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Abstract Background High resistance (the ability of the host to reduce pathogen load) and tolerance (the ability to maintain high performance at a given pathogen load) are two desirable host traits for producing animals that are resilient to infections. For Porcine Reproductive and Respiratory Syndrome (PRRS), one of the most devastating swine diseases worldwide, studies have identified substantial genetic variation in resistance of pigs, but evidence for genetic variation in tolerance has so far been inconclusive. Resistance and tolerance are usually considered as static traits. In this study, we used longitudinal viremia measurements of PRRS virus infected pigs to define discrete stages of infection based on viremia profile characteristics. These were used to investigate host genetic effects on viral load (VL) and growth at different stages of infection, to quantify genetic variation in tolerance at these stages and throughout the entire 42-day observation period, and to assess whether the single nucleotide polymorphism (SNP) WUR10000125 (WUR) with known large effects on resistance confers significant differences in tolerance. Results Genetic correlations between resistance and growth changed considerably over time. Individuals that expressed high genetic resistance early in infection tended to grow slower during that time-period, but were more likely to experience lower VL and recovery in growth by the later stage. The WUR genotype was most strongly associated with VL at early- to mid-stages of infection, and with growth at mid- to late-stages of infection. Both, single-stage and repeated measurements random regression models identified significant genetic variation in tolerance. The WUR SNP was significantly associated only with the overall tolerance slope fitted through all stages of infection, with the genetically more resistant AB pigs for the WUR SNP being also more tolerant to PRRS. Conclusions The results suggest that genetic selection for improved tolerance of pigs to PRRS is possible in principle, but may be feasible only with genomic selection, requiring intense recording schemes that involve repeated measurements to reliably estimate genetic effects. In the absence of such records, consideration of the WUR genotype in current selection schemes appears to be a promising strategy to improve simultaneously resistance and tolerance of growing pigs to PRRS.

Diagnosis of breast ductal carcinoma in situ (DCIS) presents a challenge since we cannot yet distinguish those cases that would remain indolent and not require aggressive treatment from cases that may progress to invasive ductal cancer (IDC). The purpose of this study is to determine the role of Rap1Gap, a GTPase activating protein, in the progression from DCIS to IDC. Immunohistochemistry (IHC) analysis of samples from breast cancer patients shows an increase in Rap1Gap expression in DCIS compared to normal breast tissue and IDCs. In order to study the mechanisms of malignant progression, we employed an in vitro three-dimensional (3D) model that more accurately recapitulates both structural and functional cues of breast tissue. Immunoblotting results show that Rap1Gap levels in MCF10.Ca1D cells (a model of invasive carcinoma) are reduced compared to those in MCF10.DCIS (a model of DCIS). Retroviral silencing of Rap1Gap in MCF10.DCIS cells activated extracellular regulated kinase (ERK) mitogen-activated protein kinase (MAPK), induced extensive cytoskeletal reorganization and acquisition of mesenchymal phenotype, and enhanced invasion. Enforced reexpression of Rap1Gap in MCF10.DCIS-Rap1GapshRNA cells reduced Rap1 activity and reversed the mesenchymal phenotype. Similarly, introduction of dominant negative Rap1A mutant (Rap1A-N17) in DCIS-Rap1Gap shRNA cells caused a reversion to nonmalignant phenotype. Conversely, expression of constitutively active Rap1A mutant (Rap1A-V12) in noninvasive MCF10.DCIS cells led to phenotypic changes that were reminiscent of Rap1Gap knockdown. Thus, reduction of Rap1Gap in DCIS is a potential switch for progression to an invasive phenotype. The Graphical Abstract summarizes these findings.

Understanding bottom-up growth mechanisms of 2D transition metal carbides (MXenes) may enable new synthetic routes to tailor functional properties. Here, the authors use in situ electron microscopy, density functional theory and molecular dynamics simulations to reveal the homoepitaxial growth mechanisms of a single TiC adlayer from a Ti3C2 monolayer substrate.

The unique properties of 2D materials are affected by the discontinuities posed by the structure’s edge. Here, using atomic-resolution scanning transmission electron microscopy and ab initio molecular dynamics simulations, the authors image and explain the formation of specific edge structures on Mo1 − x W x Se2 monolayers under different chemical conditions.

Introduction: In the general population, medication nonadherence contributes to poorer outcomes. However, little is known about medication adherence among adults with chronic kidney disease (CKD). We evaluated the association of self-reported medication adherence with CKD progression and all-cause death in patients with CKD. Methods: In this prospective observational study of 3305 adults with mild-to-moderate CKD enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study, the baseline self-reported medication adherence was assessed by responses to 3 questions and categorized as high, medium, and low. CKD progression (50% decline in eGFR or incident end-stage renal disease) and all-cause death were measured using multivariable Cox proportional hazards. Results: Of the patients, 68% were categorized as high adherence, 17% medium adherence, and 15% low adherence. Over a median follow-up of 6 years, there were 969 CKD progression events and 675 deaths. Compared with the high-adherence group, the low-adherence group experienced increased risk for CKD progression (hazard ratio = 1.27, 95% confidence interval = 1.05, 1.54) after adjustment for sociodemographic and clinical factors, cardiovascular medications, number of medication types, and depressive symptoms. A similar association existed between low adherence and all-cause death, but did not reach standard statistical significance (hazard ratio = 1.14 95% confidence interval = 0.88, 1.47). Conclusion: Baseline self-reported low medication adherence was associated with an increased risk for CKD progression. Future work is needed to better understand the mechanisms underlying this association and to develop interventions to improve adherence. Keywords: CKD, death, medication adherence, progression

Abstract Background Genomic prediction of the pig’s response to the porcine reproductive and respiratory syndrome (PRRS) virus (PRRSV) would be a useful tool in the swine industry. This study investigated the accuracy of genomic prediction based on porcine SNP60 Beadchip data using training and validation datasets from populations with different genetic backgrounds that were challenged with different PRRSV isolates. Results Genomic prediction accuracy averaged 0.34 for viral load (VL) and 0.23 for weight gain (WG) following experimental PRRSV challenge, which demonstrates that genomic selection could be used to improve response to PRRSV infection. Training on WG data during infection with a less virulent PRRSV, KS06, resulted in poor accuracy of prediction for WG during infection with a more virulent PRRSV, NVSL. Inclusion of single nucleotide polymorphisms (SNPs) that are in linkage disequilibrium with a major quantitative trait locus (QTL) on chromosome 4 was vital for accurate prediction of VL. Overall, SNPs that were significantly associated with either trait in single SNP genome-wide association analysis were unable to predict the phenotypes with an accuracy as high as that obtained by using all genotyped SNPs across the genome. Inclusion of data from close relatives into the training population increased whole genome prediction accuracy by 33% for VL and by 37% for WG but did not affect the accuracy of prediction when using only SNPs in the major QTL region. Conclusions Results show that genomic prediction of response to PRRSV infection is moderately accurate and, when using all SNPs on the porcine SNP60 Beadchip, is not very sensitive to differences in virulence of the PRRSV in training and validation populations. Including close relatives in the training population increased prediction accuracy when using the whole genome or SNPs other than those near a major QTL.

Purpose – The purpose of this paper is to demonstrate an improved method for forecasting the US Army recruiting. Design/methodology/approach – Time series methods, regression modeling, principle components and marketing research are included in this paper. Findings – This paper found the unique ability of multiple statistical methods applied to a forecasting context to consider the effects of inputs that are controlled to some degree by a decision maker. Research limitations/implications – This work will successfully inform the US Army recruiting leadership on how this improved methodology will improve their recruitment process. Practical implications – Improved US Army analytical technique for forecasting recruiting goals.. Originality/value – This work culls data from open sources, using a zip-code-based classification method to develop more comprehensive forecasting methods with which US Army recruiting leaders can better establish recruiting goals.

Abstract Background The WUR1000125 (WUR) single nucleotide polymorphism (SNP) can be used as a genetic marker for host response to porcine reproductive and respiratory syndrome (PRRS), PRRS vaccination, and co-infection with porcine circovirus type 2b (PCV2b). Objectives of this study were to identify genomic regions other than WUR associated with host response to PRRS vaccination and PRRSV/PCV2b co-infection and regions with a different effect on host response to co-infection, depending on previous vaccination for PRRS. Methods Commercial crossbred nursery pigs were pre-selected for WUR genotype (n = 171 AA and 198 AB pigs) where B is the dominant and favorable allele. Half of the pigs were vaccinated for PRRS and 4 weeks later, all pigs were co-infected with PRRS virus and PCV2b. Average daily gain (ADG) and viral load (VL) were quantified post vaccination (Post Vx) and post co-infection (Post Co-X). Single-SNP genome-wide association analyses were then conducted to identify genomic regions associated with response to vaccination and co-infection. Results Multiple SNPs near the major histocompatibility complex were significantly associated with PCV2b VL (−log 10 P ≥ 5.5), regardless of prior vaccination for PRRS. Several SNPs were also significantly associated with ADG Post Vx and Post Co-X. SNPs with a different effect on ADG, depending on prior vaccination for PRRS, were identified Post Vx (−log 10 P = 5.6) and Post Co-X (−log 10 P = 5.5). No SNPs were significantly associated with vaccination VL (−log10 P ≤ 4.7) or PRRS VL (−log10 P ≤ 4.3). Genes near SNPs associated with vaccination VL, PRRS VL, and PCV2b VL were enriched (P ≤ 0.01) for immune-related pathways and genes near SNPs associated with ADG were enriched for metabolism pathways (P ≤ 0.04). SNPs associated with vaccination VL, PRRS VL, and PCV2b VL showed overrepresentation of health QTL identified in previous studies and SNPs associated with ADG Post Vx of Non-Vx pigs showed overrepresentation of growth QTL. Conclusions Multiple genomic regions were associated with PCV2b VL and ADG Post Vx and Post Co-X. Different SNPs were associated with ADG, depending on previous vaccination for PRRS. Results of functional annotation analyses and novel approaches of using previously-reported QTL support the identified regions.

Abstract We reconsider a model introducing a scalar leptoquark ϕ ∼ (3 , 1 , −1/3) to explain recent deviations from the standard model in semileptonic B decays. The leptoquark can accommodate the persistent tension in the decays B ¯ → D ∗ τ ν ¯ $$ \overline{B}\to {D}^{\left(\ast \right)}\tau \overline{\nu} $$ as long as its mass is lower than approximately 10 TeV, and we show that a sizeable Yukawa coupling to the right-chiral tau lepton is necessary for an acceptable explanation. A characteristic prediction of this scenario is a value of R D * $$ {R}_{D^{*}} $$ slightly smaller than the current world average. Agreement with the measured B ¯ → D ∗ τ ν ¯ $$ \overline{B}\to {D}^{\left(\ast \right)}\tau \overline{\nu} $$ rates is mildly compromised for parameter choices addressing the tensions in b → sμμ, where the model can significantly reduce the discrepancies in angular observables, branching ratios and the lepton-flavor-universality observables R K and R K * $$ {R}_{K^{*}} $$ . The leptoquark can also reconcile the predicted and measured value of the anomalous magnetic moment of the muon and appears naturally in models of radiative neutrino mass derived from lepton-number violating effective operators. As a representative example, we incorporate the particle into an existing two-loop neutrino mass scenario derived from a dimension-nine operator. In this specific model, the structure of the neutrino mass matrix provides enough freedom to explain the small masses of the neutrinos in the region of parameter space dictated by agreement with the anomalies in B ¯ → D ∗ τ ν ¯ $$ \overline{B}\to {D}^{\left(\ast \right)}\tau \overline{\nu} $$ , but not the b → s transition. This is achieved without excessive fine-tuning in the parameters important for neutrino mass.

Wound healing is a complex biological process that involves integration of hemostasis, inflammation, proliferation and tissue remodeling. An extract of pineapple (Ananas comosus) stem demonstrates several therapeutics properties, including acceleration of wound healing. Tacorin is a water crude extract derived from the stem of A. comosus with high protein content. The effect of tacorin on wound healing in vivo was examined using rats with an induced injury. Wound closure was faster with tacorin treatment than in the untreated group. An in vitro study was conducted on mammalian cells (3T3‐L1) to observe the effect of tacorin on cell proliferation. Tacorin was first heated to inactivate its proteolytic activity. It increased the viability of 3T3‐L1 cells in a dose‐dependent manner. Excessive inflammation was suppressed by tacorin as shown by decreased tumor necrosis factor α expression. Treatment with tacorin increased the expression of transforming growth factor β, a major player in tissue remodeling. Moreover, tacorin also reduced the expression of MMP‐2 to accelerate the recovery of the wound. Taken together, tacorin is able to accelerate the wound‐healing process by increasing cell proliferation, suppressing inflammation and accelerating tissue remodeling.

Manipulating the flow of heat at the nanoscale is difficult because it requires the ability to tune the thermal properties of tiny structures. Here, the authors locally change the thermal conductivity of an individual silicon nanowire by irradiating it with helium ions.

Abstract Thin-film composite poly(amide) (PA) membranes have greatly diversified water supplies and food products. However, users would benefit from a control of the electrostatic interactions between the liquid and the net surface charge interface in order to benefit wider application. The ionic selectivity of the 100 nm PA semi-permeable layer is significantly affected by the pH of the solution. In this work, for the first time, a convenient route is presented to configure the surface charge of PA membranes by gamma ray induced surface grafting. This rapid and up-scalable method offers a versatile route for surface grafting by adjusting the irradiation total dose and the monomer concentration. Specifically, thin coatings obtained at low irradiation doses between 1 and 10 kGy and at low monomer concentration of 1 v/v% in methanol/water (1:1) solutions, dramatically altered the net surface charge of the pristine membranes from −25 mV to +45 mV, whilst the isoelectric point of the materials shifted from pH 3 to pH 7. This modification resulted in an improved water flux by over 55%, from 45.9 to up 70 L.m−2.h−1, whilst NaCl rejection was found to drop by only 1% compared to pristine membranes.

Abstract Background The breast tumor microenvironment regulates progression of ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC). However, it is unclear how interactions between breast epithelial and stromal cells can drive this progression and whether there are reliable microenvironmental biomarkers to predict transition of DCIS to IDC. Methods We used xenograft mouse models and a 3D pathomimetic model termed mammary architecture and microenvironment engineering (MAME) to study the interplay between human breast myoepithelial cells (MEPs) and cancer-associated fibroblasts (CAFs) on DCIS progression. Results Our results show that MEPs suppress tumor formation by DCIS cells in vivo even in the presence of CAFs. In the in vitro MAME model, MEPs reduce the size of 3D DCIS structures and their degradation of extracellular matrix. We further show that the tumor-suppressive effects of MEPs on DCIS are linked to inhibition of urokinase plasminogen activator (uPA)/urokinase plasminogen activator receptor (uPAR)-mediated proteolysis by plasminogen activator inhibitor 1 (PAI-1) and that they can lessen the tumor-promoting effects of CAFs by attenuating interleukin 6 (IL-6) signaling pathways. Conclusions Our studies using MAME are, to our knowledge, the first to demonstrate a divergent interplay between MEPs and CAFs within the DCIS tumor microenvironment. We show that the tumor-suppressive actions of MEPs are mediated by PAI-1, uPA and its receptor, uPAR, and are sustained even in the presence of the CAFs, which themselves enhance DCIS tumorigenesis via IL-6 signaling. Identifying tumor microenvironmental regulators of DCIS progression will be critical for defining a robust and predictive molecular signature for clinical use.

Abstract Background A host can adopt two response strategies to infection: resistance (reduce pathogen load) and tolerance (minimize impact of infection on performance). Both strategies may be under genetic control and could thus be targeted for genetic improvement. Although there is evidence that supports a genetic basis for resistance to porcine reproductive and respiratory syndrome (PRRS), it is not known whether pigs also differ genetically in tolerance. We determined to what extent pigs that have been shown to vary genetically in resistance to PRRS also exhibit genetic variation in tolerance. Multi-trait linear mixed models and random regression sire models were fitted to PRRS Host Genetics Consortium data from 1320 weaned pigs (offspring of 54 sires) that were experimentally infected with a virulent strain of PRRS virus to obtain genetic parameter estimates for resistance and tolerance. Resistance was defined as the inverse of within-host viral load (VL) from 0 to 21 (VL21) or 0 to 42 (VL42) days post-infection and tolerance as the slope of the reaction-norm of average daily gain (ADG21, ADG42) on VL21 or VL42. Results Multi-trait analysis of ADG associated with either low or high VL was not indicative of genetic variation in tolerance. Similarly, random regression models for ADG21 and ADG42 with a tolerance slope fitted for each sire did not result in a better fit to the data than a model without genetic variation in tolerance. However, the distribution of data around average VL suggested possible confounding between level and slope estimates of the regression lines. Augmenting the data with simulated growth rates of non-infected half-sibs (ADG0) helped resolve this statistical confounding and indicated that genetic variation in tolerance to PRRS may exist if genetic correlations between ADG0 and ADG21 or ADG42 are low to moderate. Conclusions Evidence for genetic variation in tolerance of pigs to PRRS was weak when based on data from infected piglets only. However, simulations indicated that genetic variance in tolerance may exist and could be detected if comparable data on uninfected relatives were available. In conclusion, of the two defense strategies, genetics of tolerance is more difficult to elucidate than genetics of resistance.

