Quality Measures Research Articles

Assessing the influence of research quality on policy citations: Quantitative analysis finds non‐academic factors more likely to influence how papers get cited in SDG policy

AbstractAddressing the United Nations Sustainable Development Goals (SDGs) requires policymakers to utilise high‐quality scholarly research effectively. This study explores the extent to which bibliometric indicators of research quality—field weighted citation index, journal CiteScore, author h‐index and mentions—influence the likelihood of SDG‐related research being cited in policy documents. Analysing metadata from over 16 million SDG‐related articles revealed modest relationships between selected bibliometric indicators and policy citations. This suggests that bibliometric measures of research quality may not adequately explain policy influence, or that these metrics might not capture the essence of research excellence from a policy perspective. Importantly, the study identifies variations in research utilisation across different SDGs and highlights the potential role of non‐academic factors in determining policy impact. Results provide a framework for understanding the extent to which policymakers are leveraging the best available evidence in their efforts to meet the SDGs.

Clinical Effects of Voice Therapy on Vocal Outcomes in Unilateral Vocal Fold Paralysis: Proof-of-Concept Study for Two SOVT-Based Treatment Protocols

BackgroundStudies on treatment efficacy in unilateral vocal fold paralysis (UVFP) often lack a predetermined treatment protocol, and little is known about the effects of specific vocal techniques on vocal outcomes and quality of life in UVFP patients. The purpose of this preliminary proof of concept study is to investigate the effects and feasibility of two intensive treatment protocols based on water-resistance therapy (WRT) and vocal function exercises (VFE). MethodsTen participants with acute or chronic UVFP/paresis were recruited in the study and randomly assigned to the WRT or VFE group. Three of these participants presented with aphonia and could not complete the program as prescribed. The remaining participants completed an intensive therapy program with the assigned vocal technique. Before, during, and after the program, a multidimensional voice assessment was performed. Maximum phonation time, acoustic, perceptual, and patient-reported outcome measures were obtained. ResultsWRT and VFE had positive clinical effects on instrumental and auditory-perceptual voice quality, glottal closure, and patient-reported outcome measures, but inter-individual variability was high. Studies with larger sample sizes are necessary to confirm or refute these findings. ConclusionThe WRT- and VFE-based therapy programs are both feasible and seem to elicit positive clinical changes in UVFP patients. Suggestions on how to improve the programs are provided, as well as considerations for implementation in clinical practice. Follow-up research is needed to examine the efficacy of both programs on group level.

The relationship between health literacy and eating awareness, diet quality, and anthropometric measurements

The relationship between health literacy and eating awareness, diet quality, and anthropometric measurements

Ranking the Relative Importance of Image Quality Features in CT by Consensus Survey

ObjectiveThis study sought to determine consensus opinions from subspecialty radiologists and imaging physicists on the relative importance of image quality features in CT. MethodsA prospective survey of subspecialty radiologists and medical physicists was conducted to collect consensus opinions on the relative importance of ten image quality features: axial sharpness, blooming, contrast, longitudinal sharpness, low contrast axial sharpness, metal artifact, motion, noise magnitude, noise texture, and streaking. The survey was first sent to subspecialty radiologists in volunteer leadership roles in the American College of Radiology and Radiological Society of North America, thereafter relying on snowball sampling. Surveyed subspecialties were abdominal, cardiac, emergency, musculoskeletal, neuro, pediatric, and thoracic radiology, and medical physics. Individual respondents’ ratings were normalized for calculation of mean normalized ratings and priority rankings for each feature within subspecialties. Also calculated were intraclass correlation coefficients across image quality features within subspecialties, and analysis of variance across subspecialties within each feature. ResultsMost subspecialties had moderate to excellent intraclass agreement. For every radiology subspecialty except musculoskeletal, motion was the most important image quality feature. There was agreement across subspecialties that axial sharpness and contrast are only moderately important. There was disagreement across subspecialties on the relative importance of noise magnitude. Blooming was highly important to cardiac radiologists, and noise texture was highly important to musculoskeletal radiologists. ConclusionImage quality preferences differ based on clinical tasks and challenges in each anatomical radiology subspecialty. CT image analysis and development of quantitative measures of quality and protocol optimization—and related policy initiatives—should be specific to radiology subspecialty.

