Explore the vast range of titles available.

The SPADE (spatio-temporal S pike PA ttern D etection and E valuation) method was developed to find reoccurring spatio-temporal patterns in neuronal spike activity (parallel spike trains). However, depending on the number of spike trains and the length of recording, this method can exhibit long runtimes. Based on a realistic benchmark data set, we identified that the combination of pattern mining (using the FP-Growth algorithm) and the result filtering account for 85–90% of the method's total runtime. Therefore, in this paper, we propose a customized FP-Growth implementation tailored to the requirements of SPADE , which significantly accelerates pattern mining and result filtering. Our version allows for parallel and distributed execution, and due to the improvements made, an execution on heterogeneous and low-power embedded devices is now also possible. The implementation has been evaluated using a traditional workstation based on an Intel Broadwell Xeon E5-1650 v4 as a baseline. Furthermore, the heterogeneous microserver platform RECS|Box has been used for evaluating the implementation on two HiSilicon Hi1616 (Kunpeng 916), an Intel Coffee Lake-ER Xeon E-2276ME, an Intel Broadwell Xeon D-D1577, and three NVIDIA Tegra devices (Jetson AGX Xavier, Jetson Xavier NX, and Jetson TX2). Depending on the platform, our implementation is between 27 and 200 times faster than the original implementation. At the same time, the energy consumption was reduced by up to two orders of magnitude.

This paper is concerned with Field Programmable Gate Arrays (FPGA)-based systems for energy-efficient high-throughput string comparison. Modern applications which involve comparisons across large data sets—such as large sequence sets in molecular biology—are by their nature computationally intensive. In this work, we present a scalable FPGA-based system architecture to accelerate the comparison of binary strings. The current architecture supports arbitrary lengths in the range 16 to 2048-bit, covering a wide range of possible applications. In our example application, we consider DNA sequences embedded in a binary vector space through Locality Sensitive Hashing (LSH) one of several possible encodings that enable us to avoid more costly character-based operations. Here the resulting encoding is a 512-bit binary signature with comparisons based on the Hamming distance. In this approach, most of the load arises from the calculation of the O ( m ∗ n ) Hamming distances between the signatures, where m is the number of queries and n is the number of signatures contained in the database. Signature generation only needs to be performed once, and we do not consider it further, focusing instead on accelerating the signature comparisons. The proposed FPGA-based architecture is optimized for high-throughput using hundreds of computing elements, arranged in a systolic array. These core computing elements can be adapted to support other string comparison algorithms with little effort, while the other infrastructure stays the same. On a Xilinx Virtex UltraScale+ FPGA (XCVU9P-2), a peak throughput of 75.4 billion comparisons per second—of 512-bit signatures—was achieved, using a design with 384 parallel processing elements and a clock frequency of 200 MHz. This makes our FPGA design 86 times faster than a highly optimized CPU implementation. Compared to a GPU design, executed on an NVIDIA GTX1060, it performs nearly five times faster.

The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propose a holistic approach that focuses on optimizing algorithms while addressing safety and security challenges inherent to AIoT systems. The foundation of this approach lies in a modular and scalable cognitive IoT hardware platform, which leverages microserver technology to enable users to configure the hardware to meet the requirements of a diverse array of applications. Heterogeneous computing is used to boost performance and energy efficiency. In addition, the full spectrum of hardware accelerators is integrated, providing specialized ASICs as well as FPGAs for reconfigurable computing. The project's contributions span across trusted computing, remote attestation, and secure execution environments, with the ultimate goal of facilitating the design and deployment of robust and efficient AIoT systems. The overall architecture is validated on use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. Ten additional use cases are integrated via an open call, broadening the range of application areas.

The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available.

Malignant mesothelioma (MM) is strongly associated with a previous asbestos exposure. To improve timely detection of MM in asbestos workers, better screening tools – like minimally-invasive biomarkers – are desirable. Between 2008 and 2018 2,769 patients with benign asbestos-related diseases were recruited to participate in annual screens. Using a nested case-control design the protein markers calretinin and mesothelin were determined by enzyme-linked immunosorbent assays in prediagnostic plasma samples of 34 MM cases as well as 136 matched controls from the cohort. Conditional on a pre-defined specificity of 98% for calretinin and 99% for mesothelin the markers reached individual sensitivities of 31% and 23%, respectively, when including the incident cases with samples taken between one and 15 months before diagnosis. The combination of both markers increased the sensitivity to 46% at 98% specificity. Marker complementation increased with earlier sampling. The marker combination improves the sensitivity of the individual markers, indicating a useful complementation and suggesting that additional markers may further improve the performance. This is the first prospective cohort study to evaluate a detection of MM by calretinin and its combination with mesothelin up to about a year before clinical diagnosis. Whether an earlier diagnosis will result in reduced mortality has yet to be demonstrated.

