A Comparative Study on the Mechanical Properties of Open-Hole Carbon Fiber-Reinforced Thermoplastic and Thermosetting Composite Materials

In this paper, the damage initiation/propagation mechanisms and failure modes of open-hole carbon fiber-reinforced thermoplastic composites and thermosetting composites with tension, compression, and bearing loads are investigated, respectively, by experiments and finite element simulations. The experimental evaluations are performed on the specimens using the Combined Loading Compression (CLC) test method, the tensile test method, and the single-shear test method. The differences in macroscopic damage initiation, evolution mode, and damage characteristics between thermoplastic composite materials and thermosetting composite material open-hole structures are obtained and analyzed under compressive load. Based on scanning electron microscope SEM images, a comparative analysis is conducted on the micro-failure modes of fibers, matrices, and fiber/matrix interfaces in the open-hole structures of thermoplastic and thermosetting composites under compressive load. The differences between thermoplastic and thermosetting composites were analyzed from the micro-failure mechanism. Finally, based on continuum damage mechanics (CDM), a damage model is also developed for predicting the initiation and propagation of damage in thermoplastic composites. The model, which can capture fiber breakage and matrix crack, as well as the nonlinear response, is used to conduct virtual compression tests, tensile test, and single-shear test, respectively. Numerical simulation results are compared with the extracted experimental results. The displacement-load curve and failure modes match the experimental result, which indicates that the finite element model has good reliability.

Development and Validation of a Prognostic Classification Model Predicting Postoperative Adverse Outcomes in Older Surgical Patients Using a Machine Learning Algorithm: Retrospective Observational Network Study

BACKGROUND

Older adults are at an increased risk of postoperative morbidity. Numerous risk stratification tools exist, but effort and manpower are required.

OBJECTIVE

This study aimed to develop a predictive model of postoperative adverse outcomes in older patients following general surgery with an open-source, patient-level prediction from the Observational Health Data Sciences and Informatics for internal and external validation.

METHODS

We used the Observational Medical Outcomes Partnership common data model and machine learning algorithms. The primary outcome was a composite of 90-day postoperative all-cause mortality and emergency department visits. Secondary outcomes were postoperative delirium, prolonged postoperative stay (≥75th percentile), and prolonged hospital stay (≥21 days). An 80% versus 20% split of the data from the Seoul National University Bundang Hospital (SNUBH) and Seoul National University Hospital (SNUH) common data model was used for model training and testing versus external validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) with a 95% CI.

RESULTS

Data from 27,197 (SNUBH) and 32,857 (SNUH) patients were analyzed. Compared to the random forest, Adaboost, and decision tree models, the least absolute shrinkage and selection operator logistic regression model showed good internal discriminative accuracy (internal AUC 0.723, 95% CI 0.701-0.744) and transportability (external AUC 0.703, 95% CI 0.692-0.714) for the primary outcome. The model also possessed good internal and external AUCs for postoperative delirium (internal AUC 0.754, 95% CI 0.713-0.794; external AUC 0.750, 95% CI 0.727-0.772), prolonged postoperative stay (internal AUC 0.813, 95% CI 0.800-0.825; external AUC 0.747, 95% CI 0.741-0.753), and prolonged hospital stay (internal AUC 0.770, 95% CI 0.749-0.792; external AUC 0.707, 95% CI 0.696-0.718). Compared with age or the Charlson comorbidity index, the model showed better prediction performance.

CONCLUSIONS

The derived model shall assist clinicians and patients in understanding the individualized risks and benefits of surgery.

Cloud-based digital healthcare development for precision medical hospital information system

Aim: This study aims to develop a cloud-based digital healthcare system for precision medical hospital information systems (P-HIS). Methods: In 2020, international standardization of P-HIS clinical terms and codes was performed. In 2021, South Korea's first tertiary hospital cloud was established and implemented successfully. Results: P-HIS was applied at Korea's first tertiary general hospital. Common data model-compatible precision medicine/medical service solutions were developed for medical support. Ultrahigh-quality medical data for precision medicine were acquired and built using big data. Joint global commercialization and dissemination/spreading were achieved using the P-HIS consortium and global common data model-based observational medical outcome partnership network. Conclusion: To provide personalized precision medical services in the future, establishing and using big medical data is essential.

Graphene-Based ESD Protection for Future ICs

On-chip electrostatic discharge (ESD) protection is required for all integrated circuits (ICs). Conventional on-chip ESD protection relies on in-Si PN junction-based device structures for ESD. However, such in-Si PN-based ESD protection solutions pose significant challenges related to ESD protection design overhead, including parasitic capacitance, leakage current, and noises, as well as large chip area consumption and difficulty in IC layout floor planning. The design overhead effects of ESD protection devices are becoming unacceptable to modern ICs as IC technologies continuously advance, which is an emerging design-for-reliability challenge for advanced ICs. In this paper, we review the concept development of disruptive graphene-based on-chip ESD protection comprising a novel graphene nanoelectromechanical system (gNEMS) ESD switch and graphene ESD interconnects. This review discusses the simulation, design, and measurements of the gNEMS ESD protection structures and graphene ESD protection interconnects. The review aims to inspire non-traditional thinking for future on-chip ESD protection.

High cysteine concentrations in cell culture media lead to oxidative stress and reduced bioprocess performance of recombinant CHO cells

Cysteine is considered an essential amino acid in the cultivation of Chinese hamster ovary (CHO) cells. An optimized cysteine supply during fed-batch cultivation supports the protein production capacity of recombinant CHO cell lines. However, we observed that CHO production cell lines seeded at low cell densities in chemically defined media enriched with, cysteine greater than 2.5 mM resulted in markedly reduced cell growth during passaging, hampering seed train performance and scale-up. To investigate the underlying mechanism, seeding cell densities and initial cysteine concentrations ranging from low to high cysteine concentrations were varied followed by an analysis of cell culture performance. Additionally, cell cycle analysis, intracellular quantification of reactive oxygen species (ROS) as well as transcriptomic analyses by next-generation sequencing were carried out. Our results demonstrate that CHO cells seeded at low cell densities at high initial cysteine concentrations encountered increased oxidative stress leading to a p21-mediated cell cycle arrest in the G1/S phase. The resulting oxidative stress caused redox imbalance in the endoplasmic reticulum and activation of the unfolded protein response as well as the major antioxidant nuclear factor-like 2 response pathways. We were able to identify potential signature genes related to oxidative stress and the inhibition of the pentose phosphate pathway. Finally, we present that seeding cells at a higher concentration counteract oxidative stress in cysteine-enriched cell culture media. This article is protected by copyright. All rights reserved.

Beyond the UNFCCC North-South divide: How newly industrializing countries collaborate to innovate in climate technologies

While the Global North is historically responsible for the majority of greenhouse gas emissions in the atmosphere, Newly Industrializing Countries (NICs) are expected to overtake developed country emissions in the coming years. At the same time, NICs are climbing the ladder of the global economy, increasing their competitiveness on the global stage and catching up with technological competencies of developed economies. Against this background, this paper explores innovation and collaboration in Climate Change Mitigation Technologies (CCMTs) in NICs. The research question is whether the propensity to innovate and diffuse CCMTs is impacted by technological collaboration with two highly developed countries, Germany and The United States. The sample of NICs includes the BRICS (Brazil, Russia, India, China, South Africa) plus Israel, Mexico, and Turkey, in a panel from 1995 to 2015. The empirical results suggest that collaboration with both Germany and the U.S. is highly significant for domestic CCMT innovation in NICs. These findings are important because, stepping beyond the literature on the merits and drawbacks of global climate governance tools such as the Clean Development Mechanism (CDM) and related UNFCCC processes, they show that collaboration for climate and environmental technologies could become a key tool to significantly improve the chances to stay in line with the Paris Climate Agreement. Finally, the policy advice for NICs and developing countries is to, above all else, focus on incubating strong technological innovation systems, including strengthening domestic Intellectual Property Rights (IPR), as well as to enhance technological collaboration with developed countries.

A Tutorial on Cognitive Diagnosis Modeling for Characterizing Mental Health Symptom Profiles Using Existing Item Responses

In research applications, mental health problems such as alcohol-related problems and depression are commonly assessed and evaluated using scale scores or latent trait scores derived from factor analysis or item response theory models. This tutorial paper demonstrates the use of cognitive diagnosis models (CDMs) as an alternative approach to characterizing mental health problems of young adults when item-level data are available. Existing measurement approaches focus on estimating the general severity of a given mental health problem at the scale level as a unidimensional construct without accounting for other symptoms of related mental health problems. The prevailing approaches may ignore clinically meaningful presentations of related symptoms at the item level. The current study illustrates CDMs using item-level data from college students (40 items from 719 respondents; 34.6% men, 83.9% White, and 16.3% first-year students). Specifically, we evaluated the constellation of four postulated domains (i.e., alcohol-related problems, anxiety, hostility, and depression) as a set of attribute profiles using CDMs. After accounting for the impact of each attribute (i.e., postulated domain) on the estimates of attribute profiles, the results demonstrated that when items or attributes have limited information, CDMs can utilize item-level information in the associated attributes to generate potentially meaningful estimates and profiles, compared to analyzing each attribute independently. We introduce a novel visual inspection aid, the lens plot, for quantifying this gain. CDMs may be a useful analytical tool to capture respondents' risk and resilience for prevention research.

Real-world incidence of endophthalmitis after intravitreal anti-VEGF injection: Common Data Model in ophthalmology

OBJECTIVES

The aim of this study was to evaluate the real-world incidence of endophthalmitis after intravitreal anti-vascular endothelial growth factor (VEGF) injections using data from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).

METHODS

Patients with endophthalmitis that developed within 6 weeks after intravitreal anti-VEGF injections were identified in 3 large OMOP CDM databases.

RESULTS

We identified 23,490 patients who received 128,123 intravitreal anti-VEGF injections. The incidence rates of endophthalmitis were 15.75 per 10,000 patients and 2.97 per 10,000 injections. The incidence rates of endophthalmitis for bevacizumab, ranibizumab, and aflibercept (per 10,000 injections) were 3.64, 1.39, and 0.76, respectively. The annual incidence has remained below 5.00 per 10,000 injections since 2011 despite the increasing number of intravitreal anti-VEGF injections. Bevacizumab presented a higher incidence rate for endophthalmitis than ranibizumab and aflibercept (incidence rate ratio, 3.17; p=0.021).

