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Objective: Intraventricular hemorrhage (IVH) is deemed to result in poor outcomes in patients with aneurysmal subarachnoid hemorrhage (SAH). The aim of this study was to explore the efficacy and safety of intraventricular injections of recombinant tissue plasminogen activator (rt-PA). Methods: We searched MEDLINE, EMBASE, and Cochrane Library from January 1980 to March 2015 for studies in English. The primary outcome was good functional improvement. The secondary outcomes were angiographic vasospasm, acute obstructive hydrocephalus, hemorrhage rate, and mortality. Results: Three observational studies and 3 randomized controlled trials (RCTs) with 217 patients were included in the present study. There is a significant difference in angiographic vasospasm (RR 0.58, 95% CI 0.16 to 0.85, P = 0.01). In the subgroup analysis, angiographic vasospasm (RR 0.37, 95% CI 0.38 to 0.88, P = 0.02) and acute obstructive hydrocephalus (RR 0.48, 95% CI 0.27 to 0.84, P = 0.01) showed significant differences in the observational studies. High dosage of rt-PA showed a significant difference in angiographic vasospasm (RR 0.60, 95% CI 0.38 to 0.97, P = 0.04). Sensitivity analysis showed that no significant differences were observed in all the outcomes after the Ramakrishna 2010 trial was excluded. Conclusion: Intraventricular rt-PA has no significant efficacy on the long-term functional recovery after aneurysmal SAH with IVH. However, high dosage of rt-PA might reduce the incidence of angiographic vasospasm. A feasible, large-scale, multi-center, placebo RCT is needed to confirm the present findings.

Intestinal pseudo-obstruction (IpsO) and acute lupus pneumonitis (ALP) are uncommon severe complications of systemic lupus erythematosus (SLE). The present study reports the case of a 26-year-old female who presented with abdominal pain, nausea and vomiting as initial symptoms. Computed tomography (CT) scanning revealed the jejunal wall was thickened and streaky, mimicking the presentation of intestinal obstruction. Following emergency surgery, the patient's general condition was aggravated, with evident limb erythematous rashes. A series of laboratory examinations revealed SLE, and combined with patient's medical history IpsO was diagnosed, with a disease Activity Index score of 10. During the therapeutic period, high fever, dyspnea and oxygen saturation (SaO2) reductions were detected, and CT scans indicated lung infiltration, excluding other causes through a comprehensive infectious work-up and a bronchoalveolar lavage examination. ALP was confirmed and treated with high-dose methylprednisolone and gamma globulin supplement. The patient responded well and was discharged in 2 weeks. In the one-year tapering period and after stopping corticosteroids, the patient recovered well with no relapse detected. In conclusion, the manifestation of IpsO in SLE is rare and represents a challenge for the surgeon to establish the correct diagnosis and avoid inappropriate surgical intervention. ALP may be the consequence of emergency surgery, and immediate high-dose glucocorticoid therapy is recommended.

Tracking in sensor networks has shown great potentials in many real world surveillance and emergency system. Due to the distributive nature and unpredictable topology structure of the randomly distributed sensor network, a good tracking algorithm must be able to aggregate large amounts of data from various unknown sources. In this paper, a distributive tracking algorithm is developed using a Markov random field (MRF) model to solve this problem. The Markov random field (MRF) utilizes probability distribution and conditional independency to identify the most relevant data from the less important data. The algorithm converts the randomly distributed network into a regularly distributed topology structure using cliques. This makes tracking in the randomly distributed network topology simple and more predictable. Simulation demonstrate that the algorithm performs well for various sensor field setting, and for various target sizes.

Recent advancement in sensor networks provides a platform for applications that requires in-network data fusion and parallel algorithms. However, processing data in parallel while propagating at low latency is very challenging. Also, implementation of these algorithms is limited by various constraints including energy, computation costs and complex network topology. In this paper, a statistical graphical model based algorithm is developed for in-network processing, which can be applied to tracking problems in the sensor networks. This algorithm represents the complex topology of a sensor network with a simple clique tree. It further utilizes the message passing algorithms to effectively make accurate inferences about the target location. The simulation shows that the algorithm can accurately track the target in a large scale random distributed sensor field with low complexity and low cost. The algorithm is also proved to be robust, as the simulation random disabled some sensors during the tracking phase.

A distributed two-tier near optimal algorithm is proposed for target tracking in sensor networks. Tier one is a multiple hypothesis tracking (MHT) algorithm where the Viterbi algorithm is used. In this tier, only binary data is used to obtain a rough region around the target. Tier two improves the accuracy of the MHT decision by localized maximum likelihood. This reduces the computational complexity and the communication costs between sensors over the global maximum likelihood approach. It also results in higher sensor power efficiency, hence longer service time of the tracking network. This two-tier system is a distributed near optimal tracking algorithm. The localized maximum likelihood tracking can tolerate errors made by the Viterbi algorithm in tier one, hence the overall algorithm is robust.

Changes in gain associated with the basilar membrane compressive nonlinearity are accompanied by changes in the bandwidth of tuning. Filters with level‐dependent bandwidth have level‐dependent phase properties. These phase properties result in level‐dependent timing of sustained phase‐locked responses of auditory‐nerve (AN) fibers at low frequencies and level‐dependent latencies at high frequencies, where phase‐locking rolls off. In the healthy ear, level‐dependent temporal aspects of AN responses carry information about stimulus level and spectral properties. Loss of compression with hearing impairment thus results not only in a reduction of amplification, but also in distortion of the temporal response pattern of the AN. The temporal aspects of compression suggest that signal‐processing schemes that attempt to correct sounds, or restore normal spatio‐temporal response patterns, should include dynamic level‐dependent phase shifts. A nonlinear signal‐processing scheme will be presented which includes dyna...