Nevin Manimala

Trop Anim Health Prod. 2021 Mar 6;53(2):197. doi: 10.1007/s11250-021-02632-3.

ABSTRACT

In the present study, an attempt was made to elucidate the genetic parameters for body weight traits of lambs from Corriedale sheep population at different ages. Data were collected from 6874 lambs born over a span of 49 years from 1969 to 2017. The traits under study included body weight at birth (BW), weaning (WW), 6 months of age (6MW), 9 months of age (9MW) and yearling stage (YW). Data were statistically analyzed using restricted maximum likelihood (REML) algorithm in WOMBAT program. A multi-variate animal model was fitted to the data incorporating season and period of lambing, sex of lamb and litter size as fixed effects. Variance and covariance components were estimated using the animal model after incorporating direct additive genetic effect of animal as random factor. Genetic and phenotypic correlations with corresponding standard errors were also estimated. The heritability estimates for BW, WW, 6MW, 9MW and YW were 0.130 ± 0.023, 0.300 ± 0.029, 0.292 ± 0.030, 0.191 ± 0.025 and 0.169 ± 0.024, respectively. The genetic correlation between different traits under study was high, except between BW and 9MW for which the estimate was moderate. Phenotypic correlation ranged from low to high for different trait combinations. Among different traits under study, only two traits showed moderate heritability i.e. WW and 6MW while heritability of other traits was low. Both these traits showed high correlation with all subsequent traits. Selection programme for Corriedale sheep should be based on WW which is expressed early in life and shall lead to moderate genetic response to selection.

PMID:33677706 | DOI:10.1007/s11250-021-02632-3

Environ Sci Pollut Res Int. 2021 Mar 6. doi: 10.1007/s11356-021-13182-4. Online ahead of print.

ABSTRACT

In recent years, the concept of sustainable development has enriched numerous scientific researches. Therefore, the combination of economic growth and the environment has been the subject of numerous econometric and statistical models. They demonstrated that there is a two-way relationship between economic growth and pollution. So, we use data from the World Bank database (1971-2014) to assess the possibility that a change (positive or negative) in pollution in India generates a gross domestic product acceleration. Through a Machine Learning approach via artificial neural network analysis, empirical findings show that a deep neural network can predict the outcome under study. The novelty of this paper is to have determined the results through a model based on a comparison with a highly developed country (Japan). The results obtained show that in a country like India, 76% of the time, a change in pollution evolves into a change in the acceleration of the economic growth.

PMID:33677670 | DOI:10.1007/s11356-021-13182-4

Eur J Nucl Med Mol Imaging. 2021 Mar 7. doi: 10.1007/s00259-021-05265-8. Online ahead of print.

ABSTRACT

PURPOSE: Probe-based dynamic (4-D) imaging modalities capture breast intratumor heterogeneity both spatially and kinetically. Characterizing heterogeneity through tumor sub-populations with distinct functional behavior may elucidate tumor biology to improve targeted therapy specificity and enable precision clinical decision making.

METHODS: We propose an unsupervised clustering algorithm for 4-D imaging that integrates Markov-Random Field (MRF) image segmentation with time-series analysis to characterize kinetic intratumor heterogeneity. We applied this to dynamic FDG PET scans by identifying distinct time-activity curve (TAC) profiles with spatial proximity constraints. We first evaluated algorithm performance using simulated dynamic data. We then applied our algorithm to a dataset of 50 women with locally advanced breast cancer imaged by dynamic FDG PET prior to treatment and followed to monitor for disease recurrence. A functional tumor heterogeneity (FTH) signature was then extracted from functionally distinct sub-regions within each tumor. Cross-validated time-to-event analysis was performed to assess the prognostic value of FTH signatures compared to established histopathological and kinetic prognostic markers.

RESULTS: Adding FTH signatures to a baseline model of known predictors of disease recurrence and established FDG PET uptake and kinetic markers improved the concordance statistic (C-statistic) from 0.59 to 0.74 (p = 0.005). Unsupervised hierarchical clustering of the FTH signatures identified two significant (p < 0.001) phenotypes of tumor heterogeneity corresponding to high and low FTH. Distributions of FDG flux, or Ki, were significantly different (p = 0.04) across the two phenotypes.

CONCLUSIONS: Our findings suggest that imaging markers of FTH add independent value beyond standard PET imaging metrics in predicting recurrence-free survival in breast cancer and thus merit further study.

PMID:33677641 | DOI:10.1007/s00259-021-05265-8

Arch Microbiol. 2021 Mar 7. doi: 10.1007/s00203-021-02224-7. Online ahead of print.

ABSTRACT

This study was aimed to investigate whether host plant species and lifestyles, and environmental conditions in the desert affect endophytic fungi composition. Endophytic fungal communities from parasitic plant Cynomorium songaricum and its host Nitraria tangutorum were investigated from three sites including Tonggu Naoer, Xilin Gaole, and Guazhou in Tengger and Badain Jaran Deserts in China using the next-generation sequencing of a ribosomal RNA gene region. Similarity and difference in endophytic fungal composition from different geographic locations were evaluated through multivariate statistical analysis. It showed that plant genetics was a deciding factor affecting endophytic fungal composition even when C. songaricum and N. tangutorum grow together tightly. Not only that, the fungal composition was also greatly affected by the local environment and rainfall. However, the distribution and richness of fungal species indicated that the geographical distance exerted little influence on characterizing the fungal composition. Overall, the findings suggested that plant species, parasitic or non-parasitic lifestyles of the plant, and local environment strongly affected the number and diversity of the endophytic fungal species, which may provide valuable insights into the microbe ecology, symbiosis specificity, and the tripartite relationship among parasitic plant, host, and endophytic fungi, especially under desert environment.

PMID:33677636 | DOI:10.1007/s00203-021-02224-7

Knee Surg Sports Traumatol Arthrosc. 2021 Mar 7. doi: 10.1007/s00167-021-06483-1. Online ahead of print.

ABSTRACT

PURPOSE: Various reconstruction techniques have been employed to restore normal kinematics to PCL-deficient knees; however, studies show that failure rates are still high. Damage to secondary ligamentous stabilizers of the joint, which commonly occurs concurrently with PCL injuries, may contribute to these failures. The main objective of this study was to quantify the biomechanical contributions of the deep medial collateral ligament (dMCL) and posterior oblique ligament (POL) in stabilizing the PCL-deficient knee, using a joint motion simulator.

METHODS: Eight cadaveric knees underwent biomechanical analysis of posteromedial stability and rotatory laxity using an AMTI VIVO joint motion simulator. Combined posterior force (100 N) and internal torque (5 Nm) loads, followed by pure internal/external torques (± 5 Nm), were applied at 0, 30, 60 and 90° of flexion. The specimens were tested in the intact state, followed by sequential sectioning of the PCL, dMCL, POL and sMCL. The order of sectioning of the dMCL and POL was randomized, providing n = 4 for each cutting sequence. Changes in posteromedial displacements and rotatory laxities were measured, as were the biomechanical contributions of the dMCL, POL and sMCL in resisting these loads in a PCL-deficient knee.

RESULTS: Overall, it was observed that POL transection caused increased posteromedial displacements and internal rotations in extension, whereas dMCL transection had less of an effect in extension and more of an effect in flexion. Although statistically significant differences were identified during most loading scenarios, the increases in posteromedial displacements and rotatory laxity due to transection of the POL or dMCL were usually small. However, when internal torque was applied to the PCL-deficient knee, the combined torque contributions of the dMCL and POL towards resisting rotation was similar to that of the sMCL.

CONCLUSION: The dMCL and POL are both important secondary stabilizers to posteromedial translation in the PCL-deficient knee, with alternating roles depending on flexion angle. Thus, in a PCL-deficient knee, concomitant injuries to either the POL or dMCL should be addressed with the aim of reducing the risk of PCL reconstruction failure.

