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Abstract Asparaginase (ASP)-containing regimens for acute lymphoblastic leukemia (ALL) are associated with venous thromboembolism (VTE). We evaluated the prevalence, risk factors, role of prophylaxis and clinical impact of VTE among adolescents and young adult (AYA) patients (15–50 years) treated on Dana-Farber Cancer Institute (DFCI) ALL protocols. The 1- and 2-year cumulative incidence of VTE were 31.9% (95% CI: 27.0%, 36.9%) and 33.5% (95% CI: 28.5%, 38.5%) respectively, with most events occurring during ASP-based consolidation phase (68.6%). VTE was more frequent in patients with overweight/obese vs. normal BMI (39.2% vs. 29.0%, p = 0.048). In a 1-year landmark analysis, the 4-year overall survival was 91.5%, without difference between patients with vs. without VTE (93.8% vs. 90.0%, p = 0.93). Relapse and non-relapse mortality rates were also similar. Among patients treated at Dana-Farber/Harvard Cancer Center, cerebral sinus vein thrombosis occurred in 3.6% of patients (8.5% of VTE events) in comparison to pulmonary embolism (32.9%) and deep vein thromboses (58.6%, 24.4% line-associated). In a Cox regression model for VTE free-time, elevated BMI was associated with shorter VTE free-time (HR 1.94 [95% CI 1.13-3.35], p = 0.018), while low molecular weight heparin (LMWH) prophylaxis as time-varying covariate was not. In conclusion, we found that VTE was frequent in AYAs treated on DFCI ALL protocols but did not impact survival outcomes. Overweight/obese BMI increased risk for VTE.

Abstract Orphaned wells are wells for which the operator is unknown or insolvent. The location of hundreds of thousands of these wells remain unknown in the United States alone. Cost-effective techniques are essential to locate orphaned wells to address environmental problems. In this paper, we present a dataset consisting of 120,948 aerial images of recently documented orphan wells. Each of these 512 × 512 images is paired with segmentation masks that indicate the presence or absence of such well. These images, sourced from the National Agriculture Imagery Program, cover the continental United States with spatial resolutions ranging from 30 centimeters to 1 meter. Additionally, we included negative examples by selecting locations uniformly across the United States. Accompanying metadata includes the IDs and spatial resolution of the original images, which are available for free through the United States Geological Survey, and the pixel coordinates of documented orphaned wells identified in these images. This dataset is intended to support the development of deep-learning models that can help locating undocumented orphan wells from such imagery, thereby blunting the environmental damage they do.

Abstract Managing fluid‐driven fracture networks is crucial for subsurface resource utilization, yet the current understanding of the key controlling factors remains insufficient. While geologic discontinuities have been shown to significantly influence fracture network complexity, this study identifies another major contributor. We conducted a new set of experiments using a transparent true triaxial cell, which enabled video recording of the temporal evolution of fluid‐driven fracture paths. Using pseudo‐2D samples without macroscale structural discontinuities, we observed multiple occurrences of hydraulic fracture curving and branching under anisotropic boundary stresses. We proposed a theoretical model demonstrating that the stress parallel to the crack line in the solid matrix near the crack tip (i.e., the T‐stress) accounts for the observed fracture curving behavior. This finding suggests that T‐stress is an additional mechanism contributing to the complexity of fluid‐driven fracture networks in the subsurface, besides the geologic discontinuities.

ABSTRACT Background Rising metastatic prostate cancer incidence has renewed debate regarding benefits of prostate‐specific antigen (PSA) screening. Identifying barriers to accessing screening for individuals at high risk of lethal prostate cancer may slow this rise. We examined associations of access barriers with receipt of PSA testing, stratified by sociodemographic factors. Methods We pooled data from male respondents to Behavior Risk Factor Surveillance Systems (BRFSS) surveys from 2006 to 2020. Questions related to affordability (insurance, cost of visits) and accommodation (regular primary care provider (PCP), physician recommending a PSA test) were considered as individual‐level barriers. For availability, we linked provider density from the 2012 Area Health Resource File and estimated driving times to closest health facility within Micropolitan and Metropolitan Statistical Area (MMSA) using Google Earth Engine. These measures were used to compute a spatial accessibility index. We fit survey‐weighted, covariate‐adjusted logistic regression models to estimate associations of barriers with receipt of PSA within the past 2 years and examined effect modification by sociodemographic factors. Results There were 185,643 participants, of whom 73% were White, 11% were Black, 4% were Asian, and 11% were Hispanic. Physician recommendation was the strongest predictor of having a PSA test (aOR: 14.5, 95% CI: 13.6, 15.6). Not having a regular PCP (aOR: 0.29, 95% CI: 0.27, 0.31), insurance (aOR: 0.64, 95% CI: 0.58, 0.71), and prohibitive cost of care (aOR: 0.82, 95% CI: 0.75, 0.90) were associated with lower PSA testing. Access barriers were stronger predictors of PSA testing for Asian and White participants compared to other groups (Phet

Abstract To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge.

Introduction: Prolidase deficiency is a rare autosomal recessive disorder caused by variants in the PEPD gene. Patients usually have multi-organ involvement and a wide range of clinical features including recurrent skin ulcers, dysmorphic facial features, recurrent infections, intellectual disability, and splenomegaly. Studies have shown that patients with prolidase deficiency may have hepatic manifestations including hepatomegaly and abnormal liver enzymes. However, there is no detailed description of liver disease in this patient population. Case Presentation: Here, we present 3 patients with prolidase deficiency with varying extents of hepatic involvement. Conclusion: Prolidase deficiency patients with liver disease should be followed up long term to understand more about the pathophysiology and the impact of liver disease on long-term outcomes.

Abstract We perform a set of reactive transport simulations in three‐dimensional fracture networks to characterize the impact of geochemical reactions on flow channelization. Flow channelization, a frequently observed phenomenon in porous and fractured subsurface rock formations, results from the spatially variable hydraulic resistance offered by a geological structure. In addition to geo‐structural features such as network connectivity, geometry, and hydraulic resistance, geochemical reactions, for example, dissolution and precipitation, can dynamically inhibit or enhance flow channelization. These geochemical processes can change the fracture permeability leading to increased flow channelization, which are localized connected regions of high volumetric flow rates that are seemingly ubiquitous in the subsurface. In our simulations, fractures partially filled with quartz are gradually dissolved until quasi‐steady state conditions are obtained. We compare the flow field's initial unreacted and final dissolved states in terms of flow and transport observations. We observe that the dissolved fracture networks provide less resistance to flow and exhibit increased flow channelization when compared to their unreacted counterparts. However, there is substantial variability in the magnitude of these changes which implies that the channelization strongly depends on the network structure. In turn, we identify the interplay between the particular network structure and the impact of geochemical dissolution on flow channelization. The presented results indicate that geological systems that have been weathering or reactive for longer times in older landscapes are likely to have increased flow channelization compared to their equivalent but younger counterparts, which implies a time dependence on flow channelization in fractured media.

IntroductionSarcoidosis is a multi-system inflammatory disease of unknown origin with heterogeneous clinical manifestations varying from a single organ non-caseating granuloma site to chronic systemic inflammation and fibrosis. Gene expression studies have suggested several genes and pathways implicated in the pathogenesis of sarcoidosis, however, due to differences in study design and variable statistical approaches, results were frequently not reproducible or concordant. Therefore, meta-analysis of sarcoidosis gene-expression datasets is of great importance to robustly establish differentially expressed genes and signalling pathways.MethodsWe performed meta-analysis on 22 published gene-expression studies on sarcoidosis. Datasets were analysed systematically using same statistical cut-offs. Differentially expressed genes were identified by pooling of p-values using Edgington’s method and analysed for pathways using Ingenuity Pathway Analysis software.ResultsA consistent and significant signature of novel and well-known genes was identified, those collectively implicated both type I and type II interferon mediated signalling pathways in sarcoidosis. In silico functional analysis showed consistent downregulation of eukaryotic initiation factor 2 signalling, whereas cytokines like interferons and transcription factor STAT1 were upregulated. Furthermore, we analysed affected tissues to detect differentially expressed genes likely to be involved in granuloma biology. This revealed that matrix metallopeptidase 12 was exclusively upregulated in affected tissues, suggesting a crucial role in disease pathogenesis.DiscussionOur analysis provides a concise gene signature in sarcoidosis and expands our knowledge about the pathogenesis. Our results are of importance to improve current diagnostic approaches and monitoring strategies as well as in the development of targeted therapeutics.

Simulations of rock damage and gas transport following underground explosions that omit preexisting fracture networks in the subsurface cannot fully characterize the influence of geo-structural variability on gas transport. Previous studies do not consider the impact that fracture network structure and variability have on gas seepage. In this study, we develop a sequentially coupled, axi-symmetric model to look at the damage pattern and resulting gas breakthrough curves following an underground explosion given different fracture network realizations. We simulate 0.327 and 0.164 kT chemical explosives with burial depths of 100 m for 90 stochastically generated fracture networks. Gases quickly reach the surface in 30% of the higher yield simulations and 5% of the lower yield simulations. The fast breakthrough can be attributed to the formation of connected pathways between fractures to the surface. The formation of a connected damage pathway to the surface is not clearly correlated with the fracture intensity (P32) in our simulations. Breakthrough curves with slower transport are highly variable depending on the fracture network sample. The variability in the breakthrough behavior indicates that ignoring the influence of fracture networks on rock damage, which strongly influences the hydraulic properties following an underground explosion, will likely lead to a large underestimation of the uncertainty in the gas transport to the surface. This work highlights the need for incorporation of fracture networks into models for accurately predicting gas seepage following underground explosions.

AbstractNeonatal sepsis is considered as alarming medical emergency and becomes the common global reason of neonatal mortality. Non-specific symptoms and limitations of conventional diagnostic methods for neonatal sepsis mandate fast and reliable method to diagnose disease for point of care application. Recently, disease specific biomarkers have gained interest for rapid diagnosis that led to the development of electrochemical biosensor with enhanced specificity, sensitivity, cost-effectiveness and user-friendliness. Other than conventional biomarker C-reactive protein to diagnose neonatal sepsis, several potential biomarkers including Procalcitonin (PCT), Serum amyloid A (SAA) and other candidates are extensively investigated. The present review provides insights on advancements and diagnostic abilities of protein and nucleotide based biomarkers with their incorporation in developing electrochemical biosensors by employing novel fabrication strategies. This review provides an overview of most promising biomarker and its capability for neonatal sepsis diagnosis to fulfil future demand to develop electrochemical biosensor for point-of-care applications.

Abstract Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models.

Background: Nutrition especially micro-mineral nutrients plays a major role in the etiology of chronic periodontitis. Serum levels of micro-mineral nutrients can be used as markers for the incidence of periodontitis and may also be used as indicators for dietary supplementation. Aims and Objectives: The aim of the study was to estimate the serum levels of Zn, Cu, Fe, and Mg of chronic periodontitis patients and normal healthy controls., to measure the clinical parameters (gingival index, probing pocket depth [PPD], and clinical attachment loss) in chronic periodontitis patients and normal healthy controls., to compare the levels of serum Zn, Cu, Fe, and Mg levels of chronic periodontitis patients and healthy controls and to correlate the levels of serum micronutrients with clinical parameters (gingival index, PPD, and clinical attachment loss) in chronic periodontitis patients and healthy controls. Materials and Methods: A total of 110 subjects, 55 subjects with chronic periodontitis and 55 healthy control subjects in the age group 35–65 years were selected for the study. Serum micronutrient levels of Cu, Fe, Zn, Mg, and the clinical parameters were measured. Results: Serum concentrations of Cu and Fe showed statistically significant increase and serum Zn and Mg showed a significant decrease in peridontitis patients as compared to normal healthy controls. Copper and Fe showed a significant positive correlation and Zn and Mg showed a significant negative correlation with clinical parameters (gingival index, PPD, and clinical attachment loss). Conclusion: The present study supports and extends the view that the assessment of serum mineral micronutrient can serve as possible biomarkers or indicators for an inflammatory condition like chronic periodontitis.

The hierarchical decision making in process industries has been traditionally viewed as having a common objective, such as the overall cost, which needs to be optimized. However, a more appropriate approach is to formulate and solve hierarchical optimization and control problems. The solution algorithms for hierarchical optimization problems have been reported in the literature. The idea is to recast each optimization sub-problem in the hierarchy into a multiparametric programming problem, considering the variables of upper-level problems as unknown parameters. In this paper, explicit Model Predictive Control (MPC) and hierarchical optimization techniques, employing multiparametric programming, are combined for hierarchical MPC. The solution algorithm for hierarchical MPC is described in detail. Note that the solution to a hierarchical MPC problem is challenging, even for the simplest case of linear-quadratic objectives. Closed-loop simulations of a thermal mixing process, under two different hierarchical MPC formulations, are performed and the control performance is studied.

Abstract We model flow and transport in fracture networks with varying degrees of fracture‐to‐fracture aperture heterogeneity and network intensity to show how changes in these properties can cause the emergence of anomalous flow and transport behavior. If fracture‐to‐fracture aperture heterogeneity is increased in sparse networks, velocity fluctuations inhibit high flow rates and solute transport can be delayed. Surprisingly, transport can be slowed even in cases where hydraulic aperture is monotonically increased. As the intensity of the networks is increased, more connected pathways allow for particles to bypass these effects. There exists transition behavior where with relatively few connected pathways in a network, first arrival times of particles are not heavily affected by fracture‐to‐fracture aperture heterogeneity, but the scaling behavior of the tails is strongly influenced. These results reinforce the importance of considering multi‐scale effects in fractured systems and can inform flow and transport processes in both natural and engineered fractured systems.

