Search results for: faster consolidation of trauma memory

Authors: C. Bel, J. Nevado, F. Ciceri, M. Ropacki, T. Hoffmann, P. Lapunzina, C. Buesa

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Background: The Phelan-McDermid syndrome (PMS) is a genetic disorder caused by the deletion of the terminal region of chromosome 22 or mutation of the SHANK3 gene. Shank3 disruption in mice leads to dysfunction of synaptic transmission, which can be restored by epigenetic regulation with both Lysine Specific Demethylase 1 (LSD1) inhibitors. PMS subjects result in a variable degree of intellectual disability, delay or absence of speech, autistic spectrum disorders symptoms, low muscle tone, motor delays and epilepsy. Vafidemstat is an LSD1 inhibitor in Phase II clinical development with a well-established and favorable safety profile, and data supporting the restoration of memory and cognition defects as well as reduction of agitation and aggression in several animal models and clinical studies. Therefore, vafidemstat has the potential to become a first-in-class precision medicine approach to treat PMS patients. Aims: The goal of this research is to perform an observational trial to psychometrically characterize individuals carrying deletions in SHANK3 and build a foundation for subsequent precision psychiatry clinical trials with vafidemstat. Methodology: This study is characterizing the clinical profile of 20 to 40 subjects, > 16-year-old, with genotypically confirmed PMS diagnosis. Subjects will complete a battery of neuropsychological scales, including the Repetitive Behavior Questionnaire (RBQ), Vineland Adaptive Behavior Scales, Escala de Observación para el Diagnostico del Autismo (Autism Diagnostic Observational Scale) (ADOS)-2, the Battelle Developmental Inventory and the Behavior Problems Inventory (BPI). Results: By March 2021, 19 patients have been enrolled. Unsupervised hierarchical clustering of the results obtained so far identifies 3 groups of patients, characterized by different profiles of cognitive and behavioral scores. The first cluster is characterized by low Battelle age, high ADOS and low Vineland, RBQ and BPI scores. Low Vineland, RBQ and BPI scores are also detected in the second cluster, which in contrast has high Battelle age and low ADOS scores. The third cluster is somewhat in the middle for the Battelle, Vineland and ADOS scores while displaying the highest levels of aggression (high BPI) and repeated behaviors (high RBQ). In line with the observation that female patients are generally affected by milder forms of autistic symptoms, no male patients are present in the second cluster. Dividing the results by gender highlights that male patients in the third cluster are characterized by a higher frequency of aggression, whereas female patients from the same cluster display a tendency toward higher repetitive behavior. Finally, statistically significant differences in deletion sizes are detected comparing the three clusters (also after correcting for gender), and deletion size appears to be positively correlated with ADOS and negatively correlated with Vineland A and C scores. No correlation is detected between deletion size and the BPI and RBQ scores. Conclusions: Precision medicine may open a new way to understand and treat Central Nervous System disorders. Epigenetic dysregulation has been proposed to be an important mechanism in the pathogenesis of schizophrenia and autism. Vafidemstat holds exciting therapeutic potential in PMS, and this study will provide data regarding the optimal endpoints for a future clinical study to explore vafidemstat ability to treat shank3-associated psychiatric disorders.

Keywords: autism, epigenetics, LSD1, personalized medicine

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Authors: Mumtaz Mooncey, Alexander Jolly, Megan Fisher, Kerry Robinson, Robert Lloyd, Dave Fielding

