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The g-C3N4/BiOI/CdS double Z-scheme heterojunction photocatalyst with I3−/I− redox pairs is prepared using simple calcination, solvothermal, and solution chemical deposition methods. The photocatalyst comprised mesoporous, thin g-C3N4 nanosheets loaded on flower-like microspheres of BiOI with CdS quantum dots. The g-C3N4/BiOI/CdS double Z-scheme heterojunction has abundant active sites and in situ redox I3−/I− mediators and shows quantum size effects, which are all conducive to enhancing the separation of photoinduced charges and increasing the photocatalytic degradation efficiency for bisphenol A, a model pollutant. Specifically, the heterojunction photocatalyst achieves a photocatalytic degradation efficiency for bisphenol A of 98.62% in 120 min and photocatalytic hydrogen production of 863.44 μmol h−1g−1 on exposure to visible light. The excellent visible-light photocatalytic performance is as a result of the Z-scheme heterojunction, which extends absorption to the visible light region, as well as the I3−/I− pairs, which accelerate photoinduced charge carrier transfer and separation, thus dramatically boosting the photocatalytic performance. In addition, the key role of the charge transfer across the indirect Z-scheme heterojunction has been elucidated and the transfer mechanism is confirmed based on the detection of intermediate I3− ions. Thus, this study provides guidelines for the design of indirect Z-scheme heterojunction photocatalysts.

Purpose: This study aims to translate the English version of the Trans Woman Voice Questionnaire (TWVQ) to simplified Chinese (TWVQ-SC) and to examine its reliability and validity. Method: Standardized translation procedures were strictly followed for the translation of the TWVQ. Two hundred sixty trans woman and 128 cis woman subjects completed sociodemographic investigation, the TWVQ-SC, and the Voice Handicap Index–10 (VHI-10) online. Internal consistency was examined by Cronbach reliability coefficient (Cronbach α). Test–retest reliability was quantified by intraclass correlation coefficient (ICC). Content validity, structural validity, and discriminant validity were examined by expert panel's judgment, factor analysis, Spearman's rank correlation coefficient, and the Mann–Whitney U test. Results: The Cronbach α of the TWVQ-SC was .969 and the ICC was .841, indicating excellent internal consistency and good test–retest reliability. The four principal factors explained 21.345%, 18.592%, 13.551%, and 12.027% of the variance respectively with the cumulative contribution rate 65.514%. There was a strong correlation between the total score of the TWVQ-SC and that of the VHI-10 ( r = .858, p < .001), indicating good structural validity. The total score of the TWVQ-SC of the trans woman subjects was significantly higher than that of the cis woman subjects ( z = 14.590, p < .05), indicating good discriminant validity. Conclusion: The TWVQ-SC exhibits overall high reliability and validity, qualified to be applied as a reliable clinical tool to evaluate trans women's voice in mainland China.

Hydrochemical research and identification of nitrate contamination are of great significant for the endorheic basin, and the Northern Huangqihai Basin (a typical endorheic basin) was comprehensively researched. The results showed that the main hydrochemical facies were HCO3–Mg·Ca and HCO3–Ca·Mg. Spatial variation coefficients of most indices were greater than 60%, which was probably caused by human activities. The hydrochemical evolution was mainly affected by rock weathering and also by cation exchange. The D–18O relationship of groundwater was δD = 5.93δ18O − 19.18, and the d–excess range was −1.60–+6.01‰, indicating that groundwater was mainly derived from precipitation and that contaminants were very likely to enter groundwater along with precipitation infiltration. The NO3(N) contents in groundwater exceeded the standard. Hydrochemical analyses indicated that precipitation, industrial activities and synthetic NO3 were unlikely to be the main sources of nitrate contamination in the study area. No obvious denitrification occurred in the transformation process of nitrate. The δ15N(NO3) values ranged from +0.29‰ to +14.39‰, and the δ18O(NO3) values ranged from −6.47‰ to +1.24‰. Based on the δ15N(NO3) – δ18O(NO3) dual isotope technique and hydrochemical methods, manure, sewage and NH4 fertilizers were identified to be the main sources of nitrate contamination. This study highlights the effectiveness of the integration of hydrochemical and isotopic data for nitrate source identification, and is significant for fully understanding groundwater hydrochemistry in endorheic basins and scientifically managing and protecting groundwater.

