Miracle-dz/UniFAR-FTUs huggingface dataset:
A Data-Driven Approach for Generating Synthetic Load Patterns and Usage Habits

Today’s electricity grid is rapidly evolving to become highly connected and automated. These advancements have been mainly attributed to the ubiquitous communication/computational capabilities in the grid and the Internet of Things paradigm that is steadily permeating modern society. Another trend is the recent resurgence of machine learning which is especially timely for smart grid applications. However, a major deterrent in effectively utilizing machine learning algorithms is the lack of labelled training data. We overcome this issue in the specific context of smart meter data by proposing a flexible framework for generating synthetic labelled load (e.g., appliance) patterns and usage habits via a non-intrusive novel data-driven approach. We leverage on recent developments in generative adversarial networks (GAN) and kernel density estimators (KDE) to eliminate model-based assumptions that otherwise result in biases. The ensuing synthetic datasets resemble real datasets and lend to rich and diverse training/testing platforms for developing effective machine learning algorithms pertaining to consumer-side energy applications. Theoretical and practical studies presented in this paper highlight the viability and superior performance of the proposed framework.
Opinions about the Use of Higher Order Thinking Skills in General, Vocational, and Academic Education Learning in Indonesia

This research aimed to find the opinions of the use of higher-order thinking skills (HOTS) in general, vocational, and academic education learning. The data samples of this study were six state universities in Java, Indonesia, while the respondents were 82 lecturers. The data collection techniques used the instrument and were analyzed using descriptive statistics and t-test. The results showed that (1) the portion of HOTS learning that needed to be applied to Elementary School was 18.21%, in Junior High School was 25.52%, in Vocational High School was 43.77%, in Senior High School was 51.30%, in Diploma-3 was 67.34%, in Diploma-4 was 75.01%, in Bachelor Degree was 79.54%, in Master Degree was 87.87%, and in Doctorate was 90.01%; (2) HOTS learning at a higher level of education showed a significantly more significant percentage, both in general, vocational and academic education; and (3) HOTS teaching in public education were considerably more significant than in vocational education just as academic education was better than vocational training.
Livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) colonisation and infection among livestock workers and veterinarians: a systematic review and meta-analysis

Objectives Methicillin-resistant Staphylococcus aureus (MRSA) is an increasing public health concern worldwide. The objective of this study was to calculate a summary odds ratio (OR) of livestock-associated MRSA colonisation and infection in humans, and to determine specific risk factors in livestock production contributing to MRSA colonisation. Methods We screened PubMed and Embase for studies published from 2005 to 2019 inclusive, reporting livestock-associated (LA)-MRSA colonisation and infection among livestock workers/veterinarians, their families, and community members not regularly exposed to livestock. The primary outcome of interest was the OR of LA-MRSA colonisation comparing exposed and control groups. Quality was assessed according to the Newcastle-Ottawa quality assessment scale. A meta-analysis using a random-effects model was conducted to calculate a pooled OR. The heterogeneity in the meta-analysis was assessed using the I² method, and publication bias was evaluated using funnel plots. Results A total of 3490 studies were identified by the search, with 37 studies including 53 matched exposed-control groups and 14 038 participants eligible for the meta-analysis. The pooled OR for LA-MRSA among livestock workers and veterinarians is 9.80 (95% CI 6.89 to 13.95; p=0.000; I2 =73.4), with no significant publication bias (Egger’s p=0.66). The OR for swine workers was highest at 15.41 (95% CI 9.24 to 25.69), followed by cattle workers (11.62, 95% CI 4.60 to 29.36), veterinarians (7.63, 95% CI 3.10 to 18.74), horse workers (7.45, 95% CI 2.39 to 23.25), livestock workers (5.86, 95% CI 1.14 to 30.16), poultry workers (5.70, 95% CI 1.70 19.11), and industrial slaughterhouse workers (4.69, 95% CI 1.10 to 20.0). Conclusions Livestock workers, particularly swine farmers, are at significantly higher risk for LA-MRSA colonisation and subsequent infection. These results support the need for preventive practices to reduce LA-MRSA risk among those who handle and treat livestock. Trial registration number CRD42019120403.
Influence of endemic versus cosmopolitan species on the local assembly of ectomycorrhizal fungal communities.

