Additional Set Research Articles

Children with Early-Onset Psychosis Have Increased Burden of Rare GRIN2A Variants.

Children and adolescents with early-onset psychosis (EOP) have more rare genetic variants than individuals with adult-onset forms of the illness, implying that fewer EOP participants are needed for genetic discovery. The Schizophrenia Exome Sequencing Meta-analysis (SCHEMA) study predicted that 10 genes with ultra-rare variation were linked to adult-onset schizophrenia. We hypothesized that rare variants predicted "High" and "Moderate" by the Variant Effect Predictor Algorithm (abbreviated as VEPHMI) in these 10 genes would be enriched in our EOP cohort. We compared rare VEPHMI variants in individuals with EOP (N = 34) with race- and sex-matched controls (N = 34) using the sequence kernel association test (SKAT). GRIN2A variants were significantly increased in the EOP cohort (p = 0.004), with seven individuals (20% of the EOP cohort) carrying a rare VEPHMI variant. The EOP cohort was then compared to three additional control cohorts. GRIN2A variants were significantly increased in the EOP cohort for two of the additional control sets (p = 0.02 and p = 0.02), and trending towards significance for the third (p = 0.06). Despite a small sample size, GRIN2A VEPHMI variant burden was increased in a cohort of individuals with EOP in comparison to controls. GRIN2A variants have been associated with a range of neuropsychiatric disorders including adult-onset psychotic spectrum disorder and childhood-onset schizophrenia. This study supports the role of GRIN2A in EOP and emphasizes its role in neuropsychiatric disorders.

Comparison of Deterministic and Probabilistic Variational Data Assimilation Methods Using Snow and Streamflow Data Coupled in HBV Model for Upper Euphrates Basin

The operation of upstream reservoirs in mountainous regions fed by snowmelt is highly challenging. This is partly due to scarce information given harsh topographic conditions and a lack of monitoring stations. In this sense, snow observations from remote sensing provide additional and relevant information about the current conditions of the basin. This information can be used to improve the model states of a forecast using data assimilation techniques, therefore enhancing the operation of reservoirs. Typical data assimilation techniques can effectively reduce the uncertainty of forecast initialization by merging simulations and observations. However, they do not take into account model, structural, or parametric uncertainty. The uncertainty intrinsic to the model simulations introduces complexity to the forecast and restricts the daily work of operators. The novel Multi-Parametric Variational Data Assimilation (MP-VarDA) uses different parameter sets to create a pool of models that quantify the uncertainty arising from model parametrization. This study focuses on the sensitivity of the parametric reduction techniques of MP-VarDA coupled in the HBV hydrological model to create model pools and the impact of the number of parameter sets on the performance of streamflow and Snow Cover Area (SCA) forecasts. The model pool is created using Monte Carlo simulation, combined with an Aggregated Distance (AD) Method, to create different model pool instances. The tests are conducted in the Karasu Basin, located at the uppermost part of the Euphrates River in Türkiye, where snowmelt is a significant portion of the yearly runoff. The analyses were conducted for different thresholds based on the observation exceedance probabilities. According to the results in comparison with deterministic VarDA, probabilistic MP-VarDA improves the m-CRPS gains of the streamflow forecasts from 57% to 67% and BSS forecast skill gains from 52% to 68% when streamflow and SCA are assimilated. This improvement rapidly increases for the first additional model parameter sets but reaches a maximum benefit after 5 parameter sets in the model pool. The improvement is notable for both methods in SCA forecasts, but the best m-CRPS gain is obtained for VarDA (31%), while the best forecast skill is detected in MP-VarDA (12%).

Five Generalized Rough Approximation Spaces Produced by Maximal Rough Neighborhoods

In rough set theory, the multiplicity of methods of calculating neighborhood systems is very useful to calculate the measures of accuracy and roughness. In line with this research direction, in this article we present novel kinds of rough neighborhood systems inspired by the system of maximal neighborhood systems. We benefit from the symmetry between rough approximations (lower and upper) and topological operators (interior and closure) to structure the current generalized rough approximation spaces. First, we display two novel types of rough set models produced by maximal neighborhoods, namely, type 2 mξ-neighborhood and type 3 mξ-neighborhood rough models. We investigate their master properties and show the relationships between them as well as their relationship with some foregoing ones. Then, we apply the idea of adhesion neighborhoods to introduce three additional rough set models, namely, type 4 mξ-adhesion, type 5 mξ-adhesion and type 6 mξ-adhesion neighborhood rough models. We establish the fundamental characteristics of approximation operators inspired by these models and discuss how the properties of various relationships relate to one another. We prove that adhesion neighborhood rough models increase the value of the accuracy measure of subsets, which can improve decision making. Finally, we provide a comparison between Yao’s technique and current types of adhesion neighborhood rough models.

Misidentification of Plasmodium Species by Cross-Reacting Primers and Cerebral Malaria Caused by Plasmodium vivax

Abstract Introduction The clinical presentation of a case as cerebral malaria with molecular identification confirming it as Plasmodium vivax underlines the importance of using molecular tools to identify the species and type of malaria. The possibility of the relationship between the complication observed during clinical diagnosis and the multifactorial molecular changes could likely be the reason for terming it cerebral malaria. Methods We report four cases analyzed using the quantitative buffy coat technique followed by classical Giemsa stained thick-film microscopy, and nested polymerase chain reaction for the genus-specific region of Plasmodium targeting 18S rDNA followed by species-specific identification with a different set of primers and products confirmation with sequencing. Results Primers targeting P. knowlesi generated the expected product size of 153 base pairs that, upon sequencing, matched with the P. vivax sequence reflecting the relatedness of the species. Likewise, primers targeting P. ovale generated a 456 product whose sequence matched the P. vivax sequence. Conclusion Infection with P. vivax can potentially cause cerebral malaria, and P. vivax can cause severe malaria complications alone or mixed with other species and can show cerebral malaria signs, which are typically associated with P. falciparum infections. The sequence relatedness reflects the genome similarity between P. knowlesi and P. ovale with P. vivax. The need to reconfirm with an additional set of newly reported primers is mandatory.

Periodic hypoxia, intermittent pain, and caffeine in male and female neonatal rats: corticosterone, insulin resistance, and hepatic gene expression.

Preterm infants experience multiple stressors including periodic neonatal hypoxia, maternal/caregiver separation, and acute pain from clinical procedures. Although neonatal hypoxia or interventional pain are associated with sexually dimorphic effects that may last into adulthood, the interaction of these common preterm stressors and caffeine pretreatment remains unknown. We hypothesize that an interaction of acute neonatal hypoxia, isolation, and pain modeling the experience of the preterm infant will augment the acute stress response and that caffeine routinely given to preterm infants will alter this response. Male and female rat pups were isolated and exposed to six cycles of periodic hypoxia (10% O2) or normoxia (room air control) and/or intermittent pain by administering needle pricks (or touch control) to the paw on postnatal (PD) days 1-4. An additional set of rat pups was pretreated with caffeine citrate (80 mg/kg ip) and studied on PD1. Plasma corticosterone, fasting glucose, and insulin were measured to calculate homeostatic model assessment for insulin resistance (HOMA-IR) (index of insulin resistance). Glucocorticoid-, insulin-, and caffeine-sensitive gene mRNAs were analyzed in the PD1 liver and hypothalamus to evaluate downstream markers of glucocorticoid action. Acute pain with periodic hypoxia led to a large increase in plasma corticosterone, which was attenuated by pretreatment with caffeine. Pain with periodic hypoxia led to a 10-fold increase in hepatic Per1 mRNA expression in males, which was attenuated with caffeine. The augmentation of corticosterone and HOMA-IR at PD1 after periodic hypoxia with pain suggests early intervention to attenuate the stress response may mitigate the programming effects of neonatal stress.

Efficient algorithms for multi-armed bandits with additional feedbacks: Modeling and algorithms

Multi-armed bandits (MAB) are widely applied to optimize networking applications such as crowdsensing and mobile edge computing. Additional feedbacks (or partial feedbacks) on some arms are usually possible to be collected in many networking applications but with certain costs. Though a variety of algorithms were proposed to utilize such feedbacks to speed up MAB learning, it is still unclear regrading: (1) How to schedule the budgets across the MAB learning period? (2) Which feedbacks to select from the additional feedback set? To fill in this gap, we design AF-UCB algorithm. The contribution of AF-UCB is a generic additional feedback processing algorithm, which is composed of an elimination module, a schedule module and a selection module. The elimination module eliminates additional feedbacks that may slow down the learning speed. It is inspired by findings from an in-depth numerical analysis, which shows that some feedbacks may slow down the learning speed (slower than not using any additional feedback at all) of two latest representative algorithms. The schedule module adaptively schedules budgets and the selection module selects appropriate feedbacks from the additional feedback set. We derive a sub-linear regret upper bound for AF-UCB. Extensive numerical experiments validate the superior performance of AF-UCB.

Systematic in vitro analysis of therapy resistance in glioblastoma cell lines by integration of clonogenic survival data with multi-level molecular data

Despite intensive basic scientific, translational, and clinical efforts in the last decades, glioblastoma remains a devastating disease with a highly dismal prognosis. Apart from the implementation of temozolomide into the clinical routine, novel treatment approaches have largely failed, emphasizing the need for systematic examination of glioblastoma therapy resistance in order to identify major drivers and thus, potential vulnerabilities for therapeutic intervention. Recently, we provided proof-of-concept for the systematic identification of combined modality radiochemotherapy treatment vulnerabilities via integration of clonogenic survival data upon radio(chemo)therapy with low-density transcriptomic profiling data in a panel of established human glioblastoma cell lines. Here, we expand this approach to multiple molecular levels, including genomic copy number, spectral karyotyping, DNA methylation, and transcriptome data. Correlation of transcriptome data with inherent therapy resistance on the single gene level yielded several candidates that were so far underappreciated in this context and for which clinically approved drugs are readily available, such as the androgen receptor (AR). Gene set enrichment analyses confirmed these results, and identified additional gene sets, including reactive oxygen species detoxification, mammalian target of rapamycin complex 1 (MTORC1) signaling, and ferroptosis/autophagy-related regulatory circuits to be associated with inherent therapy resistance in glioblastoma cells. To identify pharmacologically accessible genes within those gene sets, leading edge analyses were performed yielding candidates with functions in thioredoxin/peroxiredoxin metabolism, glutathione synthesis, chaperoning of proteins, prolyl hydroxylation, proteasome function, and DNA synthesis/repair. Our study thus confirms previously nominated targets for mechanism-based multi-modal glioblastoma therapy, provides proof-of-concept for this workflow of multi-level data integration, and identifies novel candidates for which pharmacological inhibitors are readily available and whose targeting in combination with radio(chemo)therapy deserves further examination. In addition, our study also reveals that the presented workflow requires mRNA expression data, rather than genomic copy number or DNA methylation data, since no stringent correlation between these data levels could be observed. Finally, the data sets generated in the present study, including functional and multi-level molecular data of commonly used glioblastoma cell lines, represent a valuable toolbox for other researchers in the field of glioblastoma therapy resistance.

Computational Analysis of Hemodynamic Indices Based on Personalized Identification of Aortic Pulse Wave Velocity by a Neural Network

Adequate personalized numerical simulation of hemodynamic indices in coronary arteries requires accurate identification of the key parameters. Elastic properties of coronary vessels produce a significant effect on the accuracy of simulations. Direct measurements of the elasticity of coronary vessels are not available in the general clinic. Pulse wave velocity (AoPWV) in the aorta correlates with aortic and coronary elasticity. In this work, we present a neural network approach for estimating AoPWV. Because of the limited number of clinical cases, we used a synthetic AoPWV database of virtual subjects to train the network. We use an additional set of AoPWV data collected from real patients to test the developed algorithm. The developed neural network predicts brachial–ankle AoPWV with a root-mean-square error (RMSE) of 1.3 m/s and a percentage error of 16%. We demonstrate the relevance of a new technique by comparing invasively measured fractional flow reserve (FFR) with simulated values using the patient data with constant (7.5 m/s) and predicted AoPWV. We conclude that patient-specific identification of AoPWV via the developed neural network improves the estimation of FFR from 4.4% to 3.8% on average, with a maximum difference of 2.8% in a particular case. Furthermore, we also numerically investigate the sensitivity of the most useful hemodynamic indices, including FFR, coronary flow reserve (CFR) and instantaneous wave-free ratio (iFR) to AoPWV using the patient-specific data. We observe a substantial variability of all considered indices for AoPWV below 10 m/s and weak variation of AoPWV above 15 m/s. We conclude that the hemodynamic significance of coronary stenosis is higher for the patients with AoPWV in the range from 10 to 15 m/s. The advantages of our approach are the use of a limited set of easily measured input parameters (age, stroke volume, heart rate, systolic, diastolic and mean arterial pressures) and the usage of a model-generated (synthetic) dataset to train and test machine learning methods for predicting hemodynamic indices. The application of our approach in clinical practice saves time, workforce and funds.

The recursive grey model and its application

The fixed structure parameters limit the performance of grey prediction algorithm in unsmoothed time series prediction tasks. Based on the mechanism of recursive iteration, this study proposes a recursive grey model to predict the future trend of renewable energy generation in China. The proposed method has the following improvements: the first is the priority of new information. By introducing a memory factor parameter into the objective function, recursive grey model gives more influence to new observations. The second is dynamic updating scheme of structure parameters and improved iteration base values. The proposed model uses recursive method to solve the structure parameters of each observation, and the new parameters can inherit the previous experience. The third is stable forecasting performance. Based on the principle of minimizing the prediction error of in-sample data, the memory factor optimization method is given. Without additional verification set, recursive grey model can synchronize training and test of in-sample data to avoid over fitting. Finally, the proposed model is applied to predict China's renewable energy generation. The forecast results show that in the next few years, China's hydropower, wind power and solar power generation will grow at an average annual growth rate of 2.6%, 20.7% and 24.9%, respectively. It can be predicted that China's hydropower generation, wind power generation and solar power generation will reach 1475 billion kWh, 3094 billion kWh and 1352 billion kWh, respectively in 2030.

Classification of Organic and Conventional Vegetables Using Machine Learning: A Case Study of Brinjal, Chili and Tomato

Growing organic food is becoming a challenging task with increasing demand. Food fraud activity has increased considerably with the increase in population growth. Consumers cannot visually distinguish between conventional and organically grown food products. Spectroscopic methodologies are presented to identify chemicals in food, thereby identifying organic and conventional food. Such spectroscopic techniques are laboratory-based, take more time to produce an outcome, and are costlier. Thus, this research designed a portable, low-cost multispectral sensor system to discriminate between organic and conventional vegetables. The designed multispectral sensor system uses a wavelength range (410 nm-940 nm) that includes three bands, namely visible (VIS), ultraviolet (UV) and near-infrared (NIR) spectra, to enhance the accuracy of detection. Tomato, brinjal and green chili samples are employed for the experiment. The organic and conventional discrimination problem is formulated as a classification problem and solved through random forest (RF) and neural network (NN) models, which achieve 92% and 89% accuracy, respectively. A two-stage enhancement mechanism is proposed to improve accuracy. In the first stage, the fuzzy logic mechanism generates additional feature sets. Ant colony optimization (ACO) algorithm-based parameter tuning and feature selection are employed in the second stage to enhance accuracy further. This two-stage improvement mechanism results in 100% accuracy in discriminating between organic and conventional vegetable samples. The detected adulterant is displayed on a web page through an IoT-developed application module to be accessed from anywhere.

External and Internal Microbiomes of Antarctic Nematodes are Distinct, but More Similar to each other than the Surrounding Environment.

Host-associated microbiomes have primarily been examined in the context of their internal microbial communities, but many animal species also contain microorganisms on external host surfaces that are important to host physiology. For nematodes, single strains of bacteria are known to adhere to the cuticle (e.g., Pasteuria penetrans), but the structure of a full external microbial community is uncertain. In prior research, we showed that internal gut microbiomes of nematodes (Plectus murrayi, Eudorylaimus antarcticus) and tardigrades from Antarctica's McMurdo Dry Valleys were distinct from the surrounding environment and primarily driven by host identity. Building on this work, we extracted an additional set of individuals containing intact external microbiomes and amplified them for 16S and 18S rRNA metabarcoding. Our results showed that external bacterial microbiomes were more diverse than internal microbiomes, but less diverse than the surrounding environment. Host-specific bacterial compositional patterns were observed, and external microbiomes were most similar to their respective internal microbiomes. However, external microbiomes were more influenced by the environment than the internal microbiomes were. Non-host eukaryotic communities were similar in diversity to internal eukaryotic communities, but exhibited more stochastic patterns of assembly compared to bacterial communities, suggesting the lack of a structured external eukaryotic microbiome. Altogether, we provide evidence that nematode and tardigrade cuticles are inhabited by robust bacterial communities that are substantially influenced by the host, albeit less so than internal microbiomes are.

Valuation of health benefits of green-blue areas for the purpose of ecosystem accounting: a pilot in Flanders, Belgium

In recent years, a vast amount of scientific literature has highlighted the benefits of nearby green space for physical and mental health, but the large variation in scope, methods and indicators used in these studies hampers the assessment of these benefits in the context of natural capital accounting. To our knowledge, this paper is one of the first studies to quantify and value these benefits in the context of natural capital accounting. A method was developed and applied to the Flemish Region in Belgium for 2013 and 2016. The physical supply and use accounts for health are based on a set of selected dose-effect relationships that quantify the impact of the availability of greenspace on seven specific indicators for physical and mental health, including mortality, cardio-vascular diseases, diabetes and depression. The indicator for green-blue areas is the percentage of green-blue areas in total land use, calculated for 0.5, 1 and 3 km radius from the residence, based on detailed land-use maps (10 m x 10 m) for Flanders, Belgium. The base-line data for mortality and illness are average data for the Flemish Region. These health impacts are weighted using Daly's (disability-adjusted life years) and aggregated. The total health benefits due to the availability of green-blue areas for the total Flemish population was estimated at almost 85,000 DALYs. This is 27% of the estimated total burden of disease in Flanders in 2016 for the seven selected diseases. The monetary accounts are based on a detailed assessment for mortality and morbidity of three different cost components, i.e. avoided medical costs (e.g. hospitalisation) and avoided absenteeism and welfare loss due to suffering and reduced life expectancy. Productivity gains from avoided absenteeism is valued, based on statistics on absenteeism for specific diseases for and labour market data from Belgium and account for 52% of the total monetary value of green spaces. Cost of illness is valued, based on market data and illness specific studies for Belgium or Europe and account for 36% of total values. Welfare gains from increased life expectancy are valued on the basis of European studies for the VOLY (value of a life year lost), based on the simulated exchange value for the willingness-to-pay for increased life expectancy. This accounts for 12% of the total monetary value of green space. The total monetary benefits amount to 464 Euro per inhabitant per year or 3 billion Euro per year for Flanders. This corresponds to 1.3% of the GDP, which reflects the importance of these benefits. The methodology is incomplete as not all health indicators are covered, mainly due to a lack of dose-effect relationships. The research priority for potential users of the accounts is a better indicator for contact with green space that does differentiate between ecosystems, their quality, accessibility or their use. This requires more systematic health impact studies that take these elements into account, as well as more systematic data on the daily use of green space by citizens. In the meantime, an additional set of condition accounts on these elements can be used, especially to follow changes in quality and use of green-blue areas over time.

Weakly supervised multi-class semantic video segmentation for road scenes

Weakly supervised multi-class video segmentation is one of the most challenging yet least studied research problems in computer vision. This study aims to investigate two main items: (1) effective feature update for temporal changes combined with feature reuse between temporal frames; and (2) learn object patterns in complex scenes specifically for videos under weak supervision. Associating image tags to visual appearance is not a straightforward learning task, especially for complex scenes. Therefore, in this paper, we present manifold augmentations to obtain reliable pixel labels from image tags. We propose a framework comprised of two key modules: a temporal split module for efficient video processing and a pseudo per-pixel seed generation module for precise pixel-level supervision. Particularly, in our model, we utilize and explore temporal correlations via temporal split module and temporal attention. To reuse the extracted features and incorporate temporal updates for precise and fast computation, a channel-wise temporal split mechanism between successive video frames is presented. Furthermore, we evaluated proposed modules in two additional settings: (1) fully or sparsely supervised road scene video segmentation; and (2) weakly supervised segmentation for complex road scene images. Experiments are conducted on the Cityscapes and CamVid datasets, using DeepLabv3 as segmentation network and LiteFlowNet to compute motion vectors.

Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs

BackgroundSerum microRNAs (miRNAs) are promising non-invasive biomarkers for diagnosing glioma. However, most reported predictive models are constructed without a large enough sample size, and quantitative expression levels of their constituent serum miRNAs are susceptible to batch effects, decreasing their clinical applicability.MethodsWe propose a general method for detecting qualitative serum predictive biomarkers using a large cohort of miRNA-profiled serum samples (n = 15,460) based on the within-sample relative expression orderings of miRNAs.ResultsTwo panels of miRNA pairs (miRPairs) were developed. The first was composed of five serum miRPairs (5-miRPairs), reaching 100% diagnostic accuracy in three validation sets for distinguishing glioma and non-cancer controls (n = 436: glioma = 236, non-cancers = 200). An additional validation set without glioma samples (non-cancers = 2611) showed a predictive accuracy of 95.9%. The second panel included 32 serum miRPairs (32-miRPairs), reaching 100% diagnostic performance in training set on specifically discriminating glioma from other cancer types (sensitivity = 100%, specificity = 100%, accuracy = 100%), which was reproducible in five validation datasets (n = 3387: glioma = 236, non-glioma cancers = 3151, sensitivity> 97.9%, specificity> 99.5%, accuracy> 95.7%). In other brain diseases, the 5-miRPairs classified all non-neoplastic samples as non-cancer, including stroke (n = 165), Alzheimer’s disease (n = 973), and healthy samples (n = 1820), and all neoplastic samples as cancer, including meningioma (n = 16), and primary central nervous system lymphoma samples (n = 39). The 32-miRPairs predicted 82.2 and 92.3% of the two kinds of neoplastic samples as positive, respectively. Based on the Human miRNA tissue atlas database, the glioma-specific 32-miRPairs were significantly enriched in the spinal cord (p = 0.013) and brain (p = 0.015).ConclusionsThe identified 5-miRPairs and 32-miRPairs provide potential population screening and cancer-specific biomarkers for glioma clinical practice.

"Everything in One Place": Stakeholder Perceptions of Integrated Medical and Social Care for Diabetes Patients in Western Maryland.

Patients with type 2 diabetes frequently have both medical- and health-related social needs that must be addressed for optimal disease management. Growing evidence suggests that intersectoral partnerships between health systems and community-based organizations may effectively support improved health outcomes for patients with diabetes. The purpose of this study was to describe stakeholders' perceptions of the implementation factors associated with a diabetes management program, an intervention involving coordinated clinical and social services supports to address both medical- and health-related social needs. This intervention delivers proactive care alongside community partnerships, and leverages innovative financing mechanisms. Qualitative study with semi-structured interviews. Study participants included adults (18 years or older) who were patients with diabetes and essential staff (e.g., members of a diabetes care team, health care administrators) and leaders of community-based organizations. We used the Consolidated Framework for Implementation Research (CFIR) to develop a semi-structured interview guide designed to elicit perspectives from patients and essential staff on their experiences within an outpatient center to support patients with chronic conditions (the CCR) as a part of an intervention to improve care for patients with diabetes. Interviews illuminated three key takeaways: (1) team-based care held an important role in promoting accountability across stakeholders motivating patient engagement and positive perceptions, (2) mission-driven alignment across the health care and community sectors was needed to synergize a broad range of efforts, and (3) global payment models allowing for flexible resource allocation can invaluably support the appropriate care being directed where it is needed the most whether medical or social services. The views and experiences of patient and essential staff stakeholder groups reported here thematically according to CFIR domains may inform the development of other chronic disease interventions that address medical- and health-related social needs in additional settings.

Abstract Number ‐ 97: Middle meningeal artery embolization to treat chronic subdural hematomas – Retrospective study of morphological parameters

Introduction Chronic subdural hematomas are a common neurovascular condition that involve the gradual and continual exudation of fluid from a compromised cell layer lining the dura [1]. Embolization of the middle meningeal artery (MMAE) results in lower recurrence rates as compared to traditional surgical burr hole evacuation [2‐4]. Liquid embolic agents such as cyanoacrylates or ethylene vinyl alcohol copolymers can potentially provide a distinct advantage with greater distal penetration resulting in faster hematoma resolution [5]. Their inherent radiopacity (after pre‐mixing) also allows for accurate visualization and control over embolization. The hypothesis of this study is that liquid embolic surface area would correlate with more effective cSDH volume resolution in patients. Methods Under IRB approval, we retrospectively analyzed non‐contrast head CTs and immediate post‐embolization flat‐detector CTs from 45 patients who underwent first‐line MMAE with a liquid embolic. Patients who received any neurosurgical intervention were excluded. 3D‐Slicer was used to segment and calculate hematoma volumes pre‐embolization, as well as at 1‐month, 3‐months, and 6‐months post‐embolization. The flat‐detector CT scans were also segmented to calculate the surface area of the liquid embolic. Results There was significant reduction in hematoma volume at 1‐, 3‐ and 6‐months with respect to the preoperative cSDH volume (Figure A, Pre: pre‐treatment, mo: month). The liquid embolic surface area significantly correlated with (a) pre‐embolization hematoma volume (R2 = 0.27, p = 0.0002, n = 45), (b) the reduction in volume at 1‐month post‐embolization (R2 = 0.16, p = 0.007, n = 41), (c) volume reduction at 3‐months post‐embolization (R2 = 0.29, p = 0.0015, n = 32), (d) hematoma volume resorption rate (cc/day) calculated at 1‐month post‐embolization (R2 = 0.16, p = 0.011, n = 39), and (e) hematoma volume resorption rate calculated at 3‐months post‐embolization (R2 = 0.34, p = 0.0003, n = 34, Figure B). The liquid embolic surface area was not correlated to the volume reduction at 6‐months (p = 0.3, n = 26). Conclusions The luminal surface area of MMA vasculature embolized with liquid embolics may be correlated to the reduction in cSDH volumes. The liquid embolic segmentation method used here needs further refinement to improve accuracy. Additional data sets are required to confirm the correlations reported here.

Regime Channel Bedload Transport Equation

Numerous theories have been presented over the years for quantifying bedload transport in streams and rivers. These theories have been derived using physical processes, statistical methods, and semiempirical and empirical methods and rely on a variety of hydraulic, boundary, and bed material properties. Most of these equations represent instantaneous bedload transport related to temporal hydraulic and bed conditions and are not well-suited to estimate long-term average bedload over a broad range of channels. Using a pure empirical analysis, the author has developed a dimensionally homogeneous regime channel bedload transport equation not requiring bedload measurements for calibration or verification. The new equation was developed using 45 data sets and verified using 29 additional data sets encompassing a very broad range of channel conditions and was found to rely solely on stream power and bank-full stream power or, for practical applications, flow and bank-full flow. Statistical metrics indicated better performance by the new equation than by other bedload transport equations evaluated using the same source data and statistical methods.

Dimensionless correlations for estimating the permeability of perforated packaging films to oxygen

Several methods have been applied to estimate the permeance provided by perforations for perforated-mediated modified atmosphere package design. However, no dimensionless correlations have been proposed that comply with the principles of dynamic similarity and could therefore be applied with confidence to different packaging systems. The purpose of this work was to apply the Buckingham π theorem and obtain a generic correlation that obeys the principles of dynamic similarity. A correlation between the Stanton number (St) and the Schmidt (Sc) and Peclet (Pe) numbers was proposed and fitted to a set of 46 datapoints. The results showed that the correlation could be simplified to a proportionality between St and the reciprocal of Pe with a proportionality constant of 0.863. That implied that the influence of the perforation diameter was so dominant that the mass transfer coefficient (which is the same as the permeance given by the perforations) was simply proportional to the reciprocal of the perforation diameter, within the experimental variability. This result was validated with an additional set of 10 experiments. Mass transfer coefficients varied between 1 and 6 cm/s.

Social Inclusion of Students from Disadvantaged Backgrounds: New Strategic Approaches to Educational Counselling and Communication

The need to provide education for all has continued to be at a high level even now when access to education is easier and considerably wider than in the previous historical periods. There is a general causality as well as a specific one supporting this necessity. The latter includes an additional set of reasons which connect defining characteristics of disadvantaged learners with the limitations of the pandemic crisis and its effects on communication. They also continue to manifest themselves after the health restrictions have been lifted and further hinder communication and educational counselling, thereby imposing additional barriers to inclusive education. This study is a literature review through which we aim to make a synthesis of current communication and educational counselling new strategic approaches suitable for use in the post – COVID context for facilitating access to education for disadvantaged students. The objectives of the study are as follows: O1: to identify a set of strategies which correspond to the specific communication pattern and information needs of the children and teenagers nowadays; O2: to identify current educational counselling strategies which can be successfully used in the post - COVID context to enable well-informed career decisions for disadvantaged students; O3: to develop a formative intervention model for social inclusion of students from disadvantaged backgrounds. The study findings may be useful in school and career counselling in pre-university education, but also in collaborative approaches between pre-university and academia in order to facilitate the inclusion of the disadvantaged students.

Fiscal Foresight and Perverse Distortions to Firm Behavior: Anticipatory Dips and Compensating Rebounds

We study the conditions under which fiscal foresight – forward-looking agents anticipating future policy changes – results in perverse economic behavior through unintended intertemporal tradeoffs. Somewhat surprisingly, fiscal foresight by itself is far from sufficient for policy-induced incentives to perversely distort firm behavior. Rather, we show that there are two additional sets of conditions, at least one of which must hold to generate perverse behavior: (i) storable output, diminishing returns, and a non- competitive output market; (ii) “rolling base” policy design and storable output. These conditions suggest that the estimated impacts of fiscal policies may be sensitive to underlying economic or legislative characteristics and that policies targeted to specific firms or industries with unique characteristics may not be generalizable.