Pooling Method Research Articles

Local-Global Graph Pooling via Mutual Information Maximization for Video-Paragraph Retrieval

As a task of cross-modal retrieval between long videos and paragraphs, video-paragraph retrieval is a non-trivial task. Unlike traditional video-text retrieval, the video in video-paragraph retrieval usually contains multiple clips. Each clip corresponds to a descriptive sentence; all the sentences constitute the corresponding paragraph of the video. Previous methods for video-paragraph retrieval usually encode videos and para-graphs from segment-level (clips and sentences) and overall-level (videos and paragraphs). However, there are also contents about actions and objects that exist in the segment. Hence, we propose a Local-Global Graph Pooling Network (LGGP) via Mutual Information Maximization for video-paragraph retrieval. Our model disentangles videos and paragraphs into four levels: overall-level, segment-level, motion-level, and object-level. We construct the Hierarchical Local Graph (segment-level, motion-level, and object-level) and the Hierarchical Global Graph (overall-level, segment-level, motion-level, and object-level), respectively, for semantic interaction among different levels. Meanwhile, to obtain hierarchical pooling features with fine-grained semantic information, we design hierarchical graph pooling methods to maximize the mutual information between pooling features and corresponding graph nodes. We evaluate our model on two video-paragraph retrieval datasets with three different video features. The experimental results show that our model establishes state-of-the-art results for video-paragraph retrieval. Our code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/PengchengZhang1997/LGGP</uri> .

Long Text Truncation Algorithm Based on Label Embedding in Text Classification

The long text classification task has become a hot research topic in the field of text classification due to its long length and redundant information. At present, the common processing methods for long text data, such as the truncation method and pooling method, are prone to the problem of too many sentences or loss of contextual semantic information. To deal with these issues, we present LTTA-LE (Long Text Truncation Algorithm Based on Label Embedding in Text Classification), which consists of three key steps. Firstly, we build a pretraining prefix template and a label word mapping prefix template to obtain the label word embedding, and we realize the joint training of long text and label words. Secondly, we calculate the cosine similarity between the label word embedding and the long text embedding, and we filter the redundant information of the long text to reduce the text length. Finally, a three-stage model training architecture is introduced to effectively improve the classification performance and generalization ability of the model. We conduct comparative experiments on three public long text datasets, and the results show that LTTA-LE has an average F1 improvement of 1.0518% over other algorithms, which proves that our method can achieve satisfactory performance.

A prior knowledge-embedded reinforcement learning method for real-time active power corrective control in complex power systems

With the increasing uncertainty and complexity of modern power grids, the real-time active power corrective control problem becomes intractable, bringing significant challenges to the stable operation of future power systems. To promote effective and efficient active power corrective control, a prior knowledge-embedded reinforcement learning method is proposed in this paper, to improve the performance of the deep reinforcement learning agent while maintaining the real-time control manner. The system-level feature is first established based on prior knowledge and cooperating with the equipment-level features, to provide a thorough description of the power network states. A global-local network structure is then constructed to integrate the two-level information accordingly by introducing the graph pooling method. Based on the multi-level representation of power system states, the Deep Q-learning from Demonstrations method is adopted to guide the deep reinforcement learning agent to learn from the expert policy along with the interactive improving process. Considering the infrequent corrective control actions in practice, the double-prioritized training mechanism combined with the λ-return is further developed to help the agent lay emphasis on learning from critical control experience. Simulation results demonstrate that the proposed method prevails over the conventional deep reinforcement learning methods in training efficiency and control effects, and has the potential to solve the complex active power corrective control problem in the future.

