Statistical Representation Research Articles

Prolotherapy in the Treatment of Sports-Related Tendinopathies: A Systematic Review of Randomized Controlled Trials.

As sports have increased in popularity, the incidence of tendinopathy has also grown dramatically. Nonoperative techniques and treatments used to address these pathologies continue to evolve and improve. One such treatment, prolotherapy (PrT), has become increasingly popular and may provide patients with an alternative nonoperative treatment option. To review high-quality randomized controlled trials (RCTs) that analyzed PrT treatments for the most common tendinopathies. Specifically, this review aims to provide meaningful data regarding methods and outcomes for each condition treated and guide professionals who are considering PrT as a treatment option. Systematic review; Level of evidence, 2. All RCTs published in English between January 1, 1980, and July 30, 2021, and reported in Embase, Medline, and Web of Science databases were reviewed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. After the initial search, a total of 3264 articles were identified. Studies analyzing sports medicine injuries and musculoskeletal pathologies using an RCT design were included, while case-reports, case-studies, reviews, and observational studies were excluded. Two independent researchers reviewed the search results, and conflicts were resolved by discussion of inclusion and exclusion criteria among all authors. The articles' quality was evaluated using the Cochrane tool for assessing the risk of bias. Statistical analysis and graphical representations were performed using SPSS Version 28.00. A total of 20 articles, including 1136 patients, met the inclusion criteria and were included in the study. Overall, in 85% of the studies, PrT was found to be effective in the treatment of tendinopathy. Specifically, PrT was superior to or as effective as the control in 83% (10/12) of the studies analyzing lateral epicondylitis (LE) and rotator cuff (RC) tendinopathies and in 88% (7/8) of the studies on plantar fasciitis (PF), Osgood-Schlatter disease (OSD), and Achilles tendinosis (AT). LE, RC, and PF tendinopathies were the most studied conditions (17/20 studies), while AT and OSD were the least studied (3/20 studies). Of the studies, 95% (19/20) used dextrose solutions, with only 1 using solutions of 2.5% phenol, 25% glycerin, and 25% dextrose in sterile water. Our systematic review suggests that PrT appears to be a promising alternative treatment for common tendinopathies. Most studies used a hypertonic dextrose solution. Even though further, larger randomized controlled trials comparing PrT with other orthobiologics would be beneficial, based on this review, sports medicine physiciansmay safely pursue PrT as an additional component of conservative treatment.

Combining transfer learning and statistical measures to predict performance of composite materials with limited data

AbstractPredicting the performance of composite materials is crucial for their application in civil infrastructure, yet limited experimental data often hinder the development of accurate and generalizable models. This study introduces a deep neural network (DNN) approach that combines summarizing statistics (SS) and transfer learning (TL)—termed the SSTL‐DNN approach—to address data scarcity in modeling composite materials. The computational novelty lies in the SS method's ability to extract comprehensive information from limited datasets by converting complex constitutive laws into concise statistical representations, thereby enabling efficient and effective model training. Simultaneously, the TL method enhances computational efficiency by leveraging knowledge from related tasks with abundant data to improve learning in the target task with scarce data. This combination not only reduces dependency on large datasets but also significantly improves model generalization. The proposed SSTL‐DNN approach is validated through two case studies: fiber‐reinforced polymer confined concrete and engineered cementitious composites. In both case studies, the SSTL‐DNN model reduces the required dataset size by up to 75% and decreases the validation error by 39%, compared to traditional deep learning models. These results demonstrate that the SSTL‐DNN approach not only overcomes data scarcity but also provides accurate predictions and generalization to unseen data, offering a practical solution for modeling composite materials with limited data.

What counts as productive? Redefining ‘producers’ in economic discourse, 1890–1960

In this paper, we analyse economic discourse in debates over the production boundary, asking how the boundaries around economic categories change over time. Using a sample of economic textbooks from the late nineteenth to mid-twentieth century we analyse the evolution of the category of productive labour, finding that in the mid-twentieth century economists came to cohere around a conceptualization of productive activity that framed productive labour as any utility that was paid for on a market. Central to this outcome was the development of national accounting statistics that economists came to adopt in their analytical toolkit, which rendered invisible unpaid labour activities. Through this case we can see that statistical representations of the economy can redefine theoretical concepts and redraw the boundaries around economic categories, in this case transforming the boundary of productive labour into that of paid labour.

Reshoring concepts: definitions and a structured bibliometric review

PurposeThe aim of this paper is to clearly mark the clusters in the related literature using bibliometric analysis and draw useful implications for the academic research purposes especially in the stream of Supply Chain Management (SCM).Design/methodology/approachBy employing rigorous selection criteria, sample articles were selected for synthesizing and conducting a bibliometric analysis. This paper tries to identify the significance of the concept of reshoring with the aspect of SCM, operations, logistics etc. Starting from sample selection, systematic selection criteria has been used to generate the database, using Scopus. The sample size was 184. Using VOSViewer, statistical and graphical representations were generated for clusters in the literature that will be useful for judging the trends of research under shoring concepts.FindingsFindings show that unclear difference compared to other supply chain concepts obstructs further theory development within reshoring concepts. To address this challenge, the review categorizes themes and defines boundaries. Based on the analysis findings, this paper advocates for additional research in areas that are crucial but have received limited or no attention. Specifically, there is a need for action research on motivations, governance modes, and location selection concerning reshoring decisions.Research limitations/implicationsThis review has a few limitations. The absence of publications in other language, than English, may result in a significant loss of knowledge. This research paper cited a few relevant publications in the form of conference papers, industry reports, books, and book chapters. Nonetheless, they were excluded from the organized bibliometric audit. To further advance the knowledge in this field, exploratory longitudinal case studies will bring to focus the changes on manufacturing strategies using the shoring and reshoring stages.Practical implicationsA current example of this is a French company named Stil (Terzian, 2020) which brought its glass thermometer production back to its home after the unexpected closure of its suppliers from China. Another Italian firm has restarted production of disposal bio-degradable face masks after 15 years to compete with low-cost alternatives from China (Greco, 2020). Reshoring concepts are now under consideration of organizations including Boeing, Bosch, Phillips and more.Originality/valueTo the best of our knowledge, this represents one of the earliest efforts to conceptualize and define reshoring concepts, aiming to distinguish them from one another appropriately. To accomplish this, the earlier demarcated clusters precede this section’s classification of reshoring concepts illustrated in Table 8. Based on the synthesis of definitions and thematic clusters above, we have concluded the literature review in this emerging field. It is intended to contribute to theory development and practitioner support in reshoring decisions by clarifying the dimensions.

Improving viral annotation with artificial intelligence.

Viruses of bacteria, "phages," are fundamental, poorly understood components of microbial community structure and function. Additionally, their dependence on hosts for replication positions phages as unique sensors of ecosystem features and environmental pressures. High-throughput sequencing approaches have begun to give us access to the diversity and range of phage populations in complex microbial community samples, and metagenomics is currently the primary tool with which we study phage populations. The study of phages by metagenomic sequencing, however, is fundamentally limited by viral diversity, which results in the vast majority of viral genomes and metagenome-annotated genomes lacking annotation. To harness bacteriophages for applications in human and environmental health and disease, we need new methods to organize and annotate viral sequence diversity. We recently demonstrated that methods that leverage self-supervised representation learning can supplement statistical sequence representations for remote viral protein homology detection in the ocean virome and propose that consideration of the functional content of viral sequences allows for the identification of similarity in otherwise sequence-diverse viruses and viral-like elements for biological discovery. In this review, we describe the potential and pitfalls of large language models for viral annotation. We describe the need for new approaches to annotate viral sequences in metagenomes, the fundamentals of what protein language models are and how one can use them for sequence annotation, the strengths and weaknesses of these models, and future directions toward developing better models for viral annotation more broadly.

An ontology-based text mining dataset for extraction of process-structure-property entities

While large language models learn sound statistical representations of the language and information therein, ontologies are symbolic knowledge representations that can complement the former ideally. Research at this critical intersection relies on datasets that intertwine ontologies and text corpora to enable training and comprehensive benchmarking of neurosymbolic models. We present the MaterioMiner dataset and the linked materials mechanics ontology where ontological concepts from the mechanics of materials domain are associated with textual entities within the literature corpus. Another distinctive feature of the dataset is its eminently fine-grained annotation. Specifically, 179 distinct classes are manually annotated by three raters within four publications, amounting to 2191 entities that were annotated and curated. Conceptual work is presented for the symbolic representation of causal composition-process-microstructure-property relationships. We explore the annotation consistency between the three raters and perform fine-tuning of pre-trained language models to showcase the feasibility of training named entity recognition models. Reusing the dataset can foster training and benchmarking of materials language models, automated ontology construction, and knowledge graph generation from textual data.

