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We present evidence implicating the BAF (BRG1/BRM Associated Factor) chromatin remodeler in meiotic sex chromosome inactivation (MSCI). By immunofluorescence (IF), the putative BAF DNA binding subunit, ARID1A (AT-rich Interaction Domain 1 a), appeared enriched on the male sex chromosomes during diplonema of meiosis I. Germ cells showing a Cre-induced loss of ARID1A arrested in pachynema and failed to repress sex-linked genes, indicating a defective MSCI. Mutant sex chromosomes displayed an abnormal presence of elongating RNA polymerase II coupled with an overall increase in chromatin accessibility detectable by ATAC-seq. We identified a role for ARID1A in promoting the preferential enrichment of the histone variant, H3.3, on the sex chromosomes, a known hallmark of MSCI. Without ARID1A, the sex chromosomes appeared depleted of H3.3 at levels resembling autosomes. Higher resolution analyses by CUT&RUN revealed shifts in sex-linked H3.3 associations from discrete intergenic sites and broader gene-body domains to promoters in response to the loss of ARID1A. Several sex-linked sites displayed ectopic H3.3 occupancy that did not co-localize with DMC1 (DNA meiotic recombinase 1). This observation suggests a requirement for ARID1A in DMC1 localization to the asynapsed sex chromatids. We conclude that ARID1A-directed H3.3 localization influences meiotic sex chromosome gene regulation and DNA repair.

The INO80 protein is the main catalytic subunit of the INO80-chromatin remodeling complex, which is critical for DNA repair and transcription regulation in murine spermatocytes. In this study, we explored the role of INO80 in silencing genes on meiotic sex chromosomes in male mice. INO80 immunolocalization at the XY body in pachytene spermatocytes suggested a role for INO80 in the meiotic sex body. Subsequent deletion of Ino80 resulted in high expression of sex-linked genes. Furthermore, the active form of RNA polymerase II at the sex chromosomes of Ino80-null pachytene spermatocytes indicates incomplete inactivation of sex-linked genes. A reduction in the recruitment of initiators of meiotic sex chromosome inhibition (MSCI) argues for INO80-facilitated recruitment of DNA repair factors required for silencing sex-linked genes. This role of INO80 is independent of a common INO80 target, H2A.Z. Instead, in the absence of INO80, a reduction in chromatin accessibility at DNA repair sites occurs on the sex chromosomes. These data suggest a role for INO80 in DNA repair factor localization, thereby facilitating the silencing of sex-linked genes during the onset of pachynema.

Abstract We propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers and those based on dilution, image processing, or deep learning, typically excel with homogeneous backgrounds. Yet, they often fall short in accurately detecting particles against the intricate and varied backgrounds characteristic of heterogeneous particle–substrate (HPS) interfaces in manufacturing. To address this, we've developed a flexible framework designed to detect particles in diverse environments and input types. Our modular framework hinges on model selection and AI-guided particle detection as its core, with preprocessing and postprocessing as integral components, creating a four-step process. This system is versatile, allowing for various preprocessing, AI model selections, and post-processing strategies. We demonstrate this with an entrainment-based particle delivery method, transferring various particles onto substrates that mimic the HPS interface. By altering particle and substrate properties (e.g., material type, size, roughness, shape) and process parameters (e.g., capillary number) during particle entrainment, we capture images under different ambient lighting conditions, introducing a range of HPS background complexities. In the preprocessing phase, we apply image enhancement and sharpening techniques to improve detection accuracy. Specifically, image enhancement adjusts the dynamic range and histogram, while sharpening increases contrast by combining the high pass filter output with the base image. We introduce an image classifier model (based on the type of heterogeneity), employing Transfer Learning with MobileNet as a Model Selector, to identify the most appropriate AI model (i.e., YOLO model) for analyzing each specific image, thereby enhancing detection accuracy across particle–substrate variations. Following image classification based on heterogeneity, the relevant YOLO model is employed for particle identification, with a distinct YOLO model generated for each heterogeneity type, improving overall classification performance. In the post-processing phase, domain knowledge is used to minimize false positives. Our analysis indicates that the AI-guided framework maintains consistent precision and recall across various HPS conditions, with the harmonic mean of these metrics comparable to those of individual AI model outcomes. This tool shows potential for advancing in-situ process monitoring across multiple manufacturing operations, including high-density powder-based 3D printing, powder metallurgy, extreme environment coatings, particle categorization, and semiconductor manufacturing.

Abstract Battery electric vehicles (BEVs) have emerged as a promising alternative to traditional internal combustion engine (ICE) vehicles due to benefits in improved fuel economy, lower operating cost, and reduced emission. BEVs use electric motors rather than fossil fuels for propulsion and typically store electric energy in lithium-ion cells. With rising concerns over fossil fuel depletion and the impact of ICE vehicles on the climate, electric mobility is widely considered as the future of sustainable transportation. BEVs promise to drastically reduce greenhouse gas emissions as a result of the transportation sector. However, mass adoption of BEVs faces major barriers due to consumer worries over several important battery-related issues, such as limited range, long charging time, lack of charging stations, and high initial cost. Existing solutions to overcome these barriers, such as building more charging stations, increasing battery capacity, and stationary vehicle-to-vehicle (V2V) charging, often suffer from prohibitive investment costs, incompatibility to existing BEVs, or long travel delays. In this paper, we propose Peer-to-Peer Car Charging (P2C2), a scalable approach for charging BEVs that alleviates the need for elaborate charging infrastructure. The central idea is to enable BEVs to share charge among each other while in motion through coordination with a cloud-based control system. To re-vitalize a BEV fleet, which is continuously in motion, we introduce Mobile Charging Stations (MoCS), which are high-battery-capacity vehicles used to replenish the overall charge in a vehicle network. Unlike existing V2V charging solutions, the charge sharing in P2C2 takes place while the BEVs are in-motion, which aims at minimizing travel time loss. To reduce BEV-to-BEV contact time without increasing manufacturing costs, we propose to use multiple batteries of varying sizes and charge transfer rates. The faster but smaller batteries are used for charge transfer between vehicles, while the slower but larger ones are used for prolonged charge storage. We have designed the overall P2C2 framework and formalized the decision-making process of the cloud-based control system. We have evaluated the effectiveness of P2C2 using a well-characterized simulation platform and observed dramatic improvement in BEV mobility. Additionally, through statistical analysis, we show that a significant reduction in carbon emission is also possible if MoCS can be powered by renewable energy sources.

Estradiol-17β (E) plays an important role in ovarian follicular development. Evidence indicates that some of the effect of E is mediated by the transmembrane estrogen receptor. In this study, we examined the spatio-temporal expression of recently discovered ERα36 (ESR36), a splice variant of Esr1 and a receptor for non-genomic E signaling, in the hamster ovary during the estrous cycle and the role of gonadotropins and ovarian steroid hormones in ESR36 expression. ESR36 expression was high on estrus (D1:0900 h) and declined precipitously by proestrus (D4:0900 h) and remained low up to D4:1600 h. Immunofluorescence findings corroborated immunoblot findings and revealed that ESR36 was expressed only in the cell membrane of both follicular and non-follicular cells, except the oocytes. Ovarian ESR36 was capable of binding to the E-affinity matrix, and have different molecular weight than that of the ESR1 or GPER. Hypophysectomy (Hx) resulted in a marked decline in ESR36 protein levels. FSH and LH, alone or combined, markedly upregulated ESR36 protein in Hx hamsters to the levels observed in D1 hamsters, but neither E nor P had any effect. Inhibition of the gonadotropin surge by phenobarbital treatment on D4:1100 h attenuated ESR36 expression in D1:0900 h ovaries, but the decline was restored by either FSH or LH replacement on D4 afternoon. This is the first report to show that ESR36, which is distinct from ESR1 or GPER is expressed in the plasma membrane of ovarian follicular and non-follicular cells, binds to E and its expression is regulated directly by the gonadotropins. In light of our previous findings, the results suggest that ovarian cells contain at least two distinct membrane estrogen receptors, such as GPER and ESR36, and strongly suggest for a non-genomic action of E regulating ovarian follicular functions.