Large Language Model Soft Ideologization via AI-Self-Consciousness

Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. However, few studies have addressed the LLM threat and vulnerability from an ideology perspective, especially when they are increasingly being deployed in sensitive domains, e.g., elections and education. In this study, we explore the implications of GPT soft ideologization through the use of AI-self-consciousness. By utilizing GPT self-conversations, AI can be granted a vision to "comprehend" the intended ideology, and subsequently generate finetuning data for LLM ideology injection. When compared to traditional government ideology manipulation techniques, such as information censorship, LLM ideologization proves advantageous; it is easy to implement, cost-effective, and powerful, thus brimming with risks

Serum Sodium Concentration in Patients with Portal Hypertension and Acute Gastrointestinal Bleeding Treated with Terlipressin: A Retrospective Observational Study

This retrospective observational study aimed to investigate the risk of serum sodium concentration in patients treated with terlipressin and attempted to explore the factors associated with serum sodium concentration. We included 17 patients with portal hypertension treated with terlipressin (Group 1), 7 with portal hypertension treated with somatostatin/octreotide (Group 2), 20 with acute non-variceal gastrointestinal bleeding treated with somatostatin/octreotide (Group 3), and 19 with acute pancreatitis treated with somatostatin/octreotide (Group 4). In all groups, serum sodium concentration at baseline was not significantly different from the lowest value during the infusion of terlipressin, somatostatin, or octreotide (Group 1: 136.95 ± 4.68 versus 135.52 ± 4.79, p = 0.426; Group 2: 139.64 ± 3.86 versus 138.41 ± 5.34, p = 0.813; Group 3: 138.02 ± 4.08 versus 137.69 ± 3.11, p = 0.630; Group 4: 135.96 ± 6.87 versus 134.60 ± 3.40, p = 0.098). The rate of serum sodium concentration reduction in Group 1 (8/17) was not significantly different from Group 2 (3/7, p = 1.000), Group 3 (11/20, p = 0.746), or Group 4 (14/19, p = 0.171). Age, sex, baseline MELD and Child-Pugh scores, cDDD value and duration of terlipressin, blood transfusion, and diuretics and paracentesis during terlipressin were not significantly associated with serum sodium concentration reduction in Group 1. In conclusion, serum sodium concentration is often reduced in patients treated with terlipressin. However, the association of sodium concentration reduction with terlipressin should be clarified

Evaluating Point Cloud Quality via Transformational Complexity

Full-reference point cloud quality assessment (FR-PCQA) aims to infer the quality of distorted point clouds with available references. Merging the research of cognitive science and intuition of the human visual system (HVS), the difference between the expected perceptual result and the practical perception reproduction in the visual center of the cerebral cortex indicates the subjective quality degradation. Therefore in this paper, we try to derive the point cloud quality by measuring the complexity of transforming the distorted point cloud back to its reference, which in practice can be approximated by the code length of one point cloud when the other is given. For this purpose, we first segment the reference and the distorted point cloud into a series of local patch pairs based on one 3D Voronoi diagram. Next, motivated by the predictive coding theory, we utilize one space-aware vector autoregressive (SA-VAR) model to encode the geometry and color channels of each reference patch in cases with and without the distorted patch, respectively. Specifically, supposing that the residual errors follow the multi-variate Gaussian distributions, we calculate the self-complexity of the reference and the transformational complexity between the reference and the distorted sample via covariance matrices. Besides the complexity terms, the prediction terms generated by SA-VAR are introduced as one auxiliary feature to promote the final quality prediction. Extensive experiments on five public point cloud quality databases demonstrate that the transformational complexity based distortion metric (TCDM) produces state-of-the-art (SOTA) results, and ablation studies have further shown that our metric can be generalized to various scenarios with consistent performance by examining its key modules and parameters

Synthesis and electrochemical performance of hierarchical Sb2S3 nanorod-bundles for lithium-ion batteries

Uniform hierarchical Sb2S3 nanorod-bundles were synthesised successfully by L-cysteine hydrochloride-assisted solvothermal treatment, and were then characterised by X-ray diffraction, field emission scanning electron microscopy, and high-resolution transmission electron microscopy, respectively. The electrochemical performance of the synthesised Sb2S3 nanorod-bundles was investigated by cyclic voltammetry and galvanostatic charge−discharge technique, respectively. This material was found to exhibit a high initial charge specific capacity of 803 mA h g-1 at a rate of 100 mA g-1, a good cyclability of 614 mA h g-1 at a rate of 100 mA g-1 after 30 cycles, and a good rate capability of 400 mA h g-1 at a rate of 500 mA g-1 when evaluated as an electrode candidate material for lithium-ion batteries

Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration

Legal Judgment Prediction (LJP) has become an increasingly crucial task in Legal AI, i.e., predicting the judgment of the case in terms of case fact description. Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems. Thus, it is worthwhile to explore the utilization of precedents in the LJP. Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task. These can be broken down into two categories: large language models (LLMs) and domain-specific models. LLMs are capable of interpreting and generating complex natural language, while domain models are efficient in learning task-specific information. In this paper, we propose the precedent-enhanced LJP framework (PLJP), a system that leverages the strength of both LLM and domain models in the context of precedents. Specifically, the domain models are designed to provide candidate labels and find the proper precedents efficiently, and the large models will make the final prediction with an in-context precedents comprehension. Experiments on the real-world dataset demonstrate the effectiveness of our PLJP. Moreover, our work shows a promising direction for LLM and domain-model collaboration that can be generalized to other vertical domains