CONCLUSIONS

The incidence of endophthalmitis after intravitreal anti-VEGF injections has stabilized since 2011 despite the explosive increase in anti-VEGF injections. The off-label use of bevacizumab accounted for its disproportionately high incidence of endophthalmitis. The OMOP CDM, which includes off-label uses, laboratory data, and a scalable standardized database, could provide a novel strategy to reveal real-world evidence, especially in ophthalmology.

Towards Biomanufacturing of Cell-Derived Matrices

Cell-derived matrices (CDM) are the decellularised extracellular matrices (ECM) of tissues obtained by the laboratory culture process. CDM is developed to mimic, to a certain extent, the properties of the needed natural tissue and thus to obviate the use of animals. The composition of CDM can be tailored for intended applications by carefully optimising the cell sources, culturing conditions and decellularising methods. This unique advantage has inspired the increasing use of CDM for biomedical research, ranging from stem cell niches to disease modelling and regenerative medicine. However, while much effort is spent on extracting different types of CDM and exploring their utilisation, little is spent on the scale-up aspect of CDM production. The ability to scale up CDM production is essential, as the materials are due for clinical trials and regulatory approval, and in fact, this ability to scale up should be an important factor from the early stages. In this review, we first introduce the current CDM production and characterisation methods. We then describe the existing scale-up technologies for cell culture and highlight the key considerations in scaling-up CDM manufacturing. Finally, we discuss the considerations and challenges faced while converting a laboratory protocol into a full industrial process. Scaling-up CDM manufacturing is a challenging task since it may be hindered by technologies that are not yet available. The early identification of these gaps will not only quicken CDM based product development but also help drive the advancement in scale-up cell culture and ECM extraction.

Robust Controller Design for Multi-Input Multi-Output Systems Using Coefficient Diagram Method

The coupling between variables in the multi-input multi-output (MIMO) systems brings difficulties to the design of the controller. Aiming at this problem, this paper combines the particle swarm optimization (PSO) with the coefficient diagram method (CDM) and proposes a robust controller design strategy for the MIMO systems. The decoupling problem is transformed into a compensator parameter optimization problem, and PSO optimizes the compensator parameters to reduce the coupling effect in the MIMO systems. For the MIMO system with measurement noise, the effectiveness of CDM in processing measurement noise is analyzed. This paper gives the control design steps of the MIMO systems. Finally, simulation experiments of four typical MIMO systems demonstrate the effectiveness of the proposed method.

Balancing fit and parsimony to improve Q-matrix validation

The Q-matrix identifies the subset of attributes measured by each item in the cognitive diagnosis modelling framework. Usually constructed by domain experts, the Q-matrix might contain some misspecifications, disrupting classification accuracy. Empirical Q-matrix validation methods such as the general discrimination index (GDI) and Wald have shown promising results in addressing this problem. However, a cut-off point is used in both methods, which might be suboptimal. To address this limitation, the Hull method is proposed and evaluated in the present study. This method aims to find the optimal balance between fit and parsimony, and it is flexible enough to be used either with a measure of item discrimination (the proportion of variance accounted for, PVAF) or a coefficient of determination (pseudo-R² ). Results from a simulation study showed that the Hull method consistently showed the best performance and shortest computation time, especially when used with the PVAF. The Wald method also performed very well overall, while the GDI method obtained poor results when the number of attributes was high. The absence of a cut-off point provides greater flexibility to the Hull method, and it places it as a comprehensive solution to the Q-matrix specification problem in applied settings. This proposal is illustrated using real data.

An OMOP-CDM based pharmacovigilance data-processing pipeline (PDP) providing active surveillance for ADR signal detection from real-world data sources

Background

Adverse drug reactions (ADRs) are regarded as a major cause of death and a major contributor to public health costs. For the active surveillance of drug safety, the use of real-world data and real-world evidence as part of the overall pharmacovigilance process is important. In this regard, many studies apply the data-driven approaches to support pharmacovigilance. We developed a pharmacovigilance data-processing pipeline (PDP) that utilized electronic health records (EHR) and spontaneous reporting system (SRS) data to explore pharmacovigilance signals.

Methods

To this end, we integrated two medical data sources: Konyang University Hospital (KYUH) EHR and the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). As part of the presented PDP, we converted EHR data on the Observation Medical Outcomes Partnership (OMOP) data model. To evaluate the ability of using the proposed PDP for pharmacovigilance purposes, we performed a statistical validation using drugs that induce ear disorders.

Results

To validate the presented PDP, we extracted six drugs from the EHR that were significantly involved in ADRs causing ear disorders: nortriptyline, (hazard ratio [HR] 8.06, 95% CI 2.41–26.91); metoclopramide (HR 3.35, 95% CI 3.01–3.74); doxycycline (HR 1.73, 95% CI 1.14–2.62); digoxin (HR 1.60, 95% CI 1.08–2.38); acetaminophen (HR 1.59, 95% CI 1.47–1.72); and sucralfate (HR 1.21, 95% CI 1.06–1.38). In FAERS, the strongest associations were found for nortriptyline (reporting odds ratio [ROR] 1.94, 95% CI 1.73–2.16), sucralfate (ROR 1.22, 95% CI 1.01–1.45), doxycycline (ROR 1.30, 95% CI 1.20–1.40), and hydroxyzine (ROR 1.17, 95% CI 1.06–1.29). We confirmed the results in a meta-analysis using random and fixed models for doxycycline, hydroxyzine, metoclopramide, nortriptyline, and sucralfate.

