Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 12.723
Filtrar
1.
PLoS One ; 15(11): e0241468, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33166301

RESUMO

In March of this year, COVID-19 was declared a pandemic, and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. from January 2020 to early April 2020. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations and teleworking trends regarding the pandemic propagation and the non-pharmaceutical mobility interventions. All metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states before the stay-at-home mandates implemented and becomes more stable after the order with a smaller range of fluctuation. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. As the estimated teleworking rates also continue to incline throughout the study period, the teleworking trend can be another driving factor for the growing stay-at-home population. We confirm that there exists overall mobility heterogeneity between the income or population density groups. The study suggests that public mobility trends are in line with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.


Assuntos
Infecções por Coronavirus/patologia , Movimento , Pneumonia Viral/patologia , Betacoronavirus/isolamento & purificação , Uso do Telefone Celular/estatística & dados numéricos , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/virologia , Processamento Eletrônico de Dados , Humanos , Pandemias , Pneumonia Viral/epidemiologia , Pneumonia Viral/virologia , Análise Espaço-Temporal , Estados Unidos/epidemiologia
2.
Nat Commun ; 11(1): 5454, 2020 11 03.
Artigo em Inglês | MEDLINE | ID: mdl-33144581

RESUMO

Molecular tagging is an approach to labeling physical objects using DNA or other molecules that can be used when methods such as RFID tags and QR codes are unsuitable. No molecular tagging method exists that is inexpensive, fast and reliable to decode, and usable in minimal resource environments to create or read tags. To address this, we present Porcupine, an end-user molecular tagging system featuring DNA-based tags readable within seconds using a portable nanopore device. Porcupine's digital bits are represented by the presence or absence of distinct DNA strands, called molecular bits (molbits). We classify molbits directly from raw nanopore signal, avoiding basecalling. To extend shelf life, decrease readout time, and make tags robust to environmental contamination, molbits are prepared for readout during tag assembly and can be stabilized by dehydration. The result is an extensible, real-time, high accuracy tagging system that includes an approach to developing highly separable barcodes.


Assuntos
DNA/genética , Nanoporos , Biologia Sintética/métodos , Algoritmos , Biologia Computacional , Sistemas Computacionais , Processamento Eletrônico de Dados , Aprendizado de Máquina , Análise de Sequência de DNA
3.
Vaccine ; 38(45): 7146-7155, 2020 10 21.
Artigo em Inglês | MEDLINE | ID: mdl-32943265

RESUMO

BACKGROUND: COVID-19 pandemic has affected routine immunization globally. Impact will likely be higher in low and middle-income countries with limited healthcare resources and fragile health systems. We quantified the impact, spatial heterogeneity, and determinants for childhood immunizations of 48 million population affected in the Sindh province of Pakistan. METHODS: We extracted individual immunization records from real-time provincial Electronic Immunization Registry from September 23, 2019, to July 11, 2020. Comparing baseline (6 months preceding the lockdown) and the COVID-19 lockdown period, we analyzed the impact on daily immunization coverage rate for each antigen by geographical area. We used multivariable logistic regression to explore the predictors associated with immunizations during the lockdown. RESULTS: There was a 52.5% decline in the daily average total number of vaccinations administered during lockdown compared to baseline. The highest decline was seen for Bacille Cal-mette Guérin (BCG) (40.6% (958/2360) immunization at fixed sites. Around 8438 children/day were missing immunization during the lockdown. Enrollments declined furthest in rural districts, urban sub-districts with large slums, and polio-endemic super high-risk sub-districts. Pentavalent-3 (penta-3) immunization rates were higher in infants born in hospitals (RR: 1.09; 95% CI: 1.04-1.15) and those with mothers having higher education (RR: 1.19-1.50; 95% CI: 1.13-1.65). Likelihood of penta-3 immunization was reduced by 5% for each week of delayed enrollment into the immunization program. CONCLUSION: One out of every two children in Sindh province has missed their routine vaccinations during the provincial COVID-19 lockdown. The pool of un-immunized children is expanding during lockdown, leaving them susceptible to vaccine-preventable diseases. There is a need for tailored interventions to promote immunization visits and safe service delivery. Higher maternal education, facility-based births, and early enrollment into the immunization program continue to show a positive association with immunization uptake, even during a challenging lockdown.


