Search Results (9)

This study evaluated the bone incorporation process of a screw-shaped internal fixation device made of poly (L-lactide-co-D, L-lactide) (PLDLLA). Thirty-two male Wistar rats received 32 fixation devices (2 mm × 6 mm) randomly assigned to either the right or left tibia and one implant in each animal. After 7, 14, 28, and 42 days, the rats were euthanized and the specimens were subjected to microtomographic computed tomography (microCT) and histomorphometric analyses to evaluate bone interface contact (BIC%) and new bone formation (NBF%) in cortical and cancellous bone areas. The animals euthanized on days 28 and 42 were treated with calcein and alizarin red, and confocal LASER microscopy was performed to determine the mineral apposition rate (MAR). Micro-CT revealed a higher percentage of bone volume (p < 0.006), trabecular separation (p < 0.001), and BIC in the cortical (p < 0.001) and cancellous (p = 0.003) areas at 28 and 42 days than at 7 and 14 days. The cortical NBF at 42 days was greater than that at 7 and 14 days (p = 0.022). No statistically significant differences were observed in cancellous NBF or MAR at 28 and 42 days. Based on these results, it can be seen that the PLDLLA internal fixation device is biocompatible and allows new bone formation around the screw thread. Full article

Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependent variables. Alongside this, it is increasingly recognised in many fields that combinations of algorithms through ensemble learning can lead to substantial predictive performance improvements. Still, a sufficient number of ensemble learners for improving the accuracy of satellite precipitation products and their large-scale comparison are currently missing from the literature. In this study, we work towards filling in this specific gap by proposing 11 new ensemble learners in the field and by extensively comparing them. We apply the ensemble learners to monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets that span over a 15-year period and over the entire contiguous United States (CONUS). We also use gauge-measured precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). The ensemble learners combine the predictions of six machine learning regression algorithms (base learners), namely the multivariate adaptive regression splines (MARS), multivariate adaptive polynomial splines (poly-MARS), random forests (RF), gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and Bayesian regularized neural networks (BRNN), and each of them is based on a different combiner. The combiners include the equal-weight combiner, the median combiner, two best learners and seven variants of a sophisticated stacking method. The latter stacks a regression algorithm on top of the base learners to combine their independent predictions. Its seven variants are defined by seven different regression algorithms, specifically the linear regression (LR) algorithm and the six algorithms also used as base learners. The results suggest that sophisticated stacking performs significantly better than the base learners, especially when applied using the LR algorithm. It also beats the simpler combination methods. Full article

Poly ADP-ribose polymerases (PARPs) catalyze ADP-ribosylation, a subclass of post-translational modification (PTM). Mono-ADP-ribose (MAR) moieties bind to target molecules such as proteins and nucleic acids, and are added as part of the process which also leads to formation of polymer chains of ADP-ribose. ADP-ribosylation is reversible; its removal is carried out by ribosyl hydrolases such as PARG (poly ADP-ribose glycohydrolase), TARG (terminal ADP-ribose protein glycohydrolase), macrodomain, etc. In this study, the catalytic domain of Aedes aegypti tankyrase was expressed in bacteria and purified. The tankyrase PARP catalytic domain was found to be enzymatically active, as demonstrated by an in vitro poly ADP-ribosylation (PARylation) experiment. Using in vitro ADP-ribosylation assay, we further demonstrate that the chikungunya virus (CHIKV) nsp3 (non-structural protein 3) macrodomain inhibits ADP-ribosylation in a time-dependent way. We have also demonstrated that transfection of the CHIKV nsP3 macrodomain increases the CHIKV viral titer in mosquito cells, suggesting that ADP-ribosylation may play a significant role in viral replication. Full article

Gridded satellite precipitation datasets are useful in hydrological applications as they cover large regions with high density. However, they are not accurate in the sense that they do not agree with ground-based measurements. An established means for improving their accuracy is to correct them by adopting machine learning algorithms. This correction takes the form of a regression problem, in which the ground-based measurements have the role of the dependent variable and the satellite data are the predictor variables, together with topography factors (e.g., elevation). Most studies of this kind involve a limited number of machine learning algorithms and are conducted for a small region and for a limited time period. Thus, the results obtained through them are of local importance and do not provide more general guidance and best practices. To provide results that are generalizable and to contribute to the delivery of best practices, we here compare eight state-of-the-art machine learning algorithms in correcting satellite precipitation data for the entire contiguous United States and for a 15-year period. We use monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) gridded dataset, together with monthly earth-observed precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). The results suggest that extreme gradient boosting (XGBoost) and random forests are the most accurate in terms of the squared error scoring function. The remaining algorithms can be ordered as follows, from the best to the worst: Bayesian regularized feed-forward neural networks, multivariate adaptive polynomial splines (poly-MARS), gradient boosting machines (gbm), multivariate adaptive regression splines (MARS), feed-forward neural networks and linear regression. Full article

In the search for a better and brighter future, the use of natural luminescent renewable materials as substitutes for synthetic ones in the energy field is of prime importance. The incorporation of natural pigments (e.g., xanthophylls and phycobiliproteins) is a fundamental step in a broad spectrum of applications that are presently marred by their limited stability. The incorporation of bio-based luminescent molecules into solid matrices allows the fabrication of thin films, which may dramatically increase the range of applications, including sustainable photovoltaic systems, such as luminescent solar concentrators or downshifting layers. In this work, we incorporated R-phycoerythrin (R-PE), C-phycocyanin (C-PC), and fucoxanthin (FX) into poly(vinyl alcohol) (PVA) and studied their optical properties. It was found that the emission and excitation spectra of the phycobiliproteins and FX were not modified by incorporation into the PVA matrix. Moreover, in the case of FX, the emission quantum yield (η) values also remained unaltered after incorporation, showing the suitability of the PVA as a host matrix. A preliminary photostability study was performed by exposing the solid samples to continuous AM1.5G solar radiation, which evidenced the potential of these materials for future photovoltaics. Full article

Among the post-translational modifications of proteins, ADP-ribosylation has been studied for over fifty years, and a large set of functions, including DNA repair, transcription, and cell signaling, have been assigned to this post-translational modification (PTM). This review presents an update on the function of a large set of enzyme writers, the readers that are recruited by the modified targets, and the erasers that reverse the modification to the original amino acid residue, removing the covalent bonds formed. In particular, the review provides details on the involvement of the enzymes performing monoADP-ribosylation/polyADP-ribosylation (MAR/PAR) cycling in cancers. Of note, there is potential for the application of the inhibitors developed for cancer also in the therapy of non-oncological diseases such as the protection against oxidative stress, the suppression of inflammatory responses, and the treatment of neurodegenerative diseases. This field of studies is not concluded, since novel enzymes are being discovered at a rapid pace. Full article

The paper discusses a formation of Mt–PAA composite containing a natural montmorillonite structure partially exfoliated by poly(acrylic acid) introduced through intercalation polymerization of acrylic acid. Mt–PAA was subsequently modified by controlled adsorption of Co²⁺ ions. The presence of aluminosilicate packets (clay) and carboxyl groups (hydrogel) led to the deposition of significant amounts of Co²⁺ ions, which after calcination formed the Co₃O₄ spinel particles. The conditions of the Co²⁺ ions’ deposition (pH, volume and concentration of Co(NO₃)₂ solution, as well as a type of pH-controlling agent) were widely varied. Physicochemical characterization of the prepared materials (including X-ray fluorescence (XRF), X-ray powder diffraction (XRD), low-temperature nitrogen adsorption, X-ray photoelectron spectroscopy (XPS) and temperature-programmed reduction (H₂-TPR)) revealed that the modification conditions strongly influenced the content as well as the distribution of the Co₃O₄ active phase, tuning its reducibility. The latter parameter was, in turn, very important from the point of view of catalytic activity in the combustion of aromatic volatile organic compounds (VOCs) following the Mars–van Krevelen mechanism. Full article

Nanoparticles (NPs) have been widely explored for delivering doxorubicin (DOX), an anticancer drug, to minimize cardiotoxicity. However, their efficiency is marred by a necessity to chemically modify DOX, NPs, or both and low deposition of the administered NPs on tumors. Therefore, alternative strategies should be developed to improve therapeutic efficacy and decrease toxicity. Here we report the possibility of employing a monolithic nanoporous gold (np-Au) rod as an implant for delivering DOX. The np-Au has very high DOX encapsulation efficiency (>98%) with maximum loading of 93.4 mg cm⁻³ without any chemical modification required of DOX or np-Au. We provide a plausible mechanism for the high loading of DOX in np-Au. The DOX sustained release for 26 days from np-Au in different pH conditions at 37 °C, which was monitored using UV-Vis spectroscopy. Additionally, we encased the DOX-loaded np-Au with rapamycin (RAPA)-trapped poly(D,L-lactide-co-glycolide) (PLGA) to fabricate an np-Au@PLGA/RAPA implant and optimized the combinatorial release of DOX and RAPA. Further exploiting the effect of the protein corona around np-Au and np-Au@PLGA/RAPA showed zero-order release kinetics of DOX. This work proves that the np-Au-based implant has the potential to be used as a DOX carrier of potential use in cancer treatment. Full article

Material decomposition methods are used to identify and quantify multiple tissue components in spectral CT but there is no published method to quantify the misidentification of materials. This paper describes a new method for assessing misidentification and mis-quantification in spectral CT. We scanned a phantom containing gadolinium (1, 2, 4, 8 mg/mL), hydroxyapatite (54.3, 211.7, 808.5 mg/mL), water and vegetable oil using a MARS spectral scanner equipped with a poly-energetic X-ray source operated at 118 kVp and a CdTe Medipix3RX camera. Two imaging protocols were used; both with and without 0.375 mm external brass filter. A proprietary material decomposition method identified voxels as gadolinium, hydroxyapatite, lipid or water. Sensitivity and specificity information was used to evaluate material misidentification. Biological samples were also scanned. There were marked differences in identification and quantification between the two protocols even though spectral and linear correlation of gadolinium and hydroxyapatite in the reconstructed images was high and no qualitative segmentation differences in the material decomposed images were observed. At 8 mg/mL, gadolinium was correctly identified for both protocols, but concentration was underestimated by over half for the unfiltered protocol. At 1 mg/mL, gadolinium was misidentified in 38% of voxels for the filtered protocol and 58% of voxels for the unfiltered protocol. Hydroxyapatite was correctly identified at the two higher concentrations for both protocols, but mis-quantified for the unfiltered protocol. Gadolinium concentration as measured in the biological specimen showed a two-fold difference between protocols. In future, this methodology could be used to compare and optimize scanning protocols, image reconstruction methods, and methods for material differentiation in spectral CT. Full article