LogicWiSARD: Memoryless synthesis of weightless neural networks

Weightless neural networks (WNNs) are an alternative pattern recognition technique where RAM nodes function as neurons. As both training and inference require mostly table lookups, few additions, and no multiplications, WNNs are suitable for high-performance and low-power embedded applications. This work introduces a novel approach to implement WiSARD, the leading WNN state-of-the-art architecture, completely eliminating memories and arithmetic circuits and utilizing only logic functions. The approach creates compressed minimized implementations by converting trained WNN nodes from lookup tables to logic functions. The proposed LogicWiSARD is implemented in FPGA and ASIC technologies to illustrate its suitability for edge inference. Experimental results show more than 80% reduction in energy consumption when the proposed LogicWiSARD model is compared with a multilayer perceptron network (MLP) of equivalent accuracy. Compared to previous work on FPGA implementations for WNNs, convolutional neural networks, and binary neural networks, the energy savings of LogicWiSARD range between 32.2% and 99.6%.info:eu-repo/semantics/acceptedVersio

Protein expression of transmembrane protease serine 4 in the gastrointestinal tract and in healthy, cancer, and SARS‑CoV‑2 infected lungs

Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.Copyright: © Kerslake et al. In addition to the angiotensin‑converting enzyme 2 (ACE2), a number of host cell entry mediators have been identified for severe acute respiratory syndrome coronavirus‑2 (SARS‑CoV‑2), including transmembrane protease serine 4 (TMPRSS4). The authors have recently demonstrated the upregulation of TMPRSS4 in 11 different cancers, as well as its specific expression within the central nervous system using in silico tools. The present study aimed to expand the initial observations and, using immunohistochemistry, TMPRSS4 protein expression in the gastrointestinal (GI) tract and lungs was further mapped. Immunohistochemistry was performed on tissue arrays and lung tissues of patients with non‑small cell lung cancer with concurrent coronavirus disease 2019 (COVID‑19) infection using TMPRSS4 antibody. The results revealed that TMPRSS4 was abundantly expressed in the oesophagus, stomach, small intestine, jejunum, ileum, colon, liver and pancreas. Moreover, the extensive TMPRSS4 protein expression in the lungs of a deceased patient with COVID‑19 with chronic obstructive pulmonary disease and bronchial carcinoma, as well in the adjacent normal tissue, was demonstrated for the first time, at least to the best of our knowledge. On the whole, the immunohistochemistry data of the present study suggest that TMPRSS4 may be implicated in the broader (pulmonary and extra‑pulmonary) COVID‑19 symptomatology; thus, it may be responsible for the tropism of this coronavirus both in the GI tract and lungs.Cancer Treatment and Research Trust; University Hospitals Coventry and Warwickshire NHS Trust (grant no. 12899)

ULEEN: A Novel Architecture for Ultra Low-Energy Edge Neural Networks

The deployment of AI models on low-power, real-time edge devices requires accelerators for which energy, latency, and area are all first-order concerns. There are many approaches to enabling deep neural networks (DNNs) in this domain, including pruning, quantization, compression, and binary neural networks (BNNs), but with the emergence of the "extreme edge", there is now a demand for even more efficient models. In order to meet the constraints of ultra-low-energy devices, we propose ULEEN, a model architecture based on weightless neural networks. Weightless neural networks (WNNs) are a class of neural model which use table lookups, not arithmetic, to perform computation. The elimination of energy-intensive arithmetic operations makes WNNs theoretically well suited for edge inference; however, they have historically suffered from poor accuracy and excessive memory usage. ULEEN incorporates algorithmic improvements and a novel training strategy inspired by BNNs to make significant strides in improving accuracy and reducing model size. We compare FPGA and ASIC implementations of an inference accelerator for ULEEN against edge-optimized DNN and BNN devices. On a Xilinx Zynq Z-7045 FPGA, we demonstrate classification on the MNIST dataset at 14.3 million inferences per second (13 million inferences/Joule) with 0.21 $\mu$s latency and 96.2% accuracy, while Xilinx FINN achieves 12.3 million inferences per second (1.69 million inferences/Joule) with 0.31 $\mu$s latency and 95.83% accuracy. In a 45nm ASIC, we achieve 5.1 million inferences/Joule and 38.5 million inferences/second at 98.46% accuracy, while a quantized Bit Fusion model achieves 9230 inferences/Joule and 19,100 inferences/second at 99.35% accuracy. In our search for ever more efficient edge devices, ULEEN shows that WNNs are deserving of consideration.Comment: 14 pages, 14 figures Portions of this article draw heavily from arXiv:2203.01479, most notably sections 5E and 5F.

