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We demonstrated the feasibility of die-level process monitoring using spatially resolvable optical emission spectrometers (SROESs) in a low-pressure plasma reactor. The spatially resolved Ar emission line intensities and their intensity ratios were obtained from uniformly generated and intentionally perturbed plasma by inserting conductors of known sizes at known locations. The sheath and perturbed plasma distributions were identified from the Ar emission line intensity and their intensity ratio in the lateral direction to the SROES installment orientation. However, the resolution in the axial direction was insufficient to identify the sheath and perturbed the plasma distribution. We propose the superimposition of measured plasma emission intensities from two perpendicularly aligned SROESs for die-level process monitoring. The insufficient axial resolution can be compensated for by the lateral resolution of another SROES, and die-level process monitoring using SROES can thus be realized.

The acute respiratory distress syndrome (ARDS) resulting from coronavirus disease 2019 (COVID-19) is associated with a massive release of inflammatory cytokines and high mortality. Mesenchymal stromal cells (MSCs) have anti-inflammatory properties and have shown activity in treating acute lung injury. Here the authors report a case series of 11 patients with COVID-19-associated ARDS (CARDS) requiring mechanical ventilation who were treated with remestemcel-L, an allogeneic MSC product, under individual patient emergency investigational new drug applications.Patients were eligible if they were mechanically ventilated for less than 72 h prior to the first infusion. Patients with pre-existing lung disease requiring supplemental oxygen or severe liver or kidney injury were excluded. Each patient received two infusions of remestemcel-L at a dose of 2 million cells/kg per infusion given 48-120 h apart.Remestemcel-L infusions were well tolerated in all 11 patients. At the end of the 28-day follow-up period, 10 (91%, 95% confidence interval [CI], 59-100%) patients were extubated, nine (82%, 95% CI, 48-97%) patients remained liberated from mechanical ventilation and were discharged from the intensive care unit and two (18%, 95 CI%, 2-52%) patients died. The median time to extubation was 10 days. Eight (73%, 95% CI, 34-100%) patients were discharged from the hospital. C-reactive protein levels significantly declined within 5 days of MSC infusion.The authors demonstrate in this case series that remestemcel-L infusions to treat moderate to severe CARDS were safe and well tolerated and resulted in improved clinical outcomes.

Figure S4 shows relative abundance of indicated protein levels and effect of PPARÎ' inhibitor on growth in sorafenib-sensitive and -resistant HCC cells.

Figure S3 shows relative amount of intracellular glutamine level and abundance of indicated protein levels in sorafenib-sensitive and -resistant HCC cells.

Supplementary Materials shows the figure legends of Supplementary figures and Tables.

Figure S1 shows relative protein abudance of indicated protein levels in sorafenib-sensitive HCC (Huh7 and SK-Hep1) and -resistant HCC (Huh7-R and SK-H-R) cells.

Figure S2 shows relative amount of intracellular glutamine level and abundance of indicated protein levels in sorafenib-sensitive and -resistant HCC cells.

The tyrosine kinase inhibitor sorafenib is the only therapeutic agent approved for the treatment of advanced hepatocellular carcinoma (HCC), but acquired resistance to sorafenib is high. Here, we report metabolic reprogramming in sorafenib-resistant HCC and identify a regulatory molecule, peroxisome proliferator–activated receptor-δ (PPARδ), as a potential therapeutic target. Sorafenib-resistant HCC cells showed markedly higher glutamine metabolism and reductive glutamine carboxylation, which was accompanied by increased glucose-derived pentose phosphate pathway and glutamine-derived lipid biosynthetic pathways and resistance to oxidative stress. These glutamine-dependent metabolic alterations were attributed to PPARδ, which was upregulated in sorafenib-resistant HCC cells and human HCC tissues. Furthermore, PPARδ contributed to increased proliferative capacity and redox homeostasis in sorafenib-resistant HCC cells. Accordingly, inhibiting PPARδ activity reversed compensatory metabolic reprogramming in sorafenib-resistant HCC cells and sensitized them to sorafenib. Therefore, targeting compensatory metabolic reprogramming of glutamine metabolism in sorafenib-resistant HCC by inhibiting PPARδ constitutes a potential therapeutic strategy for overcoming sorafenib-resistance in HCC.Implications: This study provides novel insight into the mechanism underlying sorafenib resistance and a potential therapeutic strategy targeting PPARδ in advanced hepatocellular carcinoma. Mol Cancer Res; 15(9); 1230–42. ©2017 AACR.

Figure S5 shows Effect of a PPARÎ' inhibitor on tumor growth in xenograft model.

Ca2+ overload-induced mitochondrial dysfunction is considered as a major contributing factor in the pathogenesis of alcohol-associated liver disease (ALD). However, the initiating factors that drive mitochondrial Ca2+ accumulation in ALD remain elusive. Here, we demonstrate that an aberrant increase in hepatic GRP75-mediated mitochondria-associated ER membrane (MAM) Ca2+-channeling (MCC) complex formation promotes mitochondrial dysfunction in vitro and in male mouse model of ALD. Unbiased transcriptomic analysis reveals PDK4 as a prominently inducible MAM kinase in ALD. Analysis of human ALD cohorts further corroborate these findings. Additional mass spectrometry analysis unveils GRP75 as a downstream phosphorylation target of PDK4. Conversely, non-phosphorylatable GRP75 mutation or genetic ablation of PDK4 prevents alcohol-induced MCC complex formation and subsequent mitochondrial Ca2+ accumulation and dysfunction. Finally, ectopic induction of MAM formation reverses the protective effect of PDK4 deficiency in alcohol-induced liver injury. Together, our study defines a mediatory role of PDK4 in promoting mitochondrial dysfunction in ALD.

