After creating an early stop codon in a critical region of the Tuberin, our results show the in vitro phenotype that occurs from a truncated Tuberin protein. Herein we demonstrate that this clinically relevant truncated form of Tuberin promotes an increased nuclear accumulation of Cyclin B1 and a subsequent increase in cell proliferation supporting the phenotypic data seen in the clinic with Tuberous Sclerosis Complex patients showing deletions within the TSC2 gene. This data provides an insight into some of the functional and molecular consequences of truncated proteins that are seen in clinical patients.
Rebirth-FTL: Lifetime Optimization via Approximate Storage for NAND Flash Memory

The lifetime of NAND flash cells significantly degrades with feature-size reductions and multilevel cell technology. On the other hand, we have more and more approximate data, such as images and videos that are more error tolerant than regular data like text. In this article, we propose Rebirth-FTL, which reuses faulty blocks that contain uncorrectable errors to store approximate data for lifetime optimization. Rebirth-FTL effectively manages two spaces, namely, the approximate space and the normal space, with an efficient address translator, a coordinated garbage collection, and a differential wear leveler. In addition, we develop an migration times restriction (MTR) policy to restrict the movement of the approximate data in the approximate space. We also develop a scheme to pass approximate information from userland to kernel space in Linux. Finally, a lifetime model is presented for lifetime analysis. Our experimental results show that Rebirth-FTL can extend the lifetime by 41.63% on average.
Thin Piezoelectric Mobile Robot Using Curved Tail Oscillation

In this paper, a novel piezoelectric material-based soft mobile robot is proposed. The robot consists of a thin film body and a soft polyvinylidene fluoride-based tail. The tail has a unique structure that is a combination of a polyethylene terephthalate fin and a curved piezoelectric film. We experimentally demonstrated the performance of a mobile robot. In particular, the resonance frequency was investigated to maximize tail movement. In addition, a parameter study of the tail shape was performed. Moreover, the repeatability of movements was tested. By using a curved piezoelectric tail with a fin, we built a mobile robot with a thin and flexible body. Furthermore, we fabricated multiple mobile robots by varying the robot configuration and analyzing their mobility. We found that our novel curved tail structure has good mobility performance at low body weights and multi-tailed conditions.
MAPEAMENTO DA PRECIPITAÇÃO NO ESTADO DE ALAGOAS POR MEIO DE TÉCNICAS GEOESTATÍSTICAS

O objetivo desta pesquisa consistiu em estudar a dinâmica espacial da precipitação no Estado de Alagoas, localizado no Nordeste do Brasil. Para responder ao objetivo da pesquisa foram empregadas técnicas geoestatísticas para construção de mapas de chuvas. Os resultados encontrados nesta pesquisa evidenciam uma grande variabilidade na distribuição das chuvas. O Leste Alagoano é o que possui maior precipitação acumulada, enquanto no Sertão as chuvas são escassas. Os resultados obtidos neste estudo podem auxiliar os órgãos responsáveis pelo gerenciamento dos recursos hídricos na tomada de decisão e na prevenção de desastres ambientais causados pelo excesso ou pela escassez das chuvas.
Computational Evaluation of Abrogation of HBx-Bcl-xL Complex with High-Affinity Carbon Nanotubes (Fullerene) to Halt the Hepatitis B Virus Replication

Hepatitis B virus (HBV) is the world’s most prevalent chronic viral infection. More than 350 million individuals are chronic carriers of the virus, with an estimated 2 billion infected persons. For instance, the role of HBx protein in attachment and infection is very obvious and consequently deemed as an important druggable target. Targeting the interface and discovering novel drugs greatly advanced the field of therapeutics development. Therefore, in the current study, HBx to Bcl-xL is abrogated on high-affinity carbon nanotubes using computational structural biology tools. Our analysis revealed that among the total 62 carbon fullerenes, only 13 compounds exhibited inhibitory activity against HBx, which was further confirmed through IFD-based rescoring. Structural dynamics investigation revealed stable binding, compactness, and hydrogen bonds reprogramming. Moreover, the binding free energy calculation results revealed that the top hits1-4 possess the total binding energy of −54.36 kcal/mol (hit1), −50.81 kcal/mol (hit2), −47.09 kcal/mol (hit3), and −45.59 kcal/mol for hit4. In addition, the predicted KD values and bioactivity scores further validated the inhibitory potential of these top hits. The identified compounds need further in vitro and in vivo validation to aid the treatment process of HBV.
Deep-Learning-Based Coronary Artery Calcium Detection from CT Image

One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible.
Research trends in online distance learning during the COVID-19 pandemic

ABSTRACT Online distance learning emerged as a solution to continue with teaching and learning during the COVID-19 pandemic, which led to more scholarly publications in the field. The current study critically analyzed the publications using a range of scientometric techniques. The journal article was the predominant form of publication, and collaborative research work was preferred by scholars in this domain. The most-cited articles were published primarily in interdisciplinary journals. Most of the papers (43.64%) used quantitative methods, followed by qualitative methods (13.33%), and mixed methods (9.09%). A large proportion of publications (33%) did not indicate a research methodology. Visualizations of research show that the field has focused largely on remote teaching as a new term to depict online distance learning but has also covered a wider spectrum of issues in relation to teaching and learning, with an emphasis on technologies for teaching and learning, assessment, teacher capacities, and institutional preparedness.
Research on Music Emotion Recognition Model of Deep Learning Based on Musical Stage Effect

The change of life style of the times has also prompted the reform of many art forms (including musicals). Nowadays, the audience can not only enjoy the wonderful performances of offline musicals but also feel the charm of musicals online. However, how to bring the emotional integrity of musicals to the audience is a technical problem. In this paper, the deep learning music emotion recognition model based on musical stage effect is studied. Firstly, there is little difference between the emotional results identified by the CRNN model test and the actual feelings of people, and the coincidence degree of emotional responses is as high as 95.68%. Secondly, the final recognition rate of the model is 98.33%, and the final average accuracy rate is as high as 93.22%. Finally, compared with other methods on CASIA emotion set, the CRNN-AttGRU has only 71.77% and 71.60% of WAR and UAR, and only this model has the highest recognition degree. This model also needs to update iteration and use other learning methods to learn at different levels so as to make this model widely used and bring more perfect enjoyment to the audience.
Prediction of Adolescent Physical Self-Concept through Autonomous Motivation and Basic Psychological Needs in Spanish Physical Education Students

Framed within the theoretical support of the Self-determination Theory (SDT) this study aimed to analyze the relationship between the satisfaction of basic psychological needs, the self-determined motivation towards physical education, and the physical self-concept of Spanish teenagers. For this, 618 students, aged from 10 to 14 years-old (M = 11.62, SD = 0.94), participated in physical education classes. The Basic Psychological Needs Satisfaction Scale (PNSE), the dimensions of intrinsic motivation and identified motivation of the Perceived Locus of Causality Scale in physical education classes (PLOC), and the Physical Self-Concept Questionnaire (PSPP) were administered. A structural equation model and test confirmatory factor analysis were used. The results indicate a positive relationship between the satisfaction of the basic psychological needs of the students and the self-determined motivation towards physical education, with this being positively associated with the physical self-concept of the students. In addition, a prediction model (χ2 = 763.23; p < 0.001; χ2/d.f. = 3.00; IFI = 0.91; CFI = 0.91; TLI = 0.90; RMSEA = 0.05) showed the satisfaction of basic psychological needs positively predicted their self-determined motivation towards physical education, and this, in turn, a greater physical self-concept of the students. The results are discussed centered on the importance of the physical education teacher contemplating experiences that work on the promotion of personal identity and student self-esteem through the improvement of quality motivation. Consequently, the greater importance of the physical self-concept of the students was explained thanks to more autonomous (intrinsic and identified regulation) motivation and higher levels of basic psychological needs (especially autonomy and competence).
Secure Boolean Spatial Keyword Query With Lightweight Access Control in Cloud Environments

Spatial keyword query has attracted wide-spread academic and industrial concerns due to the popularity of location-based services and Internet of Things. To efficiently support the online query processing, the data owners need to outsource their data to cloud platforms. However, the outsourcing services may raise privacy leaking issues. Moreover, access control, another important security concern, is largely ignored. Therefore, we first propose and formalize the problem of secure boolean spatial keyword query under lightweight access control while guaranteeing the widely accepted adaptive indistinguishability against chosen keyword attack model. Then, we devise a novel hybrid Bloom filter encoding strategy, including a linear embedding and exponent-based transformation schemes and a secure index structure, called SAGTree. They can maintain both geo-text and access policy information together in a secure way while answering the encrypted queries under access control without decryption. Finally, we present the in-depth security analysis and demonstrate the performance of our proposed algorithms.
Gene Expression of Agronomically Important Secondary Metabolites in cv. ‘USDA Cascade’ Hop (Humulus lupulus L.) Cones during Critical Developmental Stages

Abstract The transcriptome from lupulin glands and associated bracts from cone tissue of hop (Humulus lupulus) c.v. ‘Cascade’, during three stages of development: early, mid, and late or near-harvest, was sequenced. Significant increases were found in expression patterns of many genes involved in the biosynthesis of bitter acids, xanthohumol, and volatile secondary metabolites or “hop oils” during the middle stage of cone development. The biosynthesis of thiol precursors responsible for popular “tropical fruit” flavors in beer is not well known, but homologs of genes hypothesized to be involved in this process tend to be up-regulated during the late stage in hop cones. More research needs to be performed to describe the pathway of thiol precursor biosynthesis in hops. Hierarchical clustering revealed overlap of samples taken from each developmental stage, likely due to non-uniform ripening of cones on the plant. It is proposed that the mid-stage of cone development is critical for the development of important flavor-producing secondary metabolites in hops, and this is supported by previous research describing concentrations of secondary metabolites. It is hypothesized that abiotic stress during the mid-stage of cone development may be quite detrimental to the bitter acid concentrations ultimately found in hops.
Properties of Acuvue Oasys lens Care solutions and contact lens wettability

Submitted: 16 April 2016 Revised: 19 October 2016 Accepted for publication: 11 November 2016 Background: The aim of the study was to evaluate the effect of care solutions on contact lens in vivo wettability using Doane’s interferometric technique. Methods: Thirteen subjects (aged 26.6 6.3 years) participated for contact lens wettability evaluation after soaking in five care solutions namely Opti-Free EverMoist (now called puremoist), Opti-Free Express (Alcon), COMPLETE (AMO), ReNu and Biotrue (Bausch & Lomb). A new pair of lenses was soaked in the solutions for eight hours (prior to wear) or taken directly from pack solutions (as control) and worn by the subjects. The total number of pairs of lenses tested was 13 by six (78 lenses; 13 pairs of lenses wetted in five care solutions plus the pack solution). Doane’s interferometer was used to capture images of the pre-lens film on a single type of contact lens, Acuvue Oasys (Johnson & Johnson). The lens in vivo wettability was evaluated after 15 minutes and eight hours of wear by each subject. Four parameters: onset latency, drying duration, maximum speed and peak latency were used to assess the lens wettability. Results: After eight hours, the solutions showing significant reduction in contact lens wettability were the following. For onset latency: Pack solution, Biotrue Opti-Free EverMoist and Express; for drying duration: pack solution, ReNu and Opti-Free EverMoist; for peak latency; pack solution, Biotrue and Opti-Free EverMoist. Regarding the maximum speed, lenses soaked in Pack solution, ReNu and Opti-Free EverMoist showed a significant increase (worsening). The comparative study showed that there were significant differences among the performance of the care solutions. Conclusion: This novel thin film interferometric technique was able to measure, objectively, contact lens in vivo wettability, following the use of care solutions. COMPLETE was the only solution that showed no significant change in the lens wettability (with the all parameters) between the initial and the end of day.
Motor differences in autism during a human-robot imitative gesturing task.