An enduring question of community ecology is how remarkably speciose communities can coexist, particularly in light of competitive exclusion (Hutchinson, 1961). Ectomycorrhizal fungal (EMF) communities are no exception to this paradox; in a survey of old-growth Pseudotsuga menziesii stands, Kranabetter et al. (2018) reported a metacommunity (γ diversity or regional species pool) of approximately 360 EMF species on mesotrophic sites across a single watershed. Efforts to experimentally test EMF species interactions and community assembly can be insightful, but also hampered by the sheer number of taxa and difficulties in culturing an obligatory symbiosis under laboratory conditions (Kennedy, 2010; Branco, 2019). Similarly, the quantification of key morphological traits related to species distribution patterns, such as spore size and shape, can be instructive but inconclusive (Halbwachs et al., 2015; Kilvin, 2020). Given the challenges involved in an inductive approach, it may be advisable to employ perspectives from broader theoretical models to better understand the assembly of this ecologically important group. In this essay we describe how distinctions in geographical range for EMF species (regional to continental) can be used to better understand spatial processes governing local community assembly.
Immigration as a Divisive Topic: Clusters and Content Diffusion in the Italian Twitter Debate

In this work, we apply network science to analyse almost 6 M tweets about the debate around immigration in Italy, collected between 2018 and 2019, when many related events captured media outlets’ attention. Our aim was to better understand the dynamics underlying the interactions on social media on such a delicate and divisive topic, which are the actors that are leading the discussion, and whose messages have the highest chance to reach out the majority of the accounts that are following the debate. The debate on Twitter is represented with networks; we provide a characterisation of the main clusters by looking at the highest in-degree nodes in each one and by analysing the text of the tweets of all the users. We find a strongly segregated network which shows an explicit interplay with the Italian political and social landscape, that however seems to be disconnected from the actual geographical distribution and relocation of migrants. In addition, quite surprisingly, the influencers and political leaders that apparently lead the debate, do not necessarily belong to the clusters that include the majority of nodes: we find evidence of the existence of a ‘silent majority’ that is more connected to accounts who expose a more positive stance toward migrants, while leaders whose stance is negative attract apparently more attention. Finally, we see that the community structure clearly affects the diffusion of content (URLs) by identifying the presence of both local and global trends of diffusion, and that communities tend to display segregation regardless of their political and cultural background. In particular, we observe that messages that spread widely in the two largest clusters, whose most popular members are also notoriously at the opposite sides of the political spectrum, have a very low chance to get visibility into other clusters.
Role of B Cells in Multiple Sclerosis and Related Disorders

The success of clinical trials of selective B‐cell depletion in patients with relapsing multiple sclerosis (MS) and primary progressive MS has led to a conceptual shift in the understanding of MS pathogenesis, away from the classical model in which T cells were the sole central actors and toward a more complex paradigm with B cells having an essential role in both the inflammatory and neurodegenerative components of the disease process. The role of B cells in MS was selected as the topic of the 27th Annual Meeting of the European Charcot Foundation. Results of the meeting are presented in this concise review, which recaps current concepts underlying the biology and therapeutic rationale behind B‐cell–directed therapeutics in MS, and proposes strategies to optimize the use of existing anti–B‐cell treatments and provide future directions for research in this area. ANN NEUROL 2021;89:13–23
Usefulness of COVID-19 screen-and-test approach in pregnant women: an experience from a country with low COVID-19 burden

Abstract Objectives Information on the usefulness of screen-and-test strategies of pregnant women for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is lacking. Methods We retrospectively reviewed the Ljubljana Maternity Hospital database and searched for pregnant women, who were admitted to the hospital between March 15 and May 16, 2020, for a planned procedure or hospitalization. Their medical records were examined and SARS-CoV-2 test results were retrieved. Results During the two-month period analyzed, there were a total of 265 scheduled admissions of pregnant women to our hospital. Two hundred two (76.2%) were tested for SARS-CoV-2 1 day prior to admission. All tested negative for SARS-CoV-2 RNA, regardless of having coronavirus disease 2019 (COVID-19)-compatible signs or symptoms (n=28) or not (n=174). Conclusions In a population with a low SARS-CoV-2 burden, usefulness of universal testing of pregnant women before admission to the hospital is limited. We recommend that obstetric units in regions with low SARS-CoV-2 burden enforce rational use of personal protective equipment and diligent screening protocols using targeted questionnaires, whereas SARS-CoV-2 laboratory testing should be performed only in screen-positives: those with high clinical suspicion of COVID-19 and/or suspected epidemiological history.
High-amplitude cofluctuations in cortical activity drive functional connectivity

