Browse Articles

An integrated petrophysical and rock physics characterization of the Mangahewa Formation in the Pohokura field, Taranaki Basin

Abstract The Mangahewa Formation in the Pohokura gas field, Taranaki Basin, is a key reservoir for gas production in New Zealand, yet its deep and heterogeneous nature presents challenges for accurate reservoir characterization. While prior studies have explored aspects of the Mangahewa Formation such as lithology, fluid composition, and petrophysical properties, the interrelationships between these factors and their impact on hydrocarbon production remain underexamined. This study integrates detailed petrophysical and rock physics analyses to overcome these challenges. Petrophysical evaluation, based on well log data from depths of 3200–4000 m, reveals net reservoir thicknesses ranging from 164 to 479 m, with total porosity between 17 and 21% and effective porosity between 8 and 19%. Shale volume and water saturation vary from 21–28 and 22–34%, respectively. Rock physics analysis was performed using Rock Physics Templates (RPTs) to model the elastic properties of the reservoir. The Mangahewa Sandstone exhibits elastic properties consistent with the stiff sand model, with compressional sonic velocities ranging from 4100 to 5000 m/s. High correlations were achieved between measured and modeled velocities, with 97% for VP and 94% for VS. These models enabled the estimation of porosity from seismic-derived acoustic impedance, providing valuable insights in areas with limited well control. Furthermore, the RPTs effectively differentiated between gas sand, water sand, and shale facies, minimizing uncertainties in fluid and lithology prediction. These results provide a comprehensive understanding of the Mangahewa Formation, enhancing hydrocarbon prospect evaluation and supporting further exploration and development in the Pohokura field.

Scheduling optimization based on particle swarm optimization algorithm in emergency management of long-distance natural gas pipelines

This paper aims to solve the scheduling optimization problem in the emergency management of long-distance natural gas pipelines, with the goal of minimizing the total scheduling time. To this end, the objective function of the minimum total scheduling time is established, and the relevant constraints are set. A scheduling optimization model based on the particle swarm optimization (PSO) algorithm is proposed. In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. In addition, with the increase of pipeline complexity, NACFPSO can still maintain its advantages in convergence speed and scheduling time, especially in scheduling time, which further verifies the optimization effect of the algorithm in emergency management.

A frequency attention-embedded network for polyp segmentation

In Silico identification and characterization of SOS gene family in soybean: Potential of calcium in salinity stress mitigation

Leguminous crops are usually sensitive to saline stress during germination and plant growth stages. The Salt Overly Sensitive (SOS) pathway is one of the key signaling pathways involved in salt translocation and tolerance in plants however, it is obscure in soybean. The current study describes the potential of calcium application on the mitigation of salinity stress and its impact on seed germination, morphological, physiological and biochemical attributes of soybean. The seeds from previously reported salt-tolerant and salt-susceptible soybean varieties were primed with water, calcium (10 and 20 mM), and stressed under 60, 80 and 100 mM NaCl and evaluated in various combinations. Results show that germination increased by 7% in calcium primed non-stressed seeds under non-stressing, whereas an improvement of 15%-25% was observed in germination under NaCl stress. Likewise, improvement in seedling length (3%-8%), plant height (9%-18%), number of nodes (3%-14%), SOD activity (20%) and Na+/K+ concentration (3%-5% reduction) in calcium primed plants, indicates alleviation of salinity-induced negative effects. In addition, this study also included in silico identification and confirmation of presence of Arabidopsis thaliana SOS genes orthologs in soybean. The research of amino acid sequences of SOS proteins from Arabidopsis thaliana (AtSOSs) within Glycine max genome displayed protein identity (60–80%) thus these identified homologs were called as GmSOS. Further phylogeny and in silico analyses showed that GmSOS orthologs contain similar gene structures, close evolutionary relationship, and same conserved motifs, reinforcing that GmSOSs belong to SOS family and they share many common features with orthologs from other species thus may perform similar functions. This is the first study that reports role of SOSs in salt-stress mitigation in soybean.

