Multi-Feature Fusion Method Based on EEG Signal and its Application in Stroke Classification.

Electroencephalogram (EEG) analysis has been widely used in the diagnosis of stroke diseases for its low cost and noninvasive characteristics. In order to classify the EEG signals of stroke patients with cerebral infarction and cerebral hemorrhage, this paper proposes a novel EEG stroke signal classification method. This method has two highlights. The first is that a multi-feature fusion method is given by combining wavelet packet energy, fuzzy entropy and hierarchical theory. The second highlight is that a suitable classification model based on ensemble classifier is constructed for perfectly classification stroke signals. Entropy is an accessible way to measure information and uncertainty of time series. Many entropy-based methods have been developed these years. By comparing with the performances of permutation entropy, sample entropy, approximate entropy in measuring the characteristic of stroke patient's EEG signals, it can be found that fuzzy entropy has best performance in characterization stroke EEG signal. By combining hierarchical theory, wavelet packet energy and fuzzy entropy, a multi-feature fusion method is proposed. The method first calculates wavelet packet energy of EEG stroke signal, then extracts hierarchical fuzzy entropy feature by combining hierarchical theory and fuzzy entropy. The experimental results show that, compared with the fuzzy entropy feature, the classification accuracy based on the fusion feature of wavelet packet energy and hierarchical fuzzy entropy is much higher than benchmark methods. It means that the proposed multi-feature fusion method based on stroke EEG signal is an efficient measure in classifying ischemic and hemorrhagic stroke. Support vector machine (SVM), decision tree and random forest are further used as the stroke signal classification models for classifying ischemic stroke and hemorrhagic stroke. Experimental results show that, based on the proposed multi-feature fusion method, the ensemble method of random forest can get the best classification performance in accuracy among three models.

A feature-enhanced network for stroke lesion segmentation from brain MRI images.

Accurate and expeditious segmentation of stroke lesions can greatly assist physicians in making accurate medical diagnoses and administering timely treatments. However, there are two limitations to the current deep learning methods. On the one hand, the attention structure utilizes only local features, which misleads the subsequent segmentation; on the other hand, simple downsampling compromises task-relevant detailed semantic information. To address these challenges, we propose a novel feature refinement and protection network (FRPNet) for stroke lesion segmentation. FRPNet employs a symmetric encoding-decoding structure and incorporates twin attention gate (TAG) and multi-dimension attention pooling (MAP) modules. The TAG module leverages the self-attention mechanism and bi-directional attention to extract both global and local features of the lesion. On the other hand, the MAP module establishes multidimensional pooling attention to effectively mitigate the loss of features during the encoding process. Extensive comparative experiments show that, our method significantly outperforms the state-of-the-art approaches with 60.16% DSC, 36.20px HD and 85.72% DSC, 27.02px HD on two ischemic stroke datasets that contain all stroke stages and several sequences of stroke images. The excellent results that exceed those of existing methods illustrate the efficacy and generalizability of the proposed method. The source code is released on https://github.com/wu2ze2lin2/FRPNet.

TransRender: a transformer-based boundary rendering segmentation network for stroke lesions.

Vision transformer architectures attract widespread interest due to their robust representation capabilities of global features. Transformer-based methods as the encoder achieve superior performance compared to convolutional neural networks and other popular networks in many segmentation tasks for medical images. Due to the complex structure of the brain and the approximate grayscale of healthy tissue and lesions, lesion segmentation suffers from over-smooth boundaries or inaccurate segmentation. Existing methods, including the transformer, utilize stacked convolutional layers as the decoder to uniformly treat each pixel as a grid, which is convenient for feature computation. However, they often neglect the high-frequency features of the boundary and focus excessively on the region features. We propose an effective method for lesion boundary rendering called TransRender, which adaptively selects a series of important points to compute the boundary features in a point-based rendering way. The transformer-based method is selected to capture global information during the encoding stage. Several renders efficiently map the encoded features of different levels to the original spatial resolution by combining global and local features. Furthermore, the point-based function is employed to supervise the render module generating points, so that TransRender can continuously refine the uncertainty region. We conducted substantial experiments on different stroke lesion segmentation datasets to prove the efficiency of TransRender. Several evaluation metrics illustrate that our method can automatically segment the stroke lesion with relatively high accuracy and low calculation complexity.

Strategic sampling with stochastic surface walking for machine learning force fields in iron's bcc-hcp phase transitions.

This study developed a machine learning-based force field for simulating the bcc-hcp phase transitions of iron. By employing traditional molecular dynamics sampling methods and stochastic surface walking sampling methods, combined with Bayesian inference, we construct an efficient machine learning potential for iron. By using SOAP descriptors to map structural data, we find that the machine learning force field exhibits good coverage in the phase transition space. Accuracy evaluation shows that the machine learning force field has small errors compared to DFT calculations in terms of energy, force, and stress evaluations, indicating excellent reproducibility. Additionally, the machine learning force field accurately predicts the stable crystal structure parameters, elastic constants, and bulk modulus of bcc and hcp phases of iron, and demonstrates good performance in predicting higher-order derivatives and phase transition processes, as evidenced by comparisons with DFT calculations and existing experimental data. Therefore, our study provides an effective tool for investigating the phase transitions of iron using machine learning methods, offering new insights and approaches for materials science and solid-state physics research.

Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness.

Introduction: Ischemic stroke patients commonly experience disorder of consciousness (DOC), leading to poorer discharge outcomes and higher mortality risks. Therefore, the identification of applicable electrophysiological biomarkers is crucial for the rapid diagnosis and evaluation of post-stroke disorder of consciousness (PS-DOC), while providing supportive evidence for cerebral neurology. Methods: In our study, we conduct microstate analysis on resting-state electroencephalography (EEG) of 28 post-stroke patients with awake consciousness and 28 patients with PS-DOC, calculating the temporal features of microstates. Furthermore, we extract the Lempel-Ziv complexity of microstate sequences and the delta/alpha power ratio of EEG on spectral. Statistical analysis is performed to examine the distinctions in features between the two groups, followed by inputting the distinctive features into a support vector machine for the classification of PS-DOC. Results: Both groups obtain four optimal topographies of EEG microstates, but notable distinctions are observed in microstate C. Within the PS-DOC group, there is a significant increase in the mean duration and coverage of microstates B and C, whereas microstate D displays a contrasting trend. Additionally, noteworthy variations are found in the delta/alpha ratio and Lempel-Ziv complexity between the two groups. The integration of the delta/alpha ratio with microstates' temporal and Lempel-Ziv complexity features demonstrates the highest performance in the classifier (Accuracy = 91.07%). Discussion: Our results suggest that EEG microstates can provide insights into the abnormal brain network dynamics in DOC patients post-stroke. Integrating the temporal and Lempel-Ziv complexity microstate features with spectral features offers a deeper understanding of the neuro mechanisms underlying brain damage in patients with DOC, holding promise as effective electrophysiological biomarkers for diagnosing PS-DOC.

Risk or Return? The Effect of Face Consciousness Influences on the Career Construction of Chinese Rural Dwellers in Urban Areas.

This research improves our understanding of entrepreneurial intention in the Chinese cultural context. Drawing on career construction theory, we received 408 valid surveys from rural Chinese dwellers and examined the relationships rural Chinese have among "face consciousness," indecisiveness, career adaptability, and entrepreneurial intention. We found that those who are fearful of losing face are less likely to have entrepreneurial intentions, but one's desire to gain face has no significant direct impact on entrepreneurial intention. In addition, face consciousness and an indecisive personality interact to influence career adaptability and, in turn, entrepreneurial intention. In sum, this study supports the career construction perspective on understanding the formation of entrepreneurial intention and offers theoretical, practical, and policy implications for entrepreneurial career counseling and training.

