The cGMP Signaling Pathway Affects Feeding Behavior in the Necromenic Nematode Pristionchus pacificus

Background: The genetic tractability and the species-specific association with beetles make the nematode Pristionchus pacificus an exciting emerging model organism for comparative studies in development and behavior. P. pacificus differs from Caenorhabditis elegans (a bacterial feeder) by its buccal teeth and the lack of pharyngeal grinders, but almost nothing is known about which genes coordinate P. pacificus feeding behaviors, such as pharyngeal pumping rate, locomotion, and fat storage. Methodology/Principal Findings: We analyzed P. pacificus pharyngeal pumping rate and locomotion behavior on and off food, as well as on different species of bacteria (Escherichia coli, Bacillus subtilis, and Caulobacter crescentus). We found that the cGMP-dependent protein kinase G (PKG) Ppa-EGL-4 in P. pacificus plays an important role in regulating the pumping rate, mouth form dimorphism, the duration of forward locomotion, and the amount of fat stored in intestine. In addition, Ppa-EGL-4 interacts with Ppa-OBI-1, a recently identified protein involved in chemosensation, to influence feeding and locomotion behavior. We also found that C. crescentus NA1000 increased pharyngeal pumping as well as fat storage in P. pacificus. Conclusions: The PKG EGL-4 has conserved functions in regulating feeding behavior in both C. elegans and P. pacificus nematodes. The Ppa-EGL-4 also has been co-opted during evolution to regulate P. pacificus mouth form dimorphism that indirectly affect pharyngeal pumping rate. Specifically, the lack of Ppa-EGL-4 function increases pharyngeal pumping, time spent in forward locomotion, and fat storage, in part as a result of higher food intake. Ppa-OBI-1 functions upstream or parallel to Ppa-EGL-4. The beetle-associated omnivorous P. pacificus respond differently to changes in food state and food quality compared to the exclusively bacteriovorous C. elegans

Students Success Modeling: Most Important Factors

The importance of retention rate for higher education institutions has encouraged data analysts to present various methods to predict at-risk students. The present study, motivated by the same encouragement, proposes a deep learning model trained with 121 features of diverse categories extracted or engineered out of the records of 60,822 postsecondary students. The model undertakes to identify students likely to graduate, the ones likely to transfer to a different school, and the ones likely to drop out and leave their higher education unfinished. This study undertakes to adjust its predictive methods for different stages of curricular progress of students. The temporal aspects introduced for this purpose are accounted for by incorporating layers of LSTM in the model. Our experiments demonstrate that distinguishing between to-be-graduate and at-risk students is reasonably achievable in the earliest stages, and then it rapidly improves, but the resolution within the latter category (dropout vs. transfer) depends on data accumulated over time. However, the model remarkably foresees the fate of students who stay in the school for three years. The model is also assigned to present the weightiest features in the procedure of prediction, both on institutional and student levels. A large, diverse sample size along with the investigation of more than one hundred extracted or engineered features in our study provide new insights into variables that affect students success, predict dropouts with reasonable accuracy, and shed light on the less investigated issue of transfer between colleges. More importantly, by providing individual-level predictions (as opposed to school-level predictions) and addressing the outcomes of transfers, this study improves the use of ML in the prediction of educational outcomes.Comment: 15 pages, 17 figures, 1 apendi

Evaluation of the Prevalence of Congenital Heart Diseases in neonates of Ilam province of Iran, in 2019-2020

Background: Congenital heart diseases (CHDs) can cause death and severe disorders in the developmental process of children and, in most cases, are associated with other congenital defects. The current study investigates the prevalence of such defects among infants born in 2019 and 2020.Materials and Methods: This analytical cross-sectional study was conducted in Ilam province on 91 referred neonates, with the possibility of heart diseases, who were diagnosed with CHDs. Demographic, clinical, and definitive diagnoses of cardiologists were recorded and analyzed in these infants, followed by a 6-month follow-up. Data were analyzed using SPSS software with descriptive statistics, Chi-square test, and correlation coefficients.Results: A total of 91 infants out of 16,064 newborns were diagnosed with CHDs, and the prevalence of heart diseases was 5.9 in every 1000 live births. The most frequent defects were ventricular septal defects (VSDs) and PDA, with prevalence rates of 59.3% and 14.2%, respectively. Among 54 VSDs, mus VSD (n = 39) was the most common form of this disorder.Conclusion: The incidence of CHDs in Ilam province was lower than the global average, which may be attributed to the easier access of several cities to the health centers of the bordering provinces. The highlighted results of this study were the frequency of VSDs and the high rates of muscular VSD compared to membranous VSD

