Notes

Architectural depiction from the self-association area associated with take.
However, classification accuracy was consistently lower (although above chance level) for spontaneous affective behavior. The findings indicate potential shortcomings of existing out-of-the-box classifiers for measuring emotions, and highlight the need for more spontaneous facial databases that can act as a benchmark in the training and testing of automatic emotion recognition systems. We further discuss some limitations of analyzing facial expressions that have been recorded in controlled environments.With the on-going interest in implementing school policies to address the problem of childhood obesity in Malaysia, there is urgent need for information about the association between school environment and children's weight status. This study aims to investigate the association between school environmental factors (physical, economic, political and sociocultural) with BMI of school children in Terengganu. The school environment factors were assessed using a set of validated whole-school environmental mapping questionnaires, consisting of 76 criteria with four domains; physical environment (41 criteria), economic environment (nine criteria), political environment (nine criteria) and sociocultural environment (17 criteria). This involved face-to-face interview sessions with 32 teachers from 16 schools (eight rural and eight urban). In addition, 400 school children aged between 9 and 11 years of the selected schools were assessed for BMI (WHO 2007 reference chart), dietary intake (food frequency questionnaire (FFQ)) and physical activity level (physical activity questionnaire for children (PAQ-C)). Multiple regression was used to examine the association between school environment factors and BMI of the school children. Seven school environment criteria were found to be associated with BMI of school children when it was adjusted for calorie intake and physical activity level. About 33.4% of the variation in BMI of school children was explained by health professional involvement, simple exercise before class, encouragement to walk/ride bicycle to/from school, no high-calorie food sold, healthy options of foods and drinks at tuck shop, availability of policy on physical activity and training teacher as a role model. Policy makers should make urgent actions to address the obesogenic features of school environments. It should strive towards setting up healthy school environment and improving school curricula to promote healthy behaviours among the school children.Patients diagnosed with polycystic ovary syndrome (PCOS) are at high risk of developing a myriad of endocrinologic and metabolic derailments. Moreover, PCOS is a leading cause of habitual abortion, also known as recurrent pregnancy loss (RPL). Meteorin-like protein (Metrnl) is a newly discovered adipokine with the potential to counteract the metaflammation. This study aimed at determining the associations of serum Metrnl levels with homocysteine, hs-CRP, and some components of metabolic syndrome in PCOS-RPL and infertile PCOS patients.This case-control study was conducted in 120 PCOS patients (60 PCOS-RPL and 60 infertile) and 60 control. Serum hs-CRP and homocysteine were assessed using commercial kits, while adiponectin, Metrnl, FSH, LH, free testosterone and insulin levels were analyzed using ELISA technique. Serum Metrnl levels were found to be lower in PCOS patients when compared to controls (67.98 ± 26.66 vs. 96.47 ± 28.72 pg/mL, P less then 0.001)). Furthermore, serum adiponectin levels were lower, while free testosterone, fasting insulin, HOMA-IR, homocysteine, and hs-CRP were significantly higher in PCOS group compared to controls. Moreover, serum Metrnl correlated with BMI, adiponectin, and homocysteine in controls, and inversely correlated with FBG, fasting insulin, and HOMA-IR in PCOS group and subgroups. Besides, it inversely correlated with hs-CRP in control, and PCOS group and subgroups. These findings revealed a possible role of Metrnl in the pathogenesis of PCOS and RPL. Nevertheless, there is a necessity for future studies to prove this concept.A common approach to improving probabilistic forecasts is to identify and leverage the forecasts from experts in the crowd based on forecasters' performance on prior questions with known outcomes. However, such information is often unavailable to decision-makers on many forecasting problems, and thus it can be difficult to identify and leverage expertise. In the current paper, we propose a novel algorithm for aggregating probabilistic forecasts using forecasters' meta-predictions about what other forecasters will predict. We test the performance of an extremised version of our algorithm against current forecasting approaches in the literature and show that our algorithm significantly outperforms all other approaches on a large collection of 500 binary decision problems varying in five levels of difficulty. The success of our algorithm demonstrates the potential of using meta-predictions to leverage latent expertise in environments where forecasters' expertise cannot otherwise be easily identified.Coffea arabica is a highly traded commodity worldwide, and its plantations are habitat to a wide range of organisms. Coffee farmers are shifting away from traditional shade coffee farms in favor of sun-intensive, higher yield farms, which can impact local biodiversity. Using plant-associated microorganisms in biofertilizers, particularly fungi collected from local forests, to increase crop yields has gained traction among coffee producers. However, the taxonomic and spatial distribution of many fungi in coffee soil, nearby forests and biofertilizers is unknown. We collected soil samples from a sun coffee system, shade coffee system, and nearby forest from Izalco, Sonsonate, El Salvador. At each coffee system, we collected soil from the surface (upper) and 10 cm below the surface (lower), and from the coffee plant drip line (drip line) and the walkway between two plants (walkway). Forest soils were collected from the surface only. We used ITS metabarcoding to characterize fungal communities in soil and in the biofertilizer (applied in both coffee systems), and assigned fungal taxa to functional guilds using FUNGuild. In the sun and shade coffee systems, we found that drip line soil had higher richness in pathotrophs, symbiotrophs, and saprotrophs than walkway soil, suggesting that fungi select for microhabitats closer to coffee plants. Upper and lower soil depths did not differ in fungal richness or composition, which may reflect the shallow root system of Coffea arabica. MEK inhibitor Soil from shade, sun, and forest had similar numbers of fungal taxa, but differed dramatically in community composition, indicating that local habitat differences drive fungal species sorting among systems. Yet, some fungal taxa were shared among systems, including seven fungal taxa present in the biofertilizer. Understanding the distribution of coffee soil mycobiomes can be used to inform sustainable, ecologically friendly farming practices and identify candidate plant-growth promoting fungi for future studies.
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