Association between type D personality and supportive care needs in elderly patients with breast cancer: a prospective longitudinal observational study.

BACKGROUND: Elderly patients with breast cancer often have more unmet needs after receiving common treatments such as surgery and chemotherapy. Type D personality has been related to supportive care needs in the general population. However, its association with supportive care needs in elderly breast cancer patients has not been prospectively explored. This study aimed to address this gap. OBJECTIVES: The aim was to understand the impact of Type D personality on the supportive care needs of elderly breast cancer patients at diagnosis, 2 weeks postoperatively, 3 months postoperatively, and 6 months postoperatively and to analyse the impact of Type D personality on the changing trajectory of supportive care needs after controlling for confounding factors such as demographics, symptom distress and social support. METHODS: A total of 122 elderly patients (≥ 65 years) with breast cancer in Ruijin Hospital, Shanghai, China, were included from September 2021 until August 2022. Supportive care needs were measured by the Supportive Care Needs Survey Short Form and tracked at diagnosis, 2 weeks postoperatively, 3 months postoperatively, and 6 months postoperatively. To investigate changes in the supportive care needs of elderly breast cancer patients and the effect of Type D personality on these needs, a linear mixed model was applied. RESULTS: A total of 122 elderly patients participated. There was an overall decreasing trend in supportive care needs, with Type D personality patients having significantly higher levels of supportive care needs than the non-Type D personality patients at all stages. Through linear mixed models, it was found that the Type D personality group had a lower overall downward trend than the non-Type D personality group, with need levels remaining consistently higher. This difference persisted after controlling for demographic information, symptom burden, social support. CONCLUSIONS: Elderly breast cancer patients with Type D personality had higher levels of supportive care needs and a slower rate of decline that was maintained over a longer period than those with non-Type D personality.

Trajectory patterns and influencing factors of supportive care needs in Chinese elderly breast cancer patients.

AIMS AND OBJECTIVES: The aim of this study was to identify the trajectory patterns of supportive care needs in Chinese older patients with breast cancer and their predictive factors. METHODS: A total of 122 older patients with breast cancer were recruited. Demographic and disease-related information, type D personality, and supportive care needs were investigated at baseline, 3, and 6 months. Latent class growth model was used to identify the trajectory patterns of supportive care needs. Multiple logistic regression was used to determine the predictors for membership. RESULTS: Three trajectories with different characteristics of changing categories of supportive care needs were identified in the final analysis, named as "High needs decline group" (38.5%), "High needs sustained group" (51.6%), and "Low needs sustained group" (9.8%). Univariate analysis showed that age, education level, number of children, primary caregiver, pathological stage, surgical modality, treatment protocols, and personality traits were associated with the trajectory categories of supportive care needs of older patients with breast cancer. Multiple logistic regression showed that primary caregiver type, treatment protocols, and personality traits were influential factors in the trajectory of supportive care needs of older patients with breast cancer. CONCLUSION: Our study demonstrates the heterogeneity of changes in supportive care needs. The supportive care needs of older patients with breast cancer show a trajectory of change in different categories, and healthcare providers can develop individualized interventions based on the characteristics of different patients.

Mechanism of Yiqi Huoxue Huatan recipe in the treatment of coronary atherosclerotic disease through network pharmacology and experiments.