Impact Quality Indicators onto Sustainable Manufacturing

This paper dealt study effect quality indicators of crushing Limestone process in quarries onto Sustainable Manufacturing. Sustainable Environment was achieving through minimizing quality indicators which were use resources and lead time during crushing of limestone in position. Non-value added time and use resources are studied in this paper. This paper aims to show impact key performance indicators of quality which were production efficiency, efficiency of manufacturing cycle and efficiency of use resources onto Operational and environmental sustainability in order to reduce environmental and operational impacts onto environment and human health. Crushing Limestone process had been employing on one year as a case study to evaluate operational and environmental sustainability of an Iraqi industry. Program of Minitab-Version 17 was employed to analyze information of production process. Results illustrated that the rate of efficiency of utilized resources and waste of resources on one year were 95% and 5%. Conclusions shown lead time was an important performance measure of quality from sustainable Manufacturing perspective.

Optimized underwater light attenuation prior-based depth estimation and adaptive feature fusion CNN for underwater image and video enhancement

Underwater images and videos play an essential communication tool in exploring ocean resources and understanding underwater scene perception. The underwater imaging environment and the complex lighting conditions degrade the quality of the images and make them low contrast, color deviation, and blurring. These degraded images lack effective target recognition information, significantly impacting the performance of underwater application systems. To solve the above issues, a new module for underwater video as well as image enhancement is proposed. The input video is given to the frame extraction phase for converting the video into the frame. Thereafter, the blur detection and determination are done using Laplacian's variance technique for determining blurred images. The depth estimation is done using Underwater Light Attenuation Prior (ULAP), wherein coefficients are determined using the proposed Competitive Multi-Verse bird swarm Optimization (CMVBSO). The CMVBSO is combined by integrating a competitive multiverse optimizer (CMVO) and a Bird Swarm Algorithm (BSA). The blurred value, input video frame and depth estimated output are given to adaptive feature fusion CNN in which training is done utilizing Manta-Ray Foraging Lion Optimization (MRLLO). The pixel enhancement is carried out using a Type II Fuzzy system and Cuckoo Search optimization algorithm filter (T2FCS) filter. The proposed CMVBSO_ULAP provided better performance with a Peak signal to noise ratio (PSNR) of 35.037 dB, Mean square error (MSE) of 3.684, Structural similarity index (SSIM) of 0.911, underwater image quality measure (UIQM) of 5.266 and Underwater Color Image Quality Evaluation (UCIQE) of 0.818 respectively.

Integrated water quality assessment of open water bodies using empirical equations and remote sensing techniques in Bangweulu Wetland lakes, Zambia

Water quality assessment is essential for conserving aquatic ecosystems and human consumption. However, monitoring several water quality parameters in open water bodies poses significant challenges, resulting in gaps and limitations. While the Water Quality Index (WQI) integrates various water quality parameters, its spatial application has not yet been thoroughly evaluated. To address these issues, this study was conducted to establish an integrated remote sensing approach for comprehensive monitoring of water quality. Utilising Sentinel 2 data alongside on-site/lab water quality measurements, various relationships were established. The calculated mean WQI from on-site/lab data was 35, while from remotely sensed data was 41. Linear relationships (R2) between WQI and various water quality parameters such as chloride (0.712), sodium (0.705), TDS (0.705), turbidity (0.628), and EC (0.704) were observed. These relationships facilitated the transformation of water quality parameter maps into WQI maps, the WQI maps were then integrated into one WQI map showing the overall water quality status of the Bangweulu Wetland lakes. Both on-site/lab and remote sensing methods indicated that the concentrations of water quality parameters in the Bangweulu Wetland lakes is lower (better) than the local and international recommended limits, with a calculated WQI falling within the ’Good’ category. This indicates that the water is fresh, clean, and suitable for various purposes including ecological preservation, agriculture, aquaculture, recreation, industrial use, and human consumption. The findings offer insights into the general status of water quality in open water bodies, identifying hot spots and potential sources of water pollution with implications for future management practices.

Wildfire exposures and in-hospital length of stay following lung cancer surgery.

311 Background: Wildfires pose substantial health and safety threats to patients recovering from lung cancer surgery. Without specific disaster preparedness guidelines, oncologists might resort to improvisational strategies, such as increasing the length of stay (LOS), a federal care quality measure with financial implication for hospitals, to better protect the health and safety of this medically vulnerable population. Methods: Individuals aged ≥18 years who received curative-intent lobectomy or pneumonectomy for stage I-III NSCLC between 2004 and 2021 were selected from the National Cancer Database. Exposure was defined as a Federal Emergency Management Agency (FEMA) Presidential Disaster Declaration in the county of the treatment facility between date of surgery and date of discharge from the hospital. Differences in the cumulative distribution function of LOS, defined as days between date of surgery and date of discharge from the hospital, were evaluated between exposed and propensity score matched unexposed patients, who were treated at the same facility but at a time when no disasters occurred, using the product-limit method and log-rank test. In sensitivity analysis, patients with discharge date equal to date of death (in-hospital mortality) were excluded, and the association was evaluated stratified by stage. Results: Patients exposed to a wildfire disaster declaration in the county of the treating facility had longer LOS (p<0.001) than unexposed patients (9.4days compared to 7.5 days, respectively) overall and for each of the stages (I-III) for which surgery is the recommended treatment modality. In sensitivity analysis, there were no in-hospital mortality difference between exposed and unexposed patients (10.8% and 10.5%, respectively, p=0.76). Excluding patients who died in the hospital did not change the main results. Conclusions: Patients whose facility is impacted by a wildfire disaster during recovery from lung cancer surgery have longer length of stay than similar patients treated at the same facility but at times when no disaster occurred. Future studies should evaluate whether extended hospital stay improves safety and quality of surgical care. Moreover, these findings should be considered for disaster preparedness guidelines tailored to vulnerable patient population.

A procedure for assessing of machine health index data prediction quality

The paper discusses the challenge of evaluating the prognosis quality of machine health index (HI) data. Many existing solutions in machine health forecasting involve visual assessing of the quality of predictions to roughly gauge the similarity between predicted and actual samples, lacking precise measures or decisions. In this paper, we introduce a universal procedure with multiple variants and criteria. The overarching concept involves comparing predicted data with true HI time series, but each procedure variant has a specific pattern determined through statistical analysis. Additionally, a statistically established threshold is employed to classify the result as either a reliable or non-reliable prognosis. The criteria include both simple measures (MSE, MAPE) and more advanced ones (Space quantiles-inclusion factor, Kupiec’s POF, and TUFF statistics). Depending on the criterion chosen, the pattern and decision-making process vary. To illustrate effectiveness, we apply the proposed procedure to HI data sourced from the literature, covering both warning (linear degradation) and critical (exponential degradation) stages. While the method yields a binary output, there is potential for extension to a multi-class classification. Furthermore, experienced users can use the quality measure expressed in percentage for more in-depth analysis.

Improving advance care planning outcomes through social work and provider collaboration.

55 Background: For many patients living with a life-limiting illness, the ability to have authority in their end-of-life (EOL) decision making is a foundational aspect in achieving a peaceful passing. Literature shows, however, that a low percentage of adults in the United States have completed advance directive documents or had discussions indicating their wishes for EOL care. With their unique communication and advocacy skills, licensed masters-prepared social workers (MSWs) are primed to play an active and effective role in Advance Care Planning (ACP) discussions with patients. The purpose of this study aimed to explore the extent to which MSWs in an oncology setting are harnessing these skills. Methods: An online survey was dispersed to social workers working in outpatient oncology clinics in The US Oncology Network. The survey reviewed the level of education, licensure status, involvement and comfort with ACP discussions, and factors that would aid increased engagement of these conversations. Results: 41 out of 75 social workers completed the survey. 95% of respondents were masters prepared (MSW) and licensed at the masters (27%) or clinical (73%) level. Of these MSWs, 95% are having ACP conversations with their patients and loved ones, and 87% are initiating these discussions independently. Although 64% report that they are getting referrals from physicians in their practice, almost half (49%) indicated they would like more support from physicians around ACP conversations with patients. Per respondent comments, a need was expressed for providers to help initiate and normalize ACP discussions. It was also indicated that MSWs do not believe providers fully understand MSWs ability to engage in these discussions. Interestingly, only 10% of MSWs are billing for ACP discussions, yet 46% indicated they would be interested in doing so. Conclusions: Our results indicate there is a gap between the work MSWs are doing in the ACP space and how providers are utilizing the MSW’s clinical expertise in this area. The use of MSWs in a cancer care setting can enhance patient self-determination and documentation of wishes for EOL, as well as support multidisciplinary care of the patient. Additional benefits could include improvement of ACP quality measures, increased revenue, and provider satisfaction in knowing these sensitive conversations are being addressed by a trusted member of the healthcare team.

Chemical Quality (Water Content, Protein, Fiber) and Organoleptic Properties of Chicken Nuggets with Addition of Vegetables

This study aimed to assess the chemical quality (moisture content, protein, and fiber) and organoleptic properties of chicken nuggets with the addition of vegetables. The materials used included chicken breast, wheat flour, tapioca flour, bread crumbs, eggs, pepper, salt, ice water, carrots, broccoli, spinach, and young corn. Five treatments were applied: P0 (no vegetable addition), P1 (carrot addition), P2 (broccoli addition), P3 (spinach addition), and P4 (young corn addition). The research employed a Completely Randomized Design (CRD) for analyzing chemical quality and a Randomized Block Design (RBD) for examining organoleptic properties. Preferences were tested using the Kruskal-Wallis test. The chemical quality measurements included moisture content, protein, and crude fiber, while the organoleptic measurements assessed color, aroma, texture, taste, and preference. The results showed that the addition of vegetables significantly (P0.05) affected moisture content but had no significant effect (P0.05) on protein and fiber content. Furthermore, the addition of vegetables significantly (P0.05) influenced the color, aroma, and taste of the nuggets, but did not significantly affect (P0.05) texture and preference. It was concluded that incorporating 10% vegetables into chicken nuggets increased moisture content without affecting protein and fiber levels or overall preference. The addition of carrots yielded the highest scores for aroma and taste preference.

Characterization and impact of airborne particulate matter over Varanasi: A year-long study on concentration, morphology, and elemental composition

Air pollution is an important worldwide issue, especially pronounced in metropolitan and suburban regions, significantly affecting both public health and surroundings. This study investigates the particles' morphology and elemental analysis in Varanasi, a highly inhabited metropolis in the Indo-Gangetic Plain. The research was conducted over a year, from April 2019 to March 2020, utilizing Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy, Ion Chromatography, and Atomic Absorption Spectroscopy to analyse particulate matter. Results indicated that mean values of PM2.5 and PM10 were 106.5 ± 67.2μg/m³ and 180.8 ± 71.4 μg/m³, respectively. Often, these amounts exceeded the National Ambient Air Quality Standards. SEM-EDX analysis revealed diverse particle morphologies, with significant contributions from both manmade sources including industrial activities and vehicle emissions, and natural sources, like soil dust. Elemental analysis identified major components, including Carbon, Oxygen, Fluorine, Aluminium, and Silicon. IC analysis highlighted dominant ionic species, such as Ca++, SO4−-, NO3−, and Cl−, with monthly variations reflecting different emission sources. Heavy metals concentrations such as Ni, Cd, Cr, Mn, Cu, Pb, Zn, and Fe were quantified, with concentrations varying significantly across months. The findings underscore the complex nature of aerosols in Varanasi and highlight the immediate need for targeted control over air quality measures to minimize the particulate matter's detrimental effects on the local population and ecosystem.

The Cancer Equity Compass: Interactive maps showing the association between social determinants of health and cancer disparities.

149 Background: Cancer incidence and mortality in Black, Hispanic and Native American populations is over twice that in the White population for some prevalent cancers. A new, publicly available mapping tool, the Cancer Equity Compass (CEC), was developed to inform data-driven initiatives to improve equity by identifying community factors associated with cancer disparities. The tool is comprised of interactive maps that visually reveal correlations between two variables. Development of the tool began with assembling a group of nine advisors with expertise in cancer prevention, treatment and health equity. These experts inform the strategy, data sources, variables of interest, and stakeholder value propositions for the CEC. Methods: The data source for the CEC is a national, multi-level database developed using individual, health system, and community data, aggregated by county. For the three most common cancers in the U.S., breast, prostate and lung, screening and prevalence rates were collected from 2021 Medicare 5% research identifiable files and mortality rates from 2020 SEER Medicare rates. These metrics were each mapped with a variety of social determinants of health (SDOH) variables. Population rates for minorities, education, poverty, income and unemployment, and an air quality measure were from the Area Health Resources File. Environmental risks were derived from the Environmental Justice Index and included environmental burden (ranked 0-17 for pollution, toxic sites, built environment, transportation infrastructure and water pollution), air pollution (ranked 0-4), and social vulnerability (ranked 0-14 across socioeconomic variables). Results: Each map includes one cancer metric and one SDOH variable. Each variable is stratified to show the top quartile, middle 50%, and bottom quartile. The choropleth heat maps, created on Tableau, display 9 colors representing each combination of the three levels (>75%, 25-75%, <25%) for both mapped variables. The CEC currently has 140 maps presenting visualizations of county SDOH associations with cancer disparities. Conclusions: The expert advisors on the project have outlined four avenues for the CEC to aid efforts to improve cancer equity: 1) Enhance diversity in clinical trial recruitment and data registries through targeted site recruitment; 2) Identify high opportunity targets for improving equity and informing solutions; 3) Identify common correlates of disparities within and across U.S. counties to direct priorities for national policy and programs for large-scale impact; and 4) Measure change to support improvement efforts and report impact of equity initiatives. The CEC will be expanded with additional measures including healthcare access. The goal is use by stakeholders and changemakers who can apply the reported data to fuel advocacy efforts and enact positive change.

Information for Authors

The objective of this study was to examine a complex service environment-hospitals-to suggest how service quality could be reframed and measured for multiple-encounter service situations more effectively. In this cross-sectional study, a sample of 371 patients completed the survey instrument. Service quality measures were guided by the literature but allowed to flow from the respondents at the preliminary stage. Confirmatory factor analysis, along with structural equation modeling, was used to test the hypothesized relationships among key actors' performance metrics (KAPMs). Patient satisfaction is significantly influenced by perceived service quality based on KAPMs. For multiple-encounter services, service quality dimensions and measures ought to be tied to KAPMs. Primary actors-ie, doctors-need knowledge and skills about patient psychology, negotiation, handling difficult patients, and, importantly, "putting the customer first." Sensitivity training on such matters should be provided. The secondary actors are the nurses who have more frequent contact with the patients. Nurses need to be perceived as "patient advocates." Effective advocacy begins with prompt and caring services to build trust. The tertiary actors in their support role also ought to be integrated into becoming vital part of the service provided.

Assessing anthropogenic influences on the water quality of Gomati River using an innovative weighted fuzzy soft set based water pollution rating system

