Bayesian Domain Invariant Learning via Posterior Generalization of Parameter Distributions

Domain invariant learning aims to learn models that extract invariant features over various training domains, resulting in better generalization to unseen target domains. Recently, Bayesian Neural Networks have achieved promising results in domain invariant learning, but most works concentrate on aligning features distributions rather than parameter distributions. Inspired by the principle of Bayesian Neural Network, we attempt to directly learn the domain invariant posterior distribution of network parameters. We first propose a theorem to show that the invariant posterior of parameters can be implicitly inferred by aggregating posteriors on different training domains. Our assumption is more relaxed and allows us to extract more domain invariant information. We also propose a simple yet effective method, named PosTerior Generalization (PTG), that can be used to estimate the invariant parameter distribution. PTG fully exploits variational inference to approximate parameter distributions, including the invariant posterior and the posteriors on training domains. Furthermore, we develop a lite version of PTG for widespread applications. PTG shows competitive performance on various domain generalization benchmarks on DomainBed. Additionally, PTG can use any existing domain generalization methods as its prior, and combined with previous state-of-the-art method the performance can be further improved. Code will be made public

Plug-and-Play Medical Dialogue System

Medical dialogue systems aim to provide accurate answers to patients, necessitating specific domain knowledge. Recent advancements in Large Language Models (LLMs) have demonstrated their exceptional capabilities in the medical Q&A domain, indicating a rich understanding of common sense. However, LLMs are insufficient for direct diagnosis due to the absence of diagnostic strategies. The conventional approach to address this challenge involves expensive fine-tuning of LLMs. Alternatively, a more appealing solution is the development of a plugin that empowers LLMs to perform medical conversation tasks. Drawing inspiration from in-context learning, we propose PlugMed, a Plug-and-Play Medical Dialogue System that facilitates appropriate dialogue actions by LLMs through two modules: the prompt generation (PG) module and the response ranking (RR) module. The PG module is designed to capture dialogue information from both global and local perspectives. It selects suitable prompts by assessing their similarity to the entire dialogue history and recent utterances grouped by patient symptoms, respectively. Additionally, the RR module incorporates fine-tuned SLMs as response filters and selects appropriate responses generated by LLMs. Moreover, we devise a novel evaluation method based on intent and medical entities matching to assess the efficacy of dialogue strategies in medical conversations more effectively. Experimental evaluations conducted on three unlabeled medical dialogue datasets, including both automatic and manual assessments, demonstrate that our model surpasses the strong fine-tuning baselines.Comment: 9 pages, 3 figures, Possible submission to Emnlp or AAA

CD24 Expression as a Marker for Predicting Clinical Outcome in Human Gliomas

CD24 is overexpressed in glioma cells in vitro and in vivo. However, the correlation of its expression with clinicopathological parameters of gliomas and its prognostic significance in this tumor remain largely unknown. To address this problem, 151 glioma specimens and 10 nonneoplastic brain tissues were collected. Quantitative real-time PCR, immunochemistry assay, and Western blot analysis were carried out to investigate the expression of CD24. As per the results, CD24 was overexpressed in gliomas. Its expression levels in glioma tissues with higher grade (P < 0.001) and lower KPS (P < 0.001) were significantly higher than those with lower grade and higher KPS, respectively. Cox multifactor analysis showed that CD24 (P = 0.02) was an independent prognosis factor for human glioma. Our data provides convincing evidence for the first time that the overexpression of CD24 at gene and protein levels is correlated with advanced clinicopathological parameters and poor prognosis in patients with glioma

Lawyer LLaMA Technical Report

Large Language Models (LLMs), like LLaMA, have exhibited remarkable performances across various tasks. Nevertheless, when deployed to specific domains such as law or medicine, the models still confront the challenge of a deficiency in domain-specific knowledge and an inadequate capability to leverage that knowledge to resolve domain-related problems. In this paper, we focus on the legal domain and explore how to inject domain knowledge during the continual training stage and how to design proper supervised finetune tasks to help the model tackle practical issues. Moreover, to alleviate the hallucination problem during model's generation, we add a retrieval module and extract relevant articles before the model answers any queries. Augmenting with the extracted evidence, our model could generate more reliable responses. We release our data and model at https://github.com/AndrewZhe/lawyer-llama.Comment: Work in progres

Prioritized High-Confidence Risk Genes for Intellectual Disability Reveal Molecular Convergence During Brain Development

Dissecting the genetic susceptibility to intellectual disability (ID) based on de novo mutations (DNMs) will aid our understanding of the neurobiological and genetic basis of ID. In this study, we identify 63 high-confidence ID genes with q-values &lt; 0.1 based on four background DNM rates and coding DNM data sets from multiple sequencing cohorts. Bioinformatic annotations revealed a higher burden of these 63 ID genes in FMRP targets and CHD8 targets, and these genes show evolutionary constraint against functional genetic variation. Moreover, these ID risk genes were preferentially expressed in the cortical regions from the early fetal to late mid-fetal stages. In particular, a genome-wide weighted co-expression network analysis suggested that ID genes tightly converge onto two biological modules (M1 and M2) during human brain development. Functional annotations showed specific enrichment of chromatin modification and transcriptional regulation for M1 and synaptic function for M2, implying the divergent etiology of the two modules. In addition, we curated 12 additional strong ID risk genes whose molecular interconnectivity with known ID genes (q-values &lt; 0.3) was greater than random. These findings further highlight the biological convergence of ID risk genes and help improve our understanding of the genetic architecture of ID

