Probability Of Occurrence Research Articles

Identification of key potential infection processes and risk factors in the computed tomography examination process by FMEA method under COVID-19

PurposeTo identify the key infection processes and risk factors in Computed Tomography (CT) examination process within the standard prevention and control measures for the COVID-19 epidemic, aiming to mitigate cross-infection occurrences in the hospital.MethodThe case hospital has assembled a team of 30 experts specialized in CT examination. Based on the CT examination process, the potential failure modes were assessed from the perspective of severity (S), occurrence probability (O), and detectability (D); they were then combined with corresponding risk prevention measures. Finally, key infection processes and risk factors were identified according to the risk priority number (RPN) and expert analysis.ResultsThrough the application of RPN and further analysis, four key potential infection processes were identified, including “CT request form (A1),” “during the scan of CT patient (B2),” “CT room and objects disposal (C2),” and “medical waste (garbage) disposal (C3)”. In addition, eight key risk factors were also identified, including “cleaning personnel does not wear masks normatively (C32),” “nurse does not select the vein well, resulting in extravasation of the peripheral vein for enhanced CT (B25),” “patient cannot find the CT room (A13),” “patient has obtained a CT request form but does not know the procedure (A12),” “patient is too unwell to continue with the CT scan (B24),” “auxiliary staff (or technician) does not have a good grasp of the sterilization and disinfection standards (C21),” “auxiliary staff (or technician) does not sterilize the CT machine thoroughly (C22),” and “cleaning personnel lacks of knowledge of COVID-19 prevention and control (C33)”.ConclusionHospitals can publicize the precautions regarding CT examination through various channels, reducing the incidence of CT examination failure. Hospitals’ cleaning services are usually outsourced, and the educational background of the staff employed in these services is generally not high. Therefore, during training and communication, it is more necessary to provide a series of scope and training programs that are aligned with their understanding level. The model developed in this study effectively identifies the key infection prevention process and critical risk factors, enhancing the safety of medical staff and patients. This has significant research implications for the potential epidemic of major infectious diseases.

Urban aquifer health assessment and its management for sustainable water supply: an innovative approach using machine learning techniques

Water scarcity is a threat to resilient and sustainable cities as well as the resident population's well-being. This research represents a machine learning (ML) and geospatial data coupling along with groundwater sustainability management strategy (GSMS) components in a stepped methodological approach for urban, peri-urban aquifer sustainability management in Vizianagaram extended city limit in Southern India. Random forest (RF) and support vector machine (SVM) are two ML models in combination with geospatial data (hydrogeological and geo-environmental controlling factors) coupling predicted probable groundwater occurrence by categorizing different groundwater potential zones (GWPZ). The prediction accuracy determined by the area under the receiver operation curve (AUROC) demonstrated accuracy of >80 %, with the RF model being the highest at 88.40 %. Integration of GWPZ with policy and legislation, stakeholder involvement, sensitization, and linkage, along with strengths, weaknesses, opportunities, and threats (SWOT) analysis in the form of GSMS, can help policymakers, planners, and government authorities achieve sustainable measures near to reality and future adaptability.

Predicting potential occurrence of Adelges tsugae (Homoptera: Adelgidae) on a global scale under climate change scenarios using maximum entropy model

Invasive alien species pose grave threats to natural ecosystems and human society. The hemlock woolly adelgid (HWA; Adelges tsugae Annand) is an invasive alien species in several countries, causing severe damage to forest mainly composed of hemlock and spruce. This study employed a maximum entropy model to predict the global potential occurrence areas of HWA based on worldwide HWA occurrence data and four bioclimatic variables: mean diurnal range (BIO2), mean temperature of the wettest quarter (BIO8), mean temperature of the coldest quarter (BIO11), and annual precipitation (BIO12). Future climate change scenarios were used to assess the effects of climate change on HWA distribution. The developed model showed good predictive performance for HWA occurrence (AUC:0.992, F1 score: 0.876), with BIO11 being the most influential factor for HWA occurrence, followed by BIO12. The model identified high-risk areas in North America and eastern Asia including Korea, the Himalayan regions, central and western Europe, Oceania including southeastern Australia and New Zealand, and western part of South America. Additionally, most distribution areas of hemlock trees were in areas where HWA could potentially occur. According to climate change, the model predicted a northward shift in the average latitude of the predicted potential distribution areas showing that the range of HWA may potentially expand even into subarctic regions, where the occurrence probability is currently lower than that of temperate regions. The findings of this study provide valuable insights into the potential occurrence and invasion risk of HWA in current and future climates and emphasize the importance of proactive measures to monitor and manage its spread.

An Analysis of Stream Flow and Flood Frequency: A Case Study from Downstream of Kelani River Basin, Sri Lanka

River floods in Sri Lanka are mainly associated with extreme rainfall events. The Kelani and Kalu rivers are recorded the highest flood frequencies and the accompanying flood damages among the river basins in wet zone (UNDP, 2011). Therefore, the specific objective of the study is to estimate the temporal probability of occurrence of flood events in downstream of Kelani river basin. Secondary data were used for the study. Daily discharges data were obtained from Hanwella gauging station for the period of 1990 to 2019 from the Department of Irrigation, Sri Lanka. Trend analysis, normal distribution and flood frequency analysis have been used. The results of the study revealed that there was a bi-modal pattern of discharges that occurred in June and October. The results also indicated that ten return periods were covered the total period of 30 years, and there was a 97 per cent probability of flood occurrence almost annually and 64 per cent probability of occurring once every two years. This study, therefore, was recommended to design flood control structures for mitigating flood risk; and to determine the economic value of flood control projects and the effect of encroachments on flood plain. Keywords: floods, frequency, downstream, return period, probability

An Index for Snowmelt-Induced Landslide Prediction for Zavoj Lake, Serbia

In February 1963, a huge landslide (ca. 1,950,000 m3) blocked the Visočica River and, thus, formed Zavoj Lake. The primary objective of this research was to investigate the importance of snowmelt in relation to landslide occurrence and to define the critical climatic conditions that may trigger massive winter landslides. We used monthly precipitation and average monthly maximum temperature data from meteorological and precipitation stations in the Visočica River basin (Dojkinci) and in the immediate proximity of Lake Zavoj (Pirot, Dimitrovgrad and Topli Do) as data inputs to the Snow-Melt Landslide (SML) index. It considers the summed monthly precipitation for previous months that continuously have an average maximum temperature below 0 °C. According to this method, the event at Zavoj Lake stands out among all other precipitation and snowmelt values for the past 72 years. After applying the SML index, all stations showed values of >300 mm for February 1963, which we consider as the threshold value for potential landslides appearance. In addition to meteorological data, we applied the SML index to data from the Coordinated Regional Downscaling Experiment (CORDEX) regional climate model outputs for the region from 2022 to 2100. As expected, climate change will have influenced the temperature values, especially during the winter. Conversely, the study area is experiencing drastic changes in land use caused by depopulation, leading to a reduced risk of winter landslides in the Visočica basin. We suggest that future climatic conditions in the area will make it more likely to experience extreme summer precipitation events, which might trigger large landslides. The SML method can be implemented for all landscapes that experience snowy winters, providing information in a timely manner so that local residents can react properly when the probability of landslide occurrence rises. The SML index, grounded in essential meteorological principles, provides a tailor-made, data-driven methodology applicable across varied geographical settings. Its utility extends to mitigating hydro-meteorological hazards on scales ranging from local to national scales, offering diverse and effective early warning solutions.

Prediction of debris flow initiation points using high resolution DEM and GIS analysis in South Korea

This study presents a GIS analysis model that utilizes high-resolution Digital Elevation Map data to predict the initiation points of debris flow through the overlap of slope stability analysis techniques and rainfall flow analysis. The debris flow that occurred on Mount Woomyun in Seoul, South Korea in 2011 was selected as the case study for comparing and analyzing the prediction model against the actual case. In South Korea, occurrences of debris flow mostly happen during the summer monsoon season. When a substantial amount of rainfall accumulates and heavy rainfall events occur, the pore water pressure within slopes reaches saturation. Consequently, flow is initiated along “temporary streams.” In this study, areas with a combination of low slope stability and abrupt changes in velocity vectors were assumed to have the highest probability of debris flow occurrence due to the overlapping of these factors. The research model was conducted under this assumption. To evaluate the performance of the model, 3D coordinates of the model’s result point, and actual occurrence points were obtained, and a Pearson correlation analysis was conducted to compare them. With these results, R2 values of X = 0.9147, Y = 0.8625, and H = 0.8942 were obtained. These high R2 indicate a strong correlation between the model’s predictions and actual occurrences.

