Astronauts, while traveling through space, suffer rapid weight loss, but the factors responsible for this reduction in mass remain elusive. Norepinephrine stimulation, through the sympathetic nerves innervating the thermogenic tissue brown adipose tissue (BAT), promotes both the production of heat and the growth of new blood vessels within it. The effects of hindlimb unloading (HU), mimicking a weightless environment in space, on the structural and physiological modifications in brown adipose tissue (BAT), together with serological data, were examined in mice. Long-term application of HU led to the induction of brown adipose tissue thermogenesis, accomplished by enhancing the expression of mitochondrial uncoupling protein. Subsequently, peptide-conjugated indocyanine green was developed, specializing in targeting vascular endothelial cells found within brown adipose tissue. Micron-scale neovascularization in BAT of the HU group was detected by noninvasive fluorescence-photoacoustic imaging, which was further associated with elevated vessel density. The treatment of mice with HU led to a decline in serum triglyceride and glucose levels, revealing heightened heat production and energy consumption in brown adipose tissue (BAT) in comparison to the control group. Research indicated that hindlimb unloading (HU) could possibly be a strategy for preventing obesity, alongside fluorescence-photoacoustic dual-modal imaging's capacity to evaluate brown adipose tissue (BAT) activity. The activation of BAT is concomitant with the expansion of the vascular network. Employing a peptide CPATAERPC-conjugated indocyanine green, targeted towards vascular endothelial cells, fluorescence-photoacoustic imaging precisely mapped the microvascular architecture of brown adipose tissue (BAT), offering non-invasive means to assess in-situ BAT alterations.
In all-solid-state lithium metal batteries (ASSLMBs), composite solid-state electrolytes (CSEs) are fundamentally challenged by the necessity of low-energy-barrier lithium ion transport. A confinement strategy, utilizing hydrogen bonding, is proposed in this work to facilitate the construction of template channels for low-energy-barrier continuous lithium ion transport. 37 nm diameter ultrafine boehmite nanowires (BNWs) were synthesized, dispersed exceptionally well within a polymer matrix, and subsequently formed a flexible composite electrolyte (CSE). Ultrafine BNWs with expansive surface areas and abundant oxygen vacancies assist in the breakdown of lithium salts and constrain the configuration of polymer chain segments through hydrogen bonds with the polymer matrix. This constructs a polymer/ultrafine nanowire composite structure, which functions as channels for the continuous transport of dissociated lithium ions. Subsequently, the electrolytes, as prepared, displayed an acceptable ionic conductivity of 0.714 mS cm⁻¹ and a low energy barrier (1630 kJ mol⁻¹), and the assembled ASSLMB showcased remarkable specific capacity retention (92.8%) following 500 cycles. This study presents a promising approach to designing CSEs that exhibit high ionic conductivity, crucial for high-performance ASSLMBs.
Infants and the elderly are disproportionately affected by bacterial meningitis, a leading cause of illness and death. Mice serve as our model to examine the response of individual major meningeal cell types to E. coli infection in the early postnatal period, leveraging single-nucleus RNA sequencing (snRNAseq), immunostaining, and genetic and pharmacological manipulations of immune cells and signaling. To allow for optimal confocal imaging and determination of cellular abundance and forms, flat preparations of dissected dura and leptomeninges were employed. Infection prompts substantial alterations in the transcriptomic landscapes of the major meningeal cell types – endothelial cells, macrophages, and fibroblasts. Concentrations of extracellular components in the leptomeninges lead to a rearrangement of CLDN5 and PECAM1, and focal areas within the leptomeningeal capillaries show compromised blood-brain barrier. Infection-induced vascular responses are apparently significantly regulated by TLR4 signaling, as confirmed by the remarkably similar responses elicited by infection and LPS treatment, and by the reduced response in Tlr4-/- mice. Puzzlingly, the silencing of Ccr2, encoding a crucial chemoattractant for monocytes, or the rapid depletion of leptomeningeal macrophages, induced by the intracerebroventricular administration of liposomal clodronate, had an insignificant impact on the response of leptomeningeal endothelial cells to E. coli infection. Concomitantly, these data indicate that the EC's reaction to infection is largely dictated by the intrinsic EC response to LPS.
