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Reading comprehension, a fundamental cognitive ability essential for knowledge acquisition, is a complex skill, with a notable number of learners lacking proficiency in this domain. This study introduces innovative tasks for Brain-Computer Interface (BCI), predicting the relevance of words or tokens read by individuals to the target inference words. We use state-of-the-art Large Language Models (LLMs) to guide a new reading embedding representation in training. This representation, integrating EEG and eye-tracking biomarkers through an attention-based transformer encoder, achieved a mean 5-fold cross-validation accuracy of 68.7% across nine subjects using a balanced sample, with the highest single-subject accuracy reaching 71.2%. This study pioneers the integration of LLMs, EEG, and eye-tracking for predicting human reading comprehension at the word level. We fine-tune the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model for word embedding, devoid of information about the reading tasks. Despite this absence of task-specific details, the model effortlessly attains an accuracy of 92.7%, thereby validating our findings from LLMs. This work represents a preliminary step toward developing tools to assist reading.

Colorizing grayscale images offers an engaging visual experience. Existing automatic colorization methods often fail to generate satisfactory results due to incorrect semantic colors and unsaturated colors. In this work, we propose an automatic colorization pipeline to overcome these challenges. We leverage the extraordinary generative ability of the diffusion prior to synthesize color with plausible semantics. To overcome the artifacts introduced by the diffusion prior, we apply the luminance conditional guidance. Moreover, we adopt multimodal high-level semantic priors to help the model understand the image content and deliver saturated colors. Besides, a luminance-aware decoder is designed to restore details and enhance overall visual quality. The proposed pipeline synthesizes saturated colors while maintaining plausible semantics. Experiments indicate that our proposed method considers both diversity and fidelity, surpassing previous methods in terms of perceptual realism and gain most human preference.

With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine comprehension of semantic language meaning. This shift calls for interdisciplinary research that bridges cognitive science and natural language processing (NLP). This pilot study aims to provide insights into individuals' neural states during a semantic relation reading-comprehension task. We propose jointly analyzing LLMs, eye-gaze, and electroencephalographic (EEG) data to study how the brain processes words with varying degrees of relevance to a keyword during reading. We also use a feature engineering approach to improve the fixation-related EEG data classification while participants read words with high versus low relevance to the keyword. The best validation accuracy in this word-level classification is over 60\% across 12 subjects. Words of high relevance to the inference keyword had significantly more eye fixations per word: 1.0584 compared to 0.6576 when excluding no-fixation words, and 1.5126 compared to 1.4026 when including them. This study represents the first attempt to classify brain states at a word level using LLM knowledge. It provides valuable insights into human cognitive abilities and the realm of Artificial General Intelligence (AGI), and offers guidance for developing potential reading-assisted technologies.

Xiao Chen,1,* Wenjuan Zhao,2,* Jie Yuan,3 Wei Qin,1 Yuhong Zhang,1 Yuxia Zhang1 1Department of Nursing, Zhongshan Hospital of Fudan University, Shanghai, 200032, People’s Republic of China; 2Department of Nursing, Fudan University Shanghai Cancer Center, Shanghai, 200032, People’s Republic of China; 3Department of Nursing, Shanghai Fengxian District Hospital of Traditional Chinese Medicine, Shanghai, 201499, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuxia Zhang; Yuhong Zhang, Department of Nursing, Zhongshan Hospital of Fudan University, Shanghai, People’s Republic of China, Tel +86 13816881925, Fax +86 64041990, Email zhang.yx@aliyun.com; zhang.yuhong@zs-hospital.sh.cnPurpose: When creating and managing patient loyalty, medical institutes always focus on medical technicians, overall healthcare quality, and hospital brands, and the role of nursing seems to be underappreciated. Empirical evidence about the relationship between patient experience with nursing care and patient loyalty is limited, especially in the Chinese healthcare system. This study aimed to explore to what extent patient experience with nursing care influences patient loyalty to the hospital and the impact routes.Patients and Methods: This study is a multicenter cross-sectional survey. The STROBE was selected as the checklist in this study. Patient experience with nursing care, patient satisfaction and patient loyalty were collected from 1469 inpatients in three hospitals in China through a paper-based survey. Data were analyzed using SPSS and AMOS software; a structural equation model was established to explore the effect of patient experience with nursing care and satisfaction on patient loyalty using the PROCESS macro.Results: There were significant direct and indirect effects between patient experience with nursing care and patient loyalty, explaining 32.0% of patient loyalty variance. Patient experience with nursing

