The ecological risk and fate of microplastics in the environmental matrices of marine ranching area in coastal water.

The debate surrounding "source" and "sink" of microplastics (MPs) in coastal water has persisted for decades. While the transportation of MPs is influenced by surface runoff and currents, the precise transport patterns remain inadequately defined. In this study, the typical coastal habitat - marine ranching in Haizhou Bay (Jiangsu Province, China) were selected as a case study to assess the ecological risk of MPs. An enhanced framework was employed to assess the entire community characteristics of MPs in various environmental compartments, including surface water (SW), middle water (MW), bottom water (BW), sea bottom sediment (SS), and intertidal sediment (IS). The results of the assessment showed a low risk in the water column and a high risk in the sediment. PERMANOVA based on size and polymer of MPs revealed significant differences between IS and other compartments (SW, MW, BW, and SS) (P < 0.001). The co-occurrence network analysis for MP size indicated that most sites occupied central positions, while the analysis for MP polymer suggested that sites near the marine ranching area held more central positions, with sites in MW, BW, and SS being somewhat related to IS. Generalized additive model (GAM) demonstrated that MP concentration in the water correlated with Chla and nutrients, whereas MPs in sediment exhibited greater susceptibility to dissolved oxygen (DO) and salinity. We believe that except for the natural sedimentation and re-suspension of MPs in the vertical direction, MPs in bottom water may migrate to the surface water due to upwelling mediated by artificial reefs. Additionally, under the combined influence of surface runoff, currents, and tides, MPs may migrate horizontally, primarily occurring between middle and bottom water and sediments. The study recommends limiting and reducing wastewater and sewage discharge, as well as regulating fishing and aquaculture activities to control the sources and sinks of MPs in coastal water. Moreover, it advocates the implementation and strengthening of marine monitoring activities to gain a better understanding of the factors driving MP pollution in marine ranching area.

Combinatory data-independent acquisition and parallel reaction monitoring method for revealing the lipid metabolism biomarkers of coronary heart disease and its comorbidities.

Disorders of lipid metabolism are a common cause of coronary heart disease (CHD) and its comorbidities. In this study, ultra-performance liquid chromatography-high-resolution mass spectrometry in data-independent acquisition (DIA) mode was applied to collect abundant tandem mass spectrometry data, which provided valuable information for lipid annotation. For the lipid isomers that could not be completely separated by chromatography, parallel reaction monitoring (PRM) mode was used for quantification. A total of 223 plasma lipid metabolites were annotated, and 116 of them were identified for their fatty acyl chain composition and location. In addition, 152 plasma lipids in patients with CHD and its comorbidities were quantitatively analyzed. Multivariate statistical analysis and metabolic pathway analysis demonstrated that glycerophospholipid and sphingolipid metabolism deserved more attention for CHD. This study proposed a method combining DIA and PRM for high-throughput characterization of plasma lipids. The results also improved our understanding of metabolic disorders of CHD and its comorbidities, which can provide valuable suggestions for medical intervention.

Bilateral fluctuation asymmetry of otoliths of Collichthys lucidus in different functional areas of Haizhou Bay.

The fluctuating asymmetry (FA) of fish otoliths can reflect the difference in the growth and development of fish in sea areas greatly affected by environmental pressure, thus enabling the assessment of different habitats. In this study, using 113 Collichthys lucidus samples collected from different functional areas (estuary area, aquaculture area, artificial reef area and natural area) in Haizhou Bay, the square coefficient of asymmetry variation (CV2 a ) of four characters (length, width, perimeter and area) of the left and right sagittal otoliths was calculated. The results showed that the CV2 a value of otolith width was the lowest and that of otolith length was the highest. The CV2 a value had no obvious regularity with increasing fish body length. In addition, the CV2 a values of the four characteristics reached their lowest values in the artificial reef area, indicating that the construction of marine ranching dominated by artificial reefs may partly improve the aquatic environment in this functional area. We consider that the otolith FA of C. lucidus can be used as a characteristic of environmental stress between different areas/regions/habitats.