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Introduction: The integration of the WingFactors in-situ simulation programme has transformed the education landscape at the Whittington Health NHS Trust. To date, there have been a total of 90 simulations - 19 aimed at Paediatric trainees, including 2 Child and Adolescent Mental Health (CAMHS) scenarios. The opportunity for joint debriefs provided by clinical faculty and airline pilots, has created a new exciting avenue to explore human factors within psychiatry. Through the use of real clinical environments and primed actors; the benefits of high fidelity simulation, interdisciplinary and interprofessional learning has been highlighted. The use of in-situ simulation within Psychiatry is a newly emerging concept and its success here has been recognised by unanimously positive feedback from participants and acknowledgement through nomination for the Health Service Journal (HSJ) Award (Best Education Programme 2021). Methodology: The first CAMHS simulation featured a collapsed patient in the toilet with a ligature tied around her neck, accompanied by a distressed parent. This required participants to consider:; emergency physical management of the case, alongside helping to contain the mother and maintaining situational awareness when transferring the patient to an appropriate clinical area. The second simulation was based on a 17- year- old girl attempting to leave the ward after presenting with an overdose, posing potential risk to herself. The safe learning environment enabled participants to explore techniques to engage the young person and understand their concerns, and consider the involvement of other members of the multidisciplinary team. The scenarios were followed by an immediate ‘hot’ debrief, combining technical feedback with Human Factors feedback from uniformed airline pilots and clinicians. The importance of psychological safety was paramount, encouraging open and honest contributions from all participants. Key learning points were summarized into written documents and circulated. Findings: The in-situ simulations demonstrated the need for practical changes both in the Emergency Department and on the Paediatric ward. The presence of airline pilots provided a novel way to debrief on Human Factors. The following key themes were identified: -Team-briefing (‘Golden 5 minutes’) - Taking a few moments to establish experience, initial roles and strategies amongst the team can reduce the need for conversations in front of a distressed patient or anxious relative. -Use of checklists / guidelines - Principles associated with checklist usage (control of pace, rigor, team situational awareness), instead of reliance on accurate memory recall when under pressure. -Read-back - Immediate repetition of safety critical instructions (e.g. drug / dosage) to mitigate the risks associated with miscommunication. -Distraction management - Balancing the risk of losing a team member to manage a distressed relative, versus it impacting on the care of the young person. -Task allocation - The value of the implementation of ‘The 5A’s’ (Availability, Address, Allocate, Ask, Advise), for effective task allocation. Conclusion: 100% of participants have requested more simulation training. Involvement of airline pilots has led to a shift in hospital culture, bringing to the forefront the value of Human Factors focused training and multidisciplinary simulation. This has been of significant value in not only physical health, but also mental health simulation.

Keywords: human factors, in-situ simulation, inter-professional, multidisciplinary

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Authors: Anjalika Fernando, Banuka Athuraliya

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This paper presents a comprehensive approach for converting Sinhala Sign Language (SSL) into grammatically correct sentences using Natural Language Processing (NLP) techniques in real-time. While previous studies have explored various aspects of SSL translation, the research gap lies in the absence of grammar checking for SSL. This work aims to bridge this gap by proposing a two-stage methodology that leverages deep learning models to detect signs and translate them into coherent sentences, ensuring grammatical accuracy. The first stage of the approach involves the utilization of a Long Short-Term Memory (LSTM) deep learning model to recognize and interpret SSL signs. By training the LSTM model on a dataset of SSL gestures, it learns to accurately classify and translate these signs into textual representations. The LSTM model achieves a commendable accuracy rate of 94%, demonstrating its effectiveness in accurately recognizing and translating SSL gestures. Building upon the successful recognition and translation of SSL signs, the second stage of the methodology focuses on improving the grammatical correctness of the translated sentences. The project employs a Neural Machine Translation (NMT) architecture, consisting of an encoder and decoder with LSTM components, to enhance the syntactical structure of the generated sentences. By training the NMT model on a parallel corpus of Sinhala wrong sentences and their corresponding grammatically correct translations, it learns to generate coherent and grammatically accurate sentences. The NMT model achieves an impressive accuracy rate of 98%, affirming its capability to produce linguistically sound translations. The proposed approach offers significant contributions to the field of SSL translation and grammar correction. Addressing the critical issue of grammar checking, it enhances the usability and reliability of SSL translation systems, facilitating effective communication between hearing-impaired and non-sign language users. Furthermore, the integration of deep learning techniques, such as LSTM and NMT, ensures the accuracy and robustness of the translation process. This research holds great potential for practical applications, including educational platforms, accessibility tools, and communication aids for the hearing-impaired. Furthermore, it lays the foundation for future advancements in SSL translation systems, fostering inclusive and equal opportunities for the deaf community. Future work includes expanding the existing datasets to further improve the accuracy and generalization of the SSL translation system. Additionally, the development of a dedicated mobile application would enhance the accessibility and convenience of SSL translation on handheld devices. Furthermore, efforts will be made to enhance the current application for educational purposes, enabling individuals to learn and practice SSL more effectively. Another area of future exploration involves enabling two-way communication, allowing seamless interaction between sign-language users and non-sign-language users.In conclusion, this paper presents a novel approach for converting Sinhala Sign Language gestures into grammatically correct sentences using NLP techniques in real time. The two-stage methodology, comprising an LSTM model for sign detection and translation and an NMT model for grammar correction, achieves high accuracy rates of 94% and 98%, respectively. By addressing the lack of grammar checking in existing SSL translation research, this work contributes significantly to the development of more accurate and reliable SSL translation systems, thereby fostering effective communication and inclusivity for the hearing-impaired community

