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Fiber tractography (FT), which aims to reconstruct the three-dimensional trajectories of white matter (WM) fibers non-invasively, is one of the most popular approaches for analyzing diffusion tensor imaging (DTI) data given its high inter- and intra-rater reliability and scan-rescan reproducibility. The major disadvantage of manual FT segmentations, unfortunately, is that placing regions-of-interest for tract selection can be very labor-intensive and time-consuming. Although there are several methods that can identify specific WM fiber bundles in an automated way, manual FT segmentations across multiple subjects performed by a trained rater with neuroanatomical expertise are generally assumed to be more accurate. However, for longitudinal DTI analyses it may still be beneficial to automate the FT segmentation across multiple time points, but then for each individual subject separately. Both the inter-subject and intra-subject automation in this situation are intended for subjects without gross pathology. In this work, we propose such an automated longitudinal intra-subject analysis (dubbed ALISA) approach, and assessed whether ALISA could preserve the same level of reliability as obtained with manual FT segmentations. In addition, we compared ALISA with an automated inter-subject analysis. Based on DTI data sets from (i) ten healthy subjects that were scanned five times (six-month intervals, aged 7.6-8.6years at the first scan) and (ii) one control subject that was scanned ten times (weekly intervals, 12.2years at the first scan), we demonstrate that the increased efficiency provided by ALISA does not compromise the high degrees of precision and accuracy that can be achieved with manual FT segmentations. Further automation for inter-subject analyses, however, did not provide similarly accurate FT segmentations.

Instream large wood (i.e., downed trees, branches and roots larger than 1 m in length and 10 cm diameter) has essential geopmorphological and ecological functions supporting the health of river ecosystems. Still, even though its transport during floods may pose a risk, it is rarely observed and, therefore, poorly understood. This paper presents a novel approach to detect pieces of instream wood from video. The approach uses a Convolutional Neural Network to detect wood automatically. We sampled data to represent different wood transport conditions, combining 20 datasets to yield thousands of instream wood images. We designed multiple scenarios using different data subsets with and without data augmentation and analyzed the contribution of each one to the effectiveness of the model using k-fold cross-validation. The mean average precision of the model varies between 35 and 93 percent, and is highly influenced by the quality of the data which it detects. When the image resolution is low, the identified components in the labeled pieces, rather than exhibiting distinct characteristics such as bark or branches, appear more akin to amorphous masses or 'blobs'. We found that the model detects wood with a mean average precision of 67 percent when using a 418 pixels input image resolution. Also, improvements of up to 23 percent could be achieved in some instances and increasing the input resolution raised the weighted mean average precision to 74 percent. We show that the detection performance on a specific dataset is not solely determined by the complexity of the network or the training data. Therefore, the findings of this paper can be used when designing a custom wood detection network. With the growing availability of flood-related videos featuring wood uploaded to the internet, this methodology facilitates the quantification of wood transport across a wide variety of data sources. [ABSTRACT FROM AUTHOR]

As the pig industry develops rapidly, various problems are increasing both inside and outside pig houses. In particular, in the case of pig houses, it is difficult to solve the main problems even if automation and mechanization are applied with Information and Communications Technologies (ICT). The air recirculation technology can be applied as a technology that can solve these typical problems in the pig industry, such as growth environment, livestock disease, odor emission, energy cost, and pig productivity. The air recirculated ventilation system (ARVS) can minimize the inflow of air from the outdoors and recycle the internal thermal energy of the pig house. The ARVS consists of (1) an air scrubber module, (2) an external air mixing module, (3) a UV cleaning module, (4) a solar heat module, and (5) an air distribution module. In this study, the growth environment of piglets was predicted using a numerical model when the ARVS was applied. Since the concept of air recirculation was used, numerous equations for predicting the internal environment should be iteratively calculated. Furthermore, it was necessary to determine the optimum condition of the modules by applying various boundary conditions. Therefore, the model was designed for numerical analysis based on the balance equations of environmental factors inside the piglet room. For each module, the module coefficient and equations were considered based on the previous studies. The analysis was conducted according to the system diagram of each module, and the growth environment inside the piglet room was evaluated according to the various environmental conditions. As a result of calculating the numerical model, the ventilation rate of 40 CMM or more was advantageous to properly maintaining the gas environment. In the summer season, it was necessary to additionally use the cooling device and dehumidifier. In the winter season, when using a heat exchanger and solar module, was more advantageous for maintaining air temperature inside the piglet room.