The increasing pollution of river systems poses a significant challenge to water quality management and public health. This study was conducted to evaluate the water quality of the Gomati River in India, focusing on the impact of anthropogenic activities on pollution levels across different seasons. The primary objective was to develop a comprehensive assessment model using an effective weighted fuzzy soft expert system (WFSES) to derive a weighted water pollution score (WWP-score) for rating water pollution levels based on various water quality parameters. To achieve this, we employed a fuzzy soft set (FSS) methodology integrated with a weighted fuzzy soft set (WFSS) model to analyze water quality indices at six sampling stations along the Gomati River, the largest river in Tripura, India. Originating from the Raima and Sarma streams, the river flows westward through key regions, providing 249.39 million cubic meters of water annually, essential for drinking, agriculture, fishing, and transportation. The study utilized crisp data summaries of key water quality parameters, including pH, total dissolved solids, total suspended solids, electrical conductivity, biochemical oxygen demand, dissolved oxygen, total hardness, chloride, total alkalinity, and total coliform, across the pre-monsoon, monsoon, and post-monsoon seasons from May 2022 to April 2023, guided by the distribution of waste discharge points. Fuzzy membership functions were defined for these parameters, and the root mean square (RMSQR) operation was used to combine multiple FSSs. The results revealed that the WWP-scores, a measure of water quality, ranged from 0.744 to 0.872 across the sampling stations, with Srimantapur exhibiting the highest level of pollution and Udaipur showing comparatively better quality. These scores indicate that water quality at all sites falls into the poor category, with Srimantapur experiencing the most severe contamination due to high levels of coliform bacteria and organic pollutants. The analysis highlights the impact of domestic wastewater and agricultural runoff on the river's health. This research offers a novel application of FSS and WFSS theory in environmental management, providing a robust framework for evaluating and ranking water quality across multiple parameters. The findings are valuable to the scientific community as they offer detailed insights into the pollution dynamics of the Gomati River and propose a methodological approach for assessing water quality in other river systems facing similar challenges. The study's results underscore the need for targeted pollution control measures and continuous monitoring to safeguard water resources and public health.

Development of an ultrasound-based metric of muscle functional capacity for use in patients with neuromuscular disease.

Spinal muscular atrophy (SMA) and Duchenne muscular dystrophy (DMD) are progressive neuromuscular disorders characterized by severe muscle weakness and functional decline (Pillen et al., Muscle Nerve 2008; 37(6):679-693). With new therapeutics, objective methods with increased sensitivity are needed to assess muscle function. Ultrasound imaging is a promising approach for assessing muscle fat and fibrosis in neuromuscular disorders. This study builds on prior work by combining ultrasound-based measurements of muscle size, shape, and quality, relating these measures to muscle strength, and proposing a multivariable image-based estimate of muscle function. Maximum voluntary elbow flexion torque of 36 participants (SMA, DMD, and healthy controls) was measured by hand-held dynamometry and elbow flexor muscles were imaged using ultrasound. Muscle size (cross-sectional area, maximum Feret diameter or width, and thickness), quality (echogenicity, texture anisotropy index), and cross-sectional shape (diameter ratio) were measured. Multivariable regression was used to select ultrasound measurements that predict elbow flexion torque. Significant differences were observed in muscle size (decreased), shape (thinned), and quality (decreased) with increased disease severity and compared to healthy participants. CSA (brachioradialis R2 = 0.51), maximum Feret diameter (biceps R2 = 0.49, brachioradialis R2 = 0.58) and echogenicity (brachioradialis R2 = 0.61) were most correlated with torque production. Multivariable regression models identified that muscle size (CSA, maximum Feret diameter) and quality (echogenicity) were both essential to predict elbow flexion torque (R2 = 0.65). A multivariable approach combining muscle size and quality improves strength predictions over single variable approaches. These methods present a promising avenue for the development of sensitive and functionally relevant biomarkers of neuromuscular disease.

Exploring relationships between parental stress, coping, and psychological outcomes for parents of infants with CHD.

This study aimed to explore relationships between parental stress, coping, and outcomes for parents of infants with CHD, via observational approach reflecting domains of the Parental Stress and Resilience in CHD (PSRCHD) model. Fifty-five parents of 45 infants with CHD completed questionnaires with measures of parental stress, Problem-Focused Coping (PFC), Emotion-Focused Coping (EFC), Avoidant Coping (AC), mental health (symptoms of anxiety and symptoms of depression), post-traumatic growth (PTG) and quality of life (QoL). Demographic and infant clinical data were obtained. Parental stress showed significant small to medium positive correlations with MH and PTG, but no significant correlations with QoL. EFC and AC showed significant small to medium positive correlations with MH, and medium negative correlations with parental QoL. EFC and PFC had significant small to medium correlations with PTG. PFC and AC had significant small to medium correlations with infant QoL. Hierarchical multiple regression analyses indicated that parental symptoms of anxiety, PTG, parental QoL, infant QoL were significantly predicted by models comprising of parental stress, coping styles, and clinical controls (adjusted R2 = 13.0-47.9%, p range < 0.001-.048), with results for parental symptoms of depression falling marginally above significance (adjusted R2 = 12.3%, p = .056). Parental stress, coping styles, and length of hospital stay are related to psychological outcomes in parents of infants with CHD. Future research may use the PSRCHD framework to assess mechanisms underlying CHD parents' stress and coping experiences and investigate longitudinal relationships between parental factors and parent and child outcomes.

