Formal Analysis of Fairness for Optimistic Multiparty Contract Signing Protocol

Optimistic multiparty contract signing (OMPCS) protocols are proposed for exchanging multiparty digital signatures in a contract. Compared with general two-party exchanging protocols, such protocols are more complicated, because the number of protocol messages and states increases considerably when signatories increase. Moreover, fairness property in such protocols requires protection from each signatory rather than from an external hostile agent. It thus presents a challenge for formal verification. In our analysis, we employ and combine the strength of extended modeling language CSP# and linear temporal logic (LTL) to verify the fairness of OMPCS protocols. Furthermore, for solving or mitigating the state space explosion problem, we set a state reduction algorithm which can decrease the redundant states properly and reduce the time and space complexity greatly. Finally, this paper illustrates the feasibility of our approach by analyzing the GM and CKS protocols, and several fairness flaws have been found in certain computation times

TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems

Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend of task planning and the utilization of external tools, such as APIs. However, real-world complex systems present three prevalent challenges concerning task planning and tool usage: (1) The real system usually has a vast array of APIs, so it is impossible to feed the descriptions of all APIs to the prompt of LLMs as the token length is limited; (2) the real system is designed for handling complex tasks, and the base LLMs can hardly plan a correct sub-task order and API-calling order for such tasks; (3) Similar semantics and functionalities among APIs in real systems create challenges for both LLMs and even humans in distinguishing between them. In response, this paper introduces a comprehensive framework aimed at enhancing the Task Planning and Tool Usage (TPTU) abilities of LLM-based agents operating within real-world systems. Our framework comprises three key components designed to address these challenges: (1) the API Retriever selects the most pertinent APIs for the user task among the extensive array available; (2) LLM Finetuner tunes a base LLM so that the finetuned LLM can be more capable for task planning and API calling; (3) the Demo Selector adaptively retrieves different demonstrations related to hard-to-distinguish APIs, which is further used for in-context learning to boost the final performance. We validate our methods using a real-world commercial system as well as an open-sourced academic dataset, and the outcomes clearly showcase the efficacy of each individual component as well as the integrated framework

The Role of Gaseous Molecules in Traumatic Brain Injury: An Updated Review

Traumatic brain injury (TBI) affects millions of people in China each year. TBI has a high mortality and often times a serious prognosis. The causative mechanisms of TBI during development and recovery from an injury remain vague, leaving challenges for the medical community to provide treatment options that improve prognosis and provide an optimal recovery. Biological gaseous molecules including nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H2S), and molecular hydrogen (H2) have been found to play critical roles in physiological and pathological conditions in mammals. Accumulating evidence has found that these gaseous molecules can execute neuroprotection in many central nervous system (CNS) conditions due to their highly permeable properties allowing them to enter the brain. Considering the complicated mechanisms and the serious prognosis of TBI, effective and adequate therapeutic approaches are urgently needed. These four gaseous molecules can be potential attractive therapeutic intervention on TBI. In this review, we will present a comprehensive overview on the role of these four biological gasses in the development of TBI and their potential therapeutic applications

Association and interactions between mixed exposure to trace elements and the prevalence of kidney stones: a study of NHANES 2017–2018

BackgroundThe association between exposure to trace elements mixture and the prevalence of kidney stones and the interactions between elements are unclear. The aim of this study was to explore the association between exposure to trace elements mixture and the prevalence of kidney stones and the interactions between the elements.MethodsA total of 1,244 participants (139 kidney stone formers and 1,105 non-stone former participants) in NHANES 2017–2018 were included. The exposure to trace elements was evaluated by measuring their concentration in urine samples. Three methods, Logistic regression, quantile-based g computation (qgcomp), and Bayesian kernel machine regression (BKMR), were used for analysis.ResultsAccording to the results from qgcomp and BKMR, a negative association was found between exposure to the 13 trace elements and the prevalence of kidney stones [OR = 0.50 (0.32, 0.78)]. Subgroup analysis revealed that Co, As, and iodine in the whole population, Co, As, and Ni in males, and Cs, iodine, and Sb in females, were most strongly associated with kidney stones. Kidney stone was found to be positively correlated with Co and negatively correlated with the other elements. Besides, there were significant interactions between Ni and Pb in the whole population, Co and iodine in males, and Pb and iodine in females.ConclusionThere was a negative association between exposure to the mixture of 13 trace elements and the prevalence of kidney stones

OsNAR2.1 Positively Regulates Drought Tolerance and Grain Yield Under Drought Stress Conditions in Rice

Drought is an important environmental factor that severely restricts crop production. The high-affinity nitrate transporter partner protein OsNAR2.1 plays an essential role in nitrate absorption and translocation in rice. Our results suggest that OsNAR2.1 expression is markedly induced by water deficit. After drought stress conditions and irrigation, compared with wild-type (WT), the survival rate was significantly improved in OsNAR2.1 over-expression lines and decreased in OsNAR2.1 RNAi lines. The survival rate of Wuyunjing7 (WYJ), OsNRT2.1 over-expression lines and OsNRT2.3a over-expression lines was not significantly different. Compared with WT, overexpression of OsNAR2.1 could significantly increase nitrogen uptake in rice, and OsNAR2.1 RNAi could significantly reduce nitrogen uptake. Under drought conditions, the expression of OsNAC10, OsSNAC1, OsDREB2a, and OsAP37 was significantly reduced in OsNAR2.1 RNAi lines and increased substantially in OsNAR2.1 over-expression lines. Also, the chlorophyll content, relative water content, photosynthetic rate and water use efficiency were decreased considerably in OsNAR2.1 RNAi lines and increased significantly in OsNAR2.1 over-expression lines under drought conditions. Finally, compared to WT, grain yield increased by about 9.1 and 26.6%, in OsNAR2.1 over-expression lines under full and limited irrigation conditions, respectively. These results indicate that OsNAR2.1 regulates the response to drought stress in rice and increases drought tolerance

A high-throughput phenotyping assay for precisely determining stalk crushing strength in large-scale sugarcane germplasm

Sugarcane is a major industrial crop around the world. Lodging due to weak mechanical strength is one of the main problems leading to huge yield losses in sugarcane. However, due to the lack of high efficiency phenotyping methods for stalk mechanical strength characterization, genetic approaches for lodging-resistant improvement are severely restricted. This study attempted to apply near-infrared spectroscopy high-throughput assays for the first time to estimate the crushing strength of sugarcane stalks. A total of 335 sugarcane samples with huge variation in stalk crushing strength were collected for online NIRS modeling. A comprehensive analysis demonstrated that the calibration and validation sets were comparable. By applying a modified partial least squares method, we obtained high-performance equations that had large coefficients of determination (R2 > 0.80) and high ratio performance deviations (RPD > 2.4). Particularly, when the calibration and external validation sets combined for an integrative modeling, we obtained the final equation with a coefficient of determination (R2) and ratio performance deviation (RPD) above 0.9 and 3.0, respectively, demonstrating excellent prediction capacity. Additionally, the obtained model was applied for characterization of stalk crushing strength in large-scale sugarcane germplasm. In a three-year study, the genetic characteristics of stalk crushing strength were found to remain stable, and the optimal sugarcane genotypes were screened out consistently. In conclusion, this study offers a feasible option for a high-throughput analysis of sugarcane mechanical strength, which can be used for the breeding of lodging resistant sugarcane and beyond

Household, community, sub-national and country-level predictors of primary cooking fuel switching in nine countries from the PURE study

Introduction. Switchingfrom polluting (e.g. wood, crop waste, coal)to clean (e.g. gas, electricity) cooking fuels can reduce household air pollution exposures and climate-forcing emissions.While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood. Methods.We examined longitudinal survey data from 24 172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology study.We assessed household-level primary cooking fuel switching during a median of 10 years offollow up (∼2005–2015).We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching. Results. One-half of study households(12 369)reported changing their primary cookingfuels between baseline andfollow up surveys. Of these, 61% (7582) switchedfrom polluting (wood, dung, agricultural waste, charcoal, coal, kerosene)to clean (gas, electricity)fuels, 26% (3109)switched between different polluting fuels, 10% (1164)switched from clean to polluting fuels and 3% (522)switched between different clean fuels

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation