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Purpose Focal cortical dysplasia (FCD) is a common cause of epilepsy; the only treatment is surgery. Therefore, detecting FCD using noninvasive imaging technology can help doctors determine whether surgical intervention is required. Since FCD lesions are small and not obvious, diagnosing FCD through visual evaluations of magnetic resonance imaging (MRI) scans is difficult. The purpose of this study is to detect and segment histologically confirmed FCD lesions in images of normal fluid‐attenuated inversion recovery (FLAIR)‐negative lesions using convolutional neural network (CNN) technology. Methods The technique involves training a six‐layer CNN named Net‐Pos, which consists of two convolutional layers (CLs); two pooling layers (PLs); and two fully connected (FC) layers, including 60 943 learning parameters. We employed activation maximization (AM) to optimize a series of pattern image blocks (PIBs) that were most similar to a lesion image block by using the trained Net‐Pos. We developed an AM and convolutional localization (AMCL) algorithm that employs the mean PIBs combined with convolution to locate and segment FCD lesions in FLAIR‐negative patients. Five evaluation indices, namely, recall, specificity, accuracy, precision, and the Dice coefficient, were applied to evaluate the localization and segmentation performance of the algorithm. Results The PIBs most similar to an FCD lesion image block were identified by the trained Net‐Pos as image blocks with brighter central areas and darker surrounding image blocks. The technique was evaluated using 18 FLAIR‐negative lesion images from 12 patients. The subject‐wise recall of the AMCL algorithm was 83.33% (15/18). The Dice coefficient for the segmentation performance was 52.68. Conclusion We developed a novel algorithm referred to as the AMCL algorithm with mean PIBs to effectively and automatically detect and segment FLAIR‐negative FCD lesions. This work is the first study to apply a CNN‐based model to detect and segment FCD lesions in images of FLAIR‐negative lesions.

Purpose: Focal cortical dysplasia (FCD) is a malformation of cortical development that often causes pharmacologically intractable epilepsy. However, FCD lesions are frequently characterized by minor structural abnormalities that can easily go unrecognized, making diagnosis difficult. Therefore, many epileptic patients have had pathologically confirmed FCD lesions that appeared normal in pre-surgical fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) studies. Such lesions are called "FLAIR-negative." This study aimed to improve the detection of histopathologically verified FCD in a sample of patients without visually appreciable lesions. Methods: The technique first extracts a series of features from a FLAIR image. Then, three naive Bayesian classifiers with probability (NBCP) are trained based on different numbers of feature maps to classify voxels as lesional or healthy voxels and assign the lesions a probability of correct classification. This method classifies the three-dimensional (3D) images of all patients using leave-one-out cross-validation (LOOCV). Finally, the 3D lesion probability map, including epileptogenic lesions, is obtained by removing false-positive voxel outliers using the morphological method. The performance of the NBCP was assessed for quantitative analysis by specificity, accuracy, recall, precision, and Dice coefficient in subject-wise, lesion-wise, and voxel-wise manners. Results: The best detection results were obtained by using four features: cortical thickness, symmetry, K-means, and modified texture energy. There were eight lesions in seven patients. The subjectwise sensitivity of the proposed method was 85.71% (6/7). Seven out of eight lesions were detected, so the lesion-wise sensitivity was 87.50% (7/8). No significant differences in effectiveness were found between automated lesion detection using four features and lesion detection using manual segmentation, as voxels were quantitatively analyzed in terms of specificity (mean AE SD = 99.64 AE 0.13), accuracy (mean AE SD = 99.62 AE 0.14), recall (mean AE SD = 73.27 AE 26.11), precision (mean AE SD = 11.93 AE 8.16), and Dice coefficient (mean AE SD = 22.82 AE 15.57). Conclusion: We developed a novel automatic voxel-based method to improve the detection of FCD FLAIR-negative lesions. To the best of our knowledge, this study is the first to detect FCD lesions that appear normal in pre-surgical 3D high-resolution FLAIR images alone with a limited number of radiomics features. We optimized the algorithm and selected the best prior probability to improve the detection. For non-temporal lobe epilepsy (non-TLE) patients, lesions could be accurately located, although there were still false-positive areas.

To assess the effect of TDG on the survival of different sizes of pelagic fish, bighead carp (Hypophthalmichthys nobilis) were subjected to TDG supersaturated water at levels of 125, 130, 135, and 140%. The results showed that apparent abnormal behaviours and symptoms of gas bubble disease (GBD) were observed in bighead carp. The survival probability of large and small juvenile bighead carp declined with increasing TDG levels. The median survival time (ST50) values of large juvenile bighead carp were 74.97 and 31.90 h at 130% and 140% TDG, respectively. While the ST50 of small fish were 22.40 and 6.72 h at the same TDG levels. In comparison to the large juvenile bighead carp, the small juvenile bighead carp showed weaker tolerance to TDG supersaturated water. Furthermore, acute lethality experiments after chronic exposure to TDG were initiated to further investigate the effect of TDG on bighead carp. The juveniles were first subjected to 115% TDG supersaturated water for 96 h. After chronic exposure, live fish were immediately transferred to TDG supersaturated water at levels of 125, 130, 135, and 140%. The results demonstrated that no fish died under chronic exposure and few fish exhibited slight GBD symptoms. The ST50 values for bighead carp subjected to acute exposure after chronic exposure were 61.23 and 23.50 h at 130 and 140%, respectively. Compared with the bighead carp subjected to acute exposure, bighead carp subjected to multiple exposures were more vulnerable to TDG.

High total dissolved gas (TDG) levels and excessive suspended sediment (SS) concentrations pose serious threats to fish survival during flood season. However, little information is available on the effects of TDG supersaturation with varying SS concentrations on fish. In this study, laboratory experiments were performed to investigate the effects of TDG supersaturation with varying SS concentrations on five-month-old river sturgeons (Acipenser dabryanus). The test fish were exposed to combinations of SS concentrations (0, 200, 600 and 1,000 mg/L) and TDG levels (125, 130, 135 and 140%), and their mortality and median lethal time (LT50) were quantified. The fish showed abnormal behaviors (e.g., quick breathing, fast swimming and an agitated escape response) and symptoms of gas bubble disease (GBD). SS increased the mortality of river sturgeon exposed to TDG supersaturation. Furthermore, the LT50 values at 125% TDG were 4.47, 3.11, 3.07 and 2.68 h for the different SS concentrations (0, 200, 600 and 1,000 mg/L, respectively), representing a significant decrease in LT50 with increasing SS. However, at higher TDG levels (130–140%), there was no significant increase in LT50 with increasing SS. Therefore, river sturgeon showed weak tolerance of TDG-supersaturated water with SS.