One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

Delineating the cropping area of cocoa agroforests is a major challenge for quantifying the contribution of the land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multi-spectral optical images is difficult due to a similarity in the spectral characteristics of their canopy; moreover, optical sensors are largely impeded by the frequent cloud cover in the tropics. This study explores multi-season Sentinel-1 C-band SAR image to discriminate cocoa agroforests from transition forests for a heterogeneous landscape in central Cameroon. We use an ensemble classifier, random forest, to average SAR image texture features of GLCM (Grey Level Co-occurrence Matrix) across seasons; next, we compare classification performance with results from RapidEye optical data. Moreover, we assess the performance of GLCM texture feature extraction at four different grey level quantization: 32bits, 8bits, 6bits, and 4bits. The classification overall accuracy (OA) of texture-based maps outperformed that from an optical image; the highest OA of 88.8% was recorded at 6bits grey level. This quantization level, in comparison to the initial 32bits in SAR images, reduced the class prediction error by 2.9%. Although this prediction gain may be large for the landscape area, the resultant thematic map reveals the decrease and fragmentation of forest cover by cocoa agroforests. According to our classification validation, the Shannon entropy (H) or uncertainty provides a reliable validation for class predictions and reveals detail inference for discriminating inherently heterogeneous vegetation categories. The texture-based classification achieved a reliable accuracy considering the heterogeneity of the landscape and vegetation classes.

Structure and reversibility of cross-link junctions play pivotal roles in deter- mining the nature of thermoreversible gelation and dynamic mechanical prop- erties of the produced polymer networks. We attempt to explore new types of sol–gel transitions with mechanical sharpness by allowing cross-links to grow without upper bound. We consider thermoreversible gelation of the primary molecules R{Af} carrying the number f of low molecular weight functional groups (gelators) A. Gelators A are assumed to form supramolecular assemblies. Some examples are: telechelic polymers (f = 2) carrying π–π stacking benzene derivatives at their both ends, trifunctional star molecules (f = 3) bearing mul- tiple hydrogen-bonding gelators. The sol–gel transition of the primary molecules becomes sharper with the cooperativity parameter of the stepwise linear growth of the cross-links. There is a polymerization transition (crossover without singu- larity) of the junctions in the postgel region after the gel point is passed. If the gelator A tends to form supramolecular rings competitively with linear chains, there is another phase transition in the deep postgel region where the average molecular weight of the rings becomes infinite (Bose-Einstein condensation of rings). As a typical example of binary cross-links where gelators A and B form mixed junctions, we specifically consider metal-coordinated binding of ligands A by metal ions B. Two types of multi-nuclear supramolecular complexes are studied: (i) linear stacking (ladder) of the sandwich A2B units, (ii) linear train of egg-box A4B units. The average molecular weight, the gel fraction, the av- erage length of the cross-link junctions are calculated for all of these models as functions of the functionality f, the concentration of the solute molecules, and temperature. Potential candidates for the realization of these new types of thermoreversible gelation are discussed.

The objective of this study was to develop an ultrasonic-assisted procedure for the extraction of total phenolics from Citrus aurantium L. blossoms (CAB) and evaluate the free radical scavenging activity, anti-HMG-CoA reductase activity of total phenolics. In this work, a Box-Behnken design based on the single-factor experiments was used to explore the optimum extraction process. Under the optimum conditions (extraction solvent 70.31% ethanol, extraction temperature 61.94 °C, extraction time 51.73 min and liquid-to-solid ratio 35.63 mL/g), the extraction yield of total phenolics was 95.84 mg gallic acid equivalents (GAE)/g dry matter (DM), which was highly consistent with the theoretical value (96.12 mg GAE/g DM). The total phenolic extract showed excellent free radical scavenging properties against DPPH·, ABTS+·, ·OH and ·O2-, with the IC50 values of 197.007, 83.878, 218.643 and 158.885 μg/mL, respectively, and the extracts also showed good inhibition of HMG-CoA reductase activity, with the IC50 value of 117.165 μg/mL. Total phenolics from CAB could be a potential source of natural free radical scavenger and HMG-CoA reductase inhibitor.

The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) and a self-assembled (oil and gas storage tank) satellite dataset show superior performance of our method compared to the standalone state-of-the-art object detectors.

Tumour microenvironment, composed of pro- and anti-tumour immune cells, affects cancer cells behaviour. We aimed to evaluate if tumour-infiltrating lymphocyte (TIL) density and TILs subtypes in core biopsies at diagnosis of breast cancer patients could predict pathologic complete response (pCR; ypT0/is ypN0) from neoadjuvant systemic therapy (NST). The TIL subtypes were determined based on the proportions of presumably anti-tumour (CD8+, CXCL13+) and pro-tumour (PD-1+, FOXP3+) immune cells. A prospective, non-interventional study including 171 participants undergoing NST was performed. The median TIL density for the entire cohort was 10% (IQR: 3.5-23.8), and 59 (35%) patients achieved pCR. TIL density was positively associated with pCR (univariately and multivariably). In the multivariable logistic regression model, TIL density was an independent predictor of pCR (p=0.012, OR 1.27; 95% CI 1.05-1.54) when controlled for age (p=0.232), Ki-67 (p=0.001), node-negative status (p=0.024), and HER2+/triple negative vs luminal B-like subtype (p<0.001). In our sample, higher proportions of PD-1+ TILs and FOXP3+ TILs were associated with a higher probability of pCR but the association was not statistically significant. In the exploratory multivariable analysis, we showed that only higher CD8+ TILs were associated with pCR. In conclusion, TIL density and its subtypes are associated with pCR.