In recent years, with population aging and economic development, morbidity and mortality of atherosclerotic cardiovascular disease associated with atherosclerosis (AS) have gradually increased. In this study, a combination of network pharmacology and experimental verification was used to systematically explore the action mechanism of Yiqi Huoxue Huatan Recipe (YHHR) in the treatment of coronary atherosclerotic heart disease (CAD). We searched and screened the active ingredients of Coptis chinensis, Astragalus membranaceus, Salvia miltiorrhiza, and Hirudo. We also searched multiple databases for related target genes corresponding to the compounds and CAD. STRING was used to construct the protein-protein interaction (PPI) network of genes. Metascape was used to perform gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for common targets to analyze the main pathways, and finally, the molecular docking and main possible pathways were verified by experimental studies. Firstly, a total of 1480 predicted target points were obtained through the Swiss Target Prediction database. After screening, merging, and deleting duplicate values, a total of 768 targets were obtained. Secondly, "Coronary atherosclerotic heart disease" was searched in databases such as the OMIM, GeneCards, and TTD. 1844 disease-related targets were obtained. Among PPI network diagram of YHHR-CAD, SRC had the highest degree value, followed by AKT1, TP53, hsp90aa1 and mapk3. The KEGG pathway bubble diagram was drawn using Chiplot, the Signal pathways such as NF kappa B signaling pathway, Lipid and AS, and Apelin signaling pathway are closely related to the occurrence of CAD. The PCR and Western blot methods were used to detect the expression of NF-κB p65. When compared with that in the model group, the expression of NF-κB p65mRNA decreased in the low-concentration YHHR group, with P < .05, while the expression of NF-κB p65mRNA decreased significantly in the high-concentration YHHR group, with P < .01. On the other hand, when compared with that in the model group, the expression of NF-κB p65 decreased in the low-concentration YHHR group, but was not statistically significant, while the expression of NF-κB p65 was significant in the high-concentration YHHR group, and has statistical significance with P < .05. YHHR has been shown to resist inflammation and AS through the SRC/NF-κB signaling pathway.

Comparison of crop productivity, economic benefit and environmental footprints among diversified multi-cropping systems in South China.

Diversified multi-cropping system with high productivity and low environmental costs is crucial for the development of sustainable agriculture in different regions. However, the information on this practice has still been limited in the South China. This study compared different diversified multi-cropping systems including peanut-rice-fallow (P-R-F), peanut-rice-ryegrass (P-R-R), soybean-rice-ryegrass (S-R-R), feed corn-rice-milk vetch (FC-R-M), sweet corn-rice-milk vetch (SC-R-M) and zucchini -rice-milk vetch (Z-R-M), with the conventional double-rice system (CK). A newly proposed agricultural environmental footprint index (EFI) framework was introduced to quantify the comprehensive environmental costs of different systems. Results indicated that the annual productivities of P-R-R and FC-R-M rotation systems significantly increased by 39.91 % and 25.06 %, respectively, compared to the CK. The economic benefits of P-R-R and FC-R-M were 53.71 % and 16.67 % higher than the CK, respectively, with significant differences. The EFIs based on unit farmland area, crop productivity and economic benefit of the P-R-R and FC-R-M systems were 17.07 %-40.68 % lower than the CK, respectively, showing the lower environmental costs. Therefore, the P-R-R and FC-R-M were recommended as alternatives of double-rice cropping in the South China. In addition, the results indicated that the fertilization and irrigation practices were the key points for improving the rotation systems. This study provided valuable information for the transition of rice-based cropping system in South China. It was also a reference for the development of sustainable agriculture in the world's subtropical agricultural system.

The Influence of Climate Change on Droughts and Floods in the Yangtze River Basin from 2003 to 2020.

In recent decades, extreme floods and droughts have occurred frequently around the world, which seriously threatens the social and economic development and the safety of people's lives and properties. Therefore, it is of great scientific significance to discuss the causes and characteristic quantization of extreme floods and droughts. Here, the terrestrial water storage change (TWSC) derived from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) data was used to characterize the floods and droughts in the Yangtze River basin (YRB) during 2003 and 2020. To reduce the uncertainty of TWSC results, the generalized three-cornered hat and least square methods were used to fuse TWSC results from six GRACE solutions. Then combining precipitation (PPT), evapotranspiration, soil moisture (SM), runoff, and extreme climate index data, the influence of climate change on floods and droughts in the YRB was discussed and analyzed. The results show that the fused method can effectively improve the uncertainty of TWSC results. And seven droughts and seven floods occurred in the upper of YRB (UY) and nine droughts and six floods appeared in the middle and lower of YRB (MLY) during the study period. The correlation between TWSC and PPT (0.33) is the strongest in the UY, and the response time between the two is 1 month, while TWSC and SM (0.67) are strongly correlated with no delay in the MLY. The reason for this difference is mainly due to the large-scale hydropower development in the UY. Floods and droughts in the UY and MLY are more influenced by the El Niño-Southern Oscillation (ENSO) (correlation coefficients are 0.39 and 0.50, respectively) than the Indian Ocean Dipole (IOD) (correlation coefficients are 0.19 and 0.09, respectively). The IOD event is usually accompanied by the ENSO event (the probability is 80%), and the hydrological hazards caused by independent ENSO events are less severe than those caused by these two extreme climate events in the YRB. Our results provide a reference for the study on the formation, development, and recovery mechanism of regional floods and droughts on a global scale.

Risk assessment of unsafe behavior in university laboratories using the HFACS-UL and a fuzzy Bayesian network.

INTRODUCTION: Risk assessment for unsafe behavior is an important task in the management of university laboratories. Yet related research activities are still in the early stages. This paper attempts to deepen the insight and provide a basis for further research. METHOD: As traditional methods are inadequate in terms of quantitative assessment and uncertainty handling, this paper proposes a method to assess the risk of unsafe behavior in university laboratories using the human factor analysis and classification system for university laboratories (HFACS-UL)-fuzzy Bayesian network (BN) approach. A BN structure was established using the HFACS-UL model for the identification of factors influencing unsafe behavior. Using a fuzzy BN approach, parameters are learned based on prior knowledge and expert experience. The model is then applied for inference analysis to identify the main risk factors. The key agents were also analyzed along with meta-networks to determine further preventive and control measures. RESULTS: Taking chemistry laboratories of a university as an example, the results show that the probability of unacceptable unsafe behavior in chemical laboratories is 86%, indicating that commitment and cooperation from different agents are required. Of the 24 risk factors, poor organizational climate, with a sensitivity value of 24.1%, has the greatest impact on unsafe behavior. The most fundamental factor contributing to the occurrence of unsafe behavior is inadequate legislation, which in turn results in unacceptable external factors and inadequate supervision, thus forming the most likely causal chain. The functional department, lab center director, and secondary faculty leadership team are the most critical agents. CONCLUSIONS: Results from the chemistry laboratories demonstrate the credibility of the model. PRACTICAL APPLICATIONS: This study may help provide technical support for risk management in university laboratories.

Heavy Metals in the Mainstream Water of the Yangtze River Downstream: Distribution, Sources and Health Risk Assessment.

Since the mainstream of the Yangtze River lower reach is an important drinking water source for residents alongside it, it is essential to investigate the concentration, distribution characteristics and health risks of heavy metals in the water. In this study, a total of 110 water samples were collected on both the left and right banks from the upstream to the downstream. Principal component analysis (PCA) was used to determine the sources of heavy metals. Their non-carcinogenic and carcinogenic risks were studied with health risk assessment models, and uncertainties were determined through Monte Carlo simulation. Results showed that concentrations of all heavy metals were significantly lower than the relevant authoritative standards in the studied area. From the upstream to the downstream, Ni, Cu and Cr had similar concentration distribution rules and mainly originated from human industrial activities. Pb, Cd and Zn had a fluctuating but increasing trend, which was mainly due to the primary geochemistry, traffic pollution and agricultural activities. The maximum As concentration appeared in the upstream mainly because of the carbonatite weathering or mine tail water discharge. Concentrations of Zn, As, Cd and Pb on the left bank were higher than those on the right bank, while concentrations of Cu, Ni and Cr on the right bank were higher than those on the left bank. The non-carcinogenic risk index (HI) was less than 1 (except of L11), and HI on the left bank was higher than that on the right bank. The carcinogenic risk (CR) was generally larger than 1.0 × 10-4, CR on the right bank overall was higher than that on the left bank, and the health risk of kids was greater than that of adults. Furthermore, Monte Carlo simulation results and the actual calculated values were basically the same.

Optimized ratoon rice system to sustain cleaner food production in Jianghan Plain, China: a comprehensive emergy assessment.