Application of the pressure cooker technique for transarterial embolization of brain arteriovenous malformations: Factors affecting obliteration and outcomes

ObjectiveThe typical pressure cooker technique (PCT) and several modifications with similar mechanisms have been introduced to enhance the embolization of brain arteriovenous malformations (bAVMs). This study aimed to assess the effectiveness of transarterial embolization of bAVMs with the PCT.MethodFrom January 2019 to December 2021, 125 consecutive patients with bAVM managed by transarterial embolization in the prospective database on cerebral vascular diseases of a single center were retrospectively reviewed. Patient data and lesion characteristics were collected. According to the treatment strategy, the patients were assigned to the PCT group (46 patients) and conventional embolization technique (CET) group (79 patients).ResultsBaseline patient features were comparable between the two groups. After the first procedure, complete obliteration immediately was observed in 61 and 42% of patients in the PCT and CET groups, respectively. The rate was markedly elevated in the PCT group (p = 0.04). In subgroup analysis, the rate of immediate complete obliteration was starkly increased in PCT group patients with Spetzler-Martin grade I/II bAVM (86 and 53% in the PCT and CET groups, respectively; p = 0.0036). The overall complication rates were similar in the two groups (13 and 10% in the PCT and CET groups, respectively; p = 0.77). In multivariable analysis, nidus size &gt;3 cm (OR = 8.826, 95% CI: 1.250–62.312; p = 0.03) and deep location (OR = 8.576, 95% CI: 1.480–49.690; p = 0.02) were significant factors affecting complete obliteration in the PCT group.ConclusionThe PCT may yield a higher rate of immediate complete obliteration with transarterial embolization of bAVMs, without increasing the rate of procedure-related complications

Differentiation Trajectory of Limbal Stem and Progenitor Cells under Normal Homeostasis and upon Corneal Wounding

Limbal stem cells (LSCs) reside discretely at limbus surrounded by niche cells and progenitor cells. The aim of this study is to identify the heterogeneous cell populations at limbus under normal homeostasis and upon wounding using single-cell RNA sequencing in a mouse model. Two putative LSC types were identified which showed a differentiation trajectory into limbal progenitor cell (LPC) types under normal homeostasis and during wound healing. They were designated as “putative active LSCs” and “putative quiescent LSCs”, respectively, because the former type actively divided upon wounding while the later type stayed at a quiescent status upon wounding. The “putative quiescent LSCs” might contribute to a barrier function due to their characteristic markers regulating vascular and epithelial barrier and growth. Different types of LPCs at different proliferative statuses were identified in unwounded and wounded corneas with distinctive markers. Four maturation markers (Aldh3, Slurp1, Tkt, and Krt12) were screened out for corneal epithelium, which showed an increased expression along the differentiation trajectory during corneal epithelial maturation. In conclusion, our study identified two different types of putative LSCs and several types of putative LPCs under normal homeostasis and upon wounding, which will facilitate the understanding of corneal epithelial regeneration and wound healing

Precision Higgs physics at the CEPC

The discovery of the Higgs boson with its mass around 125 GeV by the ATLAS and CMS Collaborations marked the beginning of a new era in high energy physics. The Higgs boson will be the subject of extensive studies of the ongoing LHC program. At the same time, lepton collider based Higgs factories have been proposed as a possible next step beyond the LHC, with its main goal to precisely measure the properties of the Higgs boson and probe potential new physics associated with the Higgs boson. The Circular Electron Positron Collider~(CEPC) is one of such proposed Higgs factories. The CEPC is an $e^+e^-$ circular collider proposed by and to be hosted in China. Located in a tunnel of approximately 100~km in circumference, it will operate at a center-of-mass energy of 240~GeV as the Higgs factory. In this paper, we present the first estimates on the precision of the Higgs boson property measurements achievable at the CEPC and discuss implications of these measurements.Comment: 46 pages, 37 figure

Porous Ti-based bulk metallic glass orthopedic biomaterial with high strength and low Young's modulus produced by one step SPS

To alleviate the “stress shielding” of metallic biomaterials used as artificial bone, the state of art Ti45Zr10Cu31Pd10Sn4 porous bulk metallic glass (BMG) with adequate compressive strength of 180 MPa and Young's modulus of 27.2 GPa similar to nature bone was produced by a novel partial densification method using spark plasma sintering (SPS). The thermal mechanistic (sintering temperature, sintering rate and holding time) during SPS contributions to the densification behavior and the structures and mechanical properties of porous BMG were systematically explored under low loading pressure condition to obtain the optimal sintering process. With the help of this work, the potential application value of Ti–Zr–Cu–Pd–Sn BMG as substitute of human bone will be further enhanced