Min Jiang,* Bin Sun,* Yong Huang, Chengyang Liu, Yan Wang, Yanli Ren, Yuhong Zhang, Yunying Wang, Di Mu Department of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Di Mu; Yunying Wang, Department of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, No. 74 Linjiang Road, Yuzhong District, Chongqing, People’s Republic of China, Tel/Fax +86 23-62887386, Email 303921@hospital.cqmu.edu.cn; Wangyunying@hospital.cqmu.edu.cnPurpose: The aim of this study was to understand the resistance mechanism of ceftazidime/avibactam (CZA) in carbapenem-resistant Klebsiella pneumoniae under antibiotic selection pressure.Patients and Methods: Four CZA-resistant Klebsiella pneumoniae strains were isolated from two patients, and six CZA-resistant strains that were produced in vitro were screened from 25 carbapenem-resistant Klebsiella pneumoniae strains. The mechanisms of resistance to CZA of these strains were characterized by PCR and Sanger sequencing.Results: CZA-resistant Klebsiella pneumoniae with different resistance mechanisms (including upregulation of the expression of efflux pumps and KPC variants (KPC-14, KPC-44)) were isolated from the same patient (patient 1). In patient 2, the resistance mechanism of CZA-resistant Klebsiella pneumoniae was the mutation of KPC-2 to KPC-33. In addition, among the CZA-resistant Klebsiella pneumoniae that were produced in vitro, we found 3 new KPC variants: KPC-86 (D179G), KPC-87 (GT241A) and KPC-88 (G523T).Conclusion: In this study, although the CZA-resistant bacteria originated from only two clinical patients, four different mechanisms of CZA resistance were detected. In the in vitro induction experiment, the mechanisms of resistance to CZA in strains from different patients were also different. The above result implies that the mechanisms of res

In the field of intelligent education, knowledge tracing (KT) has attracted increasing attention, which estimates and traces students' mastery of knowledge concepts to provide high-quality education. In KT, there are natural graph structures among questions and knowledge concepts so some studies explored the application of graph neural networks (GNNs) to improve the performance of the KT models which have not used graph structure. However, most of them ignored both the questions' difficulties and students' attempts at questions. Actually, questions with the same knowledge concepts have different difficulties, and students' different attempts also represent different knowledge mastery. In this paper, we propose a difficulty and attempts boosted graph-based KT (DAGKT), using rich information from students' records. Moreover, a novel method is designed to establish the question similarity relationship inspired by the F1 score. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed DAGKT., Comment: 12 pages, 3figures, conference:ICONIP

Knowledge tracing (KT) is a fundamental task in educational data mining that mainly focuses on students' dynamic cognitive states of skills. The question-answering process of students can be regarded as a thinking process that considers the following two problems. One problem is which skills are needed to answer the question, and the other is how to use these skills in order. If a student wants to answer a question correctly, the student should not only master the set of skills involved in the question but also think and obtain the associative path on the skills graph. The nodes in the associative path refer to the skills needed and the path shows the order of using them. The associative path is referred to as the skill mode. Thus, obtaining the skill modes is the key to answering questions successfully. However, most existing KT models only focus on a set of skills, without considering the skill modes. We propose a KT model, called APGKT, that exploits skill modes. Specifically, we extract the subgraph topology of the skills involved in the question and combine the difficulty level of the skills to obtain the skill modes via encoding; then, through multi-layer recurrent neural networks, we obtain a student's higher-order cognitive states of skills, which is used to predict the student's future answering performance. Experiments on five benchmark datasets validate the effectiveness of the proposed model.