Abstract The important roles of plant microRNAs (miRNAs) in adaptation to nitrogen (N) deficiency in different crop species especially cereals (rice, wheat, maize) have been under discussion since last decade with little focus on potential wild relatives and landraces. Indian dwarf wheat (Triticum sphaerococcum Percival) is an important landrace native to the Indian subcontinent. Several unique features, especially high protein content and resistance to drought and yellow rust, make it a very potent landrace for breeding. Our aim in this study is to identify the contrasting Indian dwarf wheat genotypes based on nitrogen use efficiency (NUE) and nitrogen deficiency tolerance (NDT) traits and the associated miRNAs differentially expressed under N deficiency in selected genotypes. Eleven Indian dwarf wheat genotypes and a high NUE bread wheat genotype (for comparison) were evaluated for NUE under control and N deficit field conditions. Based on NUE, selected genotypes were further evaluated under hydroponics and miRNome was compared by miRNAseq under control and N deficit conditions. Among the identified, differentially expressed miRNAs in control and N starved seedlings, the target gene functions were associated with N metabolism, root development, secondary metabolism and cell-cycle associated pathways. The key findings on miRNA expression, changes in root architecture, root auxin abundance and changes in N metabolism reveal new information on the N deficiency response of Indian dwarf wheat and targets for genetic improvement of NUE.

Abstract Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it is often necessary to neglect information at small scales, an approach known as coarse-graining. For many practical applications, such as flow in porous, homogenous materials, coarse-graining offers a sufficiently-accurate approximation of the solution. Unfortunately, fractured systems cannot be accurately coarse-grained, as critical network topology exists at the smallest scales, including topology that can push the network across a percolation threshold. Therefore, new techniques are necessary to accurately model important fracture systems. Quantum algorithms for solving linear systems offer a theoretically-exponential improvement over their classical counterparts, and in this work we introduce two quantum algorithms for fractured flow. The first algorithm, designed for future quantum computers which operate without error, has enormous potential, but we demonstrate that current hardware is too noisy for adequate performance. The second algorithm, designed to be noise resilient, already performs well for problems of small to medium size (order 10–1000 nodes), which we demonstrate experimentally and explain theoretically. We expect further improvements by leveraging quantum error mitigation and preconditioning.

Abstract The polymerization/depolymerization dynamics of FtsZ play a pivotal role in cell division in the majority of the bacteria. Deinococcus radiodurans, a radiation‐resistant bacterium, shows an arrest of growth in response to DNA damage with no change in the level of FtsZ. This bacterium does not deploy LexA/RecA type of DNA damage response and cell cycle regulation, and its genome does not encode SulA homologues of Escherichia coli, which attenuate FtsZ functions in response to DNA damage in other bacteria. A radiation‐responsive Ser/Thr quinoprotein kinase (RqkA), characterized for its role in radiation resistance in this bacterium, could phosphorylate several cognate proteins, including FtsZ (drFtsZ) at Serine 235 (S235) and Serine 335 (S335) residues. Here, we reported the detailed characterization of S235 and S335 phosphorylation effects in the regulation of drFtsZ functions and demonstrated that the phospho‐mimetic replacements of these residues in drFtsZ had grossly affected its functions that could result in cell cycle arrest in response to DNA damage in D. radiodurans. Interestingly, the phospho‐ablative replacements were found to be nearly similar to drFtsZ, whereas the phospho‐mimetic mutant lost the wild‐type protein's signature characteristics, including its dynamics under normal conditions. The kinetics of post‐bleaching recovery for drFtsZ and phospho‐mimetic mutant were nearly similar at 2 h post‐irradiation recovery but were found to be different under normal conditions. These results highlighted the role of S/T phosphorylation in the regulation of drFtsZ functions and cell cycle arrest in response to DNA damage, which is demonstrated for the first time, in any bacteria.

Abstract We propose the use of reduced order modeling (ROM) to reduce the computational cost and improve the convergence rate of nonlinear solvers of full order models (FOM) for solving partial differential equations. In this study, a novel ROM-assisted approach is developed to improve the computational efficiency of FOM nonlinear solvers by using ROM’s prediction as an initial guess. We hypothesize that the nonlinear solver will take fewer steps to the converged solutions with an initial guess that is closer to the real solutions. To evaluate our approach, four physical problems with varying degrees of nonlinearity in flow and mechanics have been tested: Richards’ equation of water flow in heterogeneous porous media, a contact problem in a hyperelastic material, two-phase flow in layered porous media, and fracture propagation in a homogeneous material. Overall, our approach maintains the FOM’s accuracy while speeding up nonlinear solver by 18–73% (through suitable ROM-assisted FOMs). More importantly, the proximity of ROM’s prediction to the solution space leads to the improved convergence of FOMs that would have otherwise diverged with default initial guesses. We demonstrate that the ROM’s accuracy can impact the computational efficiency with more accurate ROM solutions, resulting in a better cost reduction. We also illustrate that this approach could be used in many FOM discretizations (e.g., finite volume, finite element, or a combination of those). Since our ROMs are data-driven and non-intrusive, the proposed procedure can easily lend itself to any nonlinear physics-based problem.

The paper is aimed to study the recent progress on lead-free piezoelectric materials, focusing on synthesis and various properties such as structural, electrical and optical of them. Lead zirconate titanate (Pb (ZrxTi1-x) O3, PZT) and its associated solid solutions are extensively used in several memory devices, sensors, actuators, and transducers in this era. The toxic nature of lead (or lead oxide) creates health and environmental problems. With the environmental concerns, researchers are very much interested in finding out environmental-friendly lead-free materials, which can substitute the said toxic materials with the same industrial applications with high-efficiency situations. Various classes of materials are now under investigation by several researchers, considered as highly potential alternatives to the above said toxic materials. The perovskite structures such as pure Bismuth Sodium Titanate (BNT), its binary and ternary solid solution systems along with their structural, electrical, and optical properties are reviewed in this paper. The observation reveals that various lead-free material compositions illustrate stability in piezoelectric results, even if it is not matching completely with the performance of lead-based material. It motivates active research on this subject matter, i.e., lead-free materials to overcome lead-based materials, cited herewith. This has stimulated us to analyze the function of lead-free material with a vision that it would provide further ignition for a wide range of industrial applications to modern scientists, continuing research in this field.

South Asia is a global hotspot for climate change with enormous pressure on land and water resources for feeding the burgeoning population. The agricultural production systems are highly vulnerable in the region and is primarily dominated by small and marginal farmers with intensive farming practices that had favored the loss of carbon (C) from soil. This review discusses the potential of soil and crop management practices such as minimum/reduced/no-tillage, use of organic manure, balanced and integrated plant nutrient application, precision land levelling, precision water and pest management, residue management, and cropping system optimization to maintain the C-equilibrium between soil and atmosphere and to enhance the C-sequestration in the long run. Results of meta-analysis show a potential 36% increase in soil organic C stock in the top 0–15 cm layer in this region which amounts to ∼18 Mg C stocks ha−1. Improved management practices across crops and environment may reduce methane em0ission by 12% resulting in an 8% reduction in global warming potential (GWP), while non-submerged condition led to a 51% GWP reduction in rice. Conservation agriculture and precision fertilization also reduced GWP by 11 and 14%, respectively. Although several innovative climate resilient technologies having significant potential for C-sequestration have been developed, there is an urgent need for their scaling and accelerated adoption to increase soil C-sequestration. Policies and programs need to be devised for incentivizing farmers to adopt more C-neutral or C-positive agricultural practices. The national governments and other agencies should work towards C farming together with global initiatives such as the “4 per 1000” Initiative and Global Soil Partnership, and regional public-private partnership initiatives on carbon credits for Regenerative Agriculture such as by Grow Indigo-CIMMYT-ICAR in India, in addition to research and policy changes. This will be vital for the success of soil C sequestration towards climate action in South Asia.

COVID-19 presents severely in persons with immunocompromised states such as diabetes mellitus. Steroids are used in the management of COVID-19. The use of steroids in immunocompromised persons increases the chances of opportunistic infections. Mucormycosis infects immunocompromised individuals. The aim of this systemic review was to study the clinico-epidemiological features of mucormycosis in patients of COVID-19. PubMed, Medline, Embase, and Google Scholar were searched for “mucormycosis”, “COVID-19”, and “SARS-Cov 2”. A total of 30 studies were included in this study, which included 22 case reports and 8 original articles. The median age of patients was 54.80 years; 79.56% (74/93) were diabetic. Steroid administration history was present in 74.19% (69/93) of patients. Rhino-orbital mucormycosis constituted 46.98% (39/83) of the cases, rhino-orbital-cerebral type in 37.35% (31/83) cases, pulmonary mucormycosis in 10.84% (9/83) cases, 1 case of gastric mucormycosis, 1 case of cutaneous mucormycosis, 1 case of palatal mucormycosis, and 1 case of disseminated mucormycosis. The median duration of presentation of mucormycosis was 14 days after hospital admission. The total number of deaths was reported in 32.26% (30/93) of the cases. The most common species found was Rhizopus, and amphotericin B was the most common antifungal administered. The COVID-19 pandemic is creating a massive pool of susceptible patients having poorly controlled diabetes mellitus and receiving steroids for COVID-19 management, thus creating an unholy alliance with mucormycosis. Mucormycosis started as a new epidemic in India, which is an otherwise rare opportunistic fungal infection.

Abstract We present a novel workflow for forecasting production in unconventional reservoirs using reduced-order models and machine-learning. Our physics-informed machine-learning workflow addresses the challenges to real-time reservoir management in unconventionals, namely the lack of data (i.e., the time-frame for which the wells have been producing), and the significant computational expense of high-fidelity modeling. We do this by applying the machine-learning paradigm of transfer learning, where we combine fast, but less accurate reduced-order models with slow, but accurate high-fidelity models. We use the Patzek model (Proc Natl Acad Sci 11:19731–19736, https://doi.org/10.1073/pnas.1313380110 , 2013) as the reduced-order model to generate synthetic production data and supplement this data with synthetic production data obtained from high-fidelity discrete fracture network simulations of the site of interest. Our results demonstrate that training with low-fidelity models is not sufficient for accurate forecasting, but transfer learning is able to augment the knowledge and perform well once trained with the small set of results from the high-fidelity model. Such a physics-informed machine-learning (PIML) workflow, grounded in physics, is a viable candidate for real-time history matching and production forecasting in a fractured shale gas reservoir.

ABSTRACT Environmental DNA uptake by certain bacteria and its integration into their genome create genetic diversity and new phenotypes. DNA processing protein A (DprA) is part of a multiprotein complex and facilitates the natural transformation (NT) phenotype in most bacteria. Deinococcus radiodurans, an extremely radioresistant bacterium, is efficient in NT, and its genome encodes nearly all of the components of the natural competence complex. Here, we have characterized the DprA protein of this bacterium (DrDprA) for the known characteristics of DprA proteins of other bacteria and the mechanisms underlying the DNA-RecA interaction. DrDprA has three domains. In vitro studies showed that purified recombinant DrDprA binds to both single-strand DNA (ssDNA) and double-strand DNA (dsDNA) and is able to protect ssDNA from nucleolytic degradation. DrDprA showed a strong interaction with DrRecA and facilitated RecA-catalyzed functions in vivo. Mutational studies identified DrDprA amino acid residues crucial for oligomerization, the interaction with DrRecA, and DNA binding. Furthermore, we showed that both oligomerization and DNA binding properties of DrDprA are integral to its support of the DrRecA-catalyzed strand exchange reaction (SER) in vitro. Together, these data suggested that DrDprA is largely structurally conserved with other DprA homologs but shows some unique structure-function features like the existence of an additional C-terminal Drosophila melanogaster Miasto-like protein 1 (DML1) domain, equal affinities for ssDNA and dsDNA, and the collective roles of oligomerization and DNA binding properties in supporting DrRecA functions. IMPORTANCE Bacteria can take up extracellular DNA (eDNA) by natural transformation (NT). Many bacteria, including Deinococcus radiodurans, have constitutive competence systems and can take up eDNA throughout their growth phase. DprA (DNA processing protein A) is a transformation-specific recombination mediator protein (RMP) that plays a role in bacterial NT, and the absence of this gene significantly reduces the transformation efficiencies of both chromosomal and plasmid DNA. NT helps bacteria survive under adverse conditions and contributes to genetic diversity in bacteria. The present work describes the characterization of DprA from D. radiodurans and will add to the existing knowledge of DprA biology.