Lipoprotein-apheresis reduces circulating microparticles in individuals with familial hypercholesterolemia

Lipoprotein-apheresis (apheresis) removes LDL-cholesterol in patients with severe dyslipidemia. However, reduction is transient, indicating that the long-term cardiovascular benefits of apheresis may not solely be due to LDL removal. Microparticles (MPs) are submicron vesicles released from the plasma membrane of cells. MPs, particularly platelet-derived MPs, are increasingly being linked to the pathogenesis of many diseases. We aimed to characterize the effect of apheresis on MP size, concentration, cellular origin, and fatty acid concentration in individuals with familial hypercholesterolemia (FH). Plasma and MP samples were collected from 12 individuals with FH undergoing routine apheresis. Tunable resistive pulse sensing (np200) and nanoparticle tracking analysis measured a fall in MP concentration (33 and 15%, respectively; P < 0.05) pre- to post-apheresis. Flow cytometry showed MPs were predominantly annexin V positive and of platelet (CD41) origin both pre- (88.9%) and post-apheresis (88.4%). Fatty acid composition of MPs differed from that of plasma, though apheresis affected a similar profile of fatty acids in both compartments, as measured by GC-flame ionization detection. MP concentration was also shown to positively correlate with thrombin generation potential. In conclusion, we show apheresis nonselectively removes annexin V-positive platelet-derived MPs in individuals with FH. These MPs are potent inducers of coagulation and are elevated in CVD; this reduction in pathological MPs could relate to the long-term benefits of apheresis

Fusidic acid and clindamycin resistance in community-associated, methicillin-resistant Staphylococcus aureus infections in children of Central Greece

<p>Abstract</p> <p>Introduction</p> <p>In Greece, fusidic acid and clindamycin are commonly used for the empiric therapy of suspected staphylococcal infections.</p> <p>Methods</p> <p>The medical records of children examined at the outpatient clinics or admitted to the pediatric wards of the University General Hospital of Larissa, Central Greece, with community-associated staphylococcal infections from January 2003 to December 2009 were reviewed.</p> <p>Results</p> <p>Of 309 children (0-14 years old), 21 (6.8%) had invasive infections and 288 (93.2%) skin and soft tissue infections (SSTIs). Thirty-five patients were ≤30 days of age. The proportion of staphylococcal infections caused by a community-associated methicillin-resistant <it>Staphylococcus aureus </it>(CA-MRSA) isolate increased from 51.5% (69 of 134) in 2003-2006 to 63.4% (111 of 175) in 2007-2009 (<it>P </it>= 0.037). Among the CA-MRSA isolates, 88.9% were resistant to fusidic acid, 77.6% to tetracycline, and 21.1% to clindamycin. Clindamycin resistance increased from 0% (2003) to 31.2% (2009) among the CA-MRSA isolates (<it>P </it>= 0.011). Over the 7-year period, an increase in multidrug-resistant CA-MRSA isolates was observed (<it>P </it>= 0.004). One hundred and thirty-one (93.6%) of the 140 tested MRSA isolates were Panton-Valentine leukocidin-positive. Multilocus sequence typing of 72 CA-MRSA isolates revealed that they belonged to ST80 (n = 61), ST30 (n = 6), ST377 (n = 3), ST22 (n = 1), and ST152 (n = 1). Resistance to fusidic acid was observed in ST80 (58/61), ST30 (1/6), and ST22 (1/1) isolates.</p> <p>Conclusion</p> <p>In areas with high rate of infections caused by multidrug-resistant CA-MRSA isolates, predominantly belonging to the European ST80 clone, fusidic acid and clindamycin should be used cautiously as empiric therapy in patients with suspected severe staphylococcal infections.</p

Description and performance of track and primary-vertex reconstruction with the CMS tracker

A description is provided of the software algorithms developed for the CMS tracker both for reconstructing charged-particle trajectories in proton-proton interactions and for using the resulting tracks to estimate the positions of the LHC luminous region and individual primary-interaction vertices. Despite the very hostile environment at the LHC, the performance obtained with these algorithms is found to be excellent. For tbar t events under typical 2011 pileup conditions, the average track-reconstruction efficiency for promptly-produced charged particles with transverse momenta of pT > 0.9GeV is 94% for pseudorapidities of |η| < 0.9 and 85% for 0.9 < |η| < 2.5. The inefficiency is caused mainly by hadrons that undergo nuclear interactions in the tracker material. For isolated muons, the corresponding efficiencies are essentially 100%. For isolated muons of pT = 100GeV emitted at |η| < 1.4, the resolutions are approximately 2.8% in pT, and respectively, 10μm and 30μm in the transverse and longitudinal impact parameters. The position resolution achieved for reconstructed primary vertices that correspond to interesting pp collisions is 10–12μm in each of the three spatial dimensions. The tracking and vertexing software is fast and flexible, and easily adaptable to other functions, such as fast tracking for the trigger, or dedicated tracking for electrons that takes into account bremsstrahlung