How International Trade and Government Integrity Affect the Structural Transformation of Lao PDR and Cambodia

This paper explores the how international trade and government integrity affect the structural transformation of Lao PDR and Cambodia. This empirical study is conducted by using the methodology based on Chenery-Syrquin model with several control groups that have impacted on structural transformation in Lao PDR and Cambodia. Moreover, the obtained data is from 1993 to 2021 to find out how these two countries transform from being agriculture dominant economy to being more industry-and services-oriented economy. This study has confirmed non-linear effects of both income and population on the sectoral shares and found that trade has facilitated structural transformation in Lao PDR but that didn’t happen in Cambodia. The political corruption index affected the sectional sectors in different ways in Lao PDR and Cambodia, but the results are not statistically significant

Sequence-Level Certainty Reduces Hallucination In Knowledge-Grounded Dialogue Generation

Model hallucination has been a crucial interest of research in Natural Language Generation (NLG). In this work, we propose sequence-level certainty as a common theme over hallucination in NLG, and explore the correlation between sequence-level certainty and the level of hallucination in model responses. We categorize sequence-level certainty into two aspects: probabilistic certainty and semantic certainty, and reveal through experiments on Knowledge-Grounded Dialogue Generation (KGDG) task that both a higher level of probabilistic certainty and a higher level of semantic certainty in model responses are significantly correlated with a lower level of hallucination. What's more, we provide theoretical proof and analysis to show that semantic certainty is a good estimator of probabilistic certainty, and therefore has the potential as an alternative to probability-based certainty estimation in black-box scenarios. Based on the observation on the relationship between certainty and hallucination, we further propose Certainty-based Response Ranking (CRR), a decoding-time method for mitigating hallucination in NLG. Based on our categorization of sequence-level certainty, we propose 2 types of CRR approach: Probabilistic CRR (P-CRR) and Semantic CRR (S-CRR). P-CRR ranks individually sampled model responses using their arithmetic mean log-probability of the entire sequence. S-CRR approaches certainty estimation from meaning-space, and ranks a number of model response candidates based on their semantic certainty level, which is estimated by the entailment-based Agreement Score (AS). Through extensive experiments across 3 KGDG datasets, 3 decoding methods, and on 4 different models, we validate the effectiveness of our 2 proposed CRR methods to reduce model hallucination

Cynaropicrin inhibits lung cancer proliferation by targeting EGFR/AKT signaling pathway

Purpose: To investigate the anti-proliferative effect of cynaropicrin on lung cancer cell lines, and the underlying molecular mechanism. Methods: The effect of cynaropicrin treatment on the viabilities of H1975 and H460 cells was measured using Cell Counting Kit-8. Apoptosis was analysed by annexin-V/FITC staining, while protein expressions were assayed by western blotting. Results: Treatment of H1975 and H460 cells with cynaropicrin at doses of 0.25 – 2.0 μM led to a marked reduction in their viability (p < 0.05). In cynaropicrin-treated H1975 and H460 cells, there was significant increase in apoptosis, when compared to control cells. Caspase-3 and caspase-9 levels were also significantly increased in H1975 and H460 cells on treatment with cynaropicrin at doses of 0.25 and 2.0 μM while treatment with cynaropicrin at doses of 0.25 - 2.0 μM significantly down-regulated the mRNA expression of CCND1 in the two cell lines (p < 0.05). Cynaropicrin markedly inhibited mRNA and protein expressions of EGFR, and also downregulated AKT in H1975 and H460 cells (p < 0.05). However, cynaropicrin significantly increased the expressions of miR-202 and miR-370. Conclusion: Cynaropicrin exerts anti-proliferative and proapoptotic effects on H1975 and H460 lung cancer cells via deactivation of EGFR/AKT signaling pathway. Moreover, it upregulated the expressions of miR-202 and miR-370 in these cells. Thus, cynaropicrin has potentials for the treatment of lung cancer

