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Pancreatic ductal adenocarcinoma (PDAC) poses significant clinical challenges, often presenting as unresectable with limited biopsy options. Here, we show that circulating tumor cells (CTCs) offer a promising alternative, serving as a "liquid biopsy" that enables the generation of in vitro 3D models and highly aggressive in vivo models for functional and molecular studies in advanced PDAC. Within the retrieved CTC pool (median 65 CTCs/5 mL), we identify a subset (median content 8.9%) of CXCR4+ CTCs displaying heightened stemness and metabolic traits, reminiscent of circulating cancer stem cells. Through comprehensive analysis, we elucidate the importance of CTC-derived models for identifying potential targets and guiding treatment strategies. Screening of stemness-targeting compounds identified stearoyl-coenzyme A desaturase (SCD1) as a promising target for advanced PDAC. These results underscore the pivotal role of CTC-derived models in uncovering therapeutic avenues and ultimately advancing personalized care in PDAC.

In the paper, we discuss the possible interpretation of the $J^P=1/2^-$ singly charm pentaquark as hadronic molecules. With the effective Lagrangian method, we further analyze the production properties of singly charm pentaquark from decays of $B$ meson, including strong coupling constants and production branching ratios of the charm pentaquark. Our numerical results show that the branching ratio of production of pentaquark from B meson can reach to order of $10^{-6}$., Comment: 16 pages, 5 figures

Pancreatic ductal adenocarcinoma (PDAC) poses significant clinical challenges, often presenting as unresectable with limited biopsy options. Here, we show that circulating tumor cells (CTCs) offer a promising alternative, serving as a "liquid biopsy" that enables the generation of in vitro 3D models and highly aggressive in vivo models for functional and molecular studies in advanced PDAC. Within the retrieved CTC pool (median 65 CTCs/5 mL), we identify a subset (median content 8.9%) of CXCR4+ CTCs displaying heightened stemness and metabolic traits, reminiscent of circulating cancer stem cells. Through comprehensive analysis, we elucidate the importance of CTC-derived models for identifying potential targets and guiding treatment strategies. Screening of stemness-targeting compounds identified stearoyl-coenzyme A desaturase (SCD1) as a promising target for advanced PDAC. These results underscore the pivotal role of CTC-derived models in uncovering therapeutic avenues and ultimately advancing personalized care in PDAC.

The Cenozoic topographic development of the Himalaya-Tibet orogen (HTO) substantially affected the paleoenvironment and biodiversity patterns of High Asia. However, concepts on the evolution and paleoenvironmental history of the HTO differ massively in timing, elevational increase and sequence of surface uplift of the different elements of the orogen. Using target enrichment of a large set of transcriptome-derived markers, ancestral range estimation and paleoclimatic niche modelling, we assess a recently proposed concept of a warm temperate paleo-Tibet in Asian spiny frogs of the tribe Paini and reconstruct their historical biogeography. That concept was previously developed in invertebrates. Because of their early evolutionary origin, low dispersal capacity, high degree of local endemism, and strict dependence on temperature and humidity, the cladogenesis of spiny frogs may echo the evolution of the HTO paleoenvironment. We show that diversification of main lineages occurred during the early to Mid-Miocene, while the evolution of alpine taxa started during the late Miocene/early Pliocene. Our distribution and niche modelling results indicate range shifts and niche stability that may explain the modern disjunct distributions of spiny frogs. They probably maintained their (sub)tropical or (warm)temperate preferences and moved out of the ancestral paleo-Tibetan area into the Himalaya as the climate shifted, as opposed to adapting in situ. Based on ancestral range estimation, we assume the existence of low-elevation, climatically suitable corridors across paleo-Tibet during the Miocene along the Kunlun, Qiangtang and/or Gangdese Shan. Our results contribute to a deeper understanding of the mechanisms and processes of faunal evolution in the HTO.

The concept of Artificial Intelligence (AI) has existed for almost 70 years, and awareness of AI among the public has increased significantly in recent years, particularly with the application of Large Language Models (LLMs) and Generative AI. One of the concrete advances enabled by the new generation of AI is to generate code in various programming languages. This study investigates how AI impacts the Software Job Landscape by analyzing the changes that AI-based developer tools bring to the Software Job landscape and uses the AI-supported GitHub Copilot as an example. The research questions we studied are how AI affects productivity and work processes; how AI alter skillsets required in the software industry; and how AI leads to changes, elimination,and creation of job types. Data was collected through an online survey with quantitative and qualitative questions and then processed using thematic analysis. The analysis results show a high expectation of AI getting integrated into current and future jobs, with a low expectation that AI will replace Software Developer jobs. The overall expectation is that AI will increase the productivity of Software Developers by 20% or more. The analysis also showed that the need for analytical skills will increase while the need for programming skills will decrease, particularly basic and entry-level programming skills. Consequently, simple and more manual tasks can be done by AI-supported tools. At the same time, more advanced cognitive processes and analyses will be driven by humans, which lift the demands for humans to better use and understand AI-supported tools. Further study should investigate the job roles most prone to AI and new job roles that will becreated.

The concept of Artificial Intelligence (AI) has existed for almost 70 years, and awareness of AI among the public has increased significantly in recent years, particularly with the application of Large Language Models (LLMs) and Generative AI. One of the concrete advances enabled by the new generation of AI is to generate code in various programming languages. This study investigates how AI impacts the Software Job Landscape by analyzing the changes that AI-based developer tools bring to the Software Job landscape and uses the AI-supported GitHub Copilot as an example. The research questions we studied are how AI affects productivity and work processes; how AI alter skillsets required in the software industry; and how AI leads to changes, elimination,and creation of job types. Data was collected through an online survey with quantitative and qualitative questions and then processed using thematic analysis. The analysis results show a high expectation of AI getting integrated into current and future jobs, with a low expectation that AI will replace Software Developer jobs. The overall expectation is that AI will increase the productivity of Software Developers by 20% or more. The analysis also showed that the need for analytical skills will increase while the need for programming skills will decrease, particularly basic and entry-level programming skills. Consequently, simple and more manual tasks can be done by AI-supported tools. At the same time, more advanced cognitive processes and analyses will be driven by humans, which lift the demands for humans to better use and understand AI-supported tools. Further study should investigate the job roles most prone to AI and new job roles that will becreated.

The concept of Artificial Intelligence (AI) has existed for almost 70 years, and awareness of AI among the public has increased significantly in recent years, particularly with the application of Large Language Models (LLMs) and Generative AI. One of the concrete advances enabled by the new generation of AI is to generate code in various programming languages. This study investigates how AI impacts the Software Job Landscape by analyzing the changes that AI-based developer tools bring to the Software Job landscape and uses the AI-supported GitHub Copilot as an example. The research questions we studied are how AI affects productivity and work processes; how AI alter skillsets required in the software industry; and how AI leads to changes, elimination,and creation of job types. Data was collected through an online survey with quantitative and qualitative questions and then processed using thematic analysis. The analysis results show a high expectation of AI getting integrated into current and future jobs, with a low expectation that AI will replace Software Developer jobs. The overall expectation is that AI will increase the productivity of Software Developers by 20% or more. The analysis also showed that the need for analytical skills will increase while the need for programming skills will decrease, particularly basic and entry-level programming skills. Consequently, simple and more manual tasks can be done by AI-supported tools. At the same time, more advanced cognitive processes and analyses will be driven by humans, which lift the demands for humans to better use and understand AI-supported tools. Further study should investigate the job roles most prone to AI and new job roles that will becreated.

The concept of Artificial Intelligence (AI) has existed for almost 70 years, and awareness of AI among the public has increased significantly in recent years, particularly with the application of Large Language Models (LLMs) and Generative AI. One of the concrete advances enabled by the new generation of AI is to generate code in various programming languages. This study investigates how AI impacts the Software Job Landscape by analyzing the changes that AI-based developer tools bring to the Software Job landscape and uses the AI-supported GitHub Copilot as an example. The research questions we studied are how AI affects productivity and work processes; how AI alter skillsets required in the software industry; and how AI leads to changes, elimination,and creation of job types. Data was collected through an online survey with quantitative and qualitative questions and then processed using thematic analysis. The analysis results show a high expectation of AI getting integrated into current and future jobs, with a low expectation that AI will replace Software Developer jobs. The overall expectation is that AI will increase the productivity of Software Developers by 20% or more. The analysis also showed that the need for analytical skills will increase while the need for programming skills will decrease, particularly basic and entry-level programming skills. Consequently, simple and more manual tasks can be done by AI-supported tools. At the same time, more advanced cognitive processes and analyses will be driven by humans, which lift the demands for humans to better use and understand AI-supported tools. Further study should investigate the job roles most prone to AI and new job roles that will becreated.

The concept of Artificial Intelligence (AI) has existed for almost 70 years, and awareness of AI among the public has increased significantly in recent years, particularly with the application of Large Language Models (LLMs) and Generative AI. One of the concrete advances enabled by the new generation of AI is to generate code in various programming languages. This study investigates how AI impacts the Software Job Landscape by analyzing the changes that AI-based developer tools bring to the Software Job landscape and uses the AI-supported GitHub Copilot as an example. The research questions we studied are how AI affects productivity and work processes; how AI alter skillsets required in the software industry; and how AI leads to changes, elimination,and creation of job types. Data was collected through an online survey with quantitative and qualitative questions and then processed using thematic analysis. The analysis results show a high expectation of AI getting integrated into current and future jobs, with a low expectation that AI will replace Software Developer jobs. The overall expectation is that AI will increase the productivity of Software Developers by 20% or more. The analysis also showed that the need for analytical skills will increase while the need for programming skills will decrease, particularly basic and entry-level programming skills. Consequently, simple and more manual tasks can be done by AI-supported tools. At the same time, more advanced cognitive processes and analyses will be driven by humans, which lift the demands for humans to better use and understand AI-supported tools. Further study should investigate the job roles most prone to AI and new job roles that will becreated.

The concept of Artificial Intelligence (AI) has existed for almost 70 years, and awareness of AI among the public has increased significantly in recent years, particularly with the application of Large Language Models (LLMs) and Generative AI. One of the concrete advances enabled by the new generation of AI is to generate code in various programming languages. This study investigates how AI impacts the Software Job Landscape by analyzing the changes that AI-based developer tools bring to the Software Job landscape and uses the AI-supported GitHub Copilot as an example. The research questions we studied are how AI affects productivity and work processes; how AI alter skillsets required in the software industry; and how AI leads to changes, elimination,and creation of job types. Data was collected through an online survey with quantitative and qualitative questions and then processed using thematic analysis. The analysis results show a high expectation of AI getting integrated into current and future jobs, with a low expectation that AI will replace Software Developer jobs. The overall expectation is that AI will increase the productivity of Software Developers by 20% or more. The analysis also showed that the need for analytical skills will increase while the need for programming skills will decrease, particularly basic and entry-level programming skills. Consequently, simple and more manual tasks can be done by AI-supported tools. At the same time, more advanced cognitive processes and analyses will be driven by humans, which lift the demands for humans to better use and understand AI-supported tools. Further study should investigate the job roles most prone to AI and new job roles that will becreated.

Jingsheng Wang,&ast; Jian Zhao,&ast; Quanqiang Lin,&ast; Xiuxiu Xu, Ke Jiang, Yuanmin Li Department of Cardiology, the Second Affiliated Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yuanmin Li, Department of Cardiology, the Second Affiliated Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, No. 366 Taishan Street, Taian, 271000, People’s Republic of China, Tel +86-13583843518, Fax +86-538-6222036, Email Liym575@126.comObjective: This study aimed to evaluate the association between red blood cell distribution width (RDW) changes and major adverse cardiovascular event (MACE) occurrences during sacubitril/valsartan treatment in patients with heart failure with reduced ejection fraction (HFrEF).Methods: This study retrospectively analyzed the medical records of patients with HFrEF hospitalized from April 2018 to February 2021. The patients were divided into two groups according to the inclusion of sacubitril/valsartan in the personal drug treatment regimen, the traditional and the sacubitril/valsartan group. RDW values before and after sacubitril/valsartan treatment were recorded respectively as RDW1 and RDW2. ΔRDW was defined as the difference between RDW2 and RDW1. The patients in the sacubitril/valsartan group were divided into two subgroups according to ΔRDW > 0 or ≤ 0. MACEs, such as readmission for HF, acute myocardial infarction, ischemic stroke, and malignant arrhythmia and death, were recorded during the 1-year follow-up period in each group.Results: MACE development was lower in patients treated with sacubitril/valsartan than those treated with conventional therapy (log-rank, P< 0.001). The incidence of cardiac events during the follow-up period was greater in the group with ΔRDW > 0 than in the group with ΔRDW ≤ 0 (Breslow, P< 0.001). Increas

Ke Jiang,1,&ast; Ling Kang,1,&ast; Andong Jiang,2 Qiang Zhao1 1Department of Cardiovascular Medicine, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, Shandong, People’s Republic of China; 2Medical Imaging Department, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, Shandong, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Qiang Zhao, Department of Cardiovascular Medicine, The Second Affiliated Hospital of Shandong First Medical University, No. 366, Taishan Street, Tai’an, Shandong, 271000, People’s Republic of China, Tel +86-13583832661, Email sydefyzq@126.comPurpose: Myocardial infarction (MI) is a common cardiovascular disease, and its underlying pathological mechanism remains unclear. We aimed to develop a diagnostic model to distinguish different subtypes of MI.Patients and Methods: The gene expression profiles of MI from the GEO database and hypoxia-related genes (HRGs) from MSigDB were downloaded. Then, the different MI subtypes based on HRGs were identified with unsupervised clustering. The difference of expression patterns and hypoxic-immune status among different subtypes of MI were investigated. The diagnostic model to distinguish the different subtypes of MI was developed and validated.Results: Based on HRGs, MI samples were divided into two subtypes, cluster A and cluster B. A total of 211 genes showed significant changes in expression between the two subtypes. Cluster A was characterized by high hypoxia status and low immunity status. Based on weighted gene co-expression network analysis, ROC analysis and LASSO regression algorithm, 5 genes were identified as potential diagnostic markers. Finally, a diagnostic model based on these 5 genes was established, which can distinguish the two subtypes well.Conclusion: The five hub genes, including ANKRD36, HLTF, KIF3A, OXCT1 and VPS13A, may be associated with the different subtypes of MI

Over the past 40 years, the climate has been changing and human disturbance has increased in the vast Qinghai¿Tibet Plateau (QTP). These 2 factors are expected to affect the distribution of a large number of endemic vertebrate species. However, quantitative relationships between range shifts and climate change and human disturbance of these species in the QTP have rarely been evaluated. We used occurrence records of 19 terrestrial vertebrate species (birds, mammals, amphibians, and reptiles) occurring in the QTP from 1980 to 2020 to quantify the effects of climate change and anthropogenic impacts on the distribution of these 4 taxonomic groups and estimated species range changes in each species. The trend in distribution changes differed among the taxonomic groups, although, generally, ranges shifted to central QTP. Climate change contributed more to range variation than human disturbance (the sum of the 4 climatic variables contributed more than the sum of the 4 human disturbance variables for all 4 taxonomic groups). Suitable geographic range increased for most mammals, amphibians, and reptiles (+27.6%, +18.4%, and +27.8% on average, respectively), whereas for birds range decreased on average by 0.9%. Quantitative evidence for climate change and human disturbance associations with range changes for endemic vertebrate species in the QTP can provide useful insights into biodiversity conservation under changing environments.

Offline reinforcement learning learns an effective policy on offline datasets without online interaction, and it attracts persistent research attention due to its potential of practical application. However, extrapolation error generated by distribution shift will still lead to the overestimation for those actions that transit to out-of-distribution(OOD) states, which degrades the reliability and robustness of the offline policy. In this paper, we propose Contextual Conservative Q-Learning(C-CQL) to learn a robustly reliable policy through the contextual information captured via an inverse dynamics model. With the supervision of the inverse dynamics model, it tends to learn a policy that generates stable transition at perturbed states, for the fact that pertuebed states are a common kind of OOD states. In this manner, we enable the learnt policy more likely to generate transition that destines to the empirical next state distributions of the offline dataset, i.e., robustly reliable transition. Besides, we theoretically reveal that C-CQL is the generalization of the Conservative Q-Learning(CQL) and aggressive State Deviation Correction(SDC). Finally, experimental results demonstrate the proposed C-CQL achieves the state-of-the-art performance in most environments of offline Mujoco suite and a noisy Mujoco setting., Comment: the work is not finished

BackgroundThe malaria protein VAR2CSA binds oncofetal chondroitin sulfate (ofCS), a unique chondroitin sulfate, expressed on almost all mammalian cancer cells. Previously, we produced a bispecific construct targeting ofCS and human T cells based on VAR2CSA and anti-CD3 (V-aCD3(Hu)). V-aCD3(Hu) showed efficacy against xenografted tumors in immunocompromised mice injected with human immune cells at the tumor site. However, the complex effects potentially exerted by the immune system as a result of the treatment cannot occur in mice without an immune system. Here we investigate the efficacy of V-aCD3(Mu) as a monotherapy and combined with immune checkpoint inhibitors in mice with a fully functional immune system.MethodsWe produced a bispecific construct consisting of a recombinant version of VAR2CSA coupled to an anti-murine CD3 single-chain variable fragment. Flow cytometry and ELISA were used to check cell binding capabilities and the therapeutic effect was evaluated in vitro in a killing assay. The in vivo efficacy of V-aCD3(Mu) was then investigated in mice with a functional immune system and established or primary syngeneic tumors in the immunologically "cold" 4T1 mammary carcinoma, B16-F10 malignant melanoma, the pancreatic KPC mouse model, and in the immunologically "hot" CT26 colon carcinoma model.ResultsV-aCD3(Mu) had efficacy as a monotherapy, and the combined treatment of V-aCD3(Mu) and an immune checkpoint inhibitor showed enhanced effects resulting in the complete elimination of solid tumors in the 4T1, B16-F10, and CT26 models. This anti-tumor effect was abscopal and accompanied by a systemic increase in memory and activated cytotoxic and helper T cells. The combined treatment also led to a higher percentage of memory T cells in the tumor without an increase in regulatory T cells. In addition, we observed partial protection against re-challenge in a melanoma model and full protection in a breast cancer model.ConclusionsOur findings suggest that V-aCD3(

BackgroundThe malaria protein VAR2CSA binds oncofetal chondroitin sulfate (ofCS), a unique chondroitin sulfate, expressed on almost all mammalian cancer cells. Previously, we produced a bispecific construct targeting ofCS and human T cells based on VAR2CSA and anti-CD3 (V-aCD3(Hu)). V-aCD3(Hu) showed efficacy against xenografted tumors in immunocompromised mice injected with human immune cells at the tumor site. However, the complex effects potentially exerted by the immune system as a result of the treatment cannot occur in mice without an immune system. Here we investigate the efficacy of V-aCD3(Mu) as a monotherapy and combined with immune checkpoint inhibitors in mice with a fully functional immune system.MethodsWe produced a bispecific construct consisting of a recombinant version of VAR2CSA coupled to an anti-murine CD3 single-chain variable fragment. Flow cytometry and ELISA were used to check cell binding capabilities and the therapeutic effect was evaluated in vitro in a killing assay. The in vivo efficacy of V-aCD3(Mu) was then investigated in mice with a functional immune system and established or primary syngeneic tumors in the immunologically "cold" 4T1 mammary carcinoma, B16-F10 malignant melanoma, the pancreatic KPC mouse model, and in the immunologically "hot" CT26 colon carcinoma model.ResultsV-aCD3(Mu) had efficacy as a monotherapy, and the combined treatment of V-aCD3(Mu) and an immune checkpoint inhibitor showed enhanced effects resulting in the complete elimination of solid tumors in the 4T1, B16-F10, and CT26 models. This anti-tumor effect was abscopal and accompanied by a systemic increase in memory and activated cytotoxic and helper T cells. The combined treatment also led to a higher percentage of memory T cells in the tumor without an increase in regulatory T cells. In addition, we observed partial protection against re-challenge in a melanoma model and full protection in a breast cancer model.ConclusionsOur findings suggest that V-aCD3(

In spite of debate, ambiguity, and tension around teacher dispositions, in the past over two decades, the place of dispositions in initial teacher education (ITE) has been widely supported among policy makers and researchers. Specifically, debate on whether dispositions are teachable has largely given way to action to foster dispositions. Adopting a two-cycle participatory action research design, this study explored ways to teach the first-year teacher candidates’ dispositions in an early childhood ITE programme in New Zealand. The intervention included eight focus dispositions and corresponding strategies to teach each focus disposition. Data collection methods included student self-assessment surveys, individual and focus group interviews with students and teaching staff, team meetings, and a variety of pedagogical documentation. Ethnographic content analysis generated three themes: legitimacy of the intervention, experiential orientation of the intervention, and effect of the intervention. The study exemplifies how dispositions intervention can be incorporated in ITE programmes.

