Open-source multi-purpose sensor for measurements in continuous capillary flow

Limited applicability and scarce availability of analytical equipment for micro- and millifluidic applications, which are of high interest in research and development, complicate process development, control, and monitoring. The low-cost sensor presented in this work is a modular, fast, non-invasive, multi-purpose, and easy to apply solution for detecting phase changes and concentrations of optically absorbing substances in single and multi-phase capillary flow. It aims at generating deeper insight into existing processes in fields of (bio-)chemical and reaction engineering. The scope of this work includes the application of the sensor to residence time measurements in a heat exchanger, a tubular reactor for concentration measurements, a tubular crystallizer for suspension detection, and a pipetting robot for flow automation purposes. In all presented applications either the level of automation has been increased or more information on the investigated system has been gained. Further applications are explained to be realized in the near future. Article highlights • An affordable multipurpose sensor for phase differentiation, concentration measurements, and process automation has been developed and characterized • The sensor is easily modified and can be applied to various tubular reaction/process units for analytical and automation purposes • Simple integration into existing process control systems is possible Graphical abstract

Accuracy of Image-Based Automated Diagnosis in the Identification and Classification of Acute Burn Injuries. A Systematic Review

Although they are a common type of injury worldwide, burns are challenging to diagnose, not least by untrained point-of-care clinicians. Given their visual nature, developments in artificial intelligence (AI) have sparked growing interest in the automated diagnosis of burns. This review aims to appraise the state of evidence thus far, with a focus on the identification and severity classification of acute burns. Three publicly available electronic databases were searched to identify peer-reviewed studies on the automated diagnosis of acute burns, published in English since 2005. From the 20 identified, three were excluded on the grounds that they concerned animals, older burns or lacked peer review. The remaining 17 studies, from nine different countries, were classified into three AI generations, considering the type of algorithms developed and the images used. Whereas the algorithms for burn identification have not gained much in accuracy across generations, those for severity classification improved substantially (from 66.2% to 96.4%), not least in the latest generation (n = 8). Those eight studies were further assessed for methodological bias and results applicability, using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. This highlighted the feasibility nature of the studies and their detrimental dependence on online databases of poorly documented images, at the expense of a substantial risk for patient selection and limited applicability in the clinical setting. In moving past the pilot stage, future development work would benefit from greater input from clinicians, who could contribute essential point-of-care knowledge and perspectives.

Progressing the development of a food literacy questionnaire using cognitive interviews

Objective: Food literacy is the knowledge, skills and behaviours needed to meet food needs and determine intake and is conceptualised as 11 components under four domains of planning and managing, selecting, preparing, and eating. Previous measures of food literacy vary in their adherence to the conceptualisation and ability to capture totality of eating. This study aimed to determine items for inclusion and exclusion in a food literacy item pool and capture the general public’s interpretation of everyday food literacy practices. Design: Beginning with an item pool from previous studies, cognitive interviews were conducted using think-aloud and verbal probing methods. Data were first analysed for applicability, clarity, ambiguity, and logic, then for emergent themes to ensure items captured the totality of the participant’s eating. Setting: Australia Participants: Australian residents over 18 years of age recruited via Facebook residential groups (n=20). Results: Of the original 116-items, 11 items had limited applicability; 13 items had unclear references; 32 items had lexical problems and 11 items had logical problems. In total, 29 items were deleted, 31 retained and 56 revised. Thematic analysis revealed participants limited their responses to consider only conventional practices such as grocery shopping, cooking and planned meals rather than the totality of their eating. An additional 84 items were developed to address eating out, incidental eating occasions and inconsistencies between participants assumed correct knowledge and that of public health guidelines. This resulted in a refined 171-item pool. Conclusion: This study progresses the development toward a comprehensive, validated food literacy questionnaire.

The Weight Problem: Overview of the Most Common Concepts for Body Mass and Fat Distribution and Critical Consideration of Their Usefulness for Risk Assessment and Practice

The prevalence of obesity already reached epidemic proportions many years ago and more people may die from this pandemic than from COVID-19. However, the figures depend on which measure of fat mass is used. The determination of the associated health risk also depends on the applied measure. Therefore, we will examine the most common measures for their significance, their contribution to risk assessment and their applicability. The following categories are reported: indices of increased accumulation of body fat; weight indices and mortality; weight indices and risk of disease; normal weight obesity and normal weight abdominal obesity; metabolically healthy obesity; the obesity paradox. It appears that BMI is still the most common measure for determining weight categories, followed by measures of abdominal fat distribution. Newer measures, unlike BMI, take fat distribution into account but often lack validated cut-off values or have limited applicability. Given the high prevalence of obesity and the associated risk of disease and mortality, it is important for a targeted approach to identify risk groups and determine individual risk. Therefore, in addition to BMI, a measure of fat distribution should always be used to ensure that less obvious but risky manifestations such as normal weight obesity are identified.

The Productivity of Horizontal Wells in an In-Line Development System in Fields with Oil Rims

The study proposes an analytical method for calculating the productivity of horizontal wells in a line-drive development pattern in fields with oil rims. The paper presents an analysis of existing techniques and compares them with the results of detailed numerical experiments. It also shows the limited applicability of existing techniques. On the basis of the obtained solution of a single-phase flow equation for a line-drive pattern of horizontal wells, an analytical formula was obtained which more accurately describes the productivity of wells beyond the limits of applicability of existing methods. The resulting formula is in good agreement with the results of a detailed numerical experiment.

Body Shape Indices in Adolescents Based on the 2009–2012 Korea National Health and Nutrition Examination Survey

A Body Shape Index (ABSI) is a recently proposed index for standardizing waist circumference (WC) for body mass index (BMI) and height in adults, using 2/3 and 1/2 as scaling exponents, respectively. However, ABSI has limited applicability to children and adolescents, as the relationship between height and weight changes with age and varies according to sex. This study aimed to investigate whether ABSI can be applied to adolescents and to analyze the relationships among BMI, WC, height, weight, and body shape index (BSI) in Korean adolescents. The data of 1023 adolescents aged 10–19 years from the 2009–2012 Korea National Health and Nutrition Examination Survey were collected. Body measurements (height, weight, WC, and BMI) were analyzed to estimate the BSI using log-linear regression. The scaling exponents for standardizing WC for weight and height were estimated according to age (per year) and sex. The scaling exponents for standardizing WC for weight and height were 0.698 and −1.090 for boys and 0.646 and −0.855 for girls, respectively. The exponents also differed according to age. BSI was negatively correlated with height, weight, and BMI in boys and girls, and these correlations differed in direction from those in adults. ABSI cannot be applied to adolescents. In adolescents, the BSI is dependent on age and sex and is associated with growth and puberty. Further studies are required to evaluate the association between BSI and other biomarkers, to improve its applicability as a parameter for predicting the risk of chronic diseases in adolescents.

MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes

The interactions between SNPs result in a complex interplay with the phenotype, known as epistasis. The knowledge of epistasis is a crucial part of understanding genetic causes of complex traits. However, due to the enormous number of SNP pairs and their complex relationship to the phenotype, identification still remains a challenging problem. Many approaches for the detection of epistasis have been developed using mutual information (MI) as an association measure. However, these methods have mainly been restricted to case–control phenotypes and are therefore of limited applicability for quantitative traits. To overcome this limitation of MI-based methods, here, we present an MI-based novel algorithm, MIDESP, to detect epistasis between SNPs for qualitative as well as quantitative phenotypes. Moreover, by incorporating a dataset-dependent correction technique, we deal with the effect of background associations in a genotypic dataset to separate correct epistatic interaction signals from those of false positive interactions resulting from the effect of single SNP×phenotype associations. To demonstrate the effectiveness of MIDESP, we apply it on two real datasets with qualitative and quantitative phenotypes, respectively. Our results suggest that by eliminating the background associations, MIDESP can identify important genes, which play essential roles for bovine tuberculosis or the egg weight of chickens.

PCNet: Cloud Detection in FY-3D True-Color Imagery Using Multi-Scale Pyramid Contextual Information

Cloud, one of the poor atmospheric conditions, significantly reduces the usability of optical remote-sensing data and hampers follow-up applications. Thus, the identification of cloud remains a priority for various remote-sensing activities, such as product retrieval, land-use/cover classification, object detection, and especially for change detection. However, the complexity of clouds themselves make it difficult to detect thin clouds and small isolated clouds. To accurately detect clouds in satellite imagery, we propose a novel neural network named the Pyramid Contextual Network (PCNet). Considering the limited applicability of a regular convolution kernel, we employed a Dilated Residual Block (DRB) to extend the receptive field of the network, which contains a dilated convolution and residual connection. To improve the detection ability for thin clouds, the proposed new model, pyramid contextual block (PCB), was used to generate global information at different scales. FengYun-3D MERSI-II remote-sensing images covering China with 14,165 × 24,659 pixels, acquired on 17 July 2019, are processed to conduct cloud-detection experiments. Experimental results show that the overall precision rates of the trained network reach 97.1% and the overall recall rates reach 93.2%, which performs better both in quantity and quality than U-Net, UNet++, UNet3+, PSPNet and DeepLabV3+.

Modularity in Biological Networks

Network modeling, from the ecological to the molecular scale has become an essential tool for studying the structure, dynamics and complex behavior of living systems. Graph representations of the relationships between biological components open up a wide variety of methods for discovering the mechanistic and functional properties of biological systems. Many biological networks are organized into a modular structure, so methods to discover such modules are essential if we are to understand the biological system as a whole. However, most of the methods used in biology to this end, have a limited applicability, as they are very specific to the system they were developed for. Conversely, from the statistical physics and network science perspective, graph modularity has been theoretically studied and several methods of a very general nature have been developed. It is our perspective that in particular for the modularity detection problem, biology and theoretical physics/network science are less connected than they should. The central goal of this review is to provide the necessary background and present the most applicable and pertinent methods for community detection in a way that motivates their further usage in biological research.

Importance sampling scheme for the stochastic simulation of quantum spin dynamics

The numerical simulation of dynamical phenomena in interacting quantum systems is a notoriously hard problem. Although a number of promising numerical methods exist, they often have limited applicability due to the growth of entanglement or the presence of the so-called sign problem. In this work, we develop an importance sampling scheme for the simulation of quantum spin dynamics, building on a recent approach mapping quantum spin systems to classical stochastic processes. The importance sampling scheme is based on identifying the classical trajectory that yields the largest contribution to a given quantum observable. An exact transformation is then carried out to preferentially sample trajectories that are close to the dominant one. We demonstrate that this approach is capable of reducing the temporal growth of fluctuations in the stochastic quantities, thus extending the range of accessible times and system sizes compared to direct sampling. We discuss advantages and limitations of the proposed approach, outlining directions for further developments.