Functional linear regression for discretely observed data: from ideal to reality

Summary Despite extensive studies on functional linear regression, there exists a fundamental gap in theory between the ideal estimation from fully observed covariate functions and the reality that one can only observe functional covariates discretely with noise. The challenge arises when deriving a sharp perturbation bound for the estimated eigenfunctions in the latter case, which renders existing techniques for functional linear regression not applicable. We use a pooling method to attain the estimated eigenfunctions and propose a sample-splitting strategy to estimate the principal component scores, which facilitates the theoretical treatment for discretely observed data. The slope function is estimated by approximated least squares, and we show that the resulting estimator attains the optimal convergence rates for both estimation and prediction when the number of measurements per subject reaches a certain magnitude of the sample size. This phase transition phenomenon differs from the known results for the pooled mean and covariance estimation, and reveals the elevated difficulty in estimating the regression function. Numerical experiments, using simulated and real data examples, yield favourable results when compared with existing methods.

Using global navigation satellite systems for modeling athletic performances in elite football players

This study aims to predict individual Acceleration-Velocity profiles (A-V) from Global Navigation Satellite System (GNSS) measurements in real-world situations. Data were collected from professional players in the Superleague division during a 1.5 season period (2019–2021). A baseline modeling performance was provided by time-series forecasting methods and compared with two multivariate modeling approaches using ridge regularisation and long short term memory neural networks. The multivariate models considered commercial features and new features extracted from GNSS raw data as predictor variables. A control condition in which profiles were predicted from predictors of the same session outlined the predictability of A-V profiles. Multivariate models were fitted either per player or over the group of players. Predictor variables were pooled according to the mean or an exponential weighting function. As expected, the control condition provided lower error rates than other models on average (p = 0.001). Reference and multivariate models did not show significant differences in error rates (p = 0.124), regardless of the nature of predictors (commercial features or extracted from signal processing methods) or the pooling method used. In addition, models built over a larger population did not provide significantly more accurate predictions. In conclusion, GNSS features seemed to be of limited relevance for predicting individual A-V profiles. However, new signal processing features open up new perspectives in athletic performance or injury occurrence modeling, mainly if higher sampling rate tracking systems are considered.

Hierarchical Graph Pooling With Self-Adaptive Cluster Aggregation

Graph neural network (GNN) introduces deep neural networks into graph structure data. It has achieved advanced performance in many fields, such as traffic prediction, recommendation systems, and computer vision, which has received extensive attention from the academic community. Most of the existing research on GNNs focuses on graph convolution, while graph pooling is usually ignored. Although there are also some graph pooling methods, most of the current pooling methods are based on top-k node selection. In the top-k-based pooling method, unselected nodes will be directly discarded, which will cause the loss of feature information during the pooling process. In this article, we propose a novel graph pooling operator, called hierarchical graph pooling with self-adaptive cluster aggregation (HGP-SACA), which uses a sparse and differentiable method to capture the graph structure. Before using top-k for cluster selection, the unselected clusters and the selected clusters in the neighbor perform an n-hop feature information aggregation. The merged clusters which contain neighborhood clusters are used for top-k selection, which can enhance the function of the unselected clusters. Through extensive theoretical analysis and experimental verification on multiple data sets, our experimental results show that combining the existing GNN architecture with HGP-SACA can achieve state-of-the-art results on multiple graph classification benchmarks, which proves the effectiveness of our proposed model.

Research on neural network algorithm in artificial intelligence recognition

With the rapid development of computer application technology, computer artificial intelligence recognition technology has also made rapid progress. Artificial intelligence was first represented by LISP language, machine theorem proof, etc., and then the intelligent medical treatment and neural network appeared, and now it realizes the popularization of AI. The purpose of this article is to explore the application of neural network algorithms in artificial intelligence recognition. Based on the classic convolutional neural network with LeNet structure, this paper constructs a reasonable convolutional neural network model by deepening and expanding, and uses it in face recognition to conduct face recognition research. LeNet is the origin of CNN, and LeNet is mainly used to identify and classify handwritten characters. This paper uses three networks on the YaleB face database for face recognition, and compares the pooling method of the maximum pooling and average pooling, the misclassification rate of the ReLU incentive function and the tanh incentive function on the three networks.YaleB is a very complete face database, divided into five subsets, ten people, and 64 images per person, including changes in illumination, expressions, and gestures. The tanh function has what is an odd function and has a soft saturation property, while the relu function has a hard saturation property. Analyze whether it can solve the problems caused by illumination factors and occlusion factors in traditional face recognition. Finally, the recognition effect is compared with the traditional face recognition method, and it is concluded that the effect of the traditional face recognition method is not as high as the misclassification rate of 0.0356 based on the convolutional neural network. The recognition method works well. Maximum pooling is also known as maximum reduced pixel sampling, and is more based on the input element map than average pooling.Through the optimization of the algorithm, the face recognition rate is improved, which helps the application and development of artificial intelligence.

Dual-branch framework: AUV-based target recognition method for marine survey

Autonomous recognition of marine targets is considered a promising technology for autonomous underwater vehicle (AUV) marine survey, and AUV equipped with side-scan sonar (SSS) for recognition is the key to surveys. As a fundamental function, SSS recognition remains unsolved due to the challenging image conditions of SSS and insufficient algorithm robustness. This paper proposes an accurate and real-time dual-branch recognition framework containing segmentation and refinement branches. Firstly, the segmentation branch uses a lightweight learning network to analyze the data comprehensively. In this branch, we propose a densely connected local attention recurrent residual (LAR2) block as the backbone, and at the same time, an atrous convolution is introduced. This branch can focus on the features of interest in the image, ensuring better feature representation with low-resolution SSS information while guiding the next branch. Secondly, the refinement branch is to adjust the previous branch’s results and combines the low-level and high-level features. We propose holistic attention (HA) block in this branch, which can further improve the target recognition performance. Finally, we adopt the feature fusion method of bilinear pooling to integrate the results of the two branches to output a high-precision recognition image. In offline experiments and sea trials, our proposed method outperforms other competing algorithms in the four indicators of semantic segmentation, and achieves a computation speed of 92.66 ms (±0.86 ms) per image on AUV dedicated hardware. The method has strong robustness, meets real-time performance, and can be widely used in AUV marine survey.

Highly scalable maximum likelihood and conjugate Bayesian inference for ERGMs on graph sets with equivalent vertices.

The exponential family random graph modeling (ERGM) framework provides a highly flexible approach for the statistical analysis of networks (i.e., graphs). As ERGMs with dyadic dependence involve normalizing factors that are extremely costly to compute, practical strategies for ERGMs inference generally employ a variety of approximations or other workarounds. Markov Chain Monte Carlo maximum likelihood (MCMC MLE) provides a powerful tool to approximate the maximum likelihood estimator (MLE) of ERGM parameters, and is generally feasible for typical models on single networks with as many as a few thousand nodes. MCMC-based algorithms for Bayesian analysis are more expensive, and high-quality answers are challenging to obtain on large graphs. For both strategies, extension to the pooled case—in which we observe multiple networks from a common generative process—adds further computational cost, with both time and memory scaling linearly in the number of graphs. This becomes prohibitive for large networks, or cases in which large numbers of graph observations are available. Here, we exploit some basic properties of the discrete exponential families to develop an approach for ERGM inference in the pooled case that (where applicable) allows an arbitrarily large number of graph observations to be fit at no additional computational cost beyond preprocessing the data itself. Moreover, a variant of our approach can also be used to perform Bayesian inference under conjugate priors, again with no additional computational cost in the estimation phase. The latter can be employed either for single graph observations, or for observations from graph sets. As we show, the conjugate prior is easily specified, and is well-suited to applications such as regularization. Simulation studies show that the pooled method leads to estimates with good frequentist properties, and posterior estimates under the conjugate prior are well-behaved. We demonstrate the usefulness of our approach with applications to pooled analysis of brain functional connectivity networks and to replicated x-ray crystal structures of hen egg-white lysozyme.

A Real-Time Sorting Robot System for Panax Notoginseng Taproots Equipped with an Improved Deeplabv3+ Model

The classification of the taproots of Panax notoginseng is conducive to improving the economic added value of its products. In this study, a real-time sorting robot system for Panax notoginseng taproots was developed based on the improved DeepLabv3+ model. The system is equipped with the improved DeepLabv3+ classification model for different grades of Panax notoginseng taproots. The model uses Xception as the taproot feature extraction network of Panax notoginseng. In the residual structure of the Xception network, a group normalization layer with deep separable convolution is adopted. Meanwhile, the global maximum pooling method is added in the Atrous Spatial Pyramid Pooling (ASPP) part to retain more texture information, and multiple shallow effective feature layers are designed to overlap in the decoding part to minimize the loss of features and improve the segmentation accuracy of Panax notoginseng taproots of all grades. The model test results show that the Xception-DeepLabv3+ model performs better than VGG16-U-Net and ResNet50-PSPNet models, with a Mean Pixel Accuracy (MPA) and a Mean Intersection over Union (MIoU) of 78.98% and 88.98% on the test set, respectively. The improved I-Xce-DeepLabv3+ model achieves an average detection time of 0.22 s, an MPA of 85.72%, and an MIoU of 90.32%, and it outperforms Xce-U-Net, Xce-PSPNet, and Xce-DeepLabv3+ models. The system control software was developed as a multi-threaded system to design a system grading strategy, which solves the problem that the identification signal is not synchronized with the grading signal. The system test results show that the average sorting accuracy of the system is 77% and the average false detection rate is 21.97% when the conveyor belt running speed is 1.55 m/s. The separation efficiency for a single-channel system is 200–300 kg/h, which can replace the manual work of three workers. The proposed method meets the requirements of current Panax notoginseng processing enterprises and provides technical support for the intelligent separation of Panax notoginseng taproots.

Novel study on the prevalence of Trichinella spiralis in farmed American minks (Neovison vison) associated with exposure to wild rats (Rattus norvegicus) in China.

Minks and brown rats are reservoir hosts for many endoparasites including those of the genus Trichinella, a group of parasite nematodes with a worldwide distribution. However, little is known about the prevalence of Trichinella sp. infection in the American mink (Neovison vison) and rats (Rattus norvegicus) in China. Therefore, we aimed to examine the prevalence of Trichinella sp. infection in farmed minks in Weihai city, Shandong province, China and infer the possible route for Trichinella transmission to farmed American minks. In total, 289 muscle samples from minks and 102 carcasses of rats were collected from Weihai City. The appearance of Trichinella sp. was examined using the pooled artificial HCl-pepsin digestion method. The results showed that muscle larvae were detected in 20 of 289 minks (6.92%) and 2 of 102 synanthropic rats (1.96%). The larval density of Trichinella sp. in mink samples ranged from 0.025 to 0.815 larvae per gram (lpg), while the average larval burden in rats was 0.17 lpg. The isolates derived from minks and rats were identified at the species level using multiplex polymerase chain reaction (PCR), which revealed that the size of the two PCR products matched that of T.spiralis at 173 bp. Furthermore, sequence analysis showed 100% identity of the 5S rDNA inter-gene spacer regions of the two isolates to that of T.spiralis. This study presents a novel report of T.spiralis-mediated infection in minks and synanthropic rats in China. We highlight the vulnerability of farmed minks to Trichinella infection through exposure to synanthropic rats, which may raise a public health concern of potential zoonotic risks for domestic animals.

DKPNet41: Directed knight pattern network-based cough sound classification model for automatic disease diagnosis.

ProblemCough-based disease detection is a hot research topic for machine learning, and much research has been published on the automatic detection of Covid-19. However, these studies are useful for the diagnosis of different diseases.AimIn this work, we collected a new and large (n=642 subjects) cough sound dataset comprising four diagnostic categories: ‘Covid-19’, ‘heart failure’, ‘acute asthma’, and ‘healthy’, and used it to train, validate, and test a novel model designed for automatic detection.MethodThe model consists of four main components: novel feature generation based on a specifically directed knight pattern (DKP), signal decomposition using four pooling methods, feature selection using iterative neighborhood analysis (INCA), and classification using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation. Multilevel multiple pooling decomposition combined with DKP yielded 41 feature vectors (40 extracted plus one original cough sound). From these, the ten best feature vectors were selected. Based on each vector's misclassification rate, redundant feature vectors were eliminated and then merged. The merged vector's most informative features automatically selected using INCA were input to a standard kNN classifier.ResultsThe model, called DKPNet41, attained a high accuracy of 99.39% for cough sound-based multiclass classification of the four categories.ConclusionsThe results obtained in the study showed that the DKPNet41 model automatically and efficiently classifies cough sounds for disease diagnosis.

A simple pooling method for variable selection in multiply imputed datasets outperformed complex methods

BackgroundFor the development of prognostic models, after multiple imputation, variable selection is advised to be applied from the pooled model. The aim of this study is to evaluate by using a simulation study and practical data example the performance of four different pooling methods for variable selection in multiple imputed datasets. These methods are the D1, D2, D3 and recently extended Median-P-Rule (MPR) for categorical, dichotomous, and continuous variables in logistic regression models.MethodsFour datasets (n = 200 and n = 500), with 9 variables and correlations of respectively 0.2 and 0.6 between these variables, were simulated. These datasets included 2 categorical and 2 continuous variables with 20% missing at random data. Multiple Imputation (m = 5) was applied, and the four methods were compared with selection from the full model (without missing data). The same analyzes were repeated in five multiply imputed real-world datasets (NHANES) (m = 5, p = 0.05, N = 250/300/400/500/1000).ResultsIn the simulated datasets, the differences between the pooling methods were most evident in the smaller datasets. The MPR performed equal to all other pooling methods for the selection frequency, as well as for the P-values of the continuous and dichotomous variables, however the MPR performed consistently better for pooling and selecting categorical variables in multiply imputed datasets and also regarding the stability of the selected prognostic models. Analyzes in the NHANES-dataset showed that all methods mostly selected the same models. Compared to each other however, the D2-method seemed to be the least sensitive and the MPR the most sensitive, most simple, and easy method to apply.ConclusionsConsidering that MPR is the most simple and easy pooling method to use for epidemiologists and applied researchers, we carefully recommend using the MPR-method to pool categorical variables with more than two levels after Multiple Imputation in combination with Backward Selection-procedures (BWS). Because MPR never performed worse than the other methods in continuous and dichotomous variables we also advice to use MPR in these types of variables.

Residual convolutional graph neural network with subgraph attention pooling

The pooling operation is used in graph classification tasks to leverage hierarchical structures preserved in data and reduce computational complexity. However, pooling shrinkage discards graph details, and existing pooling methods may lead to the loss of key classification features. In this work, we propose a residual convolutional graph neural network to tackle the problem of key classification features losing. Particularly, our contributions are threefold: (1) Different from existing methods, we propose a new strategy to calculate sorting values and verify their importance for graph classification. Our strategy does not only use features of simple nodes but also their neighbors for the accurate evaluation of its importance. (2) We design a new graph convolutional layer architecture with the residual connection. By feeding discarded features back into the network architecture, we reduce the probability of losing critical features for graph classification. (3) We propose a new method for graph-level representation. The messages for each node are aggregated separately, and then different attention levels are assigned to each node and merged into a graph-level representation to retain structural and critical information for classification. Our experimental results show that our method leads to state-of-the-art results on multiple graph classification benchmarks.

Prediction of abnormalities in heart beat sounds using convolutional neural networks

Worldwide physicians prefer to use physical stethoscope and they listen to the heart beat sound and its rhythm to diagnose various heart conditions. In this work various abnormalities that happened in heart will be reflected in the sound of the heartbeat. In this work we have created a classification system which is based on Convolutional Neural Network (CNN) to analyze the heart beat sound to predict the abnormalities. Heart beat sound is converted to spectrogram images and then CNN is trained with those images. In order to reduce the computational time, pooling is done, so that it reduces the parameters by taking particular pixel from particular part of pixels. The parameters in CNN are varied in the convolution and pooling layers to enhance the accuracy of classification of heart beat sound. Experiment is carried out by varying number of convolutional layers and by changing the pooling methods for various combinations of CNN models and the results are analyzed to find the optimal combination which can suit for sound analysis.

Dynamics of the Emerging Genogroup of Infectious Bursal Disease Virus Infection in Broiler Farms in South Korea: A Nationwide Study.

Infectious bursal disease (IBD), caused by IBD virus (IBDV), threatens the health of the poultry industry. Recently, a subtype of genogroup (G) 2 IBDV named G2d has brought a new threat to the poultry industry. To determine the current status of IBDV prevalence in South Korea, active IBDV surveillance on 167 randomly selected broiler farms in South Korea from August 2020 to July 2021 was conducted. The bursas of Fabricius from five chickens from each farm were independently pooled and screened for IBDV using virus-specific RT-PCR. As a result, 86 farms were found to be infected with the G2d variant, 13 farms with G2b, and 2 farms with G3. Current prevalence estimation of IBDV infection in South Korea was determined as 17.8% at the animal level using pooled sampling methods. G2d IBDV was predominant compared to other genogroups, with a potentially high-risk G2d infection area in southwestern South Korea. The impact of IBDV infection on poultry productivity or Escherichia coli infection susceptibility was also confirmed. A comparative pathogenicity test indicated that G2d IBDV caused severe and persistent damage to infected chickens compared with G2b. This study highlights the importance of implementation of regular surveillance programs and poses challenges for the comprehensive prevention of IBDV infections.

The comparative accuracy of pooled vs. individual blood culture sampling methods for diagnosis of catheter-related bloodstream infection

BackgroundCatheter-related bloodstream infection (CRBSI) is associated with increased morbidity, mortality, and cost of treatment in critically ill patients. A differential time to positivity (DTP) of 120 min or more between blood cultures obtained through the catheter vs. peripheral vein is an indicator of CRBSI with high sensitivity and specificity. However, it is no clear whether pooled sampling would be as efficient as individual sampling in order to reduce costs, contamination, or anemia.MethodsThis was a prospective diagnostic study conducted at the medical ICU and semi-ICU of Khon Kaen University’s Srinagarind Hospital in Thailand from May 2020 to November 2021. Fifty patients with triple-lumen central venous catheters (CVCs) who were clinically suspected of CRBSI were enrolled. 15 mL of blood was drawn through each catheter lumen, 10 mL of which was inoculated into three blood culture bottles, and the remaining 5 mL was pooled into a single bottle. Sensitivity, specificity, accuracy, and time to positivity of the pooled blood cultures were calculated using individual blood cultures as a reference.ResultsOf the 50 patients enrolled, 14 (28%) were diagnosed with CRBSI, 57.9% of whom were infected with gram-negative bacteria as the causative pathogen (57.9%). Extensively drug-resistant (XDR) Klebsiella pneumoniae was the most common organism. Sensitivity and specificity of the pooled blood sampling method were 69.23% (95% CI [0.44–0.94]) and 97.3% (95% CI [0.92–1.02]), respectively. The area under the ROC curve (AUC) was 0.83 (95% CI [0.68–0.99]). A paired T-Test to compare time to positivity of the pooled blood bottle and the first positive culture from the individual bottles indicated statistical significance (14.9 and 12.4 h, respectively). The mean difference was 2.5 [0.9–4.1] h, with a 95% CI and a p-value of 0.006.ConclusionPooled blood sampling results in a lower sensitivity and longer time to positivity for CRBSI diagnosis in patients with triple-lumen CVCs than individual lumen sampling.Trial registration Retrospectively registered at Thai Clinical Trials Registry. The study was reviewed and approved on 08/03/2022. TCTR identification number is TCTR20220308002

Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting

Photovoltaic (PV) power is highly stochastic and volatile, and PV power forecasting is a key technology to guarantee the safe and economic operation of high-penetration renewable power systems. To improve the accuracy of PV power forecasting, a quad-kernel deep convolutional neural network (QK_CNN) model is proposed to perform intra-hour PV power forecasting for the next four timesteps: four CNNs with different kernel sizes are used to extract different local cross features between sequence elements of four timesteps; a single-kernel CNN is used to further feature extraction of these features, and then the target sequence forecasting results are obtained; global maximum pooling method is used to simplify the feature extraction process and improve model learning efficiency. Operation data from a 26.52 kW PV plant in CentralAustralia is selected as the experimental data. Compared with single-kernel CNN and hybrid models (CNN_LSTM) on 5, 10, and 15 min of resolution data, respectively, the proposed model shows better forecasting performance and is able to explain 96 % to 98 % of the total variation of the forecasted PV power. All these demonstrates that the CNNs with specific design have great potential to handle the task of PV power forecasting as well.

Local Treatment of the Primary Tumor for Patients With Metastatic Cancer (PRIME-TX): A Meta-Analysis

Local treatment of the primary tumor for patients with metastases is controversial, and prospective data across many disease sites have conflicting conclusions regarding benefits. A comprehensive search was conducted in PubMed/MEDLINE including randomized controlled trials (RCTs) published in the past 50 years. Inclusion criteria were multi-institutional RCTs of patients with metastatic disease receiving systemic therapy randomized to addition of local treatment to the primary tumor. Two primary outcome measures, overall survival (OS) and progression-free survival (PFS), were quantitatively assessed using random effects, and meta-analyses were conducted using the inverse variance method for pooling. Secondary endpoints were qualitatively assessed and included toxicity and patient-reported quality of life. Exploratory analyses were performed by treatment type and volume of disease. Eleven studies comprising 4952 patients were included (1558 patients received radiation therapy and 913 patients received surgery as primary tumor treatment). OS and PFS were not significantly improved from treatment of the primary (OS: hazard ratio [HR], 0.91; 95% confidence interval [CI], 0.80-1.05; PFS: HR, 0.88; 95% CI, 0.72-1.07). Assessment of primary local treatment modality demonstrated a significant difference in summary effect size on PFS between trials using surgery (HR, 1.15; 95% CI, 0.99-1.33) compared with radiation therapy (HR, 0.73; 95% CI, 0.56-0.96) as the local treatment modality (P=.005). In low metastatic burden patients, radiation therapy was associated with significantly improved OS (HR, 0.67; 95% CI, 0.52-0.85), but surgery was not associated with improved OS compared with no local treatment (HR, 1.12; 95% CI, 0.94-1.34). In RCTs conducted to date enrolling a variety of cancer types with variable metastatic burden, there is no consistent improvement in PFS or OS from the addition of local therapy to the primary tumor in unselected patients with metastatic disease. Carefully selected patients may derive oncologic benefit and should be discussed in tumor boards. Future prospective studies should aim to further optimize patient selection and the optimal systemic and local therapy treatment types.

Accelerating COVID-19 testing: An experimental approach.

The COVID‐19 pandemic, ever since its global outbreak in 2020, has continued to wreak havoc. Governments across the world were compelled to enforce strict nation‐wide lockdowns, while emphasising on social distancing and quarantining suspected people in order to slow down the spread of the virus. During this time, there was a massive increase in demand for COVID‐19 test kits. However, given the limited supply, countries were finding it hard to test enough people. This study proposes an approach called Encoded Blending (EB) to increase the number of tests drastically, without increasing the number of test kits. EB modifies the pooled testing method; this has been followed by countries like Germany, Israel and South Korea for mass testing their citizens. EB has the potential to reduce test kits requirement by up to 85% and 80% in a population with 5% and 10% affected cases, respectively.