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BackgroundPersistent inflammation following vascular injury drives neointimal hyperplasia (NIH). Specialized lipid mediators (SPM) mediate resolution which attenuates inflammation and downstream NIH. We investigated the effects of a synthetic analogue of resolvin D1 (RvD1) on vascular cells and in a model of rat carotid angioplasty.MethodsHuman venous VSMC and endothelial cells (EC) were employed in migration, cell shape, toxicity, proliferation and p65 nuclear translocation assays. Murine RAW 264.7 cells were utilized to test the effect of pro-resolving compounds on phagocytic activity. A model of rat carotid angioplasty was used to evaluate the effects of 17R/S-benzo-RvD1 (benzo-RvD1) and 17R-RvD1 applied to the adventitia via 25% Pluronic gel. Immunostaining was utilized to examine Ki67 expression and leukocyte recruitment. Morphometric analysis was performed on arteries harvested 14 days after injury.ResultsExposure to benzo-RvD1 attenuated PDGF- stimulated VSMC migration across a range of concentrations (0.1-100 nM), similar to that observed with 17R-RvD1. Pre-treatment with either Benzo-RvD1 or 17R-RvD1 (10, 100nM) attenuated PDGF-BB-induced VSMC cytoskeletal changes to nearly baseline dimensions. Benzo-RvD1 demonstrated modest anti-proliferative activity on VSMC and EC at various concentrations, without significant cytotoxicity. Benzo-RvD1 (10nM) inhibited p65 nuclear translocation in cytokine-stimulated EC by 21% (p

Introduction: The use of theories, models and/or frameworks (TMFs) in implementation research and practice is essential for developing useful and testable implementation strategies. Recommendations and tools exist to aid implementation groups in selecting TMFs, but they do not explicitly outline a systematic method for identifying and selecting TMFs. This paper aimed to (1) propose a systematic consensus-based method to select TMFs to support implementation processes, and to (2) demonstrate the use of this novel method in the context of researching the implementation of hip protectors for fracture prevention in long-term care (LTC). Materials & methods: We developed a systematic, consensus-based method for selecting TMFs, referred to as the Implementation Theory Selection Model (ITSM). The ITSM comprises five steps: (1) identify potentially relevant TMFs; (2) narrow the pool of TMFs; (3) appraise the relevance of eligible TMFs; (4) prioritize a short-list of TMFs for further, in-depth consideration; and (5) select TMFs through consensus with investigators and research user partners. We operationalized each step of the ITSM through a project investigating determinants of hip protector use and organizational readiness for implementation in a LTC organization in Ontario, Canada. Results: Using the ITSM in our case example, we identified 66 TMFs (Step 1). Of these, 23 met our eligibility criteria (Step 2) and were appraised twice, by five appraisers (Step 3). Six TMFs (Step 4) advanced to the consensus meeting, which was attended by nine investigators and three research users, including two organizational partners and one older adult. Three rounds of voting yielded a tie between the TMFs the group felt would be most appropriate. Research users from our partner LTC organization made the final selection preferring the combination of the Practical, Robust Implementation and Sustainability Model and Consolidated Framework for Implementation Research (Step 5). Conclusions: The ITSM offers a step-by-step guide for implementation groups to adopt a rigorous, transparent and reproducible method for TMF selection. Although we have demonstrated the feasibility of operationalizing each step of the ITSM in our case example, continued research is needed to evaluate and refine the ITSM to ensure it is appropriate for a wide variety of implementation contexts. [ABSTRACT FROM AUTHOR]

Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction. Breast cancer, a significant global health concern, is particularly prevalent in Asia due to diverse factors like lifestyle, genetics, environmental exposures, and healthcare accessibility. Early detection through mammography screening is critical, but the accuracy of mammograms can vary due to factors like breast composition and tumor characteristics, leading to potential misdiagnoses. To address this, an innovative CAD system leveraging deep learning and computer vision techniques was introduced. This system enhances breast cancer diagnosis by independently identifying and categorizing breast lesions, segmenting mass lesions, and classifying them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) demonstrated the CAD system's exceptional performance, with a 99% success rate in detecting and classifying breast masses. While the accuracy of detection is 98.5%, when segmenting breast masses into separate groups for examination, the method's performance was approximately 95.39%. Upon completing all the analysis, the system's classification phase yielded an overall accuracy of 99.16% for classification. The potential for this integrated framework to outperform current deep learning techniques is proposed, despite potential challenges related to the high number of trainable parameters. Ultimately, this recommended framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain. [ABSTRACT FROM AUTHOR]

Typhoid fever, caused by Salmonella enterica serovar typhi, presents a substantial global health threat, particularly in regions with limited healthcare infrastructure. The rise of multidrug-resistant strains of S. typhi exacerbates this challenge, severely compromising conventional treatment efficacy due to over activity of efflux pumps. In our study, a comprehensive exploration of two fundamental aspects to combat MDR in S. typhi is carried out; i.e. employing advanced bioinformatics analyses and AlphaFold AI, We successfully identified and characterised a putative homologue, ABC-TPA, reminiscent of the P-glycoprotein (P-gp) known for its role in multidrug resistance in diverse pathogens. This discovery provides a critical foundation for understanding the potential mechanisms driving antibiotic resistance in S. typhi. Furthermore, employing computational methodologies, We meticulously assessed the potential of lignans, specifically Schisandrin A, B, and C, as promising Efflux Pump Inhibitors (EPIs) against the identified P-gp homologue in S. typhi. Noteworthy findings revealed robust binding interactions of Schisandrin A and B with the target protein, indicating substantial inhibitory capabilities. In contrast, Schisandrin C exhibited instability, showing varied effectiveness among the evaluated lignans. Pharmacokinetics and toxicity predictions underscored the favourable attributes of Schisandrin A, including prolonged action duration. Furthermore, high systemic stability and demanished toxicity profile of SA and SB present their therapeutic efficacy against MDR. This comprehensive investigation not only elucidates potential therapeutic strategies against MDR strains of S. typhi but also highlights the relevance of computational approaches in identifying and evaluating promising candidates. These findings lay a robust foundation for future empirical studies to address the formidable challenges antibiotic resistance poses in this clinically significant infectious diseases. [ABSTRACT FROM AUTHOR]

Short and long-term sound-induced stress on daily basis can affect the physiology of avian individuals because they are more susceptible to sound stress in an open environment. Objectives: An ex-situ study was carried out to determine the impact of noise on physiology and ptilochronology of non-breeding male domesticated quail birds. Methodology: During 60-days long trial, male quail birds, aged 5-weeks, weighing (c.100gm) were used. Out of 72 experimental birds, 18 birds were assigned to the Control Group (G1) while remaining 54 birds were divided equally into 3 treatment groups: Road Traffic noise (G2), Military activity noise (G3) and Human Activities noise (G4). Birds were housed in standard-sized separate cages (20 ×45 × 20 cm), every bird was kept apart in separate cage in open laboratory under maintained environmental conditions. Millet seeds and water were provided to all the experimental birds ad libitum. Noise originated from several sources of recorded high-intensity music (1125 Hz/ 90 dB), was administered for 5–6 hours per day. Observations were recorded in the morning and afternoon. The experiment was conducted during the non-breeding season from August to October in triplicate. Blood sampling was done after 60 days. Results: According to the current study, noise stress significantly (p<0.05) increased the concentrations of creatinine, aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), bilirubin, uric acid, cholesterol, triglycerides, total protein, and glucose while a decline in the levels of albumin was seen in treatment birds of G3. While in terms of hematology, total white blood cells count (TWBC), total red blood cells count (TRBC), mean cell volume (MCV) & packed cell volume (PCV) concentrations were raised in blood of treatment birds of G3. In terms of hormones, noise stress significantly (p<0.05) increased the serum concentrations of Corticosterone in G3 while a significant (p<0.05) decline was observed in the concentrations of luteinizing hormone (LH), thyroid stimulating hormone (TSH), and follicle stimulating hormone (FSH) in the same group. Moreover, fault bar formation in G3 was more prominent than others. Conclusion: Noise stress can significantly affect serology, hematology, hormonal physiology and ptilochronology in quail birds. [ABSTRACT FROM AUTHOR]