Keywords: Sinhala sign language, sign Language, NLP, LSTM, NMT

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Authors: Yung-Chih Kuo, Che-Yu Lin, Jay-Shake Li, Yung-I Lou

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Curcumin (CRM) and nerve growth factor (NGF) were entrapped in liposomes (LIP) with cardiolipin (CL) to downregulate the phosphorylation of mitogen-activated protein kinases for Alzheimer’s disease (AD) management. AD belongs to neurodegenerative disorder with a gradual loss of memory, yielding irreversible dementia. CL-conjugated LIP loaded with CRM (CRM-CL/LIP) and that with NGF (NGF-CL/LIP) were applied to AD models of SK-N-MC cells and Wistar rats with an insult of β-amyloid peptide (Aβ). Lipids comprising 1,2-dipalmitoyl-sn-glycero-3- phosphocholine (Avanti Polar Lipids, Alabaster, AL), 1',3'-bis[1,2- dimyristoyl-sn-glycero-3-phospho]-sn-glycerol (CL; Avanti Polar Lipids), 1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine-N- [methoxy(polyethylene glycol)-2000] (Avanti Polar Lipids), 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[carboxy(polyethylene glycol)-2000] (Avanti Polar Lipids) and CRM (Sigma–Aldrich, St. Louis, MO) were dissolved in chloroform (J. T. Baker, Phillipsburg, NJ) and condensed using a rotary evaporator (Panchum, Kaohsiung, Taiwan). Human β-NGF (Alomone Lab, Jerusalem, Israel) was added in the aqueous phase. Wheat germ agglutinin (WGA; Medicago AB, Uppsala, Sweden) was grafted on LIP loaded with CRM for (WGA-CRM-LIP) and CL-conjugated LIP loaded with CRM (WGA-CRM-CL/LIP) using 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (Sigma–Aldrich) and N-hydroxysuccinimide (Alfa Aesar, Ward Hill, MA). The protein samples of SK-N-MC cells (American Type Tissue Collection, Rockville, MD) were used for sodium dodecyl sulfate (Sigma–Aldrich) polyacrylamide gel (Sigma–Aldrich) electrophoresis. In animal study, the LIP formulations were administered by intravenous injection via a tail vein of male Wistar rats (250–280 g, 8 weeks, BioLasco, Taipei, Taiwan), which were housed in the Animal Laboratory of National Chung Cheng University in accordance with the institutional guidelines and the guidelines of Animal Protection Committee under the Council of Agriculture of the Republic of China. We found that CRM-CL/LIP could inhibit the expressions of phosphorylated p38 (p-p38), p-Jun N-terminal kinase (p-JNK), and p-tau protein at serine 202 (p-Ser202) to retard the neuronal apoptosis. Free CRM and released CRM from CRM-LIP and CRM-CL/LIP were not in a straightforward manner to effectively inhibit the expression of p-p38 and p-JNK in the cytoplasm. In addition, NGF-CL/LIP enhanced the quantities of p-neurotrophic tyrosine kinase receptor type 1 (p-TrkA) and p-extracellular-signal-regulated kinase 5 (p-ERK5), preventing the Aβ-induced degeneration of neurons. The membrane fusion of NGF-LIP activated the ERK5 pathway and the targeting capacity of NGF-CL/LIP enhanced the possibility of released NGF to affect the TrkA level. Moreover, WGA-CRM-LIP improved the permeation of CRM across the blood–brain barrier (BBB) and significantly reduced the Aβ plaque deposition and malondialdehyde level and increased the percentage of normal neurons and cholinergic function in the hippocampus of AD rats. This was mainly because the encapsulated CRM was protected by LIP against a rapid degradation in the blood. Furthermore, WGA on LIP could target N-acetylglucosamine on endothelia and increased the quantity of CRM transported across the BBB. In addition, WGA-CRM-CL/LIP could be effective in suppressing the synthesis of acetylcholinesterase and reduced the decomposition of acetylcholine for better neurotransmission. Based on the in vitro and in vivo evidences, WGA-CRM-CL/LIP can rescue neurons from apoptosis in the brain and can be a promising drug delivery system for clinical AD therapy.

