Approach For Interpretation Of Data Research Articles

Research on food safety information training system based on component algorithm

In today's world, new food safety concerns emerge and develop on a regular basis, and antimicrobial food resistance is challenged by changes in the environment, food production, and transportation, as well as new and emerging diseases. As travel and trade have grown, international pollution has become more prevalent. The Hazard Analysis Critical Control Points system is a globally recognized food safety system. This technique allows potential dangers in the food manufacturing process to be identified and controlled. By continuously monitoring and managing each of the essential control points when it comes to food production, the program focuses on preventing possible dangers. According to an increasing number of papers and studies in the food sciences addressing topics like authenticity, contamination, deception, nature, and record-keeping of foods, including the rising use of instrumental methods, principal component analysis (PCA) is by far the most common approach in data analysis and interpretation. In conclusion, the PCA program delivers two essential elements: loadings and scores. The loadings indicating which factors are significant in explaining patterns in sample grouping, and the scores offer a sample location.

A framework for occupancy detection and tracking using floor-vibration signals

In sensed buildings, information related to occupant movement helps optimize important functionalities such as caregiving, energy management, and security enhancement. Typical sensing approaches for occupant tracking rely on mobile devices and cameras. These systems compromise the privacy of building occupants and may affect their behavior. Occupant detection and tracking using floor-vibration measurements that are induced by footsteps is a non-intrusive and inexpensive sensing method. Detecting the presence of occupants on a floor is challenging due to ambient noise that may mask footstep-induced floor vibrations. In addition, spurious events such as door closing and falling objects may produce vibrations that are similar to footstep impacts. These events have to be detected and disregarded. Tracking occupants is complicated due to uncertainties associated with walking styles, walking speed, shoe type, health, and mood. Also, spatial variation in structural behavior of floor slabs adds ambiguity to the task of occupant tracking, which cannot be addressed using data-driven strategies alone. In this paper, a framework for occupant detection and tracking is developed. Occupant detection is carried out based on signal information. This method outperforms existing threshold-based methods. Support-vector-machine classifiers, trained with time and frequency-domain features, successfully distinguish footsteps from spurious events and determine the number of occupants walking simultaneously. A model-based data-interpretation approach is used for occupant tracking. Structural-mechanics models are used to identify a population of possible occupant locations and trajectories. Up to two occupants can be tracked by accommodating systematic bias and uncertainties from sources such as modeling assumptions and variability in walking gaits. A hybrid framework for occupant detection and tracking that combines model-free approaches for occupancy detection with structural behavior models for tracking is developed and tested on two full-scale case studies. These studies successfully validate the utility of the framework for buildings having sparse sensor configurations that measure floor vibrations.

Advances in Medical Wearable Biosensors: Design, Fabrication and Materials Strategies in Healthcare Monitoring.

In the past decade, wearable biosensors have radically changed our outlook on contemporary medical healthcare monitoring systems. These smart, multiplexed devices allow us to quantify dynamic biological signals in real time through highly sensitive, miniaturized sensing platforms, thereby decentralizing the concept of regular clinical check-ups and diagnosis towards more versatile, remote, and personalized healthcare monitoring. This paradigm shift in healthcare delivery can be attributed to the development of nanomaterials and improvements made to non-invasive biosignal detection systems alongside integrated approaches for multifaceted data acquisition and interpretation. The discovery of new biomarkers and the use of bioaffinity recognition elements like aptamers and peptide arrays combined with the use of newly developed, flexible, and conductive materials that interact with skin surfaces has led to the widespread application of biosensors in the biomedical field. This review focuses on the recent advances made in wearable technology for remote healthcare monitoring. It classifies their development and application in terms of electrochemical, mechanical, and optical modes of transduction and type of material used and discusses the shortcomings accompanying their large-scale fabrication and commercialization. A brief note on the most widely used materials and their improvements in wearable sensor development is outlined along with instructions for the future of medical wearables.