GOLM1 Stimulation of Glutamine Metabolism Promotes Osteoporosis via Inhibiting Osteogenic Differentiation of BMSCs

Background/Aims: Bone marrow mesenchymal stem cells (BMSCs) play an essential role in osteoporosis. However, the molecular mechanisms and the involvement of glutamine metabolism in osteogenic BMSCs differentiation and osteoporosis remain largely unclear. In this study, we investigated the role of Golgi membrane protein 1 (GOLM1) and glutamine metabolism in BMSCs differentiation and osteoporosis. Methods: Osteogenic differentiation-inducing media (Odi) was used to induce the osteogenic differentiation of BMSCs. The mRNA expression of GOLM1, ALP, Runx2, Osx, BSP and OCN was determined by qRT-PCR assay. Western blot assay was used to analyze GOLM1, p-mTOR, mTOR, p-S6 and S6 abundance in GOLM1 silencing and over-expressed BMSCs. Glutamine uptake, intracellular glutamine, glutamate and α-KG level was detected using indicated Kits. GOLM1 antibody, glutamine metabolism inhibitors EGCG and BPTES were used to treat ovariectomy (OVX)-induced osteoporosis. Bone mineral density and bone volume relative to tissue volume (%) were analyzed by micro-CT. Serum was collected from osteoporosis patients and healthy participants and subjected to GOLM1 determination using ELISA Kit. Results: GOLM1 expression and glutamine metabolism were suppressed by Odi. GOLM1 blockage or inhibition of glutamine metabolism promoted the osteogenic differentiation of BMSCs induced by Odi. GOLM1 activated glutamine metabolism depending on the mTOR signaling pathway. In vivo, GOLM1 antibody or combination of glutamine inhibitor EGCG and BPTES rescued the osteoporosis in an OVX-operated mouse model. Serum GOLM1 level was increased in the patients of osteoporosis compared with healthy people. Conclusion: GOLM1 stimulates glutamine metabolism to suppress the osteogenic differentiation of BMSCs and to promote osteoporosis. Therefore, GOLM1 activation of glutamine metabolism is a potential target for osteoporosis

Exploration of the shared pathways and common biomarker PAN3 in ankylosing spondylitis and ulcerative colitis using integrated bioinformatics analysis

BackgroundUlcerative colitis (UC) is a chronic autoimmune-related disease that causes inflammation of the intestine. Ankylosing spondylitis (AS) is a common extraintestinal complication of UC involving the sacroiliac joint. However, the pathogenesis of AS secondary to UC has not been studied. This study aimed to investigate the shared pathways and potential common biomarkers of UC and AS.MethodsMicroarray data downloaded from the Gene Expression Omnibus (GEO) database were used to screen differentially expressed genes (DEGs) in the UC and AS datasets. Weighted gene co-expression network analysis (WGCNA) was performed to identify co-expression modules related to UC and AS. Shared genes were then further analyzed for functional pathway enrichment. Next, the optimal common biomarker was selected using SVM-RFF and further validated using two independent GEO datasets. Finally, immune infiltration analysis was used to investigate the correlation of immune cell infiltration with common biomarkers in UC and AS.ResultsA total of 4428 and 2438 DEGs in UC and AS, respectively, were screened. Four modules were identified as significant for UC and AS using WGCNA. A total of 25 genes overlapped with the strongest positive and negative modules of UC and AS. KEGG analysis showed these genes may be involved in the mitogen-activated protein kinase (MAPK) signaling pathway. GO analysis indicated that these genes were significantly enriched for RNA localization. PAN3 was selected as the optimal common biomarker for UC and AS. Immune infiltration analysis showed that the expression of PAN3 was correlated with changes in immune cells.ConclusionThis study first explored the common pathways and genetic diagnostic markers involved in UC and AS using bioinformatic analysis. Results suggest that the MAPK signaling pathway may be associated with both pathogeneses and that PAN3 may be a potential diagnostic marker for patients with UC complicated by AS

Physiological roles of fatty acyl desaturases and elongases in marine fish: Characterisation of cDNAs of fatty acyl delta6 desaturase and elovl5 elongase of cobia (Rachycentron canadum)

In the present paper, we investigated the expression of fatty acyl desaturase and elongase genes in a marine teleost, cobia, a species of great interest due to its considerable aquaculture potential. A cDNA was cloned that, when expressed in yeast, was shown to result in desaturation of 18:3n-3 and 18:2n-6, indicating that it coded for a Δ6 desaturase enzyme. Very low desaturation of 20:4n-3 and 20:3n-6 indicated only trace Δ5 activity. Another cloned cDNA enabled elongation of 18:4n-3, 18:3n-6, 20:5n-3 and 20:4n-6 in the yeast expression system, indicating that it had C18-20 and C20-22 elongase activity. Sequence comparison and phylogenetic analysis confirmed that it was homologous to human ELOVL5 elongase. However, the cobia Elovl5 elongase also had low activity toward C24 HUFA. The cobia Δ6 desaturase had a preference for 18:3n-3, but the elongase was generally equally active with both n-3 and n-6 substrates. Expression of both genes was 1-2 orders of magnitude greater in brain than other tissues suggesting an important role, possibly to ensure sufficient docosahexaenoic acid (DHA, 22:6n-3) synthesis in neural tissues through elongation and desaturation of eicosapentaenoic acid (EPA; 20:5n-3)