The objective of this study was to investigate the application of dynamic contrast-enhanced CT images in the nursing of patients with gastroesophageal varices (GOV) treated by digestive endoscopy and its role in relieving bleeding symptoms. A total of 60 patients with liver cirrhosis and GOV were selected as the research objects. According to whether CT was used to evaluate the position of tissue adhesion embolism, the patients were divided into the control group (24 cases) and the observation group (36 cases). The treatment effect and bleeding situation of patients in the two groups were analyzed and compared. The results showed that the main portal vein pressure ( 17.24 ± 1.02 cmH2O), liver function recovery effect ( 2.84 ± 0.45 points), and total effective rate (100%) in observation group were better than those in control group ( 9.70 ± 1.22 cmH2O, 0.95 ± 0.72 points, and 79.17%, respectively) ( P < 0.05 ). In addition, the bleeding rate in observation group (0%) was significantly lower than that in control group (16.67%) ( P < 0.05 ). In conclusion, dynamically enhanced CT scan images combined with digestive endoscopy can help improve the therapeutic effect of GOV and reduce postoperative bleeding, which was worthy of clinical application and promotion.

How to quantitatively evaluate the groundwater environment is an important question. In this paper, the groundwater environment was defined by considering both groundwater quantity and quality, and...

Automated precise segmentation of glands from the histological images plays an important role in glandular morphology analysis, which is a crucial criterion for cancer grading and planning of treatment. However, it is non-trivial due to the diverse shapes of the glands under different histological grades and the presence of tightly connected glands. In this paper, a novel multi-scale fully convolutional network with three class classification (TCC-MSFCN) is proposed to achieve gland segmentation. The multi-scale structure can extract different receptive field features corresponding to multi-size objects. However, the max-pooling in the convolution neural network will cause the loss of global information. To compensate for this loss, a special branch called high-resolution branch in our framework is designed. Besides, for effectively separating the close glands, a three-class classification with additional consideration of edge pixels is applied instead of the conventional binary classification. Finally, the proposed method is evaluated on Warwick-QU dataset and CRAG dataset with three reliable evaluation metrics, which are applied to our method and other popular methods. Experimental results show that the proposed method achieves the-state-of-the-art performance. Discussion and conclusion are presented afterwards.

This study investigated the toxicity of trichlorfon (TCF) to the freshwater algae Chlamydomonas reinhardtii, as well as its biodegradation and metabolic fate. The growth of C. reinhardtii decreased with increasing TCF concentration, and the maximum inhibition ratio was 51.3% at 200 mg L-1 TCF compared to the control. Analyses of pigment content, chlorophyll fluorescence, and antioxidant enzymes indicated that C. reinhardtii can produce resistance and acclimatize to the presence of TCF. The variations in pH during cultivation suggested that photosynthetic microalgae have innate advantages over bacteria and fungi in remediating TCF. A 100% biodegradation rate was achieved at a maximum concentration of 100 mg L-1 TCF. Ten metabolites were identified by GC-MS, and the degradation pathways of TCF by the algae were proposed. This research demonstrated that C. reinhardtii is highly tolerant to and can efficiently degrade TCF. Thus, C. reinhardtii can be used to remove traces of TCF from natural water environments and to treat TCF-contaminated wastewater.

Medical image synthesis receives much popularity in recent years, and ample medical images can be synthesized by diverse deep learning models to alleviate the problem of lack of data in many medical imaging utilizations. However, most medical image synthesis methods still incorporate the well-known pooling operation in their convolutional neural networks-based / generative adversarial networks-based models, from which image details will be inevitably lost due to the pooling operation. In order to tackle the above problem, improved capsule-based networks, in which no pooling operation is executed and spatial details of images can be effectively preserved thanks to the equivariance characteristics of capsule models, are proposed in this paper to synthesize arterial spin labeling images, for the first time. Technically, three important issues in constructing improved capsule-based networks, including the depth of basic convolutions, the layer of capsules, and the capacity of capsules, are thoroughly investigated. Comprehensive experiments made up of region-based / voxel-based partial volume corrections and dementia diseases diagnosis based on two different datasets are conducted. The superiority of improved capsule-based networks introduced in this paper is substantiated from the statistical point of view.

Antibiotics usage in animal production is considered a primary driver of the occurrence, supply and spread of antibiotic resistance genes (ARGs) in the environment. Pig farms and fish ponds are important breeding systems in food animal production. In this study, we compared and analyzed broad ARGs profiles, mobile genetic elements (MGEs) and bacterial communities in a representative pig farm and neighboring fish ponds around Poyang Lake, the largest freshwater lake in China. The factors influencing the distribution of ARGs were also explored. The results showed widespread detection of ARGs (from 57 to 110) among 283 targeted ARGs in the collected water samples. The differences in the number and relative abundance of ARGs observed from the pig farm and neighboring fish ponds revealed that ARG contamination was more serious on the pig farm than in the fish ponds and that the water treatment plant on the pig farm was not very effective. Based on the variance partition analysis (VPA), MGEs, bacterial communities and water quality indicators (WIs) codrive the relative abundance of ARGs. Based on network analysis, we found that total phosphorus and Tp614 were the most important WIs and MGEs affecting ARG abundance, respectively. Our findings provide fundamental data on farms in lakeside districts and provide insights into establishing standards for the discharge of aquaculture wastewater.

The variation in the groundwater level, which is an indicative factor that can reflect changes in both groundwater level and groundwater quantity, was selected as the factor to be analyzed. Rainfall and groundwater exploitation were chosen as the representative factors of climate change and anthropogenic activities, respectively. By applying the elastic coefficient method and other mathematical statistical methods, the influence of climate change and anthropogenic activities on groundwater was analyzed qualitatively and quantitatively. The northern Huangqihai basin was chosen as the study area. The rainfall in the study area showed an increasing trend from 1996–2020, while most variations in groundwater level were negative. This result indicated that the positive influence of rainfall on the variation in the groundwater level in the study area was not enough to offset the negative influence of groundwater exploitation. The correlation between anthropogenic activities and variations in the groundwater level was lower in wet years than in normal years but the highest in dry years. Based on the comparative method of the slope changing ratio of cumulative quantity and the elastic coefficient method, it was determined that the contribution rate of climate change to the variation in groundwater level was 22.08% and that of human activities was 77.92%. Thus, the sustainable development of the groundwater environment can be realized by positively guiding anthropogenic activities and minimizing negative influences.

As a new type of environmental pollutant, environmental antibiotic residues have attracted widespread attention, and the degradation and removal of antibiotics has become an engaging topic for scholars. In this paper, Novozym 51003 industrialized laccase and syringaldehyde were combined to degrade sulfonamides in aquaculture wastewater. Design Expert10 software was used for multiple regression analysis, and a response surface regression model was established to obtain the optimal degradation parameters. In the actual application, the degradation system could maintain a stable performance within 9 h, and timely supplement of the mediator could achieve a better continuous degradation effect. Low concentrations of heavy metals and organic matter would not significantly affect the degradation performance of the laccase-mediator system, making the degradation system suitable for a wide range of water quality. Enzymatic reaction kinetics demonstrated a strong affinity of sulfadiazine to the substrate. Ten degradation products were speculated using high-resolution mass spectrum based on the mass/charge ratios and the publication results. Four types of possible degradation pathways of sulfadiazine were deduced. This work provides a practical method for the degradation and removal of sulfonamide antibiotics in actual sewage.

BACKGROUND: Microvascular invasion (MVI) has a significant effect on the prognosis of hepatocellular carcinoma (HCC), but its preoperative identification is challenging. Radiomics features extracted from medical images, such as magnetic resonance (MR) images, can be used to predict MVI. In this study, we explored the effects of different imaging sequences, feature extraction and selection methods, and classifiers on the performance of HCC MVI predictive models. METHODS: After screening against the inclusion criteria, 69 patients with HCC and preoperative gadoxetic acid-enhanced MR images were enrolled. In total, 167 features were extracted from the MR images of each sequence for each patient. Experiments were designed to investigate the effects of imaging sequence, number of gray levels (Ng), quantization algorithm, feature selection method, and classifiers on the performance of radiomics biomarkers in the prediction of HCC MVI. We trained and tested these models using leave-one-out cross-validation (LOOCV). RESULTS: The radiomics model based on the images of the hepatobiliary phase (HBP) had better predictive performance than those based on the arterial phase (AP), portal venous phase (PVP), and pre-enhanced T1-weighted images [area under the receiver operating characteristic (ROC) curve (AUC) =0.792 vs. 0.641/0.634/0.620, P=0.041/0.021/0.010, respectively]. Compared with the equal-probability and Lloyd-Max algorithms, the radiomics features obtained using the Uniform quantization algorithm had a better performance (AUC =0.643/0.666 vs. 0.792, P=0.002/0.003, respectively). Among the values of 8, 16, 32, 64, and 128, the best predictive performance was achieved when the Ng was 64 (AUC =0.792 vs. 0.584/0.697/0.677/0.734, P

