Alzheimer's and related dementia are associated with a gradual decline in cognitive abilities of an individual, impairing independent living abilities. Wandering, a purposeless disoriented locomotion tendency or behavior of dementia patients, requires constant caregiver supervision to reduce the risk of physical harm to patients. Integrating technology into care ecology has the potential to alleviate stress and expense. An automatic wandering detection system integrated with an intervention module may provide warnings and assistive suggestions in times of abnormal behavior. In this study, we survey existing research on technology aided methodologies and algorithms used in detection and management of wandering behavior of individuals affected with dementia. Our study provides insights into mechanisms of collecting movement data and finding patterns that distinguish wandering from normal behavior.
Automatic volume control (AVC) changes volume of TV/Radio based on surroundings noise. A number of hardware and software implementation of AVC is available. Conventional AVC methods concentrates on maintain a constant power-ratio between input of microphone and speaker-output signals. Some new methods are concentrating on preserving intelligibility of speech. They are extracting desired frequency band then comparing their power. This paper proposes Correlation based volume control for radio/Smart Television. Proposed Correlation Method adjusts volume based on linearity of input of microphone and speaker-output signals. A band-pass filter is used to filter-out unwanted frequency bands for comparing sound intelligibility. Conventional system concentrates on maintaining SNR. Proposed method adjusts volume considering delay and wave-shape of input of microphone and speaker-output signals.
Hypernym Discovery is the task of identifying potential hypernyms for a given term. A hypernym is a more generalized word that is super-ordinate to more specific words. This paper explores several approaches that rely on co-occurrence frequencies of word pairs, Hearst Patterns based on regular expressions, and word embeddings created from the UMBC corpus. Our system Babbage participated in Subtask 1A for English and placed 6th of 19 systems when identifying concept hypernyms, and 12th of 18 systems for entity hypernyms.
CRISPR-Cas9 screens facilitate the discovery of gene functional relationships and phenotype-specific dependencies. The Cancer Dependency Map (DepMap) is the largest compendium of whole-genome CRISPR screens aimed at identifying cancer-specific genetic dependencies across human cell lines. A mitochondria-associated bias has been previously reported to mask signals for genes involved in other functions, and thus, methods for normalizing this dominant signal to improve co-essentiality networks are of interest. In this study, we explore three unsupervised dimensionality reduction methods - autoencoders, robust, and classical principal component analyses (PCA) - for normalizing the DepMap to improve functional networks extracted from these data. We propose a novel onion normalization technique to combine several normalized data layers into a single network. Benchmarking analyses reveal that robust PCA combined with onion normalization outperforms existing methods for normalizing the DepMap. Our work demonstrates the value of removing low-dimensional signals from the DepMap before constructing functional gene networks and provides generalizable dimensionality reduction-based normalization tools.
Current approaches to define chemical-genetic interactions (CGIs) in human cell lines are resource-intensive. We designed a scalable chemical-genetic screen platform by generating a DNA damage response (DDR)-focused custom sgRNA library. We performed five proof-of-principle compound screens and found that the compounds' known modes-of-action (MoA) were enriched among the compounds' CGIs. These scalable screens recapitulated expected CGIs at a comparable signal-to-noise ratio (SNR) relative to genome-wide screens. Furthermore, time-resolved CGIs, captured by sequencing screens at various time points, suggested an unexpected, late time point interstrand-crosslinking (ICL) repair pathway response to camptothecin-induced DNA damage. Our approach can facilitate screening compounds at scale and produce biologically informative CGI profiles.
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