Modeling human trophoblast, the placental epithelium at the maternal fetal interface.

Appropriate human trophoblast lineage specification and differentiation is crucial for the establishment of normal placentation and maintenance of pregnancy. However, due to the lack of proper modeling systems, the molecular mechanisms of these processes are still largely unknown. Much of the early studies in this area have been based on animal models and tumor-derived trophoblast cell lines, both of which are suboptimal for modeling this unique human organ. Recent advances in regenerative and stem cell biology methods have led to development of novel in vitro model systems for studying human trophoblast. These include derivation of human embryonic and induced pluripotent stem cells and establishment of methods for the differentiation of these cells into trophoblast, as well as the more recent derivation of human trophoblast stem cells. In addition, advances in culture conditions, from traditional two-dimensional monolayer culture to 3D culturing systems, have led to development of trophoblast organoid and placenta-on-a-chip model, enabling us to study human trophoblast function in context of more physiologically accurate environment. In this review, we will discuss these various model systems, with a focus on human trophoblast, and their ability to help elucidate the key mechanisms underlying placental development and function. This review focuses on model systems of human trophoblast differentiation, including advantages and limitations of stem cell-based culture, trophoblast organoid, and organ-on-a-chip methods and their applications in understanding placental development and disease

Value-at-Risk for South-East Asian Stock Markets: Stochastic Volatility vs. GARCH

This study compares the performance of several methods to calculate the Value-at-Risk of the six main ASEAN stock markets. We use filtered historical simulations, GARCH models, and stochastic volatility models. The out-of-sample performance is analyzed by various backtesting procedures. We find that simpler models fail to produce sufficient Value-at-Risk forecasts, which appears to stem from several econometric properties of the return distributions. With stochastic volatility models, we obtain better Value-at-Risk forecasts compared to GARCH. The quality varies over forecasting horizons and across markets. This indicates that, despite a regional proximity and homogeneity of the markets, index volatilities are driven by different factors

Surveying a Diverse Pool of Microalgae as a Bioresource for Future Biotechnological Applications

Jakob G, Wolf J, Bui TVL, et al. Surveying a Diverse Pool of Microalgae as a Bioresource for Future Biotechnological Applications. Journal of Petroleum & Environmental Biotechnology. 2013;4(05):153.Resource limitation is an escalating concern given human expansion and development. Algae are increasingly recognised as a promising bioresource and the range of cultivated species and their products is expanding. Compared to terrestrial crops, microalgae are very biodiverse and offer considerable versatility for a range of biotechnological applications including the production of animal feeds, fuels, high value products and waste-water treatment. Despite their versatility and capacity for high biomass productivity on non-arable land, attempts to harness microalgae for commercial benefit have been limited. This is in large part due to capital costs and energy inputs remaining high, the necessity of identifying ‘suitable’ land with proximal resource and infrastructure availability and the need for process and strain optimisation. Microalgae represent a relatively unexplored bioresource both for native and engineered strains. Success in this area requires (1) appropriate methods to source and isolate microalgae strains, (2) efficient maintenance of motherstocks, (3) rapid strain characterisation and correct matching of strains to applications, (4) ensuring productive and stable cultivation at scale, and (5) ongoing strain development (breeding, adaptation and engineering). This article illustrates a survey and isolation of over 150 local microalgae strains as a bioresource for ongoing strain development and biotechnological applications

Effects of Harmful Algal Blooms on Fish and Shellfish Species: A Case Study of New Zealand in a Changing Environment

Harmful algal blooms (HABs) have wide-ranging environmental impacts, including on aquatic species of social and commercial importance. In New Zealand (NZ), strategic growth of the aquaculture industry could be adversely affected by the occurrence of HABs. This review examines HAB species which are known to bloom both globally and in NZ and their effects on commercially important shellfish and fish species. Blooms of Karenia spp. have frequently been associated with mortalities of both fish and shellfish in NZ and the sub-lethal effects of other genera, notably Alexandrium spp., on shellfish (which includes paralysis, a lack of byssus production, and reduced growth) are also of concern. Climate change and anthropogenic impacts may alter HAB population structure and dynamics, as well as the physiological responses of fish and shellfish, potentially further compromising aquatic species. Those HAB species which have been detected in NZ and have the potential to bloom and harm marine life in the future are also discussed. The use of environmental DNA (eDNA) and relevant bioassays are practical tools which enable early detection of novel, problem HAB species and rapid toxin/HAB screening, and new data from HAB monitoring of aquaculture production sites using eDNA are presented. As aquaculture grows to supply a sizable proportion of the world’s protein, the effects of HABs in reducing productivity is of increasing significance. Research into the multiple stressor effects of climate change and HABs on cultured species and using local, recent, HAB strains is needed to accurately assess effects and inform stock management strategies

A Feasibility Study of Three-Dimensional Empirical Orthogonal Functions From the NASA JPL Ocean General Circulation Model: Computing, Visualization and Interpretation

Existing oceanic studies on either data reconstruction or dynamics often used 2-dimensional empirical orthogonal functions (EOF) for sea surface temperature (SST) and for deep layers. However, large-scale oceanic dynamics, such as equatorial ocean upwelling and arctic ocean ventilation, imply the existence of strong covariance among the temperatures and other parameters between different layers. These ocean dynamics are not best represented in the isolated 2-dimensional layer-by-layer calculations, while the 3-dimensional EOFs have a clear advantage. The purpose of this paper is to demonstrate 3D EOF calculations based on the NASA Jet Propulsion Laboratory (JPL) ocean general circulation model (OGCM) from surface to 5,500 meters depth, with 33 depth layers, 1-degree latitude and longitude spatial resolution, and monthly temporal resolution. We also present visualizations of the 3D EOFs and make physical interpretations of the first two EOFs. Our 3D EOF results demonstrate that (i) the 3D spatial pattern of equatorial ocean upwelling is mainly reflected in the first EOF mode and has its most variabilities within the depth layer between 100 and 400 meters, (ii) the 3D El Niño Southern Oscillation (ENSO) dynamic pattern is mainly reflected in the second EOF mode and is mostly confined from surface to the depth of 150 meters, and (iii) the lead eigenvalue from the 3D EOF calculation appears to contain some signal of oceanic warming. Additionally, our method of weighted 3D EOF computation and our 3D visualization Python code may be useful tools for both climate professionals and students

Patient-reported outcome measures for monitoring primary care patients with depression: the PROMDEP cluster RCT and economic evaluation

Background: Guidelines on the management of depression recommend that practitioners use patient-reported outcome measures for the follow-up monitoring of symptoms, but there is a lack of evidence of benefit in terms of patient outcomes.// Objective: To test using the Patient Health Questionnaire-9 questionnaire as a patient-reported outcome measure for monitoring depression, training practitioners in interpreting scores and giving patients feedback.// Design: Parallel-group, cluster-randomised superiority trial; 1 : 1 allocation to intervention and control.// Setting: UK primary care (141 group general practices in England and Wales).// Inclusion criteria: Patients aged ≥ 18 years with a new episode of depressive disorder or symptoms, recruited mainly through medical record searches, plus opportunistically in consultations.// Exclusions: Current depression treatment, dementia, psychosis, substance misuse and risk of suicide.// Intervention: Administration of the Patient Health Questionnaire-9 questionnaire with patient feedback soon after diagnosis, and at follow-up 10–35 days later, compared with usual care.// Primary outcome: Beck Depression Inventory, 2nd edition, symptom scores at 12 weeks.// Secondary outcomes: Beck Depression Inventory, 2nd edition, scores at 26 weeks; antidepressant drug treatment and mental health service contacts; social functioning (Work and Social Adjustment Scale) and quality of life (EuroQol 5-Dimension, five-level) at 12 and 26 weeks; service use over 26 weeks to calculate NHS costs; patient satisfaction at 26 weeks (Medical Informant Satisfaction Scale); and adverse events.// Sample size: The original target sample of 676 patients recruited was reduced to 554 due to finding a significant correlation between baseline and follow-up values for the primary outcome measure.// Randomisation: Remote computerised randomisation with minimisation by recruiting university, small/large practice and urban/rural location.// Blinding: Blinding of participants was impossible given the open cluster design, but self-report outcome measures prevented observer bias. Analysis was blind to allocation.// Analysis: Linear mixed models were used, adjusted for baseline depression, baseline anxiety, sociodemographic factors, and clustering including practice as random effect. Quality of life and costs were analysed over 26 weeks.// Qualitative interviews: Practitioner and patient interviews were conducted to reflect on trial processes and use of the Patient Health Questionnaire-9 using the Normalization Process Theory framework.// Results: Three hundred and two patients were recruited in intervention arm practices and 227 patients were recruited in control practices. Primary outcome data were collected for 252 (83.4%) and 195 (85.9%), respectively. No significant difference in Beck Depression Inventory, 2nd edition, score was found at 12 weeks (adjusted mean difference –0.46, 95% confidence interval –2.16 to 1.26). Nor were significant differences found in Beck Depression Inventory, 2nd Edition, score at 26 weeks, social functioning, patient satisfaction or adverse events. EuroQol-5 Dimensions, five-level version, quality-of-life scores favoured the intervention arm at 26 weeks (adjusted mean difference 0.053, 95% confidence interval 0.013 to 0.093). However, quality-adjusted life-years over 26 weeks were not significantly greater (difference 0.0013, 95% confidence interval –0.0157 to 0.0182). Costs were lower in the intervention arm but, again, not significantly (–£163, 95% confidence interval –£349 to £28). Cost-effectiveness and cost–utility analyses, therefore, suggested that the intervention was dominant over usual care, but with considerable uncertainty around the point estimates. Patients valued using the Patient Health Questionnaire-9 to compare scores at baseline and follow-up, whereas practitioner views were more mixed, with some considering it too time-consuming.// Conclusions: We found no evidence of improved depression management or outcome at 12 weeks from using the Patient Health Questionnaire-9, but patients’ quality of life was better at 26 weeks, perhaps because feedback of Patient Health Questionnaire-9 scores increased their awareness of improvement in their depression and reduced their anxiety. Further research in primary care should evaluate patient-reported outcome measures including anxiety symptoms, administered remotely, with algorithms delivering clear recommendations for changes in treatment

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin