Miracle-dz/UniFAR-FTUs huggingface dataset:
Scrutinizing the Impact of Alternating Electromagnetic Fields on Molecular Features of the Model Plant Arabidopsis thaliana

Natural and anthropogenic electromagnetic fields (EMFs) are ubiquitous in the environment and interfere with all biological organisms including plants. Particularly the quality and quantity of alternating EMFs from anthropogenic sources are increasing due to the implementation of novel technologies. There is a significant interest in exploring the impact of EMFs (similar to those emitted from battery chargers of electric cars) on plants. The model plant Arabidopsis thaliana was exposed to a composite alternating EMF program for 48 h and scrutinized for molecular alterations using photosynthetic performance, metabolite profiling, and RNA sequencing followed by qRT-PCR validation. Clear differences in the photosynthetic parameters between the treated and control plants indicated either lower nonphotochemical quenching or higher reduction of the plastoquinone pool or both. Transcriptome analysis by RNA sequencing revealed alterations in transcript amounts upon EMF exposure; however, the gene ontology groups of, e.g., chloroplast stroma, thylakoids, and envelope were underrepresented. Quantitative real-time PCR validated deregulation of some selected transcripts. More profound were the readjustments in metabolite pool sizes with variations in photosynthetic and central energy metabolism. These findings together with the invariable phenotype indicate efficient adjustment of the physiological state of the EMF-treated plants, suggesting testing for more challenging growth conditions in future experiments.
Essential m6A Methylation Regulator HNRNPC Serves as a Targetable Biomarker for Papillary Renal Cell Carcinoma

m6A RNA modification is a common abundant posttranscriptional modification of mRNAs occurring in cancer growth and progression. Accumulated evidence has proved that HNRNPC, which acts as a m6A reader, plays an essential role in the promotion of cancer occurrence and development; nevertheless, the role of HNRNPC in papillary renal cell carcinoma remained to be discovered. In this study, we comprehensively identified HNRNPC as a hub gene involved in m6A modification in pRCC. Then, the expression level, survival outcomes, PPI network, function enrichment, immune cell infiltration, and single-cell analysis were performed. Finally, we found that HNRNPC significantly promoted renal cell carcinoma proliferation and migration in vitro. In conclusion, our work proved that HNRNPC may act as a momentous m6A regulator, as well as a potential targetable biomarker for pRCC.
Does Chlorination Promote Antimicrobial Resistance in Waterborne Pathogens? Mechanistic Insight into Co-Resistance and Its Implication for Public Health

Chemical agents including chlorine and antibiotics are used extensively to control infectious microorganisms. While antibiotics are mainly used to treat bacterial infections, chlorine is widely used for microbial inactivation in the post-secondary disinfection steps of water treatment. The extensive use of these agents has been acknowledged as a driving force for the expansion of antimicrobial resistance (AMR) and has prompted discourse on their roles in the evolution and proliferation of resistant pathogens in the aquatic milieus. We live in a possible “post-antibiotic” era when resistant microbes spread at startling levels with dire predictions relating to a potential lack of effective therapeutic antibacterial drugs. There have been reports of enhancement of resistance among some waterborne pathogens due to chlorination. In this context, it is pertinent to investigate the various factors and mechanisms underlying the emergence and spread of resistance and the possible association between chlorination and AMR. We, therefore, reflect on the specifics of bacterial resistance development, the mechanisms of intrinsic and acquired resistance with emphasis on their environmental and public health implications, the co-selection for antibiotic resistance due to chlorination, biofilm microbiology, and multidrug efflux activity. In-depth knowledge of the molecular basis of resistance development in bacteria will significantly contribute to the more rational utilization of these biocidal agents and aid in filling identified knowledge gap toward curbing resistance expansion.
Comparative evaluation and Immunohistochemical expression of Syndecan-1 in Ameloblastoma and Dentigerous cyst.

