Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics.

Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity.

Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics.

The impact of micro-level people's activities on urban macro-level indicators is a complex question that has been the subject of much interest among researchers and policymakers. Transportation preferences, consumption habits, communication patterns and other individual-level activities can significantly impact large-scale urban characteristics, such as the potential for innovation generation of the city. Conversely, large-scale urban characteristics can also constrain and determine the activities of their inhabitants. Therefore, understanding the interdependence and mutual reinforcement between micro- and macro-level factors is critical to defining effective public policies. The increasing availability of digital data sources, such as social media and mobile phones, has opened up new opportunities for the quantitative study of this interdependency. This paper aims to detect meaningful city clusters on the basis of a detailed analysis of the spatiotemporal activity patterns for each city. The study is carried out on a worldwide city dataset of spatiotemporal activity patterns obtained from geotagged social media data. Clustering features are obtained from unsupervised topic analyses of activity patterns. Our study compares state-of-the-art clustering models, selecting the model achieving a 2.7% greater Silhouette Score than the next-best model. Three well-separated city clusters are identified. Additionally, the study of the distribution of the City Innovation Index over these three city clusters shows discrimination of low performing from high performing cities relative to innovation. Low performing cities are identified in one well-separated cluster. Therefore, it is possible to correlate micro-scale individual-level activities to large-scale urban characteristics.

Systemic lupus erythematosus and antiphospholipid syndrome after COVID-19 vaccination. A case report.

Coronavirus disease 2019 (COVID-19) vaccines have some adverse effects, mostly mild. However, by presenting an immunological challenge to the individual, they could infrequently trigger immune-mediated diseases (IMDs). We report the case of a 42-year-old woman, with no previous medical history, who received the first dose of vaccine against COVID-19 and developed inflammatory arthralgias, associated with sudden-onset dyspnoea and hypoxemia. Pulmonary thromboembolism was documented, and the diagnosis of systemic lupus erythematosus (SLE) and secondary antiphospholipid syndrome (APS) was suspected. Autoantibodies were measured confirming this suspicion. After a few days, she presented a massive pericardial effusion with cardiac tamponade that required surgical management. Treatment with azathioprine, hydroxychloroquine, corticosteroids, and anticoagulation was indicated with improvement of all her symptoms. There is controversy regarding the potential of COVID-19 vaccines to induce autoimmunity. Studies addressing the safety of using these vaccines have reported the occurrence of mild local and systemic reactions, most frequently in young adults. So far, there are few reports of patients who have developed autoimmune or autoinflammatory diseases after getting vaccinated with any of the COVID-19 vaccines. To the best of our knowledge, to date, this is one of the first cases of new-onset SLE and secondary APS after COVID-19 vaccination.

Neuro-semantic prediction of user decisions to contribute content to online social networks.

Understanding at microscopic level the generation of contents in an online social network (OSN) is highly desirable for an improved management of the OSN and the prevention of undesirable phenomena, such as online harassment. Content generation, i.e., the decision to post a contributed content in the OSN, can be modeled by neurophysiological approaches on the basis of unbiased semantic analysis of the contents already published in the OSN. This paper proposes a neuro-semantic model composed of (1) an extended leaky competing accumulator (ELCA) as the neural architecture implementing the user concurrent decision process to generate content in a conversation thread of a virtual community of practice, and (2) a semantic modeling based on the topic analysis carried out by a latent Dirichlet allocation (LDA) of both users and conversation threads. We use the similarity between the user and thread semantic representations to built up the model of the interest of the user in the thread contents as the stimulus to contribute content in the thread. The semantic interest of users in discussion threads are the external inputs for the ELCA, i.e., the external value assigned to each choice.. We demonstrate the approach on a dataset extracted from a real life web forum devoted to fans of tinkering with musical instruments and related devices. The neuro-semantic model achieves high performance predicting the content posting decisions (average F score 0.61) improving greatly over well known machine learning approaches, namely random forest and support vector machines (average F scores 0.19 and 0.21).

Enabling anaerobic growth of Escherichia coli on glycerol in defined minimal medium using acetate as redox sink.

Glycerol has become an attractive substrate for bio-based production processes. However, Escherichia coli, an established production organism in the biotech industry, is not able to grow on glycerol under strictly anaerobic conditions in defined minimal medium due to redox imbalance. Despite extensive research efforts aiming to overcome these limitations, anaerobic growth of wild-type E. coli on glycerol always required either the addition of electron acceptors for anaerobic respiration (e.g. fumarate) or the supplementation with complex and relatively expensive additives (tryptone or yeast extract). In the present work, driven by model-based calculations, we propose and validate a novel and simple strategy to enable fermentative growth of E. coli on glycerol in defined minimal medium. We show that redox balance could be achieved by uptake of small amounts of acetate with subsequent reduction to ethanol via acetyl-CoA. Using a directed laboratory evolution approach, we were able to confirm this hypothesis and to generate an E. coli strain that shows, under anaerobic conditions with glycerol as the main substrate and acetate as co-substrate, robust growth (µ = 0.06 h-1), a high specific glycerol uptake rate (10.2 mmol/gDW/h) and an ethanol yield close to the theoretical maximum (0.92 mol per mol glycerol). Using further stoichiometric calculations, we also clarify why complex additives such as tryptone used in previous studies enable E. coli to achieve redox balance. Our results provide new biological insights regarding the fermentative metabolism of E. coli and offer new perspectives for sustainable production processes based on glycerol.

Non-Intrusive Assessment of COVID-19 Lockdown Follow-Up and Impact Using Credit Card Information: Case Study in Chile.

In this paper, we propose and validate with data extracted from the city of Santiago, capital of Chile, a methodology to assess the actual impact of lockdown measures based on the anonymized and geolocated data from credit card transactions. Using unsupervised Latent Dirichlet Allocation (LDA) semantic topic discovery, we identify temporal patterns in the use of credit cards that allow us to quantitatively assess the changes in the behavior of the people under the lockdown measures because of the COVID-19 pandemic. An unsupervised latent topic analysis uncovers the main patterns of credit card transaction activity that explain the behavior of the inhabitants of Santiago City. The approach is non-intrusive because it does not require the collaboration of people for providing the anonymous data. It does not interfere with the actual behavior of the people in the city; hence, it does not introduce any bias. We identify a strong downturn of the economic activity as measured by credit card transactions (down to 70%), and thus of the economic activity, in city sections (communes) that were subjected to lockdown versus communes without lockdown. This change in behavior is confirmed by independent data from mobile phone connectivity. The reduction of activity emerges before the actual lockdowns were enforced, suggesting that the population was spontaneously implementing the required measures for slowing virus propagation.

The development and validation of a marginalization index for inpatient psychiatry.

BACKGROUND: Marginalization is a multidimensional social construct that influences the mental health status of individuals and their use of psychiatric services. However, its conceptualization and measurement are challenging due to inconsistencies in definitions, and the lack of standard data sources to measure this construct. AIMS: To create an index for screening marginalization based on an existing comprehensive assessment system used in inpatient psychiatry. METHOD: Items anticipated to be indicative of marginalization were identified from the Resident Assessment Instrument-Mental Health (RAI-MH) that is used in all inpatient mental health beds in Ontario, Canada. Principal Component Analysis (PCA) and cluster analysis of these items was performed on a sample of 81,232 patients admitted into psychiatric care in Ontario between 1 January 2011 and 31 December 2016 to identify dimensions being measured. Various weights and scoring methods were tested to assess convergent validity on multiple outcomes of marginalization. Receiver Operating Characteristic (ROC) curve analysis was utilized to determine optimal cut-offs for the index by modeling the likelihood of different marginalization outcomes, including homelessness. RESULTS: Fifteen items were identified for the development of the Marginalization Index (MI). PCA and cluster analysis identified that the items measured five dimensions. ROC curve analysis among homeless individuals identified an Area Under the Curve of 0.76 and an optimal cut-off of five on the MI. Frequency analysis of the index by different characteristics identified homeless individuals, frequent mental health service users, persons with a history of violence and police intervention, and persons with addictions issues, as groups with the highest scores, confirming the convergent validity of the index. CONCLUSION: The MI is a valid measure of marginalization and is strong predictor of risk of homelessness among psychiatric inpatients. MI provides a resource that can be used for social and health policy, decision-support and evaluation.

Osteoarticular manifestations of systemic sclerosis: a systematic review of the literature / Manifestaciones osteoarticulares de esclerosis sistémica: una revisión sistemática de la literatura

ABSTRACT Introduction: Osteoarticular manifestations (OAM) are frequently present in patients with systemic sclerosis (SSc). These OAM are related to important functional disability and a severe impact on patient's life quality, therefore, they require special attention from clinicians. Objective: Determining the frequency of reported OAM in SSc patients, the general impact of osteoarticular manifestations in patient's condition and the tools available for diagnosis and treatment. Methodology: A systematic review of the literature was performed for information published between January 1970 and December 2018 in the medical research databases of: Medline, Embase, Lilacs, Scielo, Cochrane and clinicaltrials.gov. Results and Conclusions: A total of 116 articles were included in this review. The type and prevalence of the different OAM reported in the literature were determined. In addition to the evolution of their diagnostic methods, there are more validated methods to assess the impact of OAM in patients with SSc. Randomized clinical trials are required to establish the best treatment strategy for these patients.

RESUMEN Introducción: Las manifestaciones osteoarticulares (MOA) están presentes de forma frecuente en pacientes con esclerosis sistémica (SSc). Generan gran discapacidad funcional con un impacto importante en la calidad de vida de los pacientes, por lo que requieren especial atención por parte de los clínicos. Objetivos: Determinar la frecuencia de las MOA dentro de la SSc, su impacto en la condición general de los pacientes y las diferentes herramientas disponibles de diagnóstico y tratamiento. Metodología: Se realizó una revisión sistemática de la literatura disponible desde enero de 1970 hasta diciembre de 2018 en las bases de datos de Medline, Embase, Lilacs, Scielo, Cochrane y clinicaltrials.gov. Resultados y conclusiones: Se incluyeron un total de 116 artículos en esta revisión. Se determinaron el tipo y la prevalencia de las diferentes MOA reportadas en la literatura. Además de la evolución en los métodos diagnósticos de las mismas, cada vez existen más métodos validados para evaluar el impacto de las MOA en los pacientes con SSC. Se requieren ensayos clínicos aleatorizados que permitan establecer la mejor estrategia de tratamiento para estos pacientes.

Methodologically grounded semantic analysis of large volume of chilean medical literature data applied to the analysis of medical research funding efficiency in Chile.

BACKGROUND: Medical knowledge is accumulated in scientific research papers along time. In order to exploit this knowledge by automated systems, there is a growing interest in developing text mining methodologies to extract, structure, and analyze in the shortest time possible the knowledge encoded in the large volume of medical literature. In this paper, we use the Latent Dirichlet Allocation approach to analyze the correlation between funding efforts and actually published research results in order to provide the policy makers with a systematic and rigorous tool to assess the efficiency of funding programs in the medical area. RESULTS: We have tested our methodology in the Revista Médica de Chile, years 2012-2015. 50 relevant semantic topics were identified within 643 medical scientific research papers. Relationships between the identified semantic topics were uncovered using visualization methods. We have also been able to analyze the funding patterns of scientific research underlying these publications. We found that only 29% of the publications declare funding sources, and we identified five topic clusters that concentrate 86% of the declared funds. CONCLUSIONS: Our methodology allows analyzing and interpreting the current state of medical research at a national level. The funding source analysis may be useful at the policy making level in order to assess the impact of actual funding policies, and to design new policies.

First Detection of Toxoplasma gondii DNA in a Wild Bat from Colombia.

INTRODUCTION: Toxoplasma gondii infections have been reported for many warm-blooded animals around the world including chiropterans. However, in Colombia, the country that holds the highest taxonomic richness of this order of mammals in the Neotropics, up to date there are no reports of T. gondii in bats (Carollia brevicauda). PURPOSE: The objective of the present study was to detect T. gondii DNA from internal bat organs from Quindío, Colombia. RESULTS: We report the first detection of T. gondii DNA from internal bat organs in the department of Quindio, Central Andes of Colombia. Out of three silky short tail bat (Carollia brevicauda) specimens collected at the natural reserve "La Montaña del Ocaso", organs were recovered (lungs, liver, heart, kidneys, small and large intestine) and tested for T. gondii through PCR for B1 sequence, with 1/3 (33.3%) positive result for the presence of T. gondii DNA in bat kidney tissues. CONCLUSION: Taking into consideration the high diversity of bat species in Colombia, and the complexity of the ecological and functional relationships that these organisms establish in the ecosystems they inhabit, we discuss on the urgent need for more detailed research and surveys for Toxoplansma in bats and other mammalian wild species.

Detection and genotypes of Toxoplasma gondii DNA in feces of domestic cats in Colombia.

The high prevalence of Toxoplasma gondii in the human population in Colombia has been linked to the existence of a high density of urban stray cats, exposing the whole population to a high density of oocysts. The goal of this study was to determine the DNA prevalence of T. gondii by conventional PCR and to phylogenetically analyze ROP18 sequences from positive samples in domestic cat (Felis catus) fecal samples in the city of Armenia, Quindío. Fecal samples from 140 cats were collected from 10 districts around the city. Samples were concentrated using Ritchie's method and analyzed through optical microscopy. Concentrates were used for DNA extraction followed by nested PCR amplification for T. gondii gene B1. PCR for ROP18 was performed on all B1 positive samples; the ROP18 sequences obtained were related to the Archetype I Brazilian and Chinese strains. No oocysts were detected by optical microscopy; however, 17.8% (25/140) B1 and 24% (6/25) ROP18 PCR-positive samples were detected. Phylogenetic analyses showed that isolates clustered into a single group. We assessed whether associations existed between T. gondii positive fecal samples and survey variables such as cat healthcare and socioeconomic characteristics of owners, but no statistically significant associations were found. The presence of T. gondii in cat feces is an important factor contributing to the high prevalence in the human population of this city.

TITLE: Détection d'ADN et génotypes de Toxoplasma gondii dans les fèces de chats domestiques en Colombie. ABSTRACT: La forte prévalence de Toxoplasma gondii dans la population humaine en Colombie a été liée à l'existence d'une forte densité de chats errants urbains, exposant l'ensemble de la population à une forte densité d'oocystes. Le but de ce travail était de déterminer la prévalence de l'ADN de T. gondii par PCR conventionnelle et d'analyser phylogénétiquement les séquences ROP18 d'échantillons positifs dans des échantillons fécaux de chat domestique (Felis catus) dans la ville d'Armenia, Quindío. Des échantillons fécaux de 140 chats ont été collectés dans 10 districts de la ville. Les échantillons ont été concentrés en utilisant la méthode de Ritchie et analysés par microscopie optique. Des concentrés ont été utilisés pour l'extraction d'ADN suivie d'une amplification par PCR nichée pour le gène B1 de T. gondii. La PCR pour ROP18 a été réalisée sur tous les échantillons positifs pour B1 ; les séquences ROP18 obtenues étaient apparentées aux souches Archétype I brésiliennes et chinoises. Aucun oocyste n'a été détecté par microscopie optique mais les échantillons étaient positifs par PCR pour 17,8 % (25/140) pour B1 et 24 % (6/25) pour ROP18. Les analyses phylogénétiques ont montré que les isolats formaient un seul groupe. Nous avons évalué s'il existait des associations entre des échantillons fécaux positifs à T. gondii et des variables d'enquête telles que les soins de santé des chats et les caractéristiques socioéconomiques des propriétaires, mais aucune association statistiquement significative n'a été trouvée. La présence de T. gondii dans les excréments de chats est un facteur important contribuant à la forte prévalence dans la population humaine de cette ville.

Computational Intelligence in Remote Sensing: An Editorial.

Computational intelligence is a very active and fruitful research of artificial intelligence with a broad spectrum of applications. Remote sensing data has been a salient field of application of computational intelligence algorithms, both for the exploitation of the data and for the research/development of new data analysis tools. In this editorial paper we provide the setting of the special issue "Computational Intelligence in Remote Sensing" and an overview of the published papers. The 11 accepted and published papers cover a wide spectrum of applications and computational tools that we try to summarize and put in perspective in this editorial paper.

Machine Learning Readmission Risk Modeling: A Pediatric Case Study.

BACKGROUND: Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions. OBJECTIVE: To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile. MATERIALS: An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child's treatment administrative cost. METHODS: Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size. RESULTS: Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms. CONCLUSIONS: We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.

Rheumatoid arthritis and systemic lupus erythematosus: Pathophysiological mechanisms related to innate immune system.

Rheumatoid arthritis and systemic lupus erythematosus are two highly prevalent autoimmune diseases that generate disability and low quality of life. The innate immune system, a long-forgotten issue in autoimmune diseases, is becoming increasingly important and represents a new focus for the treatment of these entities. This review highlights the role that innate immune system plays in the pathophysiology of rheumatoid arthritis and systemic lupus erythematosus. The role of the innate immune system in rheumatoid arthritis and systemic lupus erythematosus pathophysiology is not only important in early stages but is essential to maintain the immune response and to allow disease progression. In rheumatoid arthritis, genetic and environmental factors are involved in the initial stimulation of the innate immune response in which macrophages are the main participants, as well as fibroblast-like synoviocytes. In systemic lupus erythematosus, all the cells contribute to the inflammatory response, but the complement system is the major effector of the inflammatory process. Detecting alterations in the normal function of these cells, besides its contribution to the understanding of the pathophysiology of autoimmune diseases, could help to establish new treatment strategies for these diseases.

Correction to: Antipsychotics and dementia in Canada: a retrospective cross-sectional study of four health sectors.

Following the publication of this article [1], the authors noticed that the results presented in the results section of the article were erroneously reported in the results section of the abstract.

Evaluation of an eye-pointer interaction device for human-computer interaction.

Advances in eye-tracking technology have led to better human-computer interaction, and involve controlling a computer without any kind of physical contact. This research describes the transformation of a commercial eye-tracker for use as an alternative peripheral device in human-computer interactions, implementing a pointer that only needs the eye movements of a user facing a computer screen, thus replacing the need to control the software by hand movements. The experiment was performed with 30 test individuals who used the prototype with a set of educational videogames. The results show that, although most of the test subjects would prefer a mouse to control the pointer, the prototype tested has an empirical precision similar to that of the mouse, either when trying to control its movements or when attempting to click on a point of the screen.

Geographic Clustering of Admissions to Inpatient Psychiatry among Adults with Cognitive Disorders in Ontario, Canada: Does Distance to Hospital Matter?

OBJECTIVE: This study examined relationships among hospital accessibility, socio-economic context, and geographic clustering of inpatient psychiatry admissions for adults with cognitive disorders in Ontario, Canada. METHOD: A retrospective cross-sectional analysis was conducted using admissions data from 71 hospitals with inpatient psychiatry beds in Ontario, Canada between 2011 and 2014. Data included 7,637 unique admissions for 4,550 adults with a DSM-IV diagnosis of Delirium, Dementia, Amnestic and other Cognitive Disorders. Bayesian spatial Poisson regression was employed to examine the relationship between accessibility of general hospitals with psychiatric beds and psychiatric hospitals, area-level marginalization, and hospitalization rate with the risk of admission to inpatient psychiatry among adults with cognitive disorders across 516 Forward Sortation Areas (FSA) in Ontario. RESULTS: Residential instability and the overall hospitalization rate were significantly associated with an increase in the relative risk of admissions to inpatient psychiatry. Accessibility to general hospitals and psychiatric hospitals were marginally insignificant at the 95% credible interval in the final model. Significant geographic clustering of admissions was identified where individuals residing in FSA's with the highest relative risk were 2.0 to 7.1 times more likely to be admitted to inpatient psychiatry compared to the average. CONCLUSIONS: Geographic clustering of inpatient psychiatry admissions for adults with cognitive disorders exists across the Province of Ontario, Canada. At the geographic level, the risk of admission was positively associated with residential instability and the overall hospitalization rate, but not distance to the closest general or psychiatric hospital.

Social Withdrawal Among Individuals Receiving Psychiatric Care: Derivation of a Scale Using Routine Clinical Assessment Data to Support Screening and Outcome Measurement.

Social withdrawal is a symptom experienced by individuals with an array of mental health conditions, particularly those with schizophrenia and mood disorders. Assessments of social withdrawal are often lengthy and may not be routinely integrated within the comprehensive clinical assessment of the individual. This study utilized item response and classical test theory methods to derive a Social Withdrawal Scale (SWS) using items embedded within a routine clinical assessment, the RAI-Mental Health (RAI-MH). Using data from 60,571 inpatients in Ontario, Canada, a common factor analysis identified seven items from the RAI-MH that measure social withdrawal. A graded response model found that six items had acceptable discrimination parameters: lack of motivation, reduced interaction, decreased energy, flat affect, anhedonia, and loss of interest. Summing these items, the SWS was found to have strong internal consistency (Cronbach's alpha = 0.82) and showed a medium to large effect size (d = 0.77) from admission to discharge. Fewer individuals with high SWS scores participated in social activity or reported having a confidant compared to those with lower scores. Since the RAI-MH is available across clinical subgroups in several jurisdictions, the SWS is a useful tool for screening, clinical decision support, and evaluation.

Antipsychotics and dementia in Canada: a retrospective cross-sectional study of four health sectors.

BACKGROUND: Antipsychotic medications are not recommended for the management of symptoms of dementia, particularly among persons with no behavioral or psychological symptoms. We examine patterns of antipsychotic medication use among persons with dementia across health sectors in Canada, with a focus on factors related to use among those without behavioral or psychotic symptoms. METHODS: Using a retrospective cross-sectional design, this study examines antipsychotic use among adults aged 65 or older with dementia in home care (HC), complex continuing care (CCC), long-term care (LTC), and among alternate level care patients in acute hospitals (ALC). Using clinical data from January 1, 2009 to December 31, 2014, the prevalence of antipsychotic medication use was estimated by the presence of behavioral and psychotic symptoms. Logistic regression was used to identify sector specific factors associated with antipsychotic use in the absence of behavioral and psychotic symptoms. RESULTS: The total prevalence of antipsychotic use among older adults with dementia was 19% in HC, 42% in ALC, 35% in CCC, and 37% in LTC. This prevalence ranged from 39% (HC) to 70% (ALC) for those with both behavioral and psychotic symptoms and from 12% (HC) to 32% (ALC) among those with no symptoms. The regression models identified a number of variables were related to antipsychotic use in the absence of behavior or psychotic symptoms, such as bipolar disorder (OR = 5.63 in CCC; OR = 5.52 in LTC), anxious complaints (OR = 1.54 in LTC to 2.01 in CCC), and wandering (OR = 1.83 in ALC). CONCLUSIONS: Potentially inappropriate use of antipsychotic medications is prevalent among older adults with dementia across health sectors. The variations in prevalence observed from community to facility based care suggests that system issues may exist in appropriately managing persons with dementia.

Corrigendum to "Content Patterns in Topic-Based Overlapping Communities".

[This corrects the article DOI: 10.1155/2014/105428.].