Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation between concept comprehension and mental semantic networks for scientific terms

ABSTRACT Background We adopted a theoretical framework that the acquisition of a scientific concept comprises the development of connections among conceptual elements associated with a scientific term within a mental semantic network. Given this framework, the hypothesis that the surrounding words connected with a scientific term are relevant to the comprehension of that concept was considered. Purpose Students’ mental semantic networks through a word association test were examined, which experimentally revealed whether the quantitative and qualitative connection of scientific terminology with conceptual elements is related to concept comprehension. Sample We selected five scientific concept words – ‘force’, ‘motion’, ‘speed’, ‘mass’ and ‘gravity’ – and investigated the mental semantic networks of 1,189 students regarding these five words. Design and Methods We prepared a word association test and a concept comprehension test for the 5 words; further, we asked the subjects to write 10 words that are associated with each stimulus word. The concept comprehension test was conducted with multiple-choice questions, comprising 6 questions for each stimulus word. Additionally, this study examined the relation between the concept comprehension and word association test scores. Results All the values extracted from the word association test were significantly correlated with concept comprehension. Specifically, the concept comprehension score was higher when the relation between the scientific terminology and words that form the scientific context as well as the scientific terminology and other scientific terminologies were more closely and frequently connected in the mental semantic network. Conclusion This study experimentally verifies that word-level connections are related to concept comprehension in students’ mental semantic networks. This shows that a cognitive linguistic approach to the development and modification of students’ mental semantic networks can be applied effectively in science education. Moreover, it indicates the possibility of adopting linguistic strategies in science education for the formation and modification of scientific concepts.
Adopting worst-case optimal joins in relational database systems

Worst-case optimal join algorithms are attractive from a theoretical point of view, as they offer asymptotically better runtime than binary joins on certain types of queries. In particular, they avoid enumerating large intermediate results by processing multiple input relations in a single multi-way join. However, existing implementations incur a sizable overhead in practice, primarily since they rely on suitable ordered index structures on their input. Systems that support worst-case optimal joins often focus on a specific problem domain, such as read-only graph analytic queries, where extensive precomputation allows them to mask these costs. In this paper, we present a comprehensive implementation approach for worst-case optimal joins that is practical within general-purpose relational database management systems supporting both hybrid transactional and analytical workloads. The key component of our approach is a novel hash-based worst-case optimal join algorithm that relies only on data structures that can be built efficiently during query execution. Furthermore, we implement a hybrid query optimizer that intelligently and transparently combines both binary and multi-way joins within the same query plan. We demonstrate that our approach far outperforms existing systems when worst-case optimal joins are beneficial while sacrificing no performance when they are not.
CLONAL GROWTH AND RAMET PERFORMANCE IN THE WOODLAND HERB, ASARUM CANADENSE

1 We report the results from a 7-year study on the patterns of reproduction and vegetative spread of Asarum canadense, conducted in replicate plots located in the understorey of early and late successional forest. 2 We recorded the spatial location, size, demographic performance and clonal fragment identity of all A. canadense ramets in our plots, as well as the position and fate of seedling recuits. We also collected detailed morphological data on patterns of clonal growth. 3 Recently germinated plants and the much larger, clonally produced daughter ramets had equal survivorship. Size also had little impact on rhizome production. Within sets of genetically identical ramets, flowering ramets were larger than non-flowering ramets. When there was an effect of habitat, ramets performed best in late successional forest. 4 Neither sexual nor asexual reproduction decreased the future performance of a ramet. Having connections to other ramets had little effect on survivorship or rhizome production, but often was associated with an increased probability of flowering. Clones with more ramets outperformed smaller clones and spread the risk of genet mortality. 5 Rhizome lengths were shorter in the high light, early successional forest, but no other clonal growth parameters differed between habitats. Empirically calibrated simulation and diffusion models indicated that the between-habitat differences in rhizome length were not great enough to affect the long-term expansion of clones. 6 Diffusion coefficients for the long-term spread by clonal growth or seed dispersal were compared for a number of species. Diffusion model results indicated that plants can disperse as far by clonal growth as they do by seed, although the values for A. canadense were 14 and 8895 cm2 year -1, respectively. 7 Asarum canadense appears to be under unusually strong selection for seedling survival. As has been observed for some other clonal plant species, few costs were found for either sexual or asexual reproduction.
Resources partitioning by seabirds and their relationship with other consumers at and around a small tropical archipelago

Seabirds occupy a range of trophic levels in marine foodwebs, and as top predators, they potentially may compete with large fish for prey. Here, we examine trophic segregation among seabird species breeding on São Pedro and São Paulo Archipelago (SPSPA), in the tropical Atlantic Ocean, as well as the trophic relationships between the seabirds and pelagic fish. We used stable isotopes (dN, dC) combined with a conventional dietary analysis of brown booby (Sula leucogaster), brown noddy (Anous stolidus), and black noddy (Anous minutus) and also analysed stable isotopes in the muscles of tropical two-wing flying fish (Exocoetus volitans), blackfin tuna (Thunnus atlanticus), and common dolphinfish (Coryphaena hippurus). Brown boobies shared a similar trophic level with large predatory fish probably due to their intense consumption of flying fish, but seabirds are most likely not competing with blackfin tuna and common dolphinfish, despite relying on the same prey. Food resources seem to be abundant around SPSPA, minimizing competition. Despite relying mostly on two-wing flying fish, the three seabirds studied had non-overlapping trophic niches during the breeding period, with segregation apparently occurring by prey size. Sex-related trophic differences were not found, but age-related trophic segregation was observed in brown boobies and brown noddies. Overall, although seabirds and marine pelagic fish rely on the same prey species, the overlap in their trophic niches was limited, most likely due to an overabundant food resource, i.e. flying fish, a common prey of large predators in most pelagic tropical oceans.
The Parkinson's Disease DNA Variant Browser

Parkinson's disease (PD) is a genetically complex neurodegenerative disease with ~20 genes known to contain mutations that cause PD or atypical parkinsonism. Large‐scale next‐generation sequencing projects have revolutionized genomics research. Applying these data to PD, many genes have been reported to contain putative disease‐causing mutations. In most instances, however, the results remain quite limited and rather preliminary. Our aim was to assist researchers on their search for PD‐risk genes and variant candidates with an easily accessible and open summary‐level genomic data browser for the PD research community.
Clinical and Immunological Factors That Distinguish COVID-19 From Pandemic Influenza A(H1N1)

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), is a global health threat with the potential to cause severe disease manifestations in the lungs. Although COVID-19 has been extensively characterized clinically, the factors distinguishing SARS-CoV-2 from other respiratory viruses are unknown. Here, we compared the clinical, histopathological, and immunological characteristics of patients with COVID-19 and pandemic influenza A(H1N1). We observed a higher frequency of respiratory symptoms, increased tissue injury markers, and a histological pattern of alveolar pneumonia in pandemic influenza A(H1N1) patients. Conversely, dry cough, gastrointestinal symptoms and interstitial lung pathology were observed in COVID-19 cases. Pandemic influenza A(H1N1) was characterized by higher levels of IL-1RA, TNF-α, CCL3, G-CSF, APRIL, sTNF-R1, sTNF-R2, sCD30, and sCD163. Meanwhile, COVID-19 displayed an immune profile distinguished by increased Th1 (IL-12, IFN-γ) and Th2 (IL-4, IL-5, IL-10, IL-13) cytokine levels, along with IL-1β, IL-6, CCL11, VEGF, TWEAK, TSLP, MMP-1, and MMP-3. Our data suggest that SARS-CoV-2 induces a dysbalanced polyfunctional inflammatory response that is different from the immune response against pandemic influenza A(H1N1). Furthermore, we demonstrated the diagnostic potential of some clinical and immune factors to differentiate both diseases. These findings might be relevant for the ongoing and future influenza seasons in the Northern Hemisphere, which are historically unique due to their convergence with the COVID-19 pandemic.
The Systematics and Distribution of the Lophiid Anglerfishes: II. Revisions of the Genera Lophiomus and Lophius

AXTELL, R. W. 1972. Hybridization between western collared lizards with a proposed taxonomic rearrangement. Copeia 1972:707-727. MAYR, E. 1969. Principles of systematic zoology. McGraw-Hill Book Co., New York. MONTANUCCI, R. R. 1970. Analysis of hybridization between Crotaphytus wislizenii and Crotaphytus silus (Sauria: Iguanidae) in California. Copeia 1970:104123. --, R. W. AXTELL AND H. C. DESSAUER. 1975. Evolutionary divergence among collared lizards (Crotaphytus), with comments on the status of Gambelia. Herpetologica 31:336-347. PRIETO, A. A. , JR., AND W. G. WHITFORD. 1971. Physiological responses to temperature in the horned lizards, Phrynosoma cornutum and Phrynosoma douglassi. Copeia 1971:498-504. SANBORN, S. R. , AND R. B. LooMIs. 1979. Systematics and behavior of collared lizards (Crotaphytus, Iguanidae) in southern California. Herpetologica 35:101-106. SMITH, N. M. , AND W. W. TANNER. 1972. Two new subspecies of Crotaphytus (Sauria: Iguanidae). Great Basin Nat. 32:25-34.
Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020

