Imaging hydrological dynamics in karst unsaturated zones by time-lapse electrical resistivity tomography

The hydrodynamics of karst terrain are highly complex due to the diverse fractures and reservoirs within limestone formations. The time delay between rainfall events and subsequent flow into reservoirs exhibits significant variability. However, these hydrological processes are not easily visualized in karst topography. Subsurface geophysics, specifically 2D time-lapse electrical resistivity tomography (ERT), provides an effective method for studying the relationships between hydrological and geophysical features. In our research, we adopted ERT in the Karst Critical Zone (KCZ) to visualize specific karstic zones, including cave galleries, water storage reservoirs, wetting fronts, soil layers, and potential preferential flow paths down to a depth of 20 m. To capture spatial and seasonal variations in resistivity, we presented a comprehensive approach by combining sixteen inversion models obtained between February 2020 and September 2022 above the Villars Cave in SW-France-a well-known prehistoric cave. We used a multi-dimensional statistical technique called Hierarchical Agglomerative Clustering (HAC) to create a composite model that divided the synthetic ERT image into eight clusters representing different karst critical zones. The ERT image clearly visualized the cave gallery with high resistivity values that remained consistent throughout the seasons. Our analysis revealed a close seasonal relationship between water excess and resistivity variations in most infiltration zones, with time delays increasing with depth. The karst reservoirs, located at significant depths compared to other clusters, displayed sensitivity to changes in water excess but were primarily affected by fluctuations in water conductivity, particularly during summer or dry periods. These findings have significant implications for predicting rainwater infiltration pathways into caves, thereby assisting in the conservation and preservation of prehistoric caves and their cultural heritage.

Classification of air quality zones and fine particulate matter sensitive areas by risk assessment approach

Many studies have shown that fine particulate matter can cause health problems. Thus, effectively controlling fine particulate matter concentration is an important issue around the world. The Taiwan Environmental Protection Administration (TWEPA) divides Taiwan into seven air quality zones based on counties and cities for managing air quality and analyzing pollution transmission. However, this artificial division by administrative areas relatively poorly match natural conditions and topographical and geographic factors and hence poorly represent air quality characteristics. This study proposes an air quality sensitive map analysis framework, which uses hierarchical agglomerative clustering with empirical orthogonal function and analysis of variance methods, to provide more detailed, reasonable, and township-level air quality zones incorporating the different spatial-temporal characteristics over the region. The risk concept is introduced to evaluate PM_2.5 risk sensitivity for each administrative district, combining three aspects: hazard (PM_2.5 exceedance probability), exposure (population density of sensitive groups), and vulnerability (average wind speed). Considering air quality spatial-temporal characteristics, Taiwan can be optimally divided into 14 air quality zones. PM_2.5 risk is highest for western inland towns than western coastal towns, with eastern regions exhibiting least risk. Adopting the proposed air quality zones and clarifying high risk areas allows PM_2.5 causes to be identified for different air quality zones. This allows a targeted control strategy for high risk areas to effectively improve domestic air quality. The proposed model also provides powerful reference for environmental management and environmental impact assessment for future construction and development.

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose

Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance.

Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET

Purpose

Alzheimer’s disease (AD) studies revealed that abnormal deposition of tau spreads in a specific spatial pattern, namely Braak stage. However, Braak staging is based on post mortem brains, each of which represents the cross section of the tau trajectory in disease progression, and numerous studies were reported that do not conform to that model. This study thus aimed to identify the tau trajectory and quantify the tau progression in a data-driven approach with the continuous latent space learned by variational autoencoder (VAE).

Methods

A total of 1080 [¹⁸F]Flortaucipir brain positron emission tomography (PET) images were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. VAE was built to compress the hidden features from tau images in latent space. Hierarchical agglomerative clustering and minimum spanning tree (MST) were applied to organize the features and calibrate them to the tau progression, thus deriving pseudo-time. The image-level tau trajectory was inferred by continuously sampling across the calibrated latent features. We assessed the pseudo-time with regard to tau standardized uptake value ratio (SUVr) in AD-vulnerable regions, amyloid deposit, glucose metabolism, cognitive scores, and clinical diagnosis.

Results

We identified four clusters that plausibly capture certain stages of AD and organized the clusters in the latent space. The inferred tau trajectory agreed with the Braak staging. According to the derived pseudo-time, tau first deposits in the parahippocampal and amygdala, and then spreads to the fusiform, inferior temporal lobe, and posterior cingulate. Prior to the regional tau deposition, amyloid accumulates first.

Conclusion

The spatiotemporal trajectory of tau progression inferred in this study was consistent with Braak staging. The profile of other biomarkers in disease progression agreed well with previous findings. We addressed that this approach additionally has the potential to quantify tau progression as a continuous variable by taking a whole-brain tau image into account.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05662-z.

Pharmacological treatment profiles in the FACE-BD cohort: Treatment description and complete data for bipolar subtypes

In the current study, we provide the list of pharmacological interventions applied during the one-year follow-up period of the Pharmacological treatment profiles in the FACE-BD cohort study. These data show the treatments used in the new clusters formed in this previous study and also in usual bipolarity subtypes. The proportion of each treatment used during the follow-up was calculated. Days on each treatment were also included in this dataset. The complete clinical and paraclinical data analyzed for clusters and bipolar subtypes were included in this dataset. Socio-demographic self-administered and clinician-administered scales, clinical evaluation during the follow-up, psychiatric and somatic comorbidities, and blood tests are shown in this material.

Identifying Distinct High Unmet-Need Phenotypes and Their Associated Bladder Cancer Patient Demographic, Clinical, Psychosocial, and Functional Characteristics: Results of Two Clustering Methods

OBJECTIVES

We explored phenotypes of high unmet need of patients with bladder cancer and their associated patient demographic, clinical, psychosocial, and functional characteristics.

DATA SOURCES

Patients (N=159) were recruited from the Bladder Cancer Advocacy Network and completed an online survey measuring unmet needs (BCNAS-32), quality of life (FACT-Bl), anxiety and depression (HADS), coping (BRIEF Cope), social support (SPS), and self-efficacy beliefs (GSE). Hierarchical agglomerative (HA) and partitioning clustering (PC) analyses were used to identify and confirm high unmet-need phenotypes and their associated patient characteristics. Results showed a two-cluster solution; a cluster of patients with high unmet needs (17% and 34%, respectively) and a cluster of patients with low-moderate unmet needs (83% and 66%, respectively). These two methods showed moderate agreement (κ=0.57) and no significant differences in patient demographic and clinical characteristics between the two groups. However, the high-need group identified by the HA clustering method had significantly higher psychological (81 vs 66, p < .05), health system (93 vs 74, p < .001), daily living (93 vs 74, P < .001), sexuality (97 vs 69, P < .001), logistics (84 vs 69, P < .001), and communication (90 vs 76, P < .001) needs. This group also had worse quality of life and emotional adjustment and lower personal and social resources (P < .001) compared with the group identified by the PC method.

CONCLUSION

A significant proportion of patients with bladder cancer continues to have high unique but inter-related phenotypes of needs based on the HA clustering method.

IMPLICATIONS FOR NURSING PRACTICE

Identifying characteristics of the most vulnerable patients will help tailor support programs to assist these patients with their unmet needs.

Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics

BACKGROUND

Genomic data analyses such as Genome-Wide Association Studies (GWAS) or Hi-C studies are often faced with the problem of partitioning chromosomes into successive regions based on a similarity matrix of high-resolution, locus-level measurements. An intuitive way of doing this is to perform a modified Hierarchical Agglomerative Clustering (HAC), where only adjacent clusters (according to the ordering of positions within a chromosome) are allowed to be merged. But a major practical drawback of this method is its quadratic time and space complexity in the number of loci, which is typically of the order of 10 4 to 10 5 for each chromosome.

RESULTS

By assuming that the similarity between physically distant objects is negligible, we are able to propose an implementation of adjacency-constrained HAC with quasi-linear complexity. This is achieved by pre-calculating specific sums of similarities, and storing candidate fusions in a min-heap. Our illustrations on GWAS and Hi-C datasets demonstrate the relevance of this assumption, and show that this method highlights biologically meaningful signals. Thanks to its small time and memory footprint, the method can be run on a standard laptop in minutes or even seconds.

AVAILABILITY AND IMPLEMENTATION

Software and sample data are available as an R package, adjclust, that can be downloaded from the Comprehensive R Archive Network (CRAN).

How many models/atlases are needed as priors for capturing anatomic population variations?

Many medical image processing and analysis operations can benefit a great deal from prior information encoded in the form of models/atlases to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. However, two fundamental questions have not been addressed in the literature: "How many models/atlases are needed for optimally encoding prior information to address the differing body habitus factor in that population?" and "Images of how many subjects in the given population are needed to optimally harness prior information?" We propose a method to seek answers to these questions. We assume that there is a well-defined body region of interest and a subject population under consideration, and that we are given a set of representative images of the body region for the population. After images are trimmed to the exact body region, a hierarchical agglomerative clustering algorithm partitions the set of images into a specified number of groups by using pairwise image (dis)similarity as a cost function. Optionally the images may be pre-registered among themselves prior to this partitioning operation. We define a measure called Residual Dissimilarity (RD) to determine the goodness of each partition. We then ascertain how RD varies as a function of the number of elements in the partition for finding the optimum number(s) of groups. Breakpoints in this function are taken as the recommended number of groups/models/atlases. Our results from analysis of sizeable CT data sets of adult patients from two body regions - thorax (346) and head and neck (298) - can be summarized as follows. (1) A minimum of 5 to 8 groups (or models/atlases) seems essential to properly capture information about differing anatomic forms and body habitus. (2) A minimum of 150 images from different subjects in a population seems essential to cover the anatomical variations for a given body region. (3) In grouping, body habitus variations seem to override differences due to other factors such as gender, with/without contrast enhancement in image acquisition, and presence of moderate pathology. This method may be helpful for constructing high quality models/atlases from a sufficiently large population of images and in optimally selecting the training image sets needed in deep learning strategies.

Machine learning-based identification of endogenous cellular microRNA sponges against viral microRNAs

A "miRNA sponge" is an artificial oligonucleotide-based miRNA inhibitor containing multiple binding sites for a specific miRNA. Each miRNA sponge can bind and sequester several miRNA copies, thereby decreasing the cellular levels of the target miRNA. In addition to developing artificial miRNA sponges, scientists have sought endogenous RNA transcripts and found that long non-coding RNAs, competing endogenous RNAs, pseudogenes, circular RNAs, and coding RNAs could act as miRNA sponges under precise conditions. Here we present a computational approach for the prediction of endogenous human miRNA sponge candidates targeting viral miRNAs derived from pathogenic human viruses. Viral miRNA binding sites were predicted using a newly-developed machine learning-based method, and candidate interactions between miRNAs and sponge RNAs were experimentally validated using luciferase reporter assay, western blot analysis, and flow cytometry. We found that BX649188.1 functions as a potential natural miRNA sponge against kshv-miR-K12-7-3p.

Context-driven automatic subgraph creation for literature-based discovery

BACKGROUND

Literature-based discovery (LBD) is characterized by uncovering hidden associations in non-interacting scientific literature. Prior approaches to LBD include use of: (1) domain expertise and structured background knowledge to manually filter and explore the literature, (2) distributional statistics and graph-theoretic measures to rank interesting connections, and (3) heuristics to help eliminate spurious connections. However, manual approaches to LBD are not scalable and purely distributional approaches may not be sufficient to obtain insights into the meaning of poorly understood associations. While several graph-based approaches have the potential to elucidate associations, their effectiveness has not been fully demonstrated. A considerable degree of a priori knowledge, heuristics, and manual filtering is still required.

OBJECTIVES

In this paper we implement and evaluate a context-driven, automatic subgraph creation method that captures multifaceted complex associations between biomedical concepts to facilitate LBD. Given a pair of concepts, our method automatically generates a ranked list of subgraphs, which provide informative and potentially unknown associations between such concepts.

METHODS

To generate subgraphs, the set of all MEDLINE articles that contain either of the two specified concepts (A, C) are first collected. Then binary relationships or assertions, which are automatically extracted from the MEDLINE articles, called semantic predications, are used to create a labeled directed predications graph. In this predications graph, a path is represented as a sequence of semantic predications. The hierarchical agglomerative clustering (HAC) algorithm is then applied to cluster paths that are bounded by the two concepts (A, C). HAC relies on implicit semantics captured through Medical Subject Heading (MeSH) descriptors, and explicit semantics from the MeSH hierarchy, for clustering. Paths that exceed a threshold of semantic relatedness are clustered into subgraphs based on their shared context. Finally, the automatically generated clusters are provided as a ranked list of subgraphs.

RESULTS

The subgraphs generated using this approach facilitated the rediscovery of 8 out of 9 existing scientific discoveries. In particular, they directly (or indirectly) led to the recovery of several intermediates (or B-concepts) between A- and C-terms, while also providing insights into the meaning of the associations. Such meaning is derived from predicates between the concepts, as well as the provenance of the semantic predications in MEDLINE. Additionally, by generating subgraphs on different thematic dimensions (such as Cellular Activity, Pharmaceutical Treatment and Tissue Function), the approach may enable a broader understanding of the nature of complex associations between concepts. Finally, in a statistical evaluation to determine the interestingness of the subgraphs, it was observed that an arbitrary association is mentioned in only approximately 4 articles in MEDLINE on average.

CONCLUSION

These results suggest that leveraging the implicit and explicit semantics provided by manually assigned MeSH descriptors is an effective representation for capturing the underlying context of complex associations, along multiple thematic dimensions in LBD situations.