Asymmetric ephaptic inhibition between compartmentalized olfactory receptor neurons.

In the Drosophila antenna, different subtypes of olfactory receptor neurons (ORNs) housed in the same sensory hair (sensillum) can inhibit each other non-synaptically. However, the mechanisms underlying this underexplored form of lateral inhibition remain unclear. Here we use recordings from pairs of sensilla impaled by the same tungsten electrode to demonstrate that direct electrical ("ephaptic") interactions mediate lateral inhibition between ORNs. Intriguingly, within individual sensilla, we find that ephaptic lateral inhibition is asymmetric such that one ORN exerts greater influence onto its neighbor. Serial block-face scanning electron microscopy of genetically identified ORNs and circuit modeling indicate that asymmetric lateral inhibition reflects a surprisingly simple mechanism: the physically larger ORN in a pair corresponds to the dominant neuron in ephaptic interactions. Thus, morphometric differences between compartmentalized ORNs account for highly specialized inhibitory interactions that govern information processing at the earliest stages of olfactory coding

Time-Selective RNN for Device-Free Multi-Room Human Presence Detection Using WiFi CSI

Human presence detection is a crucial technology for various applications, including home automation, security, and healthcare. While camera-based systems have traditionally been used for this purpose, they raise privacy concerns. To address this issue, recent research has explored the use of channel state information (CSI) approaches that can be extracted from commercial WiFi access points (APs) and provide detailed channel characteristics. In this thesis, we propose a device-free human presence detection system for multi-room scenarios using a time-selective conditional dual feature extract recurrent Network (TCD-FERN). Our system is designed to capture significant time features with the condition on current human features using a dynamic and static (DaS) data preprocessing technique to extract moving and spatial features of people and differentiate between line-of-sight (LoS) path blocking and non-blocking cases. To mitigate the feature attenuation problem caused by room partitions, we employ a voting scheme. We conduct evaluation and real-time experiments to demonstrate that our proposed TCD-FERN system can achieve human presence detection for multi-room scenarios using fewer commodity WiFi APs

New Insights on 30 Dor B Revealed by High-Quality Multi-wavelength Observations

The supernova remnant (SNR) 30 Dor B is associated with the \ion{H}{2} region ionized by the OB association LH99. The complex interstellar environment has made it difficult to study the physical structure of this SNR. We have used Hubble Space Telescope H$\alpha$ images to identify SNR shocks and deep Chandra X-ray observations to detect faint diffuse emission. We find that 30 Dor B hosts three zones with very different X-ray surface brightnesses and nebular kinematics that are characteristic of SNRs in different interstellar environments and/or evolutionary stages. The ASKAP 888 MHz map of 30 Dor B shows counterparts to all X-ray emission features except the faint halo. The ASKAP 888 MHz and 1420 MHz observations are used to produce a spectral index map, but its interpretation is complicated by the background thermal emission and the pulsar PSR J0537$-$6910's flat spectral index. The stellar population in the vicinity of 30 Dor B indicates a continuous star formation in the past 8--10 Myr. The observed very massive stars in LH99 cannot be coeval with the progenitor of 30 Dor B's pulsar. Adopting the pulsar's spin-down timescale, 5000 yr, as the age of the SNR, the X-ray shell would be expanding at $\sim$4000 km\,s$^{-1}$ and the post-shock temperature would be 1--2 orders of magnitude higher than that indicated by the X-ray spectra. Thus, the bright central region of 30 Dor B and the X-ray shell requires two separate SN events, and the faint diffuse X-ray halo perhaps other older SN events

PerAda: Parameter-Efficient and Generalizable Federated Learning Personalization with Guarantees

Personalized Federated Learning (pFL) has emerged as a promising solution to tackle data heterogeneity across clients in FL. However, existing pFL methods either (1) introduce high communication and computation costs or (2) overfit to local data, which can be limited in scope, and are vulnerable to evolved test samples with natural shifts. In this paper, we propose PerAda, a parameter-efficient pFL framework that reduces communication and computational costs and exhibits superior generalization performance, especially under test-time distribution shifts. PerAda reduces the costs by leveraging the power of pretrained models and only updates and communicates a small number of additional parameters from adapters. PerAda has good generalization since it regularizes each client's personalized adapter with a global adapter, while the global adapter uses knowledge distillation to aggregate generalized information from all clients. Theoretically, we provide generalization bounds to explain why PerAda improves generalization, and we prove its convergence to stationary points under non-convex settings. Empirically, PerAda demonstrates competitive personalized performance (+4.85% on CheXpert) and enables better out-of-distribution generalization (+5.23% on CIFAR-10-C) on different datasets across natural and medical domains compared with baselines, while only updating 12.6% of parameters per model based on the adapter