Evaluating Dendrolimus superans (Lepidoptera: Lasiocampidae) Occurrence and Density Modeling with Habitat Conditions

Dendrolimus superans, a prominent forest pest in northeast China, exerts detrimental effects on tree growth and development, disrupts the ecological functioning of forests, and even alters the trajectory of succession. The objective of this study was to investigate the influence of habitat conditions on the occurrence probability and density of overwintering D. superans, aiming to provide scientific insights for the effective prevention of and control measures against this pest infestation. The investigation encompassed 142 plots (20 m × 20 m) in various forest types within the primary distribution area of D. superans in the Great Xing’ an Mountains, focusing on factors such as topography, forest vegetation, and larval density. Binary logistic regression was employed to establish models for predicting the occurrence probability of D. superans, while generalized linear models (GLMs) and categorical regression (CATREG) were utilized to develop models for estimating its population size. Subsequently, an evaluation was conducted to assess the performance of these models. The occurrence probability model showed high accuracy (AUC = 0.826) in predicting infestation. The slope aspect and herb cover were the key factors affecting the occurrence of D. superans. The occurrence probability was the lowest on shady slopes and the highest on sunny slopes. The occurrence probability of D. superans increased with the increase in herb cover. The model of quantification showed that the density of D. superans was the least on shady slopes and the highest on sunny slopes. As the slope gradient increased, the density decreased. D. superans occurred most frequently on ridges. Similarly, with the increase in canopy cover or the decrease in diameter at breast height (DBH) and stand density, the density of D. superans increased. The influence of the topography factors surpassed that of the forest vegetation factors in shaping the population dynamics of D. superans, despite both being significant contributors. The study revealed that D. superans is prone to occur on sunny slopes, flat slopes, and ridges, which should be the focus of prevention and control in forest management practices, such as replanting, thinning, and regular weeding, to help restrain the growth of the population of this pest.

Molting incidents of Hyalomma spp. carrying human pathogens in Germany under different weather conditions.

Hyalomma marginatum and H. rufipes are two-host tick species, which are mainly distributed in southern Europe, Africa to central Asia but may also be found in Central and Northern Europe through introduction by migratory birds. Ticks were collected while feeding or crawling on animals and humans, or from the environment, in different regions in Germany, between 2019 and 2021 in a citizen science study and from 2022 to 2023 in the wake of this study. From 2019 to 2023, a total of 212 Hyalomma adult ticks were detected in Germany. This included 132 H. marginatum and 43 H. rufipes ticks sent to research institutions and 37 photographic records that were only identified to genus level. The number of detected ticks varied over the years, with the highest number of 119 specimens recorded in 2019, followed by 57 in 2020. Most of the specimens were collected from horses, while some were collected from other animals, humans or found crawling on human clothes or other objects inside or outside houses. The screening of 175 specimens for Crimean-Congo hemorrhagic fever virus and of 132 specimens for Babesia/Theileria spp. by PCR gave negative results, while human-pathogenic Rickettsia were detected in 44% (77/175) of the total samples. Subsequent amplicon sequencing and phylogenetic analysis of representative samples determined the species of 41 Rickettsia aeschlimannii and one R. slovaca sequences. Analysis of climatic factors indicated a significantly higher probability of Hyalomma occurrence at locations with higher average spring temperature during the years 2019 and 2020 compared to randomly generated pseudo-absence locations. Dry and hot conditions probably facilitated Hyalomma nymphs' survival and molting into adults during these years.

Improving the risk management process in quality management systems of higher education

The purpose of this paper is to improve the risk management process in the quality management system of higher education, taking into account the hazardous factors that increase the probability of occurrence and severity of consequences of undesirable events, as well as favorable factors that reduce the probability of occurrence and severity of consequences of hazardous events. The basis of risk management in the quality management systems of higher education institutions is the “Bowtie” method, which involves six main steps of identifying inconsistency, determining the impact of hazardous and favorable factors according to the impact group, ranking hazardous and favorable factors, calculating risk, substantiating precautionary measures and checking calculations. To rank hazardous and favorable factors, the authors used the “Decision Making Trial and Evaluation” method (hereinafter—DE-MATEL), which is based on paired comparison and decision-making tools based on graph theory. An improved process is proposed for risk assessment, which differs from the known ones by the presence of the identification of the cause-and-effect relationship “hazard (inconsistency)-hazardous event-consequences”, identification of hazardous and favorable factors of the internal and external environment that affect the probability and/or the degree of severity of a hazardous event—the appearance of an inconsistency, which is carried out after the inconsistency has been determined; determination of causal hazardous and favorable factors by an acceptable method. Registers of inconsistencies (hazards), hazardous and favorable factors have been developed and proposed based on the requirements for accreditation of educational programs and the international standard ISO 9001:2015, which will allow, based on a risk-oriented approach, to provide a basis for setting the goals of a higher education institution under martial law in order to guarantee effective implementation of the mission and strategy. They are proposed for decision-making in the quality management systems of educational organizations on the substantiation of precautionary or corrective measures based on ranking the risks from identified inconsistencies, which are determined taking into account the impact of the entire set of identified significant hazardous and favorable factors. The value of this paper is to improve the quality risk management process in educational organizations, taking into account the impact of hazardous and favorable factors, and to develop an appropriate step-by-step algorithm of actions and a risk assessment form.

NiCoP fibers as novel catalysts for hydrogen evolution in alkali and acidic environment

NiCoP transition metal phosphide has been recognized as a most promising candidate to alternate the noble metals in hydrogen evolution reaction. Herein, it is first time presented the facile preparation of NiCoP electrocatalyst in form of fibers with dominant 1D structure formed by needle-less electrospinning technology and finalized by defined heat treatment process. The study provides a precise method for the preparation of NiCoP or similar phosphide fibers, together with an experimental proof of their excellent electrocatalytic performance in alkali and acidic environments. The as-prepared fibrous NiCoP electrocatalyst sintered in air at 900 °C followed by sintering at 700 °C in Ar/H2 exhibits low hydrogen evolution reaction overpotential (η10) of 141 mV in alkali and 146 mV in acidic environment corresponding with low Tafel slopes of 53 and 97.8 mV/dec, respectively. The bimetallic phosphide also shows superior oxygen evolution reaction activity in alkaline medium 1 M KOH with satisfying durability in long-term stability tests. The highly innovative fibrous form of the material not only plays a significant role in regulation of the electronic structure and boosting the electrochemical active sites and thus the electrocatalytic performance in water splitting technology, but also allows tuning of the electrode electric conductivity, occurrence of the active sites probability and thus utilisation of the electrode, together with its permeability and mechanical stability.

Repeated quantum game as a stochastic game: Effects of the shadow of the future and entanglement

We present a systematic investigation of the quantum games, constructed using a novel repeated game protocol, when played repeatedly ad infinitum. We focus on establishing that such repeated games – by virtue of inherent quantum-mechanical randomness – can be mapped to the paradigm of stochastic games. Subsequently, using the setup of two-player–two-action games, we explore the pure reactive strategies belonging to the set of reactive strategies, whose support in the quantum games is no longer countably finite but rather non-denumerably infinite. We find that how two pure strategies fare against each other is crucially dependent on the discount factor (the probability of occurrence of every subsequent round) and how much entangled the quantum states of the players are. We contrast the results obtained with the corresponding results in the classical setup and find fundamental differences between them: e.g, when the underlying game is the prisoner’s dilemma, in the quantum game setup, always-defect strategy can be beaten by the tit-for-tat strategy for high enough discount factor.

Detection of small drizzle droplets in a large cloud chamber using ultrahigh-resolution radar

Abstract. A large convection–cloud chamber has the potential to produce drizzle-sized droplets, thus offering a new opportunity to investigate aerosol–cloud–drizzle interactions at a fundamental level under controlled environmental conditions. One key measurement requirement is the development of methods to detect the low-concentration drizzle drops in such a large cloud chamber. In particular, remote sensing methods may overcome some limitations of in situ methods. Here, the potential of an ultrahigh-resolution radar to detect the radar return signal of a small drizzle droplet against the cloud droplet background signal is investigated. It is found that using a small sampling volume is critical to drizzle detection in a cloud chamber to allow a drizzle drop in the radar sampling volume to dominate over the background cloud droplet signal. For instance, a radar volume of 1 cubic centimeter (cm3) would enable the detection of drizzle embryos with diameter larger than 40 µm. However, the probability of drizzle sampling also decreases as the sample volume reduces, leading to a longer observation time. Thus, the selection of radar volume should consider both the signal power and the drizzle occurrence probability. Finally, observations from the Pi Convection–Cloud Chamber are used to demonstrate the single-drizzle-particle detection concept using small radar volume. The results presented in this study also suggest new applications of ultrahigh-resolution cloud radar for atmospheric sensing.

A cross-regional analysis of red-backed shrike responses to agri-environmental schemes in Europe

Agri-environmental schemes (AES) are the main policy tool to counteract farmland biodiversity declines in Europe, but their biodiversity benefit varies across sites and is likely moderated by landscape context. Systematic monitoring of AES outcomes is lacking, and AES assessments are often based on field experiments encompassing one or few study sites. Spatial analysis methods encompassing broader areas are therefore crucial to better understand the context dependency of species’ responses to AES. Here, we quantified red-backed shrike (Lanius collurio) occurrences in relation to AES adoption in three agricultural regions: Catalonia in Spain, the Mulde River Basin in Germany, and South Moravia in the Czech Republic. We used pre-collected biodiversity datasets, comprising structured and unstructured monitoring data, to compare empirical evidence across regions. Specifically, in each region we tested whether occurrence probability was positively related with the proportion of grassland-based AES, and whether this effect was stronger in simple compared to complex landscapes. We built species distribution models using existing field observations of the red-backed shrike, which we related to topographic, climatic, and field-level land-use information complemented with remote sensing-derived land-cover data to map habitats outside agricultural fields. We found a positive relationship between AES area and occurrence probability of the red-backed shrike in all regions. In Catalonia, the relationship was stronger in structurally simpler landscapes, but we found little empirical support for similar landscape-moderated effects in South Moravia and the Mulde River Basin. Our results highlight the complexity of species’ responses to management across different regional and landscape contexts, which needs to be considered in the design and spatial implementation of future conservation measures.

Linear mixed models and latent growth curve models for group comparison studies contaminated by outliers.

The linear mixed model (LMM) and latent growth model (LGM) are frequently applied to within-subject two-group comparison studies to investigate group differences in the time effect, supposedly due to differential group treatments. Yet, research about LMM and LGM in the presence of outliers (defined as observations with a very low probability of occurrence if assumed from a given distribution) is scarce. Moreover, when such research exists, it focuses on estimation properties (bias and efficiency), neglecting inferential characteristics (e.g., power and type-I error). We study power and type-I error rates of Wald-type and bootstrap confidence intervals (CIs), as well as coverage and length of CIs and mean absolute error (MAE) of estimates, associated with classical and robust estimations of LMM and LGM, applied to a within-subject two-group comparison design. We conduct a Monte Carlo simulation experiment to compare CIs and MAEs under different conditions: data (a) without contamination, (b) contaminated by within-subject outliers, (c) contaminated by between-subject outliers, and (d) both contaminated by within- and between-subject outliers. Results show that without contamination, methods perform similarly, except CIs based on S, a robust LMM estimator, which are slightly less close to nominal values in their coverage. However, in the presence of both within- and between-subject outliers, CIs based on robust estimators, especially S, performed better than those of classical methods. In particular, the percentile CI with the wild bootstrap applied to the robust LMM estimators outperformed all other methods, especially with between-subject outliers, when we found the classical Wald-type CI based on the t statistic with Satterthwaite approximation for LMM to be highly misleading. We provide R code to compute all methods presented here. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Reliability analysis of subway sliding plug doors based on improved FMECA and Weibull distribution

Using traditional FMECA to analyze the hazard of subway sliding plug door system, there are problems such as easy-to-take repetitive values, irrational allocation of expert's weights, and failure to consider the weights of evaluation factors. Hence, this paper proposes to improve FMECA by using linear interpolation to increase the differentiation of the same fault probability occurrence among various fault modes. Apply the DUOWA to dynamically assign weights to different experts. The AHP is used to endow weights to diverse evaluation factors to make them more suitable for engineering needs. The operation records of metro trains were collected from the Shanghai subway manufacturing plant for 1836 days. The hazards of the 17 common faults of the door were first analyzed using an improved FMECA,the EDCU is the component most prone to failure. Next, use a RCM strategy to determine maintenance periods for different faults. Finally, through the Weibull distribution fitting test, the fault rate function of the door is obtained, and the RUL of the door is predicted.

Exploring the Critical Risk Factors of Public–Private Partnership City Hospital Projects in Turkey

Governments face challenges in delivering essential public services due to their limited funds. This has led to an increasing reliance on the Public–Private Partnership (PPP) model, an alternative financing model involving a long-term collaboration between the private and public sectors to provide public services. Turkey, as a developing country facing financial limitations, has embraced the PPP model to address urgent public needs. Over the past decade, the Turkish Government has extensively utilized the PPP model, particularly in executing city hospital projects. However, investors have faced challenges in project execution due to various risk factors. Therefore, the main objective of this study is to explore the critical risk factors associated with PPP city hospital projects in Turkey. In this context, a comprehensive literature review was conducted to identify potential risks related to PPP city hospital projects. A questionnaire survey was implemented to assess the probability of occurrence and the severity of the impact of these risk factors. The collected data underwent analysis to determine the priority of these risk factors. The findings revealed that the top five most critical risk factors in PPP city hospital projects in Turkey are “foreign exchange rate fluctuations”, “inflation rate volatility”, “high finance costs”, “fiscal issues”, and “economic crises”. Conversely, “unavailability of equipment” was identified as the least significant risk factor. The insights gained from this research can offer valuable guidance for prospective investors interested in participating in PPP city hospital projects in Turkey and other developing countries with similar conditions.

Improvement of performance of in-situ virtual monitoring system of the occurrence probability for high concentrations of naturally occurring radioactive materials in groundwater through the solution of the data imbalance problem

This paper presents two data-driven virtual sensors to estimate the time-series of the probability of high-concentration occurrence of naturally occurring radioactive materials (NORMs; 238U and 222Rn) in groundwater based on the in-situ groundwater quality monitoring data and geological information. The random forest was applied to estimate the NORM concentration based on the actual in-situ groundwater quality data, rock type, and the aquifer depth. Additionally, this study proposes three data sampling techniques (i.e., under-sampling, synthetic minority over-sampling, and a complex sampling) to improve the model applicability and accuracy. The developed models were validated using the actual data acquired from 201 locations in South Korea. The models for 238U and 222Rn showed estimation accuracies of 85% and 80%, respectively; the models with over-sampling showed better performance. All the results verified the usefulness of the developed models as virtual sensors for providing immediate information on the in-situ presence of NORMs in groundwater.

Scenario Planning of Pertamina Patra Niaga Business Strategy to Address the Growth of the Indonesian Electric Vehicle Market

PT Pertamina Patra Niaga is facing an era of energy transition, and one of the biggest phenomena is the adoption of electric vehicles (EVs). Massive EV adoption will impact Patra Niaga’s future business, and they must prepare a strategy to deal with these changes. This research aims to determine the factors that influence EV adoption in Indonesia and propose strategies that need to be carried out by Patra Niaga. The literature review shows that internal and external factors are very influential in determining Patra Niaga’s strategy. The research design is qualitative, with data obtained from primary sources from interviews and secondary data sources, including industry documents, reports, and journals. Using the scenario planning method with the PESTEL approach, the paper identifies four possible scenarios for the future of EV adoption in Indonesia. The possible scenarios are green tech frontier, policy-driven change, market-led innovation, and stagnant development. Environment analysis conducted both external analysis and internal analysis. The research uses Porter’s Five Forces to identify rivalry among the industry players for external analysis. Meanwhile, the internal analysis uses the use VRIO Framework to identify the competitive advantage of the company and the strength and weakness analysis to determine the opportunities and threats for the company. Finally, the research uses the BCG Matrix to develop a strategy for each scenario. Based on the BCG Matrix, the proposed strategies are to invest more in the EV infrastructure where the scenario is green tech frontier, to invest or divest where the scenario is policy-driven, to keep the EV infrastructure business running where the scenario is market-led innovation, and to s liquidated where the scenario is stagnant development. The chosen strategy is suggested to be implemented based on the probability of occurrence for each of the scenarios within a timeframe from the year 2024 to 2035 by involving various divisions in the company, including marketing, business development, and project leader of the EV division.

Synonymous Mutations Can Alter Protein Dimerization Through Localized Interface Misfolding Involving Self-entanglements

Synonymous mutations in messenger RNAs (mRNAs) can reduce protein–protein binding substantially without changing the protein’s amino acid sequence. Here, we use coarse-grain simulations of protein synthesis, post-translational dynamics, and dimerization to understand how synonymous mutations can influence the dimerization of two E. coli homodimers, oligoribonuclease and ribonuclease T. We synthesize each protein from its wildtype, fastest- and slowest-translating synonymous mRNAs in silico and calculate the ensemble-averaged interaction energy between the resulting dimers. We find synonymous mutations alter oligoribonuclease’s dimer properties. Relative to wildtype, the dimer interaction energy becomes 4% and 10% stronger, respectively, when translated from its fastest- and slowest-translating mRNAs. Ribonuclease T dimerization, however, is insensitive to synonymous mutations. The structural and kinetic origin of these changes are misfolded states containing non-covalent lasso-entanglements, many of which structurally perturb the dimer interface, and whose probability of occurrence depends on translation speed. These entangled states are kinetic traps that persist for long time scales. Entanglements cause altered dimerization energies for oligoribonuclease, as there is a large association (odds ratio: 52) between the co-occurrence of non-native self-entanglements and weak-binding dimer conformations. Simulated at all-atom resolution, these entangled structures persist for long timescales, indicating the conclusions are independent of model resolution. Finally, we show that regions of the protein we predict to have changes in entanglement are also structurally perturbed during refolding, as detected by limited-proteolysis mass spectrometry. Thus, non-native changes in entanglement at dimer interfaces is a mechanism through which oligomer structure and stability can be altered.

Relative Contributions of Large‐Scale Atmospheric Circulation Dynamics and Anthropogenic Warming to the Unprecedented 2022 Yangtze River Basin Heatwave

AbstractThe unprecedented 2022 Yangtze River Basin (YRB) heatwave is a threat to human society and natural ecology, so the understanding of its underlying drivers is critical to regional climate adaptation and resilience. Here we conducted a multi‐method attribution analysis on the contribution of atmospheric circulation change and anthropogenic impacts to the occurrence probability and intensity of this extreme heatwave. Based on the nonstationary statistical analysis, the 2022 YRB heatwave is a 1‐in‐900‐year event and a 1‐in‐110‐year event with and without considering the 2022 YRB heatwave in the fitting, respectively. The large‐scale meteorological condition analysis shows that the 2022 YRB heatwave is featured with an anomalous high‐pressure system that favors a hot and dry atmospheric column, overlaid by anomalous subsidence and clear skies which leads to warming and greater solar heating. The ensemble constructed circulation analogue analyses show that the circulation anomaly fails to explain the observed 2022 YRB SAT anomalies fully. Specifically, 46% (0.132 ± 0.027°C decade−1) of the observed SAT trend during 1979–2022 (0.290 ± 0.048°C decade−1) is caused by anthropogenic warming and the associated thermodynamic feedback, while the remaining 54% (0.157 ± 0.038°C decade−1) of the trend is caused by changes in the large‐scale atmospheric circulation. Our findings on changes in atmospheric circulation patterns associated with YRB heatwave and anthropogenic contributions to YRB heatwave could provide valuable information for climate adaptation and mitigation strategies in the context of a warming climate.