Assuntos
Infecções por Coronavirus/psicologia , Sarampo/prevenção & controle , Pandemias , Pneumonia Viral/psicologia , Quarentena , Infecções por Rotavirus/prevenção & controle , Tuberculose Pulmonar/prevenção & controle , Vacinação/estatística & dados numéricos , Vacina BCG/administração & dosagem , Betacoronavirus/patogenicidade , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/imunologia , Processamento Eletrônico de Dados , Feminino , Humanos , Programas de Imunização/estatística & dados numéricos , Lactente , Recém-Nascido , Masculino , Sarampo/epidemiologia , Sarampo/imunologia , Vacina contra Sarampo/administração & dosagem , Paquistão/epidemiologia , Pneumonia Viral/epidemiologia , Pneumonia Viral/imunologia , Sistema de Registros , Infecções por Rotavirus/epidemiologia , Infecções por Rotavirus/imunologia , Vacinas contra Rotavirus/administração & dosagem , População Rural , Tuberculose Pulmonar/epidemiologia , Tuberculose Pulmonar/imunologia , População Urbana , Vacinação/psicologia , Cobertura Vacinal/estatística & dados numéricos , Vacinas Atenuadas/administração & dosagem
5.
PLoS Biol ; 18(9): e3000860, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32960891

RESUMO

Engagement with scientific manuscripts is frequently facilitated by Twitter and other social media platforms. As such, the demographics of a paper's social media audience provide a wealth of information about how scholarly research is transmitted, consumed, and interpreted by online communities. By paying attention to public perceptions of their publications, scientists can learn whether their research is stimulating positive scholarly and public thought. They can also become aware of potentially negative patterns of interest from groups that misinterpret their work in harmful ways, either willfully or unintentionally, and devise strategies for altering their messaging to mitigate these impacts. In this study, we collected 331,696 Twitter posts referencing 1,800 highly tweeted bioRxiv preprints and leveraged topic modeling to infer the characteristics of various communities engaging with each preprint on Twitter. We agnostically learned the characteristics of these audience sectors from keywords each user's followers provide in their Twitter biographies. We estimate that 96% of the preprints analyzed are dominated by academic audiences on Twitter, suggesting that social media attention does not always correspond to greater public exposure. We further demonstrate how our audience segmentation method can quantify the level of interest from nonspecialist audience sectors such as mental health advocates, dog lovers, video game developers, vegans, bitcoin investors, conspiracy theorists, journalists, religious groups, and political constituencies. Surprisingly, we also found that 10% of the preprints analyzed have sizable (>5%) audience sectors that are associated with right-wing white nationalist communities. Although none of these preprints appear to intentionally espouse any right-wing extremist messages, cases exist in which extremist appropriation comprises more than 50% of the tweets referencing a given preprint. These results present unique opportunities for improving and contextualizing the public discourse surrounding scientific research.


Assuntos
Bases de Dados como Assunto , Publicações , Ciência , Mudança Social , Mídias Sociais , Academias e Institutos/organização & administração , Academias e Institutos/normas , Academias e Institutos/estatística & dados numéricos , Acesso à Informação , Bases de Dados como Assunto/organização & administração , Bases de Dados como Assunto/normas , Bases de Dados como Assunto/estatística & dados numéricos , Processamento Eletrônico de Dados/organização & administração , Processamento Eletrônico de Dados/normas , Processamento Eletrônico de Dados/estatística & dados numéricos , Humanos , Competência em Informação , Internet/organização & administração , Internet/normas , Internet/estatística & dados numéricos , Ativismo Político , Publicações/classificação , Publicações/normas , Publicações/estatística & dados numéricos , Publicações/provisão & distribução , Ciência/organização & administração , Ciência/normas , Ciência/estatística & dados numéricos , Mídias Sociais/organização & administração , Mídias Sociais/normas , Mídias Sociais/estatística & dados numéricos
6.
Anesth Analg ; 131(4): 1217-1227, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32925343

RESUMO

BACKGROUND: Manual processes for verifying patient identification before blood transfusion and documenting this pretransfusion safety check are prone to errors, and compliance with manual systems is especially poor in urgent operating room settings. An automated, electronic barcode scanner system would be expected to improve pretransfusion verification and documentation. METHODS: Audits were conducted of blood transfusion documentation under a manual paper system from January to October 2014. An electronic barcode scanning system was developed to streamline transfusion safety checking and automate documentation. This system was implemented in 58 operating rooms between October and December 2014, with follow-up compliance audits through December 2015. The association of barcode scanner implementation with transfusion documentation compliance was assessed using an interrupted time series analysis. Anesthesia providers were surveyed regarding their opinions on the electronic system. In mid-2016, the scanning system was modified to transfer from the Metavision medical record system to Epic OpTime. Follow-up analysis assessed performance of this system within Epic during 2017. RESULTS: In an interrupted time series analysis, the proportion of units with compliant documentation was estimated to be 19.6% (95% confidence interval [CI], 10.7-25.6) the week before scanner implementation, and 74.4% (95% CI, 59.4-87.4) the week after implementation. There was a significant postintervention level change (odds ratio 10.80, 95% CI, 6.31-18.70; P < .001) and increase in slope (odds ratio 1.14 per 1-week increase, 95% CI, 1.11-1.17; P < .001). After implementation, providers chose to use the new electronic system for 98% of transfusions. Across the 2 years analyzed (15,997 transfusions), the electronic system detected 45 potential transfusion errors in 27 unique patients, and averted transfusion of 36 mismatched blood products into 20 unique patients. A total of 69%, 86%, and 88% of providers reported the electronic system improved patient safety, blood transfusion workflow, and transfusion documentation, respectively. When providers used the barcode scanner, no transfusion errors or reactions were reported. The scanner system was successfully transferred from Metavision to Epic without retraining staff or changing workflows. CONCLUSIONS: A barcode-based system designed for easy integration to different commonly used anesthesia information management systems was implemented in a large urban academic hospital. The system allows a single user with the assistance of a software system to perform and document pretransfusion safety verification. The system improved transfusion documentation compliance, averted potential transfusion errors, and became the preferred method of blood transfusion safety checking.


Assuntos
Transfusão de Sangue/métodos , Processamento Eletrônico de Dados , Registros Eletrônicos de Saúde/organização & administração , Salas Cirúrgicas/organização & administração , Adulto , Documentação , Fidelidade a Diretrizes , Humanos , Análise de Séries Temporais Interrompida , Erros Médicos/prevenção & controle , Segurança do Paciente , Melhoria de Qualidade , Fluxo de Trabalho
7.
Nat Commun ; 11(1): 4054, 2020 08 13.
Artigo em Inglês | MEDLINE | ID: mdl-32792511

RESUMO

Many neurological and musculoskeletal diseases impair movement, which limits people's function and social participation. Quantitative assessment of motion is critical to medical decision-making but is currently possible only with expensive motion capture systems and highly trained personnel. Here, we present a method for predicting clinically relevant motion parameters from an ordinary video of a patient. Our machine learning models predict parameters include walking speed (r = 0.73), cadence (r = 0.79), knee flexion angle at maximum extension (r = 0.83), and Gait Deviation Index (GDI), a comprehensive metric of gait impairment (r = 0.75). These correlation values approach the theoretical limits for accuracy imposed by natural variability in these metrics within our patient population. Our methods for quantifying gait pathology with commodity cameras increase access to quantitative motion analysis in clinics and at home and enable researchers to conduct large-scale studies of neurological and musculoskeletal disorders.


Assuntos
Marcha/fisiologia , Aprendizado de Máquina , Processamento Eletrônico de Dados , Feminino , Humanos , Masculino , Redes Neurais de Computação , Caminhada/fisiologia
8.
PLoS One ; 15(8): e0237154, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32797055

RESUMO

Data prioritization of heterogeneous data in wireless sensor networks gives meaning to mission-critical data that are time-sensitive as this may be a matter of life and death. However, the standard IEEE 802.15.4 does not consider the prioritization of data. Prioritization schemes proffered in the literature have not adequately addressed this issue as proposed schemes either uses a single or complex backoff algorithm to estimate backoff time-slots for prioritized data. Subsequently, the carrier sense multiple access with collision avoidance scheme exhibits an exponentially increasing range of backoff times. These approaches are not only inefficient but result in high latency and increased power consumption. In this article, the concept of class of service (CS) was adopted to prioritize heterogeneous data (real-time and non-real-time), resulting in an optimized prioritized backoff MAC scheme called Class of Service Traffic Priority-based Medium Access Control (CSTP-MAC). This scheme classifies data into high priority data (HPD) and low priority data (LPD) by computing backoff times with expressions peculiar to the data priority class. The improved scheme grants nodes the opportunity to access the shared medium in a timely and power-efficient manner. Benchmarked against contemporary schemes, CSTP-MAC attained a 99% packet delivery ratio with improved power saving capability, which translates to a longer operational lifetime.


Assuntos
Redes de Comunicação de Computadores , Processamento Eletrônico de Dados/métodos , Tecnologia sem Fio , Algoritmos , Modelos Teóricos , Rádio , Design de Software
9.
Nat Commun ; 11(1): 3488, 2020 07 13.
Artigo em Inglês | MEDLINE | ID: mdl-32661261

RESUMO

In recent years, numerous applications have demonstrated the potential of deep learning for an improved understanding of biological processes. However, most deep learning tools developed so far are designed to address a specific question on a fixed dataset and/or by a fixed model architecture. Here we present Janggu, a python library facilitates deep learning for genomics applications, aiming to ease data acquisition and model evaluation. Among its key features are special dataset objects, which form a unified and flexible data acquisition and pre-processing framework for genomics data that enables streamlining of future research applications through reusable components. Through a numpy-like interface, these dataset objects are directly compatible with popular deep learning libraries, including keras or pytorch. Janggu offers the possibility to visualize predictions as genomic tracks or by exporting them to the bigWig format as well as utilities for keras-based models. We illustrate the functionality of Janggu on several deep learning genomics applications. First, we evaluate different model topologies for the task of predicting binding sites for the transcription factor JunD. Second, we demonstrate the framework on published models for predicting chromatin effects. Third, we show that promoter usage measured by CAGE can be predicted using DNase hypersensitivity, histone modifications and DNA sequence features. We improve the performance of these models due to a novel feature in Janggu that allows us to include high-order sequence features. We believe that Janggu will help to significantly reduce repetitive programming overhead for deep learning applications in genomics, and will enable computational biologists to rapidly assess biological hypotheses.


Assuntos
Aprendizado Profundo , Genômica/métodos , Animais , Biologia Computacional , Processamento Eletrônico de Dados , Humanos
11.
Farm. hosp ; 44(3): 114-121, mayo-jun. 2020. tab, graf
Artigo em Espanhol | IBECS | ID: ibc-192344

RESUMO

INTRODUCCIÓN: La tecnología sanitaria se ha convertido en la solución más aceptada para reducir los eventos adversos provocados por los medicamentos, minimizando los posibles errores humanos. La introducción de la tecnología puede mejorar la seguridad y permitir una mayor eficiencia en la clínica. Sin embargo, no elimina todos los tipos de error y puede crear otros nuevos. La administración de medicamentos con código de barras y la utilización de bombas de infusión inteligentes son dos estrategias que pueden emplearse durante la administración de medicamentos para evitar errores antes de que estos lleguen al paciente. OBJETIVO: En este artículo se han revisado diferentes tipos de errores relativos a la administración de medicamentos con código de barras y las bombas de infusión inteligentes, y se ha examinado la forma en la que se producían dichos errores al emplear la tecnología. También se exponen las recomendaciones encaminadas a evitar este tipo de errores. CONCLUSIÓN: Los hospitales deben comprender la tecnología, su funcionamiento y los errores que pretende evitar, así como analizar de qué manera cambiará los procesos clínicos. Es esencial que la dirección del hospital establezca las métricas necesarias y las monitorice regularmente para garantizar el uso óptimo de estas tecnologías. También es importante identificar y evitar desviaciones en los procesos que puedan eliminar o disminuir los beneficios de seguridad para los que fue diseñada. De igual forma, es necesario recopilar periódicamente las opiniones del profesional que la utiliza para detectar los posibles problemas que pudieran surgir. Sin embargo, la dirección debe ser consciente de que incluso con la implementación completa de la tecnología pueden surgir errores a la hora de administrar la medicación


INTRODUCTION: Healthcare-related technology has been widely accepted as a key patient safety solution to reduce adverse drug events by decreasing the risk of human error. The introduction of technology can enhance safety and support workflow; however, it does not eliminate all error types and may create new ones. Barcode medication adminis-tration and smart infusion pumps are two technologies utilized during medication administration to prevent medication errors before they reach the patient. OBJECTIVE: This article reviewed different error types with barcode medi-cation administration and smart infusion pumps and examined how these errors were able to occur while using the technology. Recommendations for preventing these types of errors were also discussed. CONCLUSION: Hospitals must understand the technology, how it is desig-ned to work, which errors it is intended to prevent, as well as understand how it will change staff workflow. It is essential that metrics are set by hospital leadership and regularly monitored to ensure optimal use of these technologies. It is also important to identify and avoid workarounds which eliminate or diminish the safety benefits that the technology was designed to achieve. Front line staff feedback should be gathered on a periodic basis to understand any struggles with utilizing the technology. Leaders must also understand that even with full implementation of technology, medication errors may still occur


Assuntos
Humanos , Erros de Medicação/prevenção & controle , Preparações Farmacêuticas/administração & dosagem , Processamento Eletrônico de Dados/métodos , Bombas de Infusão , Gestão da Segurança/métodos , Acesso a Medicamentos Essenciais e Tecnologias em Saúde , Quimioterapia Assistida por Computador/métodos
12.
PLoS One ; 15(6): e0234356, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32555656

RESUMO

In view of the strong nonlinear characteristics of the multi-packet transmission Aero-engine DCS with induced delay and random packet dropout, a neural network PID approach law sliding-mode controller using sliding window strategy and multi-kernel LS-SVM packet dropout online compensation is proposed. Firstly, the time-delay term in the system model is transformed equivalently, to establish the discrete system model of multi-packet transmission without time-delay; furthermore, the construction of multi-kernel function is transformed into kernel function coefficient optimization, and the optimization problem can be solved by the chaos adaptive artificial fish swarm algorithm, then the online predictive compensation will be made for data packet dropout of multi-packet transmission through the sliding window multi-kernel LS-SVM. After that, a sliding-mode controller design method of proportional integral differential approach law based on neural network is proposed. And online adjustment of PID approach law parameters can be achieved by nonlinear mapping of neural network. Finally, Truetime is used to simulate the method. The results shows that when the packet dropout rate is 30% and 60%, the average error of packet dropout prediction of multi-kernel LS-SVM reduces 29.21% and 44.66% compared with that of combined kernel LS-SVM, and the chattering amplitude of the proposed neural network PID approach law sliding-mode controller is decreased compared with other five approach law methods respectively. This controller can ensure a fast response speed, which shows that this method can achieve a better tracking control of the aeroengine network control system.


Assuntos
Processamento Eletrônico de Dados/métodos , Algoritmos , Simulação por Computador , Redes Neurais de Computação , Dinâmica não Linear , Máquina de Vetores de Suporte
13.
Stud Health Technol Inform ; 270: 1279-1280, 2020 Jun 16.
Artigo em Inglês | MEDLINE | ID: mdl-32570618

RESUMO

Since 2017, the Hospital Italiano de Buenos Aires, has a "Workstation on Wheels" project were nurses can access to a mobile application in order to register the drug's administration, vital signs and complete an early warning assessment scale of the hemodynamic state of the patient. Although the overall objective was to achieve at least 95% drug registration through this system, their use did not remain stable over time. Therefore, it was necessary to create an interdisciplinary team to make a diagnosis of the project situation and reasons for the low use rate. In this process, a re-implementation of the barcoding administration system was carried out, focusing on the nursing staff maintaining the use of the system over time. The aim of this paper is to describe the experience and lessons learned in the process of re-implementing the drug barcoding system at the patient's bedside.


Assuntos
Aplicativos Móveis , Processamento Eletrônico de Dados , Humanos , Recursos Humanos de Enfermagem , Sinais Vitais
14.
Stud Health Technol Inform ; 270: 1036-1040, 2020 Jun 16.
Artigo em Inglês | MEDLINE | ID: mdl-32570539

RESUMO

Health information systems (HIS) and clinical workflows generate medication errors that affect the quality of patient care. The rigorous evaluation of the medication process's error risk, control, and impact on clinical practice enable the understanding of latent and active factors that contribute to HIS-induced errors. This paper reports the preliminary findings of an evaluation case study of a 1000-bed Japanese secondary care teaching hospital using observation, interview, and document analysis methods. Findings were analysed from a process perspective by adopting a recently introduced framework known as Human, Organisation, Process, and Technology-fit. Process factors influencing risk in medication errors include template- and calendar-based systems, intuitive design, barcode check, ease of use, alert, policy, systematic task organisation, and safety culture Approaches for managing medication errors also exert an important role on error reduction and clinical workflow.


Assuntos
Sistemas de Informação em Saúde , Processamento Eletrônico de Dados , Humanos , Erros de Medicação , Sistemas de Medicação no Hospital , Fluxo de Trabalho
15.
PLoS One ; 15(6): e0234470, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32530974

RESUMO

The single nucleotide polymorphism (SNP) is the most widely studied type of genetic variation. A haplotype is defined as the sequence of alleles at SNP sites on each haploid chromosome. Haplotype information is essential in unravelling the genome-phenotype association. Haplotype assembly is a well-known approach for reconstructing haplotypes, exploiting reads generated by DNA sequencing devices. The Minimum Error Correction (MEC) metric is often used for reconstruction of haplotypes from reads. However, problems with the MEC metric have been reported. Here, we investigate the MEC approach to demonstrate that it may result in incorrectly reconstructed haplotypes for devices that produce error-prone long reads. Specifically, we evaluate this approach for devices developed by Illumina, Pacific BioSciences and Oxford Nanopore Technologies. We show that imprecise haplotypes may be reconstructed with a lower MEC than that of the exact haplotype. The performance of MEC is explored for different coverage levels and error rates of data. Our simulation results reveal that in order to avoid incorrect MEC-based haplotypes, a coverage of 25 is needed for reads generated by Pacific BioSciences RS systems.


Assuntos
Processamento Eletrônico de Dados/métodos , Haplótipos/genética , Polimorfismo de Nucleotídeo Único/genética , Erro Experimental , Análise de Dados , Genoma Humano , Humanos , Análise de Sequência de DNA/instrumentação , Análise de Sequência de DNA/métodos
16.
Emerg Top Life Sci ; 4(1): 87-97, 2020 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-32558902

RESUMO

Ecosystems are at increasing risk from the global pollination crisis. Gaining better knowledge about pollinators and their interactions with plants is an urgent need. However, conventional methods of manually recording pollinator activity in the field can be time- and cost-consuming in terms of labour. Field-deployable video recording systems have become more common in ecological studies as they enable the capture of plant-insect interactions in fine detail. Standard video recording can be effective, although there are issues with hardware reliability under field-conditions (e.g. weatherproofing), and reviewing raw video manually is a time-consuming task. Automated video monitoring systems based on motion detection partly overcome these issues by only recording when activity occurs hence reducing the time needed to review footage during post-processing. Another advantage of these systems is that the hardware has relatively low power requirements. A few systems have been tested in the field which permit the collection of large datasets. Compared with other systems, automated monitoring allows vast increases in sampling at broad spatiotemporal scales. Some tools such as post-recording computer vision software and data-import scripts exist, further reducing users' time spent processing and analysing the data. Integrated computer vision and automated species recognition using machine learning models have great potential to further the study of pollinators in the field. Together, it is predicted that future advances in technology-based field monitoring methods will contribute significantly to understanding the causes underpinning pollinator declines and, hence, developing effective solutions for dealing with this global challenge.


Assuntos
Insetos/fisiologia , Plantas/parasitologia , Polinização/fisiologia , Gravação em Vídeo/instrumentação , Animais , Ecossistema , Processamento Eletrônico de Dados , Monitoramento Ambiental , Interações Hospedeiro-Parasita
17.
Nat Commun ; 11(1): 2435, 2020 05 15.
Artigo em Inglês | MEDLINE | ID: mdl-32415206

RESUMO

The functional network of the brain continually adapts to changing environmental demands. The consequence of behavioral automation for task-related functional network architecture remains far from understood. We investigated the neural reflections of behavioral automation as participants mastered a dual n-back task. In four fMRI scans equally spanning a 6-week training period, we assessed brain network modularity, a substrate for adaptation in biological systems. We found that whole-brain modularity steadily increased during training for both conditions of the dual n-back task. In a dynamic analysis,we found that the autonomy of the default mode system and integration among task-positive systems were modulated by training. The automation of the n-back task through training resulted in non-linear changes in integration between the fronto-parietal and default mode systems, and integration with the subcortical system. Our findings suggest that the automation of a cognitively demanding task may result in more segregated network organization.


Assuntos
Mapeamento Encefálico , Memória de Curto Prazo , Rede Nervosa/fisiologia , Adolescente , Adulto , Algoritmos , Comportamento , Encéfalo/fisiologia , Cognição , Processamento Eletrônico de Dados , Feminino , Humanos , Aprendizagem , Imagem por Ressonância Magnética , Masculino , Modelos Neurológicos , Modelos Estatísticos , Processamento de Sinais Assistido por Computador , Adulto Jovem
18.
PLoS One ; 15(5): e0229845, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32379826

RESUMO

This article introduces a new formulation of, and method of computation for, the projection median. Additionally, we explore its behaviour on a specific bivariate set up, providing the first theoretical result on form of the influence curve for the projection median, accompanied by numerical simulations. Via new simulations we comprehensively compare our performance with an established method for computing the projection median, as well as other existing multivariate medians. We focus on answering questions about accuracy and computational speed, whilst taking into account the underlying dimensionality. Such considerations are vitally important in situations where the data set is large, or where the operations have to be repeated many times and some well-known techniques are extremely computationally expensive. We briefly describe our associated R package that includes our new methods and novel functionality to produce animated multidimensional projection quantile plots, and also exhibit its use on some high-dimensional data examples.


Assuntos
Simulação por Computador , Processamento Eletrônico de Dados/métodos , Modelos Teóricos , Confiabilidade dos Dados , Humanos
19.
AJR Am J Roentgenol ; 215(2): 398-405, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32406776

RESUMO

OBJECTIVE. This study assessed a machine learning-based dual-energy CT (DECT) tumor analysis prototype for semiautomatic segmentation and radiomic analysis of benign and malignant liver lesions seen on contrast-enhanced DECT. MATERIALS AND METHODS. This institutional review board-approved study included 103 adult patients (mean age, 65 ± 15 [SD] years; 53 men, 50 women) with benign (60/103) or malignant (43/103) hepatic lesions on contrast-enhanced dual-source DECT. Most malignant lesions were histologically proven; benign lesions were either stable on follow-up CT or had characteristic benign features on MRI. Low- and high-kilovoltage datasets were deidentified, exported offline, and processed with the DECT tumor analysis for semiautomatic segmentation of the volume and rim of each liver lesion. For each segmentation, contrast enhancement and iodine concentrations as well as radiomic features were derived for different DECT image series. Statistical analyses were performed to determine if DECT tumor analysis and radiomics can differentiate benign from malignant liver lesions. RESULTS. Normalized iodine concentration and mean iodine concentration in the benign and malignant lesions were significantly different (p < 0.0001-0.0084; AUC, 0.695-0.856). Iodine quantification and radiomic features from lesion rims (AUC, ≤ 0.877) had higher accuracy for differentiating liver lesions compared with the values from lesion volumes (AUC, ≤ 0.856). There was no difference in the accuracies of DECT iodine quantification (AUC, 0.91) and radiomics (AUC, 0.90) for characterizing liver lesions. CONCLUSION. DECT radiomics were more accurate than iodine quantification for differentiating solid benign and malignant hepatic lesions.


Assuntos
Hepatopatias/diagnóstico por imagem , Neoplasias Hepáticas/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Idoso , Idoso de 80 Anos ou mais , Meios de Contraste , Diagnóstico Diferencial , Processamento Eletrônico de Dados , Feminino , Humanos , Compostos de Iodo , Masculino , Pessoa de Meia-Idade , Projetos Piloto , Imagem Radiográfica a Partir de Emissão de Duplo Fóton , Estudos Retrospectivos
20.
BMC Health Serv Res ; 20(1): 367, 2020 Apr 29.
Artigo em Inglês | MEDLINE | ID: mdl-32349755

RESUMO

BACKGROUND: Electronic data capturing has the potential to improve data quality and user-friendliness compared to manually processed, paper-based documentation systems. The MyChild system uses an innovative approach to process immunization data by employing detachable vouchers integrated into a vaccination booklet which are then scanned and converted into individual-level health data. The aim was to evaluate the MyChild data capturing system by assessing the proportion of correctly processed vouchers and to compare the user-friendliness in term of time spent on documentation and health worker experiences with the standard health information system at health facilities in Uganda. METHODS: We used a mixed method approach. Documented data were manually copied and compared to processed health records to calculate the proportion of correctly registered vouchers. To compare time spend on documentation we did a continuous observational time-motion study and analyzed data using a Mann-Whitney U test. Semi-structured interviews were conducted to assess health workers' experiences and analyzed using conventional content analysis. Data was collected in 14 health facilities in two districts in Uganda using different systems. RESULTS: The MyChild system processed 97% (224 of 231) of the vouchers correctly. Recording using the MyChild system increased time spend on documentation of vaccination follow-up visits by 24 s compared to the standard system (02:25 vs. 02:01 min/child, Mann-Whitney U = 6293, n1 = 115, n2 = 151, p < 0.001 two-tailed, Z = - 3.861, r = 0.186). However, high variance between health centers using the same health information system suggests that documentation time differences can be attributed to other factors than the way information was processed. Health workers perceived both health management information systems as predominantly functional and easy to use, while the MyChild system achieved a higher level of satisfaction. CONCLUSIONS: The MyChild system electronically processes individual-level immunization data correctly without increasing significantly time spent on recording and is appreciated by health providers making it a potential solution to overcome shortcomings of present paper-based health information systems in health centers.


Assuntos
Saúde da Criança , Documentação/métodos , Processamento Eletrônico de Dados , Vacinação , Adulto , Criança , Feminino , Pessoal de Saúde/psicologia , Pessoal de Saúde/estatística & dados numéricos , Humanos , Masculino , Pessoa de Meia-Idade , Pesquisa Qualitativa , Estudos de Tempo e Movimento , Uganda
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA