Ten quick tips for effective dimensionality reduction

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks

Humans learn movements naturally, but it takes a lot of time and training to achieve expert performance in motor skills. In this review, we show how modern technologies can support people in learning new motor skills. First, we introduce important concepts in motor control, motor learning and motor skill learning. We also give an overview about the rapid expansion of machine learning algorithms and sensor technologies for human motion analysis. The integration between motor learning principles, machine learning algorithms and recent sensor technologies has the potential to develop AI-guided assistance systems for motor skill training. We give our perspective on this integration of different fields to transition from motor learning research in laboratory settings to real world environments and real world motor tasks and propose a stepwise approach to facilitate this transition.

Mapping circuit dynamics during function and dysfunction

Neural circuits can generate many spike patterns, but only some are functional. The study of how circuits generate and maintain functional dynamics is hindered by a poverty of description of circuit dynamics across functional and dysfunctional states. For example, although the regular oscillation of a central pattern generator is well characterized by its frequency and the phase relationships between its neurons, these metrics are ineffective descriptors of the irregular and aperiodic dynamics that circuits can generate under perturbation or in disease states. By recording the circuit dynamics of the well-studied pyloric circuit in Cancer borealis, we used statistical features of spike times from neurons in the circuit to visualize the spike patterns generated by this circuit under a variety of conditions. This approach captures both the variability of functional rhythms and the diversity of atypical dynamics in a single map. Clusters in the map identify qualitatively different spike patterns hinting at different dynamic states in the circuit. State probability and the statistics of the transitions between states varied with environmental perturbations, removal of descending neuromodulatory inputs, and the addition of exogenous neuromodulators. This analysis reveals strong mechanistically interpretable links between complex changes in the collective behavior of a neural circuit and specific experimental manipulations, and can constrain hypotheses of how circuits generate functional dynamics despite variability in circuit architecture and environmental perturbations.

Dietary Patterns and Risk of a New Carious Lesion Postpartum: A Cohort Study

Dental caries (cavities), one of the most common infectious diseases, is caused by a number of factors. Oral microbes, dietary practices, sociodemographic factors, and dental hygiene all inform caries risk. Assessing the impact of diet is complicated as individuals eat foods in combinations, and the interactions among the foods may alter caries risk. Our study aimed to prospectively assess the association between dietary patterns and caries risk in the postpartum period, a potentially sensitive period for caries development. We analyzed in-person dental assessments and telephone food frequency questionnaires (FFQs) from 879 Caucasian women participating in the Center for Oral Health Research in Appalachia Cohort 2 (COHRA2) that were collected biannually for up to 6 y. One-week recall of food intake frequency was assessed using a Likert scale. We used principal component analysis to summarize the FFQ data; the top 2 components described 15% and 12% of the variance in FFQ data. The first component was characterized by high consumption of fruits and vegetables, while the second component was heavily influenced by desserts and crackers. We used a modified Poisson model to predict the risk of an increase in the number of decayed, missing, and filled teeth in the postpartum period by 1) dietary patterns and 2) individual foods and beverages at the previous study visit, after controlling for other known risk factors, including history of carious lesions. Eating a dietary pattern high in desserts and crackers was associated with a 20% increase in the number of decayed, missing, and filled teeth in the postpartum period (95% confidence interval, 1.03-1.39). However, this effect was attenuated among those who also consumed a dietary pattern high in fruits and vegetables. Dietary patterns should be considered when devising interventions aimed at preventing dental caries.

Kingdom-Wide Analysis of Fungal Transcriptomes and tRNAs Reveals Conserved Patterns of Adaptive Evolution

Protein-coding genes evolved codon usage bias due to the combined but uneven effects of adaptive and nonadaptive influences. Studies in model fungi agree on codon usage bias as an adaptation for fine-tuning gene expression levels; however, such knowledge is lacking for most other fungi. Our comparative genomics analysis of over 450 species supports codon usage and transfer RNAs (tRNAs) as coadapted for translation speed and this is most likely a realization of convergent evolution. Rather than drift, phylogenetic reconstruction inferred adaptive radiation as the best explanation for the variation of interspecific codon usage bias. Although the phylogenetic signals for individual codon and tRNAs frequencies are lower than expected by genetic drift, we found remarkable conservation of highly expressed genes being codon optimized for translation by the most abundant tRNAs, especially by inosine-modified tRNAs. As an application, we present a sequence-to-expression neural network that uses codons to reliably predict highly expressed transcripts. The kingdom Fungi, with over a million species, includes many key players in various ecosystems and good targets for biotechnology. Collectively, our results have implications for better understanding the evolutionary success of fungi, as well as informing the biosynthetic manipulation of fungal genes.

Consistent changes in the intestinal microbiota of Atlantic salmon fed insect meal diets

BACKGROUND

Being part of fish's natural diets, insects have become a practical alternative feed ingredient for aquaculture. While nutritional values of insects have been extensively studied in various fish species, their impact on the fish microbiota remains to be fully explored. In an 8-week freshwater feeding trial, Atlantic salmon (Salmo salar) were fed either a commercially relevant reference diet or an insect meal diet wherein black soldier fly (Hermetia illucens) larvae meal comprised 60% of total ingredients. Microbiota of digesta and mucosa origin from the proximal and distal intestine were collected and profiled along with feed and water samples.

RESULTS

The insect meal diet markedly modulated the salmon intestinal microbiota. Salmon fed the insect meal diet showed similar or lower alpha-diversity indices in the digesta but higher alpha-diversity indices in the mucosa. A group of bacterial genera, dominated by members of the Bacillaceae family, was enriched in salmon fed the insect meal diet, which confirms our previous findings in a seawater feeding trial. We also found that microbiota in the intestine closely resembled that of the feeds but was distinct from the water microbiota. Notably, bacterial genera associated with the diet effects were also present in the feeds.

CONCLUSIONS

We conclude that salmon fed the insect meal diets show consistent changes in the intestinal microbiota. The next challenge is to evaluate the extent to which these alterations are attributable to feed microbiota and dietary nutrients, and what these changes mean for fish physiology and health.

Graph Based Feature Selection for Reduction of Dimensionality in Next-Generation RNA Sequencing Datasets

Analysis of high-dimensional data, with more features (p) than observations (N) (p>N), places significant demand in cost and memory computational usage attributes. Feature selection can be used to reduce the dimensionality of the data. We used a graph-based approach, principal component analysis (PCA) and recursive feature elimination to select features for classification from RNAseq datasets from two lung cancer datasets. The selected features were discretized for association rule mining where support and lift were used to generate informative rules. Our results show that the graph-based feature selection improved the performance of sequential minimal optimization (SMO) and multilayer perceptron classifiers (MLP) in both datasets. In association rule mining, features selected using the graph-based approach outperformed the other two feature-selection techniques at a support of 0.5 and lift of 2. The non-redundant rules reflect the inherent relationships between features. Biological features are usually related to functions in living systems, a relationship that cannot be deduced by feature selection and classification alone. Therefore, the graph-based feature-selection approach combined with rule mining is a suitable way of selecting and finding associations between features in high-dimensional RNAseq data.