Keywords: Alzheimer’s disease, β-amyloid, liposome, mitogen-activated protein kinase

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Authors: Asma Omri, Iheb Sifaoui, Sofiane Sayahi, Hichem Besbes

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Future vehicle systems demand advanced capabilities, notably in-cabin life detection and driver monitoring systems, with a particular emphasis on drowsiness detection. To meet these requirements, several techniques employ artificial intelligence methods based on real-time vital sign measurements. In parallel, Frequency-Modulated Continuous-Wave (FMCW) radar technology has garnered considerable attention in the domains of healthcare and biomedical engineering for non-invasive vital sign monitoring. FMCW radar offers a multitude of advantages, including its non-intrusive nature, continuous monitoring capacity, and its ability to penetrate through clothing. In this paper, we propose a system utilizing the AWR6843AOP radar from Texas Instruments (TI) to extract precise vital sign information. The radar allows us to estimate Ballistocardiogram (BCG) signals, which capture the mechanical movements of the body, particularly the ballistic forces generated by heartbeats and respiration. These signals are rich sources of information about the cardiac cycle, rendering them suitable for heart rate estimation. The process begins with real-time subject positioning, followed by clutter removal, computation of Doppler phase differences, and the use of various filtering methods to accurately capture subtle physiological movements. To address the challenges associated with FMCW radar-based vital sign monitoring, including motion artifacts due to subjects' movement or radar micro-vibrations, Long Short-Term Memory (LSTM) networks are implemented. LSTM's adaptability to different heart rate patterns and ability to handle real-time data make it suitable for continuous monitoring applications. Several crucial steps were taken, including feature extraction (involving amplitude, time intervals, and signal morphology), sequence modeling, heart rate estimation through the analysis of detected cardiac cycles and their temporal relationships, and performance evaluation using metrics such as Root Mean Square Error (RMSE) and correlation with reference heart rate measurements. For dataset construction and LSTM training, a comprehensive data collection system was established, integrating the AWR6843AOP radar, a Heart Rate Belt, and a smart watch for ground truth measurements. Rigorous synchronization of these devices ensured data accuracy. Twenty participants engaged in various scenarios, encompassing indoor and real-world conditions within a moving vehicle equipped with the radar system. Static and dynamic subject’s conditions were considered. The heart rate estimation through LSTM outperforms traditional signal processing techniques that rely on filtering, Fast Fourier Transform (FFT), and thresholding. It delivers an average accuracy of approximately 91% with an RMSE of 1.01 beat per minute (bpm). In conclusion, this paper underscores the promising potential of FMCW radar technology integrated with artificial intelligence algorithms in the context of automotive applications. This innovation not only enhances road safety but also paves the way for its integration into the automotive ecosystem to improve driver well-being and overall vehicular safety.

