Showing papers by "La Trobe University published in 2019"
University of Vermont1, Karolinska University Hospital2, Universidade Federal de Minas Gerais3, Universidade Católica de Pelotas4, University of Tokyo5, Fujita Health University6, Central University of Venezuela7, University of Trieste8, University of Cape Town9, Monash University10, Ohio State University11, University of Alberta12, Hospital General de México13, University of Waterloo14, American Society for Parenteral and Enteral Nutrition15, Brigham and Women's Hospital16, Saint Louis University Hospital17, Sapienza University of Rome18, La Trobe University19, Khon Kaen University20, HAN University of Applied Sciences21, Rabin Medical Center22, University of Illinois at Chicago23, Pontifical Catholic University of Chile24, University of São Paulo25, Peking Union Medical College Hospital26, University of Pennsylvania27, Free University of Brussels28
TL;DR: A consensus scheme for diagnosing malnutrition in adults in clinical settings on a global scale is proposed and it is recommended that the etiologic criteria be used to guide intervention and anticipated outcomes.
Abstract: Summary Rationale This initiative is focused on building a global consensus around core diagnostic criteria for malnutrition in adults in clinical settings Methods In January 2016, the Global Leadership Initiative on Malnutrition (GLIM) was convened by several of the major global clinical nutrition societies GLIM appointed a core leadership committee and a supporting working group with representatives bringing additional global diversity and expertise Empirical consensus was reached through a series of face-to-face meetings, telephone conferences, and e-mail communications Results A two-step approach for the malnutrition diagnosis was selected, ie, first screening to identify “at risk” status by the use of any validated screening tool, and second, assessment for diagnosis and grading the severity of malnutrition The malnutrition criteria for consideration were retrieved from existing approaches for screening and assessment Potential criteria were subjected to a ballot among the GLIM core and supporting working group members The top five ranked criteria included three phenotypic criteria (non-volitional weight loss, low body mass index, and reduced muscle mass) and two etiologic criteria (reduced food intake or assimilation, and inflammation or disease burden) To diagnose malnutrition at least one phenotypic criterion and one etiologic criterion should be present Phenotypic metrics for grading severity as Stage 1 (moderate) and Stage 2 (severe) malnutrition are proposed It is recommended that the etiologic criteria be used to guide intervention and anticipated outcomes The recommended approach supports classification of malnutrition into four etiology-related diagnosis categories Conclusion A consensus scheme for diagnosing malnutrition in adults in clinical settings on a global scale is proposed Next steps are to secure further collaboration and endorsements from leading nutrition professional societies, to identify overlaps with syndromes like cachexia and sarcopenia, and to promote dissemination, validation studies, and feedback The diagnostic construct should be re-considered every 3–5 years
885 citations
TL;DR: Age standardised mortality rates for suicide have greatly reduced since 1990, but suicide remains an important contributor to mortality worldwide and can be targeted towards vulnerable populations if they are informed by variations in mortality rates.
Abstract: Objectives To use the estimates from the Global Burden of Disease Study 2016 to describe patterns of suicide mortality globally, regionally, and for 195 countries and territories by age, sex, and Socio-demographic index, and to describe temporal trends between 1990 and 2016. Design Systematic analysis. Main outcome measures Crude and age standardised rates from suicide mortality and years of life lost were compared across regions and countries, and by age, sex, and Socio-demographic index (a composite measure of fertility, income, and education). Results The total number of deaths from suicide increased by 6.7% (95% uncertainty interval 0.4% to 15.6%) globally over the 27 year study period to 817 000 (762 000 to 884 000) deaths in 2016. However, the age standardised mortality rate for suicide decreased by 32.7% (27.2% to 36.6%) worldwide between 1990 and 2016, similar to the decline in the global age standardised mortality rate of 30.6%. Suicide was the leading cause of age standardised years of life lost in the Global Burden of Disease region of high income Asia Pacific and was among the top 10 leading causes in eastern Europe, central Europe, western Europe, central Asia, Australasia, southern Latin America, and high income North America. Rates for men were higher than for women across regions, countries, and age groups, except for the 15 to 19 age group. There was variation in the female to male ratio, with higher ratios at lower levels of Socio-demographic index. Women experienced greater decreases in mortality rates (49.0%, 95% uncertainty interval 42.6% to 54.6%) than men (23.8%, 15.6% to 32.7%). Conclusions Age standardised mortality rates for suicide have greatly reduced since 1990, but suicide remains an important contributor to mortality worldwide. Suicide mortality was variable across locations, between sexes, and between age groups. Suicide prevention strategies can be targeted towards vulnerable populations if they are informed by variations in mortality rates.
472 citations
University of Bologna1, Canadian Real Estate Association2, University of Alberta3, Leibniz Association4, National Research Council5, Tel Aviv University6, Montana State University7, United States Department of Agriculture8, La Trobe University9, University of Bari10, University of New Hampshire11, Washington State University12, University of Saskatchewan13, Agriculture and Agri-Food Canada14, Technische Universität München15
TL;DR: The assembly of the genome of durum wheat cultivar Svevo enables genome-wide genetic diversity analyses highlighting modifications imposed by thousands of years of empirical selection and breeding.
Abstract: The domestication of wild emmer wheat led to the selection of modern durum wheat, grown mainly for pasta production. We describe the 10.45 gigabase (Gb) assembly of the genome of durum wheat cultivar Svevo. The assembly enabled genome-wide genetic diversity analyses revealing the changes imposed by thousands of years of empirical selection and breeding. Regions exhibiting strong signatures of genetic divergence associated with domestication and breeding were widespread in the genome with several major diversity losses in the pericentromeric regions. A locus on chromosome 5B carries a gene encoding a metal transporter (TdHMA3-B1) with a non-functional variant causing high accumulation of cadmium in grain. The high-cadmium allele, widespread among durum cultivars but undetected in wild emmer accessions, increased in frequency from domesticated emmer to modern durum wheat. The rapid cloning of TdHMA3-B1 rescues a wild beneficial allele and demonstrates the practical use of the Svevo genome for wheat improvement. Genome assembly of durum wheat cultivar Svevo enables genome-wide genetic diversity analyses highlighting modifications imposed by thousands of years of empirical selection and breeding.