ABSTRACT Filament temperature-sensitive mutant K (FtsK)/SpoIIIE family proteins are DNA translocases known as the fastest DNA motor proteins that use ATP for their movement on DNA. Most of the studies in single chromosome-containing bacteria have established the role of FtsK in chromosome dimer resolution (CDR), connecting the bacterial chromosome segregation process with cell division. Only limited reports, however, are available on the interdependent regulation of genome segregation and cell division in multipartite genome harboring (MGH) bacteria. In this study, for the first time, we report the characterization of FtsK from the radioresistant MGH bacterium Deinococcus radiodurans R1 (drFtsK). drFtsK shows the activity characteristics of a typical FtsK/SpoIIIE/Tra family. It stimulates the site-specific recombination catalyzed by Escherichia coli tyrosine recombinases. drFtsK interacts with various cell division and genome segregation proteins of D. radiodurans. Microscopic examination of different domain deletion mutants of this protein reveals alterations in cellular membrane architecture and nucleoid morphology. In vivo localization studies of drFtsK-RFP show that it forms multiple foci on nucleoid as well as on the membrane with maximum density on the septum. drFtsK coordinates its movement with nucleoid separation. The alignment of its foci shifts from old to new septum indicating its cellular dynamics with the FtsZ ring during the cell division process. Nearly, similar positional dynamicity of FtsK was observed in cells recovering from gamma radiation exposure. These results suggest that FtsK forms a part of chromosome segregation, cell envelope, and cell division machinery in D. radiodurans. IMPORTANCE Deinococcus radiodurans show extraordinary resistance to gamma radiation. It is polyploid and harbors a multipartite genome comprised of 2 chromosomes and 2 plasmids, packaged in a doughnut-shaped toroidal nucleoid. Very little is known about how the tightly packed genome is accurately segregated and the next divisional plane is determined. Filament temperature-sensitive mutant K (FtsK), a multifunctional protein, helps in pumping the septum-trapped DNA in several bacteria. Here, we characterized FtsK of D. radiodurans R1 (drFtsK) for the first time and showed it to be an active protein. The absence of drFtsK causes many defects in morphology at both cellular and nucleoid levels. The compact packaging of the deinococcal genome and cell membrane formation is hindered in ftsK mutants. In vivo drFtsK is dynamic, forms foci on both nucleoid and septum, and coordinates with FtsZ for the next cell division. Thus, drFtsK role in maintaining the normal genome phenotype and cell division in D. radiodurans is suggested.

RB is a potato gene that provides resistance to a broad spectrum of genotypes of the late blight pathogen Phytophthora infestans. RB belongs to the CC-NB-LRR (coiled-coil, nucleotide-binding, leucine-rich repeat) class of resistance (R) genes, a major component of the plant immune system. The RB protein detects the presence of class I and II IPI-O effectors from P. infestans to initiate a hypersensitive resistance response, but this activity is suppressed in the presence of the Class III effector IPI-O4. Using natural genetic variation of RB within potato wild relatives, we identified two amino acids in the CC domain that alter interactions needed for suppression of resistance by IPI-O4. We have found that separate modification of these amino acids in RB can diminish or expand the resistance capability of this protein against P. infestans in both Nicotiana benthamiana and potato. Our results demonstrate that increased knowledge of the molecular mechanisms that determine resistance activation and R protein suppression by effectors can be utilized to tailor-engineer genes with the potential to provide increased durability.[Graphic: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

The COVID-19 pandemic has caused significant medical and psychological challenges worldwide, and not only exceeded the capacity of hospitals and intensive care units but also an individuals’ ability to cope with life. Health-care workers have continued to provide care for patients despite exhaustion, fear of transmission to themselves and their family, illness or death of friends and colleagues, and losing many patients. They have also faced additional stress and anxiety due to long shifts combined with unprecedented population restrictions, including personal isolation. In this study, we study the effect of an app-based Yoga of Immortals (YOI) intervention on mental health of healthcare workers. In this study, the health care workers were digitally recruited, and their psychological parameters were measured using validated questionaries. The participants were randomly grouped into control and test groups. The validated psychological measures were the Patient Health Questionnaire-8 (PHQ-8), Insomnia Severity Index (ISI) and generalized anxiety disorder (GAD-7) scales. The digital YOI intervention significantly reduced the anxiety, depression symptoms, and insomnia in healthcare workers of all age groups. In contrast, there was no improvement in the control group. This study details the effectiveness of an app-based YOI intervention in healthcare workers.

PURPOSEGeographic access to care is an important measure of health equity. In this study, we describe geographic access to cancer care centers (CCCs) in Rwanda with the current facilities providing care and examine how access could change with expanded care infrastructure.METHODSHealth facilities included are public hospitals administered by the Rwanda Ministry of Health. The WorldPop Project was used to estimate population distribution, and OpenStreetMap was used to determine travel routes. On the basis of geolocations of the facilities, AccessMod 5 was used to estimate the percentage of the population that live within 1 hour, 2 hours, and 4 hours of CCCs under the current (two facilities) and expanded care (seven facilities) scenarios. Variations in access by region, poverty, and level of urbanization were described.RESULTSCurrently, 13%, 41%, and 85% of Rwandans can access CCCs within one, two, and 4 hours of travel, respectively. With expansion of CCCs to seven facilities, access increases to 37%, 84%, and 99%, respectively. There is a substantial variation in current geographic access by province, with 1-hour access in Kigali at 98%, whereas access in the Western Province is 0%; care expansion could increase 2-hour access in the Western Province from 1% to 71%. Variation in access is also seen across the level of urbanization, with current 1-hour access in urban versus rural areas of 45% and 8%, respectively. Expanded care results in improvement of 1-hour access to 67% and 33%, respectively. Similar trends were also noted across poverty levels.CONCLUSIONGeographical access to CCCs varies substantially by province, level of urbanization, and poverty. These disparities can be alleviated by strategic care expansion to other tertiary care facilities across Rwanda.

Introduction While travel distance and time are important proxies of physical access to health facilities, obtaining valid measures with an appropriate modelling method remains challenging in many settings. We compared five measures of geographic accessibility in Haiti, producing recommendations that consider available analytic resources and geospatial goals.Methods Eight public hospitals within the ministry of public health and population were included. We estimated distance and time between hospitals and geographic centroids of Haiti’s section communes and population-level accessibility. Geographic feature data were obtained from public administrative databases, academic research databases and government satellites. We used validated geographic information system methods to produce five geographic access measures: (1) Euclidean distance (ED), (2) network distance (ND), (3) network travel time (NTT), (4) AccessMod 5 (AM5) distance (AM5D) and (5) AM5 travel time (AM5TT). Relative ranking of section communes across the measures was assessed using Pearson correlation coefficients, while mean differences were assessed using analysis of variance (ANOVA) and pairwise t-tests.Results All five geographic access measures were highly correlated (range: 0.78–0.99). Of the distance measures, ED values were consistently the shortest, followed by AM5D values, while ND values were the longest. ND values were as high as 2.3 times ED values. NTT models generally produced longer travel time estimates compared with AM5TT models. ED consistently overestimated population coverage within a given threshold compared with ND and AM5D. For example, population-level accessibility within 15 km of the nearest studied hospital in the Center department was estimated at 68% for ED, 50% for AM5D and 34% for ND.Conclusion While the access measures were highly correlated, there were significant differences in the absolute measures. Consideration of the benefits and limitations of each geospatial measure together with the intended purpose of the estimates, such as relative proximity of patients or service coverage, are key to guiding appropriate use.

Sheetal Pathania,1,* Mohd Imran Khan,1,* Abhishek Kumar,1 Ashish Kumar Gupta,1 Komal Rani,1 Tanvi Ramesh Parashar,1 Jnaneshwari Jayaram,2 Piyush Ranjan Mishra,2 Anurag Srivastava,2 Sandeep Mathur,3 Smriti Hari,4 Gururao Hariprasad1 1Department of Biophysics, All India Institute of Medical Sciences, New Delhi 110029, India; 2Department of Surgery, All India Institute of Medical Sciences, New Delhi 110029, India; 3Department of Pathology, All India Institute of Medical Sciences, New Delhi 110029, India; 4Department of Radiology, All India Institute of Medical Sciences, New Delhi 110029, India*These authors contributed equally to this workCorrespondence: Gururao Hariprasad Tel +91-11-26594240Fax +91-11-26588663Email g.hariprasad@rediffmail.comIntroduction: Breast cancer is the second most common cancer in women across the world. Some of the patients who present in the early stage of disease are affected by metastasis to the axillary group of lymph nodes. The first among this group that is affected is called as sentinel lymph node, and its diagnosis is crucial for the staging of cancer thereby dictating the type of surgical therapy. Therefore, the sentinel lymph node status provides the most relevant information to the surgeon and patient prognosis. The expanded utilization of breast conservation surgery has declined the morbidity associated with mastectomy and axillary lymph node surgery. Recent interest is, therefore, centered on techniques that allow accurate assessment of the sentinel lymph node metastasis. A current procedure such as sentinel lymph node biopsy (SLNB) that is used to assess axillary lymph node metastasis is neither specific nor sensitive, and besides, it is time-consuming.Objective: To compare the protein profiles between metastatic and non-metastatic lymph nodes to identify a biomarker that can flag lymph node metastasis.Materials and Methods: Women with early breast cancer were screened using mammography imaging and recruited to the study. Surgical resection was done to remove the breast tissue, and sentinel lymph node was identified using fluorescein and methylene blue tracer. Lymph node was sliced, and one set was sent for histopathology, which was considered the gold standard to assess the metastatic status of the lymph node. One set of slices was taken for proteomic experiments. Proteins were labelled with fluorescent cyanine tags and were subjected to difference gel electrophoresis experiment. Differentially expressed spots that had at least a twofold relative ratio and consistent pattern across three sets of biological replicate experiments were marked. Gel spots were trypsin digested and identified on mass spectrometry machine. Validation study was done by Western blot experiment on the same set of samples.Results: Thymidylate synthase has a twofold higher expression in the metastatic sentinel lymph nodes as compared to non-metastatic lymph nodes in early breast cancer patients.Conclusion: Differential in gel expression proteomics is an ideal platform for the identification of potential protein biomarker candidates that can differentiate metastatic from non-metastatic lymph nodes in early breast cancer. The identification of thymidylate synthase offers a scope to develop an on-table diagnostic kit to assess the status of sentinel lymph nodes during mastectomy procedure to guide surgical management of axillary lymph nodes in early breast cancer.Keywords: early breast cancer, sentinel lymph node metastasis, gel-based proteomics, difference gel expression, biomarkers, thymidylate synthase, surgical intervention

Background: Neighborhood deprivation is linked with inflammation, which may explain poorer health across populations. Behavioral risk factors are assumed to largely mediate these relationships, but few studies have examined this. We examined three neighborhood contextual factors that could exert direct effects on inflammation: (1) neighborhood socioeconomic status, (2) an index of concentration at extremes (that measures segregation), and (3) surrounding vegetation (greenness). Methods: Using blood samples and addresses collected from prospective cohorts of 7,930 male (1990–1994) and 16,183 female (1986–1990) health professionals with at least one inflammatory marker, we prospectively linked neighborhood contextual factors to inflammatory biomarkers (adiponectin, C-reactive protein, interleukin-6, soluble tumor necrosis factor receptor-2). Log-transformed, z-scaled component measures were used to calculate an inflammation score. Neighborhood socioeconomic status and index of concentration of extremes were obtained from the 1990 decennial census and linked to participant addresses. Surrounding greenness was assessed from satellite data and focal statistics were applied to generate exposures within 270 m and 1230 m of the participants’ address. We fit multiple linear regression models adjusting for demographic, clinical, and behavioral risk factors. Results: Higher neighborhood socioeconomic status was associated with lower inflammation score in women (β for interquartile range increase = −27.7%, 95% CI: −34.9%, −19.8%) and men (β = −21.2%, 95% CI: −31.0%, −10.1%). Similarly, participants in neighborhoods with higher concentrations of high-income households were associated with lower inflammation score in women (β = −27.8%, 95% CI: −35.8%, −18.7%) and men (β = -16.4%, 95% CI: −29.7%, −0.56%). Surrounding greenness within 270 m of each participant’s address was associated with lower inflammation score in women (β = -18.9%, 95% CI: −28.9%, −7.4%) but not men. Results were robust to sensitivity analyses to assess unmeasured confounding and selection bias. Discussion: Our findings support the hypothesis that adverse neighborhood environments may contribute to inflammation through pathways independent of behavioral risk factors, including psychosocial stress and toxic environments.

ObjectiveIn Botswana, cervical cancer is the leading cause of cancer death for females. With limited resources, Botswana is challenged to ensure equitable access to advanced cancer care. Botswana's capital city, Gaborone, houses the only gynecologic oncology multi-disciplinary team (MDT) and the one chemoradiation facility in the country. We aimed to identify areas where fewer women were presenting to the MDT clinic for care.MethodsThis cross-sectional study examined cervical cancer patients presenting to the MDT clinic between January 2015 and March 2020. Patients were geocoded to residential sub-districts to estimate age-standardized presentation rates. Global Moran's I and Anselin Local Moran's I tested the null hypothesis that presentation rates occurred randomly in Botswana. Community- and individual-level factors of patients living in sub-districts identified with higher (HH) and lower (LL) clusters of presentation rates were examined using ordinary least squares with a spatial weights matrix and multivariable logistic regression, respectively, with α level 0.05.ResultsWe studied 990 patients aged 22-95 (mean: 50.6). Presentation rates were found to be geographically clustered across the country (p = 0.01). Five sub-districts were identified as clusters, two high (HH) sub-district clusters and three low (LL) sub-district clusters (mean presentation rate: 35.5 and 11.3, respectively). Presentation rates decreased with increased travel distance (p = 0.033). Patients residing in LL sub-districts more often reported abnormal vaginal bleeding (aOR: 5.62, 95% CI: 1.31-24.15) compared to patients not residing in LL sub-districts. Patients in HH sub-districts were less likely to be living with HIV (aOR: 0.59; 95% CI: 0.38-0.90) and more likely to present with late-stage cancer (aOR: 1.78; 95%CI: 1.20-2.63) compared to patients not in HH sub-districts.ConclusionsThis study identified geographic clustering of cervical cancer patients presenting for care in Botswana and highlighted sub-districts with disproportionately lower presentation rates. Identified community- and individual level-factors associated with low presentation rates can inform strategies aimed at improving equitable access to cervical cancer care.

Self-identified race/ethnicity is a correlate of both genetic ancestry and socioeconomic factors, both of which may contribute to racial disparities in mortality. Investigators often hold a priori assumptions, rarely made explicit, regarding the relative importance of these factors. We studied 2,239 self-identified African Americans (SIAA) from the Prostate, Lung, Colorectal and Ovarian screening trial enrolled from 1993–1998 and followed prospectively until 2019 or until death, whichever came first. Percent African genetic ancestry was estimated using the GRAF-Pop distance-based method. A neighborhood socioeconomic status (nSES) index was estimated using census tract measures of income, housing, and employment and linked to participant residence in 2012. We used Directed Acyclic Graphs (DAGs) to represent causal models favoring (1) biomedical and (2) social causes of mortality. Hazard ratios were estimated using Cox models adjusted for sociodemographic, behavioral, and neighborhood covariates guided by each DAG. 901 deaths occurred over 40,767 person-years of follow-up. In unadjusted (biomedical) models, a 10% increase in percent African ancestry was associated with a 7% higher rate of all-cause mortality (HR: 1.07, 95% CI: 1.02, 1.12). This effect was attenuated in covariate adjusted (social) models (aHR: 1.01, 95% CI: 0.96, 1.06). Mortality was lower comparing participants in the highest to lowest nSES quintile following adjustment for covariates and ancestry (aHR: 0.74, 95% CI: 0.57, 0.98, Ptrend = 0.017). Higher African ancestry and lower nSES were associated with higher mortality, but African ancestry was not associated with mortality following covariate adjustment. Socioeconomic factors may be more important drivers of mortality in African Americans.

Understanding and responding to adverse human health impacts of global environmental change will be a major priority of 21st century public health professionals. The emerging field of planetary health aims to face this challenge by studying and promoting policies that protect the health of humans and of the Earth's natural systems that support them. Public health, drawing on its experience of guiding policies to improve population health, has contributed to planetary health's development. Yet, few public health practitioners are familiar with planetary health's systems-oriented approaches for understanding relationships between economic development, environmental degradation, and human health. In this narrative review, we present key planetary health concepts and show how systems thinking has guided its development. We discuss historical approaches to studying impacts of economic development on human health and the environment. We then review novel conceptual frameworks adopted by planetary health scientists to study and forecast impacts of policies that influence human health and Earth's natural systems at varying spatiotemporal scales. We conclude by presenting examples of how applying the “Doughnut” model (an economic framework where the needs of people are met without overshooting the world's ecological limits) could guide policies for promoting health co-benefits to humans and natural systems.

Malnutrition is a widespread problem that affects human health, society, and the economy. Traditional maize that serves as an important source of human nutrition is deficient in vitamin-E, vitamin-A, lysine, and tryptophan. Here, favorable alleles of vte4 (α-tocopherol methyl transferase), crtRB1 (β-carotene hydroxylase), lcyE (lycopene ε-cyclase), and o2 (opaque2) genes were combined in parental lines of four popular hybrids using marker-assisted selection (MAS). BC1F1, BC2F1, and BC2F2 populations were genotyped using gene-based markers of vte4, crtRB1, lcyE, and o2. Background selection using 81–103 simple sequence repeats (SSRs) markers led to the recovery of recurrent parent genome (RPG) up to 95.45%. Alpha (α)-tocopherol was significantly enhanced among introgressed progenies (16.13 μg/g) as compared to original inbreds (7.90 μg/g). Provitamin-A (proA) (10.42 μg/g), lysine (0.352%), and tryptophan (0.086%) were also high in the introgressed progenies. The reconstituted hybrids showed a 2-fold enhancement in α-tocopherol (16.83 μg/g) over original hybrids (8.06 μg/g). Improved hybrids also possessed high proA (11.48 μg/g), lysine (0.367%), and tryptophan (0.084%) when compared with traditional hybrids. The reconstituted hybrids recorded the mean grain yield of 8,066 kg/ha, which was at par with original hybrids (mean: 7,846 kg/ha). The MAS-derived genotypes resembled their corresponding original hybrids for the majority of agronomic and yield-related traits, besides characteristics related to distinctness, uniformity, and stability (DUS). This is the first report for the development of maize with enhanced vitamin-E, vitamin-A, lysine, and tryptophan.

Background: Depression, anxiety, and disordered sleep are some common symptoms associated with sub-optimal mental health. During the COVID-19 pandemic, mental health issues have grown increasingly more prevalent in the population. Due to social distancing and other limitations during the pandemic, there is a need for home-based, flexible interventions that can improve mental health. The Yoga of Immortals (YOI) mobile application provides a structured intervention that can be used on any mobile device and applied from the user's home.Methods: A total of 1,505 participants were enrolled in the study and used the YOI app for an 8-week period. Participants were asked to fill out three questionnaires: The Patient Health Questionnaire, 8 items (PHQ-8), the Generalized Anxiety Disorder questionnaire (GAD-7) and the Insomnia Severity Index (ISI). These three items were completed by 1,297 participants a total of four times: before starting YOI, two more times during use, and a fourth time after the 8-week usage period. Changes in PHQ8, GAD7 and ISI in participants were compared to a control group, who did not use the YOI app but completed all questionnaires (590 controls finished all questionnaires).Results: Participants reported significant decreases in depression and anxiety-related symptoms. Compared to baseline, PHQ-8 scores decreased 50% on average after the 8-week period. GAD-7 scores also decreased by 40–50% on average, and ISI scores decreased by 50%. These changes were significantly greater (p < 0.05) than that observed in the control group. Participants who reported a previous diagnosis of depression and generalized anxiety reported significantly larger decreases in PHQ-8 and GAD-7 as compared to participants with no prior diagnosis (p < 0.05).Conclusions: Regular use of the YOI intervention over an 8-week period led to significant decreases in symptoms of both depression and anxiety, as well as alleviation of insomnia.

DrRecA and PprA proteins function are crucial for the extraordinary resistance to γ-radiation and DNA strand break repair in Deinococcus radiodurans. DrRecA mediated homologous recombination help in DNA strand break repair and cell survival, while the PprA protein confers radio-resistance via its roles in DNA repair, genome maintenance, and cell division. Genetically recA and pprA genes interact and constitute an epistatic group however, the mechanism underlying their functional interaction is not clear. Here, we showed the physical and functional interaction of DrRecA and PprA protein both in solution and inside the cells. The absence of the pprA gene increases the recombination frequency in gamma-irradiated D. radiodurans cells and genomic instability in cells growing under normal conditions. PprA negatively regulates the DrRecA functions by inhibiting DrRecA mediated DNA strand exchange and ATPase function in vitro. Furthermore, it is shown that the inhibitory effect of PprA on DrRecA catalyzed DNA strand exchange was not due to sequestration of homologous dsDNA and was dependent on PprA oligomerization and DNA binding property. Together, results suggest that PprA is a new member of recombination mediator proteins (RMPs), and able to regulate the DrRecA function in γ-irradiated cells by protecting the D. radiodurans genome from hyper-recombination and associated negative effects.

Two different groups of solid polymer sheets: low density polyethylene (LDPE) sample of thickness 0.006 cm and 0.007 cm along with high density polyethylene (HDPE) sample of the thickness of 0.009 cm, 0.010 cm were taken in this work. The measurement of electrical properties such as dielectric constant, ε' and dielectric loss, ε'' for LDPE and HDPE polymer sheets have been measured using a dielectric cell. The dielectric cell has been fabricated which consists of two circular parallel plates of pure stainless steel each of 5 cm diameter and 2 mm thickness. An impedance bridge (GRA 650A) was used for measurement of capacitance, C, and dissipation factor, D in the audio frequency (AF) range, 100 Hz to 10 kHz. Different samples were loaded in between the two plates of the cell and the capacitance as well as the dissipation factor were estimated from the dial readings of the bridge. Effect of frequency variation on ε', ε'', relaxation time, τ , dissipation factor, tanδ and ac conductivity, σ were also discussed at audio frequency range. The complex permittivity, ε*, related to free dipole oscillating in an alternating field and loss tangent, tanδ were calculated. The frequency-dependent conductivity, dielectric behavior, and electrical modulus, both real (M') and imaginary (M") parts of LDPE and HDPE have been studied in this work. The values of the real part of the electrical modulus (M') did not equal to zero at low frequencies and it is expected that the electrode polarization may develop in both sheets. These findings reveal an increased coupling among the local dipolar motions in a short-range order localized motion. The analysis of real (ε') and imaginary (ε'') parts of dielectric permittivity and that electrical modulus real (M') and imaginary (M") parts signify poly dispersive nature of relaxation time as observed in Cole-Cole plots.

The inevitable presence of uncertain parameters in critical applications of process optimization can lead to undesirable or infeasible solutions. For this reason, optimization under parametric uncertainty was, and continues to be a core area of research within Process Systems Engineering. Multiparametric programming is a strategy that offers a holistic perspective for the solution of this class of mathematical programming problems. Specifically, multiparametric programming theory enables the derivation of the optimal solution as a function of the uncertain parameters, explicitly revealing the impact of uncertainty in optimal decision-making. By taking advantage of such a relationship, new breakthroughs in the solution of challenging formulations with uncertainty have been created. Apart from that, researchers have utilized multiparametric programming techniques to solve deterministic classes of problems, by treating specific elements of the optimization program as uncertain parameters. In the past years, there has been a significant number of publications in the literature involving multiparametric programming. The present review article covers recent theoretical, algorithmic, and application developments in multiparametric programming. Additionally, several areas for potential contributions in this field are discussed, highlighting the benefits of multiparametric programming in future research efforts.

Doctoral students in high- and low-income countries pursuing careers in global health face gaps in their training that could be readily filled through structured peer-learning activities with students based at partnering institutions in complimentary settings. We share lessons learned from the Global Cohort of Doctoral Students, a community of doctoral students based at the Harvard T. H. Chan School of Public Health, Haramaya University, University of Gondar, University of Botswana, and University of Rwanda College of Medicine and Health Sciences. Students in the Global Cohort program engage in collaborative research, forums for constructive feedback, and professional development activities. We describe the motivation for the program, core activities, and early successes.

Introduction Decisions regarding the geographical placement of healthcare services require consideration of trade-offs between equity and efficiency, but few empirical assessments are available. We applied a novel geospatial framework to study these trade-offs in four African countries.Methods Geolocation data on population density (a surrogate for efficiency), health centres and cancer referral centres in Kenya, Malawi, Tanzania and Rwanda were obtained from online databases. Travel time to the closest facility (a surrogate for equity) was estimated with 1 km resolution using the Access Mod 5 least cost distance algorithm. We studied associations between district-level average population density and travel time to closest facility for each country using Pearson’s correlation, and spatial autocorrelation using the Global Moran’s I statistic. Geographical clusters of districts with inefficient resource allocation were identified using the bivariate local indicator of spatial autocorrelation.Results Population density was inversely associated with travel time for all countries and levels of the health system (Pearson’s correlation range, health centres: −0.89 to −0.71; cancer referral centres: −0.92 to −0.43), favouring efficiency. For health centres, negative spatial autocorrelation (geographical clustering of dissimilar values of population density and travel time) was weaker in Rwanda (−0.310) and Tanzania (−0.292), countries with explicit policies supporting equitable access to rural healthcare, relative to Kenya (−0.579) and Malawi (−0.543). Stronger spatial autocorrelation was observed for cancer referral centres (Rwanda: −0.341; Tanzania: −0.259; Kenya: −0.595; Malawi: −0.666). Significant geographical clusters of sparsely populated districts with long travel times to care were identified across countries.Conclusion Negative spatial correlations suggested that the geographical distribution of health services favoured efficiency over equity, but spatial autocorrelation measures revealed more equitable geographical distribution of facilities in certain countries. These findings suggest that even when prioritising efficiency, thoughtful decisions regarding geographical allocation could increase equitable physical access to services.

CO2 capture and storage (CCS) technology is likely to be widely deployed in the coming decades in response to major climate and economics drivers: CCS is part of every clean energy pathway that limits global warming to 2 ​°C or less and receives significant CO2 tax credits in the United States. These drivers are likely to stimulate the capture, transport, and storage of hundreds of millions or billions of tonnes of CO2 annually. A key part of the CCS puzzle will be identifying and characterizing suitable storage sites for vast amounts of CO2. We introduce a new software tool called SCO2T (Sequestration of CO2 Tool, pronounced “Scott”), a dynamic CO2 injection and storage model, to rapidly characterize saline storage reservoirs. The tool is designed to rapidly screen hundreds of thousands of reservoirs, perform sensitivity and uncertainty analyses, and link sequestration engineering (injection rates, reservoir capacities, plume dimensions) to sequestration economics (costs constructed from around 70 separate economic inputs). We describe the novel science developments supporting SCO2T including a new approach to estimating CO2 injection rates and CO2 plume dimensions as well as key advances linking sequestration engineering with economics. We perform a sensitivity and uncertainty analysis of geology parameter combinations—including formation depth, thickness, permeability, porosity, and temperature—to understand the impact on carbon sequestration. Through the sensitivity analysis, we show that increasing depth and permeability both can lead to increased CO2 injection rates, increased storage potential, and reduced costs, while increasing porosity reduces costs without impacting the injection rate (CO2 is injected at a constant pressure in all cases) by increasing the reservoir capacity. Through uncertainty analysis—where formation thickness, permeability, and porosity are randomly sampled—we show that final sequestration costs are normally distributed with upper bound costs around 50% higher than the lower bound costs. While site selection decisions will ultimately require detailed site characterization and permitting, SCO2T provides an inexpensive dynamic screening tool that can help prioritize projects based on the complex interplay of reservoir, infrastructure (e.g., proximity to pipelines), and other (e.g., land use, legal) constraints on the suitability of certain regions for CCS.