Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical test of increasing genetic variation via inter‐population crossing for native plant restoration in variable environments

Restoring native plant populations is an essential component of conserving biodiversity, ecological function, and ecosystem services. Restoration using local, ecotypic source materials is largely acknowledged as best practice; however, local populations are not always available or adapted to current or future site conditions. A major challenge in restoration comes from increasingly variable and unpredictable environmental conditions that impose selective pressures and threaten restoration success. Understanding how to conserve and restore populations under changing environments requires attention to within‐species genetic diversity, which can exert a number of population‐level effects. However, given that these effects can be positive and negative, it remains unclear how increasing genetic diversity via mixing distinct populations may ultimately affect restoration in variable environments. To empirically investigate these effects, we established lineages of a native forb, Helianthus petiolaris (prairie sunflower), with higher and lower individual‐ and population‐level genetic diversity by crossing plants using seed from four distinct locations. We planted and tracked a total of 3,200 individual seeds across all lineages in replicated plots in two common gardens representing a range of environmental conditions, and measured fitness components throughout the growing season. We found that populations with increased genetic diversity had intermediate emergence and reproduction, improved survival in the poorest quality plots, and were moderately buffered against environmental variability. Overall, higher diversity led to high or intermediate and stable performance across environments. Our findings support a strategy of increasing genetic diversity when restoring populations, in particular when a lack of information hampers selection of an optimal source population.
Food intake patterns, social determinants and emotions during COVID-19 confinement: an online survey among adults in Panama.

The COVID-19 pandemic was accompanied by varying movement restriction measures across populations worldwide. These restrictions altered daily activities at all levels, including food access and intake, as well as psychological feelings during lockdown. The main objective of the present study was to evaluate health, and nutrition behaviors during confinement during the first wave of the COVID-19 pandemic in Panama. We conducted a cross-sectional study using an online survey for data collection; a total of 1,561 surveys were validated. The majority of respondents were women (74.2%) between 18 and 49 years old. Among the respondents, 83.3% indicated a university education level, and 49.9% reported a monthly family income equal to or less than 1,000 USD. In addition, more than 50% self-reported overweight or obesity. Responses were analyzed using k-means algorithms to identify food intake patterns; we found three patterns: a healthy food intake pattern, a non-healthy food intake pattern and a mixed food intake pattern. The respondents with healthy food intake and non-healthy food intake patterns reported better socioeconomic conditions. Individuals classified as having mixed food intake patterns had lower incomes, less education and higher unemployment rates. Regarding emotions, we found that women experienced more negative emotions, such as fear, worry and anxiety, during the lockdown period. Taken together, these results suggest that the mobility restriction measures imposed during the COVID-19 pandemic affected food intake patterns by exacerbating existing inequalities. We believe that directing resources towards strategies with the greatest positive impacts on public health remains key, especially in critical situations such as the COVID-19 pandemic.
Nerve excitability as a biomarker for amyotrophic lateral sclerosis: a systematic review and meta-analysis.

Objective: To determine which nerve excitability outcome measures are potential biomarkers for ALS via systematic review and meta-analysis. Methods: Potential studies were identified from the following databases: MEDLINE, PubMed Central, CINAHL Plus, EMBASE, HealthSTAR, Scopus, and Web of Science up to March 2020. Only studies performed in human participants and assessing median motor axons were included. Forest Plot analyses using a random-effects model to determine pooled effect (Z-score), heterogeneity (I2) and Cohen's d were used to identify potential biomarkers. Results: From 2866 studies, 26 (patients=942, controls=719) were used in the systematic review and 23 in the meta-analysis. Seven nerve excitability indices met the three criteria for: significant Z-score, heterogeneity I2<40% and Cohen's d >0.2 (in descending rank order): TEd 90-100 ms, strength-duration time constant (SDTC), superexcitability, TEd 40-60 ms, resting I/V slope, 50% depolarizing, and subexcitability. A sensitivity analysis restricted to patients with *early* ALS indicated that four indices are potential early biomarkers of ALS (Z ranging from 2.99 to 2.16, in descending rank order): TEd 10-20 ms, TEd 90-100 ms, superexcitability, and SDTC. Conclusion: Seven excitability indices differentiate ALS patients from healthy controls, four of which may serve as early biomarkers for ALS. The candidate biomarkers may be used to monitor disease progression, predict survivability, and measure treatment response in clinical trials. High quality diagnostic test accuracy studies are warranted to firmly establish the utility of these indices in individuals suspected of an ALS diagnosis.
Development and validation of radiomics nomograms for preoperative prediction of characteristics in non-small cell lung cancer and circulating tumor cells

