Parametric Models for Optimal Treatment Schedule Finding in Adaptive Early-Phase Clinical Trials.

Recently, a Bayesian paradigm was constructed for Phase I trial designs that allows for the evaluation and comparison of several nested treatment schedules, each consisting of a sequence of administration times. In contrast to traditional Phase I trial designs that seek to find a maximum tolerated dose (MTD), the goal of this new design was to determine a maximum tolerated schedule (MTS). Subject accrual, Bayesian estimation procedure and outcome adaptive decision-making are done in a sequential fashion as in classical Phase I trial designs. As competing approaches to the additive triangular hazard model proposed with the Bayesian paradigm, we propose several classes of parametric models for optimal treatment schedule finding by both maximum likelihood and Bayesian approaches. In part I of our research, we propose a mixture cure model to identify the MTS from a fixed number of nested treatment schedules. We model the cure rate with logistic regression and the conditional hazard function for the susceptible patients using a combination of two Weibull distributions to account for the non-monotonic nature of the hazard of toxicity. We use a modified likelihood approach to estimate parameters of interest. In part II of our research, we propose using maximum likelihood to estimate the parameters of the triangular hazard model in a single adminstration setting. We describe how to derive estimators for the change-point and boundary parameters of the triangular hazard model and discuss their large sample properties. In part III of our research, we propose a parametric non-mixture cure model to identify the optimal treatment schedule from a fixed number of nested treatment schedules. With such a model, we generate a continuous non-monotonic hazard function for the time to toxicity of each administration, as well as model the population probability of toxicity to increase with the number of administrations. Via simulation, we compare the performance of our proposed approaches to the existing method in a variety of settings motivated by an actual study in allogeneic bone marrow transplant patients. The parameters of interest are estimated by both maximum likelihood method (EM algorithm) and Bayesian approach (MCMC procedures).Ph.D.BiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57655/2/liuchang_1.pd

AGI for Agriculture

Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education. Within healthcare, AGI is being utilized to analyze clinical medical notes, recognize patterns in patient data, and aid in patient management. Agriculture is another critical sector that impacts the lives of individuals worldwide. It serves as a foundation for providing food, fiber, and fuel, yet faces several challenges, such as climate change, soil degradation, water scarcity, and food security. AGI has the potential to tackle these issues by enhancing crop yields, reducing waste, and promoting sustainable farming practices. It can also help farmers make informed decisions by leveraging real-time data, leading to more efficient and effective farm management. This paper delves into the potential future applications of AGI in agriculture, such as agriculture image processing, natural language processing (NLP), robotics, knowledge graphs, and infrastructure, and their impact on precision livestock and precision crops. By leveraging the power of AGI, these emerging technologies can provide farmers with actionable insights, allowing for optimized decision-making and increased productivity. The transformative potential of AGI in agriculture is vast, and this paper aims to highlight its potential to revolutionize the industry

MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion

Query expansion is a commonly-used technique in many search systems to better represent users' information needs with additional query terms. Existing studies for this task usually propose to expand a query with retrieved or generated contextual documents. However, both types of methods have clear limitations. For retrieval-based methods, the documents retrieved with the original query might not be accurate enough to reveal the search intent, especially when the query is brief or ambiguous. For generation-based methods, existing models can hardly be trained or aligned on a particular corpus, due to the lack of corpus-specific labeled data. In this paper, we propose a novel Large Language Model (LLM) based mutual verification framework for query expansion, which alleviates the aforementioned limitations. Specifically, we first design a query-query-document generation pipeline, which can effectively leverage the contextual knowledge encoded in LLMs to generate sub-queries and corresponding documents from multiple perspectives. Next, we employ a mutual verification method for both generated and retrieved contextual documents, where 1) retrieved documents are filtered with the external contextual knowledge in generated documents, and 2) generated documents are filtered with the corpus-specific knowledge in retrieved documents. Overall, the proposed method allows retrieved and generated documents to complement each other to finalize a better query expansion. We conduct extensive experiments on three information retrieval datasets, i.e., TREC-DL-2020, TREC-COVID, and MSMARCO. The results demonstrate that our method outperforms other baselines significantly

Chemical constituents, and pharmacological and toxicological effects of Cynomorium songaricum: An overview

Purpose: To review the chemical constituents, and the pharmacological and toxicological effects of Cynomorium songaricum (C. songaricum) and explore its potentials for further development as an alternative medicine.Methods: A large number of research articles related to “Cynomorium songaricum” “pharmacological effects”, “toxicological effects” and “chemical composition” in English and Chinese language were retrieved through an extensive literature review using various electronic databases including Medline(1966 - 2017) and EMBASE (1980 - 2017).Results: Ethyl acetate and aqueous extracts of C. songaricum have promising pharmacological activities, due to the presence of various flavonoids, triterpenes and polysaccharides. In addition to promising effects against inflammation, aging, fatigue, viruses and cancer,/ihas a protective effect on the nervous system and regulates hormones and immune functions. Oxidative regulation of hormone levels has a certain correlation with its pharmacological activities, e.g., cognitive functions, but its mechanism is not yet known, indicating the need for further research. Toxicity studies on C. songaricum have shown that it is not genotoxic to animals, but further toxicological studies are required to ascertain its safety in clinical use.Conclusion: C. songaricum is a biologically important plant which has many proven bioactivities; however, it requires further studies to determine the mechanistic aspects of its pharmacological effects.Keywords: Cynomorium, Chemical constituents, Inflammation, Aging, Fatigue, Virus, Tumor, Toxicological effec

Inhibition of RNA-binding protein HuR reduces glomerulosclerosis in experimental nephritis

