Individual Modalities Research Articles

English-Ukrainian intersemiotic translation within the configuration of modes of modality in literary texts

The article highlights the main features of intersemiotic translation of a literary text, which is considered to be an expression of the author’s modality as a set of signs of various semiotic systems. Thus, the purpose of the study is to determine the linguistic and non-linguistic means of expressing modality in a literary text, the specifics of their interaction to generate meaning, and the features of their reproduction in intersemiotic translation. The object of modality in translation within a literary text is a set of signs encoded by the author of the source text and organized into a system that is decoded and reproduced by the translator both at the phonological, lexical, and syntactic levels and the semantic, emotional, and associative ones. The analysis of the category of modality from the standpoint of semiotics in literary texts involves decoding semiotic resources, symbols, and signs, taking into account the individual author’s, social, political, cultural, and national contexts. The process of interpreting and reproducing the sign configurations of individual author modality is carried out at the syntactic, semantic, and pragmatic levels to achieve the corresponding aims. Thus, at the first stage, the forms and interactions of determinants are defined and interpreted whereas the second level involves consideration of the internal semantic plane of the research object, and, accordingly, at the third level, the influence and use of signs are outlined. It is proved that adequate literary translation is carried out based on the multimodality of the text that is viewed as a multimodal communication, within which meaning is generated by the configuration of different semiotic sign systems that require interpretation according to the principle of optimal relevance and creativity. The central concept of translation is semiosis as a process of interpretation of a sign and generation of meaning, that indicates the relationship of the sign with the outside world.

Adherence to lifestyle recommendations among Norwegian cancer survivors and the impact of traditional and complementary medicine use: the Tromsø Study 2015–2016

IntroductionAdherence to healthy lifestyle recommendations has positive effects on cancer outcomes yet adherence is low among cancer survivors. Differences in adherence between women and men, phase of survivorship, and other factors that might increase adherence, like the use of traditional and complementary medicine (T&CM), need to be explored. We aimed to study the adherence to national recommendations for a healthy diet (daily intake of ≥ 5 portions of fruit/vegetables), physical activity (150 min of moderate-intensity or 75 min of high-intensity/week), normal body mass index (BMI) (18.5–24.9 kg/m2), non-smoking, and low-risk alcohol consumption (women ≤ 10 g/day, men ≤ 20 g/day) among Norwegian cancer survivors and their associations with sex, the use of T&CM, and survivorship phase.MethodsWe used logistic regression, independent sample t-test, and chi-square test to study self-reported (diet, physical activity, smoking, alcohol consumption) and measured (BMI) adherence in 1530 cancer survivors (40 years and above, participating in the population-based Tromsø Study conducted in 2015–2016 (65% attendance). We dichotomized all assessed lifestyle recommendations (adherence = 1 point, non-adherence = 0 points), and created a score for every recommendation (0–5 points). Adherence to individual lifestyle recommendations and the use of T&CM as well as the phase of survivorship was adjusted for sex, age, income, and living with a partner.ResultsAdherence to recommendations was 7.5% for diet, 85.3% for physical activity, 30.5% for BMI, 89.3% for non-smoking, and 87.6% for alcohol consumption. In total 2.3% adhered to all five recommendations concurrently (mean score 2.96 [SD = 0.86]). Women adhered to more recommendations concurrently compared to men (3.03 [SD = 0.90] vs. 2.89 [SD = 0.80] points respectively, [p = .012]). In total, 31% reported the use of T&CM and there were no differences in adherence to individual lifestyle recommendations or concurrent adherence in overall T&CM use compared to non-use. Users of self-help techniques were more likely to adhere to the recommendations of diet (aOR 2.69, 95% CI 1.45–4.98) and physical activity (aOR 6.26, 95% CI 1.51–25.92). Users of traditional healers and users of more than one T&CM modality were less likely to adhere to the low-risk alcohol consumption recommendation, (aOR 0.32, 95% CI 0.13–0.77, and aOR 0.53, 95% CI 1.08–2.17, respectively) compared to T&CM non-users. Survivors with cancer previously (1162) had higher odds of adhering to the recommendation of diet (aOR 2.66, 95% CI 1.36–5.19) than survivors with cancer presently (n = 368), but not to other recommendations.ConclusionThe health of cancer survivors can be improved through adherence to lifestyle recommendations, yet our study found partial adherence among survivors in Norway, in accordance with findings from other countries. Although overall T&CM use was not associated with higher adherence to lifestyle recommendations, differences in adherence were seen among individual modalities like the use of self-help techniques and traditional healers. Our results suggest the need for intensified follow-up of lifestyle with attention to male survivors and diet among all survivors throughout the cancer survivorship continuum.

