Matter Of Significant Concern Research Articles

Hospitalization costs in patients with stroke in southeastern China: a retrospective population-based cohort study, 2019–2022

IntroductionStroke remains a predominant cause of mortality and accounts for one-third of all stroke-related fatalities worldwide. Increasing expenses associated with stroke are a matter of significant concern; however, this aspect has been insufficiently examined.MethodsThe purpose of this study was to analyze in-hospital stroke costs and explore potential factors influencing them across stroke subtypes. The records of stroke patients from 50 hospitals in southeastern China between 2019 and 2022 were reviewed using multistage stratified cluster random sampling. We focused on the cost patterns of four stroke types and used multivariate linear regression to identify cost determinants.ResultsA total of 417 (1.1%) patients had subarachnoid hemorrhage (SAH), 9309 (25.9%) had intracerebral hemorrhage (ICH), 22,248 (61.8%) had ischemic stroke (IS), and 4025 had transient ischemic attack (TIA). The number of stroke patients has sharply increased since the onset of COVID-19, with a majority of them being male (72.2%). Despite the fact that hospitalization costs are highest in tertiary hospitals (Chinese yuan [CNY] 30610.8/United States dollar [USD] 4551.0, interquartile range [IQR] 9944.9, 29668.4/1478.6, 4410.9), the majority of patients are admitted to tertiary hospitals (74.6%) or public hospitals (90.2%). Across all stroke subtypes, patients with SAH had the highest costs (CNY 93,454.9/USD13894.4, IQR 12273.2, 169920.0/1824.7, 25262.8), followed by those with ICH (CNY 48,724.2/USD 7244.0, IQR 16789.6, 57540.7/2496.2, 8554.8), IS (CNY 26,550.3/USD3947.4, IQR 8684.2, 28697.7/1291.1, 4266.6), and TIA (CNY 11,170.1/USD1660.7, IQR 6823.7, 12965.2/1014.5, 1927.6). Therapy fees comprised a significant portion of costs in ICH and IS cases (47.9% and 42.7%, respectively). Materials accounted for the highest proportion of expenses for SAH (56.1%), whereas patients with TIA spent more time on examinations (34.1%). Linear regression analysis revealed that length of stay (LOS), stroke subtype, hospital level, and stroke type were key factors influencing hospitalization costsDiscussionThe visiting rate and charges were highest in tertiary public hospitals, and hospitalization costs were higher in hemorrhagic types of stroke than in ischemic types of stroke; the proportion of hospitalization cost categories varied among different types of stroke, with LOS, hospital type, and level substantially affecting hospitalization costs. Enhancing medical insurance reimbursement rates for hemorrhagic strokes, implementing a hierarchical medical system, tailoring cost categories to accommodate varying stroke subtypes, and shortening LOS may help alleviate the economic burden of stroke.

CoFe2O4/carbon nitride Z-scheme heterojunction photocatalytic PMS activation for efficient tetracycline degradation: Accelerated electron transfer

The escalating consumption of antibiotics and their subsequent discharge into the water supply is a matter of significant concern. This study presents an innovative visible light (VL) activated peroxymonosulfate (PMS) system for efficient degradation of the targeted antibiotic tetracycline (TC). CoFe2O4/carbon nitride (CFO/CN) Z-scheme heterojunctions were designed and synthesized through a simple co-precipitation and calcination strategy. Surface and analytical techniques were employed to comprehensively characterize the prepared CFO/CN, demonstrating its enhanced efficiency in separating photoinduced charge carriers. CFO/CN exhibited a 27-fold enhancement in TC degradation rate constants with PMS under VL. The effects of various factors including catalyst dosage, PMS concentration, initial pH, and content of CFO on TC degradation efficiency were investigated. Reactive species quenching and EPR measurements revealed the main contribution of superoxide radicals (•O2−) made to TC degradation. TC degradation pathways were proposed based on oxidized product analysis. The assessment of acute toxicity demonstrated the reduced toxicity of the formed intermediates. A plausible mechanism for TC degradation over CFO/CN-PMS-VL was discussed. This work provides a comprehensive guide for heterojunction photocatalytic activation of PMS for efficient micropollutant degradation.

Enhanced Hydrolysis of Carbonyl Sulfide in Coking Oven Gas Utilizing an Efficient Ca-Ba-γ-Al2O3 Catalyst

China possesses a substantial capacity for coke production, resulting in the annual generation of over 100 billion standard cubic meters of the by-product coke oven gas. The comprehensive utilization of this gas has emerged as a matter of significant concern within the coking industry. The removal of carbonyl sulfide (COS) from coke oven gas is crucial for enhancing gas quality, mitigating equipment corrosion, minimizing environmental pollution, elevating the quality of recovered products, and fostering the production of high-quality steel. A novel Ca-Ba-γ-Al2O3 catalyst has been devised, employing γ-Al2O3 as the catalyst matrix and integrating calcium hydroxide (Ca(OH)2) alongside barium hydroxide octahydrate (Ba(OH)2·8H2O) as the alkaline activating components. The impact of various factors, including reaction temperature, humidity, and the number of activating components loaded, on the hydrolysis efficiency of COS has been meticulously investigated. Furthermore, the catalytic reaction mechanism has been elucidated utilizing advanced characterization techniques such as X-ray diffraction (XRD) and Brunauer–Emmett–Teller (BET) analysis. The outcomes of this research reveal that, under optimal conditions of a reaction temperature of 55 °C and a humidity of 56%, the Ca-Ba-γ-Al2O3 catalyst achieves a remarkable COS hydrolysis efficiency of 95.22%.

From green fields to housing societies: Unraveling the mysteries behind agricultural land conversion in Pakistan

Agrarian land is being converted into a non-agricultural sector, which is a matter of significant concern. This study aims to determine the factors responsible for the agrarian land conversion to the non-agricultural sector in Pakistan. Both qualitative and quantitative data were collected for analysis. Quantitative data were collected from 608 farmers through a questionnaire survey, while qualitative data by in-depth interviews and focus group discussions. The study uses a logistic regression model and thematic analysis to determine the factors responsible for land conversion. Results show that farmers’ education, total landholding, urbanization, and the provision of government facilities positively influence land conversion. The ratio of irrigated land and production per acre were negatively associated with such land conversion. The qualitative data identified the push factors such as the social network of farmers, institutional and economic, while the pull factors included commercial opportunities, industrialization, and future expectations. Farmers should be assisted in diversifying their farm operations to generate multiple income streams, reducing the financial pressure that might lead to their selling land. Strict policies are also necessary to control the conversion of agrarian land into use for non-agricultural activities.

Flood damage assessment for the Phan-Ca Lo River basin in Vinh Phuc Province, Vietnam

ABSTRACT In this research, the authors aim to evaluate the damage caused by floods in Vinh Phuc Province. The study incorporates the construction of hydro-hydraulic models and the development of damage functions for the main elements at risk. In order to ensure the accuracy of the calculated results, the hydro-hydraulic models and damage curves are verified for their reasonableness. The outcomes of this study give us an overview of flood damage in the research area. It indicates that the potential level of flood damage is a matter of significant concern, with an annual damage estimate of approximately 6.9 million USD. The research also reveals the distribution of damage by sector and by space. According to the research findings, the greatest damage was in Vinh Tuong District, where most of the damage was caused by flooding of agricultural land. In contrast, residential buildings in the area suffered relatively little damage compared to agricultural land. This information is crucial for decision-makers to implement effective and targeted mitigation measures.

The influence of nanosunflower ash and nanowalnut shell ash on sustainable lightweight self-compacting concrete characteristics

The absence of biodegradability exhibited by plastics is a matter of significant concern among environmentalists and scientists on a global scale. Therefore, it is essential to figure out potential pathways for the use of recycled plastics. The prospective applications of its utilisation in concrete are noteworthy. The use of recycled plastic into concrete, either as a partial or complete substitution for natural aggregates, addresses the issue of its proper disposal besides contributing to the preservation of natural aggregate resources. Furthermore, the use of agricultural wastes has been regarded as a very promising waste-based substance in the industry of concrete manufacturing, with the aim of fostering the creation of an environmentally sustainable construction material. This paper illustrates the impact of nano sunflower ash (NSFA) and nano walnut shells ash (NWSA) on durability (compressive strength and density after exposure to 800 °C and sulphate attack), mechanical properties (flexural, splitting tensile and compressive strength) and fresh characteristics (slump flow diameter, T50, V-funnel flow time, L-box height ratio, segregation resistance and density) of lightweight self-compacting concrete (LWSCC). The waste walnut shells and local Iraqi sunflower were calcinated at 700 ± 50 °C for 2 h and milled for 3 h using ball milling for producing NSFA and NWSA. The ball milling succeeded in reducing the particle size lower than 75 nm for NSFA and NWSA. The preparation of seven LWSCC concrete mixes was carried out to obtain a control mix, three mixtures were created using 10%, 20% and 30% NWSA, and the other three mixtures included 10%, 20% and 30% NSFA. The normal weight coarse aggregates were substituted by the plastic waste lightweight coarse aggregate with a ratio of 75%. The fresh LWSCC passing capacity, segregation resistance, and filling capability were evaluated. The hardened characteristics of LWSCC were evaluated by determining the flexural and splitting tensile strength at 7, 14 and 28 days and the compressive strength was measured at 7, 14, 28 and 60 days. Dry density and compressive strength were measured after exposing mixes to a temperature of 800 °C for 3 h and immersed in 10% magnesium sulphate attack. The results demonstrated that the LWSCC mechanical characteristics were reduced when the percentages of NWSA and NSFA increased, except for 10% NWSA substitution ratio which had an increase in splitting tensile strength test and similar flexural strength test to the control mixture. A minor change in mechanical characteristics was observed within the results of LWSCC dry density and compressive strength incorporating various NSFA and NWSA` contents after exposing to temperature 800 °C and immersed in 10% magnesium sulphate attack. Furthermore, according to the findings, it is possible to use a combination of materials consisting of 10–20% NSFA and 10–20% NWSA to produce LWSCC.

Mechanical Properties of Virgin and Recycled Polymer for Construction Pile Application

Annual polymer waste generated in Malaysia has increased significantly to more than 1 million tonnes. The prolonged degradation periods required by diverse industrial polymer waste streams are a matter of significant concern, with some taking up to 1000 years to fully degrade. Pursuing a similar environmental concern, the use of bakau piles as supports for lightweight structures in Sarawak, including drainage systems, roads, sewerage, and other water-related structures, has become a matter of concern due to the deforestation of mangrove forests. Both bakau deforestation and polymer waste issues are significant environmental and global concerns. The idea of mitigating mangrove degradation and the non-biodegradable nature of polymer waste has led to the conceptualization of an alternative solution whereby recyclable thermoplastic polymer piles are utilized to supplant bakau piles in providing support for lightweight structures during civil engineering construction projects. Therefore, the study of polymer piles is conducted to examine their mechanical properties in the form of virgin (V) and recycled (R) thermoplastic polymers. In this study, high-density polyethylene (HDPE), polypropylene (PP), and polyvinyl chloride (PVC) are considered, and the possibility of being utilized in pile application has been discussed. Based on the results, all virgin types of thermoplastic polymers (HDPE, PP, and PVC), 50%V:50%R for PP, PP(R), and PVC(R), respectively, exceed the bakau ultimate tensile strength. Thermoplastic polymer piles showed great potential to be the substitution for bakau piles to serve in the construction industry, with the recorded experimental tensile and compressive strength tests.

Towards efficient traffic crash detection based on macro and micro data fusion on expressways: A digital twin framework

AbstractEfficient detection of traffic crashes has been a significant matter of concern with regards to expressway safety management. The current challenge is that, despite collecting vast amounts of data, expressway detection equipment is plagued by low data utilization rates, unreliable crash detection models, and inadequate real‐time updating capabilities. This study is to develop an effective digital twin framework for the detection of traffic crashes on expressways. Firstly, the digital twin technology is used to create a virtual entity of the real expressway. A fusion method for macro and micro traffic data is proposed based on the location of multi‐source detectors on a digital twin platform. Then, a traffic crash detection model is developed using the ThunderGBM algorithm and interpreted by the SHAP method. Furthermore, a distributed strategy for detecting traffic crashes is suggested, where various models are employed concurrently on the digital twin platform to enhance the general detection ability and reliability of the models. Finally, the efficacy of the digital twin framework is confirmed through a case study of certain sections of the Nanjing Ring expressway. This study is expected to lay the groundwork for expressway digital twin studies and offer technical assistance for expressway traffic management.

Challenges confronting rural physical science educators in enhancing Grade 12 Performance

The quality of Physical Science education in rural South African schools is a matter of significant concern, particularly in the context of the country's development goals aimed at uplifting education standards in these areas and nurturing a more skilled workforce. Physical Science plays a pivotal role in enhancing scientific literacy across the nation. This qualitative study, framed within Vygotsky's sociocultural theory, sought to identify the challenges faced by Physical Science educators in improving Grade 12 results in rural schools in Vuwani. Data were collected through semi-structured interviews and classroom observations involving a purposive selection of ten Physical Science educators from among 49 schools within the district. Thematic analysis was applied to the collected data, yielding insightful interpretations. The findings, primarily from interview responses, reveal that Physical Science educators in Vuwani grapple with various challenges. These challenges encompass resource scarcity, excessive workloads, inadequate support from school management teams (SMT), insufficient training when curricula change, overcrowded classrooms, and suboptimal learning environments. This study underscores the importance of fostering collaborative partnerships between schools, local communities, and business sectors. In particular, it recommends that businesses extend their support to rural schools, addressing infrastructure deficiencies and resource shortages. Such collaborative initiatives between government entities and businesses can enhance the overall quality of education in rural areas, ultimately mitigating the skills deficit and contributing to the nation's socioeconomic development goals.

Evaluation of End-of-Life Vehicle Recycling in India: A Techno-Socio-Economic Analysis

In light of the rapidly advancing Indian automotive sector, the issue of end-of-life vehicles (ELVs) has emerged as a matter of significant concern. Particularly critical aspects encompass recycling procedures, environmental impacts, and social implications. With the inevitable proliferation of ELVs anticipated in the next decade, the imperative for effective and sustainable ELV management becomes paramount, necessitating a comprehensive analysis of India's burgeoning ELV recycling landscape. The present study endeavors to scrutinize India's ELV recycling system from technological, social, and economic perspectives, constituting a preliminary step toward a judicious evaluation of sustainability in ELV recycling and performing a SWOT (strengths, weaknesses, opportunities, and threats) analysis to appraise India's ELV recycling framework comprehensively. Furthermore, the ensuing recommendations shall furnish invaluable insights for the development and implementation of forthcoming regulatory and legal frameworks that adequately incorporate the economic, social, environmental, and technological aspects of ELV recycling in India. To accomplish this endeavor, the study has diligently conducted in-depth interviews with pertinent stakeholders, undertaken rigorous field investigations, and administered comprehensive surveys. Additionally, an exhaustive examination of the existing literature has been undertaken to probe the current ELV recycling system, address lingering challenges, and proffer recommendations to elevate the ELV recycling infrastructure. The outcomes of this study may assist in developing deeper comprehension of India's ELV recycling system while furnishing pragmatic recommendations to fortify its efficacy.

A Discussion of Key Aspects and Trends in Self Driving Vehicle Technology

Autonomous vehicles use remote-sensing technologies such as radar, GPS, cameras, and lidar to effectively observe their immediate environment and construct a comprehensive three-dimensional representation. The conventional constituents of this particular environment include structures, additional vehicles, people, as well as signage and traffic indicators. At now, a self-driving car is equipped with a wide array of sensors that are not found in a traditional automobile. Commonly used sensors include lasers and visual sensors, which serve the purpose of acquiring comprehensive understanding of the immediate environment. The cost of these sensors is high and they exhibit selectivity in their use requirements. The installation of these sensors in a mobile vehicle also significantly diminishes their operational longevity. Furthermore, the issue of trustworthiness is a matter of significant concern. The present article is structured into distinct parts, each of which delves into a significant aspect and obstacle pertaining to the trend and development of autonomous vehicles. The parts describing the obstacles in the development of autonomous vehicles define the conflict arising from the use of cameras and LiDAR technology, the influence of social norms, the impact of human psychology, and the legal complexities involved.

Social Construction of Population Policies and Problems in Iran Since 1963

: Population has been and continues to be a significant matter of concern and contention in Iran. A range of different claims-makers, with varying dramas, ideologies, resources, rhetorical strategies and schemas have made claims that name and frame the population problem in particular ways to influence policy processes, and the experiences, meanings, and practices of people in everyday life. As a result, since 1967, mainly in post-revolutionary Iran, there has been the rise and fall of contesting narratives of the population as a social problem. Drawing on theoretical insights from social constructionism, we track these debates. In particular, we are interested in how claims-makers use rhetorical strategies and mobilize resources to convince others and to influence policy processes through social problem games and works. In other words, we examine the claims-making activities around the population as a social problem over the last half-century in Iran, showing how an understanding of these activities can enrich our understanding of definitional processes. We also discuss how certain discourses, policies and social constructions of population and family planning have become dominant and have influenced cultural meanings, popular images and the daily life experiences of people, particularly women and their prospects of reproduction and motherhood, for the most part, in the shadow of interpretations of Islamic Sharia law enforced since the 1979 Revolution. Building on the constructionist analysis and interoperation of contesting anti-natalist and pronatalist claims and policies over the last half-century in Iran, this article highlights how images of population problems are constructed socio-culturally with actual impact on individuals’ everyday lives in society, and how people find ways to respond, resist and counter the dominant discourses and policies about family planning, population and reproduction and family. Hence, the contesting claims, definitions and discourses around population have not been merely abstract points of debate, but they have penetrated the emotions, memories, relationships, prospects and practices of people in Iran. The results reveal that there are several existing and emerging dialectics, paradoxes, and uncertainties on population policies and problems at multiple levels, with each of these definitions having key insights and implications for policy-related scientific research (science), the policy process and its outcomes, reproductive prospects and the practices of people (public).

Identification of potential Escherichia coli DNA gyrase B inhibitors targeting antibacterial therapy: an integrated docking and molecular dynamics simulation study

The alarming rise in the rate of antibiotic resistance is a matter of significant concern. DNA gyrase B (GyrB), a critical bacterial enzyme involved in DNA replication, transcription, and recombination, has emerged as a promising target for antibacterial agents. Inhibition of GyrB disrupts bacterial DNA replication, leading to cell death, making it an attractive candidate for antibiotic development. Although several classes of antibiotics targeting GyrB are currently in clinical use, the emergence of antibiotic resistance necessitates the exploration of novel inhibitors. In this study, we aimed to identify potential Escherichia coli GyrB inhibitors from a database of phytoconstituents sourced from Indian medicinal plants. Utilizing virtual screening, we performed a rigorous search to identify compounds with the most promising inhibitory properties against GyrB. Two compounds, namely Zizogenin and Cucurbitacin S, were identified based on their favorable drug likeliness and pharmacokinetic profiles. Employing advanced computational techniques, we analyzed the binding interactions of Zizogenin and Cucurbitacin S with the ATP-binding site of GyrB through molecular docking simulations. Both compounds exhibited robust binding interactions, evidenced by their high docking energy scores. To assess the stability of these interactions, we conducted extensive 100 ns molecular dynamics (MD) simulations, which confirmed the stability of Zizogenin and Cucurbitacin S when bound to GyrB. In conclusion, our study highlights Zizogenin and Cucurbitacin S as promising candidates for potential antibacterial agents targeting GyrB. Experimental validation of these compounds is warranted to further explore their efficacy and potential as novel antibiotics to combat antibiotic-resistant bacteria. Communicated by Ramaswamy H. Sarma

GD06 The grass is always greener on the nearer side

Abstract Climate change is becoming a significant matter of concern, and ways to reduce our impact on the environment are becoming an increasing priority. We assessed the difference in carbon emissions when patients attended our peripheral dermatology clinic rather than the tertiary centre. Our objective was to establish the reduction in carbon emissions when patients attended satellite clinics instead of a tertiary service. The addresses of patients attending clinics over a month-long period were compiled, and distances to both hospitals were measured. These were compared, and the difference was used to calculate the reduction in carbon emissions. The emissions produced by staff members were added to the total of emissions produced by patients travelling to peripheral clinics. In total, 245 patients were included in the study. The total distance travelled by patients for a round trip to the satellite clinic was 10 105.9 km, and the equivalent for the tertiary centre was 14 775 km. The mean distance travelled by patients to and from the peripheral clinic was 41.25 km. The mean distance travelled by patients to and from the tertiary hospital was 84.69 km. The amount of carbon emissions produced by the patients travelling to the peripheral hospital was 1.87 tonnes. The amount of carbon emissions of patients travelling to the tertiary hospital was 2.73 tonnes. This was a saving of 0.86 tonnes over a 1-month period. Annually, this amounts to a difference of 10.32 tonnes of carbon emissions. The provision of peripheral dermatology clinics has a significant positive impact on the environment and greatly reduces the distances travelled by patients. Our study suggests that satellite clinics could reduce carbon emissions by 10.32 tonnes annually. This equates to 138 flights from Dublin to London, or 12 900 takeaway coffees. This study has demonstrated that the provision of peripheral clinics can make healthcare more sustainable, by reducing the number of carbon emissions being produced.