Objective: Vast 12-lead ECGs repositories provide opportunities to develop new machine learning approaches for creating accurate and automatic diagnostic systems for cardiac abnormalities. However, most 12-lead ECG classification studies are trained, tested, or developed in single, small, or relatively homogeneous datasets. In addition, most algorithms focus on identifying small numbers of cardiac arrhythmias that do not represent the complexity and difficulty of ECG interpretation. This work addresses these issues by providing a standard, multi-institutional database and a novel scoring metric through a public competition: the PhysioNet/Computing in Cardiology Challenge 2020. Approach: A total of 66 361 12-lead ECG recordings were sourced from six hospital systems from four countries across three continents; 43 101 recordings were posted publicly with a focus on 27 diagnoses. For the first time in a public competition, we required teams to publish open-source code for both training and testing their algorithms, ensuring full scientific reproducibility. Main results: A total of 217 teams submitted 1395 algorithms during the Challenge, representing a diversity of approaches for identifying cardiac abnormalities from both academia and industry. As with previous Challenges, high-performing algorithms exhibited significant drops ( ≲ 10%) in performance on the hidden test data. Significance: Data from diverse institutions allowed us to assess algorithmic generalizability. A novel evaluation metric considered different misclassification errors for different cardiac abnormalities, capturing the outcomes and risks of different diagnoses. Requiring both trained models and code for training models improved the generalizability of submissions, setting a new bar in reproducibility for public data science competitions.
Immediate Effects of COVID-19 Outbreak on Psychiatric Outpatients: Posttraumatic Stress and Influencing Factors

We aimed to investigate the effects of COVID-19 outbreak and public health measures on the psychological well-being of patients with psychiatric disorders. This cross-sectional study assessed 436 outpatients recruited from a tertiary psychiatry clinic in Istanbul, Turkey, nearly one month after the government introduced strict measures of lockdown against the ongoing outbreak. Respondents completed a web-based survey on sociodemographic data, subjective sleep quality, and a range of psychiatric symptoms using the Impact of Events Scale-Revised (IES-R), and Hospital Anxiety and Depression Scale (HADS). Respondents reported high frequencies of clinically significant posttraumatic stress disorder (PTSD) (32.6%, IES-R score equal to or above 33), anxiety (36.4%, HADS anxiety score > 10), and depression (51%, HADS depression score > 10). 20.5% of respondents described that their psychological status worsened during the COVID-19 outbreak, and 12.1% of respondents described poor or very poor sleep in the prior month. Positive predictors of increased PTSD symptoms included the chronic medical diseases, knowing someone in the social vicinity diagnosed with the COVID-19 infection, job loss or being on temporary leave after the outbreak, and increased exposure time to TV or social media. In contrast, male gender, older age, higher educational attainment, and the psychiatric diagnoses of schizophrenia and (to a lesser degree) bipolar disorder were the negative predictors. Our results suggest that patients with psychiatric disorders are prone to substantial psychological distress during the COVID-19 outbreak, and various individual, behavioral, and social factors mediate this effect.
Clinical characteristics and Outcomes of 500 patients with COVID Pneumonia : Results from a Single center(Southend University Hospital)

Objectives To characterise the clinical features of hospitalised COVID 19 patients in a single centre during the first epidemic wave and explore potential predictive variables associated with outcomes such as mortality and the need for mechanical ventilation, using baseline clinical parameters. Methodology We conducted a retrospective review of electronic records for demographic, clinical and laboratory data, imaging and outcomes for 500 hospitalised patients between February 20th and May 7th 2020 from Southend University Hospital, Essex, UK. Multivariate logistic regression models were used to identify risk factors relevant to outcome. Results The mean age of the cohort admitted to hospital with Covid-19, was 69.4 and 290 (58%) were over 70. The majority were Caucasians, 437 (87%) with less than 2 co-morbidities 280(56%). Most common were hypertension 186(37 %), Cardiovascular disease 178(36 %) and Diabetes 128 (26 %), represented in a larger proportion on the mortality group. Mean CFS was 4 with Non Survivors had significantly higher CFS 5 vs 3 in survivors, p<0.001. In addition, Mean CRP was significantly higher 150 vs 90, p<0.001 in Non Survivors. We observed the baseline predictors for mortality were age, CFS and CRP. Conclusions In this single centre study, older and frailer patients with more comorbidities and a higher baseline CRP and creatinine were risk factors for worse outcomes. Integrated frailty and age based risk stratification are essential, in addition to monitoring SFR (Sp02/Fi02) and inflammatory markers throughout the disease course to allow for early intervention to improve patient outcomes.
Quality of life and anxiety in patients with non-muscle invasive bladder cancer: a prospective study