Conclusions

The proposed PDP could support active surveillance and the strengthening of potential ADR signals via real-world data sources. In addition, the PDP was able to generate real-world evidence for drug safety.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01520-y.

Media on-demand: Continuous reconstitution of a chemically defined media directly from solids

Chemically defined media are reconstituted batchwise and stored in hold tanks until use. To avoid large hold tanks and batchwise production of media, we developed continuous on‐demand reconstitutions directly from solids consisting of a hopper and a screw conveyor capable of feeding dry powdered media with the required precision ±5% at low dosing rates of 0.171 g min⁻¹. A commercially available dry powdered cell culture medium was continuously fed over a duration of 12 h into a mixer which was connected to a UV‐cell for monitoring and the media were compared to a batchwise production. A comparable amino acid, carbohydrate, and osmolality profile to a batchwise reconstitution could be obtained. Cell cultivation showed comparable performance of batch and continuous reconstitution for two CHO cell lines producing the antibodies adalimumab and trastuzumab on a small and benchtop scale. In‐depth analysis of the produced antibodies showed the same glycosylation pattern, other posttranslational profiles such as methionine oxidation and deamidation compared to batchwise reconstitution. Therefore, we conclude a continuous reconstitution of the medium results in the same quality of the product. A continuous on‐demand media reconstitution will impact the supply chain and significantly reduce the floor space necessary for preparation and storage.

Natural Products for the Treatment of Neurodegenerative Diseases

Neurodegenerative diseases are a heterogeneous group of disorders characterized by the progressive degeneration of the structure and function of the central nervous system or peripheral nervous system. Alzheimer's Disease (AD), Parkinson's Disease (PD) and Spinal Cord Injury (SCI) are the common neurodegenerative diseases, which typically occur in people over the age of 60. With the rapid development of an aged society, over 60 million people worldwide are suffering from these uncurable diseases. Therefore, the search for new drugs and therapeutic methods has become an increasingly important research topic. Natural products especially those from the Traditional Chinese Medicines (TCMs), are the most important sources of drugs, and have received extensive interest among pharmacist. In this review, in order to facilitate further chemical modification of those useful natural products by pharmacists, we will bring together recent studies in single natural compound from TCMs with neuroprotective effect.

Improving Robustness in Q-Matrix Validation Using an Iterative and Dynamic Procedure

In the context of cognitive diagnosis models (CDMs), a Q-matrix reflects the correspondence between attributes and items. The Q-matrix construction process is typically subjective in nature, which may lead to misspecifications. All this can negatively affect the attribute classification accuracy. In response, several methods of empirical Q-matrix validation have been developed. The general discrimination index (GDI) method has some relevant advantages such as the possibility of being applied to several CDMs. However, the estimation of the GDI relies on the estimation of the latent group sizes and success probabilities, which is made with the original (possibly misspecified) Q-matrix. This can be a problem, especially in those situations in which there is a great uncertainty about the Q-matrix specification. To address this, the present study investigates the iterative application of the GDI method, where only one item is modified at each step of the iterative procedure, and the required cutoff is updated considering the new parameter estimates. A simulation study was conducted to test the performance of the new procedure. Results showed that the performance of the GDI method improved when the application was iterative at the item level and an appropriate cutoff point was used. This was most notable when the original Q-matrix misspecification rate was high, where the proposed procedure performed better 96.5% of the times. The results are illustrated using Tatsuoka's fraction-subtraction data set.

Bibliometric Analysis of Current Drug Metabolism: The Twentieth Anniversary from 2000-2019

BACKGROUND

Scopus is regularly covering Current Drug Metabolism from 2000 onwards.

OBJECTIVE

The major objective is to perform the 1st bibliometric analysis of Current Drug Metabolism (CDM).

METHODS

The data was retrieved from Scopus in April-May 2020 for detail analysis.

RESULTS

The total number of publications was found to be 1551, with 955 reviews (61.57%) and 466 articles (30.05%). From 2000 onwards, we calculated the relative growth rate and doubling time. Based on the number of publications, total 4418 authors, 3235 institutions and 83 countries were directly involved in all publications. M.A. Kamal is the highly productive scientist with fifty-three (53 or 3.73%) publications, King Abdulaziz University is the top university with the highest number of publications (58 or 4.13%) and the USA is the top-ranked country with 365 publications (25.96%). We also provided the h-index, total citations (TC), h-index without self-citations (WSC) and total WSC of the top ten authors, universities and countries. In citations analysis, Prof. Zhou S.F. was the top scientist with the highest (1594) number of citations. In institutional category Department of Drug Metabolism, Merck Research Laboratories, Rahway, United States, is the top ranked institutes with 654 total citations. While, United States is the top-ranked country with 18409 total citations. In co-words analysis, 3387, 30564 and 17333 terms in titles of the manuscripts, abstracts and keywords were recorded, respectively. This indicated that CDM principally focused on understanding drug development ranging from its efficacy to delivery, metabolism, distribution, safety and mechanism of actions. Similarly, various specific drugs were thoroughly discussed in publications. Various enzymatic, genetics, proteins and cancer-related aspects were also described. For data presentations, we used VOSviewer graphical maps.

CONCLUSION

The data confirm that CDM showed continuous growth in the number of publications and citations. However significant measures are needed to make overall progress and improve the rankings in relevant categories.

Revisiting the land use assumptions in forest carbon projects through a case from India

Forest carbon projects are based on the premise of stable land use over long durations generally ranging from 20 to 100 years. This narrative is based on two assumptions- first, that project will be able to offset the opportunity cost of land and labour through carbon and other benefits and second assumption being that social, economic, political and biophysical factors will remain largely stable and not affect the land use significantly. This research critically evaluates these assumptions with the help of empirical evidence from a project in Haryana in India. The analysis suggests that various socioeconomic factors such as the development context of the region, changes in crop, wage and carbon prices have increased the opportunity cost of land manifold. Similarly, factors such as increased uncertainty over resource rights and biophysical factors have adversely affected the land use choices and project viability. Around 50% of the participants have changed the land use affecting the project viability. The opportunity cost of participation has been unevenly high for marginal and poor participants, who are worst affected. Hence, it can be argued that these universal land use assumptions of forest carbon projects are questionable. These findings have implications for the role and design of forest carbon projects in the broad context of climate mitigation. The land use change and forestry options through market based forest carbon projects might not offer as big a mitigation opportunity as is generally projected in the literature due to changing opportunity costs of land, complex property rights and other factors. Hence it might require recalibration of the role of these projects in the mitigation strategies at local,national and global levels. Similarly, the forest carbon projects need to be designed based on a comprehensive assessment of land use, which considers not only a dynamic and differential opportunity cost across various stakeholders, but also other sociocultural and political factors, which can impact the land use and sustainability of the project.

Fetal Macrocephaly: A Novel Sonographic Finding in Congenital Myotonic Dystrophy

Objective  Sonographic clues to the diagnosis of congenital myotonic dystrophy (CDM) are limited, particularly in the absence of family history of myotonic dystrophy (DM). We reviewed cases of CDM for unique prenatal findings. Study Design  A single-center case series of fetuses with CMD with characteristic prenatal findings confirmed postnatally. Results  Four fetuses with pre- or postnatally diagnosed CDM presented with macrocephaly in utero. While head measurements were appropriate for gestational age until midgestation, third-trimester head circumference and biparietal diameter were both >2 standard deviation (SD) above the mean in all. Abdominal and femur measurements were otherwise appropriate for gestation. Postnatally, the occipitofrontal circumference was >2 SD above the mean in all, confirming the diagnosis of macrocephaly. Conclusion  CDM should be included in the differential diagnosis of third-trimester macrocephaly, especially in the presence of additional sonographic clues and when maternal medical history and physical examination are suggestive of DM.

Reconsidering Cutoff Points in the General Method of Empirical Q-Matrix Validation

Cognitive diagnosis models (CDMs) are latent class multidimensional statistical models that help classify people accurately by using a set of discrete latent variables, commonly referred to as attributes. These models require a Q-matrix that indicates the attributes involved in each item. A potential problem is that the Q-matrix construction process, typically performed by domain experts, is subjective in nature. This might lead to the existence of Q-matrix misspecifications that can lead to inaccurate classifications. For this reason, several empirical Q-matrix validation methods have been developed in the recent years. de la Torre and Chiu proposed one of the most popular methods, based on a discrimination index. However, some questions related to the usefulness of the method with empirical data remained open due the restricted number of conditions examined, and the use of a unique cutoff point (EPS) regardless of the data conditions. This article includes two simulation studies to test this validation method under a wider range of conditions, with the purpose of providing it with a higher generalization, and to empirically determine the most suitable EPS considering the data conditions. Results show a good overall performance of the method, the relevance of the different studied factors, and that using a single indiscriminate EPS is not acceptable. Specific guidelines for selecting an appropriate EPS are provided in the discussion.

Orofacial strength, dysarthria, and dysphagia in congenital myotonic dystrophy

INTRODUCTION

Herein we present an exploratory study of orofacial function in children with congenital myotonic dystrophy (CDM) vs. healthy controls.

METHODS

We evaluated 41 children with CDM and 29 healthy controls for speech and swallow function and for lingual and labial strength.

RESULTS

The Iowa Oral Performance Instrument (IOPI), measuring tongue strength, and a lip force meter (LFM), measuring lip strength, had excellent interrater reliability with intraclass correlation coefficients (ICCs) of 0.75 (n = 19, P < 0.001) and 0.96 (n = 20, P < 0.001), respectively. Mean overall lingual strength was 3.5-fold less and labial strength was about 7-fold less in CDM patients than in healthy controls. Eighteen of 24 children with CDM demonstrated dysarthria and an additional 11 participants were nonverbal. Dysarthria correlated moderately with lingual strength, age, and dysphagia. Strength measures correlated moderately with dysphagia.

DISCUSSION

Children with CDM have impaired orofacial functioning that affects communication and swallowing. Reliability of strength measures may be useful for future therapeutic trials. Muscle Nerve 58: 413-417, 2018.

Learning to Share Health Care Data: A Brief Timeline of Influential Common Data Models and Distributed Health Data Networks in U.S. Health Care Research

The last twenty years of health care research has seen a steady stream of common health care data models implemented for multi-organization research. Each model offers a uniform interface on data from the diverse organizations that implement them, enabling the sharing of research tools and data. While the groups designing the models have had various needs and aims, and the data available has changed significantly in this time, there are nevertheless striking similarities between them. This paper traces the evolution of common data models, describing their similarities and points of departure. We believe the history of this work should be understood and preserved. The work has empowered collaborative research across competing organizations and brought together researchers from clinical practice, universities and research institutes around the planet. Understanding the eco-system of data models designed for collaborative research allows readers to evaluate where we have been, where we are going as a field, and to evaluate the utility of different models to their own work.