Xiaoxia Li,1,2,* Yixuan Xue,1,* Yuanyuan Dang,1 Wanlu Liu,1 Qingan Wang,1,2 Yi Zhao,1,2 Yuhong Zhang1,2 1Department of Epidemiology and Health Statistics, School of Public Health and Management of Ningxia Medical University, Yinchuan, People’s Republic of China; 2Key Laboratory of Environmental Factors and Chronic Disease Control, School of Public Health and Management of Ningxia Medical University, Yinchuan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuhong Zhang, Department of Epidemiology and Health Statistics School of Public Health and Management, Ningxia Medical University, 1160 Shengli Street, Xingqing District, Yinchuan, 750004, People’s Republic of China, Tel +86951-6980144, Fax +86951-6980144, Email zhabour@163.comObjective: Three non-insulin-based insulin resistance (IR) indices, ie, triglyceride-glucose (TyG) index, triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-C) ratio, and metabolic score for IR (METS-IR), were considered powerful and simplified alternatives for IR. However, evidence for the association between the three IR indices and incident type 2 diabetes mellitus (T2DM), especially impaired fasting glucose (IFG), remains limited. Therefore, this study aimed to explore the association among IR indices, incident IFG, and T2DM in a rural population cohort.Methods: We analyzed data from 2209 adults (aged 24– 75 years) at baseline and from 1205 normoglycemic participants who were followed up. Cox proportional hazards models were used to evaluate the associations between the three indices and IFG or T2DM. Restricted cubic spline curves based on the Cox regression model were used to examine the association between baseline indices and incident T2DM.Results: For the baseline data, logistic analyses demonstrated that the TyG index, TG/HDL-C ratio, and METS-IR had a significantly positive correlation with IFG or T2DM after multivariable adjustment. During a median

Recent years have witnessed increasing interest in few-shot knowledge graph completion (FKGC), which aims to infer unseen query triples for a few-shot relation using a few reference triples about the relation. The primary focus of existing FKGC methods lies in learning relation representations that can reflect the common information shared by the query and reference triples. To this end, these methods learn entity-pair representations from the direct neighbors of head and tail entities, and then aggregate the representations of reference entity pairs. However, the entity-pair representations learned only from direct neighbors may have low expressiveness when the involved entities have sparse direct neighbors or share a common local neighborhood with other entities. Moreover, merely modeling the semantic information of head and tail entities is insufficient to accurately infer their relational information especially when they have multiple relations. To address these issues, we propose a Relation-Specific Context Learning (RSCL) framework, which exploits graph contexts of triples to learn global and local relation-specific representations for few-shot relations. Specifically, we first extract graph contexts for each triple, which can provide long-term entity-relation dependencies. To encode the extracted graph contexts, we then present a hierarchical attention network to capture contextualized information of triples and highlight valuable local neighborhood information of entities. Finally, we design a hybrid attention aggregator to evaluate the likelihood of the query triples at the global and local levels. Experimental results on two public datasets demonstrate that RSCL outperforms state-of-the-art FKGC methods., Comment: 12 pages, 6 figures

Pengyi Hou,1,2 Yangyang Pi,1,2 Ziming Jiao,1 Xueyan Tian,1 Wenxuan Hu,1 Yuhong Zhang,1,2 Yi Zhao,1,2 Faxuan Wang1,2 1School of Public Health and Management, Ningxia Medical University, Yinchuan, People’s Republic of China; 2Laboratory of Environmental Factors and Chronic Disease Control, Ningxia Medical University, Yinchuan, People’s Republic of ChinaCorrespondence: Faxuan Wang, Email faxuan203@163.comBackground: In recent decades, obesity has become an epidemic worldwide and is a risk factor for many chronic diseases. Lung function is also a predictor of various chronic diseases. However, research results on the association between obesity and lung function are inconsistent and few studies have evaluated the association between central obesity indicators and lung function. Therefore, this study explored the correlation between central obesity and lung function.Methods: This study is a cross-sectional study. The basic participant characteristics were collected by questionnaire. A tape measure was used to measure waist circumference (WC) and hip circumference (HC). Body fat percentage was measured using an InBody370. Lung function parameters were measured using a digital spirometer connected to a computer (Chestgraph HI-101). R (R4.0.5) software was used for data analysis. A generalized linear model was used to analyze the association between obesity and lung function.Results: This study found that body mass index (BMI) adjusted for WC was negatively correlated with forced vital capacity (FVC) (β=− 0.05 [− 0.06, − 0.03] in men, β=− 0.05 [− 0.07, − 0.04] in women) and forced expiratory volume in 1 s (FEV1)(β=− 0.02 [− 0.03, − 0.00] in men, β=− 0.03 [− 0.04, − 0.02] in women). Body fat percentage was negatively correlated with FVC (β=− 0.01 [− 0.01, − 0.01] in men, β=− 0.01 [− 0.01, − 0.00] in women).Conclusion: Central obesity and combined central and general obesity were more strongly positively correlated with lung funct

SPPC (Super Proton-Proton Collider) is a discovery machine that is designed for energy frontier research in two decades from now, as the second stage of the CEPC-SPPC project. The main objective is to carry out experiments at 125 TeV in center-of-mass energy in a two-ring collider of 100 km in circumference and 20 T in dipole field. This white paper about SPPC describes the machine related issues, including performance, design overview, design challenges, key technologies and their maturity and required R&D, staged options and upgrades, synergies with other facilities, and environmental impacts., Comment: contribution to Snowmass 2021