Dynamic regulation of mitochondrial morphology provides cells with the flexibility required to adapt and respond to electron transport chain (ETC) toxins and mitochondrial DNA-linked disease mutations, yet the mechanisms underpinning the regulation of mitochondrial dynamics machinery by these stimuli is poorly understood. Here, we show that pyruvate dehydrogenase kinase 4 (PDK4) is genetically required for cells to undergo rapid mitochondrial fragmentation when challenged with ETC toxins. Moreover, PDK4 overexpression was sufficient to promote mitochondrial fission even in the absence of mitochondrial stress. Importantly, we observed that the PDK4-mediated regulation of mitochondrial fission was independent of its canonical function, i.e., inhibitory phosphorylation of the pyruvate dehydrogenase complex (PDC). Phosphoproteomic screen for PDK4 substrates, followed by nonphosphorylatable and phosphomimetic mutations of the PDK4 site revealed cytoplasmic GTPase, Septin 2 (SEPT2), as the key effector molecule that acts as a receptor for DRP1 in the outer mitochondrial membrane to promote mitochondrial fission. Conversely, inhibition of the PDK4-SEPT2 axis could restore the balance in mitochondrial dynamics and reinvigorates cellular respiration in mitochondrial fusion factor, mitofusin 2-deficient cells. Furthermore, PDK4-mediated mitochondrial reshaping limits mitochondrial bioenergetics and supports cancer cell growth. Our results identify the PDK4-SEPT2-DRP1 axis as a regulator of mitochondrial function at the interface between cellular bioenergetics and mitochondrial dynamics.

Recent studies in scientific paper summarization have explored a new form of structured summary for a reference paper by grouping all cited and citing sentences together by facet. This involves three main tasks: (1) identifying cited text spans for citances (i.e., citing sentences), (2) classifying their discourse facets, and (3) generating a structured summary from the cited text spans and citances. This paper focuses on the first task, and approaches the task as binary classification to distinguish relevant pairs of citances and reference sentences from irrelevant pairs. We propose a new method that integrates feature selection and classification techniques to enhance classification performance. The proposed method investigates combinations of six feature selection methods (χ2-Statistics, Information Gain, Gain Ratio, Relief-F, Significance Attribute Evaluation, and Symmetrical Uncertainty), and five classification algorithms (k-Nearest Neighbors, Decision Tree, Support Vector Machine, Naïve Bayes, and Random Forest). Additionally, to address imbalanced data during training, we apply SMOTE (Synthetic Minority Over-sampling Technique) to introduce synthetic biases towards the minority. Experiments are conducted using the CL-SciSumm corpora to compare the effect of feature selection applied to classification. The results reveal the benefits of feature selection in significantly boosting performance of F1 score metric, and show that our method is competitive to the state-of-the-art methods in the CL-SciSumm evaluations.

Recently, due to the population aging and the fast development of information and communications technology (ICT), the number of laborers has remarkably decreased. This has created a demand to improve the productivity and product quality of companies and manufacturers. Besides, Smart Factories are expected to meet those requirements as consumers' needs are diversified, demanding personalized production and rapid and accurate manufacturing innovation rather than traditional manufacturing firms. The term “Smart Factory” means an intelligent factory that integrates ICT into the traditional manufacturing industry. This applies to the entire process of planning, requirement analysis, design, production, distribution, and sales. Smart Factory broadly covers level 4 areas that deal with general information technology (IT) and level 0–3 areas that deal with operational technology (OT). Thus, information covered in OT areas can cause problems not only for a company but also for its country if it is leaked to the outside world as a company’s core asset. Therefore, it is important to identify and respond to potential security threats in a Smart Factory environment. To this end, in this paper, we research the components of major Smart Factory architecture. Subsequently, we discuss security issues and problems that may occur before the establishment of a Smart Factory. Finally, we propose a Smart Factory security model and a secure response to cyberattacks to address security issues.

