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This paper first presents CycleDance, a novel dance style transfer system that transforms an existing motion clip in one dance style into a motion clip in another dance style while attempting to preserve the motion context of the dance. CycleDance extends existing CycleGAN architectures with multimodal transformer encoders to account for the music context. We adopt a sequence length-based curriculum learning strategy to stabilize training. Our approach captures rich and long-term intra-relations between motion frames, which is a common challenge in motion transfer and synthesis work. Building upon CycleDance, we further propose StarDance, which enables many-to-many mappings across different styles using a single generator network. Additionally, we introduce new metrics for gauging transfer strength and content preservation in the context of dance movements. To evaluate the performance of our approach, we perform an extensive ablation study and a human study with 30 participants, each with 5 or more years of dance experience. Our experimental results show that our approach can generate realistic movements with the target style, outperforming the baseline CycleGAN and its variants on naturalness, transfer strength, and content preservation. Our proposed approach has potential applications in choreography, gaming, animation, and tool development for artistic and scientific innovations in the field of dance.

A man in his 60s presented with a subacute paracentral scotoma and preserved visual acuity in the left eye. He was found to have a very subtle area of deep retinal whitening at the macula and multiple retinal cholesterol emboli. Optical coherence tomography (OCT) with En face imaging revealed globular paracentral acute middle maculopathy (PAMM). A diagnosis of PAMM associated with branch artery occlusion was made and the patient was immediately transferred to the nearest stroke centre. Investigations revealed severe carotid occlusive disease for which the patient underwent carotid endarterectomy. Paracentral scotomas in patients with little clinical findings on fundus examination should raise the suspicion for PAMM, which is easily identifiable on OCT. Eye care professionals must recognise PAMM as a possible sign of acute retinal arterial ischaemia—an ocular and systemic emergency that requires immediate referral to specialised stroke centres.

Objective To characterize the multimodal retinal findings of myopic macular pits, a feature of myopic degeneration. Methods A case series of patients with myopic macular pits were studied with multimodal imaging including color fundus photography, fundus autofluorescence (FAF), near infrared reflectance (NIR), spectral domain optical coherence tomography (OCT), optical coherence tomography angiography (OCTA), fluorescein angiography (FA) and indocyanine green angiography (ICG). Results Nine eyes of 6 patients with myopic macular pit were examined. Four patients presented with multiple pits and 3 with bilateral involvement. All pits were localized in a region of severe macular chorioretinal atrophy associated with myopic posterior staphyloma. In 3 eyes, the entrance of the posterior ciliary artery through the sclera was noted at the base of the pit. Schisis overlying the pit or adjacent to the pit was identified in 3 patients. Conclusion Myopic macular pits are an additional rare sign of myopic degeneration, developing in regions of posterior staphyloma complicated by severe chorioretinal atrophy and thin sclera.

Cuticle are multifunctional hydrophobic biocomposites that protect aerial organs of plants. Along plant development, plant cuticle must accommodate different mechanical constraints combining extensibility and stiffness, the corresponding structure-function relationships are unknown. Recent data showed a fine architectural tuning of the cuticle architecture and the corresponding chemical clusters along fruit development which raise the question of their impact on the mechanical properties of the cuticle.We investigated the in-depth nanomechanical properties of tomato fruit cuticle from early development to ripening, in relation to chemical and structural heterogeneities by developing a correlative multimodal imaging approach.Unprecedented sharps heterogeneities were evidenced with the highlighting of an in-depth mechanical gradient and a ‘soft’ central furrow that were maintained throughout the plant development despite the overall increase in elastic modulus. In addition, we demonstrated that these local mechanical areas are correlated to chemical and structural gradients.This study shed light on a fine tuning of mechanical properties of cuticle through the modulation of their architecture, providing new insight for our understanding of structure-function relationships of plant cuticle and for the design of biosinpired material.

This article begins with the dialogue between critical discourse analysis (CDA) and multimodal discourse analysis (MDA). It then explores the research approach of multimodal critical discourse analysis (MCDA) which is different from multimodal semiotics, and discusses the core concepts of MCDA, introducing a social and affordance-driven approach of multimodal critical discourse and critical visual analysis. Based on the results of the analysis, a hypothetical model of MCDA for media has been proposed. As an emerging field of research, MCDA has the potential to explore in the construction of method system, the relationship between modalities and the relationship between symbolic form meaning expression, the analysis of cultural context., 多文化社会研究, 9, pp.215-227; 2023

In this article, we present a low-cost multimodal tactile sensor capable of providing accelerometer, gyroscope, and pressure data using a seven-axis chip as a sensing element. This approach reduces the complexity of the tactile sensor design and collection of multimodal data. The tactile device is composed of a top layer (a printed circuit board (PCB) and a sensing element), a middle layer (soft rubber material), and a bottom layer (plastic base) forming a sandwich structure. This approach allows the measurement of multimodal data when force is applied to different parts of the top layer of the sensor. The multimodal tactile sensor is validated with analyses and experiments in both offline and real-time. First, the spatial impulse response and sensitivity of the sensor are analyzed with accelerometer, gyroscope, and pressure data systematically collected from the sensor. Second, the estimation of contact location from a range of sensor positions and force values is evaluated using accelerometer and gyroscope data together with a convolutional neural network (CNN) method. Third, the estimation of contact location is used to control the position of a robot arm. The results show that the proposed multimodal tactile sensor has the potential for robotic applications, such as tactile perception for robot control, human-robot interaction, and object exploration.

To describe longitudinal, anatomical, and functional alterations caused by inflammatory and neovascular lesions of idiopathic multifocal choroiditis/punctate inner choroidopathy using adaptive optics imaging and microperimetry.Longitudinal case study using multiple imaging modalities, including spectral-domain optical coherence tomography, fluorescein angiography, indocyanine green angiography, optical coherence tomography angiography, flood illumination adaptive optics, and microperimetry.A 21-year-old myopic Asian man presented with blurred vision in the right eye. Clinical examination was notable for an isolated hypopigmented, perifoveal lesion in each eye. Multimodal imaging showed inflammatory lesions in the outer retina, retina pigment epithelium, and inner choroid lesions of both eyes. The right eye additionally exhibited active Type-2 macular neovascularization with loss of cone mosaic regularity that was associated with reduced sensitivity on microperimetry. The clinical picture was consistent with multifocal choroiditis/punctate inner choroidopathy. The patient was treated with oral steroids and three injections of intravitreal bevacizumab in the right eye. After therapy, imaging showed reestablishment of the cone mosaic on flood illumination adaptive optics and improvement in sensitivity on microperimetry.Adaptive optics imaging and microperimetry may detect biomarkers that help to characterize the nature and activity of multifocal choroiditis lesions and to help monitor response to therapy. With timely intervention, structural abnormalities in the outer retina and choroid can be treated, and anatomical improvements precede improvements in visual function.

Freezing of gait (FoG) is identified as a sudden and brief episode of movement cessation despite the intention to continue walking. It is one of the most disabling symptoms of Parkinson's disease (PD) and often leads to falls and injuries. Many computer-aided FoG detection methods have been proposed to use data collected from unimodal sources, such as motion sensors, pressure sensors, and video cameras. However, there are limited efforts of multimodal-based methods to maximize the value of all the information collected from different modalities in clinical assessments and improve the FoG detection performance. Therefore, in this study, a novel end-to-end deep architecture, namely graph fusion neural network (GFN), is proposed for multimodal learning-based FoG detection by combining footstep pressure maps and video recordings. GFN constructs multimodal graphs by treating the encoded features of each modality as vertex-level inputs and measures their adjacency patterns to construct complementary FoG representations, thus reducing the representation redundancy among different modalities. In addition, since GFN is devised to process multimodal graphs of arbitrary structures, it is expected to achieve superior performance with inputs containing missing modalities, compared to the alternative unimodal methods. A multimodal FoG dataset was collected, which included clinical assessment videos and footstep pressure sequences of 340 trials from 20 PD patients. Our proposed GFN demonstrates a great promise of multimodal FoG detection with an area under the curve (AUC) of 0.882. To the best of our knowledge, this is one of the first studies to utilize multimodal learning for automated FoG detection, which offers significant opportunities for better patient assessments and clinical trials in the future.

Many soils and other porous media exhibit dual- or multi-porosity type features. In a previous study (Seki et al., 2022) we presented multimodal water retention and closed-form hydraulic conductivity equations for such media. The objective of this study is to show that the proposed equations are practically useful. Specifically, dual-BC (Brooks and Corey)-CH (common head) (DBC), dual-VG (van Genuchten)-CH (DVC), and KO (Kosugi)1BC2-CH (KBC) models were evaluated for a broad range of soil types. The three models showed good agreement with measured water retention and hydraulic conductivity data over a wide range of pressure heads. Results were obtained by first optimizing water retention parameters and then optimizing the saturated hydraulic conductivity (K s ) and two parameters (p, q) or (p, r) in the general hydraulic conductivity equation. Although conventionally the tortuosity factor p is optimized and (q, r) fixed, sensitivity analyses showed that optimization of two parameters (p + r, qr) is required for the multimodal models. For 20 soils from the UNSODA database, the average R 2 for log (hydraulic conductivity) was highest (0.985) for the KBC model with r = 1 and optimization of (K s , p, q). This result was almost equivalent (0.973) to the DVC model with q = 1 and optimization of (K s , p, r); both were higher than R 2 for the widely used Peters model (0.956) when optimizing (K s , p, a, ω). The proposed equations are useful for practical applications while mathematically being simple and consistent.

This study aims to demonstrate how interactions between a Visual Analytics (VA) application and students shape an interactive and multimodal reading practice. VA is a technology offering support with analysing vast amounts of data through visualisations. Such information-rich interactive interfaces provide possibilities for students to gain insights, find correlations, and draw conclusions, but they also generate complexities concerning how to ‘read’ multimodal information on a screen. Inspired by Design-Based Research, interventions were designed and conducted in five social science secondary classrooms. The interactions between the VA application Statistics eXplorer and the students were video captured. A socio-material semiotic approach guides the analyses of how interactions between all actors (the interactive visualisations, the written text, the teachers, students, etc.) produce a reading network. The results show a reading characterised by being performative, collaborative, and dynamic. A combination of visuals and text supports the reading. However, visuals such as colour, highlighting and movement dominantly attract students’ attention, while written text often becomes subordinate and sometimes even ‘invisible’. Hence, this paper argues that it is vital for teachers to didactically support students’ visual reading skills.

Xuanfeibaidu Formula (XFBD) is a Chinese medicine used in the clinical treatment of coronavirus disease 2019 (COVID-19) patients. Although XFBD has exhibited significant therapeutic efficacy in clinical practice, its underlying pharmacological mechanism remains unclear. Here, we combine a comprehensive research approach that includes network pharmacology, transcriptomics, and bioassays in multiple model systems to investigate the pharmacological mechanism of XFBD and its bioactive substances. High-resolution mass spectrometry was combined with molecular networking to profile the major active substances in XFBD. A total of 154 compounds were identified or tentatively characterized, including flavonoids, terpenes, carboxylic acids, and other types of constituents. Based on the chemical composition of XFBD, a network pharmacology-based analysis identified inflammation-related pathways as primary targets. Thus, we examined the anti-inflammation activity of XFBD in a lipopolysaccharide-induced acute inflammation mice model. XFBD significantly alleviated pulmonary inflammation and decreased the level of serum proinflammatory cytokines. Transcriptomic profiling suggested that genes related to macrophage function were differently expressed after XFBD treatment. Consequently, the effects of XFBD on macrophage activation and mobilization were investigated in a macrophage cell line and a zebrafish wounding model. XFBD exerts strong inhibitory effects on both macrophage activation and migration. Moreover, through multimodal screening, we further identified the major components and compounds from the different herbs of XFBD that mediate its anti-inflammation function. Active components from XFBD, including Polygoni cuspidati Rhizoma, Phragmitis Rhizoma, and Citri grandis Exocarpium rubrum, were then found to strongly downregulate macrophage activation, and polydatin, isoliquiritin, and acteoside were identified as active compounds. Components of Artemisiae annuae Herba and Ephedrae Herba were found to substantially inhibit endogenous macrophage migration, while the presence of ephedrine, atractylenolide, and kaempferol was attributed to these effects. In summary, our study explores the pharmacological mechanism and effective components of XFBD in inflammation regulation via multimodal approaches, and thereby provides a biological illustration of the clinical efficacy of XFBD.

Aim: To propose a new multimodal imaging agent targeting amyloid-β (Aβ) plaques in Alzheimer’s disease. Materials & methods: A new generation of hybrid contrast agents, based on gadolinium fluoride nanoparticles grafted with a pentameric luminescent-conjugated polythiophene, was designed, extensively characterized and evaluated in animal models of Alzheimer’s disease through MRI, two-photon microscopy and synchrotron x-ray phase-contrast imaging. Results & conclusion: Two different grafting densities of luminescent-conjugated polythiophene were achieved while preserving colloidal stability and fluorescent properties, and without affecting biodistribution. In vivo brain uptake was dependent on the blood–brain barrier status. Nevertheless, multimodal imaging showed successful Aβ targeting in both transgenic mice and Aβ fibril-injected rats.

Research on multimodal communication is complex because multimodal analyses require methods and procedures that offer the possibility of disentangling the layers of meaning conveyed through different channels. We hereby propose an empirical evaluation of the Filmic Metaphor Identification Procedure (FILMIP, Bort-Mir, L. (2019). Developing, applying and testing FILMIP: the filmic metaphor identification procedure, Ph.D. dissertation. Universitat Jaume I, Castellón.), a structural method for the identification of metaphorical elements in (filmic) multimodal materials. The paper comprises two studies: (i) A content analysis conducted by independent coders, in which the reliability of FILMIP is assessed. Here, two TV commercials were shown to 21 Spanish participants for later analysis with the use of FILMIP under two questionnaires. (ii) A qualitative analysis based on a percentage agreement index to check agreement among the 21 participants about the metaphorically marked filmic components identified on the basis of FILMIP’s seven steps. The results of the two studies show that FILMIP is a valid and reliable tool for the identification of metaphorical elements in (filmic) multimodal materials. The empirical findings are discussed in relation to multimodal communication open challenges.

Liposomes are promising targeted drug delivery systems with the potential to improve the efficacy and safety profile of certain classes of drugs. Though attractive, there are unique analytical challenges associated with the development of liposomal drugs including human dose prediction given these are multi-component drug delivery systems. In this study, we developed a multimodal imaging approach to provide a comprehensive distribution assessment for an antibacterial drug, GSK2485680, delivered as a liposomal formulation (Lipo680) in a mouse thigh model of bacterial infection to support human dose prediction. Positron emission tomography (PET) imaging was used to track the in vivo biodistribution of Lipo680 over 48 h post-injection providing a clear assessment of the uptake in various tissues and, importantly, the selective accumulation at the site of infection. In addition, a pharmacokinetic model was created to evaluate the kinetics of Lipo680 in different tissues. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) was then used to quantify the distribution of GSK2485680 and to qualitatively assess the distribution of a liposomal lipid throughout sections of infected and non-infected hindlimb tissues at high spatial resolution. Through the combination of both PET and MALDI IMS, we observed excellent correlation between the Lipo680-radionuclide signal detected by PET with the GSK2485680 and lipid component signals detected by MALDI IMS. This multimodal translational method can reduce drug attrition by generating comprehensive biodistribution profiles of drug delivery systems to provide mechanistic insight and elucidate safety concerns. Liposomal formulations have potential to deliver therapeutics across a broad array of different indications, and this work serves as a template to aid in delivering future liposomal drugs to the clinic.

