The accumulation of α-synuclein (α-syn) aggregates in specific brain regions is a hallmark of synucleinopathies including Parkinson disease (PD). α-Syn aggregates propagate in a "prion-like" manner and can be transferred inside lysosomes to recipient cells through tunneling nanotubes (TNTs). However, how lysosomes participate in the spreading of α-syn aggregates is unclear. Here, by using super-resolution (SR) and electron microscopy (EM), we find that α-syn fibrils affect the morphology of lysosomes and impair their function in neuronal cells. In addition, we demonstrate that α-syn fibrils induce peripheral redistribution of lysosomes, likely mediated by transcription factor EB (TFEB), increasing the efficiency of α-syn fibrils' transfer to neighboring cells. We also show that lysosomal membrane permeabilization (LMP) allows the seeding of soluble α-syn in cells that have taken up α-syn fibrils from the culture medium, and, more importantly, in healthy cells in coculture, following lysosome-mediated transfer of the fibrils. Moreover, we demonstrate that seeding occurs mainly at lysosomes in both donor and acceptor cells, after uptake of α-syn fibrils from the medium and following their transfer, respectively. Finally, by using a heterotypic coculture system, we determine the origin and nature of the lysosomes transferred between cells, and we show that donor cells bearing α-syn fibrils transfer damaged lysosomes to acceptor cells, while also receiving healthy lysosomes from them. These findings thus contribute to the elucidation of the mechanism by which α-syn fibrils spread through TNTs, while also revealing the crucial role of lysosomes, working as a Trojan horse for both seeding and propagation of disease pathology.The large amount of biological data available in the current times, makes it necessary to use tools and applications based on sophisticated and efficient algorithms, developed in the area of bioinformatics. Further, access to high performance computing resources is necessary, to achieve results in reasonable time. To speed up applications and utilize available compute resources as efficient as possible, software developers make use of parallelization mechanisms, like multithreading. Many of the available tools in bioinformatics offer multithreading capabilities, but more compute power is not always helpful. In this study we investigated the behavior of well-known applications in bioinformatics, regarding their performance in the terms of scaling, different virtual environments and different datasets with our benchmarking tool suite BOOTABLE. The tool suite includes the tools BBMap, Bowtie2, BWA, Velvet, IDBA, SPAdes, Clustal Omega, MAFFT, SINA and GROMACS. In addition we added an application using the machineo handle their resources as efficiently as possible and raise the user community's awareness of the efficient usage of computing resources.Accuracy of infrared (IR) models to measure soil particle-size distribution (PSD) depends on soil preparation, methodology (sedimentation, laser), settling times and relevant soil features. Compositional soil data may require log ratio (ilr) transformation to avoid numerical biases. Machine learning can relate numerous independent variables that may impact on NIR spectra to assess particle-size distribution. Our objective was to reach high IRS prediction accuracy across a large range of PSD methods and soil properties. A total of 1298 soil samples from eastern Canada were IR-scanned. Spectra were processed by Stochastic Gradient Boosting (SGB) to predict sand, silt, clay and carbon. Slope and intercept of the log-log relationships between settling time and suspension density function (SDF) (R2 = 0.84-0.92) performed similarly to NIR spectra using either ilr-transformed (R2 = 0.81-0.93) or raw percentages (R2 = 0.76-0.94). Settling times of 0.67-min and 2-h were the most accurate for NIR predictions (R2 = 0.49-0.79). The NIR prediction of sand sieving method (R2 = 0.66) was more accurate than sedimentation method(R2 = 0.53). The NIR 2X gain was less accurate (R2 = 0.69-0.92) than 4X (R2 = 0.87-0.95). The MIR (R2 = 0.45-0.80) performed better than NIR (R2 = 0.40-0.71) spectra. Adding soil carbon, reconstituted bulk density, pH, red-green-blue color, oxalate and Mehlich3 extracts returned R2 value of 0.86-0.91 for texture prediction. In addition to slope and intercept of the SDF, 4X gain, method and pre-treatment classes, soil carbon and color appeared to be promising features for routine SGB-processed NIR particle-size analysis. Machine learning methods support cost-effective soil texture NIR analysis.Aspergillus fumigatus is a thermotolerant, soil-borne fungal species that is ubiquitous in the environment. Mold was nominated to the National Toxicology Program (NTP) by a private individual due to suspected adverse health effects associated with personal exposure in indoor and occupational settings. A. fumigatus is of particular concern in the biowaste industry as the species can contaminate self-heating compost piles. Because of this potential for personal and occupational exposure and the lack of available toxicity data, toxicity studies were conducted in which male and female B6C3F1/N mice were exposed to A. fumigatus conidia (spores) two times a week for 3 months. All in-life procedures, including inhalation exposure, test article preparation, and hematology analysis, were completed by the National Institute for Occupational Safety and Health (NIOSH, Morgantown, WV). Battelle (Columbus, OH) conducted terminal necropsies, measured terminal body and organ weights, and evaluated gross lesions on-site at NIOSH. Tissue processing and histopathology were completed at Battelle. Grocott's methenamine silver (GMS) staining was performed at NIOSH. Genetic toxicology studies on mouse peripheral blood erythrocytes were conducted by Integrated Laboratory Systems, LLC (Research Triangle Park, NC). (Abstract Abridged).BACKGROUND Current treatment options for progressive intrahepatic familial cholestasis type 1 (PFIC-1) comprise ursodeoxycholic acid (UDCA), partial external biliary diversion (PEBD), and liver transplantation (LTx). The role and timing of LTx in PFIC-1 remains debated. We present 2 case reports of male siblings with PFIC-1 who benefited from different treatments. CASE REPORT Both siblings harbored a homozygous truncating mutation in ATP8B1 characteristic for PFIC-1 and both underwent PEBD after unsuccessful UDCA treatment at the age of 7 and 5 months, respectively. see more The older brother, after initial improvement of symptoms, developed severe pruritus, cholestasis, and diarrhea 9 months after PEBD and underwent LTx at the age of 16 months. Chronic diarrhea and abnormal transaminases activity appeared soon after transplantation. A liver biopsy was performed 3 months after LTx and showed severe macrovesicular steatosis (95%). Sixteen months after LTx, total biliary diversion was performed, with rapid relief from diarrhea and significant regression of graft steatosis by less then 30%.