Different functional alteration in attention-deficit/hyperactivity disorder across developmental age groups: A meta-analysis and an independent validation of resting-state functional connectivity studies.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a highly complex and heterogeneous disorder. Abnormal brain connectivity in ADHD might be influenced by developmental ages which might lead to the lacking of significant spatial convergence across studies. However, the developmental patterns and mechanisms of ADHD brain connectivity remain to be fully uncovered. METHODS: In the present study, we searched PubMed, Scopus, Web of Science, and Embase for seed-based whole-brain resting-state functional connectivity studies of ADHD published through October 12th, 2020. The seeds meeting inclusion criteria were categorized into the cortex group and subcortex group, as previous studies suggested that the cortex and subcortex have different temporal patterns of development. Activation likelihood estimation meta-analysis was performed to investigate the abnormal connectivity in different age groups (all-age group, younger: <12 years, older: ≥12 years). Moreover, significant convergence of reported foci was used as seeds for validation with our independent dataset. RESULTS: As with previous studies, scarce results were found in the all-age group. However, we found that the younger group consistently exhibited hyper-connectivity between different parts of the cortex and left middle frontal gyrus, and hypo-connectivity between different parts of the cortex and left putamen/pallidus/amygdala. Whereas, the older group (mainly for adults) showed hyper-connectivity between the cortex and right precuneus/sub-gyral/cingulate gyrus. Besides, the abnormal cortico-cortical and cortico-subcortical functional connectivity in children, and the abnormal cortico-cortical functional connectivity in adults were verified in our independent dataset. CONCLUSION: Our study emphasizes the importance of developmental age effects on the study of brain networks in ADHD. Further, we proposed that cortico-cortical and cortico-subcortical connectivity might play an important role in the pathophysiology of children with ADHD, while abnormal cortico-cortical connections were more important for adults with ADHD. This work provided a potential new insight to understand the neurodevelopmental mechanisms and possible clinical application of ADHD.

Bilateral asymmetry of otoliths from Collichthys lucidus of different sizes in Haizhou Bay and Xiangshan Bay.

In recent years, the frequent occurrence of most human activities has seriously affected the structure and functioning of coastal ecosystems. The asymmetric relationship between the left and right otoliths of fish is often used to test the difference in fluctuating asymmetry (FA) reflected by the square of the coefficient of asymmetric variation (CV2 a ), which can be regarded as an important step in the study of marine environmental pressure and implementation of offshore ecological restoration. In this study, the authors tested the bilateral FA of Collichthys lucidus in the coastal waters of Haizhou Bay, Jiangsu, and Xiangshan Bay, Zhejiang, China, using four sagittal otolith characters (length, width, perimeter and area) as biological characters. The results showed that the value of CV2 a in otolith width (more sensitive to environmental pressure) of C. lucidus in Xiangshan Bay was higher than that in Haizhou Bay, indicating that the environmental pressure on Xiangshan Bay was relatively high. The authors did not find any significant differences in the FA of otoliths between different body sizes of C. lucidus, which may be related to the short-distance migration in different regions and the dietary shifts in the life history of this species. The results have conservation and management implications for this population.

Establishment and psychometric characteristics of emotional words list for suicidal risk assessment in speech emotion recognition.

Background: Emotional disturbance is an important risk factor of suicidal behaviors. To ensure speech emotion recognition (SER), a novel technique to evaluate emotional characteristics of speech, precision in labeling emotional words is a prerequisite. Currently, a list of suicide-related emotional word is absent. The aims of this study were to establish an Emotional Words List for Suicidal Risk Assessment (EWLSRA) and test the reliability and validity of the list in a suicide-related SER task. Methods: Suicide-related emotion words were nominated and discussed by 10 suicide prevention professionals. Sixty-five tape-recordings of calls to a large psychological support hotline in China were selected to test psychometric characteristics of the EWLSRA. Results: The results shows that the EWLSRA consists of 11 emotion words which were highly associated with suicide risk scores and suicide attempts. Results of exploratory factor analysis support one-factor model of this list. The Fleiss' Kappa value of 0.42 indicated good inter-rater reliability of the list. In terms of criteria validities, indices of despair (Spearman ρ = 0.54, P < 0.001), sadness (ρ = 0.37, P = 0.006), helplessness (ρ = 0.45, P = 0.001), and numbness (ρ = 0.35, P = 0.009) were significantly associated with suicidal risk scores. The index of the emotional word of numbness in callers with suicide attempt during the 12-month follow-up was significantly higher than that in callers without suicide attempt during the follow-up (P = 0.049). Conclusion: This study demonstrated that the EWLSRA has adequate psychometric performance in identifying suicide-related emotional words of recording of hotline callers to a national wide suicide prevention line. This list can be useful for SER in future studies on suicide prevention.

Emotion dysregulation in adults with ADHD: The role of cognitive reappraisal and expressive suppression.

BACKGROUND: Emotion dysregulation (ED) is a common clinical feature of attention-deficit/hyperactivity disorder (ADHD). The present study examined the role of cognitive reappraisal (CR) and expressive suppression (ES) in adults with ADHD. In addition, resting-state fMRI (rs-fMRI) data were analyzed to identify neural substrates of CR/ES-ED relationships. METHODS: A total of 309 adults with ADHD and 163 healthy controls were recruited. ED was assessed using the 'emotional control' (EC) subscale from Behavior Rating Inventory of Executive Function-Adult Version. The Emotion Regulation Questionnaire was used to measure CR and ES. The functional connectivities (FCs) with the amygdala as Region of Interest, were analyzed in a subsample to explore their association with CR, ES and EC, respectively. RESULTS: Higher EC scores (indicative of lesser emotional control), as well as lower CR and higher ES utilization were detected in adults with ADHD compared with healthy controls. CR and ES were both negatively correlated with EC in adults with ADHD. Mediation analysis detected a potential effect of ADHD diagnosis on EC via CR. In addition, a unique significant mediation effect was found between ES-related FC of the right amygdala-prefrontal cortex and ED expression in adults with ADHD, confirming the '↑ES → ↓FCs [amygR-PFC] →↓EC' relationship. LIMITATIONS: Only self-reported scales and rs-fMRI data were included in these analyses. CONCLUSIONS: Our findings provide preliminary evidence that in adults with ADHD, less frequent use of CR accounts for ED expression, while more frequent use of ES may play a unique compensatory role in emotion regulation.

Diagnosis after zooming in: A multilabel classification model by imitating doctor reading habits to diagnose brain diseases.

PURPOSE: Computed tomography (CT) has the advantages of being low cost and noninvasive and is a primary diagnostic method for brain diseases. However, it is a challenge for junior radiologists to diagnose CT images accurately and comprehensively. It is necessary to build a system that can help doctors diagnose and provide an explanation of the predictions. Despite the success of deep learning algorithms in the field of medical image analysis, the task of brain disease classification still faces challenges: Researchers lack attention to complex manual labeling requirements and the incompleteness of prediction explanations. More importantly, most studies only measure the performance of the algorithm, but do not measure the effectiveness of the algorithm in the actual diagnosis of doctors. METHODS: In this paper, we propose a model called DrCT2 that can detect brain diseases without using image-level labels and provide a more comprehensive explanation at both the slice and sequence levels. This model achieves reliable performance by imitating human expert reading habits: targeted scaling of primary images from the full slice scans and observation of suspicious lesions for diagnosis. We evaluated our model on two open-access data sets: CQ500 and the RSNA Intracranial Hemorrhage Detection Challenge. In addition, we defined three tasks to comprehensively evaluate model interpretability by measuring whether the algorithm can select key images with lesions. To verify the algorithm from the perspective of practical application, three junior radiologists were invited to participate in the experiments, comparing the effects before and after human-computer cooperation in different aspects. RESULTS: The method achieved F1-scores of 0.9370 on CQ500 and 0.8700 on the RSNA data set. The results show that our model has good interpretability under the premise of good performance. Human radiologist evaluation experiments have proven that our model can effectively improve the accuracy of the diagnosis and improve efficiency. CONCLUSIONS: We proposed a model that can simultaneously detect multiple brain diseases. The report generated by the model can assist doctors in avoiding missed diagnoses, and it has good clinical application value.

Characteristics of High Suicide Risk Messages From Users of a Social Network-Sina Weibo "Tree Hole".