International audience; Frontotemporal dementia and amyotrophic lateral sclerosis are rare neurodegenerative diseases with no effective treatment. The development of biomarkers allowing an accurate assessment of disease progression is crucial for evaluating new therapies. Concretely, neuroimaging and transcriptomic (microRNA) data have been shown useful in tracking their progression. However, no single biomarker can accurately measure progression in these complex diseases. Additionally, large samples are not available for such rare disorders. It is thus essential to develop methods that can model disease progression by combining multiple biomarkers from small samples. In this paper, we propose a new framework for computing a disease progression score (DPS) from cross-sectional multimodal data. Specifically, we introduce a supervised multimodal variational autoencoder that can infer a meaningful latent space, where latent representations are placed along a disease trajectory. A score is computed by orthogonal projections onto this path. We evaluate our framework with multiple synthetic datasets and with a real dataset containing 14 patients, 40 presymptomatic genetic mutation carriers and 37 controls from the PREV-DEMALS study. There is no ground truth for the DPS in real-world scenarios, therefore we use the area under the ROC curve (AUC) as a proxy metric. Results with the synthetic datasets support this choice, since the higher the AUC, the more accurate the predicted simulated DPS. Experiments with the real dataset demonstrate better performance in comparison with stateof-the-art approaches. The proposed framework thus leverages cross-sectional multimodal datasets with small sample sizes to objectively measure disease progression, with potential application in clinical trials.

Objective: An understanding of the complex morphology of an arteriovenous malformation (AVM) is important for successful resection. We have previously reported the utility of intra-arterial indocyanine green (ICG) videoangiography for this purpose, but that method cannot detect the angioarchitecture covered by brain tissue. 3-dimensional (3D) multimodal fusion imaging is reportedly useful for this same purpose, but cannot always visualize the exact angioarchitecture due to poor source images and processing techniques. This study examined the results of utilizing both techniques in patients with AVMs. Methods: Both techniques were applied in 12 patients with AVMs. Both images were compared with surgical views and evaluated by surgeons. Results: Although evaluations for identifying superficial feeders by ICG videoangiography were high in all cases, the more complicated the AVM, the lower the evaluation by 3D multimodal fusion imaging. Conversely, evaluation of the estimated range of the nidus was high in all cases by 3D multimodal fusion imaging, but low in all but one case by ICG videoangiography. Nidus flow reduction was recognized by Flow 800 analysis obtained after ICG videoangiography. Conclusions: These results showed that utilizing both techniques together was more useful than each modality alone in AVM surgery. This was particularly effective in identifying superficial feeders and estimating the range of the nidus. This technique is expected to offer an optimal tool for AVM surgery.

Multimodal Analytics in Big Data architectures implies compounded configurations of the data processing tasks. Each modality in data requires specific analytics that triggers specific data processing tasks. Scalability can be reached at the cost of an attentive calibration of the resources shared by the different tasks searching for a trade-off with the multiple requirements they impose. We propose a methodology to address multimodal analytics within the same data processing approach to get a simplified architecture that can fully exploit the potential of the parallel processing of Big Data infrastructures. Multiple data sources are first integrated into a unified knowledge graph (KG). Different modalities of data are addressed by specifying

Retinoma or retinocytoma is a spontaneously arrested or spontaneously regressed variant of retinoblastoma. With the advent of the latest non-invasive imaging techniques, it is possible to evaluate the microstructural and microvascular changes associated with this tumour. Although there are a few reports which describe the imaging findings in retinocytoma, information regarding retinocytoma on the multicolour imaging is lacking. Here, we describe the multimodal imaging features in a patient with classic features of retinocytoma with special emphasis on its multicolour imaging features.

Here we present a novel multimodal language model (MultiModal-MoLFormer) approach for predicting molecular properties, which combines chemical language representation embeddings derived from the recently introduced MoLFormer chemical language model and physicochemical features. Our approach employs a causal multi-stage feature selection method that selects physicochemical features based on their direct causal-effect on a specific target property to predict. Specifically, we use Mordred descriptors as physicochemical features and Markov blanket causal graphs as the inference algorithm to identify the most relevant features. Our results demonstrate that our proposed approach outperforms existing state-of-the-art algorithms, including the chemical language-based MoLFormer and graph neural networks, in predicting complex tasks such as the biodegradability of general compounds and PFAS toxicity estimation. The MultiModal-MoLFormer model resulted in a significant improvement in the classification accuracy for EPA categories of PFAS Toxicity, from 0.75 to 0.84, when compared to the base MoLFormer approach. Additionally, our proposed approach achieves an accuracy of 0.94 for the biodegradability estimation task.

Objectives: Previous studies have shown that heart rate variability is reduced in chronic pain patients. The aim of this was to investigate the effects of a 4-week multimodal pain therapy on the heart rate variability parameters in chronic pain patients and whether they will improve. Materials and methods: A retrospective pre post analysis was performed analyzing the HRV records of 30 patients who participated at a 4-week multimodal pain therapy at the Pain Centre of the Regiomed Hospital Coburg, Bavaria, Germany in the period of 9 months, from Mai 2022 till January 2023. The HRV parameters collected at the beginning and end of the therapy considered in this study are as follows: heart rate, stress index, high frequency, low frequency, HF/LF ratio, NN50, pNN50. In addition, the cortisol levels were also taken into account for a general stress profile. The collected data was analyzed with JASP (Version 0.17.2.0), and normally distributed data were compared with a paired samples Student T Test, whereas non-normally distributed the Mann-Whitney-U test (Wilcoxon signed- rank). The significance level was set to a P-value of, Ciljevi: Prethodna istraživanja su pokazala da je varijabilnost srčanog ritma smanjena kod pacijenata s kroničnom boli. Cilj ovog istraživanja bio je ispitati učinke četverotjedne multimodalne terapije boli na parametre varijabilnosti srčanog ritma kod pacijenata s kroničnom boli i provjeriti hoće li se poboljšati. Materijali i metode: Izvršena je retrospektivna pre-post analiza analizirajući zapise HRV-a 30 pacijenata koji su sudjelovali u četverotjednoj multimodalnoj terapiji boli u Centru za bol u bolnici Regiomed Coburg, Bavarska, Njemačka, u razdoblju od kolovoza 2022. do veljače 2023. HRV parametri prikupljeni na početku i kraju terapije koji su uzeti u obzir u ovom istraživanju su sljedeći: srčani ritam, indeks stresa, visoka frekvencija, niska frekvencija, HF/LF omjer, NN50, pNN50. Osim toga, razine kortizola također su uzete u obzir za opći profil stresa. Prikupljeni podaci analizirani su pomoću JASP-a (Verzija 0.17.2.0), a normalno distribuirani podaci uspoređivani su uparenim uzorcima t-testom, dok su neregularno distribuirani podaci testirani Mann-Whitney-U testom (Wilcoxonov potpisani rang). Razina značajnosti postavljena je na vrijednost P

Biometric technology is a powerful tool that relies on distinct and measurable physiological or behavioral characteristics possessed by individuals. These traits serve as reliable means to verify and authenticate individuals. However, unimodal biometric systems encounter several challenges that hinder their effectiveness. These challenges include noisy data, variations within the same class, limited degrees of freedom, non-universality, susceptibility to spoof attacks, and high error rates. To address these challenges, researchers have turned to multimodal biometric systems. These systems leverage the use of two or more biometric modalities to enhance performance and overcome the limitations of unimodal systems. In the context of the discussed system, face and fingerprint modalities are utilized. To develop and evaluate the performance of the system, various deep learning algorithms such as VGG16, VGG19, CNN, and Inception are employed. These algorithms are trained, validated, and tested using the face and fingerprint data. Performance metrics such as accuracy, precision, and f1 score are calculated for both unimodal and multimodal configurations. The results of the performance evaluation demonstrate that CNN (Convolutional Neural Network) yields higher accuracy compared to the other models tested. This finding suggests that CNN is particularly well-suited for this biometric system, as it can effectively extract meaningful features from the face and fingerprint data and provide accurate classification results. By adopting a multimodal approach and utilizing deep learning algorithms such as CNN, the system successfully addresses the challenges faced by unimodal biometric systems. This implementation demonstrates improved accuracy, making it a promising solution for reliable and secure individual authentication in various applications.

The prediction of crop yield plays a crucial role in national economic development, encompassing grain storage, processing, and grain price trends. Employing multiple sensors to acquire remote sensing data and utilizing machine learning algorithms can enable accurate, fast, and nondestructive yield prediction for maize crops. However, current research heavily relies on single-type remote sensing data and traditional machine learning methods, resulting in the limited robustness of yield prediction models. To address these limitations, this study introduces a field-scale maize yield prediction model named the convolutional neural network–attention–long short-term memory network (CNN-attention-LSTM) model, which utilizes multimodal remote sensing data collected by multispectral and light detection and ranging (LIDAR) sensors mounted on unmanned aerial vehicles (UAVs). The model incorporates meteorological data throughout the crop reproductive stages and employs the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) for the initial part of the vegetative stage (initial part of the V period), the later part of the vegetative stage (later part of the V period), the reproductive stage (R period), and the maturity stage (M period), along with LIDAR data for Point75–100 in the later part of the V period, Point80–100 in the R period, and Point50–100 in the M period, complemented by corresponding meteorological data as inputs. The resulting yield estimation demonstrates exceptional performance, with an R2 value of 0.78 and an rRMSE of 8.27%. These results surpass previous research and validate the effectiveness of multimodal data in enhancing yield prediction models. Furthermore, to assess the superiority of the proposed model, four machine learning algorithms—multiple linear regression (MLR), random forest regression (RF), support vector machine (SVM), and backpropagation (BP)—are compared to the CNN-attention-LSTM model through experimental analysis. The outcomes indicate that all alternative models exhibit inferior prediction accuracy compared to the CNN-attention-LSTM model. Across the test dataset within the study area, the R2 values for various nitrogen fertilizer levels consistently exceed 0.75, illustrating the robustness of the proposed model. This study introduces a novel approach for assessing maize crop yield and provides valuable insights for estimating the yield of other crops.

Multimodal sentiment analysis (MSA) has attracted more and more attention in recent years. This paper focuses on the representation learning of multimodal data to reach higher prediction results. We propose a model to assist in learning modality representations with multitask learning and contrastive learning. In addition, our approach obtains dynamic weights by considering the homoscedastic uncertainty of each task in multitask learning. Specially, we design two groups of subtasks, which predict the sentiment polarity of unimodal and bimodal representations, to assist in learning representation through a hard parameter-sharing mechanism in the upstream neural network. A loss weight is learned according to the homoscedastic uncertainty of each task. Moreover, a training strategy based on contrastive learning is designed to balance the inconsistency between training and inference caused by the randomness of the dropout layer. This method minimizes the MSE between two submodels. Experimental results on the MOSI and MOSEI datasets show our method achieves better performance than the current state-of-the-art methods by comprehensively considering the intramodality and intermodality interaction information.

Current approaches to breast cancer therapy include neoadjuvant systemic therapy (NST). The efficacy of NST is measured by pathologic complete response (pCR). A patient who attains pCR has significantly enhanced disease-free survival progress. The accurate prediction of pCR in response to a given treatment regimen could increase the likelihood of achieving pCR and prevent toxicities caused by treatments that are not effective. Th early prediction of response to NST can increase the likelihood of survival and help with decisions regarding breast-conserving surgery. An automated NST prediction framework that is able to precisely predict which patient undergoing NST will achieve a pathological complete response (pCR) at an early stage of treatment is needed. Here, we propose an end-to-end efficient multimodal spatiotemporal deep learning framework (deep-NST) framework to predict the outcome of NST prior or at an early stage of treatment. The deep-NST model incorporates imaging data captured at different timestamps of NST regimens, a tumor’s molecular data, and a patient’s demographic data. The efficacy of the proposed work is validated on the publicly available ISPY-1 dataset, in terms of accuracy, area under the curve (AUC), and computational complexity. In addition, seven ablation experiments were carried out to evaluate the impact of each design module in the proposed work. The experimental results show that the proposed framework performs significantly better than other recent methods.

This paper presents a systematic review and meta-analysis evaluating the effectiveness of multimodal interventions in the management of chronic low-back pain (CLBP). The study aims to synthesize the available evidence from randomized controlled trials (RCTs) to assess the impact of interventions that combine multiple treatment modalities, such as exercise, manual therapy, cognitive-behavioral therapy, and pharmacotherapy, on pain reduction, functional improvement, and quality of life outcomes in individuals with CLBP. The findings of this study provide valuable insights into the efficacy of multimodal interventions, guiding evidence-based recommendations for the management of CLBP.

Background: To perform a multimodal assessment of the ectopic inner foveal layers’ (EIFL) prognostic role on idiopathic epiretinal membrane (ERM) surgery. Methods: We retrospectively followed-up for 12 months 27 patients who underwent ERM surgery and stratified them based on EIFL presence (group 1) or absence (group 2) at baseline. Central Retinal Thickness (CRT) and best-corrected visual acuity (BCVA) were compared pre- and post-operatively at 1, 4 and 12 months, whereas fixation stability (FS), macular sensitivity (MS) and multifocal electroretinogram (mfERG) responses were confronted at baseline and 12 months. Results: In group 1, BCVA improved at 4 and 12 months (MD = 0.14 (SE = 0.04); MD = 0.13 (SE = 0.05), respectively) as well as in group 2 (MD = 0.31 (SE = 0.07); MD = 0.41 (SE = 0.08), respectively). CRT did not change in group 1, whereas it decreased in group 2 at 4 and 12 months (MD = −73.13; SE = 23.56; MD = −76.20; SE = 23.56). MS showed no changes in both groups after surgery. FS did not change in group 1, whereas group 2 improved FS 2° (+8.91 ± 13.97) and FS 4° (+4.33 ± 3.84). MfERG P1 wave did not change in group 1, while in group 2 αP1-2, αP1-3 and αP1-4 improved postoperatively (27.97 ± 27.62; 12.51 ± 17.36; 10.49 ± 17.19, respectively). Conclusions: Multimodal assessment confirmed that EIFL negatively affected ERM surgery outcomes.

In social interactions, people who are perceived as competent win more chances, tend to have more opportunities, and perform better in both personal and professional aspects of their lives. However, the process of evaluating competence is still poorly understood. To fill this gap, we developed a two-step empirical study to propose a competence evaluation framework and a predictor of individual competence based on multimodal data using machine learning and computer vision methods. In study 1, from a knowledge-driven perspective, we first proposed a competence evaluation framework composed of 4 inner traits (skill, expression efficiency, intelligence, and capability) and 6 outer traits (age, eye gaze variation, glasses, length-to-width ratio, vocal energy, and vocal variation). Then, eXtreme Gradient Boosting (XGBoost) and Shapley Additive exPlanations (SHAP) were utilized to predict and interpret individual competence, respectively. The results indicate that 8 (4 inner and 4 outer) traits (in descending order: vocal energy, age, length-to-width ratio, glasses, expression efficiency, capability, intelligence, and skill) contribute positively to competence evaluation, while 2 outer traits (vocal variation and eye gaze variation) contribute negatively. In study 2, from a data-driven perspective, we accurately predicted competence with a cutting-edge multimodal machine learning algorithm, low-rank multimodal fusion (LMF), which exploits the intra- and intermodal interactions among all the visual, vocal, and textual features of an individual’s competence behavior. The results indicate that vocal and visual features contribute most to competence evaluation. In addition, we provided a Chinese Competence Evaluation Multimodal Dataset (CH-CMD) for individual competence analysis. This paper provides a systemic competence framework with empirical consolidation and an effective multimodal machine learning method for competence evaluation, offering novel insights into the study of individual affective traits, quality, personality, etc.

RESUMO Partindo da Teoria Sistêmico-Funcional e da proposta decomposicional de Bateman, Wildfeuer e Hiippala (2017), este artigo delineia noções e procedimentos que consideramos fundamentais para a análise de textos multimodais, particularmente os digitais, fornecendo uma proposta teórico-metodológica que permite ao analista um maior comprometimento com uma organização clara e replicável do procedimento analítico. Assim, em primeiro lugar introduzimos os conceitos fundamentais de realização e instanciação e suas implicações para o estudo da multimodalidade; depois, parte-se para a noção de “tela” e para a classificação multidimensional das materialidades semióticas; em seguida, explicitam-se os passos básicos para a fundamentação da análise de textos multimodais; e, finalmente, o modelo é exemplificado com a análise sumária da composição em telas de postagens de Facebook. ABSTRACT Drawing on Systemic Functional Theory and the decompositional model proposed by Bateman, Wildfeuer and Hiippala (2017), this paper outlines notions and procedures we consider fundamental for the analysis of multimodal texts, particularly digital ones, by providing a theoretical and methodological model that enables a greater commitment to a clear and replicable organization of the analytical procedure. We introduce the fundamental systemic functional concepts of realization and instantiation and their implications for the study of multimodality; next, we present the notion of ‘canvas’ and the multidimensional classification of semiotic materialities; then, we make explicit the basic steps for the substantiation of the analysis of multimodal texts; and, finally, we exemplify the model with a cursory analysis of the canvas composition of Facebook posts.

With the increasing amounts of available data, learning simultaneously from different types of inputs is becoming necessary to obtain robust and well-performing models. With the advent of representation learning in recent years, lower-dimensional vector-based representations have become available for both images and texts, while automating simultaneous learning from multiple modalities remains a challenging problem. This paper presents an AutoML (automated machine learning) approach to automated machine learning model configuration identification for data composed of two modalities: texts and images. The approach is based on the idea of representation evolution, the process of automatically amplifying heterogeneous representations across several modalities, optimized jointly with a collection of fast, well-regularized linear models. The proposed approach is benchmarked against 11 unimodal and multimodal (texts and images) approaches on four real-life benchmark datasets from different domains. It achieves competitive performance with minimal human effort and low computing requirements, enabling learning from multiple modalities in automated manner for a wider community of researchers.

The aim of this case report is to present the multimodal imaging characteristics of pigmented paravenous retinochoroidal atrophy (PPRCA) in a pediatric patient with cystoid macular edema (CME). A 7-year-old girl was admitted to our clinic with complaints of mild blurred vision and poor night vision. Best corrected visual acuity was 10/10 in both eyes. Fundus examination showed atrophic areas around the optic nerve and along the retinal vessels in both eyes. A few small dot-shaped paravenous pigmentations were observed in the mid-peripheral retina. Fundus autofluorescence was consistent with PPRCA. Spectral-domain optical coherence tomography (OCT) revealed the presence of CME and loss of the outer retinal layers outside the macula, with intact retinal layers in the macula. OCT angiography revealed normal choriocapillaris vasculature and flow. The patient was followed up for 6 months but showed no change in CME or clinical appearance. CME without ocular inflammation is an unusual finding of PPRCA and may suggest the involvement of chronic or latent inflammation in the etiology of PPRCA. © 2022 by Turkish Ophthalmological Association.

A reliable public transport system is beneficial for people traveling in the metropolitan area. Transfer time in multimodal transit networks has been highlighted as one of the measures of public transport service quality. In this paper, we propose a novel method to estimate the passengers’ transfer time between the transit modes (i.e., train, metro, and bus) based on the 2018 Household Travel Survey in the Paris region, France. The transit trips with a single transit leg are primarily studied, wherein average wait time and mode speeds are estimated through an integrated linear regression model. Based on these inferences, transfer time is deduced within the trips of multiple transit legs. The decomposition procedure of journey time facilitates the estimation of the time components, and reveals the transfer variability in mode, time, and space. From the results, we find that the transfer to the railway modes, especially to the metro, costs less time on average than the transfer to the bus in the study area. The transfer patterns in the morning and evening peak hours are different regarding the transfer duration and locations. Lastly, the results’ reliability, method scalability, and potential applications are discussed in detail.

Drawing on video-recorded data from pre-climbing route mapping with visually impaired climbers and a sight guide, this study uses conversation analysis to investigate the situated deployment of the Italian presentative (e) hai ‘(and) you have’ within locally routinized multimodal Gestalts. The study shows that the guide uses (e) hai to progress route mapping and engage the athlete in tactile actions that target specific features of the route. In this context, (e) hai is packaged with noun phrases, silent pauses, bodily movements, and touch. The arrangement of such syntactic and embodied components is shown to follow a recurrent trajectory in which, between (e) hai and its grammatical completion, syntactic suspension creates a dedicated slot for guide and athlete to physically attain the target object. Routine embeddedness of (e) hai within such arrangement is shown to provide specific affordances to the athletes to anticipate subsequent action and engage in its embodied implementation.

Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace-based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace-based approach to study SSRL, there remains a paucity of evidence on how trace-based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes. The data collection involves secondary school students (N = 94) working collaboratively in groups through five science lessons. A multimodal data set of EDA and video data were examined to assess the relationship among shared arousals and interactions for SSRL. The results of this study inform the patterns among students’ physiological activities and their SSRL interactions to provide trace-based evidence for an adaptive and maladaptive pattern of collaborative learning. Furthermore, our findings provide evidence about how trace-based data could be utilised to predict learning outcomes in collaborative learning.

This document describes methodologies proposed by MARVEL partners during the second reporting period of the project towards the realisation of the Au- dio, Visual and Multimodal AI Subsystem of the MARVEL architecture. These meth- odologies complement the methodologies proposed by MARVEL partners during the first reporting period, and include methods for Automated Audio Captioning, Visual Crowd Counting, Visual Anomaly Detection, Audio-Visual Anomaly Detection, Audio- Visual Event Detection, privacy-preserving Audio-Visual Emotion Recognition, as well as methodologies for improving the training of dense regression models for efficient inference on standard and Gigapixel images, and on heavily compressed images. The effectiveness of these methods is compared against recent baselines, towards achieving the AI methodology-related objectives of the MARVEL project.

The metallome has been involved in the pathological investigation into ocular tissue for decades; however, as technologies advance, more information can be ascertained from individual tissue sections that were not previously possible. Herein, a demonstration of complementary techniques has been utilized to describe the distribution and concentrations of essential metals in both wildtype (WT) and rhodopsin (Rho−/−) ocular tissues. The multimodal approach described is an example of complementary datasets that can be produced when employing a multifaceted analytical approach. Heterogenous distributions of copper and zinc were observable within both WT and Rho−/− tissue by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), and the distributions of further trace elements notoriously problematic for ICP-MS analysis (phosphorous, Sulfur, chlorine, potassium, calcium, iron, and aluminum) were analysed by particle-induced X-ray emission (PIXE).

Multimodal Named Entity Recognition (MNER) and multimodal Relationship Extraction (MRE) play an important role in processing multimodal data and understanding entity relationships across textual and visual domains. However, irrelevant image information may introduce noise that misleads the recognition of information. Additionally, visual and semantic features originate from different modalities, and modal disparity hinders semantic alignment. Therefore, this paper proposes the Visual Description Augmentation Integration Network (VDAIN), which introduces an image description generation technique that allows semantic features generated from image descriptions to be presented in the same modality as the semantic features of textual information. This not only reduces the modal gap but also captures more accurately the high-level semantic information and underlying visual structure in the images. To filter out the modal noise, we use VDAIN to adaptively fuse visual features, semantic features of image descriptions, and textual information, thus eliminating irrelevant modal noise. The F1 score of the proposed model in this paper reaches 75.8% and 87.78% for the MNER task and 82.54% for the MRE task on the three public data sets, respectively, which are significantly better than the baseline model. The experimental results demonstrate the effectiveness of the proposed method in solving the modal noise and modal gap problems.

International audience; This concise volume calls attention to the instruction-giving practices of language teachers in online environments, in particular videoconferencing, employing a Multimodal (Inter)action Analysis approach to explore the challenges, affordances, and pedagogical implications of teaching in these settings.The book examines the unique competences necessary for language teachers in multimodal synchronous online environments, which require mediating a mix of modes, including spoken language gaze, gesture, posture, and textual elements. Satar and Wighamʼs innovative approach draws on Sigrid Norrisʼs work on Multimodal (Inter)action Analysis to examine variance in practices, combining in-depth micro-analytic analysis of mediation with a consideration of the modal density and complexity in the act of giving instructions. The volume shows how studying instruction giving can offer a better understanding of how online teachers mediate learning multimodally in electronic environments but also research-informed guidance for practical implementation in the classroom.This book is a valuable resource for scholars in applied linguistics, language education, and language learning and teaching as well as practicing online language teachers.Full-size versions of all Figures, Extracts, and Tables are available in colour at https://doi.org/10.25405/data.ncl.20315142

RESUMO A crise mundial da COVID-19 modificou a condução das atividades educacionais com a emergência de diversos espaços de ensino-aprendizagem organizados em ambientes virtuais. É nesse cenário que este estudo se insere, ao investigar, comparativamente, nas modalidades de ensino remoto e presencial, uma prática recorrente no contexto pedagógico: a referenciação de objetos – como uma tabela em um livro, um documento, ou um objeto não digital. Analisamos como docentes e aprendizes realizam interacionalmente essa prática, descrevendo os recursos multimodais por eles/as empregados e refletindo sobre as potencialidades e os desafios envolvidos nas duas modalidades de ensino. Para investigar as minúcias dessa prática no "aqui-e-agora" interacional do ambiente escolar, empregamos os pressupostos teórico-metodológicos da Análise da Conversa Multimodal (SACKS, 1992; MONDADA, 2014), a partir de gravações em áudio e vídeo de aulas remotas e presenciais. Os resultados evidenciam que, embora vários dos recursos empregados pelas professoras na referenciação sejam comuns a ambas as modalidades de ensino, em função das contingências impostas por cada modalidade, eles são empregados por diferentes meios. Há, contudo, diferenças cruciais e com repercussões importantes. Por exemplo, a modalidade de copresença física caracteriza-se por um contexto em que os/as aprendizes e seus respectivos objetos estão visualmente acessíveis ao/à professor/a, possibilitando seu monitoramento e, consequentemente, assegurando a completude do processo de referenciação, o que (ainda) não é possível na modalidade remota. Ademais, na modalidade remota, os/as aprendizes também precisam lidar com contingências específicas da não copresença física ao iniciarem a referenciação a algum objeto, especialmente quando é preciso sincronizar as ações para mobilizar a atenção conjunta. Assim, mostramos como essas e outras contingências implicam em esforços interacionais (frequentemente herculanos) dos/as participantes para tornar possível o processo de ensino e aprendizagem. ABSTRACT The COVID-19 global crisis has changed in many ways how educational activities happen, especially when the encounters migrated to video-mediated settings. In this paper, from a comparative perspective, we investigate online and in-person classes. We examine a recurring practice in pedagogical contexts: referencing objects – e.g. a task in a textbook, a document, or a non-digital object. We analyze how teachers and learners interactionally accomplish referencing. We describe the multimodal resources deployed and reflect on the affordances and challenges in each environment. Using Multimodal Conversation Analysis (SACKS, 1992; MONDADA, 2014), we conduct a fine-grained analysis of the unfolding of referencing practices in audio and video-recorded online and in-person classes. The analysis reveals that although many of the resources mobilized by teachers to refer to an object are common to both environments, they are accomplished through different means due to contingencies imposed by each environment. However, there are crucial differences that generate important repercussions. The in-person environment, for example, is characterized by being a context in which learners and their objects are visually accessible (i.e., independent from a camera that captures them), thus enabling their monitoring and, consequently, ensuring the full accomplishment of the referencing process, something which is not possible (yet) in the virtual environment. Furthermore, in online classes, when referencing an object, learners also need to deal with specific contingencies of the non-physical copresence, especially with those that demand synchrony of actions and visual access (both ways) to mobilize joint attention. Therefore, we show how these (and other) challenges imply interactional efforts by the participants to make the teaching and learning process possible.

As an essential part of artificial intelligence, a knowledge graph describes the real-world entities, concepts and their various semantic relationships in a structured way and has been gradually popularized in a variety practical scenarios. The majority of existing knowledge graphs mainly concentrate on organizing and managing textual knowledge in a structured representation, while paying little attention to the multimodal resources (e.g., pictures and videos), which can serve as the foundation for the machine perception of a real-world data scenario. To this end, in this survey, we comprehensively review the related advances of multimodal knowledge graphs, covering multimodal knowledge graph construction, completion and typical applications. For construction, we outline the methods of named entity recognition, relation extraction and event extraction. For completion, we discuss the multimodal knowledge graph representation learning and entity linking. Finally, the mainstream applications of multimodal knowledge graphs in miscellaneous domains are summarized.

ilustraciones, graficas, mapas La investigación desarrollada en este trabajo final de maestría presenta una aplicación técnica para la estimación de modelos de elección discreta a través del desarrollo de encuestas de Preferencias Declaradas que permitan, finalmente, la construcción de un modelo tipo logit (elección modal) para la estimación del potencial de captación de carga del proyecto Férreo Tren del Carare. En Colombia los corredores logísticos y proyectos multimodales se evalúan y priorizan principalmente a través de modelos de transporte basados en el vehículo como variable de decisión y funciones de utilidad con parámetros típicos de costo y tiempo. Por lo cual, uno de los aportes más importantes de esta investigación es estimar un modelo de elección discreta con las variables que inciden en la decisión de un medio para el transporte de mercancías, las cuales fueron identificadas a través de acercamientos directos con representantes logísticos de empresas de la región Cundiboyacense y, en donde se determinaron como preponderantes – adicional al costo y tiempo - el número de transferencias multimodales, la confiabilidad del servicio de transporte, frecuencia de envío de productos y propiedad de la flota de transporte (tercerizada o propia). El estudio de caso estima una captación de carga para el proyecto Tren del Carare de entre 40 y 45% respecto a la carga potencial (carretera) la cual maximiza su viabilidad en las operaciones de exportación e importación que permite la articulación con el Corredor Férreo del Atlántico. (Texto tomado de la fuente) The research developed in this final master's project presents a technical application for the estimation of discrete choice models through the development of Declared Preferences surveys that finally allow the construction of a logit-type model (modal choice) for the estimation of the freight capture potential of the Tren del Carare railway project. In Colombia, logistics corridors and multimodal projects are evaluated and prioritized mainly through transport models based on the vehicle as a decision variable and utility functions with typical cost and time parameters. Therefore, one of the most important contributions of this research is to estimate a discrete choice model with the variables that affect the decision of a mode of transporting goods, which were identified through direct approaches with logistics representatives of companies in the Cundiboyacense region and, in addition to cost and time, the number of multimodal transfers, the reliability of the transport service, frequency of product shipment and ownership of the transport fleet (outsourced or own) were determined as preponderant. The case study estimates a load capture for the Tren del Carare project of between 40 and 45% with respect to the potential freight (road), which maximizes its viability in export and import operations that allow the articulation with the Railway Corridor of the Atlantic. Maestría Magíster en Ingeniería - Transporte Transporte intermodal

Characterizing neuropsychiatric disorders is challenging due to heterogeneity in the population. We propose combining structural and functional neuroimaging and genomic data in a multimodal classification framework to leverage their complementary information. Our objectives are two-fold (i) to improve the classification of disorders and (ii) to introspect the concepts learned to explore underlying neural and biological mechanisms linked to mental disorders. Previous multimodal studies have focused on naïve neural networks, mostly perceptron, to learn modality-wise features and often assume equal contribution from each modality. Our focus is on the development of neural networks for feature learning and implementing an adaptive control unit for the fusion phase. Our mid fusion with attention model includes a multilayer feed-forward network, an autoencoder, a bi-directional long short-term memory unit with attention as the features extractor, and a linear attention module for controlling modality-specific influence. The proposed model acquired 92% (p&nbsp

Pain and related disability remain a major social and therapeutic problem. Comorbidities and therapies increase drug interactions and side effects making pain management more compounded especially in the elderly who are the fastest-growing pain population. Multimodal analgesia consists of using two or more drugs and/or techniques that target different sites of pain, increasing the level of analgesia and decreasing adverse events from treatment. Paracetamol enhances multimodal analgesia in experimental and clinical pain states. Strong preclinical evidence supports that paracetamol has additive and synergistic interactions with anti-inflammatory, opioid and anti-neuropathic drugs in rodent models of nociceptive and neuropathic pain. Clinical studies in young and adult elderly patients confirm the utility of paracetamol in multimodal, non-opioid or opioid-sparing, therapies for the treatment of acute and chronic pain.Opioid and anti-inflammatory drugs are essential medications to relief pain; however, they may pose a serious health risk especially in elderly patients and in patients with medical conditions. Doctors are studying ways to reduce or eliminate their use. We wanted to see how well paracetamol works together with other painkillers to manage pain. Paracetamol (or acetaminophen) is one of the most prescribed medication for fever and pain. We found strong evidence that paracetamol given in association with other analgesic drugs enhances the pain relief in adult patients and in elderly adult patients, even though more studies are warranted in the latter. The use of paracetamol in combination with other analgesics is recommended by physicians and surgeons of different specialties.

Background: Electroconvulsive therapy (ECT) is an effective treatment for severe depression and induces gray matter (GM) increases in the brain. Small-scale studies suggest that ECT also leads to changes in brain functioning, but findings are inconsistent. In this study, we investigated the influence of ECT on changes in both brain structure and function and their relation to clinical improvement using multicenter neuroimaging data from the Global ECT-MRI Research Collaboration (GEMRIC). Methods: We analyzed T1-weighted structural magnetic resonance imaging (MRI) and functional resting-state MRI data of 88 individuals (49 male) with depressive episodes before and within one week after ECT. We performed voxel-based morphometry on the structural data and calculated fractional amplitudes of low-frequency fluctuations, regional homogeneity, degree centrality, functional connectomics, and hippocampus connectivity for the functional data in both unimodal and multimodal analyses. Longitudinal effects in the ECT group were compared to repeated measures of healthy controls (n = 27). Results: Wide-spread increases in GM volume were found in patients following ECT. In contrast, no changes in any of the functional measures were observed, and there were no significant differences in structural or functional changes between ECT responders and non-responders. Multimodal analysis revealed that volume increases in the striatum, supplementary motor area and fusiform gyrus were associated with local changes in brain function. Conclusion: These results confirm wide-spread increases in GM volume, but suggest that this is not accompanied by functional changes or associated with clinical response. Instead, focal changes in brain function appear related to individual differences in brain volume increases. publishedVersion

Multimodal registration is a challenging problem in visual computing, commonly faced during medical image-guided interventions, data fusion and 3D object retrieval. The main challenge of multimodal registration is finding accurate correspondence between modalities, since different modalities do not exhibit the same characteristics. This paper explores how the coherence of different modalities can be utilized for the challenging task of 3D multimodal registration. A novel deep learning multimodal registration framework is proposed by introducing a siamese deep learning architecture, especially designed for aligning and fusing modalities of different structural and physical principles. The cross-modal attention blocks lead the network to establish correspondences between features of different modalities. The proposed framework focuses on the alignment of 3D point clouds and the micro-CT 3D volumes of the same object. A multimodal dataset consisting of real micro-CT scans and their synthetically generated 3D models (point clouds) is presented and utilized for evaluating our methodology.

Salah satu program pengembangan yang sangat penting dalam pendidikan anak usia dini adalah pengembangan bahasa. Tujuan dari penelitian ini adalah untuk mengembangkan kegiatan literasi denngan menggunakan Kegiatan Literasi Berbasis Teks Multimodal untuk anak usia dini di salah satu sekolah TK di Pematangsiantar. Metode deskriptif kualitatif diterapkan dalam penelitian ini. Sumber data penelitian ini adalah anak usia dini sebanyak 26 siswa. Observasi, wawancara, dan dokumentasi digunakan untuk memperoleh data. Triangulasi teknik dan triangulasi sumber digunakan dalam analisis data. Proses interpretasi data yang terkumpul menggunakan analisis deskriptif kualitatif. Penelitian ini mengalami peningkatan kenyamanan lingkungan belajar peserta didik dengan persentase peningkatan rata-rata sebesar 15,8% sehingga kegiatan literasi berbasis multimodal ini layak untuk dikembangkan dan dilaksanakan pada anak usia dini. Para peneliti juga menyarankan agar pihak sekolah memahami pentingnya membangun lingkungan literasi yang baik di dalam kelas dalam mendukung penerapan literasi berbasis multimodal untuk perkembangan kemampuan literasi anak

The putamen has been implicated in depressive disorders, but how its structure and function increase depression risk is not clearly understood. Here, we examined how putamen volume, neuronal density, and mood-modulated functional activity relate to family history and prospective course of depression.The study includes 115 second- and third-generation offspring at high or low risk for depression based on the presence or absence of major depressive disorder in the first generation. Offspring were followed longitudinally using semistructured clinical interviews blinded to their familial risk; putamen structure, neuronal integrity, and functional activation were indexed by structural magnetic resonance imaging (MRI), proton magnetic resonance spectroscopy (N-acetylaspartate/creatine ratio), and functional MRI activity modulated by valence and arousal components of a mood induction task, respectively.After adjusting for covariates, the high-risk individuals had lower putamen volume (standardized betas, β-Findings demonstrate abnormalities in putamen structure and function in individuals at high risk for major depressive disorder. Future studies should focus on this region as a potential biomarker for depressive illness, noting meanwhile that differences attributable to family history may peak at different ages based on which MRI modality is being used to assay them.

Background Isolated pulmonary vasculitis (IPV) is a single-organ vasculitis of unknown etiology and may mimic chronic thromboembolic pulmonary hypertension (CTEPH). The aim of this study was to review our clinical experience with pulmonary endarterectomy in patients with CTEPH secondary to IPV. Methods Data were collected prospectively for consecutive patients who underwent pulmonary endarterectomy and had a diagnosis of IPV at or after surgery. Results We identified nine patients (six female, median age 48 (23–55) years) with IPV. The diagnosis was confirmed after histopathological examination of all surgical materials. The mean duration of disease before surgery was 88.0 ±70.2 months. Exercise-induced dyspnea was the presenting symptom in all patients. Pulmonary endarterectomy was bilateral in six patients and unilateral in three. No mortality was observed, however, one patient had pulmonary artery stenosis and stent implantation was performed. All patients received immunosuppressive therapies after surgery. Mean pulmonary artery pressure decreased significantly from 30(19–67) mm Hg to 21(15–49) mm Hg after surgery (p Conclusions Isolated pulmonary vasculitis can mimic CTEPH, and these patients can be diagnosed with pulmonary endarterectomy. Furthermore, surgery has not only diagnostic but also therapeutic value for IPV when stenotic and/or thrombotic lesions are surgically accessible. A multidisciplinary experienced CTEPH team is critical for management of these unique patients.

Background & Objective: The laparoscopic sleeve gastrectomy is a popular intervention in morbidly obese patient. Postoperative pain relief is a major challenging issue in this procedure with known adverse effects on the respiratory excursion and the ability to cough by the patient. Various analgesic protocols have been tries to conquer this pain. Vitamin C is a micronutrient, water soluble vitamin with antioxidant and antinociceptive actions. We assessed the effect of vitamin C when used as a component of the multimodal analgesic technique in this cohort of the patients. Methodology: After obtaining the ethical committee approval and trial registration, 50 patients scheduled for laparoscopic sleeve gastrectomy were enrolled in this study. The patients were randomly divided into two equal groups according to the study protocol. Group C received vitamin C 500 mg every 8 h for 5 days perioperatively, while Group N received placebo in the same fashion. The surgery was performed under routine general anesthesia. We monitored hemodynamic, VAS, rescue analgesia profile, and gastrointestinal side effects in the immediately after operation, and then at 1, 2, 4, 8, 12, and at 24 h postoperatively. The postoperative morphine consumption was noted as a primary objective of the study. Independent sample T test, Mann-Whitney test, chi square test or Kruskal Wallis test were utilized to detect statistical differences between the studied groups. P < 0.05 was considered significant. Results: Postoperative morphine consumption was lower in Group C than in Group N (16.36 ± 2.37 vs. 20 ± 4.13 mg, P = 0.001). The frequency of morphine was significantly lower in Group C than in Group N (P = 0.009). VAS was lower in Group C than Group N at 4, 8, and 12 h postoperatively (P = 0.02, 0.001, 0.001), while other parameters where comparable. Abbreviation: CBC: complete blood count; IRB: Institutional Research Board; LFTs: liver function tests; LSG: laparoscopic sleeve gastrectomy; MABP: mean arterial blood pressure; NMDA: N-methyl D-aspartate; OSAS: obstructive sleep apnea syndrome; PACTR: Pan African Clinical Trial Registry; PEEP: positive end expiratory pressure.; PFT: pulmonary function tests; RFTs: renal function tests; TFTs: thyroid function test; TOF: train of four; VAS: visual analogue score. Preregistration: Institutional Research Board, Mansoura Faculty of Medicine (IRB # R.20.02.738, Feb 15-2020). Pan African clinical trial registry (PACTR- 202003565796463, date of registration: March 02-2020). Conclusion: Vitamin C via its antioxidant, antinociceptive properties and NMDA-receptor antagonist action can be used as a part of multimodal analgesic techniques. Vitamin C acts as co-analgesic, improves postoperative pain profile, and reduces the needs for other analgesic modalities. Key words: Vitamin C; Analgesia; VAS; Pain; Sleeve Gastrectomy Citation: Aboelelea M, Abedelkhalek M, Mamoun M. Role of vitamin C in multimodal analgesia for sleeve gastrectomy: a prospective randomized controlled trial. Anaesth. pain intensive care 2023;27(1):82−88. DOI: 10.35975/apic.v27i1.2110 Received: October 02, 2022; Reviewed: October 25, 2022; Accepted: December 09, 2022

One of the most innovative genres in today’s business communication is the product pitch (PP), mainly characterised by its multisemiotic nature (Daly & Davy, 2016), which makes it essential to take a multimodal approach to the analysis and teaching of this genre (Tamarit & Skorczynska, 2014; Valeiras-Jurado, 2017). However, despite the increasing importance of this oral genre in the business field, little research has been conducted on the teaching of PPs to business ESP students. The purpose of this paper is therefore to present an innovative pedagogical model consisting of a learner-led genre-based pedagogy based on a teaching-learning cycle that fosters critical thinking and multimodal awareness (Querol-Jullián & Fortanet-Gómez, 2019). Following the four stages of the cycle proposed, and with the constant scaffolding of the lecturer in the initial stages, a group of tertiary business students were requested to decode the multimodal ensembles of a YouTube PP to subsequently create their own PPs. These samples were assessed multimodally by the rest of the class (peer review) and by the teacher using an “all-mode-inclusive” rubric. This innovative pedagogical approach to a new genre increased students’ motivation and multimodal awareness, surpassing the traditionally exclusively language-bound teaching and assessment of ESP., Uno de los géneros más innovadores de la comunicación empresarial actual es el denominado “Product Pitch” (PP), caracterizado sobre todo por su naturaleza multisemiótica (Daly & Davy, 2016), que precisa un enfoque multimodal para su análisis y enseñanza (Tamarit &S korczynska, 2014; Valeiras-Jurado, 2017). Sin embargo, es escasa la investigación sobre la enseñanza de los PPs a estudiantes de inglés empresarial. El objetivo de este artículo es presentar un modelo pedagógico innovador basado en el estudio del género y dirigido por el propio aprendiz que se desarrolla en torno a un ciclo de enseñanza y aprendizaje que promueve el pensamiento crítico y la percepción de la multimodalidad (Querol-Julián & Fortanet-Gómez, 2019). Siguiendo las cuatro etapas del ciclo propuesto y con la ayuda constante del profesor, un grupo de estudiantes de negocios decodificó las agrupaciones multimodales de un PP de YouTube para posteriormente crear sus propios PP, que fueron evaluados desde el punto de vista multimodal por el resto de la clase (evaluación por pares) y por la profesora utilizando una rúbrica que incluye todos los modos. Este enfoque pedagógico innovador de un nuevo género aumentó la motivación de los estudiantes, así como su percepción multimodal, superando la enseñanza y la evaluación del inglés especializado exclusivamente centrada en el lenguaje., Ministry of Science, Innovation and Universities of the Spanish Government (grant number PGC2018-094823-B-I00), Research Promotion Plan at Universitat Jaume I (Spain) (grant ID: UJI-B2020-09)