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Structure-guided design of partial agonists at an opioid receptor
Public perception of accuracy-fairness trade-offs in algorithmic decisions in the United States
The naive approach to preventing discrimination in algorithmic decision-making is to exclude protected attributes from the model’s inputs. This approach, known as “equal treatment,” aims to treat all individuals equally regardless of their demographic characteristics. However, this practice can still result in unequal impacts across different groups. Recently, alternative notions of fairness have been proposed to reduce unequal impact. However, these alternative approaches may require sacrificing predictive accuracy. The present research investigates public attitudes toward these trade-offs in the United States. When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. Additionally, preferences polarized along partisan identities, with Democrats favoring impact parity over accuracy maximization while Republicans displayed the reverse preference. Gender and social justice orientations also significantly predicted EIA support. Overall, findings demonstrate multidimensional drivers of algorithmic fairness attitudes, underscoring divisions around equality versus equity principles. Achieving standards around fair AI requires addressing conflicting human values through good governance.
Associations between sleep duration and quality and physical frailty in community-dwelling older adults: a cross-sectional study
Controlled chain-growth polymerization via propargyl/allenyl palladium intermediates
Study on the impact of China’s urban agglomerations’ tiered spatial structure on regional economic resilience
Urban agglomerations serve as crucial spatial carriers of economic development, and their spatial structure profoundly influences regional economic resilience. This study draws on Martin’s conceptualization of economic resilience and, considering the administrative hierarchy and development stage of China’s urban system, examines the impact of the layered spatial structure of urban agglomerations on regional economic resilience. Based on data from 17 Chinese urban agglomerations from 2005 to 2019, this research employs a one-step system Generalized Method of Moments (GMM) model to empirically analyze the effects of three mechanisms – polycentricity within urban agglomerations, inter-city development disparities, and inter-city industrial gradients – on regional economic resilience. The findings reveal that both the polycentric distribution of population and economy and the coupling of dual centers significantly positively affect the economic resilience of urban agglomerations. The potential development energy difference between core and peripheral cities can be transformed into developmental momentum for peripheral cities, thereby generating positive spatial externalities that play a significant and positive role in enhancing economic resilience. As the ratio of secondary to tertiary industry employees in peripheral and core cities nears 1, urban agglomerations’ economic resilience strengthens, underscoring the importance of a balanced industrial gradient and regional collaboration in mitigating economic shocks. This study also considers the heterogeneity of whether the urban agglomerations are coastal and whether they contain a national-level central city. The research finds that for inland urban agglomerations and those with existing national-level central cities, developing a polycentric spatial structure can effectively enhance the region’s economic resilience in responding to various shocks. Furthermore, aside from a few more developed coastal urban agglomerations, other inland urban agglomerations should continue to focus on spatial agglomeration, fostering core cities to strengthen economic competitiveness. Finally, given the varying industrialization stages and structures within urban agglomerations, appropriately adjusting the industrial gradient is essential.
Nine complete chloroplast genomes of the Camellia genus provide insights into evolutionary relationships and species differentiation
Abstract The genus Camellia, known for species such as Camellia japonica, is of significant agricultural and ecological importance. However, the genetic diversity and evolutionary relationships among Camellia species remain insufficiently explored. In this study, we successfully sequenced and assembled the complete chloroplast (cp) genomes of nine Camellia accessions, including the species Camellia petelotii, and eight varieties of C. Japonica (C. Japonica ‘Massee Lane’, C. Japonica ‘L.T.Dees’, C. Japonica ‘Songzi’, C. Japonica ‘Kagirohi’, C. Japonica ‘Sanyuecha’, C. Japonica ‘Xiameng Hualin’, C. Japonica ‘Xiameng Wenqing’, and C. Japonica ‘Xiameng Xiaoxuan’). These genomes exhibited conserved lengths (~ 156,580–157,002 bp), indicating minimal variation in genome size. They consistently predicted 87 protein-coding genes, although variations were observed in the rRNA and tRNA genes. Structural and evolutionary analyses revealed the highly conserved nature of these cp genomes, with no significant inversions or gene rearrangements detected. Consistent codon usage patterns were observed across these accessions. Five hypervariable regions (rpsbK, psbM, ndhJ, ndhF, and ndhD) were identified as potential molecular markers for species differentiation. Phylogenetic analysis of 82 accessions from the Camellia genus, along with outgroup accessions revealed close genetic relationships among certain C. japonica varieties, including Songzi, Sanyuecha, L.T.Dees, and Kagirohi, which formed sister groups. Massee Lane was located within Sect. Camellia. Moreover, Xiameng Hualin, Xiameng Wenqing, Xiameng Xiaoxuan, and C. petelotii demonstrated a strong genetic affinity. These findings provide valuable insights into the structural and evolutionary dynamics of Camellia cp genomes, contributing to species identification and conservation.
