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The evolution of threats and scenarios requires continuous performance improvements of ballistic protections for armed forces. From a modeling point of view, it is necessary to use sufficiently precise material behavior models to accurately describe the phenomena observed during the impact of a projectile on a protective equipment. In this context, the goal of this paper is to characterize the behavior of a small caliber steel jacket by combining experimental and numerical approaches. The experimental method is based on the lateral compression of ring specimens directly machined from the thin and small ammunition. Various speeds and temperatures are considered in a quasi-static regime in order to reveal the strain rate and temperature dependencies of the tested material. The Finite Element Updating Method (FEMU) is used. Experimental results are coupled with an inverse optimization method and a finite element numerical model in order to determine the parameters of a constitutive model representative of the jacket material. Predictions of the present model are verified against experimental results and a parametric study as well as a discussion on the identified material parameters are proposed. The results indicate that the strain hardening parameter can be neglected and the behavior of the thin steel jacket can be described by a modeling without strain hardening sensitivity.

Abstract Background Programmed death ligand‐1 (PD‐L1) expression is a well‐known predictive biomarker of response to immune checkpoint blockade in non‐small cell lung cancer (NSCLC). However, there is limited evidence of the relationship between PD‐L1 expression, clinicopathological features, and their association with major driver mutations in NSCLC patients in Latin America. Methods This retrospective study included patients from Argentina with advanced NSCLC, and centralized evaluation of PD‐L1 expression concurrently with genomic alterations in the driver genes EGFR, ALK, ROS1, BRAF, and/or KRAS G12C in FFPE tissue samples. Results A total of 10 441 patients with advanced NSCLC were analyzed. Adenocarcinoma was the most frequent histological subtype (71.1%). PD‐L1 expression was categorized as PD‐L1 negative (45.1%), PD‐L1 positive low‐expression 1%–49% (32.3%), and PD‐L1 positive high‐expression ≥50% (22.6%). Notably, current smokers and males were more likely to have tumors with PD‐L1 tumor proportion score (TPS) ≥50% and ≥ 80% expression, respectively (p

Abstract The Li-S battery is a promising next-generation battery chemistry that offers high energy density and low cost. The Li-S battery has a unique chemistry with intermediate sulphur species readily solvated in electrolytes, and understanding their implications is important from both practical and fundamental perspectives. In this study, we utilise the solvation free energy of electrolytes as a metric to formulate solvation-property relationships in various electrolytes and investigate their impact on the solvated lithium polysulphides. We find that solvation free energy influences Li-S battery voltage profile, lithium polysulphide solubility, Li-S battery cyclability and the Li metal anode; weaker solvation leads to lower 1st plateau voltage, higher 2nd plateau voltage, lower lithium polysulphide solubility, and superior cyclability of Li-S full cells and Li metal anodes. We believe that relationships delineated in this study can guide the design of high-performance electrolytes for Li-S batteries.

Sequences with low auto-correlation sidelobes are important in diverse fields, including communications, radar, sonar, and medical imaging. The task of finding binary sequences with low auto-correlation is known as a difficult optimization problem. Recently, many computational methods have been proposed for designing long binary sequences with good auto-correlation properties. In this paper, we present a new algorithm to generate long aperiodic binary sequences with low peak sidelobe levels. Our algorithm combines the Legendre sequence to seed an initial binary sequence, and an efficient stochastic search method with a dynamic fitness function mechanism to generate a final binary sequence. Numerical examples of the obtained binary sequences with a length N up to $2^{20}-1$ and a peak sidelobe level lower than $0.8\sqrt {N}$ are provided, to demonstrate the efficiency of the proposed stochastic algorithm, that can outperform existing computational methods for designing long aperiodic binary sequences.

