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Extreme climate events are becoming more frequent, with poorly understood implications for carbon sequestration by terrestrial ecosystems. A better understanding will critically depend on accurate and precise quantification of ecosystems responses to these events. Taking the 2019 US Midwest floods as a case study, we investigate current capabilities for tracking regional flux anomalies with "top‐down" inversion analyses that assimilate atmospheric CO2 observations. For this analysis, we develop a regionally nested version of the NASA Carbon Monitoring System‐Flux system for North America (CMS‐Flux‐NA) that allows high resolution atmospheric transport (0.5° × 0.625°). Relative to a 2018 baseline, we find the 2019 US Midwest growing season net carbon uptake is reduced by 11–57 TgC (3%–16%, range across assimilated CO2 data sets). These estimates are found to be consistent with independent "bottom‐up" estimates of carbon uptake based on vegetation remote sensing (15–78 TgC). We then investigate current limitations in tracking regional carbon budgets using "top‐down" methods. In a set of observing system simulation experiments, we show that the ability of atmospheric CO2 inversions to capture regional carbon flux anomalies is still limited by observational coverage gaps for both in situ and satellite observations. Future space‐based missions that allow for daily observational coverage across North America would largely mitigate these observational gaps, allowing for improved top‐down estimates of ecosystem responses to extreme climate events. Plain Language Summary: Extreme climate events, such as floods or heatwaves, can have major impacts on the carbon cycle. For example, widespread flooding in the US Midwest during 2019 delayed the planting of crops leading to reduced plant growth and carbon uptake relative to 2018. Here, we test how well this reduction in carbon uptake can be inferred from measurements of atmospheric CO2. We find that these data can identify reduced net carbon uptake to the US Midwest during the 2019 floods, but that sparse observational coverage limits our ability to quantify the anomaly in net carbon uptake. We show that expanded space‐based observations of CO2 would fill these gaps, allowing for improved estimates of interannual variations in the US CO2 budget. Key Points: Bottom‐up and top‐down methods capture reduced 2019 US Midwest carbon uptake relative to 2018Gaps in atmospheric CO2 observations drive uncertainties in top‐down estimatesNested inversion better localizes US Midwest ΔNEE relative to coarse global model [ABSTRACT FROM AUTHOR]

Autonomous sleep tracking at home has become inevitable in today’s fast-paced world. A crucial aspect of addressing sleep-related issues involves accurately classifying sleep stages. This paper introduces a novel approach PSO–XGBoost, combining particle swarm optimisation (PSO) with extreme gradient boosting (XGBoost) to enhance the XGBoost model’s performance. Our model achieves improved overall accuracy and faster convergence by leveraging PSO to fine-tune hyperparameters. Our proposed model utilises features extracted from EEG signals, spanning time, frequency, and time–frequency domains. We employed the Pz-oz signal dataset from the sleep-EDF expanded repository for experimentation. Our model achieves impressive metrics through stratified-K-fold validation on ten selected subjects: 95.4% accuracy, 95.4% F1-score, 95.4% precision, and 94.3% recall. The experiment results demonstrate the effectiveness of our technique, showcasing an average accuracy of 95%, outperforming traditional machine learning classifications. The findings revealed that the feature-shifting approach supplements the classification outcome by 3 to 4 per cent. Moreover, our findings suggest that prefrontal EEG derivations are ideal options and could open up exciting possibilities for using wearable EEG devices in sleep monitoring. The ease of obtaining EEG signals with dry electrodes on the forehead enhances the feasibility of this application. Furthermore, the proposed method demonstrates computational efficiency and holds significant value for real-time sleep classification applications.

Indian Writing English is matured from a sapling to deep rooted tree where it shelters over millions of people by exploring the writers in various branches. Among explored writers, Amit Chaudhuri is a writer in versatile. He is a poet, novelist, critic, essayist, singer and also a good musician. Amit Chaudhuri belongs to Calcutta, his works excessively explicit the impact of a particular perspective of own domain, Calcutta. Chaudhuri's first novel A Strange and Sublime Address explores the domestic values of middle class family in the city of Calcutta where the family gives more importance to traditional and cultural values of ancestral home. Chaudhuri has described the city of Calcutta in parallel fashions as City of Joy and City of Chaos. The protagonist of the novel is twelve years old boy, Sandeep and Chaudhuri has portrayed the distinct instances of domestic obsession of the characters can be observed in the novel. Hence, the present paper focuses on domestic perception of Calcutta city conceited by Amit Chaudhuri in his AStrange and Sublime Address. [ABSTRACT FROM AUTHOR]

Aims: Models predicting the likelihood of obstructive coronary artery disease (CAD) on invasive coronary angiography (ICA) exist. However, as stable patients with new-onset chest pain frequently have lower clinical likelihood and preferably undergo index testing by noninvasive tests such as coronary computed tomography angiography (CCTA), clinical likelihood models calibrated against observed obstructive CAD at CCTA are warranted. The aim was to develop CCTA-calibrated risk-factor- and coronary artery calcium-score-weighted clinical likelihood models (i.e., RF-CLCCTA and CACS-CLCCTA models, respectively)., Methods and Results: Based on age, sex, symptoms and cardiovascular risk factors, an advanced machine learning algorithm utilized a training cohort (n=38,269) of symptomatic outpatients with suspected obstructive CAD to develop both a RF-CLCCTA and a CACS-CLCCTA model to predict observed obstructive CAD on CCTA. The models were validated in several cohorts (n=28,340) and compared to a currently endorsed basic pre-test probability (Basic PTP) model. For both the training and pooled validation cohort, observed obstructive CAD at CCTA was defined as >50% diameter stenosis.Observed obstructive CAD at CCTA was present in 6,443 (22.7%) patients in the pooled validation cohort. While the Basic PTP underestimated the prevalence of observed obstructive CAD at CCTA, the RF-CLCCTA and CACS-CLCCTA models showed superior calibration. Compared to the Basic PTP model, the RF-CLCCTA and CACS-CLCCTA models showed superior discrimination (area under the receiver-operating curves 0.71 (95% confidence interval (CI) 0.70-0.72) vs. 0.74 (95%CI0.73-0.75) and 0.87 (95%CI 0.86-0.87), p<0.001 for both comparisons)., Conclusions: CCTA-calibrated clinical likelihood models improve calibration and discrimination of observed obstructive CAD at CCTA., Clinicaltrials.gov Identifier: N/A., (© The Author(s) 2025. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Aims: To identify differences in CT-derived perivascular (PVAT) and epicardial adipose tissue (EAT) characteristics that may indicate inflammatory status differences between post-treatment acute myocardial infarction (AMI) and stable coronary artery disease (CAD) patients., Methods and Results: A cohort of 205 post-AMI patients (age 59.8±9.2, 92.2% male) was propensity-matched with 205 stable CAD patients (age 60.5±10.0, 90.2% male). Coronary CT angiography and non-contrast CT scans were performed to assess PVAT mean attenuation across major coronary segments and EAT mean attenuation and volumes, respectively. For post-AMI patients, CT scans were conducted 28.6 ± 13.8 days after the AMI incidence. Post-AMI patients showed higher non-culprit PVAT and EAT mean attenuation than stable CAD patients (8.01HU, 95% CI 5.90 to 10.11 HU, p<0.001, 2.48 HU, 95% CI 0.83 to 4.13 HU, p=0.003, respectively). The EAT volume percentage at higher attenuation levels was higher in post-AMI patients compared to stable CAD (33.93cm3, 95% CI 16.86 to 51.00 cm3, p<0.001), with the difference maximized at the -70 HU threshold (4.75%, 95% CI 3.64% to 5.87%, p<0.001). PVAT mean attenuation positively correlated with EAT mean attenuations and the percentage of EAT volume >-70 HU (p<0.001 for both)., Conclusions: Post-AMI patients showed higher PVAT and EAT attenuation than stable CAD patients, potentially indicating AMI-associated inflammatory cardiac adipose tissue changes. -70 HU can act as a potential cut-off for inflamed EAT. These findings highlight the potential of using CT-derived adipose tissue characteristics to assess inflammation and guide post-AMI management strategies., (© The Author(s) 2025. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Purpose: Coronary CT angiography (CCTA) is well established for the diagnostic evaluation and prognostication of coronary artery disease (CAD). The growing burden of CAD in Asia and the emergence of novel CT-based risk markers highlight the need for an automated platform that integrates patient data with CCTA findings to provide tailored, accurate cardiovascular risk assessments. This study aims to develop an artificial intelligence (AI)-driven platform for CAD assessment using CCTA in Singapore's multiethnic population. We will conduct a hybrid retrospective-prospective recruitment of patients who have undergone CCTA as part of the diagnostic workup for CAD, along with prospective follow-up for clinical endpoints. CCTA images will be analysed locally and by a core lab for coronary stenosis grading, Agatston scoring, epicardial adipose tissue evaluation and plaque analysis. The images and analyses will also be uploaded to an AI platform for deidentification, integration and automated reporting, generating precision AI toolkits for each parameter., Participants: CCTA images and baseline characteristics have been collected and verified for 4196 recruited patients, comprising 75% Chinese, 6% Malay, 10% Indian and 9% from other ethnic groups. Among the participants, 41% are female, with a mean age of 55±11 years. Additionally, 41% have hypertension, 51% have dyslipidaemia, 15% have diabetes and 22% have a history of smoking., Findings to Date: The cohort data have been used to develop four AI modules for training, testing and validation. During the development process, data preprocessing standardised the format, resolution and other relevant attributes of the images., Future Plans: We will conduct prospective follow-up on the cohort to track clinical endpoints, including cardiovascular events, hospitalisations and mortality. Additionally, we will monitor the long-term impact of the AI-driven platform on patient outcomes and healthcare delivery., Trial Registration Number: NCT05509010., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: The comparison of coronary computed tomography angiography plaques and perivascular adipose tissue (PVAT) between patients with acute myocardial infarction (AMI) posttreatment and patients with stable coronary artery disease is poorly understood. Our objective was to evaluate the differences in coronary computed tomography angiography-quantified plaque and PVAT characteristics in patients post-AMI and identify signs of residual inflammation., Methods and Results: We analyzed 205 patients (age, 59.77±9.24 years; 92.20% men) with AMI ≤1 month and matched them with 205 patients with stable coronary artery disease (age, 60.52±10.04 years; 90.24% men) based on age, sex, and cardiovascular risk factors. Coronary computed tomography angiography scans were assessed for nonculprit plaque and vessel characteristics, plaque volumes by composition, high-risk plaques, and PVAT mean attenuation. Both patient groups exhibited similar noncalcified plaque volumes (383.35±313.23 versus 378.63±426.25 mm 3 , P =0.899). However, multivariable analysis revealed that patients post-AMI had a greater patient-wise noncalcified plaque volume ratio (estimate, 0.089 [95% CI, 0.053-0.125], P <0.001), largely attributed to a higher fibrofatty and necrotic core volume ratio, along with higher peri-lesion PVAT mean attenuation (estimate, 3.968 [95% CI, 2.556-5.379], P <0.001). When adjusted for vessel length, patients post-AMI had more high-risk plaques (estimate, 0.417 [95% CI, 0.298-0.536], P <0.001) per patient., Conclusions: Patients post-AMI displayed heightened noncalcified plaque components, largely due to fibrofatty and necrotic core content, more high-risk plaques, and increased PVAT mean attenuation on a per-patient level, highlighting the necessity for refined risk assessment in patients with AMI after treatment.

Approximately 8100 km 2 of Alaska are leased to the oil and gas industry for exploration and extraction. According to industry estimates, subsurface expansion from these leases could cover up to 130.2 km 2 per pad. As industrial oil extraction activities increase across the thawing Alaskan permafrost, impacts on the permafrost environment will include rapid thaw, increased hydrological flux, and the release of climate warming greenhouse gases. Here, we use remote sensing and field observations to provide a first-order comparison of the direct impacts to the permafrost tundra from oil well pads, and the long-term consequences of a legacy oil pads on the warming North Slope of Alaska. We find that oil well pads on the permafrost accelerate permafrost degradation and persist despite remediation., (© 2024. The Author(s).)

Background: The use of machine learning (ML) based coronary computed tomography angiography (CCTA) derived fractional flow reserve (ML-FFR CT ), shortens the time of diagnosis of ischemia considerably and eliminates unnecessary invasive procedures, when compared to invasive coronary angiography with invasive FFR (iFFR). This systematic review aims to summarize the current evidence on the diagnostic accuracy of (ML-FFR CT ) compared with iFFR for diagnosis of patient- and vessel-level coronary ischemia., Methods: To identify suitable studies, comprehensive literature search was performed in PubMed, the Cochrane Library, Embase, up to August 2023. The index test was ML derived FFR and studies with diagnostic test accuracy data of ML-FFR CT at a threshold of 0.8 were included for the review and meta -analysis. Quality of evidence was assessed using QUADAS-2 checklist., Results: After full text review of 230 identified studies, 17 were included for analysis, which encompassed 3255 participants (age 62.0 ± 3.7). 8 studies reported patient-level data; and 12, vessel-level data. With iFFR as the reference standard, the pooled patient-level sensitivity, specificity, and area-under-curve (AUC) of ML-FFR CT were 0.86 [95 % CI: 0.79, 0.91], 0.87 [95 % CI: 0.76, 0.94], and 0.92 [95 % CI: 0.89-0.94], respectively; and pooled vessel-level sensitivity, specificity, and AUC, 0.80 [95 % CI: 0.74-0.84], 0.84 [95 % CI: 0.77-0.89), and 0.88 [95 % CI: 0.85-0.91], respectively., Conclusions: This systemic review demonstrated the favourable diagnostic performance of ML-FFR CT against standard iFFR, although heterogeneity exists, providing support for the use of ML-FFR CT as a triage tool for non-invasive screening of coronary ischemia in the clinical setting., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Background: Given the rapidly growing burden of cardiovascular disease (CVD) in Asia, this study forecasts the CVD burden and associated risk factors in Asia from 2025 to 2050., Methods: Data from the Global Burden of Disease 2019 study was used to construct regression models predicting prevalence, mortality, and disability-adjusted life years (DALYs) attributed to CVD and risk factors in Asia in the coming decades., Findings: Between 2025 and 2050, crude cardiovascular mortality is expected to rise 91.2% despite a 23.0% decrease in the age-standardised cardiovascular mortality rate (ASMR). Ischaemic heart disease (115 deaths per 100,000 population) and stroke (63 deaths per 100,000 population) will remain leading drivers of ASMR in 2050. Central Asia will have the highest ASMR (676 deaths per 100,000 population), more than three-fold that of Asia overall (186 deaths per 100,000 population), while high-income Asia sub-regions will incur an ASMR of 22 deaths per 100,000 in 2050. High systolic blood pressure will contribute the highest ASMR throughout Asia (105 deaths per 100,000 population), except in Central Asia where high fasting plasma glucose will dominate (546 deaths per 100,000 population)., Interpretation: This forecast forewarns an almost doubling in crude cardiovascular mortality by 2050 in Asia, with marked heterogeneity across sub-regions. Atherosclerotic diseases will continue to dominate, while high systolic blood pressure will be the leading risk factor., Funding: This was supported by the NUHS Seed Fund (NUHSRO/2022/058/RO5+6/Seed-Mar/03), National Medical Research Council Research Training Fellowship (MH 095:003/008-303), National University of Singapore Yong Loo Lin School of Medicine's Junior Academic Fellowship Scheme, NUHS Clinician Scientist Program (NCSP2.0/2024/NUHS/NCWS) and the CArdiovascular DiseasE National Collaborative Enterprise (CADENCE) National Clinical Translational Program (MOH-001277-01)., Competing Interests: S.T has received research grant support from Janssen, Merck, American Heart Association (USA) and the National Medical Research Council (Singapore). She has received speaker and/or consulting fees from AstraZeneca, Eli Lilly, Boehringer-Ingelheim, Merck, Novo Nordisk, Abbott, Medtronic, Dexcom, MIMS, Janssen, Novartis and Sanofi. She is also a shareholder of a health technology company (NOVI Health). D.J.H. has received consultant fees from Faraday Pharmaceuticals Inc. and Boehringer Ingelheim International GmbH, honoraria from Servier, and research funding from Astra Zeneca, Merck Sharp & Dohme Corp and Novonordisk. DJH is supported by the Duke-NUS Signature Research Programme funded by the Ministry of Health, Singapore Ministry of Health's National Medical Research Council under its Singapore Translational Research Investigator Award (MOH-STaR21jun-0003), Centre Grant scheme (NMRC CG21APR1006), and Collaborative Centre Grant scheme (NMRC/CG21APRC006). L.B. was supported by the National Medical Research Council (NMRC) of Singapore Centre Grant (Program for Transforming and Evaluating Outcomes in Cardiometabolic disease [PROTECT], Grant number: CG21APR1006), the National Medical Research Counsil (NMRC) of Singapore Transitional Award Grant (Improving Obstructive Coronary Artery Disease and Cardiovascular Risk Prediction Using Deep Learning Analysis on Coronary Artery Calcium Imaging, Grant number: TA21nov-0001), and the National Medical Research Council (NMRC) of Singapore Collaborative Centre Grant (CCG) (IMplementing a PArtnership for Cardiovacular Trials-2 [IMPACT-2] Program, Grant number: NMRC/CG2/001a/2021-NHCS). L.Z is supported by National Medical Research Council under its Clinician-Innovator Award Senior Investigator (CIASI24jan-0001) and Health Service Research Award (MOH-000358), SingHealth Duke-NUS Academic Medical Centre AM strategic fund award (07 FY2023 HTP P2 15-A2) and Industry Alignment Fund Pre-Positioning (IAFPP award) (H20c6a0035). S.L.L. is supported by the National Medical Research Council Transitional Award; she has received research grants from National University Health System, National Kidney Foundation of Singapore and Singapore Heart Foundation. She is an Associate Editor of Resuscitation and Guest Editor of Resuscitation Plus. M.Y.C. receives speaker's fees and research grants from Astra Zeneca, Abbott Technologies and Boston Scientific. N.W.S.C has received research grant support from NUHS Seed Fund (NUHSRO/2022/RO5+6/Seed-Mar/03), National Medical Research Council Research Training Fellowship (MH 095:003/008-303), National University of Singapore Yong Loo Lin School of Medicine's Junior Academic Fellowship Scheme, and the NUHS Clinician Scientist Program (NCSP2.0/2024/NUHS/NCWS)., (© 2024 The Author(s).)

Background: Most pretest probability (PTP) tools for obstructive coronary artery disease (CAD) were Western -developed. The most appropriate PTP models and the contribution of coronary artery calcium score (CACS) in Asian populations remain unknown. In a mixed Asian cohort, we compare 5 PTP models: local assessment of the heart (LAH), CAD Consortium (CAD2), risk factor-weighted clinical likelihood, the American Heart Association/American College of Cardiology and the European Society of Cardiology PTP and 3 extended versions of these models that incorporated CACS: LAH (CACS) , CAD2 (CACS) , and the CACS-clinical likelihood., Methods and Results: The study cohort included 771 patients referred for stable chest pain. Obstructive CAD prevalence was 27.5%. Calibration, area under the receiver-operating characteristic curves (AUC) and net reclassification index were evaluated. LAH clinical had the best calibration (χ 2 5.8; P =0.12). For CACS models, LAH (CACS) showed least deviation between observed and expected cases (χ 2 37.5; P <0.001). There was no difference in AUCs between the LAH clinical (AUC, 0.73 [95% CI, 0.69-0.77]), CAD2 clinical (AUC, 0.72 [95% CI, 0.68-0.76]), risk factor-weighted clinical likelihood (AUC, 0.73 [95% CI: 0.69-0.76) and European Society of Cardiology PTP (AUC, 0.71 [95% CI, 0.67-0.75]). CACS improved discrimination and reclassification of the LAH (CACS) (AUC, 0.88; net reclassification index, 0.46), CAD2 (CACS) (AUC, 0.87; net reclassification index, 0.29) and CACS-CL (AUC, 0.87; net reclassification index, 0.25)., Conclusions: In a mixed Asian cohort, Asian-derived LAH models had similar discriminatory performance but better calibration and risk categorization for clinically relevant PTP cutoffs. Incorporating CACS improved discrimination and reclassification. These results support the use of population-matched, CACS-inclusive PTP tools for the prediction of obstructive CAD.