Project Database Research Articles

Investigating the ‘what’, ‘where’ and ‘why’ of global phonological typology

AbstractWhat? Where? and Why? are the principal questions to ask in relation to linguistic typological patterns, in phonology as elsewhere. However, assembling sufficient and reliable information on a large diverse sample of languages presents challenges. Some of these issues are discussed in this paper, based on the practices seen in various database projects, including WALS and LAPSyD and in the wider literature. The challenge of recognizing areal convergence can be aided by simple mapping techniques. The most scientifically challenging issue is explaining ‘why’ as this requires considering multiple physiological, psychological, social, and other effects. Recent efforts to correlate phonological features with climatic and environmental factors offer a further potentially interesting way forward.

Dataset of theoretical multinary perovskite oxides

Perovskite oxides (ternary chemical formula ABO3) are a diverse class of materials with applications including heterogeneous catalysis, solid-oxide fuel cells, thermochemical conversion, and oxygen transport membranes. However, their multicomponent (chemical formula {A}_{x}{A}_{1-x}^{text{'}}{B}_{y}{B}_{1-y}^{text{'}}{O}_{3}) chemical space is underexplored due to the immense number of possible compositions. To expand the number of computed {A}_{x}{A}_{1-x}^{{prime} }{B}_{y}{B}_{1-y}^{{prime} }{O}_{3} compounds we report a dataset of 66,516 theoretical multinary oxides, 59,708 of which are perovskites. First, 69,407 {A}_{0.5}{A}_{0.5}^{{prime} }{B}_{0.5}{B}_{0.5}^{{prime} }{O}_{3} compositions were generated in the a−b+a− Glazer tilting mode using the computationally-inexpensive Structure Prediction and Diagnostic Software (SPuDS) program. Next, we optimized these structures with density functional theory (DFT) using parameters compatible with the Materials Project (MP) database. Our dataset contains these optimized structures and their formation (ΔHf) and decomposition enthalpies (ΔHd) computed relative to MP tabulated elemental references and competing phases, respectively. This dataset can be mined, used to train machine learning models, and rapidly and systematically expanded by optimizing more SPuDS-generated {A}_{0.5}{A}_{0.5}^{{prime} }{B}_{0.5}{B}_{0.5}^{{prime} }{O}_{3} perovskite structures using MP-compatible DFT calculations.

18. Pedicled Flaps for High-risk Open Vascular Procedures of the Lower Extremity: An Analysis of the National Surgical Quality Improvement Project (NSQIP) Database

18. Pedicled Flaps for High-risk Open Vascular Procedures of the Lower Extremity: An Analysis of the National Surgical Quality Improvement Project (NSQIP) Database

Race and sex demographics in the surgical management of facial nerve palsy.

Facial palsy affects patients of all backgrounds, yet no existing studies describe differences in its treatment patterns between demographic groups. We used the National Surgical Quality Improvement Project database to investigate whether race and sex disparities exist in facial reanimation surgery. Patients were identified using CPT codes corresponding to facial-nerve procedures. Seven hundred sixty-one patients met criteria; 681 self-identified as White (89.5%), 51 as Black (6.7%), 43 as Hispanic (5.6%), 23 as Asian (3.0%), and 5 patients as other (0.61%). White patients were more than twice as likely to undergo brow ptosis repair than Non-White patients (OR 2.49, 95% CI 1.16-6.15, p = .03). After controlling for malignancy, men had longer operative times than women (480.2 vs. 413.9 min, p = .04) and higher likelihood of free tissue transfer (OR 4.1, 95% CI 1.9-9.8), fascial free tissue transfer (OR 10.7, 95% CI 2.1-195), and ectropion repair (OR 1.8, 95% CI 1.2-2.8). Most patients undergoing facial reanimation surgery in the United States are White. Men have longer operative times and a higher likelihood of undergoing free fascial grafts and cutaneous and fascial free tissue transfer than women regardless of malignancy status. 2c.

