Backward Model Research Articles

External validation of the R2eD AVM scoring system to assess rupture risk in pediatric AVM patients.

The assessment of hemorrhagic risk is of central importance in the management of pediatric brain arteriovenous malformations (AVMs). A recently published scoring system, the R2eD AVM (race, exclusive deep location or not, AVM size < 3 or ≥ 3, venous drainage exclusively deep or not, and monoarterial feeding or >1 arterial feeders) considers patient race and AVM location, size, venous drainage, and arterial supply and was successfully externally validated for use in adults to predict the risk of AVM hemorrhagic presentation. In this study the authors sought to externally validate the R2eD AVM score for prediction of hemorrhagic risk in the pediatric AVM population. A pediatric database at Boston Children's Hospital was retrospectively reviewed for all patients with a diagnosis of intracranial AVM. Exclusion criteria included age > 21 years, multiple AVMs, and incomplete clinical/radiographic data. Demographic data and R2eD AVM score calculations were collected for all patients. Univariate binary logistic regression and multivariate stepwise backward elimination models were used to determine factors associated with hemorrhagic presentation. A total of 212 AVMs were identified in 212 patients with vascular anomalies from 1995 to 2021, and 144 patients met the inclusion criteria (74 [51.4%] male, 111 [77.1%] White), of whom 87 (60.4%) patients presented with rupture and 122 (84.7%) underwent resection. The mean age at surgery was 12 years (range 3 months to 20 years). The R2eD AVM scoring system was applied to each patient. The score components of size < 3 cm and exclusive deep venous drainage were significantly associated with rupture risk (p < 0.05). The complete model for the R2eD AVM score had an area under the curve of 0.671 (95% CI 0.586-0.757). This study demonstrated poor external validity of the R2eD AVM score in predicting pediatric AVM rupture risk. The results suggest that future studies are warranted to determine a better scoring method to capture pediatric rupture risk, given the significant differences in clinical presentation in pediatric compared with adult AVM patients.

Outcome of Primary Mediastinal Large B Cell Lymphoma Treated with RCHOP.

Primary mediastinal large B-cell Lymphoma (PMLBCL) is a rare aggressive lymphoma with unique clinical, pathological, and molecular features. The optimal frontline therapy is subject of ongoing debate. Our study aims to evaluate the outcomes of PMLBCL treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (RCHOP) at King Hussein Cancer Center. Adult patients >18 years of age with PMLBCL treated with RCHOP from January 2011 to July 2020 were identified. All demographics, disease and treatment related variables were retrospectively collected. Correlations of clinical and laboratory variables with progression-free survival (PFS) and overall survival (OS) were determined by univariate and multivariate analyses using backward stepwise Cox regression models. The PFS and OS were plotted using Kaplan‒Meier curves. 49 patients were included with a median age of 29 years. 14 (28.6%) had stage III or IV, 31 (63.3%) had mediastinal bulky disease. International prognostic index (IPI) was 0-1 in 35 (71.4%). Radiotherapy was given to 32 (65.3%) patients. End of treatment (EOT) response was complete (CR) in 32 (65.3%), partial response (PR) in 8 (16.3%) and progressive disease (PD) in 9 (18.4%). Patients who achieved CR at EOT, compared favorably with those who did not in regard to 4-year OS (92.5% vs 26.9%, p=<0.001). Overall objective response to salvage chemotherapies was 26.7%. At a median follow-up of 46 months, 4-year PFS and OS were 60% and 71% respectively. In multivariate analysis, IPI > one correlated with the EOT response (p=0.009), PFS (p=0.004) and OS (p= 0.019). In PMLBCL, RCHOP chemotherapy backbone in the frontline therapy is suboptimal but can be used in patients with low IPI. Adapting more intensive chemoimmunotherapy regimens may be considered for patients with high IPI. Salvage chemotherapy has limited activity in patients with relapsed or refractory disease.

Validation study of WindTrax reverse dispersion model coupled with a sensitivity analysis of model-specific settings

In last years, atmospheric dispersion models have reached considerable popularity in environmental research field. In this regard, given the difficulties associated to the estimation of emission rate for some kind of sources, and due to the importance of this parameter for the reliability of the results, Backward dispersion models may represent promising tools. In particular, by knowing a measured downwind concentration in ambient air, they provide a numerical value for the emission rate. This paper discusses a critical validation of the WindTrax Backward model: the investigation does not only deal with the strict reliability of the model but also assesses under which conditions (i.e. stability class, number, and location of the sensors) the model shows the greatest accuracy. For this purpose, WindTrax results have been compared to observed values obtained from available experimental datasets. In addition, a sensitivity study regarding model-specific parameters required by WindTrax to replicate the physics and the random nature of atmospheric dispersion processes is discussed. This is a crucial point, since, for these settings, indications on the numerical values to be adopted are not available. From this study, it turns out that the investigated model specific settings do not lead to a significant output variation. Concerning the validation study, a general tendency of the model to predict the observed values with a good level of accuracy has been observed, especially under neutral atmospheric conditions. In addition, it seems that WindTrax underestimates the emission rate during unstable stratification and overestimates during stable conditions. Finally, by the definition of alternative scenarios, in which only a portion of the concentration sensors was considered, WindTrax performance appears better than acceptable even with a small number of concentration sensors, as long as the positioning is in the middle of the plume and not in the strict vicinity of the source.

Risk factors for urinary incontinence during pregnancy among nulliparous women in the United Arab Emirates.

Urinary incontinence (UI), which affects the quality of life, is associated with different risk factors during pregnancy. We aimed to study the risk factors related to UI during pregnancy among nulliparous women in the UAE. This is a prospective descriptive survey, which included all nulliparous women after the first 24 weeks' gestation from 2012 to 2014 in a teaching hospital in the UAE. Participants were interviewed face-to-face, using a structured and pre-tested questionnaire and divided into 2 groups: those with UI and those without it. Factors which were statistically significant (P < .05) between the 2 groups were entered into an logistic regression backward logistic regression model to define the factors predicting UI. Five hundred one participants were interviewed. UI occurred in 106/501 (21.2%). The 2-sample comparison analysis showed that urinary tract infection (UTI) (47.2% vs 34.4%, P = .018) and its number of attacks (P = .007), chronic cough (28.3% vs 13.9%, P < .001) and chronic constipation (34.9% vs 19%, P < .001) were statistically significant between those who had UI and those who did not. The logistic regression backward logistic regression model showed that the risk factors which predicted UI were chronic constipation (P = .003), chronic cough (P = .008), and the number of UTI attacks (P = .036). UI affects one-fifth of nulliparous women in the UAE. Chronic cough, constipation, and repeated UTI infection, significantly increase the odds of UI during pregnancy. Addressing these risk factors may reduce the risk of UI.

A Prognostic Model Based on Clinical Biomarkers for Heart Failure in Adult Patients Following Allogeneic Hematopoietic Stem Cell Transplantation

Heart failure (HF) is an uncommon but serious cardiovascular complication after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Unfortunately, knowledge about early mortality prognostic factors in patients with HF after allo-HSCT is limited, and an easy-to-use prognostic model is not available. This study aimed to develop and validate a clinical-biomarker prognostic model capable of predicting HF mortality following allo-HSCT that uses a combination of variables readily available in clinical practice. To investigate this issue, we conducted a retrospective analysis at our center with 154 HF patients who underwent allo-HSCT between 2008 and 2021. The patients were separated according to the time of transplantation, with 100 patients composing the derivation cohort and the other 54 patients composing the external validation cohort. We first calculated the univariable association for each variable with 2-month mortality in the derivation cohort. We then included the variables with a P value <.1 in univariate analysis as candidate predictors in the multivariate analysis using a backward stepwise logistic regression model. Variables remaining in the final model were identified as independent prognostic factors. To predict the prognosis of HF, a scoring system was established, and scores were assigned to the prognostic factors based on the regression coefficient. Finally, 4 strongly significant independent prognostic factors for 2-month mortality from HF were identified using multivariable logistic regression methods with stepwise variable selection: pulmonary infection (P=.005), grade III to IV acute graft-versus-host disease (severe aGVHD; P=.033), lactate dehydrogenase (LDH) >426 U/L (P=.049), and brain natriuretic peptide (BNP) >1799 pg/mL (P=.026). A risk grading model termed the BLIPS score (for BNP, LDH, cardiac troponin I, pulmonary infection, and severe aGVHD) was constructed according to the regression coefficients. The validated internal C-statistic was .870 (95% confidence interval [CI], .798 to .942), and the external C-statistic was .882 (95% CI, .791-.973). According to the calibration plots, the model-predicted probability correlated well with the actual observed frequencies. The clinical use of the prognostic model, according to decision curve analysis, could benefit HF patients. The BLIPS model in our study can serve to identify HF patients at higher risk for mortality early, which might aid designing timely targeted therapies and eventually improving patients' survival and prognosis.

Health-promoting lifestyle and its determining factors among students of public and private universities in Iran.

Health-promoting lifestyle is one of the fundamental health-related components. The aim of this study was to determine the status of the health-promoting lifestyle and its determining factors among students of public and private medical universities. In this cross-sectional study conducted in 2018, 1000 students from different public and private universities of medical sciences were selected by a multi-stage, stratified random sampling procedure and were studied using health-promoting lifestyle, self-efficacy, and well-being questionnaires. Data were analyzed using t-test, Chi-square, Pearson's correlation coefficient, and backward regression model. The health-promoting lifestyle was relatively acceptable in 775 people (77.5%); 649 (64.9%) had low self-efficacy, and 560 students (56%) had abnormal health conditions. The mean scores of health-promoting lifestyle, self-efficacy, and well-being in all students were 130.74 ± 22.14, 51.19 ± 12.66, and 15.22 ± 5.43, respectively. There were statistically significant differences between the mean scores of most aspects of health-promoting lifestyle, self-efficacy, and well-being in public and private universities. There was a statistically significant relationship between well-being (r = 0.08, P = 0.02) and self-efficacy (r = -0.19, P < 0.001) with health-promoting lifestyle. Students' well-being, self-efficacy, and physical activities of students were not at an acceptable level. Moreover, the health-promoting lifestyle of students was moderate. Implementing of health-related educational, developmental, and psycho-social programs for promotion and encouraging students to promote healthy behaviors is necessary.

A Prognostic Study for the Development of Risk Prediction Model for the Success of Vaginal Birth Following a Cesarean Surgery at Felege Hiwot Comprehensive Specialized Hospital, Northwest Ethiopia.

An attempt at vaginal delivery by a woman who has previously had a cesarean section is known as a trial of labor after cesarean section. The most important issues are how to accurately anticipate successful vaginal birth after cesarean surgery and how to calculate the likelihood of success of vaginal birth after caesarean section that is suitable for women. Consequently, a tailored prediction of vaginal birth after caesarean section may result in a more effective counseling. To create a clinical risk score and prediction model for the success of vaginal birth following a previous caesarean section in women. A prognostic analysis was carried out at Felege Hiwot Comprehensive and Specialized Referral Hospital from 30 February 2017 to 30 March 2021. R statistical programming language version 4.0 was used for analysis once the data had been coded and entered into Epidata, version 3.02. Significant factors (P< 0.05) were kept in the backward multivariable logistic regression model, and variables with (P<0.25) from the bi-variable logistic regression analysis were also added. After a cesarean section, 67% of women were successful in giving birth vaginally. Previous successful vaginal birth after cesarean surgery, rupture of the membranes, and initiation time of ANC, the beginning of labor, parity and time since the previous delivery were remained in the final multivariable prediction model. The AUC of the model was 0.748 (95% CI: 0.714-0.781). Overall, this study demonstrated the likelihood of predicting vaginal birth utilizing the ideal confluence of parity, membrane rupture, and onset of labor, prior history of VBAC, inter-delivery gap, and ANC beginning time. Sixty-seven percent of VBACs were successful. As a result, this model may aid in identifying pregnant women who are candidates for VBAC and who have a better likelihood of success.

Grapevine Yield Big-data for Soil and Climate Zoning. A case study in Languedoc-Roussillon, France

New winegrower and resource datasets appear to be a great opportunity to understand which are the environmental factors involved in grapevine yield spatially. Such analysis can help regional label managers and winegrowers for the conception of local adaptation strategies to climate change, reducing yield gaps. In the present study, we aggregated yield a big dataset obtained from Pays d’Oc winegrowers (n = 96677) between 2010 and 2018 at the municipality level (n = 606), located in the Languedoc-Roussillon region, in the South of France. We used a backward stepwise model selection process using linear mixed-effect models to discriminate and select significant indicators capable of estimating grapevine yield at the municipality level, these include: Soil Available Water Capacity (SAWC), soil pH, Huglin Index, the Climate Dryness Index, the number of Very Hot Days and Days of Frost. We then determined spatial zones by creating clusters of municipalities with similar soil and climate characteristics. The seven zones presented two marked yield levels. Yet, all zones had municipalities with both high yield and high yield gaps. On each zone, grapevine yield was found to be driven by a combination of climate and soil factors, rather than just by a single environmental factor. Environmental factors at this scale largely explained yield variability across the municipalities, but they were not performant in terms of annual yield prediction. Further research is required on the interactions between environmental factors, plant material and farming practices.

A Study on the Path of Digital Technology to Improve the Level of Municipal Social Governance in Border Minority Areas—Taking Guangxi Zhuang Autonomous Region as an Example

With the deepening application of digital technologies such as big data, artificial intelligence and blockchain in the field of social governance, digital technologies provide new practical paths for improving municipal social governance. Based on the case of Guangxi, this paper explores the practice of municipal social governance under digital governance in border ethnic areas, analyzes its practices and experiences in promoting the modernization of municipal governance under digital governance, and addresses the problems of poor linkage of traditional government platforms, backward governance model, insufficient innovation of governance methods, and few professional digital technology talents and poor risk resistance in the field of municipal social governance empowered by digital technology. We propose countermeasures to increase the investment in digital governance platforms, further empower the "digital future", seize the opportunity of digital transformation, promote the diversification of governance models, use the advantages of digital technology to change governance models, promote the modernization of grassroots governance system and governance capacity, and train professional technical talents in a system.

Association between the Mediterranean Diet Index and self-reported Gingival Health Status Indicators in a population of Chilean adults: a cross-sectional study.

Despite the recognized impact of diet on non-communicable diseases, the association between the Mediterranean diet and periodontal diseases is still uncertain. This study aimed to determine the association between adherence to the Mediterranean Diet Index (MDI) and self-reported gingival health status in Chilean adults, exploring the feasibility of using validated web-based survey questionnaires. Cross-sectional data were collected from a representative sample of a population of Chilean adults (18-60 years old) using a low-cost and time-saving methodology. By the PsyToolkit platform, anonymous survey data were downloaded and analyzed in bivariate (crude) and backward stepwise selection multivariate logistic regression models adjusted for sociodemographic determinants, smoking, and dental attendance using STATA 17. Odds ratios (OR) [95% confidence intervals] were estimated. In total, 351 complete statistical data were mostly obtained from female university students who had never smoked and reported having visited a dentist in the previous year. Multivariate regression models showed an association between MDI and very good/good gingival health status (OR 1.18 [95% CI 1.04-1.34], p=0.013), absence of bleeding on toothbrushing (OR 1.12 [95% CI 1.01-1.25], p=0.035), and absence of clinical signs of gingival inflammation (OR 1.24 [95% CI 1.10-1.40], p<0.001), after controlling for age, sex, educational level, smoking, and dental attendance. We associated adherence to the Mediterranean diet with better self-reported gingival health status in a population of Chilean adults in an entirely web-based research environment. Longitudinal studies with random sampling are required to establish the effect of diet on gingival and periodontal health. Nevertheless, this evidence could contribute to the design of low-cost surveillance programs to reduce the burden of periodontal disease and related "common risk factors".

Strategy Design of PM2.5 Controlling for Northern Thailand

The emission of fine particulate matter (PM2.5) in dry season from the open biomass burning has caused a long-term negative impact on residents’ health in Northern Thailand. This study takes Chiang Mai and Chiang Rai provinces in Northern Thailand as the study areas to identify pollution episodes, analyze PM2.5 source trajectories, and finally propose pollution control strategies accordingly. PM2.5 levels during 2019–2021 of three representative air pollution monitoring stations (i.e., Chaing Mai-35T, Chiang Rai-57T, and Mae Sai-73T) in these two provinces were collected and analyzed. The Air Quality Index (AQI) defined by PM2.5 level higher than 91 µg m-3 causing serious adverse health effects was adopted to define periods having pollution levels, and the days of the air pollution episodes were identified. Based on these episodes, we applied the backward trajectory model to identify the sources of pollutants. Results showed that PM2.5 levels were significantly higher between February to April compared with other months during 2019–2021 at all three monitoring stations, indicating the severity of PM2.5 episode during the dry season. The backward trajectory demonstrated that air mass transported through the Northern Thailand or nearby mountain areas (categorized as long- or short-transport-distance) contributed up to 21.6% and 75.9% of the total air mass, respectively. Although residents in these mountainous areas are accustomed to the biomass burning, we suggested that there should be urgent needs for the improvement of the long-term air quality in these two provinces. Therefore, this study proposes some control strategies including improvement of prevention knowledge, increase of the risk perception, cultivation of the protection behavior, and intensification of the social influence. In addition to reducing biomass burning pollution, this improvement plan also has a co-benefit of achieving resources recycling concomitantly. Providing effective management strategies may reduce the adverse health effects to Thai residents.

Optimal Monetary Policy in a simple backward and forward looking Models

The growing dynamics of macroeconomic and political environment across the globe particularly in developing countries where high inflation appears persistent, and trade -off between low stable inflation and full employment are becoming more challenging for monetary and fiscal authorities and has increase the needs to review the optimal monetary policy choice by the central banks. The present study investigates the optimal monetary policy choice in a backward and forward looking model. In particular, the study adopt an explorative review where both theoretical and empirical literature on the subject were reviewed and synthesized. The findings from literature revealed that there is no consensus on whether the optimal monetary policy should be backward looking or forward looking as both have their own unique advantages. The study concludes that a hybrid of both forward and backward-looking model where more weights is assigned to forward looking variables will improve the responsiveness of monetary policy decision on real macroeconomic variables like inflation, employment and economic growth.

Classification of High-Concentration Aerosol Phenomena Using Their Physical Properties in Busan, South Korea

High concentrations of aerosols associated with various meteorological phenomena show different physical characteristics. Because these phenomena are differentiated based on the visibility observed with the eyes, the observation may be unreliable. To investigate aerosol physical properties across various meteorological phenomena and develop an algorithm to classify high-concentration aerosol events, each phenomenon was assessed by analyzing the aerosol number and mass concentrations according to particle size observed with an optical particle counter. Furthermore, the optimal probability density function for each phenomenon was derived using the mass concentration by diameter. In addition, total aerosol mass concentrations, geometric mean particle diameters, and geometric standard deviations were calculated. In the coarse mode, the total mass concentration of the Asian dust case was the highest (16.3 μg·cm−3), whereas in the accumulation mode, the haze value was greatest (22.86 μg·cm−3). Average diameters were 4.41 and 0.41 μm in the coarse and accumulation modes, respectively. A classification algorithm for high-concentration aerosol phenomena was proposed based on the determined physical properties, results of simulating long-distance transport using a backward trajectory model, and meteorological conditions. Among the nine verification cases, all the cases coincided with the observation results of the Korea Meteorological Administration.