Ecological-economic assessment of environmental quality as a natural basis for territorial sustainable development: conceptual-categorical analysis

In the context of growing global problems of resource using and environmental protection the development of a sustainable environmental-economic development strategy, including at the territorial level is becoming increasingly actual. The purpose of the research is conceptual-categorical analysis of ecological-economic assessment of environmental quality as a natural basis for territorial systems of different levels sustainable development, elaboration and justification of the conceptual assessment model and methods of its implementation. Methodological issues focus on how to comprehensively assess the environmental quality of the territory and how this assessment can be taken into account in the formation of a strategy for environmentally sustainable development of spatial-economic systems. The scientific novelty of the research results lies in the consolidation of the theories of territorial development sustainability and environmental quality environmental-economic assessment in the conceptual model, as well as in the substantiation of tools for quantitative measurement of environmental quality, increasing the accuracy and reliability of the obtained environmental-economic assessments. The significance of the research results lies in providing decision-makers with comprehensive information on the long-term consequences of environmental-economic decisions and facilitating the transition to environmentally sustainable economic development of territories. This information can be obtained as a result of systematic application of multi-dimensional environmental-economic assessment tools that take into account not only the full socio-economic value of natural environmental resources, but also the spatial characteristics of territories.

DockQ v2: improved automatic quality measure for protein multimers, nucleic acids, and small molecules.

It is important to assess the quality of modeled biomolecules to benchmark and assess the performance of different prediction methods. DockQ has emerged as the standard tool for assessing the quality of protein interfaces in model structures against given references. However, as predictions of large multimers with multiple chains become more common, DockQ needs to be updated with more functionality for robustness and speed. Moreover, as the field progresses and more methods are released to predict interactions between proteins and other types of molecules, such as nucleic acids and small molecules, it becomes necessary to have a tool that can assess all types of interactions. Here, we present a complete reimplementation of DockQ in pure Python. The updated version of DockQ is more portable, faster and introduces novel functionalities, such as automatic DockQ calculations for multiple interfaces and automatic chain mapping with multi-threading. These enhancements are designed to facilitate comparative analyses of protein complexes, particularly large multi-chain complexes. Furthermore, DockQ is now also able to score interfaces between proteins, nucleic acids, and small molecules. DockQ v2 is available online at: https://wallnerlab.org/DockQ.

Quantum refinement in real and reciprocal space using the Phenix and ORCA software.

X-ray and neutron crystallography, as well as cryogenic electron microscopy (cryo-EM), are the most common methods to obtain atomic structures of biological macromolecules. A feature they all have in common is that, at typical resolutions, the experimental data need to be supplemented by empirical restraints, ensuring that the final structure is chemically reasonable. The restraints are accurate for amino acids and nucleic acids, but often less accurate for substrates, inhibitors, small-molecule ligands and metal sites, for which experimental data are scarce or empirical potentials are harder to formulate. This can be solved using quantum mechanical calculations for a small but interesting part of the structure. Such an approach, called quantum refinement, has been shown to improve structures locally, allow the determination of the protonation and oxidation states of ligands and metals, and discriminate between different interpretations of the structure. Here, we present a new implementation of quantum refinement interfacing the widely used structure-refinement software Phenix and the freely available quantum mechanical software ORCA. Through application to manganese superoxide dismutase and V- and Fe-nitrogenase, we show that the approach works effectively for X-ray and neutron crystal structures, that old results can be reproduced and structural discrimination can be performed. We discuss how the weight factor between the experimental data and the empirical restraints should be selected and how quantum mechanical quality measures such as strain energies should be calculated. We also present an application of quantum refinement to cryo-EM data for particulate methane monooxygenase and show that this may be the method of choice for metal sites in such structures because no accurate empirical restraints are currently available for metals.