Thermoelectric Prediction from Material Descriptors Using Machine Learning Technique

In this work, we employed a machine learning framework to predict the thermoelectric power factors of materials based on their composition and structure. To generate a broad range of materials for analysis, we sourced an existing dataset from the Materials Project database. The electronic transport properties, which serve as the output variables, were obtained from the same database via a Boltzmann transport theory calculation beyond ab-initio method. These properties were used to generate input data, or material descriptors, which rely solely on atomic information and crystal structure without recourse to density functional theory calculations. The descriptors were transformed into numerical features using the open-source software Matminer. Non-linear machine learning regression models were trained and tested on the transformed datasets, and their performance was evaluated. The optimized random forest model produced the most accurate predictions, with a yield of 88%. The ultimate goals of this research were to develop material selection strategies that bypass the need for self-consumption in density functional theory calculations, and to demonstrate the potential of machine learning models to describe the thermoelectric properties of existing materials datasets.

Prediction of large magnetic moment materials with graph neural networks and random forests

Magnetic materials are crucial components of many technologies that could drive the ecological transition, including electric motors, wind turbine generators and magnetic refrigeration systems. Discovering materials with large magnetic moments is therefore an increasing priority. Here, using state-of-the-art machine learning methods, we scan the Inorganic Crystal Structure Database (ICSD) of hundreds of thousands of existing materials to find those that are ferromagnetic and have large magnetic moments. Crystal graph convolutional neural networks (CGCNN), materials graph network (MEGNet) and random forests are trained on the Materials Project database that contains the results of high-throughput DFT predictions. For random forests, we use a stochastic method to select nearly one hundred relevant descriptors based on chemical composition and crystal structure. This gives results that are comparable to those of neural networks. The comparison between these different machine learning approaches gives an estimate of the errors for our predictions on the ICSD database. Validating our final predictions by comparisons with available experimental data, we found 15 materials that are likely to have large magnetic moments and have not been yet studied experimentally.

Evaluation of large-scale highly polymorphic microhaplotypes in complex DNA mixtures analysis using RMNE method

DNA mixture interpretation is one of the most challenging problems in forensics. Complex DNA mixtures are more difficult to analyze when there are more than two contributors or related contributors. Microhaplotypes (MHs) are polymorphic genetic markers recently discovered and employed in DNA mixture analysis. However, the evidentiary interpretation of the MH genotyping data needs more debate. The Random Man Not Excluded (RMNE) method analyzes DNA mixtures without using allelic peak height data or the number of contributors (NoC) assumptions. This study aimed to assess how well RMNE interpreted mixed MH genotyping data. We classified the MH loci from the 1000 Genomes Project database into groups based on their Ae values. Then we performed simulations of DNA mixtures with 2–10 unrelated contributors and DNA mixtures with a pair of sibling contributors. For each simulated DNA mixture, incorrectly included ratios were estimated for three types of non-contributors: random men, parents of contributors, and siblings of contributors. Meanwhile, RMNE probability was calculated for contributors and three types of non-contributors, allowing loci mismatch. The results showed that the MH number, the MH Ae values, and the NoC affected the RMNE probability of the mixture and the incorrectly included ratio of non-contributors. When there were more MHs, MHs with higher Ae values, and a mixture with less NoC, the RMNE probability, and the incorrectly included ratio decreased. The existence of kinship in mixtures complicated the mixture interpretation. Contributors’ relatives as non-contributors and related contributors in the mixture increased the demands on the genetic markers to identify the contributors correctly. When 500 highly polymorphic MHs with Ae values higher than 5 were used, the four individual types could be distinguished according to the RMNE probabilities. This study reveals the promising potential of MH as a genetic marker for mixed DNA interpretation and the broadening of RMNE as a parameter indicating the relationship of a specific individual with a DNA mixture in the DNA database search.

Pan-cancer analysis reveals IGFL2 as a potential target for cancer prognosis and immunotherapy

Insulin-like growth factor like family member 2 (IGFL2) is a gene in the IGFL family, located on chromosome 19, whose role in cancer is unclear, and the aim of this study was to investigate the relevance of IGFL2 expression, prognosis, immunity, and mutation in pan-cancer. Obtaining information from The Cancer Genome Atlas and The Genotype-Tissue Expression Project (GTEx) databases for expression analysis and combining with The Gene Expression Profile Interaction Analysis database for prognostic aspects. Analysis of immune cell infiltration by TIMER and CIBERSORT algorithms. Calculation of correlation of immune-related genes with IGFL2 expression and tumor mutational burden and microsatellite instability. Mutations and DNA methylation were analyzed using the cBioPortal database and the UALCAN database, and functional enrichment was performed using Gene set enrichment analysis (GSEA). IGFL2 expression is significantly elevated in tumor tissue and high expression has a worse prognosis in most cancers. In immune correlation analysis, it was associated with most immune cells and immune-related genes. In most cancers, IGFL2 methylation is lower and the group with mutations in IGFL2 has a worse prognosis than the normal group. The GSEA analysis showed that IGFL2 was significantly enriched in signaling and metabolism. IGFL2 may be involved in the development of many types of cancer, influencing the course of cancer with different biological functions. It may also be a biomarker for tumor immunotherapy.

Lifetime Prevalence of Psychiatric Disorders in Adolescents with Unexplained Weight Loss, Underweight, or Poor Appetite.

When adolescents present with symptoms of unexplained weight loss, underweight, or poor appetite, eating disorders (EDs) are commonly on the list of differential diagnoses. However, the relationship of these symptoms to other psychiatric disorders is often less clear. Using the Rochester Epidemiology Project database, a retrospective cohort study of adolescents (13-18 years) with billing diagnoses of weight loss, underweight, or loss of appetite was conducted between January 2005 and December 2017. Patients who presented with conditions commonly associated with weight loss, underweight, or poor appetite (e.g., cancer) were excluded. This study sought to examine the proportion of patients who received ED and psychiatric diagnoses within 5 years of the index visit and patient characteristics associated with these diagnoses. Of 884 patients diagnosed with symptoms of unexplained weight loss, underweight, or poor appetite, 662 patients ( M age = 15.8; SD = 1.6; 66.0% female) met study criteria. Within 5 years of the index visit, the lifetime prevalence of all psychiatric disorders was 70% (n = 461) and of EDs was 21% (n = 141). For both psychiatric disorders and EDs, sex and race were significantly associated with receiving a diagnosis within 5 years. Decrease in body mass index (BMI) percentile was associated with receiving an ED diagnosis, whereas the highest historical BMI percentile was associated with receiving a psychiatric diagnosis. Patients presenting with symptoms of unexplained weight loss, underweight, or poor appetite are at risk not only for EDs but also for other psychiatric disorders that may require further assessment and follow-up.

Causal association of genetically predicted urinary sodium-potassium ratio and upper urinary calculi.

The causality between the urinary sodium-potassium ratio and upper urinary calculi has not been clarified and easily affected by confounders. We performed two-sample andmultivariable Mendelian randomization (MR) analysis to evaluate the potential causal role of the urinary sodium-potassium ratio in upper urinary calculi. Data of the urinary sodium-potassium ratio (N = 326,938), upper urinary calculi (N = 337,199), and confounding factors including BMI (N = 336,107), ever-smoke (N = 461,066), hypertension (N = 218,754), diabetes (N = 218,792), and alcohol intake frequency (N = 462,346) were obtained from the IEU OpenGWAS Project database. The inverse-variance weighted (IVW), weighted median, and MR-Egger methods were used to estimate MR effects. The MR-Egger intercept test, Cochran's Q test, MR-PRESSO, leave-one-out method, and funnel plot were used for sensitivity analysis. A causal relationship was found between the urinary sodium-potassium ratio and upper urinary calculi (OR = 1.008, 95% CI = 1.002-1.013, P = 0.011). FinnGen data supported this conclusion (OR = 2.864, 95% CI = 1.235-6.641, P = 0.014). The multivariable Mendelian randomization analysis result showed that after adjusting for the effects of five confounders, the urinary sodium-potassium ratio was still positively correlated with upper urinary calculi (OR = 1.005, 95% CI = 1.001-1.009, P = 0.012). This study demonstrated a positive causal association between the urinary sodium-potassium ratio and upper urinary calculi using MR analysis. Timely identification of changes in urine composition and dietary regulation of sodium and potassium intake could greatly reduce the incidence of future urinary calculi.

Association of group-level segregation with cardiovascular health in older adults: an analysis of data from the Korean Social Life, Health, and Aging Project.

The adverse health effects of individual-level social isolation (e.g., perceived loneliness) have been well documented in older adults. However, little is known about the impact of collective-level social isolation on health outcomes. We sought to examine the association of group-level segregation with cardiovascular health (CVH) in older adults. From the prospective Korean Social Life, Health, and Aging Project database, we identified 528 community-dwelling older adults who were aged ≥60 years or were married to those aged ≥60 years. Participants who belonged to smaller social groups separate from the major social group were defined as group-level-segregated. The CVH score was calculated as the number of ideal non-dietary CVH metrics (0-6), as modified from the American Heart Association's Life's Simple 7. Using ordinal logistic regression models, we assessed cross-sectional and longitudinal associations between group-level segregation and CVH. Of the 528 participants (mean age, 71.7 years; 60.0% women), 108 (20.5%) were segregated at baseline. In the cross-sectional analysis, group-level segregation was significantly associated with lower odds of having a higher CVH score at baseline after adjusting for sociodemographic factors and cognitive function (odds ratio [OR]=0.64; 95% confidence interval [CI], 0.43-0.95). Among 274 participants who completed an 8-year follow-up, group-level segregation at baseline was marginally associated with lower odds of having a higher CVH score at 8 years (OR=0.49; 95% CI, 0.24-1.02). Group-level segregation was associated with worse CVH. These findings imply that the social network structure of a community may influence its members' health status.

The challenge of studying perovskite solar cells’ stability with machine learning

Perovskite solar cells are the most dynamic emerging photovoltaic technology and attracts the attention of thousands of researchers worldwide. Recently, many of them are targeting device stability issues–the key challenge for this technology–which has resulted in the accumulation of a significant amount of data. The best example is the “Perovskite Database Project,” which also includes stability-related metrics. From this database, we use data on 1,800 perovskite solar cells where device stability is reported and use Random Forest to identify and study the most important factors for cell stability. By applying the concept of learning curves, we find that the potential for improving the models’ performance by adding more data of the same quality is limited. However, a significant improvement can be made by increasing data quality by reporting more complete information on the performed experiments. Furthermore, we study an in-house database with data on more than 1,000 solar cells, where the entire aging curve for each cell is available as opposed to stability metrics based on a single number. We show that the interpretation of aging experiments can strongly depend on the chosen stability metric, unnaturally favoring some cells over others. Therefore, choosing universal stability metrics is a critical question for future databases targeting this promising technology.

Global rare earth elements projects: New developments and supply chains

Rare earth elements (REE) are key enablers for the ongoing energy and environmental transition as they are critical raw materials in many low-carbon technologies. In the past few years, many new REE projects have been developed worldwide to replenish the market, and thus are changing the supply chains. Meanwhile, comprehensive data are lacking regarding these projects, impeding investors and policymakers from having a clear view of the global supply of REE. This study establishes a new database of REE projects, along with detailed information sources. Based on these data, we present the current situation and give some critical perspectives on the future. It is shown that there are at least total 146 advanced and mining projects, involving over 303.4 million tons of rare earth oxides around the world and some newly designed supply chains. Among those projects, carbonatite type deposits are currently the main sources and most likely will dominant future targets for exploration and mining. Asia remains the largest source of REE, but Greenland, Africa, and Europe show important resource potentials if environmental issues and processing technologies are well addressed. Finally, we review rare earth projects in terms of estimated value and heavy rare earth elements resources amounts, and further list some REE deposits that deserve more attention based on this new database.