BackgroundAllogeneic hematopoietic cell transplantation (HCT) can be curative for many hematologic diseases. However, complications such as graft-versus-host disease (GVHD) and relapse of primary malignancy remain significant and are the leading causes of morbidity and mortality. Effects of killer Ig-like receptors (KIR)-influenced NK cells on HCT outcomes have been extensively pursued over the last decade. However, the relevance of the reported algorithms on HLA matched myeloablative HCT with rabbit antithymocyte globulin (ATG) is used for GVHD prophylaxis remains elusive. Here we examined the role of KIR and KIR-ligands of donor-recipient pairs in modifying the outcomes of ATG conditioned HLA matched sibling and unrelated donor HCT.Methods and findingsThe study cohort consisted of 281 HLA matched sibling and unrelated donor-recipient pairs of first allogeneic marrow or blood stem cell transplantation allocated into 'discovery' (135 pairs) and 'validation' (146 pairs) cohorts. High resolution HLA typing was obtained from the medical charts and KIR gene repertoires were obtained by a Luminex® based SSO method. All surviving patients were followed-up for a minimum of two years. KIR and HLA class I distributions of HCT pairs were stratified as per applicable definitions and were tested for their association with cause specific outcomes [acute GVHD grade II-IV (aGVHD), chronic GVHD needing systemic therapy (cGVHD) and relapse] using a multivariate competing risks regression model as well as with survival outcomes [relapse-free survival (RFS), cGVHD & relapse free survival (cGRFS) and overall survival (OS)] by multivariate Cox proportional hazards regression model. A significant association between KIR genotype mismatching (KIR-B/x donor into KIR-AA recipient or vice versa) and cGVHD was found in both discovery (p = 0.001; SHR = 2.78; 95%CI: 1.50-5.17) and validation cohorts (p = 0.005; SHR = 2.61; 95%CI: 1.33-5.11). High incidence of cGVHD associated with KIR genotype mismatching was applicable to both sibling and unrelated donors and was specific to recipients who had one or two C1 bearing HLA-C epitopes (HLA-C1/x, p = 0.001; SHR = 2.40; 95%CI: 1.42-4.06). When compared with KIR genotype mismatched transplants, HLA-C1/x patients receiving grafts from KIR genotype matched donors had a significantly improved cGRFS (p = 0.013; HR = 1.62; 95%CI: 1.11-2.39). Although there was no effect of KIR genotype matching on survival outcomes, a significantly reduced incidence of relapse (p = 0.001; SHR = 0.22; 95%CI: 0.10-0.54) and improved relapse-free survival (p = 0.038; HR = 0.40; 95%CI: 0.17-0.95) was observed with one or more missing ligands for donor inhibitory KIR among the recipients of unrelated donor transplants.ConclusionsThe present study for the first time presents the beneficial effects of KIR genotype matching in reducing cGVHD in myeloablative transplant setting using HLA matched (sibling and unrelated) donors. The findings offer a clinically applicable donor selection strategy that can help control cGVHD without affecting the risk of relapse and/or identify patients at a high risk of developing cGVHD as potential candidates for preemptive therapy. The findings also affirm the beneficial effect of one or more missing inhibitory KIR ligands in the recipient in reducing relapse and improving a relapse free survival in unrelated donor transplants.

