Application In Practice Settings Research Articles

G-MBRMD: Lightweight liver segmentation model based on guided teaching with multi-head boundary reconstruction mapping distillation

Background and Objectives:Liver segmentation is pivotal for the quantitative analysis of liver cancer. Although current deep learning methods have garnered remarkable achievements for medical image segmentation, they come with high computational costs, significantly limiting their practical application in the medical field. Therefore, the development of an efficient and lightweight liver segmentation model becomes particularly important. Methods:In our paper, we propose a real-time, lightweight liver segmentation model named G-MBRMD. Specifically, we employ a Transformer-based complex model as the teacher and a convolution-based lightweight model as the student. By introducing proposed multi-head mapping and boundary reconstruction strategies during the knowledge distillation process, Our method effectively guides the student model to gradually comprehend and master the global boundary processing capabilities of the complex teacher model, significantly enhancing the student model’s segmentation performance without adding any computational complexity. Results:On the LITS dataset, we conducted rigorous comparative and ablation experiments, four key metrics were used for evaluation, including model size, inference speed, Dice coefficient, and HD95. Compared to other methods, our proposed model achieved an average Dice coefficient of 90.14±16.78%, with only 0.6 MB memory and 0.095 s inference speed for a single image on a standard CPU. Importantly, this approach improved the average Dice coefficient of the baseline student model by 1.64% without increasing computational complexity. Conclusion:The results demonstrate that our method successfully realizes the unification of segmentation precision and lightness, and greatly enhances its potential for widespread application in practical settings.

Progress of near-infrared spectroscopy in cerebral blood oxygenation detection: A mini review

In contrast to conventional oximeters, near-infrared spectroscopy-based brain tissue oximetry monitoring devices are capable of non-invasive, continuous, and real-time quantitative monitoring of cerebral oximetry parameters. Initially, these devices were utilized for intensive care or surgical monitoring of oxygen saturation. Due to the rapid advancement of optoelectronic sensing and measurement technologies over the past decade, the derived functional near-infrared brain imaging devices have been widely used in a variety of fields. This paper first introduces the basic principles of near-infrared spectroscopy-based cerebral oxygenation parameter detection, then focuses on the most recent developments in this field of study. Finally, a prospect on their future application in practical settings is also provided.

A Clinical Leadership Lens on Implementing Progress Feedback in Three Countries: Development of a Multidimensional Qualitative Coding Scheme.

Progress feedback, also known as measurement-based care (MBC), is the routine collection of patient-reported measures to monitor treatment progress and inform clinical decision-making. Although a key ingredient to improving mental health care, sustained use of progress feedback is poor. Integration into everyday workflow is challenging, impacted by a complex interrelated set of factors across patient, clinician, organizational, and health system levels. This study describes the development of a qualitative coding scheme for progress feedback implementation that accounts for the dynamic nature of barriers and facilitators across multiple levels of use in mental health settings. Such a coding scheme may help promote a common language for researchers and implementers to better identify barriers that need to be addressed, as well as facilitators that could be supported in different settings and contexts. Clinical staff, managers, and leaders from two Dutch, three Norwegian, and four mental health organizations in the USA participated in semi-structured interviews on how intra- and extra-organizational characteristics interact to influence the use of progress feedback in clinical practice, supervision, and program improvement. Interviews were conducted in the local language, then translated to English prior to qualitative coding. A team-based consensus coding approach was used to refine an a priori expert-informed and literature-based qualitative scheme to incorporate new understandings and constructs as they emerged. First, this hermeneutic approach resulted in a multi-level coding scheme with nine superordinate categories and 30 subcategories. Second-order axial coding established contextually sensitive categories for barriers and facilitators. The primary outcome is an empirically derived multi-level qualitative coding scheme that can be used in progress feedback implementation research and development. It can be applied across contexts and settings, with expectations for ongoing refinement. Suggestions for future research and application in practice settings are provided. Supplementary materials include the coding scheme and a detailed playbook.

Domain Adaptation: Challenges, Methods, Datasets, and Applications

Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well on another set of data (target domain), which is different but has similar properties as the source domain. Domain Adaptation (DA) strives to alleviate this problem and has great potential in its application in practical settings, real-world scenarios, industrial applications and many data domains. Various DA methods aimed at individual data domains have been reported in the last few years; however, there is no comprehensive survey that encompasses all these data domains, focuses on the datasets available, the methods relevant to each domain, and importantly the applications and challenges. To that end, this survey paper discusses how DA can help DNNs work efficiently in these settings by reviewing DA methods and techniques. We have considered five data domains: computer vision, natural language processing, speech, time-series, and multi-modal data. We present a comprehensive taxonomy, including the methods, datasets, challenges, and applications corresponding to each domain. Our goal is to discuss industrial use cases and DA implementation for those. Our final aim is to provide future research directions based on evolving methods and results, the datasets used, and industrial applications.

Evaluating the Typical Day-to-Day Variability of WHOOP-Derived Heart Rate Variability in Olympic Water Polo Athletes.

Heart rate (HR) and HR variability (HRV) can be used to infer readiness to perform exercise in athletic populations. Advancements in the photoplethysmography technology of wearable devices such as WHOOP allow for the frequent and convenient measurement of HR and HRV, and therefore enhanced application in athletes. However, it is important that the reliability of such technology is acceptable prior to its application in practical settings. Eleven elite male water polo players (age 28.8 ± 5.3 years [mean ± standard deviation]; height 190.3 ± 3.8 cm; body mass 95.0 ± 6.9 kg; international matches 117.9 ± 92.1) collected their HR and HRV daily via a WHOOP strap (WHOOP 3.0, CB Rank, Boston, MA, USA) over 16 weeks ahead of the 2021 Tokyo Olympic Games. The WHOOP strap quantified HR and HRV via wrist-based photoplethysmography during overnight sleep periods. The weekly (i.e., 7-day) coefficient of variation in lnRMSSD (lnRMSSDCV) and HR (HRCV) was calculated as a measure of day-to-day variability in lnRMSSD and HR, and presented as a mean of the entire recording period. The mean weekly lnRMSSDCV and HRCV over the 16-week period was 5.4 ± 0.7% (mean ± 95% confidence intervals) and 7.6 ± 1.3%, respectively. The day-to-day variability in WHOOP-derived lnRMSSD and HR is within or below the range of day-to-day variability in alternative lnRMSSD (~3–13%) and HR (~10–11%) assessment protocols, indicating that the assessment of HR and HRV by WHOOP does not introduce any more variability than that which is naturally present in these variables.

Estimation of nonstructural stiffness in instrumented steel frames

Lateral stiffness of nonstructural components may significantly influence the initial stiffness of the entire structure and consequently alter its dynamic characteristics. While methods for simulating structural members are well-established, approaches for modeling nonstructural components that also participate in seismic response are notably less developed. In this paper a simplified, physically-intuitive approach for estimating the stiffness of nonstructural members based on vibration recordings of buildings is presented. The method comprises two components: (1) identifying the instants during the time history wherein components of interstory velocity are negligible, such that damping forces are zero, and (2) at these instants, using static analysis to estimate story shears and story torsion in the structural members by applying the recorded displacements to the entire structure, and the accelerations to all masses above the story of interest. The method derives from first-principles of dynamics and structural analysis, and is assessed against experimental data from three datasets that include shake table data on steel frames with nonstructural walls as well as quasi-static tests on the similar walls. The results are critically discussed in the context of their prospective applications in practical settings, and limitations are summarized.

InP Quantum Dots: Synthesis and Lighting Applications.

InP quantum dots (QDs) are typical III-V group semiconductor nanocrystals that feature large excitonic Bohr radius and high carrier mobility. The merits of InP QDs include large absorption coefficient, broad color tunability, and low toxicity, which render them promising alternatives to classic Cd/Pb-based QDs for applications in practical settings. Over the past two decades, the advances in wet-chemistry methods have enabled the synthesis of small-sized colloidal InP QDs with the assistance of organic ligands. By proper selection of synthetic protocols and precursor materials coupled with surface passivation, the QYs of InP QDs are pushed to near unity with modest color purity. The state-of-the-art InP QDs with appealing optical and electronic properties have excelled in many applications with the potential for commercialization. This work focuses on the recent development of wet-chemistry protocols and various precursor materials for the synthesis and surface modification of InP QDs. Current methods for constructing light-emitting diodes using novel InP-based QDs are also summarized.

Confining Free Radicals in Close Vicinity to Contaminants Enables Ultrafast Fenton-like Processes in the Interspacing of MoS2 Membranes.

Heterogenous Fenton-like reactions are frequently proposed for treating persistent pollutants through the generation of reactive radicals. Despite great efforts to optimize catalyst activity, their broad application in practical settings has been restricted by the low efficiency of hydrogen peroxide or persulfate decomposition as well as ultrafast self-quenching of the activated radicals. Theoretical calculations predicted that two-dimensional (2D) metallic 1T phase MoS2 materials with exposed (001) surfaces and (100) edges should have remarkable affinity towards crucial intermediates in the peroxymonosulfate (PMS) activation process. X-ray photoelectron spectroscopy and in situ Raman spectroscopy were used to show that the exposed metallic Mo sites accelerate the rate-limiting step of electron transfer. A lamellar membrane made from a stack of 2D MoS2 with tunable interspacing was then designed as the catalyst. The non-linear transport between the MoS2 nanolayers leads to high water diffusivity so that the short-lived reactive radicals efficiently oxidize contaminants.