Erythropoietin (EPO) reduces symptoms of experimental autoimmune encephalomyelitis in rodents and shows neuroregenerative effects in chronic progressive multiple sclerosis. The mechanisms of action of EPO in these conditions with shared immunological etiology are still unclear. Therefore, we used a model of toxic demyelination allowing exclusion of T cell-mediated inflammation. In a double-blind (for food/injections), placebo-controlled, longitudinal four-arm design, 8-wk-old C57BL/6 mice (n = 26/group) were assigned to cuprizone-containing (0.2%) or regular food (ground chow) for 6 wks. After 3 wks, mice were injected every other day with placebo or EPO (5,000 lU/kg intraperitoneally) until the end of cuprizone feeding. Half of the mice were exposed to behavioral testing, magnetic resonance imaging (MRI) and histology immediately after treatment cessation, whereas the other half were allowed a 3-wk treatment-free recovery. Immediately after termination of cuprizone feeding, all toxin-exposed mice were compromised regarding vestibulomotor function/coordination, with EPO-treated animals performing better than placebo. Likewise, ventricular enlargement after cuprizone, as documented by MRI, was less pronounced upon EPO. After a 3-wk recovery, remarkable spontaneous improvement was observed in all mice with no measurable further benefit in the EPO group (“ceiling effect”). Histological analysis of the corpus callosum revealed attenuation by EPO of the cuprizone-induced increase in microglial numbers and amyloid precursor protein accumulations as a readout of inflammation and axonal degeneration. To conclude, EPO ameliorates neurological symptoms in the cuprizone model of demyelination, possibly by reduction of inflammation-associated axonal degeneration in white matter tracts. These findings underscore the value of future therapeutic strategies for multiple sclerosis based on EPO or EPO variants.

Severe mental illnesses have been linked to white matter abnormalities, documented by postmortem studies. However, cause and effect have remained difficult to distinguish. CNP (2′,3′‐cyclic nucleotide 3′‐phosphodiesterase) is among the oligodendrocyte/myelin‐associated genes most robustly reduced on mRNA and protein level in brains of schizophrenic, bipolar or major depressive patients. This suggests that CNP reduction might be critical for a more general disease process and not restricted to a single diagnostic category. We show here that reduced expression of CNP is the primary cause of a distinct behavioural phenotype, seen only upon aging as an additional ‘pro‐inflammatory hit’. This phenotype is strikingly similar in Cnp heterozygous mice and patients with mental disease carrying the AA genotype at CNP SNP rs2070106. The characteristic features in both species with their partial CNP ‘loss‐of‐function’ genotype are best described as ‘catatonia‐depression’ syndrome. As a consequence of perturbed CNP expression, mice show secondary low‐grade inflammation/neurodegeneration. Analogously, in man, diffusion tensor imaging points to axonal loss in the frontal corpus callosum. To conclude, subtle white matter abnormalities inducing neurodegenerative changes can cause/amplify psychiatric diseases.

Objective Calretinin is a well-known immunohistochemical tissue marker in the diagnosis of malignant mesothelioma. Promising results also indicate the use in early detection. In the present cross-sectional survey, correlations of calretinin plasma levels with clinical features were investigated. Plasma samples of 60 patients with malignant pleural mesothelioma (MPM) and 111 cancer-free controls formerly exposed to asbestos were compared. Calretinin concentrations were determined in plasma using an enzyme-linked immunosorbent assay (ELISA). Results The median concentration was higher in MPM patients than in controls (0.79 vs. 0.23 ng/ml; p  < 0.0001). Patients with epithelioid MPM or biphasic MPM had higher calretinin plasma levels than patients with sarcomatoid MPM. Strong expression of calretinin in the tumor tissue was associated with higher plasma levels. Preoperative patients showed higher levels of calretinin than patients after thoracic surgery (1.20 vs. 0.67 ng/ml; p  = 0.096). The suitability of plasma calretinin has been confirmed as a tumor marker in the differential diagnosis of epithelioid MPM. The value of plasma calretinin for therapy monitoring or as a prognostic marker should be further investigated.