A three-dimensional (3D) microstructural volume is reconstructed from a stack of two-dimensional sections which was obtained by serial sectioning coupled with electron back scattering diffraction (EBSD) mapping of a 316L austenitic stainless steel. A new alignment algorithm named linear translation by minimising the indicator (LTMI) is proposed to reduce the translational misalignments between adjacent sections by referencing to coherent twin boundaries which are flat and lying on {111} planes. The angular difference between the measured orientation of a flat twin boundary and that of the {111} plane is used as an indicator of the accuracy of the alignment operations. This indicator is minimised through linear translations of the centroids of triangular facets, which constitute grain boundaries at a distance not restricted by the in-plane step size of the EBSD maps. And hence the systematic trend in the translational misalignments can be effectively reduced. The LTMI alignment procedure proposed herein effectively corrects the misalignments remained by other methods on a 3D-EBSD data prepared using serial sectioning methods. The accuracy in distinguishing between coherent and incoherent twin boundaries is significantly improved.

Single particle cryo-electron microscopy (EM) is a method for determining the 3-D structure of macromolecules from many noisy 2-D projection images of individual macromolecules whose orientations and positions are random and unknown. The problem of orientation assignment for the images motivated work on general multireference alignment. The recently introduced non-unique games framework provides a representation theoretic approach to alignment over compact groups, and offers a convex relaxation which is formulated as semidefinite programs with certificates of global optimality under certain circumstances. One of the great opportunities in cryo-EM is studying heterogeneous samples, containing two or more distinct classes or conformations of molecules. Taking advantage of this opportunity presents an algorithmic challenge: determining both the class and orientation of each particle. We generalize multireference alignment to a problem of alignment and classification, and we propose to extend non-unique games to the problem of simultaneous alignment and classification with the goal of simultaneously classifying cryo-EM images and aligning them within their respective classes.

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of preprocessing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

The rapid development of active safety systems in the automotive industry and research in autonomous driving requires reliable, high-precision sensors that provide rich information about the surrounding environment and the behaviour of other road users. In practice, there is always some non-zero mounting misalignment, i.e., angular inaccuracy in a sensor\'s mounting on a vehicle. It is essential to accurately estimate and compensate for this misalignment further programmatically (in software). In the case of radars, imprecise mounting may result in incorrect/inaccurate target information, problems with the tracking algorithm, or a decrease in the power reflected from the target. Sensor misalignment should be mitigated in two ways: through the correction of an inaccurate alignment angle via the estimated value of the misalignment angle or alerting other components of the system of potential sensor degradation if the misalignment is beyond the operational range. This work analyses misalignment\'s influences on radar sensors and other system components. In the mathematically proven example of a vertically misaligned radar, pedestrian detectability dropped to one-third of the maximum range. In addition, mathematically derived heading estimation errors demonstrate the impact on data association in data fusion. The simulation results presented show that the angle of misalignment exponentially increases the risk of false track splitting. Additionally, the paper presents a comprehensive review of radar alignment techniques, mostly found in the patent literature, and implements a baseline algorithm, along with suggested key performance indicators (KPIs) to facilitate comparisons for other researchers.

BACKGROUND: Spatial transcriptomics (ST) is advancing our understanding of complex tissues and organisms. However, building a robust clustering algorithm to define spatially coherent regions in a single tissue slice and aligning or integrating multiple tissue slices originating from diverse sources for essential downstream analyses remains challenging. Numerous clustering, alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of comprehensive benchmark studies complicates the selection of methods and future method development.
RESULTS: In this study, we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity. We analyze the strengths and weaknesses of each method using diverse quantitative and qualitative metrics and analyses, including eight metrics for spatial clustering accuracy and contiguity, uniform manifold approximation and projection visualization, layer-wise and spot-to-spot alignment accuracy, and 3D reconstruction, which are designed to assess method performance as well as data quality. The code used for evaluation is available on our GitHub. Additionally, we provide online notebook tutorials and documentation to facilitate the reproduction of all benchmarking results and to support the study of new methods and new datasets.
CONCLUSIONS: Our analyses lead to comprehensive recommendations that cover multiple aspects, helping users to select optimal tools for their specific needs and guide future method development.

Individuals with neuromuscular pathologies are often prescribed an ankle-foot orthosis (AFO) to improve their gait mechanics by decreasing pathological movements of the ankle and lower limb. AFOs can resist or assist excessive or absent muscular forces that lead to tripping, instability, and slow inefficient gait. However, selecting the appropriate AFO with mechanical characteristics, which limit pathological ankle motion in certain phases of the gait cycle while facilitating effective ankle movement during other phases, requires careful clinical decision-making. The aim of this study is to propose an explicit methodology for the adjustment of multi-function articulated AFOs in clinical settings. A secondary aim is to outline the evidence supporting this methodology and to identify gaps in the literature as potential areas for future research. An emerging class of AFO, the multi-function articulated AFO, offers features that permit more comprehensive, iterative, and reversible adjustments of AFO ankle alignment and resistance to ankle motion. However, no standard method exists for the application and optimization of these therapeutic devices in the clinical setting. Here we propose an evidence-guided methodology applicable to the adjustment of multi-function articulated AFOs in the clinical setting. Characteristic load-deflection curves are given to illustrate the idealized yet complex resistance-angle behavior of multi-function articulated AFOs. Research is cited to demonstrate how these mechanical characteristics can help mitigate specific pathologic ankle and knee kinematics and kinetics. Evidence is presented to support the effects of systematic adjustment of high resistance, alignable, articulated AFOs to address many typical pathomechanical patterns observed in individuals with neuromuscular disorders. The published evidence supporting most decision points of the algorithm is presented with identified gaps in the evidence. In addition, two hypothetical case examples are given to illustrate the application of the method in optimizing multi-function articulated AFOs for treating specific gait pathomechanics. This method is proposed as an evidence-guided systematic approach for the adjustment of multi-function articulated AFOs. It utilizes observed gait deviations mapped to specific changes in AFO alignment and resistance settings as a clinical tool in orthotic treatment for individuals with complex neuromuscular gait disorders.

In this chapter, we outline an approach to analyzing metatranscriptomic data, focusing on the assessment of differential enzyme expression and metabolic pathway activities using a novel bioinformatics software tool, EMPathways2. The analysis pipeline commences with raw data originating from a sequencer and concludes with an output of enzyme expressions and an estimate of metabolic pathway activities. The initial step involves aligning specific transcriptomes assembled from RNA-Seq data using Bowtie2 and acquiring gene expression data with IsoEM2. Subsequently, the pipeline proceeds to quality assessment and preprocessing of the input data, ensuring accurate estimates of enzymes and their differential regulation. Upon completion of the preprocessing stage, EMPathways2 is employed to decipher the intricate relationships between genes, enzymes, and pathways. An online repository containing sample data has been made available, alongside custom Python scripts designed to modify the output of the programs within the pipeline for diverse downstream analyses. This chapter highlights the technical aspects and practical applications of using EMPathways2, which facilitates the advancement of transcriptome data analysis and contributes to a deeper understanding of the complex regulatory mechanisms underlying living systems.

In this chapter, we present an established pipeline for analyzing RNA-Seq data, which involves a step-by-step flow starting from raw data obtained from a sequencer and culminating in the identification of differentially expressed genes with their functional characterization. The pipeline is divided into three sections, each addressing crucial stages of the analysis process. The first section covers the initial steps of the pipeline, including downloading of the data of interest and performing quality control assessment. Assessment ensures that the data used for analysis is reliable and suitable for downstream analyses. In the second section, gene-level quantification is performed, which entails quantification of expression levels of genes in the samples. The third and final section is focused on differential expression analysis, which involves comparing gene expression levels between two or more conditions. This step helps identify genes that show significant differences in expression levels under different experimental conditions. To facilitate accessibility and reproducibility, we have provided an online repository containing all scripts and files. Additionally, custom scripts are available, enabling users to modify the pipeline\'s output for various downstream analyses. By following this pipeline, researchers can effectively analyze RNA-Seq data and gain valuable insights into gene expression patterns and, furthermore, the understanding of biological processes.

The controllable formation of anisotropic gel structures is presently sought for the development of foods with novel textures. Here, we used unidirectional freezing to generate agar gels consisting of a honeycomb-like porous network of elongated and aligned pores. A custom-built Peltier system allowed for control of the freezing front velocity throughout the agar gels. A higher freezing velocity (10 µm/s) led to smaller pore sizes compared to the slower freezing velocity tested (2 µm/s). Texture analysis highlighted the significantly higher Young\'s modulus in the gels when compressed in the axial vs. radial direction - a direct consequence of the unidirectional freezing. The proton spin-spin relaxation time revealed greater water mobility in the unidirectionally frozen gel with larger pores. This study serves as the basis for the development of anisotropic hydrocolloid gels with a tunable microstructure and texture.

This paper presents a study that assesses the application of chemometrics for classifying coffee samples in a quality control context. High-resolution and accurate mass measurements were utilized as input for pixel-based orthogonal partial least squares discriminant analysis (OPLS-DA) models. The compositional data were acquired through a fully automated workflow combining headspace solid-phase microextraction and gas chromatography-high-resolution mass spectrometry (GC-HRMS) using an FT-Orbitrap® mass analyzer. A workflow centered on accurate mass measurements was successfully utilized for group-type analysis, offering an alternative to methods relying solely on MS similarity searches. The predictive models underwent thorough evaluation, demonstrating robust multivariate classification performance. Five key coffee attributes, bitterness, acidity, body, intensity, and roasting level were successfully predicted using GC-HRMS data. The results revealed strong predictive accuracy across all models, ranging from 88.9 % (bitterness) to 94.4 % (roasting level). This study represents a significant advancement in automating methods for coffee quality control, notably increasing the predictive ability of the models compared to existing literature.