A novel brain-computer interface based on audio-assisted visual evoked EEG and spatial-temporal attention CNN.

Objective: Brain-computer interface (BCI) can translate intentions directly into instructions and greatly improve the interaction experience for disabled people or some specific interactive applications. To improve the efficiency of BCI, the objective of this study is to explore the feasibility of an audio-assisted visual BCI speller and a deep learning-based single-trial event related potentials (ERP) decoding strategy. Approach: In this study, a two-stage BCI speller combining the motion-onset visual evoked potential (mVEP) and semantically congruent audio evoked ERP was designed to output the target characters. In the first stage, the different group of characters were presented in the different locations of visual field simultaneously and the stimuli were coded to the mVEP based on a new space division multiple access scheme. And then, the target character can be output based on the audio-assisted mVEP in the second stage. Meanwhile, a spatial-temporal attention-based convolutional neural network (STA-CNN) was proposed to recognize the single-trial ERP components. The CNN can learn 2-dimentional features including the spatial information of different activated channels and time dependence among ERP components. In addition, the STA mechanism can enhance the discriminative event-related features by adaptively learning probability weights. Main results: The performance of the proposed two-stage audio-assisted visual BCI paradigm and STA-CNN model was evaluated using the Electroencephalogram (EEG) recorded from 10 subjects. The average classification accuracy of proposed STA-CNN can reach 59.6 and 77.7% for the first and second stages, which were always significantly higher than those of the comparison methods (p < 0.05). Significance: The proposed two-stage audio-assisted visual paradigm showed a great potential to be used to BCI speller. Moreover, through the analysis of the attention weights from time sequence and spatial topographies, it was proved that STA-CNN could effectively extract interpretable spatiotemporal EEG features.

Diagnostic Predictive Value of Tryptase, Serum Amyloid A and Lipoprotein-Associated Phospholipase A2 Biomarker Groups for Large Atherosclerotic Cerebral Infarction.

Background: There has been a gradual trend towards younger ageing of acute cerebral infarction in recent years. Atherosclerotic plaque rupture followed by dislodgement of emboli and resulting arterial embolism is an important mechanism for the development of acute cerebral infarction. Traditional independent risk factors for cerebral infarction have received attention from clinicians, but the risk factors for large artery atherosclerotic cerebral infarction are still unclear. Various blood biomarkers have an important role in the early diagnosis of large artery atherosclerotic cerebral infarction. Objective: To assess the diagnostic predictive value of a group of biomarkers for large artery atherosclerotic cerebral infarction. Methods: Lipoprotein-associated phospholipase A2 (LP-PLA2), trypsin-like protein (TPS), serum amyloid A (SAA), and supersensitive C-reactive protein (hs-CRP) levels were measured in the case group (30 cases) and control group (54 cases), respectively. Results: The differences in the general data between the two groups were not statistically significant (P > 0.05). Logistic regression and ROC curve analysis showed that Lp-PLA2, TPS, and SAA were positively associated with the diagnosis of large atherosclerotic cerebral infarction (P < 0.05). The area under the ROC curve of the multivariate model for the biomarker group reached 0.995. Conclusion: Biomarkers are closely associated with the occurrence of large atherosclerotic cerebral infarction and can be used as clinical adjuncts for diagnosis and assessment of prognosis.

Predicting the Recurrence of Operable Cervical Cancer Patients Based on Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score and Classical Clinicopathological Parameters.