Hybrid Raman and Laser-Induced Breakdown Spectroscopy for Food Authentication Applications.

The issue of food fraud has become a significant global concern as it affects both the quality and safety of food products, ultimately resulting in the loss of customer trust and brand loyalty. To address this problem, we have developed an innovative approach that can tackle various types of food fraud, including adulteration, substitution, and dilution. Our methodology utilizes an integrated system that combines laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Although both techniques emerged as valuable tools for food analysis, they have until now been used separately, and their combined potential in food fraud has not been thoroughly tested. The aim of our study was to demonstrate the potential benefits of integrating Raman and LIBS modalities in a portable system for improved product classification and subsequent authentication. In pursuit of this objective, we designed and tested a compact, hybrid Raman/LIBS system, which exhibited distinct advantages over the individual modalities. Our findings illustrate that the combination of these two modalities can achieve higher accuracy in product classification, leading to more effective and reliable product authentication. Overall, our research highlights the potential of hybrid systems for practical applications in a variety of industries. The integration and design were mainly focused on the detection and characterization of both elemental and molecular elements in various food products. Two different sets of solid food samples (sixteen Alpine-style cheeses and seven brands of Arabica coffee beans) were chosen for the authentication analysis. Class detection and classification were accomplished through the use of multivariate feature selection and machine-learning procedures. The accuracy of classification was observed to improve by approximately 10% when utilizing the hybrid Raman/LIBS spectra, as opposed to the analysis of spectra from the individual methods. This clearly demonstrates that the hybrid system can significantly improve food authentication accuracy while maintaining the portability of the combined system. Thus, the successful implementation of a hybrid Raman-LIBS technique is expected to contribute to the development of novel portable devices for food authentication in food as well as other various industries.

Prediction of lymph node metastasis in primary gastric cancer from pathological images and clinical data by multimodal multiscale deep learning

Background:Gastric cancer is the third most common cause of cancer-related death. Accurate preoperative prediction of lymph node metastasis (LNM) in primary gastric cancer strongly influences the choice of surgical approach and the prognosis of gastric cancer patients. Objective:To develop and validate a deep learning-based model to analyze routine histological slides of patients with primary gastric cancer as well as clinical data to predict the occurrence of LNM preoperatively. Patients and Methods:Radical surgery slides from 309 patients and biopsy slides from 157 patients were collected from Fujian Cancer Hospital, along with radical surgery slides from 306 patients from The Cancer Genome Atlas (TCGA). Clinical data, including age, gender, lauren classification, and tumor location, were collected. These datasets were used to develop and validate a deep learning-based model. Results:Five models were trained via cross-validation, with a mean area under the receiver operating characteristic curve (AUC) (standard deviation [SD]) of 0.877 (0.048) achieved. There was a significant difference in scores between both classes (LNM positive [N+] and LNM negative [N0]) ( p<0.001). we validated the performance of the model on biopsy slides and achieved a mean AUC (SD) of 0.725 (0.020). In the analysis of clinical data, the lauren classification was demonstrated to be an independent risk factor for predicting LNM. Conclusion:Our study confirmed that deep learning-based image analysis could preoperatively predict LNM in patients with primary gastric cancer, combining histological slides at different magnification scales and relevant clinical data, showing superiority over individual modality prediction.