443 citations
TL;DR: Vesiclepedia is a web-based compendium of RNA, proteins, lipids and metabolites that are identified in EVs from both published and unpublished studies aiding biomedical scientists in assessing the quality of the EV preparation and the corresponding data obtained.
Abstract: Extracellular vesicles (EVs) are membranous vesicles that are released by both prokaryotic and eukaryotic cells into the extracellular microenvironment. EVs can be categorised as exosomes, ectosomes or shedding microvesicles and apoptotic bodies based on the mode of biogenesis. EVs contain biologically active cargo of nucleic acids, proteins, lipids and metabolites that can be altered based on the precise state of the cell. Vesiclepedia (http://www.microvesicles.org) is a web-based compendium of RNA, proteins, lipids and metabolites that are identified in EVs from both published and unpublished studies. Currently, Vesiclepedia contains data obtained from 1254 EV studies, 38 146 RNA entries, 349 988 protein entries and 639 lipid/metabolite entries. Vesiclepedia is publicly available and allows users to query and download EV cargo based on different search criteria. The mode of EV isolation and characterization, the biophysical and molecular properties and EV-METRIC are listed in the database aiding biomedical scientists in assessing the quality of the EV preparation and the corresponding data obtained. In addition, FunRich-based Vesiclepedia plugin is incorporated aiding users in data analysis.
411 citations
Johns Hopkins University School of Medicine1, Utrecht University2, University of Turin3, Agency for Science, Technology and Research4, University of Modena and Reggio Emilia5, Paracelsus Private Medical University of Salzburg6, La Trobe University7, St. George's University8, Health Sciences Authority9, Boston Children's Hospital10, University of Pittsburgh11, Kyoto University12, National University of Singapore13, University of Vermont14, Southern Medical University15, University of Duisburg-Essen16
TL;DR: MSC-sEVs should be defined by quantifiable metrics to identify the cellular origin of the sEVs in a preparation, presence of lipid-membrane vesicles, and the degree of physical and biochemical integrity of the vesicle.
Abstract: Small extracellular vesicles (sEVs) from mesenchymal stromal/stem cells (MSCs) are transiting rapidly towards clinical applications. However, discrepancies and controversies about the biology, functions, and potency of MSC-sEVs have arisen due to several factors: the diversity of MSCs and their preparation; various methods of sEV production and separation; a lack of standardized quality assurance assays; and limited reproducibility of in vitro and in vivo functional assays. To address these issues, members of four societies (SOCRATES, ISEV, ISCT and ISBT) propose specific harmonization criteria for MSC-sEVs to facilitate data sharing and comparison, which should help to advance the field towards clinical applications. Specifically, MSC-sEVs should be defined by quantifiable metrics to identify the cellular origin of the sEVs in a preparation, presence of lipid-membrane vesicles, and the degree of physical and biochemical integrity of the vesicles. For practical purposes, new MSC-sEV preparations might also be measured against a well-characterized MSC-sEV biological reference. The ultimate goal of developing these metrics is to map aspects of MSC-sEV biology and therapeutic potency onto quantifiable features of each preparation.
342 citations
TL;DR: This survey evaluated the techniques of deep learning in developing SDN-based Network Intrusion Detection Systems (NIDS) and covered tools that can be used to develop NIDS models in SDN environment.
Abstract: Software Defined Networking Technology (SDN) provides a prospect to effectively detect and monitor network security problems ascribing to the emergence of the programmable features. Recently, Machine Learning (ML) approaches have been implemented in the SDN-based Network Intrusion Detection Systems (NIDS) to protect computer networks and to overcome network security issues. A stream of advanced machine learning approaches – the deep learning technology (DL) commences to emerge in the SDN context. In this survey, we reviewed various recent works on machine learning (ML) methods that leverage SDN to implement NIDS. More specifically, we evaluated the techniques of deep learning in developing SDN-based NIDS. In the meantime, in this survey, we covered tools that can be used to develop NIDS models in SDN environment. This survey is concluded with a discussion of ongoing challenges in implementing NIDS using ML/DL and future works.
341 citations
TL;DR: The tumour-modulating activities of STAT3 are reviewed in light of its role as a signalling node integrating inflammatory responses during wound healing, and how excessive signal transducer and activator of transcription 3 (STAT3) activation within cancer cells and cells of the tumour microenvironment can be viewed as a neoplastic mimic of an inflammation-driven repair response that promotes tumour progression.
Abstract: The tightly orchestrated temporal and spatial control of signal transducer and activator of transcription 3 (STAT3) activity in epithelial, immune and stromal cells is critical for wound healing and tissue repair. Excessive STAT3 activation within cancer cells and cells of the tumour microenvironment can be viewed as a neoplastic mimic of an inflammation-driven repair response that collectively promotes tumour progression. In addition to the canonical transcriptional pathways by which STAT3 promotes stem cell-like characteristics, survival, proliferation, metastatic potential and immune evasion, cytoplasmic STAT3 activity fuels tumour growth by metabolic and other non-transcriptional mechanisms. Here, we review the tumour-modulating activities of STAT3 in light of its role as a signalling node integrating inflammatory responses during wound healing. Accordingly, many of the cytokines that contribute to the para-inflammatory state of most solid malignancies converge on and underpin dysregulated STAT3 activity. Targeting of these cytokines, their cognate receptors and associated signalling cascades in clinical trials is beginning to demonstrate therapeutic efficacy, given that interference with STAT3 activity is likely to simultaneously curb the growth of cancer cells and augment antitumour immunity. This Review discusses how excessive signal transducer and activator of transcription 3 (STAT3) activation within cancer cells and cells of the tumour microenvironment can be viewed as a neoplastic mimic of an inflammation-driven repair response that promotes tumour progression.
313 citations
Erasmus University Rotterdam1, University Hospital Southampton NHS Foundation Trust2, University of Southampton3, University of Porto4, Paris Descartes University5, Sorbonne6, University of Crete7, Maastricht University8, University of Southern California9, National and Kapodistrian University of Athens10, University Medical Center Groningen11, Université de Sherbrooke12, Norwegian Institute of Public Health13, University of Bologna14, Nofer Institute of Occupational Medicine15, University of California, Davis16, Harvard University17, University of Illinois at Chicago18, University of Valencia19, National Institutes of Health20, University of Turku21, University of Bristol22, Helmholtz Centre for Environmental Research - UFZ23, Jagiellonian University Medical College24, Åbo Akademi University25, Harokopio University26, Public Health Research Institute27, University of Copenhagen28, University of Southern Denmark29, La Trobe University30, University of Helsinki31, University of Turin32, Radboud University Nijmegen33, University of Trieste34, University of Bergen35, Ludwig Maximilian University of Munich36, Slovak Medical University37, Utrecht University38, Pompeu Fabra University39
TL;DR: In this meta-analysis of pooled individual participant data from 25 cohort studies, the risk for adverse maternal and infant outcomes varied by gestational weight gain and across the range of prepregnancy weights, however, the optimal gestations weight gain ranges had limited predictive value for the outcomes assessed.
Abstract: Importance Both low and high gestational weight gain have been associated with adverse maternal and infant outcomes, but optimal gestational weight gain remains uncertain and not well defined for all prepregnancy weight ranges. Objectives To examine the association of ranges of gestational weight gain with risk of adverse maternal and infant outcomes and estimate optimal gestational weight gain ranges across prepregnancy body mass index categories. Design, Setting, and Participants Individual participant-level meta-analysis using data from 196 670 participants within 25 cohort studies from Europe and North America (main study sample). Optimal gestational weight gain ranges were estimated for each prepregnancy body mass index (BMI) category by selecting the range of gestational weight gain that was associated with lower risk for any adverse outcome. Individual participant-level data from 3505 participants within 4 separate hospital-based cohorts were used as a validation sample. Data were collected between 1989 and 2015. The final date of follow-up was December 2015. Exposures Gestational weight gain. Main Outcomes and Measures The main outcome termedany adverse outcomewas defined as the presence of 1 or more of the following outcomes: preeclampsia, gestational hypertension, gestational diabetes, cesarean delivery, preterm birth, and small or large size for gestational age at birth. Results Of the 196 670 women (median age, 30.0 years [quartile 1 and 3, 27.0 and 33.0 years] and 40 937 were white) included in the main sample, 7809 (4.0%) were categorized at baseline as underweight (BMI Conclusions and Relevance In this meta-analysis of pooled individual participant data from 25 cohort studies, the risk for adverse maternal and infant outcomes varied by gestational weight gain and across the range of prepregnancy weights. The estimates of optimal gestational weight gain may inform prenatal counseling; however, the optimal gestational weight gain ranges had limited predictive value for the outcomes assessed.
286 citations
TL;DR: The prevalence of WPV against healthcare workers is high, especially in Asian and North American countries, psychiatric and emergency department settings, and among nurses and physicians, and there is a need for governments, policymakers and health institutions to take actions to address WPV towards healthcare professionals globally.
Abstract: We aim to quantitatively synthesise available epidemiological evidence on the prevalence rates of workplace violence (WPV) by patients and visitors against healthcare workers. We systematically searched PubMed, Embase and Web of Science from their inception to October 2018, as well as the reference lists of all included studies. Two authors independently assessed studies for inclusion. Data were double-extracted and discrepancies were resolved by discussion. The overall percentage of healthcare worker encounters resulting in the experience of WPV was estimated using random-effects meta-analysis. The heterogeneity was assessed using the I2 statistic. Differences by study-level characteristics were estimated using subgroup analysis and meta-regression. We included 253 eligible studies (with a total of 331 544 participants). Of these participants, 61.9% (95% CI 56.1% to 67.6%) reported exposure to any form of WPV, 42.5% (95% CI 38.9% to 46.0%) reported exposure to non-physical violence, and 24.4% (95% CI 22.4% to 26.4%) reported experiencing physical violence in the past year. Verbal abuse (57.6%; 95% CI 51.8% to 63.4%) was the most common form of non-physical violence, followed by threats (33.2%; 95% CI 27.5% to 38.9%) and sexual harassment (12.4%; 95% CI 10.6% to 14.2%). The proportion of WPV exposure differed greatly across countries, study location, practice settings, work schedules and occupation. In this systematic review, the prevalence of WPV against healthcare workers is high, especially in Asian and North American countries, psychiatric and emergency department settings, and among nurses and physicians. There is a need for governments, policymakers and health institutions to take actions to address WPV towards healthcare professionals globally.
279 citations
VU University Amsterdam1, University of Leeds2, University of Duisburg-Essen3, University of Texas MD Anderson Cancer Center4, Semmelweis University5, Austrian Academy of Sciences6, Duke University7, Broad Institute8, Harvard University9, Case Western Reserve University10, Medical University of Vienna11, Northwestern University12, University of Liverpool13, University of Connecticut Health Center14, St James's University Hospital15, James Cancer Hospital16, Brigham and Women's Hospital17, Yale University18, Medical College of Wisconsin19, University of California, San Francisco20, German Cancer Research Center21, Erasmus University Rotterdam22, University of Melbourne23, La Trobe University24, National Institutes of Health25, Laval University26, Columbia University Medical Center27, University of Sydney28, Henry Ford Health System29, University of New South Wales30, Samsung Medical Center31, The Chinese University of Hong Kong32, University of Luxembourg33, University of Colorado Denver34, Seoul National University Hospital35, Columbia University36, University of Düsseldorf37, University Hospitals of Cleveland38, Hospital for Sick Children39, University of Tübingen40, Emory University41, University of Birmingham42, University of Zurich43, University Health Network44
TL;DR: The results suggest that the strongest selective pressures occur during early glioma development and that current therapies shape this evolution in a largely stochastic manner.
Abstract: The evolutionary processes that drive universal therapeutic resistance in adult patients with diffuse glioma remain unclear1,2. Here we analysed temporally separated DNA-sequencing data and matched clinical annotation from 222 adult patients with glioma. By analysing mutations and copy numbers across the three major subtypes of diffuse glioma, we found that driver genes detected at the initial stage of disease were retained at recurrence, whereas there was little evidence of recurrence-specific gene alterations. Treatment with alkylating agents resulted in a hypermutator phenotype at different rates across the glioma subtypes, and hypermutation was not associated with differences in overall survival. Acquired aneuploidy was frequently detected in recurrent gliomas and was characterized by IDH mutation but without co-deletion of chromosome arms 1p/19q, and further converged with acquired alterations in the cell cycle and poor outcomes. The clonal architecture of each tumour remained similar over time, but the presence of subclonal selection was associated with decreased survival. Finally, there were no differences in the levels of immunoediting between initial and recurrent gliomas. Collectively, our results suggest that the strongest selective pressures occur during early glioma development and that current therapies shape this evolution in a largely stochastic manner.
264 citations
TL;DR: It is demonstrated that the proposed channel estimation in OTFS significantly outperforms OFDM with known channel information, and extensions of the proposed schemes to multiple-input multiple-output (MIMO) and multi-user uplink/downlink are presented.
Abstract: Orthogonal time frequency space (OTFS) modulation was shown to provide significant error performance advantages over orthogonal frequency division multiplexing (OFDM) in delay–Doppler channels. In order to detect OTFS modulated data, the channel impulse response needs to be known at the receiver. In this paper, we propose embedded pilot-aided channel estimation schemes for OTFS. In each OTFS frame, we arrange pilot, guard, and data symbols in the delay–Doppler plane to suitably avoid interference between pilot and data symbols at the receiver. We develop such symbol arrangements for OTFS over multipath channels with integer and fractional Doppler shifts, respectively. At the receiver, channel estimation is performed based on a threshold method and the estimated channel information is used for data detection via a message passing algorithm. Thanks to our specific embedded symbol arrangements, both channel estimation and data detection are performed within the same OTFS frame with minimum overhead. We compare through simulations the error performance of OTFS using the proposed channel estimation and OTFS with ideally known channel information and observe only a marginal performance loss. We also demonstrate that the proposed channel estimation in OTFS significantly outperforms OFDM with known channel information. Finally, we present extensions of the proposed schemes to multiple-input multiple-output (MIMO) and multi-user uplink/downlink.
Erasmus University Medical Center1, Medical University of Warsaw2, University of Valencia3, University of Porto4, Stockholm County Council5, Paris Descartes University6, Sorbonne7, Maastricht University8, University of Southern California9, University of Crete10, French Institute of Health and Medical Research11, National and Kapodistrian University of Athens12, University Medical Center Groningen13, University of Southampton14, Liverpool School of Tropical Medicine15, Norwegian Institute of Public Health16, Karolinska Institutet17, University of Bologna18, University Hospital Southampton NHS Foundation Trust19, Ludwig Maximilian University of Munich20, Nofer Institute of Occupational Medicine21, University of California, Davis22, University of Illinois at Chicago23, University of Western Australia24, National Institutes of Health25, University College Cork26, University of Bristol27, University of Turku28, Helmholtz Centre for Environmental Research - UFZ29, Jagiellonian University Medical College30, Åbo Akademi University31, Harokopio University32, University College Dublin33, University of Calgary34, Public Health Research Institute35, University of Copenhagen36, University of Southern Denmark37, La Trobe University38, Harvard University39, University of Helsinki40, University of Turin41, University of Trieste42, University of Bergen43, Slovak Medical University44, Boston Children's Hospital45, Utrecht University46, Pompeu Fabra University47, Bradford Royal Infirmary48
TL;DR: In this article, the authors conducted an individual participant data meta-analysis of data from 162,129 mothers and children from 37 pregnancy and birth cohort studies from Europe, North-America and Australia, using multilevel binary logistic regression models with a random intercept at cohort level adjusted for maternal socio-demographic and life style related characteristics.
Abstract: Background:
Maternal obesity and excessive gestational weight gain may have persistent effects on offspring fat development. However, it remains unclear whether these risks differ by severity of obesity, and whether these effects are restricted to the extremes of maternal body mass index (BMI) and gestational weight gain. We aimed to assess the separate and combined associations of maternal BMI and gestational weight gain with the risk of overweight/obesity throughout childhood, and their population impact.
Methods and Findings:
We conducted an individual participant data meta-analysis of data from 162,129 mothers and children from 37 pregnancy and birth cohort studies from Europe, North-America and Australia. We assessed the individual and combined associations of maternal pre-pregnancy BMI and gestational weight gain, both in clinical categories and across their full ranges with the risks of overweight/obesity in early- (2.0-5.0 years), mid- (5.0-10.0 years) and late childhood (10.0-18.0 years), using multilevel binary logistic regression models with a random intercept at cohort level adjusted for maternal socio-demographic and life style related characteristics. We observed that a higher maternal pre-pregnancy BMI and gestational weight gain both in clinical categories and across their full ranges were associated with higher risks of childhood overweight/obesity, with the strongest effects in late childhood (Odds Ratios (OR) for overweight/obesity in early-, mid- and late childhood, respectively: 1.66 (95% Confidence Interval (CI): 1.56, 1.78), OR 1.91 (95% CI: 1.85, 1.98), and OR 2.28 (95% CI: 2.08, 2.50) for maternal overweight, OR 2.43 (95% CI: 2.24, 2.64), OR 3.12 (95% CI: 2.98, 3.27), and OR 4.47 (95% CI: 3.99, 5.23) for maternal obesity, and OR 1.39 (95% CI: 1.30, 1.49), OR 1.55 (95% CI: 1.49, 1.60), and 1.72 (95% CI: 1.56, 1.91) for excessive gestational weight gain. The proportions of childhood overweight/obesity prevalence attributable to maternal overweight, maternal obesity and excessive gestational weight gain ranged from 10.2 to 21.6%. Relative to the effect of maternal BMI, excessive gestational weight gain only slightly increased the risk of childhood overweight/obesity within each clinical BMI category (P-values for interactions of maternal BMI with gestational weight gain: p=0.038, p<0.001 and p=0.637, in early-, mid- and late childhood, respectively). Limitations of this study include the self-report of maternal BMI and gestational weight gain for some of the cohorts, and the potential of residual confounding. Also, as this study only included participants from Europe, North-America and Australia, results need to be interpreted with caution with respect to other populations.
Conclusions:
In this study, higher maternal pre-pregnancy BMI and gestational weight gain were associated with an increased risk of childhood overweight/obesity, with the strongest effects at later ages. The additional effect of gestational weight gain in women who are overweight or obese before pregnancy is small. Given the large population impact, future intervention trials aiming to reduce the prevalence of childhood overweight and obesity should focus on maternal weight status before pregnancy, in addition to weight gain during pregnancy.
TL;DR: This review discusses the growing number of applications of SuFEx, which can be found in nearly all areas of modern chemistry; from drug discovery to materials science.
Abstract: SuFEx (Sulfur Fluoride Exchange) is a modular, next generation family of click reactions, geared towards the rapid and reliable assembly of functional molecules. This review discusses the growing number of applications of SuFEx, which can be found in nearly all areas of modern chemistry; from drug discovery to materials science.
TL;DR: Wang et al. as mentioned in this paper examined the relationships among HPWS, employee resilience and engagement, using a sample of 2040 employees in the Chinese banking industry, and developed three hypotheses to test the relationship between HPWS and employee resilience, resilience and employee engagement.
Abstract: There is now growing interest in employee resilience in the organizational context and its contribution to organizational performance. However, little is known the extent to which high-performance work system (HPWS) contributes towards enhancing employee’s resilience as well as their levels of engagement. This study examines the relationships among HPWS, employee resilience and engagement, using a sample of 2040 employees in the Chinese banking industry. Drawing on the job demands-resources model and strategic/high-performance human resource management theory, we develop three hypotheses to test the relationship between HPWS and employee resilience, resilience and employee engagement, and the mediating effect of resilience on the relationship between HPWS and engagement. All hypotheses are supported and suggest that HPWS can be used as a job resource to positively affect resilience and subsequently employee engagement. The key message of the paper is that employee resilience can be viewed as a set...
TL;DR: The results suggest that the perovskite QDs are ideal candidates for the detection of soft X-rays and for large-area flat or flexible panels with tremendous application potential in multidimensional and different architectures imaging technologies.
Abstract: Metal halide perovskites represent a family of the most promising materials for fascinating photovoltaic and photodetector applications due to their unique optoelectronic properties and much needed simple and low-cost fabrication process. The high atomic number (Z) of their constituents and significantly higher carrier mobility also make perovskite semiconductors suitable for the detection of ionizing radiation. By taking advantage of that, the direct detection of soft-X-ray-induced photocurrent is demonstrated in both rigid and flexible detectors based on all-inorganic halide perovskite quantum dots (QDs) synthesized via a solution process. Utilizing a synchrotron soft-X-ray beamline, high sensitivities of up to 1450 µC Gyair-1 cm-2 are achieved under an X-ray dose rate of 0.0172 mGyair s-1 with only 0.1 V bias voltage, which is about 70-fold more sensitive than conventional α-Se devices. Furthermore, the perovskite film is printed homogeneously on various substrates by the inexpensive inkjet printing method to demonstrate large-scale fabrication of arrays of multichannel detectors. These results suggest that the perovskite QDs are ideal candidates for the detection of soft X-rays and for large-area flat or flexible panels with tremendous application potential in multidimensional and different architectures imaging technologies.
TL;DR: Various Decision Engine (DE) approaches are described, including new ensemble learning and deep learning approaches, and cyber kill chain models and cyber-attacks that compromise network systems are explained.
TL;DR: This special issue contains 12 publications—nine review articles and three original research articles that cover diverse areas of mitochondrial biology and function and how defects in these areas can lead to disease.
Abstract: Mitochondria are best known as the sites for production of respiratory ATP and are essential for eukaryotic life. They have their own genome but the great majority of the mitochondrial proteins are encoded by the nuclear genome and are imported into the mitochondria. The mitochondria participate in critical central metabolic pathways and they are fully integrated into the intracellular signalling networks that regulate diverse cellular functions. It is not surprising then that mitochondrial defects or dysregulation have emerged as having key roles in ageing and in the cytopathological mechanisms underlying cancer, neurodegenerative and other diseases. This special issue contains 12 publications—nine review articles and three original research articles. They cover diverse areas of mitochondrial biology and function and how defects in these areas can lead to disease. In addition, the articles in this issue highlight how model organisms have contributed to our understanding of these processes.
Indian Council of Agricultural Research1, University of Newcastle2, Kansas State University3, Indian Agricultural Research Institute4, National Academy of Agricultural Research Management5, Kerala Forest Research Institute6, Sejong University7, University of Wuppertal8, Korea University9, Foshan University10, La Trobe University11, Tianjin University12, Central State University13
TL;DR: In this article, the authors review the progress of work on soil organic carbon dynamics in the major ecosystems of the world and provide an overview of the information that can enrich understanding of carbon sequestration and mitigation strategies.
Abstract: Global climate change has resulted in changes to the earth's geological, ecological, and biological ecosystems, which pose a severe threat to the existence of human civilization and sustenance of agricultural productivity vis-a-vis food security. In the last several decades, climate change has been linked to erratic rainfall distribution patterns and large variations in diurnal temperatures, because of a rise in atmospheric CO2 concentration. This, in turn, is thought to make world agricultural production systems more prone to failure. Soil organic carbon (SOC) is an important component for the functioning of agro-ecosystems, and its presence is central to the concept of sustainable maintenance of soil health. Soil is the largest terrestrial carbon sink and contains 2- and 3-times more carbon than the carbon in the atmosphere and vegetation, respectively. Therefore, a meager change in soil carbon sequestration will have a drastic impact on the global carbon cycle and climate change. The SOC has different pools and fractions including total organic carbon (TOC), particulate organic carbon (POC), microbial biomass carbon (MBC), dissolved organic carbon (DOC), permanganate oxidizable carbon (KMnO4-C), and mineral associated organic carbon (MOC). Each has a varying degree of decomposition rate and stability. Researchers have identified many ways to offset the effect of climate change through modification of carbon sequestration in the soil. Identification of location-specific, suitable land use and management practices is one of the options to mitigate the impact of the climate change. It can be done by re-balancing different carbon pools and emission fluxes. Labile organic carbon pools including MBC, POC, and KMnO4-C are the most sensitive indicators for assessing soil quality after the adoption of alternate land use and management practices. Information on soil aggregation and SOC stabilization helps for long-term sequestration of carbon in the soil. Here we review the progress of work on SOC dynamics in the major ecosystems of the world. The information should enrich understanding of carbon sequestration and climate change mitigation strategies.
TL;DR: The challenges associated with discovering and developing anticancer agents designed specifically to prevent or delay the metastatic outgrowth of cancer are described and guidance on how these challenges might be overcome is provided.
Abstract: Most cancer-related deaths are a result of metastasis, and thus the importance of this process as a target of therapy cannot be understated. By asking ‘how can we effectively treat cancer?’, we do not capture the complexity of a disease encompassing >200 different cancer types — many consisting of multiple subtypes — with considerable intratumoural heterogeneity, which can result in variable responses to a specific therapy. Moreover, we have much less information on the pathophysiological characteristics of metastases than is available for the primary tumour. Most disseminated tumour cells that arrive in distant tissues, surrounded by unfamiliar cells and a foreign microenvironment, are likely to die; however, those that survive can generate metastatic tumours with a markedly different biology from that of the primary tumour. To treat metastasis effectively, we must inhibit fundamental metastatic processes and develop specific preclinical and clinical strategies that do not rely on primary tumour responses. To address this crucial issue, Cancer Research UK and Cancer Therapeutics CRC Australia formed a Metastasis Working Group with representatives from not-for-profit, academic, government, industry and regulatory bodies in order to develop recommendations on how to tackle the challenges associated with treating (micro)metastatic disease. Herein, we describe the challenges identified as well as the proposed approaches for discovering and developing anticancer agents designed specifically to prevent or delay the metastatic outgrowth of cancer.
TL;DR: Targeted re-sequencing of 890 diverse accessions of hexaploid and tetraploid wheat identifies regions showing the signals of wild emmer introgression, thus suggesting that historic wild-relative gene flow shaped modern bread wheat's adaptive diversity.
Abstract: Introgression is a potential source of beneficial genetic diversity. The contribution of introgression to adaptive evolution and improvement of wheat as it was disseminated worldwide remains unknown. We used targeted re-sequencing of 890 diverse accessions of hexaploid and tetraploid wheat to identify wild-relative introgression. Introgression, and selection for improvement and environmental adaptation, each reduced deleterious allele burden. Introgression increased diversity genome wide and in regions harboring major agronomic genes, and contributed alleles explaining a substantial proportion of phenotypic variation. These results suggest that historic gene flow from wild relatives made a substantial contribution to the adaptive diversity of modern bread wheat.
TL;DR: The current understanding of the role of EVs in neurological diseases is discussed and some of the limitations of the current understandings of this field are raised.
Abstract: Extracellular vesicles (EVs) include exosomes and microvesicles and have been shown to have roles in the CNS ranging from the removal of unwanted biomolecules to intercellular communication to the spread of pathogenic proteins associated with neurodegenerative diseases. EVs carry protein, lipid, and genetic cargo, and research over more than a decade has shown that they contain the misfolded forms of proteins associated with Alzheimer's, Parkinson's, and the prion diseases. Altered genetic cargo, usually in the form of miRNAs, have also been identified in EVs patients with these diseases, suggesting that EVs may be a source of disease biomarkers. Whether EVs play a key role in the pathogenesis of neurological diseases remains to be firmly established because most current research is performed using cell culture and transgenic animal models. If EVs are identified as a key pathological contributor to neurological conditions, they will form a novel target for therapeutic intervention. This Dual Perspectives article will discuss the current understanding of the role of EVs in neurological diseases and raise some of the limitations of our current understandings of this field.
TL;DR: In this paper, the adsorption of tetracycline (TC), cadmium [Cd(II)] and arsenate [As(V)] onto magnetic graphene oxide (MGO), magnetic chemically-reduced graphene (MCRG) and magnetic annealing-reducing graphene (MARG) was investigated to understand the adsoreption properties and molecular mechanisms.
TL;DR: Evidence that supports a role for the immune system in the pathogenesis of hypertension is presented, including the immune cell subsets involved and the means by which these immune cells become activated throughout the course of the disease.
Abstract: Hypertension affects 30% of adults and is the leading risk factor for heart attack and stroke. Traditionally, hypertension has been regarded as a disorder of two systems that are involved in the regulation of salt-water balance and cardiovascular function: the renin-angiotensin-aldosterone system (RAAS) and the sympathetic nervous system (SNS). However, current treatments that aim to limit the influence of the RAAS or SNS on blood pressure fail in ~40% of cases, which suggests that other mechanisms must be involved. This Review summarizes the clinical and experimental evidence supporting a contribution of immune mechanisms to the development of hypertension. In this context, we highlight the immune cell subsets that are postulated to either promote or protect against hypertension through modulation of cardiac output and/or peripheral vascular resistance. We conclude with an appraisal of knowledge gaps still to be addressed before immunomodulatory therapies might be applied to at least a subset of patients with hypertension.
TL;DR: A review of the known causes and consequences of endothelial cell death during atherosclerosis along with highlighting current methodological approaches to studying EndoEVs and the potential roles of Endo-EVs in atherogenic development is provided in this paper.
Abstract: To maintain physiological homeostasis, cell turnover occurs every day in the body via a form of programmed cell death called apoptosis. During apoptosis, cells undergo distinct morphological changes culminating in the disassembly of the dying cell into smaller fragments known as apoptotic bodies (ApoBDs). Dysregulation of apoptosis is associated with diseases including infection, cancer and atherosclerosis. Although the development of atherosclerosis is largely attributed to the accumulation of lipids and inflammatory debris in vessel walls, it is also associated with apoptosis of macrophages, smooth muscle cells (SMCs) and endothelial cells. During cellular activation and apoptosis, endothelial cells can release several types of membrane-bound extracellular vesicles (EVs) including exosomes, microvesicles (MVs)/microparticles and ApoBDs. Emerging evidence in the field suggests that these endothelial cell-derived EVs (EndoEVs) can contribute to intercellular communication during the development of atherosclerosis via the transfer of cellular contents such as protein and microRNA, which may prevent or promote disease progression depending on the context. This review provides an up-to-date overview of the known causes and consequences of endothelial cell death during atherosclerosis along with highlighting current methodological approaches to studying EndoEVs and the potential roles of EndoEVs in atherosclerosis development.
TL;DR: The ProTrip RS is a health-centric RS which is capable of suggesting the food availability through considering climate attributes based on user’s personal choice and nutritive value, and the developed food recommendation approach is evaluated for the real-time IoT-based healthcare support system.
Abstract: The recent developments of internet technology have created premium space for recommender system (RS) to help users in their daily life. An effective personalized recommendation of a travel recommender system can reduce time and travel cost of the travellers. ProTrip RS addresses the personalization problem through exploiting user interests and preferences to generate suggestions. Data considered for the recommendations include travel sequence, actions, motivations, opinions and demographic information of the user. ProTrip is completely designed to be intelligent and in addition, the ProTrip is a health-centric RS which is capable of suggesting the food availability through considering climate attributes based on user’s personal choice and nutritive value. A novel functionality of ProTrip supports travellers with long-term diseases and followers of strict diet. The ProTrip is built on the pillars of ontological knowledge base and tailored filtering mechanisms. The gap between heterogeneous user profiles and descriptions is bridged using semantic ontologies. The effectiveness of recommendations is enhanced through a hybrid model of blended filtering approaches, and results prove that the proposed ProTrip to be a proficient system. The developed food recommendation approach is evaluated for the real-time IoT-based healthcare support system. We also present a detailed case study on the food recommendation-based health management. The proposed system is evaluated on real-time dataset, and analysis of the results shows improved accuracy and efficiency compared to existing models.
TL;DR: The development and application of a sampling framework to sample studies from among those eligible for inclusion in a qualitative evidence synthesis on vaccination communication and ensured that studies representing a wide geographic spread, rich data and a focus that closely resembled the authors' synthesis objective were included.
Abstract: In a qualitative evidence synthesis, too much data due to a large number of studies can undermine our ability to perform a thorough analysis. Purposive sampling of primary studies for inclusion in the synthesis is one way of achieving a manageable amount of data. The objective of this article is to describe the development and application of a sampling framework for a qualitative evidence synthesis on vaccination communication. We developed and applied a three-step framework to sample studies from among those eligible for inclusion in our synthesis. We aimed to prioritise studies that were from a range of settings, were as relevant as possible to the review, and had rich data. We extracted information from each study about country and study setting, vaccine, data richness, and study objectives and applied the following sampling framework:
We assessed 79 studies as eligible for inclusion in the synthesis and sampled 38 of these. First, we sampled all nine studies that were from low and middle-income countries. These studies contributed to the least number of findings. We then sampled an additional 24 studies that scored high for data richness. These studies contributed to a larger number of findings. Finally, we sampled an additional five studies that most closely matched our synthesis objectives. These contributed to a large number of findings. Our approach to purposive sampling helped ensure that we included studies representing a wide geographic spread, rich data and a focus that closely resembled our synthesis objective. It is possible that we may have overlooked primary studies that did not meet our sampling criteria but would have contributed to the synthesis. For example, two studies on migration and access to health services did not meet the sampling criteria but might have contributed to strengthening at least one finding. We need methods to cross-check for under-represented themes.
TL;DR: It is shown that patients with metastatic cutaneous melanoma have an improved survival rate if their tumor shows evidence of NK cell infiltration, and survival effects are enhanced in tumors that show higher expression of genes that encode NK cell stimuli such as the cytokine IL15.