Objective: The purpose of this study was to evaluate the prognostic value of hemoglobin, albumin, lymphocyte, and platelet (HALP) score in patients with operable cervical cancer, and on this basis, combined with classical clinicopathological parameters to predict the recurrence of patients. Methods: A total of 1580 patients with stage IA-IIA cervical cancer were randomly divided into training cohort (n=1054) and validation cohort (n=526) according to the predefined ratio of 2:1. In the training cohort, the receiver operating characteristic (ROC) curve and Youden index were used to determine the optimal threshold of HALP score for predicting cervical cancer recurrence. On this basis, the independent related factors with cervical cancer recurrence were screened through univariate and multivariate Cox regression analysis, and then a nomogram model was further established. The internal and external validation of the model was carried out in the training cohort and the validation cohort respectively through the consistency index (C-index) and calibration curve. Results: ROC curve and Youden index showed that the optimal threshold of HALP score for predicting cervical cancer recurrence was 39.50. Multivariate analysis confirmed that HALP score and some other classic clinicopathological parameters were independently associated with cervical cancer recurrence. Based on the results of multivariate analysis, a nomogram model for predicting cervical cancer recurrence was successfully constructed. The internal and external calibration curves showed that the fitting degree of the model was good, and the C-index (the C-index of the training cohort and the validation cohort were 0.862 and 0.847, respectively) showed that the prediction accuracy of the model proposed in this study was better than other similar models. Conclusion: HALP score may be a novel predictor for predicting the cervical cancer recurrence. Nomogram model based on HALP score and classical clinicopathological parameters can better predict the recurrence of cervical cancer.

Self-management Strategies for Risk Reduction of Subclinical and Mild Stage of Breast Cancer-Related Lymphedema: A Longitudinal, Quasi-experimental Study.

BACKGROUND: Early intervention with self-management strategies can potentially reduce the risk of progression of breast cancer-related lymphedema (BCRL). OBJECTIVE: To determine if The-Optimal-Lymph-Flow (TOLF) program focused on self-management strategies applied to patients with a subclinical or mild stage of BCRL can improve lymphedema-related behaviors, symptom experience, and limb circumference changes. METHODS: A total of 41 women with subclinical or mild lymphedema were enrolled in TOLF program. Lymphedema-related behaviors and lymphedema-related symptom experiences were measured by the Breast Cancer and Lymphedema Symptom Experience Index, and limb circumference changes were measured by sequential circumferential limb measurements at baseline and 1, 3, 6, and 12 months after the intervention. Generalized estimating equations were used to estimate the effects of the intervention on outcomes. RESULTS: Generalized estimating equations revealed that lymphedema-related behaviors and the number and severity of lymphedema-related symptoms were significantly improved at 4 postintervention test points compared with baseline (all P < .001). Reduced lymphedema-related symptom distress in functional, social, emotional, and psychological and self-perception (all P < .01) also resulted. The majority (77.5%) of patients maintained their preintervention lymphedema status; 17.5% of them reversed from mild lymphedema to subclinical lymphedema; 5.0% of them had lymphedema status progression. CONCLUSION: Positive outcomes in terms of lymphedema-related behaviors, relieving lymphedema-related symptom experience, and halting the progression of lymphedema status were documented following TOLF. IMPLICATIONS FOR PRACTICE: Nurses could educate patients to incorporate the self-management strategies of TOLF program into daily life to help patients prevent or reverse subclinical or mild stage of BCRL.

The Co-Expression of Melanoma-Antigen Family a Proteins and New York Esophageal Squamous Cell Carcinoma-1 in Breast Cancer: A Pilot Study.

