Tag: nevin manimala

PLoS One. 2021 Aug 30;16(8):e0250332. doi: 10.1371/journal.pone.0250332. eCollection 2021.

ABSTRACT

This study reports comprehensive clinical pathology data for hematology, serum, and plasma biochemistry reference intervals for 174 apparently healthy common bottlenose dolphins (Tursiops truncatus) and reference values for 27 Indo-Pacific bottlenose dolphins (Tursiops aduncus), 13 beluga whales (Delphinapterus leucas), and 6 Pacific white-sided dolphins (Lagenorhynchus obliquidens) in zoos and aquariums accredited by the Alliance for Marine Mammal Parks and Aquariums and the Association of Zoos & Aquariums. Blood samples were collected as part of a larger study titled “Towards understanding the welfare of cetaceans in zoos and aquariums” (colloquially called the Cetacean Welfare Study). Two blood samples were collected following a standardized protocol, and two veterinarian examinations were conducted approximately six months apart between July to November 2018 and January to April 2019. Least square means, standard deviations, and 95% confidence intervals were calculated for hematology, serum, and plasma biochemical variables. Comparisons by age, gender, and month revealed statistically significant differences (p < 0.01) for several variables. Reference intervals and values were generated for samples tested at two laboratories for up to 56 hematologic, serum, and plasma biochemical variables. To apply these data, ZooPhysioTrak, an iOS mobile software application, was developed to provide a new resource for cetacean management. ZooPhysioTrak provides species-specific reference intervals and values based on user inputs of individual demographic and sample information. These data provide a baseline from which to compare hematological, serum, and plasma biochemical values in cetaceans in zoos and aquariums.

PMID:34460864 | DOI:10.1371/journal.pone.0250332

PLoS One. 2021 Aug 30;16(8):e0241512. doi: 10.1371/journal.pone.0241512. eCollection 2021.

ABSTRACT

BACKGROUND: Tobacco policies, including clean indoor air laws and cigarette taxes, increase smoking cessation in part by stimulating the use of cessation treatments. We explored whether the associations between tobacco policies and treatment use varies across sociodemographic groups.

METHODS: We used data from 62,165 U.S. adult participants in the 2003 and 2010/11 Tobacco Use Supplement to the Current Population Survey (TUS-CPS) who reported smoking cigarettes during the past-year. We built on prior structural equation models used to quantify the degree to which smoking cessation treatment use (prescription medications, nicotine replacement therapy, counseling/support groups, quitlines, and internet resources) mediated the association between clean indoor air laws, cigarette excise taxes, and recent smoking cessation. In the current study, we added selected moderators to each model to investigate whether associations between tobacco polices and smoking cessation treatment use varied by sex, race/ethnicity, education, income, and health insurance status.

RESULTS: Associations between clean indoor air laws and the use of prescription medication and nicotine replacement therapies varied significantly between racial/ethnic, age, and education groups in 2003. However, none of these moderation effects remained significant in 2010/11. Higher cigarette excise taxes in 2010/2011 were associated with higher odds of using counseling among older adults and higher odds of using prescription medications among younger adults. No other moderator reached statistical significance. Smoking cessation treatments did not mediate the effect of taxes on smoking cessation in 2003 and were not included in these analyses.

CONCLUSIONS: Sociodemographic differences in associations between clean indoor air laws and smoking cessation treatment use have decreased from 2003 to 2010/11. In most cases, policies appear to stimulate smoking cessation treatment use similarly across varied sociodemographic groups.

PMID:34460821 | DOI:10.1371/journal.pone.0241512

PLoS Genet. 2021 Aug 30;17(8):e1009713. doi: 10.1371/journal.pgen.1009713. Online ahead of print.

ABSTRACT

Genome-wide association studies (GWASs) have uncovered a wealth of associations between common variants and human phenotypes. Here, we present an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link with biological mechanisms. Our framework incorporates multitrait association mapping along with an investigation of the breakdown of genetic associations into clusters of variants harboring similar multitrait association profiles. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster assignment and the success of drug targets in randomized controlled trials.

PMID:34460823 | DOI:10.1371/journal.pgen.1009713

J Imaging. 2021 Aug 20;7(8):155. doi: 10.3390/jimaging7080155.

ABSTRACT

Medical image analysis research requires medical image datasets. Nevertheless, due to various impediments, researchers have been described as “data starved”. We hypothesize that implicit evolving community standards require researchers to use ever-growing datasets. In Phase I of this research, we scanned the MICCAI (Medical Image Computing and Computer-Assisted Intervention) conference proceedings from 2011 to 2018. We identified 907 papers involving human MRI, CT or fMRI datasets and extracted their sizes. The median dataset size had grown by 3-10 times from 2011 to 2018, depending on imaging modality. Statistical analysis revealed exponential growth of the geometric mean dataset size with an annual growth of 21% for MRI, 24% for CT and 31% for fMRI. Thereupon, we had issued a forecast for dataset sizes in MICCAI 2019 well before the conference. In Phase II of this research, we examined the MICCAI 2019 proceedings and analyzed 308 relevant papers. The MICCAI 2019 statistics compare well with the forecast. The revised annual growth rates of the geometric mean dataset size are 27% for MRI, 30% for CT and 32% for fMRI. We predict the respective dataset sizes in the MICCAI 2020 conference (that we have not yet analyzed) and the future MICCAI 2021 conference.

PMID:34460791 | DOI:10.3390/jimaging7080155

Rev Invest Clin. 2021 Aug 30. doi: 10.24875/RIC.21000120. Online ahead of print.

ABSTRACT

BACKGROUND: Hospital bed saturation has been one of the problems to solve during the SARS-CoV-2 pandemic. However, not every patient who is admitted requires close monitoring or specific therapeutics. Mild cases could be managed in the outpatient setting.

OBJECTIVE: Our study aimed to analyze the accuracy of the oxygen saturation/respiratory rate (sat/RR) index, NEWS2, CURB65, and quick Sequential Organ Failure Assessment (qSOFA) scores to predict supplemental oxygen requirement and prolonged hospital stay in patients with mild coronavirus disease 2019 (COVID-19).

METHODS: A prospective cohort study in an academic medical center. We compared the values of these scores according to the occurrence or not of each outcome. When differences between groups were statistically significant, the discriminatory capacity of the score for that outcome was analyzed.

RESULTS: We included 271 patients. Of them, 11.07% required supplemental oxygen, showing significantly higher values of NEWS2 score and qSOFA score, and lower values of Sat/RR index. About 38% presented prolonged hospital stay, with significantly higher values of NEWS2 score and lower values of sat/RR index. The ROC curve area under the curve (AUC) of sat/RR index to discriminate the requirement of supplemental oxygen was 0.72 (CI 95% 0.61-0.84). The ROC curve of NEWS2 and qSOFA for the same outcome was 0.75 (95% [95% CI 0.65-0.85]) and 0.66 (95% CI 0.57-0.76), respectively. The ability of the Sat/RR index to discriminate the requirement of prolonged hospitalization showed an AUC of 0.67 (95% [95% CI 0.60- 0.73]). The NEWS2 score showed an AUC of 0.63 (CI 95% 0.56-0.70) for the same outcome.

CONCLUSIONS: sat/RR index and NEWS2 score have a good capacity to discriminate patients at risk of clinical worsening, being the Sat/RR index simpler and easier to calculate.

PMID:34460808 | DOI:10.24875/RIC.21000120

J Imaging. 2020 Sep 17;6(9):97. doi: 10.3390/jimaging6090097.

ABSTRACT

Deep learning (DL) convolutional neural networks (CNNs) have been rapidly adapted in very high spatial resolution (VHSR) satellite image analysis. DLCNN-based computer visions (CV) applications primarily aim for everyday object detection from standard red, green, blue (RGB) imagery, while earth science remote sensing applications focus on geo object detection and classification from multispectral (MS) imagery. MS imagery includes RGB and narrow spectral channels from near- and/or middle-infrared regions of reflectance spectra. The central objective of this exploratory study is to understand to what degree MS band statistics govern DLCNN model predictions. We scaffold our analysis on a case study that uses Arctic tundra permafrost landform features called ice-wedge polygons (IWPs) as candidate geo objects. We choose Mask RCNN as the DLCNN architecture to detect IWPs from eight-band Worldview-02 VHSR satellite imagery. A systematic experiment was designed to understand the impact on choosing the optimal three-band combination in model prediction. We tasked five cohorts of three-band combinations coupled with statistical measures to gauge the spectral variability of input MS bands. The candidate scenes produced high model detection accuracies for the F1 score, ranging between 0.89 to 0.95, for two different band combinations (coastal blue, blue, green (1,2,3) and green, yellow, red (3,4,5)). The mapping workflow discerned the IWPs by exhibiting low random and systematic error in the order of 0.17-0.19 and 0.20-0.21, respectively, for band combinations (1,2,3). Results suggest that the prediction accuracy of the Mask-RCNN model is significantly influenced by the input MS bands. Overall, our findings accentuate the importance of considering the image statistics of input MS bands and careful selection of optimal bands for DLCNN predictions when DLCNN architectures are restricted to three spectral channels.

PMID:34460754 | DOI:10.3390/jimaging6090097

J Imaging. 2021 Jul 30;7(8):128. doi: 10.3390/jimaging7080128.

ABSTRACT

To properly contrast the Deepfake phenomenon the need to design new Deepfake detection algorithms arises; the misuse of this formidable A.I. technology brings serious consequences in the private life of every involved person. State-of-the-art proliferates with solutions using deep neural networks to detect a fake multimedia content but unfortunately these algorithms appear to be neither generalizable nor explainable. However, traces left by Generative Adversarial Network (GAN) engines during the creation of the Deepfakes can be detected by analyzing ad-hoc frequencies. For this reason, in this paper we propose a new pipeline able to detect the so-called GAN Specific Frequencies (GSF) representing a unique fingerprint of the different generative architectures. By employing Discrete Cosine Transform (DCT), anomalous frequencies were detected. The β statistics inferred by the AC coefficients distribution have been the key to recognize GAN-engine generated data. Robustness tests were also carried out in order to demonstrate the effectiveness of the technique using different attacks on images such as JPEG Compression, mirroring, rotation, scaling, addition of random sized rectangles. Experiments demonstrated that the method is innovative, exceeds the state of the art and also give many insights in terms of explainability.

PMID:34460764 | DOI:10.3390/jimaging7080128

J Imaging. 2021 Mar 12;7(3):53. doi: 10.3390/jimaging7030053.

ABSTRACT

Digital images represent the primary tool for diagnostics and documentation of the state of preservation of artifacts. Today the interpretive filters that allow one to characterize information and communicate it are extremely subjective. Our research goal is to study a quantitative analysis methodology to facilitate and semi-automate the recognition and polygonization of areas corresponding to the characteristics searched. To this end, several algorithms have been tested that allow for separating the characteristics and creating binary masks to be statistically analyzed and polygonized. Since our methodology aims to offer a conservator-restorer model to obtain useful graphic documentation in a short time that is usable for design and statistical purposes, this process has been implemented in a single Geographic Information Systems (GIS) application.

PMID:34460709 | DOI:10.3390/jimaging7030053

J Imaging. 2020 Aug 24;6(9):84. doi: 10.3390/jimaging6090084.

ABSTRACT

We present a numerical illumination model to calculate direct as well as diffuse or Hapke scattered radiation scenarios on arbitrary planetary surfaces. This includes small body surfaces such as main belt asteroids as well as e.g., the lunar surface. The model is based on the ray tracing method. This method is not restricted to spherical or ellipsoidal shapes but digital terrain data of arbitrary spatial resolution can be fed into the model. Solar radiation is the source of direct radiation, wavelength-dependent effects (e.g. albedo) can be accounted for. Mutual illumination of individual bodies in implemented (e.g. in binary or multiple systems) as well as self-illumination (e.g. crater floors by crater walls) by diffuse or Hapke radiation. The model is validated by statistical methods. A χ2 test is utilized to compare simulated images with DAWN images acquired during the survey phase at small body 4 Vesta and to successfully prove its validity.

PMID:34460741 | DOI:10.3390/jimaging6090084

J Imaging. 2021 Apr 28;7(5):81. doi: 10.3390/jimaging7050081.

ABSTRACT

The COVID-19 pandemic has been deemed a global health pandemic. The early detection of COVID-19 is key to combating its outbreak and could help bring this pandemic to an end. One of the biggest challenges in combating COVID-19 is accurate testing for the disease. Utilizing the power of Convolutional Neural Networks (CNNs) to detect COVID-19 from chest X-ray images can help radiologists compare and validate their results with an automated system. In this paper, we propose a carefully designed network, dubbed CORONA-Net, that can accurately detect COVID-19 from chest X-ray images. CORONA-Net is divided into two phases: (1) The reinitialization phase and (2) the classification phase. In the reinitialization phase, the network consists of encoder and decoder networks. The objective of this phase is to train and initialize the encoder and decoder networks by a distribution that comes out of medical images. In the classification phase, the decoder network is removed from CORONA-Net, and the encoder network acts as a backbone network to fine-tune the classification phase based on the learned weights from the reinitialization phase. Extensive experiments were performed on a publicly available dataset, COVIDx, and the results show that CORONA-Net significantly outperforms the current state-of-the-art networks with an overall accuracy of 95.84%.

PMID:34460677 | DOI:10.3390/jimaging7050081