Obesity is now recognized as a disease. This study revealed a novel role for pyruvate dehydrogenase kinase (PDK) in diet-induced hypertrophic obesity. Mice with global or adipose tissue-specific PDK2 deficiency were protected against diet-induced obesity. The weight of adipose tissues and the size of adipocytes were reduced. Adipocyte-specific PDK2 deficiency slightly increased insulin sensitivity in HFD-fed mice. In studies with 3T3-L1 preadipocytes, PDK2 and PDK1 expression was strongly increased during adipogenesis. Evidence was found for epigenetic induction of both PDK1 and PDK2. Gain- and loss-of-function studies with 3T3-L1 cells revealed a critical role for PDK1/2 in adipocyte differentiation and lipid accumulation. PDK1/2 induction during differentiation was also accompanied by increased expression of hypoxia-inducible factor-1α (HIF1α) and enhanced lactate production, both of which were absent in the context of PDK1/2 deficiency. Exogenous lactate supplementation increased the stability of HIF1α and promoted adipogenesis. PDK1/2 overexpression-mediated adipogenesis was abolished by HIF1α inhibition, suggesting a role for the PDK-lactate-HIF1α axis during adipogenesis. In human adipose tissue, the expression of PDK1/2 was positively correlated with that of the adipogenic marker PPARγ and inversely correlated with obesity. Similarly, PDK1/2 expression in mouse adipose tissue was decreased by chronic high-fat diet feeding. We conclude that PDK1 and 2 are novel regulators of adipogenesis that play critical roles in obesity., Obesity: Effects of different forms of key enzyme The discovery that two forms of a key enzyme appear to play a critical role in fat production triggered by overeating might lead to new approaches to prevent and treat obesity. Hyeon-Ji Kang at Kyungpook National University, Daegu, South Korea, and colleagues in South Korea and the USA examined the role of the enzymes pyruvate dehydrogenase kinase types 1 and 2 (PDK1/2). PDK enzymes regulate the activity of a multi-enzyme complex that catalyzes a key step in the use of glucose to provide energy stores for cells. Mice deficient in PDK2 were protected from diet-induced obesity, and PDK 1 and 2 activity was increased during the generation of fat cells. Studies using mice and human fat tissue confirmed that the enzymes regulate the development and growth of fat cells. Drugs inhibiting PDK enzymes might combat obesity.

Muscle atrophy, leading to muscular dysfunction and weakness, is an adverse outcome of sustained period of glucocorticoids usage. However, the molecular mechanism underlying this detrimental condition is currently unclear. Pyruvate dehydrogenase kinase 4 (PDK4), a central regulator of cellular energy metabolism, is highly expressed in skeletal muscle and has been implicated in the pathogenesis of several diseases. The current study was designed to investigated and delineate the role of PDK4 in the context of muscle atrophy, which could be identified as a potential therapeutic avenue to protect against dexamethasone-induced muscle wasting.The dexamethasone-induced muscle atrophy in C2C12 myotubes was evaluated at the molecular level by expression of key genes and proteins involved in myogenesis, using immunoblotting and qPCR analyses. Muscle dysfunction was studied in vivo in wild-type and PDK4 knockout mice treated with dexamethasone (25 mg/kg body weight, i.p., 10 days). Body weight, grip strength, muscle weight and muscle histology were assessed. The expression of myogenesis markers were analysed using qPCR, immunoblotting and immunoprecipitation. The study was extended to in vitro human skeletal muscle atrophy analysis.Knockdown of PDK4 was found to prevent glucocorticoid-induced muscle atrophy and dysfunction in C2C12 myotubes, which was indicated by induction of myogenin (0.3271 ± 0.102 vs 2.163 ± 0.192, ****P 0.0001) and myosin heavy chain (0.3901 ± 0.047 vs. 0.7222 ± 0.082, **P 0.01) protein levels and reduction of muscle atrophy F-box (10.77 ± 2.674 vs. 1.518 ± 0.172, **P 0.01) expression. In dexamethasone-induced muscle atrophy model, mice with genetic ablation of PDK4 revealed increased muscle strength (162.1 ± 22.75 vs. 200.1 ± 37.09 g, ***P 0.001) and muscle fibres (54.20 ± 11.85% vs. 84.07 ± 28.41%, ****P 0.0001). To explore the mechanism, we performed coimmunoprecipitation and liquid chromatography-mass spectrometry analysis and found that myogenin is novel substrate of PDK4. PDK4 phosphorylates myogenin at S43/T57 amino acid residues, which facilitates the recruitment of muscle atrophy F-box to myogenin and leads to its subsequent ubiquitination and degradation. Finally, overexpression of non-phosphorylatable myogenin mutant using intramuscular injection prevented dexamethasone-induced muscle atrophy and preserved muscle fibres.We have demonstrated that PDK4 mediates dexamethasone-induced skeletal muscle atrophy. Mechanistically, PDK4 phosphorylates and degrades myogenin via recruitment of E3 ubiquitin ligase, muscle atrophy F-box. Rescue of muscle regeneration by genetic ablation of PDK4 or overexpression of non-phosphorylatable myogenin mutant indicates PDK4 as an amenable therapeutic target in muscle atrophy.

OBJECTIVE To assess whether epilepsy is associated with increased odds of 30-day readmission due to psychiatric illness during the postpartum period. METHODS The 2014 Nationwide Readmissions Database and the International Classification of Disease, Ninth Revision, Clinical Modification codes were used to identify postpartum women up to 50 years old in the United States, including the subgroup with epilepsy. The primary outcome was 30-day readmission and was categorized as (1) readmission due to psychiatric illness, (2) readmission due to all other causes, or (3) no readmission. Secondary outcome was diagnosis at readmission. The association of the primary outcome and presence of epilepsy was examined using multinomial logistic regression. RESULTS Of 1 558 875 women with admissions for delivery identified, 6745 (.45%) had epilepsy. Thirteen of every 10 000 women had 30-day psychiatric readmissions in the epilepsy group compared to one of every 10 000 in the no-epilepsy group (p

Many object re-identification (Re-ID) methods that depend on large-scale training datasets have been proposed in recent years. However, the performance of these methods degrades dramatically when insufficient training data are available. To address this challenging problem, we propose a few-shot object re-identification (FSOR) method that enhances the generalization and discrimination abilities of object Re-ID models trained on small datasets. This method applies two novel techniques: reparameterization for feature vectors and dual-distance metric learning. The reparameterization mechanism transforms the primary feature vector of each input image into a Gaussian distribution to enhance the robustness of the FSOR method when performing object Re-ID tasks. The dual-distance metric learning technique, called H&C learning, considers both the hard mining distance and the center-point distance between each query sample and each support set of different object identities. H&C learning extracts the characteristics of the entire training dataset more precisely than other approaches and thus improves the discriminative abilities of object Re-ID models. Extensive experiments on both person and vehicle Re-ID datasets, such as Market-1501, DukeMTMC-ReID, CUHK03, and VeRi-776, show that the FSOR method has improved performance and outperforms state-of-the-art methods when the amount of labeled training data is small.

Background Prior work has shown that combining bootstrap imputation with tree-based machine learning variable selection methods can provide good performances achievable on fully observed data when covariate and outcome data are missing at random (MAR). This approach however is computationally expensive, especially on large-scale datasets. Methods We propose an inference-based method, called RR-BART, which leverages the likelihood-based Bayesian machine learning technique, Bayesian additive regression trees, and uses Rubin’s rule to combine the estimates and variances of the variable importance measures on multiply imputed datasets for variable selection in the presence of MAR data. We conduct a representative simulation study to investigate the practical operating characteristics of RR-BART, and compare it with the bootstrap imputation based methods. We further demonstrate the methods via a case study of risk factors for 3-year incidence of metabolic syndrome among middle-aged women using data from the Study of Women’s Health Across the Nation (SWAN). Results The simulation study suggests that even in complex conditions of nonlinearity and nonadditivity with a large percentage of missingness, RR-BART can reasonably recover both prediction and variable selection performances, achievable on the fully observed data. RR-BART provides the best performance that the bootstrap imputation based methods can achieve with the optimal selection threshold value. In addition, RR-BART demonstrates a substantially stronger ability of detecting discrete predictors. Furthermore, RR-BART offers substantial computational savings. When implemented on the SWAN data, RR-BART adds to the literature by selecting a set of predictors that had been less commonly identified as risk factors but had substantial biological justifications. Conclusion The proposed variable selection method for MAR data, RR-BART, offers both computational efficiency and good operating characteristics and is utilitarian in large-scale healthcare database studies.

This article examines Cold War icons transfigured in new media ecology by looking at the South Korean webtoon (web cartoon) Secretly, Greatly. In featuring the superfluous existence of kanch’ŏp (North Korean spies) dispatched to the South, the graphic narrative interweaves the legacy of national division with the sensibilities of the millennial generation. Feeling that their lives are rendered ingyŏ (surplus) under neoliberal governance, the precarious youth empathetically relate to the history-laden image of spies, whose belonging in society has been disavowed or forsaken. Appropriating earlier aesthetic conventions, Secretly, Greatly thus offers content and a channel through which the surplus generation can express and share their sense of misplacement in the present. In addressing such remediation of inherited memories, this study critically attends to the webtoon’s formal qualities that evoke affective engagement and connective practices in the digital space. By fostering multisensory interactions with the kanch’ŏp-ingyŏ personas on the screen, the graphic power of comics invites the viewer to participate in the characters’ boundary-crossing movements against the geopolitical backdrop of divided Korea. Nonetheless, the webtoon ultimately leaves the survival of North Korean others beyond the realm of the visible because their ordinary coexistence in the South is yet to be imagined.

Objective To derive and validate a comorbidity-based delirium risk index (DRI) to predict postoperative delirium. Data source/study setting Data of 506 438 hip fracture repair surgeries from 2006 to 2016 were collected to derive DRI and perform internal validation from the Premier Healthcare Database, which provided billing information on 20-25 percent of hospitalizations in the USA. Additionally, data of 1 130 569 knee arthroplasty surgeries were retrieved for external validation. Study design Thirty-six commonly seen comorbidities were evaluated by logistic regression with the outcome of postoperative delirium. The hip fracture repair surgery cohort was separated into a training dataset (60 percent) and an internal validation (40 percent) dataset. The least absolute shrinkage and selection operator (LASSO) procedure was applied for variable selection, and weights were assigned to selected comorbidities to quantify corresponding risks. The newly developed DRI was then compared to the Charlson-Deyo Index for goodness-of-fit and predictive ability, using the Akaike information criterion (AIC), Bayesian information criterion (BIC), area under the ROC curve (AUC) for goodness-of-fit, and odds ratios for predictive performance. Additional internal validation was performed by splitting the data by four regions and in 4 randomly selected hospitals. External validation was conducted in patients with knee arthroplasty surgeries. Data collection Hip fracture repair surgeries, knee arthroplasty surgeries, and comorbidities were identified by using ICD-9 codes. Postoperative delirium was defined by using ICD-9 codes and analyzing billing information for antipsychotics (specifically haloperidol, olanzapine, and quetiapine) typically recommended to treat delirium. Principal findings The derived DRI includes 14 comorbidities and assigns comorbidities weights ranging from 1 to 6. The DRI outperformed the Charlson-Deyo Comorbidity Index with better goodness-of-fit and predictive performance. Conclusions Delirium risk index is a valid comorbidity index for covariate adjustment and risk prediction in the context of postoperative delirium. Future work is needed to test its performance in different patient populations and varying definitions of delirium.

OBJECTIVE Identifying adverse outcomes and examining trends and causes of nonelective admissions among persons with epilepsy would be beneficial to optimize patient care and reduce health services utilization. We examined the association of epilepsy with discharge status, in-hospital mortality, length-of-stay, and charges. We also examined 10-year trends and causes of hospital admissions among those with and without epilepsy. METHODS Nonelective hospital admission in persons with epilepsy was identified in the 2005-2014 National Inpatient Sample (NIS) using a validated International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) case definition. The NIS is the largest US all-payer database including patient and hospital-level variables, and represents hospitalizations in the general population. Descriptive statistics on trends and causes of admissions and multivariable regression analysis summarizing the association of epilepsy with the outcomes of interest are presented. RESULTS Of 4 718 178 nonelective admissions in 2014, 3.80% (n = 179 461) were in persons with epilepsy. Admissions in persons with epilepsy increased from 14 636 to 179 461 (P

Biological subtypes have been strongly correlated with loco-regional recurrence after definitive treatment for localized breast cancer. Brain metastases (BM) occur in 15% of patient diagnosed with breast cancer (BC). Therefore, we sought to determine whether biologic subtype is predictive of local control (LC) in BC patients with BM treated with stereotactic radiosurgery (SRS). This is a single-center retrospective study conducted with Institutional Review Board approval for the period January 1, 2004–December 31, 2018. Eligibility criteria included (1) histopathologic proven BC with documented biologic subtype, (2) presence of BM treated with SRS (single fraction), (3) follow-up MRIs, and (4) clinical follow-up. Patients were subdivided into three biologic subtypes: ER+/HER2- (“luminal”), HER2+, and ER-/PR-/HER2 (TN). All patients were treated with LINAC-based SRS with dose according to published guidelines. Cox proportional hazard model and Kaplan-Meier were used for statistical analysis of LC and overall survival (OS). With a median follow-up of 11.1 months, 17 BC BM in 50 consecutive patients were included in this study. The median disease-specific GPA was 2.0, and all patients received systemic chemotherapy and/or hormonal therapy. The 12-month LC rates for the entire cohort were 85%, 87%, and 49% for luminal, HER2+, and TN, respectively, with a significantly shorter time to local failure for the TN subtype (p = 0.014). The 12-month OS rates were 83%, 88%, and 80% for luminal, HER2+, and TN, respectively, with a trend toward shorter OS in the TN group. This study shows that in BC patients with BM treated with SRS, biologic subtype affects LC. Consideration of radiation treatment intensification or altered fractionation to improve LC may be indicated for the TN subtype. Further multicenter studies are necessary to corroborate our results.

We used a rigorous PRoBE (prospective-specimen collection, retrospective-blinded-evaluation) study design to compare the ability of biomarkers of systemic inflammation and biomarkers of gastrointestinal (GI) tissue damage to predict response to corticosteroid treatment, the incidence of clinically severe disease, 6-month nonrelapse mortality (NRM), and overall survival in patients with acute graft-versus-host disease (GVHD). We prospectively collected serum samples of newly diagnosed GVHD patients (n = 730) from 19 centers, divided them into training (n = 352) and validation (n = 378) cohorts, and measured TNFR1, TIM3, IL6, ST2, and REG3α via enzyme-linked immunosorbent assay. Performances of the 4 strongest algorithms from the training cohort (TNFR1 + TIM3, TNFR1 + ST2, TNFR1 + REG3α, and ST2 + REG3α) were evaluated in the validation cohort. The algorithm that included only biomarkers of systemic inflammation (TNFR1 + TIM3) had a significantly smaller area under the curve (AUC; 0.57) than the AUCs of algorithms that contained ≥1 GI damage biomarker (TNFR1 + ST2, 0.70; TNFR1 + REG3α, 0.73; ST2 + REG3α, 0.79; all P < .001). All 4 algorithms were able to predict short-term outcomes such as response to systemic corticosteroids and severe GVHD, but the inclusion of a GI damage biomarker was needed to predict long-term outcomes such as 6-month NRM and survival. The algorithm that included 2 GI damage biomarkers was the most accurate of the 4 algorithms for all endpoints.

Additional file 1 Web-based supplementary materials. Supplementary Section 1. Random Forest based Imputation Algorithm - missForest. Supplementary Section 2. Simulation Setup. Supplementary Section 3. Additional tables (Supplementary Table 1–12) and figures (Supplementary Figure 1–2) for simulation study and case study.

AIMS: The aim of this study was to improve medication reconciliation and reduce the occurrence of duplicate prescriptions by pharmacists and physicians within 72 hours of hospital admission using an intelligent prescription system combined with the National Health Insurance PharmaCloud system to integrate the database with the medical institution computerized physician order entry (CPOE) system. METHODS: This 2‐year intervention study was implemented in the geriatric ward of a hospital in Taiwan. We developed an integrated CPOE system linked with the PharmaCloud database and established an electronic platform for coordinated communication with all healthcare professionals. Patients provided written informed consent to access their PharmaCloud records. We compared the intervention effectiveness within 72 hours of admission for improvement in pharmacist medication reconciliation, increased at‐home medications documentation and decreased costs from duplicated at‐home prescriptions. RESULTS: The medication reconciliation rate within 72 hours of admission increased from 44.0% preintervention to 86.8% postintervention (relative risk = 1.97, 95% confidence interval [CI]: 1.69–2.31; P

College students' physical education plays an important role in physical activity and cultivates the concept of independent health management. At present, what kind of learning attitude do Taiwan college students face in physical education? What motivation does the student influence the attitude of the physical education? What is the relevance? All of the above are the purpose of this study. The research method adopts the questionnaire survey method, and the survey data adopts descriptive statistical analysis, independent sample t test, single factor variance analysis, LSD post hoc comparison method, and typical correlation analysis. Research results: 1. The different background variables of Taiwanese college students are that the main motivation factor of physical education is to obtain good health fitness for "physical health". 2. Taiwanese college students have different background variables. They all think that the "cognitive learning" of physical education is the main factor of attitude, that is, the knowledge about health care and sports skills. 3. There is a positive correlation between learning motivation and learning attitude (ρ=.90). Learning motivation is one of the important factors affecting learning attitude. Research conclusions: 1. The factors of Taiwanese male and female college students' motivation for learning in physical education are mainly based on "physical health". 2. Freshmen have higher motivations and learning attitudes in physical education than second-grade to fourth-grade. 3. Taiwan female college students average 1 or 2 times per week, male college students have the most athletes 2 to 3 times per week, more than 90% of college students like sports. 4. There is a positive correlation between learning motivation and learning attitude, indicating that the stronger the attribute of learning motivation "physical health", the higher the student's learning attitude. 5. Satisfying students' motivation for learning helps students to learn positively. 6. Another important task of the college physical education class is to prepare students for future lifelong sports.

Science teachers use a large number of visual representations and models in science classes to guide students to understand complex phenomena and to learn to conduct scientific inquiry. Fluent formation and use of visual representation involves metavisualization, which is a process related to metacognition and visualization. However, what kinds of knowledge and skills are involved and interact during successful metavisualization need further research. Moreover, teachers’ metavisualization should be a focus of research since teachers play a mediating role in guiding students to become proficient performers of visualization in science. Therefore, in this study, we investigated how an experienced science teacher performed metavisualization via qualitative data collection techniques including think-aloud tasks and a follow-up retrospective interview. We identified the relevant knowledge and skills that were involved in the teacher’s metavisualization. Moreover, by focusing on the interaction among the knowledge and skills, we observed three aspects of the teacher’s performance that were salient to her metavisualization, including the use of metavisual strategies, judgement criteria, and the encountered critical points. Drawing upon previous perspectives and this study’s findings, we propose a model of metavisualization by extending an existing model for further research. The findings also provide insight into teacher professional development programs.

The present study demonstrates, via a numerical simulation, the feasibility of achieving enhanced forced convection heat transfer of laminar water flow in an isoflux heated circular tube by inserting a concentric circular tube and controlling the concurrent flow distribution through the resulting concentric double-tube duct. Under identical operation conditions to those for the parent single-tube flow configuration, including the inlet fluid temperature, the total volumetric flow rate, the length of heated section, as well as the wall heat flux imposed, numerical simulations have been undertaken for the thermally developing convective heat transfer characteristics of water flow in the concentric double-tube duct featuring geometrically by three different relative radius ratio ro (= 1.2, 1.5, 1.8), compared with its parent single-tube duct of lh,ST = 0.1 at ReST = 100, 500, and 1000, respectively. In terms of the local and length-averaged heat transfer effectiveness gauged against that obtained for the parent single-tube duct, numerical results clearly demonstrate that the double-tube duct of smaller ro operating with relative larger flow rate than that in the inner tube can serve as highly effective heat transfer enhancement configuration.

Paenibacillus konkukensis sp. nov., SK3146 is a novel strain isolated from a pig feed. Here, we present complete genome sequence of SK3146. The genome consists of a single circular genome measuring 7,968,964 bp in size with an average guanine + cytosine (G+C) content of 53.4%. Genomic annotation revealed that the strain encodes 151 proteins related to hydrolases (EC3), which was higher than those in Bacillus subtilis and Escherichia coli. Diverse kinds of hydrolases including galactosidase, glucosidase, cellulase, lipase, xylanase, and protease were found in the genome of SK3146, coupled with one bacteriocin encoding gene. The complete genome sequence of P. konkukensis SK3146 indicates the immense probiotic potential of the strain with nutrient digestibility and antimicrobial activity functions.

