Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of a coastal North Sea fish community: Comparison of aquatic environmental DNA concentrations to fish catches

Analyses of environmental DNA has the potential to become an integrated tool in fish research and management. We performed a pilot study during the spring migration of fishes from the North Sea into the Wadden Sea and present comparative results from daily fyke catches (20 mm mesh) in the Marsdiep tidal inlet on two locations and results from weekly fish eDNA analyses on three locations, all within 2 km distance. Fish catches did not differ significantly between the two locations, whereas the eDNA composition showed a significant location effect. However, when eDNA analysis was restricted to species that were caught with the fykes, differences among locations became insignificant. Over ten weeks, from late April to early July, presence-absence calls of fishes based on weekly eDNA sampling significantly agreed with calls based on seven days of fyke fishing 1 km westwards. Fish eDNA compositions differed significantly among sample days and months but not between tides. Over the season, patterns in eDNA concentration (12S rRNA gene copies/L) inferred from quantitative PCR and Illumina HiSeq community composition, corresponded to patterns in wet mass for the eight most abundant fish species in the fyke (>6 weeks present) despite changes in water temperature and changes in fish size class. Small sandeel and gobies, which are important prey for large fishes and birds, were typically missed with the fyke but contributed up to 25-40% of the fish eDNA depending on the sample location.
Factors Explaining the Hypothetical Bias: How to Improve Models for Meta-analyses

Meta-analyses are getting more common in economics. However, little information is available regarding the choice of econometric models and its impact on results. Moreover, outlier data are common in meta-analyses and some authors have simply chosen to remove arbitrarily such data. We use the rich literature of meta-analysis on hypothetical bias (HB) related to contingent valuation methods to illustrate our point. More specifically, we review and update the meta-analyses of HB using a Meta-Regression Hierarchical Mixed Effect (MRHME) model and we apply a Bayesian Gibbs Sampling as classical results robustness check. A set of 462 observations from 87 economic valuation studies is used to this effect. The findings indicate that MRHME model is more efficient to explain HB. While respondents overstate their stated willingness-to-pay for a good by a factor of two, cheap talk, certainty correction, Ex Ante and Ex Post mitigation techniques significantly reduce the HB. Notwithstanding, mitigation techniques are more effective in private goods economic valuation.
How Do Anthocyanins Affect Peripheral Blood Flow: A Systematic Review

Foods rich in polyphenols, a type of antioxidant, have many health benefits. The largest and most important subgroup of polyphenols are flavonoids, of which anthocyanins are one. In vitro studies have shown that anthocyanin induced vasorelaxation is dependent on Nitrous Oxide and a functional endothelium. The anthocyanin effect on change in blood flow could have potential therapeutic implications. However it is unclear if the results can be extrapolated to humans. The number of studies investigating the effects of anthocyanins within humans is fairly limited and the results are discordant. The conflicting evidence surrounding anthocyanins and blood flow highlights the need for a future research in this area.
Seasonal variability of calcium carbonate precipitation and dissolution in shallow coral reef sediments

Shallow, permeable calcium carbonate (CaCO3) sediments make up a large proportion of the benthic cover on coral reefs and account for a large fraction of the standing stock of CaCO3. There have been a number of laboratory, mesocosm, and in situ studies examining shallow sediment metabolism and dissolution, but none of these have considered seasonal variability. Advective benthic chambers were used to measure in situ net community calcification (NCC) rates of CaCO3 sediments on Heron Island, Australia (Great Barrier Reef) over an annual cycle. Sediments were, on average, net precipitating during the day and net dissolving at night throughout the year. Night dissolution rates (−NCCNIGHT) were highest in the austral autumn and lowest in the austral winter driven by changes in respiration (R) and to a lesser extent temperature and Ωarag/pH. Similarly, precipitation during the day (+NCCDAY) was highest in March and lowest in winter, driven primarily by benthic net primary production (NPP) and temperature. On average, sediments were net precipitating over a diel cycle (NCC24h) but shifted to net dissolving in July and December. This shift was largely caused by the differential effects of seasonal cycles in organic metabolism and carbonate chemistry on NCCDAY and NCCNIGHT. The results from this study highlight the large variability in sediment CaCO3 dynamics and the need to include repeated measurements over different months and seasons, particularly in shallow reef systems that can experience large swings in light, temperature, and carbonate chemistry.
Use of content mastery service using role playing approach improve student's emotional intelligence