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Livestock manure is recycled to agricultural land as organic fertilizer. Due to the extensive usage of antibiotics in conventional animal farming, antibiotic-resistant bacteria are highly prevalent in feces and manure. The spread of wind-driven particulate matter (PM) with potentially associated harmful bacteria through manure application may pose a threat to environmental and human health. We studied whether PM was aerosolized during the application of solid and dried livestock manure and the functional relationship between PM release, manure dry matter content (DM), treatment and animal species. In parallel, manure and resulting PM were investigated for the survival of pathogenic and antibiotic-resistant bacterial species. The results showed that from manure with a higher DM smaller particles were generated and more PM was emitted. A positive correlation between manure DM and PM aerosolization rate was observed. There was a species-dependent critical dryness level (poultry: 60% DM, pig: 80% DM) where manure began to release PM into the environment. The maximum PM emission potentials were 1 and 3 kg t−1 of applied poultry and pig manure, respectively. Dried manure and resulting PM contained strongly reduced amounts of investigated pathogenic and antibiotic-resistant microorganisms compared to fresh samples. An optimal manure DM regarding low PM emissions and reduced pathogen viability was defined from our results, which was 55–70% DM for poultry manure and 75–85% DM for pig manure. The novel findings of this study increase our detailed understanding and basic knowledge on manure PM emissions and enable optimization of manure management, aiming a manure DM that reduces PM emissions and pathogenic release into the environment.

This study aimed to investigate the impact of decreased crude protein (CP) levels (by 2% units) or acidifying diets (by adding 10 g benzoic acid/kg diet in combination with replacing a part of CaCO3 by about 10 g Ca-formate/kg diet) on urine, feces and manure composition and ammonia emissions from growing and finishing pig houses. Yorkshire x F1(Landrace x Yorkshire) pigs (n = 576) with an initial body weight of 24.9 ± 3.4 kg were randomly allocated to four treatments of (i) a control diet with normal protein content and no acidifying components added; (ii) a diet with 2% units CP reduction; (iii) a diet with an acidifying effect on the manure; (iv) or a diet consisting of a combination of diet (ii) and (iii). Pigs were housed in four mechanically ventilated and temperature-controlled rooms. Results showed that decreasing the dietary CP levels by 2% units reduced the ammonia emission from the floor by 46% (p = 0.06) and from the pig house by 31% (p = 0.08). Decreased CP diets reduced the total N in feces and in manure and NH4-N in the manure, as well as the ammonia concentration at 1 cm and 10 cm above the manure surface (p < 0.05). However, acidifying diets failed to reduce ammonia emissions from the floor and the pig house (p > 0.05). Reducing dietary crude protein is, therefore, a solution to reducing ammonia emissions from pig houses.