Introduction We evaluated anxiety and quality of life (QoL) in patients who were operated for non-muscle invasive bladder cancer (NMIBC) Methods The present study is a prospective analysis of patients with histopathologically confirmed NMIBCs after they were submitted to transurethral resection of the tumour (TURBT). Eligible were all adult patients with a single or multiple NMIBCs. All included patients followed therapy with either BCG or Epirubicin instillations. The SF-36 questionnaire Physical and Mental health aspects were used for QoL assessment. Similarly, the STAI-Y was introduced for the state (STAI-Y1) and trait anxiety (STAI-Y2) evaluation. Results In total, 117 eligible patients were included. Regarding SF-36 Physical a 6 months decrease was followed by an improvement at 12 months. Similarly, an increase of the SF-36 Mental health score was identified. In contrast to STAI-Y2, a long-term reduction of the state anxiety was identified. Preoperative SF-36 Physical was inversely correlated with age, while absence of alcohol was associated with lower mental health. Overall, patient characteristics, habits and the administered treatment did not affect the postoperative QoL and anxiety. Conclusions Patient QoL and anxiety improved during follow up. Although certain characteristics were related to QoL and anxiety, further larger scale studies are required.
Prediction of asymptotic rates of response from selection on multiple traits using univariate and multivariate best linear unbiased predictors.

Predicted rate of genetic response for multiple trait breeding objectives from multivariate analyses is compared with that from univariate analyses for a range of breeding schemes, A selection index that approximates a multipl trait best linear unbiased prediction (BLUP) animal model is utilized. Changes in genetic parameters due to linkage disequilibrium generated by selection are accounted for. This study shows that asymptotic response in the aggregate breeding value, and its component traits, to selection on multivariate predictions, can easily be calculated from first generation responses, which use ancestral information. For two-path breeding schemes with equal accuracy for males and females, proportional reductions in responses depend only on selection intensity. In general, benefit from multivariate over univariate analyses is slightly smaller at the asymptote than in the first generation. Multivariate analyses give substantially more response than univariate analyses on untransformed data, when correlations between traits are high and when genetic and phenotypic correlations have opposite signs. The advantage of multivariate analyses decreases with increasing family size. When three random factors (additive genetic, common and individual environmental effects) are included in the model, the benefit of multivariate analysis is negligible if univariate analyses are performed on canonical traits. In this case, the advantage of multivariate analyses is never higher than 1% for all cases studied. These results are of particular relevance to the design of genetic evaluation programmes.
Towards safer risperidone prescribing in Alzheimer's disease

Background In the treatment of psychosis, agitation and aggression in Alzheimer's disease, guidelines emphasise the need to ‘use the lowest possible dose’ of antipsychotic drugs, but provide no information on optimal dosing. Aims This analysis investigated the pharmacokinetic profiles of risperidone and 9-hydroxy (OH)-risperidone, and how these related to treatment-emergent extrapyramidal side-effects (EPS), using data from The Clinical Antipsychotic Trials of Intervention Effectiveness in Alzheimer's Disease study (Clinicaltrials.gov identifier: NCT00015548). Method A statistical model, which described the concentration–time course of risperidone and 9-OH-risperidone, was used to predict peak, trough and average concentrations of risperidone, 9-OH-risperidone and ‘active moiety’ (combined concentrations) (n = 108 participants). Logistic regression was used to investigate the associations of pharmacokinetic biomarkers with EPS. Model-based predictions were used to simulate the dose adjustments needed to avoid EPS. Results The model showed an age-related reduction in risperidone clearance (P < 0.0001), reduced renal elimination of 9-OH-risperidone (elimination half-life 27 h), and slower active moiety clearance in 22% of patients, (concentration-to-dose ratio: 20.2 (s.d. = 7.2) v. 7.6 (s.d. = 4.9) ng/mL per mg/day, Mann–Whitney U-test, P < 0.0001). Higher trough 9-OH-risperidone and active moiety concentrations (P < 0.0001) and lower Mini-Mental State Examination (MMSE) scores (P < 0.0001), were associated with EPS. Model-based predictions suggest the optimum dose ranged from 0.25 mg/day (85 years, MMSE of 5), to 1 mg/day (75 years, MMSE of 15), with alternate day dosing required for those with slower drug clearance. Conclusions Our findings argue for age- and MMSE-related dose adjustments and suggest that a single measure of the concentration-to-dose ratio could be used to identify those with slower drug clearance.
Influenza epidemic model with dynamic social networks of agents with individual behaviour: A self organize perspective

It's well known the existence of an interplay between the spread of an infectious disease like influenza and behavioral changes of individuals. An outbreak can trigger behavioral responses, at the group and individual levels, which in turn can influence the course of the epidemic. Daily life interactions can be modeled by adaptive temporal networks in an explicit contact space through an agent-based model, where each agent represents the interacting individuals. In this paper we introduce an individual-based model where the behavior of each individual is determined both by the external stimuli and its own appreciation of the environment and can be built as a combination of three interacting blocks: i) individual behavior, ii) social behavior and iii) epidemic state or epidemiological behavior. We fit the model for a real influenza epidemic and perform the model validation, comparing the results with the classical approaches.