Emotional intelligence is the ability possessed by individuals to be able to recognize, understand, manage and manage emotions and understand the emotions of others. There are many problems that arise related to emotional intelligence, especially in the school environment. To be able to overcome this and increase the emotional intelligence of BK teacher students can carry out content mastery services that aim to help students develop and learn new aspects or competencies related to emotional intelligence. Implementation of content mastery services can also be done with a role playing approach by playing the role associated with increasing emotional intelligence.
Gestational weight gain and modifiable risk factors of severe maternal morbidity in a hospital-based, retrospective cohort

PSevere maternal morbidity affects nearly 50,000 women every year and its incidence has risen over the past 3 decades. However, there remain several gaps in the epidemiologic literature. Our goal was to quantify the burden that modifiable risk factors place on severe maternal morbidity, with a focus on gestational weight gain because of its amenability to intervention during pregnancy. We used two, retrospective cohorts of delivery hospitalizations at Magee-Womens Hospital in Pittsburgh, PA to address three specific aims: 1) determine the association between total gestational weight gain and the risk of severe maternal morbidity, 2) determine the association between early gestational weight gain and the risk of severe maternal morbidity, and 3) calculate the population attributable fraction of known, modifiable risk factors of severe maternal morbidity. A total gestational weight gain z-score of +2 (31kg at 40 weeks gestation among normal weight women) was associated with 1.0 (0.46, 1.5)) excess cases of severe maternal morbidity per 100 delivery hospitalizations compared with a z-score of 0 (16kg at 40 weeks among normal weight). Very low weight gain was also associated with an increased risk, though the magnitude of association was smaller. The relationship between early gestational weight gain and risk of severe maternal morbidity followed an inverted-U distribution, though the divergent findings with Specific Aim #1 were likely due to differences in sample characteristics. For Specific Aim #3, we found that optimizing eight, known risk factors concurrently could prevent 36% (626 cases) of the severe maternal morbidities in this sample. High gestational weight gain, high body mass index, advanced maternal age, preexisting hypertension, and lack of a college degree had population attributable fractions ranging from 4.5% to 13%. Our results suggest that optimizing individual-level risk factors, including gestational weight gain, would have modest impacts on reducing risk of severe maternal morbidity and that the burden of severe maternal morbidity is likely due to a constellation of components. This is significant for future public health efforts because, while additional research should confirm and extend our findings, the greatest change will likely come through addressing larger, population-level factors and disparities.
Determinants of individuals’ intention to use mobile health: insights from India

Purpose This study aims to examine the determinants of mobile health technology and applications use intention. The factors are delineated from prior literature and theories of individual traits and adoption characteristics, technology acceptance and health belief. Design/methodology/approach Data from 409 respondents were collected from Indian participants through a questionnaire survey. The construct “use intention” was measured using individual traits to mobile services, subjective norm, health consciousness, awareness and perceived usefulness, and the model was tested. Findings The study found that mobile health technology and the applications awareness and personal innovativeness influence intention to use. Originality/value Previous studies have often looked at technology adoption and acceptance models separately and are less adequately discussed in the Indian context. The components that determine mobile health technology and the applications’ acceptance by literature and theories of individual traits and adoption characteristics, technology acceptance and health beliefs were also inadequately discussed. The significant contribution of this research also includes policy recommendations for improving mobile health acceptance in India.
Climate explains geographic and temporal variation in mosquito-borne disease dynamics on two continents

Climate drives population dynamics, but when the underlying mechanisms are unresolved, studies can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive vector-borne diseases such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. In this study, our objective was to test the extent to which a mathematical model, parameterized with climate-driven mosquito physiology measured in laboratory studies, predicts observed vector and disease dynamics in the field across ecologically and culturally distinct settings in Ecuador and Kenya. The model incorporates different rainfall functions and time lags. We show that the climate-driven model captures three key epidemic characteristics across settings: the number, timing, and duration of outbreaks. In addition, the model generates a range of disease dynamics consistent with observations of Aedes aegypti abundances and laboratory-confirmed arboviral incidence with varying levels of accuracy (28 – 85% for vector dynamics, 36 – 88% for human disease dynamics). Further, we find that the model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and a larger proportion of homes without window screens and made of cement. A mechanistic model with limited calibration to local data that robustly captures the influence of climate on viruses transmitted by Aedes aegypti provides critical information to help guide future intervention efforts and improve climate change predictions.
Morphotypes or distinct species? A multilocus assessment of two East Asian scimitar babblers (Aves, Timaliidae)