OBJECTIVE: The aim of this study was to quantify the expression of melanoma-antigen family A proteins (MAGE-A) and New York esophageal squamous cell carcinoma-1 (NY-ESO-1) in breast cancer and establish the prognosis of breast cancer patients with MAGE-A and NY-ESO-1 co-expression. METHODS: A total of 122 patients with breast cancer were recruited for this study. Their clinicopathological data were collected retrospectively, and the MAGE-A and NY-ESO-1 expressions in paraffin-embedded specimens from the 122 patients were evaluated using immunohistochemical analysis. In addition, the survival states of the patients were recorded. RESULTS: Fifty-four patients (44.26%) were MAGE-A positive and 46 (37.70%) were NY-ESO-1 positive. Interestingly, 22 of the 46 NY-ESO-1-positive cases co-expressed MAGE-A. The expression of MAGE-A was positively associated with estrogen-receptor status (χ2 = 4.026, p = 0.045) and human epidermal growth factor receptor 2 status (χ2 = 5.482, p = 0.019), while the expression of NY-ESO-1 was positively associated with p53 expression (χ2 = 4.541, p = 0.033). Of the 122 patients, the lowest survival rate was observed in patients with NY-ESO-1 (+)/MAGE-A (+), with a 5-year survival rate of 59.09% and a median survival of 97 months. CONCLUSION: The results showed that MAGE-A and NY-ESO-1 were frequently expressed in breast cancer patients. The co-expression of MAGE-A and NY-ESO-1 occurred in about 18% of these patients, and it may indicate a poor prognosis.

Developing and validating a prediction model for lymphedema detection in breast cancer survivors.

PURPOSE: Early detection and intervention of lymphedema is essential for improving the quality of life of breast cancer survivors. Previous studies have shown that patients have symptoms such as arm tightness and arm heaviness before experiencing obvious limb swelling. Thus, this study aimed to develop a symptom-warning model for the early detection of breast cancer-related lymphedema. METHODS: A cross-sectional study was conducted at a tertiary hospital in Beijing between April 2017 and December 2018. A total of 24 lymphedema-associated symptoms were identified as candidate predictors. Circumferential measurements were used to diagnose lymphedema. The data were randomly split into training and validation sets with a 7:3 ratio to derive and evaluate six machine learning models. Both the discrimination and calibration of each model were assessed on the validation set. RESULTS: A total of 533 patients were included in the study. The logistic regression model showed the best performance for early detection of lymphedema, with AUC = 0.889 (0.840-0.938), sensitivity = 0.771, specificity = 0.883, accuracy = 0.825, and Brier scores = 0.141. Calibration was also acceptable. It has been deployed as an open-access web application, allowing users to estimate the probability of lymphedema individually in real time. The application can be found at https://apredictiontoolforlymphedema.shinyapps.io/dynnomapp/. CONCLUSION: The symptom-warning model developed by logistic regression performed well in the early detection of lymphedema. Integrating this model into an open-access web application is beneficial to patients and healthcare providers to monitor lymphedema status in real-time.

A scoring system for predicting the risk of breast cancer-related lymphedema.

OBJECTIVE: We aimed to establish a scoring system to predict the risk of breast cancer-related lymphedema. METHODS: From April 2017 to December 2018, 533 patients who previously underwent surgery for breast cancer were enrolled in this cross-sectional study. Univariate analysis was performed to explore and define the risk factors. A scoring system was then established on the basis of odds ratio values in the regression analysis. RESULTS: The additive scoring system values ranged from 6 to 22. The receiver operating characteristic (ROC) curve of this scoring system showed a sensitivity and specificity of 83.3% and 57.3%, respectively, to predict the risk of lymphedema at a cut-off of 15.5 points; the area under the curve was 0.736 (95% confidence interval: 0.662-0.811), with χ 2 = 5.134 (P = 0.274) for the Hosmer-Lemeshow test. CONCLUSIONS: The predictive efficiency and accuracy of the scoring system were acceptable, and the system could be used to predict and screen groups at high risk for breast cancer-related lymphedema.

A nurse-administered 3-Minute diagnostic interview for CAM-defined Delirium (3D-CAM Chinese version) in hospitalized elderly patients: A validation study.

BACKGROUND: Delirium is a common complication in hospitalized elderly patients, yet often remains unrecognized in the clinical care unit. The newly developed 3-Minute Diagnostic Interview for CAM-Defined Delirium (3D-CAM) has high sensitivity and specificity when administered by trained researchers. However, diagnostic characteristics of the 3D-CAM as performed in routine practice are unclear. OBJECTIVE: To determine the diagnostic characteristics of a Chinese version of the 3D-CAM administered to hospitalized elderly patients by bedside nurses. DESIGN: A prospective cohort study. SETTING: A tertiary geriatric hospital in China. PARTICIPANTS: 323 patients and 49 bedside nurses. METHODS: This prospective cohort study was conducted at a tertiary geriatric hospital in China. A Chinese version of 3D-CAM was administered by trained bedside nurses and compared against the reference standard performed by experienced neurologists using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. All assessors were blinded to each other's assessment results. Sensitivity and specificity of the 3D-CAM among bedside nurses were analysed by comparing with the reference standard to test the ability of 3D-CAM in detecting delirium. RESULTS: A total of 323 patients were included in the study, of whom 64 were classified as delirium positive by the neurologists. The 3D-CAM had a sensitivity of 92% (95% confidence interval, 82-97%), specificity of 89% (95% confidence interval, 84-92%), respectively. CONCLUSION: The Chinese version of 3D-CAM demonstrated good performance as a bedside nurse-administered screening tool for delirium detection among hospitalized Chinese geriatric patients. Registration number: ChiCTR-IOR-17,010,368.

Histological and ultrastructural observation reveals significant cellular differences between Agrobacterium transformed embryogenic and non-embryogenic calli of cotton.

Over the past few decades genetic engineering has been applied to improve cotton breeding. Agrobacterium medicated transformation is nowadays widely used as an efficient approach to introduce exogenous genes into cotton for genetically modified organisms. However, it still needs to be improved for better transformation efficiency and higher embryogenic callus induction ratios. To research further the difference of mechanisms for morphogenesis between embryogenic callus and non-embryogenic callus, we carried out a systematical study on the histological and cellular ultrastructure of Agrobacterium transformed calli. Results showed that the embryogenic callus developed nodule-like structures, which were formed by small, tightly packed, hemispherical cells. The surface of some embryogenic callus was covered with a fibrilar-like structure named extracellular matrix. The cells of embryogenic calli had similar morphological characteristics. Organelles of embryogenic callus cells were located near the nucleus, and chloroplasts degraded to proplastid-like structures with some starch grains. In contrast, the non-embryogenic calli were covered by oval or sphere cells or small clusters of cells. It was observed that cells had vacuolation of cytoplasm and plastids with a well organized endomembrane system. This study aims to understand the mechanisms of embryogenic callus morphogenesis and to improve the efficiency of cotton transformation in future.

MiR-144-3p: a novel tumor suppressor targeting MAPK6 in cervical cancer.

Cervical cancer is the third most common gynecologic cancer in the world. Exploration of the molecular mechanism underlying cervical cancer pathogenesis will provide new insights into the development of novel therapies. In this study, we were aimed to characterize a novel miRNA in cervical cancer tumorigenesis. First, we measured the expressional change of miR-144-3p in clinical tissues and cancer cells. Second, we employed cell proliferation, cell migration, and invasion assays to understand its functional role in cervical cancer. Then, we confirmed in vitro findings in xenograft cancer model. Last, we mapped out a downstream target of miR-144-3p and validated its functional role in cancer cells. In the results, miR-144-3p was found significantly downregulated in cervical cancer cells and tissues. Over-expressing miR-144-3p suppressed cancer cells growth and metastasis. Consistent with in vitro results, over-expressing miR-144-3p led to tumor growth inhibition in vivo. Further on, MAPK6 was identified as an endogenous target of miR-144-3p in cervical cancer. Knocking down MAPK6 inhibited cervical cancer cells proliferation, migration, and invasion potential. Our investigation was the first time to report miR-144-3p as a tumor suppressive miRNA in cervical cancer. It inhibited tumor growth by targeting MAKP6. The newly identified signalling axis may serve as novel therapeutic targets to manage cervical cancer.