Immune checkpoint therapies (ICT) have transformed the treatment of cancer over the past decade. However, many patients do not respond or suffer relapses. Successful immunotherapy requires epitope spreading, but the slow or inefficient induction of functional antitumoral immunity delays the benefit to patients or causes resistances. Therefore, understanding the key mechanisms that support epitope spreading is essential to improve immunotherapy. In this review, we highlight the major role played by B-cells in breaking immune tolerance by epitope spreading. Activated B-cells are key Antigen-Presenting Cells (APC) that diversify the T-cell response against self-antigens, such as ribonucleoproteins, in autoimmunity but also during successful cancer immunotherapy. This has important implications for the design of future cancer vaccines.

Abstract Metastatic uveal melanomas are highly resistant to all existing treatments. To address this critical issue, we performed a kinome‐wide CRISPR‐Cas9 knockout screen, which revealed the LKB1‐SIK2 module in restraining uveal melanoma tumorigenesis. Functionally, LKB1 loss enhances proliferation and survival through SIK2 inhibition and upregulation of the sodium/calcium (Na+/Ca2+) exchanger SLC8A1. This signaling cascade promotes increased levels of intracellular calcium and mitochondrial reactive oxygen species, two hallmarks of cancer. We further demonstrate that combination of an SLC8A1 inhibitor and a mitochondria‐targeted antioxidant promotes enhanced cell death efficacy in LKB1‐ and SIK2‐negative uveal melanoma cells compared to control cells. Our study also identified an LKB1‐loss gene signature for the survival prognostic of patients with uveal melanoma that may be also predictive of response to the therapy combination. Our data thus identify not only metabolic vulnerabilities but also new prognostic markers, thereby providing a therapeutic strategy for particular subtypes of metastatic uveal melanoma.

Un varón de 51 años que se encontraba con neutropenia profunda y prolongada luego de 12 días del inicio de su quimioterapia por una leucemia mieloide aguda desarrolló una lesión nodular, eritematosa y con centro necrótico en la base del cuello, asociada a fiebre, escalofríos y mialgias. Se diagnosticó infección fúngica invasiva luego del desarrollo de Candia tropicalis en los hemocultivos. Evolucionó con múltiples lesiones papulares rojizas concentradas principalmente en el tronco, aunque también extendidas a las extremidades. Las lesiones cutáneas más frecuentes de la candidiasis diseminada son pápulas eritematosas-violáceas con centros vesiculares, que, en algunos casos, pueden evolucionar a necrosis. Otras formas de presentación cutánea de la candidiasis invasiva son lesiones similares a ectima gangrenoso, placas o bullas hemorrágicas, erupción que resembla foliculitis, y nódulos subcutáneos.

Electrochemical pH-swing strategies offer a promising avenue for cost-effective and energy-efficient carbon dioxide (CO2) capture, surpassing the traditional thermally activated processes and humidity-sensitive techniques. The concept of elevating seawater's alkalinity for scalable CO2 capture without introducing additional chemical as reactant is particularly intriguing due to its minimal environmental impact. However, current commercial plants like chlor-alkali process or water electrolysis demand high thermodynamic voltages of 2.2 V and 1.23 V, respectively, for the production of sodium hydroxide (NaOH) from seawater. These high voltages are attributed to the asymmetric electrochemical reactions, where two completely different reactions take place at the anode and cathode. Here, we developed a symmetric electrochemical system for seawater alkalization based on a highly reversible and identical reaction taking place at the anode and cathode. We utilize hydrogen evolution reaction at the cathode, where the generated hydrogen is looped to the anode for hydrogen oxidation reaction. Theoretical calculations indicate an impressively low energy requirement ranging from 0.07 to 0.53 kWh/kg NaOH for established pH differences of 1.7 to 13.4. Experimentally, we achieved the alkalization with an energy consumption of 0.63 kWh/kg NaOH, which is only 38% of the theoretical energy requirements of the chlor-alkali process (1.64 kWh/kg NaOH). Further tests demonstrated the system's potential of enduring high current densities (~20 mA/cm²) and operating stability over an extended period (>110 h), showing its potential for future applications. Notably, the CO2 adsorption tests performed with alkalized seawater exhibited remarkably improved CO2 capture dictated by the production of hydroxide compared to the pristine seawater. [ABSTRACT FROM AUTHOR]

