Miracle-dz/UniFAR-FTUs huggingface dataset:
A Transdiagnostic Approach to Sexual Distress and Sexual Pleasure: A Preliminary Mediation Study with Repetitive Negative Thinking

Sexual distress is a core characteristic of sexual dysfunction; however, little is known about its correlates. In the current study, we aimed to contribute to the understanding of both sexual distress and its positive counterpart, sexual pleasure, by taking a transdiagnostic approach to sexual distress using two types of repetitive negative thinking: worry and rumination. Because sexual activity mostly occurs in a dyadic context, we also looked at the potential mediating effect of co-worry and co-rumination, and we used them as mediators. Our preliminary exploratory quantitative study used a cross-sectional design, with a sample of 206 partnered heterosexual people. We used path analysis with parallel mediation, with structural equation modelling being performed using lavaan designed for R environment. Overall, our results show that repetitive negative thinking is associated with both sexual distress and sexual pleasure, and that neither co-rumination nor co-worry mediates these associations. The exception is the indirect effect of rumination on sexual pleasure that is mediated by co-rumination. These results demonstrate that a transdiagnostic approach to sexual distress is a new field worth exploring, and they contribute to establishing the relevance of a cognitive approach to sexual dysfunction.
A Systematic Review of Interventions to Improve Mental Health and Substance Use Outcomes for Individuals with Prenatal Alcohol Exposure and Fetal Alcohol Spectrum Disorder

Individuals with fetal alcohol spectrum disorder (FASD) experience remarkably high rates of mental health and substance use challenges, beginning early in life and extending throughout adulthood. Proactive intervention can help to mitigate some of these negative experiences. Although the literature on FASD intervention is growing, there is currently a lack of consolidated evidence on interventions that may improve mental health and substance use outcomes in this population. Informed by a life course perspective, we undertook a systematic review of the literature to identify interventions that improve mental wellness through all developmental stages for people with prenatal alcohol exposure (PAE) and FASD. A total of 33 articles were identified, most of which were focused on building skills or strategies that underlie the well‐being of children with PAE and FASD and their families. Other interventions were geared toward supporting child and family wellness and responding to risk or reducing harm. There was a notable lack of interventions that directly targeted mental health and substance use challenges, and a major gap was also noted in terms of interventions for adolescents and adults. Combined, these studies provide preliminary and emerging evidence for a range of intervention approaches that may support positive outcomes for individuals with FASD across the life course.
Leguminous pre-crops improved quality of organic winter and spring cereals

ABSTRACT The aim of this research was to assess the effect of leguminous pre-crop species on quality characteristics of subsequent spring and winter cereals. The experiment was carried out in an organic crop rotation in north-eastern Europe. The influence of biomass dry matter yield, carbon and nitrogen content of perennial red clover (Trifolium pratense L.), alsike clover (Trifolium hybridum L.), Washington lupin (Lupinus polyphyllus Lindl.), biennial white sweet clover (Melilotus albus L.), annual crimson clover (Trifolium incarnatum L.) and Alexandria clover (Trifolium alexandrinum L.) on protein concentration, bulk density and thousand grain weight of subsequent cereals were assessed. Barley, oats, winter rye, spring and winter wheat were grown in the first post-legume year and barley and oats in the second post-legume year. In the first year after perennial and biennial pre-crops, the protein concentration of the cereals, except for rye, increased by 0.8–2.6 percentage points compared with the control. The largest increases in protein concentration of the cereals were after red and alsike clovers. The legumes increased the bulk density of all of the cereals, while thousand kernel weights were increased only for barley, spring and winter wheat. All the legume species had a positive second-year after-effect on the protein concentration of barley and oats, with the largest effect after red and alsike clover at 1.0–1.3 percentage points. The results showed that all of the leguminous pre-crops were suitable for increasing the quality of cereals. The effect was greater after perennial and biennial species compared with the annual species.
Impact of COVID-19 pandemic, national lockdown, and unlocking on an apex tertiary care ophthalmic institute