Satellite radar interferometry: Potential and limitations for structural assessment and monitoring

The present paper analyzes potential and limitations of multi-temporal Differential Synthetic Aperture Radar Interferometry (DInSAR) techniques applied for the structural monitoring and assessment of constructions affected by different external actions. A comprehensive approach for the processing, interpretation and proper use of satellite radar interferometry data for structural analyses at urban and single-construction scales is proposed. Following a brief literature review, highlighting the most relevant applications in this field, some aspects concerning DInSAR techniques are shortly summarized, particularly focusing on the Small BAseline Subset (SBAS) approach applied to perform advanced analyses at structural level. Then, the main issues of the DInSAR measurements exploitation for structural applications are discussed, focusing on the use of both one or two datasets relevant to ascending and descending satellite passes. Analysis at the scale of single construction (i.e., building, bridge, etc.) of displacements time series and mean deformation velocities is discussed, in order to identify scenarios that may become potentially critical for the structures. Some application examples are provided, employing COSMO-SkyMed (CSK) datasets relative to the urban area of Rome (Italy), acquired during the 2011–2019 time interval and processed through the SBAS algorithm. Specific considerations related to the use of DInSAR measurements for structural monitoring and assessment are then proposed, also discussing possible approaches for preliminary and detailed evaluation of structural damages.

Evaluation of candidate reference genes for quantitative real-time PCR analysis in a male rat model of dietary iron deficiency

BackgroundQuantitative real-time polymerase chain reaction (qPCR) is a reliable and efficient method for quantitation of gene expression. Due to the increased use of qPCR in examining nutrient-gene interactions, it is important to examine, develop, and utilize standardized approaches for data analyses and interpretation. A common method used to normalize expression data involves the use of reference genes (RG) to determine relative mRNA abundance. When calculating the relative abundance, the selection of RG can influence experimental results and has the potential to skew data interpretation. Although common RG may be used for normalization, often little consideration is given to the suitability of RG selection for an experimental condition or between various tissue or cell types. In the current study, we examined the stability of gene expression using BestKeeper, comparative delta quantitation cycle, NormFinder, and RefFinder in a variety of tissues obtained from iron-deficient and pair-fed iron-replete rats to determine the optimal selection among ten candidate RG.ResultsOur results suggest that several commonly used RG (e.g., Actb and Gapdh) exhibit less stability compared to other candidate RG (e.g., Rpl19 and Rps29) in both iron-deficient and iron-replete pair-fed conditions. For all evaluated RG, Tfrc expression significantly increased in iron-deficient animal livers compared to the iron-replete pair-fed controls; however, the relative induction varied nearly 4-fold between the most suitable (Rpl19) and least suitable (Gapdh) RG.ConclusionThese results indicate the selection and use of RG should be empirically determined and RG selection may vary across experimental conditions and biological tissues.

Experiences of School Counselor Trainees in Group Supervision During Practicum: A Phenomenological Study

In this phenomenological study, we explored experience of students (N=6) in group supervision within a practicum field experience course. Using a narrative-focused approach for data analysis and interpretation of journal entries, were created a thematic structure of the participants’ lived experiences using the Critical Incident Questionnaire (CIQ). Discussion and implications of this journal writing activity are offered to help reveal and understand the instructional effectiveness of a field practicum counseling course.

Experiences of School Counselor Trainees in Group Supervision During Practicum: A Phenomenological Study

In this phenomenological study, we explored experience of students (N=6) in group supervision within a practicum field experience course. Using a narrative-focused approach for data analysis and interpretation of journal entries, were created a thematic structure of the participants’ lived experiences using the Critical Incident Questionnaire (CIQ). Discussion and implications of this journal writing activity are offered to help reveal and understand the instructional effectiveness of a field practicum counseling course.

Recent advances in glycopeptide enrichment and mass spectrometry data interpretation approaches for glycoproteomics analyses

