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In the last decade, the detection and attribution science that links climate change to extreme weather and climate events has emerged as a growing field of research with an increasing body of literature. This paper overviews the methods for extreme event attribution (EEA) and discusses the new insights that EEA provides for infrastructure adaptation. We found that EEA can inform stakeholders about current climate risk, support vulnerability-based and hazard-based adaptations, assist in the development of cost-effective adaptation strategies, and enhance justice and equity in the allocation of adaptation resources. As engineering practice shifts from a retrospective approach to a proactive, forward-looking risk management strategy, EEA can be used together with climate projections to enhance the comprehensiveness of decision making, including planning and preparing for unprecedented extreme events. Additionally, attribution assessment can be more useful for adaptation planning when the exposure and vulnerability of communities to past events are analyzed, and future changes in the probability of extreme events are evaluated. Given large uncertainties inherent in event attribution and climate projections, future research should examine the sensitivity of engineering design to climate model uncertainties, and adapt engineering practice, including building codes, to uncertain future conditions. While this study focuses on adaptation planning, EEA can also be a useful tool for informing and enhancing decisions related to climate mitigation.

Semantic segmentation of Remote Sensing (RS) images involves the classification of each pixel in a satellite image into distinct and non-overlapping regions or segments. This task is crucial in various domains, including land cover classification, autonomous driving, and scene understanding. While deep learning has shown promising results, there is limited research that specifically addresses the challenge of processing fine details in RS images while also considering the high computational demands. To tackle this issue, we propose a novel approach that combines convolutional and transformer architectures. Our design incorporates convolutional layers with a low receptive field to generate fine-grained feature maps for small objects in very high-resolution images. On the other hand, transformer blocks are utilized to capture contextual information from the input. By leveraging convolution and self-attention in this manner, we reduce the need for extensive downsampling and enable the network to work with full-resolution features, which is particularly beneficial for handling small objects. Additionally, our approach eliminates the requirement for vast datasets, which is often necessary for purely transformer-based networks. In our experimental results, we demonstrate the effectiveness of our method in generating local and contextual features using convolutional and transformer layers, respectively. Our approach achieves a mean dice score of 80.41%, outperforming other well-known techniques such as UNet, Fully-Connected Network (FCN), Pyramid Scene Parsing Network (PSP Net), and the recent Convolutional vision Transformer (CvT) model, which achieved mean dice scores of 78.57%, 74.57%, 73.45%, and 62.97% respectively, under the same training conditions and using the same training dataset.

Chromium (Cr) is classified as an ecological toxin, and irrigation of soil with raw industrial effluents causes its concentration in soil and plants to reach fatal levels. Due to reduced metal mobility caused by organic amendment and availability of food for microbial activity, use of Cr-tolerant rhizobacteria in conjunction with compost has proven to be useful in supporting growth and survival of plants. We checked the simultaneous effects of rhizobacteria and compost on spinach (Spinacia oleracea) growth, physiology, biochemistry as well as uptake of Cr. Plants were treated with compost (1 % w/w) and subsequently inoculated with two different PGPR strains under Cr contamination. Among tested inoculants , inoculation of 'JS2′ strain along with compost significantly ameliorated the adverse effects of Cr on spinach, and improved root and shoot lengths (84 and 197 %), as well as their dry (104 and 89 %) and fresh (89 and 167 %) weights , along with chlorophyll content (84 %) as compared to relative control. In addition, combined application led to a significant increase in antioxidant activities, such as SOD (128 %), CAT (40 %), POD (73 %) and APX (57 %) concomitant with decreased MDA contents (68 %). Data regarding Cr movement from soil to roots and aboveground plant parts, as well as the results of translocation and bioconcentration factors, revealed that combined application of PGPR and compost reduced Cr entry to aboveground plant parts and led to its phytostabilization. Hence, compost and Cr-tolerant PGPR can efficiently immobilize Cr in soil, and reduce its harmful effects on spinach growth, physiology and other important attributes.

This research aimed to develop a jam using orange-fleshed sweet potato puree (OFSPP) and pineapple pulp (PP) and to assess nutritional, gelling, sensory, and microbiological qualities. Four jam formulations of OFSPP: PP (70%:30, 50%:50%, and 30%:70) and 100% PP were developed and evaluated. Increasing the level of OFSPP resulted in a significant (P

Background and objectives: The pandemic of novel coronavirus disease 2019 (COVID-19) has severely impacted human society with a massive death toll worldwide. There is an urgent need for early and reliable screening of COVID-19 patients to provide better and timely patient care and to combat the spread of the disease. In this context, recent studies have reported some key advantages of using routine blood tests for initial screening of COVID-19 patients. In this article, first we present a review of the emerging techniques for COVID-19 diagnosis using routine laboratory and/or clinical data. Then, we propose ERLX which is an ensemble learning model for COVID-19 diagnosis from routine blood tests. Method: The proposed model uses three well-known diverse classifiers, extra trees, random forest and logistic regression, which have different architectures and learning characteristics at the first level, and then combines their predictions by using a second level extreme gradient boosting (XGBoost) classifier to achieve a better performance. For data preparation, the proposed methodology employs a KNNImputer algorithm to handle null values in the dataset, isolation forest (iForest) to remove outlier data, and a synthetic minority oversampling technique (SMOTE) to balance data distribution. For model interpretability, features importance are reported by using the SHapley Additive exPlanations (SHAP) technique. Results: The proposed model was trained and evaluated by using a publicly available data set from Albert Einstein Hospital in Brazil, which consisted of 5644 data samples with 559 confirmed COVID-19 cases. The ensemble model achieved outstanding performance with an overall accuracy of 99.88% [95% CI: 99.6–100], AUC of 99.38% [95% CI: 97.5–100], a sensitivity of 98.72% [95% CI: 94.6–100] and a specificity of 99.99% [95% CI: 99.99–100]. Discussion: The proposed model revealed better performance when compared against existing state-of-the-art studies (Banerjee et al., 2020; de Freitas Barbosa et al., 2020; de Moraes Batista et al., 2020; Soares et al., 2020) [3,22,56,71] for the same set of features employed by them. As compared to the best performing Bayes Net model (de Freitas Barbosa et al., 2020) [22] average accuracy of 95.159%, ERLX achieved an average accuracy of 99.94%. In comparison with AUC of 85% reported by the SVM model (de Moraes Batista et al., 2020) [56], ERLX obtained AUC of 99.77% in addition to improvements in sensitivity, and specificity. As compared with ER-COV model (Soares et al., 2020) [71] average sensitivity of 70.25% and specificity of 85.98%, ERLX model achieved sensitivity of 99.47% and specificity of 99.99%. The ERLX model obtained a considerably higher score as compared with ANN model (Banerjee et al., 2020) [3] in all performance metrics. Therefore, the model presented is robust and can be deployed for reliable early and rapid screening of COVID-19 patients.

Over the last decades, geo-structures are experiencing a rapid development in China. The potential risks inherent in the huge amount of construction and asset operation projects in China were well managed in the major project, i.e. the project of Shanghai Yangtze tunnel in 2002. Since then, risk assessment of geo-structures has been gradually developed from a qualitative manner to a quantitative manner. However, the current practices of risk management have been paid considerable attention to the assessment, but little on risk control. As a result, the responses to risks occurrences after a comprehensive assessment are basically too late. In this paper, a smart system for risk sensing incorporating the wireless sensor network (WSN) on-site visualization techniques and the resilience-based repair strategy was proposed. The merit of this system is the real-time monitoring for geo-structural performance and dynamic pre-warning for safety of on-site workers. The sectional convergence, joint opening, and seepage of segmental lining of shield tunnel were monitored by the micro-electro-mechanical systems (MEMS) based sensors. The light emitting diode (LED) coupling with the above WSN system was used to indicate different risk levels on site. By sensing the risks and telling the risks in real time, the geo-risks could be controlled and the safety of geo-structures could be assured to a certain degree. Finally, a resilience-based analysis model was proposed for designing the repair strategy by using the measured data from the WSN system. The application and efficiency of this system have been validated by two cases including Shanghai metro tunnel and underwater road tunnel.

The utilization of embryonic stem cells has ethical problems regarding the use of embryos in cell therapy and regenerative medicine, especially in neurodegenerative diseases. To overcome these ethical issues, induced Pluripotent Stem Cells and then transdifferentiation have presented to the science world which can be a good shortcut and solution to the ethical issues of traditional methods. Neurodegenerative diseases are difficult puzzles to combine and with modeling of these diseases by somatic cells reprogramming such as induced pluripotent stem cells induction or direct differentiation techniques, this could be solved. In the present study, we briefly review the techniques which used for neurodegenerative diseases’ researches.

Fibroblast growth factor-21 (FGF21) levels are elevated in patients with primary mitochondrial disorders but have not been studied in patients with inborn errors of metabolism (IEM) known to have secondary mitochondrial dysfunction. We measured plasma FGF21 by ELISA in patients with and without IEM. FGF21 levels were higher in patients with IEM compared to without IEM (370 pg/dL vs. 0–65 pg/dL). Further study of FGF21 as a biomarker in IEM is warranted.