Background: People with suicidal ideation post suicide-related information on social media, and some may choose collective suicide. Sina Weibo is one of the most popular social media platforms in China, and "Zoufan" is one of the largest depression "Tree Holes." To collect suicide warning information and prevent suicide behaviors, researchers conducted real-time network monitoring of messages in the "Zoufan" tree hole via artificial intelligence robots. Objective: To explore characteristics of time, content and suicidal behaviors by analyzing high suicide risk comments in the "Zoufan" tree hole. Methods: Knowledge graph technology was used to screen high suicide risk comments in the "Zoufan" tree hole. Users' level of activity was analyzed by calculating the number of messages per hour. Words in messages were segmented by a Jieba tool. Keywords and a keywords co-occurrence matrix were extracted using a TF-IDF algorithm. Gephi software was used to conduct keywords co-occurrence network analysis. Results: Among 5,766 high suicide risk comments, 73.27% were level 7 (suicide method was determined but not the suicide date). Females and users from economically developed cities are more likely to express suicide ideation on social media. High suicide risk users were more active during nighttime, and they expressed strong negative emotions and willingness to end their life. Jumping off buildings, wrist slashing, burning charcoal, hanging and sleeping pills were the most frequently mentioned suicide methods. About 17.55% of comments included suicide invitations. Negative cognition and emotions are the most common suicide reason. Conclusion: Users sending high risk suicide messages on social media expressed strong suicidal ideation. Females and users from economically developed cities were more likely to leave high suicide risk comments on social media. Nighttime was the most active period for users. Characteristics of high suicide risk messages help to improve the automatic suicide monitoring system. More advanced technologies are needed to perform critical analysis to obtain accurate characteristics of the users and messages on social media. It is necessary to improve the 24-h crisis warning and intervention system for social media and create a good online social environment.

Association between blood glucose levels and Glasgow Outcome Score in patients with traumatic brain injury: secondary analysis of a randomized trial.

BACKGROUND: Blood glucose levels that are too high or too low after traumatic brain injury (TBI) negatively affect patient prognosis. This study aimed to demonstrate the relationship between blood glucose levels and the Glasgow Outcome Score (GOS) in TBI patients. METHODS: This study was based on a randomized, dual-center, open-label clinical trial. A total of 208 patients who participated in the randomized controlled trial were followed up for 5 years. Information on the disease, laboratory examination, insulin therapy, and surgery for patients with TBI was collected as candidate variables according to clinical importance. Additionally, data on 5-year and 6-month GOS were collected as primary and secondary outcomes, respectively. For multivariate analysis, a generalized additive model (GAM) was used to investigate relationships between blood glucose levels and GOS. The results are presented as odds ratios (ORs) with 95% confidence intervals (95% CIs). We further applied a two- piecewise linear regression model to examine the threshold effect of blood glucose level and GOS. RESULTS: A total of 182 patients were included in the final analysis. Multivariate GAM analysis revealed that a bell-shaped relationship existed between average blood glucose level and 5-year GOS score or 6-month GOS score. The inflection points of the average blood glucose level were 8.81 (95% CI: 7.43-9.48) mmol/L considering 5-year GOS as the outcome and were 8.88 (95% CI 7.43-9.74) mmol/L considering 6-month GOS score as the outcome. The same analysis revealed that there was also a bell relationship between average blood glucose levels and the favorable outcome group (GOS score ≥ 4) at 5 years or 6 months. CONCLUSION: In a population of patients with traumatic brain injury, blood glucose levels were associated with the GOS. There was also a threshold effect between blood glucose levels and the GOS. A blood glucose level that is either too high or too low conveys a poor prognosis. TRIAL REGISTRATION: ClinicalTrials.gov NCT02161055 . Registered on 11 June 2014.

SoySNP618K array: A high-resolution single nucleotide polymorphism platform as a valuable genomic resource for soybean genetics and breeding.

