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Traumatic brain injury (TBI) is a major public health problem. Despite considerable research deciphering injury pathophysiology, precision therapies remain elusive. Here, we present large-scale data sharing and machine intelligence approaches to leverage TBI complexity. The Open Data Commons for TBI (ODC-TBI) is a community-centered repository emphasizing Findable, Accessible, Interoperable, and Reusable data sharing and publication with persistent identifiers. Importantly, the ODC-TBI implements data sharing of individual subject data, enabling pooling for high-sample-size, feature-rich data sets for machine learning analytics. We demonstrate pooled ODC-TBI data analyses, starting with descriptive analytics of subject-level data from 11 previously published articles (N = 1250 subjects) representing six distinct pre-clinical TBI models. Second, we perform unsupervised machine learning on multi-cohort data to identify persistent inflammatory patterns across different studies, improving experimental sensitivity for pro- versus anti-inflammation effects. As funders and journals increasingly mandate open data practices, ODC-TBI will create new scientific opportunities for researchers and facilitate multi-data-set, multi-dimensional analytics toward effective translation.

Objective: For selecting minimally invasive surgery (i.e. laparoscopic ovarian cystectomy) for treating ovarian tumours (OTs) in premenopausal patients, the pre-operative differentiation of benign ovarian tumours (Be-OTs) based on magnetic resonance imaging (MRI) interpretation is important. This paper describes the authors’ 8-year experience of approximately 1000 OT cases, and provides information about a diagnostic algorithm to help other hospitals. Study design: The medical records of 901 patients aged

Introduction. Academic stress (AS) is a prevalent challenge faced by university students, potentially affecting molecular indicators such as brain-derived neurotrophic factor (BDNF) and global DNA methylation (G-DNA-M). These indicators could illuminate the physiological ramifications of academic stress. Study Design and Methods. This research followed a quantitative, non-experimental, longitudinal panel design spanning two academic semesters, observing phenomena in their natural context. Students from the Medical Technology program at Universidad de Concepción, Chile were involved, with assessments at the beginning and during heightened academic stress periods. Sample. Of the total participants, 63.0% were females, with an average age of 21.14 years at baseline, and 36.92% were males, averaging 21.36 years. By the study's conclusion, female participants averaged 21.95 years, and males 22.13 years. Results. Significant differences were observed between initial and final assessments for the SISCO-II Inventory of Academic Stress and Beck Depression Inventory-II, notably in stressor scores, and physical, and psychological reactions. Gender differences emerged in the final physical and psychological reactions. No significant changes were detected between the two assessments in plasma BDNF or G-DNA-M values. A refined predictive model showcased that, on average, there was a 3.56% decrease in females' plasma BDNF at the final assessment and a 17.14% decrease in males. In the sample, the G-DNA-M percentage at the final assessment increased by 15.06% from the baseline for females and 18.96% for males. Conclusions. The study underscores the physiological impact of academic stress on university students, evidenced by changes in markers like BDNF and G-DNA-M. These findings offer an in-depth understanding of the intricate mechanisms regulating academic stress responses and highlight the need for interventions tailored to mitigate its physiological and psychological effects. [ABSTRACT FROM AUTHOR]

Introduction Academic stress (AS) is a prevalent challenge faced by university students, potentially affecting molecular indicators such as brain-derived neurotrophic factor (BDNF) and global DNA methylation (G-DNA-M). These indicators could illuminate the physiological ramifications of academic stress. Study Design and Methods This research followed a quantitative, non-experimental, longitudinal panel design spanning two academic semesters, observing phenomena in their natural context. Students from the Medical Technology program at Universidad de Concepción, Chile were involved, with assessments at the beginning and during heightened academic stress periods. Sample Of the total participants, 63.0% were females, with an average age of 21.14 years at baseline, and 36.92% were males, averaging 21.36 years. By the study’s conclusion, female participants averaged 21.95 years, and males 22.13 years. Results Significant differences were observed between initial and final assessments for the SISCO-II Inventory of Academic Stress and Beck Depression Inventory-II, notably in stressor scores, and physical, and psychological reactions. Gender differences emerged in the final physical and psychological reactions. No significant changes were detected between the two assessments in plasma BDNF or G-DNA-M values. A refined predictive model showcased that, on average, there was a 3.56% decrease in females’ plasma BDNF at the final assessment and a 17.14% decrease in males. In the sample, the G-DNA-M percentage at the final assessment increased by 15.06% from the baseline for females and 18.96% for males. Conclusions The study underscores the physiological impact of academic stress on university students, evidenced by changes in markers like BDNF and G-DNA-M. These findings offer an in-depth understanding of the intricate mechanisms regulating academic stress responses and highlight the need for interventions tailored to mitigate its physiological and psychological effects.

In brain-based communication, voluntarily modulated brain signals (instead of motor output) are utilized to interact with the outside world. The possibility to circumvent the motor system constitutes an important alternative option for severely paralyzed. Most communication brain-computer interface (BCI) paradigms require intact visual capabilities and impose a high cognitive load, but for some patients, these requirements are not given. In these situations, a better-suited, less cognitively demanding information-encoding approach may exploit auditorily-cued selective somatosensory attention to vibrotactile stimulation. Here, we propose, validate and optimize a novel communication-BCI paradigm using differential fMRI activation patterns evoked by selective somatosensory attention to tactile stimulation of the right hand or left foot. Using cytoarchitectonic probability maps and multi-voxel pattern analysis (MVPA), we show that the locus of selective somatosensory attention can be decoded from fMRI-signal patterns in (especially primary) somatosensory cortex with high accuracy and reliability, with the highest classification accuracy (85.93%) achieved when using Brodmann area 2 (SI-BA2) at a probability level of 0.2. Based on this outcome, we developed and validated a novel somatosensory attention-based yes/no communication procedure and demonstrated its high effectiveness even when using only a limited amount of (MVPA) training data. For the BCI user, the paradigm is straightforward, eye-independent, and requires only limited cognitive functioning. In addition, it is BCI-operator friendly given its objective and expertise-independent procedure. For these reasons, our novel communication paradigm has high potential for clinical applications.

