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Objective Social determinants of health (SDOH) are nonclinical, socioeconomic conditions that influence patient health and quality of life. Identifying SDOH may help clinicians target interventions. However, SDOH are more frequently available in narrative notes compared to structured electronic health records. The 2022 n2c2 Track 2 competition released clinical notes annotated for SDOH to promote development of NLP systems for extracting SDOH. We developed a system addressing 3 limitations in state-of-the-art SDOH extraction: the inability to identify multiple SDOH events of the same type per sentence, overlapping SDOH attributes within text spans, and SDOH spanning multiple sentences. Materials and Methods We developed and evaluated a 2-stage architecture. In stage 1, we trained a BioClinical-BERT-based named entity recognition system to extract SDOH event triggers, that is, text spans indicating substance use, employment, or living status. In stage 2, we trained a multitask, multilabel NER to extract arguments (eg, alcohol “type”) for events extracted in stage 1. Evaluation was performed across 3 subtasks differing by provenance of training and validation data using precision, recall, and F1 scores. Results When trained and validated on data from the same site, we achieved 0.87 precision, 0.89 recall, and 0.88 F1. Across all subtasks, we ranked between second and fourth place in the competition and always within 0.02 F1 from first. Conclusions Our 2-stage, deep-learning-based NLP system effectively extracted SDOH events from clinical notes. This was achieved with a novel classification framework that leveraged simpler architectures compared to state-of-the-art systems. Improved SDOH extraction may help clinicians improve health outcomes.

We aimed to develop a data-driven machine learning model for predicting critical deterioration events from routinely collected EHR data in hospitalized children.This retrospective cohort study included all pediatric inpatients hospitalized on a medical or surgical ward between 2014-2018 at a quaternary children's hospital.We developed a large data-driven approach and evaluated three machine learning models to predict pediatric critical deterioration events. We evaluated the models using a nested, stratified 10-fold cross-validation. The evaluation metrics included C-statistic, sensitivity, and positive predictive value. We also compared the machine learning models with patients identified as high-risk Watchers by bedside clinicians.The study included 57,233 inpatient admissions from 34,976 unique patients. 3,943 variables were identified from the EHR data. The XGBoost model performed best (C-statistic=0.951, CI: 0.946 ∼ 0.956).Our data-driven machine learning models accurately predicted patient deterioration. Future sociotechnical analysis will inform deployment within the clinical setting.

Objective: We aimed to develop a data-driven machine learning model for predicting critical deterioration events from routinely collected EHR data in hospitalized children. Materials: This retrospective cohort study included all pediatric inpatients hospitalized on a medical or surgical ward between 2014–2018 at a quaternary children’s hospital. Methods: We developed a large data-driven approach and evaluated three machine learning models to predict pediatric critical deterioration events. We evaluated the models using a nested, stratified 10-fold cross-validation. The evaluation metrics included C-statistic, sensitivity, and positive predictive value. We also compared the machine learning models with patients identified as high-risk Watchers by bedside clinicians. Results: The study included 57,233 inpatient admissions from 34,976 unique patients. 3,943 variables were identified from the EHR data. The XGBoost model performed best (C-statistic=0.951, CI: 0.946 ∼ 0.956). Conclusions: Our data-driven machine learning models accurately predicted patient deterioration. Future sociotechnical analysis will inform deployment within the clinical setting.

