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In a previous work, a hole lifetime of 1.4 ns for GaAsCr sensors was determined for the sensors of the 2016 sensor batch, explaining the so-called "crater effect" which describes the occurrence of negative signals in the pixels around a pixel with a photon hit due to the missing hole contribution to the overall signal causing an incomplete signal induction. In this publication, the "crater effect" is further elaborated by measuring GaAsCr sensors using the sensors from 2017. The hole lifetime of these sensors was 2.5 ns. A focused photon beam was used to illuminate well defined positions along the pixels in order to corroborate the findings from the previous work and to further characterize the consequences of the "crater effect" on the detector operation.The tumor-microenvironment (TME) is an amalgamation of various factors derived from malignant cells and infiltrating host cells, including cells of the immune system. One of the important factors of the TME is microRNAs (miRs) that regulate target gene expression at a post transcriptional level. MiRs have been found to be dysregulated in tumor as well as in stromal cells and they emerged as important regulators of tumorigenesis. In fact, miRs regulate almost all hallmarks of cancer, thus making them attractive tools and targets for novel anti-tumoral treatment strategies. Tumor to stroma cell cross-propagation of miRs to regulate protumoral functions has been a salient feature of the TME. MiRs can either act as tumor suppressors or oncogenes (oncomiRs) and both miR mimics as well as miR inhibitors (antimiRs) have been used in preclinical trials to alter cancer and stromal cell phenotypes. Owing to their cascading ability to regulate upstream target genes and their chemical nature, which allows specific pharmacological targeting, miRs are attractive targets for anti-tumor therapy. In this review, we cover a recent update on our understanding of dysregulated miRs in the TME and provide an overview of how these miRs are involved in current cancer-therapeutic approaches from bench to bedside.Intracellular divalent cations control the molecular function of transmembrane protein 16 (TMEM16) family members. Both anion channels (such as TMEM16A) and phospholipid scramblases (such as TMEM16F) in this family are activated by intracellular Ca2+ in the low µM range. In addition, intracellular Ca2+ or Co2+ at mM concentrations have been shown to further potentiate the saturated Ca2+-activated current of TMEM16A. In this study, we found that all alkaline earth divalent cations in mM concentrations can generate similar potentiation effects in TMEM16A when applied intracellularly, and that manipulations thought to deplete membrane phospholipids weaken the effect. In comparison, mM concentrations of divalent cations minimally potentiate the current of TMEM16F but significantly change its cation/anion selectivity. We suggest that divalent cations may increase local concentrations of permeant ions via a change in pore electrostatic potential, possibly acting through phospholipid head groups in or near the pore. Monovalent cations appear to exert a similar effect, although with a much lower affinity. Our findings resolve controversies regarding the ion selectivity of TMEM16 proteins. The physiological role of this mechanism, however, remains elusive because of the nearly constant high cation concentrations in cytosols.Despite Degenerative Cervical Myelopathy (DCM) being the most common form of spinal cord injury, effective methods to evaluate patients for its presence and severity are only starting to appear. Evaluation of patient images, while fast, is often unreliable; the pathology of DCM is complex, and clinicians often have difficulty predicting patient prognosis. Automated tools, such as the Spinal Cord Toolbox (SCT), show promise, but remain in the early stages of development. To evaluate the current state of an SCT automated process, we applied it to MR imaging records from 328 DCM patients, using the modified Japanese Orthopedic Associate scale as a measure of DCM severity. We found that the metrics extracted from these automated methods are insufficient to reliably predict disease severity. Such automated processes showed potential, however, by highlighting trends and barriers which future analyses could, with time, overcome. This, paired with findings from other studies with similar processes, suggests that additional non-imaging metrics could be added to achieve diagnostically relevant predictions. Although modeling techniques such as these are still in their infancy, future models of DCM severity could greatly improve automated clinical diagnosis, communications with patients, and patient outcomes.Bovine digital dermatitis (DD) is a contagious infectious cause of lameness in cattle with unknown definitive etiologies. Many of the bacterial species detected in metagenomic analyses of DD lesions are difficult to culture, and their antimicrobial resistance status is largely unknown. Recently, a novel proximity ligation-guided metagenomic approach (Hi-C ProxiMeta) has been used to identify bacterial reservoirs of antimicrobial resistance genes (ARGs) directly from microbial communities, without the need to culture individual bacteria. The objective of this study was to track tetracycline resistance determinants in bacteria involved in DD pathogenesis using Hi-C. A pooled sample of macerated tissues from clinical DD lesions was used for this purpose. Selleckchem RP-6306 Metagenome deconvolution using ProxiMeta resulted in the creation of 40 metagenome-assembled genomes with ≥80% complete genomes, classified into five phyla. Further, 1959 tetracycline resistance genes and ARGs conferring resistance to aminoglycoside, beta-lactams, sulfonamide, phenicol, lincosamide, and erythromycin were identified along with their bacterial hosts. In conclusion, the widespread distribution of genes conferring resistance against tetracycline and other antimicrobials in bacteria of DD lesions is reported for the first time. Use of proximity ligation to identify microorganisms hosting specific ARGs holds promise for tracking ARGs transmission in complex microbial communities.