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Article ; Online: Switching Go

Yang, Song / Song, Chen

Journal of chemical theory and computation

2024 Volume 20, Issue 6, Page(s) 2618–2629

Abstract: Proteins are dynamic biomolecules that can transform between different conformational states when exerting physiological functions, which is difficult to simulate using all-atom methods. Coarse-grained (CG) Go̅-like models are widely used to investigate ... ...

Abstract	Proteins are dynamic biomolecules that can transform between different conformational states when exerting physiological functions, which is difficult to simulate using all-atom methods. Coarse-grained (CG) Go̅-like models are widely used to investigate large-scale conformational transitions, which usually adopt implicit solvent models and therefore cannot explicitly capture the interaction between proteins and surrounding molecules, such as water and lipid molecules. Here, we present a new method, named
MeSH term(s)	Molecular Dynamics Simulation ; Protein Conformation ; Proteins/chemistry ; Solvents/chemistry ; Lipids
Chemical Substances	Proteins ; Solvents ; Lipids
Language	English
Publishing date	2024-03-06
Publishing country	United States
Document type	Journal Article
ISSN	1549-9626
ISSN (online)	1549-9626
DOI	10.1021/acs.jctc.3c01222
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Reliability and minimal detectable change of the Short Physical Performance Battery in older adults with mild cognitive impairment.

Lin, Yi-Te / Song, Chen-Yi

Geriatric nursing (New York, N.Y.)

2024 Volume 57, Page(s) 91–95

Abstract: Objectives: Reliability of the Short Physical Performance Battery (SPPB) are rarely examined among older adults with mild cognitive impairment (MCI). This study aimed to investigate the test-retest reliability and minimal detectable change (MDC) of the ... ...

Abstract	Objectives: Reliability of the Short Physical Performance Battery (SPPB) are rarely examined among older adults with mild cognitive impairment (MCI). This study aimed to investigate the test-retest reliability and minimal detectable change (MDC) of the SPPB in older adults with MCI. Methods: Participants included 100 older adults with MCI. The SPPB was assessed with the first 2 assessments separated by a 20-min interval and the third separated by a 1-week interval. The intraclass correlation coefficient (ICC) and MDC values were estimated. Results: The intraday ICC was 0.73 for the SPPB score, 0.90 for the 4-m walk time (4mwt), and 0.95 for the 5-times chair stand time (5cst); the corresponding interday ICC values were 0.76, 0.89, and 0.91, respectively. The MDC values ranged from 1.1 to 1.2 for the SPPB score, from 0.77 to 0.80 s for the 4mwt, and from 1.32 to 1.77 for the 5cst. Conclusions: The SPPB had satisfactory reliability among older adults with MCI. The test-retest reliability of the SPPB is sufficient (>0.7) for group comparisons. Moreover, the test-retest reliability for the 4mwt and 5cst subscale performances is acceptable (> 0.9) for individual-level measurements over time.
Language	English
Publishing date	2024-04-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	632559-2
ISSN	1528-3984 ; 0197-4572
ISSN (online)	1528-3984
ISSN	0197-4572
DOI	10.1016/j.gerinurse.2024.04.004
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Article ; Online: The value discovery of foreign direct investment

Song Chen / Meixu Ren / Chuanzhen Li

Journal of Innovation & Knowledge, Vol 9, Iss 1, Pp 100461- (2024)

Based on the study of the crowding-out effect of zombie enterprises on investment

2024

Abstract: Foreign direct investment (FDI) is of great significance to the economic development of developing countries; it infuses capital into enterprises, while propeling both technological advancement and economic efficiency. We examine whether zombie ... ...