Physiologic and Behavioral Correlates of Energy Intake in Emerging Adulthood: Cross-Sectional Analysis of the RIGHT Track Health Study

BackgroundConsistent findings have reported that FFM is associated with EI. However, conjoint assessments of physiologic (body composition, fasting serum leptin) and behavioral [eating behaviors and physical activity (PA)] correlates of EI during emerging adulthood have not been examined. ObjectivesWe assessed associations between physiologic and behavioral correlates of EI within the context of one another in emerging adults (18–28 years old). We also assessed these associations in a subsample after the removal of probable EI underreporters. MethodsCross-sectional data from 244 emerging adults (age = 19.6 ± 1.4 y; BMI = 26.4 ± 6.6 kg/m2; 56.6% female) from the RIGHT Track Health study were used. Measures included body composition (BOD POD), eating behaviors (Three-Factor Eating Questionnaire), objective and subjective PA (accelerometer-derived total activity counts and Godin-Shephard Leisure-Time Exercise Questionnaire), fasting serum leptin, and EI (three 24-hour dietary recalls). Correlates independently associated with EI were entered into a backward stepwise linear regression model. Correlates that met the criteria of P < 0.05 were retained. Analyses were repeated in a subsample after removing probable EI underreporters (n = 48). Effect modification by sex (male and female) and BMI (BMI < 25 kg/m2, BMI ≥ 25 kg/m2) categories was also assessed. ResultsIn the full sample, FFM (β: 18.4; 95% CI: 9.9, 26.8), leptin (β: −84.8; 95% CI: −154.3, −15.4), dietary restraint (β: −35.2; 95% CI: −59.1, −11.3), and subjective PA (β: 2.5; 95% CI: 0.04, 4.9) were significantly associated with EI. After the removal of probable underreporters, only FFM remained significantly associated with EI (β: 43.9; 95% CI: 27.2, 60.6). No evidence of effect modification by sex or BMI categories was noted. ConclusionsAlthough physiologic and behavioral correlates were associated with EI in the full sample, only FFM remained a robust correlate of EI in a subsample of emerging adults after removing probable EI underreporters.

Affective temperaments mediate aggressive dimensions in bipolar disorders: A cluster analysis from a large, cross-sectional, international study

BackgroundAffective temperaments show potential for aggressive behavior (AB) preventive strategies in bipolar disorder (BD). We aim to define intra-diagnostic subgroups of patients with BD based on homogeneous behaviors related to AB. Subsequently, to assess whether affective temperament dimensions may contribute to the presence and severity of AB. MethodsPatients with BD were recruited. AB was evaluated through the modified overt aggression scale (MOAS); affective temperaments were assessed with the TEMPS-A. A cluster analysis was conducted based on TEMPS-A and MOAS scores. Stepwise backward logistic regression models were used to identify the predictive factors of cluster membership. Results799 patients with BD were enrolled. Three clusters were determined: non-aggressive (55.5 %), self-aggressive (18 %), and hetero-aggressive (26.5 %). Depressive, irritable, and anxious temperament scores significantly increased from the non-aggressive (lower) to the self-aggressive (intermediate) and the hetero-aggressive group (highest). A positive history of a suicide attempt (B = 5.131; OR = 169.2, 95 % CI 75.9; 377) and rapid cycling (B = −0.97; OR = 0.40, 95 % CI 0.17; 0.95) predicted self-aggressive cluster membership. Atypical antipsychotics (B = 1.19; OR = 3.28, 95 % CI 2.13; 5.06) or SNRI treatment (B = 1.09; OR = 3, 95 % CI 1.57; 5.71), psychotic symptoms (B = 0.73; OR = 2.09, 95 % CI 1.34; 3.26), and history of a suicide attempt (B = −1.56; OR = 0.20, 95 % CI 0.11; 0.38) predicted hetero-aggressive cluster membership. LimitationsRecall bias might have affected the recollection of AB. ConclusionsClinical factors orientate the prevention of different ABs in BD. Affective temperaments might play a role in preventing AB since patients with more pronounced affective temperaments might have an increased risk of showing AB, in particular hetero-AB.

A Prediction Model for Rapid Identification of Ischemic Stroke: Application of Serum Soluble Corin.

Rapid identification is critical for ischemic stroke due to the very narrow therapeutic time window. The objective of this study was to construct a diagnostic model for the rapid identification of ischemic stroke. A mixture population constituted of patients with ischemic stroke (n = 481), patients with hemorrhagic stroke (n = 116), and healthy individuals from communities (n = 2498) were randomly resampled into training (n = 1547, mean age: 55 years, 44% males) and testing (n = 1548, mean age: 54 years, 43% males) samples. Serum corin was assayed using commercial ELISA kits. Potential risk factors including age, sex, education level, cigarette smoking, alcohol consumption, obesity, blood pressure, lipids, glucose, and medical history were obtained as candidate predictors. The diagnostic model of ischemic stroke was developed using a backward stepwise logistic regression model in the training sample and validated in the testing sample. The final diagnostic model included age, sex, cigarette smoking, family history of stroke, history of hypertension, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, fasting glucose, and serum corin. The diagnostic model exhibited good discrimination in both training (AUC: 0.910, 95% CI: 0.884-0.936) and testing (AUC: 0.907, 95% CI: 0.881-0.934) samples. Calibration curves showed good concordance between the observed and predicted probability of ischemic stroke in both samples (all P>0.05). We developed a simple diagnostic model with routinely available variables to assist rapid identification of ischemic stroke. The effectiveness and efficiency of this model warranted further investigation.

Scanning a compressed ordered representation of the future.

Several authors have suggested a deep symmetry between the psychological processes that underlie our ability to remember the past and make predictions about the future. The judgment of recency (JOR) task measures temporal order judgments for the past by presenting pairs of probe stimuli; participants choose the probe that was presented more recently. We performed a short-term relative JOR task and introduced a novel judgment of imminence (JOI) task to study temporal order judgments for the future. In the JOR task, participants were presented with a sequence of stimuli and asked to choose which of two probe stimuli was presented closer to the present. In the JOI task, participants were trained on a probabilistic sequence. After training, the sequence was interrupted with probe stimuli. Participants were asked to choose which of two probe stimuli was expected to be presented closer to the present. Replicating prior work on JOR, we found that RT results supported a backward self-terminating search model operating on a temporally organized representation of the past. We also showed that RT distributions are consistent with this model and that the temporally organized representation is compressed. Critically, results for the JOI task probing expectations of the future suggest a forward self-terminating search model operating on a temporally organized representation of the future. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Causal stabilisation of strictly causal multidimensional discrete objects

The paper presents a solution to the problem of constructing the set of causal discrete controllers for strictly causal multidimensional discrete objects based on the application of determinant polynomial equations (DPE). It is shown that the properties of the solutions of the corresponding DPE depend on the type of the mathematical description of the controlled object. If the object is described by a backward polynomial model in terms of the B-shift operator ζ, it is shown that the corresponding DPE generates only causal solutions. This allows us to construct a parameterisation of the set of transfer matrices of causal stabilising controllers in a form that corresponds to the multidimensional Youla-Kučera parameterisation. For the case where the object is given as a forward polynomial model in the F-shift operator z, special techniques are developed to extract the set of all stabilising controllers from the subset of causal solutions.

Explaining the natural and anthropogenic factors driving glacier recession in Kashmir Himalaya, India.

Glaciers across the Kashmir Himalayan region are melting at an accelerated pace compared to other regions across the Himalayan arc. This study analyzed the recession patterns of nine glaciers in the Kashmir Himalaya region over 28years between 1992 and 2020 using satellite images and field measurements. The recession patterns were correlated with debris cover, topographic factors, and ambient black carbon (BC) concentration at glacier sites. HYSPLIT model was used to track the air mass sources at a 7-day time-step from September 1, 2014, to September 28, 2014, over the selected region. All nine glaciers revealed high recession as indicated by changes in the area (average recession: 20.8%) and snout position (~ 14m a-1). The relative percentage of debris on each glacier varied between ~ 0% (clean glacier) and 43%. Although the investigated glaciers lie in the same climatological regime, their topographical behavior is dissimilar with mean altitude ranging between 4000 and ~ 4700m asl and the average slope varying from 17 to 24°. All the investigated glaciers are north-facing except G3 (southerly aspect). Our results indicate anomalously high ambient BC concentrations, ranging from 500 to 1364ngm-3, at the glacier sites, higher than previously studied for glaciers in the Himalayas and neighboring Tibetan Plateau. The backward air-mass trajectory modeling indicated both local and global sources of particulate matter in the study area. A comparative analysis of BC measurements and glacier recession with the studies conducted across high Asia indicated the influence of BC in accelerating the melting of glaciers in the Kashmir region.

Development and Validation of a Prognostic Model Based on Clinical Biomarkers for Heart Failure in Adult Patients Following Allogeneic Hematopoietic Stem Cell Transplantation

Introduction: Heart failure (HF) is an uncommon but one of the most serious types of cardiovascular complications after allogeneic hematopoietic stem cell transplantation (allo-HSCT). However, the prognostic factors of HF after allo-HSCT remain unknown, and there is currently no prognostic model for HF after allo-HSCT. In this study, we investigated the clinical features and prognosis of patients with HF after allo-HSCT. A prognostic model based on clinical biomarkers, which combined variables that are readily available in clinical practice, that is capable of predicting 2-month mortality of HF patients after allo-HSCT was developed and validated. Methods: In our retrospective study, 154 patients with HF after allo-HSCT from 2008 to 2021 were identified. One hundred patients were assigned to the derivation cohort and the remaining 54 patients were assigned to the external validation cohort according to the time of transplantation. We first calculated the univariable association of each variable with 2-month mortality in the derivation cohort. Second, the variables with p values less than 0.1 in the univariate analysis were further included as candidate predictors in the multivariate analysis using a backward stepwise logistic regression model. The variables that remained in the final model based on the outcomes of the multivariate analysis in the derivation cohort were identified as independent prognostic factors. A scoring system to predict the prognosis of HF after allo-HSCT was also established, and scores were assigned to the prognostic factors based on the regression coefficient. Results: Using multivariable logistic regression methods with stepwise variable selection, four highly significant independent prognostic factors for the two-month mortality of HF were identified: pulmonary infection (p=0.005; odds ratio [OR], 6.89; 95% confidence interval [CI], 1.93-29.13), Grades III to IV acute graft-versus-host disease (severe aGVHD) (p=0.033; odds ratio [OR], 3.62; 95% confidence interval [CI], 1.15-12.69), lactate dehydrogenase (LDH)>426 U/L (p=0.049; odds ratio [OR], 3.02; 95% confidence interval [CI], 1.00-9.32) and brain natriuretic peptide (BNP)>1799 pg/ml (p=0.026; odds ratio [OR], 3.44; 95% confidence interval [CI], 1.16-10.56). We also included cardiac troponin I (cTNI) (p=0.069; odds ratio [OR], 2.93, 95% confidence interval [CI], 0.94-29.13) in our final 5-predictor prognostic model. A risk grading model termed the BLIPS score (BNP, LDH, cardiac troponin I, pulmonary infection, severe aGVHD) was constructed according to the regression coefficients. BNP level>1799 pg/mL, LDH level>426 U/L, cTNI level>100 pg/ml, and severe aGVHD were each assigned one point, and pulmonary infection was assigned two points. The points scored for each of these five factors were added to yield the overall risk score, which ranged from zero to six. Patients were stratified into a low-risk group (0-2 points), an intermediate-risk group (3-4 points) and a high-risk group (5-6 points). The validated internal c-statistic was 0.870 (95% CI, 0.798-0.942), and the external c-statistic was 0.882 (95% CI, 0.791-0.973). According to the calibration plots, the model-predicted probabilities showed a good correlation with the actual observed frequencies. Decision curve analysis indicated that the clinical implementation of the prognostic model could benefit HF patients. The Kaplan‒Meier estimations of overall survival revealed good separation between these risk groups. We generated a risk heatmap for the BLIPS model to visualize the risk estimates derived from the individual prognostic factor profile. Conclusions: Patients who develop HF after allo-HSCT have a poor prognosis. An integrated prognostic model based on clinical biomarkers (BLIPS) was developed and externally validated, and this is the first straightforward scoring model that incorporates clinical and laboratory risk factors to evaluate the 2-month mortality of patients with HF after receiving allo-HSCT to treat hematological malignancies. This model can be effectively utilized to help improve the survival and prognosis of HF patients by accelerating the early identification of patients at a high risk of mortality and contributing to the appropriate implementation of urgent medical support. Disclosures: No relevant conflicts of interest to declare.