SKYQUANT 3D: Quantifying Vascular Anatomy With an Open-Source Workflow for Comprehensive Analysis of Volumetric Optoacoustic Angiography Data.

Efficient visualization of the vascular system is of key importance in biomedical research into tumor angiogenesis, cerebrovascular alterations, and other angiopathies. Optoacoustic (OA) angiography offers a promising solution combining molecular optical contrast with high resolution and deep penetration of ultrasound. However, its hybrid nature implies complex data collection and processing workflows, with significant variability in methodologies across developers and users. To streamline interoperability, we introduce SKYQUANT 3D, a Python-based set of instructions for the Thermo Fisher Scientific Amira/Avizo 3D Visualization & Analysis Software. Our workflow simplifies the batch processing of volumetric optoacoustic angiography images, extracting meaningful quantitative information while also providing statistical analysis and graphical representation of the results. Quantification performance of SKYQUANT 3D is demonstrated using functional preclinical and clinical in vivo 3D OA angiographic tests involving ambient temperature variations and repositioning of the imaged limb.

Scaffolding Fifth Graders’ Learning with a Digital Multi-Representation Applet: Design Research on Focusing Multiplicative Structures with Dynamic Dot Arrays

AbstractDigital multi-representation applets offer promising opportunities for promoting students’ conceptual understanding by dynamically converting graphical and symbolic representations and for promoting discoveries and justifications of mathematical properties. However, learning effects of open-ended multi-representation applets seem to depend on targeted scaffolds for students’ processes of conversions, treatments, and conversions of treatments. While these design challenges have come to be well-understood for dynamic geometry and functions, more design research is needed for multi-representation applets for multiplicative reasoning. We report on a design research study in Grade 5 in which the multi-representation applet of a dynamic dot array was integrated into a digital teaching–learning environment with sequenced tasks and more targeted scaffolds. The qualitative analysis of 30 students’ processes revealed multiple potentials of the dynamic array to (a) connect static representations by articulating the underlying multiplicative unit structures (conversions), (b) describe systematic variations (coordinated treatments), (c) explain systematic variations in representations by articulating unit structures (conversions of variations), (d) discover multiplicative commutativity (treatments), and (e) go towards justifying multiplicative commutativity by restructuring the array and its unit structures (conversions of treatments). Design experiment Cycle 1 revealed that all potentials were partially addressed by some students. To enable students to exploit more of these multiple potentials, attention scaffolds and language scaffolds were integrated. In Cycle 2, more potentials were exploited while still leaving room for improvements. In total, the study shows how design research can contribute to embed multi-representation applets into teaching–learning environments with well-sequenced tasks and targeted scaffolds.

Taking an argumentation approach to statistical investigations: developing student data-ing practices

AbstractResearch literature has provided considerable insight as to how young students might come to deeper understandings of statistical concepts, processes and representations through investigations. However, ‘data-ing’—the critical thinking aspects of decision-making while engaging in statistical investigations—has shown to be more difficult to grasp. Data-ing aspects of statistical investigations identified in literature as particularly challenging include: generating researchable questions, knowing when or how to apply statistical knowledge, seeing a need for evidence to support conclusions, and knowing how to use data as evidence. Argumentation practices necessitate a focus on the use of evidence to logically support a claim, thereby offering potential to focus students on the sufficiency and quality of evidence and the conclusions that can be drawn. As such, argumentation offers potential to support students to engage in data decision-making practices. In the research described here, an argumentation focus was used to guide a statistical investigation with a class of 9–10-year-old students in a suburban Australian school. Analysis of classroom interactions and children's work samples suggested that taking an argument-based focus supported students’ developing capacity to overcome the aforementioned challenges and to engage in critical, decision-making practices with authentic, messy data within the context of the investigation.

Interference of mid-level sound statistics underlie human speech recognition sensitivity in natural noise.