Recent deep learning-based methods have achieved promising performance for computed tomography metal artifact reduction (CTMAR). However, most of them suffer from two limitations: (i) the domain knowledge is not fully embedded into the network training; (ii) metal artifacts lack effective representation models. The aforementioned limitations leave room for further performance improvement. Against these issues, we propose a novel triple domain model-driven CTMAR network, termed as TriDoNet, whose network training exploits triple domain knowledge, i.e., the knowledge of the sinogram, CT image, and metal artifact domains. Specifically, to explore the non-local repetitive streaking patterns of metal artifacts, we encode them as an explicit tight frame sparse representation model with adaptive thresholds. Furthermore, we design a contrastive regularization (CR) built upon contrastive learning to exploit clean CT images and metal-affected images as positive and negative samples, respectively. Experimental results show that our TriDoNet can generate superior artifact-reduced CT images., Comment: 6 pages, 3 figures

Cache side-channel attacks exhibit severe threats to software security and privacy, especially for cryptosystems. In this paper, we propose CaType, a novel refinement type-based tool for detecting cache side channels in crypto software. Compared to previous works, CaType provides the following advantages: (1) For the first time CaType analyzes cache side channels using refinement type over x86 assembly code. It reveals several significant and effective enhancements with refined types, including bit-level granularity tracking, distinguishing different effects of variables, precise type inferences, and high scalability. (2) CaType is the first static analyzer for crypto libraries in consideration of blinding-based defenses. (3) From the perspective of implementation, CaType uses cache layouts of potential vulnerable control-flow branches rather than cache states to suppress false positives. We evaluate CaType in identifying side channel vulnerabilities in real-world crypto software, including RSA, ElGamal, and (EC)DSA from OpenSSL and Libgcrypt. CaType captures all known defects, detects previously-unknown vulnerabilities, and reveals several false positives of previous tools. In terms of performance, CaType is 16X faster than CacheD and 131X faster than CacheS when analyzing the same libraries. These evaluation results confirm the capability of CaType in identifying side channel defects with great precision, efficiency, and scalability., Comment: Accepted by the 29th ACM Conference on Computer and Communications Security (CCS), 2022

Cache side-channel attacks exhibit severe threats to software security and privacy, especially for cryptosystems. In this paper, we propose CaType, a novel refinement type-based tool for detecting cache side channels in crypto software. Compared to previous works, CaType provides the following advantages: (1) For the first time CaType analyzes cache side channels using refinement type over x86 assembly code. It reveals several significant and effective enhancements with refined types, including bit-level granularity tracking, distinguishing different effects of variables, precise type inferences, and high scalability. (2) CaType is the first static analyzer for crypto libraries in consideration of blinding-based defenses. (3) From the perspective of implementation, CaType uses cache layouts of potential vulnerable control-flow branches rather than cache states to suppress false positives. We evaluate CaType in identifying side channel vulnerabilities in real-world crypto software, including RSA, ElGamal, and (EC)DSA from OpenSSL and Libgcrypt. CaType captures all known defects, detects previously-unknown vulnerabilities, and reveals several false positives of previous tools. In terms of performance, CaType is 16X faster than CacheD and 131X faster than CacheS when analyzing the same libraries. These evaluation results confirm the capability of CaType in identifying side channel defects with great precision, efficiency, and scalability. © 2022 ACM.

A direct band gap that solely exists in monolayer semiconducting transition metal dichalcogenides (TMDs) endows strong photoluminescence (PL) features, whereas multilayer TMD structures exhibit quenched PL due to the direct-to-indirect band gap transition. We demonstrate multi-layer TMD (such as MoS2 and WS2) nanoscrolls with a preserved direct band gap fabricated by an effective and facile method of solvent-driven self-assembly. The resultant multi-layer nanoscrolls, exhibiting up to 11 times higher PL intensity than the remanent monolayer, are carefully characterized using PL spectroscopy. Significantly enlarged interlayer distances and modulated interlayer coupling in the fabricated nanostructures are unveiled by cross-sectional scanning transmission electron microscopy, atomic force microscopy, and Raman spectroscopy. The preservation of direct band gap features is further evidenced by density functional theory calculations using the simplified bilayer model with an experimentally obtained 15 & ANGS; interlayer distance. The modulation of the PL intensity as an indicator of the band gap crossover in the TMD nanoscrolls is demonstrated by removing the acetone molecules trapped inside the interlayer space. The general applicability of the method presents an opportunity for large-scale fabrication of a plethora of multilayer TMD nanoscrolls with direct band gaps.

In spite of debate, ambiguity, and tension around teacher dispositions, in the past over two decades, the place of dispositions in initial teacher education (ITE) has been widely supported among policy makers and researchers. Specifically, debate on whether dispositions are teachable has largely given way to action to foster dispositions. Adopting a two-cycle participatory action research design, this study explored ways to teach the first-year teacher candidates’ dispositions in an early childhood ITE programme in New Zealand. The intervention included eight focus dispositions and corresponding strategies to teach each focus disposition. Data collection methods included student self-assessment surveys, individual and focus group interviews with students and teaching staff, team meetings, and a variety of pedagogical documentation. Ethnographic content analysis generated three themes: legitimacy of the intervention, experiential orientation of the intervention, and effect of the intervention. The study exemplifies how dispositions intervention can be incorporated in ITE programmes.