The present paper investigates panoramic image reflection removal, targeting the clarification of the content overlapping between the reflected layer and the transmitted scene. Though a section of the reflected scene is captured in the comprehensive image, yielding further insights for reflection reduction, directly applying this knowledge to eliminate undesirable reflections is challenging due to the misalignment of the panoramic view with the reflection-laden image. This problem demands a holistic solution, thus we propose an integrated system from start to finish. By systematically addressing the misalignments in adaptive modules, the reflection layer and transmission scenes are successfully recovered with high fidelity. We present a new data generation methodology, based on a physics-based model of how mixed images form, and the in-camera dynamic range clipping technique, aiming to minimize the divergence between simulated and genuine datasets. Results from experiments showcase the proposed method's strength and its applicability to both mobile and industrial settings.
The task of locating the specific time spans of actions in untrimmed videos using solely video-level action labels, a problem known as weakly supervised temporal action localization (WSTAL), has become a subject of heightened research focus over the past few years. While a model trained with such labels will lean towards portions of the video most important for the video-level categorization, it invariably produces localization results that are inaccurate and incomplete. This paper approaches the problem of relation modeling from a novel angle, proposing a method we call Bilateral Relation Distillation (BRD). Biobehavioral sciences Our method's essence lies in learning representations by simultaneously considering relational aspects of categories and sequences. Compound pollution remediation Latent segment representations specific to each category are first generated using individual embedding networks, one per category. To capture category-level relationships, we process the knowledge obtained from a pre-trained language model, leveraging correlation alignment and category-aware contrast, both within and between videos. To model inter-segment relations at the sequence level, we develop a gradient-driven feature enhancement approach, ensuring the learned latent representation of the augmented feature aligns with that of the original. read more A comprehensive set of experiments reveals that our strategy attains leading performance on the THUMOS14 and ActivityNet13 datasets.
LiDAR-based 3D object detection's contribution to long-range perception in autonomous driving escalates as the sensing range of LiDAR systems extends. Mainstream 3D object detectors often build dense feature maps, which lead to computational costs that grow quadratically with the range of perception, thereby impeding scalability to long distances. A fully sparse object detector, FSD, is introduced as a method for achieving efficient long-range detection. FSD's design is built from a foundation of a general sparse voxel encoder and the addition of a novel sparse instance recognition (SIR) module. SIR's method involves grouping points into instances and performing highly-efficient feature extraction at the instance level. The design deficiency in fully sparse architectures, caused by the missing center feature, is offset by the instance-wise grouping approach. Capitalizing on the full advantage of the sparse characteristic, we use temporal information to reduce data redundancy and propose FSD++, a highly sparse detector. FSD++ commences by calculating residual points, which depict the alterations in point positions between successive frames. The super sparse input data is built from residual points and some selected foreground points from prior iterations, greatly decreasing data redundancy and computational overhead. Employing the vast Waymo Open Dataset, we meticulously evaluate our method, ultimately reporting state-of-the-art results. We implemented experiments on the Argoverse 2 Dataset, to verify our method's exceptional long-range detection ability; its range of 200 meters greatly surpasses the 75-meter limit of the Waymo Open Dataset. Open-sourced code for the SST project resides on GitHub, accessible via this link: https://github.com/tusen-ai/SST.
This article highlights an ultra-miniaturized implant antenna, having a volume of 2222 mm³, intended for integration with a leadless cardiac pacemaker. Its operational frequency band is the Medical Implant Communication Service (MICS) from 402 to 405 MHz. The proposed antenna, with its planar spiral geometry and a faulty ground plane, reaches 33% radiation efficiency in a lossy medium. Simultaneously, more than 20 dB of forward transmission enhancement is observed. Further optimization of coupling can be achieved by adjusting the antenna's insulation thickness and size, contingent on the target application. The implanted antenna's performance, as measured, reveals a bandwidth of 28 MHz, which extends beyond the needs of the MICS band. Across a vast frequency range, the implanted antenna's different operational behaviors are detailed by the proposed circuit model of the antenna. Radiation resistance, inductance, and capacitance, components of the circuit model, are key to understanding the antenna's interactions within human tissues and the improved performance characteristics of electrically small antennas.