Innovations in genomics have enabled the development of low-cost, high-resolution, single nucleotide polymorphism (SNP) genotyping arrays that accelerate breeding progress and support basic research in crop science. Here, we developed and validated the SoySNP618K array (618,888 SNPs) for the important crop soybean. The SNPs were selected from whole-genome resequencing data containing 2,214 diverse soybean accessions; 29.34% of the SNPs mapped to genic regions representing 86.85% of the 56,044 annotated high-confidence genes. Identity-by-state analyses of 318 soybeans revealed 17 redundant accessions, highlighting the potential of the SoySNP618K array in supporting gene bank management. The patterns of population stratification and genomic regions enriched through domestication were highly consistent with previous findings based on resequencing data, suggesting that the ascertainment bias in the SoySNP618K array was largely compensated for. Genome-wide association mapping in combination with reported quantitative trait loci enabled fine-mapping of genes known to influence flowering time, E2 and GmPRR3b, and of a new candidate gene, GmVIP5. Moreover, genomic prediction of flowering and maturity time in 502 recombinant inbred lines was highly accurate (>0.65). Thus, the SoySNP618K array is a valuable genomic tool that can be used to address many questions in applied breeding, germplasm management, and basic crop research.

Correction: Distant Supervision for Mental Health Management in Social Media: Suicide Risk Classification System Development Study.

[This corrects the article DOI: 10.2196/26119.].

Distant Supervision for Mental Health Management in Social Media: Suicide Risk Classification System Development Study.

BACKGROUND: Web-based social media provides common people with a platform to express their emotions conveniently and anonymously. There have been nearly 2 million messages in a particular Chinese social media data source, and several thousands more are generated each day. Therefore, it has become impossible to analyze these messages manually. However, these messages have been identified as an important data source for the prevention of suicide related to depression disorder. OBJECTIVE: We proposed in this paper a distant supervision approach to developing a system that can automatically identify textual comments that are indicative of a high suicide risk. METHODS: To avoid expensive manual data annotations, we used a knowledge graph method to produce approximate annotations for distant supervision, which provided a basis for a deep learning architecture that was built and refined by interactions with psychology experts. There were three annotation levels, as follows: free annotations (zero cost), easy annotations (by psychology students), and hard annotations (by psychology experts). RESULTS: Our system was evaluated accordingly and showed that its performance at each level was promising. By combining our system with several important psychology features from user blogs, we obtained a precision of 80.75%, a recall of 75.41%, and an F1 score of 77.98% for the hardest test data. CONCLUSIONS: In this paper, we proposed a distant supervision approach to develop an automatic system that can classify high and low suicide risk based on social media comments. The model can therefore provide volunteers with early warnings to prevent social media users from committing suicide.

Feature Ranking and Screening for Class-Imbalanced Metabolomics Data Based on Rank Aggregation Coupled with Re-Balance.

Feature screening is an important and challenging topic in current class-imbalance learning. Most of the existing feature screening algorithms in class-imbalance learning are based on filtering techniques. However, the variable rankings obtained by various filtering techniques are generally different, and this inconsistency among different variable ranking methods is usually ignored in practice. To address this problem, we propose a simple strategy called rank aggregation with re-balance (RAR) for finding key variables from class-imbalanced data. RAR fuses each rank to generate a synthetic rank that takes every ranking into account. The class-imbalanced data are modified via different re-sampling procedures, and RAR is performed in this balanced situation. Five class-imbalanced real datasets and their re-balanced ones are employed to test the RAR's performance, and RAR is compared with several popular feature screening methods. The result shows that RAR is highly competitive and almost better than single filtering screening in terms of several assessing metrics. Performing re-balanced pretreatment is hugely effective in rank aggregation when the data are class-imbalanced.

Screening of lipid metabolism biomarkers in patients with coronary heart disease via ultra-performance liquid chromatography-high resolution mass spectrometry.

Coronary heart disease (CHD) has a high mortality worldwide. This study aimed to screen lipid metabolism biomarkers in patients with coronary heart disease via ultra-performance liquid chromatography-high resolution mass spectrometry. Extraction and reconstitution solvents, liquid chromatographic and mass spectrometry conditions were optimized to detect more plasma lipid metabolites. In this study, the chromatographic and mass spectra characteristics of lipid metabolites were summarized. A total of 316 lipid metabolites were annotated via diagnostic fragment ion filtration, nitrogen rule filtration, and neutral loss filtration. Glycerophospholipid metabolism and sphingolipid metabolism were revealed as the main lipid disorders of CHD. This study provides a novel insight for high-throughput detection of lipid metabolites in plasma and provides a further understanding of the occurrence of CHD, which can provide valuable suggestions for the prevention of CHD.