The toxicity of nanoplastics to aquatic organisms has been widely studied in terms of biochemical indicators. However, there is little discussion about the underlying toxic mechanism of nanoplastics on microalgae. Therefore, the chronic effect of polystyrene (PS) nanoplastics (80 nm) on Chlorella pyrenoidosa was investigated, in terms of responses at the biochemical and molecular/omic level. It was surprising that both inhibitory and promoting effects of nanoplastcis on C. pyrenoidosa were found during chronic exposure. Before 13 days, the maximum growth inhibition rate was 7.55% during 10 mg/L PS nanoplastics treatment at 9 d. However, the inhibitory effect gradually weakened with the prolongation of exposure time. Interestingly, algal growth was promoted for 1–5 mg/L nanoplastics during 15–21 d exposure. Transcriptomic analysis explained that the inhibitory effect of nanoplastics could be attributed to suppressed gene expression of aminoacyl-tRNA synthetase that resulted in the reduced synthesis of related enzymes. The promotion phenomenon may be due to that C. pyrenoidosa defended against nanoplastics stress by promoting cell proliferation, regulating intracellular osmotic pressure, and accelerating the degradation of damaged proteins and organs. This study is conducive to provide theoretical basis for evaluating the actual hazard of nanoplastics to aquatic organisms.

The glottis’s morphology not only reflects vocal and respiratory information, but also plays an important role in the diagnosis of laryngeal diseases. The glottis segmentation is a primary step in computer-aided diagnostic system, however is challenging due to various shapes of glottis, low contrast with surrounding tissues, the existence of laryngeal diseases and so on. In this paper, a deep attention network based on U-Net with color normalization operation (CN-DA-Unet) is proposed to achieve an end-to-end segmentation of the glottal area for the first time. The original images are first processed by color normalization to reduce the adverse effects of low contrast and large differences in colors between different images. The normalized images are then sent to the proposed DA-Unet for feature extraction. In this network, residual structure is incorporated to extract rich features from deep neural networks. After extracting features, a feature pyramid attention (FPA) module is applied to enhance the semantic information of the glottal area. These features are up-sampled and added to the features from the corresponding encoding layer for several times to obtain the final segmented image. The proposed approach is tested on laryngeal images of an in–house dataset including images from healthy subjects and pathologic subjects. Its performance is evaluated by several reliable and popular evaluation metrics, achieving the dice coefficient of 92.9%, sensitivity of 93.5% and precision of 92.6%. These results demonstrate the effectiveness of our proposed approach and the better performance comparing with several popular networks.

Foreground targets localization in video sequences receives much popularity in computer vision during the past few years, and its studies are highly related toward machine learning techniques. Driven by the recent popular deep learning techniques in machine learning, many contemporary localization studies are equipped with popular deep learning methods, and their performance has been benefited a lot by the prominent generalization capability of deep learning methods. In this study, inspired by deep metric learning, which is a new trend in deep learning, a novel single-target localization method is proposed. This new method is composed of two steps. First, an offline deep-ranked metric learning step is fulfilled and its gradient at the end-to-end learning procedure of the whole deep learning model is derived for realizing the conventional stochastic gradient algorithm. Also, an alternative proximal gradient algorithm is introduced to boost the efficiency as well. Second, an online models updating step is employed by the consecutive updating manner as well as the incremental updating manner, in order to make the offline learned outcome more adaptive during the progression of video sequences, in which challenging circumstances, such as sudden illumination changes, obstacles, shape transformation, complex background, etc., are likely to occur. This new single-target localization method has been compared with several shallow learning-based or deep learning-based localization methods in a large video database. Both qualitative and quantitative analysis have been comprehensively conducted to reveal the superiority of the new single-target localization method from the statistical point of view.

Moving human localization is the first pre-requisite step of human activity analysis in video surveillance. Identifying human targets accurately and efficiently is always of high demands in computer vision studies. Also, learning is often indispensable in contemporary moving human localization, and unknown parameters of proposed methods need to be properly adjusted to guarantee the final localization performance. Such a task can be facilitated with the help of popular deep learning techniques, especially when enormous surveillance video clips become commonly seen nowadays. In this study, the metric learning problem in moving human localization is emphasized, and a new deep multi-channel residual networks-based metric learning method is introduced for the first time. Specifically, the deep metric learning problem in this new method is solved within a ranking procedure via both the conventional stochastic gradient descent algorithm and a more efficient proximal gradient descent algorithm. Comprehensive experiments are conducted and this new method is compared with several other popular deep learning-based approaches. Qualitative and quantitative analysis are conducted from the statistical perspective, to evaluate all localization outcomes obtained by all compared methods based on two specific measurements. The localization performance of this new method is suggested to be promising after the comprehensive analysis.

