Buckling instability of lipid tubules with multibilayer walls under local radial indentation

The mechanical behavior of self-assembled lipid tubules is an important property which determines their suitability for technological applications. We study the instability of multibilayer lipid tubules (with wall thickness t and external radius R(ext)) beyond elastic response under local radial atomic force microscopy indentations. A discontinuity in force-distance curves associated with the buckling instability of lipid tubules is observed. The critical force at which lipid tubules undergo a buckling transition linearly scales as t/R(ext). In addition, a reduced critical buckling force is found to extend a distance of similar to 1 mu m from the end of lipid tubules

Cooperative Classification and Rationalization for Graph Generalization

Graph Neural Networks (GNNs) have achieved impressive results in graph classification tasks, but they struggle to generalize effectively when faced with out-of-distribution (OOD) data. Several approaches have been proposed to address this problem. Among them, one solution is to diversify training distributions in vanilla classification by modifying the data environment, yet accessing the environment information is complex. Besides, another promising approach involves rationalization, extracting invariant rationales for predictions. However, extracting rationales is difficult due to limited learning signals, resulting in less accurate rationales and diminished predictions. To address these challenges, in this paper, we propose a Cooperative Classification and Rationalization (C2R) method, consisting of the classification and the rationalization module. Specifically, we first assume that multiple environments are available in the classification module. Then, we introduce diverse training distributions using an environment-conditional generative network, enabling robust graph representations. Meanwhile, the rationalization module employs a separator to identify relevant rationale subgraphs while the remaining non-rationale subgraphs are de-correlated with labels. Next, we align graph representations from the classification module with rationale subgraph representations using the knowledge distillation methods, enhancing the learning signal for rationales. Finally, we infer multiple environments by gathering non-rationale representations and incorporate them into the classification module for cooperative learning. Extensive experimental results on both benchmarks and synthetic datasets demonstrate the effectiveness of C2R. Code is available at https://github.com/yuelinan/Codes-of-C2R.Comment: Accepted to WWW 202

FedJudge: Federated Legal Large Language Model

Large Language Models (LLMs) have gained prominence in the field of Legal Intelligence, offering potential applications in assisting legal professionals and laymen. However, the centralized training of these Legal LLMs raises data privacy concerns, as legal data is distributed among various institutions containing sensitive individual information. This paper addresses this challenge by exploring the integration of Legal LLMs with Federated Learning (FL) methodologies. By employing FL, Legal LLMs can be fine-tuned locally on devices or clients, and their parameters are aggregated and distributed on a central server, ensuring data privacy without directly sharing raw data. However, computation and communication overheads hinder the full fine-tuning of LLMs under the FL setting. Moreover, the distribution shift of legal data reduces the effectiveness of FL methods. To this end, in this paper, we propose the first Federated Legal Large Language Model (FedJudge) framework, which fine-tunes Legal LLMs efficiently and effectively. Specifically, FedJudge utilizes parameter-efficient fine-tuning methods to update only a few additional parameters during the FL training. Besides, we explore the continual learning methods to preserve the global model's important parameters when training local clients to mitigate the problem of data shifts. Extensive experimental results on three real-world datasets clearly validate the effectiveness of FedJudge. Code is released at https://github.com/yuelinan/FedJudge.Comment: Accepted to DASFAA 202

Dose and formulation of azithromycin in mass drug administration studies: a systematic review protocol

Introduction: Azithromycin has been given for tropical infectious diseases such as trachoma and yaws by mass drug administration (MDA). As well as controlling the infectious disease in question, MDA may have a beneficial effect in reducing mortality in young children. However, the dose, formulation, frequency and duration of azithromycin used in certain infectious diseases may vary in different studies, and these differences may have impacts on the effectiveness of azithromycin MDA. Furthermore, whether the dose, formulation, frequency and duration are associated with the effectiveness of azithromycin for reducing child mortality—if indeed this effect can be confirmed—remain unknown. In this study, we will investigate whether different strategies such as different dose, formulation, frequency and duration affect the effectiveness of azithromycin MDA on the prevalence of certain infectious diseases or child mortality.Methods and analysis: A narrative systematic review will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. PubMed, Embase, the Cochrane Central Register of Controlled Trials, Web of Science, ClinicalTrials.gov and WHO International Clinical Trials Registry Platform will be searched. No language restrictions will be applied. All randomised/quasi-controlled trials, observational studies (cross-sectional studies, cohort studies and case–control studies), case series and registered protocols will be considered. Dose, duration, frequency, rounds and formulations of azithromycin used in MDA will be collected and reviewed. The outcomes will be disease prevalence/control in children and child mortality. Data from the individual studies will not be pooled

Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks

To protect privacy and meet legal regulations, federated learning (FL) has gained significant attention for training speech-to-text (S2T) systems, including automatic speech recognition (ASR) and speech translation (ST). However, the commonly used FL approach (i.e., \textsc{FedAvg}) in S2T tasks typically suffers from extensive communication overhead due to multi-round interactions based on the whole model and performance degradation caused by data heterogeneity among clients.To address these issues, we propose a personalized federated S2T framework that introduces \textsc{FedLoRA}, a lightweight LoRA module for client-side tuning and interaction with the server to minimize communication overhead, and \textsc{FedMem}, a global model equipped with a $k$-nearest-neighbor ($k$NN) classifier that captures client-specific distributional shifts to achieve personalization and overcome data heterogeneity. Extensive experiments based on Conformer and Whisper backbone models on CoVoST and GigaSpeech benchmarks show that our approach significantly reduces the communication overhead on all S2T tasks and effectively personalizes the global model to overcome data heterogeneity.Comment: ICASSP 202

Zero-1-to-3: Domain-level Zero-shot Cognitive Diagnosis via One Batch of Early-bird Students towards Three Diagnostic Objectives

Cognitive diagnosis seeks to estimate the cognitive states of students by exploring their logged practice quiz data. It plays a pivotal role in personalized learning guidance within intelligent education systems. In this paper, we focus on an important, practical, yet often underexplored task: domain-level zero-shot cognitive diagnosis (DZCD), which arises due to the absence of student practice logs in newly launched domains. Recent cross-domain diagnostic models have been demonstrated to be a promising strategy for DZCD. These methods primarily focus on how to transfer student states across domains. However, they might inadvertently incorporate non-transferable information into student representations, thereby limiting the efficacy of knowledge transfer. To tackle this, we propose Zero-1-to-3, a domain-level zero-shot cognitive diagnosis framework via one batch of early-bird students towards three diagnostic objectives. Our approach initiates with pre-training a diagnosis model with dual regularizers, which decouples student states into domain-shared and domain-specific parts. The shared cognitive signals can be transferred to the target domain, enriching the cognitive priors for the new domain, which ensures the cognitive state propagation objective. Subsequently, we devise a strategy to generate simulated practice logs for cold-start students through analyzing the behavioral patterns from early-bird students, fulfilling the domain-adaption goal. Consequently, we refine the cognitive states of cold-start students as diagnostic outcomes via virtual data, aligning with the diagnosis-oriented goal. Finally, extensive experiments on six real-world datasets highlight the efficacy of our model for DZCD and its practical application in question recommendation. The code is publicly available at https://github.com/bigdata-ustc/Zero-1-to-3.Comment: Accepted by AAAI202

4-Nonylphenol induces autophagy and attenuates mTOR-p70S6K/4EBP1 signaling by modulating AMPK activation in Sertoli cells

The estrogenic chemical 4-nonylphenol (NP) is known to impair testicular devolopment and spermatogenesis in rodents. The objective of this study was to explore the effects of NP on autophagy induction and AMPK-mTOR signaling pathway in Sertoli cells (SCs), which are the “nursemaid cells” for meiosis of spermatocytes. In this study we exposed 7-week-old male rats to NP by intra-peritoneal injection at 0, 20, 50 or 100 mg/kg body weight/2 days for 20 consecutive days. Our results showed that exposure to NP dose-dependently induces the formation of autophagosomes in SCs, increases the expression of Beclin-1, the conversion of LC3-I to LC3-II and the mRNA expression of Atg3, Atg5, Atg7 and Atg12 in testis, and these effects are concomitant with the activation of AMPK, and the suppression of TSC2-mTOR-p70S6K/4EBP1 signaling cascade in testis. Furthermore, 10 µM Compound C or AMPKα1 siRNA pre-treatment effectively attenuated autophagy and reversed AMPK-mTOR-p70S6K/4EBP1 signaling in NP-treated SCs. Co-treatment with 1 mM AICAR remarkably strengthened NP-induced autophagy and mTOR inhibition in SCs. Together, these data suggest that NP stimulates Sertoli cell autophagy and inhibits mTOR-p70S6K/4EBP1 activity through AMPK activation, which is the potential mechanism responsible for the regulation of testis function and differentiation following NP exposure

Buckling Of Lipid Tubules In Shrinking Liquid Droplets

Self-assembled hollow lipid tubules are interesting and potentially useful supramolecular structures. Here, we study the deformation of lipid tubules of 1,2-bis(tricosa-10,12-diynoyl)-sn-glycero-3-phosphocholine (DC8,9PC) trapped inside liquid droplets on glass substrates. The interface tension of the shrinking liquid droplets exerts a compression force on the ends of the trapped lipid tubules, and causes them to buckle. This provides a method to measure their mechanical properties. The Young\u27s modulus of the DC8,9PC lipid tubules is estimated to ∼1.07 GPa. As the strain energy of the buckled tubules builds up, they poke through the interface of shrinking liquid droplets and then adhere onto glass substrates to form looplike shapes. © 2007 American Chemical Society

Radial Elasticity Of Self-Assembled Lipid Tubules

Self-assembled lipid tubules with crystalline bilayer walls represent useful supramolecular architectures which hold promise as vehicles for the controlled release of preloaded drugs and templates for the synthesis of one-dimensional inorganic materials. We study the local elasticity of lipid tubules of 1,2-bis(tricosa-10, 12-diynoyl)-sn-glycero-3-phosphocholine by radial atomic force microscope indentation, coupled with finite element analysis. A reduced stiffness is found to extend a distance of ∼600 nm from the ends of lipid tubules. The middle section of lipid tubules is homogeneous in terms of their radial elasticity with a Young\u27s modulus of ∼703 MPa. The inhomogeneous radial elasticity likely arises from the variation of lipid packing density near the tubule ends. © 2008 American Chemical Society