Abstract Background In low-immunological risk kidney transplant recipients (KTRs), reduced exposure to calcineurin inhibitor (CNI) appears particularly attractive for avoiding adverse events, but may increase the risk of developing de novo Donor Specific Antibodies (dnDSA). Methods CNI exposure was retrospectively analyzed in 247 non-HLA immunized first KTRs by taking into account trough levels (C0) collected during follow-up. Reduced exposure to CNI was defined as follows: C0 less than the lower limit of the international targets for ≥50% of follow-up. Results During a mean follow-up of 5.0 ± 2.0 years, 39 patients (15.8%) developed dnDSA (MFI ≥1000). Patients with DSA were significantly younger (46.6 ± 13.8 vs. 51.7 ± 14.0 years, p = 0.039), received more frequently poorly-matched grafts (59% with 6–8 A-B-DR-DQ HLA mismatches vs. 34.6%, p = 0.016) and had more frequently a reduced exposure to CNI (92.3% vs. 62.0%, p = 0.0002). Reduced exposure to CNI was associated with an increased risk of dnDSA (multivariable HR = 9.77, p = 0.002). Reduced exposure to CNI had no effect on patient survival, graft loss from any cause including death, or post-transplant cancer. Conclusions Even in a low-immunological risk population, reduced exposure to CNI is associated with increased risk of dnDSA. Benefits and risks of under-immunosuppression must be carefully evaluated before deciding on CNI minimization.

Abstract In the Mauritian macaque experimentally inoculated with SIV, gene polymorphisms potentially associated with the plasma virus load at a set point, approximately 100 days post inoculation, were investigated. Among the 42 animals inoculated with 50 AID50 of the same strain of SIV, none of which received any preventive or curative treatment, nine individuals were selected: three with a plasma virus load (PVL) among the lowest, three with intermediate PVL values and three among the highest PVL values. The complete genomes of these nine animals were then analyzed. Initially, attention was focused on variants with a potential functional impact on protein encoding genes (non-synonymous SNPs (NS-SNPs) and splicing variants). Thus, 424 NS-SNPs possibly associated with PVL were detected. The 424 candidates SNPs were genotyped in these 42 SIV experimentally infected animals (including the nine animals subjected to whole genome sequencing). The genes containing variants most probably associated with PVL at a set time point are analyzed herein.

Background: The optimal management of immunosuppressive therapy (IT) after kidney allograft failure (KAF) remains controversial. Although maintaining IT may reduce HLA‐sensitization and improve access to retransplantation, it may also increase the rate of immunosuppression‐related complications. The overall impact on patient mortality is unknown. The main objective of this study was to compare the evolution of HLA‐sensitization 6 months after KAF according to IT management. Methods: Individual clinical and health care data were extracted from the French national end‐stage kidney disease registry (Renal Epidemiology and Information Network [REIN]) and the French National Health Data system (SNDS), respectively. Patients aged > 18 years returning to dialysis after KAF between January 2008 and December 2019 in Lorraine were included. Patients were classified into two groups, IT continuation or IT discontinuation. HLA‐sensitization was defined as an increase in incompatible graft rate (IGR) between KAF and 6 months post‐KAF (change to a higher predefined category (0%–5%), (5%–20%), (20%–50%), (50%–85%), (85%–95%), (95%–98%), (98%–100%)). Secondary outcome was patient survival according to IT management. Results: A total of 121 patients were included, 35 (29%) of whom continued IT. HLA‐sensitization after KAF tended to be higher in the "IT discontinuation" group (57% vs. 38% in the "IT continuation" group, p =.07). In multivariate analysis, IT continuation was associated with a lower increase in IGR (OR.37, 95% CI [.14;.93]). IT management was not associated with patient mortality. Conclusions: Continuation of IT after KAF was associated with less change in IGR and was not associated with excess mortality. [ABSTRACT FROM AUTHOR]

Ammonia emissions from naturally ventilated livestock buildings (NVLBs) pose a serious environmental problem. However, the mechanisms that control these emissions are still not fully understood. One promising method for understanding these mechanisms is physical modelling in wind tunnels. This paper reviews studies that have used this method to investigate flow or pollutant dispersion within or from NVLBs. The review indicates the importance of wind tunnels for understanding the flow and pollutant dispersion processes within and from NVLBs. However, most studies have investigated the flow, while only few studies have focused on pollutant dispersion. Furthermore, only few studies have simulated all the essential parameters of the approaching boundary layer. Therefore, this paper discusses these shortcomings and provides tips and recommendations for further research in this respect.