Using Distributed Temperature Sensing to monitor field scale dynamics of ground surface temperature and related substrate heat flux

We present one of the first studies of the use of Distributed Temperature Sensing (DTS) along fibre-optic cables to purposely monitor spatial and temporal variations in ground surface temperature (GST) and soil temperature, and provide an estimate of the heat flux at the base of the canopy layer and in the soil. Our field site was at a groundwater-fed wet meadow in the Netherlands covered by a canopy layer (between 0-0.5 m thickness) consisting of grass and sedges. At this site, we ran a single cable across the surface in parallel 40 m sections spaced by 2 m, to create a 40×40 m monitoring field for GST. We also buried a short length (≈10 m) of cable to depth of 0.1±0.02 m to measure soil temperature. We monitored the temperature along the entire cable continuously over a two-day period and captured the diurnal course of GST, and how it was affected by rainfall and canopy structure. The diurnal GST range, as observed by the DTS system, varied between 20.94 and 35.08◦C; precipitation events acted to suppress the range of GST. The spatial distribution of GST correlated with canopy vegetation height during both day and night. Using estimates of thermal inertia, combined with a harmonic analysis of GST and soil temperature, substrate and soil-heat fluxes were determined. Our observations demonstrate how the use of DTS shows great promise in better characterising area-average substrate/soil heat flux, their spatiotemporal variability, and how this variability is affected by canopy structure. The DTS system is able to provide a much richer data set than could be obtained from point temperature sensors. Furthermore, substrate heat fluxes derived from GST measurements may be able to provide improved closure of the land surface energy balance in micrometeorological field studies. This will enhance our understanding of how hydrometeorological processes interact with near-surface heat fluxes

Urban Microclimate, a Study of Energy Balance and Fluid Dynamics /

Improvements in building energy use, air quality in urban canyons and in general urban microclimates requires understanding of the complex interaction between urban morphology, materials, and climate as well as their interaction with the flow dynamics in urban canyons. The review of the literature indicates that despite a long history of valuable urban microclimate studies, more comprehensive approaches of investigating energy, heat and flow in urban areas are needed. In this research an indoor -outdoor dynamically coupled urban model, the Temperature of Urban Facets Indoor-Outdoor Building Energy Simulator (TUF-IOBES), has been developed and carefully validated. It is a building-to-canopy model that simulates indoor and outdoor building surface temperatures and heat fluxes in an urban area to estimate cooling/heating loads and energy use in buildings. The effects of a large number of parameters such as different ground surface albedo, building condition, window size and type, seasonal climate, and canopy aspect ratio on building thermal loads were investigated. The results presented in this dissertation highlight the fact that the interaction of urban materials (e.g. reflective pavements) with surrounding buildings must be considered in the energy analysis of urban areas. Although reflective pavements have been proposed as a mitigation measure for urban heat island since they reduce urban air temperatures, the increased solar reflectivity which transports solar radiation into (through fenestrations) and onto adjacent buildings increases building energy use. To investigate a more comprehensive and realistic simulation of the diurnally varying street canyon flow and associated heat transport, TUF-IOBES three -dimensional surface heat flux distribution were used as thermal boundary conditions in large-eddy simulation (LES). Compared to previous analyses which used uniformly distributed thermal forcing on urban surfaces, the present analysis shows that non-uniform thermal forcing can result in complex local air flow patterns. Strong horizontal pressure gradients were detected in streamwise and spanwise canyons throughout the daytime which motivate larger turbulent velocity fluctuations in the horizontal directions rather than in the vertical direction. This dissertation demonstrates that only local simulations for specific neighborhoods and urban climates can elucidate specific effects of urban mitigation measures; with often surprising outcome