Anomaly Detection on Time Series Data

Anomaly detection is an important problem that has been researched within diverse application domains. Detection of anomalies in the time series domain finds extensive application in monitoring system status, mal-ware/spam detection, credit-card fraud etc. In this work we explore methods to detect anomalies in multivariate as well as uni variate time-series and proposed a novel method using Dictionary Learning, Sparse Representation, Singular Value Decomposition and Topological anomaly detection(TAD). We have tested the proposed method on real as well as synthetic data sets. Our novel method brings down the false positive rates as compared to the existing methods

The interaction between astrocytes and excitatory synapses in health and disease

Synaptic transmission forms the basis of neuronal activity and astrocytes play an integral part in this process. Glutamate is the major excitatory neurotransmitter in the brain and an important function of astrocyte during excitatory synaptic activity involves the uptake of glutamate through astrocyte glutamate transporters (EAATs) and hence shaping the excitatory neurotransmission. Recently astrocytes have also been shown to affect a sustained form of inhibition. In this thesis, we study these aspects of astrocyte functions and their role in affecting the excitatory synapse. In addition, using animal models of diseases we describe how the astrocyte synapse interaction is affected in diseased conditions. In the Paper I, we confirmed previous studies showing that astrocytes respond by a longlasting depolarization upon synaptic stimulation, mediated by an increase in extracellular potassium ions. We found that this long-lasting depolarization is enhanced when astrocytic glutamate transporters are blocked, whereas the neuronal EPSC is reduced under these conditions. Blocking the glutamate transporters reduces the AMPA receptor response whereas the NMDA receptor activation is increased, causing the enhancement seen in the astrocytic long-lasting depolarization. Since astrocyte glutamate transporters are impaired in many neurodegenerative diseases, this study gives us an idea about how the impairment of astrocytic glutamate transporters can influence synapse activity. In the Paper II and III we used animal models of depression and AD respectively to understand the role of astrocytes in affecting synaptic transmission. In Paper II we used the well characterized FSL rat model of depression and investigated how reactive astrocytes affect inhibition by producing and releasing GABA. We found that tonic inhibition of pyramidal neurons is increased in the FSL rat while the synaptic plasticity is impaired. We also found that this tonic inhibition was reduced by blocking the astrocytic GABA synthesis or by chelating intracellular Ca[superscript2+] in astrocytes in slices from the FSL rat, giving evidence for increased astrocytic involvement in tonic inhibition in an animal model of depression. Furthermore, blocking of astrocytic GABA synthesis restored the impaired synaptic plasticity seen in FSL rats. In the Paper III, we explored the astrocyte mediated glutamate uptake in Alzheimer’s disease model using a knock in AD mouse model App[superscript NL-G-F]. Astrocytes displayed a reactive morphology, with swollen cell bodies and increased number of processes. We found that though there was an increase in the protein expression levels of astrocytic glutamate transporters (EAATs), they were functionally impaired as reflected by the glutamate transporter current recordings

Interactive Fashion Content Generation Using LLMs and Latent Diffusion Models

Fashionable image generation aims to synthesize images of diverse fashion prevalent around the globe, helping fashion designers in real-time visualization by giving them a basic customized structure of how a specific design preference would look in real life and what further improvements can be made for enhanced customer satisfaction. Moreover, users can alone interact and generate fashionable images by just giving a few simple prompts. Recently, diffusion models have gained popularity as generative models owing to their flexibility and generation of realistic images from Gaussian noise. Latent diffusion models are a type of generative model that use diffusion processes to model the generation of complex data, such as images, audio, or text. They are called "latent" because they learn a hidden representation, or latent variable, of the data that captures its underlying structure. We propose a method exploiting the equivalence between diffusion models and energy-based models (EBMs) and suggesting ways to compose multiple probability distributions. We describe a pipeline on how our method can be used specifically for new fashionable outfit generation and virtual try-on using LLM-guided text-to-image generation. Our results indicate that using an LLM to refine the prompts to the latent diffusion model assists in generating globally creative and culturally diversified fashion styles and reducing bias.Comment: Third Workshop on Ethical Considerations in Creative applications of Computer Vision (EC3V) at CVPR 2023. arXiv admin note: substantial text overlap with arXiv:2301.02110 by other author

Purification and characterization of a thermoalkaline, cellulase free thermostable xylanase from a newly isolated Anoxybacillus sp. Ip-C from hot spring of Ladakh

An alkaline, highly thermostable cellulase free xylanase was purified from a thermophilic Anoxybacillus sp. Ip-C, newly isolated from hot spring of Ladakh. The enzyme was purified using ammonia sulphate precipitation followed by Sephadex G-75. The molecular weight of the xylanase was about 45 kDa, as analyzed by SDS-PAGE. The enzyme had optimum activity at pH 9.0 and 70ºC temperature; the enzyme retained 90% of its original activity for 96 hrs at 70 ºC. Vmax and Km of the enzyme were found to be 13.5 µmol min-1 mg-1 protein and 4.59 mg ml-1, respectively. Metal ions, Ca+2, Fe+2 and Mg+2 highly enhance the enzyme activity to 122.45, 119.06 and 118.98% respectively; whereas SDS and Hg+2 completely inhibit (0 U/ml) the enzyme activity. TLC analysis of enzymatic hydrolysis products showed that this xylanase is an endoxylanase, and generates xylooligosaccharides. Thus, it provides a potential thermostable alkaline xylanase for industrial applications

Technology-guided assessment of vocalisations and their diagnostic value as pain indicators for people living with dementia

Background: during pain assessment in persons unable to self-report, such as people living with dementia, vocalisations are commonly used as pain indicators. However, there is a lack of evidence from clinical practice regarding their diagnostic value and relationship with pain. We aimed to explore vocalisations and pain in people with dementia undergoing pain assessments in clinical practice settings. Methods: a total of 22,194 pain assessments were reviewed in people with dementia (n = 3,144) from 34 different Australian aged care homes and two dementia specific programs. Pain assessments were conducted by 389 purposely trained health care professionals and cares using PainChek pain assessment tool. Vocalised expressions were determined based on nine vocalisation features included in the tool. Linear mixed models were used to examine the relationship of pain scores with vocalisation features. Using a single pain assessment for each of the 3,144 people with dementia, additional data analysis was conducted via Receiver Operator Characteristic (ROC) analysis and Principal Component Analysis. Results: vocalisation scores increased with increasing pain intensity. High pain scores were more likely with the presence of sighing and screaming (8 times). The presence of vocalisation features varied depending on the intensity of pain. The ROC optimal criterion for the voice domain yielded a cut-off score of ≥2.0 with a Youden index of 0.637. The corresponding sensitivity and specificity were 79.7% [confidence interval (CI): 76.8–82.4%] and 84.0% (CI: 82.5–85.5%), respectively. Conclusion: we describe vocalisation features during presence of different levels of pain in people with dementia unable to self-report, therefore providing evidence in regard to their diagnostic value in clinical practice

Technology-guided assessment of vocalisations and their diagnostic value as pain indicators for people living with dementia

Background during pain assessment in persons unable to self-report, such as people living with dementia, vocalisations are commonly used as pain indicators. However, there is a lack of evidence from clinical practice regarding their diagnostic value and relationship with pain. We aimed to explore vocalisations and pain in people with dementia undergoing pain assessments in clinical practice settings. Methods a total of 22,194 pain assessments were reviewed in people with dementia (n = 3,144) from 34 different Australian aged care homes and two dementia specific programs. Pain assessments were conducted by 389 purposely trained health care professionals and cares using PainChek pain assessment tool. Vocalised expressions were determined based on nine vocalisation features included in the tool. Linear mixed models were used to examine the relationship of pain scores with vocalisation features. Using a single pain assessment for each of the 3,144 people with dementia, additional data analysis was conducted via Receiver Operator Characteristic (ROC) analysis and Principal Component Analysis. Results vocalisation scores increased with increasing pain intensity. High pain scores were more likely with the presence of sighing and screaming (8 times). The presence of vocalisation features varied depending on the intensity of pain. The ROC optimal criterion for the voice domain yielded a cut-off score of ≥2.0 with a Youden index of 0.637. The corresponding sensitivity and specificity were 79.7% [confidence interval (CI): 76.8–82.4%] and 84.0% (CI: 82.5–85.5%), respectively. Conclusion we describe vocalisation features during presence of different levels of pain in people with dementia unable to self-report, therefore providing evidence in regard to their diagnostic value in clinical practice

Sensing Technology to Monitor Behavioral and Psychological Symptoms and to Assess Treatment Response in People With Dementia. A Systematic Review

Background: The prevalence of dementia is expected to rapidly increase in the next decades, warranting innovative solutions improving diagnostics, monitoring and resource utilization to facilitate smart housing and living in the nursing home. This systematic review presents a synthesis of research on sensing technology to assess behavioral and psychological symptoms and to monitor treatment response in people with dementia. Methods: The literature search included medical peer-reviewed English language publications indexed in Embase, Medline, Cochrane library and Web of Sciences, published up to the 5th of April 2019. Keywords included MESH terms and phrases synonymous with “dementia”, “sensor”, “patient”, “monitoring”, “behavior”, and “therapy”. Studies applying both cross sectional and prospective designs, either as randomized controlled trials, cohort studies, and case-control studies were included. The study was registered in PROSPERO 3rd of May 2019. Results: A total of 1,337 potential publications were identified in the search, of which 34 were included in this review after the systematic exclusion process. Studies were classified according to the type of technology used, as (1) wearable sensors, (2) non-wearable motion sensor technologies, and (3) assistive technologies/smart home technologies. Half of the studies investigated how temporarily dense data on motion can be utilized as a proxy for behavior, indicating high validity of using motion data to monitor behavior such as sleep disturbances, agitation and wandering. Further, up to half of the studies represented proof of concept, acceptability and/or feasibility testing. Overall, the technology was regarded as non-intrusive and well accepted. Conclusions: Targeted clinical application of specific technologies is poised to revolutionize precision care in dementia as these technologies may be used both by patients and caregivers, and at a systems level to provide safe and effective care. To highlight awareness of legal regulations, data risk assessment, and patient and public involvement, we propose a necessary framework for sustainable ethical innovation in healthcare technology. The success of this field will depend on interdisciplinary cooperation and the advance in sustainable ethic innovation.publishedVersio

Affective Computing for Late-Life Mood and Cognitive Disorders

Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer\u27s disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed

Impact of COVID-19 restrictions on behavioural and psychological symptoms in home-dwelling people with dementia: A prospective cohort study (PAN.DEM)

Objectives To investigate the impact of the COVID-19 restrictions on behavioural and psychological symptoms of dementia (BPSD). Design Prospective cohort study (PAN.DEM) nested within the halted parent trial ([email protected]). Setting Households in Norway immediate before and 6–9 weeks into the COVID-19 restrictions. Participants 104 dyads (persons with mild to moderate dementia aged ≥65 and their informal carers) completed both prepandemic and pandemic assessments, among 237 in the parent trial. Mini-Mental Status Examination score 15–26 or Functional Assessment Staging score 3–7 covered dementia severity. Main outcome measures Neuropsychiatric Inventory (NPI-12) total (range 0–144), psychosis (range 0–24), hyperactive behaviour (range 0–60) and mood subsyndrome (range 0–48) scores; Cornell Scale for Depression in Dementia (CSDD) total score (range 0–38). Results We found an overall increase in BPSD by NPI-12 total score comparing prepandemic to pandemic levels (median 16 IQR (4.5–29) to 20 (7–32.5), p=0.03) over a mean of 86 days (SD 19). NPI-12 total score worsened in 57 (55%) of people with dementia and was associated with postponed or averted contacts with healthcare professionals (logistic regression, OR 3.96, 95% CI 1.05 to 14.95). Psychosis subsyndrome levels increased (0 (0–3) to 0.5 (0–6), p=0.01) in 37 (36%) persons; this worsening was associated with partial insight (9.57, 1.14 to 80.71) and reduced informal carer contact (4.45, 1.01 to 19.71). Moreover, depressive symptoms increased as assessed by CSDD total score (5 (3–9) to 7 (4–12), p=0.01) and worsened for 56 (54%), which was inversely associated with psychotropic drugs on-demand (0.16, 0.03 to 0.75). Conclusions BPSD worsened during the first months of the COVID-19 restrictions, most pronounced for psychosis and depression. These BPSD exacerbations have implications for pandemic policies, emphasising that restrictions must balance COVID-19 morbidity and mortality against dementia deterioration.publishedVersio