Purpose: To develop and validate three radiomics nomograms for preoperative prediction of pathological and progression diagnosis in non-small cell lung cancer (NSCLC) as well as circulating tumor cells (CTCs). Patients and methods: A total of 224 and 134 patients diagnosed with NSCLC were respectively gathered in 2018 and 2019 in this study. There were totally 1,197 radiomics features that were extracted and quantified from the images produced by computed tomography (CT). Then we selected the radiomics features with predictive value by LASSO and combined them into radiomics signature. Logistic regression models were built using radiomics signature as the only predictor, which were then converted to nomograms for individualized predictions. Finally, the performance of the nomograms was assessed on both two cohorts. Results: As for discrimination, the AUC of pathological diagnosis nomogram and progression diagnosis nomogram in NSCLC were both higher than 90% in the training cohort and higher than 80% in the validation cohort. The performance of the CTC-diagnosis nomogram was somehow unexpected where the AUC were range from 60% to 70% in both two cohorts. As for calibration, nonsignificant statistics (p>0.05) yielded by Hosmer-Lemeshow tests suggested no departure between model prediction and perfect fit. Additionally, decision curve analyses demonstrated the clinically usefulness of the nomograms. Conclusion: We developed radiomics-based nomograms for pathological, progression and CTC diagnosis prediction in NSCLC, respectively. Nomograms for pathological and progression diagnosis were demonstrated well performed to facilitate the preoperative individualized prediction, while the nomogram for CTC-diagnosis prediction needed improvement.
Psychological Effects of COVID-19 in Medical Students.

BACKGROUND COVID-19 spread rapidly around the world, causing an acute infectious pneumonia pandemic. COVID-19 first seen in our country since 11 March 2020 in Turkey have also taken an epidemic form. Many social restrictions have been imposed to slow the spread, such as quarantine practices, curfews, restrictions on travel between countries / cities, restrictions on collective activities and social distance practices. In our country, formal education was suspended by the Higher Education Council and it was recommended that all processes including assessment and evaluation be carried out online. In this study, it was aimed to evaluate the psychological effects of Süleyman Demirel University Faculty of Medicine students due to the COVID-19 outbreak. METHOD 607 students gave opinions for the study (n=607). Generalized Anxiety Disorder-7 (GAD-7) Test was preferred in the study because it was preferred in international studies. RESULTS Students' mean GAD-7 score was calculated as 10.24±3.95. 3 (0.5%) of the students were mild, 339 (55.8) were moderate, 168 (27.7%) were high and 97 (16%) were in serious anxiety. The average of anxiety of preclinical students (10.50±4.06) was calculated to be higher than the clinical period students (9.80±3.72) and a statistically significant difference was found between the two groups (p=0.028). 61 of the participants (10%) had COVID-19 positivity in themselves or in their relatives. The average of the students who had COVID-19 positivity in themselves or their close environment (11.37±4.34) was higher than the students who did not (10.12±3.89), and there was a statistically significant difference between the two groups (p=0.034). In our cross-sectional descriptive study, students' GAD-7 mean score was 10.24±3.95, which shows that students of Süleyman Demirel University Faculty of Medicine also had high prevalent anxiety disorders during the COVID-19 epidemic, in line with the current literature. As a result of this study in which we evaluated the psychological conditions of our students during the pandemic period. CONCLUSION Considering that the long-term effects of anxiety disorders will continue, we believe that it will be appropriate to plan activities for post-epidemic anxiety rehabilitation of our students in the evaluation of our post-epidemic education program.
Determination of Attitude and Stress Levels of Midwives, Nurses and Physicians Working in Obstetrics and Gynecology Clinics Regarding COVID-19 Pandemic.

BACKGROUND This study aimed to determine the attitude and stress levels of midwives, nurses and physicians working in gynecology and obstetrics clinics towards the COVID-19 pandemic. SUBJECTS AND METHODS This descriptive and cross-sectional study was conducted with 134 healthcare workers in obstetrics and gynecology clinics. The Personal Information Form, the Attitude form for the COVID-19 pandemic and the Perceived Stress Scale were used for collecting the data. RESULTS It was found that the total average score of the healthcare workers' attitude scale was 55.61±5.97. The total score average of healthcare workers on the perceived stress scale was found to be 28.28±7.43. CONCLUSION It was determined that the attitudes of healthcare professionals towards the COVID-19 pandemic and the perceived stress for the COVID-19 pandemic were at moderate levels.
Cognitive Aspects in Multiple Sclerosis.

