Automated detection of focal cortical dysplasia based on magnetic resonance imaging and positron emission tomography.

PURPOSE: Focal cortical dysplasia (FCD) is a common etiology of drug-resistant focal epilepsy. Visual identification of FCD is usually time-consuming and depends on personal experience. Herein, we propose an automated type II FCD detection approach utilizing multi-modal data and 3D convolutional neural network (CNN). METHODS: MRI and positron emission tomography (PET) data of 82 patients with FCD were collected, including 55 (67.1%) histopathologically, and 27 (32.9%) radiologically diagnosed patients. Three types of morphometric feature maps and three types of tissue maps were extracted from the T1-weighted images. These maps, T1, and PET images formed the inputs for CNN. Five-fold cross-validations were carried out on the training set containing 62 patients, and the model behaving best was chosen to detect FCD on the test set of 20 patients. Furthermore, ablation experiments were performed to estimate the value of PET data and CNN. RESULTS: On the validation set, FCD was detected in 90.3% of the cases, with an average of 1.7 possible lesions per patient. The sensitivity on the test set was 90.0%, with 1.85 possible lesions per patient. Without the PET data, the sensitivity decreased to 80.0%, and the average lesion number increased to 2.05 on the test set. If an artificial neural network replaced the CNN, the sensitivity decreased to 85.0%, and the average lesion number increased to 4.65. SIGNIFICANCE: Automated detection of FCD with high sensitivity and few false-positive findings is feasible based on multi-modal data. PET data and CNN could improve the performance of automated detection.

MsVRL: Self-Supervised Multiscale Visual Representation Learning via Cross-Level Consistency for Medical Image Segmentation.

Automated medical image segmentation for organs or lesions plays an essential role in clinical diagnoses and treatment plannings. However, training an accurate and robust segmentation model is still a long-standing challenge due to the time-consuming and expertise-intensive annotations for training data, especially 3-D medical images. Recently, self-supervised learning emerges as a promising approach for unsupervised visual representation learning, showing great potential to alleviate the expertise annotations for medical images. Although global representation learning has attained remarkable results on iconic datasets, such as ImageNet, it can not be applied directly to medical image segmentation, because the segmentation task is non-iconic, and the targets always vary in physical scales. To address these problems, we propose a Multi-scale Visual Representation self-supervised Learning (MsVRL) model, to perform finer-grained representation and deal with different target scales. Specifically, a multi-scale representation conception, a canvas matching method, an embedding pre-sampling module, a center-ness branch, and a cross-level consistent loss are introduced to improve the performance. After pre-trained on unlabeled datasets (RibFrac and part of MSD), MsVRL performs downstream segmentation tasks on labeled datasets (BCV, spleen of MSD, and KiTS). Results of the experiments show that MsVRL outperforms other state-of-the-art works on these medical image segmentation tasks.

Black TiO2-Based Dual Photoanodes Boost the Efficiency of Quantum Dot-Sensitized Solar Cells to 11.7.

Quantum dot-sensitized solar cells (QDSSC) have been regarded as one of the most promising candidates for effective utilization of solar energy, but its power conversion efficiency (PCE) is still far from meeting expectations. One of the most important bottlenecks is the limited collection efficiency of photogenerated electrons in the photoanodes. Herein, we design QDSSCs with a dual-photoanode architecture, and assemble the dual photoanodes with black TiO2 nanoparticles (NPs), which were processed by a femtosecond laser in the filamentation regime, and common CdS/CdSe QD sensitizers. A maximum PCE of 11.7% with a short circuit current density of 50.3 mA/cm2 is unambiguously achieved. We reveal both experimentally and theoretically that the enhanced PCE is mainly attributed to the improved light harvesting of black TiO2 due to the black TiO2 shells formed on white TiO2 NPs.

From Green Super Rice to green agriculture: Reaping the promise of functional genomics research.

Producing sufficient food with finite resources to feed the growing global population while having a smaller impact on the environment has always been a great challenge. Here, we review the concept and practices of Green Super Rice (GSR) that have led to a paradigm shift in goals for crop genetic improvement and models of food production for promoting sustainable agriculture. The momentous achievements and global deliveries of GSR have been fueled by the integration of abundant genetic resources, functional gene discoveries, and innovative breeding techniques with precise gene and whole-genome selection and efficient agronomic management to promote resource-saving, environmentally friendly crop production systems. We also provide perspectives on new horizons in genomic breeding technologies geared toward delivering green and nutritious crop varieties to further enhance the development of green agriculture and better nourish the world population.

Effects of rivastigmine hydrogen tartrate and donepezil hydrochloride on the cognitive function and mental behavior of patients with Alzheimer's disease.

The present study aimed to examine the effects of rivastigmine hydrogen tartrate and donepezil hydrochloride on the cognitive function and mental behavior of patients with Alzheimer's disease (AD). For this purpose, a total of 126 patients with AD admitted to Luoyang Central Hospital from January, 2018 to December, 2018 were enrolled. Patients were divided into different groups according to the treatment they selected. Patients treated with single-agent donepezil were separated into a monotherapy group (n=56), and patients receiving donepezil plus rivastigmine were placed in the combination group (n=70). Before and after treatment, the cognitive functions, mental behavior and quality of life of the patients in the two groups were respectively evaluated by the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), the Mini-Mental State Examination (MMSE), the Blessed-Roth Dementia Scale (BRDS) and the QOL-AD. In addition, the serum bradykinin level was detected by enzyme-linked immunosorbent assay. Following treatment, the MMSE score, BRDS, ADAS-Cog and QOL-AD scores were improved compared with those before treatment (P<0.05). However, following treatment, the 4 scores in the combination group were significantly higher than those in the monotherapy group (P<0.05). No significant differences were observed in the incidence of adverse reactions between the 2 groups (P>0.05). Following treatment, the bradykinin level in both groups was significantly decreased (P<0.05), although the decrease in the combination group was more significant than that in the monotherapy group (P<0.05). On the whole, the findings of the present study indicate that rivastigmine hydrogen tartrate used in combination with donepezil hydrochloride relieves the symptoms and improves the quality of life of patients with AD more effectively, which may be related to the reduction of the bradykinin level in these patients.

Low-Complexity Adaptive Signal Detection for Mobile Molecular Communication.

Currently, most of the researches in molecular communication (MC) domain focus on the static MC scenarios. However, some envisioned important MC applications require mobile MC system. The investigation on mobile MC, especially the signal detection of mobile MC is limited. This work considers the problem of signal detection for mobile MC scenarios where the receiver nano-machine performs random movement. Due to the random movement of the receiver, the channel impulse response (CIR) changes over time which makes the received signal stochastic and complicated. This further complicates the signal detection in mobile MC and leads to that the state-of-the-art signal detection schemes for static MC scenarios fail for the mobile MC scenarios. To solve this issue, an adaptive detection scheme has been proposed by our group previously, based on dynamic estimation of the stochastically varying distance between the transmitter and receiver and the reconstruction of CIR in each interval. However, its computational complexity is high. Limited capability of current nano-machines desire low-complexity detection algorithm. In this work, we further propose an adaptive detection scheme for mobile MC with a low computational complexity by utilizing the local convex property of the CIR. With on-off keying (OOK) modulation, the signal of symbol "1" presents local convex property while that of symbol "0" presents local concave property. The convexity extent varies with the stochastic distance. A simple indicator, local maximum convexity is proposed which adapts to the stochastic distance. By comparing the adaptive indicator with an adaptive threshold within each symbol interval, the signal is detected without the need to estimate the stochastically changing distance or to reconstruct the CIR. Therefore, the computational load is effectively reduced. Numerical simulations are performed to evaluate the proposed scheme. The results show that the proposed scheme achieves good detection accuracy with low computational complexity and it could be a promising detection scheme for mobile MC scenarios.

Preparation of chitosan­silicon dioxide/BCSG1­siRNA nanoparticles to enhance therapeutic efficacy in breast cancer cells.

Breast cancer is one of the most serious diseases, posing threats to women's physical and mental health. Gene therapy has been gradually regarded as an important part of tumor therapeutics. In the present study, the breast cancer­specific gene 1­small interference RNA (BCSG1­siRNA) plasmid was designed, then encapsulated by chitosan­silicon dioxide nanometer carriers. The results demonstrated a successful encapsulation of BCSG1­siRNA in chitosan­silicon dioxide nanoparticles (encapsulation efficiency exceeded 90%). BCSG1­siRNA was released slowly (the release rate was almost 30% after 24 h). The cytotoxic effect on MCF­7 cells was enhanced by increasing the concentration of nanoparticle (the proliferation rate was reduced to 13.4±5.3% and apoptosis rate was increased to 71.5±6.8%). Therefore, the materials presented in the current study acted as successful gene carriers and exhibited significant antitumor effects in breast cancer cells.

Dosimetric comparison of intensity-modulated radiotherapy and three-dimensional conformal radiotherapy for cerebral malignant gliomas.

PURPOSE: To compare the dose distribution characteristics of tumor target area, normal tissues and organs at risk in patients with malignant gliomas treated with intensity- modulated radiotherapy (IMRT) and three-dimensional conformal radiotherapy (3DCRT). METHODS: Plans of IMRT and 3DCRT were designed for each of the 96 included patients with malignant gliomas. Tumor dose was 60 Gy, and the dose distribution differences between the target area and normal tissues were compared using dose-volume histogram (DVH). RESULTS: Gross tumor volume (GTV) doses for 95% of the volume in the plans of IMRT and 3DCRT were as follows: 59.82±0.43, 57.68±0.62 Gy (p<0.05); clinical target volume (CTV): 58.16±0.48, 54.47±0.28 Gy (p<0.05); and planning treatment volume (PTV): 57.38±0.74, 54.21±0.48 Gy (p<0.05). The conformal index (CI) values of IMRT and 3DCRT plans were 0.92±0.15 and 0.73±0.12, respectively (p<0.05), whereas the homogeneity index (HI) values variability of IMRT and 3DCRT were 0.78±0.12 and 1.13±0.09 respectively (p<0.05). For normal brain tissues pituitary and optic chiasm, the maximum dose (Dmax) and the mean dose (Dmean) of lens exposure differed significantly between thw two plans (p<0.05). CONCLUSION: The target dose distribution of IMRT was superior to that of 3DCRT in terms of rationality, uniformity and conformal nature. IMRT may be better in protecting normal tissue and increasing the tumor radiation dose compared with 3DCRT.

Synuclein-γ suppression mediated by RNA interference inhibits the clonogenicity and invasiveness of MCF-7 cells.

The aim of the present study was to investigate the effects of synuclein-γ (SNCG) downregulation by RNA interference (RNAi) on the clonogenicity and invasiveness of MCF-7 breast cancer cells. This study used four pairs of SNCG-specific siRNAs which were designed and cloned into the pGPU6 plasmid for introduction into an MCF-7 cell line. The SNCG knockdown efficacies of the four siRNAs were compared using the reverse transcription polymerase chain reaction (RT-PCR) and immunocytochemistry. The cells' clonogenic and invasive phenotypes were examined with clonogenic and Boyden chamber assays. In comparison with the non-specific siRNA and empty vector controls, all four SNCG siRNAs were observed to significantly inhibit SNCG expression at the mRNA and protein levels (F=481.06, P<0.001; F=147.42, P<0.0001). SNCG suppression mediated by RNAi successfully inhibited the clonogenicity (P=0.002) and invasiveness (P<0.001) of transfected MCF-7 cells. According to the results of the present study, we concluded that SNCG suppression mediated by RNAi significantly suppressed SNCG expression at the mRNA and protein levels, suggesting that SNCG suppression mediated by an RNAi strategy may become a novel approach for treating advanced breast cancer.

Risk perceptions for avian influenza virus infection among poultry workers, China.

To determine risk for avian influenza virus infection, we conducted serologic surveillance for H5 and H9 subtypes among poultry workers in Beijing, China, 2009-2010, and assessed workers' understanding of avian influenza. We found that poultry workers had considerable risk for infection with H9 subtypes. Increasing their knowledge could prevent future infections.

[Investigation on status of Oncomelania hupensis in schistosomiasis transmission-interrupted areas of Huangshan City].

OBJECTIVE: To explore a new monitoring method for schistosomiasis transmission interrupted areas, so as to evaluate the monitoring results in counties (districts) to improve the quality. METHODS: Specialized technicians were selected as survey teams to investigate the status of Oncomelania hupensis snails with the random sampling method combined with environmental sampling method every year from 1997 to 2013. RESULTS: From 1997 to 2013, 118 villages of 38 townships of 21 counties were investigated for the snail status, and there were 18 environments with snails in 10 townships of 7 counties. The snail area was 10.91 hm2. CONCLUSION: There are still small snail areas. Therefore, the snail monitoring should still be strengthened by specialized technicians.