This study analyzes "selective exposure" to online news media (i.e., preferential exposure to congenial and trusted media) by comparing the behavioral and declarative data of 1,536 participants over a one-month period. We recorded their Internet activity over a one-month period and then asked them about their level of trust in several news media outlets, the frequency with which they had been exposed to them, and their political position and orientation along a progressive-conservative axis.. Results show that 1) participants' trust in the different news media varies according to their political attitudes, 2) participants' probability of declaring exposure to a specific news media is impacted by their trust in this media, as well as by their political attitudes, 3) these effects are much weaker when considering participants' effective exposure to news media. Overall, these results indicate that selective exposure to online news media exists, but it is exaggerated when studied only by means of declarative data. [ABSTRACT FROM AUTHOR]

Simple Summary: Recent research has helped us understand more about the role of microbes in the health and disease of the lungs. The detection of microbes and microbial products in sputum may improve early lung cancer diagnosis. The monitoring of the microbiome of the lungs over time may help predict the response to and side effects of treatment. However, studies have not yet examined how diet and air pollution affect the lung microbiome and how it might be linked to the development and progression of lung cancer. By examining the lung microbiome, dietary patterns, and air pollutants, we hope to prevent and manage lung cancer in the future. The past several years have provided a more profound understanding of the role of microbial species in the lung. The respiratory tract is a delicate ecosystem of bacteria, fungi, parasites, and viruses. Detecting microbial DNA, pathogen-associated molecular patterns (PAMPs), and metabolites in sputum is poised to revolutionize the early diagnosis of lung cancer. The longitudinal monitoring of the lung microbiome holds the potential to predict treatment response and side effects, enabling more personalized and effective treatment options. However, most studies into the lung microbiota have been observational and have not adequately considered the impact of dietary intake and air pollutants. This gap makes it challenging to establish a direct causal relationship between environmental exposure, changes in the composition of the microbiota, lung carcinogenesis, and tumor progression. A holistic understanding of the lung microbiota that considers both diet and air pollutants may pave the way to improved prevention and management strategies for lung cancer. [ABSTRACT FROM AUTHOR]

In this paper, we present a computational search for best-known merit factors of longer binary sequences with an odd length. Finding low autocorrelation binary sequences with optimal or suboptimal merit factors is a very difficult optimization problem. An improved version of the heuristic algorithm is presented and tackled to search for aperiodic binary sequences with good autocorrelation properties. High-performance computations with the execution of our stochastic algorithm to search skew-symmetric binary sequences with high merit factors. After experimental work, as results, we present new binary sequences with odd lengths between 201 and 303 that are skew-symmetric and have the merit factor F greater than 8.5. Moreover, an example of a binary sequence having F > 8 has been found for all odd lengths between 201 and 303. The longest binary sequence with F > 9 found to date is of length 255.

This paper proposes a hybrid machine translation (HMT) system that improves the quality of neural machine translation (NMT) by incorporating statistical machine translation (SMT). Therefore, two NMT systems and two SMT systems were built for the Slovenian–English language pair, each for translation in one direction. We used a multilingual language model to embed the source sentence and translations into the same vector space. From each vector, we extracted features based on the distances and similarities calculated between the source sentence and the NMT translation, and between the source sentence and the SMT translation. To select the best possible translation, we used several well-known classifiers to predict which translation system generated a better translation of the source sentence. The proposed method of combining SMT and NMT in the hybrid system is novel. Our framework is language-independent and can be applied to other languages supported by the multilingual language model. Our experiment involved empirical applications. We compared the performance of the classifiers, and the results demonstrate that our proposed HMT system achieved notable improvements in the BLEU score, with an increase of 1.5 points and 10.9 points for both translation directions, respectively.