"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters

Large Language Models (LLMs) have recently emerged as an effective tool to assist individuals in writing various types of content, including professional documents such as recommendation letters. Though bringing convenience, this application also introduces unprecedented fairness concerns. Model-generated reference letters might be directly used by users in professional scenarios. If underlying biases exist in these model-constructed letters, using them without scrutinization could lead to direct societal harms, such as sabotaging application success rates for female applicants. In light of this pressing issue, it is imminent and necessary to comprehensively study fairness issues and associated harms in this real-world use case. In this paper, we critically examine gender biases in LLM-generated reference letters. Drawing inspiration from social science findings, we design evaluation methods to manifest biases through 2 dimensions: (1) biases in language style and (2) biases in lexical content. We further investigate the extent of bias propagation by analyzing the hallucination bias of models, a term that we define to be bias exacerbation in model-hallucinated contents. Through benchmarking evaluation on 2 popular LLMs- ChatGPT and Alpaca, we reveal significant gender biases in LLM-generated recommendation letters. Our findings not only warn against using LLMs for this application without scrutinization, but also illuminate the importance of thoroughly studying hidden biases and harms in LLM-generated professional documents.Comment: Accepted to EMNLP 2023 Finding

Screening and clinical characteristics analysis of familial hypercholesterolemia in a tertiary public hospital

Background and aimsFamilial hypercholesterolemia (FH) is becoming a global burden. However, it remains underdiagnosed and undertreated worldwide. This study aimed to observe the screening rate of FH patients and department distribution among hospitalized patients using different diagnostic criteria.MethodsA total of 45,410 inpatients with LDL-C ≥3.5 mmol/L between 2008 and 2019 were included from The Second Affiliated Hospital of Nanchang University. Inpatients are diagnosed and divided into groups by Dutch Lipid Clinic Network (DLCN) criteria, Chinese-modified DLCN criteria and Chinese expert consensus (CEC) criteria.ResultsThere were 172, 1,076 and 115 inpatients included in the DLCN group, Chinese-modified DLCN group and CEC group, respectively (screening rates: 0.38%, 2.37% and 0.25%). These FH patients had a very high risk of atherosclerotic cardiovascular disease (ASCVD) (55.7%–74.4%), especially in the DLCN group and CEC group (70.4%–74.4%). More than half of the patients were in the Department of Cardiology, and other high-risk departments included Neurology, Nephrology, Vascular Surgery, Otolaryngology & Head Neck Surgery and Traditional Chinese Medicine (24.35%–31.51%). Overall, hypertension, coronary heart disease, carotid arteriosclerosis, hepatic cyst, arrhythmia, and nonalcoholic fatty liver disease were common accompanying diseases with FH.ConclusionsIt is necessary to establish appropriate diagnostic criteria and more positive treatment strategies for the FH inpatient population. In addition, promoting awareness of FH among doctors from other departments is also necessary. Therefore, developing a comprehensive management strategy for FH disease is very important

LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected?

With the rapid development and widespread application of Large Language Models (LLMs), the use of Machine-Generated Text (MGT) has become increasingly common, bringing with it potential risks, especially in terms of quality and integrity in fields like news, education, and science. Current research mainly focuses on purely MGT detection without adequately addressing mixed scenarios, including AI-revised Human-Written Text (HWT) or human-revised MGT. To tackle this challenge, we define mixtext, a form of mixed text involving both AI and human-generated content. Then, we introduce MixSet, the first dataset dedicated to studying these mixtext scenarios. Leveraging MixSet, we executed comprehensive experiments to assess the efficacy of prevalent MGT detectors in handling mixtext situations, evaluating their performance in terms of effectiveness, robustness, and generalization. Our findings reveal that existing detectors struggle to identify mixtext, particularly in dealing with subtle modifications and style adaptability. This research underscores the urgent need for more fine-grain detectors tailored for mixtext, offering valuable insights for future research. Code and Models are available at https://github.com/Dongping-Chen/MixSet.Comment: Accepted by NAACL 202

MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use

Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities. Recently, many studies have focused on the tool utilization ability of LLMs. They primarily investigated how LLMs effectively collaborate with given specific tools. However, in scenarios where LLMs serve as intelligent agents, as seen in applications like AutoGPT and MetaGPT, LLMs are expected to engage in intricate decision-making processes that involve deciding whether to employ a tool and selecting the most suitable tool(s) from a collection of available tools to fulfill user requests. Therefore, in this paper, we introduce MetaTool, a benchmark designed to evaluate whether LLMs have tool usage awareness and can correctly choose tools. Specifically, we create a dataset called ToolE within the benchmark. This dataset contains various types of user queries in the form of prompts that trigger LLMs to use tools, including both single-tool and multi-tool scenarios. Subsequently, we set the tasks for both tool usage awareness and tool selection. We define four subtasks from different perspectives in tool selection, including tool selection with similar choices, tool selection in specific scenarios, tool selection with possible reliability issues, and multi-tool selection. We conduct experiments involving nine popular LLMs and find that the majority of them still struggle to effectively select tools, highlighting the existing gaps between LLMs and genuine intelligent agents. However, through the error analysis, we found there is still significant room for improvement. Finally, we conclude with insights for tool developers that follow ChatGPT to provide detailed descriptions that can enhance the tool selection performance of LLMs

Modular polyoxometalate-intercalated layered double hydroxide membranes for molecular sieving and in situ regeneration

The design and synthesis of two-dimensional membranes with ultra-high permeability, selectivity, and antifouling properties have been a significant challenge. Herein, we propose a facile approach to design modular polyoxometalate-intercalated layered double hydroxide membranes using a charge-driven self-assembly process. The resultant MgAl-SiW12 membrane shows 4 times higher water permeance (>130 L m−2 h−1 bar−1) than that of its MgAl-NO3 precursor. Excellent retention of >99% for Congo red and Evans blue is achieved by the MgAl-SiW12 membrane, which can be regenerated (permeance recovery > 95%) via a simple UV-vis irradiation cycle. Insertion of the SiW12 cluster into layered double hydroxide allows precise control and modulation of the interlayer’s spacing and hydrophilicity and promotes spontaneous electron migration and interfacial charge carrier separation. Moreover, the ·OH and ·O2− radicals forming during the irradiation process are responsible for the degradation of contaminants

Intermediate role of gut microbiota in vitamin B nutrition and its influences on human health

Vitamin B consists of a group of water-soluble micronutrients that are mainly derived from the daily diet. They serve as cofactors, mediating multiple metabolic pathways in humans. As an integrated part of human health, gut microbiota could produce, consume, and even compete for vitamin B with the host. The interplay between gut microbiota and the host might be a crucial factor affecting the absorbing processes of vitamin B. On the other hand, vitamin B supplementation or deficiency might impact the growth of specific bacteria, resulting in changes in the composition and function of gut microbiota. Together, the interplay between vitamin B and gut microbiota might systemically contribute to human health. In this review, we summarized the interactions between vitamin B and gut microbiota and tried to reveal the underlying mechanism so that we can have a better understanding of its role in human health

Genome-Wide Association Study Reveals Both Overlapping and Independent Genetic Loci to Control Seed Weight and Silique Length in Brassica napus

Seed weight (SW) is one of three determinants of seed yield, which positively correlates with silique length (SL) in Brassica napus (rapeseed). However, the genetic mechanism underlying the relationship between seed weight (SW) and silique length (SL) is largely unknown at present. A natural population comprising 157 inbred lines in rapeseed was genotyped by whole-genome re-sequencing and investigated for SW and SL over four years. The genome-wide association study identified 20 SNPs in significant association with SW on A01, A04, A09, C02, and C06 chromosomes and the phenotypic variation explained by a single locus ranged from 11.85% to 34.58% with an average of 25.43%. Meanwhile, 742 SNPs significantly associated with SL on A02, A03, A04, A07, A08, A09, C01, C03, C04, C06, C07, and C08 chromosomes were also detected and the phenotypic variation explained by a single locus ranged from 4.01 to 48.02% with an average of 33.33%, out of which, more than half of the loci had not been reported in the previous studies. There were 320 overlapping or linked SNPs for both SW and SL on A04, A09, and C06 chromosomes. It indicated that both overlapping and independent genetic loci controlled both SW and SL in B. napus. On the haplotype block on A09 chromosome, the allele variants of a known gene BnaA.ARF18.a controlling both SW and SL were identified in the natural population by developing derived cleaved amplified polymorphic sequence (dCAPS) markers. These findings are valuable for understanding the genetic mechanism of SW and SL and also for rapeseed molecular breeding programs