A guide to machine learning for biologists

The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.

Learning Target Class Feature Subspace (LTC-FS) Using Eigenspace Analysis and N-ary Search-Based Autonomous Hyperparameter Tuning for OCSVM

Existing dimensionality reduction (DR) techniques such as principal component analysis (PCA) and its variants are not suitable for target class mining due to the negligence of unique statistical properties of class-of-interest (CoI) samples. Conventionally, these approaches utilize higher or lower eigenvalued principal components (PCs) for data transformation; but the higher eigenvalued PCs may split the target class, whereas lower eigenvalued PCs do not contribute significant information and wrong selection of PCs leads to performance degradation. Considering these facts, the present research offers a novel target class-guided feature extraction method. In this approach, initially, the eigendecomposition is performed on variance–covariance matrix of only the target class samples, where the higher- and lower-valued eigenvectors are rejected via statistical analysis, and the selected eigenvectors are utilized to extract the most promising feature subspace. The extracted feature-subset gives a more tighter description of the CoI with enhanced associativity among target class samples and ensures the strong separation from nontarget class samples. One-class support vector machine (OCSVM) is evaluated to validate the performance of learned features. To obtain optimized values of hyperparameters of OCSVM a novel [Formula: see text]-ary search-based autonomous method is also proposed. Exhaustive experiments with a wide variety of datasets are performed in feature-space (original and reduced) and eigenspace (obtained from original and reduced features) to validate the performance of the proposed approach in terms of accuracy, precision, specificity and sensitivity.

Fabrication approaches for high-throughput and biomimetic disease modeling

There is often a tradeoff between in vitro disease modeling platforms that capture pathophysiologic complexity and those that are amenable to high-throughput fabrication and analysis. However, this divide is closing through the application of a handful of fabrication approaches-parallel fabrication, automation, and flow-driven assembly-to design sophisticated cellular and biomaterial systems. The purpose of this review is to highlight methods for the fabrication of high-throughput biomaterial-based platforms and showcase examples that demonstrate their utility over a range of throughput and complexity. We conclude with a discussion of future considerations for the continued development of higher-throughput in vitro platforms that capture the appropriate level of biological complexity for the desired application. STATEMENT OF SIGNIFICANCE: There is a pressing need for new biomedical tools to study and understand disease. These platforms should mimic the complex properties of the body while also permitting investigation of many combinations of cells, extracellular cues, and/or therapeutics in high-throughput. This review summarizes emerging strategies to fabricate biomimetic disease models that bridge the gap between complex tissue-mimicking microenvironments and high-throughput screens for personalized medicine.

Paths and timings of the peopling of Polynesia inferred from genomic networks

Polynesia was settled in a series of extraordinary voyages across an ocean spanning one third of the Earth1, but the sequences of islands settled remain unknown and their timings disputed. Currently, several centuries separate the dates suggested by different archaeological surveys2-4. Here, using genome-wide data from merely 430 modern individuals from 21 key Pacific island populations and novel ancestry-specific computational analyses, we unravel the detailed genetic history of this vast, dispersed island network. Our reconstruction of the branching Polynesian migration sequence reveals a serial founder expansion, characterized by directional loss of variants, that originated in Samoa and spread first through the Cook Islands (Rarotonga), then to the Society (Tōtaiete mā) Islands (11th century), the western Austral (Tuha'a Pae) Islands and Tuāmotu Archipelago (12th century), and finally to the widely separated, but genetically connected, megalithic statue-building cultures of the Marquesas (Te Henua 'Enana) Islands in the north, Raivavae in the south, and Easter Island (Rapa Nui), the easternmost of the Polynesian islands, settled in approximately AD 1200 via Mangareva.

Subsidy as An Economic Instrument for Environmental Protection: A Case of Global Fertilizer Use

Fertilizer subsidies may constitute a key economic tool with which to provide food for the growing population. Therefore, this work aimed to (i) assess the effectiveness of subsidized chemical (NPK) fertilizer use in food production by comparing the crop output between developed and developing regions and (ii) examine the benefits of organic fertilizer and the need for its use in developing regions such as Africa. Secondary data from 2000 to 2019 on global subsidized fertilizer use, crop production, income, and other agro-environmental parameters, such as climate and soil, were collected from the international databases of the World Bank, Food and Agriculture Organization (FAO), Forest Resources Assessment (FRA), National Aeronautics and Space Administration (NASA), and World Income Inequalities Database (WID), as well as countries’ national statistics. Data were analyzed using qualitative, quantitative, and geospatial software and techniques, such as SPSS, averages, multivariate analysis, and spatial analytical Geographic Information System (GIS) tools. The results reveal that the total global fertilizer use continuously increased from 79 million tonnes in 2000 to 125 million tonnes in 2019. Subsidized fertilizer use and crop production increased with countries’ economic status. For example, countries or regions with more economic resources tended to have higher fertilizer subsidies. More than 95% of North American and European countries recorded the highest total chemical fertilizer use, ranging from 855,160 to 18,224,035 kg ha−1. In terms of organic fertilizer production, the percentage contribution in Africa relative to global production was only 2%, which was about 932,538 million tonnes below the production yield in North America. More organic fertilizer and less inorganic fertilizer should be encouraged instead of the total eradication of chemical fertilizers. This is especially applicable to developing countries, where food production is low due to poor soil and high food demand owing to a harsh environment and rapid population growth.

Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry

Most high-dimensional datasets are thought to be inherently low-dimensional-that is, data points are constrained to lie on a low-dimensional manifold embedded in a high-dimensional ambient space. Here, we study the viability of two approaches from differential geometry to estimate the Riemannian curvature of these low-dimensional manifolds. The intrinsic approach relates curvature to the Laplace-Beltrami operator using the heat-trace expansion and is agnostic to how a manifold is embedded in a high-dimensional space. The extrinsic approach relates the ambient coordinates of a manifold's embedding to its curvature using the Second Fundamental Form and the Gauss-Codazzi equation. We found that the intrinsic approach fails to accurately estimate the curvature of even a two-dimensional constant-curvature manifold, whereas the extrinsic approach was able to handle more complex toy models, even when confounded by practical constraints like small sample sizes and measurement noise. To test the applicability of the extrinsic approach to real-world data, we computed the curvature of a well-studied manifold of image patches and recapitulated its topological classification as a Klein bottle. Lastly, we applied the extrinsic approach to study single-cell transcriptomic sequencing (scRNAseq) datasets of blood, gastrulation, and brain cells to quantify the Riemannian curvature of scRNAseq manifolds.

Novel Metrics to Characterize In Vitro Pollen Tube Growth Performance of Apple Cultivars

In vitro germination assays are frequently used in screening trials to evaluate the pollen viability of pollinizers. To be effective, screening trials must have defined threshold criteria, from which individuals can then be assessed. However, despite decades of research on pollen viability, no established threshold is available to categorize apple cultivars based on their in vitro pollen tube lengths. This study aimed to identify and characterize the subgroups of cultivars based on their pollen tube growth performance. In vitro pollen tube lengths of 41 individuals were determined by incubating samples on artificial germination media at 15 and 25 °C. A six-number summary statistic was calculated, and hierarchical clustering on principal component (HCPC) analysis was used to determine and characterize subgroups. Furthermore, a decision tree model was used to predict class membership for future datasets. HCPC analysis partitioned the 41 individuals into three subgroups with different performances. The decision tree quickly predicted the cluster membership based on the second quartile at 15 °C and the third quartile at 25 °C. The thresholds from the decision tree can be used to characterize new observations. The use of the methods will be demonstrated using a case study with 29 apple accessions.

The Atlantic salmon's stress- and immune-related transcriptional responses to moderate hypoxia, an incremental temperature increase, and these challenges combined

The marine environment is predicted to become warmer, and more hypoxic, and these conditions may negatively impact the health and survival of coastal fish species, including wild and farmed Atlantic salmon (Salmo salar). Thus, we examined how: (1) moderate hypoxia (∼70% air saturation) at 12°C for 3 weeks; (2) an incremental temperature increase from 12°C to 20°C (at 1°C week-1) followed by 4 weeks at 20°C; and (3) treatment "2" combined with moderate hypoxia affected transcript expression in the liver of post-smolts as compared to control conditions (normoxia, 12°C). Specifically, we assessed the expression of 45 genes related to the heat shock response, oxidative stress, apoptosis, metabolism and immunity using a high-throughput qPCR approach (Fluidigm Biomark™ HD). The expression profiles of 27 "stress"-related genes indicated that: (i) moderate hypoxia affected the expression of several stress genes at 12°C; (ii) their expression was impacted by 16°C under normoxic conditions, and this effect increased until 20°C; (iii) the effects of moderate hypoxia were not additive to those at temperatures above 16°C; and (iv) long-term (4 weeks) exposure to 20°C, with or without hypoxia, resulted in a limited acclimatory response. In contrast, the expression of 15 immune-related genes was not greatly affected until temperatures reached 20°C, and this effect was particularly evident in fish exposed to the added challenge of hypoxia. These results provide valuable information on how these two important environmental factors affect the "stress" physiology and immunology of Atlantic salmon, and we identify genes that may be useful as hypoxia and/or temperature biomarkers in salmonids and other fishes.

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

A Statistical Perspective on the Challenges in Molecular Microbial Biology

High throughput sequencing (HTS)-based technology enables identifying and quantifying non-culturable microbial organisms in all environments. Microbial sequences have enhanced our understanding of the human microbiome, the soil and plant environment, and the marine environment. All molecular microbial data pose statistical challenges due to contamination sequences from reagents, batch effects, unequal sampling, and undetected taxa. Technical biases and heteroscedasticity have the strongest effects, but different strains across subjects and environments also make direct differential abundance testing unwieldy. We provide an introduction to a few statistical tools that can overcome some of these difficulties and demonstrate those tools on an example. We show how standard statistical methods, such as simple hierarchical mixture and topic models, can facilitate inferences on latent microbial communities. We also review some nonparametric Bayesian approaches that combine visualization and uncertainty quantification. The intersection of molecular microbial biology and statistics is an exciting new venue. Finally, we list some of the important open problems that would benefit from more careful statistical method development.

A Sight on Single-Cell Transcriptomics in Plants Through the Prism of Cell-Based Computational Modeling Approaches: Benefits and Challenges for Data Analysis

Single-cell technology is a relatively new and promising way to obtain high-resolution transcriptomic data mostly used for animals during the last decade. However, several scientific groups developed and applied the protocols for some plant tissues. Together with deeply-developed cell-resolution imaging techniques, this achievement opens up new horizons for studying the complex mechanisms of plant tissue architecture formation. While the opportunities for integrating data from transcriptomic to morphogenetic levels in a unified system still present several difficulties, plant tissues have some additional peculiarities. One of the plants' features is that cell-to-cell communication topology through plasmodesmata forms during tissue growth and morphogenesis and results in mutual regulation of expression between neighboring cells affecting internal processes and cell domain development. Undoubtedly, we must take this fact into account when analyzing single-cell transcriptomic data. Cell-based computational modeling approaches successfully used in plant morphogenesis studies promise to be an efficient way to summarize such novel multiscale data. The inverse problem's solutions for these models computed on the real tissue templates can shed light on the restoration of individual cells' spatial localization in the initial plant organ-one of the most ambiguous and challenging stages in single-cell transcriptomic data analysis. This review summarizes new opportunities for advanced plant morphogenesis models, which become possible thanks to single-cell transcriptome data. Besides, we show the prospects of microscopy and cell-resolution imaging techniques to solve several spatial problems in single-cell transcriptomic data analysis and enhance the hybrid modeling framework opportunities.

The dimensionality and structure of species trait spaces

Trait-based ecology aims to understand the processes that generate the overarching diversity of organismal traits and their influence on ecosystem functioning. Achieving this goal requires simplifying this complexity in synthetic axes defining a trait space and to cluster species based on their traits while identifying those with unique combinations of traits. However, so far, we know little about the dimensionality, the robustness to trait omission and the structure of these trait spaces. Here, we propose a unified framework and a synthesis across 30 trait datasets representing a broad variety of taxa, ecosystems and spatial scales to show that a common trade-off between trait space quality and operationality appears between three and six dimensions. The robustness to trait omission is generally low but highly variable among datasets. We also highlight invariant scaling relationships, whatever organismal complexity, between the number of clusters, the number of species in the dominant cluster and the number of unique species with total species richness. When species richness increases, the number of unique species saturates, whereas species tend to disproportionately pack in the richest cluster. Based on these results, we propose some rules of thumb to build species trait spaces and estimate subsequent functional diversity indices.

Informal coercion during childbirth: risk factors and prevalence estimates from a nationwide survey of women in Switzerland

BACKGROUND

In many countries, the increase in facility births is accompanied by a high rate of obstetric interventions. Lower birthrates or elevated risk factors such as women's higher age at childbirth and an increased need for control and security cannot entirely explain this rise in obstetric interventions. Another possible factor is that women are coerced to agree to interventions, but the prevalence of coercive interventions in Switzerland is unknown.

METHODS

In a nationwide cross-sectional online survey, we assessed the prevalence of informal coercion during childbirth, women's satisfaction with childbirth, and the prevalence of women at risk of postpartum depression. Women aged 18 years or older who had given birth in Switzerland within the previous 12 months were recruited online through Facebook ads or through various offline channels. We used multivariable logistic regression to estimate the risk ratios associated with multiple individual and contextual factors.

RESULTS

In total, 6054 women completed the questionnaire (a dropout rate of 16.2%). An estimated 26.7% of women experienced some form of informal coercion during childbirth. As compared to vaginal delivery, cesarean section (CS) and instrumental vaginal birth were associated with an increased risk of informal coercion (planned CS risk ratio [RR]: 1.52, 95% confidence interval [1.18,1.96]; unplanned CS RR: 1.92 [1.61,2.28]; emergency CS RR: 2.10 [1.71,2.58]; instrumental vaginal birth RR: 2.17 [1.85,2.55]). Additionally, migrant women (RR: 1.45 [1.26,1.66]) and women for whom a self-determined vaginal birth was more important (RR: 1.15 [1.06,1.24]) more often reported informal coercion. Emergency cesarean section (RR: 1.32 [1.08,1.62]), being transferred to hospital (RR: 1.33 [1.11,1.60]), and experiencing informal coercion (RR: 1.35 [1.19,1.54]) were all associated with a higher risk of postpartum depression. Finally, women who had a non-instrumental vaginal birth reported higher satisfaction with childbirth while women who experienced informal coercion reported lower satisfaction.

CONCLUSIONS

One in four women experience informal coercion during childbirth, and this experience is associated with a higher risk of postpartum depression and lower satisfaction with childbirth. To prevent traumatic after-effects, health care professionals should make every effort to prevent informal coercion and to ensure sensitive aftercare for all new mothers.

Ecosystem services show variable responses to future climate conditions in the Colombian páramos

Background

The páramos, the high-elevation ecosystems of the northern Andes, are well-known for their high species richness and provide a variety of ecosystem services to local subsistence-based communities and regional urbanizations. Climate change is expected to negatively affect the provision of these services, but the level of this impact is still unclear. Here we assess future climate change impact on the ecosystem services provided by the critically important páramos of the department of Boyacá in Colombia, of which over 25% of its territory is páramo.

Methods

We first performed an extensive literature review to identify useful species of Boyacá, and selected 103 key plant species that, based on their uses, support the provision of ecosystem services in the páramos. We collated occurrence information for each key species and using a Mahalanobis distance approach we applied climate niche modelling for current and future conditions.

Results

We show an overall tendency of reduction in area for all ecosystem services under future climate conditions (mostly a loss of 10% but reaching up to a loss of 40%), but we observe also increases, and responses differ in intensity loss. Services such as Food for animals, Material and Medicinal, show a high range of changes that includes both positive and negative outcomes, while for Food for humans the responses are mostly substantially negative. Responses are less extreme than those projected for individual species but are often complex because a given ecosystem service is provided by several species. As the level of functional or ecological redundancy between species is not yet known, there is an urgency to expand our knowledge on páramos ecosystem services for more species. Our results are crucial for decision-makers, social and conservation organizations to support sustainable strategies to monitor and mitigate the potential consequences of climate change for human livelihoods in mountainous settings.

Radiomic biomarkers of tumor immune biology and immunotherapy response

Immunotherapies are leading to improved outcomes for many cancers, including those with devastating prognoses. As therapies like immune checkpoint inhibitors (ICI) become a mainstay in treatment regimens, many concurrent challenges have arisen - for instance, delineating clinical responders from non-responders. Predicting response has proven to be difficult given a lack of consistent and accurate biomarkers, heterogeneity of the tumor microenvironment (TME), and a poor understanding of resistance mechanisms. For the most part, imaging data have remained an untapped, yet abundant, resource to address these challenges. In recent years, quantitative image analyses have highlighted the utility of medical imaging in predicting tumor phenotypes, prognosis, and therapeutic response. These studies have been fueled by an explosion of resources in high-throughput mining of image features (i.e. radiomics) and artificial intelligence. In this review, we highlight current progress in radiomics to understand tumor immune biology and predict clinical responses to immunotherapies. We also discuss limitations in these studies and future directions for the field, particularly if high-dimensional imaging data are to play a larger role in precision medicine.

Socio-Economic Analysis of Wood Charcoal Production as a Significant Output of Forest Bioeconomy in Africa

Wood charcoal (WCH) is a sustainable biofuel for rural and urban users because of its higher energy density and emission of marginal smoke when compared with firewood. Besides helping the poor majority who cannot afford kerosene, electricity or liquid petroleum gas (LPG), WCH is a key source of income and livelihood. This work aimed at quantifying the volume of WCH production as well as appraising its socio-economics, including environmental impacts, especially the impact of long-term deforestation and forest degradation in Africa. Historically robust data from the databases of UN-FAO, FAOSTAT, International Energy Agency (IEA), United Nations Statistics Division, UN-DESA energy statistics yearbook, and the Forest Resources Assessment (FRA) were used. The data analysis involved descriptive statistics, multivariate analysis, and geospatial techniques. The result revealed that East Africa had the highest average wood charcoal production which was 32,058,244 tonnes representing 43.2% of the production whereas West Africa had 23,831,683 tonnes denoting 32.1%. Others were North Africa (8,650,207 tonnes), Middle Africa (8,520,329 tonnes), and South Africa (1,225,062 tonnes) representing 11.6%, 11.5% and 1.6% respectively. The correlation matrix showed that WCH production for the three decades had a significant positive correlation with all the measured parameters (such as areas of forest cover, export quantity, export value, GDP, human population, climate season, average income per citizen, and literacy rate). Wood charcoal is an essential livelihood support system. New policies including commercial wood charcoal production and licensing for revenue and ecological sustainability are required. Enterprise-based approaches for poverty reduction, smallholders’ tree-growing, wood charcoal-energy conserving technologies, improved electricity supply and agricultural productivity are encouraged. The novelty of this study can also be explained by the diverse parameters examined in relation to WCH production which no other studies in the region have done.

COVID-19 trial graph: a linked graph for COVID-19 clinical trials

OBJECTIVE

Clinical trials are an essential part of the effort to find safe and effective prevention and treatment for COVID-19. Given the rapid growth of COVID-19 clinical trials, there is an urgent need for a better clinical trial information retrieval tool that supports searching by specifying criteria, including both eligibility criteria and structured trial information.

MATERIALS AND METHODS

We built a linked graph for registered COVID-19 clinical trials: the COVID-19 Trial Graph, to facilitate retrieval of clinical trials. Natural language processing tools were leveraged to extract and normalize the clinical trial information from both their eligibility criteria free texts and structured information from ClinicalTrials.gov. We linked the extracted data using the COVID-19 Trial Graph and imported it to a graph database, which supports both querying and visualization. We evaluated trial graph using case queries and graph embedding.

RESULTS

The graph currently (as of October 5, 2020) contains 3392 registered COVID-19 clinical trials, with 17 480 nodes and 65 236 relationships. Manual evaluation of case queries found high precision and recall scores on retrieving relevant clinical trials searching from both eligibility criteria and trial-structured information. We observed clustering in clinical trials via graph embedding, which also showed superiority over the baseline (0.870 vs 0.820) in evaluating whether a trial can complete its recruitment successfully.

CONCLUSIONS

The COVID-19 Trial Graph is a novel representation of clinical trials that allows diverse search queries and provides a graph-based visualization of COVID-19 clinical trials. High-dimensional vectors mapped by graph embedding for clinical trials would be potentially beneficial for many downstream applications, such as trial end recruitment status prediction and trial similarity comparison. Our methodology also is generalizable to other clinical trials.

Utilizing machine learning dimensionality reduction for risk stratification of chest pain patients in the emergency department

BACKGROUND

Chest pain is among the most common presenting complaints in the emergency department (ED). Swift and accurate risk stratification of chest pain patients in the ED may improve patient outcomes and reduce unnecessary costs. Traditional logistic regression with stepwise variable selection has been used to build risk prediction models for ED chest pain patients. In this study, we aimed to investigate if machine learning dimensionality reduction methods can improve performance in deriving risk stratification models.

METHODS

A retrospective analysis was conducted on the data of patients > 20 years old who presented to the ED of Singapore General Hospital with chest pain between September 2010 and July 2015. Variables used included demographics, medical history, laboratory findings, heart rate variability (HRV), and heart rate n-variability (HRnV) parameters calculated from five to six-minute electrocardiograms (ECGs). The primary outcome was 30-day major adverse cardiac events (MACE), which included death, acute myocardial infarction, and revascularization within 30 days of ED presentation. We used eight machine learning dimensionality reduction methods and logistic regression to create different prediction models. We further excluded cardiac troponin from candidate variables and derived a separate set of models to evaluate the performance of models without using laboratory tests. Receiver operating characteristic (ROC) and calibration analysis was used to compare model performance.

RESULTS

Seven hundred ninety-five patients were included in the analysis, of which 247 (31%) met the primary outcome of 30-day MACE. Patients with MACE were older and more likely to be male. All eight dimensionality reduction methods achieved comparable performance with the traditional stepwise variable selection; The multidimensional scaling algorithm performed the best with an area under the curve of 0.901. All prediction models generated in this study outperformed several existing clinical scores in ROC analysis.

CONCLUSIONS

Dimensionality reduction models showed marginal value in improving the prediction of 30-day MACE for ED chest pain patients. Moreover, they are black box models, making them difficult to explain and interpret in clinical practice.

The transcriptomic responses of Atlantic salmon (Salmo salar) to high temperature stress alone, and in combination with moderate hypoxia

BACKGROUND

Increases in ocean temperatures and in the frequency and severity of hypoxic events are expected with climate change, and may become a challenge for cultured Atlantic salmon and negatively affect their growth, immunology and welfare. Thus, we examined how an incremental temperature increase alone (Warm & Normoxic-WN: 12 → 20 °C; 1 °C week- 1), and in combination with moderate hypoxia (Warm & Hypoxic-WH: ~ 70% air saturation), impacted the salmon's hepatic transcriptome expr\ession compared to control fish (CT: 12 °C, normoxic) using 44 K microarrays and qPCR.

RESULTS

Overall, we identified 2894 differentially expressed probes (DEPs, FDR < 5%), that included 1111 shared DEPs, while 789 and 994 DEPs were specific to WN and WH fish, respectively. Pathway analysis indicated that the cellular mechanisms affected by the two experimental conditions were quite similar, with up-regulated genes functionally associated with the heat shock response, ER-stress, apoptosis and immune defence, while genes connected with general metabolic processes, proteolysis and oxidation-reduction were largely suppressed. The qPCR assessment of 41 microarray-identified genes validated that the heat shock response (hsp90aa1, serpinh1), apoptosis (casp8, jund, jak2) and immune responses (apod, c1ql2, epx) were up-regulated in WN and WH fish, while oxidative stress and hypoxia sensitive genes were down-regulated (cirbp, cyp1a1, egln2, gstt1, hif1α, prdx6, rraga, ucp2). However, the additional challenge of hypoxia resulted in more pronounced effects on heat shock and immune-related processes, including a stronger influence on the expression of 14 immune-related genes. Finally, robust correlations between the transcription of 19 genes and several phenotypic traits in WH fish suggest that changes in gene expression were related to impaired physiological and growth performance.

CONCLUSION

Increasing temperature to 20 °C alone, and in combination with hypoxia, resulted in the differential expression of genes involved in similar pathways in Atlantic salmon. However, the expression responses of heat shock and immune-relevant genes in fish exposed to 20 °C and hypoxia were more affected, and strongly related to phenotypic characteristics (e.g., growth). This study provides valuable information on how these two environmental challenges affect the expression of stress-, metabolic- and immune-related genes and pathways, and identifies potential biomarker genes for improving our understanding of fish health and welfare.

Neuropsychological profile in the preclinical stages of dementia: principal component analysis approach

The preclinical stages of dementia include subtle neurocognitive changes that are not easily detected in standard clinical evaluations. Neuropsychological evaluation is important for the classification and prediction of deterioration in all the phases of dementia.

TractLearn: A geodesic learning framework for quantitative analysis of brain bundles

Deep learning-based convolutional neural networks have recently proved their efficiency in providing fast segmentation of major brain fascicles structures, based on diffusion-weighted imaging. The quantitative analysis of brain fascicles then relies on metrics either coming from the tractography process itself or from each voxel along the bundle. Statistical detection of abnormal voxels in the context of disease usually relies on univariate and multivariate statistics models, such as the General Linear Model (GLM). Yet in the case of high-dimensional low sample size data, the GLM often implies high standard deviation range in controls due to anatomical variability, despite the commonly used smoothing process. This can lead to difficulties to detect subtle quantitative alterations from a brain bundle at the voxel scale. Here we introduce TractLearn, a unified framework for brain fascicles quantitative analyses by using geodesic learning as a data-driven learning task. TractLearn allows a mapping between the image high-dimensional domain and the reduced latent space of brain fascicles using a Riemannian approach. We illustrate the robustness of this method on a healthy population with test-retest acquisition of multi-shell diffusion MRI data, demonstrating that it is possible to separately study the global effect due to different MRI sessions from the effect of local bundle alterations. We have then tested the efficiency of our algorithm on a sample of 5 age-matched subjects referred with mild traumatic brain injury. Our contributions are to propose: 1/ A manifold approach to capture controls variability as standard reference instead of an atlas approach based on a Euclidean mean. 2/ A tool to detect global variation of voxels' quantitative values, which accounts for voxels' interactions in a structure rather than analyzing voxels independently. 3/ A ready-to-plug algorithm to highlight nonlinear variation of diffusion MRI metrics. With this regard, TractLearn is a ready-to-use algorithm for precision medicine.

Stability test of canonical correlation analysis for studying brain-behavior relationships: The effects of subject-to-variable ratios and correlation strengths

Canonical correlation analysis (CCA), a multivariate approach to identifying correlations between two sets of variables, is becoming increasingly popular in neuroimaging studies on brain‐behavior relationships. However, the CCA stability in neuroimaging applications has not been systematically investigated. Although it is known that the number of subjects should be greater than the number of variables due to the curse of dimensionality, it is unclear at what subject‐to‐variable ratios (SVR) and at what correlation strengths the CCA stability can be maintained. Here, we systematically assessed the CCA stability, in the context of investigating the relationship between the brain structural/functional imaging measures and the behavioral measures, by measuring the similarity of the first‐mode canonical variables across randomly sampled subgroups of subjects from a large set of 936 healthy subjects. Specifically, we tested how the CCA stability changes with SVR under two different brain‐behavior correlation strengths. The same tests were repeated using an independent data set (n = 700) for validation. The results confirmed that both SVR and correlation strength affect greatly the CCA stability—the CCA stability cannot be guaranteed if the SVR is not sufficiently high or the brain‐behavior relationship is not sufficiently strong. Based on our quantitative characterization of CCA stability, we provided a practical guideline to help correct interpretation of CCA results and proper applications of CCA in neuroimaging studies on brain‐behavior relationships.

Distinct temporal features of genuine and deliberate facial expressions of surprise

The physical properties of genuine and deliberate facial expressions remain elusive. This study focuses on observable dynamic differences between genuine and deliberate expressions of surprise based on the temporal structure of facial parts during emotional expression. Facial expressions of surprise were elicited using multiple methods and video recorded: senders were filmed as they experienced genuine surprise in response to a jack-in-the-box (Genuine), other senders were asked to produce deliberate surprise with no preparation (Improvised), by mimicking the expression of another (External), or by reproducing the surprised face after having first experienced genuine surprise (Rehearsed). A total of 127 videos were analyzed, and moment-to-moment movements of eyelids and eyebrows were annotated with deep learning-based tracking software. Results showed that all surprise displays were mainly composed of raising eyebrows and eyelids movements. Genuine displays included horizontal movement in the left part of the face, but also showed the weakest movement coupling of all conditions. External displays had faster eyebrow and eyelid movement, while Improvised displays showed the strongest coupling of movements. The findings demonstrate the importance of dynamic information in the encoding of genuine and deliberate expressions of surprise and the importance of the production method employed in research.

Reproducible analysis of disease space via principal components using the novel R package syndRomics

Biomedical data are usually analyzed at the univariate level, focused on a single primary outcome measure to provide insight into systems biology, complex disease states, and precision medicine opportunities. More broadly, these complex biological and disease states can be detected as common factors emerging from the relationships among measured variables using multivariate approaches. ‘Syndromics’ refers to an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns. A key part of the syndromic workflow is the interpretation, the visualization, and the study of robustness of the main components that characterize the disease space. We present a new software package, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis. We document the implementation of syndRomics and illustrate the use of the package in case studies of neurological trauma data.

Are British urban foxes (Vulpes vulpes) "bold"? The importance of understanding human-wildlife interactions in urban areas

Human-wildlife interactions are believed to be increasing in urban areas. In Britain, numerous media reports have stated that urban foxes (Vulpes vulpes) are becoming "bolder," thereby posing a risk to public safety. However, such claims overlook how an individual's personality might influence urban fox behavior. Personality determines multiple aspects of an animal's interactions with both conspecifics and its environment, and can have a significant impact on how people perceive wildlife. Furthermore, describing urban foxes as "bold" confounds two different but inter-related behaviors, both of which influence an animal's propensity to take risks. Neophobia affects an animal's reaction to novelty, wariness its reaction to potential threats. Since urban wildlife frequently encounters both novel and threatening stimuli, a highly adaptable species such as the red fox might be predicted to exhibit reduced neophobia and wariness. We investigated how social status influenced both behaviors in Bristol's fox population. Dominant foxes were significantly more neophobic and warier than subordinates, which adopt a more exploratory and risk-taking lifestyle to meet their energetic and other needs. We found no seasonal effect on neophobia and wariness, although this may be due to sample size. The presence of conspecifics decreased neophobia for dominants, and wariness for both dominants and subordinates. We highlight the importance of considering animal social status and personality when planning management protocols, since interventions that destabilize fox social groups are likely to increase the number of subordinate foxes in the population, thereby increasing rather than decreasing the number of interactions between humans and urban foxes.

D-EE: Distributed software for visualizing intrinsic structure of large-scale single-cell data

BACKGROUND

Dimensionality reduction and visualization play vital roles in single-cell RNA sequencing (scRNA-seq) data analysis. While they have been extensively studied, state-of-the-art dimensionality reduction algorithms are often unable to preserve the global structures underlying data. Elastic embedding (EE), a nonlinear dimensionality reduction method, has shown promise in revealing low-dimensional intrinsic local and global data structure. However, the current implementation of the EE algorithm lacks scalability to large-scale scRNA-seq data.

RESULTS

We present a distributed optimization implementation of the EE algorithm, termed distributed elastic embedding (D-EE). D-EE reveals the low-dimensional intrinsic structures of data with accuracy equal to that of elastic embedding, and it is scalable to large-scale scRNA-seq data. It leverages distributed storage and distributed computation, achieving memory efficiency and high-performance computing simultaneously. In addition, an extended version of D-EE, termed distributed optimization implementation of time-series elastic embedding (D-TSEE), enables the user to visualize large-scale time-series scRNA-seq data by incorporating experimentally temporal information. Results with large-scale scRNA-seq data indicate that D-TSEE can uncover oscillatory gene expression patterns by using experimentally temporal information.

CONCLUSIONS

D-EE is a distributed dimensionality reduction and visualization tool. Its distributed storage and distributed computation technique allow us to efficiently analyze large-scale single-cell data at the cost of constant time speedup. The source code for D-EE algorithm based on C and MPI tailored to a high-performance computing cluster is available at https://github.com/ShaokunAn/D-EE.

Performance of family health teams for tackling chronic diseases in a state of the Amazon

The most common cause of death worldwide is noncommunicable diseases. A cross-sectional study was conducted to evaluate the adequacy of the work process among family health teams and compare differences in regional adequacy in the state of Tocantins, in the Amazonian Region, Brazil. Categorical principal components analysis was used, and scores of each principal component extracted in the analysis were compared among health regions in Tocantins. A post hoc analysis was performed to compare the heath region pairs. The adequacy of family health teams’ work process was evaluated with respect to the Strategic Action Plan to Tackle NCDs. The results showed that the family health teams performed actions according to the Strategic Action Plan to Tackle NCDs. However, overall, the adequacy percentages of these actions according to the axes of the Plan are very uneven in Tocantins, with large variations among health regions. The family health teams in the Bico do Papagaio (Region 1), Médio Norte Araguaia (Region 2), Cantão (Region 4) and Capim Dourado (Region 5) regions have adequacy percentages ≥ 50% with the Strategic Action Plan to Tackle NCDs, whereas all other regions have percentages <50%. Health teams perform surveillance actions, health promotion, and comprehensive care for NCDs in accordance with the guidelines of the Strategic Action Plan to Tackle NCDs. The challenge of NCDs in primary care requires a care model that is tailored to users’ needs and has the power to reduce premature mortality and its determinants.

Answerable and Unanswerable Questions in Risk Analysis with Open-World Novelty

Decision analysis and risk analysis have grown up around a set of organizing questions: what might go wrong, how likely is it to do so, how bad might the consequences be, what should be done to maximize expected utility and minimize expected loss or regret, and how large are the remaining risks? In probabilistic causal models capable of representing unpredictable and novel events, probabilities for what will happen, and even what is possible, cannot necessarily be determined in advance. Standard decision and risk analysis questions become inherently unanswerable ("undecidable") for realistically complex causal systems with "open-world" uncertainties about what exists, what can happen, what other agents know, and how they will act. Recent artificial intelligence (AI) techniques enable agents (e.g., robots, drone swarms, and automatic controllers) to learn, plan, and act effectively despite open-world uncertainties in a host of practical applications, from robotics and autonomous vehicles to industrial engineering, transportation and logistics automation, and industrial process control. This article offers an AI/machine learning perspective on recent ideas for making decision and risk analysis (even) more useful. It reviews undecidability results and recent principles and methods for enabling intelligent agents to learn what works and how to complete useful tasks, adjust plans as needed, and achieve multiple goals safely and reasonably efficiently when possible, despite open-world uncertainties and unpredictable events. In the near future, these principles could contribute to the formulation and effective implementation of more effective plans and policies in business, regulation, and public policy, as well as in engineering, disaster management, and military and civil defense operations. They can extend traditional decision and risk analysis to deal more successfully with open-world novelty and unpredictable events in large-scale real-world planning, policymaking, and risk management.

Associations between dietary patterns and gene expression pattern in peripheral blood mononuclear cells: A cross-sectional study

BACKGROUND AND AIMS

Diet may alter gene expression in immune cells involved in atherosclerotic cardiovascular disease susceptibility. However, we still lack a robust understanding of the association between diet and immune cell-related gene expression in humans. Therefore, we examined associations between dietary patterns (DPs) and gene expression profiles in peripheral blood mononuclear cells (PBMCs) in a population of healthy, Norwegian adults (n = 130 women and 105 men).

METHODS AND RESULTS

We used factor analysis to define a posteriori DPs from food frequency questionnaire-based dietary assessment data. In addition, we derived interpretable features from microarray-based gene expression data (13 967 transcripts) using two algorithms: CIBERSORT for estimation of cell subtype proportions, and weighted gene co-expression network analysis (WGCNA) for cluster discovery. Finally, we associated DPs with either CIBERSORT-predicted PBMC leukocyte distribution or WGCNA gene clusters using linear regression models. We detected three DPs that broadly reflected Western, Vegetarian, and Low carbohydrate diets. CIBERSORT-predicted percentage of monocytes associated negatively with the Vegetarian DP. For women, the Vegetarian DP associated with a large gene cluster consisting of 600 genes mainly involved in regulation of DNA transcription, whereas for men, the Western DP inversely associated with a smaller cluster of 36 genes mainly involved in regulation of metabolic and inflammatory processes. A subsequent protein-protein interaction network analysis suggested that genes within these clusters might physically interact in biological networks.

CONCLUSIONS

Although the present findings are exploratory, our analysis pipeline serves as a useful framework for studying the association between diet and gene expression.

Exploring high-dimensional biological data with sparse contrastive principal component analysis

MOTIVATION

Statistical analyses of high-throughput sequencing data have re-shaped the biological sciences. In spite of myriad advances, recovering interpretable biological signal from data corrupted by technical noise remains a prevalent open problem. Several classes of procedures, among them classical dimensionality reduction techniques and others incorporating subject-matter knowledge, have provided effective advances. However, no procedure currently satisfies the dual objectives of recovering stable and relevant features simultaneously.

RESULTS

Inspired by recent proposals for making use of control data in the removal of unwanted variation, we propose a variant of principal component analysis (PCA), sparse contrastive PCA that extracts sparse, stable, interpretable and relevant biological signal. The new methodology is compared to competing dimensionality reduction approaches through a simulation study and via analyses of several publicly available protein expression, microarray gene expression and single-cell transcriptome sequencing datasets.

AVAILABILITY AND IMPLEMENTATION

A free and open-source software implementation of the methodology, the scPCA R package, is made available via the Bioconductor Project. Code for all analyses presented in this article is also available via GitHub.

CONTACT

philippe_boileau@berkeley.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Generating wall shear stress for coronary artery in real-time using neural networks: Feasibility and initial results based on idealized models

Computational fluid dynamics (CFD) and medical imaging can be integrated to derive some important hemodynamic parameters such as wall shear stress (WSS). However, CFD suffers from a relatively long computational time that usually varies from dozens of minutes to hours. Machine learning is a popular tool that has been applied to many fields, and it can predict outcomes fast and even instantaneously in most applications. This study aims to use machine learning as an alternative to CFD for generating hemodynamic parameters in real-time diagnosis during medical examinations. To perform the feasibility study, we used CFD to model the blood flow in 2000 idealized coronary arteries, and the calculated WSS values in these models were used as the dataset for training and testing. The preparation of the dataset was automated by scripts programmed in Python, and OpenFOAM was used as the CFD solver. We have explored multivariate linear regression, multi-layer perceptron, and convolutional neural network architectures to generate WSS values from coronary artery geometry directly without CFD. These architectures were implemented in TensorFlow 2.0. Our results showed that these algorithms were able to generate results in less than 1 s, proving its capability in real-time applications, in terms of computational time. Based on the accuracy, convolutional neural network outperformed the other architectures with a normalized mean absolute error of 2.5%. Although this study is based on idealized models, to the best of our knowledge, it is the first attempt to predict WSS in a stenosed coronary artery using machine learning approaches.

A cautionary note on the use of unsupervised machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices

Genetic surveillance of malaria parasites supports malaria control programmes, treatment guidelines and elimination strategies. Surveillance studies often pose questions about malaria parasite ancestry (e.g. how antimalarial resistance has spread) and employ statistical methods that characterise parasite population structure. Many of the methods used to characterise structure are unsupervised machine learning algorithms which depend on a genetic distance matrix, notably principal coordinates analysis (PCoA) and hierarchical agglomerative clustering (HAC). PCoA and HAC are sensitive to both the definition of genetic distance and algorithmic specification. Importantly, neither algorithm infers malaria parasite ancestry. As such, PCoA and HAC can inform (e.g. via exploratory data visualisation and hypothesis generation), but not answer comprehensively, key questions about malaria parasite ancestry. We illustrate the sensitivity of PCoA and HAC using 393 Plasmodium falciparum whole genome sequences collected from Cambodia and neighbouring regions (where antimalarial resistance has emerged and spread recently) and we provide tentative guidance for the use and interpretation of PCoA and HAC in malaria parasite genetic epidemiology. This guidance includes a call for fully transparent and reproducible analysis pipelines that feature (i) a clearly outlined scientific question; (ii) a clear justification of analytical methods used to answer the scientific question along with discussion of any inferential limitations; (iii) publicly available genetic distance matrices when downstream analyses depend on them; and (iv) sensitivity analyses. To bridge the inferential disconnect between the output of non-inferential unsupervised learning algorithms and the scientific questions of interest, tailor-made statistical models are needed to infer malaria parasite ancestry. In the absence of such models speculative reasoning should feature only as discussion but not as results.

Morphological Diversity and Genetic Relationships in Pulque Production Agaves in Tlaxcala, Mexico, by Means of Unsupervised Learning and Gene Sequencing Analysis

Pulque is one of the oldest fermented beverages, with its origins dating back to pre-Hispanic Mexico. Recently, public consumption has increased. However, the majority of Agave plantations for pulque production have disappeared or been abandoned in recent decades. To create strategies for the conservation and production of pulque agaves, it is necessary to first determine their taxonomic identities and to better understand their genetic and morphological diversity. Despite the historical importance of pulque in Mexico, little attention has been placed on the study of Agave plants used for its production. Therefore, we analyzed the morphological diversity of vegetative characters of nine landraces of two Agave species (A. salmiana and A. mapisaga) which are widely cultivated for pulque production in Tlaxcala, Mexico. The analysis of morphological characters showed that the landraces largely clustered based on classic taxonomic relationships. One cluster of landraces associated with Agave mapisaga var. mapisaga and another with A. salmiana subsp. salmiana, but with the exception of A. salmiana subsp. salmiana "Ayoteco", which is more closely related with A. mapisaga var. mapisaga. Additionally, we analyzed the genetic relationships between 14 landraces and wild individuals using molecular markers (trnL and ITS). The identified genetic variants or haplotypes and genetic pools mainly corresponded with the species. In the case of "Ayoteco", incongruence between markers was observed. Low selection intensity, genetic flow events, and the plasticity of morphological traits may explain the high number of landraces without clear differences in their morphological diversity (vegetative characters) or genetic pools. The use of reproductive traits and massive sequencing might be useful for identifying possible morphological and genetic changes in the Agave landraces used for pulque production.

Ontario Neurodegenerative Disease Research Initiative (ONDRI): Structural MRI Methods and Outcome Measures

The Ontario Neurodegenerative Research Initiative (ONDRI) is a 3 years multi-site prospective cohort study that has acquired comprehensive multiple assessment platform data, including 3T structural MRI, from neurodegenerative patients with Alzheimer's disease, mild cognitive impairment, Parkinson's disease, amyotrophic lateral sclerosis, frontotemporal dementia, and cerebrovascular disease. This heterogeneous cross-section of patients with complex neurodegenerative and neurovascular pathologies pose significant challenges for standard neuroimaging tools. To effectively quantify regional measures of normal and pathological brain tissue volumes, the ONDRI neuroimaging platform implemented a semi-automated MRI processing pipeline that was able to address many of the challenges resulting from this heterogeneity. The purpose of this paper is to serve as a reference and conceptual overview of the comprehensive neuroimaging pipeline used to generate regional brain tissue volumes and neurovascular marker data that will be made publicly available online.

A CT-Based Radiomics Approach for the Differential Diagnosis of Sarcomatoid and Clear Cell Renal Cell Carcinoma

This study was aimed at building a computed tomography- (CT-) based radiomics approach for the differentiation of sarcomatoid renal cell carcinoma (SRCC) and clear cell renal cell carcinoma (CCRCC). It involved 29 SRCC and 99 CCRCC patient cases, and to each case, 1029 features were collected from each of the corticomedullary phase (CMP) and nephrographic phase (NP) image. Then, features were selected by using the least absolute shrinkage and selection operator regression method and the selected features of the two phases were explored to build three radiomics approaches for SRCC and CCRCC classification. Meanwhile, subjective CT findings were filtered by univariate analysis to construct a radiomics model and further selected by Akaike information criterion for integrating with the selected image features to build the fifth model. Finally, the radiomics models utilized the multivariate logistic regression method for classification and the performance was assessed with receiver operating characteristic curve (ROC) and DeLong test. The radiomics models based on the CMP, the NP, the CMP and NP, the subjective findings, and the combined features achieved the AUC (area under the curve) value of 0.772, 0.938, 0.966, 0.792, and 0.974, respectively. Significant difference was found in AUC values between each of the CMP radiomics model (0.0001 ≤ p ≤ 0.0051) and the subjective findings model (0.0006 ≤ p ≤ 0.0079) and each of the NP radiomics model, the CMP and NP radiomics model, and the combined model. Sarcomatoid change is a common pathway of dedifferentiation likely occurring in all subtypes of renal cell carcinoma, and the CT-based radiomics approaches in this study show the potential for SRCC from CCRCC differentiation.