Supplemental material, sj-pdf-1-smm-10.1177_09622802211046385 for Variable selection with missing data in both covariates and outcomes: Imputation and machine learning by Liangyuan Hu, Jung-Yi Joyce Lin and Jiayi Ji in Statistical Methods in Medical Research

Supplemental material, sj-pdf-1-smm-10.1177_09622802211046385 for Variable selection with missing data in both covariates and outcomes: Imputation and machine learning by Liangyuan Hu, Jung-Yi Joyce Lin and Jiayi Ji in Statistical Methods in Medical Research

The aims were to develop an integrated electronic medication reconciliation (ieMR) platform, evaluate its effects on preventing potential duplicated medications, analyze the distribution of the potential duplicated medications by the Anatomical Therapeutic and Chemical (ATC) code for all inpatients, and determine the rate of 30-day medication-related hospital revisits for a geriatric unit. The study was conducted in a tertiary medical center in Taiwan and involved a retrospective quasi pre-intervention (July 1–November 30, 2015) and post-intervention (October 1–December 31, 2016) study design. A multidisciplinary team developed the ieMR platform covering the process from admission to discharge. The ieMR platform included six modules of an enhanced computer physician order entry system (eCPOE), Pharmaceutical-care, Holistic Care, Bedside Display, Personalized Best Possible Medication Discharge Plan, and Pharmaceutical Care Registration System. The ieMR platform prevented the number of potential duplicated medications from pre (25,196 medications, 2.3%) to post (23,413 medications, 3.8%) phases (OR 1.71, 95% CI, 1.68–1.74; p

Background Previous studies have found that Black patients with multiple myeloma undergo autologous stem-cell transplantation (ASCT) less frequently than their white counterparts, although the factors leading to decreased access and utilization have not been fully elucidated. Patients and Methods To identify whether racial differences in transplantation timing played a role in these disparities, we retrospectively analyzed 410 Black and white patients who received their first transplant at The Mount Sinai Hospital between 2011 and 2016 (260 white and 150 Black patients). We compared the time from initial diagnosis to stem-cell collection and the time from collection to transplantation between the 2 races while controlling for age, socioeconomic status, and functional status. Results Between Blacks and whites, time from diagnosis to collection was higher in Black patients (median 238, vs. 195 days, respectively, P = .051). Functional status, socioeconomic status, and age were also significantly associated with time to collection, and after controlling for these covariates, the effect of race was not significant (P = .0625). Conversely, time from collection to transplantation was increased in white patients compared to Black. Conclusion Increased time from diagnosis to stem-cell collection for Black patients was driven in part by socioeconomic status and baseline functional status.

Purpose To assess the therapeutic effects of fursultiamine on choroidal neovascularization (CNV) through its modulation of inflammation and metabolic reprogramming in the retinal pigment epithelium (RPE). Methods The anti-angiogenic effects of fursultiamine were assessed by measuring vascular leakage and CNV lesion size in the laser-induced CNV mouse model. Inflammatory responses were evaluated by quantitative polymerase chain reaction, western blot, and ELISA in both CNV eye tissues and in vitro cell cultures using ARPE-19 cells or primary human RPE (hRPE) cells under lipopolysaccharide (LPS) treatment or hypoxia. Mitochondrial respiration was assessed by measuring oxygen consumption in ARPE-19 cells treated with LPS with or without fursultiamine, and lactate production was measured in ARPE-19 cells subjected to hypoxia with or without fursultiamine. Results In laser-induced CNV, fursultiamine significantly decreased vascular leakage and lesion size, as well as the numbers of both choroidal and retinal inflammatory cytokines, including IL-1β, IL-6, IL-8, and TNF-α. In LPS-treated ARPE-19 cells, fursultiamine decreased proinflammatory cytokine secretion and nuclear factor kappa B phosphorylation. Furthermore, fursultiamine suppressed LPS-induced upregulation of IL-6, IL-8, and monocyte chemoattractant protein-1 in a dose-dependent and time-dependent manner in primary hRPE cells. Interestingly, fursultiamine significantly enhanced mitochondrial respiration in the LPS-treated ARPE-19 cells. Additionally, fursultiamine attenuated hypoxia-induced aberrations, including lactate production and inhibitory phosphorylation of pyruvate dehydrogenase. Furthermore, fursultiamine attenuated hypoxia-induced VEGF secretion and mitochondrial fission in primary hRPE cells that were replicated in ARPE-19 cells. Conclusions Our findings show that fursultiamine is a viable putative therapeutic for neovascular age-related macular degeneration by modulating the inflammatory response and metabolic reprogramming by enhancing mitochondrial respiration in the RPE.

Background While the concept of oligometastatic disease is increasingly recognized as a distinct clinical disease state, the concept of oligoprogression is less well-characterized. Oligoprogression may be particularly relevant in the context of immune checkpoint inhibitors (CPI) given the underlying mechanism of action and insights regarding acquired resistance. In this study, we sought to characterize the incidence of oligoprogression in patients on CPI and explore the impact of local therapy. Materials and Methods We performed a retrospective analysis of all patients with advanced solid tumors (excluding glioblastoma multiforme) who received a PD-1, PD-L1, or CTLA-4 inhibitor at a single institution between 2011 and 2017. Oligoprogression was defined as progression at ≤3 metastatic lesions outside of the brain after achieving at least stable disease on CPI for 3 months. Progression-free survival (PFS) was calculated using the Kaplan-Meier method. Results Among 425 patients treated with CPI, 390 had advanced primary solid tumors outside of the central nervous system. 321 of these patients were evaluable for response, among whom 102 achieved at least stable disease. Oligoprogression was observed in 4.1% of the entire cohort and 15.7% of patients achieving at least stable disease on CPI. Among 16 patients experiencing oligoprogression, 15 received local therapy to the oligoprogressive lesions, many of whom continued CPI. At a median follow-up of 25.8 months, the median PFS for patients with oligoprogression after local therapy was 15.4 months. Conclusions Oligoprogression occurs in a subset of patients after an initial response to CPI. However, patients receiving local therapy to oligoprogressive sites may experience durable disease control. Further study is warranted. Microabstract Oligoprogression was observed in 4.1% of the entire cohort of patients on immune checkpoint inhibitors in this study and 15.7% of patients achieving at least stable disease. Among 16 patients experiencing oligoprogression, 15 received local therapy. At a median follow-up of 25.8 months, the median progression-free survival for patients with oligoprogression after local therapy was 15.4 months and zero patients had died. Oligoprogression occurs in a subset of patients after an initial response to CPI and local therapy to oligoprogressive sites may result in durable disease control.

PURPOSE Discharges against medical advice (DAMA) are associated with adverse patient outcomes among those with epilepsy. Our goal was to examine trends and factors associated with DAMA among those living with epilepsy. METHODS A retrospective cross-sectional study was performed using the 2003-2014 National Inpatient Sample database. ICD-9-CM diagnosis codes were used to identify admissions of patients with epilepsy. Following outcomes were examined among epilepsy patients: proportion and predictors of DAMA, 12-year DAMA trends and causes of admissions. RESULTS In 2014, of the 187,850 admissions in patients with epilepsy, 3783 (2.01 %) were DAMA. Male sex, Black race, younger age, lower household income, Medicaid/self-pay/other as primary payer, lower Elixhauser comorbidities index, weekend admission, non-elective admission, hospital in northeast region, and urban nonteaching hospital were all associated with DAMA. There was a significant increase in the proportion of DAMA in people with epilepsy from 2003 to 2014 (1.13 %-2.01 %, p

The inhibitory effect of fresh or dried daylilies extract on the generation of nitric oxide, and the effects of sulfur dioxide in dried daylily on nitric oxide production and DNA damage in Raw 264.7 macrophages were examined. The extracts from sulfitetreated and untreated dried daylily flowers exhibited stronger scavenging effects on nitric oxide production by sodium nitroprusside (SNP) than that of fresh flower. Fresh and untreated dried daylily flowers exhibited strong inhibitory effect on nitric oxide induced by lipopolysaccharide (LPS) in macrophages, but the sulfite-treated dried daylily did not. The scavenging effect of sulfite on nitric oxide production by SNP was dose-dependent, and sulfite also exhibited low inhibitory effect on nitric oxide induced by LPS in macrophages. Sodium hydrogen sulfite also slightly induced DNA damage in macrophages. Although sulfur dioxide in dried flowers is volatilized during heating, its residual adverse effects should still be concerned.

Background The Oncology Care Model (OCM) was developed as a payment model to encourage participating practices to provide better-quality care for cancer patients at a lower cost. The risk-adjustment model used in OCM is a Gamma generalized linear model (Gamma GLM) with log-link. The predicted value of expense for the episodes identified for our academic medical center (AMC), based on the model fitted to the national data, did not correlate well with our observed expense. This motivated us to fit the Gamma GLM to our AMC data and compare it with two other flexible modeling methods: Random Forest (RF) and Partially Linear Additive Quantile Regression (PLAQR). We also performed a simulation study to assess comparative performance of these methods and examined the impact of non-linearity and interaction effects, two understudied aspects in the field of cost prediction. Methods The simulation was designed with an outcome of cost generated from four distributions: Gamma, Weibull, Log-normal with a heteroscedastic error term, and heavy-tailed. Simulation parameters both similar to and different from OCM data were considered. The performance metrics considered were the root mean square error (RMSE), mean absolute prediction error (MAPE), and cost accuracy (CA). Bootstrap resampling was utilized to estimate the operating characteristics of the performance metrics, which were described by boxplots. Results RF attained the best performance with lowest RMSE, MAPE, and highest CA for most of the scenarios. When the models were misspecified, their performance was further differentiated. Model performance differed more for non-exponential than exponential outcome distributions. Conclusions RF outperformed Gamma GLM and PLAQR in predicting overall and top decile costs. RF demonstrated improved prediction under various scenarios common in healthcare cost modeling. Additionally, RF did not require prespecification of outcome distribution, nonlinearity effect, or interaction terms. Therefore, RF appears to be the best tool to predict average cost. However, when the goal is to estimate extreme expenses, e.g., high cost episodes, the accuracy gained by RF versus its computational costs may need to be considered.