CRS-FL: Conditional Random Sampling for Communication-Efficient and Privacy-Preserving Federated Learning

Federated Learning (FL), a privacy-oriented distributed ML paradigm, is being gaining great interest in Internet of Things because of its capability to protect participants data privacy. Studies have been conducted to address challenges existing in standard FL, including communication efficiency and privacy-preserving. But they cannot achieve the goal of making a tradeoff between communication efficiency and model accuracy while guaranteeing privacy. This paper proposes a Conditional Random Sampling (CRS) method and implements it into the standard FL settings (CRS-FL) to tackle the above-mentioned challenges. CRS explores a stochastic coefficient based on Poisson sampling to achieve a higher probability of obtaining zero-gradient unbiasedly, and then decreases the communication overhead effectively without model accuracy degradation. Moreover, we dig out the relaxation Local Differential Privacy (LDP) guarantee conditions of CRS theoretically. Extensive experiment results indicate that (1) in communication efficiency, CRS-FL performs better than the existing methods in metric accuracy per transmission byte without model accuracy reduction in more than 7% sampling ratio (# sampling size / # model size); (2) in privacy-preserving, CRS-FL achieves no accuracy reduction compared with LDP baselines while holding the efficiency, even exceeding them in model accuracy under more sampling ratio conditions

Identifying the proton loading site cluster in the ba₃ cytochrome c oxidase that loads and traps protons

Cytochrome c Oxidase (CcO) is the terminal electron acceptor in aerobic respiratory chain, reducing O₂ to water. The released free energy is stored by pumping protons through the protein, maintaining the transmembrane electrochemical gradient. Protons are held transiently in a proton loading site (PLS) that binds and releases protons driven by the electron transfer reaction cycle. Multi-Conformation Continuum Electrostatics (MCCE) was applied to crystal structures and Molecular Dynamics snapshots of the B-type Thermus thermophilus CcO. Six residues are identified as the PLS, binding and releasing protons as the charges on heme b and the binuclear center are changed: the heme a₃ propionic acids, Asp287, Asp372, His376 and Glu126B. The unloaded state has one proton and the loaded state two protons on these six residues. Different input structures, modifying the PLS conformation, show different proton distributions and result in different proton pumping behaviors. One loaded and one unloaded protonation states have the loaded/unloaded states close in energy so the PLS binds and releases a proton through the reaction cycle. The alternative proton distributions have state energies too far apart to be shifted by the electron transfers so are locked in loaded or unloaded states. Here the protein can use active states to load and unload protons, but has nearby trapped states, which stabilize PLS protonation state, providing new ideas about the CcO proton pumping mechanism. The distance between the PLS residues Asp287 and His376 correlates with the energy difference between loaded and unloaded states

Pathway and kinetics of cyhalothrin biodegradation by Bacillus thuringiensis strain ZS-19

10.1038/srep08784Scientific Reports

Secure multi-path routing for Internet of Things based on trust evaluation

In the realm of the Internet of Things (IoT), ensuring the security of communication links and evaluating the safety of nodes within these links remains a significant challenge. The continuous threat of anomalous links, harboring malicious switch nodes, poses risks to data transmission between edge nodes and between edge nodes and cloud data centers. To address this critical issue, we propose a novel trust evaluation based secure multi-path routing (TESM) approach for IoT. Leveraging the software-defined networking (SDN) architecture in the data transmission process between edge nodes, TESM incorporates a controller comprising a security verification module, a multi-path routing module, and an anomaly handling module. The security verification module ensures the ongoing security validation of data packets, deriving trust scores for nodes. Subsequently, the multi-path routing module employs multi-objective reinforcement learning to dynamically generate secure multiple paths based on node trust scores. The anomaly handling module is tasked with handling malicious switch nodes and anomalous paths. Our proposed solution is validated through simulation using the Ryu controller and P4 switches in an SDN environment constructed with Mininet. The results affirm that TESM excels in achieving secure data forwarding, malicious node localization, and the secure selection and updating of transmission paths. Notably, TESM introduces a minimal 12.4% additional forwarding delay and a 5.46% throughput loss compared to traditional networks, establishing itself as a lightweight yet robust IoT security defense solution

