Investigating the Nonlinear Relationship Between Car Dependency and the Built Environment

Car-dominated daily travel has caused many severe and urgent urban problems across the world, and such travel patterns have been found to be related to the built environment. However, few existing studies have uncovered the nonlinear relationship between the built environment and car dependency using a machine learning method, thus failing to provide policymakers with nuanced evidence-based guidance on reducing car dependency. Using data from Puget Sound regional household travel surveys, this study analyzes the complicated relationship between car dependency and the built environment using the gradient boost decision tree method. The results show that people living in high-density areas are less likely to rely on private cars than those living in low-density neighborhoods. Both threshold and nonlinear effects are observed in the relationships between the built environment and car dependency. Increasing road density promotes car usage when the road density is below 6 km/km2. However, the positive association between road density and car use is not observed in areas with high road density. Increasing pedestrian-oriented road density decreases the likelihood of using cars as the main mode. Such a negative effect is most effective when the pedestrian-oriented road density is over 14.5 km/km2. More diverse land use also discourages people’s car use, probably because those areas are more likely to promote active modes. Destination accessibility has an overall negative effect and a significant threshold effect on car dependency. These findings can help urban planners formulate tailored land-use interventions to reduce car dependency

RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit

Although Large Language Models (LLMs) have demonstrated extraordinary capabilities in many domains, they still have a tendency to hallucinate and generate fictitious responses to user requests. This problem can be alleviated by augmenting LLMs with information retrieval (IR) systems (also known as retrieval-augmented LLMs). Applying this strategy, LLMs can generate more factual texts in response to user input according to the relevant content retrieved by IR systems from external corpora as references. In addition, by incorporating external knowledge, retrieval-augmented LLMs can answer in-domain questions that cannot be answered by solely relying on the world knowledge stored in parameters. To support research in this area and facilitate the development of retrieval-augmented LLM systems, we develop RETA-LLM, a {RET}reival-{A}ugmented LLM toolkit. In RETA-LLM, we create a complete pipeline to help researchers and users build their customized in-domain LLM-based systems. Compared with previous retrieval-augmented LLM systems, RETA-LLM provides more plug-and-play modules to support better interaction between IR systems and LLMs, including {request rewriting, document retrieval, passage extraction, answer generation, and fact checking} modules. Our toolkit is publicly available at https://github.com/RUC-GSAI/YuLan-IR/tree/main/RETA-LLM.Comment: Technical Report for RETA-LL

Periostin identified as a potential biomarker of prostate cancer by iTRAQ-proteomics analysis of prostate biopsy

<p>Abstract</p> <p>Background</p> <p>Proteomics may help us better understand the changes of multiple proteins involved in oncogenesis and progression of prostate cancer(PCa) and identify more diagnostic and prognostic biomarkers. The aim of this study was to screen biomarkers of PCa by the proteomics analysis using isobaric tags for relative and absolute quantification(iTRAQ).</p> <p>Methods</p> <p>The patients undergoing prostate biopsies were classified into 3 groups according to pathological results: benign prostate hyperplasia (BPH, n = 20), PCa(n = 20) and BPH with local prostatic intraepithelial neoplasm(PIN, n = 10). Then, all the specimens from these patients were analyzed by iTRAQ and two-dimensional liquid chromatography-tandem mass spectrometry (2DLC-MS/MS). The Gene Ontology(GO) function and the transcription regulation networks of the differentially expressed were analyzed by MetaCore software. Western blotting and Immunohistochemical staining were used to analyze the interesting proteins.</p> <p>Result</p> <p>A total of 760 proteins were identified from 13787 distinct peptides, including two common proteins that enjoy clinical application: prostate specific antigen (PSA) and prostatic acid phosphatase(PAP). Proteins that expressed differentially between PCa and BPH group were further analyzed. Compared with BPH, 20 proteins were significantly differentially up-regulated (>1.5-fold) while 26 were significantly down-regulated in PCa(<0.66-fold). In term of GO database, the differentially expressed proteins were divided into 3 categories: cellular component(CC), molecular function (MF) and biological process(BP). The top 5 transcription regulation networks of the differentially expressed proteins were initiated through activation of SP1, p53, YY1, androgen receptor(AR) and c-Myc The overexpression of periostin in PCa was verified by western blotting and immunohistochemical staining.</p> <p>Conclusion</p> <p>Our study indicates that the iTRAQ technology is a new strategy for global proteomics analysis of the tissues of PCa. A significant up-regulation of periostin in PCa compared to BPH may provide clues for not only a promising biomarker for the prognosis of PCa but also a potential target for therapeutical intervention.</p

