Prospects of the hydraulic air conditioning of mold air on the basis of pulse wave effects

The results of analytical and technical substantiation of promising solutions for the development of tools for hydraulic conditioning of mine air in deep mine conditions that combine cooling and purification of air based on pulse-wave effects created by diffuser and confuser elements are presented

Penggantian Slave Arm Ms-manipulator Hotcell Uji 02 Dan 03 Irm

─ The replacement of the failure slave arm unit of MS-manipulator in hot cell 02 and 03 of Radiometallurgy Installations (IRM) has been done. This replacement purposes in order to refunctioned the failure MS-manipulator (5 units) in examination hot cell 02 and 03. The slave arm unit of MS-manipulator in examination hot cell 02 and 03 using a hanging device so it is impossible to pulled the slave arm units out to the operating area. The handling process cover; setting zero position on master arm unit, release the locking mechanism between slave arm and wall tube unit, detached the failure slave arm unit that used the hanging device in hot cell using the power manipulator, installation of new booting on new slave arm unit, the drawing of wall tube and master arm unit to the operating area using manipulator stager, fixing the new slave arm unit to wall tube and the complete manipulator to the hot cell. A green house is make to cover the working area in the operating area to limit the contaminated area. Very low radiation exposure to the operating area as well as the level of contamination at the end of the slave arm. The handling of replacement techniques can be performed by the staff of Bidang Uji Radiometalurgi by following stages as has been stated in Work Instruction No. PR 15 E05 018. The MS-manipulators in examination hot cell 02 and 03 are ready for use. Key Words – Radiometallurgy Installations (IRM), manipulator, slave arm unit replacemen

Foodborne illness among school children in Ga east, Accra

Background: A food borne illness was reported in Ga- East district of Greater Accra Region among school children in May, 2007 after eating food provided at school. The objective of the investigation was to determine the source, mode of contamination and the causative agent.Methods: A case-control study was conducted, cases were schoolchildren with abdominal symptoms and controls were children of the same sex and class without any symptom during the same period. The school children were selected by systematic sampling. Food handlers and the children were interviewed by a structured questionnaire. Food handlers were physically examined and their stools and blood examined. The kitchen for food preparation was inspected. Risks of food borne infection from the foods eaten were determined using attack rates .Results: The minimum, peak and maximum incubation periods were 2, 11 and 61 hours respectively. The source was rice and groundnut soup (with the highest attack rate difference). Stool and blood samples of food handlers were not infective. Storage facility for food items was poor. No food samples were available for organism isolation. A protocol to prevent such outbreaks was nonexistent.Conclusion: The short incubation period and symptoms presented suggest an infective origin. The storage of the meat may potentially have been the point of contamination. The study showed that the schoolchildren ate contaminated food although the investigation could not determine the causative agent. Protocols to prevent such outbreaks need to be developed for the schools.Keywords: Food borne, illness, contaminated food, school children, Accr

ORCHIDEE-PEAT (revision 4596), a model for northern peatland CO2, water, and energy fluxes on daily to annual scales

Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO2 fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5 degrees grid cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation within a grid cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model was evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation (V-cmax) being optimized at each site. Regarding short-term day-to-day variations, the model performance was good for gross primary production (GPP) (r(2) = 0.76; Nash-Sutcliffe modeling efficiency, MEF = 0.76) and ecosystem respiration (ER, r(2) = 0.78, MEF = 0.75), with lesser accuracy for latent heat fluxes (LE, r(2) = 0.42, MEF = 0.14) and and net ecosystem CO2 exchange (NEE, r(2) = 0.38, MEF = 0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high r(2) values (0.57-0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, r(2) values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted (r(2) <0.1), likely due to the uncertain water input to the peat from surrounding areas. However, the poor performance of WT simulation did not greatly affect predictions of ER and NEE. We found a significant relationship between optimized V-cmax and latitude (temperature), which better reflects the spatial gradients of annual NEE than using an average V-cmax value.Peer reviewe

Diagnostic classification of childhood cancer using multiscale transcriptomics

The causes of pediatric cancers’ distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types