MP07-04 OUTCOMES AND PATIENT CHARACTERISTICS FOR SACROCOLPOPEXIES PERFORMED BY UROLOGY VS GYNECOLOGY

You have accessJournal of UrologyCME1 Apr 2023MP07-04 OUTCOMES AND PATIENT CHARACTERISTICS FOR SACROCOLPOPEXIES PERFORMED BY UROLOGY VS GYNECOLOGY Rishabh Simhal, Kerith Wang, Maria D'Amico, Courtney Capella, Radhika Ragam, Yash Shah, Joon Yau Leong, Whitney Smith, Patrick Shenot, and Alana Murphy Rishabh SimhalRishabh Simhal More articles by this author , Kerith WangKerith Wang More articles by this author , Maria D'AmicoMaria D'Amico More articles by this author , Courtney CapellaCourtney Capella More articles by this author , Radhika RagamRadhika Ragam More articles by this author , Yash ShahYash Shah More articles by this author , Joon Yau LeongJoon Yau Leong More articles by this author , Whitney SmithWhitney Smith More articles by this author , Patrick ShenotPatrick Shenot More articles by this author , and Alana MurphyAlana Murphy More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003222.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Sacrocolpopexies (SCP) are performed by both urologists and gynecologists; however, the majority are performed by gynecologists. We aim to elucidate if there is a difference in patient demographics and surgical outcomes based on surgeon specialty. METHODS: Open and minimally invasive SCPs performed between 2006-2020 were identified in the American College of Surgeons National Surgical Quality Improvement Project (ACS-NSQIP) database using CPTs 57280 and 57425. Frailty was calculated using the NSQIP modified frailty index (mFI-5), a previously validated 5-item score including points for diabetes, functional status, chronic obstructive pulmonary disease, heart failure, and hypertension, and the revised surgical Risk Analysis Index (RAI-rev), a validated score which accounts for patient comorbidities, age, and functional status. Univariate analysis was performed using student’s t-test and Pearson’s chi-square to compare operative parameters, patient frailty and demographics, surgical outcomes, and mortality. RESULTS: We identified 8,944 SCPs from 2006-2020. Gynecology performed 81% of cases, and urology performed 19% (p<.001). Urologists tended to perform more minimally invasive cases compared to gynecologists (84% vs 77%, p<.001). Between urologists and gynecologists, there were no significant differences in surgical outcomes, minor complications (surgical site infection, pneumonia, urinary tract infection, bleeding, or acute kidney injury), major complications (deep vein thrombosis, stroke, renal failure, myocardial infarction, pulmonary embolism, septic shock, deep wound infections, or cardiac arrest), 30-day reoperations, 30-day hospital readmissions, nor 30-day mortality. However, urology patients tended to be older (65 vs 61 years, p<.001) and frailer by mFI-5 (0.7 vs 0.5, p<.001, higher score indicating more frailty) and RAI-rev (19.4 vs 17.1, p<.001, higher score indicating more frailty). CONCLUSIONS: We found no difference in 30-day surgical outcomes for SCPs performed by urologists compared to gynecologists. However, urologists tend to perform more minimally invasive procedures and operate on older, more frail patients. This finding highlights differences in practice patterns between two specialties and warrants further studies. Source of Funding: n/a © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e85 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Rishabh Simhal More articles by this author Kerith Wang More articles by this author Maria D'Amico More articles by this author Courtney Capella More articles by this author Radhika Ragam More articles by this author Yash Shah More articles by this author Joon Yau Leong More articles by this author Whitney Smith More articles by this author Patrick Shenot More articles by this author Alana Murphy More articles by this author Expand All Advertisement PDF downloadLoading ...

