AMPA receptor and synaptic plasticity

Long-term changes in synaptic strength, such as long-term potentiation (LTP) and long-term depression (LTD), have been proposed to be the cellular correlates of learning and memory formation. In the hippocampus, an area of the brain associated with memory formation, LTP and LTD require functional modification of AMPA receptors (AMPARs). Since AMPARs are the major ionotropic glutamate receptors in the brain, changing the single channel properties and/or the number at synapses can greatly affect excitatory synaptic function. Recent studies highlight that functional recruitment of Ca2+-permeable AMPARs (CP-AMPARs) at synapses is another key regulatory mechanism that alter excitatory synaptic transmission. By combining electrophysiology, biochemistry, and imaging methods, I found that phosphorylation of the GluR1 subunit of AMPAR on the serine-845 site (GluR1-S845) is critical for the functional recruitment of CP-AMPARs. This has functional consequences as CP-AMPARs can be expressed at synapses by various neuronal activities both in vitro and in vivo, such as by LTP, sensory experiences, brain diseases and drug addiction. On the other hand, dephosphorylation of the GluR1-S845 is necessary for producing long-term synaptic depression, which is accompanied by a loss in functional CP-AMPARs. Interestingly, the GluR1-S845 site is not required for the plasticity of dendritic spine structures, which is considered an important mechanism for long-term synaptic plasticity as well as learning and memory formation. These results suggest that the functional change in synaptic transmission and the structural synaptic plasticity may utilize separate signaling cascades. In a parallel study, I demonstrated that the beta-site cleaving enzyme 1 (BACE1), which cleaves the amyloid precursor protein (APP) to release the amyloid beta peptide (Abeta), is also involved in regulating synaptic plasticity. Using mice lacking the BACE1 gene, I found that BACE1 is involved in specific forms of synaptic plasticity as well as presynaptic function. Abnormal accumulation of Abeta by excessive BACE1 activity is thought responsible for triggering the pathology of Alzheimer's disease (AD). However, my results caution the development of AD therapeutics targeting the BACE1 activity. In summary, my studies demonstrate that the function of AMPA receptors can be regulated in multiple ways, including phosphorylation of a single amino acid, and is critically involved in synaptic plasticity that underlies learning and memory formation

Schrodinger Bridges Beat Diffusion Models on Text-to-Speech Synthesis

In text-to-speech (TTS) synthesis, diffusion models have achieved promising generation quality. However, because of the pre-defined data-to-noise diffusion process, their prior distribution is restricted to a noisy representation, which provides little information of the generation target. In this work, we present a novel TTS system, Bridge-TTS, making the first attempt to substitute the noisy Gaussian prior in established diffusion-based TTS methods with a clean and deterministic one, which provides strong structural information of the target. Specifically, we leverage the latent representation obtained from text input as our prior, and build a fully tractable Schrodinger bridge between it and the ground-truth mel-spectrogram, leading to a data-to-data process. Moreover, the tractability and flexibility of our formulation allow us to empirically study the design spaces such as noise schedules, as well as to develop stochastic and deterministic samplers. Experimental results on the LJ-Speech dataset illustrate the effectiveness of our method in terms of both synthesis quality and sampling efficiency, significantly outperforming our diffusion counterpart Grad-TTS in 50-step/1000-step synthesis and strong fast TTS models in few-step scenarios. Project page: https://bridge-tts.github.io

Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach

Data augmentation is a critical contributing factor to the success of deep learning but heavily relies on prior domain knowledge which is not always available. Recent works on automatic data augmentation learn a policy to form a sequence of augmentation operations, which are still pre-defined and restricted to limited options. In this paper, we show that a prior-free autonomous data augmentation's objective can be derived from a representation learning principle that aims to preserve the minimum sufficient information of the labels. Given an example, the objective aims at creating a distant "hard positive example" as the augmentation, while still preserving the original label. We then propose a practical surrogate to the objective that can be optimized efficiently and integrated seamlessly into existing methods for a broad class of machine learning tasks, e.g., supervised, semi-supervised, and noisy-label learning. Unlike previous works, our method does not require training an extra generative model but instead leverages the intermediate layer representations of the end-task model for generating data augmentations. In experiments, we show that our method consistently brings non-trivial improvements to the three aforementioned learning tasks from both efficiency and final performance, either or not combined with strong pre-defined augmentations, e.g., on medical images when domain knowledge is unavailable and the existing augmentation techniques perform poorly. Code is available at: https://github.com/kai-wen-yang/LPA3}{https://github.com/kai-wen-yang/LPA3.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS 2022

Passive SSH Key Compromise via Lattices

We demonstrate that a passive network attacker can opportunistically obtain private RSA host keys from an SSH server that experiences a naturally arising fault during signature computation. In prior work, this was not believed to be possible for the SSH protocol because the signature included information like the shared Diffie-Hellman secret that would not be available to a passive network observer. We show that for the signature parameters commonly in use for SSH, there is an efficient lattice attack to recover the private key in case of a signature fault. We provide a security analysis of the SSH, IKEv1, and IKEv2 protocols in this scenario, and use our attack to discover hundreds of compromised keys in the wild from several independently vulnerable implementations

