A spectral framework to map QTLs affecting joint differential networks of gene co-expression.

Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype → expression → phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype → network → phenotype mechanism. Here, we develop a network-based method, called snQTL, to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback (Gasterosteus aculeatus) data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.

Differential diagnosis of rare adrenal cellular schwannomas: A case report.

BACKGROUND: Adrenal cellular schwannomas are exceptionally rare stromal tumors that are often misdiagnosed due to the lack of specific radiological, serological, or clinical features. In this report, we describe the differential diagnosis of a rare adrenal cellular schwannoma. METHODS: A 69-year-old man with a history of persistent hypertension, chronic kidney disease, hypertensive heart disease, and cardiac insufficiency was hospitalized due to bilateral lower extremity edema lasting for 3 months. Plain computed tomography at that time revealed a space-occupying lesion in the right adrenal gland. As serum levels of catecholamines, cortisol, and adrenocorticotropic hormone were within normal ranges, the edema was attributed to the chronic kidney disease and cardiac insufficiency, and the patient was referred to our hospital for surgical treatment. Contrast-enhanced computed tomography revealed heterogeneous enhancement in the adrenal mass indicating pheochromocytoma. An irregularly shaped 5 cm mass with a complete capsule in the right adrenal gland was laparoscopically resected. The postoperative histopathological diagnosis was adrenal cellular schwannoma. RESULTS: The postoperative course was unremarkable and the tumor did not recur during 5 years of follow-up. CONCLUSION: Adrenal cellular schwannoma is a very rare tumor that is extremely difficult to preoperatively diagnose. Histological and immunohistochemical analyses are required for differential diagnosis and confirmation. Cellular schwannomas can transform into malignant peripheral nerve sheath tumors, but not often. Consequently, regular postoperative follow-up is required for such patients, especially imaging.

Promoting In Vivo NIR-II Fluorescent Imaging for Lipid in Lipid Metabolism Diseases Diagnosis.

Lipid metabolism diseases have become a tremendous risk worldwide, along with the development of productivity and particular attention to public health. It has been an urgent necessity to exploit reliable imaging strategies for lipids and thus to monitor fatty liver diseases. Herein, by converting the NIR-I signal to the NIR-II signal with IR1061 for the monitoring of lipid, the in vivo imaging of fatty liver disease was promoted on the contrast and visual effect. The main advantages of the imaging promotion in this work included a long emission wavelength, rapid response, and high signal-background-ratio (SBR) value. After promoting the NIR-I signal to NIR-II signal, IR1061 achieved higher SBR value and exhibited a dose-dependent fluorescence intensity at 1100 nm along with the increase of the EtOH proportion as well as steady and selective optical responses toward liposomes. IR1061 was further applied in the in vivo imaging of lipid in fatty liver diseases. In spite of the differences in body weight gain and TC level between healthy mice and fatty liver diseases two models, IR1061 achieved high-resolution imaging in the liver region to monitor the fatty liver disease status. This work might be informatic for the clinical diagnosis and therapeutical treatments of fatty liver diseases.

Prevalence and Source Tracing of PFAS in Shallow Groundwater Used for Drinking Water in Wisconsin, USA.

Samples from 450 homes with shallow private wells throughout the state of Wisconsin (USA) were collected and analyzed for 44 individual per- and polyfluoroalkyl substances (PFAS), general water quality parameters, and indicators of human waste as well as agricultural influence. At least one PFAS was detected in 71% of the study samples, and 22 of the 44 PFAS analytes were detected in one or more samples. Levels of PFOA and/or PFOS exceeded the proposed Maximum Contaminant Levels of 4 ng/L, put forward by the U.S. Environmental Protection Agency (EPA) in March 2023, in 17 of the 450 samples, with two additional samples containing PFHxS ≳ 9 ng/L (the EPA-proposed hazard index reference value). Those samples above the referenced PFAS levels tend to be associated with developed land and human waste indicators (artificial sweeteners and pharmaceuticals), which can be released to groundwater via septic systems. For a few samples with levels of PFOA, PFOS, and/or PFHxS > 40 ng/L, application of wastes to agricultural land is a possible source. Overall, the study suggests that human waste sources, septic systems in particular, are important sources of perfluoroalkyl acids, especially ones with ≤8 perfluorinated carbons, in shallow groundwater.

Application of Multimodal MRI in the Early Diagnosis of Autism Spectrum Disorders: A Review.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder in children. Early diagnosis and intervention can remodel the neural structure of the brain and improve quality of life but may be inaccurate if based solely on clinical symptoms and assessment scales. Therefore, we aimed to analyze multimodal magnetic resonance imaging (MRI) data from the existing literature and review the abnormal changes in brain structural-functional networks, perfusion, neuronal metabolism, and the glymphatic system in children with ASD, which could help in early diagnosis and precise intervention. Structural MRI revealed morphological differences, abnormal developmental trajectories, and network connectivity changes in the brain at different ages. Functional MRI revealed disruption of functional networks, abnormal perfusion, and neurovascular decoupling associated with core ASD symptoms. Proton magnetic resonance spectroscopy revealed abnormal changes in the neuronal metabolites during different periods. Decreased diffusion tensor imaging signals along the perivascular space index reflected impaired glymphatic system function in children with ASD. Differences in age, subtype, degree of brain damage, and remodeling in children with ASD led to heterogeneity in research results. Multimodal MRI is expected to further assist in early and accurate clinical diagnosis of ASD through deep learning combined with genomics and artificial intelligence.

Polarization modulation with optical lock-in detection reveals universal fluorescence anisotropy of subcellular structures in live cells.

The orientation of fluorophores can reveal crucial information about the structure and dynamics of their associated subcellular organelles. Despite significant progress in super-resolution, fluorescence polarization microscopy remains limited to unique samples with relatively strong polarization modulation and not applicable to the weak polarization signals in samples due to the excessive background noise. Here we apply optical lock-in detection to amplify the weak polarization modulation with super-resolution. This novel technique, termed optical lock-in detection super-resolution dipole orientation mapping (OLID-SDOM), could achieve a maximum of 100 frames per second and rapid extraction of 2D orientation, and distinguish distance up to 50 nm, making it suitable for monitoring structural dynamics concerning orientation changes in vivo. OLID-SDOM was employed to explore the universal anisotropy of a large variety of GFP-tagged subcellular organelles, including mitochondria, lysosome, Golgi, endosome, etc. We found that OUF (Orientation Uniformity Factor) of OLID-SDOM can be specific for different subcellular organelles, indicating that the anisotropy was related to the function of the organelles, and OUF can potentially be an indicator to distinguish normal and abnormal cells (even cancer cells). Furthermore, dual-color super-resolution OLID-SDOM imaging of lysosomes and actins demonstrates its potential in studying dynamic molecular interactions. The subtle anisotropy changes of expanding and shrinking dendritic spines in live neurons were observed with real-time OLID-SDOM. Revealing previously unobservable fluorescence anisotropy in various samples and indicating their underlying dynamic molecular structural changes, OLID-SDOM expands the toolkit for live cell research.

Lower cretaceous avian-dominated, theropod, thyreophoran, pterosaur and turtle track assemblages from the Tugulu Group, Xinjiang, China: ichnotaxonomy and palaeoecology.

Rich tetrapod ichnofaunas, known for more than a decade, from the Huangyangquan Reservoir (Wuerhe District, Karamay City, Xinjiang) have been an abundant source of some of the largest Lower Cretaceous track collections from China. They originate from inland lacustrine clastic exposures of the 581-877 m thick Tugulu Group, variously divided into four formations and subgroups in the northwestern margin of the Junggar Basin. The large Huangyangquan track assemblages occur in the Lower layer/Subgroup II. Similarly-composed track assemblages also occur at the smaller Asphaltite site in the Upper Layer/Subgroup III. The Huangyangquan assemblages have yielded more than 1,500 identified tracks including abundant tracks of avian and non-avian theropods, pterosaurs and turtles and less abundant tracks of stegosaurs. Previous avian track identifications have been reassessed to conclude that Moguiornipes robustus is a taphotaxon and Koreanaornis dodsoni might be better accommodated in the ichnogenus Aquatilavipes which appears to be the dominant avian ichnotaxon. The avian track Ignotornis is also recognized and represents the first occurrence of this ichnogenus in China. Although the Huangyangquan assemblages lack some of the larger components (e.g., sauropodan and ornithopodan tracks) known from other Lower Cretaceous localities, the association of abundant tracks of smaller tetrapods (avian and non-avian theropods, pterosaurs and turtles) appears to be representative of lacustrine basin faunas of this region, and are an excellent example of the shorebird ichnocoenosis/ichnofacies concept. This is the first comprehensive review and re-analysis of an important Lower Cretaceous ecosystem.

Advances of super-resolution fluorescence polarization microscopy and its applications in life sciences.

Fluorescence polarization microscopy (FPM) analyzes both intensity and orientation of fluorescence dipole, and reflects the structural specificity of target molecules. It has become an important tool for studying protein organization, orientational order, and structural changes in cells. However, suffering from optical diffraction limit, conventional FPM has low orientation resolution and observation accuracy, as the polarization information is averaged by multiple fluorescent molecules within a diffraction-limited volume. Recently, novel super-resolution FPMs have been developed to break the diffraction barrier. In this review, we will introduce the recent progress to achieve sub-diffraction determination of dipole orientation. Biological applications, based on polarization analysis of fluorescence dipole, are also summarized, with focus on chromophore-target molecule interaction and molecular organization.

Plasmonics meets super-resolution microscopy in biology.

Super-resolution microscopy can reveal the subtle biological processes hidden behind the optical diffraction barrier. Plasmonics is a key nanophotonic that combines electronics and photonics through the interaction of light with the metallic nanostructure. In this review, we survey the recent progresses on plasmonic-assisted super-resolution microscopy. The strong electromagnetic field enhancement trapped near metallic nanostructures offers a unique opportunity to manipulate the illumination scheme for overcoming the diffraction limit. Plasmonic nanoprobes, exploited as surface-enhanced Raman scattering (SERS) and plasmon-enhanced fluorescence nanoparticles, are a major category of contrast agent in super-resolution microscopy. The outstanding challenges, future developments, and potential biological applications are also discussed.

Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality.

We consider the problem of decomposing a higher-order tensor with binary entries. Such data problems arise frequently in applications such as neuroimaging, recommendation system, topic modeling, and sensor network localization. We propose a multilinear Bernoulli model, develop a rank-constrained likelihood-based estimation method, and obtain the theoretical accuracy guarantees. In contrast to continuous-valued problems, the binary tensor problem exhibits an interesting phase transition phenomenon according to the signal-to-noise ratio. The error bound for the parameter tensor estimation is established, and we show that the obtained rate is minimax optimal under the considered model. Furthermore, we develop an alternating optimization algorithm with convergence guarantees. The efficacy of our approach is demonstrated through both simulations and analyses of multiple data sets on the tasks of tensor completion and clustering.

Group-Sparsity-Based Super-Resolution Dipole Orientation Mapping.

The dipole orientation of fluorophores could be resolved by fluorescence polarization microscopy (FPM), which in turn reveals structural specificity for the labeled organelles. Conventional FPM can detect only the averaged fluorescence anisotropy collected from dipoles within the diffraction-limited volume. Super-resolution dipole orientation mapping (SDOM) method, which applies sparse deconvolution and least square estimation to fluorescence polarization modulation data, achieves the dipole orientation measurement within a sub-diffraction focal area. However, during SDOM analysis, some pixels with fluorescence signal are not resolved with orientation for relatively small adjusted R2. Here we report group-sparsity-based SDOM (GS-SDOM), which utilizes the relevance of modulation sequences to effectively improve the SDOM reconstruction model. More credible resolved dipole orientations with higher adjusted R2 can be mapped and false positive estimation for local dipole orientation is vitally corrected. In addition to achieving the same spatial super-resolution as SDOM does, GS-SDOM accesses more morphological information with more credible orientations and more accurate local dipole distribution estimation. During the GS-SDOM analysis of actin filaments in mammalian kidney cells, the dipole orientation of fluorescence is detected always parallel to the direction of the actin filaments. Also with dipole orientation information extracted by GS-SDOM, the reconstructed visual circle from intensity dimension is discerned as jointed by double close filaments and 3-dimensional co-localization is accomplished in the intersection of actin filaments.

THREE-WAY CLUSTERING OF MULTI-TISSUE MULTI-INDIVIDUAL GENE EXPRESSION DATA USING SEMI-NONNEGATIVE TENSOR DECOMPOSITION.

The advent of high-throughput sequencing technologies has led to an increasing availability of large multi-tissue data sets which contain gene expression measurements across different tissues and individuals. In this setting, variation in expression levels arises due to contributions specific to genes, tissues, individuals, and interactions thereof. Classical clustering methods are ill-suited to explore these three-way interactions and struggle to fully extract the insights into transcriptome complexity contained in the data. We propose a new statistical method, called MultiCluster, based on semi-nonnegative tensor decomposition which permits the investigation of transcriptome variation across individuals and tissues simultaneously. We further develop a tensor projection procedure which detects covariate-related genes with high power, demonstrating the advantage of tensor-based methods in incorporating information across similar tissues. Through simulation and application to the GTEx RNA-seq data from 53 human tissues, we show that MultiCluster identifies three-way interactions with high accuracy and robustness.

Polarization-based super-resolution imaging of surface-enhanced Raman scattering nanoparticles with orientational information.

Raman scattering provides key information of the biological environment through light-molecule interaction; yet, it is generally very weak to detect. Surface-enhanced Raman scattering (SERS) can boost the Raman signal by several orders-of-magnitude, and thus is highly attractive for biochemical sensing. However, conventional super-resolution imaging of SERS is challenging as the Raman signal is generated from the virtual state which cannot be easily modulated as fluorescence. Here, we demonstrate super-resolution microscopy with a surface-enhanced Raman scattering (SERS) signal, with a resolution of approximately 50 nm. By modulating the polarization angle of the excitation laser, the SERS nanorods display a dramatic anisotropy effect, allowing nanoscale orientation determination of multiple dipoles with dense concentration. Furthermore, a well-established defocused analysis was performed to reconfirm the orientation accuracy of super-resolved SERS nanorods. Sub-diffraction resolution was achieved in the imaging of SERS nanorod labeled vesicles in fixed macrophages. Finally, we demonstrate dynamic SERS nanorod tracking in living macrophages, which provides not only the particle trajectory with high spatial resolution but also the rotational changes at the nanometer scale. This pioneering study paves a new way for subcellular super-resolution imaging with the SERS effect, shedding light on wider biological applications.

Two-way mixed-effects methods for joint association analysis using both host and pathogen genomes.

Infectious diseases are often affected by specific pairings of hosts and pathogens and therefore by both of their genomes. The integration of a pair of genomes into genome-wide association mapping can provide an exquisitely detailed view of the genetic landscape of complex traits. We present a statistical method, ATOMM (Analysis with a Two-Organism Mixed Model), that maps a trait of interest to a pair of genomes simultaneously; this method makes use of whole-genome sequence data for both host and pathogen organisms. ATOMM uses a two-way mixed-effect model to test for genetic associations and cross-species genetic interactions while accounting for sample structure including interactions between the genetic backgrounds of the two organisms. We demonstrate the applicability of ATOMM to a joint association study of quantitative disease resistance (QDR) in the Arabidopsis thaliana-Xanthomonas arboricola pathosystem. Our method uncovers a clear host-strain specificity in QDR and provides a powerful approach to identify genetic variants on both genomes that contribute to phenotypic variation.

Developing novel methods to image and visualize 3D genomes.

To investigate three-dimensional (3D) genome organization in prokaryotic and eukaryotic cells, three main strategies are employed, namely nuclear proximity ligation-based methods, imaging tools (such as fluorescence in situ hybridization (FISH) and its derivatives), and computational/visualization methods. Proximity ligation-based methods are based on digestion and re-ligation of physically proximal cross-linked chromatin fragments accompanied by massively parallel DNA sequencing to measure the relative spatial proximity between genomic loci. Imaging tools enable direct visualization and quantification of spatial distances between genomic loci, and advanced implementation of (super-resolution) microscopy helps to significantly improve the resolution of images. Computational methods are used to map global 3D genome structures at various scales driven by experimental data, and visualization methods are used to visualize genome 3D structures in virtual 3D space-based on algorithms. In this review, we focus on the introduction of novel imaging and visualization methods to study 3D genomes. First, we introduce the progress made recently in 3D genome imaging in both fixed cell and live cells based on long-probe labeling, short-probe labeling, RNA FISH, and the CRISPR system. As the fluorescence-capturing capability of a particular microscope is very important for the sensitivity of bioimaging experiments, we also introduce two novel super-resolution microscopy methods, SDOM and low-power super-resolution STED, which have potential for time-lapse super-resolution live-cell imaging of chromatin. Finally, we review some software tools developed recently to visualize proximity ligation-based data. The imaging and visualization methods are complementary to each other, and all three strategies are not mutually exclusive. These methods provide powerful tools to explore the mechanisms of gene regulation and transcription in cell nuclei.

OPERATOR NORM INEQUALITIES BETWEEN TENSOR UNFOLDINGS ON THE PARTITION LATTICE.

Interest in higher-order tensors has recently surged in data-intensive fields, with a wide range of applications including image processing, blind source separation, community detection, and feature extraction. A common paradigm in tensor-related algorithms advocates unfolding (or flattening) the tensor into a matrix and applying classical methods developed for matrices. Despite the popularity of such techniques, how the functional properties of a tensor changes upon unfolding is currently not well understood. In contrast to the body of existing work which has focused almost exclusively on matricizations, we here consider all possible unfoldings of an order-k tensor, which are in one-to-one correspondence with the set of partitions of {1, …, k}. We derive general inequalities between the lp -norms of arbitrary unfoldings defined on the partition lattice. In particular, we demonstrate how the spectral norm (p = 2) of a tensor is bounded by that of its unfoldings, and obtain an improved upper bound on the ratio of the Frobenius norm to the spectral norm of an arbitrary tensor. For specially-structured tensors satisfying a generalized definition of orthogonal decomposability, we prove that the spectral norm remains invariant under specific subsets of unfolding operations.

G-STRATEGY: Optimal Selection of Individuals for Sequencing in Genetic Association Studies.

In a large-scale genetic association study, the number of phenotyped individuals available for sequencing may, in some cases, be greater than the study's sequencing budget will allow. In that case, it can be important to prioritize individuals for sequencing in a way that optimizes power for association with the trait. Suppose a cohort of phenotyped individuals is available, with some subset of them possibly already sequenced, and one wants to choose an additional fixed-size subset of individuals to sequence in such a way that the power to detect association is maximized. When the phenotyped sample includes related individuals, power for association can be gained by including partial information, such as phenotype data of ungenotyped relatives, in the analysis, and this should be taken into account when assessing whom to sequence. We propose G-STRATEGY, which uses simulated annealing to choose a subset of individuals for sequencing that maximizes the expected power for association. In simulations, G-STRATEGY performs extremely well for a range of complex disease models and outperforms other strategies with, in many cases, relative power increases of 20-40% over the next best strategy, while maintaining correct type 1 error. G-STRATEGY is computationally feasible even for large datasets and complex pedigrees. We apply G-STRATEGY to data on high-density lipoprotein and low-density lipoprotein from the AGES-Reykjavik and REFINE-Reykjavik studies, in which G-STRATEGY is able to closely approximate the power of sequencing the full sample by selecting for sequencing a only small subset of the individuals.

Super-resolution dipole orientation mapping via polarization demodulation.

Fluorescence polarization microscopy (FPM) aims to detect the dipole orientation of fluorophores and to resolve structural information for labeled organelles via wide-field or confocal microscopy. Conventional FPM often suffers from the presence of a large number of molecules within the diffraction-limited volume, with averaged fluorescence polarization collected from a group of dipoles with different orientations. Here, we apply sparse deconvolution and least-squares estimation to fluorescence polarization modulation data and demonstrate a super-resolution dipole orientation mapping (SDOM) method that resolves the effective dipole orientation from a much smaller number of fluorescent molecules within a sub-diffraction focal area. We further apply this method to resolve structural details in both fixed and live cells. For the first time, we show that different borders of a dendritic spine neck exhibit a heterogeneous distribution of dipole orientation. Furthermore, we illustrate that the dipole is always perpendicular to the direction of actin filaments in mammalian kidney cells and radially distributed in the hourglass structure of the septin protein under specific labelling. The accuracy of the dipole orientation can be further mapped using the orientation uniform factor, which shows the superiority of SDOM compared with its wide-field counterpart as the number of molecules is decreased within the smaller focal area. Using the inherent feature of the orientation dipole, the SDOM technique, with its fast imaging speed (at sub-second scale), can be applied to a broad range of fluorescently labeled biological systems to simultaneously resolve the valuable dipole orientation information with super-resolution imaging.

The development and application of a quantitative peptide microarray based approach to protein interaction domain specificity space.

Protein interaction domain (PID) linear peptide motif interactions direct diverse cellular processes in a specific and coordinated fashion. PID specificity, or the interaction selectivity derived from affinity preferences between possible PID-peptide pairs is the basis of this ability. Here, we develop an integrated experimental and computational cellulose peptide conjugate microarray (CPCMA) based approach for the high throughput analysis of PID specificity that provides unprecedented quantitative resolution and reproducibility. As a test system, we quantify the specificity preferences of four Src Homology 2 domains and 124 physiological phosphopeptides to produce a novel quantitative interactome. The quantitative data set covers a broad affinity range, is highly precise, and agrees well with orthogonal biophysical validation, in vivo interactions, and peptide library trained algorithm predictions. In contrast to preceding approaches, the CPCMAs proved capable of confidently assigning interactions into affinity categories, resolving the subtle affinity contributions of residue correlations, and yielded predictive peptide motif affinity matrices. Unique CPCMA enabled modes of systems level analysis reveal a physiological interactome with expected node degree value decreasing as a function of affinity, resulting in minimal high affinity binding overlap between domains; uncover that Src Homology 2 domains bind ligands with a similar average affinity yet strikingly different levels of promiscuity and binding dynamic range; and parse with unprecedented quantitative resolution contextual factors directing specificity. The CPCMA platform promises broad application within the fields of PID specificity, synthetic biology, specificity focused drug design, and network biology.