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Nocardia farcinica is the most pathogenic Nocardia, which is easy to disseminate. It can be caused by trauma, and even lead to severe lung or central nervous system infection. This report covers a case of Nocardia brain abscess in an HIV patient, who underwent resection of the brain abscess, followed by anti-infective therapy with sulfamethoxazole and meropenem, and eventually made a good recovery. The mortality rate of Nocardia farcinica brain abscess has been attributed to the severity of the underlying disease, the difficulty in identifying the pathogen, and its inherent resistance to antibiotics, leading to inappropriate or late initiation of treatment. Medication should follow the principle of sufficient dosage and sufficient course of treatment. [ABSTRACT FROM AUTHOR]

Background: Chlamydia abortus, as a pathogen of atypical pneumonia, is rare in humans, especially in HIV infection patients. Case presentation: We present the case of a 48-year-old man with a history of HIV infection who started high fever and developed pneumonia. The pathogen-targeted next-generation sequencing (ptNGS) results of bronchial lavage fluid showed Chlamydia abortus infection. Conclusion: This is the first report of Chlamydia abortus infection presented as atypical pneumonia in an AIDS patient. [ABSTRACT FROM AUTHOR]

Introduction: Kaposi sarcoma (KS) incidence has decreased since the initiation of combination antiretroviral therapy (cART), but it remains the most common cancer in people with HIV/AIDS (PWHA). PWHA with advanced immunosuppression who initiate antiretroviral therapy are susceptible to the occurrence of an immune reconstitution inflammatory syndrome (IRIS). Case Presentation: This report covers the case of a 25-year-old male with AIDS-related KS who relapsed after Liposomal Doxorubicin, but recovered well after administration of nab-paclitaxel (Nab-PTX). Conclusion: This is a rare case in choosing Nab-PTX to treat relapsed AIDS-KS and get good feedback. We report the case to provide a possible solution to treat AIDS-KS. [ABSTRACT FROM AUTHOR]

Background: Nontuberculous mycobacteria disease is a common invasive infectious disease in patients with HIV. However, Mycobacterium thermoresistibile association with lymphadenectasis is unusual in AIDS patients. Case Presentation: This report covers the case of a 25-year-old male AIDS patient infected with Mycobacterium thermoresistibile. The case was identified via pathogen-targeted next-generation sequencing (ptNGS). Conclusion: This is the first report of disseminated M. thermoresistibile infection presented with lymphadenectasis in an AIDS patient. Prompt diagnosis and antimicrobial treatment are crucial. [ABSTRACT FROM AUTHOR]

Introduction: Talaromycosis is a common invasive fungal disease in patients with HIV. However, its association with bone destruction is unusual in AIDS patients with talaromycosis. Case Presentation: This report covers the case of a 38-year-old male AIDS patient coinfected with Talaromyces marneffei and Salmonella. The case, which involved bone destruction, was identified via metagenomic next-generation sequencing (mNGS). Following treatment with a combination of amphotericin B and piperacillin-tazobactam, the patient's elbow motion noticeably improved. Imaging findings revealed that the progression of bony destruction had halted. Conclusion: Bone damage due to Talaromyces marneffei infection is infrequent in HIV-positive patients. Therefore, healthcare professionals must be vigilant for potential bone lesions associated with this type of infection. Prompt diagnosis and antimicrobial treatment are crucial. [ABSTRACT FROM AUTHOR]

Wide models such as generalized linear models and factorization-based models have been extensively used in various predictive applications, e.g., recommendation, CTR prediction, and image recognition. Due to the memory bounded property of the models, the performance improvement on CPU is reaching the limitation. GPU is known to have many computation units and high memory bandwidth, and becomes a promising platform for training machine learning models. However, the GPU training for the wide models is far from optimal due to the sparsity and irregularity in wide models. The existing GPU-based wide models are even slower than the ones using CPU. The classical training schema of the wide models does not optimized for the GPU architecture, which suffers from large amount of random memory accesses and redundant read/write of intermediate values. In this paper, we propose an efficient GPU-training framework for the large-scale wide models, named cuWide. To fully benefit from the memory hierarchy of GPU, cuWide applies a new flow-based schema for training, which leverages the spatial and temporal locality of wide models to drastically reduce the amount of communication with GPU global memory. To do so, we adopt a bigraph computation model to efficiently realize the flow-based schema and exploit three flexible interfaces for programming. Further, we use the 2D partition of mini-batch (in sample and feature dimensions) with proposed graph abstraction to optimize GPU memory access for sparse data, and apply several spatial-temporal caching mechanisms (importance-based model caching and cross-stage accumulation caching mechanisms) to achieve a high performance kernel. To efficiently implement cuWide, we also propose several GPU-oriented optimizations, including feature-oriented data layout to enhance the data locality, replication mechanism to reduce update conflicts in shared memory, and multi-stream scheduling to overlap data transferring and kernel computing. We show that cuWide can be up to more than 20× faster than the state-of-the-art GPU solutions and multi-core CPU solutions. [ABSTRACT FROM AUTHOR]

