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"Sharma, Shivani"
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The use of social robots with children and young people on the autism spectrum: A systematic review and meta-analysis
Robot-mediated interventions show promise in supporting the development of children on the autism spectrum.
In this systematic review and meta-analysis, we summarize key features of available evidence on robot-interventions for children and young people on the autism spectrum aged up to 18 years old, as well as consider their efficacy for specific domains of learning.
PubMed, Scopus, EBSCOhost, Google Scholar, Cochrane Library, ACM Digital Library, and IEEE Xplore. Grey literature was also searched using PsycExtra, OpenGrey, British Library EThOS, and the British Library Catalogue. Databases were searched from inception until April (6th) 2021.
Searches undertaken across seven databases yielded 2145 articles. Forty studies met our review inclusion criteria of which 17 were randomized control trials. The methodological quality of studies was conducted with the Quality Assessment Tool for Quantitative Studies. A narrative synthesis summarised the findings. A meta-analysis was conducted with 12 RCTs.
Most interventions used humanoid (67%) robotic platforms, were predominantly based in clinics (37%) followed home, schools and laboratory (17% respectively) environments and targeted at improving social and communication skills (77%). Focusing on the most common outcomes, a random effects meta-analysis of RCTs showed that robot-mediated interventions significantly improved social functioning (g = 0.35 [95%CI 0.09 to 0.61; k = 7). By contrast, robots did not improve emotional (g = 0.63 [95%CI -1.43 to 2.69]; k = 2) or motor outcomes (g = -0.10 [95%CI -1.08 to 0.89]; k = 3), but the numbers of trials were very small. Meta-regression revealed that age accounted for almost one-third of the variance in effect sizes, with greater benefits being found in younger children.
Overall, our findings support the use of robot-mediated interventions for autistic children and youth, and we propose several recommendations for future research to aid learning and enhance implementation in everyday settings.
Our methods were preregistered in the PROSPERO database (CRD42019148981).
Journal Article
Impact of isolation methods on the biophysical heterogeneity of single extracellular vesicles
Extracellular vesicles (EVs) have raised high expectations as a novel class of diagnostics and therapeutics. However, variabilities in EV isolation methods and the unresolved structural complexity of these biological-nanoparticles (sub-100 nm) necessitate rigorous biophysical characterization of single EVs. Here, using atomic force microscopy (AFM) in conjunction with direct stochastic optical reconstruction microscopy (dSTORM), micro-fluidic resistive pore sizing (MRPS), and multi-angle light scattering (MALS) techniques, we compared the size, structure and unique surface properties of breast cancer cell-derived small EVs (sEV) obtained using four different isolation methods. AFM and dSTORM particle size distributions showed coherent unimodal and bimodal particle size populations isolated via centrifugation and immune-affinity methods respectively. More importantly, AFM imaging revealed striking differences in sEV nanoscale morphology, surface nano-roughness, and relative abundance of non-vesicles among different isolation methods. Precipitation-based isolation method exhibited the highest particle counts, yet nanoscale imaging revealed the additional presence of aggregates and polymeric residues. Together, our findings demonstrate the significance of orthogonal label-free surface characteristics of single sEVs, not discernable via conventional particle sizing and counts alone. Quantifying key nanoscale structural characteristics of sEVs, collectively termed ‘EV-nano-metrics’ enhances the understanding of the complexity and heterogeneity of sEV isolates, with broad implications for EV-analyte based research and clinical use.
Journal Article
IoT based car accident detection and notification algorithm for general road accidents
With an increase in population, there is an increase in the number of accidents that happen every minute. These road accidents are unpredictable. There are situations where most of the accidents could not be reported properly to nearby ambulances on time. In most of the cases, there is the unavailability of emergency services which lack in providing the first aid and timely service which can lead to loss of life by some minutes. Hence, there is a need to develop a system that caters to all these problems and can effectively function to overcome the delay time caused by the medical vehicles. The purpose of this paper is to introduce a framework using IoT, which helps in detecting car accidents and notifying them immediately. This can be achieved by integrating smart sensors with a microcontroller within the car that can trigger at the time of an accident. The other modules like GPS and GSM are integrated with the system to obtain the location coordinates of the accidents and sending it to registered numbers and nearby ambulance to notify them about the accident to obtain immediate help at the location.
Journal Article
An evolutionary computation-based sensitive pattern hiding model under a multi-threshold constraint in healthcare
In the domain of collaborative frequent pattern mining, the preservation of privacy has emerged as a critical area of investigation as the data procured by the analytical arm of business organizations may contain critical sensitive patterns. Further, this investigation may cause the disclosure of sensitive information. Various Evolutionary techniques have been proposed in the past to efficiently investigate such sensitive patterns while preserving data privacy. These techniques utilized various nature-inspired evolutionary-based algorithms like Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for masking such confidential information before sharing data to the business organizations. However, most of them either choose to delete entire sensitive transactions for masking confidential information or by selecting a victim item and its subsequent deletion based on a single parameter such as the length or frequency of a sensitive item. This may cause various side effects, and hence reduces the utility of sanitized datasets. In this paper, we propose a novel and innovative Particle Swarm Optimization (PSO) based algorithm specifically designed to address the challenge of concealing sensitive patterns within the constraints of a multi-threshold framework. The proposed algorithm emphasizes enhancing the utility of sanitized datasets by selecting victim items based on multiple parameters, unlike the schemes proposed in the past. For making the scheme suitable for real applications, we propose to introduce the dynamic multithreshold-based framework, the algorithm utilizes the bi-variate normal distribution to determine the dynamic threshold value for each sensitive item set. This helps in improving the utility of the underlying dataset while making lesser modifications for hiding sensitive knowledge. The empirical findings substantiate the effectiveness of the proposed algorithm in concealing sensitive patterns over benchmark FIMI datasets, Heart Disease, and Heart Attack Prediction datasets. The experimental results show the superiority of the proposed scheme by reducing the side-effect with minimum loss of data over the existing PSO and ACO-based algorithms. We noted that the Failure-to-Hide(FTH) named side effect is significantly lower in most of the cases for our algorithm in comparison to the existing algorithms.