Analysis of Iron Requirements and Siderophore Production

This chapter describes the methods for inducing, detecting, and purifying the Legionella pneumophila siderophore. The first protocol details the methods by which L. pneumophila is cultured to facilitate production of the siderophore, rhizoferrin. This chapter then describes how to purify siderophore from culture supernatants through sequential reversed-phase/weak-anion exchange chromatography and high-performance liquid chromatography. The next section describes assays which allow the detection of the iron-binding capability and the biological activity of the purified siderophore. Lastly, this chapter describes the growth of L. pneumophila in chemically defined liquid medium (CDM) containing various iron sources as a method to assess the iron requirements of L. pneumophila.

Introducing the General Polytomous Diagnosis Modeling Framework

Although considerable developments have been added to the cognitive diagnosis modeling literature recently, most have been conducted for dichotomous responses only. This research proposes a general cognitive diagnosis model for polytomous responses—the general polytomous diagnosis model (GPDM), which combines the G-DINA modeling process for dichotomous responses with the item-splitting process for polytomous responses. The polytomous items are specified similar to dichotomous items in the Q-matrix, and the MML estimation is implemented using an EM algorithm. Under the general framework, different saturated forms, and some reduced forms, can be transformed linearly. Model assessment and adjustment under the dichotomous context can be extended to polytomous responses. This simulation study demonstrates the effectiveness of the model when comparing the two response types. The real-data example further illustrates how the proposed model can make a difference in practice.

Effect of a rehabilitation-based chronic disease management program targeting severe COPD exacerbations on readmission patterns

Background

Pulmonary rehabilitation (PR) is recommended after a severe COPD exacerbation, but its short- and long-term effects on health care utilization have not been fully established.

Aims

The aims of this study were to evaluate patient compliance with a chronic disease management (CDM) program incorporating home-based exercise training as the main component after a severe COPD exacerbation and to determine its effects on health care utilization in the following year.

Materials and methods

COPD patients with a severe exacerbation were included in a case-cohort study at admission. An intervention group participated in a nurse-supervised CDM program during the 2 months after discharge, comprising of home-based PR with exercise components directly supervised by a physiotherapist, while the remaining patients followed usual care.

Results

Nineteen of the twenty-one participants (90.5%) were compliant with the CDM program and were compared with 29 usual-care patients. Compliance with the program was associated with statistically significant reductions in admissions due to respiratory disease in the following year (median [interquartile range]: 0 [0–1] vs 1 [0–2.5]; P=0.022) and in days of admission (0 [0–7] vs 7 [0–12]; P=0.034), and multiple linear regression analysis confirmed the protective effect of the CDM program (β coefficient −0.785, P=0.014, and R²=0.219).

Conclusion

A CDM program incorporating exercise training for COPD patients without limiting comorbidities after a severe exacerbation achieves high compliance and reduces admissions in the year following after the intervention.

Emission factors of health- and climate-relevant pollutants measured in home during a carbon-finance-approved cookstove intervention in rural India

We present results of an emission characterization effort, completed as part of a larger intervention trial, of a carbon-finance-approved program replacing traditional cookstoves with "rocket"-style natural draft stoves. The 100 emission tests were conducted across 31 households in control and intervention groups, with repeated tests in most households during preintervention and postintervention periods. While mean fine particulate matter (PM_2.5) emission factor for intervention stoves was significantly lower than for traditional stoves in baseline measurements, they were only marginally lower than traditional stoves during follow-up. Intervention stove PM_2.5 emissions had a larger contribution from light-absorbing (elemental) carbon than traditional stoves. Repeated measurements in control households provide evidence for strong seasonality, likely due to differences in fuel moisture/types, in traditional stove emissions, with important implications for study design. Seasonality observed in control household emission factors (baseline > follow-up) was in the opposite direction as that observed in indoor PM_2.5 concentrations (baseline < follow-up), highlighting that seasonally varying conditions (e.g., ventilation rates) may modify the link between emissions and exposures. Emission factor differences in paired (pre/post) tests from the same households were similar to differences in the medians of entire groups, suggesting variability is dominated by test-to-test variation. Emission reductions from intervention stoves were significantly smaller than laboratory performance would suggest or that are required to strongly reduce exposures. Field emissions assessment like that presented here should be prioritized early in technology assessment and development to provide rigorous estimates of the benefits reasonably expected from interventions with the potential for substantial benefits to human health and the environment.

The declared barriers of the large developing countries waste management projects: The STAR model

The aim of this study is to investigate and describe the barriers system that precludes the feasibility, or limits the performance of the waste management projects through the analysis of which are the declared barriers at the 432 large waste management projects registered as CDM during the period 2004-2014. The final product is a waste management barriers conceptual model proposal (STAR), supported by literature and corroborated by projects design documents. This paper uses the computer assisted qualitative content analysis (CAQCA) methodology with the qualitative data analysis (QDA) software NVivo®, by 890 fragments, to investigate the motives to support our conclusions. Results suggest the main barriers classification in five types: sociopolitical, technological, regulatory, financial, and human resources constraints. Results also suggest that beyond the waste management industry, projects have disadvantages added related to the same barriers inherent to others renewable energies initiatives. The STAR model sheds some light over the interactivity and dynamics related to the main constraints of the industry, describing the mutual influences and relationships among each one. Future researches are needed to better and comprehensively understand these relationships and ease the development of tools to alleviate or eliminate them.