Exploring salivary metabolome alterations in people with HIV: towards early diagnostic markers

BackgroundThe human immunodeficiency virus (HIV) remains a critical global health issue, with a pressing need for effective diagnostic and monitoring tools.MethodologyThis study explored distinctions in salivary metabolome among healthy individuals, individuals with HIV, and those receiving highly active antiretroviral therapy (HAART). Utilizing LC–MS/MS for exhaustive metabolomics profiling, we analyzed 90 oral saliva samples from individuals with HIV, categorized by CD4 count levels in the peripheral blood.ResultsOrthogonal partial least squares-discriminant analysis (OPLS-DA) and other analyses underscored significant metabolic alterations in individuals with HIV, especially in energy metabolism pathways. Notably, post-HAART metabolic profiles indicated a substantial presence of exogenous metabolites and changes in amino acid pathways like arginine, proline, and lysine degradation. Key metabolites such as citric acid, L-glutamic acid, and L-histidine were identified as potential indicators of disease progression or recovery. Differential metabolite selection and functional enrichment analysis, combined with receiver operating characteristic (ROC) and random forest analyses, pinpointed potential biomarkers for different stages of HIV infection. Additionally, our research examined the interplay between oral metabolites and microorganisms such as herpes simplex virus type 1 (HSV1), bacteria, and fungi in individuals with HIV, revealing crucial interactions.ConclusionThis investigation seeks to contribute understanding into the metabolic shifts occurring in HIV infection and following the initiation of HAART, while tentatively proposing novel avenues for diagnostic and treatment monitoring through salivary metabolomics

Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages

IntroductionUnmanned aerial vehicles (UAVs) equipped with visible and multispectral cameras provide reliable and efficient methods for remote crop monitoring and above-ground biomass (AGB) estimation in rice fields. However, existing research predominantly focuses on AGB estimation based on canopy spectral features or by incorporating plant height (PH) as a parameter. Insufficient consideration has been given to the spatial structure and the phenological stages of rice in these studies. In this study, a novel method was introduced by fully considering the three-dimensional growth dynamics of rice, integrating both horizontal (canopy cover, CC) and vertical (PH) aspects of canopy development, and accounting for the growing days of rice.MethodsTo investigate the synergistic effects of combining spectral, spatial and temporal parameters, both small-scale plot experiments and large-scale field testing were conducted in Jiangsu Province, China from 2021 to 2022. Twenty vegetation indices (VIs) were used as spectral features, PH and CC as spatial parameters, and days after transplanting (DAT) as a temporal parameter. AGB estimation models were built with five regression methods (MSR, ENet, PLSR, RF and SVR), using the derived data from six feature combinations (VIs, PH+CC, PH+CC+DAT, VIs+PH +CC, VIs+DAT, VIs+PH+CC+DAT).ResultsThe results showed a strong correlation between extracted and ground-measured PH (R2 = 0.89, RMSE=5.08 cm). Furthermore, VIs, PH and CC exhibit strong correlations with AGB during the mid-tillering to flowering stages. The optimal AGB estimation results during the mid-tillering to flowering stages on plot data were from the PLSR model with VIs and DAT as inputs (R2 = 0.88, RMSE=1111kg/ha, NRMSE=9.76%), and with VIs, PH, CC, and DAT all as inputs (R2 = 0.88, RMSE=1131 kg/ha, NRMSE=9.94%). For the field sampling data, the ENet model combined with different feature inputs had the best estimation results (%error=0.6%–13.5%), demonstrating excellent practical applicability.DiscussionModel evaluation and feature importance ranking demonstrated that augmenting VIs with temporal and spatial parameters significantly enhanced the AGB estimation accuracy. In summary, the fusion of spectral and spatio-temporal features enhanced the actual physical significance of the AGB estimation models and showed great potential for accurate rice AGB estimation during the main phenological stages

SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. Previously, the delineation of GTVs and OARs was performed by experienced radiation oncologists. Recently, deep learning has achieved promising results in many medical image segmentation tasks. However, for NPC OARs and GTVs segmentation, few public datasets are available for model development and evaluation. To alleviate this problem, the SegRap2023 challenge was organized in conjunction with MICCAI2023 and presented a large-scale benchmark for OAR and GTV segmentation with 400 Computed Tomography (CT) scans from 200 NPC patients, each with a pair of pre-aligned non-contrast and contrast-enhanced CT scans. The challenge's goal was to segment 45 OARs and 2 GTVs from the paired CT scans. In this paper, we detail the challenge and analyze the solutions of all participants. The average Dice similarity coefficient scores for all submissions ranged from 76.68\% to 86.70\%, and 70.42\% to 73.44\% for OARs and GTVs, respectively. We conclude that the segmentation of large-size OARs is well-addressed, and more efforts are needed for GTVs and small-size or thin-structure OARs. The benchmark will remain publicly available here: https://segrap2023.grand-challenge.orgComment: A challenge report of SegRap2023 (organized in conjunction with MICCAI2023