Most previous studies have focused on the continuous exposure of aquatic organisms to nanoplastics. However, persistent pollutants in natural aquatic surroundings are a threat, and their concentrations are continuously increasing. The discussion and research into the effects of accumulative exposure to these materials are limited. Therefore, this study aimed to compare the effects of continuous and accumulative exposure to polystyrene (PS) nanoplastics (80 nm) on Chlorella pyrenoidosa during chronic toxicity. The results indicated that under conditions of continuous exposure, this alga exhibited self-recovery to defend against the negative effects of PS nanoplastics during 15-21 days of exposure (the 21-d inhibitory rate was 1.41%). However, one unanticipated finding was that during the same period of accumulative exposure, nanoplastics retained a substantial and stable inhibitory effect on the algal growth (the 21-d inhibitory rate was 6.79% in accumulative exposure for twice), indicating the invalid self-recovery of algae. The results of scanning electron microscopy demonstrated that on day 21, the degree of damage to the algal cells under accumulative exposure was more severe than that under continuous exposure. Hence, nanoplastics exerted an irreversibly negative effect on aquatic organisms depending on the pattern, frequency, concentration, and duration of exposure. This project evaluated the practical significance of nanoplastics in aquatic ecosystems.

The automatic diagnosis method based on speech signal analysis is able to realize the detection and classification of pathological voices. It plays an important role in the early diagnosis and auxiliary treatment of voice pathology, which effectively relief the discomfort of patients and reduce the workload of doctors. Therefore, the automatic diagnosis method based on speech signal analysis is of great research value. Meanwhile, high accuracy, high precision and stability are the pursuit goals. In this paper, a novel computer-aided assessment based on speech signal analysis for pathological voice classification (CS-PVC) system is proposed. This model focuses on the areas with large differences between different pathological voices and healthy voices, while ignore the negative impact of insignificant information on the performance of the model. Two databases were used in the experiments, one is the Saarbruecken Voice database (SVD), and the other is the self-built Shenzhen People’s Hospital voice database (SZUPD). The pathological voice detection accuracy of the proposed system on the above two databases are 81.6% and 82.2% respectively. The experimental results show that the proposed framework is not data-dependence. In other words, it has the potential to be universally applicable in medical framework in the future.

Most previous studies have focused on the toxicity of microplastics on aquatic organisms. However, research on nanoplastics is still limited and poses significant threat to aquatic organisms than microplastics. Therefore, this study investigated the effects of nanoplastics (80 nm) on the microalgae Chlorella pyrenoidosa. One unanticipated finding was that inhibition and detoxification effects existed in the interaction between nanoplastics and C. pyrenoidosa. Nanoplastics contributed the maximum inhibition rates of 27.73%, 29.64%, and 11.76% on algal growth, chlorophyll a, and Fv/Fm, respectively, which were much higher than those of microplastics. However, the inhibitory effect of nanoplastics gradually decreased with prolonged exposure time after reaching a maximum. The transcriptomic analysis explained that the inhibition effect of nanoplastics was due to the blockage of the gene expression of aminoacyl tRNA synthetase and the synthesis of related enzymes and proteins at low concentrations (10 mg·L-1). Moreover, it affected DNA damage repair and hindered photosynthesis at high concentrations (50 mg·L-1). The detoxification phenomenon is attributed to the promotion of cell proliferation, the acceleration of the degradation of damaged proteins and organs, and the regulation of intracellular osmotic pressure in algae. The results of this study provide an understanding of the mechanism underlying the interaction between nanoplastics and microalgae.

SPE-UPLC-MS/MS was used to investigate the occurrence of 18 target antibiotics in the surface water of Poyang Lake over different seasons of 2014-2015. The maximum concentrations of sulfadiazine, oxytetracycline, and doxycycline were 56.2, 48.7, and 39.7 ng/L, respectively. Compared with those in the other lakes or surface waters, the surface water of Poyang Lake contained moderate or below-average levels of antibiotics. The significantly lower concentrations (P 0.01) of roxithromycin in June 2015 likely resulted from the dilution effect of water flow during the flood season. Antibiotic concentrations were higher in site P3-1 than in other sites (P 0.01), whereas those in other sites (P1-1, P2-1, P5-1, P6-1, P7-1, P13-1, P16-1, P17-1, P18-1) were not significantly different (P 0.05). Given that tetracyclines and sulfonamides are common veterinary medicines, the high concentrations of oxytetracycline, doxycycline, and sulfadiazine in site P3-1 might be closely related to agricultural production in the surrounding areas. The risk assessment of the main antibiotic contaminants revealed that the majority of the risk quotients of the target antibiotics were below 0.01, thereby indicating the minimal risk of these antibiotics to organisms at three different trophic levels. Sulfadimidine and sulfadiazine were identified as the main antibiotics that contribute to ecological risk in Poyang Lake, and that the daphnid is the main model organism exposed to these risks. This study provides important data for antibiotic pollution control and environmental protection in the study area and enriches environmental monitoring data on a global scale.