蛋白质糖基化是生物体内最重要的翻译后修饰之一,在蛋白质稳定性、细胞内和细胞间信号转导、激素活化或失活和免疫调节等生理过程和病理进程中发挥重要作用。而异常的蛋白质糖基化往往和多种疾病的发生发展密切相关,目前应用于临床检测的多种肿瘤生物标志物大多属于糖蛋白或者糖抗原。因此在组学层次系统分析蛋白质糖基化的变化对阐明生物体内糖基化修饰的调控机理和发现新型疾病标志物都非常重要。基于质谱的蛋白质组学技术为全面分析蛋白质及其修饰提供了有效的分析手段。在自下而上的蛋白质组学研究中,由于完整糖基化肽段同时存在性质各异的肽段骨架和糖链结构、糖肽的相对丰度和离子化效率较低以及糖基化修饰有高度异质性等特点,完整糖肽的分析比其他翻译后修饰更加困难。近年来,为了更全面、系统地分析蛋白质糖基化,研究人员发展了一些新技术,包括完整糖肽的富集技术、质谱的碎裂模式和数据采集模式、质谱数据的解析方法和定量策略等等,大力推进了该领域的研究水平,也为研究蛋白质糖基化相关的生物标志物提供了技术支持。该篇综述主要关注近年来基于质谱的糖蛋白质组学研究中的新进展,重点介绍针对完整N-和O-糖基化肽段的富集新技术和谱图解析新方法,并讨论其在肿瘤早期诊断方面的应用潜力。

Assessing smallholder sustainable intensification in the Ethiopian highlands

CONTEXTSustainable intensification is one approach to increasing food production without undermining sustainability goals. In recent years new tools and indicators have been developed for broad-based assessment of sustainable intensification. However, most of these tools have been applied at field level and assessing individual technologies, while integrated assessments of multiple novel practices at farm-to-village level are lacking. OBJECTIVEIn this study we develop and apply a data collection, analysis, and interpretation approach that results in a replicable and rapid method for a multi-variate assessment of sustainable intensification. METHODSDrawing on a survey of 779 participant farmers, and using the Sustainable Intensification Assessment Framework, we quantified 27 indicators grouped into five domains: agricultural production, economics, environment, human welfare, and social. We applied an expert-led threshold setting exercise to re-scale indicators, permitting aggregated and dis-aggregated visualisation onto a common axis. We developed a graphic evaluation approach to communicate the multiple domain and indicator scores and applied this approach to quantify trade-offs and synergies related to agricultural productivity in four contrasting sites in Ethiopia. RESULTS AND CONCLUSIONSIn each site there was a notable and significant gradient of production intensity, although average crop and livestock productivity remained well below attainable levels. Higher levels of productivity were correlated with higher scores in the economic, social and agricultural domains, and in some sites they were also positively correlated with the human welfare and environmental domains. In no case was increased production intensity correlated with lower scores in any of the sustainability domains, indicating that in these relatively low-intensity systems increases in productivity will go hand-in-hand with improvements in most other sustainability domains. Synergies can overrule trade-offs in these smallholder systems in Ethiopia, if managed well. SIGNIFICANCEThis is one of very few studies of multiple sustainable intensification technologies implemented concurrently at the household to community level. Most studies focus on individual technologies or practices within specific niches of the farming system. The method could be developed further for efficient application to various large-scale development or intensification projects, and could potentially make use of existing smallholder information databases.

Prospects and challenges of cancer systems medicine: from genes to disease networks.

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

GPR Data Interpretation Approaches in Archaeological Prospection

This article focuses on the possible drawbacks and pitfalls in the GPR data interpretation process commonly followed by most GPR practitioners in archaeological prospection. Standard processing techniques aim to remove some noise, enhance reflections of the subsurface. Next, one has to calculate the instantaneous envelope and produce C-scans which are 2D amplitude maps showing high reflectivity surfaces. These amplitude maps are mainly used for data interpretation and provide a good insight into the subsurface but cannot fully describe it. The main limitations are discussed while studies aiming to overcome them are reviewed. These studies involve integrated interpretation approaches using both B-scans and C-scans, attribute analysis, fusion approaches, and recent attempts to automatically interpret C-scans using Deep Learning (DL) algorithms. To contribute to the automatic interpretation of GPR data using DL, an application of Convolutional Neural Networks (CNNs) to classify GPR data is also presented and discussed.