8 BERD Challenges and opportunities in the new translational science landscape

OBJECTIVES/GOALS: The goal of this presentation is to highlight the role of and challenges to the Biostatistics, Epidemiology and Research Design (BERD) Core as it transitions into (i) the resources and services module (ii) and also possibly a regional center of translational science. METHODS/STUDY POPULATION: BERD cores play key roles in translational research missions, often in the form of long-term collaborative relationships focusing on project conception and design, interim monitoring and review, analysis and dissemination. The resources and services module encourages consolidation of all resources and services under the single modular umbrella, which can pose challenges to BERD autonomy and function. CTSAs transitioning from local to regional centers can also threaten to overwhelm BERD workloads and resources. Our CTSA planned an outsource-insource model to make the BERD a central feature of the new module and transplant its functionality to partner institutions. RESULTS/ANTICIPATED RESULTS: The team has planned a centralized, web-based entry-point for both self-guided inquiry and electronic requests of all resources and services, which aides project database creation and provides notification for review. The review process adds two new approval types: guided assistance to help navigate connections to relevant resources, and triaged referrals to training modules for remediation and skills development. Our BERD has added a Consulting Laboratory for projects of sufficient but non-priority merit that would otherwise be waitlisted, and established connections with regional partner institutions to whom they can refer investigators. DISCUSSION/SIGNIFICANCE: We have consolidated all resources and services into a single, accessible location, emphasizing tailored guidance to maximize limited resources. We also planned a transportable regional model that accounts for local resources and capacity to keep from overstretching the BERD and other hubs.

Performance Comparison of Three Classification Algorithms for Non-alcoholic Fatty Liver Disease Patients Using Data Mining Tool

This study aims to carry out a comparative analysis of the three classification algorithms used in research on Nonalcoholic Fatty Liver Disease (NAFLD) Patients. NAFLD is a liver condition associated with the accumulation of fat in the liver in individuals who do not consume excessive alcohol. The algorithms used in the analysis are Decision Tree, Naïve Bayes, and k-Nearest Neighbor (k-NN), with data processing using RapidMiner software. The data used is sourced from Kaggle which comes from the Rochester Epidemiology Project (REP) database with research conducted in Olmsted, Minnesota, United States. The measurement results show that the Decision Tree algorithm has an accuracy of 92.56%, a precision of 93.24%, and a recall of 99.08%. The Naïve Bayes algorithm has an accuracy of 89.93%, a precision of 95.40% and a recall of 93.56%. While the k-Nearest Neighbor algorithm has an accuracy of 91.33%, a precision of 91.94%, and a recall of 99.27%. ROC curve analysis, all algorithms show "Excellent" classification quality. However, only the k-NN algorithm reached 1.0, showing excellent classification results in solving the problem of classifying Nonalcoholic Fatty Liver Disease patients. This study concluded that the k-NN algorithm is a better choice in solving the problem of classifying Non-alcoholic Fatty Liver Disease patients compared to the Decision Tree and Naïve Bayes algorithms. This study provides valuable insights in the development of classification methods for the early diagnosis and management of NAFLD.

Evaluating the harmonic approximation for the prediction of thermodynamic formation properties of solids

Thermodynamic stability is regularly quantified using the enthalpy of formation (ΔfH) and the Gibbs free energy of formation (ΔfG). Although knowledge of these properties is crucial for many applications, literature data are often missing for many solids. We evaluate the harmonic approximation (HA) for the prediction of ΔfH and ΔfG at constant volume and pressure, based on phonon calculations. Using density functional theory to carry out phonon calculations, we show that the HA excellently describes the temperature-dependence of ΔfH and ΔfG for 14 compounds; mean absolute error (MAE) of 2.1 kJ⋅mol−1 and 1.1 kJ⋅mol−1, respectively, in the temperature interval 0–2500 K. Moreover, the performance of the HA was evaluated using computational data from the Materials Project database for 69 compounds yielding an MAE of 1.1 kJ⋅mol−1 and 1.1 kJ⋅mol−1, respectively, in the temperature interval 100–800 K. Very good performance is also observed for a number of additional compounds, including several hydrated salts, at 298.15 K. The model is subsequently applied to a number of phase-transition phenomena that demonstrate the strengths and weaknesses of the model. In addition, it is demonstrated that the HA model can provide quantitative performance that rivals that of the conventional quasi-harmonic approximation for the prediction of formation properties, at a significantly reduced computational effort (∼5–10 times faster).

New Electrogenic Microorganism Citrobacter sp. Isolated from Microbial Fuel Cell and Bacterial Characteristics Determination

The generation of energy through the transformation of polluting waste is a widely explored field and offers advances in green technologies. One of the promising technologies is Microbial Fuel Cells (MFCs). These cells can contain electroactive microorganisms that transform organic waste into electricity by transferring electrons from their metabolism. In this study, a new bacterium capable of producing electricity from the waste of the poultry sector and using copper electrodes, called Av_G1, was identified and isolated. It is phylogenetically related to Citrobacter freundii and Citrobacter Murlinae. This new strain was identified molecularly, biochemically, and phylogenetically; its physiological and morphological characteristics were also studied through a Scanning Electron Microscope (SEM). Biochemical determination was performed using Simmons Citrate Agar, Lysine Iron Medium (L.I.A.), Motility/Ornithine Test, Methyl Red indicator, Enzymes: oxidase and catalase, and Gram stain test. The phylogenetic inference was deduced by bioinformatics tools (MEGA X, JalView, Clustal Omega) and the genetic databases The Ribosomal Database Project—RDP and the National Center for Biotechnology Information (NCBI). A maximum current potential of 0.645 V, a maximum current density of 168.72 ± 14.07 mA/m2, and a power density of 31.05 mW/m2 were recorded. During the monitoring, the physicochemical parameters were taken: pH, Oxide Reduction Potential (ORP), Dissolved Oxygen (DO), conductivity, Total Solids (TDS), and average temperature were recorded. Therefore, the present study shows a new Gram-negative electrogenic bacterium, which can be used for electrochemical processes and applied in MFC with copper electrodes.

Compressed Stabilized Earthen Blocks and Their Use in Low-Cost Social Housing

Earthen techniques have been historically used in construction of housing in Africa, Asia and Latin America. In the past two decades the interest in earthen material has grown considerably, leading to the development of sustainable materials such as compressed earth blocks (CEB), compressed stabilized earth blocks (CSEB) and interlocking stabilized soil blocks (ISSB). Scientific publications from various countries and context have examined the physical aspects of these earthen building materials, but so far, the results are not well connected to housing practices for and by low-income households with their self-organizing skills. This research sought to close this gap by documenting the housing projects where earth blocks are applied in participatory social housing. The study provides an overview of relevant practical examples from the three world regions (Africa, Asia and Latin America), with their cultural and climatic differences, and an analysis of similarities and possibilities. Based on the lessons learned from these examples, recommendations are made on further research on sustainable building materials within social housing practices, which can benefit the scientific community. We propose to set up a worldwide database of housing projects where earthen techniques have been applied responsibly. We conclude and recommend that more high-quality pilot projects with CSEB and ISSB are needed to get a broader picture on the potential of these materials for social housing, and the necessary support for local communities wishing to be involved in these sustainable housing practices.

The driving factors of corporate carbon emissions: an application of the LASSO model with survey data

Corporate carbon performance is a key driver of achieving corporate sustainability. The identification of factors that influence corporate carbon emissions is fundamental to promoting carbon performance. Based on the carbon disclosure project (CDP) database, we integrate the least absolute shrinkage and selection operator (LASSO) regression model and the fixed effects model to identify the determinants of carbon emissions. Furthermore, we rank determining factors according to their importance. We find that Capx enters the models under all carbon contexts. For Scope 1 and Scope 2, financial-level factors play a greater role. For Scope 3, corporate internal incentive policies and emission reduction behaviors are important. Different from absolute carbon emissions, for relative carbon emissions, the financial-level factors’ debt-paying ability is a vital reference indicator for the impact of corporate carbon emissions.