Recognizing speech in noise, such as in a busy restaurant, is an essential cognitive skill where the task difficulty varies across environments and noise levels. Although there is growing evidence that the auditory system relies on statistical representations for perceiving 1-5 and coding4,6-9 natural sounds, it's less clear how statistical cues and neural representations contribute to segregating speech in natural auditory scenes. We demonstrate that human listeners rely on mid-level statistics to segregate and recognize speech in environmental noise. Using natural backgrounds and variants with perturbed spectro-temporal statistics, we show that speech recognition accuracy at a fixed noise level varies extensively across natural backgrounds (0% to 100%). Furthermore, for each background the unique interference created by summary statistics can mask or unmask speech, thus hindering or improving speech recognition. To identify the neural coding strategy and statistical cues that influence accuracy, we developed generalized perceptual regression, a framework that links summary statistics from a neural model to word recognition accuracy. Whereas a peripheral cochlear model accounts for only 60% of perceptual variance, summary statistics from a mid-level auditory midbrain model accurately predicts single trial sensory judgments, accounting for more than 90% of the perceptual variance. Furthermore, perceptual weights from the regression framework identify which statistics and tuned neural filters are influential and how they impact recognition. Thus, perception of speech in natural backgrounds relies on a mid-level auditory representation involving interference of multiple summary statistics that impact recognition beneficially or detrimentally across natural background sounds.

Reduced Markovian models of dynamical systems

Leveraging recent work on data-driven methods for constructing a finite state space Markov process from dynamical systems, we address two problems for obtaining further reduced statistical representations. The first problem is to extract the most salient reduced-order dynamics for a given timescale by using a modified clustering algorithm from network theory. The second problem is to provide an alternative construction for the infinitesimal generator of a Markov process that respects statistical features over a large range of time scales. We demonstrate the methodology on three low-dimensional dynamical systems with stochastic and chaotic dynamics. We then apply the method to two high-dimensional dynamical systems, the Kuramoto-Sivashinky equations and data sampled from fluid-flow experiments via Particle Image Velocimetry. We show that the methodology presented herein provides a robust reduced-order statistical representation of the underlying system.

An Evaluation of Cloud‐Precipitation Structures in Mixed‐Phase Stratocumuli Over the Southern Ocean in Kilometer‐Scale ICON Simulations During CAPRICORN

AbstractA persistent shortwave radiative bias of Southern Ocean (SO) clouds in climate models is strongly associated with incorrect cloud phase representation, which impacts precipitation. Measurements characterizing precipitation in low‐level mixed‐phase clouds, which frequently form over the SO, are rare, and our understanding of precipitation efficacy within these clouds remains limited. The simulated surface precipitation bias has an indirect effect on determining global climate sensitivity and a direct impact on the hydrological cycle. This study investigates the representation of low clouds, cloud variability, and precipitation statistics over the SO in real‐case Icosahedral Nonhydrostatic (ICON) simulations at the kilometer scale. The simulations are contrasted with 48 hr of continuous shipborne observations of open and closed‐cell stratocumuli, south of Tasmania. Our simulations show the significance of heavily rimed particle formation, their in‐cloud growth, and subcloud melting to capture the observed cloud‐precipitation vertical structure. In addition, supercooled drizzle formation impacts the vertical structure and precipitation statistics. ICON captures the observed intermittency of precipitation even at a standard vertical resolution of 200 m in the boundary layer but only captures the observed sparse distribution of intense precipitation (>1 mm hr−1) when the maximum vertical resolution is reduced to 100 m. However, the simulations of the 2‐day accumulated precipitation and the radiative effect are largely insensitive to the vertical resolution. The cloud reflectivity of the broken cloud deck is underestimated due to negative biases in cloud optical depth.

2D/3D Expression Generation Using Advanced Learning Techniques and the Emotion Wheel

Facial expression analysis is a critical component in numerous applications, ranging from human- computer interaction to digital character animation. Despite the availability of extensive datasets, most focus predominantly on basic emotions, limiting the expressiveness and applicability of models trained on them. This article introduces a novel approach to generating compound emotional expressions by leveraging the Emotion Wheel, a principle that captures the complex interrelations between basic emotions. Our method integrates the EMOCA (Emotion Driven Monocular Face Capture and Animation) [1] framework, which enhances 3D facial reconstruction by incorporating emotion recognition, stacking models [2, 54] with a sophisticated expression blending algorithm to synthesize nuanced 2D and 3D facial animations. Utilizing the VKIST dataset, which includes high-resolution facial images of Vietnamese individuals, we build a comprehensive database of emotion parameters. Through principal component analysis (PCA) and correlation-driven blending, our approach not only enhances the realism of generated facial expressions but also preserves the subtle nuances that are characteristic of human emotions. Experimental results demonstrate that our method consistently outperforms traditional linear interpolation techniques, producing more distinct and recognizable blended expressions. A user study further validates the naturalness and quality of the generated expressions, with average ratings indicating a strong preference for the proposed method over existing approaches. These findings suggest significant potential for improving emotional expressiveness in both static and dynamic digital character representations.

The contribution of movement to social network structure and spreading dynamics under simple and complex transmission.

The structure of social networks fundamentally influences spreading dynamics. In general, the more contact between individuals, the more opportunity there is for the transmission of information or disease to take place. Yet, contact between individuals, and any resulting transmission events, are determined by a combination of spatial (where individuals choose to move) and social rules (who they choose to interact with or learn from). Here, we examine the effect of the social-spatial interface on spreading dynamics using a simulation model. We quantify the relative effects of different movement rules (localized, semi-localized, nomadic and resource-based movement) and social transmission rules (simple transmission, anti-conformity, proportional, conformity and threshold rules) to both the structure of social networks and spread of a novel behaviour. Localized movement created weakly connected sparse networks, nomadic movement created weakly connected dense networks, and resource-based movement generated strongly connected modular networks. The resulting rate of spreading varied with different combinations of movement and transmission rules, but-importantly-the relative rankings of transmission rules changed when running simulations on static versus dynamic representations of networks. Our results emphasize that individual-level social and spatial behaviours influence emergent network structure, and are of particular consequence for the spread of information under complex transmission rules.This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Teaching protein structure and function through molecular visualization.

The function of proteins is governed by their three-dimensional structure. This structure is determined by the chemical characteristics and atomic interactions of amino acids. Students of biochemistry, with a particular focus on protein chemistry, benefit from looking at protein structures and understanding how proteins are built and fold. Due to their three-dimensional nature, static two-dimensional representations in textbooks can be limiting to student learning. Here, we developed a series of tutorials that introduce students to molecular graphics software. The students are challenged to apply the software to look at proteins and to get a deeper understanding of how amino acid properties are linked to structure. We also familiarize students with some of the latest tools in computational structural biology. Students performed the tutorials with visual enthusiasm and reported general satisfaction in being able to visualize theoretical concepts learned during lectures. We further stimulated student engagement by allowing space for self-exploration. We share the tutorial instructions for other teachers to build on them, and we also offer suggestions for further improvement based on student feedback. In summary, we present a series of tutorials aimed at students of an advanced course in protein biochemistry to enable them to explore the universe of protein structures and how those relate to function.

AlphaFold2 Predicts Alternative Conformation Populations in Green Fluorescent Protein Variants.

Artificial intelligence-based protein structure prediction methods such as AlphaFold2 have emerged as powerful tools for characterizing proteins sequence-structure relationship offering unprecedented opportunities for the molecular interpretation of biological and biochemical phenomena. While initially confined to providing a static representation of proteins through their global free-energy minimum, AlphaFold2 has demonstrated the ability to partially sample conformational landscapes, providing insights into protein dynamics, which is fundamental for interpreting and potentially tuning the function of natural and artificial proteins. In this study, we show that targeted column masking of AlphaFold2's multiple sequence alignment enables the characterization and estimation of the population ratio of the two main conformations of engineered green fluorescent proteins with alternative β-strands. The possibility of quickly estimating relative populations through AlphaFold2 predictions is expected to speed-up the computational design of related systems for sensing applications.

Auroral Image Classification Based on Second-Order Convolutional Network and Channel Attention Awareness

Accurate classification of ground-based auroral images is essential for studying variations in auroral morphology and uncovering magnetospheric mechanisms. However, distinguishing subtle morphological differences among different categories of auroral images presents a significant challenge. To excavate more discriminative information from ground-based auroral images, a novel method named learning representative channel attention information from second-order statistics (LRCAISS) is proposed. The LRCAISS is highlighted with two innovative techniques: a second-order convolutional network and a novel second-order channel attention block. The LRCAISS extends from Resnet50 architecture by incorporating a second-order convolutional network to capture more detailed statistical representation. Meanwhile, the novel second-order channel attention block effectively recalibrates these features. LACAISS is evaluated on two public ground-based auroral image datasets, and the experimental results demonstrate that LRCAISS achieves competitive performance compared to existing methods.

Marked Point Process Secretory Events Statistically Characterize Leptin Pulsatile Dynamics.

Recent studies have highlighted leptin, a key hormone that regulates energy intake and induces satiety, due to the worldwide prevalence of obesity. In this study, we analyzed plasma leptin measurements from 18 women with premenopausal obesity before and after bromocriptine treatment. By using underlying pulses recovered through deconvolution, we modeled the leptin secretory pulses as marked point processes and applied statistical distributions to evaluate the dynamics of leptin, including the interpulse intervals and amplitudes of the secretion. We fit the generalized inverse Gaussian and lognormal distributions to the intervals and the Gaussian, lognormal, and gamma distributions to the amplitudes of pulses. We evaluated the models' goodness of fit using statistical metrics including Akaike's information criterion, Kolmogorov-Smirnov plots, and quantile-quantile plots. Our evaluation results revealed the effectiveness of these statistical distributions in modeling leptin secretion. Although the lognormal and gamma distributions performed the best based on the metrics, we found all distributions capable of accurately modeling the timing of secretory events, leading us to a better understanding of the physiology of leptin secretion and providing a basis for leptin monitoring. In terms of pulse amplitude, the evaluation metrics indicated the gamma distribution as the most accurate statistical representation. We found no statistically significant effect of bromocriptine intake on the model parameters except for one distribution model.

A machine-learning enabled digital-twin framework for next generation precision agriculture and forestry

This work utilizes the modern synergy between flexible, rapid, simulations and quick assimilation of data in order to develop next-generation tools for precise biomass management of large-scale agricultural and forestry systems. Additionally, when integrated with satellite and drone-based digital elevation technologies, the results lead to digital replicas of physical systems, or so-called digital-twins, which offer a powerful framework by which to optimally manage agricultural and forestry assets. Specifically, this enables the investigation of inverse problems seeking to ascertain ideal parameter combinations, such as the number of plants/trees, plant/tree spacing, light intensity, water availability, soil resources, available planting surface area, initial seedling size, genetic variation, etc. to obtain optimal system performance. Towards this goal, a digital-twin framework is developed, consisting of a rapid computational physics engine to simulate an agricultural installation, containing thousands of growing, interacting, plants/trees. This model is then driven by a machine-learning algorithm to obtain optimal parameter sets that match observed statistical representations of a time series of growing agricultural canopy surfaces, measured by digital elevation models. Model simulations are provided to illustrate the approach and to show how such a tool can be used for large-scale biomass management.

Systematic Literature Review of Heuristic-Optimized Microgrids and Energy-Flexible Factories

Decentralized renewable energy generation and consumption through microgrids, coupled with short- and long-term storage systems and enhanced demand flexibility, represent a promising strategy for mitigating grid stress and reducing emissions in the industrial sector. However, transitioning into a sustainable industry often poses challenges in terms of economic feasibility. This review surveys current optimization approaches and simulation functionalities to enhance feasibility. It follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, covering 1066 studies from 2016 to 2023 across three research areas: optimal system sizing of microgrids (OSS), optimization of electrical energy distribution to storage systems and consumers (EED), and energy flexibilization of factories (EF). As a result, 24 filtered sources from these areas were analyzed. Quantitative analysis indicated that evolutionary and swarm-inspired metaheuristics are predominantly applied in OSS, whereas exact linear problem solvers are favored for EED and EF optimization. A range of functionalities is available, and approaches often prioritize individual functionalities, such as load forecasting, dynamic electricity pricing, and statistical representation of energy generation, rather than comprehensively integrating them. Furthermore, no current approach simultaneously integrates optimization and simulation models across all three research areas.