Transverse permeability measurements of gas shales under replicated in-situ flow conditions: Mathematical modeling and laboratory testing

Fast and accurate characterization of unconventional gas reservoirs is of great significance for estimation of gas-in-place and enhancement of gas recovery; however, it remains a big challenge to efficiently and effectively determine the permeability of tight-structure reservoirs in the nano-Darcy scale, especially for those reservoirs with strong gas sorption potential. A novel pressure transient technique is proposed to replicate the in-situ gas flow behavior, and thus to reduce the permeability errors from gas compressive storage, gas sorption, and gas compressibility, which are the three primary error sources widely existing in various permeability measurement techniques. The feasibility of the technique is firstly verified via numerical solutions of a partial differential equation (PDE) model established to closely represent the proposed technique. The results of the numerical study showed that the effects of the error sources on the measured permeability can be minimized and meanwhile, high accuracy and efficiency can be achieved in comparison with the conventional method. A specially designed apparatus for experimental tests via the proposed technique is then used to investigate the stress-dependent permeability of gas shales. The consistency of the experimental results with the findings from the numerical study further validates the applicability of the technique. Considering the complexity of the analytical solution of the PDE model for experimental data interpretation, shale permeability is computed via a new data interpretation approach which is developed by combining the curve-matching and the finite difference method. The experimental results showed that shale permeability is strongly dependent upon the applied stresses and easily affected by gas sorption, gas compressibility and stress inhomogeneity. Without consideration of these factors, the permeability error can reach up to 28.1 % at low stresses. The proposed technique provides a great tool to get insights into the in-situ gas flow behavior and characterize tight-structure gas reservoirs.

Transcriptome dynamics during cholesterol-induced transdifferentiation of human coronary artery smooth muscle cells: A Gene Ontology-centric clustering approach

Macrophage-like cells derived from vascular smooth muscle cells (SMCs) play critical roles in atherogenesis, and DNA hydroxymethylation was implicated in transdifferentiation. We examined transcriptomes and (hydroxy)methylomes of human coronary artery SMCs during cholesterol-induced transdifferentiation. A unique approach of exhaustive identification of differentially expressed genes followed by Gene Ontology-centric clustering facilitated deeper understanding of multifaceted modulations of genes involved in extracellular matrix organization, angiogenesis, cell migration, hypoxia response, and cholesterol biosynthesis. Intriguingly, type I interferon response was transiently activated, presumably forming an immuno-metabolic circuit with cholesterol metabolism. Neither global nor DEG-proximal changes were evident in (hydroxy)methylation. These results would not only provide a unique data resource for atherosclerosis research but present a potentially useful approach in transcriptome data interpretation.

Unlocking the full potential of voltammetric data analysis: A novel peak recognition approach for (bio)analytical applications

Bridging the gap between complex signal data output and clear interpretation by non-expert end-users is a major challenge many scientists face when converting their scientific technology into a real-life application. Currently, pattern recognition algorithms are the most frequently encountered signal data interpretation algorithms to close this gap, not in the least because of their straight-forward implementation via convenient software packages. Paradoxically, just because their implementation is so straight-forward, it becomes cumbersome to integrate the expert's domain-specific knowledge. In this work, a novel signal data interpretation approach is presented that uses this domain-specific knowledge as its fundament, thereby fully exploiting the unique expertise of the scientist. The new approach applies data preprocessing in an innovative way that transcends its usual purpose and is easy to translate into a software application. Multiple case studies illustrate the straight-forward application of the novel approach. Ultimately, the approach is highly suited for integration in various (bio)analytical applications that require interpretation of signal data.

Comparative Social Life Cycle Assessment of Two Biomass-to-Electricity Systems.

Biomass plays a fundamental role in numerous decarbonisation strategies that seek to mitigate the short- and long-term effects of climate change. Within this context, decision-makers’ choices need to comprehensively consider potential sustainability effects associated with bioenergy systems. In particular, due to the lack of studies addressing the social sustainability of bioelectricity, the present work applies the Social Life Cycle Assessment (S-LCA) methodology to compare the social performance of two biomass-to-electricity systems located in Portugal based on either fluidised-bed or grate furnace technology. S-LCA involves a comprehensive approach for holistic evaluation and data interpretation of social aspects. Six social indicators were benchmarked: child labour, forced labour, gender wage gap, women in the sectoral labour force, health expenditure, and contribution to economic development. The results show that the implementation of fluidised-bed furnaces as a more efficient conversion technology could reduce by 15–19% the selected negative social impacts, except women in the sectoral labour force. When enlarging the interpretation to a sustainability perspective, the general suitability of the fluidised-bed furnace system would be further emphasised under environmental aspects while jointly providing valuable insights for informed decision-making and sustainability reporting.

Uncertainties in Structural Behavior for Model-Based Occupant Localization Using Floor Vibrations

In sensed buildings, information related to occupant movement helps optimize functions such as security, energy management, and caregiving. Due to privacy needs, non-intrusive sensing approaches for tracking occupants inside buildings, such as vibration sensors, are often preferred over intrusive strategies that involve use of cameras and wearable devices. Current sensor-based occupant-localization approaches are data-driven techniques that do not account for structural behavior and limited to slabs on grade. Varying-rigidity floors and inherent variability in walking gaits lead to ambiguous interpretations of floor vibrations when performing model-free occupant localization. In this paper, an extensive analysis of vibrations induced by a range of occupants is described. Firstly, the need for a structural-behavior model for occupant localization is assessed using two full-scale case studies. Structural behavior is found to significantly influence floor vibrations induced by footstep impacts. Since a simple relationship between distances from footstep-impact to sensor locations cannot be assured, the use of physics-based models is necessary for accurate occupant localization. Secondly, measured data are interpreted using physics-based models and information related to uncertainties from multiple sources. There are two types of uncertainties: modelling uncertainties and measurement uncertainties, including variability in walking gaits. Error-domain model falsification (EDMF) and residual minimization (RM) are model-based approaches for data interpretation. Unlike RM, EDMF explicitly accounts for the presence of systematic errors in parameters and overall model bias. In this paper, model-based occupant localization is carried out using EDMF and RM on a full-scale case study. By explicitly accounting for the presence of uncertainties and the influence of structural behavior, EDMF, unlike RM, accurately reveals possible occupant locations on floor slabs.

A time domain approach for data interpretation from long‐term static monitoring of historical structures

A time domain approach for data interpretation from long‐term static monitoring of historical structures

The application of threshold gold values based on geochemical exploration in complex regolith terrains: A case study at Lawra Greenstone Belt of Northwest Ghana

coming....

Influence of fermentation temperature on in situ heteropolysaccharide formation (Lactobacillus plantarum TMW 1.1478) and texture properties of raw sausages.

This study puts a focus on the influence of microbial in situ heteropolysaccharide (HePS) formation on the quality of raw fermented sausages (salami). Since exopolysaccharide‐production is often triggered by sub‐optimal growth conditions, the influence of different fermentation temperatures was also investigated. For this reason, the sausage batter was inoculated with (Lactobacillus plantarum TMW 1.1478) or without (L. sakei TMW 1.2037; control) a HePS‐producing starter culture (inoculation concentration ~108 CFU/g), and the sausages fermented at either 10, 16, or 24°C (7 days), followed by a drying period at 14°C until the final weight loss of 31% was reached. Microbial growth, pH, and weight loss development were monitored and the products further characterized using texture profile analysis and a sensory test. HePS in the salami matrix were determined using confocal laser scanning microscopy and a semi‐quantitative data interpretation approach. Sausages containing L. plantarum were found to be significantly (p < .05) softer compared with control samples, which was also confirmed in the sensory analysis. The different fermentation temperatures had an influence on the drying speed. Here, sausages produced with L. plantarum needed more time to reach the final weight loss of 31% as compared to control samples, which could be attributed to the presence of exopolysaccharides in the matrix (p < .05). Using HePS‐forming starter cultures in raw fermented sausage manufacturing can lead to products with a softer texture (undesired in Europe) depending on the strain and processing conditions used, highlighting the importance of a suitable starter culture selection in food processing.

Site-Specific N-glycosylation Analysis of Recombinant Proteins by LC/MSE.

The glycosylation process is extremely heterogeneous, dynamic, and complex compared with any other post-translational modification of protein. In the context of recombinant glycoproteins, glycosylation is a critical attribute as glycans could dramatically alter protein functions and properties including activity, half-life, in vivo localization, stability, and, last but not least, immunogenicity. Liquid chromatography combined to mass spectrometry constitutes the most powerful analytical approach to achieve the comprehensive glycan profile description or comparison of glycoproteins. This chapter details a versatile yet straightforward LC-MS approach for sample preparation, analysis, and data interpretation, enabling the evaluation of site-specific N-glycosylation of recombinant glycoproteins.