Using multiple sequence alignment to extract daily activity routines of the elderly living alone

Promoting healthy movement behaviours among children during the COVID-19 pandemic

Daily foraging routines in food-caching mountain chickadees are associated with variation in environmental harshness

Examining daily activity routines of older adults using workflow

BackgroundA fundamental assumption of all widely-used multiple sequence alignment techniques is that the left- and right-most positions of the input sequences are relevant to the alignment. However, the position where a sequence starts or ends can be totally arbitrary due to a number of reasons: arbitrariness in the linearisation (sequencing) of a circular molecular structure; or inconsistencies introduced into sequence databases due to different linearisation standards. These scenarios are relevant, for instance, in the process of multiple sequence alignment of mitochondrial DNA, viroid, viral or other genomes, which have a circular molecular structure. A solution for these inconsistencies would be to identify a suitable rotation (cyclic shift) for each sequence; these refined sequences may in turn lead to improved multiple sequence alignments using the preferred multiple sequence alignment program.ResultsWe present MARS, a new heuristic method for improving Multiple circular sequence Alignment using Refined Sequences. MARS was implemented in the C++ programming language as a program to compute the rotations (cyclic shifts) required to best align a set of input sequences. Experimental results, using real and synthetic data, show that MARS improves the alignments, with respect to standard genetic measures and the inferred maximum-likelihood-based phylogenies, and outperforms state-of-the-art methods both in terms of accuracy and efficiency. Our results show, among others, that the average pairwise distance in the multiple sequence alignment of a dataset of widely-studied mitochondrial DNA sequences is reduced by around 5% when MARS is applied before a multiple sequence alignment is performed.ConclusionsAnalysing multiple sequences simultaneously is fundamental in biological research and multiple sequence alignment has been found to be a popular method for this task. Conventional alignment techniques cannot be used effectively when the position where sequences start is arbitrary. We present here a method, which can be used in conjunction with any multiple sequence alignment program, to address this problem effectively and efficiently.

The response to COVID-19: Occupational resilience and the resilience of daily occupations in action.

Past research has demonstrated that older adults tend to use daily activities as cues to remember to take medications. However, they may still experience medication non-adherence because they did not select adequate contextual cues or face situations that interfere with their medication routines. This work addresses two research questions: (1) How does the association that older adults establish between their daily routines and their medication taking enable them to perform it consistently? (2) What problems do they face in associating daily routines with medication taking? For 30 days, using a mixed-methods approach, we collected quantitative and qualitative data from four participants aged 70–73 years old about their medication taking. We confirm that older adults who matched their medication regimens to their habitual routines obtained better results on time-based consistency measures. The main constraints for using daily routines as contextual cues were the insertion of medication taking into broad daily routines, the association of multiple daily routines with medication taking, the lack of strict daily routines, and the disruption of daily routines. We argue that the strategies proposed by the literature for forming medication-taking habits should support their formulation by measuring patients’ dosage patterns and generating logs of their daily activities.

Although multiple sequence alignments (MSAs) are essential for a wide range of applications from structure modeling to prediction of functional sites, construction of accurate MSAs for distantly related proteins remains a largely unsolved problem. The rapidly increasing database of spatial structures is a valuable source to improve alignment quality. We explore the use of 3D structural information to guide sequence alignments constructed by our MSA program PROMALS. The resulting tool, PROMALS3D, automatically identifies homologs with known 3D structures for the input sequences, derives structural constraints through structure-based alignments and combines them with sequence constraints to construct consistency-based multiple sequence alignments. The output is a consensus alignment that brings together sequence and structural information about input proteins and their homologs. PROMALS3D can also align sequences of multiple input structures, with the output representing a multiple structure-based alignment refined in combination with sequence constraints. The advantage of PROMALS3D is that it gives researchers an easy way to produce high-quality alignments consistent with both sequences and structures of proteins. PROMALS3D outperforms a number of existing methods for constructing multiple sequence or structural alignments using both reference-dependent and reference-independent evaluation methods.

Daily routines, including in-person school and extracurricular activities, are important for maintaining healthy physical activity and sleep habits in children. The COVID-19 pandemic significantly disrupted daily routines as in-person school and activities closed to prevent spread of SARS-CoV-2. We aimed to examine and assess differences in objectively measured physical activity levels and sleep patterns from wearable sensors in children with obesity before, during, and after a period of school and extracurricular activity closures associated with the COVID-19 pandemic. We compared average step count and sleep patterns (using the Mann Whitney U Test) before and during the pandemic-associated school closures by using data from activity tracker wristbands (Garmin VivoFit 3). Data was collected from 94 children (aged 5–17) with obesity, who were enrolled in a randomized controlled trial testing a community-based lifestyle intervention for a duration of 12-months. During the period that in-person school and extracurricular activities were closed due to the COVID-19 pandemic, children with obesity experienced objectively-measured decreases in physical activity, and sleep duration. From March 15, 2020 to March 31, 2021, corresponding with local school closures, average daily step count decreased by 1,655 steps. Sleep onset and wake time were delayed by about an hour and 45 minutes, respectively, while sleep duration decreased by over 12 minutes as compared with the pre-closure period. Step counts increased with the resumption of in-person activities. These findings provide objective evidence for parents, clinicians, and public health professionals on the importance of in-person daily activities and routines on health behaviors, particularly for children with pre-existing obesity. We demonstrate the utility of wearable sensors in objectively measuring longitudinal physical activity and sleep behavior patterns in children with obesity and in quantifying changes in their health behaviors due to disruption of structured, daily routines following in-person school closures during the COVID-19 pandemic.Trial Registration:Clinical trial registration: NCT03339440.

BackgroundMultiple Sequence Alignment (MSA) is a basic tool for bioinformatics research and analysis. It has been used essentially in almost all bioinformatics tasks such as protein structure modeling, gene and protein function prediction, DNA motif recognition, and phylogenetic analysis. Therefore, improving the accuracy of multiple sequence alignment is important for advancing many bioinformatics fields.ResultsWe designed and developed a new method, MSACompro, to synergistically incorporate predicted secondary structure, relative solvent accessibility, and residue-residue contact information into the currently most accurate posterior probability-based MSA methods to improve the accuracy of multiple sequence alignments. The method is different from the multiple sequence alignment methods (e.g. 3D-Coffee) that use the tertiary structure information of some sequences since the structural information of our method is fully predicted from sequences. To the best of our knowledge, applying predicted relative solvent accessibility and contact map to multiple sequence alignment is novel. The rigorous benchmarking of our method to the standard benchmarks (i.e. BAliBASE, SABmark and OXBENCH) clearly demonstrated that incorporating predicted protein structural information improves the multiple sequence alignment accuracy over the leading multiple protein sequence alignment tools without using this information, such as MSAProbs, ProbCons, Probalign, T-coffee, MAFFT and MUSCLE. And the performance of the method is comparable to the state-of-the-art method PROMALS of using structural features and additional homologous sequences by slightly lower scores.ConclusionMSACompro is an efficient and reliable multiple protein sequence alignment tool that can effectively incorporate predicted protein structural information into multiple sequence alignment. The software is available at http://sysbio.rnet.missouri.edu/multicom_toolbox/.

Multiple sequence alignment (MSA) is fundamental to many biological applications. But most classical MSA algorithms are difficult to handle large-scale multiple sequences, especially long sequences. Therefore, some recent aligners adopt an efficient divide-and-conquer strategy to divide long sequences into several short sub-sequences. Selecting the common segments (i.e. anchors) for division of sequences is very critical as it directly affects the accuracy and time cost. So, we proposed a novel algorithm, FMAlign, to improve the performance of multiple nucleotide sequence alignment. We use FM-index to extract long common segments at a low cost rather than using a space-consuming hash table. Moreover, after finding the longer optimal common segments, the sequences are divided by the longer common segments. FMAlign has been tested on virus and bacteria genome and human mitochondrial genome datasets, and compared with existing MSA methods such as MAFFT, HAlign and FAME. The experiments show that our method outperforms the existing methods in terms of running time, and has a high accuracy on long sequence sets. All the results demonstrate that our method is applicable to the large-scale nucleotide sequences in terms of sequence length and sequence number. The source code and related data are accessible in https://github.com/iliuh/FMAlign.

Early detection of neurodegenerative diseases, such as Alzheimers and Parkinsons, is essential for timely intervention, which can improve patients' quality of life and slow down disease progression. Traditional diagnostic methods rely heavily on clinical tests, which can be infrequent and may not capture slight behavioral changes that indicate early cognitive or motor decline. This work presents a novel approach using smart home data to detect early signs of neurodegeneration through continuous monitoring of sleep patterns and daily activity routines. In a smart home environment, sensors passively monitor daily routines, sleep quality, and mobility patterns of the elderly persons. This paper introduces a novel framework combining the Sleep Deviation Patterns (SDP) and the Weighted Activity Deviation Index (WADI) to comprehensively analyze deviations in sleep and daily routines. The SDP framework captures deviations in sleep onset, duration, interruptions, and consistency using metrics such as Sleep Onset Deviation, Sleep Duration Deviation, Sleep Interruption Index, and Sleep Consistency Index-aggregated into a weighted Sleep Deviation Score. WADI quantifies deviations in daily activities by computing weighted absolute deviations of activity proportions relative to a reference routine. Thus, we applied our framework to real-world smart home datasets (TM001-TM004) from the CASAS project, which include labeled activity data from both single- and multi-resident households. Experimental findings reveal a distinct stratification: TM001 and TM002 exhibited Low WADI and Low SDP with an average of >0.015 - >0.2 scores, suggesting stable routines, whereas TM003 and TM004 demonstrated elevated with an average of >0.03 - >0.4 values, indicating disrupted behaviors. In TM004, up to 28% of days were flagged as anomalous, correlating with patterns consistent with early neurodegeneration such as fragmented sleep and disorganized activity routines. Finally, experimental results demonstrate that the combined SDP and WADI frameworks effectively identify irregularities in sleep and activity patterns on real-world datasets. The proposed approach offers a robust and scalable solution for health monitoring, with potential applications in neurodegenerative disease detection, personalized healthcare, and smart home systems.

In this study, we explored the impact of an occupational therapy wellness program on daily habits and routines through the perspectives of youth and their parents. Data were collected through semistructured interviews with children and their parents, the Pizzi Healthy Weight Management Assessment(©), and program activities. Three themes emerged from the interviews: Program Impact, Lessons Learned, and Time as a Barrier to Health. The most common areas that both youth and parents wanted to change were time spent watching television and play, fun, and leisure time. Analysis of activity pie charts indicated that the youth considerably increased active time in their daily routines from Week 1 to Week 6 of the program. An occupational therapy program focused on health and wellness may help youth and their parents be more mindful of their daily activities and make health behavior changes.

Purpose: The purpose of this study was to investigate parent implementation of home-based therapy regimens, and the relationship between implementation and family-level outcomes. Method: A stratified (by child age group) random sample of 538 families raising children with disabilities in Alberta, Canada took part. Participants completed the Family Life Survey, which incorporated child and family measures, and items measuring parent implementation of home-based therapy regimens. Results: Parents are more likely to implement therapeutic regimens when these are “enfolded” into other daily activities and routines. If parents have to “find a slot” in the daily routine to implement therapy, they will sacrifice personal leisure, participation in paid work, and time spent with other family members. Greater parent sacrifices/trade-offs was negatively associated with family well-being. Conclusion: As a general rule, children do well when their families do well, and families do well when they have the resources they need to juggle work and family and care demands. Recruiting parents as interventionists can tax family resources. Rehabilitation professionals must weigh up the pros and cons of parent-mediated intervention, and look to enfolding therapy into the everyday family routine.Implications for RehabilitationParents are more likely to implement prescribed therapeutic activities and exercises when these are “enfolded” into the everyday family routine.Higher levels of parental adherence and implementation of home-based therapy programs is associated with poorer family well-being.Rehabilitation professionals must weigh the pros and cons of parent-mediated intervention, and help families find ways to enfold intervention into their daily routine.

Children with autism often experience considerable challenges and one of them is the difficulty in understanding, structuring and predicting their daily life activities and routines. Several methodologies have been studied and implemented to help autistic children with these routine activities and tasks, and one of those methods is the use of visual schedules. For this, mobile apps and related technology have been considered as an excellent tool in supporting autistic children’s development. But despite the technological resources and the variety of mobile apps available today, the authors could not find such needed resources available for the Portuguese speaking autistic children population, especially in relation to visual schedules/routines, which are considered very important for the child’s development. Therefore, based on the literature and in some apps available in other countries for autistic children, the authors propose a set of mock-ups of a visual schedule application for smartphone. The visual mock-ups represent the idea of the app that we intend to implement in a near future to be used by Portuguese autistic children aged between 4 to 10 years old to support them in their daily routine and the performance of related tasks.

Navigating the time–space context of HIV and AIDS:: daily routines and access to care