Nursing is a high-risk occupation with high exposure to stress. The physical and mental health of nurses is directly related to the quality of medical services. Therefore, the sleep quality of nurses should not be ignored. In this study, the method of cluster random sampling was adopted from May to September 2019, and a questionnaire survey was conducted among 521 surgical nurses from five affiliated hospitals of Xinjiang Medical University. The relationship between mental health and sleep quality was analyzed, and 20% of the participants with sleep disorders were randomly selected. The sleep disorders used 1 : 1 matching, finally providing a sample with 60 cases and 60 controls for measurement of the CLOCK gene (rs1801260, rs6850524), to analyze the effect of the interaction between mental health and the CLOCK gene on sleep. The mental health and sleep quality of the surgical nurses were evaluated using the Symptom Checklist 90 (SCL-90) and Pittsburgh Sleep Quality Index (PSQI). The study found that surgical nurses had poor sleep, and there were differences associated with age, years working, frequency of night shifts, and incidence of sleep disorders under marital status (p<0.05). The PSQI scores of the positive psychological symptoms were higher than those of the negative psychological symptoms. The rank sum test was used to compare the sleep quality scores of different genotypes in CLOCK rs1801260 and rs6850524; the results indicated that the PSQI scores were different among different genotypes at the rs1801260 and rs6850524 loci. The logistic regression results suggested that CLOCK gene rs1801260 (TC) and positive psychological symptoms were influential factors for sleep disorders, and the interaction of positive psychological symptoms∗rs1801260 (TT) was a risk factor for sleep disorders (OR=10.833, 95% CI: 2.987–39.288). The sleep quality of nurses is not only affected by demographic characteristics but also affected by mental health status and the CLOCK gene.

Human cervical cancer is the fourth most common carcinoma in women in the world. The JAK/STAT3 signaling pathways crucially regulate cell growth and apoptosis. It is a significant target signaling pathway for the development of novel antitumor medicine. This study intended to explore whether lycorine could prevent HT-3 proliferation and induce apoptosis by targeting the JAK/STAT3 signaling cascade. The HT-3 cells were treated with various lycorine dosages and we analyzed cell growth, lipid peroxidation, antioxidants, mitochondrial membrane potential (ΔΨm), DNA damage, apoptosis markers by different in vitro methodologies. Our results revealed that lycorine substantially reserved cell growth via decreased antioxidants, augmented reactive oxygen species (ROS) generation which leads to loss of ΔΨm, increased nuclear crumbling and chromatin condensation, thus resulting in representative increased apoptotic cell death. Furthermore, we analyzed that the molecular mechanical action of lycorine considerably repressed JAK1/STAT3 transactional activation and decrease its downstream molecules Bcl-2, and enhances the expressional activity of Bax, cytochrome c, caspase 3 and 9 in HT-3 cells. Finally, the fact that N-acetylcysteine inhibits lycorine-induced ROS-mediated apoptosis was confirmed in HT-3 cells. Thus, the results indicate that lycorine efficiently enhances apoptosis and inhibits HT-3 cell proliferation. These outcomes collectively proposed that lycorine could be a beneficial chemotherapeutic agent for treating and managing human cervical carcinoma.

To develop and evaluate a high-dimensional, data-driven model to identify patients at high risk of clinical deterioration from routinely collected electronic health record (EHR) data.In this single-center, retrospective cohort study, 488 patients with single-ventricle and shunt-dependent congenital heart disease6 months old were admitted to the cardiac intensive care unit before stage 2 palliation between 2014 and 2019. Using machine-learning techniques, we developed the Intensive care Warning Index (I-WIN), which systematically assessed 1028 regularly collected EHR variables (vital signs, medications, laboratory tests, and diagnoses) to identify patients in the cardiac intensive care unit at elevated risk of clinical deterioration. An ensemble of 5 extreme gradient boosting models was developed and validated on 203 cases (130 emergent endotracheal intubations, 34 cardiac arrests requiring cardiopulmonary resuscitation, 10 extracorporeal membrane oxygenation cannulations, and 29 cardiac arrests requiring cardiopulmonary resuscitation onto extracorporeal membrane oxygenation) and 378 control periods from 446 patients.At 4 hours before deterioration, the model achieved an area under the receiver operating characteristic curve of 0.92 (95% confidence interval, 0.84-0.98), 0.881 sensitivity, 0.776 positive predictive value, 0.862 specificity, and 0.571 Brier skill score. Performance remained high at 8 hours before deterioration with 0.815 (0.688-0.921) area under the receiver operating characteristic curve.I-WIN accurately predicted deterioration events in critically-ill infants with high-risk congenital heart disease up to 8 hours before deterioration, potentially allowing clinicians to target interventions. We propose a paradigm shift from conventional expert consensus-based selection of risk factors to a data-driven, machine-learning methodology for risk prediction. With the increased availability of data capture in EHRs, I-WIN can be extended to broader applications in data-rich environments in critical care.