BACKGROUND Cognitive dysfunction appears all through the course of multiple sclerosis (MS). Mild and moderate cognitive impairment is present in up to 40% of MS patients and severe cognitive decline affects more than 50% of patients in progressive course of the disease. The most common cognitive disorders in MS include diminished information processing speed, compromised word fluency, complex attention deficit and executive dysfunction. METHODS In this mini review, we present the reader with the most common neuropsychological assessments for the evaluation of cognition in MS, addressing the question of cognitive relapse. Source of data presented in this review is PubMed search of the recently published literature on cognitive decline in MS. RESULTS Patients with cognitive relapse often fail to meet diagnostic criteria for classical relapse in MS. Although, cognitive decline relates poorly to functional disability in MS, it correlates well with neuropsychological testing and with neuroimaging parameters of the disease. CONCLUSIONS Cognitive decline might be considered as additional indicator of MS activity, and therefore evaluated routinely, irrespective of clinical presentation. Brief cognitive assessment, with confirmed psychometric qualities, might be useful in detection of cognitive relapse in MS patient.
Flexible TALEs for an expanded use in gene activation, virulence and scaffold engineering

Abstract Transcription activator-like effectors (TALEs) are bacterial proteins with a programmable DNA-binding domain, which turned them into exceptional tools for biotechnology. TALEs contain a central array of consecutive 34 amino acid long repeats to bind DNA in a simple one-repeat-to-one-nucleotide manner. However, a few naturally occurring aberrant repeat variants break this strict binding mechanism, allowing for the recognition of an additional sequence with a −1 nucleotide frameshift. The limits and implications of this extended TALE binding mode are largely unexplored. Here, we analyse the complete diversity of natural and artificially engineered aberrant repeats for their impact on the DNA binding of TALEs. Surprisingly, TALEs with several aberrant repeats can loop out multiple repeats simultaneously without losing DNA-binding capacity. We also characterized members of the only natural TALE class harbouring two aberrant repeats and confirmed that their target is the major virulence factor OsSWEET13 from rice. In an aberrant TALE repeat, the position and nature of the amino acid sequence strongly influence its function. We explored the tolerance of TALE repeats towards alterations further and demonstrate that inserts as large as GFP can be tolerated without disrupting DNA binding. This illustrates the extraordinary DNA-binding capacity of TALEs and opens new uses in biotechnology.
Religious Characteristics of Romanian Physicians: Towards an Integrative Support of Religious Psychiatric Patients.

BACKGROUND The growing closeness between psychiatry and theology may impact positively the healthcare of the religious psychiatric patients. However, some significant divergences regarding the health care religious methods and the concept and believe in the demonic possession of psychiatric patients continue to shape the relationships between these professionals. While the religions generally admit the demonic or spirit possession as real, the current views of physicians and psychiatric patients are just taken for granted and therefore demands new investigations. In this study, we have performed a targeted survey on this subject. SUBJECTS AND METHODS The survey was based on a questionnaire addressed to 216 psychiatrists and 201 non-psychiatrists, and 408 psychiatric patients. For physicians, the questionnaire was randomized sent to hospitals in Romania. The patients received the questionnaire on paper. Except for patients with dementia and those in the acute phase of a psychiatric illness, all psychiatric disorders available at the time of the investigation were randomized included in the study. RESULTS The results showed that about 20% of physicians and 60% of psychiatric patients considered that demonic possession might be associated to a psychiatric illness, while the later would like a priest in the therapeutic team (89.4%, CI: 0.86-0.92). In addition, the psychiatrists declared a lower attendance of religious services, although the majority would accept a priest in the therapeutic team (p>0.05, CI: 0.61-0.70). CONCLUSION These findings invite to a more practical collaboration between psychiatrists, clinical psychologists, and theologians/priests with training in psychiatry for a more integrative mental care of the religious psychiatric patients. The results call as well for more efficient practical solutions for psychiatric patients, raise awareness towards the personal religious needs and critical beliefs of such patients, and finally might narrow the gap of the controversy between psychiatrists, non-psychiatrists, psychologists and theologians/priests on the addressed issues.
Structural characterization of the ANTAR antiterminator domain bound to RNA

Abstract Regulated transcription termination provides an efficient and responsive means to control gene expression. In bacteria, rho-independent termination occurs through the formation of an intrinsic RNA terminator loop, which disrupts the RNA polymerase elongation complex, resulting in its dissociation from the DNA template. Bacteria have a number of pathways for overriding termination, one of which is the formation of mutually exclusive RNA motifs. ANTAR domains are a class of antiterminator that bind and stabilize dual hexaloop RNA motifs within the nascent RNA chain to prevent terminator loop formation. We have determined the structures of the dimeric ANTAR domain protein EutV, from Enterococcus faecialis, in the absence of and in complex with the dual hexaloop RNA target. The structures illustrate conformational changes that occur upon RNA binding and reveal that the molecular interactions between the ANTAR domains and RNA are restricted to a single hexaloop of the motif. An ANTAR domain dimer must contact each hexaloop of the dual hexaloop motif individually to prevent termination in eubacteria. Our findings thereby redefine the minimal ANTAR domain binding motif to a single hexaloop and revise the current model for ANTAR-mediated antitermination. These insights will inform and facilitate the discovery of novel ANTAR domain RNA targets.
A Model of Pedestrian Gap Selecting in Crosswalks at Unsignalized Intersections