We investigate the growth, purity, grain structure/morphology, and electrical resistivity of 3D platinum nanowires synthesized via electron beam induced deposition with and without an in situ pulsed laser assist process which photothermally couples to the growing Pt–C deposits. Notably, we demonstrate: 1) higher platinum concentration and a coalescence of the otherwise Pt–C nanogranular material, 2) a slight enhancement in the deposit resolution and 3) a 100-fold improvement in the conductivity of suspended nanowires grown with the in situ photothermal assist process, while retaining a high degree of shape fidelity.

Chronic kidney disease (CKD) is associated with an increased risk of cognitive decline, but the mechanisms remain poorly defined. We sought to determine the relation between serum inflammatory markers and risk of cognitive decline among adults with CKD. Methods: We studied 757 adults aged ≥55 years with CKD participating in the Chronic Renal Insufficiency Cohort Cognitive study. We measured interleukin (IL)−1β, IL-1 receptor antagonist, IL-6, tumor necrosis factor (TNF)−α, high-sensitivity C-reactive protein (hs-CRP), and fibrinogen in baseline plasma samples. We assessed cognitive function at regular intervals in 4 domains and defined incident impairment as a follow-up score more than 1 SD poorer than the group mean. Results: The mean age of the sample was 64.3 ± 5.6 years, and the mean follow-up was 6.2 ± 2.5 years. At baseline, higher levels of each inflammatory marker were associated with poorer age-adjusted performance. In analyses adjusted for baseline cognition, demographics, comorbid conditions, and kidney function, participants in the highest tertile of hs-CRP, the highest tertile of fibrinogen, and the highest tertile of IL-1β had an increased risk of impairment in attention compared to participants in the lowest tertile of each marker. Participants in the highest versus lowest tertile of TNF-α had a lower adjusted risk of impairment in executive function. There was no association between other inflammatory markers and change in cognitive function. Discussion: Among adults with CKD, higher levels of hs-CRP, fibrinogen, and IL-1β were associated with a higher risk of impairment in attention. Higher levels of TNF-α were associated with a lower risk of impaired executive function.

Simultaneous micronization and purification of DLBS3233 bioactive fraction, a combination of two Indonesian herbals Lagerstroemia speciosa and Cinnamomum burmannii has been successfully performed via supercritical anti-solvent (SAS) technology. The objective of the present study was to investigate the effectiveness of SAS technology to micronize and reduce coumarin content of DLBS3233. The effects of four SAS process parameters, i.e. pressure, temperature, concentration and solution flow rate on particle formation were investigated. In SAS process, DLBS3233 was dissolved in dimethylformamide (DMF) as the liquid solvent. The solution was then pumped through a nozzle into a chamber simultaneously with supercritical carbon dioxide (SC-CO2) which acts as the anti-solvent, resulting in DLBS3233 precipitation. Physicochemical properties of unprocessed DLBS3233 and SAS-processed DLBS3233 particles were analyzed using scanning electron microscopy (SEM) and high pressure liquid chromatography (HPLC). Total polyphenol content (TPC) was also analyzed.Particles with mean particle size ranging from 0.107±0.028 μm to 0.298±0.138 μm were obtained by varying the process parameters. SAS-processed DLBS3233 particles showed no coumarin content in all experiments studied in this work. Results of TPC analysis revealed no significant change in SAS-processed DLBS3233 particles compared to unprocessed DLBS3233. Nano-sized DLBS3233 particles with no coumarin content have been successfully produced using SAS process. This study demonstrates the ability of SAS for processing herbal medicine in single step process.

In 2015 the Lancet Commission on Global Surgery (LCoGS) argued that surgical care is important to national health systems along with the economic viability of countries. Gajewski and colleagues outlined how the Commission’s blueprint has been implemented in sub-Saharan Africa, including two funded research projects that were integrated into national surgical plans. Here, we outline how the five processes proposed by Gajewski and colleagues are critical to integrate research, policy, and on-the-ground implementation. We also propose that, moving forward, the most pressing adjunct in many low- and middle-income countries (LMICs) may be a better characterization of rural surgical practices through rigorous research along with models that enable lessons to inform national policy.

Persistent global disparities in maternal and neonatal outcomes exist, in part, due to a lack of access to safe surgery. This commentary examines the relative need for increased focus on access to safe maternal and pediatric surgery globally, starting with a focus on cost-effective surgeries. There is a need to understand context-specific surgeries for regions, including understanding regional versus tertiary development. Most important is a need to understand the crucial role of supply chain management (SCM) in developing better access to maternal and pediatric surgery in limited resource settings. We evaluate the role of SCM in global surgery and global health, and the current landscape of inefficiency. We outline specific findings and takeaways from recent solutions developed in pediatric and maternal surgery to address SCM inefficiencies. We then examine the applicability to other settings and look at the future. Our goal is to summarize the challenges that exist today in a global setting to provide better access to maternal and pediatric surgery and outline solutions relying on structural, SCM-related framework. Key words: • Pediatric surgery • Neonatal outcomes • Supply-chain management • Limited resource settings Copyright © 2020 Navarro et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The operation pressure levels of refinery hydrogenation units (e.g., hydrocrackers and hydro-treaters) and hydrogen supply units (i.e., hydrogen plant and continuous catalyst reforming unit) are normally different. The hydrogen compressors (i.e., make-up hydrogen and recycle hydrogen compressors) are widely utilized to raise the pressure of the hydrogen-rich streams. The valves are also used to reduce the pressure of hydrogen-rich streams, and this leads to the waste of power. Hydrogen turbines have been successfully applied in practical industrial plants to recover power. This paper presents a novel superstructure of refinery hydrogen networks integrated with hydrogen turbines to recovery the power. The superstructure consists of a hydrogen production plant, a continuous catalyst reforming unit, other hydrogen sources and sinks, hydrogen compressors and turbines. The mathematical model including mass balance and logical constraints is proposed, with the objectives of minimizing flowrate of hydrogen utility and minimizing the difference between consumed power in compressors and recovered power using turbines. A simplified industrial case study is solved to illustrate the proposed methodology. Results show that 1 MW of power can be recovered via the integrated turbines.

Currently, a wide variety of energy storage alternatives are available, each with a unique set of characteristics advantageous on selective applications. Current studies focus only on levelized costs on predicting the best-fit technology for specific applications. The study addresses this limitation by considering multiple factors on the selection process among technologies for specific applications. A systematic approach on the selection of energy storage technologies based on multiple and possible conflicting factors was proposed in this study for two specific applications: frequency regulation and load levelling. Fuzzy Analytic Hierarchy Process was utilized to generate the relative importance of each criterion. Monte Carlo simulations were performed to reflect the effect of battery characteristics and operating parameters uncertainties on the resulting scores of technologies. Grey Relational Analysis was used to aggregate the performance attributes of alternatives into a single score reflecting the desirability of alternatives. The levelized costs dominated all other criteria for both applications. Lithium ion battery dominated all technologies for both applications resulting from its well-rounded performance across all considered attributes. Results emphasized the importance of considering socio-economic indicators alongside techno-economic parameters on selecting the technology for future deployment. Thorough analysis on the results is important not only for decision-makers but for developers and innovators as well to direct future research.

Process Integration provides an excellent engineering toolbox for optimising industrial systems to achieve sustainability gains. However, effective large-scale use of such tools depends on effective education of new generations of engineers with the correct training and mind-set. Learning strategies refer to the different combinations of activities the learners utilise in their process of learning. Chemical engineering students often face the challenges in processing key concepts integral in the understanding of Process Integration, optimisation and sustainability. A preliminary survey of students from the undergraduate program from the Philippines identifies learning strategies they employ in better understanding Process Integration, optimisation and sustainability. These strategies help learners in analysing concepts, monitoring the learning process, linking concepts with one another, inferring meanings from context, and managing their behaviour towards learning. Educational best practices are suggested based on insights drawn from the responses.

Pinch Analysis (PA) methodology was originally developed for determining feasible Heat Integration targets in process plants, and for designing optimal Heat Exchanger Networks to achieve the previously determined energy budget. This powerful methodology has since been extended to a wide range of applications, such as Mass Integration, Carbon Management and Financial PA. All these extensions have the common feature of utilizing a stream quality index that defines direction of flow, in the same way that temperature differences determine heat transfer. In this paper, a graphical PA approach to cash flow analysis and management in engineering projects is proposed in ensuring project sustainability. This method considers the positive and negative cash flows that occur over time in an engineering project. A case study is used to illustrate how this approach can provide insights for managers to synchronize the operations of a single project to stay within the firm’s cash flow limits. Such strategies can potentially affect the sustainability of construction projects. There were three solutions considered in the case study. Based on the results, it was found that from the hypothetical example, the ideal solution is to allot 50 % or 75 % of the total inflow assigned to labour outflow with a pinch period of 1 month. It resulted in a minimum loan value 61.2 %-months for materials outflow.

A trend arises among industrial and government sectors to transition from the conventional economic system to the new Circular Economy. Its benefit of material security, resource efficiency, and economic growth has attracted government institutions and business sectors to adopt the new trend. However, its challenge falls on the real complexities of economic systems. Adoption of the Circular Economy requires careful consideration of possible challenges. Previous works have aimed to identify the drivers of Circular Economy through surveys based on the frequency of data. The results provided useful information for the decision making of the transition. However, it is also limiting as it does not address a plausible chain-like effect of the drivers which can aid stakeholders determine which course of action is necessary for an efficient transition. Hence, this study is focused in determining these causal drivers by using the DEMATEL approach. DEMATEL is a methodology that identifies the cause and effect relationship between drivers, of which, it can then determine the top causal driver. The study uses a case study in the Philippines to illustrate the capability of the methodology of determining the causality between drivers of Circular Economy. The results of the case study were able to identify ‘economic attractiveness’, with a net cause/effect value of 1.22, and ‘consumer demand’, with a net cause/effect value of 0.87, as the main causal driver while ‘company culture’, with a net cause/effect value of -1.22, as the main effect. The result implies that the improvement in the circular business models and increase in customer awareness are the top priority for the transition. The application of this work is intended to provide researchers an alternative approach in identifying the critical causal drivers of Circular Economy.

Co-firing of biomass in fossil fuel-fired power plants is a mature technology option for reducing greenhouse gas emissions. Up to 10 % of fossil fuel energy input can be displaced by biomass without the need for major retrofits. Co-firing may be done directly or indirectly. However, direct co-firing has the disadvantage of being less flexible with the use of different types of biomass. Indirect co-firing, on the other hand, overcomes this limitation by using a gasifier which converts the biomass into syngas and biochar. The syngas can then be used for co-firing, while biochar can be applied to soil as a form of carbon sequestration. This process stores the carbon initially fixed by biomass through photosynthesis in the soil, and results in the net transfer of part of the carbon in biomass from the atmosphere to the soil. Biochar thus acts as a negative emissions technology with potential for scale-up in the near future. The utilization of biomass for co-firing in a fleet of thermal power plants can be optimized as a carbon management network, subject to biomass availability and the presence of suitable biochar sinks. In this work, a fuzzy mixed integer linear programming model is developed to minimize net carbon emissions in such a system, taking into account parametric uncertainties in the storage capacities of the biochar sinks. It is assumed that there is the option to use direct, indirect or no co-firing in each power plant in the system. The model is illustrated using a case study.

This study develops a graphical decision-making tool inspired by the break-even concept. It facilitates the selection of biomass utilisation where the highest possible profit and lowest GHG emissions indicate the optimal choice. The GHG intensity (kg CO2eq/MWh) and GHG price (USD/kg CO2eq) comprise the y-axis and x-axis of the Break-even Based Decision Making (BBDM) tool. The applicability of the tool is demonstrated by a case study where the biomass (switchgrass and wheat straw) is treated with slow pyrolysis for energy generation and/or biochar production. Burning for electricity is generally a preferable option, especially for countries with high GHG intensity. Biochar application as a form of carbon sequestration is suggested at low GHG intensity. This application becomes the preferred option when the GHG price is higher than 0.03 USD/kg CO2eq for switchgrass and higher than 0.01 USD/kg CO2eq for wheat straw. Wheat straw, as agricultural waste, has the advantage of lower GHG footprint while switchgrass offers a higher energy output. In general, the choice of the best use of biomass can be different under different circumstances. The developed tool is able to provide a rigorous basis for decision support when the GHG intensity and GHG price are defined.

Designers of energy systems often face challenges in balancing the trade-off between cost and reliability. In literature, several papers have presented mathematical models for optimizing the reliability and cost of energy systems. However, the previous models only addressed reliability implicitly, i.e., based on availability and maintenance planning. Others focused on allocation of reliability based on individual equipment requirements via non-linear models that require high computational effort. This work proposes a novel mixed-integer linear programming (MILP) model that combines the use of both input-output (I-O) modelling and linearized parallel system reliability expressions. The proposed MILP model can optimize the design and reliability of energy systems based on equipment function and operating capacity. The model allocates equipment with sufficient reliability to meet system functional requirements and determines the required capacity. A simple pedagogical example is presented in this work to illustrate the features of proposed MILP model. The MILP model is then applied to a polygeneration case study consisting of two scenarios. In the first scenario, the polygeneration system was optimized based on specified reliability requirements. The technologies chosen for Scenario 1 were the CHP module, reverse osmosis unit and vapour compression chiller. The total annualized cost (TAC) for Scenario 1 was 53.3 US$ million/year. In the second scenario, the minimum reliability level for heat production was increased. The corresponding results indicated that an additional auxiliary boiler must be operated to meet the new requirements. The resulting TAC for the Scenario 2 was 5.3% higher than in the first scenario.

In contrast to other cancers, the presence of tumor-infiltrating lymphocytes (TILs) in uveal melanoma is associated with a poor prognosis. However, how TILs may promote disease progression and what regulates their infiltration has not yet been established. To address these clinically relevant outstanding questions, T cell, immune regulatory, and chemokine gene expression profiles of 57 enucleated uveal melanoma tumors were compared, encompassing 27 with TILs and 30 without,. Tumors with infiltrating lymphocytes expressed more CD8A mRNA, as well as IFNG, TGFB1, and FOXP3 transcripts. Other T helper associated cytokines and T helper transcription factors were not differentially expressed, nor were mediators of lymphocyte cytotoxicity. The immune inhibitors INDO, PDCA1, CTLA4, and LAG3, and the non-classical MHC Class I target of CD8+ T regulatory cells, HLA‑E, were significantly higher in tumors with TILs. FAS was also significantly higher. The C-C chemokine ligands CCL4, CCL5, and CCL20 were higher in tumors with TILs. Levels of CCL5 were most strongly correlated with levels of CD8A. Chemokine receptors were not differentially expressed. Molecular profiling of uveal melanoma tumors with TILs supports the existence of an immunosuppressive tumor microenvironment and suggests roles for CD8+ regulatory T cells, as well as specific chemokines, in fostering uveal melanoma disease progression.

We develop a linear programming (LP) model for optimizing enhanced weathering (EW) networks. EW is a negative emissions technology (NET) that involves mining and grinding of naturally occurring CO2-reactive rocks, and subsequently applying the resulting powder to soil to provide ample surface area for contact with the atmosphere and thus accelerate carbon fixation. Use of EW as a carbon management strategy at scale will result in the need to properly match sources (rock crushing plants) with sinks (application sites); the result is a special case of a supply chain optimization problem. The model proposed here can determine optimal matches of sources and sinks in EW-based CMNs, considering material flow and temporal constraints. A case study is solved to illustrate the model. Keywords: Negative emissions technology (NET), Carbon dioxide removal (CDR), Supply chain, Optimization, Carbon management network (CMN)

The rapid evolution of porcine reproductive and respiratory syndrome viruses (PRRSV) poses a major challenge to effective disease control since available vaccines show variable efficacy against divergent strains. Knowledge of the antigenic targets of virus-neutralizing antibodies that confer protection against heterologous PRRSV strains would be a catalyst for the development of next-generation vaccines. Key to discovering these epitopes is the isolation of neutralizing monoclonal antibodies (mAbs) from immune pigs. To address this need, we sought to establish systems to enable the isolation of PRRSV neutralizing porcine mAbs. We experimentally produced a cohort of immune pigs by sequential challenge infection with four heterologous PRRSV strains spanning PRRSV-1 subtypes and PRRSV species. Whilst priming with PRRSV-1 subtype 1 did not confer full protection against a subsequent infection with a PRRSV-1 subtype 3 strain, animals were protected against a subsequent PRRSV-2 infection. The infection protocol resulted in high serum neutralizing antibody titers against PRRSV-1 Olot/91 and significant neutralization of heterologous PRRSV-1/-2 strains. Enriched memory B cells isolated at the termination of the study were genetically programmed by transduction with a retroviral vector expressing the Bcl-6 transcription factor and the anti-apoptotic Bcl-xL protein, a technology we demonstrated efficiently converts porcine memory B cells into proliferating antibody-secreting cells. Pools of transduced memory B cells were cultured and supernatants containing PRRSV-specific antibodies identified by flow cytometric staining of infected MARC-145 cells and in vitro neutralization of PRRSV-1. Collectively, these data suggest that this experimental system may be further exploited to produce a panel of PRRSV-specific mAbs, which will contribute both to our understanding of the antibody response to PRRSV and allow epitopes to be resolved that may ultimately guide the design of immunogens to induce cross-protective immunity.

Background: Central aortic blood pressures and arterial stiffness are better indicators of cardiovascular outcomes than brachial blood pressures. However, their response to renal denervation (RDN) in patients with Stage 3 and Stage 4 chronic kidney disease (CKD) has not yet been examined. Objective: To evaluate the impact of RDN on central blood pressures (CBP), brachial (office and ambulatory) blood pressures, arterial stiffness, glomerular filtration rate (GFR), 24-hour urine protein, and selective cardiac parameters observed on echocardiograms. Design: Single-center, single-arm with pre-/post-RDN follow-up. Setting: Patients were recruited from the multidisciplinary CKD clinic, Regina General Hospital, Canada. Patients: About 25 consecutive patients with Stage 3 or Stage 4 CKD and resistant hypertension, with no radiological or laboratory evidence of secondary causes of hypertension. Measurements: The key measurements were CBP, pulse wave velocity, ambulatory 24-hour blood pressure, office blood pressures on BP Tru, GFR, 24-hour urine protein and sodium, dose and number of blood pressure medication and doses. Methods: The primary outcome measure was the change in CBP from baseline to 6 months post-RDN. Secondary outcome measures included changes in CBP, office blood pressure, 24-hour ambulatory pressures, pulse wave velocity, kidney function (eGFR and 24-hour protein excretion), and the change in the number and dose of medications during the 2-year follow-up period. The primary outcome and the secondary outcomes were evaluated using a Friedman’s analysis of variance (ANOVA) and Wilcoxon signed-rank test for changes from post RDN procedure. Bonferroni correction was used to adjust P values for multiple testing. A two-sided alpha of .05 was used. Results: Median central blood pressures (mm Hg) were 127/75 at baseline versus 118/70 at 6 months and 118/67 at 24 months ( P = .13). Median office blood pressures (mm Hg) were 148/76 at baseline versus 135/75 at 6 months and 133/75 at 24 months ( P ≤ .001). Median ambulatory 24-hour day (mm Hg) was 148/64 at baseline and 146/68 at 6 months and 152/67 at 24 months ( P = .60). Median pulse wave velocity (m/s) at baseline was 13.8 at baseline versus 13.3 m/s at 6 months and 12.3 at 12 months’ time ( P = .62). Estimated glomerular filtration rate (mL/min/1.73m 2 ) at baseline was 37, at 6 months was 36 and 34 at 24 months ( P = .33). Limitations: Single-center study, with no sham arm. Conclusions: Our study demonstrates that there was a significant improvement in office blood pressures from baseline to 6 months, maintained to 24 months. There was a numerical improvement in central pressures, and pulse wave velocity at 6 and 24 months, with no sustained changes noted in 24-hour blood pressure. Kidney function remained at or near baseline throughout the 24 months of observation. Trial registration: ClinicalTrials.gov (NCT01832233).

Background Heterogeneity in the underlying processes that contribute to heart failure with preserved ejection fraction (HFpEF) is increasingly recognized. Diabetes mellitus is a frequent comorbidity in HFpEF, but its impact on left ventricular and arterial structure and function in HFpEF is unknown. Methods and Results We assessed the impact of diabetes mellitus on left ventricular cellular and interstitial hypertrophy (assessed with cardiac magnetic resonance imaging, including T1 mapping pregadolinium and postgadolinium administration), arterial stiffness (assessed with arterial tonometry), and pulsatile arterial hemodynamics (assessed with in‐office pressure‐flow analyses and 24‐hour ambulatory monitoring) among 53 subjects with HFpEF (32 diabetic and 21 nondiabetic subjects). Despite few differences in clinical characteristics, diabetic subjects with HFpEF exhibited a markedly greater left ventricular mass index (78.1 [95% CI, 70.4–85.9] g versus 63.6 [95% CI, 55.8–71.3] g; P=0.0093) and indexed extracellular volume (23.6 [95% CI, 21.2–26.1] mL/m2 versus 16.2 [95% CI, 13.1–19.4] mL/m2; P=0.0008). Pronounced aortic stiffening was also observed in the diabetic group (carotid‐femoral pulse wave velocity, 11.86 [95% CI, 10.4–13.1] m/s versus 8.8 [95% CI, 7.5–10.1] m/s; P=0.0027), with an adverse pulsatile hemodynamic profile characterized by increased oscillatory power (315 [95% CI, 258–373] mW versus 190 [95% CI, 144–236] mW; P=0.0007), aortic characteristic impedance (0.154 [95% CI, 0.124–0.183] mm Hg/mL per second versus 0.096 [95% CI, 0.072–0.121] mm Hg/mL per second; P=0.0024), and forward (59.5 [95% CI, 52.8–66.1] mm Hg versus 40.1 [95% CI, 31.6–48.6] mm Hg; P=0.0010) and backward (19.6 [95% CI, 16.2–22.9] mm Hg versus 14.1 [95% CI, 10.9–17.3] mm Hg; P=0.0169) wave amplitude. Abnormal pulsatile hemodynamics were also evident in 24‐hour ambulatory monitoring, despite the absence of significant differences in 24‐hour systolic blood pressure between the groups. Conclusions Diabetes mellitus is a key determinant of left ventricular remodeling, arterial stiffness, adverse pulsatile hemodynamics, and ventricular‐arterial interactions in HFpEF. Clinical Trial Registration URL: https://www.clinicaltrials.gov. Unique identifier: NCT01516346.

Background Central arteriovenous fistula (AVF) creation is under investigation for treatment of severe hypertension. We evaluated the effects of AVF for initiation of hemodialysis on systolic, diastolic, and mean arterial blood pressure in patients with end‐stage renal disease. Methods and Results Data search included PubMed, Web of Science, and the Cochrane Library. A systematic review and meta‐analysis of peer‐reviewed studies reporting the effects of the creation/ligation of an AVF on blood pressure in patients with end‐stage renal disease was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analysis), PRISMA‐P (PRISMA for systematic review protocols), and ROBINS‐I (Risk of Bias in Non‐Randomized Studies) criteria by the Cochrane Bias Methods Group. All studies in which the results could have been biased by hemodialysis were excluded. A total of 14 trials including 412 patients with end‐stage renal disease (AVF creation, n=185; AVF ligation, n=227) fulfilled the criteria and were subsequently analyzed. Average blood pressure in patients with no/closed AVF was 140.5/77.6 mm Hg with a mean arterial blood pressure of 96.1 mm Hg. Following creation of AVF, systolic blood pressure significantly decreased by 8.7 mm Hg (P

Biochar is the stable, carbon-rich solid co-product of thermochemical biomass conversion. It has recently gained considerable interest, driven by the need to mitigate climate change through carbon sequestration in soil. During pyrolysis, much of the carbon in biomass is transformed into recalcitrant form, so that biochar applied to soils results in storage of carbon for hundreds of years and simultaneous improvement of soil fertility. Biochar can display a wide range of properties such as pH, cation exchange capacity (CEC) and elemental composition due to the assortment of raw materials and reaction conditions. The suitability of soil to biochar application depends on these properties. This has led to the “designer biochar” concept, wherein biochar could be tailored with relevant properties to address specific soil quality improvements. This aspect and the promising results of biochar application to soil can be potentially optimized through biochar-based carbon management networks (CMN) with the aid of mathematical programming. In this work, a bi-objective mixed-integer linear programming (MILP) model is developed for the high-level planning of biochar-based CMN. Additional parameters, variables and constraints are given to account for the incompatibilities of biochar sources and sinks. An illustrative case study is presented here to demonstrate the applicability of the developed model.

Microarrays have proven to be useful in rapid detection of many viruses and bacteria. Pathogen detection microarrays have been used to diagnose viral and bacterial infections in clinical samples and to evaluate the safety of biological drug materials. In this study, the Axiom Microbiome Array was evaluated to determine its sensitivity, specificity and utility in microbiome analysis of veterinary clinical samples. The array contains probes designed to detect more than 12,000 species of viruses, bacteria, fungi, protozoa and archaea, yielding the most comprehensive microbial detection platform built to date. The array was able to detect Shigella and Aspergillus at 100 genome copies, and vaccinia virus DNA at 1,000 genome copies. The Axiom Microbiome Array made correct species-level calls in mock microbial community samples. When tested against serum, tissue, and fecal samples from pigs experimentally co-infected with porcine reproductive and respiratory syndrome virus and porcine circovirus type 2, the microarray correctly detected these two viruses and other common viral and bacterial microbiome species. This cost-effective and high-throughput microarray is an efficient tool to rapidly analyze large numbers of clinical and environmental samples for the presence of multiple viral and bacterial pathogens.

[This corrects the article DOI: 10.1371/journal.pone.0194509.].

The incursion of African swine fever virus (ASFV) into Eurasia presents a threat to the world’s swine industry. Highly sensitive and specific diagnostic assays are urgently needed for rapid detection during an outbreak, post-outbreak investigation, and disease surveillance. In this study, a highly specific and repeatable blocking ELISA (bELISA) was developed using a recombinant p30 protein as the antigen combined with biotinylated mAb against p30 as the detection antibody. Initial test validation included sera from 810 uninfected animals and 106 animals experimentally inoculated with ASFV or recombinant alphavirus/adenovirus expressing p30. Receiver operating characteristic (ROC) analysis of the data calculated an optimal percentage of inhibition (PI) cutoff value of 45.92%, giving a diagnostic sensitivity of 98.11% and diagnostic specificity of 99.42%. The coefficient of variation of an internal quality control serum was 6.81% for between runs, 6.71% for within run, and 6.14% for within plate. A time course study of infected pigs showed that bELISA was able to detect seroconversion as early as 7 days post-inoculation. Taken together, these results demonstrate that bELISA can be used as an alternative serological test for detecting ASFV infection.

Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University–Oil Well Object Detection Version 1.0 (NEPU–OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing.

BackgroundScalable PTSD screening strategies must be brief, accurate and capable of administration by a non-specialized workforce.MethodsWe used PTSD as determined by the structured clinical interview as our gold standard and considered predictors sets of (a) Posttraumatic Stress Checklist-5 (PCL-5), (b) Primary Care PTSD Screen for the DSM-5 (PC-PTSD) and, (c) PCL-5 and PC-PTSD questions to identify the optimal items for PTSD screening for public sector settings in Kenya. A logistic regression model using LASSO was fit by minimizing the average squared error in the validation data. Area under the receiver operating characteristic curve (AUROC) measured discrimination performance.ResultsPenalized regression analysis suggested a screening tool that sums the Likert scale values of two PCL-5 questions-intrusive thoughts of the stressful experience (#1) and insomnia (#21). This had an AUROC of 0.85 (using hold-out test data) for predicting PTSD as evaluated by the MINI, which outperformed the PC-PTSD. The AUROC was similar in subgroups defined by age, sex, and number of categories of trauma experienced (all AUROCs>0.83) except those with no trauma history- AUROC was 0.78.ConclusionIn some East African settings, a 2-item PTSD screening tool may outperform longer screeners and is easily scaled by a non-specialist workforce.