Trifunctional protein deficiency/Long-chain hydroxyacyl-CoA dehydrogenase deficiency (LCHAD/TFP) deficiency is a disorder of fatty acid oxidation and ketogenesis. Severe neonatal lactic acidosis, cardiomyopathy, and hepatic dysfunction are caused by the accumulation of toxic long-chain acylcarnitines. The feasibility of umbilical cord blood use in screening for acylcarnitine analysis and free carnitine has been hypothesized but not reported in LCHAD/TFP neonates. We present a 4 week old female who was at risk of inheriting LCHAD/TFP deficiency and was diagnosed at the time of delivery using umbilical cord blood. Umbilical cord blood was collected at delivery and sent for acylcarnitine analysis. Treatment was started immediately. Acylcarnitine analysis demonstrated findings that are consistent with a biochemical diagnosis of LCHAD/TFP deficiency. Patients with LCHAD/TFP deficiency should have treatment initiated as early as possible to avoid acute decompensation and minimize the long-term complications of the disorder including cardiomyopathy. In pregnancies at risk of having a child with LCHAD/TFP deficiency, umbilical cord blood sample is an efficient method to diagnose an inborn error of metabolism such as LCHAD/TFP deficiency.

Fibroblasts from a Familial Mediterranean Fever (FMF) patient were reprogrammed with episomal vectors by using the Neon Transfection System for the generation of integration-free induced pluripotent stem cells (iPSCs). The resulting iPSC line was characterized to determine the expression of pluripotency markers, proper differentiation into three germ layers, the presence of normal chromosomal structures as well as the lack of genomic integration. A homozygous missense mutation in the MEFV gene (p.Met694Val), which lead to typical FMF phenotype, was shown to be present in the generated iPSC line.

Engineering structures are often subjected to the influences of performance deterioration and multiple hazards during their service lives, and consequently may suffer from damage/failure as a result of external loads. Structural reliability and resilience assessment is a powerful tool for quantifying the structural ability to withstand these environmental or operational attacks. This paper proposes new formulas for structural time-dependent reliability and resilience analyses in the presence of multiple hazards, which are functions of the duration of the reference period of interest. The joint impacts of nonstationarities in multiple hazards due to a changing environment, as well as the deterioration of structural performance, are explicitly incorporated. The correlation between the structural resistances/capacities associated with different hazard types is modeled by employing a copula function. It is observed that, under the context of multiple hazards and aging effects, the time-dependent resilience takes a generalized form of time-dependent reliability. The proposed formulas can be used to guide the adaptive design of structures, where adaptive strategies are identified across a range of possible future service conditions. An example is presented to demonstrate the applicability of the proposed method for structural reliability and resilience analyses.

Climate change can accelerate infrastructure deterioration in coastal areas because increased temperature and humidity can promote steel corrosion. This study (1) projects corrosion rate changes for reinforced concrete and steel structures in 223 coastal counties, (2) assesses the impact of corrosion rate changes on the useful life of structures, and (3) evaluates direct economic losses due to shortened useful life of highway bridges over the period 2000-2100. The results show that the useful life of concrete structures may decrease by 1.7-2.7% under the representative concentration pathway (RCP) 8.5 and decrease by 0.7-1.1% under RCP 4.5 by the end of the 21st century. The useful life of steel structures may decrease by 7.9-15.9% under RCP 8.5 and 3.3-6.7% under RCP 4.5. Concrete bridges may suffer an average loss of $6.5-11.7/m2 under RCP 8.5 and $3.3-16.5/m2 under RCP 4.5 due to shortened useful life. Steel bridges may suffer an average loss of $73.4-111.3/m2 under RCP 8.5 and $46.9-81.2/m2 under RCP 4.5. In both climate scenarios, 10% of counties may have negative losses and 10% of counties may have losses greater than $20 million due to corrosion rate changes for concrete and steel bridges. The results reveal the spatial difference of climate change impacts on infrastructural deterioration and suggest the importance of developing regional specific adaptation strategies.

Sir2, an evolutionarily conserved NAD+-dependent deacetylase, has been implicated as a key factor in mediating organismal life span. However, recent contradictory findings have brought into question the role of Sir2 and its orthologs in regulating organismal longevity. In this study, we report that Drosophila Sir2 (dSir2) in the adult fat body regulates longevity in a diet-dependent manner. We used inducible Gal4 drivers to knock down and overexpress dSir2 in a tissue-specific manner. A diet-dependent life span phenotype of dSir2 perturbations (both knockdown and overexpression) in the fat body, but not muscles, negates the effects of background genetic mutations. In addition to providing clarity to the field, our study contrasts the ability of dSir2 in two metabolic tissues to affect longevity. We also show that dSir2 knockdown abrogates fat-body dFOXO-dependent life span extension. This report highlights the importance of the interplay between genetic factors and dietary inputs in determining organismal life spans.