A potential association of RNF219-AS1 with ADHD: Evidence from categorical analysis of clinical phenotypes and from quantitative exploration of executive function and white matter microstructure endophenotypes.

AIMS: Attention-deficit/hyperactivity disorder (ADHD) is a neuropsychiatric disorder of substantial heritability, yet emerging evidence suggests that key risk variants might reside in the noncoding regions of the genome. Our study explored the association of lncRNAs (long noncoding RNAs) with ADHD as represented at three different phenotypic levels guided by the Research Domain Criteria (RDoC) framework: (i) ADHD caseness and symptom dimension, (ii) executive functions as functional endophenotype, and (iii) potential genetic influence on white matter architecture as brain structural endophenotype. METHODS: Genotype data of 107 tag single nucleotide polymorphisms (SNP) from 10 candidate lncRNAs were analyzed in 1040 children with ADHD and 630 controls of Chinese Han descent. Executive functions including inhibition and set-shifting were assessed by STROOP and trail making tests, respectively. Imaging genetic analyses were performed in a subgroup of 33 children with ADHD and 55 controls using fractional anisotropy (FA). RESULTS: One SNP rs3908461 polymorphism in RNF219-AS1 was found to be significantly associated with ADHD caseness: with C-allele detected as the risk genotype in the allelic model (P = 8.607E-05) and dominant genotypic model (P = 9.628E-05). Nominal genotypic effects on inhibition (p = 0.020) and set-shifting (p = 0.046) were detected. While no direct effect on ADHD core symptoms was detected, mediation analysis suggested that SNP rs3908461 potentially exerted an indirect effect through inhibition function [B = 0.21 (SE = 0.12), 95% CI = 0.02-0.49]. Imaging genetic analyses detected significant associations between rs3908461 genotypes and FA values in corpus callosum, left superior longitudinal fasciculus, left posterior limb of internal capsule, left posterior thalamic radiate (include optic radiation), and the left anterior corona radiate (P FWE corrected  < 0.05). CONCLUSION: Our present study examined the potential roles of lncRNA in genetic etiological of ADHD and provided preliminary evidence in support of the potential RNF219-AS1 involvement in the pathophysiology of ADHD in line with the RDoC framework.

A multi-label classification model for full slice brain computerised tomography image.

BACKGROUND: Screening of the brain computerised tomography (CT) images is a primary method currently used for initial detection of patients with brain trauma or other conditions. In recent years, deep learning technique has shown remarkable advantages in the clinical practice. Researchers have attempted to use deep learning methods to detect brain diseases from CT images. Methods often used to detect diseases choose images with visible lesions from full-slice brain CT scans, which need to be labelled by doctors. This is an inaccurate method because doctors detect brain disease from a full sequence scan of CT images and one patient may have multiple concurrent conditions in practice. The method cannot take into account the dependencies between the slices and the causal relationships among various brain diseases. Moreover, labelling images slice by slice spends much time and expense. Detecting multiple diseases from full slice brain CT images is, therefore, an important research subject with practical implications. RESULTS: In this paper, we propose a model called the slice dependencies learning model (SDLM). It learns image features from a series of variable length brain CT images and slice dependencies between different slices in a set of images to predict abnormalities. The model is necessary to only label the disease reflected in the full-slice brain scan. We use the CQ500 dataset to evaluate our proposed model, which contains 1194 full sets of CT scans from a total of 491 subjects. Each set of data from one subject contains scans with one to eight different slice thicknesses and various diseases that are captured in a range of 30 to 396 slices in a set. The evaluation results present that the precision is 67.57%, the recall is 61.04%, the F1 score is 0.6412, and the areas under the receiver operating characteristic curves (AUCs) is 0.8934. CONCLUSION: The proposed model is a new architecture that uses a full-slice brain CT scan for multi-label classification, unlike the traditional methods which only classify the brain images at the slice level. It has great potential for application to multi-label detection problems, especially with regard to the brain CT images.

Hellinger distance-based stable sparse feature selection for high-dimensional class-imbalanced data.