Vacuolin-1 potently and reversibly inhibits autophagosome-lysosome fusion by activating RAB5A

published_or_final_versio

Functional and Structural Brain Plasticity in Adult Onset Single-Sided Deafness

Single-sided deafness (SSD) or profound unilateral hearing loss obligates the only serviceable ear to capture all acoustic information. This loss of binaural function taxes cognitive resources for accurate listening performance, especially under adverse environments or challenging tasks. We hypothesized that adults with SSD would manifest both functional and structural brain plasticity compared to controls with normal binaural hearing. We evaluated functional alterations using magnetoencephalographic imaging (MEGI) of brain activation during performance of a moderately difficult auditory syllable sequence reproduction task and assessed structural integrity using diffusion tensor imaging (DTI). MEGI showed the SSD cohort to have increased induced oscillations in the theta band over the left superior temporal cortex and decreased induced gamma band oscillations over the frontal and parietal cortices between 175 and 475 ms following stimulus onset. DTI showed the SSD cohort to have extensive fractional anisotropy (FA) reduction in both auditory and non-auditory tracts and regions. Overlaying functional and structural changes revealed by the two imaging techniques demonstrated close registration of cortical areas and white matter tracts that expressed brain plasticity. Hence, complete loss of input from one ear in adulthood triggers both functional and structural alterations to dorsal temporal and frontal-parietal areas

A Biologically Interpretable Two-Stage Deep Neural Network (BIT-DNN) for Vegetation Recognition From Hyperspectral Imagery

Spectral-spatial-based deep learning models have recently proven to be effective in hyper-spectral image (HSI) classification for various earth monitoring applications such as land cover classification and agricultural monitoring. However, due to the nature of ``black-box'' model representation, how to explain and interpret the learning process and the model decision, especially for vegetation classification, remains an open challenge. This study proposes a novel interpretable deep learning model--a biologically interpretable two-stage deep neural network (BIT-DNN), by incorporating the prior-knowledge (i.e., biophysical and biochemical attributes and their hierarchical structures of target entities)-based spectral-spatial feature transformation into the proposed framework, capable of achieving both high accuracy and interpretability on HSI-based classification tasks. The proposed model introduces a two-stage feature learning process: in the first stage, an enhanced interpretable feature block extracts the low-level spectral features associated with the biophysical and biochemical attributes of target entities; and in the second stage, an interpretable capsule block extracts and encapsulates the high-level joint spectral-spatial features representing the hierarchical structure of biophysical and biochemical attributes of these target entities, which provides the model an improved performance on classification and intrinsic interpretability with reduced computational complexity. We have tested and evaluated the model using four real HSI data sets for four separate tasks (i.e., plant species classification, land cover classification, urban scene recognition, and crop disease recognition tasks). The proposed model has been compared with five state-of-the-art deep learning models. The results demonstrate that the proposed model has competitive advantages in terms of both classification accuracy and model interpretability, especially for vegetation classification

Corticostriatal functional connectivity of bothersome tinnitus in single-sided deafness

Subjective tinnitus is an auditory phantom perceptual disorder without an objective biomarker. Bothersome tinnitus in single-sided deafness (SSD) is particularly challenging to treat because the deaf ear can no longer be stimulated by acoustic means. We contrasted an SSD cohort with bothersome tinnitus (TIN; N = 15) against an SSD cohort with no or non-bothersome tinnitus (NO TIN; N = 15) using resting-state functional magnetic resonance imaging (fMRI). All study participants had normal hearing in one ear and severe or profound hearing loss in the other. We evaluated corticostriatal functional connectivity differences by placing seeds in the caudate nucleus and Heschl’s Gyrus (HG) of both hemispheres. The TIN cohort showed increased functional connectivity between the left caudate and left HG, and left and right HG and the left caudate. Within the TIN cohort, functional connectivity between the right caudate and cuneus was correlated with the Tinnitus Functional Index (TFI) relaxation subscale. And, functional connectivity between the right caudate and superior lateral occipital cortex, and the right caudate and anterior supramarginal gyrus were correlated with the TFI control subscale. These findings support a striatal gating model of tinnitus and suggest tinnitus biomarkers to monitor treatment response and to target specific brain areas for innovative neuromodulation therapies