Abstract	Foreign direct investment (FDI) is of great significance to the economic development of developing countries; it infuses capital into enterprises, while propeling both technological advancement and economic efficiency. We examine whether zombie enterprises crowd out FDI inflows and whether the institutional environment can alleviate this effect. Using the Chinese Industrial Enterprise Database from 2003 to 2013 to calculate zombie shares at the prefecture-level cities and applying the OLS and instrumental variable estimation, we find that the larger the zombie shares, the less FDI inflows. That is, zombies do crowd out FDI inflows—a finding robust to specifications. We also find that the geographical advantage and greater openness to the outside world can alleviate the crowding-out effect. Further, we investigate the moderating effect of the institutional environment on the crowding-out effect. We find that market segmentation and government corruption will aggravate the crowding-out effect while strengthening intellectual property protection, improving the trust environment, and factor market development will alleviate it. Overall, our study explains impediments to FDI inflows by accounting for zombie enterprises. It provides insights for local governments seeking to clean up zombies and strengthen the institutional environment to attract FDI inflows.
Keywords	F21 ; O17 ; R38 ; History of scholarship and learning. The humanities ; AZ20-999 ; Social sciences (General) ; H1-99
Subject code	338
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Three-Dimensional Imaging of Emulsion Separation through Liquid-Infused Membranes Using Confocal Laser Scanning Microscopy.

Song, Chen / Rutledge, Gregory C

Langmuir : the ACS journal of surfaces and colloids

2023 Volume 39, Issue 32, Page(s) 11468–11480

Abstract: The removal of emulsified oils from water has always been a challenge due to the kinetic stability resulting from the small droplet size and the presence of stabilizing agents. Membrane technology can treat such mixtures, but fouling of the membrane ... ...

Abstract	The removal of emulsified oils from water has always been a challenge due to the kinetic stability resulting from the small droplet size and the presence of stabilizing agents. Membrane technology can treat such mixtures, but fouling of the membrane leads to dramatic reductions in the process capacity. Liquid-infused membranes (LIMs) can potentially resolve the issue of fouling. However, their low permeate flux compared with conventional hydrophilic membranes remains a limitation. To gain insight into the mechanism of transport, we use 3D images acquired by confocal laser scanning microscopy (CLSM) to reconstruct the sequence of events occurring during startup and operation of the LIM for removal of dispersed oil from oil-in-water emulsions. We find evidence for coalescence of oil droplets on the surface of and formation of oil channels within the LIM. Using image analysis, we find that the rate at which oil channels are formed within the membrane and the number of channels ultimately govern the permeate flux of oil through the LIMs. Oil concentration in the feed affects the rate of coalescence of oil on the surface of the LIM, which, in turn, affects the channel formation dynamics. The channel formation dynamics also depend on the viscosity of the infused liquid and the operating pressure. A higher affinity to the pore wall for infused liquid than permeating liquid is essential to antifouling behavior. Overall, this work offers insight into the selective permeation of a dispersed liquid phase through a LIM.
Language	English
Publishing date	2023-08-04
Publishing country	United States
Document type	Journal Article
ZDB-ID	2005937-1
ISSN	1520-5827 ; 0743-7463
ISSN (online)	1520-5827
ISSN	0743-7463
DOI	10.1021/acs.langmuir.3c01477
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Article ; Online: Prognostic factors for surgical site infection in patients with spinal metastases and following surgical treatment.

Song, Chen / Zhang, Wanxi / Luo, Cheng / Zhao, Xiaoyong

Medicine

2024 Volume 103, Issue 11, Page(s) e37503

Abstract: There were few articles reviewed prognostic factors of surgical site infection (SSI) in patients with spinal metastases following surgery. The purpose of the present study was to systematically: (1) investigate the incidence rates of SSI following spinal ...

Abstract	There were few articles reviewed prognostic factors of surgical site infection (SSI) in patients with spinal metastases following surgery. The purpose of the present study was to systematically: (1) investigate the incidence rates of SSI following spinal metastases surgery; (2) identify the factors which were independently associated with postoperative wound infection. One hundred sixty-seven consecutive adult patients with spinal metastases and underwent surgical treatment were retrospectively enrolled from January 2011 to February 2022. Demographic data, disease and operation-related indicators were extracted and analyzed. Univariate and multivariate logistic analysis model were performed respectively to determine independent risk factors of SSI. 17 cases infection were collected in this study. The overall incidence of SSI after surgery of spinal metastases patients was 10.2%. Univariate regression analysis showed that age (P = .028), preoperative ALB level (P = .024), operation time (P = .041), intraoperative blood loss (P = .030), Karnofsky Performance Status score (P = .000), body mass index (P = .013), American Society of Anesthesiologists > 2 (P = .010), Tobacco consumption (P = .035), and number of spinal levels involved in surgical procedure (P = .007) were associated with wound infection. Finally, the multivariate logistic model demonstrated that body mass index (P = .043; OR = 1.038), preoperative ALB level (P = .018; OR = 1.124), and number of spinal levels (P = .003; OR = 1.753) were associated with SSI occurrence. Surgery on multiple vertebral levels for spinal metastases significantly increases the risk of SSI and weight management, nutritional support and palliative surgery have the positive significance in reducing wound complications. Orthopedist should focus on identifying such high-risk patients and decrease the incidence of wound infection by formulating comprehensive and multi-disciplinary care strategy.
MeSH term(s)	Adult ; Humans ; Surgical Wound Infection/epidemiology ; Surgical Wound Infection/etiology ; Retrospective Studies ; Spinal Neoplasms/surgery ; Spinal Neoplasms/complications ; Prognosis ; Spine/surgery ; Risk Factors
Language	English
Publishing date	2024-03-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	80184-7
ISSN	1536-5964 ; 0025-7974
ISSN (online)	1536-5964
ISSN	0025-7974
DOI	10.1097/MD.0000000000037503
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Article ; Online: Synchronous bilateral breast cancer diagnosed with primary breast apocrine carcinoma and invasive breast cancer: A rare case report.

Lv, Huiyun / Kong, Jixia / Liu, Wei / Song, Chen

Asian journal of surgery

2024

Language	English
Publishing date	2024-03-26
Publishing country	Netherlands
Document type	Letter
ZDB-ID	1068461-x
ISSN	0219-3108 ; 1015-9584
ISSN (online)	0219-3108
ISSN	1015-9584
DOI	10.1016/j.asjsur.2024.03.098
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Article ; Online: BERT-Log

Song Chen / Hai Liao

Applied Artificial Intelligence, Vol 36, Iss

Anomaly Detection for System Logs Based on Pre-trained Language Model

2022 Volume 1

Abstract: Logs are primary information resource for fault diagnosis and anomaly detection in large-scale computer systems, but it is hard to classify anomalies from system logs. Recent studies focus on extracting semantic information from unstructured log messages ...

Abstract	Logs are primary information resource for fault diagnosis and anomaly detection in large-scale computer systems, but it is hard to classify anomalies from system logs. Recent studies focus on extracting semantic information from unstructured log messages and converting it into word vectors. Therefore, LSTM approach is more suitable for time series data. Word2Vec is the up-to-date encoding method, but the order of words in sequences is not taken into account. In this article, we propose BERT-Log, which regards the log sequence as a natural language sequence, use pre-trained language model to learn the semantic representation of normal and anomalous logs, and a fully connected neural network is utilized to fine-tune the BERT model to detect abnormal. It can capture all the semantic information from log sequence including context and position. It has achieved the highest performance among all the methods on HDFS dataset, with an F1-score of 99.3%. We propose a new log feature extractor on BGL dataset to obtain log sequence by sliding window including node ID, window size and step size. BERT-Log approach detects anomalies on BGL dataset with an F1-score of 99.4%. It gives 19% performance improvement compared to LogRobust and 7% performance improvement compared to HitAnomaly.
Keywords	Electronic computers. Computer science ; QA75.5-76.95 ; Cybernetics ; Q300-390
Subject code	006
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	Taylor & Francis Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: An Unsupervised Detection Method for Multiple Abnormal Wi-Fi Access Points in Large-Scale Wireless Network

Song Chen / Hai Liao

Applied Artificial Intelligence, Vol 36, Iss

2022 Volume 1

Abstract: The probability of a single access point (AP) failure is very small. In addition, APs communicate with each other; therefore, it is considered that these failures have little impact on the wireless network. Only when a large number of APs are abnormal ... ...

Abstract	The probability of a single access point (AP) failure is very small. In addition, APs communicate with each other; therefore, it is considered that these failures have little impact on the wireless network. Only when a large number of APs are abnormal offline, do we consider that the wireless network is faulty and needs to be recovered immediately. Network breakdown, network congestion, and AP management software shutdown may cause numerous APs in aborted status. In this article, we utilize DBSCAN algorithm to detect abnormal Wi-Fi APs. Compared with other research works, our proposed unsupervised method can distinguish between normal and abnormal offline APs. This study proposes a new date dimension to calculate the number of online APs together with the time dimension, and it provides new insights to set up thresholds of online APs automatically. Experimental results show that this 3-D model based on date and time is more accurate than the traditional 2-D model only based on time. With regard to the sampling method of random forest, this paper carries out repetitive random sampling to form small sample sets and finally to obtain the mean feature plane, which can reduce the interference of abnormal points to our algorithm.
Keywords	wireless network management ; anomaly detection ; feature plane ; Electronic computers. Computer science ; QA75.5-76.95 ; Cybernetics ; Q300-390
Subject code	000
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	Taylor & Francis Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Prediction of the Probability and Risk Factors of Early Abdominal Aortic Aneurysm Using the Gradient Boosted Decision Trees Model

Song Chen / Chuan-Jun Liao

Applied Artificial Intelligence, Vol 36, Iss

2022 Volume 1

Abstract: Currently, abdominal aortic aneurysm (AAA) diagnosis mainly relies on the analysis of the image data, such as Doppler ultrasonic and computed tomography (CT). Once AAA has formed, it may rupture and lead to death at any time. Surgical or endovascular ... ...

Abstract	Currently, abdominal aortic aneurysm (AAA) diagnosis mainly relies on the analysis of the image data, such as Doppler ultrasonic and computed tomography (CT). Once AAA has formed, it may rupture and lead to death at any time. Surgical or endovascular treatment was the only method, but it has a high complication rate and poses a huge economic burden to patients. The gradient boosted decision trees (GBDT) model proposed in this paper is used to predict the probability and risk factors that lead to AAA, and the prediction accuracy of the algorithm is able to reach as high as 96%. This study selected 15 related AAA features as training samples. After the training, age, triglycerides (TG), blood pressure (BP), low-density lipoprotein cholesterol (LDL-C), blood glucose (Glu), and body mass index (BMI) are found to have a direct impact on AAA. For individuals with a high AAA probability, the risk factors that contribute the most to the AAA probability can be determined with the GBDT model. This study presents the GBDT model that effectively predicts the probability and risk factors of early AAA, which enables an early intervention and control of these risk factors against incidence of AAA.
Keywords	Electronic computers. Computer science ; QA75.5-76.95 ; Cybernetics ; Q300-390
Subject code	610
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	Taylor & Francis Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: China Biographical Database (CBDB)

Song Chen / Hongsu Wang

Journal of Open Humanities Data, Vol

A Relational Database for Prosopographical Research of Pre-Modern China

2022 Volume 8

Abstract: The China Biographical Database (CBDB) is the largest prosopographical database for the study of Chinese history. We use regular expressions and neural network models to systematically harvest data from primary and secondary sources and employ an entity- ... ...

Abstract	The China Biographical Database (CBDB) is the largest prosopographical database for the study of Chinese history. We use regular expressions and neural network models to systematically harvest data from primary and secondary sources and employ an entity-relationship model to organize our data. As a relational database with both online and offline versions, CBDB provides freely accessible, structured data for macroscopic, quantitative studies of premodern China. The data in CBDB is continuously disambiguated and readily formatted for statistical, social network, and spatial analyses, and also has value for tagging named entities in historical texts and contextualizing other data collections.
Keywords	chinese history ; relational database ; prosopography ; geographical information system ; social network analysis ; History of scholarship and learning. The humanities ; AZ20-999 ; Language and Literature ; P
Subject code	950
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Ubiquity Press
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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