Guided Weak Supervision for Action Recognition with Scarce Data to Assess Skills of Children with Autism

Diagnostic and intervention methodologies for skill assessment of autism typically requires a clinician repetitively initiating several stimuli and recording the child's response. In this paper, we propose to automate the response measurement through video recording of the scene following the use of Deep Neural models for human action recognition from videos. However, supervised learning of neural networks demand large amounts of annotated data that are hard to come by. This issue is addressed by leveraging the `similarities' between the action categories in publicly available large-scale video action (source) datasets and the dataset of interest. A technique called guided weak supervision is proposed, where every class in the target data is matched to a class in the source data using the principle of posterior likelihood maximization. Subsequently, classifier on the target data is re-trained by augmenting samples from the matched source classes, along with a new loss encouraging inter-class separability. The proposed method is evaluated on two skill assessment autism datasets, SSBD and a real world Autism dataset comprising 37 children of different ages and ethnicity who are diagnosed with autism. Our proposed method is found to improve the performance of the state-of-the-art multi-class human action recognition models in-spite of supervision with scarce data.Comment: AAAI 202

Identification of novel host-oriented targets for Human Immunodeficiency Virus type 1 using Random Homozygous Gene Perturbation

<p>Abstract</p> <p>Background</p> <p>Human Immunodeficiency Virus (HIV) is a global threat to public health. Current therapies that directly target the virus often are rendered ineffective due to the emergence of drug-resistant viral variants. An emerging concept to combat drug resistance is the idea of targeting host mechanisms that are essential for the propagation of the virus, but have a minimal cellular effect.</p> <p>Results</p> <p>Herein, using Random Homozygous Gene Perturbation (RHGP), we have identified cellular targets that allow human MT4 cells to survive otherwise lethal infection by a wild type HIV-1_NL4-3. These gene targets were validated by the reversibility of the RHGP technology, which confirmed that the RHGP itself was responsible for the resistance to HIV-1 infection. We further confirmed by siRNA knockdowns that the RHGP-identified cellular pathways are responsible for resistance to infection by either CXCR4 or CCR5 tropic HIV-1 variants. We also demonstrated that cell clones with these gene targets disrupted by RHGP were not permissible to the replication of a drug resistant HIV-1 mutant.</p> <p>Conclusion</p> <p>These studies demonstrate the power of RHGP to identify novel host targets that are essential for the viral life cycle but which can be safely perturbed without overt cytotoxicity. These findings suggest opportunities for the future development of host-oriented therapeutics with the broad spectrum potential for safe and effective inhibition of HIV infection.</p

Computer Vision-Based Assessment of Autistic Children: Analyzing Interactions, Emotions, Human Pose, and Life Skills

In this paper, the proposed work implements and tests the computer vision applications to perform the skill and emotion assessment of children with Autism Spectrum Disorder (ASD) by extracting various bio-behaviors, human activities, child-therapist interactions, and joint pose estimations from the recorded videos of interactive single- or two-person play-based intervention sessions. A comprehensive data set of 300 videos is amassed from ASD children engaged in social interaction, and three novel deep learning-based vision models are developed, which are explained as follows: (i) activity comprehension to analyze child-play partner interactions (activity comprehension model); (ii) an automatic joint attention recognition framework using head and hand pose; and (iii) emotion and facial expression recognition. The proposed models are also tested on children&#x2019;s real-world, 68 unseen videos captured from the clinic, and public datasets. The activity comprehension model has an overall accuracy of 72.32&#x0025;, the joint attention recognition models have an accuracy of 97&#x0025; for follow eye gaze and 93.4&#x0025; for hand pointing, and the facial expression recognition model has an overall accuracy of 95.1&#x0025;. The proposed models could extract behaviors of interest, events of activities, emotions, and social skills from free-play and intervention session videos of long duration and provide temporal plots for session monitoring and assessment, thus empowering clinicians with insightful data useful in diagnosis, assessment, treatment formulation, and monitoring ASD children with limited supervision

Identification and Characterization of Wilt and Salt Stress-Responsive MicroRNAs in Chickpea through High-Throughput Sequencing

<div><p>Chickpea (<i>Cicer arietinum</i>) is the second most widely grown legume worldwide and is the most important pulse crop in the Indian subcontinent. Chickpea productivity is adversely affected by a large number of biotic and abiotic stresses. MicroRNAs (miRNAs) have been implicated in the regulation of plant responses to several biotic and abiotic stresses. This study is the first attempt to identify chickpea miRNAs that are associated with biotic and abiotic stresses. The wilt infection that is caused by the fungus <i>Fusarium oxysporum</i> f.sp. <i>ciceris</i> is one of the major diseases severely affecting chickpea yields. Of late, increasing soil salinization has become a major problem in realizing these potential yields. Three chickpea libraries using fungal-infected, salt-treated and untreated seedlings were constructed and sequenced using next-generation sequencing technology. A total of 12,135,571 unique reads were obtained. In addition to 122 conserved miRNAs belonging to 25 different families, 59 novel miRNAs along with their star sequences were identified. Four legume-specific miRNAs, including miR5213, miR5232, miR2111 and miR2118, were found in all of the libraries. Poly(A)-based qRT-PCR (Quantitative real-time PCR) was used to validate eleven conserved and five novel miRNAs. miR530 was highly up regulated in response to fungal infection, which targets genes encoding zinc knuckle- and microtubule-associated proteins. Many miRNAs responded in a similar fashion under both biotic and abiotic stresses, indicating the existence of cross talk between the pathways that are involved in regulating these stresses. The potential target genes for the conserved and novel miRNAs were predicted based on sequence homologies. miR166 targets a HD-ZIPIII transcription factor and was validated by 5′ RLM-RACE. This study has identified several conserved and novel miRNAs in the chickpea that are associated with gene regulation following exposure to wilt and salt stress.</p></div