Hearing aids (HAs) only partially restore the ability of older hearing impaired (OHI) listeners to understand speech in noise, due in large part to persistent deficits in consonant identification. Here, we investigated whether adaptive perceptual training would improve consonant-identification in noise in sixteen aided OHI listeners who underwent 40 hours of computer-based training in their homes. Listeners identified 20 onset and 20 coda consonants in 9,600 consonant-vowel-consonant (CVC) syllables containing different vowels (/ɑ/, /i/, or /u/) and spoken by four different talkers. Consonants were presented at three consonant-specific signal-to-noise ratios (SNRs) spanning a 12 dB range. Noise levels were adjusted over training sessions based on d' measures. Listeners were tested before and after training to measure (1) changes in consonant-identification thresholds using syllables spoken by familiar and unfamiliar talkers, and (2) sentence reception thresholds (SeRTs) using two different sentence tests. Consonant-identification thresholds improved gradually during training. Laboratory tests of d' thresholds showed an average improvement of 9.1 dB, with 94% of listeners showing statistically significant training benefit. Training normalized consonant confusions and improved the thresholds of some consonants into the normal range. Benefits were equivalent for onset and coda consonants, syllables containing different vowels, and syllables presented at different SNRs. Greater training benefits were found for hard-to-identify consonants and for consonants spoken by familiar than unfamiliar talkers. SeRTs, tested with simple sentences, showed less elevation than consonant-identification thresholds prior to training and failed to show significant training benefit, although SeRT improvements did correlate with improvements in consonant thresholds. We argue that the lack of SeRT improvement reflects the dominant role of top-down semantic processing in processing simple sentences and that greater transfer of benefit would be evident in the comprehension of more unpredictable speech material.

The most common complaint of older hearing impaired (OHI) listeners is difficulty understanding speech in the presence of noise. However, tests of consonant-identification and sentence reception threshold (SeRT) provide different perspectives on the magnitude of impairment. Here we quantified speech perception difficulties in 24 OHI listeners in unaided and aided conditions by analyzing (1) consonant-identification thresholds and consonant confusions for 20 onset and 20 coda consonants in consonant-vowel-consonant (CVC) syllables presented at consonant-specific signal-to-noise (SNR) levels, and (2) SeRTs obtained with the Quick Speech in Noise Test (QSIN) and the Hearing in Noise Test (HINT). Compared to older normal hearing (ONH) listeners, nearly all unaided OHI listeners showed abnormal consonant-identification thresholds, abnormal consonant confusions, and reduced psychometric function slopes. Average elevations in consonant-identification thresholds exceeded 35 dB, correlated strongly with impairments in mid-frequency hearing, and were greater for hard-to-identify consonants. Advanced digital hearing aids (HAs) improved average consonant-identification thresholds by more than 17 dB, with significant HA benefit seen in 83% of OHI listeners. HAs partially normalized consonant-identification thresholds, reduced abnormal consonant confusions, and increased the slope of psychometric functions. Unaided OHI listeners showed much smaller elevations in SeRTs (mean 6.9 dB) than in consonant-identification thresholds and SeRTs in unaided listening conditions correlated strongly (r = 0.91) with identification thresholds of easily identified consonants. HAs produced minimal SeRT benefit (2.0 dB), with only 38% of OHI listeners showing significant improvement. HA benefit on SeRTs was accurately predicted (r = 0.86) by HA benefit on easily identified consonants. Consonant-identification tests can accurately predict sentence processing deficits and HA benefit in OHI listeners.

Twitter, the largest microblogging platform, has reported more than 330 million active users in recent years. Many users express their sentiments about politics, sports, products, personalities, etc. Sentiment analysis has emerged as a specialized branch of machine learning in which tweets are binary-classified to provide sentimental insights. A major step in sentiment classification is feature selection, which primarily revolves around parts of speech (POS). Few techniques merely focused on single features such as adjectives, adverbs, and verbs, while other techniques examined types of these features, such as comparative adjectives, superlative adjectives, or general adverbs. Furthermore, POS as linguistic entities have also been studied and extensively classified by researchers, such as CLAWS-C7. For sentiment analysis, none of the studies conceptualized all possible POS features under similar conditions to draw firm conclusion. This research is centered on the following objectives: 1) examining the impact of various types of adjectives and adverbs that have not been previously explored for sentiment classification; 2) analyzing potential combinations of adjectives and adverbs types 3) conducting a comparison with a benchmark dataset for better classification accuracy. To assess the concept, a renowned human annotated dataset of tweets is investigated. Results showed that classification accuracy for adjectives is improved up to 83% based on the general superlative adjective whereas for adverbs, comparative general adverb also depicted significant accuracy improvement. Their combination with general adjectives and general adverbs also played a substantial role. The unexplored potential of adjectives and adverb types proved better in accuracy against state-of-the-art probabilistic model. In comparison to lexicon-based model, proposed research model overruled the dependency of lexicon-based dictionary where each term first needs to be matched for semantic orientation. The evident outcomes also help in time reduction aspect where huge volume of data need to be processed swiftly. This noteworthy contribution brought up significant knowledge and direction for domain experts. In the future, the proposed technique will be explored for other types of textual data across different domains. [ABSTRACT FROM AUTHOR]

