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1.
Sensors (Basel) ; 21(9)2021 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-34062944

RESUMO

The economic and personal consequences that a car accident generates for society have been increasing in recent years. One of the causes that can generate a car accident is the stress level the driver has; consequently, the detection of stress events is a highly desirable task. In this article, the efficacy that statistical time features (STFs), such as root mean square, mean, variance, and standard deviation, among others, can reach in detecting stress events using electromyographical signals in drivers is investigated, since they can measure subtle changes that a signal can have. The obtained results show that the variance and standard deviation coupled with a support vector machine classifier with a cubic kernel are effective for detecting stress events where an AUC of 0.97 is reached. In this sense, since SVM has different kernels that can be trained, they are used to find out which one has the best efficacy using the STFs as feature inputs and a training strategy; thus, information about model explain ability can be determined. The explainability of the machine learning algorithm allows generating a deeper comprehension about the model efficacy and what model should be selected depending on the features used to its development.


Assuntos
Automóveis , Máquina de Vetores de Suporte , Algoritmos , Eletromiografia , Aprendizado de Máquina
2.
Sensors (Basel) ; 21(9)2021 May 05.
Artigo em Inglês | MEDLINE | ID: mdl-34063055

RESUMO

Exploiting hand gestures for non-verbal communication has extraordinary potential in HCI. A data glove is an apparatus widely used to recognize hand gestures. To improve the functionality of the data glove, a highly stretchable and reliable signal-to-noise ratio sensor is indispensable. To do this, the study focused on the development of soft silicone microchannel sensors using a Eutectic Gallium-Indium (EGaIn) liquid metal alloy and a hand gesture recognition system via the proposed data glove using the soft sensor. The EGaIn-silicone sensor was uniquely designed to include two sensing channels to monitor the finger joint movements and to facilitate the EGaIn alloy injection into the meander-type microchannels. We recruited 15 participants to collect hand gesture dataset investigating 12 static hand gestures. The dataset was exploited to estimate the performance of the proposed data glove in hand gesture recognition. Additionally, six traditional classification algorithms were studied. From the results, a random forest shows the highest classification accuracy of 97.3% and a linear discriminant analysis shows the lowest accuracy of 87.4%. The non-linearity of the proposed sensor deteriorated the accuracy of LDA, however, the other classifiers adequately overcame it and performed high accuracies (>90%).


Assuntos
Gálio , Gestos , Algoritmos , Análise Discriminante , Mãos , Humanos , Índio , Silicones
3.
Sensors (Basel) ; 21(10)2021 May 15.
Artigo em Inglês | MEDLINE | ID: mdl-34063354

RESUMO

A dual liquid-crystal variable retarder Mueller polarimetry system incorporating a gold-based surface plasmon resonance prism coupler was proposed for extracting the optical properties of serum albumin protein media in the reflectance configuration. The feasibility of the proposed system was demonstrated by measuring the circular dichroism and circular birefringence properties of glucose tissue phantom solutions with different albumin concentrations. The results showed that the circular dichroism increased with albumin concentration, while the optical rotation angle increased with glucose concentration. Both properties reduced over time as a result of the protein glycation effect, which led to a gradual reduction in the glucose content of the sample.


Assuntos
Algoritmos , Albumina Sérica , Birrefringência , Imagens de Fantasmas , Análise Espectral
4.
Sensors (Basel) ; 21(9)2021 May 03.
Artigo em Inglês | MEDLINE | ID: mdl-34063576

RESUMO

During the pandemic of coronavirus disease-2019 (COVID-19), medical practitioners need non-contact devices to reduce the risk of spreading the virus. People with COVID-19 usually experience fever and have difficulty breathing. Unsupervised care to patients with respiratory problems will be the main reason for the rising death rate. Periodic linearly increasing frequency chirp, known as frequency-modulated continuous wave (FMCW), is one of the radar technologies with a low-power operation and high-resolution detection which can detect any tiny movement. In this study, we use FMCW to develop a non-contact medical device that monitors and classifies the breathing pattern in real time. Patients with a breathing disorder have an unusual breathing characteristic that cannot be represented using the breathing rate. Thus, we created an Xtreme Gradient Boosting (XGBoost) classification model and adopted Mel-frequency cepstral coefficient (MFCC) feature extraction to classify the breathing pattern behavior. XGBoost is an ensemble machine-learning technique with a fast execution time and good scalability for predictions. In this study, MFCC feature extraction assists machine learning in extracting the features of the breathing signal. Based on the results, the system obtained an acceptable accuracy. Thus, our proposed system could potentially be used to detect and monitor the presence of respiratory problems in patients with COVID-19, asthma, etc.


Assuntos
Processamento de Sinais Assistido por Computador , Algoritmos , Humanos , Respiração
5.
Sensors (Basel) ; 21(9)2021 May 03.
Artigo em Inglês | MEDLINE | ID: mdl-34063578

RESUMO

The use of deep learning models to identify lesions on cotton leaves on the basis of images of the crop in the field is proposed in this article. Cultivated in most of the world, cotton is one of the economically most important agricultural crops. Its cultivation in tropical regions has made it the target of a wide spectrum of agricultural pests and diseases, and efficient solutions are required. Moreover, the symptoms of the main pests and diseases cannot be differentiated in the initial stages, and the correct identification of a lesion can be difficult for the producer. To help resolve the problem, the present research provides a solution based on deep learning in the screening of cotton leaves which makes it possible to monitor the health of the cotton crop and make better decisions for its management. With the learning models GoogleNet and Resnet50 using convolutional neural networks, a precision of 86.6% and 89.2%, respectively, was obtained. Compared with traditional approaches for the processing of images such as support vector machines (SVM), Closest k-neighbors (KNN), artificial neural networks (ANN) and neuro-fuzzy (NFC), the convolutional neural networks proved to be up to 25% more precise, suggesting that this method can contribute to a more rapid and reliable inspection of the plants growing in the field.


Assuntos
Aprendizado Profundo , Algoritmos , Redes Neurais de Computação , Folhas de Planta , Máquina de Vetores de Suporte
6.
Sensors (Basel) ; 21(10)2021 May 11.
Artigo em Inglês | MEDLINE | ID: mdl-34064615

RESUMO

In motor control studies, the 90% thresholding of variance accounted for (VAF) is the classical way of selecting the number of muscle synergies expressed during a motor task. However, the adoption of an arbitrary cut-off has evident drawbacks. The aim of this work is to describe and validate an algorithm for choosing the optimal number of muscle synergies (ChoOSyn), which can overcome the limitations of VAF-based methods. The proposed algorithm is built considering the following principles: (1) muscle synergies should be highly consistent during the various motor task epochs (i.e., remaining stable in time), (2) muscle synergies should constitute a base with low intra-level similarity (i.e., to obtain information-rich synergies, avoiding redundancy). The algorithm performances were evaluated against traditional approaches (threshold-VAF at 90% and 95%, elbow-VAF and plateau-VAF), using both a simulated dataset and a real dataset of 20 subjects. The performance evaluation was carried out by analyzing muscle synergies extracted from surface electromyographic (sEMG) signals collected during walking tasks lasting 5 min. On the simulated dataset, ChoOSyn showed comparable performances compared to VAF-based methods, while, in the real dataset, it clearly outperformed the other methods, in terms of the fraction of correct classifications, mean error (ME), and root mean square error (RMSE). The proposed approach may be beneficial to standardize the selection of the number of muscle synergies between different research laboratories, independent of arbitrary thresholds.


Assuntos
Músculo Esquelético , Caminhada , Algoritmos , Eletromiografia , Humanos
7.
Sensors (Basel) ; 21(10)2021 May 11.
Artigo em Inglês | MEDLINE | ID: mdl-34064750

RESUMO

Autistic people face many challenges in various aspects of daily life such as social skills, repetitive behaviors, speech, and verbal communication. They feel hesitant to talk with others. The signs of autism vary from one individual to another, with a range from mild to severe. Autistic children use fewer communicative gestures compared with typically developing children (TD). With time, the parents may learn their gestures and understand what is occurring in their child's mind. However, it is difficult for other people to understand their gestures. In this paper, we propose a wearable-sensors-based platform to recognize autistic gestures using various classification techniques. The proposed system defines, monitors, and classifies the gestures of the individuals. We propose using wearable sensors that transmit their data using a Bluetooth interface to a data acquisition and classification server. A dataset of 24 gestures is created by 10 autistic children performing each gesture about 10 times. Time- and frequency-domain features are extracted from the sensors' data, which are classified using k-nearest neighbor (KNN), decision tree, neural network, and random forest models. The main objective of this work is to develop a wearable-sensor-based IoT platform for gesture recognition in children with autism spectrum disorder (ASD). We achieve an accuracy of about 91% with most of the classifiers using dataset cross-validation and leave-one-person-out cross-validation.


Assuntos
Transtorno do Espectro Autista , Dispositivos Eletrônicos Vestíveis , Algoritmos , Transtorno do Espectro Autista/diagnóstico , Criança , Gestos , Humanos , Aprendizado de Máquina
8.
Sensors (Basel) ; 21(10)2021 May 11.
Artigo em Inglês | MEDLINE | ID: mdl-34065035

RESUMO

Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant's head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant's left/right hand side. This identification process is based on "Levenberg-Marquardt" backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.


Assuntos
Eletroencefalografia , Mãos , Algoritmos , Encéfalo , Humanos , Redes Neurais de Computação
9.
Sensors (Basel) ; 21(9)2021 May 09.
Artigo em Inglês | MEDLINE | ID: mdl-34065067

RESUMO

This study innovatively proposes a feature fusion technique to determine fatty acid content during rice storage. Firstly, a self-developed olfactory visualization sensor was used to capture the odor information of rice samples at different storage periods and a portable spectroscopy system was employed to collect the near-infrared (NIR) spectra during rice storage. Then, principal component analysis (PCA) was performed on the pre-processed olfactory visualization sensor data and the NIR spectra, and the number of the best principal components (PCs) based on the single technique model was optimized during the backpropagation neural network (BPNN) modeling. Finally, the optimal PCs were fused at the feature level, and a BPNN detection model based on the fusion feature was established to achieve rapid measurement of fatty acid content during rice storage. The experimental results showed that the best BPNN model based on the fusion feature had a good predictive performance where the correlation coefficient (RP) was 0.9265, and the root mean square error (RMSEP) was 1.1005 mg/100 g. The overall results demonstrate that the detection accuracy and generalization performance of the feature fusion model are an improvement on the single-technique data model; and the results of this study can provide a new technical method for high-precision monitoring of grain storage quality.


Assuntos
Oryza , Algoritmos , Ácidos Graxos , Análise dos Mínimos Quadrados , Redes Neurais de Computação , Espectroscopia de Luz Próxima ao Infravermelho
10.
Sensors (Basel) ; 21(10)2021 May 20.
Artigo em Inglês | MEDLINE | ID: mdl-34065360

RESUMO

Scene sonification is a powerful technique to help Visually Impaired People (VIP) understand their surroundings. Existing methods usually perform sonification on the entire images of the surrounding scene acquired by a standard camera or on the priori static obstacles acquired by image processing algorithms on the RGB image of the surrounding scene. However, if all the information in the scene are delivered to VIP simultaneously, it will cause information redundancy. In fact, biological vision is more sensitive to moving objects in the scene than static objects, which is also the original intention of the event-based camera. In this paper, we propose a real-time sonification framework to help VIP understand the moving objects in the scene. First, we capture the events in the scene using an event-based camera and cluster them into multiple moving objects without relying on any prior knowledge. Then, sonification based on MIDI is enabled on these objects synchronously. Finally, we conduct comprehensive experiments on the scene video with sonification audio attended by 20 VIP and 20 Sighted People (SP). The results show that our method allows both participants to clearly distinguish the number, size, motion speed, and motion trajectories of multiple objects. The results show that our method is more comfortable to hear than existing methods in terms of aesthetics.


Assuntos
Algoritmos , Pessoas com Deficiência Visual , Humanos , Processamento de Imagem Assistida por Computador , Movimento (Física)
11.
Sensors (Basel) ; 21(10)2021 May 20.
Artigo em Inglês | MEDLINE | ID: mdl-34065434

RESUMO

In unpredictable disaster scenarios, it is important to recognize the situation promptly and take appropriate response actions. This study proposes a cloud computing-based data collection, processing, and analysis process that employs a crowd-sensing application. Clustering algorithms are used to define the major damage types, and hotspot analysis is applied to effectively filter critical data from crowdsourced data. To verify the utility of the proposed process, it is applied to Icheon-si and Anseong-si, both in Gyeonggi-do, which were affected by heavy rainfall in 2020. The results show that the types of incident at the damaged site were effectively detected, and images reflecting the damage situation could be classified using the application of the geospatial analysis technique. For 5 August 2020, which was close to the date of the event, the images were classified with a precision of 100% at a threshold of 0.4. For 24-25 August 2020, the image classification precision exceeded 95% at a threshold of 0.5, except for the mudslide mudflow in the Yul area. The location distribution of the classified images showed a distribution similar to that of damaged regions in unmanned aerial vehicle images.


Assuntos
Algoritmos , Smartphone , Computação em Nuvem , República da Coreia
12.
Int J Mol Sci ; 22(9)2021 May 10.
Artigo em Inglês | MEDLINE | ID: mdl-34068835

RESUMO

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, most important methodological advancements in more than half century of molecular modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but results are not transferable. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science. This framework is based on a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems.


Assuntos
Modelos Moleculares , Simulação de Dinâmica Molecular , Algoritmos , Termodinâmica
13.
Nat Commun ; 12(1): 3407, 2021 06 07.
Artigo em Inglês | MEDLINE | ID: mdl-34099685

RESUMO

Single-Molecule Localization Microscopy (SMLM) provides the ability to determine molecular organizations in cells at nanoscale resolution, but in complex biological tissues, where sample-induced aberrations hamper detection and localization, its application remains a challenge. Various adaptive optics approaches have been proposed to overcome these issues, but the exact performance of these methods has not been consistently established. Here we systematically compare the performance of existing methods using both simulations and experiments with standardized samples and find that they often provide limited correction or even introduce additional errors. Careful analysis of the reasons that underlie this limited success enabled us to develop an improved method, termed REALM (Robust and Effective Adaptive Optics in Localization Microscopy), which corrects aberrations of up to 1 rad RMS using 297 frames of blinking molecules to improve single-molecule localization. After its quantitative validation, we demonstrate that REALM enables to resolve the periodic organization of cytoskeletal spectrin of the axon initial segment even at 50 µm depth in brain tissue.


Assuntos
Encéfalo/patologia , Óptica e Fotônica/métodos , Imagem Individual de Molécula/métodos , Algoritmos , Animais , Células COS , Chlorocebus aethiops , Microscopia de Fluorescência/instrumentação , Ratos , Imagem Individual de Molécula/instrumentação , Software
14.
Nat Commun ; 12(1): 3423, 2021 06 08.
Artigo em Inglês | MEDLINE | ID: mdl-34103507

RESUMO

Chromatin architecture plays an important role in gene regulation. Recent advances in super-resolution microscopy have made it possible to measure chromatin 3D structure and transcription in thousands of single cells. However, leveraging these complex data sets with a computationally unbiased method has been challenging. Here, we present a deep learning-based approach to better understand to what degree chromatin structure relates to transcriptional state of individual cells. Furthermore, we explore methods to "unpack the black box" to determine in an unbiased manner which structural features of chromatin regulation are most important for gene expression state. We apply this approach to an Optical Reconstruction of Chromatin Architecture dataset of the Bithorax gene cluster in Drosophila and show it outperforms previous contact-focused methods in predicting expression state from 3D structure. We find the structural information is distributed across the domain, overlapping and extending beyond domains identified by prior genetic analyses. Individual enhancer-promoter interactions are a minor contributor to predictions of activity.


Assuntos
DNA/genética , Aprendizado Profundo , Drosophila melanogaster/genética , Elementos Facilitadores Genéticos , Regiões Promotoras Genéticas , Transcrição Genética , Algoritmos , Animais , Cromatina/genética , Simulação por Computador , Regulação da Expressão Gênica , Inativação Gênica , Genoma de Inseto , Família Multigênica , Redes Neurais de Computação
15.
Nat Commun ; 12(1): 3445, 2021 06 08.
Artigo em Inglês | MEDLINE | ID: mdl-34103512

RESUMO

To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems.


Assuntos
Algoritmos , Biomarcadores/análise , Genômica , Doença de Alzheimer/genética , Neoplasias da Mama/genética , Bases de Dados Genéticas , Feminino , Humanos
16.
BMC Bioinformatics ; 22(1): 317, 2021 Jun 10.
Artigo em Inglês | MEDLINE | ID: mdl-34112081

RESUMO

BACKGROUND: To assign structural and functional annotations to the ever increasing amount of sequenced proteins, the main approach relies on sequence-based homology search methods, e.g. BLAST or the current state-of-the-art methods based on profile Hidden Markov Models, which rely on significant alignments of query sequences to annotated proteins or protein families. While powerful, these approaches do not take coevolution between residues into account. Taking advantage of recent advances in the field of contact prediction, we propose here to represent proteins by Potts models, which model direct couplings between positions in addition to positional composition, and to compare proteins by aligning these models. Due to non-local dependencies, the problem of aligning Potts models is hard and remains the main computational bottleneck for their use. METHODS: We introduce here an Integer Linear Programming formulation of the problem and PPalign, a program based on this formulation, to compute the optimal pairwise alignment of Potts models representing proteins in tractable time. The approach is assessed with respect to a non-redundant set of reference pairwise sequence alignments from SISYPHUS benchmark which have lowest sequence identity (between [Formula: see text] and [Formula: see text]) and enable to build reliable Potts models for each sequence to be aligned. This experimentation confirms that Potts models can be aligned in reasonable time ([Formula: see text] in average on these alignments). The contribution of couplings is evaluated in comparison with HHalign and independent-site PPalign. Although Potts models were not fully optimized for alignment purposes and simple gap scores were used, PPalign yields a better mean [Formula: see text] score and finds significantly better alignments than HHalign and PPalign without couplings in some cases. CONCLUSIONS: These results show that pairwise couplings from protein Potts models can be used to improve the alignment of remotely related protein sequences in tractable time. Our experimentation suggests yet that new research on the inference of Potts models is now needed to make them more comparable and suitable for homology search. We think that PPalign's guaranteed optimality will be a powerful asset to perform unbiased investigations in this direction.


Assuntos
Algoritmos , Proteínas , Sequência de Aminoácidos , Humanos , Proteínas/genética , Alinhamento de Sequência , Homologia de Sequência
17.
BMC Med Inform Decis Mak ; 21(1): 174, 2021 06 02.
Artigo em Inglês | MEDLINE | ID: mdl-34078363

RESUMO

BACKGROUND: Validation of the autoverification function is one of the critical steps to confirm its effectiveness before use. It is crucial to verify whether the programmed algorithm follows the expected logic and produces the expected results. This process has always relied on the assessment of human-machine consistency and is mostly a manually recorded and time-consuming activity with inherent subjectivity and arbitrariness that cannot guarantee a comprehensive, timely and continuous effectiveness evaluation of the autoverification function. To overcome these inherent limitations, we independently developed and implemented a laboratory information system (LIS)-based validation system for autoverification. METHODS: We developed a correctness verification and integrity validation method (hereinafter referred to as the "new method") in the form of a human-machine dialog. The system records personnel review steps and determines whether the human-machine review results are consistent. Laboratory personnel then analyze the reasons for any inconsistency according to system prompts, add to or modify rules, reverify, and finally improve the accuracy of autoverification. RESULTS: The validation system was successfully established and implemented. For a dataset consisting of 833 rules for 30 assays, 782 rules (93.87%) were successfully verified in the correctness verification phase, and 51 rules were deleted due to execution errors. In the integrity validation phase, 24 projects were easily verified, while the other 6 projects still required the additional rules or changes to the rule settings. Taking the Hepatitis B virus test as an example, from the setting of 65 rules to the automated releasing of 3000 reports, the validation time was reduced from 452 (manual verification) to 275 h (new method), a reduction in validation time of 177 h. Furthermore, 94.6% (168/182) of laboratory users believed the new method greatly reduced the workload, effectively controlled the report risk and felt satisfied. Since 2019, over 3.5 million reports have been automatically reviewed and issued without a single clinical complaint. CONCLUSION: To the best of our knowledge, this is the first report to realize autoverification validation as a human-machine interaction. The new method effectively controls the risks of autoverification, shortens time consumption, and improves the efficiency of laboratory verification.


Assuntos
Sistemas de Informação em Laboratório Clínico , Algoritmos , Humanos , Laboratórios
18.
BMC Med Inform Decis Mak ; 21(1): 175, 2021 06 02.
Artigo em Inglês | MEDLINE | ID: mdl-34078366

RESUMO

BACKGROUND: Accurate, comprehensive, cause-specific mortality estimates are crucial for informing public health decision making worldwide. Incorrectly or vaguely assigned deaths, defined as garbage-coded deaths, mask the true cause distribution. The Global Burden of Disease (GBD) study has developed methods to create comparable, timely, cause-specific mortality estimates; an impactful data processing method is the reallocation of garbage-coded deaths to a plausible underlying cause of death. We identify the pattern of garbage-coded deaths in the world and present the methods used to determine their redistribution to generate more plausible cause of death assignments. METHODS: We describe the methods developed for the GBD 2019 study and subsequent iterations to redistribute garbage-coded deaths in vital registration data to plausible underlying causes. These methods include analysis of multiple cause data, negative correlation, impairment, and proportional redistribution. We classify garbage codes into classes according to the level of specificity of the reported cause of death (CoD) and capture trends in the global pattern of proportion of garbage-coded deaths, disaggregated by these classes, and the relationship between this proportion and the Socio-Demographic Index. We examine the relative importance of the top four garbage codes by age and sex and demonstrate the impact of redistribution on the annual GBD CoD rankings. RESULTS: The proportion of least-specific (class 1 and 2) garbage-coded deaths ranged from 3.7% of all vital registration deaths to 67.3% in 2015, and the age-standardized proportion had an overall negative association with the Socio-Demographic Index. When broken down by age and sex, the category for unspecified lower respiratory infections was responsible for nearly 30% of garbage-coded deaths in those under 1 year of age for both sexes, representing the largest proportion of garbage codes for that age group. We show how the cause distribution by number of deaths changes before and after redistribution for four countries: Brazil, the United States, Japan, and France, highlighting the necessity of accounting for garbage-coded deaths in the GBD. CONCLUSIONS: We provide a detailed description of redistribution methods developed for CoD data in the GBD; these methods represent an overall improvement in empiricism compared to past reliance on a priori knowledge.


Assuntos
Confiabilidade dos Dados , Saúde Global , Algoritmos , Brasil , Causas de Morte , Feminino , França , Humanos , Japão , Masculino
19.
BMC Med Inform Decis Mak ; 21(1): 177, 2021 06 03.
Artigo em Inglês | MEDLINE | ID: mdl-34082739

RESUMO

BACKGROUND: The ability to prioritize people living with HIV (PLWH) by risk of future transmissions could aid public health officials in optimizing epidemiological intervention. While methods exist to perform such prioritization based on molecular data, their effectiveness and accuracy are poorly understood, and it is unclear how one can directly compare the accuracy of different methods. We introduce SEPIA (Simulation-based Evaluation of PrIoritization Algorithms), a novel simulation-based framework for determining the effectiveness of prioritization algorithms. SEPIA expands upon prior related work by defining novel metrics of effectiveness with which to compare prioritization techniques, as well as by creating a simulation-based tool with which to perform such effectiveness comparisons. Under several metrics of effectiveness that we propose, we compare two existing prioritization approaches: one phylogenetic (ProACT) and one distance-based (growth of HIV-TRACE transmission clusters). RESULTS: Using all proposed metrics, ProACT consistently slightly outperformed the transmission cluster growth approach. However, both methods consistently performed just marginally better than random, suggesting that there is significant room for improvement in prioritization tools. CONCLUSION: We hope that, by providing ways to quantify the effectiveness of prioritization methods in simulation, SEPIA will aid researchers in developing novel risk prioritization tools for PLWH.


Assuntos
Infecções por HIV , Sepia , Algoritmos , Animais , Simulação por Computador , Humanos , Filogenia
20.
Sensors (Basel) ; 21(11)2021 May 21.
Artigo em Inglês | MEDLINE | ID: mdl-34063738

RESUMO

The communication in quarantined areas, e.g., due to the new COVID-19 pandemic, between isolated areas and in areas with technical damage has resulted in a great deal of interest concerning the safety of the population. A new method for ensuring communication between different areas, using unmanned aerial vehicle (UAV) networks with a well-established mobility schedule is proposed. UAVs fly based on a mission plan using regular polygons covering an area from a map. The area is considered to be equidistantly covered with points, grouped in triangles which are further grouped into hexagons. In this paper, UAVs, including battery charging or battery swapping stations and light weight Wi-Fi boards, are used for the data transfer among drones and stations using delivery protocols. UAV network analysis and evaluation (lengths of the arcs in seconds) based on experimental preliminary flight tests are proposed. Multiple simulations are performed based on six DTN algorithms, single-copy, and multiple-copies algorithms, and the efficiency of data transmission (delivery rate and latency) is analyzed. A very good delivery rate of 0.973 is obtained using the newly introduced TD-UAV Dijkstra algorithm.


Assuntos
COVID-19 , Desastres , Aeronaves , Algoritmos , Humanos , Pandemias , SARS-CoV-2
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