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Impacts of different boundary layer parameterization schemes on simulation of meteorology over Himalaya
No full textThe boundary layer plays a vital role in governing the local atmospheric dynamics and meteorology, including the vertical transport of moisture, momentum, energy, and air pollutants. Hence, accurate parameterization of the boundary layer processes is essential in atmospheric models such as the regional climate models (RCMs). However, evaluations of different boundary layer schemes within the Weather Research and Forecasting (WRF) model remain nearly non-existing over complex terrains of the Himalayan region. In this regard, six different planetary boundary layer (PBL) schemes: Yonsei University (YSU), Mellor-Yamada-Nakanishi-Niino level 3 (MYNN3), Shin-Hong Scale-aware (SHSS), Mellor-Yamada-Janjić (MYJ), Asymmetric Convective Model version 2 (ACM2), and Quasi Normal Scale Elimination (QNSE) in the WRF model have been evaluated against observations during the Ganges Valley Aerosol Experiment (GVAX). The evaluation is carried out for clear-sky conditions during spring (March 22–27, 2012) against surface-based, balloon-borne, and radar wind profiler (RWP) observations. The MYJ, YSU, and SHSS performed well in simulating the temperature and specific humidity, whereas the ACM2 (MYNN3) shows lower (higher) bias in T2 and higher (lower) bias in Q2. Model performance is limited in reproducing wind field (r ∼ 0.3) with different PBL schemes over this region. Model performance is seen to vary significantly with the choice of PBL schemes; nevertheless, all schemes could typically capture the daytime maxima in boundary layer height with some overestimation. The competing effects of synoptic versus local circulation were found to control the boundary layer evolution over the Himalayas, affecting the model performance. The model better reproduced the boundary layer evolution during strong surface flow regimes, while a prominent nocturnal peak in boundary layer height is observed during the weak mean flow conditions associated with topography-induced local circulations. The model performance is limited for reproducing such mountain meteorological features in contrast to the homogeneous terrains where such nocturnal peaks are absent. This study may serve as a reference for selecting a suitable PBL scheme for regional climate modeling studies in the future
Synthesis, characterization, degradation and applications of vinyl polyperoxides
No full textPolymeric peroxide is an equimolar alternating copolymer formed by the reaction of a monomer with molecular oxygen (O2). Various polyperoxides have been successfully synthesized using different techniques, such as free radical polymerization, condensation polymerization, and insertion polymerization in the solid state. A wide variety of physical and chemical characteristics are displayed by these polyperoxides, making them attractive candidates for various applications. Due to their high exothermal degrading behavior and autocombustibility, polyperoxides are a viable alternative to fuels derived from petroleum. Additionally, polyperoxides have a wide range of applications, such as free radical initiators, curatives, biocompatible drug carriers, coating materials, dismantlable adhesives, and molding precursors. In this focused review, we report on recent efforts in developing vinyl homo- and copolyperoxides, their physicochemical behaviors, and various applications. Finally, the existing opportunities, possible challenges, and some viewpoints on future directions in vinyl polyperoxide research are highlighted
A shorter induction corticosteroid reduces acute lymphoblastic leukaemia induction deaths in the icicle-all-14 randomised trial
No full textBackground and Aim - Treatment-related toxicity poses an obstacle to improving acute lymphoblastic leukaemia (ALL) outcomes. The ICiCLe-ALL-14 randomised clinical trial investigated the toxicity impact of a shortened induction corticosteroid course in young children with non-high-risk ALL.
Method - Between October 2016 and August 2022, patients 1-10 years old with newly diagnosed standard-risk (SR) and intermediate-risk (IR) B-precursor ALL were `randomised to receive a long (28 days, continuous with 5-day taper; Long-Pred) or short (21 days, pulsed, no taper; Short-Pred) course of prednisolone 60 mg/m2/day during induction as part of the ICiCLe-ALL-14 trial. End-induction toxicity (deaths and Grades 3-4 NCI-CTCAE toxicities) was the primary endpoint. Treatment response (including minimum residual disease [MRD] levels), relapse, and survival outcomes represented secondary objectives. All analysis was by intention-to-treat.
Results - Of 1,307 eligible patients, 1,246(95%) received randomised induction corticosteroid (623/arm, matched for conventional risk factors). Thirty (2·4%) died in induction, all sepsis-related. Significantly more deaths were recorded in the Long-Pred group: 22/623 (3·5%) versus 8/623 (1·3%) (p=0·0095). More deaths were similarly observed in Long-Pred SR [7(88%)/8] and IR [15(68%)/22] sub-groups. Using Cox multivariable regression, induction mortality was significantly lower with the Short-Pred regimen (HR[hazard ratio], 3·12; 95% CI[confidence interval]1·38-7·04; p=0·0062), especially in younger patients (HR decrease of 0·83/unit increase in age; 95%CI, 0·68-0·99; p=0·0474). Rates of Grades 3-4 toxicity, predominantly sepsis and drug-induced hypertension, were comparable between the randomised groups (Long-Pred:44% versus Short-Pred:45%). Rates of induction-remission and MRD response were similarly comparable. So were treatment-related deaths post-induction (overall, 4%) and relapses (overall, 20%). At a median of 30 months, the estimated 3-year event-free survival was similar between Short-Pred (72%, 95%CI 68-76%) and Long-Pred (73%, 95%CI 68-77%) groups.
Conclusions - Reducing corticosteroid duration during induction therapy in young children with non-high-risk acute lymphoblastic leukaemia significantly decreases treatment-related deaths without compromising treatment response or relapse risk (Clinical Trials Registry India CTRI/2015/12/006434).
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Determinants of tumor necrosis and its impact on outcome in patients with Localized osteosarcoma uniformly treated with a response adapted regimen without high dose Methotrexate– A retrospective institutional analysis
No full textPurpose
Response to neoadjuvant chemotherapy in form of tumor necrosis predicts outcome in osteosarcoma; although response-adapted treatment escalation failed to improve outcome among patients treated with high-dose methotrexate-based (HDMTx) chemotherapy. This study aimed to identify factors predicting tumor necrosis and its impact on survival among patients with non-metastatic osteosarcoma treated with a response-adapted non-HDMTx regimen.
Methods
A retrospective single-institutional study was conducted among non-metastatic osteosarcoma patients treated with neoadjuvant therapy between 2004–2019. Patients were treated uniformly with three cycles of neoadjuvant cisplatin/doxorubicin. Post-operatively, patients with favourable necrosis (≥90 %) received 3 cycles of cisplatin/doxorubicin, while patients with poor necrosis (<90 %) received escalated treatment with alternating six cycles of cisplatin/doxorubicin and ifosfamide/etoposide. Propensity score matching (PSM) analyses were conducted to ascertain independent impact of necrosis on event-free survival (EFS) and overall survival (OS).
Results
Of 594 registered osteosarcoma patients, 280 patients (median age 17 years; male 67.1 %) were included for analysis. 73 patients (26.1 %) achieved favourable necrosis. Patients with smaller tumor size (≤10 cm) (aOR = 2.28; p = 0.030), lower serum alkaline phosphatase (≥450 IU/L) (aOR = 2.10; p = 0.035), and who had surgery earlier (<115 days) (aOR = 2.28; p = 0.016) were more likely to have favourable necrosis. On 1:2 PSM analysis, patients not achieving favourable necrosis demonstrated inferior EFS (HR = 2.68; p = 0.003) and OS (HR = 3.42; p = 0.003).
Conclusions
Patients of osteosarcoma with smaller tumor, lower serum alkaline phosphatase and earlier surgery are more likely to achieve favourable necrosis. Tumor necrosis independently predicts outcome in osteosarcoma, and response-adapted treatment escalation fails to overcome the adverse impact of poor necrosis in non-HDMTx based regimen
Trusted Personalized and Contextualized E-Commerce Services for Consumer Electronics over Wireless Sensor Network
No full textWireless sensor network-based edge computing (WEC) technology enables e-commerce to provide reliable and real-time services over the wireless sensor network (WSN). To enable efficient pseudonym updates and pseudonym tracking, an access point pseudonym management method for wireless edge computing is described in personalization and contextualization e-commerce services. Instead of the central cloud, the edge cloud with high real-time performance authenticates the access point identity, which increases identity authentication and pseudonym update. This study presents an access point pseudonym management strategy for edge computing based on wireless sensor network, addressing the security concern of leakage of SNU pseudonym information in the edge data center. To maintain the confidentiality of pseudonym information, it is homomorphically encrypted. Each WSN pseudonym table is associated with a search word, and the system’s highest authority can calculate the search word from pseudonym table ciphertext to reveal enemy SNUs’ true identities and achieve pseudonym traceability. According to established security theory, the approach is indistinguishable during a chosen-plaintext attack. Access point anonymity, message integrity, and nonrepudiation security studies in the system satisfy Internet of Consumer Electronics Access Point standards for identity privacy protection. The performance of identity authentication, pseudonym request, and homomorphic encryption is studied and simulated
Functional and Neuropsychological Outcome After Surgical Treatment of Moyamoya Disease
No full textMoyamoya disease (MMD) is a rare cerebrovascular disease characterized by progressive stenosis of the supraclinoid internal carotid artery. As a result of chronically decreased brain perfusion, eloquent areas of the brain become hypoperfused, leading to cognitive changes in patients. Repeated infarcts and bleeds produce clinically apparent neurologic deficits.
Objectives
1) To study the functional and neuropsychological outcome in MMD after revascularization surgery. 2) To find postrevascularization correlation between functional and neuropsychological improvement and radiologic improvement.
Methods
A single-center prospective and analytic study was carried out including 21 patients with MMD during the study period from March 2021 to December 2022. Patients were evaluated and compared before and after revascularization for functional, neuropsychological, and radiologic status.
Results
Postoperative functional outcome in terms of modified Rankin Scale score showed improvement in 33.33% of cases (P = 0.0769). An overall improving trend was observed in different neuropsychological domains in both adult and pediatric age groups. However, the trend of neuropsychological improvement was better in adults compared with pediatric patients. Radiologic outcome in the form of the Angiographic Outcome Score (AOS) significantly improved after revascularization (P = 0.0001). There was a trend toward improvement in magnetic resonance imaging (MRI) perfusion in the middle cerebral artery and anterior cerebral artery territories, 4.7% (P = 0.075) and 9.33% (P = 0.058) respectively, compared with preoperative MRI perfusion.
Conclusions
After revascularization, significant improvement occurred in functional and neuropsychological status. This result was also shown radiologically as evidenced by improvement in MRI perfusion and cerebral angiography
STAT1 mediated downregulation of the tumor suppressor gene PDCD4, is driven by the atypical cadherin FAT1, in glioblastoma
No full textSTAT1 (Signal Transducer and Activator of Transcription 1), belongs to the STAT protein family, essential for cytokine signaling. It has been reported to have either context dependent oncogenic or tumor suppressor roles in different tumors. Earlier, we demonstrated that Glioblastoma multiforme (GBMs) overexpressing FAT1, an atypical cadherin, had poorer outcomes. Overexpressed FAT1 promotes pro-tumorigenic inflammation, migration/invasion by downregulating tumor suppressor gene, PDCD4. Here, we demonstrate that STAT1 is a novel mediator downstream to FAT1, in downregulating PDCD4 in GBMs. In-silico analysis of GBM databases as well as q-PCR analysis in resected GBM tumors showed positive correlation between STAT1 and FAT1 mRNA levels. Kaplan-Meier analysis showed poorer survival of GBM patients having high FAT1 and STAT1 expression. SiRNA-mediated knockdown of FAT1 decreased STAT1 and increased PDCD4 expression in glioblastoma cells (LN229 and U87MG). Knockdown of STAT1 alone resulted in increased PDCD4 expression. In silico analysis of the PDCD4 promoter revealed four putative STAT1 binding sites (Site1-Site4). ChIP assay confirmed the binding of STAT1 to site1. ChIP-PCR revealed decrease in the binding of STAT1 on the PDCD4 promoter after FAT1 knockdown. Site directed mutagenesis of Site1 resulted in increased PDCD4 luciferase activity, substantiating STAT1 mediated PDCD4 inhibition. EMSA confirmed STAT1 binding to the Site 1 sequence. STAT1 knockdown led to decreased expression of pro-inflammatory cytokines and EMT markers, and reduced migration/invasion of GBM cells. This study therefore identifies STAT1 as a novel downstream mediator of FAT1, promoting pro-tumorigenic activity in GBM, by suppressing PDCD4 expression
Strategic decision-making for sustainable production and distribution in automotive industry: a machine learning enabled dynamic multi-objective optimisation
No full textOver the last decade, numerous researchers have disclosed that major automotive companies do not conform to regulatory or societal expectations regarding their environmental and social performances. This paper explores the dynamic capabilities of production distribution within the sustainability practices of automotive industries. It offers insights to better grasp and articulate the environmental, economic, and social dimensions of sustainable supply chains. The research framework encloses all supply chain phases, from raw material sourcing to retailing finished products. Three conflicting objective functions are identified: social advantages maximisation, cost minimisation, and emission minimisation. Specifically, the study tackles a dynamic multi-objective optimisation model where each automobile type faces a series of dynamic demands. The dynamic nature of the problem poses significant challenges to conventional evolutionary algorithms for detecting the optimal solutions over time. Therefore, we introduce an interconnected prediction-based dynamic non-dominated sorting algorithm (ICP-DNSGA-II). Finally, extensive computational experiments are conducted to assess the effectiveness of this holistic approach. The findings offer valuable insights for automotive industry stakeholders and policymakers, illustrating its potential to enhance operational efficiency and sustainability performance across the supply chain. Most importantly, this paper proposes an automated decision-making approach to generate optimal solutions with dynamic changes in market demands
Fuzzy Deep Learning for the Diagnosis of Alzheimer's Disease: Approaches and Challenges
No full textAlzheimer's disease (AD) is the leading neurodegenerative disorder and primary cause of dementia. Researchers are increasingly drawn to automated diagnosis of AD using neuroimaging analyses. Conventional deep learning (DL) models excel in constructing learning classifiers in early-stage AD diagnosis. However, they often struggle with AD diagnosis due to uncertainties stemming from unclear annotations by experts, challenges in data collection, such as data harmonization issues, and limitations in equipment resolution. These factors contribute to imprecise data, hindering accurate analysis, interpretation of obtained results, and understanding of complex symptoms. In response, the integration of fuzzy logic into DL, forming fuzzy deep learning (FDL), effectively manages imprecise data and provides interpretable insights, offering a valuable advancement in AD. Therefore, exploring recent advancements in integrating DL with fuzzy logic is crucial for improving AD diagnosis. In this review, we explore the contributions of fuzzy logic within FDL models, focusing on fuzzy-based image preprocessing, segmentation, and classification. Moreover, in exploring research directions, we discuss the possibility of the fusion of multimodal data with fuzzy logic, addressing challenges in AD diagnosis. Leveraging fuzzy logic and membership while integrating diverse datasets, such as genomics, proteomics, and metabolomics may provide an effective development of a DL classifier. In addition, fuzzy explainable DL promises more accurate and linguistically interpretable decision support systems for AD diagnosis. The primary objective of this article is to serve as a comprehensive and authoritative resource for newcomers, researchers, and clinicians interested in employing FDL models for AD diagnosis
Constraints on compact dark matter from the nonobservation of gravitational-wave strong lensing
No full textWe use the nonobservation of strong lensing of gravitational waves (GWs) in the first three observation runs of the LIGO and Virgo detectors to constrain the fraction of dark matter in the form of compact objects (COs) in the mass range 106−109M⊙. Using a Bayesian formalism supplemented by astrophysical simulations of strong lensing of GWs, we constrain the compact dark matter fraction to ≤0.4−0.6 with currently available data and show that they may get significantly tighter in the future. We find that multiple lensing—i.e., GWs getting deflected by multiple COs on their way to us—is possible. By ignoring this, we underestimate the constraints by a few percent