Bela Negara dalam Persepektif Iman Kristen sebagai Makna Ketundukan terhadap Pemerintah

Abstract. Hoak, radicalism and various kinds of behavior of modern humans in society sometimes lead to disintegration and horizontal conflicts that can lead to situations that threaten the sovereignty of the Indonesian nation and the integrity of the country. For this reason, the purpose of writing this paper is to provide an understanding of the concept of state defense in the perspective of the Christian faith as a meaning to respect and live in submission to the government as input to be applied. Through descriptive qualitative research with literary literature, researchers can find that the concept that defending the state in the perspective of Christian faith as a meaning of submission to the government can describe how believers can understand the notion of defending the country and submitting to the government in biblical studies so that the concept of state defense becomes the obligation of believers to be responsible for maintaining nationalism towards the nation in accordance with what is written in the foundation of God's Word. Abstrak. Hoak, radikalisme dan berbagai macam prilaku manusia modern dalam bermasyarakat terkadang menimbulkan disintegrasi dan konflik horizontal yang dapat memunculkan situasi yang mengancam kedaulatan bangsa Indonesia dan keutuhan negara. Menjadi tujuan penulisan paper ini agar dapat memberikan pemahaman konsep bela negara dalam persepetif iman Kristen sebagai makna untuk menghormatai dan hidup dalam ketundukan terhadap pemerintah menjadi masukan untuk dapat diterapkan. Melalui penelitian kulaitatif deskriftif dengan literature pustaka, peneliti dapat menemukan konsep bahwa bela negara dalam persepektif iman Kristen sebagai makna ketundukan terhadap pemerintah. Hal ini dapat di deskripsikan bagaimana orang percaya dapat memahami pengertian tentang bela negara dan ketundukan terhadap pemerintah dalam kajian Alkitabiah sehingga konsep bela negara menjadi kewajiban orang percaya, dan bertanggung jawab untuk menjaga nasionalisme terhadap bangsa sesuai dengan apa yang tertulis dalam landasan Firman Tuhan

A Clinical Tool to Identify Candidates for Stress-First Myocardial Perfusion Imaging

Objectives: This study sought to develop a clinical model that identifies a lower-risk population for coronary artery disease that could benefit from stress-first myocardial perfusion imaging (MPI) protocols and that can be used at point of care to risk stratify patients. Background: There is an increasing interest in stress-first and stress-only imaging to reduce patient radiation exposure and improve patient workflow and experience. Methods: A secondary analysis was conducted on a single-center cohort of patients undergoing single-photon emission computed tomography (SPECT) and positron emission tomography (PET) studies. Normal MPI was defined by the absence of perfusion abnormalities and other ischemic markers and the presence of normal left ventricular wall motion and left ventricular ejection fraction. A model was derived using a cohort of 18,389 consecutive patients who underwent SPECT and was validated in a separate cohort of patients who underwent SPECT (n = 5,819), 1 internal cohort of patients who underwent PET (n=4,631), and 1 external PET cohort (n = 7,028). Results: Final models were made for men and women and consisted of 9 variables including age, smoking, hypertension, diabetes, dyslipidemia, typical angina, prior percutaneous coronary intervention, prior coronary artery bypass graft, and prior myocardial infarction. Patients with a score ≤1 were stratified as low risk. The model was robust with areas under the curve of 0.684 (95% confidence interval [CI]: 0.674 to 0.694) and 0.681 (95% CI: 0.666 to 0.696) in the derivation cohort, 0.745 (95% CI: 0.728 to 0.762) and 0.701 (95% CI: 0.673 to 0.728) in the SPECT validation cohort, 0.672 (95% CI: 0.649 to 0.696) and 0.686 (95% CI: 0.663 to 0.710) in the internal PET validation cohort, and 0.756 (95% CI: 0.740 to 0.772) and 0.737 (95% CI: 0.716 to 0.757) in the external PET validation cohort in men and women, respectively. Men and women who scored ≤1 had negative likelihood ratios of 0.48 and 0.52, respectively. Conclusions: A novel model, based on easily obtained clinical variables, is proposed to identify patients with low probability of having abnormal MPI results. This point-of-care tool may be used to identify a population that might qualify for stress-first MPI protocols

Clarifying the murk: unveiling bacterial dynamics in response to crude oil pollution, Corexit-dispersant, and natural sunlight in the Gulf of Mexico

The 2010 Deepwater Horizon (DwH) Oil spill released an enormous volume of oil into the Gulf of Mexico (GoM), prompting the widespread use of chemical dispersants like Corexit® EC9500A. The ecological consequences of this treatment, especially when combined with natural factors such as sunlight, remain unexplored in the context of marine bacterial communities’ dynamics. To address this knowledge gap, our study employed a unique metaproteomic approach, investigating the combined effects of sunlight, crude Macondo surrogate oil, and Corexit on GoM microbiome across different mesocosms. Exposure to oil and/or Corexit caused a marked change in community composition, with a decrease in taxonomic diversity relative to controls in only 24 hours. Hydrocarbon (HC) degraders, particularly those more tolerant to Corexit and phototoxic properties of crude oil and/or Corexit, proliferated at the expense of more sensitive taxa. Solar radiation exacerbated these effects in most taxa. We demonstrated that sunlight increased the dispersant’s toxicity, impacting on community structure and functioning. These functional changes were primarily directed by oxidative stress with upregulated proteins and enzymes involved in protein turnover, general stress response, DNA replication and repair, chromosome condensation, and cell division. These factors were more abundant in chemically treated conditions, especially in the presence of Corexit compared to controls. Oil treatment significantly enhanced the relative abundance of Alteromonas, an oil-degrading Gammaproteobacteria. In combined oil-Corexit treatments, the majority of identified protein functions were assigned to Alteromonas, with strongly expressed proteins involved in membrane transport, motility, carbon and amino acid metabolism and cellular defense mechanisms. Marinomonas, one of the most active genera in dark conditions, was absent from the light treatment. Numerous metabolic pathways and HC-degrading genes provided insights into bacterial community adaptation to oil spills. Key enzymes of the glyoxylate bypass, enriched in contaminant-containing treatments, were predominantly associated with Rhodobacterales and Alteromonadales. Several proteins related to outer membrane transport, photosynthesis, and nutrient metabolisms were characterized, allowing predictions of the various treatments on biogeochemical cycles. The study also presents novel perspectives for future oil spill clean-up processes

Virtual Special Issue on Catalysis at the U.S. Department of Energy’s National Laboratories

Catalysis research at the U.S. Department of Energy’s (DOE’s) National Laboratories covers a wide range of research topics in heterogeneous catalysis, homogeneous/molecular catalysis, biocatalysis, electrocatalysis, and surface science. Since much of the work at National Laboratories is funded by DOE, the research is largely focused on addressing DOE’s mission to ensure America’s security and prosperity by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions. The catalysis research carried out at the DOE National Laboratories ranges from very fundamental catalysis science, funded by DOE’s Office of Basic Energy Sciences (BES), to applied research and development (R&D) in areas such as biomass conversion to fuels and chemicals, fuel cells, and vehicle emission control with primary funding from DOE’s Office of Energy Efficiency and Renewable Energy. National Laboratories are home to many DOE Office of Science national scientific user facilities that provide researchers with the most advanced tools of modern science, including accelerators, colliders, supercomputers, light sources, and neutron sources, as well as facilities for studying the nanoworld and the terrestrial environment. National Laboratory research programs typically feature teams of researchers working closely together, often joining scientists from different disciplines to tackle scientific and technical problems using a variety of tools and techniques available at the DOE national scientific user facilities. Along with collaboration between National Laboratory scientists, interactions with university colleagues are common in National Laboratory catalysis R&D. In some cases, scientists have joint appointments at a university and a National Laboratory. This ACS Catalysis Virtual Special Issue {http://pubs.acs.org/page/accacs/vi/doe-national-labs} was motivated by Christopher Jones and Rhea Williams, who sent out the invitations to all of DOE’s National Laboratories where catalysis research is conducted. All manuscripts submitted went through the standard rigorous peer review required for publication in ACS Catalysis. A total of 29 papers are published in this virtual special issue, which features some of the recent catalysis research at 11 of DOE’s National Laboratories: Ames Laboratory (Ames), Argonne National Laboratory (ANL), Brookhaven National Laboratory (BNL), Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), National Energy Technology Laboratory (NETL), National Renewable Energy Laboratory (NREL), Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory (PNNL), Sandia National Laboratory (SNL), and SLAC National Accelerator Laboratory (SLAC). In this preface, we briefly discuss the history and impact of catalysis research at these particular DOE National Laboratories, where the majority of catalysis research continues to be conducted

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

PURPOSE Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone

Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria.

Differentiation of naïve CD4+ T cells into functionally distinct T helper subsets is crucial for the orchestration of immune responses. Due to extensive heterogeneity and multiple overlapping transcriptional programs in differentiating T cell populations, this process has remained a challenge for systematic dissection in vivo. By using single-cell transcriptomics and computational analysis using a temporal mixtures of Gaussian processes model, termed GPfates, we reconstructed the developmental trajectories of Th1 and Tfh cells during blood-stage Plasmodium infection in mice. By tracking clonality using endogenous TCR sequences, we first demonstrated that Th1/Tfh bifurcation had occurred at both population and single-clone levels. Next, we identified genes whose expression was associated with Th1 or Tfh fates, and demonstrated a T-cell intrinsic role for Galectin-1 in supporting a Th1 differentiation. We also revealed the close molecular relationship between Th1 and IL-10-producing Tr1 cells in this infection. Th1 and Tfh fates emerged from a highly proliferative precursor that upregulated aerobic glycolysis and accelerated cell cycling as cytokine expression began. Dynamic gene expression of chemokine receptors around bifurcation predicted roles for cell-cell in driving Th1/Tfh fates. In particular, we found that precursor Th cells were coached towards a Th1 but not a Tfh fate by inflammatory monocytes. Thus, by integrating genomic and computational approaches, our study has provided two unique resources, a database www.PlasmoTH.org, which facilitates discovery of novel factors controlling Th1/Tfh fate commitment, and more generally, GPfates, a modelling framework for characterizing cell differentiation towards multiple fates

Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning:a retrospective observational study

BACKGROUND: Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction.METHODS: Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction.FINDINGS: Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36).INTERPRETATION: Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results.FUNDING: This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002].</p