Discovering Physical Interaction Vulnerabilities in IoT Deployments

Internet of Things (IoT) applications drive the behavior of IoT deployments according to installed sensors and actuators. It has recently been shown that IoT deployments are vulnerable to physical interactions, caused by design flaws or malicious intent, that can have severe physical consequences. Yet, extant approaches to securing IoT do not translate the app source code into its physical behavior to evaluate physical interactions. Thus, IoT consumers and markets do not possess the capability to assess the safety and security risks these interactions present. In this paper, we introduce the IoTSeer security service for IoT deployments, which uncovers undesired states caused by physical interactions. IoTSeer operates in four phases (1) translation of each actuation command and sensor event in an app source code into a hybrid I/O automaton that defines an app's physical behavior, (2) combining apps in a novel composite automaton that represents the joint physical behavior of interacting apps, (3) applying grid-based testing and falsification to validate whether an IoT deployment conforms to desired physical interaction policies, and (4) identification of the root cause of policy violations and proposing patches that guide users to prevent them. We use IoTSeer in an actual house with 13 actuators and six sensors with 37 apps and demonstrate its effectiveness and performance

Recombinant VP1, an Akt Inhibitor, Suppresses Progression of Hepatocellular Carcinoma by Inducing Apoptosis and Modulation of CCL2 Production

BACKGROUND: The application of viral elements in tumor therapy is one facet of cancer research. Recombinant capsid protein VP1 (rVP1) of foot-and-mouth disease virus has previously been demonstrated to induce apoptosis in cancer cell lines. Here, we aim to further investigate its apoptotic mechanism and possible anti-metastatic effect in murine models of hepatocellular carcinoma (HCC), one of the most common human cancers worldwide. METHODOLOGY/PRINCIPAL FINDINGS: Treatment with rVP1 inhibited cell proliferation in two murine HCC cell lines, BNL and Hepa1-6, with IC₅₀ values in the range of 0.1-0.2 µM. rVP1 also induced apoptosis in these cells, which was mediated by Akt deactivation and dissociation of Ku70-Bax, and resulted in conformational changes and mitochondrial translocation of Bax, leading to the activation of caspases-9, -3 and -7. Treatment with 0.025 µM rVP1, which did not affect the viability of normal hepatocytes, suppressed cell migration and invasion via attenuating CCL2 production. The production of CCL2 was modulated by Akt-dependent NF-κB activation that was decreased after rVP1 treatment. The in vivo antitumor effects of rVP1 were assessed in both subcutaneous and orthotopic mouse models of HCC in immune-competent BALB/c mice. Intratumoral delivery of rVP1 inhibited subcutaneous tumor growth as a result of increased apoptosis. Intravenous administration of rVP1 in an orthotopic HCC model suppressed tumor growth, inhibited intra-hepatic metastasis, and prolonged survival. Furthermore, a decrease in the serum level of CCL2 was observed in rVP1-treated mice. CONCLUSIONS/SIGNIFICANCE: The data presented herein suggest that, via inhibiting Akt phosphorylation, rVP1 suppresses the growth, migration, and invasion of murine HCC cells by inducing apoptosis and attenuating CCL2 production both in vitro and in vivo. Recombinant protein VP1 thus has the potential to be developed as a new therapeutic agent for HCC

Promoter methylation and downregulation of SLC22A18 are associated with the development and progression of human glioma

<p>Abstract</p> <p>Background</p> <p>Downregulation of the putative tumor suppressor gene <it>SLC22A18 </it>has been reported in a number of human cancers. The aim of this study was to investigate the relationship between <it>SLC22A18 </it>downregulation, promoter methylation and the development and progression of human glioma.</p> <p>Method</p> <p><it>SLC22A18 </it>expression and promoter methylation was examined in human gliomas and the adjacent normal tissues. U251 glioma cells stably overexpressing <it>SLC22A18 </it>were generated to investigate the effect of <it>SLC22A18 </it>on cell growth and adherence <it>in vitro </it>using the methyl thiazole tetrazolium assay. Apoptosis was quantified using flow cytometry and the growth of <it>SLC22A18 </it>overexpressing U251 cells was measured in an <it>in viv</it>o xenograft model.</p> <p>Results</p> <p><it>SLC22A18 </it>protein expression is significantly decreased in human gliomas compared to the adjacent normal brain tissues. <it>SLC22A18 </it>protein expression is significantly lower in gliomas which recurred within six months after surgery than gliomas which did not recur within six months. <it>SLC22A18 </it>promoter methylation was detected in 50% of the gliomas, but not in the adjacent normal tissues of any patient. SLC22A18 expression was significantly decreased in gliomas with <it>SLC22A18 </it>promoter methylation, compared to gliomas without methylation. The <it>SLC22A18 </it>promoter is methylated in U251 cells and treatment with the demethylating agent 5-aza-2-deoxycytidine increased <it>SLC22A18 </it>expression and reduced cell proliferation. Stable overexpression of <it>SLC22A18 </it>inhibited growth and adherence, induced apoptosis <it>in vitro </it>and reduced <it>in vivo </it>tumor growth of U251 cells.</p> <p>Conclusion</p> <p><it>SLC22A18 </it>downregulation via promoter methylation is associated with the development and progression of glioma, suggesting that <it>SLC22A18 </it>is an important tumor suppressor in glioma.</p