Bailong Hu,1,2,&ast; Ke Jiang,1,&ast; Wenyan Shi,3 Shixiang Xiao,4 Shili Zhang,5 Yongguo Zhang,6 Yuancheng Zhou,7 Chengwei Tan,8 Songjiang Tan,9 Xiaohua Zou1,2 1Department of Anesthesiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, People’s Republic of China; 2College of Anesthesiology, Guizhou Medical University, Guiyang, People’s Republic of China; 3Department of Anesthesiology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, People’s Republic of China; 4Department of Anesthesiology, The First People’ s Hospital of Bijie City, Bijie, People’s Republic of China; 5Department of Anesthesiology, Dali Bai Autonomous Prefecture People’s Hospital, Dali, People’s Republic of China; 6Department of Anesthesiology, The People’s Hospital of Qiannan, Qiannan, People’s Republic of China; 7Department of Anesthesiology, The First People’s Hospital of Guiyang, Guiyang, People’s Republic of China; 8Department of Anesthesiology, The Second People’s Hospital of Guiyang, Guiyang, People’s Republic of China; 9Department of Anesthesiology, The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiaohua Zou; Songjiang Tan, Fax +86-851-86771013, Email zouxiaohuazxh@163.com; 214459348@qq.comBackground: Remimazolam tosilate (RT) is a new type of γ-aminobutyric acid subtype A (GABAA) receptor agonist, having the possibility to be an ideal sedative drug for procedural sedation. At present, there are few studies on the effect of RT on respiratory depression in elderly patients. We aimed to evaluate the effect of RT on respiratory depression in elderly patients undergoing gastroscopy.Methods: This prospective, randomized, single-blinded trial recruited patients from eight centers in China between May 2022 and July 2022. A total of 346 elderly patients undergoing gastroscopy were rando

In this article, a novel multiple-input multiple-out (MIMO) transmission scheme, called generalized polarized enhanced spatial modulation (GPESM), is proposed for dual-polarized land mobile satellite (LMS) communications. We first introduce the enhanced spatial modulation (ESM) technique for dual-polarized LMS communications, in which polarization dimension, spatial dimension and multiple signal constellations are used to transmit information and obtain substantial performance gain. Meanwhile, the theoretical upper bound for the average bit error probability (ABEP) of the proposed GPESM scheme is derived. In order to further improve the reliability of the system, we also propose two novel power allocation (PA) algorithms for GPESM system, which are the optimization-driven approximated max-min distance (AMMD)-based PA algorithm and the data-driven deep neural network (DNN)-based PA algorithm. To achieve an enhanced spatial diversity gain, we consider to apply a reconfigurable intelligent surface (RIS) to the GPESM system as a relay to assist in transmitting information. In this way, the user can receive the information transmitted by the satellite on one hand, and the information sent by the satellite via the RIS relay on the other hand. We also extend the above-mentioned two PA algorithms to the RIS-assisted GPESM systems. Our simulation results show that the RIS-assisted GPESM systems are capable of obtaining high bit error rate (BER) performance gain (up to 10 dB) compared to the standard GPESM system and two PA algorithms can further improve the performance to the systems., QC 20210720

Yongjun Liu,1,&ast; Yuee Chen,2,&ast; Jie Chen,3 Yukung Kuang,4 Niandi Tan,5 Ke Jiang,6 Shuihui Peng,7 Chunlin Hu2 1Department of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People’s Republic of China; 2Department of Emergency Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People’s Republic of China; 3Department of Critical Care Medicine, Dongguan People’s Hospital, Dongguan, Province Guangdong, 523059, People’s Republic of China; 4Division of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People’s Republic of China; 5Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China; 6Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People’s Republic of China; 7Department of Pharmacy Intravenous Admixture Services, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Chunlin HuDepartment of Emergency Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People’s Republic of ChinaEmail huchunl@mail.sysu.edu.cnShuihui PengDepartment of Pharmacy Intravenous Admixture Services, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People’s Republic of ChinaEmail pengshh@mail.sysu.edu.cnObjective: To observe hemodynamic characteristics in a series of patients with myocardial injury caused by severe COVID-19-related pneumonia.Materials and Methods: We continuously collected clinical data from severe COVID-19-related pneumonia patients from the We

Making sound food and agriculture decisions is important for global society and the environment. Experts tend to view crop genetic engineering, a technology that can improve yields and minimize impacts on the environment, more favorably than the public. Because there is a causal relationship between public opinion and public policy, it is important to understand how opinions about genetically engineered (GE) crops are influenced. The public increasingly seeks science information on the Internet. Here, semantic network analysis is performed to characterize the presentation of the term "GMO (genetically modified organism)," a proxy for food developed from GE crops, on the web. Texts from three sources are analyzed: U.S. federal websites, top pages from a Google search, and online news titles. We found that the framing and sentiment (positive, neutral, or negative attitudes) of "GMO" varies across these sources. It is described how differences in the portrayal of GE food by each source might affect public opinion. A current understanding of the types of information individuals may encounter online can provide insight into public opinion toward GE food. In turn, this knowledge can guide teaching and communication efforts by the scientific community to promote informed decision-making about agricultural biotechnologies.

Defects from grain interiors and boundaries of perovskite films cause significant nonradiative recombination energy loss, and thus perovskite films with controlled crystallinity and large grains is critical for improvement of both photovoltaic performance and stability for perovskite-based solar cells. Here, a methylamine (MA0) gas-assisted crystallization method is developed for fabrication of methylammonium lead iodide (MAPbI3) perovskite films. In the process, the perovskite film is formed via controlled release of MA0 gas molecules from a liquid intermediate phase MAPbI3·xMA0. The resulting perovskite film comprises millimeter-sized grains with (110)-uniaxial crystallographic orientation, exhibiting much low trap density, long carrier lifetime, and excellent environmental stability. The corresponding perovskite solar cell exhibits a power conversion efficiency (PCE) of ~ 21.36%, which is among the highest reported for MAPbI3-based devices. This method provides important progress towards the fabrication of high-quality perovskite thin films for low-cost, highly efficient and stable perovskite solar cells. © 2020, The Author(s).

The low toxicity, narrow bandgaps, and high charge-carrier mobilities make tin perovskites the most promising light absorbers for low-cost perovskite solar cells (PSCs). However, the development of the Sn-based PSCs is seriously hampered by the critical issues of poor stability and low power conversion efficiency (PCE) due to the facile oxidation of Sn2+ to Sn4+ and poor film formability of the perovskite films. Herein, a synthetic strategy is developed for the fabrication of methylammonium tin iodide (MASnI3) film via ion exchange/insertion reactions between solid-state SnF2 and gaseous methylammonium iodide. In this way, the nucleation and crystallization of MASnI3 can be well controlled, and a highly uniform pinhole-free MASnI3 perovskite film is obtained. More importantly, the detrimental oxidation can be effectively suppressed in the resulting MASnI3 film due to the presence of a large amount of remaining SnF2. This high-quality perovskite film enables the realization of a PCE of 7.78%, which is among the highest values reported for the MASnI3-based solar cells. Moreover, the MASnI3 solar cells exhibit high reproducibility and good stability. This method provides new opportunities for the fabrication of low-cost and lead-free tin-based halide perovskite solar cells. © 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

In this report, emplectite (CuBiS2) semiconductor has been deposited on mesoporous TiO2 using gas–solid reaction method. For the first time, CuCl2 and BiCl3 are solution-coated on mesoporous TiO2 films, and thereafter reacted with H2S gas in an H2S atmosphere. The CuBiS2 film is further confirmed using X-ray diffraction; thus, demonstrating the pure phase of CuBiS2. CuBiS2 film shows high spectral absorption with an energy gap (E g) of 2.18 eV. Furthermore, devices have a structure consisting of FTO/compact-TiO2/mesoporous-TiO2/CuBiS2/P3HT/Ag have been fabricated and hence exhibit high photoresponse performance.

Ke Jiang,1 Genpeng Li,1 Wenjie Chen,1 Linlin Song,1 Tao Wei,1 Zhihui Li,1 Rixiang Gong,1 Jianyong Lei,1 Hubing Shi,2 Jingqiang Zhu1 1Thyroid and Parathyroid Surgery Center, West China Hospital of Sichuan University, Chengdu 610041, People’s Republic of China; 2State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu 610041, Sichuan, People’s Republic of ChinaCorrespondence: Jianyong LeiThyroid and Parathyroid Surgery Center, West China Hospital of Sichuan University, Chengdu 610041, People’s Republic of ChinaTel/Fax +86-28-85423822Email leijianyong11@163.comHubing ShiState Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu 610041, Sichuan, People’s Republic of ChinaEmail shihb@scu.edu.cnBackground: Lymph node metastasis (LNM) is associated with increased risk of recurrence and poor prognosis in papillary thyroid cancer (PTC). Novel non-invasive biomarkers for the prediction of LNM in PTC patients are still urgently needed. In this study, the relationship between the expression of plasma exosomal microRNAs (miRNAs) and LNM was analyzed. Further, we aimed to explore if exosomal miRNAs can serve as indicators of LNM in PTC patients.Methods: A total of 64 PTC patients who underwent total thyroidectomy and neck dissection from June 2018 to July 2018 in West China Hospital were enrolled in this study. Plasma exosomes were isolated by exoRNeasy Serum/Plasma Maxi Kit. The levels of selected exosomal miRNAs were detected by real-time quantitative PCR (qRT-PCR). Cox proportional hazard analyses and receiver operating characteristic (ROC) curves were conducted to evaluate the predictive efficiency. Furthermore, PTC cell lines with transfection of miRNA mimics/inhibitors were used to verify the functions of exosomal miRNAs.Results: 49 PTC patients with LNM and 15 without LNM were included in the present study. Ex

The deposition of a uniform and dense tin-based perovskite layer with low defect-caused background carrier density is crucial for achieving efficient tin perovskite solar cells (PSCs). These defects are mainly caused by the rapid oxidation of Sn2+ to Sn4+ in tin perovskite during device fabrication. Herein, trihydrazine dihydriodide ((N2H4)3(HI)2) is used as an additive for solution deposition of a formamidinium tin iodide (FASnI3) perovskite layer. The resultant FASnI3 layer is homogeneous with full surface coverage; moreover, the content of Sn4+ is significantly reduced in the film from the SnI2 precursor owing to the reductive property of (N2H4)3(HI)2. With the high-quality FASnI3 layer as a light absorber, planar-heterojunction perovskite solar cells are fabricated, exhibiting a maximum power conversion efficiency of 8.48% and good reproducibility. This work opens new possibilities for achieving efficient lead-free tin-based perovskite solar cells. © 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Methylammonium bismuth iodide (MA3Bi2I9) has been recently investigated as a light absorber in lead-free perovskite solar cells. However, the MA3Bi2I9 film fabricated via conventional one-step spin coating methods usually has poor surface morphology, limiting the device performance. Herein, a cation displacement approach was employed for the fabrication of MA3Bi2I9 film, where (CH3CH2CH2NH3)3Bi2I9 (PA3Bi2I9) film was first deposited from a solution containing CH3CH2CH2NH3I and BiI3 and then transformed into MA3Bi2I9 film in a methylamine atmosphere. With the technique, the MA3Bi2I9 film was realized with smooth, uniform, and compact surface morphology. Using the MA3Bi2I9 film as a light absorber, a mesoporous photovoltaic device was fabricated with a power conversion efficiency of 0.33%, which is about two times higher than the value (0.15%) obtained for the one-step spin coating MA3Bi2I9 device. Moreover, the facile film fabrication strategy utilized in this work paves the way for high reproducibility of lead-free organic–inorganic halide films and devices.

Tin-based halide perovskite materials have been successfully employed in lead-free perovskite solar cells, but the overall power conversion efficiencies (PCEs) have been limited by the high carrier concentration from the facile oxidation of Sn2+ to Sn4+. Now a chemical route is developed for fabrication of high-quality methylammonium tin iodide perovskite (MASnI3) films: hydrazinium tin iodide (HASnI3) perovskite film is first solution-deposited using presursors hydrazinium iodide (HAI) and tin iodide (SnI2), and then transformed into MASnI3 via a cation displacement approach. With the two-step process, a dense and uniform MASnI3 film is obtained with large grain sizes and high crystallization. Detrimental oxidation is suppressed by the hydrazine released from the film during the transformation. With the MASnI3 as light harvester, mesoporous perovskite solar cells were prepared, and a maximum power conversion efficiency (PCE) of 7.13 % is delivered with good reproducibility.

Temperature measurement by infrared thermal imager is an attractive technique in many fields, and it is of great importance to ensure the measurement accuracy of the infrared thermal imager. Aiming at the influence of dust on the temperature measurement of infrared thermal imager, this paper summarized the dust influence into three categories: dust on the surface of the measured object, dust on the infrared thermal imager’s lens and dust in the optical path between the measured object and the infrared thermal imager, and conducted three dust experiments. To quantify the measurement errors caused by dust, the infrared thermal image features that are affected by dust are extracted and a compensation model is established based on polynomial regression. The results indicate that dust can introduce measurement errors of infrared thermal imager and the proposed compensation method can compensate for the measurement errors caused by dust and improve the accuracy of infrared thermal imager.

A high performance electronic hair (EH) sensor with multiple responsibilities is fabricated via fully mimicking the sensory hair cell structure and properties of human skin. The designed EH sensor consists of nylon fibers as hairs for mechanical signal amplification and polydimethylsiloxane (PDMS) resin as the human skin for sensor encapsulation. Two carbonized papers are used as piezo-resistive mechanoreceptors (M1 and M2). The nylon fibers used have a diameter and Young's modulus close to those of hairs and PDMS has a Young's modulus close to that of human skin. The structure of human hairs is verified to be optimal for maximizing the sensing ability, and the structure of the EH sensor is then optimized in terms of the structure of human hairs. Unlike conventional single-mode EH sensors, the EH sensor obtained by fully mimicking human skin is capable of detecting multiple signals of pressure, surface roughness, the airflow rate, etc. just like human skin. Moreover, the EH sensor is also effective in identifying the airflow direction. Because of its simple structure, low cost, good flexibility and multiple functionalities, the EH sensor is expected to find widespread application in e-skins, wearable devices, robotics, human machine interfaces, etc.

Temperature measurement by infrared thermal imager is an attractive technique in many fields, and it is of great importance to ensure the measurement accuracy of the infrared thermal imager. Aiming at the influence of dust on the temperature measurement of infrared thermal imager, this paper summarized the dust influence into three categories: dust on the surface of the measured object, dust on the infrared thermal imager’s lens and dust in the optical path between the measured object and the infrared thermal imager, and conducted three dust experiments. To quantify the measurement errors caused by dust, the infrared thermal image features that are affected by dust are extracted and a compensation model is established based on polynomial regression. The results indicate that dust can introduce measurement errors of infrared thermal imager and the proposed compensation method can compensate for the measurement errors caused by dust and improve the accuracy of infrared thermal imager.

Linlin Song, 1 Jianyong Lei, 1 Ke Jiang, 1 Yali Lei, 2 Yuting Tang, 2 Jingqiang Zhu, 1 Zhihui Li, 1 Huairong Tang 2 1Thyroid and Parathyroid Surgery Center, West China Hospital of Sichuan University, Chengdu 610041, People’s Republic of China; 2Health and Management Center, West China Hospital of Sichuan University, Chengdu 610041, People’s Republic of ChinaCorrespondence: Huairong TangHealth and Management Center, West China Hospital of Sichuan University, Chengdu 610041, People’s Republic of ChinaTel/Fax +86-28-85422933Email thrhuaxi11@163.comBackground and Aim: The relationship between subclinical hypothyroidism (SHYPO) and sleep quality is still unclear. Our objective was to compare the sleep quality between SHYPO patients and a control group with normal thyroid function.Methods: A total of 2224 patients with SHYPO and 12,622 euthyroid (EUTH) control group patients were included in the present study. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. The sleep outcomes were compared to explore the association between SHYPO and sleep quality. Furthermore, we tried to identify the risk factors of poor sleep in SHYPO patients.Results: Compared to the EUTH control patients, SHYPO patients had a higher PSQI score (6.83 ± 2.67 vs 6.64 ± 2.63, p =0.004) and a higher proportion of poor sleepers (67.09% vs 64.75%, p =0.033). Moreover, subjects with SHYPO were associated with poorer sleep (Odd Ratio (OR) 1.120, 95% Confidence Intervals (CI) 1.016 to 1.235, p =0.023), longer sleep latency (OR 1.162, 95% CI 1.053 to 1.282, p =0.003), and shorter sleep duration (OR 1.148, 95% CI 1.019 to 1.293, p =0.023) after adjusting for potential confounders. Furthermore, we found that lower age, lower body mass index, and women were risk factors for poor sleep quality in SHYPO patients.Conclusion: Our findings suggest a relationship between SHYPO and poor sleep quality in a large Chinese population.Keywords: su

Email has remained a principal form of communication among people, both in enterprise and social settings. With a deluge of emails crowding our mailboxes daily, there is a dire need of smart email systems that can recover important emails and make personalized recommendations. In this work, we study the problem of predicting user triage actions to incoming emails where we take the reply prediction as a working example. Different from existing methods, we formulate the triage action prediction as a recommendation problem and focus on the content-based approach, where the users are represented using the content of current and past emails. We also introduce additional similarity features to further explore the affinities between users and emails. Experiments on the publicly available Avocado email collection demonstrate the advantages of our proposed recommendation framework and our method is able to achieve better performance compared to the state-of-the-art deep recommendation methods. More importantly, we provide valuable insight into the effectiveness of different textual and user representations and show that traditional bag-of-words approaches, with the help from the similarity features, compete favorably with the more advanced neural embedding methods., Comment: User representations, Personalization, Email response prediction, Similarity features

Herein, new organic-inorganic hybrid perovskite single crystals of complex 1 with the formula of (n-C3H7NH3)6Pb4I14 are synthesized from an aqueous HI solution containing PbI2 and propylammonium iodide. The single crystal X-ray crystallographic study shows that the structure consists of two-dimensional (2D) sheets of corner- and face-sharing [Pb2I7]3- octahedra, and the inorganic layers are separated by layers of 3-propylammonium cations. The 2D perovskite material features good film formability upon simple solution deposition. This 2D phase can be easily converted into 3D CH3NH3PbI3 (MAPbI3) perovskite via an organic-cation displacement approach, exhibiting a compact, ultrasmooth, and textured morphology. Using the film as a light absorber, planar CH3NH3PbI3 perovskite solar cells are fabricated, and the best power conversion efficiency of 19.19% is obtained with good reproducibility. © 2018 The Royal Society of Chemistry.

Methylammonium iodide (MAI) and lead iodide (PbI2) have been extensively employed as precursors for solution-processed MAPbI3 perovskite solar cells (PSCs). However, the MAPbI3 perovskite films directly deposited from the precursor solutions, usually suffer from poor surface coverage due to uncontrolled nucleation and crystal growth of the perovskite during the film formation, resulting in low photovoltaic conversion efficiency and poor reproducibility. Herein, propylammonium iodide and PbI2 are employed as precursors for solution deposition of propylammonium lead iodide (PAPbI3) perovskite film. It is found that the precursors have good film formability, enabling the deposition of a large-area and homogeneous PAPbI3 perovskite film by a scalable dip-coating technique. The dip-coated PAPbI3 film is then subjected to an organic-cation displacement reaction, resulting in MAPbI3 film with high surface coverage and crystallinity. With the MAPbI3 film as the light absorber, planar PSCs are fabricated, and stabilized power conversion efficiencies of 19.27% and 15.68% can be achieved for the devices with active areas of 0.09 and 5.02 cm2, respectively. The technology reported here provides a robust and efficient approach to fabricate large-area and high-efficiency perovskite cells for practical application.

Existing studies on mixed-criticality systems are usually safety-oriented, which seriously ignore the security and energy related requirements. In this paper we are interested in the design of security-sensitive mixed-criticality real-time systems. We first establish the system model to capture security-critical applications in mixed-criticality systems. Higher security-criticality protection always results in significant time and energy overhead in mixed-criticality systems. Thus, this paper proposes a system-level design framework for energy optimization of security-sensitive mixed-criticality system with hard real-time constraints. Since the time complexity of finding optimal solutions grows exponentially as problem size grows, a GA (Genetic Algorithm) based on efficient heuristic algorithm is devised to address the system-level optimization problem. Extensive experiments and a real-life case study have been conducted to show the efficiency of the proposed technique, which can obtain balanced minimal energy consumption while satisfying strict security and timing constraints. The proposed approach can save up to 28.9% energy consumption compared with other three candidates. (C) 2018 Elsevier Inc. All rights reserved., Funding Agencies|National Natural Science Foundation of China [61003032]; Research Fund of National Key Laboratory of Computer Architecture [CARCH201501]; Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing [2016A09]

Kunpeng Shu,1,* Yu Zheng,2,* Junru Chen,1 Wenbin Li,3 Ke Jiang4 1Department of Urology, Institute of Urology, 2Department of Thoracic Surgery, 3Huaxi MR Research Center (HMRRC), Department of Radiology, 4Thyroid and Parathyroid Surgery Center, West China Hospital of Sichuan University, Chengdu, Sichuan, People’s Republic of China *These authors contributed equally to this work Background: This study investigated the prognostic value of inflammation-based scores in patients with high-risk localized prostate cancer who underwent radical prostatectomy with or without neoadjuvant androgen deprivation therapy (ADT). Methods: Inflammation-based scores included the neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), platelet-to-lymphocyte ratio (PLR), prognostic nutritional index (PNI), and plasma fibrinogen. A total of 440 patients (380 patients treated without neoadjuvant ADT and 60 patients treated with neoadjuvant ADT) were retrospectively evaluated in our medical center. Receiver operating characteristic (ROC) curves and Kaplan–Meier analyses were performed to compare the prognostic value of these scores. Univariate and multivariate Cox regression analyses were also performed. Results: For all patients, dNLR and PNI were predictive of biochemical recurrence (P=0.041 and <0.001, respectively). Subgroup analysis of neoadjuvant strategies was also performed. For patients treated with neoadjuvant ADT, no selected inflammation-based scores were significantly correlated with biochemical recurrence (P>0.05). In contrast, for patients treated without neoadjuvant ADT, NLR (area under the ROC curve [AUC] =0.576, P=0.033), dNLR (P=0.585 and 0.017), PLR (AUC =0.582, P=0.024), and PNI (AUC =0.622, P<0.001) were predictive of biochemical recurrence. Kaplan–Meier analyses showed that dNLR (P=0.044), PLR (P=0.028), and PNI (P=0.004) were significantly associated with biochemical recurrence. Based on multivariable models, PNI wa

Honghao Duan, Ke Jiang, Dengke Wei, Lijun Zhang, Deliang Cheng, Min Lv, Yuben Xu, Aimin He Department of Hand Surgery, Honghui Hospital, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, People’s Republic of China Abstract: This study aimed to analyze epigenetically and genetically altered genes in melanoma to get a better understanding of the molecular circuitry of melanoma and identify potential gene targets for the treatment of melanoma. The microarray data of GSE31879, including mRNA expression profiles (seven melanoma and four melanocyte samples) and DNA methylation profiles (seven melanoma and five melanocyte samples), were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and differentially methylated positions (DMPs) were screened using the linear models for microarray data (limma) package in melanoma compared with melanocyte samples. Gene ontology (GO) and pathway enrichment analysis of the DEGs were carried out using the Database for Annotation, Visualization, and Integrated Discovery. Moreover, differentially methylated genes (DMGs) were identified, and a transcriptional regulatory network was constructed using the University of California Santa Cruz genome browser database. A total of 1,215 DEGs (199 upregulated and 1,016 downregulated) and 14,094 DMPs (10,450 upregulated and 3,644 downregulated) were identified in melanoma compared with melanocyte samples. Additionally, the upregulated and downregulated DEGs were significantly associated with different GO terms and pathways, such as pigment cell differentiation, biosynthesis, and metabolism. Furthermore, the transcriptional regulatory network showed that DMGs such as Aristaless-related homeobox (ARX), damage-specific DNA binding protein 2 (DDB2), and myelin basic protein (MBP) had higher node degrees. Our results showed that several methylated genes (ARX, DDB2, and MBP) may be involved in melanoma progression. Keyword

Nowadays, embedded systems have been widely used in all types of application areas, some of which belong to the safety and reliability critical domains. The functional correctness and design robustness of the embedded systems involved in such domains are crucial for the safety of personal/enterprise property or even human lives. Thereby, a holistic design procedure that considers all the important design concerns is essential. In this article, we approach embedded systems design from an integral perspective. We consider not only the classic real-time and quality of service requirements, but also the emerging security and power efficiency demands. Modern embedded systems are not any more developed for a fixed purpose, but instead designed for undertaking various processing requests. This leads to the concept of multimode embedded systems, in which the number and nature of active tasks change during runtime. Under dynamic situations, providing high performance along with various design concerns becomes a really difficult problem. Therefore, we propose a novel power-aware secure embedded systems design framework that efficiently solves the problem of runtime quality optimization with security and power constraints. The efficiency of our proposed techniques are evaluated in extensive experiments., Funding Agencies|Swedish Research Council [B0621501]

Embedded systems (ESs) have been widely used in various application domains. It is very important to design ESs that guarantee functional correctness of the system under strict timing constraints. Such systems are known as the real-time embedded systems (RTESs). More recently, RTESs started to be utilized in safety and reliability critical areas, which made the overlooked security issues, especially confidentiality of the communication, a serious problem. Differential power analysis attacks (DPAs) pose serious threats to confidentiality protection mechanisms, i.e., implementations of cryptographic algorithms, on embedded platforms. In this work, we present a scheduling policy, SPARTA, that thwarts DPAs. Theoretical guarantees and preliminary experimental results are presented to demonstrate the efficiency of the SPARTA scheduler.

In this paper, we target on energy-efficient design of soft real-time and reliable applications on uniprocessor embedded systems. We consider soft real-time tasks with stochastic execution times with given distribution. Instead of guaranteeing hard real-time constraint, the application may be finished after their deadlines with a certain probability. We utilize Dynamic Voltage and Frequency Scaling (DVFS) to save energy, and also take into account of the impact of DVFS on reliability. Our objective is to minimize the expected energy consumption of the system subject to statistical reliability and deadline constraints. Due to the huge complexity of solving the problem exactly, we develop a fast bi-search approach based on dynamic programming, which can find the near-optimal solution with energy cost at most (1+beta) times of the optimal energy and has polynomial time complexity. Extensive experiments and a real-life application were conducted to evaluate the efficiency of the proposed techniques.

Topic models have emerged as fundamental tools in unsupervised machine learning. Most modern topic modeling algorithms take a probabilistic view and derive inference algorithms based on Latent Dirichlet Allocation (LDA) or its variants. In contrast, we study topic modeling as a combinatorial optimization problem, and propose a new objective function derived from LDA by passing to the small-variance limit. We minimize the derived objective by using ideas from combinatorial optimization, which results in a new, fast, and high-quality topic modeling algorithm. In particular, we show that our results are competitive with popular LDA-based topic modeling approaches, and also discuss the (dis)similarities between our approach and its probabilistic counterparts., Comment: 19 pages