Ratoon rice (RR) is regarded as a labor-saving and efficient approach to rice cultivation; however, sub-optimal production techniques (fertilization, irrigation, harvesting) may lead to serious environmental problems and unsustainable agriculture. In this study, emergy analysis was combined with indicators of soil fertility, global warming potential (GWP), and profitability to comprehensively assess the sustainability performance of three cultivation modes: (i) traditional farm practice (TRA), (ii) optimized mode (OPT), and (iii) OPT plus green manure planting (OPTM). Over 2 years, compared with the TRA mode, OPT and OPTM modes increased total rice yield by 10% and 19% on average and improved profit by 233.7 and 456.5 Yuan ha-1, respectively. Single emergy analysis results showed that, compared with the TRA mode, OPT and OPTM (2-year average value) modes increased production efficiency by 10% and 8%, reduced renewable fraction and emergy sustainability index by 14-19% and 18-23%, respectively, and increased environmental loading ratio by 31% and 22%. Multiple EMA analysis results showed that, compared with the TRA mode, OPT and OPTM (2-year average value) modes reduced UEVNmin by 23% and 21% and increased UEVGWP 32% and 51%, respectively. The UEVTotal revenue and UEVBenefit of OPT and OPTM increased by 8-29% and 4-37%, respectively, compared with TRA mode. The comprehensive assessment indicated that, despite OPT and OPTM modes have a range of improvements and dis-improvements versus the TRA mode, OPTM was the more sustainable mode of RR production overall. However, some sustainability indicators remained poor, and there remains scope for further optimization via, e.g., precision application of enhanced-efficiency fertilizers, application of a straw-decomposing inoculant to improve soil fertility, and use of new improved rice varieties with high regenerative ability to improve the yield of ratoon crops.

A Labeling Method for Financial Time Series Prediction Based on Trends.

Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called "continuous trend labeling" is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation was given, the definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data.

Identification of dissolved metal contamination of major rivers in the southeastern hilly area, China: distribution, source apportionment, and health risk assessment.

Metal pollution has been a serious problem facing river systems worldwide, which can adversely affect human health through food chain. The goal of this study was to determine the distribution, sources, and health risk of dissolved metals (Cr, Mn, Ni, Cu, Zn, As, Cd, Pb, Al, Sb, V, Co, Mo) in major rivers located in the hilly area of southeastern China (namely, Jiulongjiang River (JL), Minjiang River (MJ), Oujiang River (OJ), Qiantangjiang River (QT), Raohe River (RR), Fuhe River (FR), Xinjiang River (XR), Ganjiang River (GR), Xiushui River (XS), Xiangjiang River (XJ), Zishui River (ZR), Yuanjiang River (YR), Lishui River (LR)) during dry and wet seasons. Results indicated that metal concentrations were spatially and temporally distinct. Metals of Mn, Ni, Cu, Cd, Sb were significantly higher in specific rivers than other metals. And metals of Cr, Ni, As, and Al concentrations were obviously higher in dry season than in wet season. The comparison with drinking water guidelines of China and other countries indicated that waterbodies were polluted higher by Al than other metals at most sites. Metals in JL and XJ may have health risk to local adults with hazard index (HI) > 1. While special attention should be paid to As, which had a potential carcinogenic risk to adults in study area with CR higher than the critical value. Source analysis with statistical method indicated that point and non-point pollutants from anthropogenic activities are the main sources of metals, with the exception of Al, Ni, V, Cr, and Mo that were mainly from natural processes. This study could be useful for the management and protection of the China's Southern hilly area river systems.

Ecological risk assessment and source apportionment of metals in the surface sediments of river systems in Lake Taihu Basin, China.

In this study, the concentrations of Zn, Cr, Ni, Cu, Pb, As, Cd, and Hg in the surface sediments of 94 sites sampled from six water systems in the Lake Taihu Basin in China were measured, and the pollution risks and sources of the metals were identified. The results showed that the mean concentrations of Zn, Cr, Ni, Cu, Pb, As, Cd, and Hg in the riverine surface sediments were 163.6, 102.5, 45.5, 44.7, 37.0, 13.3, 0.5, and 0.1 mg/kg, respectively, higher than the corresponding background values (except for Hg). According to the geoaccumulation index (Igeo), the Pb, Ni, Zn, Cu, and Cd concentrations in the riverine surface sediments were generally at low levels of pollution. Based on a pollution load index (PLI) evaluation, the Pb, Ni, Zn, and Cu concentrations in the riverine surface sediments were generally at moderate levels of pollution. According to the thresholds of potential ecological risk, the Cd and Hg concentrations in the riverine surface sediments exhibited moderate potential ecological risks. Multivariate statistical analysis indicated that the Pb in the riverine surface sediments primarily originated from domestic sewage, agricultural wastewater discharge, and petroleum combustion; the concentrations of Cr, Ni, and Zn were influenced by the electroplating and alloy manufacturing industries; the concentrations of Cu and As mainly originated from pesticide use and industrial wastewater discharge; and those of Cd and Hg primarily stemmed from industrial wastewater discharge. This research provides information regarding metallic contamination and the possible associated ecological risks to benthic organisms in the surface sediments of river systems and is useful for developing sustainable strategies for environmental pollution control and management in the Lake Taihu Basin.

Environmental and economic consequences analysis of cropping systems from fragmented to concentrated farmland in the North China Plain based on a joint use of life cycle assessment, emergy and economic analysis.

The policy of land rental activity from fragmented to concentrated farmland has been overwhelming encouraged by the Chinese government. The land management policy has paid more attention on the investigation of its economic and social performances of land rental activity, while information on its environmental consequence is still lacking. This study, therefore, compared the environmental and economic performances of small (SF) and large scale (LF) farms based on emergy evaluation (EME), life cycle assessment (LCA) methods, and economic analysis (EA), which reflected a land rental activity from fragmented to concentrated farmland in the North China Plain (NCP). The EME results showed that the environmental loading ratio of the LF was 5.0% lower, while the emergy yield ratio and emergy sustainability index of the LF were 1.48% and 8.0% higher, respectively, than that of the SFs. The LCA results demonstrated that the area-based and yield-based environmental impact indices of the LF were 28.8% and 18.3% lower than that of the SF, respectively. These results indicate that the environmental consequences of the cropping system were improved when the farmland was managed in a concentrated model instead of a fragmented model. In addition, the EA results showed that the income to cost ratio of the LF was reduced by 47.46% compared to that of the SF, due to high land rental costs in the LF. Nevertheless, the total profit of the LF was 1719.3% higher than that of the SF due to its lager farm scale. Also, the owner's total profit of the SF was increased by 195.5% compared to the farming by themselves in their own farmland instead of renting them out. These results showed that scale management can promote both managers who rented out and into the farmland to increase their annual total incomes. In conclusion, the concentrated farmland would be a platform for the improvement of environmental consequences of cropping systems in the NCP.

Index of biotic integrity based on phytoplankton and water quality index: Do they have a similar pattern on water quality assessment? A study of rivers in Lake Taihu Basin, China.

Our study illustrated the ecological conditions in the rivers of Lake Taihu Basin (LTB) using an index of biotic integrity based on phytoplankton (P-IBI), and its performance was compared with the previously developed water quality index (WQI). Samples were collected seasonally at 96 sites covering the major rivers from September 2014 to January 2016. Three critical ecological indices, i.e., phytoplankton density, chlorophyll a (chl a), and Menhinick, were selected from a pool of 22 candidate indices mainly according to the correlations among indices and environmental parameters. The results indicated that the ecological status of LTB based on P-IBI was significantly different at spatial (especially between Tiaoxi and the other 5 river systems) and seasonal scales. Furthermore, the proposed P-IBI effectively identified the major environmental parameters (total nitrogen, ammonium, total phosphorus, and permanganate index) associated with each level (bad, low, and moderate). Moreover, the P-IBI was closely and positively correlated with the WQI at the spatial scale regardless of season. However, the ecological conditions were significantly worse according to the P-IBI at both the spatial (P < 0.001) and seasonal scales (P values of 0.018 in winter and < 0.001 in other seasons, respectively), and the seasonal distribution pattern differed between the two methods. Our study suggests that the P-IBI provides an essential supplement for the assessment of ecological conditions of rivers and that the selected critical indices (phytoplankton density, chl a, and Menhinick) are suitable for river ecosystems. Additionally, compared with WQI, the water quality condition was generally worse when using P-IBI, and this phenomenon requires further attention during water quality assessments, as well as different seasonal distribution patterns.

Ecological risk assessment and distribution of potentially harmful trace elements in lake sediments of Songnen Plain, NE China.

In order to understand the distribution and the ecological risk of the potentially harmful trace elements (PHTEs) in lake sediments of Songnen Plain, northeast (NE) China, an integrated survey of PHTEs (As, Cd, Cr, Cu, Ni, Pb, Zn and Ti) was conducted in July 2015 in 11 shallow lakes adjacent to Qiqihar and Daqing. The enrichment factor (EF) and Index of geoaccumulation (Igeo) results showed that Cd was obviously enriched in all lakes and reached the moderate pollution level. A comparison of PHTE concentrations in the lake sediments from 2005 to 2015 found the PHTEs pollution status doubled. Multivariate statistical analysis identified the heavy industries of petroleum and steel in the cities close to lakes and excessive agricultural fertilizing in the region as possible pollution sources of the PHTEs. The Håkanson index method (RI) and the sediment quality guidelines (SQGs) were used to assess the potential risk of PHTEs in sediments. The risk degree of 11 lakes had reached a medium level of potential ecological risk except for one lake which had a low potential ecological risk status. The Songnen Plain has been significantly affected by anthropogenic activities and this study provides an effective reference for the environmental protection and management of lakes (heavy metal pollution and control) around the heavy industrial cities of China and the world.

Heavy metal pollution in reservoirs in the hilly area of southern China: Distribution, source apportionment and health risk assessment.

Reservoirs play more and more important role in providing potable water in many developing countries, including the China. In the present study, pollution characteristics, source and health risk of dissolved heavy metals in 19 representative reservoirs in the hilly area of southern China were investigated. The results presented great spatial heterogeneity in heavy metal concentrations among the studied reservoirs due to the disturbances by different anthropogenic activities. Several reservoirs had significantly higher concentrations of heavy metals compared with others, FengTan (FT) reservoir (0.34µg/L in dry season), Cr in reservoirs of HuangShi (HS) and ZheLin (ZL) with values of 4.16µg/L and 3.45µg/L in dry season respectively, and Al in reservoirs of JiaoKou (JK), GuTian (GT) and DouShui (DS) with values of 1011µg/L, 1036µg/L and 1001µg/L in wet season, respectively. Furthermore, there was a great difference in the seasonal variation of heavy metals, especially for Al and Pb in wet season characterized with relatively high values of 643µg/L and 0.67µg/L, respectively. Accordingly, Al was identified with a great health risk to living beings in view of its mean value in wet season, which greatly exceeded the criteria for drinking water of China, WHO and US EPA. Furthermore, As might be the greatest concern of health risk in this region considering its high carcinogenic risk to the local residents around the reservoirs of OuYangHai (OYH), DS, ZhiXi (ZX) and HS. Multivariate statistical analysis suggested that there was great heterogeneity in the sources of these heavy metals in the hilly area of southern China. Therefore, specific measures, such as controls on point source pollution control and tailings, should be taken for maintaining drinking water safety and aquatic ecosystem health.

Water quality assessment based on the water quality index method in Lake Poyang: The largest freshwater lake in China.

Twenty-four samplings were conducted every 3 months at 15 sites from January 2009 to October 2014 in Lake Poyang, and 20 parameters were analyzed and classified into three groups (toxic metals, easily treated parameters, and others). The assessment results based on water quality index (WQI) showed that the water quality in Lake Poyang was generally "moderate", according to the classification of the surface water quality standard (GB3838-2002) in China, but a deteriorating trend was observed at the interannual scale. Seasonally, the water quality was best in summer and worst in winter. Easily treated parameters generally determined the WQI value in the assessment, especially total nitrogen (TN) and total phosphorus (TP), while toxic metals and other parameters in Lake Poyang were generally at low and safe levels for drinking water. Water level (WL) has a net positive effect on water quality in Lake Poyang through dilution of environmental parameters, which in practice means TN. Consequently, local management agencies should pay more attention to nutrient concentrations during the monitoring schedule, as well as during the low-water periods which manifest a relatively bad water quality state, especially with the prevailing low WL observed recently in Lake Poyang.

De-identification of clinical notes via recurrent neural network and conditional random field.

De-identification, identifying information from data, such as protected health information (PHI) present in clinical data, is a critical step to enable data to be shared or published. The 2016 Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-scale and RDOC Individualized Domains (N-GRID) clinical natural language processing (NLP) challenge contains a de-identification track in de-identifying electronic medical records (EMRs) (i.e., track 1). The challenge organizers provide 1000 annotated mental health records for this track, 600 out of which are used as a training set and 400 as a test set. We develop a hybrid system for the de-identification task on the training set. Firstly, four individual subsystems, that is, a subsystem based on bidirectional LSTM (long-short term memory, a variant of recurrent neural network), a subsystem-based on bidirectional LSTM with features, a subsystem based on conditional random field (CRF) and a rule-based subsystem, are used to identify PHI instances. Then, an ensemble learning-based classifiers is deployed to combine all PHI instances predicted by above three machine learning-based subsystems. Finally, the results of the ensemble learning-based classifier and the rule-based subsystem are merged together. Experiments conducted on the official test set show that our system achieves the highest micro F1-scores of 93.07%, 91.43% and 95.23% under the "token", "strict" and "binary token" criteria respectively, ranking first in the 2016 CEGS N-GRID NLP challenge. In addition, on the dataset of 2014 i2b2 NLP challenge, our system achieves the highest micro F1-scores of 96.98%, 95.11% and 98.28% under the "token", "strict" and "binary token" criteria respectively, outperforming other state-of-the-art systems. All these experiments prove the effectiveness of our proposed method.