Background: Air pollution has several negative health effects. Particulate matter (PM) is a pollutant that is often linked to health adversities. PM2.5 (PM with an aerodynamic diameter of ≤2.5μm) exposure has been associated with negative cardiovascular (CV) outcomes. However, the impact of PM10 (PM with an aerodynamic diameter of ≤10μm) exposure is often overlooked due to its limited ability to pass the alveolar barrier. This study aims to assess the association between PM10 exposure and risk of myocardial infarction (MI) amongst adults (≥18 years of age) as this has been poorly studied. Methods: The study protocol was published on the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42023409796) on March 31, 2023. Literature searches were conducted on 4 databases (Ovid Medline, Embase, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Web of Science) on January 17, 2023, for studies looking at associations between PM and MI. English studies from all time periods were assessed. Studies selected for review were time-series, case-crossover, and cohort studies which investigated the risk of MI as an outcome upon PM10 exposure. The quality of evidence was assessed using Cochrane's Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Data for different risk outcomes (risk ratio (RR), odds ratio (OR), hazard ratio (HR)) and 3 lags was meta-analyzed using an inverse variance statistical analysis using a random effects model. The pooled effect sizes and the 95% confidence intervals (CIs) were reported in forest plots. Results: Among the 1,099 studies identified, 41 were included for review and 23 were deemed eligible for meta-analysis. Our analysis revealed that there is an increased risk (OR = 1.01; 95% CI:1.00–1.02) of MI with a 10 μg/m3 increase in PM10 after a lag 0 and lag 1 delay. Conclusions: Our findings indicate that PM10 exposure is associated with an increased risk of MI. This can aid in informing environmental policy-making, personal-level preventative measures, and global public health action. [ABSTRACT FROM AUTHOR]

Background: Intimate partner violence (IPV) is a global public health problem. The objectives of this study were to analyze the prevalence and correlates of IPV perpetrated by men against women from the recent nationally representative Pakistan Demographic and Health Survey (PDHS) 2017–18, and to analyze levels and trends of IPV perpetrated by current/former husbands from PDHS conducted in 2012–13, in the four provinces and the capital city. Methods: Association of having ever experienced IPV, defined as either emotional, physical and/or sexual violence, by ever married women aged 15–49, with 12 explanatory socio-demographic, attitudinal, and experiences were analyzed using simple and multiple logistic regression models. Results: The prevalence of having ever experienced IPV was 33.48% (95% CI: 30.76–36.32). In the final multivariable model, number of living children, having knowledge of parental physical IPV, husband's use of alcohol, and marital control were statistically significantly associated with IPV. Proportions and trend analysis of emotional and physical IPV between the PDHS 2017–18 and PDHS 2012–13, showed that in general, rural areas of provinces reported higher prevalence of emotional and physical IPV, compared with urban areas, and in general, emotional, and physical IPV prevalence declined from PDHS2012-13 to PDHS2017-18. Conclusions: The prevalence of having experienced physical and/or sexual intimate partner violence in Pakistan was lower than the prevalence for the WHO Eastern Mediterranean region. However, IPV burden at the provincial urban-rural residency status underscore the need for location specific strategies to effectively address IPV in Pakistan. [ABSTRACT FROM AUTHOR]