The conflicts between crowed pedestrians and vehicles greatly decrease the capacity of intersection,and lead to safety problems.This paper analyzes the characteristics of pedestrian-vehicle conflicts based on the video surveys.The cubic polynomial function is used to model the relationship between the probability of pedestrian choice behaviors and safety margins by using the measured data.The model is calibrated and validated.At last,this model is compared with the existing binary Logit approach.
Thermodynamic Calculation of a Rotary Engine with External Heat Supply Based on the Ideal Rallis Cycle

Abstract The design and kinematic scheme of the operation of a rotary external combustion engine with offset shafts have been developed. Expressions are obtained that make it possible to calculate the values of the increasing and decreasing functions of the working volume of the hot and cold cavities with a change in the angle of rotation of the rotor. An expression is obtained for calculating the compression ratio in the cold cavity of a rotary heat engine with an external heat supply. An expression has been determined that makes it possible to calculate the total torque of a rotary external combustion engine. A comparative analysis of the torque values of a rotary heat engine with an external heat supply and a Wankel engine is carried out. An assessment of the efficiency of an external combustion engine with offset shafts is carried out. Based on the thermodynamic calculations using ideal Erickson and Rallis cycles for a rotary external combustion engine, the processes occurring inside the hot and cold cavities of a heat engine are described. The thermodynamic condition parameters at the characteristic points of the cycle are determined and expressions are obtained that determine the thermal efficiency of the ideal Erickson and Rallis cycles in relation to the considered external combustion engine. A method for calculating the ideal cycle for an external combustion engine with offset shafts is presented.
Front-end Based Robust Speech Recognition Methods: A Review

Improving the robustness of speech recognition against environmental noise for speech recognition has been a long research problem. Even with many developments in the field, it remains a notable challenge. A plethora of methods have been proposed to deal with these problems. Some of them deal with noise on the signal levels at the front-end of speech recognition while others adapt the model at the back-end to the noisy conditions. In the past, applying various signal processing techniques has been dominant for front-end-based models, while model adaptations are often applied at the back-end. Currently, data-driven based such as machine learning and deep learning have been increasingly more popular approaches. Both supervised and unsupervised methods have been employed for this task. In this study, our main objective is to review various challenges of solving the robustness issues of speech recognition against environmental noise. We focus on front-end-based methods since their improvements are more tractable for deep learning-based speech recognition. Furthermore, we also summarize and describe various techniques for robust speech recognition and the trends for future methods with the emphasis on data-driven-based methods. Finally, we discuss the advantages and disadvantages of these methods and report several important results in the field.
Rapid Grading of Mangosteen Peel Defect Using Extreme Learning Machine

Even during the pandemic, mangosteen is still the prima donna of foreign market demand and the highest exotic fruit export commodity in Indonesia. Only good quality mangosteen that meets the requirements can be exported, including the factor of mangosteen peel defect. Therefore, in this research, rapid grading was carried out to measure the level of mangosteen peel defect by distinguishing the skin texture into 3 levels, namely Fine (F), Medium (M), and Rough (R). Image data retrieval using 300 mangosteens with different maturity levels, where 240 image data for training and 60 image data for testing. Until 20 features of texture extraction based Gray Level Co-occurrence Matrix (GLCM) has been evaluated. Because of non-iterative weighted learning (non-epoch), the Extreme Learning Machine (ELM) can be forced to find the optimum performance by the number of hidden neurons. The results show that the training performance value reaches 99.58%, the testing performance value reaches 96.67%, and the 5-fold cross-validation accuracy until 97%. With the training time is under 0.3 seconds, so the extreme learning machine is very suitable for rapid grading applications.
Room Mapping and Robot Localization Using RGB-D Camera and IMU Sensor

Room mapping and localization of mobile robots is an important and fundamental problem in robotics research. Environmental perception and identification of the robot’s position and orientation to a global coordinate are basic prerequisites for autonomous navigation on mobile robots. In situations where autonomous mobile robots must operate in an unknown environment, localization and mapping are required simultaneously. Thus, simultaneous localization and mapping (SLAM) on mobile robots in unknown environments has become a topic that is widely discussed due to its important theoretical value and possible application. The SLAM is considered the key to realizing the autonomous mobile robot. This also applies to domestic service robots (DSR) that operate in a dynamic environment. In order to accomplish its duties in this dynamic environment, DSR must be able to recognize the environment and the various changes that occur around it. In this paper, we develop the domestic service robots (DSR) and implement SLAM that is based on visual information captured from the RGB-D (i.e., color and depth) camera combined with an inertial measurement unit (IMU). To test the developed systems, we have conducted several experiments. As the preliminary study, we achieved good results.