Evolution of the strange-metal scattering in momentum space of electron-doped La2−xCexCuO4{\rm La}_{2-x}{\rm Ce}_x{\rm CuO}_4

The linear-in-temperature resistivity is one of the important mysteries in the strange metal state of high-temperature cuprate superconductors. To uncover this anomalous property, the energy-momentum-dependent imaginary part of the self-energy Im ${\rm \Sigma}(k, \omega)$ holds the key information. Here we perform systematic doping, momentum, and temperature-dependent angle-resolved photoemission spectroscopy measurements of electron-doped cuprate ${\rm La}_{2-x}{\rm Ce}_x{\rm CuO}_4$ and extract the evolution of the strange metal scattering in momentum space. At low doping levels and low temperatures, Im ${\rm\Sigma} \propto \omega$ dependence dominates the whole momentum space. For high doping levels and high temperatures, Im ${\rm\Sigma} \propto \omega^2$ shows up, starting from the antinodal region. By comparing with the hole-doped cuprates ${\rm La}_{2-x}{\rm Sr}_x{\rm CuO}_4$ and ${\rm Bi}_2{\rm Sr}_2{\rm CaCu}_2{\rm O}_8$, we find a dichotomy of the scattering rate exists along the nodal and antinodal direction, which is ubiquitous in the cuprate family. Our work provides new insight into the strange metal state in cuprates

A DOD-SOH balancing control method for dynamic reconfigurable battery systems based on DQN algorithm

This article presents a DOD-SOH equalization method for a DRB system based on the Deep DQN algorithm. The proposed method utilizes DQN to learn the operational processes of the system. By integrating the advantages of DRB with SOH equalization theory and the DQN algorithm from the perspective of DOD, our method significantly improve battery performance and ensure cell balancing. To begin with, we present a dynamic reconfigurable battery system with a simple topological structure and outline its switching control process. Additionly, we provide an analysis of the SOH balancing principle and elaborate on the control process of DQN algorithm. Finally, subsequent simulations are carried out, and the simulation results demonstrate outstanding performances in reducing the variance of SOHs, which indicates an enhancement in the level of SOH balancing as well

Oncogenic PIK3CA Mutations Reprogram Glutamine Metabolism in Colorectal Cancer

Cancer cells often require glutamine for growth, thereby distinguishing them from most normal cells. Here we show that PIK3CA mutations reprogram glutamine metabolism by upregulating glutamate pyruvate transaminase 2 (GPT2) in colorectal cancer (CRC) cells, making them more dependent on glutamine. Compared with isogenic wild-type (WT) cells, PIK3CA mutant CRCs convert substantially more glutamine to alpha-ketoglutarate to replenish the tricarboxylic acid cycle and generate ATP. Mutant p110 alpha upregulates GPT2 gene expression through an AKT-independent, PDK1-RSK2-ATF4 signalling axis. Moreover, aminooxyacetate, which inhibits the enzymatic activity of aminotransferases including GPT2, suppresses xenograft tumour growth of CRCs with PIK3CA mutations, but not with WT PIK3CA. Together, these data establish oncogenic PIK3CA mutations as a cause of glutamine dependency in CRCs and suggest that targeting glutamine metabolism may be an effective approach to treat CRC patients harbouring PIK3CA mutations

Oncogenic PIK3CA Mutations Reprogram Glutamine Metabolism in Colorectal Cancer

Cancer cells often require glutamine for growth, thereby distinguishing them from most normal cells. Here we show that PIK3CA mutations reprogram glutamine metabolism by upregulating glutamate pyruvate transaminase 2 (GPT2) in colorectal cancer (CRC) cells, making them more dependent on glutamine. Compared with isogenic wild-type (WT) cells, PIK3CA mutant CRCs convert substantially more glutamine to alpha-ketoglutarate to replenish the tricarboxylic acid cycle and generate ATP. Mutant p110 alpha upregulates GPT2 gene expression through an AKT-independent, PDK1-RSK2-ATF4 signalling axis. Moreover, aminooxyacetate, which inhibits the enzymatic activity of aminotransferases including GPT2, suppresses xenograft tumour growth of CRCs with PIK3CA mutations, but not with WT PIK3CA. Together, these data establish oncogenic PIK3CA mutations as a cause of glutamine dependency in CRCs and suggest that targeting glutamine metabolism may be an effective approach to treat CRC patients harbouring PIK3CA mutations