Background: Syndecans are type-1 heparan sulphate proteoglycans which play significant role in cell-cell and cell-extracellular matrix interaction. Syndecans are involved in tooth development and differentiation of mesenchymal cells. Amongst odontogenic lesions, ameloblastomas and dentigerous cysts are routinely encountered lesions with difference in treatment modality based on its aggressiveness. The objective of the present research was to study and compare immunohistochemical expression of syndecan-1 in ameloblastoma and dentigerous cyst. Method: 40 retrospectively diagnosed cases of ameloblastomas and dentigerous cysts were immunohistochemically stained against syndecan-1. The intensity of immunostaining and percentage of positive cells was assessed by three independent blind observers. Weighted kappa test was used to find out inter-observer reliability. Comparative evaluation of syndecan-1 expression between the two lesions was done using student t-test. Results: There was statistically significant difference between the mean of score for intensity, mean of score for percentage of positive cells and total mean score of syndecan-1 between ameloblastoma and dentigerous cyst. Conclusion: Syndecan-1 may be involved in aetiopathogenesis of odontogenic lesions like ameloblastoma and dentigerous cyst. Also, weak expression in ameloblastoma indicates that tumor invasion and aggressiveness is related to cell adhesion molecule like syndecan-1.
Developing a Multimodal Monitoring System for Geriatric Depression

The Internet of Medical Things is promising for monitoring depression symptoms. Therefore, it is necessary to develop multimodal monitoring systems tailored for elderly individuals with high feasibility and usability for further research and practice. This study comprised two phases: (1) methodological development of the system; and (2) system validation to evaluate its feasibility. We developed a system that includes a smartphone for facial and verbal expressions, a smartwatch for activity and heart rate monitoring, and an ecological momentary assessment application. A sample of 21 older Koreans aged 65 years and more was recruited from a community center. The 4-week data were collected for each participant (n = 19) using self-report questionnaires, wearable devices, and interviews and were analyzed using mixed methods. The depressive group (n = 6) indicated lower user acceptance relative to the nondepressive group (n = 13). Both groups experienced positive emotions, had regular life patterns, increased their self-interest, and stated that a system could disturb their daily activities. However, they were interested in learning new technologies and actively monitored their mental health status. Our multimodal monitoring system shows potential as a feasible and useful measure for acquiring mental health information about geriatric depression.
Motherhood and Me (Mom-Me): The Development of an Acceptance-Based Group for Women with Postpartum Mood and Anxiety Symptoms

Untreated postpartum mood and anxiety disorders (PMADs) place women and their families at risk for negative biopsychosocial sequelae. Innovative and tailored treatments are needed to address potential disruptions in maternal functioning. Third-wave cognitive–behavioral approaches, including acceptance and commitment therapy (ACT) and dialectical behavioral therapy (DBT), hold promise for optimizing functioning given the focus on values-based living, rather than symptom reduction. Purpose: The purpose of this paper is to describe the development of an innovative psychotherapy group for women with symptoms of PMADs. Methods: This seven-session group, Motherhood and Me (Mom-Me), includes selected skills training from ACT, DBT, and Emotion-Centered Problem-Solving Therapy. Results: Mom-Me group sessions are described, and an outline of key information (session goals, content, and homework assignments) is provided to facilitate practical implementation. Conclusion: In line with third-wave approaches, this group was developed to enhance maternal functioning, which, in turn, may help women cope with psychological distress during the transition to motherhood.
Intelligence of Autonomous Vehicles: A Concise Revisit

Artificial intelligence- (AI-) empowered machines are devised to mimic human actions. In the automotive industry, AI plays a significant role in the development of vehicular technology. AI joins hands with the field of mechatronics to assist in the accurate execution of the vehicle functionalities. Autonomous vehicles get the scene information by using onboard sensors such as laser, radar, lidar, Global Positioning System (GPS), and vehicular communication networks. The data obtained is then used for various path planning and control techniques to make the vehicles capable of autonomously driving in complex environments. Autonomous vehicles use very up-to-date AI algorithms to localize themselves in known and unknown environments. AI algorithms are also exploited for perception, path planning, and motion control. A concise review of the state-of-the-art techniques to improve the performance of autonomous vehicles is presented.
Fine-Tuning BERT Models to Classify Misinformation on Garlic and COVID-19 on Twitter