Costs of oropharyngeal squamous cell cancer treatment in Finland

BackgroundOropharyngeal squamous cell carcinoma (OPSCC) can be treated with definitive (chemo)radiotherapy ((C)RT) or primary surgical treatment (PST) with or without postoperative oncologic treatment. The prognosis of OPSCC does not essentially depend on the treatment modality, which allows to consider secondary decision-making aspects such as treatment costs when recommending an individual treatment modality. We attempted to analyze the costs associated with definitive (C)RT and PST in the treatment of OPSCC in Finland.Materials and methodsWe included 73 patients diagnosed with OPSCC at the Helsinki University Hospital (HUS) (Helsinki, Finland) in 2019 and 2020. Treatment costs were defined as the costs incurred in the specialized medical care during the first 12 months after the diagnosis was established.ResultsDefinitive RT and definitive CRT were on a 1-year horizon associated with median costs of approximately 10 700€ and 13 300€, respectively; while, the median costs of PST equaled about 40 600€. The costs of definitive (C)RT mostly consisted of the costs of (chemo)radiotherapy sessions; while, the operating room costs and the costs of intensive care and stay on a ward drove the costs of PST.ConclusionsPST is associated with 2–3 times higher median costs than definitive (C)RT in Finland. The finding differs from the results previously reported in North America, which is related, e.g., to differences in the treatment practices as well as in the regulation of the health care systems.

MFIRA: Multimodal Fusion Intent Recognition Algorithm for AR Chemistry Experiments

The current virtual system for secondary school experiments poses several issues, such as limited methods of operation for students and an inability of the system to comprehend the users’ operational intentions, resulting in a greater operational burden for students and hindering the goal of the experimental practice. However, many traditional multimodal fusion algorithms rely solely on individual modalities for the analysis of users’ experimental intentions, failing to fully utilize the intention information for each modality. To rectify these issues, we present a new multimodal fusion algorithm, MFIRA, which intersects and blends intention probabilities between channels by executing parallel processing of multimodal information at the intention layer. Additionally, we developed an augmented reality (AR) virtual experiment platform based on the Hololens 2, which enables students to conduct experiments using speech, gestures, and vision. Employing the MFIRA algorithm, the system captures users’ experimental intent and navigates or rectifies errors to guide students through their experiments. The experimental results indicate that the MFIRA algorithm boasts a 97.3% accuracy rate in terms of interpreting users’ experimental intent. Compared to existing experimental platforms, this system is considerably more interactive and immersive for students and is highly applicable in secondary school experimental chemistry classrooms.

Network Capacity Bound for Personalized PageRank in Multimodal Networks

In a former paper [1] the concept of Bipartite PageRank was introduced and a theorem on the limit of authority flowing between nodes for personalized PageRank has been generalized. In this paper we want to extend those results to multimodal networks. In particular we deal with a hypergraph type that may be used for describing multimodal network where a hyperlink connects nodes from each of the modalities. We introduce a generalisation of PageRank for such graphs and define the respective random walk model that can be used for computations. We state and prove theorems on the limit of outflow of authority for cases where individual modalities have identical and distinct damping factors.

Electromagnetic source imaging predicts surgical outcome in children with focal cortical dysplasia

ObjectiveTo evaluate the diagnostic accuracy of electromagnetic source imaging (EMSI) in localizing spikes and predict surgical outcome in children with drug resistant epilepsy (DRE) due to focal cortical dysplasia (FCD). MethodsWe retrospectively analyzed magnetoencephalography (MEG) and high-density (HD-EEG) data from 23 children with FCD-associated DRE who underwent intracranial EEG and surgery. We localized spikes using equivalent current dipole (ECD) fitting, dipole clustering, and dynamical statistical parametric mapping (dSPM) on EMSI, electric source imaging (ESI), and magnetic source imaging (MSI). We calculated the distance from the seizure onset zone (DSOZ) and resection (DRES). We estimated receiver operating characteristic (ROC) curves with Youden’s index (J) to predict outcome. ResultsEMSI presented shorter DSOZ (15.18 ± 9.06 mm) and DRES (8.56 ± 6.24 mm) compared to ESI (DSOZ: 25.04 ± 16.20 mm, p < 0.009; DRES: 18.88 ± 17.30 mm, p < 0.03) and MSI (DSOZ: 23.37 ± 8.98 mm, p < 0.03; DRES: 15.51 ± 10.11 mm, p < 0.02) for clustering in patients with good outcome. Clustering showed shorter DSOZ and DRES compared to ECD fitting and dSPM (p < 0.05). EMSI had higher performance as outcome predictor (J = 70.63%) compared to ESI (J = 41.27%) and MSI (J = 33.33%) for clustering. ConclusionsEMSI provides superior localization and improved predictive performance than individual modalities. SignificanceEMSI can help the surgical planning and facilitate the localization of epileptogenic foci.

Audio–Visual Fusion for Emotion Recognition in the Valence–Arousal Space Using Joint Cross-Attention

Automatic emotion recognition (ER) has recently gained lot of interest due to its potential in many real-world applications. In this context, multimodal approaches have been shown to improve performance (over unimodal approaches) by combining diverse and complementary sources of information, providing some robustness to noisy and missing modalities. In this paper, we focus on dimensional ER based on the fusion of facial and vocal modalities extracted from videos, where complementary audio-visual (A-V) relationships are explored to predict an individual’s emotional states in valence-arousal space. Most state-of-the-art fusion techniques rely on recurrent networks or conventional attention mechanisms that do not effectively leverage the complementary nature of A-V modalities. To address this problem, we introduce a joint cross-attentional model for A-V fusion that extracts the salient features across A-V modalities, that allows to effectively leverage the inter-modal relationships, while retaining the intra-modal relationships. In particular, it computes the cross-attention weights based on correlation between the joint feature representation and that of the individual modalities. By deploying the joint A-V feature representation into the cross-attention module, it helps to simultaneously leverage both the intra and inter modal relationships, thereby significantly improving the performance of the system over the vanilla cross-attention module. The effectiveness of our proposed approach is validated experimentally on challenging videos from the RECOLA and AffWild2 datasets. Results indicate that our joint cross-attentional A-V fusion model provides a cost-effective solution that can outperform state-of-the-art approaches, even when the modalities are noisy or absent. Code is available at https://github.com/praveena2j/Joint-Cross-Attention-for-Audio-Visual-Fusion.

Geographic variation in the utilisation of specialist healthcare for patients with severe mental illness in Norway: a population-based registry study

PurposeThe aim of this study is to measure geographic variations in mental healthcare service utilisation among patients with severe mental illness in Norway.MethodWe analysed data from the Norwegian patient registry for 2014–2018 for patients with severe mental illness. The outcomes measured in this study were: outpatient contact, admission, bed days and total contact rates. Total contacts were calculated as the sum of observed outpatient contacts plus four times the hospital bed days for each hospital catchment area based on the Norwegian health director’s report on clinical activity and patient treatment cost. Geographic variations were measured using extreme quotient (EQ), coefficient of variation (CV) and systematic component of variation (SCV). Maps, figures, and tables were used to visualise geographic variation.ResultsThe geographic variations saw a six-fold increase in the outpatient contact rate and a three-fold increase in the admission rate between the areas with lowest rate and areas with the highest rate. However, there was low geographic variation in calculated total contact rates (Eqs. 5 − 95 =1.77). The low-level geographic variation in the total calculated contact rate was also confirmed with an SCV of less than three.ConclusionThe levels of geographic variations in the utilisation of outpatient and inpatient mental healthcare services among patients with severe mental illness are high. However, the geographic variation in total services provided by hospital catchment areas calculating the two service modalities together using their treatment cost ratio, is low. This may reflect the relatively equal performance of hospital catchment areas in terms of resource utilisation regardless which service modality they prioritise. Factors contributing to high geographic variation in individual service modalities need further investigation.

Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing

Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack types, generic solutions are still challenging due to the diversity of spoof characteristics. Recently, the spoof trace disentanglement framework has shown great potential for coping with both seen and unseen spoof scenarios, but the performance is largely restricted by the single-modal input. This paper focuses on this issue and presents a multi-modal disentanglement model which targetedly learns polysemantic spoof traces for more accurate and robust generic attack detection. In particular, based on the adversarial learning mechanism, a two-stream disentangling network is designed to estimate spoof patterns from the RGB and depth inputs, respectively. In this case, it captures complementary spoofing clues inhering in different attacks. Furthermore, a fusion module is exploited, which recalibrates both representations at multiple stages to promote the disentanglement in each individual modality. It then performs cross-modality aggregation to deliver a more comprehensive spoof trace representation for prediction. Extensive evaluations are conducted on multiple benchmarks, demonstrating that learning polysemantic spoof traces favorably contributes to anti-spoofing with more perceptible and interpretable results.

Losing sight of Luck: Automatic approach tendencies toward gambling cues in Canadian moderate- to high-risk gamblers – A replication study

Evidence for approach bias tendencies to underly automatic behavioural impulses towards seeking out gambling activities in the presence of appetitive salient cues was first shown by Boffo et al. (2018) in a Dutch sample. Relative to non-problem gamblers, moderate-to-high-risk gamblers demonstrated stronger approach tendencies towards gambling-related stimuli compared with neutral ones. Moreover, gambling approach bias was associated with past-month gambling behaviour and predictive of gambling activity persistence over time. The current study aimed to replicate these findings within a Canadian sample evaluating the concurrent and longitudinal correlates of gambling approach bias. The study was conducted online, available throughout Canada. Twenty-seven non-treatment-seeking moderate-to-high-risk gamblers and 26 non-problem gamblers community-recruited via multiple channels (i.e., internet and newspaper advertisements, land-based flyers, and university recruitment portals). Participants completed two online assessment sessions 6-months apart. Each session included (1) self-report measures of gambling behaviour (frequency, duration, and expenditure), (2) self-report assessment of problem gambling severity (PGSI), and (3) a gambling approach-avoidance task, utilising culturally relevant stimuli tailored to individual gambling habits. However, our study failed to replicate Boffo et al. (2018) findings in a Canadian sample. Relative to non-problem gamblers, moderate-to-high-risk gamblers did not exhibit greater approach bias tendencies towards gambling-related stimuli compared to neutral stimuli. Moreover, gambling approach bias was not predictive of prospective gambling behaviour (frequency, duration, or expenditure) or severity of gambling problems. Reported results do not provide evidence for approach tendencies contributing to problematic gambling behaviour in a Canadian sample of moderate-to-high-risk gamblers compared to non-problematic gambler controls. Further replications on the topic are needed. Future research should evaluate approach tendencies within the gambling context, considering the potential impact of task reliability to assess approach bias in light of individual gambling modality preferences.

Pathway-level multi-omics analysis of the molecular mechanisms underlying the toxicity of long-term tacrolimus exposure

Tacrolimus (TAC)-based treatment is associated with nephrotoxicity and hepatotoxicity; however, the underlying molecular mechanisms responsible for this toxicity have not been fully explored. This study elucidated the molecular processes underlying the toxic effects of TAC using an integrative omics approach. Rats were sacrificed after 4 weeks of daily oral TAC administration at a dose of 5 mg/kg. The liver and kidney underwent genome-wide gene expression profiling and untargeted metabolomics assays. Molecular alterations were identified using individual data profiling modalities and further characterized by pathway-level transcriptomics-metabolomics integration analysis. Metabolic disturbances were mainly related to an imbalance in oxidant-antioxidant status, as well as in lipid and amino acid metabolism in the liver and kidney. Gene expression profiles also indicated profound molecular alterations, including in genes associated with a dysregulated immune response, proinflammatory signals, and programmed cell death in the liver and kidney. Joint-pathway analysis indicated that the toxicity of TAC was associated with DNA synthesis disruption, oxidative stress, and cell membrane permeabilization, as well as lipid and glucose metabolism. In conclusion, our pathway-level integration of transcriptome and metabolome and conventional analyses of individual omics profiles, provided a more comprehensive picture of the molecular changes resulting from TAC toxicity. This study also serves as a valuable resource for subsequent investigations aiming to understand the mechanism underlying the molecular toxicology of TAC.

Association of deep phenotyping with diagnostic yield of prenatal exome sequencing for fetal brain abnormalities

PurposeTo evaluate whether deep prenatal phenotyping of fetal brain abnormalities (FBAs) increases diagnostic yield of trio-exome sequencing (ES) compared with standard phenotyping. MethodsRetrospective exploratory analysis of a multicenter prenatal ES study. Participants were eligible if an FBA was diagnosed and subsequently found to have a normal microarray. Deep phenotyping was defined as phenotype based on targeted ultrasound plus prenatal/postnatal magnetic resonance imaging, autopsy, and/or known phenotypes of other affected family members. Standard phenotyping was based on targeted ultrasound alone. FBAs were categorized by major brain findings on prenatal ultrasound. Cases with positive ES results were compared with those that have negative results by available phenotyping, as well as diagnosed FBAs. ResultsA total of 76 trios with FBAs were identified, of which 25 (33%) cases had positive ES results and 51 (67%) had negative results. Individual modalities of deep phenotyping were not associated with diagnostic ES results. The most common FBAs identified were posterior fossa anomalies and midline defects. Neural tube defects were significantly associated with receipt of a negative ES result (0% vs 22%, P = .01). ConclusionDeep phenotyping was not associated with increased diagnostic yield of ES for FBA in this small cohort. Neural tube defects were associated with negative ES results.

Self-powered smart skins for multimodal tactile perception based on triboelectric and hygroelectric working principles

Human being perceives multiple tactile modalities in the process of sensation on the skin and interpretation in the brain. To date, several sensing techniques facilitate the accurate measurement of individual tactile modality, but multimodal static and dynamic sensing remain challenging. Moreover, low-cost and highly efficient interpretation techniques are still required for tactile perception. Herein, we present cost-effective and high-performing self-powered smart skins that mimic multimodal tactile perception, enabling accurate perception of pressure, vibration, and humidity in the process of sensation on the smart skin and interpretation by machine learning. The dynamic and static stimuli are encoded by triboelectric and hygroelectric principles in the smart skins, respectively, while the hygroscopic nature empowers humidity sensation capability in the smart skin with an accuracy rate as high as 84.0%−100.0%. We believe our smart skin will enable the smooth transition of e-skin into practical applications, such as robotics, prosthetics, healthcare, and intelligent industry.

Multimodal communication development in semiwild chimpanzees

Human language is characterized by the integration of multiple signal modalities, including speech, facial and gestural signals. While language likely has deep evolutionary roots that are shared with some of our closest living relatives, studies of great ape communication have largely focused on each modality separately, thus hindering insights into the origins of its multimodal nature. Studying when multimodal signals emerge during great ape ontogeny can inform about both the proximate and ultimate mechanisms underlying their communication systems, shedding light on potential evolutionary continuity between humans and other apes. To this end, the current study investigated developmental patterns of multimodal signal production by 28 semiwild chimpanzees, Pan troglodytes, ranging in age from infancy to early adolescence. We examined the production of facial expressions, gestures and vocalizations across a range of behavioural contexts, both when produced separately and as part of multimodal signal combinations (henceforth multimodal). Overall, we found that while unimodal signals were produced consistently more often than multimodal combinations across all ages and contexts, the frequency of multimodal combinations increased significantly in older individuals and most within the aggression and play contexts, where the costs of signalling ambiguity may be higher. Furthermore, older individuals were more likely to produce a multimodal than a unimodal signal and, again, especially in aggressive contexts. Variation in production of individual signal modalities across ages and contexts are also presented and discussed. Overall, evidence that multimodality increases with age in chimpanzees is consistent with patterns of developing communicative complexity in human infancy, revealing apparent evolutionary continuity. Findings from this study contribute novel insights into the evolution and development of multimodality and highlight the importance of adopting a multimodal approach in the comparative study of primate communication.

Stretching and Releasing of Iliotibial Band Complex in Patients with Iliotibial Band Syndrome: A Narrative Review.

Iliotibial band syndrome (ITBS) is one of the most common overuse syndromes causing knee pain; it is especially prevalent in runners and also common in cyclists, rowers, and field athletes, with occasional cases occurring in non-athletes too. ITBS symptoms can negatively affect not only knee function, but also mental and physical aspects of health-related quality of life. Although various conservative treatment options have been investigated and discussed, there is still no consensus on a standard of care for ITBS. Moreover, the literature on the etiology and risk factors of ITBS, which could help in selecting appropriate treatment methods, is conflicting and inconclusive. The role of individual treatment modalities such as stretching and releasing techniques has not been extensively studied and remains unclear. In this article, we will critically review the available evidence for the benefits of ITB stretching and "release" methods in the treatment of ITBS. In addition to the direct evidence (clinical studies examining the effects of ITB stretching and other methods that purportedly stretch or "release" the ITB), we present several additional lines of reasoning that discuss the rationale for ITB stretching/releasing in terms of the etiology of ITBS, the mechanical properties and behavior of the ITB, and the risk factors for ITBS development. We conclude that the current literature provides some evidence for the inclusion of stretching or other "release" methods in the early rehabilitation of ITBS. Long-term interventions typically include ITB stretching; however, it remains unclear to what extent stretching within a multimodal treatment actually contributes to resolving the symptoms. At the same time, there is no direct evidence to suggest that stretching and "release" methods have any negative effects.

Pinakas: A Methodology for Deep Analysis of Tables in Technical Documents

The holistic understanding of the information contained in technical documents depends on the understanding of the document’s individual modalities. These modalities are tables, graphics, diagrams, formulas, etc. and each of them is a standalone research topic that requires a different way of processing and understanding. These modalities, processed and combined with the document text, can introduce new techniques for visual question answering in documents. Thus, in this paper we present Pinakas, a methodology for automatic analysis of the internal tabular information that appears in technical documents and its modeling to stochastic Petri-net graphs. We focus only on tables that strictly abide by the IEEE format rules. The methodology presented here is divided into the following steps: (1) table detection, (2) table recognition, (3) table understanding. Qualitative results of Pinakas are demonstrated as proof of concept for the accurate extraction of information from three different types of tables.

Education modalities for serious illness communication training: A scoping review on the impact on clinician behavior and patient outcomes.

e24003 Background: Several clinician training interventions that address serious illness communication have been developed over the past decade. While numerous studies report the impact on clinician attitude, confidence and intention to change practice, less is reported on individual education modalities and their impact on actual behavior change and patient outcomes. This study's aim was to examine what is known about the education modalities used in serious illness communication training and their impact on clinician behaviors and patient outcomes. Methods: A scoping review using the Joanna Briggs Methods Manual for Scoping Reviews was conducted of studies that specifically measured clinician behaviors or patient outcomes. Ovid MEDLINE and EMBASE databases were searched for English-language studies published between January 2011, and November 2021. Results: The search identified 1132 articles: 60 met inclusion criteria describing 49 unique interventions. Common education modalities used were a single workshop (n = 18), multiple workshops (n = 8), single workshop with coaching (n = 7), multiple workshops with coaching (n = 5); though they were inconsistently structured. Studies reporting improved clinician skills tended to be in simulation settings with neither clinical practice nor patient outcomes explored. While studies reporting behavior changes or improved patient outcomes did not confirm improvements in skills. As multiple modalities were commonly used and often embedded within larger quality improvement initiatives, the impact of individual modalities could not be determined. Conclusions: To advance the evidence of the benefits of serious illness communication training, well-defined education modalities and consistent outcome measures for behavior change and patient outcomes are needed.

Diagnostic Yield and Utility of Radiographic Imaging in the Evaluation of Pulsatile Tinnitus: A Systematic Review.

The objective of this study is to assess diagnostic yield of imaging modalities used to evaluate patients presenting with pulsatile tinnitus (PT). PubMed, Embase, and Scopus were queried using the search terms "pulsatile tinnitus," "pulse-synchronous tinnitus," and "pulse synchronous tinnitus" with no date limitations. Studies that reported diagnostic imaging for patients presenting with PT were included. Data were reviewed for sample size, gender, age, imaging study, indications, and diagnoses. The primary outcome measure from aggregated data was the yield of positive diagnoses made with each imaging modality. The quality of evidence was assessed for risk of bias. From an initial search of 1145 articles, 17 manuscripts met inclusion criteria, of which 12 studies evaluated individual imaging modalities. The number of unique patients included was 1232. The diagnostic yield varied between modalities: carotid ultrasound (21%, 95% confidence interval [CI]: 12%-35%), CT temporal bone (65%, CI: 20%-93%), computed tomographic angiography (86%, CI: 80%-90%), and MRI/magnetic resonance angiography (58%, CI: 43%-72%). Studies on the diagnostic approach to PT are limited by heterogeneity in both inclusion criteria and reporting standards. A wide range of imaging modalities are used in practice during the initial evaluation of PT, and the diagnostic yield for imaging can be improved by utilizing more specific clinical indications.