Arterial spin labeling is a recently emerging imaging modality in functional magnetic resonance, and it is widely acknowledged to be effective in directly measuring the cerebral blood flow of patients while being scanned, which makes it a promising indicator in contemporary dementia disease diagnosis studies. However, partial volume effects mainly caused by signal cross-contamination due to pixel heterogeneity and limited spatial resolution of the arterial spin labeling scanning protocol often prevent the cerebral blood flow from being accurately measured. In order to correct the partial volume effects, contemporary studies usually rely on neighboring pixles to solve indefinite equations of partial volume correction, which makes shortcomings of blurring and brain tissue details loss inevitable in their correction outcomes. In this study, a novel pixel-wise correction method is proposed to tackle partial volume effects in arterial spin labeling images. The main idea is to formalize the correction problem as a series of quadratic programming sub-problems using split-Bregman iterations, then formulate each sub-problem via the regularization of least absolute shrinkage and selection operator, and finally solve the regularization sub-problem via the fast proximal gradient descent approach. A real-patients database composed of 360 demented patients is incorporated for experimental evaluation of the pixel-wise method. Extensive experiments and comprehensive statistical analysis are carried out to demonstrate the superiority of the pixel-wise method with comparisons towards the popular region-based method. Promising results are reported from the statistical point of view.

The motion intent of individual finger is recognized based on mechanomyogram.The mechanomyogram signal is captured by inertial sensor.Time and transform domain feature sets are effectively applied for recognition.The effectiveness of single and combined feature sets are evaluated.The best average recognition rate of 95.20% can be achieved. The mechanomyogram (MMG) signals detected from forearm muscle group contain abundant information which can be utilized to predict finger motion intention. Few works have been reported in this area especially for the recognition of individual finger motions, which however is crucial for many applications such as prosthesis control. In this paper, a MMG based finger gesture recognition system is designed to identify the motions of each finger. In this system, three kinds of feature sets, wavelet packet transform (WPT) coefficients, stationary wavelet transform (SWT) coefficients, and the time and frequency domain hybrid (TFDH) features, are adopted and processed by a support vector machine (SVM) classifier. The experimental results show that the average accuracy rates of recognition using the WPT, SWT and TFDH features are 91.64%, 94.31%, and 91.56%, respectively. Furthermore, the average rate of 95.20% can be achieved when above three feature sets are combined to use in the proposed recognition system.

Medical Images data is widely acknowledged for its large volumes, high complexities and limited labels information, which makes learning-based tasks challenging to obtain promising performance based on such images data. Also, Alzheimer’s disease, one of the five most severe non-communicable diseases all around the world, receives much attention in both academic research and clinical diagnosis for the time being. It is generally acknowledged that magnetic resonance imaging, a popular imaging tool because of its free of radiation exposure in terms of patients safety issues, has been vastly employed in Alzheimer’s disease diagnosis in recent years. However, most contemporary clinical diagnosis efforts still heavily rely on clinicians’ expertise to realize the disease diagnosis task. In this study, inspired by the recent rapid development of deep learning and low-resource learning techniques, a novel image-based disease diagnosis scheme is proposed to automatically fulfil the diagnosis of Alzheimer’s disease based on arterial spin labeling magnetic resonance images. This new end-to-end low-resource learning scheme is conducted following four steps. First, raw arterial spin labeling images need to be acquired and essential pre-processing steps should be carried out on raw images. Second, the popular convolutional neural network technique is adopted to automatically extract latent trainable features from processed arterial spin labelling images, instead of traditional hand-crafted features. Third, a novel low-resource pair-wise learning method inspired by the semi-supervised learning theory is carried out based on latent trainable features for automatically determining disease similarity functions. Forth, learned outcomes are then fed into a multi-nominal logistic regression model and a regression process with the highest posterior probability is capable to reveal the corresponding disease diagnosis outcome. Extensive experiments based on a database composed of magnetic resonance images acquired from 350 real demented patients are carried out with the newly poroposed scheme being compared towards several other well-known diagnosis tools. All diagnosis results have undergone rigorous and comprehensive statistical analysis composed of analysis of variance and multiple comparison tests. The superiority of the new scheme has been demonstrated therein.