Confining Free Radicals in Close Vicinity to Contaminants Enables Ultrafast Fenton‐like Processes in the Interspacing of MoS2 Membranes

AbstractHeterogenous Fenton‐like reactions are frequently proposed for treating persistent pollutants through the generation of reactive radicals. Despite great efforts to optimize catalyst activity, their broad application in practical settings has been restricted by the low efficiency of hydrogen peroxide or persulfate decomposition as well as ultrafast self‐quenching of the activated radicals. Theoretical calculations predicted that two‐dimensional (2D) metallic 1T phase MoS2 materials with exposed (001) surfaces and (100) edges should have remarkable affinity towards crucial intermediates in the peroxymonosulfate (PMS) activation process. X‐ray photoelectron spectroscopy and in situ Raman spectroscopy were used to show that the exposed metallic Mo sites accelerate the rate‐limiting step of electron transfer. A lamellar membrane made from a stack of 2D MoS2 with tunable interspacing was then designed as the catalyst. The non‐linear transport between the MoS2 nanolayers leads to high water diffusivity so that the short‐lived reactive radicals efficiently oxidize contaminants.

Metal Sulfides as Excellent Co-catalysts for H2O2 Decomposition in Advanced Oxidation Processes

Summary Advanced oxidation processes (AOPs) are widely proposed for treating persistent pollutants by the ⋅ OH radicals generated from H 2 O 2 decomposition. However, their broad applications in practical settings have been hampered by the low efficiency of H 2 O 2 decomposition. Here, we report that metal sulfides (MoS 2 , WS 2 , Cr 2 S 3 , CoS 2 , PbS, or ZnS) can serve as excellent co-catalysts to greatly increase the efficiency of H 2 O 2 decomposition and significantly decrease the required dosage of H 2 O 2 and Fe 2+ in AOPs. Unsaturated S atoms on the surface of metal sulfides can capture protons to form H 2 S and expose metallic active sites with reductive properties to accelerate the rate-limiting step of Fe 3+ /Fe 2+ conversion. The efficiency of AOPs involving co-catalysts can be further enhanced by visible-light illumination thanks to the sensitization of organic pollutants. This discovery is expected to drive great advances in the use of AOPs for large-scale practical applications such as environmental remediation.

Three-dimensional reciprocal space x-ray coherent scattering tomography of two-dimensional object.

X-ray coherent scattering tomography is a powerful tool in discriminating biological tissues and bio-compatible materials. Conventional x-ray scattering tomography framework can only resolve isotropic scattering profile under the assumption that the material is amorphous or in powder form, which is not true especially for biological samples with orientation-dependent structure. Previous tomography schemes based on x-ray coherent scattering failed to preserve the scattering pattern from samples with preferred orientations, or required elaborated data acquisition scheme, which could limit its application in practical settings. Here, we demonstrate a simple imaging modality to preserve the anisotropic scattering signal in three-dimensional reciprocal (momentum transfer) space of a two-dimensional sample layer. By incorporating detector movement along the direction of x-ray beam, combined with a tomographic data acquisition scheme, we match the five dimensions of the measurements with the five dimensions (three in momentum transfer domain, and two in spatial domain) of the object. We employed a collimated pencil beam of a table-top copper-anode x-ray tube, along with a panel detector to investigate the feasibility of our method. We have demonstrated x-ray coherent scattering tomographic imaging at a spatial resolution ~2 mm and momentum transfer resolution 0.01 Å-1 for the rotation-invariant scattering direction. For any arbitrary, non-rotation-invariant direction, the same spatial and momentum transfer resolution can be achieved based on the spatial information from the rotation-invariant direction. The reconstructed scattering profile of each pixel from the experiment is consistent with the x-ray diffraction profile of each material. The three-dimensional scattering pattern recovered from the measurement reveals the partially ordered molecular structure of Teflon wrap in our sample. We extend the applicability of conventional x-ray coherent scattering tomography to the reconstruction of two-dimensional samples with anisotropic scattering profile by introducing additional degree of freedom on the detector. The presented method has the potential to achieve low-cost, high-specificity material discrimination based on x-ray coherent scattering.

Getting the Word Out: New Approaches for Disseminating Public Health Science.

The gap between discovery of public health knowledge and application in practice settings and policy development is due in part to ineffective dissemination. This article describes (1) lessons related to dissemination from related disciplines (eg, communication, agriculture, social marketing, political science), (2) current practices among researchers, (3) key audience characteristics, (4) available tools for dissemination, and (5) measures of impact. Dissemination efforts need to take into account the message, source, audience, and channel. Practitioners and policy makers can be more effectively reached via news media, social media, issue or policy briefs, one-on-one meetings, and workshops and seminars. Numerous “upstream” and “midstream” indicators of impact include changes in public perception or awareness, greater use of evidence-based interventions, and changes in policy. By employing ideas outlined in this article, scientific discoveries are more likely to be applied in public health agencies and policy-making bodies.

Doing mutual understanding. Calibrating with micro-sequences in face-to-face dialogue

“Mutual understanding” has both cognitive and social, interactive meanings. Interlocutors can have or share a (cognitive) mutual understanding. We propose they also do mutual understanding in an observable, reciprocal, three-step micro-process called calibrating. Following Mead (1934) and several subsequent authors, calibrating sequences require three steps: The speaker introduces new information, the addressee responds, and the speaker follows up with evidence that the addressee's response displayed sufficient understanding for current purposes. Without the third step, the addressee would not have evidence of mutual understanding. We developed a microanalysis for reliably identifying calibrating sequences, then applied it to a random sample of face-to-face getting-acquainted dialogues. The results confirmed our three hypotheses: (a) calibrating sequences were continuous throughout these dialogues; 97% of the 1232 utterances that introduced new information were followed by the second and third steps. (b) The micro-sequences were short, averaging 5s each. (c) They were also efficient, with almost two-thirds of the utterances playing a role in more than one sequence. Several factors enhanced generalizability, including the unstructured and diverse content of getting-acquainted dialogues, random sampling, high inter-analyst agreement, and replication of an earlier study. Calibration can link cognitive theories of mutual understanding with observable social interaction. We also describe applications in practical settings.

Joint Inverse Problems for Signal Reconstruction via Dictionary Splitting

Sparse signal recovery from limited and/or degraded samples is fundamental to many applications, such as medical imaging, remote sensing, astronomical and seismic imaging. Discrete wavelet transform (DWT) has been commonly used for sparse representation of signals; nevertheless, due to its shift-variant nature, pseudo-Gibbs artifacts are present in the recovered signals. Using the redundant shift-invariant wavelet transform (SWT) is the ideal solution to obtain shift invariance; however, high redundancy factor of SWT limits its application in practical settings. We propose a dictionary splitting approach for sparse recovery from incomplete data, which leverages the ideas of cycle spinning in combination with Bregman splitting. The proposed method significantly improves the conventional signal reconstruction with DWT, offers the advantages of SWT, and overcomes high redundancy factor of SWT. We solve parallel sparse recovery problems with orthogonal dictionaries (DWT and its permuted versions), while we impose consistency between the results by updating the recovered image at each iteration. Our experiments demonstrate that few shifts are sufficient to achieve reconstruction accuracy as high as recovery with SWT, and significantly reduces its computational cost and redundancy factor.

Optimum Diffraction-Corrected Frequency-Shift Estimator of the Ultrasonic Attenuation Coefficient.

The ultrasonic attenuation coefficient is an important parameter that has been studied extensively in Quantitative Ultrasound and Tissue Characterization. There are various methods described in the literature that estimate this parameter by measuring either spectral difference (i.e., decay) or spectral shift of the backscattered echo signal. Under ideal conditions, i.e., in the absence of abrupt changes in tissue backscattering, Spectral Difference methods can produce estimates with high accuracy and precision. On the other hand, diffraction-corrected Spectral Shift methods (e.g., the Hybrid method) are better suited for application in practical settings using clinical ultrasound scanners. However, current Spectral Shift methods use inefficient frequency shift estimators that ultimately degrade the quality of attenuation coefficient estimates. In this paper, a probabilistic model of the backscattered radiofrequency (RF) echo is used to derive the Cramér-Rao lower bound (CRLB) on estimation variance of the spectral centroid. Next, an efficient correlation-based shift estimator is presented that achieves the CRLB. Used in conjunction with a well-characterized reference phantom to correct for diffraction and other system-related effects, this estimator greatly improves the accuracy and precision of Spectral- Shift attenuation estimation. A theoretical analysis of this method is provided, and its performance is quantitatively compared with that of the Hybrid method using simulated and experimental phantom studies. A minimum of 3-fold reduction in the standard deviation of attenuation coefficient estimates is observed using the new method.

Emulating facial biomechanics using multivariate partial least squares surrogate models.

A detailed biomechanical model of the human face driven by a network of muscles is a useful tool in relating the muscle activities to facial deformations. However, lengthy computational times often hinder its applications in practical settings. The objective of this study is to replace precise but computationally demanding biomechanical model by a much faster multivariate meta-model (surrogate model), such that a significant speedup (to real-time interactive speed) can be achieved. Using a multilevel fractional factorial design, the parameter space of the biomechanical system was probed from a set of sample points chosen to satisfy maximal rank optimality and volume filling. The input-output relationship at these sampled points was then statistically emulated using linear and nonlinear, cross-validated, partial least squares regression models. It was demonstrated that these surrogate models can mimic facial biomechanics efficiently and reliably in real-time.

Harnessing and Understanding Feedback Technology in Applied Settings

Research on the influence of augmented feedback effects on both skill learning and performance has been examined from two differing positions, generally reflective of two core movement science disciplines: motor learning and biomechanics. The motor learning approach has been to examine the content and timing of feedback under tightly controlled laboratory settings, with a focus on simple tasks and the influence of movement outcome feedback. At the other end of the spectrum are biomechanical approaches, which have been primarily devoted to demonstrating the capacity of measurement technology to quantify and report on movement pattern effectiveness. This review highlights the gap left by these two approaches and argues that advancement of our understanding of feedback application in practical settings requires a shift towards a multi-disciplinary focus. A particular focus of the review is on how researchers and practitioners need to harness our understanding and subsequent application of the emergent feedback technologies most prevalent in elite sport settings and clinical sports medicine. We highlight important considerations for future applied multidisciplinary research driven by relevant theory and methodological design to more comprehensively capture how feedback systems can be used to facilitate the development of skilled performance.

Pattern Matching in Indeterminate and Arc-Annotated Sequences

In this paper, we present efficient algorithms for finding indeterminate Arc-Annotated patterns in indeterminate Arc-Annotated references. Our algorithms run in O(m+ (nm) w) time where n and m are respectively the length of our reference and pattern strings and w is the target machine word size. Here we have assumed the alphabet size to be constant, because, indeterminate Arc-Annotated sequences are used to model biological sequences. Clearly, for short patterns, our algorithms run in linear time and efficient algorithms for matching short patterns to reference genomes have huge applications in practical settings. We have also applied our algorithms to scan the ncRNAs without pseudoknots. We scanned three whole human chromosomes and it took only 2.5 - 4 minutes to scan one whole chromosome for an ncRNA family. Some relevant patents are discussed in.

Safety in numbers 2: Competency modelling and diagnostic error assessment in medication dosage calculation problem-solving

Accurately defining and modelling competence in medication dosage calculation problem-solving (MDC-PS) is a fundamental pre-requisite to measuring competence, diagnosing errors and determining the necessary design and content of professional education programmes. In this paper we advance an MDC-PS competence model that illustrates the relationship between conceptual competence (dosage problem-understanding), calculation competence (dosage-computation) and technical measurement competence (dosage-measurement). To facilitate bridging of the theory–practice gap it is critical that such models are operationalised within a wider education framework that supports the learning, assessment and synthesis of cognitive competence (the knowing that and knowing why of MDC-PS) and functional competence (the know-how and skills associated with the professional practice of MDC-PS in clinical settings).Within the context of supporting the learning and diagnostic assessment of MDC-PS we explore PhD fieldwork that challenges the value of pedagogical approaches that focus solely on abstract information, that isolate the process of knowledge construction from its application in practice settings and contribute to the generation of conceptual errors. We consider misconceptions theory and the concept of mathematical ‘dropped stitches’ and offer an assessment model and program designed to diagnose flawed arithmetical operation and computation constructs.

Integration of treatment innovation planning and implementation: Strategic process models and organizational challenges.

Sustained and effective use of evidence-based practices in substance abuse treatment services faces both clinical and contextual challenges. Implementation approaches are reviewed that rely on variations of plan-do-study-act (PDSA) cycles, but most emphasize conceptual identification of core components for system change strategies. A two-phase procedural approach is therefore presented based on the integration of Texas Christian University (TCU) models and related resources for improving treatment process and program change. Phase 1 focuses on the dynamics of clinical services, including stages of client recovery (cross-linked with targeted assessments and interventions), as the foundations for identifying and planning appropriate innovations to improve efficiency and effectiveness. Phase 2 shifts to the operational and organizational dynamics involved in implementing and sustaining innovations (including the stages of training, adoption, implementation, and practice). A comprehensive system of TCU assessments and interventions for client and program-level needs and functioning are summarized as well, with descriptions and guidelines for applications in practical settings.