Ionic liquid pretreatment of biomass has been shown to greatly reduce the recalcitrance of lignocellulosic biomass, resulting in improved sugar yields after enzymatic saccharification. However, even under these improved saccharification conditions the cost of enzymes still represents a significant proportion of the total cost of producing sugars and ultimately fuels from lignocellulosic biomass. Much of the high cost of enzymes is due to the low catalytic efficiency and stability of lignocellulolytic enzymes, especially cellulases, under conditions that include high temperatures and the presence of residual pretreatment chemicals, such as acids, organic solvents, bases, or ionic liquids. Improving the efficiency of the saccharification process on ionic liquid pretreated biomass will facilitate reduced enzyme loading and cost. Thermophilic cellulases have been shown to be stable and active in ionic liquids but their activity is typically at lower levels. Cel5A_Tma, a thermophilic endoglucanase from Thermotoga maritima, is highly active on cellulosic substrates and is stable in ionic liquid environments. Here, our motivation was to engineer mutants of Cel5A_Tma with higher activity on 1-ethyl-3-methylimidazolium acetate ([C2mim][OAc]) pretreated biomass. We developed a robotic platform to screen a random mutagenesis library of Cel5A_Tma. Twelve mutants with 25-42% improvement in specific activity on carboxymethyl cellulose and up to 30% improvement on ionic-liquid pretreated switchgrass were successfully isolated and characterized from a library of twenty thousand variants. Interestingly, most of the mutations in the improved variants are located distally to the active site on the protein surface and are not directly involved with substrate binding.

Background: Variability in spine kinematics is a common motor adaptation to pain, which has been measured in various ways. However, it remains unclear whether low back pain (LBP) is typically characterised by increased, decreased or unchanged kinematic variability. Therefore, the aim of this review was to synthesise the evidence on whether the amount and structure of spine kinematic variability is altered in people with chronic non-specific LBP (CNSLBP). Methods: Electronic databases, grey literature, and key journals were searched from inception up to August 2022, following a published and registered protocol. Eligible studies must investigated kinematic variability in CNSLBP people (adults ≥18 years) while preforming repetitive functional tasks. Two reviewers conducted screening, data extraction, and quality assessment independently. Data synthesis was conducted per task type and individual results were presented quantitatively to provide a narrative synthesis. The overall strength of evidence was rated using the Grading of Recommendations, Assessment, Development and Evaluation guidelines. Findings: Fourteen observational studies were included in this review. To facilitate the interpretation of the results, the included studies were grouped into four categories according to the task preformed (i.e., repeated flexion and extension, lifting, gait, and sit to stand to sit task). The overall quality of evidence was rated as a very low, primarily due to the inclusion criteria that limited the review to observational studies. In addition, the use of heterogeneous metrics for analysis and varying effect sizes contributed to the downgrade of evidence to a very low level. Interpretation: Individuals with chronic non-specific LBP exhibited altered motor adaptability, as evidenced by differences in kinematic movement variability during the performance of various repetitive functional tasks. However, the direction of the changes in movement variability was not consistent across studies. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) emissions have become a critical aspect of the economic and sustainable development indicators of every country. In Pakistan, where there is a substantial increase in the population, industrialization, and demand for electricity production from different resources, the fear of an increase in CO2 emissions cannot be ignored. This study explores the link that betwixt CO2 emissions with different significant economic indicators in Pakistan from 1960 to 2018 using the autoregressive distributed lag (ARDL) modelling technique. We implemented the covariance proportion, coefficient of determination, the Durbin Watson D statistics, analysis of variance (ANOVA), variance inflating factor (VIF), the Breusch-Pagan test, the Theil's inequality, the root mean quare error (RMSE), the mean absolute percentage error (MAPE), and the mean absolute error (MAE) for the diagnostics, efficiency, and validity of our model. Our results showed a significant association between increased CO2 emissions and increased electricity production from oil, gas, and other sources. An increase in electricity production from coal resources was seen to have resulted in a decrease in CO2 emissions. We observed that an increase in the gross domestic product (GDP) and population growth significantly contributed to the increased CO2 emissions. The increment in CO2 emissions resulting from industrial growth was not significant. The increment in CO2 emissions in the contemporary year is significantly associated with the preceding year's increase. The rate of increase was very alarming, a sign that no serious efforts have been channelled in this regard to reduce this phenomenon. We call for policy dialogue to devise energy-saving and CO2 emission reduction strategies to minimize the impact of climate change on industrialization, population growth, and GDP growth without deterring economic and human growth. Electricity production from different sources with no or minimal CO2 emissions should be adopted. We also recommend rigorous tree planting nationwide to help reduce the concentration of CO2 in the atmosphere as well as environmental pollution. [ABSTRACT FROM AUTHOR]