BACKGROUND The aim of the present work was to evaluate FOXA2 expression in ovarian cancer and to use integrated bioinformatics analysis to correlate it with patient prognosis. MATERIAL AND METHODS FOXA2 expression was evaluated in multiple cancers in The Cancer Genome Atlas database. A protein-protein interaction (PPI) network relevant to FOXA2 was constructed using the Search Tool for Retrieval of Interacting Genes/Proteins (STRIN). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed of FOXA2 and relevant genes. Correlations between overall survival (OS), disease-free survival, and FOXA2 expression were evaluated. An immunohistochemical assay (IHC) was used to test for FOXA2 protein expression in 79 ovarian cancer specimens. RESULTS FOXA2 mRNA was upregulated in colorectal, stomach, liver, and endometrial cancers. In the PPI network, 21 protein nodes and 533 edges were constructed with a local clustering coefficient of 0.698, which indicated significant PPI enrichment (P

Objective Limited research exists in predicting first-time suicide attempts that account for two-thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data-driven approach that applies natural language processing (NLP) and machine learning (ML) to unstructured (narrative) clinical notes and structured electronic health record (EHR) data. Methods This case-control study included patients aged 10–75 years who were seen between 2007 and 2016 from emergency departments and inpatient units. Cases were first-time suicide attempts from coded diagnosis; controls were randomly selected without suicide attempts regardless of demographics, following a ratio of nine controls per case. Four data-driven ML models were evaluated using 2-year historical EHR data prior to suicide attempt or control index visits, with prediction windows from 7 to 730 days. Patients without any historical notes were excluded. Model evaluation on accuracy and robustness was performed on a blind dataset (30% cohort). Results The study cohort included 45 238 patients (5099 cases, 40 139 controls) comprising 54 651 variables from 5.7 million structured records and 798 665 notes. Using both unstructured and structured data resulted in significantly greater accuracy compared to structured data alone (area-under-the-curve [AUC]: 0.932 vs. 0.901 P Conclusions Our large data-driven approach using both structured and unstructured EHR data demonstrated accurate and robust first-time suicide attempt prediction, and has the potential to be deployed across various populations and clinical settings.

BACKGROUND This study investigated the effect of occupational stress and circadian clock gene polymorphism on sleep disorder of oil workers in Xinjiang, China. MATERIAL AND METHODS We enrolled 2300 Xinjiang oil workers who had been working for at least 1 year. The Chinese revised version of the Occupational Stress Questionnaire (OSI-R), the Pittsburgh Sleep Quality Index (PSQI), and General Survey Questionnaire were used. A total of 308 subjects were selected for stress hormone measurements and gene polymorphism analysis of the circadian clock genes CLOCK, PER2, and PER3. RESULTS The occupational stress scores were influenced by sex, smoking, marital status, age, and work type. Different work shift groups and different professional title groups had statistically significant sleep disorder incidences (P