BACKGROUND: Cocos nucifera (L.) is an important plantation crop with immense but untapped nutraceutical potential. Despite its bioactive potential, the biochemical features of testa oils of various coconut genotypes are poorly understood. Hence, in this study, the physicochemical characteristics of testa oils extracted from six coconut genotypes – namely West Coast Tall (WCT), Federated Malay States Tall (FMST), Chowghat Orange Dwarf (COD), Malayan Yellow Dwarf (MYD), and two Dwarf × Dwarf (D × D hybrids) viz., Cameroon Red Dwarf (CRD) × Ganga Bondam Green Dwarf (GBGD) and MYD × Chowghat Green Dwarf (CGD) – were analyzed. RESULTS: The proportion of testa in the nuts (fruits) (1.29–3.42%), the proportion of oil in the testa (40.97–50.56%), and biochemical components in testa oils – namely proxidant elements Fe (34.17–62.48 ppm) and Cu (1.63–2.77 ppm), and the total phenolic content (6.84–8.67 mg GAE/100 g), and phytosterol content (54.66–137.73 mg CE/100 g) varied depending on the coconut genotypes. The saturated fatty acid content of testa oils (67.75 to 78.78%) was lower in comparison with that of coconut kernel oils. Similarly, the lauric acid (26.66–32.04%), myristic (18.31–19.60%), and palmitic acid (13.43–15.71%,) content of testa oils varied significantly in comparison with the coconut kernel oils (32–51%, 17–21% and 6.9–14%, respectively). Liquid chromatography–mass spectrometry (LC–MS) analysis revealed the presence of 18 phenolic acids in coconut testa oil. Multivariate analysis revealed the biochemical attributes that defined the principal components loadings. Hierarchical clustering analysis of the genotypes showed two distinct clusters. CONCLUSION: This study reveals the genotypic variations in the nutritionally important biochemical components of coconut testa oils. The relatively high concentration of polyunsaturated fatty acids (PUFA) and polyphenol content in testa oils warrant further investigation to explore their nutraceutical potential. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) is a major public health problem. Despite considerable research deciphering injury pathophysiology, precision therapies remain elusive. Here, we present large-scale data sharing and machine intelligence approaches to leverage TBI complexity. The Open Data Commons for TBI (ODC-TBI) is a community-centered repository emphasizing Findable, Accessible, Interoperable, and Reusable data sharing and publication with persistent identifiers. Importantly, the ODC-TBI implements data sharing of individual subject data, enabling pooling for high-sample-size, feature-rich data sets for machine learning analytics. We demonstrate pooled ODC-TBI data analyses, starting with descriptive analytics of subject-level data from 11 previously published articles (N?=?1250 subjects) representing six distinct pre-clinical TBI models. Second, we perform unsupervised machine learning on multi-cohort data to identify persistent inflammatory patterns across different studies, improving experimental sensitivity for pro- versus anti-inflammation effects. As funders and journals increasingly mandate open data practices, ODC-TBI will create new scientific opportunities for researchers and facilitate multi-data-set, multi-dimensional analytics toward effective translation.

We used pattern classifiers to extract features related to recognition memory retrieval from the temporal information in single-trial electroencephalography (EEG) data during attempted memory retrieval. Two-class classification was conducted on correctly remembered trials with accurate context (or source) judgments vs. correctly rejected trials. The average accuracy for datasets recorded in a single session was 61% while the average accuracy for datasets recorded in two separate sessions was 56%. To further understand the basis of the classifier's performance, two other pattern classifiers were trained on different pairs of behavioral conditions. The first of these was designed to use information related to remembering the item and the second to use information related to remembering the contextual information (or source) about the item. Mollison and Curran (2012) had earlier shown that subjects' familiarity judgments contributed to improved memory of spatial contextual information but not of extrinsic associated color information. These behavioral results were similarly reflected in the event-related potential (ERP) known as the FN400 (an early frontal effect relating to familiarity) which revealed differences between correct and incorrect context memories in the spatial but not color conditions. In our analyses we show that a classifier designed to distinguish between correct and incorrect context memories, more strongly involves early activity (400-500 ms) over the frontal channels for the location distinctions, than for the extrinsic color associations. In contrast, the classifier designed to classify memory for the item (without memory for the context), had more frontal channel involvement for the color associated experiments than for the spatial experiments. Taken together these results argue that location may be bound more tightly with the item than an extrinsic color association. The multivariate classification approach also showed that trial-by-trial variation in EEG corresponding to these ERP components were predictive of subjects' behavioral responses. Additionally, the multivariate classification approach enabled analysis of error conditions that did not have sufficient trials for standard ERP analyses. These results suggested that false alarms were primarily attributable to item memory (as opposed to memory of associated context), as commonly predicted, but with little previous corroborating EEG evidence.

Following a case study, community adaptation plans are a bottom-up approach that focus on increasing climate-vulnerable communities' engagement in local adaptation planning and policy design, prioritization, and implementation in Nepal. This paper explains how Community-Based Adaptation Action Plan (CAPA) groups are being studied to assess the climate vulnerability of the local socio-ecosystem and to develop community-level adaptation measures. However, there is insufficient research to differentiate local vulnerabilities caused by climate change. This paper, therefore, examines climate change vulnerability with respect to community vulnerability and potential adaptation measures to increase community resilience and adaptive capacity through CAPAs. The study compares differences by gender, caste/ethnicity, and wealth in relation to specific climate-related hazards (exposure, sensitivity, and adaptive capacity) of communities. The study draws on secondary sources of information along with field observations, 73 household interviews, 13 key-informant interviews, consultations, and 9 interactive meetings in 3 districts of Nepal. Differential impact analysis refers to the exposure, sensitivity, and adaptive capacity of local socio-ecological systems. In addition, multivariate analysis was conducted using the Canoco program to analyze the role of actors with respect to climate vulnerability. The results conclude that the degree of vulnerability varies widely at the household level and is strongly influenced by socio-economic characteristics such as gender, caste/ethnicity, and wealth. Immediate and focused attention is needed to improve access to government resources for vulnerable households, requiring positive support from decision makers. Equally important is improving the chain of communication, which includes information, skills, knowledge, capacity, and institutional arrangements. Analysis of the differential vulnerability and the adaptive capacity of a vulnerable community is more appropriate for the design of local adaptation plans. Therefore, the study suggests that engagement of local partners, including local authorities, in addressing vulnerability and adaptation is required to confront the social process, new institutional arrangements, local adaptation, and capacity-building with technical solutions. [ABSTRACT FROM AUTHOR]

In the current aging society, there has been a marked increase in the incidence of cervical spinal cord injury (CSCI) without major bone injury. This multi-center study aimed to identify predictors of neurological improvement in elderly patients with CSCI without major bone injury. The participants were 591 patients aged ≥65 years with CSCI without major bone injury and a minimum follow-up period of three months. Neurologic status was defined using the American Spinal Injury Association (ASIA) impairment scale (AIS). Univariate and multi-variate analyses were performed to identify prognostic factors for walking recovery in AIS A–C cases and full upper extremity motor recovery in AIS D cases. In AIS A–C cases, body mass index (odds ratio (OR): 1.112), magnetic resonance imaging signal change (OR: 0.240), AIS on admission (OR: 3.497), comorbidity of dementia/delirium (OR: 0.365), and post-injury pneumonia (OR: 0.194) were identified as independent prognostic factors for walking recovery. The prevalence of ossification of the posterior longitudinal ligament (OR: 0.494) was also found to be an independent prognostic factor in AIS B and C cases only. In AIS D cases, age (OR: 0.937), upper extremity ASIA motor score on admission (OR: 1.230 [per 5 scores]), and operation (OR: 0.519) were independent prognostic factors for full motor recovery. The severity of AIS at admission was the strongest predictor of functional outcomes. Promoting rehabilitation, however, through measures to reduce cognitive changes, post-injury pneumonia, and unhealthy body weight changes can contribute to greater neurological improvement in AIS A–C cases. [ABSTRACT FROM AUTHOR]

