ATLAS detector with cosmic rays and expected performance with early data

Cosmic muons have helped to understand the ATLAS detector in terms of DAQ, trigger, alignment and calibration. I briefly review the performance of the ATLAS Inner Detector, Calorimeters and Muon Spectrometer systems with cosmic rays. The expected performance with first LHC collisions will also be reviewed

Membrane Surface Engineering for Biochemical Applications

Synthetic membranes have been frequently used for many fields, such as, the food and beverage, biopharmaceutical, and biofuel industries. In the beer industry, microfiltration frequently suffers from fouling due to the interaction between different species. It is shown that polyphenols can form cross-links with protein molecules, forming insoluble aggregates. However, by adding an optimal amount of polysaccharides these aggregates can be disrupted thus reducing fouling by the aggregates. Confocal laser scanning microscopy (CLSM) is a powerful technique to locate the foulants inside the wet membrane in order to understand more about the behavior of fouling in microfiltration. Membrane surface modification is used to impart desirable membrane surface properties. Here membrane surface modification is used to develop membrane adsorbers for protein purification. Hydrophobic interaction membrane chromatography (HIMC) has gained interest due to its excellent performance in purification humanized monoclonal antibodies. HIMC affords all the advantages of membrane adsorbers, which is dynamic capacities that are independent of flow rate, higher throughput and easy to scale up. Unique inverse colloidal crystal (ICC) membranes were developed with highly periodic structures, high porosity, and fully interconnected pores. ICC membranes offer a very high binding capacity for IgG4. On the other hand, salt responsive membranes were developed by grafting responsive ligand, poly vinylcaprolactam (PVCL), from the surface of the membrane by atom transfer radical polymerization (ATRP). The nanostructure can vary its conformation and hydrophobicity when the temperature changes. After modification, membrane is able to provide a very high recovery and yield. Membrane modification is also well applied for biofuel industry. Duel nanostructures, poly styrene sulfonic acid (PSSA) and poly ionic liquid (PIL) were grafted separately and neighborly from the surface of ceramic membrane substrate by control ATRP and UV initiated radical polymerization. Modified membrane substrates were challenged with cellulose and corn-stovers biomass hydrolysis in pure ionic liquid (IL) and mixture of IL and co-solvent. High yield in total reducing sugar (TRS), 95% and 60% for cellulose and corn-stovers biomass respectively, indicates strong activity of polymeric solid acid catalysts

Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis

Breast Ultrasound plays a vital role in cancer diagnosis as a non-invasive approach with cost-effective. In recent years, with the development of deep learning, many CNN-based approaches have been widely researched in both tumor localization and cancer classification tasks. Even though previous single models achieved great performance in both tasks, these methods have some limitations in inference time, GPU requirement, and separate fine-tuning for each model. In this study, we aim to redesign and build end-to-end multi-task architecture to conduct both segmentation and classification. With our proposed approach, we achieved outstanding performance and time efficiency, with 79.8% and 86.4% in DeepLabV3+ architecture in the segmentation task.Comment: 7 pages, 3 figure

Clinical Epidemiological Characteristics and Risk Factors for Severity of SARS-CoV-2 Pneumonia in Pediatric Patients: A Hospital-Based Study in Vietnam

Introduction Coronavirus disease (COVID-19) is an infectious disease caused by SARS-CoV-2, which can cause organ failure in several organs, cardiac problems, or acute respiratory distress syndrome (ARDS). Identifying clinical epidemiological characteristics and risk factors for complications of COVID-19 allows clinicians to diagnose and treat promptly. Objectives This study aims to describe the clinical epidemiological characteristics of COVID-19 and assess risk factors for the severity of SARS-CoV-2 pneumonia in children treated at Haiphong Children\u27s Hospital. Methods A descriptive cross-sectional study was conducted in Haiphong Children\u27s Hospital, Haiphong, Vietnam, for one year, from January 1, 2022, to December 31, 2022. Results In our study, 540 children were evaluated; the male-to-female ratio was 1.48/1; the median age was 23 months (IQR=6-74); Children aged under one year accounted for the highest proportion (n=202; 37.4%); 40 (7.4%) children had underlying illnesses. The number of admitted patients diagnosed with COVID-19 peaked in February 2022. Regarding severity, 380 (70.4%) cases were mild, 136 (25.2%) were moderate, only 24 (4.4%) cases were severe, and no children died. Common symptoms were fever in 483 (89.4%), coughing in 399 (73.9%), and tachypnea in 163 (30.2%) children. Laboratory features: white blood cell count, platelet count, serum CRP, and coagulation test showed little change. Around 116 (21.5%) had lymphopenia and 148 (27.4%) had pneumonia. Patients under one year were approximately 1.64 times more likely to experience pneumonia complications from COVID-19 than those without such a history (OR=1.64, 95%CI = 1.12 - 2.41, p=0.0112). Patients with underlying conditions were approximately 2.08 times more likely to experience pneumonia complications from COVID-19 compared to those without such conditions (OR=2.08, 95%CI =1.08 - 4.02, p=0.0289). Conclusion In COVID-19 pediatric patients, the severity of the disease was mild to moderate without any mortality. Children aged under one year accounted for the highest proportion of all COVID-19 patients. This study found that age under one year and underlying illnesses are related to pneumonia in COVID-19 pediatric patients

The millipede family Polydesmidae Leach, 1816 (Diplopoda, Polydesmida) from Vietnam, with a description of a new cavernicolous species

The millipede family Polydesmidae Leach, 1816 is reviewed in the scope of the Vietnamese fauna. The distribution of the species, Polydesmus vietnamicus Nguyen, 2009 is extended northward to Ha Giang Province. A new cavernicolous polydesmid, Pacidesmus tuachua sp. nov., is described from two caves in northwestern Vietnam, representing the first record of the genus from Vietnam. Extensive illustrations and DNA barcodes are provided for both species, a revised key is presented to all 12 species of Pacidesmus Golovatch, 1991, as well as a key to all eight genera of Asian Polydesmidae

Conditional expectation with regularization for missing data imputation

Missing data frequently occurs in datasets across various domains, such as medicine, sports, and finance. In many cases, to enable proper and reliable analyses of such data, the missing values are often imputed, and it is necessary that the method used has a low root mean square error (RMSE) between the imputed and the true values. In addition, for some critical applications, it is also often a requirement that the imputation method is scalable and the logic behind the imputation is explainable, which is especially difficult for complex methods that are, for example, based on deep learning. Based on these considerations, we propose a new algorithm named "conditional Distribution-based Imputation of Missing Values with Regularization" (DIMV). DIMV operates by determining the conditional distribution of a feature that has missing entries, using the information from the fully observed features as a basis. As will be illustrated via experiments in the paper, DIMV (i) gives a low RMSE for the imputed values compared to state-of-the-art methods; (ii) fast and scalable; (iii) is explainable as coefficients in a regression model, allowing reliable and trustable analysis, makes it a suitable choice for critical domains where understanding is important such as in medical fields, finance, etc; (iv) can provide an approximated confidence region for the missing values in a given sample; (v) suitable for both small and large scale data; (vi) in many scenarios, does not require a huge number of parameters as deep learning approaches; (vii) handle multicollinearity in imputation effectively; and (viii) is robust to the normally distributed assumption that its theoretical grounds rely on