Keywords: ballistocardiogram, FMCW Radar, vital sign monitoring, LSTM

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Authors: Ramazan Bakir, Gizem Kayar

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It is known that Alzheimer and Dementia are the two most common types of Neurodegenerative diseases and their visibility is getting accelerated for the last couple of years. As the population sees older ages all over the world, researchers expect to see the rate of this acceleration much higher. However, unfortunately, there is no known pharmacological cure for both, although some help to reduce the rate of cognitive decline speed. This is why we encounter with non-pharmacological treatment and tracking methods more for the last five years. Many researchers, including well-known associations and hospitals, lean towards using non-pharmacological methods to support cognitive function and improve the patient’s life quality. As the dementia symptoms related to mind, learning, memory, speaking, problem-solving, social abilities and daily activities gradually worsen over the years, many researchers know that cognitive support should start from the very beginning of the symptoms in order to slow down the decline. At this point, life of a patient and caregiver can be improved with some daily activities and applications. These activities include but not limited to basic word puzzles, daily cleaning activities, taking notes. Later, these activities and their results should be observed carefully and it is only possible during patient/caregiver and M.D. in-person meetings in hospitals. These meetings can be quite time-consuming, exhausting and financially ineffective for hospitals, medical doctors, caregivers and especially for patients. On the other hand, digital support systems are showing positive results for all stakeholders of healthcare systems. This can be observed in countries that started Telemedicine systems. The biggest potential of our system is setting the inter-user communication up in the best possible way. In our project, we propose Machine Learning based digitalization of validated traditional cognitive tests (e.g. MOCA, Afazi, left-right hemisphere), their analyses for high-quality follow-up and communication systems for all stakeholders. R. Bakir and G. Kayar are with Gefeasoft, Inc, R&D – Software Development and Health Technologies company. Emails: ramazan, gizem @ gefeasoft.com This platform has a high potential not only for patient tracking but also for making all stakeholders feel safe through all stages. As the registered hospitals assign corresponding medical doctors to the system, these MDs are able to register their own patients and assign special tasks for each patient. With our integrated machine learning support, MDs are able to track the failure and success rates of each patient and also see general averages among similarly progressed patients. In addition, our platform also supports multi-player technology which helps patients play with their caregivers so that they feel much safer at any point they are uncomfortable. By also gamifying the daily household activities, the patients will be able to repeat their social tasks and we will provide non-pharmacological reminiscence therapy (RT – life review therapy). All collected data will be mined by our data scientists and analyzed meaningfully. In addition, we will also add gamification modules for caregivers based on Naomi Feil’s Validation Therapy. Both are behaving positively to the patient and keeping yourself mentally healthy is important for caregivers. We aim to provide a therapy system based on gamification for them, too. When this project accomplishes all the above-written tasks, patients will have the chance to do many tasks at home remotely and MDs will be able to follow them up very effectively. We propose a complete platform and the whole project is both time and cost-effective for supporting all stakeholders.

Keywords: alzheimer’s, dementia, cognitive functionality, cognitive tests, serious games, machine learning, artificial intelligence, digitalization, non-pharmacological, data analysis, telemedicine, e-health, health-tech, gamification

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Authors: Ziyad S. Haidar

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Millions of teeth are removed annually, and dental extraction is one of the most commonly performed surgical procedures globally. Whether due to caries, periodontal disease or trauma, exodontia and the ensuing wound healing and bone remodeling processes of the resultant socket (hole in the jaw bone) usually result in serious deformities of the residual alveolar osseous ridge and surrounding soft tissues (reduced height/width). Such voluminous changes render the placement of a proper conventional bridge, denture or even an implant-supported prosthesis extremely challenging. Further, most extractions continue to be performed with no regard for preventing the onset of alveolar osteitis (also known as dry socket, a painful and difficult-to-treat/-manage condition post-exodontia). Hence, such serious resorptive morphological changes often result in significant facial deformities and a negative impact on the overall Quality of Life (QoL) of patients (and oral health-related QoL), alarming, particularly for the geriatric with compromised healing and in light of the thriving longevity statistics. Opportunity: Despite advances in tissue/wound grafting, serious limitations continue to exist, including efficacy and clinical outcome predictability, cost, treatment time, expertise and risk of immune reactions. For cases of dry sockets, specifically, the commercially-available and often-prescribed home remedies are highly lacking. Indeed, most are not recommended for use anymore. Alveogyl is a fine example. Hence, there is a great market demand and need for alternative solutions. Solution: Herein, SockGEL/PLUG (patent pending), an all-natural, drug-free and injectable stimuli-responsive hydrogel, was designed, formulated, characterized and evaluated as an osteogenic, angiogenic, anti-microbial and pain-soothing suture-free intra-alveolar dressing, safe and efficacious for use in several oro-dental and cranio-maxillo-facial interventional applications; for example: in fresh dental extraction sockets, immediately post-exodontia. It is composed of FDA-approved, biocompatible and biodegradable polymers, self-assembled electro-statically to formulate a scaffolding matrix to (a) prevent the onset of alveolar osteitis via securing the fibrin-clot in situ and protecting/sealing the socket from contamination/infection; and (b) endogenously promote/accelerate wound healing and bone remodeling to preserve the volume of the alveolus. Findings: The intrinsic properties of the SockGEL/PLUG hydrogel were evaluated physico-chemico-mechanically for safety (cell viability), viscosity, rheology, bio-distribution and essentially, capacity to induce wound healing and osteogenesis (small defect, in vivo) without any signaling cues from exogenous cells, growth factors or drugs. The performed animal model of cranial critical-sized and non-vascularized bone defects shall provide vitally critical insights into the role and mechanism of the employed natural bio-polymer blend and gel product in endogenous reparative regeneration of soft tissues and bone morphogenesis. Alongside, the fine-tuning of our modified formulation method will further tackle appropriateness, reproducibility, scalability, ease and speed in producing stable, biodegradable and sterilizable stimuli (thermo-sensitive and photo-responsive) matrices (3-dimensional interpenetrating yet porous polymeric network) suitable for an intra-socket application, and beyond. Conclusions and Perspective: Findings are anticipated to provide sufficient evidence to translate into pilot clinical trials and validate the bionanomaterial before engaging the market for feasibility, acceptance and cost-effectiveness studies. The SockGEL/PLUG platform is patent pending: SockGEL is a bio-inspired drug-free hydrogel; SockPLUG is a drug-loaded hydrogel designed for complex indications.

Keywords: hydrogel, injectable, dentistry, craniomaxillofacial complex, bioinspired, nanobiotechnology, biopolymer, sol-gel, stimuli-responsive, matrix, tissue engineering, regenerative medicine

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Authors: Chiu-Yin Leung

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This research investigates a complex assemblage among indigenous identities, socio-political organization and national apparatus in the border landscape of post-colonial Hong Kong. This former British colony had designated a transient mode of governance in its New Territories and particularly the northernmost borderland in 1951-2012. With a discriminated system of land provisions for the indigenous villagers, the place has been inherited with distinctive village-based culture, historic monuments and agrarian practices until its sovereignty return into the People’s Republic of China. In its latest development imperatives by the national strategic planning, the frontier area of Hong Kong has been identified as a strategy site for regional economic integration in South China, with cross-border projects of innovation and technology zones, mega-transport infrastructure and inter-jurisdictional arrangement. Contemporary literature theorizes borders as the material and discursive production of territoriality, which manifest in state apparatus and the daily lives of its citizens and condense in the contested articulations of power, security and citizenship. Drawing on the concept of assemblage, this paper attempts to tract how the border regime and infrastructure in Hong Kong as a city are deeply ingrained in the everyday lived spaces of the local communities but also the changing urban and regional strategies across different longitudinal moments. Through an intensive ethnographic fieldwork among the borderland villages since 2008 and the extensive analysis of colonial archives, new development plans and spatial planning frameworks, the author navigates the genealogy of the border landscape in Ta Kwu Ling frontier area and its implications as the milieu for new state space, covering heterogeneous fields particularly in indigenous rights, heritage preservation, rural sustainability and regional economy. Empirical evidence suggests an apparent bias towards indigenous power and colonial representation in classifying landscape values and conserving historical monuments. Squatter and farm tenants are often deprived of property rights, statutory participation and livelihood option in the planning process. The postcolonial bureaucracies have great difficulties in mobilizing resources to catch up with the swift, political-first approach of the mainland counterparts. Meanwhile, the cultural heritage, lineage network and memory landscape are not protected altogether with any holistic view or collaborative effort across the border. The enactment of land resumption and compensation scheme is furthermore disturbed by lineage-based customary law, technocratic bureaucracy, intra-community conflicts and multi-scalar political mobilization. As many traces of colonial misfortune and tyranny have been whitewashed without proper management, the author argues that postcolonial justice is yet reconciled in this fragmented border landscape. The assemblage of border in mainstream representation has tended to oversimplify local struggles as a collective mist and setup a wider production of schizophrenia experiences in the discussion of further economic integration among Hong Kong and other mainland cities in the Pearl River Delta Region. The research is expected to shed new light on the theorizing of border regions and postcolonialism beyond Eurocentric perspectives. In reassembling the borderland experiences with other arrays in state governance, village organization and indigenous identities, the author also suggests an alternative epistemology in reconciling socio-spatial differences and opening up imaginaries for positive interventions.

Keywords: heritage conservation, indigenous communities, post-colonial borderland, regional development, rural sustainability

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Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

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Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

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Authors: Alba Prats-Bisbe, Jaume López-Carballo, David Leno-Colorado, Alberto García Molina, Alicia Romero Marquez, Elena Hernández Pena, Eloy Opisso Salleras, Raimon Jané Campos

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Neurological disorders are a leading cause of disability and premature mortality worldwide. Neurorehabilitation (NRHB) is a clinical process aimed at reducing functional impairment, promoting societal participation, and improving the quality of life for affected individuals. Virtual reality (VR) technology is emerging as a promising NRHB support tool. Its immersive nature fosters a strong sense of agency and embodiment, motivating patients to engage in meaningful tasks and increasing adherence to therapy. However, the clinical benefits of VR interventions are challenging to determine due to the high heterogeneity among health applications. This study explores a stepwise development approach for creating VR-based tools to assist individuals with neurological disorders in medical practice, aiming to enhance reproducibility, facilitate comparison, and promote the generalization of findings. Building on previous research, the step-by-step methodology encompasses: Needs Identification– conducting cross-disciplinary meetings to brainstorm problems, solutions, and address barriers. Intervention Definition– target population, set goals, and conceptualize the VR system (equipment and environments). Material Selection and Placement– choose appropriate hardware and software, place the device within the hospital setting, and test equipment. Co-design– collaboratively create VR environments, user interfaces, and data management strategies. Prototyping– develop VR prototypes, conduct user testing, and make iterative redesigns. Usability and Feasibility Assessment– design protocols and conduct trials with stakeholders in the hospital setting. Efficacy Assessment– conduct clinical trials to evaluate outcomes and long-term effects. Cost-Effectiveness Validation– assess reproducibility, sustainability, and balance between costs and benefits. NRHB is complex due to the multifaceted needs of patients and the interdisciplinary healthcare architecture. VR has the potential to support various applications, such as motor skill training, cognitive tasks, pain management, unilateral spatial neglect (diagnosis and treatment), mirror therapy, and ecologically valid activities of daily living. Following this methodology was crucial for launching a VR-based system in a real hospital environment. Collaboration with neuropsychologists lead to develop A) a VR-based tool for cognitive rehabilitation in patients with acquired brain injury (ABI). The system comprises a head-mounted display (HTC Vive Pro Eye) and 7 tasks targeting attention, memory, and executive functions. A desktop application facilitates session configuration, while database records in-game variables. The VR tool's usability and feasibility were demonstrated in proof-of-concept trials with 20 patients, and effectiveness is being tested through a clinical protocol with 12 patients completing 24-session treatment. Another case involved collaboration with nurses and paediatric physiatrists to create B) a VR-based distraction tool during invasive techniques. The goal is to alleviate pain and anxiety associated with botulinum toxin (BTX) injections, blood tests, or intravenous placements. An all-in-one headset (HTC Vive Focus 3) deploys 360º videos to improve the experience for paediatric patients and their families. This study presents a framework for developing clinically relevant and technologically feasible VR-based support tools for hospital settings. Despite differences in patient type, intervention purpose, and VR system, the methodology demonstrates usability, viability, reproducibility and preliminary clinical benefits. It highlights the importance approach centred on clinician and patient needs for any aspect of NRHB within a real hospital setting.

Keywords: neurological disorders, neurorehabilitation, stepwise development approach, virtual reality

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