The authors got the motivation for writing the article based on an issue, with which developers of the newly developed nature-inspired algorithms are usually confronted today: How to select the test benchmark such that it highlights the quality of the developed algorithm most fairly? In line with this, the CEC Competitions on Real-Parameter Single-Objective Optimization benchmarks that were issued several times in the last decade, serve as a testbed for evaluating the collection of nature-inspired algorithms selected in our study. Indeed, this article addresses two research questions: (1) How the selected benchmark affects the ranking of the particular algorithm, and (2) If it is possible to find the best algorithm capable of outperforming all the others on all the selected benchmarks. Ten outstanding algorithms (also winners of particular competitions) from different periods in the last decade were collected and applied to benchmarks issued during the same time period. A comparative analysis showed that there is a strong correlation between the rankings of the algorithms and the benchmarks used, although some deviations arose in ranking the best algorithms. The possible reasons for these deviations were exposed and commented on.

Binary sequences are widely used in many practical fields, such as radar applications, telecommunications and cryptography. Finding low autocorrelation binary sequences with good peak side-lobe level (PSL) values is a difficult optimization problem. In this paper we present an improved heuristic algorithm for searching low autocorrelation PSL sequences. A heuristic algorithm can find a sequence with a PSL value, which is not necessarily optimal, but is usually near optimal, and the algorithm finds it in a reasonable amount of time. In the experimental work we applied our algorithm to find binary sequences with low PSL values, and made a comparison with the state-of-the-art algorithms from literature. With our algorithm many sequences with the currently best-known PSL values have been improved. We found new sequences with better, i.e., lower, PSL values.

Genomics and transcriptomics have led to the widely-used molecular classification of breast cancer (BC). However, heterogeneous biological behaviors persist within breast cancer subtypes. Metabolomics is a rapidly-expanding field of study dedicated to cellular metabolisms affected by the environment. The aim of this study was to compare metabolomic signatures of BC obtained by 5 different unsupervised machine learning (ML) methods. Fifty-two consecutive patients with BC with an indication for adjuvant chemotherapy between 2013 and 2016 were retrospectively included. We performed metabolomic profiling of tumor resection samples using liquid chromatography-mass spectrometry. Here, four hundred and forty-nine identified metabolites were selected for further analysis. Clusters obtained using 5 unsupervised ML methods (PCA k-means, sparse k-means, spectral clustering, SIMLR and k-sparse) were compared in terms of clinical and biological characteristics. With an optimal partitioning parameter k = 3, the five methods identified three prognosis groups of patients (favorable, intermediate, unfavorable) with different clinical and biological profiles. SIMLR and K-sparse methods were the most effective techniques in terms of clustering. In-silico survival analysis revealed a significant difference for 5-year predicted OS between the 3 clusters. Further pathway analysis using the 449 selected metabolites showed significant differences in amino acid and glucose metabolism between BC histologic subtypes. Our results provide proof-of-concept for the use of unsupervised ML metabolomics enabling stratification and personalized management of BC patients. The design of novel computational methods incorporating ML and bioinformatics techniques should make available tools particularly suited to improving the outcome of cancer treatment and reducing cancer-related mortalities.