Real-time monitoring of wound healing remains a major challenge in clinical tissue regeneration, calling the need for the development of biomaterial-guided on-site monitoring wound healing technology. In this study, multifunctional double colorimetry-integrated polyacrylamide-quaternary ammonium chitosan-carbon quantum dots (CQDs)-phenol red hydrogels are presented, aiming to simultaneously detect the wound pH level, reduce bacterial infection, and promote wound healing. The hybridization of CQDs and pH indicator (phenol red) with the hydrogels enables their high responsiveness, reversibility, and accurate indication of pH variability to reflect the dynamic wound status in the context of both ultraviolet and visible light. Furthermore, these visual images can be collected by smartphones and converted into on-site wound pH signals, allowing for a real-time evaluation of the wound dynamic conditions in a remote approach. Notably, the hydrogels exhibit excellent hemostatic and adhesive properties, maintain sufficient wound moisture, and promote wound healing via their high antibacterial activity (against Staphylococcus Aureus, and Escherichia Coli) and skin repair function. Overall, the resulting hydrogels have high potential as a novel smart and flexible wound dressing platform for theranostic skin regeneration.

Faxuan Wang,1,2,* Binxia Wang,3,* Xiyuan Chen,1,2 Wanlu Liu,1,2 Guoqi Wang,1,2 Xiaoxia Li,1,2 Xiuying Liu,1,2 Nan Li,1,2 Jiaxing Zhang,1,2 Ting Yin,1,2 Jinyun Jing,1,2 Xiaoyu Chang,4 Yanan Jin,5 Yuhong Zhang,1,2 Yi Zhao1 1School of Public Health and Management, Ningxia Medical University, Yinchuan, Ningxia, People’s Republic of China; 2Key Laboratory of Environmental Factors and Chronic Disease Control, Yinchuan, Ningxia, People’s Republic of China; 3Second People’s Hospital of Gansu Province, Lanzhou, Gansu, People’s Republic of China; 4Editorial Department of the Journal of Ningxia Medical University, Ningxia Medical University, Yinchuan, Ningxia, People’s Republic of China; 5Centers for Disease Control and Prevention in Ningxia, Yinchuan, Ningxia, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yi ZhaoSchool of Public Health and Management, Ningxia Medical University Tel +86 139 9501 1690Fax +86 951-6980144Email zhaoyi751114@hotmail.comBackground: Recently, the association between lifestyle-related diseases and free amino acids in the blood plasma—especially branched-chain amino acids (BCAAs) and aromatic amino acids (AAAs)—has been highlighted. However, few studies have been carried out on 24-hour urine samples. Therefore, we aimed to explore the relationships between 24-hour urinary BCAAs and AAAs excretion rate and blood pressure (BP) in elderly patients with hypertension.Methods and Results: Each of the 322 elderly patients with hypertension completed an in-person questionnaire interview, underwent a physical examination, and provided a 24-hour urine specimen. We measured their BCAAs and AAAs excretion rate, and used multiple linear regression analysis with variable selection to construct models describing the relationships between their BCAAs and AAAs excretion rate and BP. After adjusted for age, gender, height, and weight, valine was inversely associated with both systolic blo

Completion through the embedding representation of the knowledge graph (KGE) has been a research hotspot in recent years. Realistic knowledge graphs are mostly related to time, while most of the existing KGE algorithms ignore the time information. A few existing methods directly or indirectly encode the time information, ignoring the balance of timestamp distribution, which greatly limits the performance of temporal knowledge graph completion (KGC). In this paper, a temporal KGC method is proposed based on the direct encoding time information framework, and a given time slice is treated as the finest granularity for balanced timestamp distribution. A large number of experiments on temporal knowledge graph datasets extracted from the real world demonstrate the effectiveness of our method., Comment: 14 pages, 1 figures

The Salt Substitute and Stroke Study is an ongoing 5-year large-scale cluster randomized trial investigating the effects of potassium-enriched salt substitute compared to usual salt on the risk of stroke. The study involves 600 villages and 20,996 individuals in rural China. Intermediate risk markers were measured in a random subsample of villages every 12 months over 3 years to track progress against key assumptions underlying study design. Measures of 24-hour urinary sodium, 24-hour urinary potassium, blood pressure and participants' use of salt substitute were recorded, with differences between intervention and control groups estimated using generalized linear mixed models. The primary outcome of annual event rate in the two groups combined was determined by dividing confirmed fatal and non-fatal strokes by total follow-up time in the first 2 years. The mean differences (95% CI) were -0.32 g (-0.68 to 0.05) for 24-hour urinary sodium, +0.77 g (+0.60 to +0.93) for 24-hour urinary potassium, -2.65 mmHg (-4.32 to -0.97) for systolic blood pressure and +0.30 mmHg (-0.72 to +1.32) for diastolic blood pressure. Use of salt substitute was reported by 97.5% in the intervention group versus 4.2% in the control group (P<.0001). The overall estimated annual event rate for fatal and non-fatal stroke was 3.2%. The systolic blood pressure difference and the annual stroke rate were both in line with the statistical assumptions underlying study design. The trial should be well placed to address the primary hypothesis at completion of follow-up.

Suppression of radar-to-radar jammers, especially the mainbeam jammers, has been an urgent demand in vehicular sensing systems with the expected increased number of vehicles equipped with radar systems. This paper deals with the suppression of mainbeam deceptive jammers with frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radar, utilizing its extra degrees-of-freedom (DOFS) in the range domain. At the modelling stage, false targets, which lag several pulses behind the true target, are considered as a typical form of mainbeam jammers. To this end the data-independent beamforming is performed to suppress false targets by nulling at the equivalent transmit beampattern with an appropriate frequency increment. However, the suppression performance degrades in the presence of transmit spatial frequency mismatch, which could be induced by quantization errors, angle estimation errors and frequency increment errors. To solve this problem, a preset broadened nulling beamformer (PBN-BF) is proposed by placing artificial interferences with appropriate powers around the nulls of the equivalent transmit beampattern. In such a way, effective suppression of deceptive jammer can be guaranteed owing to the broadened notches. At the analysis stage, numerical results in a scenario with multiple unmanned aerial vehicles (UAVs) are provided to illustrate the effectiveness of the devised data-independent BF, and the signal-to-interference-plus-noise ratio is improved compared with the conventional data-independent BF., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Microwave Sensing, Signals & Systems

A novel cloud data center (DC) model is studied here with cognitive capabilities for real-time (or online) flow compared to the batch tasks. Here, a DC can determine the cost of using resources and an online user or the user with batch tasks may decide whether or not to pay for getting the services. The online service tasks have a higher priority in getting the service over batch tasks. Both types of tasks need a certain number of virtual machines (VM). By targeting on the maximization of total discounted reward, an optimal policy for admitting task tasks is finally verified to be a state-related control limit policy. Next, a lower and an upper bound for such an optimal policy are derived, respectively, for the estimation and utilization in reality. Finally, a comprehensive set of experiments on the various cases to validate this proposed model and the solution is conducted. As a demonstration, the machine learning method is adopted to show how to obtain the optimal values by using a feed-forward neural network model. The results achieved in this paper will be expectedly utilized in various cloud data centers with cognitive characteristics in an economically optimal strategy., Comment: 11 pages, 1 figure

Yuhong Zhang, Bo Huang, Zhi Chen, Shiming Yang Department of General Surgery, Shanxi Provincial People’s Hospital, Taiyuan, Shanxi 030012, People’s Republic of ChinaCorrespondence: Zhi ChenDepartment of General Surgery, Shanxi Provincial People’s Hospital, No 29 Shuangtasi Street, Taiyuan, Shanxi, People’s Republic of ChinaTel +86 351-4960336Email vznoaj@163.comBackground: Hepatocellular carcinoma (HCC) is the most common form of liver cancer. Radioresistance is a significant obstacle in HCC therapy. Long non-coding RNA 473 (LINC00473) has been found to impair the effect of radiotherapy. This study aimed to explore the function and molecular basis of LINC00473 in the radiosensitivity of HCC cells.Methods: The levels of LINC00473, miR-345-5p and Forkhead Box P1 (FOXP1) were determined by quantitative real-time polymerase chain reaction. Cell viability was assessed by 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay. Survival fraction was calculated by colony survival assay after exposure to different doses of radiation. Cell apoptosis was evaluated by flow cytometry. The interaction among LINC00473, miR-345-5p and FOXP1 was confirmed by dual-luciferase reporter assay. The protein level of FOXP1 was detected by Western blot assay.Results: LINC00473 and FOXP1 were up-regulated, while miR-345-5p was down-regulated in HCC tissues and cells. Radiation elevated LINC00473 expression in a dose- and time-dependent manner. Depletion of LINC00473 inhibited proliferation and heightened radiosensitivity and apoptosis in HCC cells. In addition, LINC00473 was a sponge of miR-345-5p. Also, miR-345-5p overexpression sensitized HCC cells to radiation. Moreover, miR-345-5p directly targeted FOXP1. MiR-345-5p inhibition or FOXP1 up-regulation reversed the enhanced radiosensitivity caused by LINC00473 knockdown.Conclusion: LINC00473 contributed to radioresistance in HCC via modulating the miR-345-5p/FOXP1 axis, which mi