Cadmium (Cd) is a toxic metal and found in various soils, including forest soils. The great spatial heterogeneity in soil Cd makes it difficult to determine its distribution. Both traditional soil surveys and spatial modeling have been used to study the natural distribution of Cd. However, traditional methods are highly labor-intensive and expensive, while modeling is often encumbered by the need to select the proper predictors. In this study, based on intensive soil sampling (385 soil pits plus 64 verification soil pits) in subtropical forests in Yunfu, Guangdong, China, we examined the impacting factors and the possibility of combining existing soil information with digital elevation model (DEM)-derived variables to predict the Cd concentration at different soil depths along the landscape. A well-developed artificial neural network model (ANN), multi-variate analysis, and principal component analysis were used and compared using the same dataset. The results show that soil Cd concentration varied with soil depth and was affected by the top 0-20 cm soil properties, such as soil sand or clay content, and some DEM-related variables (e.g., slope and vertical slope position, varying with depth). The vertical variability in Cd content was found to be correlated with metal contents (e.g., Cu, Zn, Pb, Ni) and Cd contents in the layer immediately above. The selection of candidate predictors differed among different prediction models. The ANN models showed acceptable accuracy (around 30% of predictions have a relative error of less than 10%) and could be used to assess the large-scale Cd impact on environmental quality in the context of intensifying industrialization and climate change, particularly for ecosystem management in this region or other regions with similar conditions. [ABSTRACT FROM AUTHOR]

Material properties is one of the most significant variables in terms of efficiency. The sediment layer in a coal pit mine has a possibility of sticking to the equipment bucket and reducing its productivity, especially in the disposal area. Consequently, stickiness has a close definition of adhesivity level; thus it may be associated with geomechanical properties. Various soil classification in the disposal area was investigated to identify the relationship between adhesivity and geomechanical properties such as water content, density, cohesion, and internal friction angles. Multivariate regression analysis and statistical test (F-test and t-test) were used to investigate geomechanical properties related to adhesivity on each disposal area. Primary data was taken from a standard and modified laboratory testing. The results showed that disposal materials were high-plasticity materials with different grain-sizes. The dominant grain size on disposal 1, 2, and 3 were clay, sand, and clay, respectively. Based on regression analysis, the adhesivity on each disposal was increased along with the water content until its optimum value. Using a statistical test with a significance level of 95% (P-value 0.04), water content, cohesion and internal friction angle affected the adhesivity level on disposal 1 by 99% (adjusted R² 0.99). Adhesivity level in disposal 2 was only affected with density by 63% (adjusted R² 0.63). Meanwhile, in disposal 3, the significance level of 33% (P-value 0.50) was used to define that water content, cohesion, and internal friction angle as parameters affecting adhesivity level by 33% (adjusted R² 0.33). [ABSTRACT FROM AUTHOR]

Aims: Vegetation sampling typically involves the use of quadrats to estimate species cover abundance. Such surveys do not generally record small‐scale vegetation patch structure at sub‐quadrat scales. Here we test a simple method to quantify patch structure that complements conventional techniques. We compare the two methods, and analyse metrics derived from small‐scale patch surveys with environment/management data. Location: Northumberland, United Kingdom. Methods: We recorded cover abundance of all species in an upland moor with 1‐m2 quadrats. These were divided into 100 'sub‐quadrats', 10 cm × 10 cm, and the dominant and subdominant species identified. Patch metrics (number, area and shape) for individual species recorded as dominant or subdominant in the subquadrat survey were analysed using multi‐variate generalised linear models with environmental and management data. Sub‐quadrat data were also aggregated for each quadrat, to create species composition data. The two sets of compositional data, from whole‐quadrat and sub‐quadrat aggregations, were compared via Procrustes rotation of ordination scores. Results: Patch number, area and shape for dominant and subdominant species were all significantly affected by soil pH, soil water content, slope and elevation. Effects of proximity to sheep tracks and drainage ditches were less consistent amongst species. Ordinations of vegetation data from conventional and sub‐quadrats were similar, with significant Procrustes R2 of 67% and 70% for dominant and subdominant species respectively. Conclusions: Sub‐quadrat surveys can easily be used to complement existing whole‐quadrat surveys at little cost in time or resources. Their patch metrics can provide additional insights into the environmental and management drivers that may affect the growth of individual plants or clumps, potentially in relation to plant traits, and thus alter the overall community composition. The methods we describe can readily be adapted to other sizes of quadrats and sub‐quadrats in a wide range of vegetation communities. [ABSTRACT FROM AUTHOR]

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Background: The COVID-19 pandemic has led to an unprecedented demand for testing - for diagnosis and prognosis - as well as for investigation into the impact of the disease on the host metabolism. Sebum sampling has the potential to support both needs by looking at what the virus does to us, rather than looking for the virus itself. Methods: In this pilot study, sebum samples were collected from 67 hospitalised patients (30 COVID-19 positive and 37 COVID-19 negative) by gauze swab. Lipidomics analysis was carried out using liquid chromatography mass spectrometry, identifying 998 reproducible features. Univariate and multivariate statistical analyses were applied to the resulting feature set. Findings: Lipid levels were depressed in COVID-19 positive participants, indicative of dyslipidemia; p-values of 0·022 and 0·015 were obtained for triglycerides and ceramides respectively, with effect sizes of 0·44 and 0·57. Partial Least Squares-Discriminant Analysis showed separation of COVID-19 positive and negative participants with sensitivity of 57% and specificity of 68%, improving to 79% and 83% respectively when controlled for confounding comorbidities. Interpretation: COVID-19 dysregulates many areas of metabolism; in this work we show that the skin lipidome can be added to the list. Given that samples can be provided quickly and painlessly, we conclude that sebum is worthy of future consideration for clinical sampling. Funding: The authors acknowledge funding from the EPSRC Impact Acceleration Account for sample collection and processing, as well as EPSRC Fellowship Funding EP/R031118/1, the University of Surrey and BBSRC BB/T002212/1. Mass Spectrometry was funded under EP/P001440/1.