Recent identification of an RNA-binding protein (HuR) that regulates mRNA turnover and translation of numerous transcripts via binding to an ARE in their 3′-UTR involved in inflammation and is abnormally elevated in varied kidney diseases offers a novel target for the treatment of renal inflammation and subsequent fibrosis. Thus, we hypothesized that treatment with a selective inhibition of HuR function with a small molecule, KH-3, would down-regulate HuR-targeted proinflammatory transcripts thereby improving glomerulosclerosis in experimental nephritis, where glomerular cellular HuR is elevated. Three experimental groups included normal and diseased rats treated with or without KH-3. Disease was induced by the monoclonal anti-Thy 1.1 antibody. KH-3 was given via daily intraperitoneal injection from day 1 after disease induction to day 5 at the dose of 50 mg/kg BW/day. At day 6, diseased animals treated with KH-3 showed significant reduction in glomerular HuR levels, proteinuria, podocyte injury determined by ameliorated podocyte loss and podocin expression, glomerular staining for periodic acid-Schiff positive extracellular matrix proteins, fibronectin and collagen IV and mRNA and protein levels of profibrotic markers, compared with untreated disease rats. KH-3 treatment also reduced disease-induced increases in renal TGFβ1 and PAI-1 transcripts. Additionally, a marked increase in renal NF-κB-p65, Nox4, and glomerular macrophage cell infiltration observed in disease control group was largely reversed by KH-3 treatment. These results strongly support our hypothesis that down-regulation of HuR function with KH-3 has therapeutic potential for reversing glomerulosclerosis by reducing abundance of pro-inflammatory transcripts and related inflammation

Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification

With the popularity of deep neural networks (DNNs), model interpretability is becoming a critical concern. Many approaches have been developed to tackle the problem through post-hoc analysis, such as explaining how predictions are made or understanding the meaning of neurons in middle layers. Nevertheless, these methods can only discover the patterns or rules that naturally exist in models. In this work, rather than relying on post-hoc schemes, we proactively instill knowledge to alter the representation of human-understandable concepts in hidden layers. Specifically, we use a hierarchical tree of semantic concepts to store the knowledge, which is leveraged to regularize the representations of image data instances while training deep models. The axes of the latent space are aligned with the semantic concepts, where the hierarchical relations between concepts are also preserved. Experiments on real-world image datasets show that our method improves model interpretability, showing better disentanglement of semantic concepts, without negatively affecting model classification performance

Stable Reference Gene Selection for RT-qPCR Analysis in Nonviruliferous and Viruliferous \u3cem\u3eFrankliniella occidentalis\u3c/em\u3e

Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) is a reliable technique for measuring and evaluating gene expression during variable biological processes. To facilitate gene expression studies, normalization of genes of interest relative to stable reference genes is crucial. The western flower thrips Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae), the main vector of tomato spotted wilt virus (TSWV), is a destructive invasive species. In this study, the expression profiles of 11 candidate reference genes from nonviruliferous and viruliferous F. occidentalis were investigated. Five distinct algorithms, geNorm, NormFinder, BestKeeper, the ΔCt method, and RefFinder, were used to determine the performance of these genes. geNorm, NormFinder, BestKeeper, and RefFinder identified heat shock protein 70 (HSP70), heat shock protein 60 (HSP60), elongation factor 1 α, and ribosomal protein l32 (RPL32) as the most stable reference genes, and the ΔCt method identified HSP60, HSP70, RPL32, and heat shock protein 90 as the most stable reference genes. Additionally, two reference genes were sufficient for reliable normalization in nonviruliferous and viruliferous F. occidentalis. This work provides a foundation for investigating the molecular mechanisms of TSWV and F. occidentalis interactions

Hexaaqua­manganese(II) dipicrate dihydrate

In the title compound, [Mn(H2O)6](C6H2N3O7)2·2H2O, the manganese cation, on an inversion centre, is coordinated by six water mol­ecules, but the picrate anion has no coordinative inter­action with the manganese cation. The anions in the stack are linked via short inter­molecular O⋯C (3.013 and 2.973 Å) and C⋯C (3.089 and 3.065 Å) contacts and hydrogen bonds

MiR-543 Promotes Migration, Invasion and Epithelial-Mesenchymal Transition of Esophageal Cancer Cells by Targeting Phospholipase A2 Group IVA

Background/Aims: The aim of this study was to investigate the roles of miR-543 and phospholipase A2 group IVA (PLA2G4A) in cell mobility and the invasiveness cascade in esophageal squamous cell carcinoma (ESCC) and to validate the interactive relationship between miR-543 and PLA2G4A. Methods: Microarray analysis showed the different expression levels of PLA2G4A in two ESCC cell lines (KYSE30 and KYSE180). The expression levels of miR-543 and PLA2G4A in ESCC tissues were confirmed by qRT-PCR and Western blotting. The targeted relationship between miR-543 and PLA2G4A was studied and verified by a luciferase activity assay. Then, the invasion and metastasis ability of ESCC cell lines transfected with miR-543 mimics, miR-543 inhibitor, or PLA2G4A and miR-543 mimics were analyzed separately by Transwell migration and invasion assays. In addition, the roles of miR-543 and PLA2G4A in the expression of E-cadherin and vimentin were also investigated. Results: PLA2G4A up-regulated the level of E-cadherin and down-regulated the level of vimentin, which curbed ESCC cell mobility and invasion. In ESCC cells, the expression of miR-543 was significantly higher, whereas the expression of PLA2G4A was markedly lower. MiR-543 facilitated ESCC cell mobility and invasion by repressing PLA2G4A. Conclusions: MiR-543 enhanced the cell mobility and the invasiveness cascade in ESCC cells via the down-regulation of PLA2G4A expression