Introduction: Aside from the reverse transcription-PCR tests for the diagnosis of the COVID-19 in routine clinical care and population-scale screening, there is an urgent need to increase the number and the efficiency for full viral genome sequencing to detect the variants of SARS-CoV-2. SARS-CoV-2 variants assessment should be easily, rapidly, and routinely available in any academic hospital.Materials and Methods: SARS-CoV-2 full genome sequencing was performed retrospectively in a single laboratory (LPCE, Louis Pasteur Hospital, Nice, France) in 103 SARS-CoV-2 positive individuals. An automated workflow used the Ion Ampliseq SARS-CoV-2 panel on the Genexus Sequencer. The analyses were made from nasopharyngeal swab (NSP) (n = 64) and/or saliva (n = 39) samples. All samples were collected in the metropolitan area of the Nice city (France) from September 2020 to March 2021.Results: The mean turnaround time between RNA extraction and result reports was 30 h for each run of 15 samples. A strong correlation was noted for the results obtained between NSP and saliva paired samples, regardless of low viral load and high (>28) Ct values. After repeated sequencing runs, complete failure of obtaining a valid sequencing result was observed in 4% of samples. Besides the European strain (B.1.160), various variants were identified, including one variant of concern (B.1.1.7), and different variants under monitoring.Discussion: Our data highlight the current feasibility of developing the SARS-CoV-2 next-generation sequencing approach in a single hospital center. Moreover, these data showed that using the Ion Ampliseq SARS-CoV-2 Assay, the SARS-CoV-2 genome sequencing is rapid and efficient not only in NSP but also in saliva samples with a low viral load. The advantages and limitations of this setup are discussed.

In this paper we present an approach which given only a set of rules is able to learn to play the game of Checkers. We utilize neural networks and reinforced learning combined with Monte Carlo Tree Search and alpha-beta pruning. Any human influence or knowledge is removed by generating needed data, for training neural network, using self-play. After a certain number of finished games, we initialize the training and transfer better neural network version to next iteration. We compare different obtained versions of neural networks and their progress in playing the game of Checkers. Every new version of neural network represented a better player.

ABSTRACT Due to increased demand for testing, as well as restricted supply chain resources, testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection continues to face many hurdles. Pooling several samples has been proposed as an alternative approach to address these issues. We investigated the feasibility of pooling nasopharyngeal swab (NPS) or saliva samples for SARS-CoV-2 testing with a commercial assay (Idylla SARS-CoV-2 test; Biocartis). We evaluated the 10-pool and 20-pool approaches for 149 subjects, with 30 positive samples and 119 negative samples. The 10-pool approach had sensitivity of 78.95% (95% confidence interval [CI], 54.43% to 93.95%) and specificity of 100% (95% CI, 71.51% to 100%), whereas the 20-pool approach had sensitivity of 55.56% (95% CI, 21.20% to 86.30%) and specificity of 100% (95% CI, 25% to 100%). No significant difference was observed between the results obtained with pooled NPS and saliva samples. Given the rapidity, full automation, and practical advantages of the Idylla SARS-CoV-2 assay, pooling of 10 samples has the potential to significantly increase testing capacity for both NPS and saliva samples, with good sensitivity. IMPORTANCE To control outbreaks of coronavirus disease 2019 (COVID-19) and to avoid reagent shortages, testing strategies must be adapted and maintained for the foreseeable future. We analyzed the feasibility of pooling NPS and saliva samples for SARS-CoV-2 testing with the Idylla SARS-CoV-2 test, and we found that sensitivity was dependent on the pool size. The SARS-CoV-2 testing capacity with both NPS and saliva samples could be significantly expanded by pooling 10 samples; however, pooling 20 samples resulted in lower sensitivity.

Tubular or pipe structures are commonly found in various engineering applications, such as offshore pipelines, parts in the nuclear and automotive industries. Standard mechanical testing methods like compression tests on cylindrical samples or tensile tests on dog-bone samples are often out of questions due to the geometry restraints. Carrying out standardized mechanical characterization on ring-shaped samples could thus be a solution. The goal of this paper is to successfully conduct mechanical characterization of copper alloy rings, which are extracted directly from the jackets of small calibre ammunition. Firstly, an experimental protocol has been established to investigate their mechanical behaviour in the quasi-static regime. The strain rate and temperature sensitivities have also been evaluated through the same experimental process. Furthermore, a Johnson-Cook constitutive material model has been identified using an inverse analysis of lateral compressive experiments. This identification is based upon the ABAQUS/Standard ® finite element software coupled with the optimization software LS-OPT®.As a result, a low anisotropic material behaviour is demonstrated. Finite element simulations using identified parameters are in accordance with the experimental measurements from compressive tests in axial direction.