A review of Garra (Teleostei: Cypriniformes) from two rivers in West Yunnan, China with description of a new species.

Species of the genus Garra from the Irrawaddy and Salween River basins in west Yunnan, China are difficult to identify and their taxonomy remains problematic. We conducted a systematic review of the genus using a combination of molecular and morphological approaches to address these issues and describe a new species, G. surgifrons. Phylogenetic analyses indicate that 15 species of Garra do not form a monophyletic group. Garra imberba and G. nujiangensis form a distinct clade that is not sister to the remaining species of Garra. According to the combination of morphological characters of the proboscis and rostrum, Garra from western Yunnan Province can be divided into 4 groups: (1) smooth snout; (2) unilobed proboscis; (3) bi-lobed proboscis; and (4) tri-lobed proboscis. The types of snout shapes in Garra are consistent with the topological structure of the phylogenetic tree. Garra surgifrons can be distinguished from all other congeners occurring in China and Southeast Asia by a combination of the following characters: presence of a transverse lobe and a tri-lobed proboscis; both dorsal and caudal fins without black markings; the proboscis short and broad, a roughly quadrate shape from the dorsal view with a poorly developed and slightly protuberant middle lobe, with the front tip not suspended from the lateral view and the width almost equal to the width of the lateral lobes; the posterior margin of mental adhesive disc extending close to or reaching the vertical through the central line of the eye and never reaching the posterior margin of the eye; two pairs of barbels; 16 circumpeduncular scales; and 32-34 lateral line scales.

AtWuschel promotes formation of the embryogenic callus in Gossypium hirsutum.

Upland cotton (Gossypium hirsutum) is one of the most recalcitrant species for in vitro plant regeneration through somatic embryogenesis. Callus from only a few cultivars can produce embryogenic callus (EC), but the mechanism is not well elucidated. Here we screened a cultivar, CRI24, with high efficiency of EC produce. The expression of genes relevant to EC production was analyzed between the materials easy to or difficult to produce EC. Quantitative PCR showed that CRI24, which had a 100% EC differentiation rate, had the highest expression of the genes GhLEC1, GhLEC2, and GhFUS3. Three other cultivars, CRI12, CRI41, and Lu28 that formed few ECs expressed these genes only at low levels. Each of the genes involved in auxin transport (GhPIN7) and signaling (GhSHY2) was most highly expressed in CRI24, with low levels in the other three cultivars. WUSCHEL (WUS) is a homeodomain transcription factor that promotes the vegetative-to-embryogenic transition. We thus obtained the calli that ectopically expressed Arabidopsis thaliana Wus (AtWus) in G. hirsutum cultivar CRI12, with a consequent increase of 47.75% in EC differentiation rate compared with 0.61% for the control. Ectopic expression of AtWus in CRI12 resulted in upregulation of GhPIN7, GhSHY2, GhLEC1, GhLEC2, and GhFUS3. AtWus may therefore increase the differentiation potential of cotton callus by triggering the auxin transport and signaling pathways.

[A fish-based biotic integrity index selection for rivers in Hechi Prefecture, Guangxi and their environmental quality assessment].

This study aims to develop a multi-metric, fish-based Index of Biological Integrity (IBI) for assessing the environmental quality of rivers in Hechi Prefecture, Guangxi Province, South China. Data on fish assemblage were collected via electrofishing We screened 21 candidate metrics representing attributes of fish assemblage. Among these, six metrics were finally selected for the IBI. It was shown that the fish-based biological integrity was good in Xiaohuanjiang River , fair in Hongshuihe, Longjiang and Dahuanjiang Rivers, but poor in Diaojiang River. The results of assessment were consistent with the actual situation of human disturbance. The assessment system constructed by this study could be used or used for reference in the area around Hechi. Biotic integrity assessment was an important means of water environment quality monitoring. To enable the biotic integrity assessment more scientific and more objective, much more research work is required to focus on the relationship between biotic integrity and environmental factors.