In order to study the clinical characteristics of intraventricular meningiomas, we analyzed retrospectively 25 patients and reviewed the literature with regard to incidence, clinical manifestation, imaging features, preoperative diagnosis, surgical findings, and histopathological results. Intraventricular meningiomas are quite rare, but they represent an important differential diagnosis of intraventricular neoplasms. Computed tomography or magnetic resonance imaging enable a correct diagnosis of intraventricular meningiomas in most of the cases. The tumors often grow slowly to a substantial size before they become symptomatic. The operative route should be selected according to the tumor's location. Out of the 24 lateral ventricular meningiomas in our series, 20 were resected via a posterior parieto-occipital transcortical approach, two were resected via a transcallosal approach, and another two tumors, located in the frontal horn and body of the lateral ventricle, were resected via a frontal approach. A median suboccipital craniotomy was performed for the fourth ventricular meningioma. The parieto-occipital route for lateral ventricular meningiomas is a safe surgical approach, which is not necessarily associated with postoperative visual deficits. Piecemeal removal of the tumor can be safely and easily performed and special attention should be paid to the choroidal vessels intraoperatively.

Trichinellosis is a foodborne zoonotic disease caused by Trichinella spp., including Trichinella spiralis. In the present study, T. spiralis membrane-associated progesterone receptor component-2 (Ts-MAPRC2) gene was cloned and characterized using protein sequencing analysis. Furthermore, the expression, purification, immunoblot assay, binding ability with progesterone antibody, and immunofluorescence assay were performed. A direct effect of progesterone (P4) and mifepristone (RU486) on the Ts-MAPRC2 gene was determined using in vitro cell culture that showed different expression levels at all developmental stages (muscle larvae (ML), female adult worm (F-AL), male adult worm (M-AL), and newborn larvae (NBL)). Subsequently, the in vitro phenotypic effects of P4, RU486, and rTs-MAPRC2-Ab on F-AL and ML stages were measured. Later, the in vivo phenotypic effect and relative mRNA expression of mifepristone on the F-AL stage were studied. Our results revealed that the Ts-MAPRC2 gene is critical to maintaining pregnancy in the female adult worm (F-AL) of T. spiralis. The 300 ng/mL of P4 and 100 ng/mL of RU486 showed downregulation of the Ts-MAPRC2 gene in F-AL (p ≤ 0.05). This plays an important role in abortion and possibly decreases the worm burden of T. spiralis in the host. Only 30 ng/mL P4 showed significant upregulation in F-AL (p ≤ 0.05). The current study provides new insights regarding the antihormone (P4 and RU486) drug design and vaccine therapy of recombinant (rTs-MAPRC2) protein as well as their combined effects to control T. spiralis infection.

Using voxel-based morphometry (VBM), we studied cortical gray matter volume changes in patients with cervical spondylotic myelopathy (CSM) before and after cervical cord surgical decompression. We then discussed the structural damage mechanisms and the neural plasticity mechanisms involved in postsurgical CSM. Forty-five presurgical CSM patients, 41 of the same group followed-up 6 months after decompression surgery and 45 normal controls (NC) matched for age, sex and level of education underwent high-resolution 3-dimensional T1-weighted scans by 3.0 T MR. Then, VBM measurements were compared and cortical gray matter volume alterations were assessed among pre- or postsurgical CSM patients and NC, as well as correlations with clinical indexes by Pearson correlation. Compared with NC, presurgical CSM patients showed reduced gray matter volume in the left caudate nucleus and the right thalamus. After 6 months, postsurgical CSM patients had lower gray matter volume in the bilateral cerebellar posterior lobes but had higher gray matter volume in the brain-stem than did presurgical CSM patients. Postsurgical CSM patients had significantly lower gray matter volume in the left caudate nucleus but greater regional gray matter volume in the right inferior temporal gyrus, the right middle orbitofrontal cortex (OFC) and the bilateral lingual gyrus / precuneus /posterior cingulate cortex than did NC. Abnormal areas gray volume in presurgical CSM and postsurgical CSM patients showed no significant correlation with clinical data ( P > .05). Myelopathy in the cervical cord may cause chronic cerebral structural damage before and after the decompression stage, markedly in outlier brain regions involving motor execution/control, vision processing and the default mode network and in areas associated with brain compensatory plasticity to reverse downstream spinal cord compression and respond to spinal cord surgical decompression.