BACKGROUND Difficulty with imitative gesturing is frequently observed as a clinical feature of autism. Current practices for assessment of imitative gesturing ability-behavioral observation and parent report-do not allow precise measurement of specific components of imitative gesturing performance, instead relying on subjective judgments. Advances in technology allow researchers to objectively quantify the nature of these movement differences, and to use less socially stressful interaction partners (e.g., robots). In this study, we aimed to quantify differences in imitative gesturing between autistic and neurotypical development during human-robot interaction. METHODS Thirty-five autistic (n = 19) and neurotypical (n = 16) participants imitated social gestures of an interactive robot (e.g., wave). The movements of the participants and the robot were recorded using an infrared motion-capture system with reflective markers on corresponding head and body locations. We used dynamic time warping to quantify the degree to which the participant's and robot's movement were aligned across the movement cycle and work contribution to determine how each joint angle was producing the movements. FINDINGS Results revealed differences between autistic and neurotypical participants in imitative accuracy and work contribution, primarily in the movements requiring unilateral extension of the arm. Autistic individuals imitated the robot less accurately and used less work at the shoulder compared to neurotypical individuals. INTERPRETATION These findings indicate differences in autistic participants' ability to imitate an interactive robot. These findings build on our understanding of the underlying motor control and sensorimotor integration mechanisms that support imitative gesturing in autism which may aid in identifying appropriate intervention targets.
Mechanism Analysis of Applying Blockchain Technology to Forestry Carbon Sink Projects Based on the Differential Game Model

As an important way to reduce emission, forestry carbon sink (FCS) has not been implemented effectively. Therefore, this paper aims to analyze the effectiveness and mechanism of applying blockchain technology in FCS projects by utilizing the differential game model. A Stackelberg differential game model between forest farmers and emission-controlled enterprises (ECEs) is developed to analyze the optimal emission reduction efforts and the optimal trajectory of forest farmers and ECEs before and after introducing blockchain technology. It is found that: (1) At the initial stage of the utilization of blockchain technology, if blockchain technology takes a leading role in stabilizing carbon prices, the ECEs prefer to purchase FCS instead of reducing emissions by their own technology. On the contrary, if blockchain technology takes a leading role in stimulating the vitality of the carbon trading market, ECEs tend to use emission abatement technology to meet the carbon quote requirements. (2) In the later stage, the incentive and stabilizing effects of blockchain technology on carbon prices tend to be balanced, and the emission reduction efforts of ECEs are lower than the efforts before applying blockchain technology. (3) The application of blockchain technology increases forest farmers’ willingness to reduce emissions because of its effection of cost reduction and efficiency improvement. Meanwhile, blockchain technology reduces abatement costs by influencing carbon prices. Therefore, blockchain technology improves forest farmers’ emission reduction efforts on the whole.
Enhanced Observation: How Therapeutic Are They Within A Medium Secure Forensic Mental Health Setting?

ABSTRACT Enhanced observations (EO) have been integral to mental health nursing care for decades. However, research examining patient’s experiences of EO within forensic mental health settings is limited. This study explored patients’ lived experiences of EO within a medium secure hospital as part of a service review. Four patients and one staff member completed semi-structured interviews. The data were analysed using Interpretive Phenomenological Analysis (IPA). The four themes that emerged were: 1) The importance of the therapeutic relationship; 2) Focus on staff’s interpersonal qualities; 3) EO experienced as loss; and 4) Factors to improve the therapeutic experience. Staff awareness training and development of guidelines (embedded within service policy) were suggested recommendations; with the aim of enhancing patient focused practice and altering a ritualistic task to become a recovery orientated therapeutic experience.
Toward an Adaptive Threshold on Cooperative Bandwidth Management Based on Hierarchical Reinforcement Learning

With the increase in Internet of Things (IoT) devices and network communications, but with less bandwidth growth, the resulting constraints must be overcome. Due to the network complexity and uncertainty of emergency distribution parameters in smart environments, using predetermined rules seems illogical. Reinforcement learning (RL), as a powerful machine learning approach, can handle such smart environments without a trainer or supervisor. Recently, we worked on bandwidth management in a smart environment with several fog fragments using limited shared bandwidth, where IoT devices may experience uncertain emergencies in terms of the time and sequence needed for more bandwidth for further higher-level communication. We introduced fog fragment cooperation using an RL approach under a predefined fixed threshold constraint. In this study, we promote this approach by removing the fixed level of restriction of the threshold through hierarchical reinforcement learning (HRL) and completing the cooperation qualification. At the first learning hierarchy level of the proposed approach, the best threshold level is learned over time, and the final results are used by the second learning hierarchy level, where the fog node learns the best device for helping an emergency device by temporarily lending the bandwidth. Although equipping the method to the adaptive threshold and restricting fog fragment cooperation make the learning procedure more difficult, the HRL approach increases the method’s efficiency in terms of time and performance.