Integrative Approach to Child and Adolescent Mental Health

The prevalence of mental disorders between children and adolescents is 10–20% worldwide. Research has shown that most mental disorders begin at childhood and adolescence. Neurodevelopmental disorders are classified by which the development of the central nervous system is disturbed and are associated with varying degrees of consequences in one’s mental, emotional, physical, and economic states. Recently, research in mental health, neurobiology, and early childhood development supported the case for early intervention and prevention. The causes of mental disorders in children and adolescents are not currently known, but research suggests that a combination of factors that include heredity, biology, psychological trauma, spiritual well-being, and environmental stress might be involved. There are many factors that play into child and adolescent mental health and disorders; therefore, individualized, personalized, and integrative approaches are necessary in therapeutic interventions and prevention. Thus, by ensuring that the needed mental health care competencies are made available in each primary health care team and by assuring fully integrated mental health and other types of health care, primary health care teams would best provide early, efficient, effective, and optimal recovery-based care

Integration of the Production Logging Tool and Production Data for Post-Fracturing Evaluation by the Ensemble Smoother

A post-fracturing evaluation is essential to optimize a fracturing design for a multi-stage fractured well located in unconventional reservoirs. To accomplish this task, a production logging tool (PLT) can be utilized to provide the oil production rate of each fracturing stage. In this research, a practical method is proposed to integrate PLT and surface production data into a reservoir model. It applies the ensemble smoother for history-matching to integrate various kinds of dynamic data. To investigate the validity of the proposed method, three cases are designed according to the frequency of PLT surveys. Each fracture half-length calibrated by PLT data is similar to the true value, and the dynamic behavior also has the same trend as true production behavior. Integration with PLT data can reduce error ratios for fracture half-length down to 48%. In addition, it presents the applicability of reserve prediction and uncertainty assessment. It has been proven that the more frequently PLTs are surveyed, the more accurate the results. By sensitivity analysis of PLT frequency—a cost-effective strategy—a combination of only one PLT survey and continuous surface production data is employed to demonstrate this proposed concept

Expansion of Geological CO₂ Storage Capacity in a Closed Aquifer by Simultaneous Brine Production with CO₂ Injection

Structural trapping is the primary mechanism for intensive CO2 sequestration in saline aquifers. This is the foundation for increasing global CO2 storage; gradual switch to preferable trapping mechanisms, such as residual saturation, dissolution, and mineral trapping, will require a long-time scale. The major constraints limiting the storage capacity of structural trapping are formation pressure and structure size. Over-pressure owing to CO2 injection causes a disruption of seal integrity indicating a failure in geological sequestration. The other constraint on storage capacity is a spill point determining geological storage volume. Overflowing CO2, after filling the storage volume, migrates upward along the aquifer geometry with buoyancy. This study proposes a methodology to maximize CO2 storage capacity of a geological site with a substructure created by an interbedded calcareous layer below spill point. This study provides various conceptual schemes, i.e., no brine production, simultaneous brine production and pre-injection brine production, for geological CO2 storage. By the comparative analysis, location of brine producer, production rate, and distance between injector and producer are optimized. Therefore, the proposed scheme can enhance CO2 storage capacity by 68% beyond the pressure and migration limits by steering CO2 plume and managing formation pressure

Reaction to the COVID-19 pandemic in Seoul with biostatistics

This paper discusses our collaboration work with government officers in the health department of Seoul during the COVID-19 pandemic. First, we focus on short-term fore-casting for the number of new confirmed cases and severe cases. Second, we focus on understanding how much of the current infections has been affected by external influx from neighborhood areas or internal transmission within the area. This understanding may be important because it is linked to the government policy determining non-pharmaceutical interventions. To obtain the decomposition of the effect, districts of Seoul should be considered simultaneously, and multivariate time series models are used. Third, we focus on predicting the number of new weekly confirmed cases for each district in Seoul. This detailed prediction may be important to the government policy on resource allocation. We consider an ensemble method to overcome poor prediction performance of simple models. This paper presents the methodological details and analysis results of the study.(c) 2022 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).N

Mobilization of monocytic myeloid-derived suppressor cells is regulated by PTH1R activation in bone marrow stromal cells

Abstract Myeloid-derived suppressor cells (MDSCs) are bone marrow (BM)-derived immunosuppressive cells in the tumor microenvironment, but the mechanism of MDSC mobilization from the BM remains unclear. We investigated how BM stromal cell activation by PTH1R contributes to MDSC mobilization. PTH1R activation by parathyroid hormone (PTH) or PTH-related peptide (PTHrP), a tumor-derived counterpart, mobilized monocytic (M-) MDSCs from murine BM without increasing immunosuppressive activity. In vitro cell-binding assays demonstrated that α4β1 integrin and vascular cell adhesion molecule (VCAM)-1, expressed on M-MDSCs and osteoblasts, respectively, are key to M-MDSC binding to osteoblasts. Upon PTH1R activation, osteoblasts express VEGF-A and IL6, leading to Src family kinase phosphorylation in M-MDSCs. Src inhibitors suppressed PTHrP-induced MDSC mobilization, and Src activation in M-MDSCs upregulated two proteases, ADAM-17 and MMP7, leading to VCAM1 shedding and subsequent disruption of M-MDSC tethering to osteoblasts. Collectively, our data provide the molecular mechanism of M-MDSC mobilization in the bones of tumor hosts

Detecting Incongruity between News Headline and Body Text via a Deep Hierarchical Encoder

Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume. This research introduces million-scale pairs of news headline and body text dataset with incongruity label, which can uniquely be utilized for detecting news stories with misleading headlines. On this dataset, we develop two neural networks with hierarchical architectures that model a complex textual representation of news articles and measure the incongruity between the headline and the body text. We also present a data augmentation method that dramatically reduces the text input size a model handles by independently investigating each paragraph of news stories, which further boosts the performance. Our experiments and qualitative evaluations demonstrate that the proposed methods outperform existing approaches and efficiently detect news stories with misleading headlines in the real world

Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction

Abstract Machine learning (ML) has been suggested to improve the performance of prediction models. Nevertheless, research on predicting the risk in patients with acute myocardial infarction (AMI) has been limited and showed inconsistency in the performance of ML models versus traditional models (TMs). This study developed ML-based models (logistic regression with regularization, random forest, support vector machine, and extreme gradient boosting) and compared their performance in predicting the short- and long-term mortality of patients with AMI with those of TMs with comparable predictors. The endpoints were the in-hospital mortality of 14,183 participants and the three- and 12-month mortality in patients who survived at discharge. The performance of the ML models in predicting the mortality of patients with an ST-segment elevation myocardial infarction (STEMI) was comparable to the TMs. In contrast, the areas under the curves (AUC) of the ML models for non-STEMI (NSTEMI) in predicting the in-hospital, 3-month, and 12-month mortality were 0.889, 0.849, and 0.860, respectively, which were superior to the TMs, which had corresponding AUCs of 0.873, 0.795, and 0.808. Overall, the performance of the predictive model could be improved, particularly for long-term mortality in NSTEMI, from the ML algorithm rather than using more clinical predictors