In this study, g-C3N4 was synthesized by thermal polycondensation of melamine and was characterized by X-ray powder diffraction, X-ray photoelectron spectroscopy, UV–visible diffuse reflection spectroscopy, and scanning electron microscopy. Results showed that g-C3N4 degraded sulfadimidine (SMD) under visible light, in which the adsorption and photocatalytic degradation was influenced by pH. The maximum adsorption capacity was achieved at approximately pH 5. The highest degradation rate constant was obtained at strong acid and alkali. In addition, the degradation mechanism of g-C3N4 was evaluated with the help of quencher agents. The intermediates, degradation pathways, and mineralization of SMD were also determined to evaluate the degradation and oxidation ability of g-C3N4.

This thesis focuses on the development of speech enhancement algorithms in the transform domain. The motivation and objectives of the work are first stated and various effects of noise on speech are discussed. A literature review of various speech enhancement algorithms with an emphasis on those implemented in the transform domain is presented. Some of the important speech enhancement algorithms are outlined and various transform methods are compared and discussed. Since the primary aim is to attenuate the noise component of a noisy speech in order to enhance its quality using transform based filtering algorithms, three proposed transform domain speech enhancement algorithms are introduced in details. The first one is DCT-based algorithm which reduces the variance between frames and achieves a better noise reduction. Next a DFT-based speech algorithm is introduced which considers the 2D relations of DFT coefficients by incorporating image processing type of techniques. The last one is a DFT-based post-processor which is proposed for over-attenuated speech components regeneration. For the purpose of evaluation, several objective measures are utilized together with two novel proposed objective measures. The proposed objective measures are able to give specific judgements on speech distortion and noise reduction. All the proposed algorithms are compared in terms of their computational cost, inherent delay times as well. The strengths and weaknesses of various proposed algorithms are analyzed and recommendations for future work are presented. DOCTOR OF PHILOSOPHY (EEE)

Here we report a set of experiments in which water blooming cyanobacteria Microcystis aeruginosa was repeatedly exposed to erythromycin. Growth inhibition increased with increasing erythromycin concentration (1–150 μg/L) upon first exposure. Maximum inhibition rate (76.06%), occurred under 150 μg/L erythromycin. Moreover, 96-h 50% effective concentration (EC50) was 22.97 μg/L, indicating that the growth of M. aeruginosa was affected by erythromycin under common environmental concentrations. Photosynthesis was hindered by chlorophyll and photosystem II limitations. Malondialdehyde, reactive oxygen species, and superoxide dismutase contents increased significantly under certain concentrations of erythromycin, but superoxide dismutase was suppressed by 150 μg/L erythromycin. Synthesis of intracellular and extracellular microcystins was promoted by 10–60 and by 20–60 μg/L erythromycin, respectively, but both were inhibited by 100–150 μg/L. Principal component analysis and Pearson’s correlation revealed the accumulation of reactive oxygen species as the dominant mechanism of erythromycin toxicity to cells. M. aeruginosa repeatedly subjected to erythromycin exposure showed obvious resistance against the antibiotic, especially when treated twice with 60 μg/L erythromycin. The 96-h EC50 was 81.29 μg/L. As compared to the first exposure to erythromycin, photosynthetic and antioxidant activities increased, while growth inhibition and oxidation stress decreased upon multiple exposures. Production and release of microcystins were enhanced by repeated exposure to the antibiotic. Thus, erythromycin persistence in water should be examined, as repeated exposure may lead to serious environmental and human health hazards.

Adequate medical images are often indispensable in contemporary deep learning-based medical imaging studies, although the acquisition of certain image modalities may be limited due to several issues including high costs and patients issues. However, thanks to recent advances in deep learning techniques, the above tough problem can be substantially alleviated by medical images synthesis, by which various modalities including T1/T2/DTI MRI images, PET images, cardiac ultrasound images, retinal images, and so on, have already been synthesized. Unfortunately, the arterial spin labeling (ASL) image, which is an important fMRI indicator in dementia diseases diagnosis nowadays, has never been comprehensively investigated for the synthesis purpose yet. In this paper, ASL images have been successfully synthesized from structural magnetic resonance images for the first time. Technically, a novel unbalanced deep discriminant learning-based model equipped with new ResNet sub-structures is proposed to realize the synthesis of ASL images from structural magnetic resonance images. The extensive experiments have been conducted. Comprehensive statistical analyses reveal that: 1) this newly introduced model is capable to synthesize ASL images that are similar towards real ones acquired by actual scanning; 2) synthesized ASL images obtained by the new model have demonstrated outstanding performance when undergoing rigorous tests of region-based and voxel-based corrections of partial volume effects, which are essential in ASL images processing; and 3) it is also promising that the diagnosis performance of dementia diseases can be significantly improved with the help of synthesized ASL images obtained by the new model, based on a multi-modal MRI dataset containing 355 demented patients in this paper.