With the solid-state battery (vs. Li) application, the overlithiation mechanism of the different cathode materials is worthy to investigate. In this study, both LiMn2O4 and LiFePO4 cathode materials at different over-discharge conditions were tested using half cell (vs. Li) and anode-free systems. The cells were dismantled to study the electrode structure, surface morphology, and compositional changes. The study shows that LiMn2O4 and LiFePO4 still maintain good crystal morphology during the deep over-discharge process, showing better over-discharge resistance capability with different overlithiation mechanisms. As shown by X-ray diffraction and X-ray photoelectron spectroscopy with Ar-ion etching, the new phase, Li2Mn2O4, appears starting from 2.5 V. Until the voltage is less than 0.2 V, the framework structures of LiMn2O4 are deteriorated, and further overlithiation caused decomposition into Li2MnO2 and Li2O. LiFePO4 essentially maintains its olivine-type structure, but below 0.2 V, direct overlithiation causes decomposition into Li2O and Fe metal. Furthermore, overlithiated decomposition of LiMn2O4 and LiFePO4 occurs at very low voltages approximately 0.43 and 0.56 V, respectively. Additionally, the deep over-discharge also leads to the decay of the electrolyte structure, associated with LiF, Li2CO3 and LixPOyFz by-products. The detailed overlithiation mechanism will provide important theoretical guidance for practical applications. [ABSTRACT FROM AUTHOR]

Lessons Learned: Administration of autologous invariant natural killer T (iNKT) cells was safe and well‐tolerated in patients with hepatocellular carcinoma (Barcelona Clinic Liver Cancer stage B/C).Expanded iNKT cells produced T‐helper 1–like responses with possible antitumor activity.No severe adverse events were observed in any of the enrolled patients, including one patient who received 1010 in vitro–expanded autologous iNKT cells as a single infusion. Background: Invariant natural killer T cells co‐express T‐cell antigen receptor and natural killer (NK) cell receptors. Invariant natural killer T (iNKT) cells exhibit antitumor activity, but their numbers and functions are impaired in patients with hepatocellular carcinoma (HCC). The adoptive transfer of iNKT cells might treat advanced HCC. Methods: This phase I study (NCT03175679) enrolled 10 patients with HCC (Barcelona Clinic Liver Cancer [BCLC] stage B/C) at Beijing YouAn Hospital (April 2017 to May 2018). iNKT cells isolated from peripheral blood mononuclear cells (PBMCs) were expanded and alpha‐galactosylceramide (α‐GalCer)–pulsed. Dosage escalated from 3 × 107 to 6 × 107 to 9 × 107 cells/m2 (3+3 design). An exploratory dose trial (1 × 1010 cells/m2) was conducted in one patient. Results: Expanded iNKT cells produced greater quantities of T‐helper 1 (Th1) cytokines (e.g., interferon‐gamma, perforin, and granzyme B) but less interleukin‐4 than nonexpanded iNKT cells. Circulating numbers of iNKT cells and activated NK cells were increased after iNKT cell infusion. Most treatment‐related adverse events were grade 1–2, and three grade 3 adverse events were reported; all resolved without treatment. Four patients were progression‐free at 5.5, 6, 7, and 11 months after therapy, and one patient was alive and without tumor recurrence at the last follow‐up. Five patients died at 1.5 to 11 months after treatment. Conclusion: Autologous iNKT cell treatment is safe and well‐tolerated. Expanded iNKT cells produce Th1‐like responses with possible antitumor activity. The antitumor effects of iNKT cell infusion in patients with advanced HCC merit further investigation. [ABSTRACT FROM AUTHOR]

Topic models, such as LDA and its variants, are popular probabilistic models for discovering the abstract “topics” that occur in a collection of documents. However, the performance of topic models may vary a lot for different workloads, and it is not a trivial task to achieve a well-optimized implementation. In this paper, we systematically study all recently proposed samplers over LDA: AliasLDA, F+LDA, LightLDA, and WarpLDA, and discover a novel system tradeoff by considering the diversity and skewness of workloads. Then, we propose a hybrid sampler which can cleverly choose an efficient sampler with the tradeoff, and apply the hybrid sampler to LDA and its variants, including STM, TOT and CTM. Finally, we build a fast and general topic modeling system Sys-TM, which provides a unified topic modeling framework by integrating the hybrid sampler. Based on our empirical studies, the hybrid sampler outperforms the state-of-the-art samplers by up to 2 × over various topic models, and with carefully engineered implementation, Sys-TM is able to outperform the existing systems by up to 10 × . [ABSTRACT FROM AUTHOR]

PoCel12A, PoCel12B, and PoCel12C are genes that encode glycoside hydrolase family 12 (GH12) enzymes in Penicillium oxalicum. PoCel12A and PoCel12B are typical GH12 enzymes that belong to fungal subfamilies 12-1 and 12-2, respectively. PoCel12C contains a low-complexity region (LCR) domain, which is not found in PoCel12A or PoCel12B and independent of fungal subfamily 12-1 or 12-2. Recombinant enzymes (named rCel12A, rCel12B and rCel12C) demonstrate existing diversity in the substrate specificities. Although most members in GH family 12 are typical endoglucanases and preferentially hydrolyze β-1,4-glucan (e.g., carboxymethylcellulose), recombinant PoCel12A is a non-typical endo-(1-4)-β-glucanase; it preferentially hydrolyzes mix-linked β-glucan (barley β-glucan, β-1,3-1,4-glucan) and slightly hydrolyzes β-1,4-glucan (carboxymethylcellulose). Recombinant PoCel12B possesses a significantly high activity against xyloglucan. A specific activity of rCel12B toward xyloglucan (239 µmol/min/mg) is the second-highest value known. Recombinant PoCel12C shows low activity toward β-glucan, carboxymethylcellulose, or xyloglucan. All three enzymes can degrade phosphoric acid-swollen cellulose (PASC). However, the hydrolysis products toward PASC by enzymes are different: the main hydrolysis products are cellotriose, cellotetraose, and cellobiose for rCel12A, rCel12B, and rCel12C, correspondingly. A synergistic action toward PASC among rCel12A and rCel12B is observed, thereby suggesting a potential application for preparing enzyme cocktails used in lignocellulose hydrolysis. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) techniques now are ubiquitous tools to extract structural information from data collections. With the increasing volume of data, large-scaleML applications require an efficient implementation to accelerate the performance. Existing systems parallelize algorithms through either data parallelism or model parallelism. But data parallelism cannot obtain good statistical efficiency due to the conflicting updates to parameters while the performance is damaged by global barriers in model parallel methods. In this paper, we propose a new system, named Angel, to facilitate the development of large-scale ML applications in production environment. By allowing concurrent updates to model across different groups and scheduling the updates in each group, Angel can achieve a good balance between hardware efficiency and statistical efficiency. Besides, Angel reduces the network latency by overlapping the parameter pulling and update computing and also utilizes the sparseness of data to avoid the pulling of unnecessary parameters. We also enhance the usability of Angel by providing a set of efficient tools to integrate with application pipelines and provisioning efficient fault tolerance mechanisms.We conduct extensive experiments to demonstrate the superiority of Angel. [ABSTRACT FROM AUTHOR]

With the rapid development of social media applications, lots of users are connected with friends online, and their daily life and opinions are recorded. Social media provides us an unprecedented way to collect and analyze billions of users' information. Proper user attribute identification or profile inference becomes increasingly attractive and feasible. However, the flourishing social records also pose great challenge in effective feature selection and integration for user profile inference, which is mainly caused by the text diversity and complex community structures. In this paper, we propose a comprehensive framework to infer the user occupation from his/her social activities recorded in the micro-blog system, which is a multi-source integration framework that combines both content and network information. We first identify some beneficial content features, and propose a machine learning classification model, named content model. We proceed to exploit the social network information, which tailors a community discovery based latent dimension solution to extract community-based feature, and utilizes the neighbor predictions for inference updating. Extensive empirical studies are conducted on a large real-life micro-blog dataset. The experimental results demonstrate the superiority of our integrated model for the occupation inference task, verify the effect of homophily in user interaction records, and reveal different effects of heterogeneous interactive networks. [ABSTRACT FROM AUTHOR]