Similar chiral phenomena occur in cell cultures and human crowds
Pickering emulsions with low interface coverage but enhanced stability for emulsion interface catalysis and SERS-based detection
Sine-G family of distributions in Bayesian survival modeling: A baseline hazard approach for proportional hazard regression with application to right-censored oncology datasets using R and STAN
In medical research and clinical practice, Bayesian survival modeling is a powerful technique for assessing time-to-event data. It allows for the incorporation of prior knowledge about the model’s parameters and provides a more comprehensive understanding of the underlying hazard rate function. In this paper, we propose a Bayesian survival modeling strategy for proportional hazards regression models that employs the Sine-G family of distributions as baseline hazards. The Sine-G family contains flexible distributions that can capture a wide range of hazard forms, including increasing, decreasing, and bathtub-shaped hazards. In order to capture the underlying hazard rate function, we examine the flexibility and effectiveness of several distributions within the Sine-G family, such as the Gompertz, Lomax, Weibull, and exponentiated exponential distributions. The proposed approach is implemented using the R programming language and the STAN probabilistic programming framework. To evaluate the proposed approach, we use a right-censored survival dataset of gastric cancer patients, which allows for precise determination of the hazard rate function while accounting for censoring. The Watanabe Akaike information criterion and the leave-one-out information criterion are employed to evaluate the performance of various baseline hazards.
Developing a seasonal-adjusted machine-learning-based hybrid time‑series model to forecast heatwave warning
How a vast digital twin of the Yangtze River could prevent flooding in China
Addendum: Trade-off between critical metal requirement and transportation decarbonization in automotive electrification
Exploring a long short-term memory for mountain flood forecasting based on watershed-internal knowledge graph and large language model
The water levels associated with mountain floods exhibit rapid fluctuations within small watersheds, necessitating extensive data on various factors influencing such disasters to facilitate real-time forecasting. This study investigates the application of Long Short-Term Memory (LSTM) networks for mountain flood forecasting, designing a watershed-internal Knowledge Graph (KG) and Large Language Model (LLM) that encompass watershed relationships and internal information structures. We have developed a hydrological KG for the Qixi Reservoir and Qiaodongcun forecasting points located in Zhejiang Province, China, to systematically organize water conservancy data, identify significant disaster-related factors, optimize the input hydrological data, and determine the most effective combination of input data for forecasting water levels. Additionally, we have implemented Recurrent Neural Networks (RNN) and Gated Recurrent Units (GRU) for comparative analysis with LSTM. The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.
Adropin ameliorates reproductive dysfunctions in letrozole-induced PCOS mouse
Alcohol and cancer risk: what you need to know
Integration of therapeutic cargo into the human genome with programmable type V-K CAST
A novel flexible near-infrared endoscopic device that enables real-time artificial intelligence fluorescence tissue characterization
Real-time endoscopic rectal lesion characterization employing artificial intelligence (AI) and near-infrared (NIR) imaging of the fluorescence perfusion indicator agent Indocyanine Green (ICG) has demonstrated promise. However, commercially available fluorescence endoscopes do not possess the flexibility and anatomical reach capabilities of colonoscopy while commercial flexible scopes do not yet provide beyond visible spectral imaging. This limits the application of this AI-NIR classification technology. Here, to close this technical gap, we present our development of a colonoscope-compatible flexible imaging probe for NIR-ICG visualization combined with a full field of view machine learning (ML) algorithm for fluorescence quantification and perfusion pattern cross-correlation (including first in human testing). The imaging probe is capable of 133µm minimum object resolution, with a maximum working distance of 50mm and an excitation illumination power of 52mW with 75o average field of illumination (meaning minimum device tip distance from target is 13 mm for a 2 cm polyp). The system demonstrated ex-vivo and in-vivo NIR visualization of clinically relevant concentrations of ICG in both resected and in situ (extracorporeally) colon in patients undergoing colorectal resection. A previously developed AI-NIR perfusion quantification algorithm was applied to videos of a bench model of varying ICG flow captured with the developed flexible system with added ML features generated full field of view pixel-level fluorescence time-series measurements capable of distinguishing distinct ICG flow regions in the image via correlative dynamic fluorescence intensity profiles. Jaccard Index comparison of the AI -generated flow regions against manually delineated flow regions resulted in 79% accuracy. While further clinical validation of the AI-NIR polyp classification method is on-going (in the Horizon Europe Awarded CLASSICA project), other use case applications of NIR colonoscopy include simpler perioperative perfusion assessment in patients undergoing colorectal resection and combination with targeted agents in development thus encouraging continuing development and design optimization of this flexible NIR imaging probe to enable clinical and commercial translation.