BACKGROUND: Feature selection in class-imbalance learning has gained increasing attention in recent years due to the massive growth of high-dimensional class-imbalanced data across many scientific fields. In addition to reducing model complexity and discovering key biomarkers, feature selection is also an effective method of combating overlapping which may arise in such data and become a crucial aspect for determining classification performance. However, ordinary feature selection techniques for classification can not be simply used for addressing class-imbalanced data without any adjustment. Thus, more efficient feature selection technique must be developed for complicated class-imbalanced data, especially in the context of high-dimensionality. RESULTS: We proposed an algorithm called sssHD to achieve stable sparse feature selection applied it to complicated class-imbalanced data. sssHD is based on the Hellinger distance (HD) coupled with sparse regularization techniques. We stated that Hellinger distance is not only class-insensitive but also translation-invariant. Simulation result indicates that HD-based selection algorithm is effective in recognizing key features and control false discoveries for class-imbalance learning. Five gene expression datasets are also employed to test the performance of the sssHD algorithm, and a comparison with several existing selection procedures is performed. The result shows that sssHD is highly competitive in terms of five assessment metrics. In addition, sssHD presents limited differences between performing and not performing re-balance preprocessing. CONCLUSIONS: sssHD is a practical feature selection method for high-dimensional class-imbalanced data, which is simple and can be an alternative for performing feature selection in class-imbalanced data. sssHD can be easily extended by connecting it with different re-balance preprocessing, different sparse regularization structures as well as different classifiers. As such, the algorithm is extremely general and has a wide range of applicability.

Spatio-temporal features of microplastics pollution in macroalgae growing in an important mariculture area, China.

Macroalgae are being consumed by a growing number of people as functional food. Therefore, they are intensively cultivated to meet the rising demand. Mariculture is a potential source of microplastics (MPs). However, as a potential source of microplastics, little is known regarding the MPs pollution in macroalgae of open sea macriculture. Here we investigated the MPs characteristics in macroalgae in three sections of Haizhou Bay, an important mariculture area in China, during Pyropia culture (Pyropia yezoensis) and non-culture periods (Ulva prolifera, Sargassum horneri, Cladophora sp., Undaria pinnatifida, Ulva pertusa). It was found that P. yezoensis during the culture period had higher MPs abundance (0.17 ± 0.08 particles g-1fresh weight) than other macroalgae (0.12 ± 0.09 particles g-1 fresh weight) during the non-culture period, particularly for the nearshore sections. There were more fiber MPs in P. yezoensis (90.43%) in culture period compared to macroalgae (84.46%) in non-culture period. Highly similar spectrum of plastics in culture gears and macroalgae was verified. Pyropia culture gears released about 1, 037 tons plastics into the environment annually and the MPs abundances in seawater during the culture and non-culture periods were 1.04 ± 0.32 and 1.86 ± 0.49 particles L-1, respectively. The gap of MPs abundance between the two periods can be attributed to the tremendous trapping by massive biomass of P. yezoensis during the culture period and the continuous plastic release during the non-culture period. This study indicates that culture gears of macroalgae could be an important MPs source and the MPs can be transferred to human by edible macroalgae, and meanwhile macroalgae may be ideal biomonitors for MPs pollution in seawater due to their unbiased trapping and immovability.

A Comparison of Sparse Partial Least Squares and Elastic Net in Wavelength Selection on NIR Spectroscopy Data.

Elastic net (Enet) and sparse partial least squares (SPLS) are frequently employed for wavelength selection and model calibration in analysis of near infrared spectroscopy data. Enet and SPLS can perform variable selection and model calibration simultaneously. And they also tend to select wavelength intervals rather than individual wavelengths when the predictors are multicollinear. In this paper, we focus on comparison of Enet and SPLS in interval wavelength selection and model calibration for near infrared spectroscopy data. The results from both simulation and real spectroscopy data show that Enet method tends to select less predictors as key variables than SPLS; thus it gets more parsimony model and brings advantages for model interpretation. SPLS can obtain much lower mean square of prediction error (MSE) than Enet. So SPLS is more suitable when the attention is to get better model fitting accuracy. The above conclusion is still held when coming to performing the strongly correlated NIR spectroscopy data whose predictors present group structures, Enet exhibits more sparse property than SPLS, and the selected predictors (wavelengths) are segmentally successive.