The internet usage increases fear of infection with Covid-19

Factors influencing sepsis associated thrombocytopenia (SAT): A multicenter retrospective cohort study

Introduction Sepsis associated thrombocytopenia (SAT) is a common complication of sepsis. We designed this study to investigate factors influencing SAT. Methods Patients with sepsis (2984 in Peking union medical college hospital [PUMCH] database, 13165 in eICU Collaborative Research [eICU] database, 11101 in Medical Information Mart for Intensive Care IV [MIMIC-IV] database) were enrolled. Variables included basic information, comorbidities, and organ functions. Multi-variable logistic regression models and artificial neural network model were applied to determine the factors related to SAT. Main results Age and body mass index (BMI) were inversely correlated with the incidence of SAT (p-value 0.175 and 0.049 [PUMCH], p-value 0.000 and 0.000 [eICU], p-value 0.000 and 0.000 [MIMIC-IV]). Hematologic malignancies and other malignancies were positively correlated with the incidence of SAT (p-value 0.000 and 0.000 [PUMCH], p-value 0.000 and 0.000 [eICU], p-value 0.000 and 0.020 [MIMIC-IV]) except other malignancies was inversely correlated with the incidence of SAT in PUMCH database. Norepinephrine (NE) equivalents, total bilirubin (TBIL) and creatinine were positively correlated with the incidence of SAT (p-value 0.000, 0.000 and 0.011 [PUMCH], p-value 0.028, 0.000 and 0.013 [eICU], p-value 0.028, 0.000 and 0.027 [MIMIC-IV]). PaO2 / FiO2 was inversely correlated with the incidence of SAT in PUMCH database (p-value 0.021 [PUMCH]), while it was positively correlated with the incidence of SAT (p-value 0.000 [MIMIC-IV]). PaO2 / FiO2 and SAT was not related (p-value 0.111 [eICU]). TBIL, hematologic malignancies, PaO2 / FiO2 and NE equivalents ranked in the top five significant variables in all three datasets. Conclusions Hematologic malignancies and other malignancies were positively correlated with the incidence of SAT. NE equivalents, TBIL and creatinine were positively correlated with the incidence of SAT. TBIL, hematologic malignancies, PaO2 / FiO2 and NE equivalents ranked in the top significant variables in factors influencing SAT.

Glucagon-like peptide-1 receptor agonists improve metabolic dysfunction-associated steatotic liver disease outcomes

Type 1 diabetes incidence during COVID-19 pandemic has not been influenced by COVID-19 vaccination in northern Italy region, Lombardy

Objective To describe the trends of type 1 diabetes(T1D) incidence in 0–17-year-olds over the years 2020–2023, and the COVID-19 vaccination uptake in Lombardy region. Methods Data about children and adolescents aged 0–17 years who received a diagnosis of T1D from 2020 to 2023 were extracted from the public computerized registry of the healthcare system of the Lombardy Region (Italy). After calculating the annual T1D incidence, the incidence in 2020, prior to the availability of vaccination, was compared to subsequent years. A separate analysis was conducted for the 12–17 age group, the first to receive vaccination. Results One thousand two hundred seventy-three T1D onsets were recorded. The distribution of T1D showed no significant annual variation by sex (p-trend = 0.338), mean age (9 years, p = 0.537) and age distribution (p-trend = 0.563). T1D incidence [95% CI/100.000] did not significantly change comparing 2020 [18.94/100.000 (CI 16.88–21.18)] with 2021 [21.82/100.000 (CI 18.90–23.44)], 2022 [20.77/100.000 (CI 18.59–23.13)] and 2023 [19.68/100.000 (CI 16.61–20.94)]. No differences in incidence were observed in the 12–17 age group during 2021–2023 when COVID-19 vaccination was available when compared to 2020 (p-wald > 0.05). The COVID-19 vaccination coverage was lower in children with diabetes onset compared to the same-age general population (38 vs 42%). Conclusions The incidence of T1D in children remained stable during the COVID-19 pandemic, regardless of the uptake of the vaccination.

