Category: Statistics

PLoS One. 2022 Jan 24;17(1):e0262143. doi: 10.1371/journal.pone.0262143. eCollection 2022.

ABSTRACT

Paint technology, namely paint preparation and application procedures, is an important aspect of painting traditions. With the expansion of archaeometric studies and in situ non-destructive analytical methods, a renewal of technological studies is being observed in rock art. In situ analyses have several limitations that are widely discussed in the literature, however. It is not yet clear whether they provide accurate information on paint technology, except under certain conditions. Here, we evaluated digital microscopic and pXRF in situ analyses for the characterisation of a large set of red and yellow paintings from the El Castillo cave, Cantabria, Spain. We have set experiments and used statistical methods to identify differences between paint components and determine factors impacting pXRF measurements. We found that the compositional heterogeneity of the paintings’ environment, especially variations in secondary deposits, was responsible for most of the differences observed between the pXRF signals recorded on the paintings. We concluded that the El Castillo cave environment is not suitable for non-destructive technological studies, but that more favourable contexts might exist. Following previous works and our own results, we advocate a combination of both in situ and laboratory invasive analyses for the study of paint composition and paint technology. Our research protocol, based on the comparison of rock paintings, their substrate, experimental paintings and Fe-normalisation of the signals can improve the reliability of pXRF results. We also propose to include more systematic characterisation of rock wall heterogeneity and the use of microscopic analyses in non-destructive approaches.

PMID:35073338 | DOI:10.1371/journal.pone.0262143

PLoS One. 2022 Jan 24;17(1):e0262545. doi: 10.1371/journal.pone.0262545. eCollection 2022.

ABSTRACT

Insight into the metabolic biosignature of tuberculosis (TB) may inform clinical care, reduce adverse effects, and facilitate metabolism-informed therapeutic development. However, studies often yield inconsistent findings regarding the metabolic profiles of TB. Herein, we conducted an untargeted metabolomics study using plasma from 63 Korean TB patients and 50 controls. Metabolic features were integrated with the data of another cohort from China (35 TB patients and 35 controls) for a global functional meta-analysis. Specifically, all features were matched to a known biological network to identify potential endogenous metabolites. Next, a pathway-level gene set enrichment analysis-based analysis was conducted for each study and the resulting p-values from the pathways of two studies were combined. The meta-analysis revealed both known metabolic alterations and novel processes. For instance, retinol metabolism and cholecalciferol metabolism, which are associated with TB risk and outcome, were altered in plasma from TB patients; proinflammatory lipid mediators were significantly enriched. Furthermore, metabolic processes linked to the innate immune responses and possible interactions between the host and the bacillus showed altered signals. In conclusion, our proof-of-concept study indicated that a pathway-level meta-analysis directly from metabolic features enables accurate interpretation of TB molecular profiles.

PMID:35073339 | DOI:10.1371/journal.pone.0262545

PLoS Negl Trop Dis. 2022 Jan 24;16(1):e0010071. doi: 10.1371/journal.pntd.0010071. Online ahead of print.

ABSTRACT

The dengue virus affects millions of people every year worldwide, causing large epidemic outbreaks that disrupt people’s lives and severely strain healthcare systems. In the absence of a reliable vaccine against dengue or an effective treatment to manage the illness in humans, most efforts to combat dengue infections have focused on preventing its vectors, mainly the Aedes aegypti mosquito, from flourishing across the world. These mosquito-control strategies need reliable disease activity surveillance systems to be deployed. Despite significant efforts to estimate dengue incidence using a variety of data sources and methods, little work has been done to understand the relative contribution of the different data sources to improved prediction. Additionally, scholarship on the topic had initially focused on prediction systems at the national- and state-levels, and much remains to be done at the finer spatial resolutions at which health policy interventions often occur. We develop a methodological framework to assess and compare dengue incidence estimates at the city level, and evaluate the performance of a collection of models on 20 different cities in Brazil. The data sources we use towards this end are weekly incidence counts from prior years (seasonal autoregressive terms), weekly-aggregated weather variables, and real-time internet search data. We find that both random forest-based models and LASSO regression-based models effectively leverage these multiple data sources to produce accurate predictions, and that while the performance between them is comparable on average, the former method produces fewer extreme outliers, and can thus be considered more robust. For real-time predictions that assume long delays (6-8 weeks) in the availability of epidemiological data, we find that real-time internet search data are the strongest predictors of dengue incidence, whereas for predictions that assume short delays (1-3 weeks), in which the error rate is halved (as measured by relative RMSE), short-term and seasonal autocorrelation are the dominant predictors. Despite the difficulties inherent to city-level prediction, our framework achieves meaningful and actionable estimates across cities with different demographic, geographic and epidemic characteristics.

PMID:35073316 | DOI:10.1371/journal.pntd.0010071

PLoS One. 2022 Jan 24;17(1):e0258904. doi: 10.1371/journal.pone.0258904. eCollection 2022.

ABSTRACT

This study was conducted to evaluate the effect of genetic selection (Lines A and B; Line A pigs have a greater proportion of Pietrain genes than those from Line B and therefore, selected for improved lean tissue accretion) on the feeding behavior of group-housed pigs exposed to daily cyclic high ambient temperatures. Feeding behavior of 78 barrows housed together in a single room was recorded in real time by five automatic feeders. The feeders registered each visit of each pig (day, hour, min, and second) and the amount of feed requested. Daily cyclic high ambient temperature was induced exposing pigs at 22°C from 18.00 to 10.00 h and 30°C from 10.01 to 17.59 h. From this temperature variation, day-period was divided into: 22°C(06-10h), from 6.00 to 10.00 h; 30°C(10-18h), from 10.01 to 17.59 h; and 22°C(18-06h), from 18.00 to 5.59 h. Meal criteria was estimated based on the probability of animals starting a new feeding event within the next minute since the last visit (Pstart). After defining the meal criteria, the number of meals (n), feed intake rate (g/min), feed intake (g/meal), feeder occupancy (min/meal), and interval between meals (min) of each animal were calculated. Greatest probability of starting to feed was observed at 22°C(06-10h), followed by 30°C(10-18h) and then 22°C(18-06h). Regardless of time period, pigs from line A had greater feed intake rate and lower feed intake, feed occupancy per meal and probability of starting a meal when compared with line B pigs. Only line A pigs had greater feed intake and feeder occupancy per meal at 22°C(18-06h) than remainder of the day. This indicates that pig feeding pattern is strongly related to the circadian rhythm. However, the genetic selection for improved lean tissue accretion may modulate pigs feeding behavior under daily cyclic high ambient temperatures.

PMID:35073329 | DOI:10.1371/journal.pone.0258904

PLoS Comput Biol. 2022 Jan 24;18(1):e1009813. doi: 10.1371/journal.pcbi.1009813. Online ahead of print.

ABSTRACT

Foraging entails finding multiple targets sequentially. In humans and other animals, a key observation has been a tendency to forage in ‘runs’ of the same target type. This tendency is context-sensitive, and in humans, it is strongest when the targets are difficult to distinguish from the distractors. Many important questions have yet to be addressed about this and other tendencies in human foraging, and a key limitation is a lack of precise measures of foraging behaviour. The standard measures tend to be run statistics, such as the maximum run length and the number of runs. But these measures are not only interdependent, they are also constrained by the number and distribution of targets, making it difficult to make inferences about the effects of these aspects of the environment on foraging. Moreover, run statistics are underspecified about the underlying cognitive processes determining foraging behaviour. We present an alternative approach: modelling foraging as a procedure of generative sampling without replacement, implemented in a Bayesian multilevel model. This allows us to break behaviour down into a number of biases that influence target selection, such as the proximity of targets and a bias for selecting targets in runs, in a way that is not dependent on the number of targets present. Our method thereby facilitates direct comparison of specific foraging tendencies between search environments that differ in theoretically important dimensions. We demonstrate the use of our model with simulation examples and re-analysis of existing data. We believe our model will provide deeper insights into visual foraging and provide a foundation for further modelling work in this area.

PMID:35073315 | DOI:10.1371/journal.pcbi.1009813

PLoS Comput Biol. 2022 Jan 24;18(1):e1009659. doi: 10.1371/journal.pcbi.1009659. Online ahead of print.

ABSTRACT

Since their introduction in the 50’s, variance component mixed models have been widely used in many application fields. In this context, ReML estimation is by far the most popular procedure to infer the variance components of the model. Although many implementations of the ReML procedure are readily available, there is still need for computational improvements due to the ever-increasing size of the datasets to be handled, and to the complexity of the models to be adjusted. In this paper, we present a Min-Max (MM) algorithm for ReML inference and combine it with several speed-up procedures. The ReML MM algorithm we present is compared to 5 state-of-the-art publicly available algorithms used in statistical genetics. The computational performance of the different algorithms are evaluated on several datasets representing different plant breeding experimental designs. The MM algorithm ranks among the top 2 methods in almost all settings and is more versatile than many of its competitors. The MM algorithm is a promising alternative to the classical AI-ReML algorithm in the context of variance component mixed models. It is available in the MM4LMM R-package.

PMID:35073307 | DOI:10.1371/journal.pcbi.1009659

J Thromb Haemost. 2022 Jan 23. doi: 10.1111/jth.15653. Online ahead of print.

ABSTRACT

BACKGROUND: Haemostasis evaluation in chronic kidney disease (CKD) is critical for optimal management of thrombotic and bleeding events. Standard coagulation screens are inadequate for predicting coagulopathy in CKD.

OBJECTIVE: Evaluate haemostasis parameters in patients with different stages of CKD using novel coagulation assays.

PATIENTS/METHODS: Cross-sectional study of 30 healthy controls (HC) and 120 CKD patients (10 Stage-2, 20 Stage-3, 20 Stage-4, 20 Stage-5 not requiring renal replacement, 20 transplant, 10 newly started on haemodialysis (HD), 20 established on HD). Standard laboratory tests were performed in addition to rotational thromboelastometry (ROTEM), multiple electrode aggregometry (MEA), thrombin generation assays, d-dimer, and markers of thrombogenesis (thrombin-antithrombin (TAT)), fibrinolysis, and endothelial activation (intercellular adhesion molecule-1 (ICAM-1)).

RESULTS: D-dimer, TAT and ICAM-1 concentrations were significantly higher in patients with CKD than HC (p<0.01). ROTEM Maximum Clot Firmness was significantly higher in patients than in HC (p<0.01). In CKD Stage 5 patients (pre-HD and started HD) adenosine diphosphate and thrombin receptor activating peptide MEA tests were significantly lower than HC indicating platelet aggregation defect (p<0.05). Multivariate analysis confirmed the direct effect of eGFR in the variance of ROTEM and MEA tests. Endogenous thrombin potential and peak thrombin were not statistically different between groups, but Stage 5 CKD patients had prolonged lag time (7.91 vs 6.33, p<0.001) and time to thrombin peak (10.8 vs 9.5, p<0.05) compared to HC.

CONCLUSIONS: Patients with CKD exhibit features of concomitant hypercoagulability measured by ROTEM and platelet dysfunction measured with MEA. eGFR was an independent determinant of platelet dysfunction and hypercoagulability.

PMID:35068080 | DOI:10.1111/jth.15653

Adv Clin Exp Med. 2022 Jan 24. doi: 10.17219/acem/144638. Online ahead of print.

ABSTRACT

BACKGROUND: During minimally invasive spine surgery, nerve root decompression is challenging due to the anatomical division and uncertainty in lumbar lateral recess (LLR).

OBJECTIVES: To evaluate the outcome and safety of foraminoplasty using percutaneous transforaminal endoscopic decompression (PTED) (performed with an aid of a trephine) in the treatment of lumbar lateral recess stenosis (LLRS).

MATERIAL AND METHODS: All operations were performed under local anesthesia and in prone position. The puncture point was 10-14 cm away from the midline of the spinous process. One hundred eight individuals with LLRS who underwent PTED from September 2016 to December 2020 in our hospital were enrolled in the study. Visual Analog Scale (VAS) and Oswestry Disability Index (ODI) scores were collected preoperatively after 1 day, 7 days, 1 month and at the final follow-up (June 2021). Low back pain and leg pain were measured using VAS score. Functional outcomes were assessed with ODI and modified Macnab criteria.

RESULTS: After the surgery, the VAS score and ODI were statistically significant at all follow-up points compared with the pre-surgery (both p < 0.05). Based on the modified Macnab scores at the final follow-up, the satisfaction rate at postoperative 1 month was 96.3% and the satisfaction rate at postoperative 7 days was 70.38%. A significant difference was observed between the 2 groups (p < 0.05).

CONCLUSIONS: Foraminoplasty using PTED performed with a trephine is one of the safe and effective, minimally invasive methods to treat LLRS.

PMID:35068091 | DOI:10.17219/acem/144638

Obesity (Silver Spring). 2022 Jan 23. doi: 10.1002/oby.23339. Online ahead of print.

ABSTRACT

OBJECTIVE: This study evaluated associations between neighborhood-level characteristics and gestational weight gain (GWG) in a population-level study of 2015 New York City births.

METHODS: Generalized linear mixed-effects models were used to estimate odds ratios (ORs) for associations between neighborhood-level characteristics (poverty, food environment, walkability) within 1 km of a residential Census block centroid and excessive or inadequate GWG compared with recommended GWG. All models were adjusted for individual-level sociodemographic characteristics.

RESULTS: Among the sample of 106,285 births, 41.8% had excessive GWG, and 26.3% had inadequate GWG. Residence in the highest versus lowest quartile of neighborhood poverty was associated with greater odds of excessive GWG (OR: 1.17, 95% CI: 1.08-1.26). Residence in neighborhoods in the quartile of highest walkability compared with the quartile of lowest walkability was associated with lower odds of excessive GWG (OR: 0.87, 95% CI: 0.81-0.93). Adjustment for prepregnancy BMI attenuated the associations for neighborhood poverty, but not for walkability. Neighborhood variables were not associated with inadequate GWG.

CONCLUSIONS: These analyses indicate that greater neighborhood walkability is associated with lower odds of excessive GWG, potentially from differences in pedestrian activity during pregnancy. This research provides further evidence for using urban design to support healthy weight status during pregnancy.

PMID:35068077 | DOI:10.1002/oby.23339

Int J Nurs Pract. 2022 Jan 23:e13038. doi: 10.1111/ijn.13038. Online ahead of print.

ABSTRACT

AIM: The aim of this work is to identify the pressure ulcer risk profiles of hospitalized patients with reference to Braden Scale subscales.

METHODS: A total of 2996 hospitalized Portuguese participants were screened using the Braden Scale. A hierarchical and nonhierarchical cluster analysis was conducted, with ethical approval.

RESULTS: Five risk profiles (clusters) based on the first risk assessments were identified. Regarding the Braden Scale total score, two profiles with high risk and three profiles with low risk of pressure ulcer development were identified. All clusters were statistically significantly different in terms of sociodemographic and clinical variables. When the first and the last risk assessments were compared, all the clusters improved the Braden Scale total score on the last risk assessment, except Cluster 4 (low-risk category). Clusters 3, 4 and 5, which were classified as low risk, decreased in several Braden subscales at the last risk assessment.

CONCLUSIONS: The classification of low risk may misguide the early identification of patients with individual risk factors. Increasing the awareness of health care professionals for the importance of risk assessment of each Braden subscale is necessary for pressure ulcer prevention. We recommend the implementation of strategies for early identification of patients at risk at local and national levels.

PMID:35068026 | DOI:10.1111/ijn.13038