These lecture notes provide a quick review of basic concepts in statistical analysis and probability theory for data science. We survey general description of single- and multi-variate data, and derive regression models by means of the method of least squares. As theoretical backgrounds we provide basic knowledge of probability theory which is indispensable for further study of mathematical statistics and probability models. We show that the regression line for a multi-variate normal distribution coincides with the regression curve defined through the conditional density function. In Appendix matrix operations are quickly reviewed. These notes are based on the lectures delivered in Graduate Program in Data Science (GP-DS) and Data Sciences Program (DSP) at Tohoku University in 2018-2020. [ABSTRACT FROM AUTHOR]

Confocal Raman microspectral imaging (CRMI) is a versatile non-invasive technique that allows in vitro cell imaging without any chemical fixation, fluorescent markers or other contrast-enhancing chemicals. In this study, dorsal root ganglion (DRG) neuron and its affiliated Schwann cells (SCs) were co-cultured to unveil their underlying sub-cellular constitutional and structural nature. Both DRG neurons and SCs were derived from neonatal Sprague-Dawley rats and seeded on CaF 2 subtracts for spectral analysis. After acquiring Raman hyperspectral datasets, multivariate data analyses, including K-mean cluster analysis (KCA) and principal component analysis (PCA), were successively adopted to study the subcellular structural and compositional information of the measured cells. Univariate spectral analysis was adopted to emphasize the spatial distribution of subcellular constitutions based on the acquired spectral characteristics. Results have shown Raman spectral characteristics of DRG neurons (cell membrane, cytoplasm, organelles, nucleus) and its affiliated SCs (myelin, cell membrane, cytoplasm, nucleus), as well as information on the subcellular distribution pattern of major biochemical components (proteins, cytochrome c, nucleic acids, lipids, carbohydrates). This in vitro spectral-imaging work provides a proof of principle of an analytical method for future studies on the developmental mechanisms of DRG neurons and their molecular bases for the treatment of diseases of the peripheral nervous system. [Display omitted] • CRMI was used to study the potential subcellular composition and structural properties of DRG neurons and SCs. • Use multivariate data analysis to describe the subcellular structure and composition information of the tested cell. • Univariate spectral analysis was adopted to emphasize the spatial distribution of subcellular constitutions. [ABSTRACT FROM AUTHOR]

Previously, we have established two distinct progressive multiple sclerosis (MS) models by induction of experimental autoimmune encephalomyelitis (EAE) with myelin oligodendrocyte glycoprotein (MOG) in two mouse strains. A.SW mice develop ataxia with antibody deposition, but no T cell infiltration, in the central nervous system (CNS), while SJL/J mice develop paralysis with CNS T cell infiltration. In this study, we determined biomarkers contributing to the homogeneity and heterogeneity of two models. Using the CNS and spleen microarray transcriptome and cytokine data, we conducted computational analyses. We identified up-regulation of immune-related genes, including immunoglobulins, in the CNS of both models. Pro-inflammatory cytokines, interferon (IFN)-γ and interleukin (IL)-17, were associated with the disease progression in SJL/J mice, while the expression of both cytokines was detected only at the EAE onset in A.SW mice. Principal component analysis (PCA) of CNS transcriptome data demonstrated that down-regulation of prolactin may reflect disease progression. Pattern matching analysis of spleen transcriptome with CNS PCA identified 333 splenic surrogate markers, including Stfa2l1, which reflected the changes in the CNS. Among them, we found that two genes (PER1/MIR6883 and FKBP5) and one gene (SLC16A1/MCT1) were also significantly up-regulated and down-regulated, respectively, in human MS peripheral blood, using data mining.

Biofilms are communities of bacteria living embedded in a highly hydrated matrix composed of polysaccharides, proteins, and extracellular DNA. This life style confers numerous advantages to bacteria including protection against external threats. However, they also contribute to increase bacterial resistance against antimicrobials, an issue particularly relevant in dangerous infections. Due to the complexity of the matrix, few information is present in the literature on details of its architecture including the spatial distribution of the macromolecular components which might give hints on the way the biofilm scaffold is built up by bacteria. In this study, we investigated the possibility to combine well-established microbiological procedures with advanced microscopies to get information on composition and distribution of the macromolecular components of biofilm matrices. To this, confocal microscopy, diffraction-limited infrared (IR) spectral imaging, and atomic force microscopy (AFM) were used to explore biofilm produced by a clinical strain of Klebsiella pneumoniae. IR imaging permitted to have clues on how the biofilm grows and spreads on surfaces, and the local distribution of the components within it. Through the analysis of the pure component spectra, it was possible to assess the chemical and structural composition of the saccaridic matrix, confirming the data obtained by NMR. It was also possible to follow the time course of biofilm from 6 up to 48 h when the biofilm grew into a 3-dimensional multi-layered structure, characteristic of colonies of bacteria linked together by a complex matrix. In addition, nanoFTIR and AFM investigations allowed the estimation of biofilm growth in the vertical direction and the morphological analysis of bacterial colonies at different time points and the evaluation of the chemical composition at the nanoscale. [ABSTRACT FROM AUTHOR]

Confocal Raman microspectral imaging (CRMI) is an advanced cell-imaging method that maps endogenous molecular compositions with their unique spectral fingerprint indicators. The aim of this work was to provide a visualized understanding of subcellular features of live osteosarcoma cells using a 532-nm laser excitation without the use of dyes or molecular probes. Both malignant osteoblast and spindle osteosarcoma cells derived from the BALB/c mouse osteosarcoma cell line K7M2 were investigated in this work. After preprocessing the obtained spectral dataset, K-means cluster analysis (KCA) is employed to reconstruct Raman spectroscopic maps of single biological cells by identifying regions of the cellular membrane, cytoplasm, organelles, and nucleus with their corresponding mean spectra. Principal component analysis (PCA) was further employed to indicate variables of significant influence on the separation of the spectra of each cellular component. The biochemical components of the two cell types were then extracted by showing the spectral and distribution features attributed to proteins, lipids, and DNA. Using this standardized CRMI technique and multivariate analysis approaches, the results obtained could be a sound foundation for a typical Raman imaging protocol of live cellular biomedical analysis. [ABSTRACT FROM AUTHOR]

Current flood vulnerability analyses rely on deterministic methods (e.g., stage–damage functions) to quantify resulting damage and losses to the built environment. While such approaches have been used extensively by communities, they do not enable the propagation of uncertainty into a risk- or resilience-informed decision process. In this paper, a method that allows the development of building fragility and building loss functions is articulated and applied to develop an archetype portfolio that can be used to model buildings in a typical community. The typical single-variable flood vulnerability function, normally based on flood depth, is extended to a multi-variate flood vulnerability function, which is a function of both flood depth and flood duration, thereby creating fragility surfaces. The portfolio presented herein consists of 15 building archetypes that can serve to populate a community-level model to predict damage and resulting functionality from a scenario flood event. The prediction of damage and functionality of buildings within a community is the first step in developing risk-informed mitigation decisions to improve community resilience.

