Videos of sipuleucel-T programmed T cells lysing cells that express prostate cancer target antigens

Sipuleucel-T, an autologous cellular immunotherapy, was approved to treat metastatic castration-resistant prostate cancer in 2010 in the United States. Treatment with sipuleucel-T primes the immune system to target prostate acid phosphatase, which is expressed by prostate cancer cells, potentially leading to lysis of cancer cells. Expanding on previously reported indirect evidence of cell killing with sipuleucel-T treatment, we sought to provide direct evidence of cell lysis through visualization. We used advanced video technology and available samples of peripheral blood mononuclear cells from subjects enrolled in the STAMP trial (NCT01487863). Isolated CD8+ T cells were used as effector cells and cocultured with autologous monocytes pulsed with control or target antigens. Differentially stained effector and target cells were then video recorded during coculture. Here, we present video recordings and analyses of T cells from sipuleucel-T-treated subjects showing-for the first time-direct lysis of cells that express prostate cancer target antigens, prostate acid phosphatase, or prostate-specific antigen

The Meigs Creek no. 9 coal bed in Ohio

The Meigs Creek no. 9 coal bed in Ohio: Part 1 - Geology and reserves, by William H. Smith, Russell A. Brant, and Fred Amos. Part 2 - Washability characteristics and other properties, by Peter O. Krumin.The location of the Meigs Creek coal deposits is shown on Map L (See following page.) As calculated in this study this bed extends in mineable thickness over 1040 square miles, and contains 3,973,331,000 tons of coal reserves. These remaining reserves in the Meigs Creek bed are believed to be the largest in any of Ohio's easily available coal deposits, except perhaps in the Pittsburgh #8 seam, The coal lies near the ~face and is easily accessible by stripping. This has caused a 400% rise in the production of coal from the seam during the past 8 years. Quality wise, the cool in the #9 seam does not compare well with other Ohio coals, so that to date its chief utilization has been in the production of electrical power, In much of the field, the seam occurs as two beds, or benches, separated by as much as 30 inches of clay parting which adds to the difficulty in mining and cleaning. This has necessitated the compilation of reserve tonnage separately for each of the benches. Part II of the report discusses laboratory investigations of methods of improving the quality of the coal by mechanical cleaning. Part I contains a discussion of the geology of the seam and gives the reserves by thickness (14 -28" , 28"-42", 42"-54", etc.) and by reliability category (proven, probable, and inferred) for each township in which mineable Meigs Creek coal occurs

Diversity, host specialization, and geographic structure of filarial nematodes infecting Malagasy bats

We investigated filarial infection in Malagasy bats to gain insights into the diversity of these parasites and explore the factors shaping their distribution. Samples were obtained from 947 individual bats collected from 52 sites on Madagascar and representing 31 of the 44 species currently recognized on the island. Samples were screened for the presence of micro-and macro-parasites through both molecular and morphological approaches. Phylogenetic analyses showed that filarial diversity in Malagasy bats formed three main groups, the most common represented by Litomosa spp. infecting Miniopterus spp. (Miniopteridae); a second group infecting Pipistrellus cf. hesperidus (Vespertilionidae) embedded within the Litomosoides cluster, which is recognized herein for the first time from Madagascar; and a third group composed of lineages with no clear genetic relationship to both previously described filarial nematodes and found in M. griveaudi, Myotis goudoti, Neoromicia matroka (Vespertilionidae), Otomops madagascariensis (Molossidae), and Paratriaenops furculus (Hipposideridae). We further analyzed the infection rates and distribution pattern of Litomosa spp., which was the most diverse and prevalent filarial taxon in our sample. Filarial infection was disproportionally more common in males than females in Miniopterus spp., which might be explained by some aspect of roosting behavior of these cave-dwelling bats. We also found marked geographic structure in the three Litomosa clades, mainly linked to bioclimatic conditions rather than host-parasite associations. While this study demonstrates distinct patterns of filarial nematode infection in Malagasy bats and highlights potential drivers of associated geographic distributions, future work should focus on their alpha taxonomy and characterize arthropod vectors

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers