The current research aims to aid policymakers and healthcare service providers in estimating expected long-term costs of medical treatment, particularly for chronic conditions characterized by disease transition. The study comprised two phases , in which we developed linear optimization-based mathematical frameworks to ascertain the expected long-term treatment cost per patient considering the integration of various related dimensions such as the progression of the medical condition, the accuracy of medical treatment, treatment decisions at respective severity levels of the medical condition, and randomized/deterministic policies. At the qualitative research stage, we conducted the data collection and validation of various cogent hypotheses acting as inputs to the prescriptive modeling stage. We relied on data collected from 115 different cardio-vascular clinicians to understand the nuances of disease transition and related medical dimensions. The framework developed was implemented in the context of a multi-specialty hospital chain headquartered in the capital city of a state in Eastern India, the results of which have led to some interesting insights. For instance, at the prescriptive modeling stage, though one of our contributions related to the development of a novel medical decision-making framework, we illustrated that the randomized versus deterministic policy seemed more cost-competitive. We also identified that the expected treatment cost was most sensitive to variations in steady-state probability at the “major” as opposed to the “severe” stage of a medical condition, even though the steady-state probability of the “severe” state was less than that of the “major” state. According to the world health organization (WHO), the work on healthcare costing and accompanying efficiencies explores questions around the usage of healthcare resources, particularly in the public health sector WHO, . StrategGlobal healthcare spending has been spiraling since the early 2000s, as per a report released by the WHO in 2019 , there are important accompanying downsides, both at the macro and micro levels. At the macro level, healthcare spending has been accompanied by a significant increase in catastrophic and out-of-pocket spending (OOPS) as a share of total health expenditure in the past decade . There would be a certain level of treatment decision in each of these states depending on the extent of medical, medicinal, and surgical interventions. In such transitions, the expected costs, therefore, likely depend on whether treatment decisions are deterministic or randomized in nature. A deterministic decision reflects when the healthcare service provider prescribes an appropriate level of treatment depending on past experiences in that a certain expected cost of medical treatment would result.On the other hand, a randomized policy pertains to an attempt by the medical service provider so that certain acceptable treatment decisions can be mapped to individual states of the medical condition to minimize the expected treatment costs. Further, long-term steady-state probabilities , would significantly impact the expected costs of treatment under both deterministic and randomized policies. Furthermore, data related to the accuracy of diagnosis can also impact the long-term steady-state probabilities and, therefore, treatment costs , we employed the theoretical construct of thematic analysis to identify and classify major cardiovascular condition states. The clinicians for this classification were 115 retired cardiovascular specialists and surgeons . Using the clinicians’ expert inputs, we also obtained a sense of the broad frequencies of each of these states, transitions, and accuracy of diagnoses. We further conducted statistical validations to eliminate outliers to ensure that the data from the remaining clinicians were not statistically different. Following this, in the second stage of our research , using the linear programming (LP)-based exact-method technique, we modeled the frameworks of the minimization of expected medical costs of treatment under both deterministic and randomized policies. Using the data-related costs, transition probabilities, and so on, we illustrated the workings of the models developed. Figure From Fig. The remainder of the paper is organized as follows: Sect. The two primary research streams around which our research revolves are a) the cost and economic modeling of healthcare using analytical and statistical methods; b) the cost-dominant medical decision-making in healthcare. We now present the extant research literature related to both streams.Using the Bayesian Markov-chain Monte Carlo simulation method, Cooper et al.  devised In a study related to healthcare budgets and the decision rules of cost-effective healthcare providers, Baal et al.  convergeMost current studies, including those by Lin et al. , Morid eAlthough de Gues et al.  deployedPrior to developing the prescriptive framework based on linear optimization models, we collected real-life and empirically validated data on cardiovascular conditions from retired medical professionals (experts) who had experience diagnosing and treating such conditions. In particular, data on states of the said medical condition, transitions, and treatment accuracy were systematically collected during primary data collection. Further, we relied on robust statistical validation, thereby eliminating outliers and attempting to ensure that the data did not remain statistically dissimilar. All these nuances related to data and hypothesis formalized at the qualitative stage stage 1) were key to establishing and validating the prescriptive model developed in stage 2. Therefore, an important contribution associated with our study is that, instead of evolving a prescriptive model characterized only by theoretical underpinnings, our model is grounded in real-life data backed further by strong statistical validation. Table  were keyMost current studies, including those by Cooper et al. , Lin et This section presents the research gaps addressed in the current study. Though studies such as Beaulieu and Bentahar , Lin et Practicality dictates that the most important determinants of research methodology are often formulated research questions  over video calls (primarily due to the ongoing COVID-19 pandemic). The objective of such interviews as a primary method of data collection is anchored in the fact that such a method enables (1) insightful discussions with experienced medical professionals, thus obtaining richness in primary first-hand data; (2) the uncovering of newer knowledge through allowing clinicians to express their ideas freely; (3) the privacy of clinicians who are not willing to share personal experiences in front of peers; and (4) two-way communication between interviewer and interviewee. Following this, the use of thematic analysis helped identify, analyze, and report themes within the data. Further, thematic analysis has shown itself to be flexible and tangible, particularly in the context of qualitative data  Are there any statistical differences in the transitions of different states of medical conditions?; and (c) How can we work with the means of different parameters that are statistically similar by filtering out the outliers?At the end of the information-gathering phase, it was broadly expected that the major states of the medical condition along with its accompanying transitions would be finalized. These thematic inputs then acted as an input to collect the pertinent objective data, followed by rigorous statistical testing and validation to ensure that elemental relationships were identified and verified. In particular, we aimed to substantiate a few important questions: (a) Once the rigorous statistical validation was conducted, we moved to the second stage of the work , wherein we conducted detailed modeling of deterministic and randomized policies, as detailed in Sect. The indices, parameters, and decision variables of the model are presented in Table A(mt) requires the corresponding treatment. The treatment would be appropriate only if the corresponding treatment is T(mt). This means that for the specific state A(mt), only T(mt) would be appropriate, and no other treatment decisions such as T(m1), T(m2), … T(mt-1), …, T(mt+1),…. T(mT) would be acceptable. If A(mt) is mapped to T(mt), then the treatment decision would be termed ‘true treatment.’ Otherwise, the treatment would be termed ‘false treatment.’ Table A(mt) and T(mt), the treatment is true, and the corresponding probability of treatment corresponding to medical condition A(mt) is p{ A(mt), T(mt)}.Suppose a patient suffering from a specific medical condition, state A(m1) denotes a “minor condition”. A(m1) can remain at this state itself with a probability of p{A(m1)}. A(m1) can further deteriorate to lower states of the medical condition with individual probabilities. For instance, state A(m1) can deteriorate to a lower state A(m2) with a mean transition probability of Another dimension of medical treatment is related to the Markovian property in that the state of a specific medical condition can evolve to other severe states of the specific medical condition. For example, an individual with a specific “minor” cardiovascular condition can remain in this same condition with a probability of 1/2 or can deteriorate to a “major” and “severe” conditions over a period of time with probabilities of 3/8 or 1/8, respectively. This means that, on average, there is a 50% likelihood of the patient remaining steady at the same “minor” condition. The patient’s “minor” condition would deteriorate to “major” and “severe” with a 37.5% and 12.5% likelihood, respectively. Referring to Table A(m1), A(m2), A(.), A(mt), A(mT), A(..), A(mT) are all possible exhaustive states of a medical condition such that A(m1) >  > A(m2) >  > A(.) >  > A(mt) >  > A(mT) >  > A(..) >  > A(mT) with >  > depicting progressively degenerate states, then the sum of the corresponding mean transition probabilities corresponding to all the rows would be equal to 1. For instance, within the medical condition transition matrix If A(m2) cannot transition back to better prior states. Therefore, the values of such transition probabilities would be equal to zero.Further, a particular degenerate state, for instance, Similarly, for the remainder of the row, the following mathematical expressions can be used:Equations –5) ensu ensu5) eA(mT), will not improve from any previous state and will remain in this state only with a probability of 1. Therefore, the value of p{A(mT)} would be equal to 1. The interplay of likelihoods of states of a medical condition and corresponding medical treatments and transition probabilities would have a combined effect in that the resulting probabilities would be indicative of uncertainties associated with a specific state of medical condition/corresponding treatment while at the same time taking into account deterioration to a degenerate state of a medical condition. This means that the effective transition probability would be a key input in subsequent modeling.Notably, the last state, When we consider the mean effective transition probability, we can ascertain the elements of the effective transition probability using the independent property of the two probability element sets depicted in Table A(m1)—is expressed as x{A(m1)} and can be determined using the following mathematical expression:The effective transition probability can be represented using two types. The first is a transition within the same state , and the second is a transition from one state to another . The effective transition probability in the same state of a medical condition—for instance, related to state A(m1) transitions to A(mT), is expressed as The effective transition probability when the state of a medical condition transitions to some other state, for instance, when state p would be replaced by x.Similar to the approach specified in Eq. , the remA(mT) represents the absorbing state in that once the state of medical condition finally transitions to A(mT), it becomes an infeasible state and needs to be brought back to state A(m1). For instance, if the patient goes into a “severe” state in certain cardiovascular conditions, the patient might have to be administered a pacemaker. Therefore, the corresponding probability for A(m1) would assume a value of 1.Once the elements of the matrix If Equations –13) ens ens13) eSince Equation  ensures c{A(m1)}, c{A(m2)}, c{A(.)},..C{A(mt), C{A(..)}..C{A(mT) represent the average costs of treatment corresponding to states A(m1), A(m2), A(.),..A(mt), A(..), A(mT), respectively, then the deterministic long-term average cost of treatment per patient can be ascertained as per Eq. (If  per Eq. :15\documD(1), D(2), D(.), D(k), D(..), and D(K) are the various treatment decisions, then these treatment decisions could be mapped with individual states of the medical condition in binary terms, as shown in Table D ϵ . The rationale for this is that depending on the actual state of a medical condition, different treatment decisions can be taken depending on the patient. For instance, for a “minor” cardiovascular condition, a relatively healthy patient’s treatment might be accompanied by non-surgical and lifestyle change-oriented approaches. On the other side, for a “minor” cardiovascular condition for a patient with comorbidities, a moderate surgical procedure with some medicinal interventions might be more appropriate. However, the flip side to a deterministic policy of a treatment decision is that the medical service provider would not have any leeway to consider different treatment decisions corresponding to different states of a medical condition in a commensurate manner. Therefore, a probability distribution should be employed to map a particular treatment decision with a certain medical condition state.If A(mT) and treatment decision D(k), let y be a steady-state unconditional probability, which can be interpreted as the following:A randomized policy matrix with the mapping of states of a medical condition and decision is shown in Table The abovementioned steady-state unconditional probability holds the form of a joint probability containing both the state of a medical condition and the medical decision corresponding to that state.y is closely related to D{k, mt) such that the following mathematical expression formulated in Eq. 19) captuThere exist three sets of constraints for Equations  and 21)21) ensur(b) From the results on steady-state probabilities, the following can be formulated:Equation  ensures Hence, the long-term expected cost Equation  expressey so as toHence, the linear programming model chooses Equation  signifieThis is subject to the constraintEquation  capturesEquation  establisEquation  ensures (MT + 2) functional constraints and K(MT + 1) decision variables. Because the above LP model can be solved using commercial solvers, once the The model represented by mathematical Eqs. –29) rep rep29) rIn order to illustrate our methodology, we consider the data obtained from 115 clinicians (experts) who had been affiliated with a multi-specialty hospital chain in Eastern India as cardiovascular specialists and are now retired. These hospital chains are part of a large private multi-specialty hospital chain headquartered in the capital city of an Eastern Indian state. Being a reputable hospital in Eastern India, patients from both the native state and other states often flock to this hospital due to its affordable health care costs. To preserve anonymity, we do not explicitly mention the names of the hospital chain and 115 clinicians. The pertinent data related to the study and experts’ profiles are listed in Table A belief about certainty (or uncertainty) by an expert about some state of a medical condition is often grounded in data or experience gained during professional practice, or both  opined that they had typically observed four major states of cardiovascular conditions, which often form the basis for the corresponding level of medical diagnosis and treatment. These levels also form the basis for corresponding administrative procedures, including those of the hospital and insurance companies (in cases wherein the patients were insured). Table Referring to Table Thereafter, the clinicians were asked to fill out a Google form-based survey created to capture the inputs related to cardiovascular conditions during the questionnaire phase. The survey essentially captured three broad dimensions. First, what were the typical frequencies of reported diagnosis of the four states of the medical condition? Second, what were the frequencies of the accuracy of medical treatment corresponding to each of the four states of the diagnosed condition? Third, what were and to what extent did the transitions from a particular state of medical condition impact itself and subsequent degenerative states? Table When we observed the reported frequencies of the four states of a diagnosed cardiovascular condition, we ascertained that out of 115, 13 clinicians reported that frequencies were outliers. In the case of the remaining 102 clinicians’ data, there was a clear pattern in the reported frequencies. This pattern manifested clearly in that frequency of diagnosis of the “minor” state was greater than that of the “moderate” state, which in turn was greater than that of the “major” state. Finally, the “severe” state had the lowest reported frequencies. Therefore, we only considered the inputs of 102 clinicians. In particular, one of the primary variables of interest was the mean probability of a diagnosed state (based on the mean frequency of 102 considered clinicians). Figure p-value was greater than 0.05, indicating that the probabilities corresponding to each state of diagnosis were approximately normally distributed. Thereafter, we carried out an analysis of variance (ANOVA) one-factor test at p < 0.05 for the data from the 102 clinicians (leaving aside the outlier data from 13 clinicians), where the following hypothesis represented the null hypothesis:H(0): Reported frequencies of diagnosed states remain statistically the same for the twelve clinicians within a cutoff percentage of 2.However, before assuming that we could obtain and use these mean values for the subsequent modeling stage, it was essential to ascertain whether the mean frequency (probability) of a particular state of diagnosis was statistically the same across the 102 clinicians considered . Before performing this, we examined the normality of sample data corresponding to each state of diagnosis, and we tested for normality of the sample at a 95-percent confidence interval. We first laid out the data graphically and looked at the histogram and Q-Q plots, which indicated a normal distribution. Further, we also performed the Shapiro–Wilk test for the reported probabilities. In this test, the p-value and t statistic, we failed to reject the null hypothesis of frequencies at a 95-percent confidence level.Based on the H(0): The reported frequencies of the diagnosed states are such that order remains: frequency of “Minor” state > frequency of “Moderate” state > frequency of “Major” state > frequency of “Severe” state for all 102 clinicians.In order to test the order of the mean probabilities of the reported states (such that the mean probability of diagnosis at a minor state was greater than that at a moderate state and so forth), the following null hypothesis was postulated.t-tests were carried out at a 95-percent confidence level . For the sake of brevity, we report two such comparisons, which are provided in Table In order to test the aforementioned hypothesis, a total of six pair-wise two-tailed p-value and t statistic, it was verified that the mean probability of diagnosis at the minor stage > the mean probability of diagnosis at the moderate state > the mean probability of diagnosis at the major state > the mean probability of diagnosis at the severe state. We also concluded that the frequencies (probabilities) of finding a particular state of the cardiovascular condition remained statistically the same for the 102 clinicians. Therefore, we used the mean medical condition probabilities p{A(mt), T(mt)}s and mean transition probabilities Based on the Notably, all statistical tests performed to validate the postulated hypotheses were parametric in nature. There were inherent assumptions about the population parameters from which the 102 relevant (and workable) samples were drawn. Since we were also interested in the important nuances related to the sample data (such as the order of reported frequencies at each state), we naturally preferred parametric tests over non-parametric tests, even though parametric tests often require normality tests and non-parametric tests do not necessarily require such tests on the sample.Table Referring to Table Table Using the values given in Table p (minor) was determined as follows (transition to own state):For instance, Another instance of transition to a different state—that is, Along similar lines, the remaining elements of the matrix If Solving the set of equations given in Eq.  on ILOG EDet(c) was determined to be $4859.84.The treatment costs corresponding to each of the four medical states  were $570, $1590, $6500, and $13,500, respectively, as determined by the average costs of the previous three years. Considering these costs and the optimal steady-state probabilities, and using Eq. , the clinicians revealed that decisions about a specific medical condition also have certain subjectivities and, therefore, there cannot always be clear one-to-one mapping. The experts reasoned that other dimensions related to patients, such as comorbidities , the extent of physical fitness, and a history of specific medical conditions, also play a major role in warranting one decision over another.ys, were determined. The mapping of these variables with the state of medical conditions and corresponding decisions is illustrated in Table ERand(c) can be expressed as follows:Using the information provided in Table It is worth mentioning that, in the above equation, though decision D1 corresponding to the “minor” state does not have a clear treatment/medical cost, as per the description of this decision in Table Following this convention, as formalized by Eq. , the folCorresponding to Eqs.  and 29)29), therThe developed mathematical model belongs to linear programming and was solved using ILOG CPLEX Optimization Studio. The following results were obtained:Ds were determined:Using equation number 19, the following ERand(c) was determined to be $3031.42.Comparing the results from both the deterministic and randomized policy produced a couple of important points. First, as opposed to any lack of mapping of the pertinent decision policy for the various states of the medical condition, the randomized policy resulted in the steady-state probabilities and the mapping of the pertinent decision policy with respect to the various states of the medical condition. Second, the expected cost of treatment under the randomized policy was superior to that of the deterministic policy, indicating there might be a preference for the randomized medical treatment policy over a long horizon from an economic standpoint. Finally, the randomized policy was slightly more predisposed than the deterministic policy toward dealing with severe states of the medical condition, considering that the steady-state probability in the case of the randomized policy was higher than that of the deterministic policy.In this section, we thematically discuss the important findings and accompanying nuances related to both stage 1 and stage 2 of the study.The data reported by the remaining 102 clinicians were fairly homogenous in that the probability of the cardiovascular condition remaining in any particular state, as reported by 102 of the 115 clinicians, lay within the 2% range. An important reason for this relative homogeneity is that most of these clinicians belonged to the same facility of the hospital chain, while several others belonged to a different facility (in a different city) within the state. Post-study follow-up discussions with clinicians showed that the vast majority (more than 85%) of the patients belonged to the same geographic regions  and were predominantly aged 55 or over. Further, a fairly low and uniform population sample size also supports the relative homogeneity of the probabilities of different states remaining in a narrow range.Further, referring to Fig. Referring to Table A comparison of the randomized policy and deterministic policy of medical treatment revealed that the expected long-term cost of treatment in the case of the randomized policy was significantly lower than that of the deterministic policy. An important reason for this difference is that in the case of the randomized policy, the optimal steady-state unconditional probability is less skewed toward the “severe” state than a deterministic policy, which is more skewed toward the “severe” state. The cost of diagnosis, treatment, surgeries, and post-treatment in a “severe” state was found to be significantly higher in the case of a “severe” state as opposed to the other states. Because the treatment decision in the deterministic policy revolves around the healthcare service provider prescribing an appropriate level of treatment depending on the history of the case, a certain expected cost of the medical treatment would result. On the other hand, the randomized policy revolves around an attempt by the service provider in such a manner that certain acceptable treatment decisions can be mapped to individual states of a medical condition such that the expected treatment costs can be minimized. The adoption of randomized medical treatment policies can be of particular value to large developing countries such as India, which are often characterized by limits on governmental spending, inadequate healthcare infrastructure (relative to developed countries), and variable quality of healthcare  is varied from 90 to 100% of its optimal value, the higher side of the expected cost would result when θ(moderate) and θ(major) remains constant, and θ(severe) varies based on variation in θ. When θ(minor) is varied from 101 to 110% of its optimal value, a higher side of the expected cost will result when θ(major) and θ(severe) remain constant, and π(moderate) varies based on variation in θ(minor). When θ (minor) is varied from 90 to 100% of its optimal value, the lower side of the expected cost will result when θ(major) and θ(severe) remain constant, and θ(moderate) varies based on variation in θ(minor) . When θ(minor) is varied from 101 to 110% of its optimal value, the lower side of the expected cost will result when θ(major) and θ(major) remain constant, and θ(severe) varies based on variation in π(minor). Table To understand the impact of the changes in the steady-state probabilities on the total expected treatment cost spread over time, we conducted a sensitivity analysis for the deterministic policy. In particular, we varied the steady-state probability corresponding to the states “minor”, “moderate”, “major”, and “severe” one at a time within ± 10%. Table θ(minor) from 90 to 110% resulted in a decreasing trend in the expected cost of treatment. Referring to Fig. θ(severe) from 90 to 110% resulted in an increasing trend in the expected cost of treatment.Figure It can also be observed that the expected cost of treatment was most sensitive to variations in the steady-state probability at the “major” stage of a medical condition as opposed to the “severe” stage of a medical condition, though the steady-state probability of the “severe” state was less than that of the “major” state.From a managerial implications perspective, our study can aid medical service providers such as private and public hospitals, practitioners, and surgeons in several important ways. First, the study enables such entities to approach the cost modeling of treatment costs in a structured and scientific manner while considering real-life data. The robust statistical validation performed at the qualitative stage (stage 1) ensures that samples taken are subject to reasonable testing and validation. Such an approach can be helpful to medical service providers in that large countries  are often associated with challenges to data collection in a structured manner. It remains impossible in many cases to collect data at a larger sample level or even as part of the population level. Thus, our study shows a possible way in which data pertaining to disease prevalence at various stages of severity, accompanying transitions with accompanying frequencies, and true/false treatment can be collected and handled, leading to meaningful conclusions. Second, the analytical part of the study (stage 2) enables parametrization of specific medical decisions to be made under both the randomized and generalized policy. This parametrization can aid medical service providers in developing some guiding decision support systems such that, by including the contextual factors such as the medical history of a patient in line with treatment cost rationalization, optimal medical treatment decisions can be reached. In particular, such an approach can be extremely beneficial in those cases wherein the doctors’/clinicians’ discretion, depending on different patients, is lower.Our study augments the extant research in several important ways at a policy level. First, in the context of low- and middle-income countries (LMICs) and from a resource planning perspective, there has been increasing interest in understanding the cost of healthcare programs that can deliver the desired healthcare services to patients (Clarke-Deeler et al., In this two-stage research, we developed mathematical frameworks to determine the expected cost of treatment per patient in the long-term, considering the integration of various interrelated nuances such as the transition of the medical condition, accuracy of medical treatment, and medical decisions taken at various severity levels of the medical conditions. Further, LP-based and exact method-oriented modeling approaches were deployed to ascertain the steady-state probabilities corresponding to the respective severity levels of the medical condition under both the deterministic and randomized policy. However, before delving into the modeling aspect of the study, thereby taking into context the prescriptive setting and at the qualitative stage of the study, we also focused on the data collection and validation of various cogent hypotheses, thus providing input to the modeling stage of research. To this end and to ensure a strong empirical underpinning to the research, we relied on the data collected from 115 different cardiovascular medical professionals to understand the nuances related to disease transition, the accuracy of medical treatment, and treatment decisions about individual disease severity levels. In particular, we relied on semi-structured interviews and thematic analysis to understand the characteristics related to the empirical setting. Based on a few key hypotheses developed and their subsequent validation, we utilized the empirical data as an input to the modeling stage of the study. Thematically, there were four broad severity levels of the cardiovascular condition identified: “minor,” “moderate,” “major,” and “severe.”At both the qualitative and prescriptive modeling stage of the study, several interesting insights emerged based on the case example of a history of cardiovascular treatment at the service facilities of a well-known multi-specialty hospital chain in Eastern India. For instance, at the qualitative stage of the research, it was determined that treatment accuracy was better in more severe states of cardiovascular conditions and inferior in relatively less severe states of the condition. Counter-intuitively, it was also determined that the probabilistic value of the transition from “moderate” to “severe” was higher compared with the transition from “major” to “severe”. At the prescriptive modeling stage, though one of our primary contributions relates to developing the novel mathematical framework, with subsequent optimization runs, we illustrated that the randomized policy seems to be cost-competitive compared with the deterministic policy. Further, using a sensitivity analysis, we showcased the impact of the varying steady-state probabilities of the respective states of a medical condition on the expected cost of treatment. Finally, there are several ways our study can be aligned with favorable policy implications, one of which is possibly considering a randomized treatment policy in LMIC countries to treat pervasive but less life-threatening conditions.Like any study, ours is also not devoid of limitations. First, our research only considered direct costs when modeling the expected cost of treatment. Other indirect costs, such as wages, administrative costs, and so on, were not considered. This implication is particularly important for countries like the US, wherein administrative costs typically constitute a significant proportion of overall healthcare expenditure. Second, in demonstrating our modeling framework, the sample data used was rather limited and primarily belonged to the same geographical setting and similar age group. Therefore, to further generalize the study's findings, it is imperative that the framework developed to be tested in a larger setting with a more heterogeneous population. Third, at the qualitative stage of our study, we relied on respondents’ inputs to ascertain the transitions pertinent to the medical condition and the accuracy of the treatment. An implicit assumption here is that the data did not significantly suffer from the clinicians’ biases in that the IDEA framework works effectively in such cases. Fourth, another future research direction would be to explore the application of more advanced supervised learning methods such as deep learning and structure analysis to improve the performance of cost prediction methods. Such forecasts can be conducted with respect to certain extant data over a sufficiently long period. Specifically, adding the features of medical treatment and benefiting from their predictive and explanatory power can be an important step in such approaches.


Escherichia coli transporter knockout strains exposed to sub-inhibitory concentrations of 18 diverse antimicrobials. We found numerous knockout strains that showed more resistant or sensitive phenotypes to specific antimicrobials, suggestive of transport pathways. We highlight several specific drug-transporter interactions that we identified and provide the full dataset, which will be a useful resource in further research on antimicrobial transport pathways. Overall, we determined that transporters are involved in modulating the efficacy of almost all the antimicrobial compounds tested and can, thus, play a major role in the development of antimicrobial resistance.Antibiotic resistance is a major global healthcare issue. Antibiotic compounds cross the bacterial cell membrane via membrane transporters, and a major mechanism of antibiotic resistance is through modification of the membrane transporters to increase the efflux or reduce the influx of antibiotics. Targeting these transporters is a potential avenue to combat antibiotic resistance. In this study, we used an automated screening pipeline to evaluate the growth of a library of 447  Antibiotic resistance is a major global healthcare burden, with over 1 million deaths attributable to antibiotic resistance in 2019, and with the World Health Organization listing antimicrobial resistance as one of the top 10 global public health threats facing humanity , albeit In order to exert their effects, most antibiotics must first cross the bacterial cell membrane. There is now strong evidence to suggest that in order to enter cells, nearly all compounds must pass through membrane transporters and that “passive bilayer diffusion is negligible” ,7,8. TheWhile modelling suggests that mutations that cause antibiotic resistance will predominantly be in exporters rather than importers , there aEscherichia coli K-12 chromosome, an estimated 598 encode established or predicted membrane transporters [Of the ~4401 genes in the sporters . Despitesporters . We hyposporters . In thisE. coli transporters, we investigated the previously developed library of 447 E. coli transporter knockouts [In the present study, which forms part of an ongoing project to deorphanize all orphan nockouts ,20 for gWe selected a range of compounds that were available and readily soluble in our LB media. We endeavored to include compounds from several major antibiotic classes as well as compounds with activity that have previously not been tested extensively against Gram-negative bacteria (ornidazole and paraquat). Cefiderocol was very recently approved, so was included due to novelty. E. coli growth in a two-fold serial dilution of the relevant antibiotic in LB. We defined MIC values as those with OD levels at 48 h less than 10% of the antibiotic-free media condition. For screening of the transporter library, we selected concentrations that showed some inhibition of growth in the WT strain without causing full inhibition. Generally, this was a concentration half that of the MIC; however, when effects on growth  could be clearly seen at concentrations significantly lower than that, then these concentrations were used. The 18 antimicrobial compounds included in this study, as well as the MIC and screening concentrations, are listed in The workflow for the screening of each compound  initiall−1. The WT growth rate was slightly higher than the mean growth rate, possibly due to the burden of expression of the constitutively active kanamycin cassette present in all knockout strains [Generally, the growth of nearly all transporter knockout strains in LB without supplementation was similar. The empirical area under the curve (AUC)  had a mean of 30.0 and an interquartile range (IQR) of 1.90, while the maximum growth rate had a mean of 1.19 and an IQR of 0.14 h strains ,22. HistCompared to growth in LB, we saw a much larger degree of variation in growth in the presence of the antimicrobial compounds, consistent with multiple transporter knockouts influencing the level of intracellular antibiotic accumulation. We observed that many transporter knockouts had effects on the sensitivity to the compounds tested. Due to the large volume of data generated, detailed descriptions of all relevant results are beyond the scope of this paper. As we intend to perform targeted follow-up validation experiments, given that this is an initial exploratory study, we are hesitant to define specific criteria for sensitivity or resistance. Indeed, one advantage of our study over previous high-throughput growth assays is the generation of full growth curves rather than the reduction in growth of a single metric, as we elaborate on in the discussion.Selected specific results are discussed below. Generally, when searching for novel results, we looked at strains ranked highest or lowest in normalized growth rate or AUC and then manually inspected the growth curves. We selected those highlighted in this paper based on interactions with y-genes or where we felt there were plausible and interesting mechanisms for discussion. macB) did not result in azithromycin sensitivity, and we also could not see evidence of increased sensitivity to azithromycin in a previously published dataset [Our study replicated some previously established drug–transporter interactions. First, we observed that knockout of acrB, a promiscuous drug-efflux protein well-known to play a role in export of many xenobiotics , caused  dataset .E. coli BW1556 of 800 mg/L  is a widely used herbicide, which exerts toxic effects, after conversion to a superoxide radical, once inside the cell . We foun 32 mg/L A. We scrts ∆cusA B and ∆fipermease C. The mop values for the correlation and found a peak near zero . These are shown in sapB, a putrescine exporter knockout, have a high correlation with the knockout of orphan transporter knockout ∆ydjE  PCR to confirm the strain is correctly labelled, and (2) MIC determination to validate the observed result . Work toA limitation to the approach described in this study for transporter pathway identification is that growth is used as a proxy for transport with no direct measurement. While the simplest mechanism by which a transporter knockout changes growth in the presence of an antimicrobial is through the direct alteration of transport, there are other possible mechanisms. Gene knockouts are well-known to cause pleiotropic effects, with knockouts of a single gene often causing altered expression in many other genes that may affect transporter sensitivity . FurtherE. coli knockouts under different conditions (including numerous antibiotics) on solid media [There are studies that have investigated the growth of the full Keio collection of id media ,38,39. Wid media ,38, fittid media . There is a large degree of homology found between transporters of different pathogenic bacterial families. Given this, as well as the recent advances in prediction of protein structure from sequence ,42, we eAntibiotics were sourced from Sigma, apart from rifampicin, ceftriaxone and azithromycin, which were purchased from Tokyo Chemical Industries .Plates were prepared by first inoculating deep-well plates with 1 mL Lucia Broth (LB) from glycerol stocks of the transporter library. The inoculated LB was grown overnight under agitation at 37 °C. Following overnight growth, the cultures were mixed with 1 mL 50% glycerol. CR1496c polystyrene plates  were prepared for growth assays, by dispensing a 3 µL droplet of the culture and glycerol mixture into the bottom of the well. These loaded plates were then stored at −20 °C for up to 3 weeks or −80 °C for longer storage. E. coli), with pictures taken every 20 min.The growth assays were initiated by adding 297 µL LB, containing the appropriate dose of antibiotics to the pre-inoculated plates, and sealing with CR1396b Sandwich covers . Growth was assessed using the Growth Profiler 960 , which uses camera-based measurements to estimate growth rates simultaneously in up to 10 96-well plates. As our library encompassed 5 plates, this allowed us to run the full library in duplicate. The Growth Profiler 960 was set to 37 °C with 225 rpm shaking (recommended settings for Inoculation and media loading were performed using an Opentrons OT2 robot fitted with a 20 µL multichannel pipette and a 300 µL multichannel pipette. Scripts used in operation can be found at github.com/ljm176/TransporterScreening (11 August 2022). Growth plates were sterilized between uses by washing and UV in accordance with the instructions of the manufacturer.G-values were obtained from the plate images using the manufacturer’s software. G-values were converted to OD600 values using the formula:a = 0.0158 and b = 0.9854, which were found by measurement of a standard curve in accordance with the instructions of the manufacturer.With the predetermined values tN is total cell population, K is the carrying capacity, initial cell population is N0 and r is the intrinsic growth rate. For strains that showed growth in only a single replicate, the replicate without growth was filtered from analysis, as this was determined to be the result of missed inoculation during automated loading. Data analysis and generation of figures was performed in R (version 4.1.2). Growth rates were determined by using the R package Growthcurver . Growthcgithub.com/ljm176/TransporterScreening (11 August 2022). Figures were generated using ggplot2 for R. The R script used in data analysis and figure generation is available at


This article presents a novel optimization algorithm for large array thinning. The algorithm is based on Discrete Particle Swarm Optimization (DPSO) integrated with some different search strategies. It utilizes a global learning strategy to improve the diversity of populations at the early stage of optimization. A dispersive solution set and the gravitational search algorithm are used during particle velocity updating. Then, a local search strategy is enabled in the later stage of optimization. The particle position is adaptively adjusted by the mutation probability, and its motion state is monitored by two observation parameters. The peak side-lobe level (PSLL) performance, effectiveness and robustness of the improved PSO algorithm are verified by several representative examples. Thinned arrays have been the focus of research in recent years for their lower cost, lower energy consumption and lighter weight compared with the conventional uniform arrays. The main purpose of array thinning is to obtain a lower peak side-lobe level (PSLL) on the condition that the antenna array satisfies gain demand. Planar array thinning can be achieved by adjusting the “ON” or “OFF” states of each element in a uniform array.To suppress the PSLL, several optimization methods have been proposed. As suggested by Liu in , the thiBenefitting from excellent global search performance, some intelligent optimization algorithms, such as the real genetic algorithm (RGA)  and asymFirst proposed by Eberhart and Kennedy in 1995 , particlIn general, in order to avoid the rapid loss of particle distribution in solution space, the existing PSO methods give a variety of evaluation strategies for the diversity of particle population distribution. However, these methods mainly focus on improving the algorithm efficiency and ignore the balance between global search and local search, resulting in the lack of ability to adjust the search focus dynamically in different search stages.In this paper, a new novel optimization algorithm for large array thinning is proposed. The innovative part of this algorithm is the combined usage of different particle learning strategies with discrete particle swarm optimization, which enhances the ability of global search and effectiveness of the algorithm.The rest of this paper is organized as follows: The planar array structure and optimization problem model are briefly outlined in d along M columns and N rows, as shown in Assume a large planar array with elements arranged in square grids with a spacing of A and B are set as:mna and mnb are the excitation of element  and the “ON” or “OFF” state of the element , respectively. So, mnb is 1 or 0. In θ and φ are the elevation and the azimuth angles in the spherical coordinate, respectively. u and v are direction cosines defined by u = sin θ cos φ and v = sin θ sin φ. If  is the desired main beam direction, the radiation beam pattern F can be expressed as:Matrices S denotes the angular region excluding the main beam. Considering the constraint of the array filling rate, the sum of all elements in matrix B should be a definite constant. If the aperture remains the same, the four corner elements of the planar array must be “ON”. The model of optimization can be represented as:K denotes the number of elements that turned “ON”.The fitness function considered in the present study is the PSLL of the radiation beam pattern, which is desired to be as low as possible. The PSLL of the planar array can be computed as:This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.The fundamental PSO algorithm assumes a swarm of particles in the solution space, and the positions of these particles indicate possible solutions to the variables defined for a specific optimization problem. The particles move in directions based on update equations impacted by their own local best positions and the global best position of the entire swarm.D-dimensional solution space, where the position xi and velocity vi of ith particle can be expressed as:Assume a swarm composed of NP particles is uniformly dispersed in a i = 1, 2, …, NP, j = 1, 2, …, D. Obviously, NP represents the number of particles, and D represents the number of dimensions. c1 and c2 are the acceleration constants, and r1 and r2 are two random numbers within the range . ijv(t) and ijx(t) are the velocity and position along the thj dimension for the thi particle at tht iteration, respectively. ijpBest(t) is the best position along the thj dimension for the thi particle at tht iteration, also called “personal best’’. Finally, “global best’’ igBest(t) is the best position found by the swarm along the thj dimension at tht iteration.The velocity update equation is given below:thj dimension for the thi particle is given bys(ijv) denotes a function that maps the particle velocity to the probability of the particle position, and r represents a random number within the range .The value of a position along each dimension for each particle is limited to “0” and “1” in discrete algorithms. The position update equation along the The improved PSO algorithm proposed in this paper consists of three main strategies, which are the global learning strategy based on niche technique, dispersed solution sets, the local mutation search strategy and the motion state monitoring strategy. The execution of the corresponding strategies is adaptively adjusted in different search stages of the optimization.It is necessary to maintain as high a swarm diversity as possible in the early stage of optimization to avoid premature convergence. So, two strategies are utilized in the early stage, which are the niche technique and the gravitational search algorithm.G is proposed in this paper, as shown in G consist of two kinds. One is the optimal solutions of all particles, the other is the eliminated optimal solutions of some particles with excellent fitness function values. Preserving the possibility of interaction between each particle and its neighbors, particles can also learn from PG directly. This structure will substitute for the role of the global best position gBest in (8).The niche technique proposed in  can formG may be uniformly distributed in the whole solution space, particles under the guidance of elements in PG have a considerable number of potential motion directions, which improves the diversity of the population, and avoids premature convergence.Because the positions of elements in PipBest in (8) to enhance the correlation between particles.The gravitational search algorithm (GSA) mentioned in  is a novtht iteration, the gravitational attraction of particle q on particle i in the thk dimension can be defined as:qM(t) represents the inertial mass of the particle applying force, iM(t) represents the inertial mass of the particle subjected to the force, iqR = ||xi(t) − xq(t)||, iqR denotes the Euclidean distance between particle i and particle q, and ε is a small constant to make sure the denominator is not zero. G(t) is the gravitational constant whose value changes dynamically, which can be expressed as:α represents the attenuation rate, T is the max iteration times, and G0 is the initial value.At the tht iteration, the resultant force thi particle in the thk dimension, so:qrand is a random number within the range .At the According to Newton’s second law, the acceleration produced by this resultant force can be expressed as:i(t) represents the fitness function value of particle i at tht iteration, and gWorst(t) is the worst position found by the swarm at tht iteration.The inertial mass of each particle is calculated from its fitness function value and the updating equation of inertial mass is given below:d = 1, 2, …, D, D represents the number of dimensions, C denotes a set of q dimensions randomly selected from all D dimensions, idpGood(t) is the best position of l candidate solution randomly selected from PG and neighborhood particles along the thd dimension for the thi particle at tht iteration, ida(t) is the acceleration along the thd dimension for the thi particle at tht iteration.Therefore, after applying the global learning strategy, the updating equation of particle velocity can be rewritten as:An algorithm should have strong local search ability to improve the convergence efficiency in the later stage of optimization, especially when it is applied to the optimal design of large-scale array. The flowchart of the local search strategy is shown in i can be regarded as moving from a certain position xi to another position ’xi in the solution space when xi along some dimensions are mutated. Therefore, we propose a local search method that the neighborhood of the optimal position of a particle i is searched by only changing partial elements of its position xi under the guidance of a Gaussian random variable. The position moving the equation along the thj dimension for the thi particle is given by:ijX  represents a Gaussian random variable with a mean value of 0 and standard deviation of σ, and ir is a random number within the range .It can be considered that a particle xi if it has a better fitness function value, and the standard deviation σ should be amplified to increase the moving distance of particles. Otherwise, keep xi unchanged and reduce σ. The updating equation of standard deviation σ can be expressed as:k represents the number of variations, ρ is the expansion parameter and ρ > 1, μ is the contraction parameter and 0 < μ < 1. If the size of the standard deviation σ is less than a preset threshold eσ, that means there is no better solution for particle i in the adjacent position. So, the update should be stopped.The mutated position In order to avoid some particles falling into local optimum prematurely and to improve the performance of the optimization algorithm, a condition monitoring strategy is proposed to adjust the position of the particle according to the moving state of the particle.i to trigger a mutation operation. The first one is that the optimal solution of particle i has not been updated for u iterations, which can be expressed as:i represents the number of iterations of the optimal solution that have not been updated. The second precondition is that the moving distance of the position xi is less than the set value ε for successive h iterations, which can be expressed as:i represents the number of iterations in which the moving distance of the position xi is less than the set value ε. The moving distance is defined as the number of different elements in all dimensions before and after the particle position changes.There are two preconditions for a particle i has fallen into local optimum when both preconditions are met, and then an individual variation probability aγ is used to vary all dimensions of position xi. The equation of variation can be represented as:d = 1, 2, …, D, D represents the number of dimensions, and dr is a random number within the range .It can be assumed that particle Algorithm 1: DPSO with hybrid search strategiesipBest, gWorst, and gBest. Initialize observation parameters Tpbi and iTxb. Initialize the solution set PG. 2. Calculate the fitness function value of each particle and update ipBest, gWorst, gBest, and Tpbi. Replenish the set PG with the good solutions that have been eliminated. 3. Update the velocity and position of the particle according to (16), (9), and (10). Determine whether the number of iterations t is larger than tL. If so, go to Step 4. Otherwise, go to Step 5. 4. Initiate the local search strategy. 5. Determine whether Tpbi> u and Txbi> h are both valid. If so, update the position of the particle according to (21). Otherwise, go to Step 6. 6. Constrain the particle position according to the constraint condition and update the parameter Txbi. 7. Do boundary treatment for particle velocity. 8. Output gBest. Determine whether the termination conditions are met. If so, end the optimization. Otherwise, t = t + 1, and return to Step 2.1. Generate the initial particle swarm that satisfying the conditions. Initialize The new Algorithm 1 exploits the hybrid search strategies (HSS) described above to improve the performance of a fundamental DPSO. Therefore, it is called DPSO-HSS. The detailed steps of the improved algorithm are summarized as follows.In this section, several examples are presented to compare the performance, effectiveness and robustness of the DPSO-HSS algorithm and some contrast algorithms.The algorithms tested include the proposed DPSO-HSS algorithm, RDPSO algorithm in , and NPSThe five typical functions used to compare the performance of each algorithm are shown in The mean value of multiple simulation results;The variance of multiple simulation results;The minimum value of multiple simulations.The three statistical characteristics as evaluation criteria are:T, the dimension D, the learning factors c1 and c2, and the inertia weight w of the three algorithms are the same. The simulation results are shown in The population size NP, the iteration times Among the five test functions, the proposed algorithm outperforms the other two algorithms in the mean and variance of Ackley, Rastrigin, and Sphere. In Rosenbrock’s test, DPSO-HSS has a better variance. In Griewank’s test, the test results of DPSO-HSS and RDPSO are close to the same. Considering the test results of the above five functions comprehensively, the proposed DPSO-HSS algorithm performs well in both mean and variance compared with the RDPSO and NPSOWM algorithm. The low mean indicates the excellent global search ability and convergence effect of the algorithm, whereas the low variance indicates that the stability of the algorithm results in multiple runs.D), where D is the number of dimensions. By substituting this result into the optimization of the DPSO-HSS algorithm, the time complexity of DPSO-HSS can be calculated as followsD represents the number of dimensions, p is the swarm population, q denotes the number of dimensions randomly selected from all D dimensions in (16), and t is the number of iterations. It can be seen that, compared with the general PSO algorithm, the improved algorithm has higher time complexity.The time complexity of each test function is O  = , that is,  = . The filling rate is 50%, so there are 200 elements in the “ON” state. The population size NP = 100, iteration times T = 500, and dimensions D = 400 are the same for all algorithms used.Consider a planar array consisting of 20 × 20 elements with equal spacing The convergence curve of PSLL using four algorithms for array thinning is shown in u, v) =  with the normalized amplitude of 0 dB, and the PSLL is −18.32 dB. These results mean the result of the array thinning design is correct.The elements distribution and beam pattern diagram of the optimized array of DPSO-HSS algorithm are shown in u-cut of the beam pattern diagram of a thinned array optimized by four kinds of algorithms. The main lobe width of the beam pattern of each algorithm is identical. The PSLL of the DPSO-HSS algorithm is −18.32 dB, whereas those of RDPSO, NPSOWM, and MOPSO-CO are −17.15 dB, −17.42 dB, and −17.66 dB, respectively. Compared with the other three algorithms, the PSLL of DPSO-HSS is decreased by 6.82%, 5.17%, and 3.74%, respectively.We can see the PSLL performance in details in v-cut of the radiation beam pattern diagram is shown in θ, φ) = ,  = , and  = . The PSLLs are −18.32 dB, −18.30 dB, and −18.31 dB, respectively. The difference is less than 0.1 dB, which is consistent with the conclusion in [In the above example, we set the main beam to a normal direction. Reference  proved tusion in .The array thinning effect may be affected by both array aperture and array filling rate. The results of PSLL under different filling rates show that the filling rate does have a certain influence on the optimization effect of thinned array and the optimization effect will be reduced if the number of open elements is too many or too few. The PSLL is improved when the filling rate is a constant of 60% and the aperture is changed from 5λ to 10λ, which indicates that the aperture also has a certain influence on the performance of array thinning. As shown in A novel optimization algorithm for large array thinning based on DPSO with hybrid search strategies is proposed to improve the performance of large planar array thinning. The proposed algorithm, named DPSO-HSS, utilizes a global learning strategy to improve the diversity of populations at the early stage of optimization. A dispersive solution set and the gravitational search algorithm are used during particle velocity updating. Then, a local mutation strategy is employed in the later stage of optimization so that the local convergence is enhanced by continuous search around the best position of the particle. Several of the above-mentioned representative examples of large planar arrays thinning are provided to demonstrate the effectiveness and robustness of the DPSO-HSS algorithm. The brief comparison results are shown in the


We find a diminishing knowledgegap: people with low previous knowledge catch up on the better informed, butoverall knowledge remained low and learning was limited. This suggests a ceilingeffect: possibly journalistic media did not provide enough new information forthe well-informed. Closing knowledge gaps may also be explained by the mediasystem with public television and regional newspapers reaching broad segments ofthe population. Higher knowledge was predicted less by media use than byeducation, concern, and being male.A basic understanding of climate politics is necessary for citizens to assesstheir government’s policies. Media use is supposed to enable learning, whilewidening knowledge gaps. We analyze whether such a gap opened up in times ofintense media coverage during the 2015 climate conference in Paris and explainlearning through hierarchical regression analyses, drawing on a 3-month panelsurvey ( Thus, the conditions were ideally suited for exploringlearning from media content: media attention was guaranteed and highly relevantpolitical decisions on climate politics were taken—yet, we were intrigued to find onlymodest learning and a diminishing rather than widening knowledge gap.We apply this framework to the case of learning about To explain why some people learn more from media use than others, one of the mostcommon reference points is the knowledge gap hypothesis . In its sociodemographic factors, thatis, formal education. In many studies, formal education is used asa proxy for previous knowledge, since it is easy to ascertain in a survey. Indeed,many empirical studies have shown a relevant positive correlation between formaleducation and political and scientific knowledge levels . This event was chosen because the UNclimate conferences are main triggers of media coverage on climate change , andserWhile most previous studies focus on the US context, our survey was conducted inGermany. Here, climate change is seen as an important and barely controversial topic, and medThe panel survey took part 2 weeks before, during, and 4 weeks after the UNclimate conference 2015 (COP 21). For the following analyses, we only usedata from the first and the last wave (conducted in November 2015 andJanuary 2016) to avoid conclusions based on short-term changes during theconference period, since we are interested in learning as a long-termeffect.n = 1121 participants (wave 1:n = 2098).The external panel provider respondi (certified according to ISO 26362)recruited the respondents via an online access panel with 100,000respondents in Germany. First, participants from the panel were randomlyinvited to take part in the survey. Second, a quota regarding age andgender, federal state, and formal education was applied to the sample forthe first survey wave to represent the distribution of these variableswithin the German population aged 18–69. The final sample after the thirdwave comprised politics, butconcentrated on knowledge of causes, consequences, and individualcounter-measures of climate change  cover the three maindimensions of climate politics as defined by 2 emissions ofstates). Apart from two items (concerning the Kyoto Protocol and emissionstrading), which were modified from a study on political knowledge  and knowledge that is more closely related to the specific summit (e.g.asking for the key objective of COP 21). The seven items are multiple-choicequestions with four alternative answers plus the option to respond with“don’t know.” For the analyses presented in this article, correct answerswere coded as 1, while incorrect and “don’t know” answers were coded as 0.The learning effect is operationalized as the difference between the firstwave and the last wave of the survey, with knowledge in wave 3 representingthe dependent variable of the regression.The questions vary in their level of difficulty, as shown by a qualitativepre-test with graduate students. In addition, the questions were validatedby an independent expert from the Climate Service Center Germany of theHelmholtz-Zentrum Geesthacht. Our knowledge scale includes both basicbackground knowledge .Gender (H1) was captured as a dichotomous variable ; foreducation (H2), we used a 5-point Likert-type scale from 1 “no graduation(yet)” to 5 “university diploma” .Topic-specific previous knowledge (H6) sums up the correct answers in thefirst wave of the survey .The motivation to process information about climate change was measured bytaking personal relevance of the topic (H3) as a proxy .The resM = 4.84,SD = 1.84), commercial television, national printnewspaper , weeklynewspaper or magazine , regional print newspaper, tabloid newspaper“BILD-Zeitung” , themost widely used online newspapers spiegel.de  and bild.de , other online newspaper and interpersonaldiscussions .Media use (H7 and H9) was included in form of habitual media use, since weexpect an effect on knowledge from the media use aggregated over time ratherthan from single occasions. We measured the habitual use of media sourceswith a 7-point Likert-type scale ranging from 1 (“never”) to 7 . For all types of media, examples were provided. The sourcesincluded were public television  and monthlyincome (Mdn = 2000–2999€).The models used in the analysis included the control variables age. Comparing the group of peoplewith low previous knowledge (0–2 correct answers) to the group of people with higherthan average knowledge in T1 (3–7 correct answers), we found thatthe former had a significantly lower formal education and less income —a typical set of factorsaccompanying knowledge gaps (t(1120) = 5.42,p < .001).We found a rather low initial level of public knowledge about climate politics = 10.28, p < .001), contrary tothe assumption of the knowledge gap hypothesis.In wave 1, the mean difference between both groups was 2.66 correct answers. In wave2, the difference was only 1.84 correct answers, since the “low previous knowledge”group had improved their score, while the “high knowledge” group had given lesscorrect answers in the mean. To test whether this difference between the two groupswas significant, a new variable for the learning effect was calculated (a sum indexof the difference between the first and second waves per item), which indicateswhether a person performed better, equally well, or worse after the climate summitthan before. Subsequently, the learning effect was compared between the two groupsby an unpaired F = 47.348,p < .001. The R² for the final model was .483(adjusted R² = .473), thus explaining roughly half of the variance,indicative for a high goodness-of-fit according to To explain these differences in learning, in the following, we present the results ofour hierarchical regression analysis. The final model fulfills all necessarypreconditions for a multiple regression and was able to statistically significantlypredict learning, We see that the sociodemographic variables gender and education have a significanteffect in all models. A gender difference is obvious (H1): Males have learnt moreabout climate politics by the end of the climate summit. There is also a positiveeffect of formal education (supporting H2), consistent with the knowledge gaphypothesis. However, the sociodemographic variables only explain less than 2% of thevariance.not support our sixth hypothesis that higher knowledge willlead to more learning, as we will argue below when discussing this result in lightof the prior result of a diminishing knowledge gap. The set of topic-specificindividual factors raises the explained variance of the model by 30%.Motivational and cognitive factors prove most important to explain higher knowledgelevels after the climate summit. A higher personal importance of climate changecorrelates with a higher knowledge on the topic, as expected by Hypothesis 3.However, climate change skepticism has no effect; thus, Hypothesis 4 is rejected.Higher information literacy concerning climate change coverage is also associatedwith higher knowledge levels (H5 is supported). Prior knowledge about climatepolitics positively correlates with the final knowledge level. Yet this doesIn the final regression model, we additionally included media use. Reading regionalprint newspapers and the online newspaper spiegel.de has a small positive effect onknowledge . While public television has no significant effect (H8 isrejected), commercial television even seems to interfere with learning. No othersources show a significant effect . All media effects are small and significant only on a low level—all typesof media use combined only add less than 2% of explained variance to the model.To sum up the analysis and findings: We conducted a regression analysis on panelsurvey data to find out how public knowledge about climate politics evolves in timesof high media coverage, and which factors influence learning about climate politics.Based on the knowledge gap hypothesis and its advancements, we includedsociodemographic, cognitive, and motivational factors as well as a differentiatedmeasurement of media use in our analysis. Prior knowledge was the most importantfactor to predict knowledge after the COP 21. This is not surprising as the overalllearning effect was small and knowledge levels remained mostly stable. Learning ispositively influenced by higher education and  gender. Higher motivation toprocess climate information and self-assessed information literacy generated amoderate positive effect. The use of regional newspapers and spiegel.de had a minorpositive effect, while private television had a minor negative effect.These findings advance our understanding of learning from media content in three waysby advancing and challenging some assumptions related to the knowledge gaphypothesis: We find that factors explaining learning and the evolution of knowledgegaps are far more dependent on the sociocultural context than prior discussionssuggest—both the media system and issue-specific national opinion cultures play arole.widening knowledge gap as a media effect.Our first and most important finding challenges the assumption of a wideningknowledge gap during a period of intense media coverage. With regards to thedistribution of knowledge about climate policy among the German public, there is nowidening gap. Instead, the gap between those who are ignorant about a number ofsubstantial aspects of climate policy and those who are better informed shrinks.Media coverage may thus have served as a leveler of knowledge—albeit on a fairly lowlevel of knowledge before and after the summit and fairly small overall learningeffects. Being one of few studies that have actually tested the evolution ofknowledge over time e.g. , and theOne explanation could be that knowledge gap research has (a) neglected the contextualfactor of the media system and (b) often not measured media effects in adifferentiated way, distinguishing different types of, for example, television oronline sources. In our data, the regional press and spiegel.de have fosteredknowledge gains, while private television correlates negatively. Both positivefactors reach very broad audiences in Germany, so that they were able to diminishthe knowledge gap. For example, in the United States, public television is veryweak, the regional press and elite outlets are less widely read today, and privatetelevision  is very strong, leading to differenteffects on knowledge gaps. Thus, it is plausible that media coverage, representingthe total output of a media system, may have different effects in countries withdifferent media systems.Second, the knowledge gap hypothesis is to some degree not a media effect hypothesis,but rather describes an “education gap” effect, since education predicts learningabout climate politics far better than media use—confirming the findings of mostother studies on the important influence of education.Third, we find that national issue cultures influence the power of certainexplanatory factors. Surprisingly, political orientation and climate change denialwere not relevant to explain learning about climate politics. This is interestingbecause in many studies analyzing knowledge about and attitudes toward climatechange, these variables are significant predictors e.g. . HoweverIn line with prior research e.g. , those wTable b in the supplemental material)—of course, those persons witha “perfect” previous knowledge score were not able to learn in the course of ourstudy, but their share is extremely minor.Finally, it is important to discuss our descriptive finding of an overall smalllearning effect. Knowledge levels remained mostly stable, and well-informed peoplelearnt even less than people with low prior knowledge. To understand why people witha high previous knowledge on climate politics did not learn more, ceiling effectscome into play. First, a methodological ceiling effect comes to mind, but does notseem plausible: Only 0.4% of the participants (five persons out of 1121) were ableto answer all questions correctly in the first wave of the survey  have a small positive effect. Prior knowledge positively predictsknowledge levels after the summit—however, the difference in knowledge betweenwell-informed and less informed people diminishes in a media environment that alsoprovides some basic knowledge to the less educated publics.This shows that potential knowledge gap effects can be overshadowed by a media systemleading to leveling of knowledge and ceiling effects due to limited provision ofin-depth information in the most commonly used media outlets. However, thisassumption of a media ceiling effect needs to be confirmed with a content analysis.A combination of a panel survey with a content analysis would be especiallyinsightful.We measured factual knowledge with close-ended multiple-choice items. Within a panelsurvey like ours, other types of knowledge, such as structural knowledge, aredifficult to include, but would be relevant to analyze learning in itsentirety—other studies could go deeper in this regard. Furthermore, our items are anormative selection of facts we deem relevant to understand climate politics. Yet,we do not claim to have covered all facets of climate politics. Furthermore, thereis no objective standard as to which aspects are most important to know for informedcitizens (see, e.g. Although we used a panel survey design, the timeframe for learning was rather short,covering only 3 months. Thus, future studies could look into learning effects over amuch longer period of time. We still expect media to contribute a relevant effect tolearning in the long term, in a dynamic process combined with other factors, andmaybe more on issues not covered by our items. The effect of media use seems to beweak in the case of the relatively short-term event-specific learning. Qualitativestudies analyzing a concrete learning process could also contribute valuableinsights into how individuals make sense of the information they receive in theirmedia repertoires and thus acquire new knowledge.Our results show that the learning process from media use cannot be explained bydisregarding the information environment provided by media systems, issue-specificpolitical discourse cultures and differences between types of media. Going beyondthe limits of this study, by combining long-term panel studies with qualitativeanalyses, is worth further research.Click here for additional data file.Supplemental material, sj-docx-1-pus-10.1177_09636625211068635 for Learning aboutclimate politics during COP 21: Explaining a diminishing knowledge gap by FenjaDe Silva-Schmidt, Michael Brüggemann, Imke Hoppe and Dorothee Arlt in PublicUnderstanding of Science


CENP-SX is a histone-fold complex that is involved in chromosome segregation and DNA repair. Biochemical and crystallization analysis suggested that multiple molecules of CENP-SX may be involved in DNA binding. P21 and C2, where the volume of the P21 asymmetric unit is twice as large as that of the C2 asymmetric unit. Analysis of the self-rotation function revealed the presence of twofold and fourfold symmetry in both crystals. This suggests that there may be multiple molecules of CENP-SX and DNA within the asymmetric unit with respective symmetry. Structure determination of the present crystals should reveal details of the DNA-binding properties of CENP-SX.The CENP-SX (MHF) complex is a conserved histone-fold protein complex that is involved in chromosome segregation and DNA repair. It can bind to DNA on its own as well as in complex with other proteins such as CENP-TW and FANCM to recognize specific substrates. CENP-SX binds nonspecifically to dsDNA, similar to other histone-fold proteins. Several low-resolution structures of CENP-SX in complex with DNA are known, but a high-resolution structure is still lacking. The DNA-binding properties of CENP-SX and FANCM–CENP-SX complexes with various lengths of dsDNA were compared and the band-shift patterns and migration positions were found to differ. To confirm the DNA-binding properties in detail, CENP-SX–DNA and FANCM–CENP-SX–DNA complexes were crystallized. Analysis of the crystals revealed that they all contained the CENP-SX–DNA complex, irrespective of the complex that was used in crystallization. Detailed diffraction data analyses revealed that there were two types of crystal with different space groups,  Eukaryotes, in particular, undergo mitotic and meiotic cell cycles to proliferate and produce the next generation. Chromosome segregation and DNA repair play pivotal roles in these processes. The CENP-S (MHF1)–CENP-X (MHF2) (CENP-SX) complex  is a conserved histone-fold complex that participates in these processes  was incubated with dsDNA of various lengths (1.25 µM) at 42°C for 60 min in binding buffer . The mixtures were analyzed by 10–20% gradient native PAGE (Wako) and stained with ethidium bromide.EMSA was performed as described previously  using 31 bp DNA and Natrix 2 condition No. 29  using 19–49 bp DNA. For diffraction analysis, 1,4-dioxane was replaced by 30% MPD and the crystals were cryoprotected using 30% ethylene glycol.To form a protein–DNA complex, a mixture of protein and DNA was incubated at 20°C for 60 min. Initial crystallization screenings for FANCM–CENP-SX–dsDNA were performed using Natrix and Natrix 2 (Hampton Research) by the sitting-drop vapor-diffusion technique in a 96-well format crystallization plate. The final volume of the drop was 0.2 µl, with 0.1 µl of the reservoir solution and the protein–DNA complex, and the plate was incubated at a constant temperature of 20°C. Initial crystals were obtained in two conditions: Natrix condition No. 12 .CENP-SX–dsDNA crystallization was performed using 29–31 bp DNA. To improve the crystallization, the mixing ratio of protein and DNA, the DNA length and the overhang structures were varied. The optimized crystal condition was 20 mConditions for the production of CENP-SX–dsDNA crystals with improved diffraction quality are summarized in Table 22.3.HKL-2000 package (HKL Research) or XDS  synchrotron facility (KEK) and were processed with the 3.et al., 2012et al., 2014et al., 2014et al., 2012Chicken and human CENP-SX bind a single dsDNA at regular intervals, whereas human FANCM–CENP-SX prefers branched molecules (Nishino a). Analysis by SDS–PAGE revealed that the crystals contained CENP-S and CENP-X, whereas FANCM was absent . FANCM was present as a film-like structure in the air–liquid interface of the crystal droplet. This situation is similar to a previous report where FANCM was observed to detach from CENP-SX in the presence of organic solvent and oxidative conditions . Irrespective of the length of DNA used, FANCM–CENP-SX–DNA crystals appeared in the presence of 30% 1,4-dioxane. The shapes of the crystals differed according to the length of the DNA Fig. 2. Rectangnt Fig. 3b. FANCMP21, with unit-cell parameters a = 101, b = 84, c = 112 Å, α = 90, β = 105, γ = 90°. The other crystal belonged to space group C2, with unit-cell parameters a = 128, b = 81, c = 100 Å, α = 90, β = 124, γ = 90° (Table 3C2 and P21 crystals contain ∼80 000 and ∼160 000 Da, respectively, with a calculated Matthews coefficient of 2.7 Å3 Da−1 and a solvent content of 60%. These results suggest that multiple CENP-SX heterodimers and DNA are present in the asymmetric unit. The situation resembles previous low-resolution CENP-SX–DNA complex crystal structures, in which several different crystals were formed and multiple molecules were present in the asymmetric units.The initial crystals diffracted to ∼7 Å resolution with high mosaicity. Optimization of the DNA and cryoprotectant improved the resolution Fig. 4. Data an° Table 3. The volP21 and C2 crystals, twofold peaks in the ac plane and fourfold peaks in the 90° plane (Fig. 5et al., 2014To analyze the relationship between the multiple molecules of CENP-SX and DNA within the crystal, the self-rotation function was calculated. In both the ne Fig. 5 were obs


This paper addresses whether supervisory responsibility is a challenging job demand in the Job Demands-Resources (JD-R) model in different cultural contexts. We investigate how job satisfaction responds to a supervisory role with job control and selected cultural dimensions using a cross-cultural dataset of 14 countries with more than 43,000 adults using ordered logit regression models. We find that a supervisory role enhances job satisfaction and appears to be a challenging job demand. However, no studied cultural dimension, masculinity, power distance, individualism, or uncertainty avoidance, increases job satisfaction derived from this kind of responsibility. Our study indicates that there might be stereotypical assumptions about cultural dimensions concerning the job satisfaction of supervisors. There is a consensus that employee wellbeing necessitates sufficient resources that are required to perform one's job. This can be witnessed in the increasing popularity of the Job Demands-Resources (JD-R) model , covering 22 countries. The PIAAC, a survey produced by the Organization for Economic Co-operation and Development (OECD), provides comprehensive information on adults' work-life and demographic characteristics. Even though the acknowledgment of potential cultural effects on JD-R predictions has increased , reported persistently higher job satisfaction in the former group  contribute to <10 percent of the observations, these categories are merged into one.In the PIAAC questionnaire, the following question was asked from respondents: “Do you manage or supervise other employees? By managing or supervising other employees, we mean that a person is in some way responsible for how other employees do their work”. Supervisory responsibility is a dummy variable and takes a value of 1 if the respondent is supervising at least one subordinate and 0 otherwise.The variable reflecting how much control an employee has over their own work and the working environment is calculated as the mean of four items. The example questions are “To what extent can you choose or change the sequence of your tasks?” and “To what extent can you choose or change your working hours?” The scale ranged from 1  to 5 (to a very high extent). Cronbach's alpha for job control was 0.816.Scores for MAS, PD, IND, and UA were obtained from a publicly available database using European Social Survey and European Values Survey indicators, calculated by Kaasa et al. 3 for 14 age, tenure, health conditions, number of children, wage, and hours worked, are vital factors that directly influence the wellbeing of a person. Another important characteristic is gender; while job demands and resources are positively related in male respondents, it tends to be negative in females . Employee-specific characteristics, particularly n et al.  reveals This study employs the ordered logit estimation model with robust standard errors to examine if supervisory responsibility is associated with job satisfaction and the role of moderators in this link. The choice of model is justified because the job satisfaction variable is an ordered choice  variable. Alternative models, such as the logit regression for binary variables and OLS regression for continuous variables, are not suitable here. By using stepwise regression modeling, the first stage of analysis estimates the direct effect of the supervisory role on job satisfaction. The second stage of investigation looks at the challenging demand hypothesis by adding job control to the regression. The third stage will predict how country-level cultural dimensions and their interactions with individual-level variables are associated with job satisfaction. Furthermore, all cultural dimensions  are standardized by using the sample mean and standard deviation (the process of subtracting the mean and dividing it by the standard deviation), and their z-scores are used in regressions. In models without cultural dimensions, country fixed effects are included. Further, an empirical analysis was performed using STATA 15.1 software.b = 0.17, p < 0.01; see Model 1). Model 2 tests if supervisory responsibility together with job control enhances job satisfaction. We find that the supervisory role coupled with job control is indeed positive and significant . Thus, H1a and H1b are confirmed, and we can conclude that the supervisory role can be deemed a challenging job demand and a positive contributor to job satisfaction by itself.First, we investigated if the supervisory role contributes to job satisfaction while holding the control variables fixed see , Model 1b = 0.005), but insignificant.Before moving on to cross-level interactions, we note that, in line with previous research, cultural dimensions themselves determine the level of job satisfaction. People are more satisfied with their jobs when located in higher IND countries and less satisfied with higher PD, MAS, and UA countries. In Models 3–6, we present the analysis of the moderating role of cultural dimensions. In H2, we suggested that job satisfaction decreases with supervisory responsibility in MAS cultures. Model 3 interaction term is positive . Most surprisingly, the interaction coefficient for IND is negative and significant , which is opposite to H4. UA is insignificant, and hence H5 is not confirmed. Therefore, our results indicate that none of the cultural dimensions favor job satisfaction from supervisory responsibility.In H3, we proposed that the supervisory role has a stronger positive effect on job satisfaction in high PD cultures. Model 4 shows that the interaction effect is positive but insignificant , see b = 0.09, p < 0.01). The moderating role of cultural dimensions changes on some occasions: for MAS, the interaction coefficient becomes negative but is still insignificant. For PD, the coefficient is negative and significant . IND remains negative but has become insignificant, similarly to UA. Hence, none of our hypotheses would hold under the described variable specification.Although empirical studies show that job satisfaction is more affected by the managerial yes/no status rather than the number of subordinates  is associated with job satisfaction, even though, similarly to earlier findings (Bless and Granato, We expected that a more powerful role should be appreciated in high PD countries, but our results refute this intuition. Bless and Granato  offer anMoreover, there might be fewer job resources available for managers in high PD cultures. For example, feedback provided to managers in high PD cultures is almost absent, and the manager's subjective feedback to employees is feared (Hwang and Francesco, We expected that security is also more important to job satisfaction in MAS cultures (Hauff et al., We expected that an IND cultural context would facilitate a supervisor's job satisfaction because challenging and interesting work is valued positively in IND culture (Hauff et al., Finally, conforming to the general notion, higher UA does not enhance job satisfaction from the supervisory role, but no significant negative effect was found either. UA can be considered a litmus test for potential stress factors (Naseer et al., Therefore, an implication of our study is that the design of corporate incentive strategies may use stereotypical assumptions about cultural dimensions that should be revised. Assigning a supervisory role in high PD and IND environments maybe even detrimental to the person's job satisfaction in the long term. Based on the general notion that being a manager is reputable in high PD cultures, it does not, however, follow that highly respected managers are more satisfied as well. Similarly, high IND promotes employee job satisfaction in general but does not mean that supervisory responsibility is more satisfying in this environment.Regarding this study's limitations, PIAAC comprises self-reported data and standard caveats applied in this respect, including accuracy and social desirability bias. Due to cross-sectional nature of the data there are potential endogeneity issues between variables. We thus cannot claim for certain that the supervisory role increases job satisfaction, it may well be that more satisfied employees accept supervisory roles.Job satisfaction was a single-item measure in the questionnaire, and its reliability may be criticized. However, this approach in JD-R research had been adopted by Demerouti et al. , FarndalCultural dimensions were assumed on a national level, though the dimensions may vary to a large extent within one country (Kaasa et al., PIAAC covers only OECD countries, therefore, we must be careful in making global generalizations. OECD countries are aging societies with labor shortage in many industries, making employers more willing to design jobs that ensures employees' job satisfaction.Finally, our data is from 2011 to 2012, and drastic changes in the work environment have occurred since. Changes like digitalization and the gig economy have profoundly affected managerial work. Future studies should explore the second cycle of PIAAC data anticipated to be available by 2024.https://www.oecd.org/skills/piaac/ and https://lepo.it.da.ut.ee/~akaasa/culturaldistances/datasources.html.Publicly available datasets were analyzed in this study. This data can be found here: All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.This work was supported by the Estonian Research Council grants PRG1513 and PRG791. Also, the support by the European Union Horizon 2020 research and innovation program grant agreement no. 822781 GROWINPRO is acknowledged.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Over the past decades, a growing body of evidence has demonstrated the impact of the psychosocial work environment on workers’ health, safety and wellbeing. These factors may also affect employees ’job satisfaction.To explore psychosocial determinants of job satisfaction among workers in a Tunisian electricity and gas company.A cross-sectional survey was conducted among male workers in a Tunisian electricity and gas company. The Copenhagen Psychosocial Questionnaire (COPSOQ), the Job Content Questionnaire and the general health questionnaire (GHQ12) were used to assess psychosocial risk factors at work. A principal component analysis (PCA) was used to assess correlations between instruments ’scores. Multiple linear regression analysis was applied to explore the specific factors associated with job satisfaction. Data were analysed using R software.A total of 83 workers participated in the survey (the age range: 21-60 years). Job satisfaction score varied from 0 to 100% with a mean of 73.09 %. In the PCA, job satisfaction had a positive correlation with high social support and a negative one with work-family conflicts, a high psychological demand, stress, burnout and quantitative demands. In multivariate analysis, factors negatively associated with job satisfaction were: age, stress and low social support. In contrast, seniority was positively associated with job satisfaction.Job satisfaction is deeply influenced by the psychosocial work environment. Therefore, it is necessary to provide supervision, communication, and social support for these workers to increase or maintain a high level of job satisfaction.No significant relationships.