Laryngeal tumor is a typical head and neck disease that may be cancerous, causing harm to human health. Automatic laryngeal tumor detection in laryngeal endoscopic images is beneficial to the further analysis of tumor characteristics to aid in treatment, such as computer assisted surgery. However, there have been very few attempts to automatically detect the tumor of larynx. In this paper, three commonly used object detection models based on convolutional neural networks (CNNs) are used to achieve automatic detection of the tumors on a dataset of laryngeal endoscopic images. As far as we know, this is the first time that object detection models based on CNNs have been used to create end-to-end detection of laryngeal tumors. The experimental results show that all of the three methods have good performances and single shot multibox detector (SSD) is more suitable for laryngeal tumor detection in terms of our evaluation metrics.

Arterial spin labeling (ASL) images begin to receive much popularity in dementia diseases diagnosis recently, yet it is still not commonly seen in well-established image datasets for investigating dementia diseases. Hence, synthesizing ASL images from available data is worthy of investigations. In this study, a novel locally-constrained WGAN-GP model ensemble is proposed to realize ASL images synthesis from structural MRI for the first time. Technically, this new WGAN-GP model ensemble is unique in its constrained optimization task, in which diverse local constraints are incorporated. In this way, more details of synthesized ASL images can be obtained after incorporating local constraints in this new ensemble. The effectiveness of the new WGAN-GP model ensemble for synthesizing ASL images has been substantiated both qualitatively and quantitatively through rigorous experiments in this study. Comprehensive analyses reveal that, this new WGAN-GP model ensemble is superior to several state-of-the-art GAN-based models in synthesizing ASL images from structural MRI in this study.

A novel WGAN-GP-based model is proposed in this study to fulfill bi-directional synthesis of medical images for the first time. GMM-based noise generated from the Glow model is newly incorporated into the WGAN-GP-based model to better reflect the characteristics of heterogeneity commonly seen in medical images, which is beneficial to produce high-quality synthesized medical images. Both the conventional “down-sampling”-like synthesis and the more challenging “up-sampling”-like synthesis are realized through the newly introduced model, which is thoroughly evaluated with comparisons towards several popular deep learning-based models both qualitatively and quantitatively. The superiority of the new model is substantiated based on a series of rigorous experiments using a multi-modal MRI database composed of 355 real demented patients in this study, from the statistical perspective.

Moxifloxacin (MOX) and gatifloxacin (GAT) are fourth-generation fluoroquinolone antibiotics that are frequently detected in surface water environments and pose a threat to aquatic organisms. However, research into their toxicity to Microcystis aeruginosa, a cyanobacterium, has thus far been limited. In the present study, we investigated the effects of these antibiotics on M. aeruginosa growth, photosynthesis, oxidative stress, and microcystin (MC) release. The results of the 96 h EC50 values of MOX and GAT were 60.34 and 25.30 μg/L, respectively, and the risk quotients calculated indicated that these antibiotics could pose considerable ecological risks at actual environmental concentrations. Photosynthetic fluorescence intensity was shown to decline markedly, and Fv/Fm significantly decreased without any evidence of recovery, suggesting that the organism’s photosystems were irreversibly damaged. Chlorophyll a and carotenoid content decreased, whereas the ratio of carotenoids to chlorophyll a increased, indicating that carotenoids were less susceptible to damage than chlorophyll a. The reactive oxygen species and malondialdehyde content significantly increased, as well as the superoxide dismutase and catalase activities, indicating that exposure caused serious oxidative stress. Additionally, MC release increased. These results demonstrate that the environmental risks posed by MOX and GAT should be given serious consideration, particularly as their use is increasing.

Antibiotics in the aquatic environment present potential risks to aquatic ecosystems. The adsorption of tetracycline (TC) and two sulfonamides (SAs), sulfadiazine (SD) and sulfamethoxazole (SMZ), from an aqueous solution of synthesized amorphous nano-carbon (ANC) was investigated. The ANC had a large adsorption capacity for TC (125.55 mg/g), SD (620.74 mg/g), and SMZ (332.21 mg/g). Several mechanisms (i.e. cation exchange, electrostatic interaction, hydrophobic effect, H-bonding, and π–π interactions) are potentially involved in the adsorption process. SAs were more strongly adsorbed on ANC because their molecules are smaller than those of TC. Adsorption nearly reached equilibrium within 2 h, with a removal rate of over 75%. Temperature and pH of the solution positively affected the adsorption process. Cations (Na+ and Ca2+) in solution had a slight effect on adsorption; Ca2+ inhibited adsorption, while Na+ promoted it. The adsorption process was fitted to pseudo-second-order kinetic and Freundlic...