Lifespan in rodents with MYT1L heterozygous mutation

Correction: A peripheral subepithelial network for chemotactile processing in the predatory sea slug Pleurobranchaea californica

Circulating lncRNA HOTAIR is a biomarker for pediatric acute lymphoblastic leukemia and mediator of miR-326 exosomal export

Removal of sulfate pollutant from different samples of a river water using nanozeolite technology, case study: Gamasiab River, Iran

Human activities significantly impact on river water quality as a crucial water source. A study in the Gamasiab River analyzed samples from 16 points at three time periods, assessing element concentrations. The most polluted station was identified using spectrophotometric testing and treated with natural and modified zeolite nanoparticles for purification. Various acid and base combinations modified the nanoparticles, optimizing their effectiveness as adsorbents through tests under different conditions. Utilizing the Design Expert model, theoretical adsorption values were determined based on pH and adsorbent-pollutant ratio. The modified samples demonstrated 77% efficiency with 0.2 molar nitric and sulfuric acid. Interaction studies showed how phosphate and nitrate ions affected sulfate adsorption. Optimal adsorption conditions were defined at pH = 9.6 and D/C = 17.01, achieving 86.5% pollutant adsorption. The Freundlich isotherm, with a coefficient of determination of 0.92, was chosen over the Langmuir isotherm (0.79) for its superior performance. Therefore, applying zeolite nanoparticles efficiently eliminated sulfate pollutants from surface water resources at the laboratory.

Berberine attenuates obesity-induced skeletal muscle atrophy via regulation of FUNDC1 in skeletal muscle of mice

Impact of parental education on number of under five children death per mother in Bangladesh

One of the leading challenges of social development is the reduction of children’s deaths under the age of five. The primary focus of this research is to study the potential impact of parental education on under five children death in Bangladesh utilizing a secondary dataset extracted from the Bangladesh Demographic and Health Survey (BDHS), 2017–18. The total count of deceased children within a family is a non-negative numerical variable. The mean number of under five children death per 100 mothers is found to be 20 with variance of around 27, which indicates the presence of overdispersion. As the response variable exhibits 84.2% zero counts, we have considered three regression models in this research; Poisson model, zero-inflated Poisson model, and zero-inflated negative binomial model. Finally, zero-inflated negative binomial model, exhibiting the lowest AIC value, indicates that both maternal and paternal education have significant protective impact on under five children death. Specifically, greater levels of formal education achieved by the parents are associated with a decreased rate of children death.

A Compact 2-D photonic crystal biomedical sensor for enhanced glucose concentration detection in urine

Abstract This study introduces a 2-D Photonic Crystal (PhC) biosensor designed, simulated, and evaluated for detecting glucose concentrations in urine by utilizing refractive index variations. The sensor demonstrates exceptional performance, achieving a sensitivity of 20,040.30 nm/RIU for glucose levels ranging from 0–15 mg/dl, a quality factor of 10,424.55, and a detection limit as low as 8 × 10−10, surpassing benchmarks reported in the literature. With compact dimensions of 16.8 × 17.6 µm2 and compatibility with modern fabrication techniques, the proposed design is well suited for integration into portable diagnostic devices. A comprehensive comparative analysis underscores its superior sensitivity, ultra-high quality factor, and compact design, establishing it as a major advancement in glucose detection technology.

Climate change effects on ecosystem services: Disentangling drivers of mixed responses

Climate change is a pervasive hazard that impacts the supply and demand of ecosystem goods and services (EGS) that maintain human well-being. A recent review found that the impacts of climate change on EGS are sometimes mixed, posing challenges for managers who need to adapt to these changes. We expand on earlier work by exploring drivers of varying responses of EGS to climate within studies. We conducted a systematic review of English-language papers directly assessing climate change impacts on the supply, demand, or monetary value of ‘provisioning EGS’, ‘regulating EGS’, or ‘cultural EGS’. Ultimately, 44 papers published from December 2014 to March 2018 were analyzed. Nearly 66% of EGS were assessed for higher-income countries despite how lower-income countries disproportionately face negative climate impacts. Around 59% of observations or projections were mixed responses of EGS to climate change. Differences in climate impacts to EGS across space or climate scenarios were the most common causes of mixed responses, followed by mixed responses across time periods assessed. Disaggregating findings by drivers is valuable because mixed responses were often due to multiple drivers of variation. Carefully considering the decision context and desired outcome of a study will help select appropriate methodology to detect EGS variation. Although studies have often assessed relevant drivers of variation, assessing interactions of other sources of uncertainty and both climate and non-climate drivers may support more effective management decisions that holistically account for different values in the face of uncertainty.