HIRAX:A Probe of Dark Energy and Radio Transients

The Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX) is a new 400-800MHz radio interferometer under development for deployment in South Africa. HIRAX will comprise 1024 six meter parabolic dishes on a compact grid and will map most of the southern sky over the course of four years. HIRAX has two primary science goals: to constrain Dark Energy and measure structure at high redshift, and to study radio transients and pulsars. HIRAX will observe unresolved sources of neutral hydrogen via their redshifted 21-cm emission line (`hydrogen intensity mapping'). The resulting maps of large-scale structure at redshifts 0.8-2.5 will be used to measure Baryon Acoustic Oscillations (BAO). HIRAX will improve upon current BAO measurements from galaxy surveys by observing a larger cosmological volume (larger in both survey area and redshift range) and by measuring BAO at higher redshift when the expansion of the universe transitioned to Dark Energy domination. HIRAX will complement CHIME, a hydrogen intensity mapping experiment in the Northern Hemisphere, by completing the sky coverage in the same redshift range. HIRAX's location in the Southern Hemisphere also allows a variety of cross-correlation measurements with large-scale structure surveys at many wavelengths. Daily maps of a few thousand square degrees of the Southern Hemisphere, encompassing much of the Milky Way galaxy, will also open new opportunities for discovering and monitoring radio transients. The HIRAX correlator will have the ability to rapidly and eXperimentciently detect transient events. This new data will shed light on the poorly understood nature of fast radio bursts (FRBs), enable pulsar monitoring to enhance long-wavelength gravitational wave searches, and provide a rich data set for new radio transient phenomena searches. This paper discusses the HIRAX instrument, science goals, and current status.Comment: 11 pages, 5 figure

A Gammaherpesviral Internal Repeat Contributes to Latency Amplification

BACKGROUND: Gammaherpesviruses cause important infections of humans, in particular in immunocompromised patients. The genomes of gammaherpesviruses contain variable numbers of internal repeats whose precise role for in vivo pathogenesis is not well understood. METHODOLOGY/PRINCIPAL FINDINGS: We used infection of laboratory mice with murine gammaherpesvirus 68 (MHV-68) to explore the biological role of the 40 bp internal repeat of MHV-68. We constructed several mutant viruses partially or completely lacking this repeat. Both in vitro and in vivo, the loss of the repeat did not substantially affect lytic replication of the mutant viruses. However, the extent of splenomegaly, which is associated with the establishment of latency, and the number of ex vivo reactivating and genome positive splenocytes were reduced. Since the 40 bp repeat is part of the hypothetical open reading frame (ORF) M6, it might function as part of M6 or as an independent structure. To differentiate between these two possibilities, we constructed an N-terminal M6STOP mutant, leaving the repeat structure intact but rendering ORF M6 unfunctional. Disruption of ORF M6 did neither affect lytic nor latent infection. In contrast to the situation in lytically infected NIH3T3 cells, the expression of the latency-associated genes K3 and ORF72 was reduced in the latently infected murine B cell line Ag8 in the absence of the 40 bp repeat. CONCLUSIONS/SIGNIFICANCE: These data suggest that the 40 bp repeat contributes to latency amplification and might be involved in the regulation of viral gene expression

Chemokine Binding Protein M3 of Murine Gammaherpesvirus 68 Modulates the Host Response to Infection in a Natural Host

Murine γ-herpesvirus 68 (MHV-68) infection of Mus musculus-derived strains of mice is an attractive model of γ-herpesvirus infection. Surprisingly, however, ablation of expression of MHV-68 M3, a secreted protein with broad chemokine-binding properties in vitro, has no discernable effect during experimental infection via the respiratory tract. Here we demonstrate that M3 indeed contributes significantly to MHV-68 infection, but only in the context of a natural host, the wood mouse (Apodemus sylvaticus). Specifically, M3 was essential for two features unique to the wood mouse: virus-dependent inducible bronchus-associated lymphoid tissue (iBALT) in the lung and highly organized secondary follicles in the spleen, both predominant sites of latency in these organs. Consequently, lack of M3 resulted in substantially reduced latency in the spleen and lung. In the absence of M3, splenic germinal centers appeared as previously described for MHV-68-infected laboratory strains of mice, further evidence that M3 is not fully functional in the established model host. Finally, analyses of M3's influence on chemokine and cytokine levels within the lungs of infected wood mice were consistent with the known chemokine-binding profile of M3, and revealed additional influences that provide further insight into its role in MHV-68 biology

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe