scholarly journals BOVIDS: A deep learning-based software for pose estimation to evaluate nightly behavior and its application to Common Elands (Tragelaphus oryx) in zoos

2021 ◽  
Author(s):  
Jennifer Gübert ◽  
Max Hahn-Klimroth ◽  
Paul W. Dierkes
Keyword(s):  
Deep Learning ◽  
Small Sample ◽  
Future Research ◽  
Good Opportunity ◽  
Nocturnal Behavior ◽  
Zoo Animals ◽  
Learning Software ◽  
Small Sample Sizes ◽  
Species Specific ◽  
Video Material

Only a few studies on the nocturnal behavior of African ungulates exist so far, with mostly small sample sizes. For a comprehensive understanding of nocturnal behavior, this database needs to be expanded. Zoo animals offer a good opportunity to lay the corresponding foundations. The results can provide clues for the study of wild animals and furthermore contribute to a better understanding of animal welfare and better husbandry conditions in zoos. To tackle this open question, we developed a stand-alone open-source software based on deep learning techniques, named BOVIDS (Behavioral Observations by Videos and Images using a Deep-Learning Software). This software is used to identify ungulates in their enclosure and to determine crucial behavioral poses on video material with an accuracy of 99.4%. A case study on 25 Common Elands (Tragelaphus oryx) out of 5 EAZA zoos with a total of 11,411 hours video material out of 822 nights is conducted, yielding the first detailed description of the nightly behavior of Common Elands. Our results indicate that age and sex are influencing factors on the nocturnal activity budget, the length of behavioral phases as well as the number of phases per behavioral state during the night. Finally, the results suggest the existence of species-specific rhythms that open future research directions.

scholarly journals Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review

Data ◽  
2018 ◽  
Vol 3 (3) ◽  
pp. 28 ◽  
Author(s):  
Kasthurirangan Gopalakrishnan
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
Asset Management ◽  
Network Architecture ◽  
Crack Detection ◽  
Future Research ◽  
Deep Convolutional Neural Networks ◽  
Pavement Distress ◽  
Learning Software

Deep learning, more specifically deep convolutional neural networks, is fast becoming a popular choice for computer vision-based automated pavement distress detection. While pavement image analysis has been extensively researched over the past three decades or so, recent ground-breaking achievements of deep learning algorithms in the areas of machine translation, speech recognition, and computer vision has sparked interest in the application of deep learning to automated detection of distresses in pavement images. This paper provides a narrative review of recently published studies in this field, highlighting the current achievements and challenges. A comparison of the deep learning software frameworks, network architecture, hyper-parameters employed by each study, and crack detection performance is provided, which is expected to provide a good foundation for driving further research on this important topic in the context of smart pavement or asset management systems. The review concludes with potential avenues for future research; especially in the application of deep learning to not only detect, but also characterize the type, extent, and severity of distresses from 2D and 3D pavement images.

Next-generation sequencing (NGS) in an advanced hepatocellular carcinoma (HCC) cohort: Analyses of TP53 and CTNNB1.

2016 ◽  
Vol 34 (4_suppl) ◽  
pp. 286-286
Author(s):  
Robin Kate Kelley ◽  
John Dozier Gordan ◽  
Kimberley Evason ◽  
Paige M. Bracci ◽  
Nancy M. Joseph ◽  
...  
Keyword(s):  
Early Stage ◽  
Small Sample ◽  
Reference Sequence ◽  
Future Research ◽  
Prognostic Impact ◽  
Advanced Hepatocellular Carcinoma ◽  
Advanced Hcc ◽  
Significant Difference ◽  
Small Sample Sizes ◽  
Ctnnb1 Mutation

286 Background: Mutations in TP53 and CTNNB1 are common in early stage HCC resection samples. The frequency and prognostic impact of these mutations in advanced HCC is not known. We conducted this retrospective analysis using a large NGS panel to explore for association between tumor genetics, clinicopathologic features, and prognosis in an advanced HCC cohort. Methods: Eligible cases had diagnosis of unresectable HCC or mixed HCC-cholangiocarcinoma and were enrolled on NCT01008917 or NCT01687673 clinical trials of sorafenib plus temsirolimus with informed consent for specimen banking for future research including genetic testing. Paired tumor and germline (blood) DNA samples were sequenced using a capture-based NGS cancer panel to allow for determination of somatic variants. Analysis was based on the human reference sequence UCSC build hg19. Variants were called using GATK Unified Genotyper software. Somatic, non-synonymous, and exonic calls were curated using COSMIC, cBioPortal, and Pubmed. Results: Cases with HCC (n = 21) and mixed HCC-cholangiocarcinoma (n = 2) comprised the cohort (N = 23). Male/female: 83%/17%. Race: White 56%, Asian 39%. BCLC stage: B 35%, C 65%. Etiology: HBsAg+ 26%, HCV+ 39%. Immune infiltrates ( ≥ 1 on scale 0-3) were present in 7/12 (58%) evaluable tumor samples. TP53 mutations were present in 14/23 (61%, 95% CI: 38.5, 80.0). CTNNB1 mutations were present in 7/23 (30%, 95% CI: 13.2, 52.9). There was no significant difference between HBsAg+ and HCV+. Both TP53 and CTNNB1 mutation were present in 4/23 (17%). CTNNB1 mutation was present in 2/7 (29%) cases with immune infiltrate score ≥ 1, and 1/5 (20%) with score < 1 (not significant). Other mutations and variants will be reported. Conclusions: NGS in this advanced HCC cohort suggests a higher incidence of TP53 and coexisting TP53 plus CTNNB1 mutations than has been reported in early stage HCC which requires confirmation in a larger cohort. There was no clear relationship between these mutations, HCC etiology, or tumor immune infiltrates though interpretation is limited by small sample sizes. Analyses are ongoing to explore for association between TP53 and CTNNB1 mutations and prognosis in this advanced HCC cohort.

scholarly journals Patient's experiences of involuntary hospital admission and treatment: A review of qualitative studies

2007 ◽  
Vol 16 (2) ◽  
pp. 172-178 ◽  
Author(s):  
Christina Katsakou ◽  
Stefan Priebe
Keyword(s):  
Psychiatric Patients ◽  
Small Sample ◽  
Qualitative Studies ◽  
Future Research ◽  
Involuntary Admission ◽  
Involuntary Treatment ◽  
Emerging Issues ◽  
Patient Groups ◽  
Perceived Impact ◽  
Small Sample Sizes

SUMMARYAims - This study aimed to explore psychiatric patients' experiences of involuntary admission and treatment by reviewing qualitative studies. Method - Qualitative studies investigating patients' experiences of involuntary treatment were identified. Relevant databases were searched and authors were contacted. Thematic analysis was applied for the synthesis of emerging issues. Results - Five studies fulfilled the inclusion criteria. The main areas that appear to be of importance are: patients' perceived autonomy and participation in decisions for themselves, their feeling of whether or not they are being cared for and their sense of identity. In these areas both negative and positive consequences from involuntary admission were mentioned. However, methodological weaknesses were also found, such as small sample sizes. Furthermore, it is not described whether these themes are mentioned by all participants as negative and positive aspects of their experience or whether they reflect views supported by distinct groups. Conclusions - Although the perceived impact of involuntary treatment is fairly clearly described, differences between distinct patient groups are not examined. Future research should investigate such differences in order to inform relevant policy decisions for particular groups.Declaration of Interest: None.

scholarly journals Sleep Spindle Characteristics in Children with Neurodevelopmental Disorders and Their Relation to Cognition

2016 ◽  
Vol 2016 ◽  
pp. 1-27 ◽  
Author(s):  
Reut Gruber ◽  
Merrill S. Wise
Keyword(s):  
Cognitive Function ◽  
Neurodevelopmental Disorders ◽  
Sleep Problems ◽  
Small Sample ◽  
Future Research ◽  
Sleep Spindles ◽  
Sleep Spindle ◽  
High Prevalence ◽  
Future Research Directions ◽  
Small Sample Sizes

Empirical evidence indicates that sleep spindles facilitate neuroplasticity and “off-line” processing during sleep, which supports learning, memory consolidation, and intellectual performance. Children with neurodevelopmental disorders (NDDs) exhibit characteristics that may increase both the risk for and vulnerability to abnormal spindle generation. Despite the high prevalence of sleep problems and cognitive deficits in children with NDD, only a few studies have examined the putative association between spindle characteristics and cognitive function. This paper reviews the literature regarding sleep spindle characteristics in children with NDD and their relation to cognition in light of what is known in typically developing children and based on the available evidence regarding children with NDD. We integrate available data, identify gaps in understanding, and recommend future research directions. Collectively, studies are limited by small sample sizes, heterogeneous populations with multiple comorbidities, and nonstandardized methods for collecting and analyzing findings. These limitations notwithstanding, the evidence suggests that future studies should examine associations between sleep spindle characteristics and cognitive function in children with and without NDD, and preliminary findings raise the intriguing question of whether enhancement or manipulation of sleep spindles could improve sleep-dependent memory and other aspects of cognitive function in this population.

scholarly journals Crowdsourcing Neighbourhood Delineations in Wellington, New Zealand

2021 ◽  
Author(s):  
◽  
Hayden Rickard
Keyword(s):  
New Zealand ◽  
Personal Characteristics ◽  
Practical Method ◽  
Small Sample ◽  
Future Research ◽  
Web Based ◽  
Aotearoa New Zealand ◽  
Small Sample Sizes ◽  
Quantitative And Qualitative Methods ◽  
Neighbourhood Perceptions

<p>Neighbourhoods are important in our everyday lives, but physical definitions of neighbourhoods are often ambiguous. Official representations of neighbourhood boundaries used to present geographic outcomes poorly reflect individuals’ perceptions of their neighbourhoods. Existing methods of collecting neighbourhood delineations commonly consist of small sample sizes or stratified surveys on residents of individual neighbourhoods. By reducing effort and potentially increasing response rates, a crowdsourcing approach may be effective in collecting neighbourhood delineations across an entire city.  This thesis presents results from a web-based application used to crowdsource neighbourhood delineations from residents of Wellington City, Aotearoa-New Zealand. Over eight hundred responses were analysed to investigate how personal characteristics impact neighbourhood boundaries, determine areas of shared neighbourhood geographies based on overlapping demarcations, and examine how participants neighbourhood delineations compare to official representations of neighbourhoods. Case studies of a range of geographic features are provided to explore how they impact neighbourhood delineations.  This thesis found transport choices significantly impact perceived neighbourhood area; neighbourhood areas differ markedly in terms of consensus about their boundaries; and there are both similarities and discrepancies between official and perceived neighbourhood boundaries. A variety of geographic features were found to function as different perceptual elements in informing neighbourhood delineations. Crowdsourcing was a practical method to collect neighbourhood perceptions with possible implications for official neighbourhood boundaries. Finally, recommendations for future research aiming to crowdsource neighbourhood delineations were made with a combination of quantitative and qualitative methods being of high value.</p>

scholarly journals Crowdsourcing Neighbourhood Delineations in Wellington, New Zealand

2021 ◽  
Author(s):  
◽  
Hayden Rickard
Keyword(s):  
New Zealand ◽  
Personal Characteristics ◽  
Practical Method ◽  
Small Sample ◽  
Future Research ◽  
Web Based ◽  
Aotearoa New Zealand ◽  
Small Sample Sizes ◽  
Quantitative And Qualitative Methods ◽  
Neighbourhood Perceptions

<p>Neighbourhoods are important in our everyday lives, but physical definitions of neighbourhoods are often ambiguous. Official representations of neighbourhood boundaries used to present geographic outcomes poorly reflect individuals’ perceptions of their neighbourhoods. Existing methods of collecting neighbourhood delineations commonly consist of small sample sizes or stratified surveys on residents of individual neighbourhoods. By reducing effort and potentially increasing response rates, a crowdsourcing approach may be effective in collecting neighbourhood delineations across an entire city.  This thesis presents results from a web-based application used to crowdsource neighbourhood delineations from residents of Wellington City, Aotearoa-New Zealand. Over eight hundred responses were analysed to investigate how personal characteristics impact neighbourhood boundaries, determine areas of shared neighbourhood geographies based on overlapping demarcations, and examine how participants neighbourhood delineations compare to official representations of neighbourhoods. Case studies of a range of geographic features are provided to explore how they impact neighbourhood delineations.  This thesis found transport choices significantly impact perceived neighbourhood area; neighbourhood areas differ markedly in terms of consensus about their boundaries; and there are both similarities and discrepancies between official and perceived neighbourhood boundaries. A variety of geographic features were found to function as different perceptual elements in informing neighbourhood delineations. Crowdsourcing was a practical method to collect neighbourhood perceptions with possible implications for official neighbourhood boundaries. Finally, recommendations for future research aiming to crowdsource neighbourhood delineations were made with a combination of quantitative and qualitative methods being of high value.</p>

scholarly journals Physical interventions for people with more advanced dementia – a scoping review

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Abigail J. Hall ◽  
Samantha Febrey ◽  
Victoria A. Goodwin
Keyword(s):  
Scoping Review ◽  
Data Extraction ◽  
Cost Effective ◽  
Small Sample ◽  
Future Research ◽  
Advanced Dementia ◽  
Physical Decline ◽  
Effective Interventions ◽  
Small Sample Sizes ◽  
Physical Outcomes

Abstract Background Dementia is a neuro-degenerative condition resulting in cognitive and physical decline over time. In the early stages of the condition, physical decline may be slow, but in the later stages, it may become more pronounced. Physical interventions may be employed to try and reduce the physical decline that people experience, yet it is unclear what interventions may be effective. The aim of this study was to explore the breadth and quantity of evidence that exists in relation to the delivery of physical interventions for people with advanced dementia. Methods We undertook a scoping review in order to map the current literature. All types of study design were included in the search in order to gain a comprehensive scope of the literature. We searched a variety of databases from inception until March 2021, focusing on physical interventions. Double screening and data extraction were employed in order to increase the reliability of the results. Results Our review found four studies which focused on physical interventions aimed at improving physical outcomes for people with more advanced dementia. The majority of studies were excluded as their interventions were not specific to people with advanced dementia. The studies that were included incorporated functional activities and, despite small sample sizes, suggested statistically significant improvements in outcomes for people with advanced dementia. Conclusion There is currently limited evidence relating to physical rehabilitation interventions for people with more advanced dementia, however, the evidence we presented suggests potential benefits for physical outcomes. Future research should focus on robust research to determine the most effective and cost-effective interventions that meet the needs of this population.

scholarly journals Side Scan Sonar Shipwreck Image Recognition Algorithm based on Transfer Learning and Deep Learning

2021 ◽  
Vol 290 ◽  
pp. 02020
Author(s):  
Boyu Zhang ◽  
Xiao Wang ◽  
Shudong Li ◽  
Jinghua Yang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Image Recognition ◽  
Correlated Data ◽  
Small Sample ◽  
Side Scan Sonar ◽  
Transfer Training ◽  
Small Sample Sizes

Current underwater shipwreck side scan sonar samples are few and difficult to label. With small sample sizes, their image recognition accuracy with a convolutional neural network model is low. In this study, we proposed an image recognition method for shipwreck side scan sonar that combines transfer learning with deep learning. In the non-transfer learning, shipwreck sonar sample data were used to train the network, and the results were saved as the control group. The weakly correlated data were applied to train the network, then the network parameters were transferred to the new network, and then the shipwreck sonar data was used for training. These steps were repeated using strongly correlated data. Experiments were carried out on Lenet-5, AlexNet, GoogLeNet, ResNet and VGG networks. Without transfer learning, the highest accuracy was obtained on the ResNet network (86.27%). Using weakly correlated data for transfer training, the highest accuracy was on the VGG network (92.16%). Using strongly correlated data for transfer training, the highest accuracy was also on the VGG network (98.04%). In all network architectures, transfer learning improved the correct recognition rate of convolutional neural network models. Experiments show that transfer learning combined with deep learning improves the accuracy and generalization of the convolutional neural network in the case of small sample sizes.

scholarly journals Spatial-Aware Network for Hyperspectral Image Classification

2021 ◽  
Vol 13 (16) ◽  
pp. 3232
Author(s):  
Yantao Wei ◽  
Yicong Zhou
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
Hyperspectral Image ◽  
Main Idea ◽  
Small Sample ◽  
Hierarchical Architecture ◽  
Edge Preserving ◽  
Side Window ◽  
Training Samples ◽  
Small Sample Sizes

Deep learning is now receiving widespread attention in hyperspectral image (HSI) classification. However, due to the imbalance between a huge number of weights and limited training samples, many problems and difficulties have arisen from the use of deep learning methods in HSI classification. To handle this issue, an efficient deep learning-based HSI classification method, namely, spatial-aware network (SANet) has been proposed in this paper. The main idea of SANet is to exploit discriminative spectral-spatial features by incorporating prior domain knowledge into the deep architecture, where edge-preserving side window filters are used as the convolution kernels. Thus, SANet has a small number of parameters to optimize. This makes it fit for small sample sizes. Furthermore, SANet is able not only to aware local spatial structures using side window filtering framework, but also to learn discriminative features making use of the hierarchical architecture and limited label information. The experimental results on four widely used HSI data sets demonstrate that our proposed SANet significantly outperforms many state-of-the-art approaches when only a small number of training samples are available.

scholarly journals Resting heart rate and psychopathy: Findings from the Add Health Survey

2017 ◽  
Author(s):  
Nicholas Kavish ◽  
Q. John Fu ◽  
Michael G. Vaughn ◽  
Zhengmin Qian ◽  
Brian B. Boutwell
Keyword(s):  
Heart Rate ◽  
Small Body ◽  
Psychopathic Traits ◽  
Small Sample ◽  
Future Research ◽  
Alternative Measure ◽  
Resting Heart Rate ◽  
Nationally Representative ◽  
Small Sample Sizes ◽  
The Relationship

AbstractDespite the prior linkages of low resting heart rate to antisocial behavior broadly defined, less work has been done examining possible associations between heart rate to psychopathic traits. The small body of research on the topic that has been conducted so far seems to suggest an inverse relationship between the two constructs. A smaller number of studies have found the opposite result, however, and some of the previous studies have been limited by small sample sizes and unrepresentative samples. The current study attempts to help clarify the relationship between resting heart rate and psychopathic traits in a large, nationally representative sample (analytical N ranged from 14,173-14,220) using an alternative measure of psychopathic traits that is less focused on antisocial processes, and rooted in personality traits. No significant relationship between heart rate and psychopathic traits, or heart rate and a measure of cold heartedness, was found after controlling for age, sex, and race. Implications of the findings, study limitations, and directions for future research are discussed.

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