We used pattern classifiers to extract features related to recognition memory retrieval from the temporal information in single-trial electroencephalography (EEG) data during attempted memory retrieval. Two-class classification was conducted on correctly remembered trials with accurate context (or source) judgments vs. correctly rejected trials. The average accuracy for datasets recorded in a single session was 61% while the average accuracy for datasets recorded in two separate sessions was 56%. To further understand the basis of the classifier’s performance, two other pattern classifiers were trained on different pairs of behavioral conditions. The first of these was designed to use information related to remembering the item and the second to use information related to remembering the contextual information (or source) about the item. Mollison and Curran (2012) had earlier shown that subjects’ familiarity judgments contributed to improved memory of spatial contextual information but not of extrinsic associated color information. These behavioral results were similarly reflected in the event-related potential (ERP) known as the FN400 (an early frontal effect relating to familiarity) which revealed differences between correct and incorrect context memories in the spatial but not color conditions. In our analyses we show that a classifier designed to distinguish between correct and incorrect context memories, more strongly involves early activity (400–500 ms) over the frontal channels for the location distinctions, than for the extrinsic color associations. In contrast, the classifier designed to classify memory for the item (without memory for the context), had more frontal channel involvement for the color associated experiments than for the spatial experiments. Taken together these results argue that location may be bound more tightly with the item than an extrinsic color association. The multivariate classification approach also showed that trial-by-trial variation in EEG corresponding to these ERP components were predictive of subjects’ behavioral responses. Additionally, the multivariate classification approach enabled analysis of error conditions that did not have sufficient trials for standard ERP analyses. These results suggested that false alarms were primarily attributable to item memory (as opposed to memory of associated context), as commonly predicted, but with little previous corroborating EEG evidence.

Paddy rice is one of the most important cereal crops worldwide, so it is very important to accurately monitor its growth status and photosynthetic efficiency. The nitrogen (N) level is a key factor closely related to crop growth. In this study, laser-induced fluorescence (LIF) technology combined with multi-variate analysis was applied to investigate the effect of paddy rice variety on N fertilizer level monitoring. Principal components analysis was conducted to extract the variables of the main fluorescence characteristics to identify N levels. Experimental results demonstrated that no nitrogen fertilizer can be completely identified for each paddy rice variety. In addition, other N levels can also be well classified based on the fluorescence characteristics. The relationship between the fluorescence ratio (F735/F685 : F735, and F685 denote the fluorescence intensity at 735 nm, and 685 nm, respectively) and leaf N content of different paddy rice varieties is also discussed. Experimental results revealed that LIF technology is an effective method of monitoring the N fertilizer and leaf biochemical components of paddy rice.

Objective: For selecting minimally invasive surgery (i.e. laparoscopic ovarian cystectomy) for treating ovarian tumours (OTs) in premenopausal patients, the pre-operative differentiation of benign ovarian tumours (Be-OTs) based on magnetic resonance imaging (MRI) interpretation is important. This paper describes the authors' 8-year experience of approximately 1000 OT cases, and provides information about a diagnostic algorithm to help other hospitals., Study Design: The medical records of 901 patients aged < 50 years with OTs from 1 January 2015-31 March 31 2023 were reviewed. First, the accuracy of pre-operative differentiation between Be-OTs and borderline/malignant ovarian tumours (Bo/Ma-OTs) was compared in each type of OT. Second, to identify the factors influencing differentiation between Be-OTs and Bo/Ma-OTs in 164 serous/mucinous ovarian tumours (SM-OTs), a multi-variate logistic regression analysis was performed to assess the effect of 13 factors, including MRI findings, OT size and tumour markers., Results: In the comparison of diagnostic accuracy of pre-operative MRI for each OT type, accuracy was found to be notably high for ovarian endometrial cyst (OEC) ( n  = 409), ovarian mature cystic teratoma (OMCT) ( n  = 308), ovarian endometrioid adenocarcinoma (OEA) ( n  = 6) and ovarian clear cell adenocarcinoma (OCCA) ( n  = 14). On the other hand, discrepancies between MRI and pathological findings often occurred in SM-OTs, including ovarian serous cystadenoma ( n  = 86), ovarian mucinous adenocarcinoma ( n  = 61), ovarian serous adenocarcinoma ( n  = 12) and ovarian mucinous adenocarcinoma ( n  = 5). In the multi-variate logistic regression analysis of the latter 164 patients, in addition to MRI findings, OT size and carbohydrate antigen 125 also had an effect to some extent. The combination of MRI interpretation and OT size may enhance differentiation of Be-OTs and Bo/Ma-OTs., Conclusions: Among four types of OTs (OEC, OMCT, OEA and OCCA), MRI interpretation was able to differentiate between Be-OTs and Bo/Ma-OTs almost perfectly. Additionally, to mitigate the difficulty in differentiating SM-OTs, OT size may be useful in combination with MRI findings, although further accumulation and analysis of OT cases is needed., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (© 2023 The Authors. Published by Elsevier B.V.)

Last year, in 2017, The Behaviormetric Society conducted a symposium entitled "Be-haviormetrika and Dr. Chikio Hayashi--Celebrating his 100 years from his birthday--." This essay was revised and enlarged edition of my lecture at that symposium. In this essay, I tried to describe and analyze why Dr. Hayashi's methods, including "Idea of Quantification" and "Quantification theories" were so widely accepted and highly evaluated in many fields of academic societies, and influenced many areas of human societies. On group of "Quantification Theories", the author has opinions that "Quantification Theory Family IV and Family V" are specially unique and superb, hence contribute to policy decisions and social life in many ways. So, the author discussed on these two models, introducing several interesting research examples. On the naming for "Quantification theories" by myself, Use and Up-use of naming were discussed, which made clear Dr. Hayashi's intentions of developing his theories and methods. Finally, the author picking up especially MDA-OR and MDA-UO by Dr. Hayashi, tried to discuss the unique and useful models of MDS, Multi-Dimensional-Scaling. [ABSTRACT FROM AUTHOR]

Background: During the last years, fluorescence spectroscopy has been used as a potential tool for the evaluation and characterization of tissues with different disease conditions due to its low cost, high sensitivity, and minimally or noninvasive character. Objective: In this study, fluorescence spectroscopy was used to study 19 paraffin blocks containing human liver tissue from biopsies. Methods and results: All samples were previously analyzed by two senior pathologists in a single-blind trial. After their evaluation, four liver sampleswere classified as nonfibrosis (F0), four as initial fibrosis (F1-F2), four as advanced fibrosis (F3), and six as cirrhosis (F4). The fluorescence was induced at different wavelengths as follows: 330, 365, and 405nm using a portable fiber-optic system. The fluorescence spectra were recorded in the range of 400-750 nm. A distinctive correlation between the shape of each spectrum and the level of fibrosis in the liver sample was detected. A multi-variate statistical analysis based on principal component analysis followed by linear discrimination analysis was applied to develop algorithms able to distinguish different stages of fibrosis based on the characteristics of fluorescence spectra. Pairwise comparisons were performed: F0 versus F1-F2, F1-F2 versus F3, F3 versus F4, and F1-F2 versus F4. The algorithms applied to each set of data yielded values of sensitivity and specificity that were higher than 90% and 95%, respectively, in all the analyzed cases. Conclusions: With this study, it is concluded that fluorescence spectroscopy can be used as a complementary tool for the assessment of liver fibrosis in liver tissue samples, which sets the stage for subsequent clinical trials. [ABSTRACT FROM AUTHOR]

Aims To measure and model Australian, Cypriot and Italian nurses' beliefs about what care is missed and how frequently it occurs within their settings. Background This study expands on previous MISSCARE research but now applies and predicts missed care within three countries. Methods Multivariate analysis was performed to estimate 1,896 nurses' consensus scores about missed care activities based on Alfaro-Lefevre's conceptual framework of care priorities. Results Five latent variables have direct predictor effects on missed care frequencies. Another four variables including the nurses' age, highest qualifications, absenteeism rate and workplace type, contributed to explaining the overall variance of missed care scores. The nurses' gender had no influence on missed care. Conclusion Cross country comparisons of missed nursing care allow for a more refined identification of strategies for remediation for both managers and clinicians. Implications for Nursing Management Reliable consensus estimates about the types and frequencies of missed care can be scaled with variables identified to predict missed care across three different countries. Comparative international studies build on the foundations for understanding missed care in terms of nursing practices, policies and related social policies. [ABSTRACT FROM AUTHOR]

• Locus of somatosensory attention can be reliably decoded from fMRI activation. • Attention to right hand/left foot is a convenient fMRI-BCI communication paradigm. • Effective communication can be obtained with limited amount of training data. • Using cytoarchitectonic maps for MVPA is an objective and timesaving approach. In brain-based communication, voluntarily modulated brain signals (instead of motor output) are utilized to interact with the outside world. The possibility to circumvent the motor system constitutes an important alternative option for severely paralyzed. Most communication brain-computer interface (BCI) paradigms require intact visual capabilities and impose a high cognitive load, but for some patients, these requirements are not given. In these situations, a better-suited, less cognitively demanding information-encoding approach may exploit auditorily-cued selective somatosensory attention to vibrotactile stimulation. Here, we propose, validate and optimize a novel communication-BCI paradigm using differential fMRI activation patterns evoked by selective somatosensory attention to tactile stimulation of the right hand or left foot. Using cytoarchitectonic probability maps and multi-voxel pattern analysis (MVPA), we show that the locus of selective somatosensory attention can be decoded from fMRI-signal patterns in (especially primary) somatosensory cortex with high accuracy and reliability, with the highest classification accuracy (85.93%) achieved when using Brodmann area 2 (SI-BA2) at a probability level of 0.2. Based on this outcome, we developed and validated a novel somatosensory attention-based yes/no communication procedure and demonstrated its high effectiveness even when using only a limited amount of (MVPA) training data. For the BCI user, the paradigm is straightforward, eye-independent, and requires only limited cognitive functioning. In addition, it is BCI-operator friendly given its objective and expertise-independent procedure. For these reasons, our novel communication paradigm has high potential for clinical applications. [ABSTRACT FROM AUTHOR]

Non-alcoholic fatty liver disease is a highly prevalent condition worldwide that increases the risk to develop liver fibrosis, cirrhosis, and hepatocellular carcinoma. Thus, it is imperative to develop novel diagnostic tools that together with liver biopsy help to differentiate mild and advanced degrees of steatosis. Ex-vivo liver samples were collected from mice fed a methionine-choline deficient diet for two or eight weeks, and from a control group. The degree of hepatic steatosis was histologically evaluated, and fat content was assessed by Oil-Red O staining. On the other hand, fluorescence spectroscopy was used for the assessment of the steatosis progression. Fluorescence spectra were recorded at excitation wavelengths of 330, 365, 385, 405, and 415 nm by establishing surface contact of the fiber optic probe with the liver specimens. A multi-variate statistical approach based on principal component analysis followed by quadratic discriminant analysis was applied to spectral data to obtain classifiers able to distinguish mild and moderate stages of steatosis at the different excitation wavelengths. Receiver Operating Characteristic (ROC) curves were computed to compare classifier’s performances for each one of the five excitation wavelengths and steatosis stages. Optimal sensitivity and specificity were calculated from the corresponding ROC curves using the Youden index. Intensity in the endogenous fluorescence spectra at the given wavelengths progressively increased according to the time of exposure to diet. The area under the curve of the spectra was able to discriminate control liver samples from those with steatosis and differentiate among the time of exposure to the diet for most of the used excitation wavelengths. High specificities and sensitivities were obtained for every case; however, fluorescence spectra obtained by exciting with 405 nm yielded the best results distinguishing between the mentioned classes with a total classification error of 1.5% and optimal sensitivities and specificities better than 98.6% and 99.3%, respectively.

Background: Catheter-associated urinary tract infections (CAUTIs) are the most common hospital acquired infections and a leading cause of morbidity and mortality in hospitalized patients with various life threatening complications. Hence, this study was aimed to determine the incidence, risk factors of CAUTI in a tertiary care hospital so as to find out better preventive measures to reduce the prevalence of CAUTI and their complications so as to reduce the hospital stay and mortality. Materials and methods: Present study included 200 adult patients who received indwelling urethral foley’s catheter and urinary drainage system in various wards in Government General Hospital, Nizamabad from Jan 2015 - May 2016. Patients were diagnosed to have CAUTI according to CDC guidelines to study its incidence and associated risk factors which were analyzed using multi variate analysis. Results: Overall incidence of CAUTI was 59%. The incidence of CAUTI was maximum (70.58%) in the age group of 51-70 years. The incidence of CAUTI was more (69.44%) in females and was directly proportional to the duration of catheterization. The high incidence in the present study reflects the practice of frequent disconnections of urinary closed systems. Multi-variate analysis shows age, duration of catheterization, catheter- tubing disconnections, absence of antibiotic use and renal insufficiencies as important risk factors for CAUTI. 88.66% of CAUTIs were asymptomatic among 75 clinically evaluable CAUTIs. Conclusion: CAUTI is an important preventable hospital acquired infection seen in all age groups however incidence increases with age, common in both sexes, incidence can be reduced by minimizing the catheter procedures, taking the maximum aseptic precautions, reducing the duration of catheterization and avoiding frequent disconnections, this becomes more significant in patients with underlying renal disease , prophylactic antibiotics prevent CAUTI. As most of the CAUTI are asymptomatic, all catheterized patients should be screened for CAUTI and be treated depending upon antibiotic sensitivity of uropathogens [ABSTRACT FROM AUTHOR]

Flood is a frequent natural hazard that has significant financial consequences for Australia. In Australia, physical losses caused by floods are commonly estimated by stage-damage functions. These methods usually consider only the depth of the water and the type of buildings at risk. However, flood damage is a complicated process, and it is dependent on a variety of factors which are rarely taken into account. This study explores the interaction, importance, and influence of water depth, flow velocity, water contamination, precautionary measures, emergency measures, flood experience, floor area, building value, building quality, and socioeconomic status. The study uses tree-based models (regression trees and bagging decision trees) and a dataset collected from 2012 to 2013 flood events in Queensland, which includes information on structural damages, impact parameters, and resistance variables. The tree-based approaches show water depth, floor area, precautionary measures, building value, and building quality to be important damage-influencing parameters. Furthermore, the performance of the tree-based models is validated and contrasted with the outcomes of a multi-parameter loss function (FLFArs) from Australia. The tree-based models are shown to be more accurate than the stage-damage function. Consequently, considering more parameters and taking advantage of tree-based models is recommended. The outcome is important for improving established Australian flood loss models and assisting decision-makers and insurance companies dealing with flood risk assessment. [ABSTRACT FROM AUTHOR]

Raman spectroscopy was used on 95 samples comprising mainly of uranium ore concentrates as well as some UF4 and UO2 samples, in order to classify uranium compounds for nuclear forensic purposes, for the first time. This technique was selected as it is non-destructive and rapid. The spectra obtained from 9 different classes of chemical compounds were subjected to multivariate data analysis such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA) and Fisher Discriminant Analysis (FDA). These classes were ammonium diuranate (ADU), sodium diuranate (SDU), ammonium uranyl carbonate (AUC), uranyl hydroxide (UH), UO2, UO3, UO4, U3O8 and UF4. Unsupervised PCA of full spectra shows fairly good distinction among the classes with some overlaps observed with ADU and UH. These overlaps are also reflected in the poorer specificities determined by PLS-DA. Higher values of sensitivities and specificities of remaining compounds were obtained. Supervised FDA based on reduced dataset of only 40 variables shows similar results to that of PCA but with closer clustering of ADU, UH, SDU, AUC. As a rapid and non-destructive technique, Raman spectroscopy is useful and complements existing techniques in multi-faceted nuclear forensics. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to extract and validate the dimensions of service quality in retail banking services in India by adopting an integrated and hierarchical perspective of service quality determinants. Design/methodology/approach – The paper empirically validates a parsimonious (multi-dimensional and multi-level) model of service quality in retail banking services in India. The analysis was conducted using structural equation modeling. A hypothesized second-order model was tested and compared with a first-order model of service quality. The dimensions were extracted through exploratory factor analysis and validated through confirmatory factor analysis. Findings – The second-order service quality model was accepted based on parsimony as it consisted of five primary dimensions: Service delivery (describing the efficiency with which the service is provided), tangibles (the quality of physical service environment), reliability (the promise of right service being provided), core service (the attributes and features of the service product) and competence (the capability of employees and systems for providing the service). The second-order model enhances the understanding of the structure of service quality for retail banking services in India. The most important dimension was tangibles, especially the physical environment which facilitates efficient delivery of service. Research limitations/implications – The research provides support for a multi-dimensional second-order model of service quality in retail banking service in India. The results show that customers form perceptions of overall service quality which are reflected by five primary dimensions. The primary dimension of tangibles is the most influential. Practical implications – Organizations need to measure and manage overall service quality perceptions to build trust and reinforce loyalty intentions among their customers. Banks need to adopt a multi-level approach to managing service quality perceptions, i.e. both at the dimensional level and organizational level. Social implications – This study would contribute to the enhancement of service quality outcomes in retail banking services in India which has a crucial role in the economic development. Originality/value – The study proposes and validates a parsimonious and hierarchical model of service quality in the context of retail banking in Indian cultural context. Thus this research provides support to existing knowledge of service quality measurement and management and extends the understanding of its structure by validating the multi-level model in an emerging market context. [ABSTRACT FROM AUTHOR]

A novel multi-scale approach for quantifying both inter- and intra-component dependence of a complex system is introduced. This is achieved using empirical mode decomposition (EMD), which, unlike conventional scale-estimation methods, obtains a set of scales reflecting the underlying oscillations at the intrinsic scale level. This enables the datadriven operation of several standard data-association measures (intrinsic correlation, intrinsic sample entropy (SE), intrinsic phase synchrony) and, at the same time, preserves the physical meaning of the analysis. The utility of multi-variate extensions of EMD is highlighted, both in terms of robust scale alignment between system components, a pre-requisite for inter-component measures, and in the estimation of feature relevance. We also illuminate that the properties of EMD scales can be used to decouple amplitude and phase information, a necessary step in order to accurately quantify signal dynamics through correlation and SE analysis which are otherwise not possible. Finally, the proposed multi-scale framework is applied to detect directionality, and higher order features such as coupling and regularity, in both synthetic and biological systems. [ABSTRACT FROM AUTHOR]

Flood is a frequent natural hazard that has significant financial consequences for Australia. In Australia, physical losses caused by floods are commonly estimated by stage-damage functions. These methods usually consider only the depth of the water and the type of buildings at risk. However, flood damage is a complicated process, and it is dependent on a variety of factors which are rarely taken into account. This study explores the interaction, importance, and influence of water depth, flow velocity, water contamination, precautionary measures, emergency measures, flood experience, floor area, building value, building quality, and socioeconomic status. The study uses tree-based models (regression trees and bagging decision trees) and a dataset collected from 2012 to 2013 flood events in Queensland, which includes information on structural damages, impact parameters, and resistance variables. The tree-based approaches show water depth, floor area, precautionary measures, building value, and building quality to be important damage-influencing parameters. Furthermore, the performance of the tree-based models is validated and contrasted with the outcomes of a multi-parameter loss function (FLFArs) from Australia. The tree-based models are shown to be more accurate than the stage-damage function. Consequently, considering more parameters and taking advantage of tree-based models is recommended. The outcome is important for improving established Australian flood loss models and assisting decision-makers and insurance companies dealing with flood risk assessment.

We formulate and perform principal component analysis (PCA) of mechanically stirred fields, based on tuner sweep data collected across a frequency band at a single location of a sensor inside a reverberation chamber. Both covariance- and correlation-based PCA in undermoded and overmoded regime are performed and intercompared. The nonstationarity of the stir performance as a function of the angular position of the stirrer is demonstrated. It is shown that this nonuniformity can be quantified and exploited to select a set of optimal stir angles. The rotated principal components are found to be interpretable as energy stirred by specific angular sectors of the stirrer and are related to the correlation structure of the data. The analysis leads to the concept of eigenstirrings (stir modes), which form an orthonormal set of empirical basis functions for expanding stir data. [ABSTRACT FROM PUBLISHER]

Copyright of Archives of Mining Sciences is the property of Polish Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: This paper investigates the factors that affect travel behaviour within neighbourhoods in Tyne and Wear, North East England while accounting for differences in attitudes and perceptions. Ten different neighbourhoods have been carefully selected to characterise the two different types of traditional and suburban neighbourhood street layouts. A self-administered questionnaire has been delivered to 2200 households to capture neighbourhood design, travel patterns, travel attitudes and socio-economic characteristics. Multivariate analysis of cross-sectional data shows that some socio-economic variables as well as travel attitudes and neighbourhood design preferences can explain the differences in travel patterns between the two distinct neighbourhood designs. The results show additionally that the traditional neighbourhood group is more sensitive to factors of perception and attitudes in relation to neighbourhood design that lead to walking, cycling and public transport use travel patterns, suggesting that land-use policy designed to accommodate lower carbon-based travel together with measures to encourage active travel will have greater impact on the traditional group than the suburban group. This finding suggests that generic measures imposed by many governments, and certainly implied by current UK land-use policy, to promote sustainable mobility should be selectively targeted. [Copyright &y& Elsevier]

Purpose – The purpose of this paper is to explore and map the intellectual structure of biofuel research. Design/methodology/approach – The study attempts to present the structure of biofuel research through document co-citation patterns of core references. Document co-citation analysis was performed using the Web of Science of the Thomson-ISI database. A sample of 26 cited references was identified and the co-citation frequencies were analyzed and represented them systematically within groups of similar researched topics. Findings – The study shows the co-citation analysis method suitable for depicting structure of biofuel research in document clusters by performing multivariate analysis: cluster analysis, factor analysis, multidimensional scaling and network analysis. Research limitations/implications – The study is limited to research articles and co-citation data for the first author only. For the co-citation analysis, the cited references rather than the cited authors were used as the units for analysis. Practical implications – Co-citational analysis using multivariate tools provides a useful technique to explore and document the development of the field supplementing the insights normally available from the routine co-citational analysis. Originality/value – Specialties in biofuel research are identified and this may provide a valuable building block for future research. [ABSTRACT FROM AUTHOR]

A novelthree-stage methodology for the detection of fetal heart rate (fHR) from multivariate abdominal ECG recordings is introduced. In the first stage, the maternal R-peaks and fiducial points (maternal QRS onset and offset) are detected, using band-pass filtering and phase space analysis. The maternal fiducial points are useki to eliminate the maternal QRS complexes from the abdominal ECG recordings. In the second stage, two denoising procedures are applied to enhance the fetal QRS complexes. The phase space characteristics are employed to identify fetal heart beats not overlapping with the maternal QRSs, which are eliminated in the first stage. The extraction of the fHR is accomplished in the third stage, using a histogram-based technique in order to identify the location of the fatal heart beats that overlap with the maternal QRSs. The methodology is evaluated on simulated multichannel ECG signals, generated by a recently proposed model with various SNRs, and on real signals, recorded from pregnant women in various weeks during gestation. In both cases, the obtained results indicate high performance; in the simulated ECGs, the accuracy ranges from 72.78% to 98.61%, depending on the employed SNR, while in the real recordings, the average accuracy is 95.45%. The proposed methodology is advantageous since it copes with the existence of noise from various sources while it is applicable in multichannel abdominal recordings. [ABSTRACT FROM AUTHOR]

Abstract: Quantitative X-ray powder diffraction (XRPD) data obtained from intact, consolidated samples affords the opportunity to analyze mixtures that simulate pharmaceutical drug products without the need for reversion back to powders; an analytical preparation step that destroys the contextual solid-state information intrinsic to intact consolidated samples. Traditional, standardless quantitative methods generally involve sophisticated pattern refinement procedures (e.g., Rietveld refinement) and are limited to crystalline materials. Methods that incorporate an internal standard are not optimal for compact analysis, and may often be susceptible to prediction errors associated with intensity attenuation. Chemometric-based XRPD utilizes full-pattern methods that combine analyses of both Bragg diffraction and diffuse scatter, thereby allowing for enhancement of signal-to-noise, sensitivity, and selectivity. Classical least-squares (CLS) regression, principal components regression (PCR) and partial least squares (PLS) regression are three chemometric algorithms commonly employed in spectroscopy. In the present work, quantification of a consolidated four-component system, composed of two crystalline materials and two disordered materials was analyzed intact, using two different XRPD optics geometries. Calibrations constructed for the prediction of individual constituent concentrations using the aforementioned three multi-variate algorithms were statistically compared with traditional diffraction–absorption univariate calibration. PLS regression modeling of data collected in transmission geometry provided the best statistical results for the quantification of constituent concentration. Further, this calibration was minimally affected by diffraction pattern anomalies traditionally corrected prior to phase quantification. [Copyright &y& Elsevier]

Abstract: The aim of this study was to provide an analysis of vegetation and environment relationships in the Alxa Plateau of Inner Mongolia, China. Seventy species representing 46 genera and 16 families were collected. The largest families were Leguminosae, Polygonaceae, Gramineae and Compositae, representing 20.0%, 15.7%, 14.3% and 14.3% of the total flora, respectively. Phanerophytes and hemicryptophytes were the most abundant life-form. Classification of the vegetation was analysed using two-way indicator species analysis (TWINSPAN) techniques. This resulted in the recognition of six vegetation groups, each of definite vegetation and soil characteristics, which could be linked to a specific geomorphologic unit. Ordination techniques as detrended correspondence analysis (DCA) and canonical correspondence analysis (CCA) were used to examine the relationship between the vegetation and environmental parameters. Analysis with DCA gave results similar to CCA, suggesting that there is a relatively strong correspondence between vegetation and environmental factors. CCA axis 1 was highly associated with organic matter, total nitrogen, silt and clay content, elevation, Kira moisture index and relative humidity, while CCA axis 2 showed significant correlation with chlorine, calcium ion, magnesium ion and electrolytic conductivity. These gradients were related closely to the first two canonical axes, and accounted for 60% of the species–environment relationship in the study sites. Understanding relationships between environmental variables and vegetation distribution in this area helps us to apply these findings in management, reclamation, and development of arid and semi-arid grassland ecosystems. [Copyright &y& Elsevier]

This study compares the disciplinary process for police vs. nonpolice public employees. A random sample of 200 cases was drawn from the 806 public sector discipline cases published in volumes 111–118 of Labor Arbitration Reports. Case characteristics, classification of conduct unbecoming, proposed discipline, arbitrator behavior, reasons for reversing management’s penalty other than conduct unbecoming, procedural outcomes and findings were analyzed. The frequency of the finding for the employer was actually higher for police than for the nonpolice cases. Managerial actions that resulted in reversing the disciplinary penalty were not as suggested by the literature. Multivariate analyses of the case characteristics led to the conclusion that police discipline may have been distinctive from nonpolice public employee discipline in the past, but is no longer so distinctive. [ABSTRACT FROM AUTHOR]