Domain adaptation aims to exploit the knowledge in source domain to promote the learning tasks in target domain, which plays a critical role in real-world applications. Recently, lots of deep learning approaches based on autoencoders have achieved a significance performance in domain adaptation. However, most existing methods focus on minimizing the distribution divergence by putting the source and target data together to learn global feature representations, while they do not consider the local relationship between instances in the same category from different domains. To address this problem, we propose a novel Semi-Supervised Representation Learning framework via Dual Autoencoders for domain adaptation, named SSRLDA. More specifically, we extract richer feature representations by learning the global and local feature representations simultaneously using two novel autoencoders, which are referred to as marginalized denoising autoencoder with adaptation distribution (MDAad) and multi-class marginalized denoising autoencoder (MMDA) respectively. Meanwhile, we make full use of label information to optimize feature representations. Experimental results show that our proposed approach outperforms several state-of-the-art baseline methods., Comment: This paper has been accepted by the journal of KNOWLEDGE-BASED SYSTEMS (KBS) 2019

One-bit radar, performing signal sampling and quantization by a one-bit ADC, is a promising technology for many civilian applications due to its low-cost and low-power consumptions. In this paper, problems encountered by one-bit LFMCW radar are studied and a two-stage target detection method termed as the dimension-reduced generalized approximate message passing (DR-GAMP) approach is proposed. Firstly, the spectrum of one-bit quantized signals in a scenario with multiple targets is analyzed. It is indicated that high-order harmonics may result in false alarms (FAs) and cannot be neglected. Secondly, based on the spectrum analysis, the DR-GAMP approach is proposed to carry out target detection. Specifically, linear preprocessing methods and target predetection are firstly adopted to perform the dimension reduction, and then, the GAMP algorithm is utilized to suppress high-order harmonics and recover true targets. Finally, numerical simulations are conducted to evaluate the performance of one-bit LFMCW radar under typical parameters. It is shown that compared to the conventional radar applying linear processing methods, one-bit LFMCW radar has about $1.3$ dB performance gain when the input signal-to-noise ratios (SNRs) of targets are low. In the presence of a strong target, it has about $1.0$ dB performance loss., Comment: Paper has been accepted in IEEE TAES

Following the discovery of the Higgs boson at LHC, new large colliders are being studied by the international high-energy community to explore Higgs physics in detail and new physics beyond the Standard Model. In China, a two-stage circular collider project CEPC-SPPC is proposed, with the first stage CEPC (Circular Electron Positron Collier, a so-called Higgs factory) focused on Higgs physics, and the second stage SPPC (Super Proton-Proton Collider) focused on new physics beyond the Standard Model. This paper discusses this second stage.

Xijin Wang,1 Qiang Guan,2 Meihua Wang,1 Liu Yang,1 Jie Bai,1 Zhiqiang Yan,3 Yuhong Zhang,4 Zhenguo Liu11Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 2Department of Neurology, Tongji Hospital, Tongji University, 3Shanghai Laboratory Animal Center, Chinese Academy of Sciences, 4Department of Neurology, Shanghai Tenth People’s Hospital, Tongji University, Shanghai, People’s Republic of ChinaAbstract: Aging is one of the strongest risk factors for Parkinson’s disease (PD). SIRT2 has been implicated in the aging process. It is pertinent to investigate the role of SIRT2 in aging-related dopaminergic neurotoxicity and to develop effective therapeutic strategies for PD through the use of aging animals. In this study, we observed that rotenone induced significant behavior abnormality and striatal dopamine depletion in aging rats, while it did not do so in young rats. No significant change in striatal serotonin level was observed in the aging rats after rotenone administration. There was also aging-related rotenone-induced increase in substantia nigra (SN) SIRT2 expression in the rats. In addition, there was aging-related rotenone-induced SN malondialdehyde (MDA) increase and glutathione (GSH) decrease in the rats. No significant changes in cerebellar SIRT2, MDA, or GSH levels were observed in the aging rats after rotenone administration. Striatal dopamine content was significantly inversely correlated with SN SIRT2 expression in the rats. AK-7 significantly diminished striatal dopamine depletion and improved behavior abnormality in the rotenone-treated aging rats. Furthermore, AK-7 significantly decreased MDA content and increased GSH content in the SN of rotenone-treated aging rats. Finally, the effect of AK-7 on dopaminergic neurons and redox imbalance was supported by the results from primary mesencephalic cultures. Our study helps to elucidate the mechanism for the

BACKGROUND: Changed temperature not only threaten agricultural production, but they also affect individual biological behavior, population and community of many insects, and consequently reduce the stability of our ecosystem. Insect's ability to respond to temperature stress evolved through a complex adaptive process, thus resulting in varied temperature tolerance among different insects. Both high and low extreme temperatures are detrimental to insect development since they constitute an important abiotic stress capable of inducing abnormal biological responses. Many studies on heat or cold tolerance of ladybirds have focused on measurements of physiological and biochemical indexes such as supercooling point, higher/lower lethal temperatures, survival rate, dry body weight, water content, and developmental duration. And studies of the molecular mechanisms of ladybird responses to heat or cold stress have focused on single genes, such as those encoding heat shock proteins, but has not been analyzed by transcriptome profiling RESULTS: In this study, we report the use of Digital Gene Expression (DGE) tag profiling to gain insight into transcriptional events associated with heat- and cold-stress in C. montrouzieri. About 6 million tags (49 bp in length) were sequenced in a heat stress group, a cold stress group and a negative control group. We obtained 687 and 573 genes that showed significantly altered expression levels following heat and cold shock treatments, respectively. Analysis of the global gene expression pattern suggested that 42 enzyme-encoding genes mapped to many Gene Ontology terms are associated with insect's response to heat- and cold-stress CONCLUSIONS: These results provide a global assessment of genes and molecular mechanisms involved in heat and cold tolerance

In this paper, we develop a novel Distance-weighted k-nearest Neighbor rule (DWKNN), using the dual distance-weighted function. The proposed DWKNN is motivated by the sensitivity problem of the selection of the neighborhood size k that exists in k-nearest Neighbor rule (KNN), with the aim of improving classification performance. The experiment results on twelve real data sets demonstrate that our proposed classifier is robust to different choices of k to some degree, and yields good performance with a larger optimal k, compared to the other state-of-art KNN-based methods.

A new technique for detection of stationary or slow-moving over-resolved targets on the ground, based upon a Hotelling T-squared test/generalized inner product (GIP) approach to signal analysis is presented. This robust detector separates radar returns from interference via a coherent process. This GIP-based processing can be applied to single-receiver channel systems, multiple spatial channel systems multiple polarization channel systems or systems with multiple spatial and polarization channels., See also ADM002322. Presented at the IEEE International Radar Conference (9th), held in Arlington, VA, on 10-14 May 2010. Published in the proceedings of the conference, p359-363, 2010. Prepared in cooperation with Stiefvater Consultants, Rome, NY. Sponsored in part by the Navy. The original document contains color images.

Protein misfolding in the endoplasmic reticulum (ER) activates a set of intracellular signaling pathways, collectively termed the Unfolded Protein Response (UPR). UPR signaling promotes cell survival by reducing misfolded protein levels. If homeostasis cannot be restored, UPR signaling promotes cell death. The molecular basis for the switch between prosurvival and proapoptotic UPR function is poorly understood. The ER-resident proteins, PERK and IRE1, control two key UPR signaling pathways. Protein misfolding concomitantly activates PERK and IRE1 and has clouded insight into their contributions toward life or death cell fates. Here, we employed chemical-genetic strategies to activate individually PERK or IRE1 uncoupled from protein misfolding. We found that sustained PERK signaling impaired cell proliferation and promoted apoptosis. By contrast, equivalent durations of IRE1 signaling enhanced cell proliferation without promoting cell death. These results demonstrate that extended PERK and IRE1 signaling have opposite effects on cell viability. Differential activation of PERK and IRE1 may determine life or death decisions after ER protein misfolding.

