Tag: statistics

PLoS One. 2021 Oct 8;16(10):e0257962. doi: 10.1371/journal.pone.0257962. eCollection 2021.

ABSTRACT

The objective of the paper is to diagnose organisational culture of selected universities and analyse its impact on the innovation processes within them. The subject matter of the study was organisational culture and innovation at universities. The subjects were four selected universities in Poland, Austria, Germany, and Ukraine. The paper provided a definition of organisational culture and its typology. It further discussed the organisational culture of universities and the relationships between organisational culture and innovativeness. The literature review provided foundations for building a model for the formation of a type of organisational culture at universities that is innovation-friendly, which is the added value of the paper. It offers actions worth taking to shape innovation-friendly culture at universities. It is particularly important during difficult time of changing labour market, when universities greatly impact the attitudes of young people. The knowledge of how to shape innovation-friendly organisational culture at universities is necessary for academia to profile future employees in times of continuous changes. To investigate the relationship between organisational culture and the innovativeness of universities, we designed an original survey questionnaire [S1 File]. Organisational culture was diagnosed with the Organizational Culture Assessment Instrument by K.S. Cameron and R.E. Quinn. The analyses were conducted in Dell Statistica v. 13.1 (StatSoft Polska). We normalised data from the Likert rating scale using Kaufman’s and Rousseeuw’s formula. We used Spearman’s correlation coefficient and Kendall’s W to calculate correlations. The research shows that the investigated Polish and Austrian universities are dominated by hierarchy and market cultures. On the other hand, the German and Ukrainian universities host all cultures, but clan and adhocracy dominate there. Moreover, the analyses demonstrated that although the adhocracy culture was the least visible in the investigated organisations, it contributes to university innovativeness the most. The conclusions were used to build a model for promoting innovation-friendly organisational culture at universities. The model contains answers to the research questions. In addition, it offers guidelines for shaping organisational culture to bolster innovation at universities. The research identified relationships between organisational culture and university innovativeness and components that create innovation opportunities at universities as its contribution to management theory. When applied in practice, the guidelines can help form the university’s organisational culture bottom-up.

PMID:34624041 | DOI:10.1371/journal.pone.0257962

PLoS One. 2021 Oct 8;16(10):e0258347. doi: 10.1371/journal.pone.0258347. eCollection 2021.

ABSTRACT

INTRODUCTION: Breastfeeding is one of the most effective ways to ensure infant health and survival. Inadequate breastfeeding practices, and knowledge among adolescent mothers have led to unprecedented infant and child morbidity and mortality. Given, the high global prevalence of adolescent mothers it is imperative to understand how the knowledge of breastfeeding practices operates among adolescent girls across different socio-economic settings.

MATERIALS & METHODS: Data was carried out from Understanding the Lives of Adolescents and Young Adults (UDAYA) survey, conducted in 2015-16. Descriptive statistics along with bivariate analysis was done to examine the preliminary results. For analysing the association between the binary outcome variable and other explanatory variables, binary logistic regression method was used. The explanatory variables were educational status of the respondent, media exposure, working status, ever pregnant status (only for married adolescent girls), sex and age of the household head, educational status of the head of the household, caste, religion, wealth index, residence and states.

RESULTS: About 42%, 50%, and 42% of married adolescent girls had knowledge of immediate breastfeeding, yellowish milk, and exclusive breastfeeding respectively. The odds of knowledge about immediate breastfeeding [married-AOR: 1.57; CI: 1.09-2.28 and unmarried-AOR: 1.30; CI: 1.08-1.55], yellowish milk feeding [married-AOR: 2.09; CI: 1.46-3.01 and unmarried-AOR: 1.39; CI: 1.17-1.66], and exclusive breastfeeding [married-AOR: 1.74; CI: 1.2-2.52 and unmarried-AOR: 1.46; CI: 1.22-1.76] were significantly more among adolescent girls aged 19 years old compared to 15 years old girls. Adolescent married and unmarried girls with 10 & above years of schooling were 1.82 times [AOR: 1.82; CI: 1.52-2.18] and 2.69 times [AOR: 2.69; CI: 2.08-3.47] more likely to have knowledge about immediate breastfeeding, 1.74 times [AOR: 1.74; CI: 1.45-2.09] and 2.10 times [AOR: 2.10; CI: 1.68-2.62] more likely to have knowledge about yellowish milk feeding, and 3.13 times [AOR: 3.13; CI: 2.6-3.78] and 3.87 times [AOR: 3.87; CI: 2.95-5.08] more likely to have knowledge about exclusive breastfeeding respectively than girls with no schooling.

CONCLUSION: Breastfeeding practices and interpersonal counselling from elders in the household should be encouraged. Ongoing breastfeeding promotion programs of the government should promote high education of adolescent girls. Mass media interventions should be encouraged.

PMID:34624069 | DOI:10.1371/journal.pone.0258347

PLoS One. 2021 Oct 8;16(10):e0258215. doi: 10.1371/journal.pone.0258215. eCollection 2021.

ABSTRACT

Agricultural index insurance contracts increasingly use remote sensing data to estimate losses and determine indemnity payouts. Index insurance contracts inevitably make errors, failing to detect losses that occur and issuing payments when no losses occur. The quality of these contracts and the indices on which they are based, need to be evaluated to assess their fitness as insurance, and to provide a guide to choosing the index that best protects the insured. In the remote sensing literature, indices are often evaluated with generic model evaluation statistics such as R2 or Root Mean Square Error that do not directly consider the effect of errors on the quality of the insurance contract. Economic analysis suggests using measures that capture the impact of insurance on the expected economic well-being of the insured. To bridge the gap between the remote sensing and economic perspectives, we adopt a standard economic measure of expected well-being and transform it into a Relative Insurance Benefit (RIB) metric. RIB expresses the welfare benefits derived from an index insurance contract relative to a hypothetical contract that perfectly measures losses. RIB takes on its maximal value of one when the index contract offers the same economic benefits as the perfect contract. When it achieves none of the benefits of insurance it takes on a value of zero, and becomes negative if the contract leaves the insured worse off than having no insurance. Part of our contribution is to decompose this economic well-being measure into an asymmetric loss function. We also argue that the expected well-being measure we use has advantages over other economic measures for the normative purpose of insurance quality ascertainment. Finally, we illustrate the use of the RIB measure with a case study of potential livestock insurance contracts in Northern Kenya. We compared 24 indices that were made with 4 different statistical models and 3 remote sensing data sources. RIB for these indices ranged from 0.09 to 0.5, and R2 ranged from 0.2 to 0.51. While RIB and R2 were correlated, the model with the highest RIB did not have the highest R2. Our findings suggest that, when designing and evaluating an index insurance program, it is useful to separately consider the quality of a remote sensing-based index with a metric like the RIB instead of a generic goodness-of-fit metric.

PMID:34624022 | DOI:10.1371/journal.pone.0258215

PLoS One. 2021 Oct 8;16(10):e0256868. doi: 10.1371/journal.pone.0256868. eCollection 2021.

ABSTRACT

Ecological Niche Modeling is a process by which spatiotemporal, climatic, and environmental data are analyzed to predict the distribution of an organism. Using this process, an ensemble ecological niche model for West Nile virus habitat prediction in the state of Florida was developed. This model was created through the weighted averaging of three separate machine learning models-boosted regression tree, random forest, and maximum entropy-developed for this study using sentinel chicken surveillance and remote sensing data. Variable importance differed among the models. The highest variable permutation value included mean dewpoint temperature for the boosted regression tree model, mean temperature for the random forest model, and wetlands focal statistics for the maximum entropy mode. Model validation resulted in area under the receiver curve predictive values ranging from good [0.8728 (95% CI 0.8422-0.8986)] for the maximum entropy model to excellent [0.9996 (95% CI 0.9988-1.0000)] for random forest model, with the ensemble model predictive value also in the excellent range [0.9939 (95% CI 0.9800-0.9979]. This model should allow mosquito control districts to optimize West Nile virus surveillance, improving detection and allowing for a faster, targeted response to reduce West Nile virus transmission potential.

PMID:34624026 | DOI:10.1371/journal.pone.0256868

PLoS Comput Biol. 2021 Oct 8;17(10):e1009451. doi: 10.1371/journal.pcbi.1009451. Online ahead of print.

ABSTRACT

Recent advances in two-photon fluorescence microscopy (2PM) have allowed large scale imaging and analysis of blood vessel networks in living mice. However, extracting network graphs and vector representations for the dense capillary bed remains a bottleneck in many applications. Vascular vectorization is algorithmically difficult because blood vessels have many shapes and sizes, the samples are often unevenly illuminated, and large image volumes are required to achieve good statistical power. State-of-the-art, three-dimensional, vascular vectorization approaches often require a segmented (binary) image, relying on manual or supervised-machine annotation. Therefore, voxel-by-voxel image segmentation is biased by the human annotator or trainer. Furthermore, segmented images oftentimes require remedial morphological filtering before skeletonization or vectorization. To address these limitations, we present a vectorization method to extract vascular objects directly from unsegmented images without the need for machine learning or training. The Segmentation-Less, Automated, Vascular Vectorization (SLAVV) source code in MATLAB is openly available on GitHub. This novel method uses simple models of vascular anatomy, efficient linear filtering, and vector extraction algorithms to remove the image segmentation requirement, replacing it with manual or automated vector classification. Semi-automated SLAVV is demonstrated on three in vivo 2PM image volumes of microvascular networks (capillaries, arterioles and venules) in the mouse cortex. Vectorization performance is proven robust to the choice of plasma- or endothelial-labeled contrast, and processing costs are shown to scale with input image volume. Fully-automated SLAVV performance is evaluated on simulated 2PM images of varying quality all based on the large (1.4×0.9×0.6 mm3 and 1.6×108 voxel) input image. Vascular statistics of interest (e.g. volume fraction, surface area density) calculated from automatically vectorized images show greater robustness to image quality than those calculated from intensity-thresholded images.

PMID:34624013 | DOI:10.1371/journal.pcbi.1009451

PLoS Comput Biol. 2021 Oct 8;17(10):e1009432. doi: 10.1371/journal.pcbi.1009432. Online ahead of print.

ABSTRACT

Measuring the activity of neuronal populations with calcium imaging can capture emergent functional properties of neuronal circuits with single cell resolution. However, the motion of freely behaving animals, together with the intermittent detectability of calcium sensors, can hinder automatic monitoring of neuronal activity and their subsequent functional characterization. We report the development and open-source implementation of a multi-step cellular tracking algorithm (Elastic Motion Correction and Concatenation or EMC2) that compensates for the intermittent disappearance of moving neurons by integrating local deformation information from detectable neurons. We demonstrate the accuracy and versatility of our algorithm using calcium imaging data from two-photon volumetric microscopy in visual cortex of awake mice, and from confocal microscopy in behaving Hydra, which experiences major body deformation during its contractions. We quantify the performance of our algorithm using ground truth manual tracking of neurons, along with synthetic time-lapse sequences, covering a wide range of particle motions and detectability parameters. As a demonstration of the utility of the algorithm, we monitor for several days calcium activity of the same neurons in layer 2/3 of mouse visual cortex in vivo, finding significant turnover within the active neurons across days, with only few neurons that remained active across days. Also, combining automatic tracking of single neuron activity with statistical clustering, we characterize and map neuronal ensembles in behaving Hydra, finding three major non-overlapping ensembles of neurons (CB, RP1 and RP2) whose activity correlates with contractions and elongations. Our results show that the EMC2 algorithm can be used as a robust and versatile platform for neuronal tracking in behaving animals.

PMID:34624016 | DOI:10.1371/journal.pcbi.1009432

JMIR Med Inform. 2021 Sep 10. doi: 10.2196/30467. Online ahead of print.

ABSTRACT

BACKGROUND: In 2020, the COVID-19 pandemic put the world in crisis on both physical and psychological health. Simultaneously, a myriad of unverified information flowed on social media and online outlets. The situation was so severe that the World Health Organization identified it as an infodemic in February 2020.

OBJECTIVE: We want to study the propagation patterns and textual transformation of COVID-19 related rumors on a closed-platform.

METHODS: We obtained a dataset of suspicious text messages collected on Taiwan’s most popular instant messaging platform, LINE, between January and July 2020. We proposed a Classification-based Clustering algorithm that could efficiently cluster messages into groups, with each group representing a rumor. For ease of understanding, a group is referred to as a “rumor group”. Messages in a rumor group could be identical or within limited textual differences with one another. Therefore, each message in a rumor group is a form of the rumor.

RESULTS: A total of 936 rumor groups with at least 10 messages were discovered among 114,124 text messages collected from LINE. Among 936 rumors, 396 (42.3%) were related to COVID-19. Of 396 COVID-related rumors, 134 (33.8%) had been fact-checked by IFCN-certified agencies in Taiwan to be false or misleading. Studying the prevalence of Simplified Chinese characters or phrases that originated in China in the messages, we found that COVID-related messages, compared to non-COVID-related messages, were more likely to have been written by non-Taiwanese. The association was statistically significant with p < .01 by the chi-squared independence test. The qualitative investigations of the 3 most popular COVID-19 rumors revealed that key authoritative figures, mostly medical personnel, were often misquoted in the messages. In addition, these rumors resurfaced multiple times after being fact-checked, usually preceded by major societal events or textual transformations.

CONCLUSIONS: To fight infodemic, it is crucial that we first understand why and how a rumor becomes popular. While social media gives rise to an unprecedented number of unverified rumors, it also provides a unique opportunity for us to study rumor propagations and the interactions with society. Therefore, we must put more effort in the areas.

PMID:34623954 | DOI:10.2196/30467

J Occup Environ Med. 2021 Oct 7. doi: 10.1097/JOM.0000000000002411. Online ahead of print.

ABSTRACT

OBJECTIVE: There is a scarcity of data on the impact of the pandemic in farmers.

METHODS: Cross-sectional survey of certified organic producers through a 28-item self-reported paper or electronic survey. Analysis included descriptive statistics, Cronbach α to measure the internal consistency of a six-item prevention scale, and correlation and regression analyses.

RESULTS: A total of 344 records were computed. Infection rate among producers was 6.4%. Sex and farm size were the most statistically significant predictors of prevention behaviors. Women reported more use of prevention methods (β = 0.333, P < 0.001) and those with 50 or more certified organic acres reporting less use of prevention methods (β = -0.228, P < 0.001). Mask wearing was significantly related to lower COVID-19 prevalence.

CONCLUSIONS: Determining prevalence and understanding how farmers follow prevention behaviors is essential for health care and public health interventions and policies.

PMID:34623976 | DOI:10.1097/JOM.0000000000002411

Sci Total Environ. 2021 Sep 7;804:150151. doi: 10.1016/j.scitotenv.2021.150151. Online ahead of print.

ABSTRACT

We measured SARS-CoV-2 RNA load in raw wastewater in Attica, Greece, by RT-qPCR for the environmental surveillance of COVID-19 for 6 months. The lag between RNA load and pandemic indicators (COVID-19 hospital and intensive care unit (ICU) admissions) was calculated using a grid search. Our results showed that RNA load in raw wastewater is a leading indicator of positive COVID-19 cases, new hospitalization and admission into ICUs by 5, 8 and 9 days, respectively. Modelling techniques based on distributed/fixed lag modelling, linear regression and artificial neural networks were utilized to build relationships between SARS-CoV-2 RNA load in wastewater and pandemic health indicators. SARS-CoV-2 mutation analysis in wastewater during the third pandemic wave revealed that the alpha-variant was dominant. Our results demonstrate that clinical and environmental surveillance data can be combined to create robust models to study the on-going COVID-19 infection dynamics and provide an early warning for increased hospital admissions.

PMID:34623953 | DOI:10.1016/j.scitotenv.2021.150151

Cancer Control. 2021 Jan-Dec;28:10732748211045275. doi: 10.1177/10732748211045275.

ABSTRACT

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has overwhelmed the capacity of healthcare systems worldwide. Cancer patients, in particular, are vulnerable and oncology departments drastically needed to modify their care systems and established new priorities. We evaluated the impact of SARS-CoV-2 on the activity of a single cancer center.

METHODS: We performed a retrospective analysis of (i) volumes of oncological activities (2020 vs 2019), (ii) patients’ perception rate of the preventive measures, (iii) patients’ SARS-CoV-2 infections, clinical signs thereof, and (iv) new diagnoses made during the SARS-CoV-2 pandemic.

RESULTS: As compared with a similar time frame in 2019, the overall activity in total numbers of outpatient chemotherapy administrations and specialist visits was not statistically different (P = .961 and P = .252), while inpatient admissions decreased for both medical oncology and thoracic oncology (18% (P = .0018) and 44% (P < .0001), respectively). Cancer diagnosis plummeted (-34%), but no stage shift could be demonstrated.Acceptance and adoption of hygienic measures was high, as measured by a targeted questionnaire (>85%). However, only 46.2% of responding patients regarded telemedicine, although widely deployed, as an efficient surrogate to a consultation.Thirty-three patients developed SARS-CoV-2, 27 were hospitalized, and 11 died within this time frame. These infected patients were younger, current smokers, and suffered more comorbidities.

CONCLUSIONS: This retrospective cohort analysis adds to the evidence that continuation of active cancer therapy and specialist visits is feasible and safe with the implementation of telemedicine. These data further confirm the impact of SARS-CoV-2 on cancer care management, cancer diagnosis, and impact of infection on cancer patients.

PMID:34623943 | DOI:10.1177/10732748211045275