Purpose: To evaluate the impact of 2019 COVID-19 pandemic, national lockdown, and unlocking on ophthalmic care provided by the government-funded apex health institute of India. Methods: Retrospective review of electronic medical records of all patients presenting to the ophthalmology department from March 23, 2020, to July 15, 2020, was compared with that from March 23, 2019, to July 15, 2019. The data between March 23, 2020, to May 31, 2020 (lockdown) and June 06, 2020, to July 15, 2020 (unlock) was compared separately. Parameters evaluated were age, gender, presenting complaints, final diagnosis, treatment advised, and surgical interventions. Results: During the lockdown, routine outpatient flow reduced by 97.14% (P < 0.001), the median age of presentation decreased to 29 years (55 years last year) and males increased by 4.7% (from 61.51% to 66.21%) in 2020. Emergency services decreased by 35.25%, percentage of children decreased by 4.28% (from 34.28% to 30%) and males increased by 13.53% (from 59.97% to 73.5%). Mechanical trauma, microbial keratitis, and conjunctivitis were the most common reasons for presentation. The former lessened by 41.75% while the latter two amplified by 1.25 times and 2 times, respectively. While sanitizer-associated chemical injury increased in proportion, endophthalmitis, and postoperative complications declined. The number of donor corneas collected and emergency therapeutic keratoplasties performed decreased by 99.61% and 92.39%, respectively (P < 0.001). During the unlocking phase, routine patient consultations were 71 ± 19/day, significantly lower than 978 ± 109/day of last year (P < 0.001). No voluntary eye donation was reported during this period. Conclusion: COVID-19 pandemic and national lockdown severely hampered the delivery of ophthalmic care by the apex-ophthalmic institute. Unlike anticipated, lifting of pandemic-associated lockdown served only minimally in improving patient inflow in its initial phases.
Learning probabilistic representations with randomly connected neural circuits

The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a new model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficiently learnable, and realistic neural implementation. This model’s performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable or better than that of current models. Importantly, the model can be learned using a small number of samples, and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation.
The pneumococcal social network

Gram-positive bacteria employ an array of secreted peptides to control population-level behaviors in response to environmental cues. We review mechanistic and functional features of secreted peptides produced by the human pathogen Streptococcus pneumoniae. We discuss sequence features, mechanisms of transport, and receptors for 3 major categories of small peptides: the double-glycine peptides, the Rap, Rgg, NprR, PlcR, and PrgX (RRNPP)-binding peptides, and the lanthionine-containing peptides. We highlight the impact of factors that contribute to carriage and pathogenesis, specifically genetic diversity, microbial competition, biofilm development, and environmental adaptation. A recent expansion in pneumococcal peptide studies reveals a complex network of interacting signaling systems where multiple peptides are integrated into the same signaling pathway, allowing multiple points of entry into the pathway and extending information content in new directions. In addition, since peptides are present in the extracellular milieu, there are opportunities for crosstalk, quorum sensing (QS), as well as intra- and interstrain and species interactions. Knowledge on the manner that population-level behaviors contribute to disease provides an avenue for the design and development of anti-infective strategies.
Twenty-four hour face mask sampling in pulmonary tuberculosis reveals three distinct patterns of bacterial aerosol production dissociated from conventional markers of transmission risk

Rationale: Although tuberculosis (TB) is transmitted by Mycobacterium tuberculosis (Mtb) in aerosols, little is known of the dynamic characteristics of spontaneous output of bacilli in this form. We have developed and implemented a mask aerosol sampling system (MASS) for longitudinal capture and study of spontaneous aerosol. Objective: To determine patterns of Mtb output in aerosols, captured using the MASS over 24 hours and their association with existing criteria used to assess transmission risk in patients with pulmonary TB. Methods: Twenty-four hospitalised patients with newly diagnosed pulmonary TB recruited in Pretoria, South Africa, wore FFP1 masks for one hour out of every three for 24 hours. Aerosol was captured in a gelatine filter processed for Mtb quantitation by PCR. Serial sputum was collected and objective cough frequency monitoring performed over the same period. Measurements and Main Results: Mtb was detected in 86.5% of 192 mask samples and 20.7% of 38 assessable sputum samples obtained from the cohort. Mtb was detected by MASS in all but two patients. Three dynamic patterns of expression were identifiable in Mtb aerosol producers: i. variable high; ii. consistent; and iii. variable low. No diurnal variation was apparent and there was no correlation between mask Mtb and either sputum Mtb levels or cough frequency. Sputum smear status, culture time to positivity and chest radiographic characteristics also failed to associate with MASS bacillary output. Conclusions: Conventional markers of tuberculosis case infectivity do not predict bacillary aerosols detected by the MASS. The MASS provides a novel, non-invasive tool for tuberculosis diagnosis and control.
Overcoming the trade‐off between grain weight and number in wheat by the ectopic expression of expansin in developing seeds leads to increased yield potential

Summary Wheat is the most widely grown crop globally, providing 20% of all human calories and protein. Achieving step changes in genetic yield potential is crucial to ensure food security, but efforts are thwarted by an apparent trade‐off between grain size and number. Expansins are proteins that play important roles in plant growth by enhancing stress relaxation in the cell wall, which constrains cell expansion. Here, we describe how targeted overexpression of an α‐expansin in early developing wheat seeds leads to a significant increase in grain size without a negative effect on grain number, resulting in a yield boost under field conditions. The best‐performing transgenic line yielded 12.3% higher average grain weight than the control, and this translated to an increase in grain yield of 11.3% in field experiments using an agronomically appropriate plant density. This targeted transgenic approach provides an opportunity to overcome a common bottleneck to yield improvement across many crops.
The MSP-Conversation Corpus