Nursing is a high-risk occupation with high exposure to stress. The physical and mental health of nurses is directly related to the quality of medical services. Therefore, the sleep quality of nurses should not be ignored. In this study, the method of cluster random sampling was adopted from May to September 2019, and a questionnaire survey was conducted among 521 surgical nurses from five affiliated hospitals of Xinjiang Medical University. The relationship between mental health and sleep quality was analyzed, and 20% of the participants with sleep disorders were randomly selected. The sleep disorders used 1 : 1 matching, finally providing a sample with 60 cases and 60 controls for measurement of the CLOCK gene (rs1801260, rs6850524), to analyze the effect of the interaction between mental health and the CLOCK gene on sleep. The mental health and sleep quality of the surgical nurses were evaluated using the Symptom Checklist 90 (SCL-90) and Pittsburgh Sleep Quality Index (PSQI). The study found that surgical nurses had poor sleep, and there were differences associated with age, years working, frequency of night shifts, and incidence of sleep disorders under marital status (p < 0.05). The PSQI scores of the positive psychological symptoms were higher than those of the negative psychological symptoms. The rank sum test was used to compare the sleep quality scores of different genotypes in CLOCK rs1801260 and rs6850524; the results indicated that the PSQI scores were different among different genotypes at the rs1801260 and rs6850524 loci. The logistic regression results suggested that CLOCK gene rs1801260 (TC) and positive psychological symptoms were influential factors for sleep disorders, and the interaction of positive psychological symptoms∗rs1801260 (TT) was a risk factor for sleep disorders (OR = 10.833, 95% CI: 2.987–39.288). The sleep quality of nurses is not only affected by demographic characteristics but also affected by mental health status and the CLOCK gene.

The advancement of the wireless body area networks (WBAN) and sensor technologies allows us to collect a variety of physiological and behavioral data from human body. And appropriate application of machine learning methods can greatly promote the development of e-health. Nevertheless, the collected data contains personal privacy information. When using the machine learning methods to analyze the collected data, some information of the training data will be stored in the learning models unconsciously. To handle such information disclosure problem, we propose a differentially private classification algorithm based on ensemble decision tree with high utility for wireless body area networks. In order to improve the accuracy and stableness of classification, the bagging framework of ensemble learning is used in our algorithm. We aggregate the results of multiple private decision trees as the final classification in a weight-based voting way. For each private decision tree trained on the bootstrap samples, we offer a novel privacy budget allocation strategy that allows the nodes in larger depth to get more privacy budget, which can mitigate the problem of excessive noise introduced to leaf nodes to some extent. The better classification accuracy and stableness of this new algorithm, especially on small dataset, are demonstrated by simulation experiments. The work is partially supported by the National Natural Science Foundation of China under Grant 61972148, the National Key R and D Program of China under grant 2018YFC0831404, Beijing Natural Science Foundation under grant 4182060.

Since the emergence of memristors (or memristive devices), how to integrate them into arrays has been widely investigated. After years of research, memristor crossbar arrays have been proposed and realized with potential applications in nonvolatile memory, logic and neuromorphic computing systems. Despite the promising prospects of memristor crossbar arrays, one of the main obstacles for their development is the so-called sneak-path current causing cross-talk interference between adjacent memory cells and thus may result in misinterpretation which greatly influences the operation of memristor crossbar arrays. Solving the sneak-path current issue, the power consumption of the array will immensely decrease, and the reliability and stability will simultaneously increase. In order to suppress the sneak-path current, various solutions have been provided. So far, some reviews have considered some of these solutions and established a sophisticated classification, including 1D1M, 1T1M, 1S1M (D: diode, M: memristor, T: transistor, S: selector), self-selective and self-rectifying memristors. Recently, a mass of studies have been additionally reported. This review thus attempts to provide a survey on these new findings, by highlighting the latest research progress realized for relieving the sneak-path issue. Here, we first present the concept of the sneak-path current issue and solutions proposed to solve it. Consequently, we select some typical and promising devices, and present their structures and properties in detail. Then, the latest research activities focusing on single-device structures are introduced taking into account the mechanisms underlying these devices. Finally, we summarize the properties and perspectives of these solutions.

Since the traditional teaching mode ignores the cultivation of students' innovative and entrepreneurial ability, this paper proposes a new “full process, open and project driven course mode”. This new teaching model was piloted in the course of “Experiment of Optoelectronic Materials and Devices”. With the new course model, students will complete a project practice in group. Through this kind of practice, students have more solid knowledge of round management, and their interdisciplinary learning ability, application knowledge ability, team cooperation ability and pressure resistance ability can be cultivated. This course mode is conducive to the cultivation of university students with the ability of innovation and entrepreneurship.