Model Similarity, Model Selection, and Attribute Classification

Selecting the most appropriate cognitive diagnosis model (CDM) for an item is a challenging process. Although general CDMs provide better model-data fit, specific CDMs have more straightforward interpretations, are more stable, and can provide more accurate classifications when used correctly. Recently, the Wald test has been proposed to determine at the item level whether a general CDM can be replaced by specific CDMs without a significant loss in model-data fit. The current study examines the practical consequence of the test by evaluating whether the attribute-vector classification based on CDMs selected by the Wald test is better than that based on general CDMs. Although the Wald test can detect the true underlying model for certain CDMs, it is yet unclear how effective it is at distinguishing among the wider range of CDMs found in the literature. This study investigates the relative similarity of the various CDMs through the use of the newly developed dissimiliarity index, and explores the implications for the Wald test. Simulations show that the Wald test cannot distinguish among additive models due to their inherent similarity, but this does not impede the ability of the test to provide higher correct classification rates than general CDMs, particularly when the sample size is small and items are of low quality. An empirical example is included to demonstrate the viability of the procedure.

A correction in the CDM methodological tool for estimating methane emissions from solid waste disposal sites

Solid waste disposal sites (SWDS) - especially landfills - are a significant source of methane, a greenhouse gas. Although having the potential to be captured and used as a fuel, most of the methane formed in SWDS is emitted to the atmosphere, mainly in developing countries. Methane emissions have to be estimated in national inventories. To help this task the Intergovernmental Panel on Climate Change (IPCC) has published three sets of guidelines. In addition, the Kyoto Protocol established the Clean Development Mechanism (CDM) to assist the developed countries to offset their own greenhouse gas emissions by assisting other countries to achieve sustainable development while reducing emissions. Based on methodologies provided by the IPCC regarding SWDS, the CDM Executive Board has issued a tool to be used by project developers for estimating baseline methane emissions in their project activities - on burning biogas from landfills or on preventing biomass to be landfilled and so avoiding methane emissions. Some inconsistencies in the first two IPCC guidelines have already been pointed out in an Annex of IPCC latest edition, although with hidden details. The CDM tool uses a model for methane estimation that takes on board parameters, factors and assumptions provided in the latest IPCC guidelines, while using in its core equation the one of the second IPCC edition with its shortcoming as well as allowing a misunderstanding of the time variable. Consequences of wrong ex-ante estimation of baseline emissions regarding CDM project activities can be of economical or environmental type. Example of the first type is the overestimation of 18% in an actual project on biogas from landfill in Brazil that harms its developers; of the second type, the overestimation of 35% in a project preventing municipal solid waste from being landfilled in China, which harms the environment, not for the project per se but for the undue generated carbon credits. In a simulated landfill - the same amount of waste for 20 years -, the error would be an overestimation of 25% if the CDM project activity starts from the very first year or an underestimation of 15% if it starts just after the landfill closure. Therefore, a correction in the tool to calculate emissions from landfills as adopted by the CDM Executive Board is needed. Moreover, in countries not using the latest IPCC guidelines, which provides clear formulas to prevent misunderstandings, inventory compilers can also benefit from this paper by having more accurate results in national GHG inventories related to solid waste disposal, especially when increasing amounts of waste are landfilled, which is the case of the developing countries.

The financial attractiveness assessment of large waste management projects registered as clean development mechanism

This study illustrates the financial analyses for demonstration and assessment of additionality presented in the project design (PDD) and enclosed documents of the 431 large Clean Development Mechanisms (CDM) classified as the 'waste handling and disposal sector' (13) over the past ten years (2004-2014). The expected certified emissions reductions (CER) of these projects total 63.54 million metric tons of CO2eq, where eight countries account for 311 projects and 43.36 million metric tons. All of the projects declare themselves 'not financially attractive' without CER with an estimated sum of negative results of approximately a half billion US$. The results indicate that WM benchmarks and indicators are converging and reducing in variance, and the sensitivity analysis reveals that revenues have a greater effect on the financial results. This work concludes that an extensive financial database with simple standards for disclosure would greatly diminish statement problems and make information more comparable, reducing the risk and capital costs of WM projects.

Contribution of cooperative sector recycling to greenhouse gas emissions reduction: a case study of Ribeirão Pires, Brazil

Solid waste, including municipal waste and its management, is a major challenge for most cities and among the key contributors to climate change. Greenhouse gas emissions can be reduced through recovery and recycling of resources from the municipal solid waste stream. In São Paulo, Brazil, recycling cooperatives play a crucial role in providing recycling services including collection, separation, cleaning, stocking, and sale of recyclable resources. The present research attempts to measure the greenhouse gas emission reductions achieved by the recycling cooperative Cooperpires, as well as highlight its socioeconomic benefits. Methods include participant observation, structured interviews, questionnaire application, and greenhouse gas accounting of recycling using a Clean Development Mechanism methodology. The results show that recycling cooperatives can achieve important energy savings and reductions in greenhouse gas emissions, and suggest there is an opportunity for Cooperpires and other similar recycling groups to participate in the carbon credit market. Based on these findings, the authors created a simple greenhouse gas accounting calculator for recyclers to estimate their emissions reductions.

Efficient definitive endoderm induction from mouse embryonic stem cell adherent cultures: a rapid screening model for differentiation studies

Definitive endoderm (DE) differentiation from mouse embryonic stem cell (mESC) monolayer cultures has been limited by poor cell survival or low efficiency. Recently, a combination of TGFβ and Wnt activation with BMP inhibition improved DE induction in embryoid bodies cultured in suspension. Based on these observations we developed a protocol to efficiently induce DE cells in monolayer cultures of mESCs. We obtained a good cell yield with 54.92% DE induction as shown by Foxa2, Sox17, Cxcr4 and E-Cadherin expression. These DE-cells could be further differentiated into posterior foregut and pancreatic phenotypes using a culture protocol initially developed for human embryonic stem cell (hESC) differentiation. In addition, this mESC-derived DE gave rise to hepatocyte-like cells after exposure to BMP and FGF ligands. Our data therefore indicate a substantial improvement of monolayer DE induction from mESCs and support the concept that differentiation conditions for mESC-derived DE are similar to those for hESCs. As mESCs are easier to maintain and manipulate in culture compared to hESCs, and considering the shorter duration of embryonic development in the mouse, this method of efficient DE induction on monolayer will promote the development of new differentiation protocols to obtain DE-derivatives, like pancreatic beta-cells, for future use in cell replacement therapies.

Octopaminergic modulation of contrast sensitivity

Sensory systems adapt to prolonged stimulation by decreasing their response to continuous stimuli. Whereas visual motion adaptation has traditionally been studied in immobilized animals, recent work indicates that the animal's behavioral state influences the response properties of higher-order motion vision-sensitive neurons. During insect flight octopamine is released, and pharmacological octopaminergic activation can induce a fictive locomotor state. In the insect optic ganglia, lobula plate tangential cells (LPTCs) spatially pool input from local elementary motion detectors (EMDs) that correlate luminosity changes from two spatially discrete inputs after delaying the signal from one. The LPTC velocity optimum thereby depends on the spatial separation of the inputs and on the EMD's delay properties. Recently it was shown that behavioral activity increases the LPTC velocity optimum, with modeling suggesting this to originate in the EMD's temporal delay filters. However, behavior induces an additional post-EMD effect: the LPTC membrane conductance increases in flying flies. To physiologically investigate the degree to which activity causes presynaptic and postsynaptic effects, we conducted intracellular recordings of Eristalis horizontal system (HS) neurons. We constructed contrast response functions before and after adaptation at different temporal frequencies, with and without the octopamine receptor agonist chlordimeform (CDM). We extracted three motion adaptation components, where two are likely to be generated presynaptically of the LPTCs, and one within them. We found that CDM affected the early, EMD-associated contrast gain reduction, temporal frequency dependently. However, a CDM-induced change of the HS membrane conductance disappeared during and after visual stimulation. This suggests that physical activity mainly affects motion adaptation presynaptically of LPTCs, whereas post-EMD effects have a minimal effect.

Biodiversity information platforms: From standards to interoperability

One of the most serious bottlenecks in the scientific workflows of biodiversity sciences is the need to integrate data from different sources, software applications, and services for analysis, visualisation and publication. For more than a quarter of a century the TDWG Biodiversity Information Standards organisation has a central role in defining and promoting data standards and protocols supporting interoperability between disparate and locally distributed systems.Although often not sufficiently recognized, TDWG standards are the foundation of many popular Biodiversity Informatics applications and infrastructures ranging from small desktop software solutions to large scale international data networks. However, individual scientists and groups of collaborating scientist have difficulties in fully exploiting the potential of standards that are often notoriously complex, lack non-technical documentations, and use different representations and underlying technologies. In the last few years, a series of initiatives such as Scratchpads, the EDIT Platform for Cybertaxonomy, and biowikifarm have started to implement and set up virtual work platforms for biodiversity sciences which shield their users from the complexity of the underlying standards. Apart from being practical work-horses for numerous working processes related to biodiversity sciences, they can be seen as information brokers mediating information between multiple data standards and protocols.The ViBRANT project will further strengthen the flexibility and power of virtual biodiversity working platforms by building software interfaces between them, thus facilitating essential information flows needed for comprehensive data exchange, data indexing, web-publication, and versioning. This work will make an important contribution to the shaping of an international, interoperable, and user-oriented biodiversity information infrastructure.

Data management Redefined

Core perspectives on the traditional approach to CDM are rapidly changing and EDC and new eclincal initiatives are redefining the face of data management. Associated with EDC are not only the higher efficiencies, resulting in lower study costs, but its applications in key areas such as adaptive trials and clinical event adjudication; however the cost and effort involved in deployment and integration remain a deterrent. The role of the data manager may change to that of a data broker who manages the exchange of data from multiple sources, and semantic interoperability, data standards and data privacy will prove to be the defining factors. Simulation modeling, pharmacogenomics, personalized medicine and EHRs will no longer exist as silos and seamless data flows will be the drivers of healthcare solutions.