PMID:33677624 | DOI:10.1007/s00167-021-06483-1

Oper Neurosurg (Hagerstown). 2021 Mar 2:opab051. doi: 10.1093/ons/opab051. Online ahead of print.

ABSTRACT

BACKGROUND: Surgery of insular glial tumors remains a challenge because of high incidence of postoperative neurological deterioration and the complex anatomy of the insular region.

OBJECTIVE: To explore the prognostic role of our and Berger-Sanai classifications on the extent of resection (EOR) and clinical outcome.

METHODS: From 2012 to 2017, a transsylvian removal of insular glial tumors was performed in 79 patients. The EOR was assessed depending on magnetic resonance imaging scans performed in the first 48 h after surgery.

RESULTS: The EOR ≥90% was achieved in 30 (38%) cases and <90% in 49 (62.0%) cases. In the early postoperative period, the new neurological deficit was observed in 31 (39.2%) patients, and in 5 patients (6.3%), it persisted up to 3 mo.We proposed a classification of insular gliomas based on its volumetric and anatomical characteristics. A statistically significant differences were found between proposed classes in tumor volume before and after surgery (P < .001), EOR (P = .02), rate of epileptic seizures before the surgical treatment (P = .04), and the incidence of persistent postoperative complications (P = .03).In the logistic regression model, tumor location in zone II (Berger-Sanai classification) was the predictor significantly related to less likely EOR of ≥90% and the maximum rate of residual tumor detection (P = .02).

CONCLUSION: The proposed classification of the insular gliomas was an independent predictor of the EOR and persistent postoperative neurological deficit. According to Berger-Sanai classification, zone II was a predictor of less EOR through the transsylvian approach.

PMID:33677610 | DOI:10.1093/ons/opab051

J Am Med Inform Assoc. 2020 Mar 2:ocab008. doi: 10.1093/jamia/ocab008. Online ahead of print.

ABSTRACT

OBJECTIVE: Public Health Announcements (PHAs) on television are a means of raising awareness about risk behaviors and chronic conditions. PHAs’ scarce airtime puts stress on their target audience reach. We seek to help health campaigns select television shows for their PHAs about smoking, binge drinking, drug overdose, obesity, diabetes, STDs, and other conditions using available statistics.

MATERIALS AND METHODS: Using Nielsen’s TV viewership database for the entire US panel, we presented a novel show discovery methodology for PHAs that combined (i) pattern discovery from high-dimensional data (ii) nonparametric tests for validation, and (iii) online experiments on Facebook.

RESULTS: The nonparametric tests verified the robustness of the discovered associations between the popularity of certain shows and health conditions. Findings from fifty (independent) online experiments (where our awareness messages were seen by nearly 1.5 million American adults) empirically demonstrated the value of the methodology.

DISCUSSION: For 2016, the methodology identified several shows whose popularities were genuinely associated with certain health conditions, opening up the possibility of health agencies embracing both big data and large-scale experimentation to address an old problem in a new way.

CONCLUSION: Policy makers can repeatedly apply the methodology as new data streams in, with perhaps different feature sets, pattern discovery techniques, and online experiments running over longer periods. The comparatively lower initial investment in the methodology can pay off by identifying several shows for a potentially national television campaign. As simply a by-product, the initial investment also results in awareness messages that might reach millions of individuals.

PMID:33677589 | DOI:10.1093/jamia/ocab008

Eur J Radiol. 2021 Feb 25;138:109617. doi: 10.1016/j.ejrad.2021.109617. Online ahead of print.

ABSTRACT

PURPOSE: Cancer treatments with immune checkpoint inhibitors (ICI) are associated with a unique set of drug toxicities called immune-related adverse events (irAES). The aim of the present study was to describe the radiological manifestation of irAES detectable by CT.

METHOD: Retrospective analysis of 284 patients treated with ICI for various types of advanced cancer; of them, 129 patients were selected, all having been treated with single-agent ICI, and all with a baseline CT scan and follow-up scans available at our Institute. CT examinations were reviewed by two radiologists involved in the study with a consensus reading. Imaging findings consistent with irAES were reported and correlated with clinical-laboratory data.

RESULTS: Immune-related adverse events were found in 25/129 (19.4 %) patients. No statistically significant differences were found in either the prevalence of irAES or in the time of onset of tumour type. Thoracic complications were detected in 14/25 (56.0 %) patients consisting in: 3 radiation recall pneumonia, 3 Transient Asymptomatic Pulmonary Opacities (TAPOs), 3 hypersensitivity pneumonia, 2 diffuse alveolar damage, 2 organizing pneumonia, 1 sarcoid-like reaction. In the remaining 11/25 (44.0 %), there were extra-pulmonary complications: 3 colitis, 4 cholecystitis, 2 pancreatitis and 2 cases of visceral ischemia.

CONCLUSIONS: Radiologists should be aware of the wide spectrum of irAES as they could affect the outcome. Pneumonia is the most frequent irAES; however, the international classification for interstitial lung disease does not seem to be capable of describing all possible drug-related pulmonary toxicities. Additional findings included TAPOs, radiation recall pneumonia and sarcoid-like reaction.

PMID:33676358 | DOI:10.1016/j.ejrad.2021.109617

Bioresour Technol. 2021 Feb 27;329:124905. doi: 10.1016/j.biortech.2021.124905. Online ahead of print.

ABSTRACT

Parameters of temperature-phased anaerobic digestion (TPAD) were varied to study their effects on hydrolysis, biomethane potential (BMP), and microbial diversity of microalgae biodegradation. Anaerobic pretreatments at 85 °C demonstrated the release of soluble carbohydrate and protein molecules under low microbial metabolic activity. However, at 55 °C, anaerobic pretreatments showed superior performance in methane yield, nutrient release, and volatile fatty acids (VFAs) production due to dominant Clostridium. Furthermore, the highest destruction of volatile solids (VS) was observed during aerobic pretreatments at 55 °C under the influence of various quantities of these genera – Luteimonas, Symbiobacterium, Soehngenia, Thermobacillus, and Ureibacillus. Statistical analysis revealed that hydrolysis and BMP were not correlated. However, soluble nitrogen and phosphorous showed strong correlation with methane (r = 0.623 and 0.948, respectively) under thermo-anaerobic pretreatment, while VS removal and concentrations of acetic and butyric acids and lipids were positively correlated with each other under thermo-aerobic pretreatment.

PMID:33676351 | DOI:10.1016/j.biortech.2021.124905

Neural Netw. 2021 Feb 23;138:179-197. doi: 10.1016/j.neunet.2021.02.012. Online ahead of print.

ABSTRACT

We derive the fast convergence rates of a deep neural network (DNN) classifier with the rectified linear unit (ReLU) activation function learned using the hinge loss. We consider three cases for a true model: (1) a smooth decision boundary, (2) smooth conditional class probability, and (3) the margin condition (i.e., the probability of inputs near the decision boundary is small). We show that the DNN classifier learned using the hinge loss achieves fast rate convergences for all three cases provided that the architecture (i.e., the number of layers, number of nodes and sparsity) is carefully selected. An important implication is that DNN architectures are very flexible for use in various cases without much modification. In addition, we consider a DNN classifier learned by minimizing the cross-entropy, and show that the DNN classifier achieves a fast convergence rate under the conditions that the noise exponent and margin exponent are large. Even though they are strong, we explain that these two conditions are not too absurd for image classification problems. To confirm our theoretical explanation, we present the results of a small numerical study conducted to compare the hinge loss and cross-entropy.

PMID:33676328 | DOI:10.1016/j.neunet.2021.02.012