Deep learning (DL) technology has been widely deployed in many fields and achieved great success, but it is not absolutely safe and reliable. It has been proved that research on adversarial attacks can reveal the vulnerability of deep neural networks (DNN). Although many methods of adversarial attack and defense have been proposed in the field of images, the research on textual adversarial samples is still few. It is challenging because text samples are sparse and discrete and the added perturbation might lead to grammatical errors and semantic changes. Thus, there are some special restrictions on textual adversarial samples. We propose a synonyms substitution-based adversarial text generation via Probability Determined Word Saliency (PDWS). In our method PDWS, the word saliency and the optimal substitution word are determined by the optimal replace-ment effect. The replacement effect is the probability change caused by replacing one word with its substitution word. We evaluate our attack method on two popular text classification tasks using CNN and LSTM. The experimental results show that our method gets higher misleading rate and less perturbation rate than the baseline methods.

Social determinants of health (SDOH) are non-medical factors that can profoundly impact patient health outcomes. However, SDOH are rarely available in structured electronic health record (EHR) data such as diagnosis codes, and more commonly found in unstructured narrative clinical notes. Hence, identifying social context from unstructured EHR data has become increasingly important. Yet, previous work on using natural language processing to automate extraction of SDOH from text (a) usually focuses on an ad hoc selection of SDOH, and (b) does not use the latest advances in deep learning. Our objective was to advance automatic extraction of SDOH from clinical text by (a) systematically creating a set of SDOH based on standard biomedical and psychiatric ontologies, and (b) training state-of-the-art deep neural networks to extract mentions of these SDOH from clinical notes.A retrospective cohort study.Data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. The corpus comprised 3,504 social related sentences from 2,670 clinical notes.We developed a framework for automated classification of multiple SDOH categories. Our dataset comprised narrative clinical notes under the "Social Work" category in the MIMIC-III Clinical Database. Using standard terminologies, SNOMED-CT and DSM-IV, we systematically curated a set of 13 SDOH categories and created annotation guidelines for these. After manually annotating the 3,504 sentences, we developed and tested three deep neural network (DNN) architectures - convolutional neural network (CNN), long short-term memory (LSTM) network, and the Bidirectional Encoder Representations from Transformers (BERT) - for automated detection of eight SDOH categories. We also compared these DNNs to three baselines models: (1) cTAKES, as well as (2) L2-regularized logistic regression and (3) random forests on bags-of-words. Model evaluation metrics included micro- and macro- F1, and area under the receiver operating characteristic curve (AUC).All three DNN models accurately classified all SDOH categories (minimum micro-F1 = 0.632, minimum macro-AUC = 0.854). Compared to the CNN and LSTM, BERT performed best in most key metrics (micro-F1 = 0.690, macro-AUC = 0.907). The BERT model most effectively identified the "occupational" category (F1 = 0.774, AUC = 0.965) and least effectively identified the "non-SDOH" category (F = 0.491, AUC = 0.788). BERT outperformed cTAKES in distinguishing social vs non-social sentences (BERT F1 = 0.87 vs. cTAKES F1 = 0.06), and outperformed logistic regression (micro-F1 = 0.649, macro-AUC = 0.696) and random forest (micro-F1 = 0.502, macro-AUC = 0.523) trained on bag-of-words.Our study framework with DNN models demonstrated improved performance for efficiently identifying a systematic range of SDOH categories from clinical notes in the EHR. Improved identification of patient SDOH may further improve healthcare outcomes.

With the development of Internet of Things and the increasingly rampant malicious network activities, higher requirements are put forward for security to detect malicious behavior and prevent attackers from obtaining sensitive data in the smart home environment. In this paper, an intrusion detection system is proposed to detect and classify abnormal behavior in the smart home environment. The two-layer feature processing method based on random forest and principal component analysis can reduce the loss of data information and is suitable for massive data. The three-layer detection model can detect four common attacks with binary classifiers and effectively improve the accuracy. The experimental evaluation of the proposed model is conducted using the real smart home traffic dataset and achieves a classification accuracy of 95.90%. The experimental results show that our model has a good performance in detecting and classifying malicious attacks in the smart home.

This work describes a sensitive electrochemical biosensor for detection of carbamate pesticides based on immobilization of acetylcholinesterase (AChE) on the electrochemically inducing porous graphene oxide network (e-pGON) which is prepared by scanning the GO modified electrode using successive cyclic voltammetry method. The e-pGON effectively promotes the electron transfer rate and facilitates the access of substrates to the active centers. The as-prepared biosensor shows high affinity to acetylthiocholine (ATCl) with a Michaelis-Menten constant value of 0.45 mmol L −1 . Under optimum conditions, the inhibition of carbaryl is proportional to its concentration ranging from 0.3 to 6.1 ng/mL. The detection limit is 0.15 ng/mL. The developed biosensor exhibits good performance such as reproducibility and stability, thus providing a promising tool for the analysis of enzyme inhibitors.

A sensitive electrochemical biosensor for determining organophosphates (OPs) and carbamate pesticides has been achieved by immobilizing acetylcholinesterase (AChE) on electrochemically inducing 3D graphene oxide network/multi-walled carbon nanotube composites (e-GON–MWCNTs). The nanocomposites of e-GON–MWCNTs can provide a favorable environment for the immobilized AChE and improve the electron transfer speed between the analyte and electrode surface. The fabricated AChE biosensors show a favorable affinity to acetylthiocholine chloride (ATCl) with a Michaelis–Menten constant of 0.43 mmol L−1. In the optimal conditions, the biosensor exhibits a linear range of 0.03–0.81 ng mL−1 for detecting carbofuran, and two linear ranges of 0.05–1 ng mL−1 and 1–104 ng mL−1 for detecting paraoxon. Furthermore, the detection limits for carbofuran and paraoxon can reach 0.015 and 0.025 ng mL−1, respectively. The AChE biosensor exhibits good reproducibility and high stability, which demonstrates good efficiency in real sample analysis.

The electrical energy consumed by university laboratories accounts for a large proportion of total electrical energy. How to effectively monitor the use of laboratory electrical energy has become extremely important. Taking the lighting circuit as an example, a targeted mobile-based lighting monitoring system was developed. The lighting monitoring system uses Visual Basic as the development language, MySQL as the background database, and OPC technology as the data transmission communication protocol and B/S architecture mode, which realizes the energy consumption of voltage, current and power in the laboratory lighting circuit on the mobile side. The results show that the designed laboratory lighting power monitoring system has real-time and simplicity, and the system runs smoothly during the testing process, showing that the system functions are more reasonable and perfect.

We aimed to predict an individual suicide risk level from longitudinal posts on Reddit discussion forums. Through participating in a shared task competition hosted by CLPsych2019, we received two annotated datasets: a training dataset with 496 users (31,553 posts) and a test dataset with 125 users (9610 posts). We submitted results from our three best-performing machine-learning models: SVM, Naive Bayes, and an ensemble model. Each model provided a user’s suicide risk level in four categories, i.e., no risk, low risk, moderate risk, and severe risk. Among the three models, the ensemble model had the best macro-averaged F1 score 0.379 when tested on the holdout test dataset. The NB model had the best performance in two additional binary-classification tasks, i.e., no risk vs. flagged risk (any risk level other than no risk) with F1 score 0.836 and no or low risk vs. urgent risk (moderate or severe risk) with F1 score 0.736. We conclude that the NB model may serve as a tool for identifying users with flagged or urgent suicide risk based on longitudinal posts on Reddit discussion forums.