In a multi-static radar (MSR) the transmit/receive aperture is divided into a number of sub-apertures that can be placed in various locations relative to each other. These locations can be chosen to optimize the performance of the radar in terms of some specific task. Two multi-static approaches have been investigated: 1. Closely spaced apertures distributed aperture radar (DAR) 2. Widely spaced apertures Realizing the greater capability of MSR's requires unique waveform and signal processing approaches. A computer simulation has been developed that permits the analysis of MSR signal processing. This paper presents the results of a series of experiments to validate the results of that simulation., See also ADM002187. Presented at the Army Science Conference (26th) held in Orlando, Florida on 1-4 December 2008. Published in the Proceedings of the Army Science Conference (26th), 2008. The original document contains color images.

The high false alarm associated with conventional alongtrack interferometric synthetic aperture radar (ATI-SAR) is a big concern for any valuable military radar. To reduce the false alarm rate, this paper proposes a dual-threshold approach that combines the conventional interferometric phase detection with the SAR image amplitude detection. This yields two results: (1) the interferometric phase map (including target velocity information) obtained by applying the interferometric phase detection only to the pixels selected by the amplitude detection, and (2) the amplitude map (including target strength information) obtained by applying the amplitude detection only to the pixels selected by the interferometric phase detection. The concept is illustrated by the results obtained using the Jet Propulsion Laboratory's (JPL) AirSAR ATI data, collected in the Monterey Bay area, California. The ATI-SAR processing requires also a precise calibration of the platform's crab angle. This paper presents a simple blind-calibration method that does not require any knowledge of the background and/or the actual crab angle., See also ADM002017. Presented at the 2005 IEEE International Radar Conference held in Arlington, VA on 9-12 May 2005. Published in the Proceedings of the 2005 IEEE International Radar Conference, May 2005. The original document contains color images.

This thesis concentrates on two subjects related to wave digital filter design and realization. Jarmasz provided an efficient cascade synthesis algorithm of lossless two-port networks by extracting elementary sections step by step. Following this approach, based on the factorization of the transfer matrix, necessary and sufficient conditions for cascade synthesis of lossless two-port networks from a given canonic set of scattering polynomials is presented. An algorithm to realize a digital filter with ladder structure based on the cascade decomposition and an illustrative example are also provided. Fettweis proposed another approach to the cascade decomposition of lossless two-port networks based on the factorization of the scattering matrix. A proof that this approach can be applied to FIR filters is provided and at the same time a realization structure and an algorithm in a very general form is developed. Several other realization structures and algorithms for FIR filters are derived directly from this general form, including Fettweis' two structures. An analytical formula method and an optimization method for the design of bireciprocal filters are presented. (Abstract shortened by UMI.)

We use the quantum Brownian model to derive the uncertainty relation for a quantum open system in an arbitrarily-squeezed initial state interacting with an environment at finite temperature. We examine the relative importance of the quantum and thermal fluctuations in the evolution of the system towards equilibrium with the aim of clarifying the meaning of quantum, classical and thermal. We show that upon contact with the bath the system evolves from a quantum-dominated state to a thermal-dominated state in a time which is the same as the decoherence time calculated before in the context of quantum to classical transitions. We also use these results to deduce the conditions when the two basic postulates of quantum statistical mechanics become valid., Comment: physics preprint pp93-161, TEX, 12 pages e-mail addresses: hu@umdhep.bitnet, zhang@helix.nih.gov

We derive the uncertainty relation for a quantum open system comprised of a Brownian particle interacting with a bath of quantum oscillators at finite temperature. We examine how the quantum and thermal fluctuations of the environment contribute to the uncertainty in the canonical variables of the system. We show that upon contact with the bath (assumed ohmic in this paper) the system evolves from a quantum-dominated state to a thermal-dominated state in a time which is the same as the decoherence time in similar models in the discussion of quantum to classical transition. This offers some insight into the physical mechanisms involved in the environment-induced decoherence process. We obtain closed analytic expressions for this generalized uncertainty relation under the conditions of high temperature and weak damping separately. We also consider under these conditions an arbitrarily-squeezed initial state and show how the squeeze parameter enters in the generalized uncertainty relation. Using these results we examine the transition of the system from a quantum pure state to a nonequilibrium quantum statistical state and to an equilibrium quantum statistical state. The three stages are marked by the decoherence time and the relaxation time respectively. With these observations we explicate the physical conditions when the two basic postulates of quantum statistical mechanics become valid. We also comment on the inappropriateness in the usage of the word classicality in many decoherence studies of quantum to classical transition., Comment: 36 pages,Tex,umdpp93-162,(submitted to Phys. Rev. A)