Tag: nevin manimala

Reprod Domest Anim. 2021 Aug 9. doi: 10.1111/rda.14001. Online ahead of print.

ABSTRACT

Based on different surveys, dairy farmers are concerned about extensive use of exogenous oxytocin in buffaloes, which is being held responsible for reproductive problems, including; irregular estrous cycle and delayed ovulation. For these concerns, effects of oxytocin injection on postpartum follicular dynamics, postpartum estrous interval (PEI), estrus length, the interval from onset of estrus to ovulation, and blood progesterone (P4) were studied in Nili-Ravi buffaloes. For this purpose, 23 animals within one week after calving were randomly divided into three groups: without oxytocin (CON; n=7), 10 i.u. oxytocin (LOW; n=8), 30 i.u. oxytocin – (HIGH; n=8), and used to record the PEIfor the study period of 154 days. At subsequent estrus, three buffaloes from each group (not served) were selected randomly to monitor two cycles for six weeks. Trans-rectal ultrasonography was performed to evaluate follicular and corpus luteum (CL) development, while blood sampling was done for progesterone (P4) analysis. These results revealed that postpartum estrous interval (PEI) decreased significantly in oxytocin-treated groups. The number of small, medium and total follicles on the left ovary was significantly higher in HIGH group. However, an overall number of small and total follicles on both right and left ovaries was significantly higher in CON and HIGH groups. On the other hand, there was no difference in the number of follicles on right ovary among all treatment groups. The same was true for the size of pre-ovulatory follicles, CL, P4 concentrations and estrous cycle length. The intervals from onset of estrus to ovulation and from standing estrus to ovulation were increased considerably in HIGH group. It is concluded that exogenous oxytocin administration resulted in the shortening of PEIbut triggered a delay in ovulation. Moreover, a higher dose of oxytocin could stimulate the growth of small, medium, and total follicles in postpartum Nili-Ravi buffaloes.

PMID:34370879 | DOI:10.1111/rda.14001

Med Phys. 2021 Aug 9. doi: 10.1002/mp.15155. Online ahead of print.

ABSTRACT

PURPOSE: The current practice in Intensity Modulated Radiation Therapy (IMRT) planning almost always includes different dose calculation strategies for plan optimization and final dose verification. The accurate Monte Carlo (MC) dose algorithm is considered to be time-consuming for the optimization. Thus a fast, simplified dose algorithm is used in the optimization. The significant differences between the optimized dose and the delivered dose lead to tediously-planning loops and potentially-suboptimal solutions. This work aims to develop an IMRT optimization algorithm to minimize the dose discrepancy so that the delivered dose can be optimized in a holistic, end-to-end manner.

METHODS: The proposed algorithm, namely NeuralDAO, integrates a neural dose network into the Column-Generation (CG) Direct Aperture Optimization (DAO) formulation for step-and-shoot IMRT planning. The neural dose network is designed and trained to produce doses of MC-level accuracy within few milliseconds. Its differentiability is fully exploited to compute gradients for identifying potential aperture shapes. A prototype of NeuralDAO was developed in PyTorch and available to the public. Five lung patient cases have been studied. Dosimetric accuracy was compared with the MC dose. Plan quality and time were compared with a state-of-the-art (SoA) dose-correct algorithm. Statistical analysis was performed by Wilcoxon signed-rank test.

RESULTS: The average gamma passing rate at 2mm2% is 99.7% between the optimized and delivered dose. The Convergence process produced by NeuralDAO is virtually identical to that produced by an MC-based DAO. The average dose calculation time is 12.1 milliseconds for an aperture on GPU. One session of optimization took 10 minutes to 36 minutes. Compared with the SoA, a better conformity index and homogeneity index were observed for the target. The esophagus was significantly spared. Significant reductions were observed for the re-planning number and the planning time.

CONCLUSIONS: A new DAO algorithm based on the neural dose network has been developed. The results suggest this algorithm minimizes the discrepancy between the optimized and delivered dose, which offers a promising approach to reduce the time and effort required in IMRT planning. This work demonstrates the possibility of applying the neural network in IMRT optimization. It is of great potential to extend this algorithm to other treatment modalities. This article is protected by copyright. All rights reserved.

PMID:34370880 | DOI:10.1002/mp.15155

Drug Alcohol Rev. 2021 Aug 9. doi: 10.1111/dar.13367. Online ahead of print.

ABSTRACT

Sexuality and gender diverse Australians are a priority population in federal and state-based alcohol and other drug (AOD) strategies. Research evidence shows higher prevalence of AOD use by lesbian, gay, bisexual, transgender and queer (LGBTQ) people, riskier use and a higher proportion have accessed AOD treatment. Despite these disparities, Australian AOD treatment services do not routinely collect data on sexuality or gender identity. As a result, the treatment needs, experiences and outcomes of LGBTQ people remain largely invisible. The Australian Bureau of Statistics’ recently released standardised indicators for the recording of sex, gender, variations of sex characteristics and sexual orientation presents an opportunity for the AOD sector to implement inclusive data collection as a foundational step towards achieving policy priorities for LGBTQ people. This commentary includes an implementation case study from the New South Wales non-government AOD treatment sector, where sexuality and gender identity indicators have been collected since 2016.

PMID:34370883 | DOI:10.1111/dar.13367

J Am Anim Hosp Assoc. 2021 Aug 9. doi: 10.5326/JAAHA-MS-7060. Online ahead of print.

ABSTRACT

Packed cell volume (PCV) is commonly used to assess and monitor red blood cell count in animals, but the results can be altered if inappropriate ratios of anticoagulant/blood are used. The purpose of this study was to determine the effect of ideally filled, overfilled, and underfilled K3 ethylenediaminetetraacetic acid (EDTA) tubes with various volumes of healthy dog blood on centrifuged PCV. Six milliliters of blood was obtained from 94 blood donors each. Initial distribution was injected into two nonheparinized microhematocrit tubes. The remainder was instilled into 1.3 mL K3 EDTA spray-dried tubes as 1.5 mL, 1.3 mL, 0.75 mL, 0.5 mL, and 0.25 mL aliquots. Normality was determined using the D’agostino-Pearson method and by visual examination of histograms. Data were analyzed using a repeated-measures analysis of variance with post hoc testing using Tukey’s test. There is a statistically significant decrease in the PCV between all groups with progressive underfilling of tubes (P < .0001). The closest difference is between 1.5 and 1.3 mL (P = .0138). Our study suggested that underfilling K3 EDTA tubes significantly and negatively influences the PCV in healthy dogs. Using underfilled K3 EDTA tubes result in a lower PCV compared with directly filled microhematocrit tubes without anticoagulant.

PMID:34370848 | DOI:10.5326/JAAHA-MS-7060

PLoS One. 2021 Aug 9;16(8):e0256189. doi: 10.1371/journal.pone.0256189. eCollection 2021.

ABSTRACT

[This corrects the article DOI: 10.1371/journal.pone.0251107.].

PMID:34370793 | DOI:10.1371/journal.pone.0256189

PLoS Pathog. 2021 Aug 9;17(8):e1009838. doi: 10.1371/journal.ppat.1009838. Online ahead of print.

ABSTRACT

Through evolution, Hepatitis B Virus (HBV) developed highly intricate mechanisms exploiting host resources for its multiplication within a constrained genetic coding capacity. Yet a clear picture of viral hitchhiking of cellular processes with spatial resolution is still largely unsolved. Here, by leveraging bDNA-based fluorescence in situ hybridization (FISH) combined with immunofluorescence, we developed a microscopic approach for multiplex detection of viral nucleic acids and proteins, which enabled us to probe some of the key aspects of HBV life cycle. We confirmed the slow kinetics and revealed the high variability of viral replication at single-cell level. We directly visualized HBV minichromosome in contact with acetylated histone 3 and RNA polymerase II and observed HBV-induced degradation of Smc5/6 complex only in primary hepatocytes. We quantified the frequency of HBV pregenomic RNAs occupied by translating ribosome or capsids. Statistics at molecular level suggested a rapid translation phase followed by a slow encapsidation and maturation phase. Finally, the roles of microtubules (MTs) on nucleocapsid assembly and virion morphogenesis were analyzed. Disruption of MTs resulted in the perinuclear retention of nucleocapsid. Meanwhile, large multivesicular body (MVB) formation was significantly disturbed as evidenced by the increase in number and decrease in volume of CD63+ vesicles, thus inhibiting mature virion secretion. In conclusion, these data provided spatially resolved molecular snapshots in the context of specific subcellular activities. The heterogeneity observed at single-cell level afforded valuable molecular insights which are otherwise unavailable from bulk measurements.

PMID:34370796 | DOI:10.1371/journal.ppat.1009838

Br J Surg. 2021 Aug 9:znab217. doi: 10.1093/bjs/znab217. Online ahead of print.

ABSTRACT

INTRODUCTION: Over half of women with surgically managed breast cancer in the UK undergo breast-conserving treatment (BCT). While photographs are shown prior to reconstructive surgery or complex oncoplastic procedures, standard practice prior to breast conservation is to simply describe the likely aesthetic changes. Patients have expressed the desire for more personalized information about likely appearance after surgery. The hypothesis was that viewing a three-dimensional (3D) simulation improves patients’ confidence in knowing their likely aesthetic outcome after surgery.

METHODS: A randomized, controlled trial of 117 women planning unilateral BCT was undertaken. The randomization was three-way: standard of care (verbal description alone, control group), viewing two-dimensional (2D) photographs, or viewing a 3D simulation before surgery. The primary endpoint was the comparison between groups’ median answer on a visual analogue scale (VAS) for the question administered before surgery: ‘How confident are you that you know how your breasts are likely to look after treatment?’

RESULTS: The median VAS in the control group was 5.2 (i.q.r. 2.6-7.8); 8.0 (i.q.r. 5.7-8.7) for 2D photography, and 8.9 (i.q.r. 8.2-9.5) for 3D simulation. There was a significant difference between groups (P < 0.010) with post-hoc pairwise comparisons demonstrating a statistically significant difference between 3D simulation and both standard care and viewing 2D photographs (P < 0.010 and P = 0.012, respectively).

CONCLUSIONS: This RCT has demonstrated that women who viewed an individualized 3D simulation of likely aesthetic outcome for BCT were more confident going into surgery than those who received standard care or who were shown 2D photographs of other women. The impact on longer-term satisfaction with outcome remains to be determined.Registration number: NCT03250260 (http://www.clinicaltrials.gov).

PMID:34370833 | DOI:10.1093/bjs/znab217

PLoS One. 2021 Aug 9;16(8):e0255718. doi: 10.1371/journal.pone.0255718. eCollection 2021.

ABSTRACT

Regardless of all efforts on community discovery algorithms, it is still an open and challenging subject in network science. Recognizing communities in a multilayer network, where there are several layers (types) of connections, is even more complicated. Here, we concentrated on a specific type of communities called seed-centric local communities in the multilayer environment and developed a novel method based on the information cascade concept, called PLCDM. Our simulations on three datasets (real and artificial) signify that the suggested method outstrips two known earlier seed-centric local methods. Additionally, we compared it with other global multilayer and single-layer methods. Eventually, we applied our method on a biological two-layer network of Colon Adenocarcinoma (COAD), reconstructed from transcriptomic and post-transcriptomic datasets, and assessed the output modules. The functional enrichment consequences infer that the modules of interest hold biomolecules involved in the pathways associated with the carcinogenesis.

PMID:34370784 | DOI:10.1371/journal.pone.0255718

PLoS One. 2021 Aug 9;16(8):e0255857. doi: 10.1371/journal.pone.0255857. eCollection 2021.

ABSTRACT

In the current study, based on the national fire statistics from 2003 to 2017, we analyzed the 24-hour occurrence regularity of fire in China to study the occurrence regularity and influencing factors of fire and provide a reference for scientific and effective fire prevention. The results show that the frequency of fire is low from 0 to 6 at night, accounting for about 13.48%, but the death toll due to fire is relatively high, accounting for about 39.90%. Considering the strong seasonal characteristics of the time series of monthly fire frequency, the SARIMA model predicts the fire frequency. According to the characteristics of time series data and prediction results, an optimized Seasonal Autoregressive Integrated Moving Average Model (SARIMA) model based on Quantile outlier detection method and similar mean interpolation method is proposed, and finally, the optimal model is constructed as SARIMA (1,1,1) (1,1,1) 12 for prediction. The results show that: according to the optimized SARIMA model to predict the number of fires in 2018 and 2019, the root mean square error of the fitting results is 2826.93, which is less than that of the SARIMA model, indicating that the improved SARIMA model has a better fitting effect. The accuracy of the results is increased by 11.5%. These findings verified that the optimized SARIMA model is an effective improvement for the series with quantile outliers, and it is more suitable for the data prediction with seasonal characteristics. The research results can better mine the law of fire aggregation and provide theoretical support for fire prevention and control work of the fire department.

PMID:34370785 | DOI:10.1371/journal.pone.0255857

PLoS One. 2021 Aug 9;16(8):e0255675. doi: 10.1371/journal.pone.0255675. eCollection 2021.

ABSTRACT

Dealing with a system of first-order reactions is a recurrent issue in chemometrics, especially in the analysis of data obtained by spectroscopic methods applied on complex biological systems. We argue that global multiexponential fitting, the still common way to solve such problems, has serious weaknesses compared to contemporary methods of sparse modeling. Combining the advantages of group lasso and elastic net-the statistical methods proven to be very powerful in other areas-we created an optimization problem tunable from very sparse to very dense distribution over a large pre-defined grid of time constants, fitting both simulated and experimental multiwavelength spectroscopic data with high computational efficiency. We found that the optimal values of the tuning hyperparameters can be selected by a machine-learning algorithm based on a Bayesian optimization procedure, utilizing widely used or novel versions of cross-validation. The derived algorithm accurately recovered the true sparse kinetic parameters of an extremely complex simulated model of the bacteriorhodopsin photocycle, as well as the wide peak of hypothetical distributed kinetics in the presence of different noise levels. It also performed well in the analysis of the ultrafast experimental fluorescence kinetics data detected on the coenzyme FAD in a very wide logarithmic time window. We conclude that the primary application of the presented algorithms-implemented in available software-covers a wide area of studies on light-induced physical, chemical, and biological processes carried out with different spectroscopic methods. The demand for this kind of analysis is expected to soar due to the emerging ultrafast multidimensional infrared and electronic spectroscopic techniques that provide very large and complex datasets. In addition, simulations based on our methods could help in designing the technical parameters of future experiments for the verification of particular hypothetical models.

PMID:34370771 | DOI:10.1371/journal.pone.0255675