Rapid Discrimination of Neuromyelitis Optica Spectrum Disorder and Multiple Sclerosis Using Machine Learning on Infrared Spectra of Sera

Neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) are both autoimmune inflammatory and demyelinating diseases of the central nervous system. NMOSD is a highly disabling disease and rapid introduction of the appropriate treatment at the acute phase is crucial to prevent sequelae. Specific criteria were established in 2015 and provide keys to distinguish NMOSD and MS. One of the most reliable criteria for NMOSD diagnosis is detection in patient’s serum of an antibody that attacks the water channel aquaporin-4 (AQP-4). Another target in NMOSD is myelin oligodendrocyte glycoprotein (MOG), delineating a new spectrum of diseases called MOG-associated diseases. Lastly, patients with NMOSD can be negative for both AQP-4 and MOG antibodies. At disease onset, NMOSD symptoms are very similar to MS symptoms from a clinical and radiological perspective. Thus, at first episode, given the urgency of starting the anti-inflammatory treatment, there is an unmet need to differentiate NMOSD subtypes from MS. Here, we used Fourier transform infrared spectroscopy in combination with a machine learning algorithm with the aim of distinguishing the infrared signatures of sera of a first episode of NMOSD from those of a first episode of relapsing-remitting MS, as well as from those of healthy subjects and patients with chronic inflammatory demyelinating polyneuropathy. Our results showed that NMOSD patients were distinguished from MS patients and healthy subjects with a sensitivity of 100% and a specificity of 100%. We also discuss the distinction between the different NMOSD serostatuses. The coupling of infrared spectroscopy of sera to machine learning is a promising cost-effective, rapid and reliable differential diagnosis tool capable of helping to gain valuable time in patients’ treatment.     

CRIM’s content-based audio copy detection system for TRECVID 2009

We report results on audio copy detection for TRECVID 2009 copy detection task. This task involves searching for transformed audio queries in over 385?h of test audio. The queries were transformed in seven different ways, three of them involved mixing unrelated speech to the original query, making it a much more difficult task. We give results with two different audio fingerprints and show that mapping each test frame to the nearest query frame (nearest-neighbor fingerprint) results in robust audio copy detection. The most difficult task in TRECVID 2009 was to detect audio copies using predetermined thresholds computed from 2008 data. We show that the nearest-neighbor fingerprints were robust to even this task and gave actual minimal normalized detection cost rate (NDCR) of around 0.06 for all the transformations. These results are close to those obtained by using the optimal threshold for each transform. This result shows the robustness of the nearest-neighbor fingerprints. These nearest-neighbor fingerprints can be efficiently computed on a graphics processing unit, leading to a very fast search.     

On Analyzing the Potential of a Layer-2 Multi-hop Authentication and Credential Delivery Scheme for Vehicular Communications

In this paper we introduce a new security architecture and security mechanisms for vehicular networks providing a layer-2 multi-hop authentication and credential delivery scheme between vehicles and the network operator authentication server. We present a performance analysis of the proposed solution under various vehicles and Access Points (APs) densities, showing the potential of our scheme in providing a more efficient, robust and scalable authentication and credential delivery to vehicles when compared against the classical single-hop scheme.     

Organic photovoltaics for low light applications

Here we report on organic photovoltaic's (OPV) suitable for low light applications. In this paper, we illustrate the impact of R_s and R_p for indoor and outdoor applications. In addition, we propose a simple physics approach to predict the behavior of organic solar cells under various illumination intensities through electrical modeling. The combination of simulation and modeling allows to define a set of design rules for OPVs under low light illumination. The performance of various organic solar cells under low light intensity is compared with our predictions and excellent correlation is found. OPV shows high performance under low light conditions.     

New temperature dependent thermal conductivity data for water-based nanofluids

This paper presents effective thermal conductivity measurements of alumina/water and copper oxide/water nanofluids. The effects of particle volume fraction, temperature and particle size were investigated. Readings at ambient temperature as well as over a relatively large temperature range were made for various particle volume fractions up to 9%. Results clearly show the predicted overall effect of an increase in the effective thermal conductivity with an increase in particle volume fraction and with a decrease in particle size. Furthermore, the relative increase in thermal conductivity was found to be more important at higher temperatures. Obtained results compare favorably with certain data sets and theoretical models found in current literature.     

Simulation of the deflected cutting tool trajectory in complex surface milling

Since industry is rapidly developing, either locally or globally, manufacturers witness harder challenges due to the growing competitivity. This urges them to better consider the four factors linked to production and output: quality, quantity, cost and price, quality being of course the most important factor which constitutes their main concern. Efforts will be concentrated??in this research??on improving the quality and securing more accuracy for a machined surface in ball-end milling. Quality and precision are two essential criteria in industrial milling. However, milling errors and imperfections, due mainly to the cutting tool deflection, hinder the full achieving of these targets. Our task, all along this paper, consists in studying and realizing the simulation of the deflected cutting tool trajectory, by using the methods which are available. In a future stage, and in the frame of a deeper research, the simulation process will help to carry out the correction and the compensation of the errors resulting from the tool deflection. The corrected trajectory which is obtained by the method mirror will be sent to the machine. To achieve this goal, the next process consists??as a first step??in selecting a model of cutting forces for a ball-end mill. This allows to define??later on??the behavior of this tool, and the emergence of three methods namely the analytical model, the finite elements method, and the experimental method. It is possible to tackle the cutting forces simulation, all along the tool trajectory, while this latter is carrying out the sweeping of the part to be machined in milling and taking into consideration the cutting conditions, as well as the geography of the workpiece. A simulation of the deflected cutting tool trajectory dependent on the cutting forces has been realized.     

Synthesis and Biological Evaluation of Chromium Bioorganometallics Based on the Antibiotic Platensimycin Lead Structure

The recent discovery of the natural product platensimycin as a new antibiotic lead structure has triggered the synthesis of numerous organic derivatives for structure–activity relationship studies. Herein, we describe the synthesis, characterization and biological evaluation of the first organometallic antibiotic inspired by platensimycin. Two bioorganometallic compounds containing (η⁶‐pentamethylbenzene)Cr(CO)₃ ( 2 ) and (η⁶‐benzene)Cr(CO)₃ ( 3 ), linked by an amide bond to the aromatic part of platensimycin, were synthesized. Their antibiotic activities were tested against B. subtilis 168 (Gram positive) and E. coli W3110 (Gram negative) bacterial strains. Both compounds were found to be inactive against E. coli but derivative 2 inhibits B. subtilis growth at a moderate MIC value of 0.15 mM . To test the intrinsic toxicity of chromium, several chromium salts along with {η⁶‐(3‐pentamethylphenyl propionic acid)}Cr(CO)₃ ( 5 ) and {η⁶‐(3‐phenyl propionic acid)}Cr(CO)₃ ( 6 ) were tested against both bacterial strains. No activity was observed against E. coli for any of the compounds; B. subtilis growth was not inhibited by Cr(NO₃)₃ and only very weakly by 5 , K₂Cr₂O₇ and Na₂CrO₄ at MIC values of 0.5, 0.68 and 1.24 mM , respectively. Compounds 2 , 3 , 5 and 4 (the pure organic analogue of 2 ) show similar cytotoxicity against HeLa, HepG2 and HT‐29 mammalian cell lines. Furthermore, the cellular uptake and the intracellular distribution of compounds 2 , 3 and Cr(NO₃)₃ in B. subtilis were studied using atomic absorption spectroscopy to gain insight in to the possible cellular targets. Compound 2 was found to be readily taken up and distributed almost equally among cytosol, cell debris and cell membrane in B. subtilis.     

Machine learning to design full-reference image quality assessment algorithm

A crucial step in image compression is the evaluation of its performance, and more precisely, available ways to measure the quality of compressed images. In this paper, a machine learning expert, providing a quality score is proposed. This quality measure is based on a learned classification process in order to respect human observers. The proposed method namely Machine Learning-based Image Quality Measure (MLIQM) first classifies the quality using multi-Support Vector Machine (SVM) classification according to the quality scale recommended by the ITU. This quality scale contains 5 ranks ordered from 1 (the worst quality) to 5 (the best quality). To evaluate the quality of images, a feature vector containing visual attributes describing images content is constructed. Then, a classification process is performed to provide the final quality class of the considered image. Finally, once a quality class is associated to the considered image, a specific SVM regression is performed to score its quality. Obtained results are compared to the one obtained applying classical Full-Reference Image Quality Assessment (FR-IQA) algorithms to judge the efficiency of the proposed method.     

Antibacterial activity of peptides extracted from tryptic hydrolyzate of whey protein by nanofiltration

Ultrafiltration permeate of whey protein tryptic hydrolyzate was processed by nanofiltration (NF) to obtain retentate (NFR) and permeate (NFP) that were then tested as inhibitors of Listeria, Staphylococcus aureus and Escherichia coli. NFR at 20 mg mL^?1 was most effective as an inhibitor (P < 0.001); whereas E. coli was relatively resistant, the effect on Listeria and S. aureus was greater at 20 mg mL^?1 than at 10 mg mL^?1 (P < 0.01). Peptide analysis revealed that NFR was rich in anionic peptides over eight amino acid residues in length. The antibacterial activity of two anionic peptides (84–91 and 125–135) and a cationic peptide (36–42) derived from β-lactoglobulin was tested. Peptide 125–135 was more inhibitory (P < 0.05) than peptide 84–91 against Listeria monocytogenes and S. aureus; peptide 36–42 was not inhibitory. NFR appears to have potential as a natural bio-preservative.     

Numerical modelling of geothermal vertical heat exchangers for the short time analysis using the state model size reduction technique

According to the introduction of a dynamic operating mode in ground-coupled heat pump systems, a short time analysis within and around borehole heat exchangers is required in the modern geothermal system simulation. A numerical modelling could be a proper answer for this challenge. However, the numerical model is time consuming and necessitates a large memory particularly in such large systems. Therefore, the state model size reduction technique has been applied in this paper with various numerical techniques particularly in the finite elements method. As a result, the reduced model developed is: (a) relevant with a validation using a traditional analytical model (using 100% modes) and (b) efficient in calculation time, only using 6% modes and consequently reducing time consumption up to 95%.