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Background/Aims: Risk factor studies for acute kidney injury (AKI) in China are lacking, especially those regarding non-traditional risk factors, such as laboratory indicators. Methods: All adult patients admitted to 38 tertiary and 22 secondary hospitals in China in any one month between July and December 2014 were surveyed. AKI patients were screened according to the Kidney Disease: Improving Global Outcomes’ definition of AKI. Logistic regression was used to analyze the risk factors for AKI, and Cox regression was used to analyze the risk of in-hospital mortality for AKI patients; additionally, a propensity score analysis was used to reconfirm the risk factors among laboratory indicators for mortality. Results: The morbidity of AKI was 0.97%. Independent risk factors for AKI were advancing age, male gender, hypertension, and chronic kidney disease. All-cause mortality was 16.5%. The predictors of mortality in AKI patients were advancing age, tumor, higher uric acid level and increases in Acute Physiologic Assessment and Chronic Health Evaluation II and Sequential Organ Failure Assessment scores. The hazard ratio (HR) for mortality with uric acid levels > 9.1 mg/dl compared with ≤ 5.2 mg/dl was 1.78 (95% CI: 1.23 to 2.58) for the AKI patients as a group, and was 1.73 (95% CI: 1.24 to 2.42) for a propensity score-matched set. Conclusion: In addition to traditional risk factors, uric acid level is an independent predictor of all-cause mortality after AKI.

Offline reinforcement learning leverages previously-collected offline datasets to learn optimal policies with no necessity to access the real environment. Such a paradigm is also desirable for multi-agent reinforcement learning (MARL) tasks, given the increased interactions among agents and with the enviroment. Yet, in MARL, the paradigm of offline pre-training with online fine-tuning has not been studied, nor datasets or benchmarks for offline MARL research are available. In this paper, we facilitate the research by providing large-scale datasets, and use it to examine the usage of the Decision Transformer in the context of MARL. We investigate the generatlisation of MARL offline pre-training in the following three aspects: 1) between single agents and multiple agents, 2) from offline pretraining to the online fine tuning, and 3) to that of multiple downstream tasks with few-shot and zero-shot capabilities. We start by introducing the first offline MARL dataset with diverse quality levels based on the StarCraftII environment, and then propose the novel architecture of multi-agent decision transformer (MADT) for effective offline learning. MADT leverages transformer's modelling ability of sequence modelling and integrates it seamlessly with both offline and online MARL tasks. A crucial benefit of MADT is that it learns generalisable policies that can transfer between different types of agents under different task scenarios. On StarCraft II offline dataset, MADT outperforms the state-of-the-art offline RL baselines. When applied to online tasks, the pre-trained MADT significantly improves sample efficiency, and enjoys strong performance both few-short and zero-shot cases. To our best knowledge, this is the first work that studies and demonstrates the effectiveness of offline pre-trained models in terms of sample efficiency and generalisability enhancements in MARL.

In this paper, we propose MixSpeech, a simple yet effective data augmentation method based on mixup for automatic speech recognition (ASR). MixSpeech trains an ASR model by taking a weighted combination of two different speech features (e.g., mel-spectrograms or MFCC) as the input, and recognizing both text sequences, where the two recognition losses use the same combination weight. We apply MixSpeech on two popular end-to-end speech recognition models including LAS (Listen, Attend and Spell) and Transformer, and conduct experiments on several low-resource datasets including TIMIT, WSJ, and HKUST. Experimental results show that MixSpeech achieves better accuracy than the baseline models without data augmentation, and outperforms a strong data augmentation method SpecAugment on these recognition tasks. Specifically, MixSpeech outperforms SpecAugment with a relative PER improvement of 10.6% on TIMIT dataset, and achieves a strong WER of 4.7% on WSJ dataset.

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