BackgroundCentral nervous system (CNS) tuberculoma is a rare disease with severe neurological deficits. This retrospective research is to review the data of patients diagnosed as CNS tuberculoma. Surgeries were performed in all patients. The clinical features especially the neurological image and the anatomical characters of the tuberculomas were concerned.MethodsTotally 11 patients diagnosed as CNS tuberculoma were admitted in Guangzhou First People’s Hospital (7cases) and Changzheng Hospital (4 cases) during 2006–2015. The data including preoperative condition, neurological imaging, and surgical findings was collected and analyzed.ResultsThe lesions of nine patients (9/11) were totally or subtotally excised and two (2/11) were partially excised. Neurological functions of all patients were improved after surgery without secondary infection. Lesions of nine (9/11) patients preoperatively progressed as a result of paradoxical reaction. Of the 9 patients demonstrated paradoxical progression, all lesions were partially or totally located at the cisterns or the subarachnoid space. Preoperative ATTs lasted 2 to 12 months and tuberculomas were not eliminated. The arachnoid was found thickened and tightly adhered to the lesions during surgeries. Of the 2 cases that paradoxical reaction were excluded, both patients (case 6, intramedullary tuberculoma; case 11, intradural extramedullary tuberculoma) were admitted at onset of the disease. ATTs were preoperatively given for 1 week as neurological deficits aggravated. The tuberculous lesions of CNS or other system showed no obvious change and paradoxical reaction could not be established in both cases.ConclusionsExudates of tuberculosis is usually accumulated in the cisterns and frequently results in the paradoxical formation of tuberculoma. Intracisternal tuberculoma is closely related to paradoxical reaction and refractory to anti-tuberculosis therapy. Micro-surgical excision is safe and effective. Early surgical intervention may be considered in the diagnosis of intracisternal tuberculoma especially when paradoxical reaction participates in the development of tuberculoma.

Raised-floor data centers usually suffer from the local hotspots resulted from uneven cool air delivery. These hotspots not only degrade server performance, but also threat equipment reliability. The commonly used industrial practice of increasing the Computer Room Air Conditioner (CRAC) blower speed for removing hotspots is energy inefficient and may lead to overcooling of some servers. In this paper, we explore the potential of active tiles in data center cooling management. In particular, we deploy a prototype of active tile in a production data center and conduct extensive experiments to investigate the cooling performance. It is shown that deploying the active tiles with even 10% fan speed increases the tile flow by 49%, and sealing the under-rack gap reduces the rack bottom temperature by up to 6°C. Moreover, three machine learning techniques, i.e., Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Multivariate Linear Regression (MLR) are employed to construct end-to-end data-driven thermal models for the active tile. Using field measured data as training and testing data sets, it is concluded that GPR and ANN are competent for accurate thermal modeling of active tiles. Specifically, GPR achieves the smallest prediction error which is around 0.3°C.

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