Occurrence of Perfluorooctanoic Acid and Perfluorooctane Sulfonate in Milk and Yogurt and Their Risk Assessment.

Although perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) have been identified in milk and dairy products in many regions, knowledge on their occurrence in Xinjiang (China) is rare. This study was conducted to measure the levels of PFOA and PFOS in milk and yogurt from Xinjiang and to investigate the average daily intake (ADI) of these two compounds. PFOA and PFOS levels were analyzed using ultrasonic extraction with methanol and solid-phase extraction followed by liquid chromatography-mass spectrometry. Retail milk and yogurt samples present higher detection rates (39.6% and 48.1%) and mean concentrations (24.5 and 31.8 ng/L) of PFOS than those of PFOA (33.0% and 37.0%; 16.2 and 22.6 ng/L, respectively). For raw milk samples, only PFOS was detected. The differences in the levels of the two compounds between samples from the north and south regions were observed, and northern regions showed higher pollution levels than southern regions. On the basis of the retail milk measurements and consumption data, the ADIs of PFOA and PFOS for Xinjiang adults were calculated to be 0.0211 and 0.0318 ng/kg/day, respectively. Furthermore, the estimated intakes of PFOA and PFOS varied among different groupings (age, area, gender, and race) and increased with increasing age. Relevant hazard ratios were found to be far less than 1.0, and this finding suggested that no imminent health damages were produced by PFOA and PFOS intake via milk and yogurt consumption in the Xinjiang population.

Protein binding site prediction by combining hidden Markov support vector machine and profile-based propensities.

Identification of protein binding sites is critical for studying the function of the proteins. In this paper, we proposed a method for protein binding site prediction, which combined the order profile propensities and hidden Markov support vector machine (HM-SVM). This method employed the sequential labeling technique to the field of protein binding site prediction. The input features of HM-SVM include the profile-based propensities, the Position-Specific Score Matrix (PSSM), and Accessible Surface Area (ASA). When tested on different data sets, the proposed method showed promising results, and outperformed some closely relative methods by more than 10% in terms of AUC.

Using amino acid physicochemical distance transformation for fast protein remote homology detection.

Protein remote homology detection is one of the most important problems in bioinformatics. Discriminative methods such as support vector machines (SVM) have shown superior performance. However, the performance of SVM-based methods depends on the vector representations of the protein sequences. Prior works have demonstrated that sequence-order effects are relevant for discrimination, but little work has explored how to incorporate the sequence-order information along with the amino acid physicochemical properties into the prediction. In order to incorporate the sequence-order effects into the protein remote homology detection, the physicochemical distance transformation (PDT) method is proposed. Each protein sequence is converted into a series of numbers by using the physicochemical property scores in the amino acid index (AAIndex), and then the sequence is converted into a fixed length vector by PDT. The sequence-order information can be efficiently included into the feature vector with little computational cost by this approach. Finally, the feature vectors are input into a support vector machine classifier to detect the protein remote homologies. Our experiments on a well-known benchmark show the proposed method SVM-PDT achieves superior or comparable performance with current state-of-the-art methods and its computational cost is considerably superior to those of other methods. When the evolutionary information extracted from the frequency profiles is combined with the PDT method, the profile-based PDT approach can improve the performance by 3.4% and 11.4% in terms of ROC score and ROC50 score respectively. The local sequence-order information of the protein can be efficiently captured by the proposed PDT and the physicochemical properties extracted from the amino acid index are incorporated into the prediction. The physicochemical distance transformation provides a general framework, which would be a valuable tool for protein-level study.