Abstract: Natural killer (NK) cell activity is essential for initiating antitumor responses and may be linked to immunotherapy success. NK cells and other innate immune components could be exploitable for cancer treatment, which drives the need for tools and methods that identify therapeutic avenues. Here, we extend our gene-set scoring method singscore to investigate NK cell infiltration by applying RNA-seq analysis to samples from bulk tumors. Computational methods have been developed for the deconvolution of immune cell types within solid tumors. We have taken the NK cell gene signatures from several such tools, then curated the gene list using a comparative analysis of tumors and immune cell types. Using a gene-set scoring method to investigate RNA-seq data from The Cancer Genome Atlas (TCGA), we show that patients with metastatic cutaneous melanoma have an improved survival rate if their tumor shows evidence of NK cell infiltration. Furthermore, these survival effects are enhanced in tumors that show higher expression of genes that encode NK cell stimuli such as the cytokine IL15 Using this signature, we then examine transcriptomic data to identify tumor and stromal components that may influence the penetrance of NK cells into solid tumors. Our results provide evidence that NK cells play a role in the regulation of human tumors and highlight potential survival effects associated with increased NK cell activity. Our computational analysis identifies putative gene targets that may be of therapeutic value for boosting NK cell antitumor immunity.
TL;DR: A review of the Ni(II)-surface interaction mechanism at the solid-water interface is presented in this article, which can fill the lacuna of researchers who would like to do more research in this related area in depth.
Abstract: Water polluted with heavy-metal ion has been a major problem in recent years. Among various metal ions, nickel(II) is a priority pollutant commonly found in industrial wastewater. As a highly toxic element at an elevated concentration, Ni(II) can pose a serious threat to our ecological environment as well as human being. Ni(II) adsorption from wastewater is a must for environmental management and sustainability. Remediation of Ni(II) contaminated water is possible through adsorption onto various innovative adsorbents from the aquatic environment. The current review looks at the present status of the research done so far Ni(II) adsorption using various adsorbents from wastewater. Ni(II) adsorption kinetics, edges, isotherm, thermodynamic parameters, and Ni(II) adsorption mechanism have also been talked over. Efforts have also been made to steer out of the advantages and disadvantages of adsorbents and the future research need in Ni(II) adsorption by adsorbents. Agricultural based substrates and nanosized metal oxides have been found a hopeful alternative for Ni(II) adsorption from wastewater. The Ni(II) primarily adsorbed onto a homogeneous substrate forming a monolayer. Ni(II) generally formed outer-sphere complexes at low pH values while it formed inner-sphere complexes at higher pH. More than one species is being sorbed, or more than one type of surface site is involved in Ni(II) adsorption process or both. However, significant research is needed to understand Ni(II)-surface interaction mechanism at the solid-water interface. This review can fill the lacuna of researchers who would like to do more research in this related area in depth.
TL;DR: Recognition of DNA activity associated expertise to be distinct from expertise associated with the identification of individuals is advocated, to be supported by dedicated training, competency testing, authorisation, and regular fit for purpose proficiency testing.
Abstract: Understanding the variables impacting DNA transfer, persistence, prevalence and recovery (DNA-TPPR) has become increasingly relevant in investigations of criminal activities to provide opinion on how the DNA of a person of interest became present within the sample collected. This review considers our current knowledge regarding DNA-TPPR to assist casework investigations of criminal activities. There is a growing amount of information available on DNA-TPPR to inform the relative probabilities of the evidence given alternative scenarios relating to the presence or absence of DNA from a specific person in a collected sample of interest. This information should be used where relevant. However, far more research is still required to better understand the variables impacting DNA-TPPR and to generate more accurate probability estimates of generating particular types of profiles in more casework relevant situations. This review explores means of achieving this. It also notes the need for all those interacting with an item of interest to have an awareness of DNA transfer possibilities post criminal activity, to limit the risk of contamination or loss of DNA. Appropriately trained forensic practitioners are best placed to provide opinion and guidance on the interpretation of profiles at the activity level. However, those requested to provide expert opinion on DNA-related activity level issues are often insufficiently trained to do so. We advocate recognition of DNA activity associated expertise to be distinct from expertise associated with the identification of individuals. This is to be supported by dedicated training, competency testing, authorisation, and regular fit for purpose proficiency testing. The possibilities for experts to report on activity-related issues will increase as our knowledge increases through further research, access to relevant data is enhanced, and tools to assist interpretations are better exploited. Improvement opportunities will be achieved sooner, if more laboratories and agencies accept the need to invest in these aspects as well as the training of practitioners.
TL;DR: This study aims to analyze the effectiveness of various machine learning classification models for predicting personalized usage utilizing individual’s phone log data and presents the empirical evaluations of Artificial Neural Network based classification model, which is frequently used in deep learning and makes comparative analysis in this context-aware study.
Abstract: Due to the increasing popularity of recent advanced features and context-awareness in smart mobile phones, the contextual data relevant to users’ diverse activities with their phones are recorded through the device logs. Modeling and predicting individual’s smartphone usage based on contexts, such as temporal, spatial, or social information, can be used to build various context-aware personalized systems. In order to intelligently assist them, a machine learning classifier based usage prediction model for individual users’ is the key. Thus, we aim to analyze the effectiveness of various machine learning classification models for predicting personalized usage utilizing individual’s phone log data. In our context-aware analysis, we first employ ten classic and well-known machine learning classification techniques, such as ZeroR, Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Adaptive Boosting, Repeated Incremental Pruning to Produce Error Reduction, Ripple Down Rule Learner, and Logistic Regression classifiers. We also present the empirical evaluations of Artificial Neural Network based classification model, which is frequently used in deep learning and make comparative analysis in our context-aware study. The effectiveness of these classifier based context-aware models is examined by conducting a range of experiments on the real mobile phone datasets collected from individual users. The overall experimental results and discussions can help both the researchers and applications developers to design and build intelligent context-aware systems for smartphone users.