Human-computer interactions can be very effective, especially if computers can automatically recognize the emotional state of the user. A key barrier for effective speech emotion recognition systems is the lack of large corpora annotated with emotional labels that reflect the temporal complexity of expressive behaviors, especially during multiparty interactions. This paper introduces the MSP-Conversation corpus, which contains interactions annotated with time-continuous emotional traces for arousal (calm to active), valence (negative to positive), and dominance (weak to strong). Time-continuous annotations offer the flexibility to explore emotional displays at different temporal resolutions while leveraging contextual information. This is an ongoing effort, where the corpus currently contains more than 15 hours of speech annotated by at least five annotators. The data is sourced from the MSP-Podcast corpus, which contains speech data from online audio-sharing websites annotated with sentence-level emotional scores. This data collection scheme is an easy, affordable, and scalable approach to obtain natural data with diverse emotional content from multiple speakers. This study describes the key features of the corpus. It also compares the time-continuous evaluations from the MSPConversation corpus with the sentence-level annotations of the MSP-Podcast corpus for the speech segments that overlap between the two corpora.
Hand and Oral Hygiene Practices among Adolescents in Dominican Republic, Suriname and Trinidad and Tobago: Prevalence, Health, Risk Behavior, Mental Health and Protective Factors

Objective: The study aimed to estimate the prevalence and correlates of oral hygiene (OH) and hand hygiene (HH) behavior among school adolescents in three Caribbean countries. Method: In all, 7476 school adolescents (median age 14 years) from the Dominican Republic, Suriname, and Trinidad and Tobago responded to the cross-sectional Global School-Based Student Health Survey (GSHS) in 2016–2017. Results: The prevalence of poor OH (tooth brushing < 2 times/day) was 16.9%, poor HH (not always before meals) was 68.2%, poor HH (not always after toilet) was 28.4%, and poor HH (not always with soap) was 52.7%. In the adjusted logistic regression analysis, current cannabis use, inadequate fruit and vegetable intake, poor mental health, and low parental support increased the odds for poor OH. Rarely or sometimes experiencing hunger, trouble from alcohol use, inadequate fruit and vegetable intake, poor mental health, and low parental support were associated with poor HH (before meals and/or after the toilet, and/or with soap). Conclusion: The survey showed poor OH and HH behavior practices. Several sociodemographic factors, health risk behaviors, poor mental health, and low parental support were associated with poor OH and/or HH behavior that can assist with tailoring OH and HH health promotion.
Making a Distinction Between Schizophrenia and Bipolar Disorder Based on Temporal Parameters in Spontaneous Speech

Schizophrenia is a heterogeneous chronic and severe mental disorder. There are several different theories for the development of schizophrenia from an etiological point of view: neurochemical, neuroanatomical, psychological and genetic factors may also be present in the background of the disease. In this study, we examined spontaneous speech productions by patients suffering from schizophrenia (SCH) and bipolar disorder (BD). We extracted 15 temporal parameters from the speech excerpts and used machine learning techniques for distinguishing the SCH and BD groups, their subgroups (SCH-S and SCH-Z) and subtypes (BD-I and BD-II). Our results indicated, that there is a notable difference between spontaneous speech productions of certain subgroups, while some appears to be indistinguishable for the used classification model. Firstly, SCH and BD groups were found to be different. Secondly, the results of SCH-S subgroup were distinct from BD. Thirdly, the spontaneous speech of the SCH-Z subgroup was found to be very similar to the BDI, however, it was sharply distinct from BD-II. Our detailed examination highlighted the indistinguishable subgroups and led to us to make our S and Z theory more clarified.
Adventitious Respiratory Classification Using Attentive Residual Neural Networks

Every year, respiratory diseases affect millions of people world-wide, becoming one of the main causes of death in nowadays society. Currently, the COVID-19—known as a novel respiratory illness—has triggered a global health crisis, which has been identiﬁed as the greatest challenge of our time since the Second World War. COVID-19 and many other respiratory diseases present often common symptoms, which impairs their early diagnosis; thus, restricting their prevention and treatment. In this regard, in order to encourage a faster and more accurate detection of these kinds of diseases, the automatic identiﬁcation of respiratory illness through the application of machine learning methods is a very promising area of research aimed to support clinicians. With this in mind, we apply attention-based Convolutional Neural Networks for the recognition of adventitious respiratory cycles on the International Conference on Biomedical Health Informatics 2017 challenge database. Experimental results indicate that the architecture of residual networks with attention mechanism achieves a signiﬁcant improvement w.r. t. the baseline models.
Exploring Eating Disorder Topics on Twitter: Machine Learning Approach

Background Eating disorders (EDs) are a group of mental illnesses that have an adverse effect on both mental and physical health. As social media platforms (eg, Twitter) have become an important data source for public health research, some studies have qualitatively explored the ways in which EDs are discussed on these platforms. Initial results suggest that such research offers a promising method for further understanding this group of diseases. Nevertheless, an efficient computational method is needed to further identify and analyze tweets relevant to EDs on a larger scale. Objective This study aims to develop and validate a machine learning–based classifier to identify tweets related to EDs and to explore factors (ie, topics) related to EDs using a topic modeling method. Methods We collected potential ED-relevant tweets using keywords from previous studies and annotated these tweets into different groups (ie, ED relevant vs irrelevant and then promotional information vs laypeople discussion). Several supervised machine learning methods, such as convolutional neural network (CNN), long short-term memory (LSTM), support vector machine, and naïve Bayes, were developed and evaluated using annotated data. We used the classifier with the best performance to identify ED-relevant tweets and applied a topic modeling method—Correlation Explanation (CorEx)—to analyze the content of the identified tweets. To validate these machine learning results, we also collected a cohort of ED-relevant tweets on the basis of manually curated rules. Results A total of 123,977 tweets were collected during the set period. We randomly annotated 2219 tweets for developing the machine learning classifiers. We developed a CNN-LSTM classifier to identify ED-relevant tweets published by laypeople in 2 steps: first relevant versus irrelevant (F1 score=0.89) and then promotional versus published by laypeople (F1 score=0.90). A total of 40,790 ED-relevant tweets were identified using the CNN-LSTM classifier. We also identified another set of tweets (ie, 17,632 ED-relevant and 83,557 ED-irrelevant tweets) posted by laypeople using manually specified rules. Using CorEx on all ED-relevant tweets, the topic model identified 162 topics. Overall, the coherence rate for topic modeling was 77.07% (1264/1640), indicating a high quality of the produced topics. The topics were further reviewed and analyzed by a domain expert. Conclusions A developed CNN-LSTM classifier could improve the efficiency of identifying ED-relevant tweets compared with the traditional manual-based method. The CorEx topic model was applied on the tweets identified by the machine learning–based classifier and the traditional manual approach separately. Highly overlapping topics were observed between the 2 cohorts of tweets. The produced topics were further reviewed by a domain expert. Some of the topics identified by the potential ED tweets may provide new avenues for understanding this serious set of disorders.
The Reflection Effect for Higher-Order Risk Preferences

Abstract Higher-order risk preferences are important determinants of economic behavior. We apply insights from behavioral economics: we measure higher-order risk preferences for pure gains and losses. We find a reflection effect not only for second-order risk preferences, as did Kahneman and Tversky (1979), but also for higher-order risk preferences: we find risk aversion, prudence and intemperance for gains and much more risk-loving preferences, imprudence and temperance for losses. These findings are at odds with a universal preference for combining good with bad or good with good, which previous results suggest may underlie higher-order risk preferences.
Morphological Characteristics of Some Pepper Genotypes (Capsicum annuum L.) Grown in Conventional and Organic Agricultural Systems: Comparative Analysis

Long-term research in pepper organic production are scarce in Republic of Macedonia. Plant and fruit morphological traits of important pepper varieties organically produced were compared to pepper produced in conventional systems. Conceivably, this is the first morphological traits comparative study of locally important pepper genotypes (Capsicum annuum L.) in Republic of Macedonia in organic and conventional open-field production. The aim of the research is to determine the differences in pepper morphological traits when produced organically in comparison to conventional production. Eight plants and fruits were used for morphological traits determination of plant and fruit in six locally important pepper genotypes (Strumicka Kapija, Strumicka Vezena, Original Research Article Bicikliski et al.; ARRB, 28(3): 1-11, 2018; Article no.ARRB. 43308 2 Piran, Zupska Rana, Duga Bela and Kurtovska Kapija) grown in an organic and conventional production system. The traits number or branches per plant, fruit width and fruit index were significantly affected by the production manner resulting in less branches per plant, lower fruit width and lower fruit index in organic production. The traits fruit weight, pericarp thickness and fruit flesh percentage, which are the most important for pepper crop yield and utilisation of pepper fruits were not significantly affected by the production practice. Organic production has limited influence on pepper plant and fruit traits, but not in a manner to decrease the production, which generally is the main fear of conversion from conventional to organic production in vegetables. Appropriate selection of pepper varieties with application of suitable cultivation and management practices can contribute to successful organic production resulting in high quality pepper production.