Journal Article
Elucidating diversity of exosomes: biophysical and molecular characterization methods
Exosomes are cell-secreted nanovesicles present in biological fluids in normal and diseased conditions. Owing to their seminal role in cell-cell communication, emerging evidences suggest that exosomes are fundamental regulators of various diseases. Due to their potential usefulness in disease diagnosis, robust isolation and characterization of exosomes is critical in developing exosome-based assays. In the last few years, different exosome characterization methods, both biophysical and molecular, have been developed to characterize these tiny vesicles. Here, in this review we summarize: first, biophysical techniques based on spectroscopy (e.g., Raman spectroscopy, dynamic light scattering) and other principles, for example, scanning electron microscopy, atomic force microscopy; second, antibody-based molecular techniques including flow cytometry, transmission electron microscopy and third, nanotechnology-dependent exosome characterization methodologies.
Journal Article
Cryo- EM structure of the mycobacterial 70S ribosome in complex with ribosome hibernation promotion factor RafH
Ribosome hibernation is a key survival strategy bacteria adopt under environmental stress, where a protein, hibernation promotion factor (HPF), transitorily inactivates the ribosome.
Mycobacterium tuberculosis
encounters hypoxia (low oxygen) as a major stress in the host macrophages, and upregulates the expression of RafH protein, which is crucial for its survival. The RafH, a dual domain HPF, an orthologue of bacterial long HPF (HPF
long
), hibernates ribosome in 70S monosome form, whereas in other bacteria, the HPF
long
induces 70S ribosome dimerization and hibernates its ribosome in 100S disome form. Here, we report the cryo- EM structure of
M. smegmatis
, a close homolog of
M. tuberculosis
, 70S ribosome in complex with the RafH factor at an overall 2.8 Å resolution. The N- terminus domain (NTD) of RafH binds to the decoding center, similarly to HPF
long
NTD. In contrast, the C- terminus domain (CTD) of RafH, which is larger than the HPF
long
CTD, binds to a distinct site at the platform binding center of the ribosomal small subunit. The two domain-connecting linker regions, which remain mostly disordered in earlier reported HPF
long
structures, interact mainly with the anti-Shine Dalgarno sequence of the 16S rRNA.
Ribosome hibernation is a key survival strategy bacteria adapt under stress. Here, cryo- EM structure of mycobacterial 70S ribosome with hypoxia stress-induced factor RafH suggests the molecular mechanism of RafH-induced ribosome hibernation.
Journal Article
An improved finegrained ciphertext policy based temporary keyword search on encrypted data for secure cloud storage
We present a temporary keyword search over sensitive and confidential health data in a cloud environment. The cloud constitutes a semi-trusted domain, making it necessary for data owners to secure their data before outsourcing it through techniques like encryption. Attribute-based keyword search techniques tend to perform a search operation using a search token generated by an authorized user. These search tokens can lead to serious privacy threats, as they can extract all ciphertexts that may have been generated along with their keyword. Therefore, restricting search tokens to extract ciphertexts generated within a time interval is a more promising solution. In this paper, we present a novel ciphertext policy fine-grained temporary keyword that prevents the misuse of these search tokens. Further, it mitigates the risk of insider threats within healthcare organizations by limiting the window of opportunity for unauthorized access to minimum. To assess the security, our proposed scheme is formally proven to be secure against Selectively Chosen Keyword Attacks in the generic bilinear group model. Additionally, we demonstrate that the encryption algorithm’s complexity is linear in relation to the number of attributes. Our scheme’s significance and practicality are revealed by the performance evaluation.
Journal Article
Transformer enabled multi-modal medical diagnosis for tuberculosis classification
Recently, multimodal data analysis in medical domain has started receiving a great attention. Researchers from both computer science, and medicine are trying to develop models to handle multimodal medical data. However, most of the published work have targeted the homogeneous multimodal data. The collection and preparation of heterogeneous multimodal data is a complex and time-consuming task. Further, development of models to handle such heterogeneous multimodal data is another challenge. This study presents a cross modal transformer-based fusion approach for multimodal clinical data analysis using medical images and clinical data. The proposed approach leverages the image embedding layer to convert image into visual tokens, and another clinical embedding layer to convert clinical data into text tokens. Further, a cross-modal transformer module is employed to learn a holistic representation of imaging and clinical modalities. The proposed approach was tested for a multi-modal lung disease tuberculosis data set. Further, the results are compared with recent approaches proposed in the field of multimodal medical data analysis. The comparison shows that the proposed approach outperformed the other approaches considered in the study. Another advantage of this approach is that it is faster to analyze heterogeneous multimodal medical data in comparison to existing methods used in the study, which is very important if we do not have powerful machines for computation.
Journal Article