Garlic-related misinformation is prevalent whenever a virus outbreak occurs. With the outbreak of COVID-19, garlic-related misinformation is spreading through social media, including Twitter. Bidirectional Encoder Representations from Transformers (BERT) can be used to classify misinformation from a vast number of tweets. This study aimed to apply the BERT model for classifying misinformation on garlic and COVID-19 on Twitter, using 5929 original tweets mentioning garlic and COVID-19 (4151 for fine-tuning, 1778 for test). Tweets were manually labeled as ‘misinformation’ and ‘other. ’ We fine-tuned five BERT models (BERTBASE, BERTLARGE, BERTweet-base, BERTweet-COVID-19, and BERTweet-large) using a general COVID-19 rumor dataset or a garlic-specific dataset. Accuracy and F1 score were calculated to evaluate the performance of the models. The BERT models fine-tuned with the COVID-19 rumor dataset showed poor performance, with maximum accuracy of 0.647. BERT models fine-tuned with the garlic-specific dataset showed better performance. BERTweet models achieved accuracy of 0.897–0.911, while BERTBASE and BERTLARGE achieved accuracy of 0.887–0.897. BERTweet-large showed the best performance with maximum accuracy of 0.911 and an F1 score of 0.894. Thus, BERT models showed good performance in classifying misinformation. The results of our study will help detect misinformation related to garlic and COVID-19 on Twitter.
EgSPEECHLESS Responses to Salt Stress by Regulating Stomatal Development in Oil Palm

Oil palm is the most productive oil producing plant. Salt stress leads to growth damage and a decrease in yield of oil palm. However, the physiological responses of oil palm to salt stress and their underlying mechanisms are not clear. RNA-Seq was conducted on control and leaf samples from young palms challenged under three levels of salts (100, 250, and 500 mM NaCl) for 14 days. All three levels of salt stress activated EgSPCH expression and increased stomatal density of oil palm. Around 41% of differential expressed genes (DEGs) were putative EgSPCH binding target and were involved in multiple bioprocesses related to salt response. Overexpression of EgSPCH in Arabidopsis increased the stomatal production and lowered the salt tolerance. These data indicate that, in oil palm, salt activates EgSPCH to generate more stomata in response to salt stress, which differs from herbaceous plants. Our results might mirror the difference of salt-induced stomatal development between ligneous and herbaceous crops.
Super-Twisting Sliding Mode Control for Differential Steering Systems in Vehicular Yaw Tracking Motion

This paper deals with the modelling and control of differential steering for vehicular motions with the help of Sliding Mode Control (SMC) with a Super-Twisting Algorithm (STA). A single-track mechanical model of the vehicular dynamics is proposed and validated by simulations. A control strategy based on the STA is proposed for the yaw dynamics to improve travel comfort and the stability of the vehicular motion. The desired yaw trajectory is given and the equivalent and corrective control parts of the STA are calculated by the controller. Simulation results show the effectiveness of the proposed control strategy.
Shape monitoring and damage identification in stiffened plates using inverse finite element method and Bayesian learning

Stiffened plate structures are common structural forms that are widely used in various fields. Structural Health Monitoring (SHM) is an important tool to maintain their safe operation. The inverse finite element method (iFEM) is a state-of-the-art methodology for SHM that can precisely reconstruct full-field displacements through limited strain sensors. In this paper, we study the stiffened cantilever plate and propose a complete damage localization and quantification system based on iFEM. By solving the damage location index, we can recognize the damage locations in real time, independent of loads and load combinations. Moreover, the strain modes at strain sensors are obtained by modal tests, which can be transformed into full degrees of freedom (DOFs) mode shapes by the iFEM. The damage severities can be further determined using the Bayesian learning (BL) algorithm. In this way, the mode shapes required for damage identification are transformed into strain modes which are easier to obtain, especially for structures with rotational DOFs such as plates and shells. Considering different damage scenarios, the proposed method can detect true locations and severities of damage even with a limited number of strain sensors and under measurement noise.
Medical, Endoscopic and Surgical Management of Stricturing Crohn’s Disease: Current Clinical Practice

The development of fibrostenotic intestinal disease occurs in approximately one-third of patients with Crohn’s disease and is associated with increased morbidity. Despite introducing new biologic agents, stricturing Crohn’s disease remains a significant clinical challenge. Medical treatment is considered the first-line treatment for inflammatory strictures, and anti-TNF agents appear to provide the most considerable benefit among the available medical treatments. However, medical therapy is ineffective on strictures with a mainly fibrotic component, and a high proportion of patients under anti-TNF will require surgery. In fibrotic strictures or cases refractory to medical treatment, an endoscopic or surgical approach should be considered depending on the location, length, and severity of the stricture. Both endoscopic balloon dilatation and endoscopic stricturoplasty are minimally invasive and safe, associated with a small risk of complications. On the other hand, the surgical approach is indicated in patients not suitable for endoscopic therapy. This review aimed to present and analyze the currently available medical, endoscopic, and surgical management of stricturing Crohn’s disease.
An Unusual Case of Delayed Hemolytic Transfusion Reaction With Hyperhemolysis Syndrome Due to Anti-Jkb and Anti-Fya Alloantibodies

Delayed hemolytic transfusion reaction (DHTR) is a complication appearing a few days to weeks due to alloimmunization following packed red blood cells (RBCs) transfusion, a pregnancy, or transplantation. Hyperhemolysis syndrome (HS) is a severe form of DHTR defined by a drop of hemoglobin to a level lower than before the transfusion, reflecting a destruction of the patient’s own RBCs not presenting the targeted antigen as well as the transfused RBCs. Usually seen in sickle cell disease (SCD) patients, HS remains very rare in patients without a hematologic disorder. We report the case of an 82-year-old Caucasian woman who presented with a DHTR with HS after being transfused packed RBC twice in the context of rectal bleeding. The patient was not known for any hemoglobinopathy and did not have a history of massive transfusions nor multiple pregnancies putting her at risk of alloimmunization. Our patient developed anti-C, anti-Fya and anti-Jkb antibodies, known to be harmful antibodies. First line of treatment after avoidance of further transfusions is intravenous immunoglobulins for 3 to 5 days and high-dose corticosteroids. Exceptional in the non-SCD population, this complication should be recalled by clinicians as it can be fatal if not treated appropriately. We performed a review of the literature using the words “delayed hemolytic transfusion reaction” and “hyperhemolysis syndrome” for similar cases. Finally, we describe how to diagnose, manage, and prevent this potentially fatal complication, which is still underrecognized even within the SCD population.
CAE‐WANN: A novel anomaly detection method for gas turbines via search space extension

Anomaly detection of gas turbines is a typical binary classification problem under small sample size. In essence, the goal of classification is to discover the mapping that can map samples in different categories to disjoint codomains. The neural network has strong nonlinear mapping ability thus it has great potentialities in classification tasks. However, how to design the architecture of the neural network is still a challenging problem. The inappropriate network architecture might cause either overfitting or underfitting problems. To alleviate the problem, this paper presents an effective anomaly detection model for gas turbines by combining the convolutional auto‐encoder (CAE) and weight agnostic neural network (WANN) search. More specifically, the CAE is used for the search space extension by obtaining high‐dimensional latent representations of the raw data. Then, the WANN search finds a suitable neural network architecture for anomaly detection in the extended search space. Moreover, a novel method based on maximum likelihood estimation is proposed for threshold selection, which is also effective in the case of unbalanced datasets. In the end, the real‐life monitoring data of gas turbines validate the effectiveness of the presented anomaly detection model.
Preparing to be future early childhood teachers: undergraduate students’ perceptions of their identity

ABSTRACT Teachers’ professional identities, or the professional images teachers have of themselves, play an essential role in teachers’ pedagogical knowledge and their propensity to stay in the profession. Even though with a shared recognition of the importance of professional identities among teachers, research is limited in terms of examining preservice teachers’ professional identities in early childhood education (ECE). This study, thus, investigated Chinese ECE undergraduate students’ perception of their identities as future teachers and the factors perceived to influence their professional identities. Altogether 182 preservice early childhood (EC) teachers were surveyed, and six graduates from the teacher training programmes were interviewed. Latent profile analyses generated three profiles of professional identity: low, medium, and high profiles. Regression analyses revealed that the year of study negatively predicted preservice teachers’ profile of professional identity, while the academic competency positively predicted the profile. Further interviews indicated that the social images of EC teachers, professional learning in universities, and requirements in kindergartens were perceived as the main obstacles to achieving a high identity, which refers to a strong, emotional recognition of ECE, satisfaction with the teacher education programme, and positive career prospects.
A Geometric-Based LSGDM Method for Tourism Project Decision Optimization with Trust–Distrust Relationships

In this paper, we discuss the decision optimization of tourism projects in Hebei Province, China. To improve the process of analyzing tourism projects, we introduce a model that includes multiple decision makers as subjects based on a standard four-dimensional evaluation system. In order to improve the effectiveness of decision-making results, we will increase the number of decision makers to 40. A novel large-scale group decision-making (LSGDM) algorithm that incorporates the trust–distrust asymmetric relationships between decision makers is proposed. This model contains three main innovations: firstly, in the evaluation of decision makers’ social network relations, the trust–distrust value is introduced as a new carrier, and a weighted directed network and data integration operator are constructed based on the evaluation between decision makers; secondly, an extended Girvan-Newman (GN) algorithm is constructed to cluster the decision makers from this weighted network; thirdly, the interval-valued intuitionistic fuzzy number (IVIFN) is used to evaluate the alternatives, studying the IVIFN’s geometric significance by placing in a rectangular coordinate system. Finally, a new LSGDM model is proposed. Using the development of a cultural tourism project in a township as an example, the effectiveness of the proposed model is illustrated. By comparing the results of our method to those of a LSGDM algorithm that does not incorporate trust relationships, we assess the performance of the new model.
Impacto de la Covid 19 en las barreras percibidas para practicar actividad física en las personas usuarias de centros fitness en España Impact of Covid 19 on perceived barriers to practice physical activity in users of fitness centers in Spain

This research intends to know the perceived barriers, as well as their predictor variables, in people who practice Physical Activity in fitness centers and were home confined due to the COVID-19 pandemic. The sample was conformed of 8,087 physical activity practitioners. An online questionnaire was administered by email. A descriptive analysis of the perceived barriers was done, and logistic regression analysis to determine possible predictor variables. The most important perceived barriers were price and a lack of safety. Age, gender, the vigorous physical practice frequency and the observation of the WHO recommendations would be the predictor variables to a larger extend for the perceived barriers. The results are relevant for sport organizations to plan their services offer post lockdown.
Faster scale-invariant object detection

We aim to investigate the feasibility of a scale-invariant object detection model, using a single deep neural network. We aim to achieve this by designing a novel layer which we call as "zoom" layer. The aim of these layers is to determine the scale of the input images, and transform them to a standard scale. This allows further convolution layers to focus on learning high level features for object detection without worrying about different image scales. In addition, they don’t have to deal with noisy or background part of the image. We use statistical methods to calculate the relevant object region from the feature maps. Our approach did not work as expected, and we try to highlight some of the issues we faced during training, and potential things to try.