Significance Despite widespread applications, the origins of functional connectivity remain elusive. Here we analyze human functional neuroimaging data. We decompose resting-state functional connectivity across time to assess the contributions of moment-to-moment activity cofluctuations to the overall connectivity pattern. We show that functional connectivity is driven by a small number of high-amplitude frames. We show that these frames are underpinned by a specific mode of brain activity; that the topography of this mode gets modulated during in-scanner tasks; and that high-amplitude frames encode personalized, subject-specific information. In summary, our parameter-free method provides an exact mathematical link between functional connectivity and frame-wise cofluctuations, creating opportunities for studying both static and time-varying functional brain networks. Resting-state functional connectivity is used throughout neuroscience to study brain organization and to generate biomarkers of development, disease, and cognition. The processes that give rise to correlated activity are, however, poorly understood. Here we decompose resting-state functional connectivity using a temporal unwrapping procedure to assess the contributions of moment-to-moment activity cofluctuations to the overall connectivity pattern. This approach temporally resolves functional connectivity at a timescale of single frames, which enables us to make direct comparisons of cofluctuations of network organization with fluctuations in the blood oxygen level-dependent (BOLD) time series. We show that surprisingly, only a small fraction of frames exhibiting the strongest cofluctuation amplitude are required to explain a significant fraction of variance in the overall pattern of connection weights as well as the network’s modular structure. These frames coincide with frames of high BOLD activity amplitude, corresponding to activity patterns that are remarkably consistent across individuals and identify fluctuations in default mode and control network activity as the primary driver of resting-state functional connectivity. Finally, we demonstrate that cofluctuation amplitude synchronizes across subjects during movie watching and that high-amplitude frames carry detailed information about individual subjects (whereas low-amplitude frames carry little). Our approach reveals fine-scale temporal structure of resting-state functional connectivity and discloses that frame-wise contributions vary across time. These observations illuminate the relation of brain activity to functional connectivity and open a number of directions for future research.
Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing

Background Many factors involved in the onset and clinical course of the ongoing COVID-19 pandemic are still unknown. Although big data analytics and artificial intelligence are widely used in the realms of health and medicine, researchers are only beginning to use these tools to explore the clinical characteristics and predictive factors of patients with COVID-19. Objective Our primary objectives are to describe the clinical characteristics and determine the factors that predict intensive care unit (ICU) admission of patients with COVID-19. Determining these factors using a well-defined population can increase our understanding of the real-world epidemiology of the disease. Methods We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling) to analyze the electronic health records (EHRs) of patients with COVID-19. We explored the unstructured free text in the EHRs within the Servicio de Salud de Castilla-La Mancha (SESCAM) Health Care Network (Castilla-La Mancha, Spain) from the entire population with available EHRs (1,364,924 patients) from January 1 to March 29, 2020. We extracted related clinical information regarding diagnosis, progression, and outcome for all COVID-19 cases. Results A total of 10,504 patients with a clinical or polymerase chain reaction–confirmed diagnosis of COVID-19 were identified; 5519 (52.5%) were male, with a mean age of 58.2 years (SD 19.7). Upon admission, the most common symptoms were cough, fever, and dyspnea; however, all three symptoms occurred in fewer than half of the cases. Overall, 6.1% (83/1353) of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm, we identified that a combination of age, fever, and tachypnea was the most parsimonious predictor of ICU admission; patients younger than 56 years, without tachypnea, and temperature <39 degrees Celsius (or >39 ºC without respiratory crackles) were not admitted to the ICU. In contrast, patients with COVID-19 aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnea and delayed their visit to the emergency department after being seen in primary care. Conclusions Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnea with or without respiratory crackles) predicts whether patients with COVID-19 will require ICU admission.
Come Out, Get Out: Relations Among Sexual Minority Identification, Microaggressions, and Retention in Higher Education*

Heterosexism, in the form of microaggressions, contributes to hostile, anti-lesbian, gay, bisexual, transgender, and queer (LGBTQ) college campus climates, thereby limiting sexual and gender minority students’ social engagement and academic persistence. Using Tinto’s model of institutional departure, we examined the degree to which experiencing microaggressions affects sexual minority college students’ retention as a function of their feelings of discomfort in the classroom. Mediation analyses were performed on a subsample of data from 152 self-identified LGBTQ college students at a southern university in the United States to examine relations among experiences of self-reported microaggressions, self-rated classroom discomfort, and expressed intentions to transfer from the university. Self-reported discomfort in the classroom accounts for the relation between experiences of microaggressions and LGBTQ students’ intentions to transfer from the university. Specifically, LGBTQ students who experienced microaggressions more often reported greater discomfort in their classrooms and reported fewer intentions to continue studying at their university. Universities should strive to implement campus-wide programs that help minimize microaggressions, encourage cultural competency and comfort in the classroom, and combat anti-LGBTQ prejudice to better support students in their day-to-day academic endeavors. LGBTQ students who feel safe and supported in the classroom may be protected from heterosexism and social isolation and, thereby, may be more likely to persevere in their academic pursuits.
Self-management strategies and comorbidities in chronic disease patients: associations with quality of life and depression

ABSTRACT Self-management strategies are essential for improving prognosis in chronic illnesses. This study aimed to investigate the association between comorbidity and self-management strategies. A total of 700 patients with one or more chronic diseases including diabetes, dyslipidemia, hypertension, osteoporosis, chronic lung disease, chronic kidney disease and arthritis were enrolled. A questionnaire including the Smart Management Strategy for Health Assessment Tool Short Form (SAT), the Short Form-12, the McGill Quality of Life questionnaire, and the Patient Health Questionnaire-9 was administered to participants. The trend of each SAT according to number of comorbidities was evaluated, and the difference in quality of life and depression according to self-management strategies was examined in the model classified by the number of diseases. Self-management strategy scores tended to decrease as the number of comorbidities increased from one to four (p-value: 0.001 to 0.008). Regardless of the number of comorbidities, the MQOL score was higher in the good self-management strategy group (p: <0.001 to 0.016). The prevalence of mild depression was higher in patients with low self-management strategy, but the differences were not significant. Based on these findings, self-management strategies should be evaluated multidimensionally, and patients should be encouraged to develop effective self-management strategies to manage multiple chronic diseases.
Feasibility of unattended home sleep apnea testing in a cognitively impaired clinic population.

STUDY OBJECTIVES Obstructive sleep apnea (OSA) increases the risk of developing dementia. Home sleep apnea testing (HSAT) is a convenient and validated method to screen for OSA among cognitively well individuals, however, it is unknown if it is a clinically feasible and practical approach in clinic patients with cognitive impairment. We evaluated if HSAT was a feasible and practical approach to screen for OSA in clinic patients with cognitive impairment. METHODS Patients with cognitive impairment due to neurodegenerative and/or vascular etiologies completed OSA screening using HSAT. HSAT was considered a feasible technique if ≥80% of those who attempted HSAT obtained analyzable data (i.e. ≥4 hours of flow, effort and oxygen evaluation), and a practical technique if ≥50% of all patients approached for study inclusion obtained analyzable data. RESULTS Of the 119 patients who were approached for participation, 83 were enrolled and offered HSAT; 5 did not complete HSAT screening and the remaining 78 patients attempted HSAT; mean age (±SD) of 72.86 (±9.89) years and 46% were male. In those that attempted HSAT, 85.9% (67/78) obtained analyzable data and 56.3% (67/119) of eligible patients approached for study inclusion obtained analyzable data. CONCLUSIONS HSAT is a feasible and practical technique in a cognitively impaired clinic population. As OSA is a modifiable risk factor for patients with dementia, HSAT has the potential to lead to expedited treatment for OSA, which may potentially improve health related outcomes such as cognition.
SON and SRRM2 are essential for nuclear speckle formation

Nuclear speckles (NS) are among the most prominent biomolecular condensates. Despite their prevalence, research on the function of NS is virtually restricted to colocalization analyses, since an organizing core, without which NS cannot form, remains unidentified. The monoclonal antibody SC35, raised against a spliceosomal extract, is frequently used to mark NS. Unexpectedly, we found that this antibody was mischaracterized and the main target of SC35 mAb is SRRM2, a spliceosome-associated protein that sharply localizes to NS. Here we show that, the core of NS is likely formed by SON and SRRM2, since depletion of SON leads only to a partial disassembly of NS, while co-depletion of SON and SRRM2 or depletion of SON in a cell-line where intrinsically disordered regions (IDRs) of SRRM2 are genetically deleted, leads to a near-complete dissolution of NS. This work, therefore, paves the way to study the role of NS under diverse physiological and stress conditions.
Interphase-arrested Drosophila embryos activate zygotic gene expression and initiate mid-blastula transition events at a low nuclear-cytoplasmic ratio

Externally deposited eggs begin development with an immense cytoplasm and a single overwhelmed nucleus. Rapid mitotic cycles restore normality as the ratio of nuclei to cytoplasm (N/C) increases. A threshold N/C has been widely proposed to activate zygotic genome transcription and onset of morphogenesis at the mid-blastula transition (MBT). To test whether a threshold N/C is required for these events, we blocked N/C increase by down-regulating cyclin/Cdk1 to arrest early cell cycles in Drosophila. Embryos that were arrested two cell cycles prior to the normal MBT activated widespread transcription of the zygotic genome including genes previously described as N/C dependent. Zygotic transcription of these genes largely retained features of their regulation in space and time. Furthermore, zygotically regulated post-MBT events such as cellularization and gastrulation movements occurred in these cell cycle–arrested embryos. These results are not compatible with models suggesting that these MBT events are directly coupled to N/C. Cyclin/Cdk1 activity normally declines in tight association with increasing N/C and is regulated by N/C. By experimentally promoting the decrease in cyclin/Cdk1, we uncoupled MBT from N/C increase, arguing that N/C-guided down-regulation of cyclin/Cdk1 is sufficient for genome activation and MBT.