The rapid development of data analysis technologies and the easily accessible datasets have enabled the construction of a comprehensive analytics model, which can facilitate the decision makings involved in services. Meanwhile, the individual privacy preservation is of great necessity. Decision tree is a common method in medical prediction and diagnose, known for its simplicity of understanding and interpreting. However, the process of building a decision tree might cause individual privacy disclosure. Differential privacy provides a rigorous mathematical definition of privacy by controlling the risk of privacy leakage in a manageable range while maintaining the statistical characteristics. In this paper, we propose a Differentially Private Greedy Decision Forest with high utility (DPGDF) to build a privacy-preserving decision forest. In DPGDF, we design a novel budget allocation strategy that allows the nodes in greater depth get more privacy budgets in the decision tree construction process, which can, to some extent, mitigate the problem of excessive noises introduced to the leaf nodes. To aggregate multiple trees into a forest, we propose a selective aggregation method based on the prediction accuracy of the decision forest. In addition, we develop an iterative method to speed up the process of selective aggregation. Finally, we experimentally prove that the proposed DPGDF can achieve a better performance on two practical datasets compared with other algorithms.

The authors systematically reviewed peer-reviewed studies done with LENA (Language ENvironment Analysis) technology, guided by three research questions: (a) What types of studies have been conducted, and with which populations, since the launch of LENA technology? (b) What challenges related to use of LENA technology were identified? (c) What are the implications for practice and future research using LENA technology? Electronic databases, the LENA Research Foundation website, and bibliographies of already-included studies were searched; 38 studies were identified. The authors selected studies on the basis of purpose, design, participant characteristics, application of LENA technology, and results. They found that LENA technology was used with a range of populations to yield a variety of information. Though challenges and limitations are associated with LENA technology, great potential exists for further research and a resultant increase in evidence-based understanding of early language development and interventions on its behalf.

In many electronic applications, the surface properties of hydrogen-terminated diamond films determine the eventual performance of the electronic device. In this work, diamond films were grown by hot-filament chemical vapor deposition method and hydrogenated films were obtained by hydrogen plasma treatment. X-ray photoelectron spectroscopy analysis was carried out to evaluate the film surface after annealing treatment. The C1s XPS spectrum showed the C1s spectrum changed after annealing treatment. In air atmosphere, hydrogen decreased and oxygen absorption increased. The interaction on the surface changed drastically after annealed in argon atmosphere and phase transition would happen when the annealing temperature increased to 600 °C.

High quality freestanding diamond (FSD) films with smooth nucleation surfaces were grown by microwave plasma chemical vapor deposition (MPCVD) method. A p-type hydrogenated surface conductivity of FSD film was obtained by using hydrogen plasma treatment. The annealing process in vacuum on the p-type behavior of FSD nucleation surfaces was investigated by Hall effect measurement. H-terminated diamond phototransistors were fabricated and the results suggest that they may be ideally suited for ultraviolet (UV) switching applications.

Objectives This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases. Methods A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients’ diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH). At each site, we manually built a rule-based NLP and trained a Bayesain network classifier from over 40,000 ED encounters between Jan. 2008 and May. 2010 using feature selection, machine learning, and expert debiasing approach. Transferability of a BCD in this study may be impacted by seven factors: development (source) institution, development parser, application (target) institution, application parser, NLP transfer, BN transfer, and classification task. We employed an ANOVA analysis to study their impacts on BCD performance. Results Both BCDs discriminated well between influenza and non-influenza on local test cases (AUCs > 0.92). When tested for transferability using the other institution’s cases, BCDUPMC discriminations declined minimally (AUC decreased from 0.95 to 0.94, p

NUCC-3 choriocarcinoma cell line was used as anin vitro placental cell model to investigate the effects of follistatin on GnRH-stimulated hCG secretion and its subunit mRNA level and stability. Follistatin alone did not affect basal hCG secretion and its subunit mRNA level. GnRH increased hCG secretion and hCG β-mRNA level in a dose-dependent manner. Follistatin significantly suppressed GnRH-stimulated hCG secretion and hCG α- and β-mRNA level. It inhibited hCG secretion in response to PMA, and did not affect the stability of hCG α- and β-mRNA. The authors suggest that follistatin inhibits GnRH-stimulated hCG secretion as well as hCG α- and β-RNA level by decreasing the rate of transcription through the second messenger transduction system —protein kinase-C.

This paper describes LEARN, a software system for computer assisted foreign language vocabulary acquisition. LEARN uses unrestricted text to assist in learning because its potential value is clear: unrestricted text can be chosen by the learner to suit the learner's own interests. LEARN processes English unrestricted text by translating selected English words in it into foreign words before presenting the text to the learner. Learners can then practice their foreign language vocabulary in the course of reading the text of their choice. Currently LEARN can translate a significant number of words into Chinese and Bengali. Ambiguity in translation is addressed by ‘word experts’, miniature expert systems, each of which translates some word from English into a particular language by examining its context. The natural path for the future of LEARN is to extend it by adding more languages, having it take input in languages other than English, adding more word experts, adding more extensive interactive hi...

To investigate the modulating effect on lipid and gene expressions of CPT I A caused by berberine (Ber) in experimental hyperlipidemia rats.Male SD rats were randomly divided into 5 groups according to the blood lipid values: normal group, hyperlipidemia group, 300 mg x kg(-1) x d(-1) Ber-treated group, 60 mg x kg(-1) x d(-1) Ber-treated group, and 7.2 mg x kg(-1) x d(-1) lovastatin-treated group. Normal group were fed with base diet and other groups were fed with high fat and cholesterol diet. 12 weeks after drugs were given the TC, TG, LDL-C, and HDL-C from rat blood samples were tested by automatic biochemistry analyzer. Gene expressions of CPT I A and PPARalpha were evaluated by RT-PCR and Western blot, respectively.It was shown that Ber significantly decreased TC and LDL-C, but increased HDL-C in dose-dependent manner, elevated expressions of CPT I A mRNA and protein without influence on PPARalpha expression. Similar effects from lovastatin on lipidemia were observed except the Ber effect on CPT I A gene expression.Ber has modulating effect on the lipid metabolism, the mechanism of which may be by promoting the CPT I A gene expression.

OBJECTIVE To investigate the modulating effect on lipid and gene expressions of CPT I A caused by berberine (Ber) in experimental hyperlipidemia rats. METHOD Male SD rats were randomly divided into 5 groups according to the blood lipid values: normal group, hyperlipidemia group, 300 mg x kg(-1) x d(-1) Ber-treated group, 60 mg x kg(-1) x d(-1) Ber-treated group, and 7.2 mg x kg(-1) x d(-1) lovastatin-treated group. Normal group were fed with base diet and other groups were fed with high fat and cholesterol diet. 12 weeks after drugs were given the TC, TG, LDL-C, and HDL-C from rat blood samples were tested by automatic biochemistry analyzer. Gene expressions of CPT I A and PPARalpha were evaluated by RT-PCR and Western blot, respectively. RESULT It was shown that Ber significantly decreased TC and LDL-C, but increased HDL-C in dose-dependent manner, elevated expressions of CPT I A mRNA and protein without influence on PPARalpha expression. Similar effects from lovastatin on lipidemia were observed except the Ber effect on CPT I A gene expression. CONCLUSION Ber has modulating effect on the lipid metabolism, the mechanism of which may be by promoting the CPT I A gene expression.

Freestanding diamond (FSD) film with p-type hydrogen-terminated nucleation surface was prepared by microwave plasma chemical vapour deposition (MPCVD) method. The post-treatment (wet chemical etch and annealing process) on the property of diamond film was investigated. The preparation and characterization of hydrogen-terminated diamond film p-type channel metal-semiconductor field effect transistors (MESFETs) was studied. The device was also used for photodetector application. The results showed the potential of high switching speed and high sensitivity to ultraviolet (UV).

We have correlated luminescence studies of epitaxially laterally overgrown GaN with microstructure and local carrier concentration measurements. We have found that the luminescence characteristics of the coherently grown regions are considerably different from those of the sidewall facets. We find that these differences are related to the growth-front and not the dislocation density. The differences appears to be due to a variation in the incorporation of Ga vacancies for different facets. The ELO GaN (ELOG) structures were grown using metalorganic chemical vapor deposition, with a parallel stripe pattern of SiO/sub 2/ mask along (1100) direction.