IVC treatment between primary and second TURBT may improve the prognosis of high-risk NMIBC patients receiving BCG treatment

Automated gait event detection for exoskeleton-assisted walking using a long short-term memory model with ground reaction force and heel marker data

Traditional gait event detection methods for heel strike and toe-off utilize thresholding with ground reaction force (GRF) or kinematic data, while recent methods tend to use neural networks. However, when subjects’ walking behaviors are significantly altered by an assistive walking device, these detection methods tend to fail. Therefore, this paper introduces a new long short-term memory (LSTM)-based model for detecting gait events in subjects walking with a pair of custom ankle exoskeletons. This new model was developed by multiplying the weighted output of two LSTM models, one with GRF data as the input and one with heel marker height as input. The gait events were found using peak detection on the final model output. Compared to other machine learning algorithms, which use roughly 8:1 training-to-testing data ratio, this new model required only a 1:79 training-to-testing data ratio. The algorithm successfully detected over 98% of events within 16ms of manually identified events, which is greater than the 65% to 98% detection rate of previous LSTM algorithms. The high robustness and low training requirements of the model makes it an excellent tool for automated gait event detection for both exoskeleton-assisted and unassisted walking of healthy human subjects.

Contrast-enhanced magnetic resonance imaging based calf muscle perfusion and machine learning in peripheral artery disease

Classifying COVID-19 hospitalizations in epidemiology cohort studies: The C4R study

Rationale Robust COVID-19 outcomes classification is important for ongoing epidemiology research on acute and post-acute COVID-19 conditions. Protocolized medical record review is an established method to validate endpoints for clinical trials and cardiovascular epidemiology cohorts; however, a protocol to adjudicate hospitalizations for COVID-19 among epidemiology cohorts was lacking. Objectives We developed a protocol to ascertain and adjudicate hospitalized COVID-19 across a meta-cohort of 14 US prospective cohort studies. This report describes the first three years of protocol implementation (October 1, 2020—October 1, 2023) and evaluates its repeatability and performance compared to classification by administrative codes. Methods The protocol was adapted from cohort approaches to clinical cardiovascular events ascertainment and adjudication. Potential COVID-19 hospitalizations and deaths were identified by self-/proxy-report and, in some cases, active surveillance. Medical records were requested from hospitals and adjudicated for COVID-19 outcomes by clinically trained personnel according to a standardized rubric. Inter-rater agreement was assessed. The sensitivity and specificity of discharge diagnosis codes was compared to adjudicated diagnoses. Measurements and main results The study obtained medical records for 1,167 potential COVID-19 hospitalizations, which underwent protocolized adjudication. Adjudication confirmed COVID-19 infection was present for 1,030 (88%) events, of which COVID-19 was not the cause of hospitalization for 78 (8%). Of 952 hospitalizations determined by adjudicators to be caused by COVID-19, 319 (34%) participants were critically ill and 210 (22%) died. Pneumonia was confirmed in 822 (86%) and acute kidney injury in 350 (37%); other cardiovascular and thrombotic complications were rare (2–5%). Interrater reliability among adjudicators was high (kappa = 0.85–1.00) except for myocardial infarction (kappa = 0.60). Compared to adjudication, sensitivity of discharge diagnosis codes was higher for pneumonia (84%) and pulmonary embolism (81%) than for other complications (48–70%). Conclusions Protocolized adjudication confirmed four out of five COVID-19 hospitalizations in a US meta-cohort and confirmed cases of pneumonia, pulmonary embolism, and other conditions that were not indicated by discharge diagnosis codes. These results highlight the importance of validating health outcomes for use in research on COVID-19 and post-COVID-19 conditions, and some limitations of claims-based data.