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Plastic shopping bags are often discarded as litter and can be carried away from roadsides and become tangled on cotton plants in farm fields. This rubbish plastic can end up in the cotton at the gin if not removed before harvest. These bags may not only cause problems in the ginning process but might also become embedded in cotton fibers, reducing the quality and marketable value. Therefore, detecting, locating, and removing the bags before the cotton is harvested is required. Manually detecting and locating these bags in cotton fields is a tedious, time-consuming, and costly process. To solve this, this paper shows the application of YOLOv5 to detect white and brown colored plastic bags tangled at three different heights in cotton plants (bottom, middle, top) using Unmanned Aircraft Systems (UAS)-acquired Red, Green, Blue (RGB) images. It was found that an average white and brown bag could be detected at 92.35% and 77.87% accuracies and a mean average precision (mAP) of 87.68%. Similarly, the trained YOLOv5 model, on average, could detect 94.25% of the top, 49.58% of the middle, and only 5% of the bottom bags. It was also found that both the color of the bags (p < 0.001) and their height on cotton plants (p < 0.0001) had a significant effect on detection accuracy. The findings reported in this paper can help in the autonomous detection of plastic contaminants in cotton fields and potentially speed up the mitigation efforts, thereby reducing the amount of contaminants in cotton gins.

We present PALLAS, a practical method for gene regulatory network (GRN) inference from time series data, which employs penalized maximum likelihood and particle swarms for optimization. PALLAS is based on the Partially-Observable Boolean Dynamical System (POBDS) model and thus does not require ad-hoc binarization of the data. The penalty in the likelihood is a LASSO regularization term, which encourages the resulting network to be sparse. PALLAS is able to scale to networks of realistic size under no prior knowledge, by virtue of a novel continuous-discrete Fish School Search particle swarm algorithm for efficient simultaneous maximization of the penalized likelihood over the discrete space of networks and the continuous space of observational parameters. The performance of PALLAS is demonstrated by a comprehensive set of experiments using synthetic data generated from real and artificial networks, as well as real time series microarray and RNA-seq data, where it is compared to several other well-known methods for gene regulatory network inference. The results show that PALLAS can infer GRNs more accurately than other methods, while being capable of working directly on gene expression data, without need of ad-hoc binarization. PALLAS is a fully-fledged program, written in python, and available on GitHub (https://github.com/yukuntan92/PALLAS).

Background Excessive complement activation has been implicated in the pathogenesis of coronavirus disease 2019 (COVID-19), but the mechanisms leading to this response remain unclear. Methods We measured plasma levels of key complement markers, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA and antibodies against SARS-CoV-2 and seasonal human common cold coronaviruses (CCCs) in hospitalized patients with COVID-19 of moderate (n = 18) and critical severity (n = 37) and in healthy controls (n = 10). Results We confirmed that complement activation is systemically increased in patients with COVID-19 and is associated with a worse disease outcome. We showed that plasma levels of C1q and circulating immune complexes were markedly increased in patients with severe COVID-19 and correlated with higher immunoglobulin (Ig) G titers, greater complement activation, and higher disease severity score. Additional analyses showed that the classical pathway was the main arm responsible for augmented complement activation in severe patients. In addition, we demonstrated that a rapid IgG response to SARS-CoV-2 and an anamnestic IgG response to the nucleoprotein of the CCCs were strongly correlated with circulating immune complex levels, complement activation, and disease severity. Conclusions These findings indicate that early, nonneutralizing IgG responses may play a key role in complement overactivation in severe COVID-19. Our work underscores the urgent need to develop therapeutic strategies to modify complement overactivation in patients with COVID-19.

Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and accuracy. In addition, we also demonstrate the effect of height of plastic bags on the detection accuracy. It was found that color of bags had significant effect (p < 0.001) on accuracy across all the four variants while it did not show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p = 0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags, but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001). Additionally, height of plastic bags had significant effect (p < 0.0001) on overall detection accuracy. The findings reported in this paper can be useful in speeding up removal of plastic bags from cotton fields before harvest and thereby reducing the amount of contaminants that end up at cotton gins., preprint

Machine Learning Requires Probability and Statistics The contemporary practice of machine learning often involves the application of deterministic, computationally intensive algorithms to iteratively minimize a criterion of fit between a discriminant and sample data. There is often little interest in using probability to model the uncertainty in the problem and statistics to characterize the behavior of predictors derived from data, with the emphasis being on computation and coding. It follows that little can be stated about performance on future data, beyond perhaps a simple error count on a given test set. In this article, we argue that the knowledge imparted by deterministic computational methods is not rigorously related to the real world and, in particular, future events. This connection requires rigorous probabilistic modeling and statistical inference as well as an understanding of the proper role of computation and an appreciation of epistemological issues.

Uncovering links between processing conditions, microstructure, and properties is a central tenet of materials analysis. It is well known that microstructure determines properties, but expressing these structural features in a universal quantitative fashion has proved to be extremely difficult. Recent efforts have focused on training supervised learning algorithms to place microstructure images into predefined classes, but this approach assumes a level of a priori knowledge that may not always be available. In this paper, we expand this idea to the semi-supervised context in which class labels are known with confidence for only a fraction of the microstructures that represent the material system. It is shown that classifiers which perform well on both the high-confidence labeled data and the unlabeled, ambiguous data can be constructed by relying on the labeling consensus of a collection of semi-supervised learning methods. We also demonstrate the use of novel error estimation approaches for unlabeled data to establish robust confidence bounds on the classification performance over the entire microstructure space.

For most diseases, building large databases of labeled genetic data is an expensive and time-demanding task. To address this, we introduce genetic Generative Adversarial Networks (gGAN), a semi-supervised approach based on an innovative GAN architecture to create large synthetic genetic data sets starting with a small amount of labeled data and a large amount of unlabeled data. Our goal is to create a mechanism able to increase the sample size of the labeled data and generalize learning over different populations while keeping awareness of the quality of its own predictions. The proposed model achieved satisfactory results using real genetic data from different datasets and populations, in which the test populations may not have the same genetic profiles. The proposed model is self-aware and capable of determining whether a new genetic profile has enough compatibility with the data on which the network was trained and is thus suitable for prediction. The code and datasets used can be found at https://github.com/caio-davi/gGAN

Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model for COVID-19 transmission, based on a discrete-time spatio-temporal susceptible/exposed/infected/recovered/deceased (SEIRD) model. The proposed framework allows the hidden SEIRD states and unknown transmission parameters to be estimated from noisy, incomplete time series of reported epidemiological data, by application of unscented Kalman filtering (UKF), maximum-likelihood adaptive filtering, and metaheuristic optimization. Experiments using both synthetic data and real data from the Fall 2020 Covid-19 wave in the state of Texas demonstrate the effectiveness of the proposed model., 17 pages, 5 figures

Dengue has become one of the most important worldwide arthropod-borne diseases. Dengue phenotypes are based on laboratorial and clinical exams, which are known to be inaccurate. Objective : We present a machine learning approach for the prediction of dengue fever severity based solely on human genome data. Methods : One hundred and two Brazilian dengue patients and controls were genotyped for 322 innate immunity single nucleotide polymorphisms (SNPs). Our model uses a support vector machine algorithm to find the optimal loci classification subset and then an artificial neural network (ANN) is used to classify patients into dengue fever or severe dengue. Results : The ANN trained on 13 key immune SNPs selected under dominant or recessive models produced median values of accuracy greater than 86%, and sensitivity and specificity over 98% and 51%, respectively. Conclusion : The proposed classification method, using only genome markers, can be used to identify individuals at high risk for developing the severe dengue phenotype even in uninfected conditions. Significance : Our results suggest that the genetic context is a key element in phenotype definition in dengue. The methodology proposed here is extendable to other Mendelian based and genetically influenced diseases.

Nonlinear state-space models are ubiquitous in modeling real-world dynamical systems. Sequential Monte Carlo (SMC) techniques, also known as particle methods, are a well-known class of parameter estimation methods for this general class of state-space models. Existing SMC-based techniques rely on excessive sampling of the parameter space, which makes their computation intractable for large systems or tall data sets. Bayesian optimization techniques have been used for fast inference in state-space models with intractable likelihoods. These techniques aim to find the maximum of the likelihood function by sequential sampling of the parameter space through a single SMC approximator. Various SMC approximators with different fidelities and computational costs are often available for sample-based likelihood approximation. In this paper, we propose a multi-fidelity Bayesian optimization algorithm for the inference of general nonlinear state-space models (MFBO-SSM), which enables simultaneous sequential selection of parameters and approximators. The accuracy and speed of the algorithm are demonstrated by numerical experiments using synthetic gene expression data from a gene regulatory network model and real data from the VIX stock price index.

Control of gene regulatory networks (GRNs) to shift gene expression from undesirable states to desirable ones has received much attention in recent years. Most of the existing methods assume that the cost of intervention at each state and time point, referred to as the immediate cost function, is fully known. In this paper, we employ the Partially-Observed Boolean Dynamical System (POBDS) signal model for a time sequence of noisy expression measurement from a Boolean GRN and develop a Bayesian Inverse Reinforcement Learning (BIRL) approach to address the realistic case in which the only available knowledge regarding the immediate cost function is provided by the sequence of measurements and interventions recorded in an experimental setting by an expert. The Boolean Kalman Smoother (BKS) algorithm is used for optimally mapping the available gene-expression data into a sequence of Boolean states, and then the BIRL method is efficiently combined with the Q-learning algorithm for quantification of the immediate cost function. The performance of the proposed methodology is investigated by applying a state-feedback controller to two GRN models: a melanoma WNT5A Boolean network and a p53-MDM2 negative feedback loop Boolean network, when the cost of the undesirable states, and thus the identity of the undesirable genes, is learned using the proposed methodology.

This paper proposes a methodology to monitor and control gene regulatory networks (GRNs) via noisy measurements in an infinite observation space. Toward this end, we employ the partially observed Boolean dynamical system (POBDS) signal model. The proposed methodology consists of offline and online steps. In the offline step, a family of point-based methods is applied to the POBDS model to gather the necessary control policy prior to the online (execution) step. This is accomplished by developing efficient backup and belief expansion processes to make the computation scale with the log of the number of states, as opposed to the complexity of existing point-based methods, which grows with the number of states. In the online step, simultaneous monitoring and control is achieved by a one-step look-ahead search procedure using the optimal state estimation algorithm for the POBDS model, known as the Boolean Kalman filter (BKF), as well as the information gathered in the offline step. The online one-step look-ahead process confers robustness to changes in system dynamics, possibility of starting the execution process before the completion of the offline step. The use of the BKF for simultaneous monitoring and control during the online stage can be key in assessing possible side effects of intervention. The performance of the proposed methodology is investigated through a comprehensive set of numerical experiments using synthetic gene expression data generated from a melanoma GRN.

Motor imagery brain computer interface designs are considered difficult due to limitations in subject-specific data collection and calibration, as well as demanding system adaptation requirements. Recently, subject-independent (SI) designs received attention because of their possible applicability to multiple users without prior calibration and rigorous system adaptation. SI designs are challenging and have shown low accuracy in the literature. Two major factors in system performance are the classification algorithm and the quality of available data. This paper presents a comparative study of classification performance for both SS and SI paradigms. Our results show that classification algorithms for SS models display large variance in performance. Therefore, distinct classification algorithms per subject may be required. SI models display lower variance in performance but should only be used if a relatively large sample size is available. For SI models, LDA and CART had the highest accuracy for small and moderate sample size, respectively, whereas we hypothesize that SVM would be superior to the other classifiers if large training sample-size was available. Additionally, one should choose the design approach considering the users. While the SS design sound more promising for a specific subject, an SI approach can be more convenient for mentally or physically challenged users.

We propose a new algorithm for inference of protein-protein interaction (PPI) networks from noisy time series of Liquid-Chromatography Mass-Spectrometry (LC-MS) proteomic expression data based on Approximate Bayesian Computation - Sequential Monte Carlo sampling (ABC-SMC). The algorithm is an extension of our previous framework PALLAS. The proposed algorithm can be easily modified to handle other complex models of expression data, such as LC-MS data, for which the likelihood function is intractable. Results based on synthetic time series of cytokine LC-MS measurements corresponding to a prototype immunomic network demonstrate that our algorithm is capable of inferring the network topology accurately.

There are few data considering human genetics as an important risk factor for birth abnormalities related to ZIKV infection during pregnancy, even though sub-Saharan African populations are apparently more resistant to CZS as compared to populations in the Americas. We hypothesized that single nucleotide variants (SNVs), especially in innate immune genes, could make some populations more susceptible to Zika congenital complications than others. Differences in the SNV frequencies among continental populations provide great potential for Machine Learning techniques. We explored a key immune genomic gradient between individuals from Africa, Asia and Latin America, working with complex signatures, using 297 SNVs. We employed a two-step approach. In the first step, decision trees (DTs) were used to extract the most discriminating SNVs among populations. In the second step, machine learning algorithms were used to evaluate the quality of the SNV pool identified in step one for discriminating between individuals from sub-Saharan African and Latin-American populations. Our results suggest that 10 SNVs from 10 genes (CLEC4M, CD58, OAS2, CD80, VEPH1, CTLA4, CD274, CD209, PLAAT4, CREB3L1) were able to discriminate sub-Saharan Africans from Latin American populations using only immune genome data, with an accuracy close to 100%. Moreover, we found that these SNVs form a genome gradient across the three main continental populations. These SNVs are important elements of the innate immune system and in the response against viruses. Our data support the Human Immune Genome Complex Gradient hypothesis as a new theory that may help to explain the CZS catastrophe in Brazil.

We present PALLAS, a practical method for gene regulatory network (GRN) inference from time series data, which employs penalized maximum likelihood and particle swarms for optimization. PALLAS is based on the Partially-Observable Boolean Dynamical System (POBDS) model and thus does not require ad-hoc binarization of the data. The penalty in the likelihood is a LASSO regularization term, which encourages the resulting network to be sparse. PALLAS is able to scale to large networks under no prior knowledge, by virtue of a novel continuous-discrete Fish School Search particle swarm algorithm for efficient simultaneous maximization of the penalized likelihood over the discrete space of networks and the continuous space of observational parameters. The performance of PALLAS is demonstrated by a comprehensive set of experiments using synthetic data generated from real and artificial networks, as well as real time series microarray and RNA-seq data, where it is compared to several other well-known methods for gene regulatory network inference. The results show that PALLAS can infer GRNs efficiently and accurately. PALLAS is a fully-fledged program with a commandline user interface, written in python, and available on GitHub (https://github.com/yukuntan92/PALLAS).

Optimal classification requires full knowledge of the feature-label distribution. In practice, that is a relatively rare scenario, and a combination of distributional knowledge and sample data must be employed to obtain a classifier. In this chapter, we introduce the basic concepts related to samplebased classification, including designed classifiers and error rates, and consistency. The chapter includes a section showing that distribution-free classification rules have important limitations. The material in this chapter provides the foundation for the next several chapters on sample-based classification.

All the classification rules seen so far were plug-in rules, that is, they could be viewed as distribution estimation using training data. We consider now a different idea: iteratively adjusting a discriminant (decision boundary) to the training data by optimizing an error criterion. This is reminiscent in some ways to the process of human learning. Not surprisingly, this is the basic idea behind many popular classification rules: support vector machines, neural networks, decision trees, and rankbased classifiers. We examine each of these classification rules in this chapter.

In this chapter, we address an obvious question about classification: which classification rule should one choose for a given problem? A related question is how to pick the free parameters of a classification rule.

If one knew the feature-label distribution of a problem, then one could in principle compute the exact error of a classifier. In many practical cases, such knowledge is not available, and it is necessary to estimate the error of a classifier using sample data. If the sample size is large enough, it is possible to divide the available data into training and testing samples, design a classifier on the training set, and evaluate it on the test set. In this chapter, it is shown that this produces a very accurate error estimator, provided that there is an abundance of test points. However, large test samples are not possible if the overall sample size is small, in which case training and testing has to be performed on the same data. This chapter provides a comprehensive survey of different classification error estimation procedures and how to assess their performance.

This paper proposes an approach for finite-horizon control of partially-observed Boolean dynamical systems (POBDS) with uncertain continuous control input and infinite observation space. To cope with the partial observability of states, the proposed method first maps the POBDS to an unnormalized belief space. The nonlinear dynamics in this continuous belief space are linearized over a nominal trajectory. Then, the optimal feedback controller is derived, based on the well-known linear quadratic regulator (LQR), to push the system to follow the nominal trajectory. This nominal trajectory is computed in a planning stage before starting execution, and updated efficiently during execution, whenever the system is found to deviate from the nominal trajectory. We prove that, under mild regularization conditions, the proposed controller approaches the cost of the nominal trajectory as the linearization error approaches zero. The performance of the proposed controller is demonstrated by numerical experiments with a Melanoma gene regulatory network observed through noisy gene expression measurements.

We present a framework for the simultaneous estimation of state and parameters of partially observed Boolean dynamical systems (POBDS). Simultaneous state and parameter estimation is achieved through the combined use of the Boolean Kalman filter and Boolean Kalman smoother, which provide the minimum mean-square error state estimators for the POBDS model, and maximum-likelihood (ML) parameter estimation; in the presence of continuous parameters, ML estimation is performed using the expectation–maximization algorithm. The performance of the proposed ML adaptive filter is demonstrated by numerical experiments with a POBDS model of gene regulatory networks observed through noisy next-generation sequencing (RNA-seq) time series data using the well-known p53-MDM2 negative-feedback loop gene regulatory model.