Assisting the Human Embryo Viability Assessment by Deep Learning for In Vitro Fertilization

The increasing global infertility rate is a matter of significant concern. In vitro fertilization (IVF) significantly minimizes infertility by providing an alternative clinical means of becoming pregnant. The success of IVF mainly depends on the assessment and analysis of human blastocyst components such as the blastocoel (BC), zona pellucida (ZP), inner cell mass (ICM), and trophectoderm (TE). Embryologists perform a morphological assessment of the blastocyst components for the selection of potential embryos to be used in the IVF process. Manual assessment of blastocyst components is time-consuming, subjective, and prone to errors. Therefore, artificial intelligence (AI)-based methods are highly desirable for enhancing the success rate and efficiency of IVF. In this study, a novel feature-supplementation-based blastocyst segmentation network (FSBS-Net) has been developed to deliver higher segmentation accuracy for blastocyst components with less computational overhead compared with state-of-the-art methods. FSBS-Net uses an effective feature supplementation mechanism along with ascending channel convolutional blocks to accurately detect the pixels of the blastocyst components with minimal spatial loss. The proposed method was evaluated using an open database for human blastocyst component segmentation, and it outperformed state-of-the-art methods in terms of both segmentation accuracy and computational efficiency. FSBS-Net segmented the BC, ZP, ICM, TE, and background with intersections over union (IoU) values of 89.15, 85.80, 85.55, 80.17, and 95.61%, respectively. In addition, FSBS-Net achieved a mean IoU for all categories of 87.26% with only 2.01 million trainable parameters. The experimental results demonstrate that the proposed method could be very helpful in assisting embryologists in the morphological assessment of human blastocyst components.

A Deep Convolutional Neural Network for the Early Detection of Heart Disease

Heart disease is one of the key contributors to human death. Each year, several people die due to this disease. According to the WHO, 17.9 million people die each year due to heart disease. With the various technologies and techniques developed for heart-disease detection, the use of image classification can further improve the results. Image classification is a significant matter of concern in modern times. It is one of the most basic jobs in pattern identification and computer vision, and refers to assigning one or more labels to images. Pattern identification from images has become easier by using machine learning, and deep learning has rendered it more precise than traditional image classification methods. This study aims to use a deep-learning approach using image classification for heart-disease detection. A deep convolutional neural network (DCNN) is currently the most popular classification technique for image recognition. The proposed model is evaluated on the public UCI heart-disease dataset comprising 1050 patients and 14 attributes. By gathering a set of directly obtainable features from the heart-disease dataset, we considered this feature vector to be input for a DCNN to discriminate whether an instance belongs to a healthy or cardiac disease class. To assess the performance of the proposed method, different performance metrics, namely, accuracy, precision, recall, and the F1 measure, were employed, and our model achieved validation accuracy of 91.7%. The experimental results indicate the effectiveness of the proposed approach in a real-world environment.

Choices of (in)action in obesity: Implications for research on treatment and prevention.

The obesity epidemic has crossed social-demographic barriers and is a matter of significant concern. Why do individuals fail to restrain from eating high-calorie foods and fail to follow treatment routines that reduce the risk of health complications? These questions have been addressed through behavioral and brain imaging studies on prefrontal cortex inhibitory mechanisms. Failure to inhibit undesirable behaviors has become a hallmark of obesity. In many life situations, obesity risk is increased by inaction (e.g., not taking blood pressure medication, not following a healthy diet). Risk by inaction has been defined as passive risk-taking, and it is correlated with traits such as procrastination, future time perspective, and cognitive avoidance. To the present, passive tendencies, specifically in the context of risk-taking behaviors, have not been addressed in the obesity literature. We introduce a framework in which active and passive risk-taking behaviors are integrated within the scope of bidirectional models of obesity that describe the brain as both the cause and the consequence of obesity vulnerability. The present perspective aims to foster new research on treatment and prevention, and also on the neurobiology of passive behaviors in obesity and other metabolic conditions.

In Silico Screening of Synthetic and Natural Compounds to Inhibit the Binding Capacity of Heavy Metal Compounds against EGFR Protein of Lung Cancer.

Inorganic pollutant, specifically heavy metals' contamination, is a significant matter of concern and is one of the key contributors in various health disorders including cancer. However, the interaction of heavy metals (HMs) with lung cancer has rarely been explored yet. Therefore, the present study was intended with the aim to identify the interactions of HMs with the target protein “epidermal growth factor receptor (EGFR)” of lung cancer and explore potential drug candidates, which could inhibit the active site of EGFR against HM exposure. The molecular operating environment (MOE) tool was used to study the interactions of HMs with EGFR protein. The drug-drug interaction (DDI) network approach was used to identify the potential drug candidates, which were further confirmed and compared with the commercial medicines/control group. Various compounds of twenty-three HMs were docked with EGFR protein. Out of which tinidazole, thallium bromodimethyl, and silver acetate (Sn, Ti, and Ag compounds) showed strong interactions with EGFR based on lowest-scoring values (-20.42, -7.86, and -7.74 kcal/mol, respectively). Among 1280 collected drug candidates, three synthetic compounds viz., ZINC00602803, ZINC00602685, and ZINC06718468 and three natural compounds (berberine chloride, transresveratrol, and ellagic acid) depicted strong binding capacity with EGFR. Specifically, the scoring value of ZINC00602803 (-30.99 kcal/mol) was even lowest than standard lung cancer drugs (afatinib, erlotinib, and gefitinib). Our findings revealed that both natural and synthetic compounds having strong associations with EGFR protein could be potential candidates to inhibit the interaction between HMs and lung cancer protein and can also be used as an alternative for the prevention and treatment of lung cancer. However, in vitro and in vivo studies should be conducted to validate the aforementioned natural and synthetic compounds.

Interactions between the Arctic Mediterranean and the Atlantic Meridional Overturning Circulation: A Review

The Atlantic Meridional Overturning Circulation (AMOC) plays a significant role in the global climate system, and its behavior in a warming climate is a matter of significant concern. The AMOC is thought to be driven largely by ocean heat loss in the subpolar North Atlantic Ocean, but recent research increasingly emphasizes the importance of the Arctic Mediterranean for the AMOC. In turn, the AMOC may influence the Arctic heat budget through its impact on poleward heat transport. Hence, understanding the processes that link the AMOC and the Arctic is critical for our ability to project how both may evolve in a warming climate. In this paper we review some of the recent research that is shaping our thinking about the AMOC and its two-way interactions with the Arctic.

Directional Pattern based Clustering for Quantitative Survey Data: Method and Application

Analysis of survey data is a matter of significant concern as it plays a key role in organizational and behavioral research. Quantitative survey data possesses several distinct characteristics i.e., fixed small range of ordinal values, importance of respondent category labels etc. Due to such reasons quantitative survey data is not appropriate for existing analysis methods involving aggregate statistics. Literature has advised to utilize pattern based analysis tools instead of aggregate statistics since patterns are more informative and efficient in reflecting respondents’ preferences. Thus, we introduce a specialized pattern based clustering technique for survey data that uses the convention of direction instead of magnitude. Further, it does not require manual setting of clustering parameters whereas it automatically identifies respondent categories and their representative features with the help of an adaptive procedure. We apply proposed method over an original academic survey dataset and compare its results with K-Means clustering method in terms of interpretability and usability. We utilize benchmark stakeholder theory to verify the results. Results suggest that proposed pattern clustering method performs far better in segregating survey responses according to the stakeholder theory and the clusters made by it are much more meaningful. Hence, results empirically validates that pattern based analysis methods are more suitable for analyzing quantitative survey data.