In birds, widespread species complexes often exhibit dramatic plumage differences across their distribution, which can give rise to discordance between morphotypes and phylogroups. Accurate identification of species diversity may require an integrated approach in which multilocus genetic data are used for inference and further corroborated by ecological, morphometric or behavioural data. Pomatorhinus gravivox and P. swinhoei, which were formerly considered component races of P. erythrogenys, were recently split using a delimitation system that mainly relied on the quantification of differences in phenotypic traits. We therefore carried out a reassessment of this taxonomic recommendation by conducting phylogenetic and population genetic analyses with multilocus genetic data. A deep mitochondrial split with a Kimura 2‐parameter distance of 0.061 was observed, mainly corresponding to the two morphologically defined species. However, one individual from P. swinhoei harboured a haplotype of P. gravivox. Individual‐based analyses of nuclear loci identified two distinct clusters that were exactly congruent with the two species. BPP delimitation also provided support for the separation. These congruencies support the notion that these two taxa are best regarded as two separate species. The presence of the P. gravivox mitochondrial haplotype in P. swinhoei was most likely a result of hybridization, due to the clear separation of nuclear loci. The speciation might be attributed to paleoclimatic changes but requires further evaluation due to the likelihood of ecological speciation. This study was in accordance with the results inferred from the quantitative system but highlighted the importance of sampling various data, especially in contact zones, in the study of taxonomy and speciation history.
A New Combinatory Approach for Wind Power Forecasting

Wind power generation is considerably dependent on the weather condition and it is correlated closely with the air density, wind speed, and its direction through considering the transformer tap change cost. Therefore, an accurate prediction model is required to forecast and adopt with the complicated signals. In this article, an accurate prediction method trained with improved wavelet transform (IWT) to decompose the original signal to subsignals, new feature selection based on the maximum dependence, maximum relevancy, and minimum redundancy to filter the signal and select the best candidate inputs and a synthetic forecasting engine with minimum forecasting error is proposed. The proposed method of forecasting engine composed two-dimensional convolution neural network (TDCNN) and trained by improved optimization algorithm based on particle swarm optimization. The proposed improved optimization algorithm will fine-tune the weights of TDCNN to increase the prediction accuracy of the forecast engine. The training optimization processes and effectual fast classification are the main duty of the proposed intelligent algorithm. The efficiency of the suggested prediction approach is widely evaluated and compared to other prediction approaches using the practical power market data. A simulation study is conducted over the trained model and the results are discussed in detail. Obtained numerical results and also analysis in short-term and long-term forecasting horizons validate the high performance and advantages of the introduced approach.
Post-operative pain assessment in dogs

Abstract The development of the Registered Veterinary Nurse’s (RVN’s) role has progressed greatly within recent years with the establishment of the Veterinary Nurse Charter in February 2015, which enforces and strengthens the profession making RVNs autonomous and self-regulating. With this newly achieved autonomy, RVNs need to provide gold-standard care relative to recent, appropriate literature. An important aspect of this is monitoring pain in post-operative patients. As veterinary professionals there is an ethical and legal obligation to identify and treat pain where required.
Takeover Law Enforcement and Acquirer Returns

This paper examines the relationship between legal shareholder rights and acquirer returns. Europe is an ideal context to explore this link because various institutional differences make the European Takeover Directive a suitable focus for a natural experiment. We find that an improvement of legal shareholder rights entails an increase in acquirer returns, supporting the hypothesis that strong shareholder rights constrain the discretion of corporate insiders, leading to better investment decisions. However, this value creation is partly consumed by the costs of the reform. The gains from improving legal shareholder rights are decreasing in the relative disruption of prevailing governance practices.
Predictors of mental health risks in newly resettled North Korean refugee women

This study aimed to find factors that predict high risk groups for mental health condition in recently immigrated North Korean refugee women. The number of North Korean refugee women has increased in the recent years and currently makes up about 3 out of 4 entrants. The rise in their number, greater susceptibility of mental health in women, and multiple trauma these women are suspected to have been exposed to such as human trafficking and sexual violence, call for attention to their mental health. Multinomial logistical regression analysis was conducted to identify the variables determining the likelihood of an individual to be in varying risk groups that were clustered into 4 groups: (1) non-clinical group; (2) PTSD only group; (3) depression/anxiety only group; (4) co-morbidity of PTSD and depression/anxiety group. The findings of the analysis include: trauma exposure increased the odds of an individual to be in one of three clinical groups with the highest odds to be in co-morbidity group. Living with a family increased the odds for favorable mental health status, whereas having a chronic medical problem elevated the odds for poor mental health. Childhood trauma raised the risk of an individual developing depression/anxiety while not affecting the odds against PTSD or mental health co-morbidity. Depression/anxiety seems to be a greater threat to the psychological adjustment of North Korean women than PTSD, looking at the prevalence rates of two clinical conditions. Implications of the study findings and recommendations were made. Keywords: Human trafficking Language: ko
Role of Trust in Adopting Consumer Social Responsible Behaviour in the Context of Water Use in Domestic Households

Abstract The central theme in this article is Consumer Social Responsibility (CnSR) in the context of sustainable water consumption at home. In this paper, a conceptual SEM model will be tested based on the Theory of Planned Behaviour. This model focuses on the role of trust mediated by CnSR attitude, in influencing water conservation behaviour. Using a sample of 493 respondents, SEM analysis results show that trust in water companies and general trust disposition have significant effect on CnSR attitude. Attitude is approached via environmental, economic and social angle, covering three (triple) mentioned domains of sustainability each with an associated minimum (bottom) required level (line) also known as the Triple Bottom Line (TBL).
Modeling of Precast Concrete Load-Bearing Walls Exposed to Fire

The performance of precast concrete load-bearing walls exposed to fire is investigated using 3-D thermal and structural models. The thermal model is validated by comparing the predicted temperatures with results from the fire tests carried out in the present study, previous experimental studies, and Eurocode 2. The structural model is validated by comparing the predicted displacements and crack patterns with results from the fire tests carried out in the present study. It is found that the results obtained from the thermal model and the structural model are in line with the experimental data and Eurocode 2 results. The validated 3-D model of precast concrete load-bearing walls is used to study the effect of load level, slenderness ratio and boundary conditions on the fire performance of the walls. The results show that the fire resistance rating of load-bearing walls decreases with increasing load level and slenderness ratio, while the fire resistance rating of the walls increases when rotational restraints are imposed at the ends of the walls. Finally, equations are presented for estimating the fire resistance rating of the walls with different support conditions.
Residual Agroforestry Biomass–Thermochemical Properties

Research Highlights: Biomass from Mediterranean agroforestry vegetation may be a potential source of renewable energy. However, due to the high heterogeneity of this type of resource, the study of its characteristics becomes necessary for its efficient use. Objectives: The aim of this study was to evaluate the thermal and chemical properties of 14 different kinds of agroforestry biomass groups: shrubs, forest, and agricultural wastes. Materials and Methods: The higher heating value (HHV), the elemental analysis (C, H, O, N, S), ashes, mineral elements (Na, K, Ca, Mg, and P), trace elements (Mn, Fe, Zn, Ni, Cu, Cr, and Cd) and halogen elements (F and Cl) were quantified and compared with CEN/TS 147775 and CENS/TS 14961 standards, looking forward to future use for energy purposes, namely through combustion processes, as an alternative to fossil fuels. Results: The shrubs present the highest values of higher heating value (20.5 MJ kg−1), followed by the forest wastes (19.2 MJ kg−1) and the lowest in the agricultural wastes (18.5 MJ kg−1). Concerning the elemental analysis, the difference between groups C, H, and O are very small and not statistically significant, while for N, S and ashes values are higher in agricultural than shrubs and forestry wastes. The same tendency was found for the mineral nutrients. For the trace elements, the lowest content of Mn, Fe, and Zn is found in agricultural, Ni, and Cr content in the shrubs and Cu in the forest wastes. The halogen elements are present in greater amount in shrubs than agricultural and forest wastes. Conclusions: Although the high values of the halogen elements which may raise sintering problems and corrosive effect on metal parts in furnace and boiler, in general the shrubs biomass are those with better characteristics for energy uses, namely through combustion processes.
Tumor Necrosis Factor Alpha: A Major Cytokine of Brain Neuroinflammation

Tumor necrosis factor (TNF) is one of the most extensively studied cytokine with about 19 distinct superfamily members and many more to be found. Prominent among these members is tumor necrosis factor alpha (TNF- α ) that is known to be a potent promoter of inflammation, as well as many normal physiological functions in homeostasis and health and antimicrobial immunity. Nuclear factor kappa-light-chain enhancer of activated B cells (NF κ B) is one of the most important transcription factors that activate transcription of many proinflammatory genes, and the unraveling of TNF- α induced NF κ B activation forms the foundation of TNF- α as major cytokine of neuroinflammation. This review discusses summary of literature on unique role of TNF- α in neuroinflammation and various agents that mediate neuroinflammation via TNF- α modulation.