Distributed deformation based on Fiber Bragg Grating sensors or other kinds of strain sensors can be used to evaluate safety in operating periods of bridges. However, most of the published researches about distributed deformation monitoring are focused on solid rectangular beam rather than box girder—a kind of typical hollow beam widely employed in actual bridges. Considering that the entire deformation of a single-cell box girder contains not only bending deflection but also two additional deformations respectively caused by shear lag and shearing action, this paper again revises the improved conjugated beam method (ICBM) based on the LFBG sensors to satisfy the requirements for monitoring two mentioned additional deformations. The best choice for the LFBG sensor placement in box gilder is also proposed in this paper due to strain fluctuation on flange caused by shear lag effect. Results from numerical simulations show that most of the theoretical monitoring errors of the revised ICBM are 0.3%~1.5%, and the maximum error is 2.4%. A loading experiment for a single-cell box gilder monitored by LFBG sensors show that most of the practical monitoring errors are 6%~8%, and the maximum error is 11%.

This study investigated the impact of seven-strain probiotic supplementation on the frequency of T helper 17 (Th17) cells, regulatory T (T-reg) cells, Treg/Th17 ratio, and their gene expression in individuals with severe traumatic brain injury (STBI). Probiotics can influence the gut microbiota, modulate the immune response, and prevent or alleviate certain immune diseases. However, it is necessary to clarify whether probiotics can effectively impact Th cells in STBI patient. Despite the great attention to improving the immune system in most diseases, there is currently no accurate mechanism for the use of probiotics in diseases with damage to the head injury, which can be a basis for the use of probiotics. The primary objective of this study is to address this research gap. The present study was a randomized, double-blind, placebo-controlled trial that enrolled 48 STBI patients. They were treated with probiotics or placebo: 4 capsule/day for 14 days. The pre- and post-study evaluation included examining the frequency of Treg, Th17 cells, as well as the expression of associated immunological parameters (Foxp3 and RORγt) and Treg/Th17 ratio in both groups. No significant differences were found between the two groups in terms of subject characteristics (P>0.05). GCS scores at 14 days were significantly higher in the probiotic compared to the placebo group. The probiotic group showed a significant decrease in Th17 cell and RORγt gene expression (P = 0.022 and P< 0.001, respectively), a notable increase in T-reg and FoxP3 gene expression (P= 0.025 and P< 0.001, respectively), and a higher Treg/Th17 ratio compared to the placebo group. Our study provides promising evidence of the beneficial effects of seven-strain probiotic supplementation on Th cells in individuals with STBI.

Rett syndrome (RTT), a neurodevelopmental disorder, is mainly caused by mutations in methyl CpG-binding protein 2 (MECP2), which alter the functions of domains to either bind to methylated DNA or interact with a transcriptional co-repressor complex. It has been established that alterations in cyclin-dependent kinase-like 5 (CDKL5) or forkhead box protein G1 (FOXG1) correspond to distinct neurodevelopmental disorders, given that a series of studies have indicated that RTT is also caused by alterations in either one of these genes. We tried to elucidate RTT through evolution and structure assessment of MeCP2, CDKL5, and FOXG1, by focusing on their binding partners and disordered structures. Here, we provide insight into the similarities of the FOXG1 and MECP2 binding partners evolution and function. On the other hand, we suggest that CDKL5 could be a potential candidate for a classical RTT treatment, particularly based on its disordered structure that spans after the catalytic domain to the C-terminus, which shows abundant linear motifs that can bind to molecules with divergent structures of similar affinity. Additionally, we provide insight into the relationship between disordered structure and disease.

Rett syndrome (RTT) is mainly caused by mutations in methyl CpG-binding protein 2, cyclin-dependent kinase-like 5, or forkhead box protein G1. These RTT-causing proteins harbor an intrinsically disordered region (IDR) whose conformation exhibits spatiotemporal heterogeneity, which not only confer versatility to the protein, but also implicates them in diseases. The IDR generally evolves more rapidly than an ordered structure. In this study, we examined the relationship between pathogenic RTT-associated point mutations in RTT-causing proteins and the evolutionary dynamics of sequence features including structural order–disorder, phosphorylation sites, and evolutionary rates. We also analyzed the molecular properties and evolution of proteins that interact with RTT-causing proteins in terms of phylogenetic profiles, tissue specificity, subcellular localization, expression level, and functions. The results indicate that constrained IDRs may function by forming contacts with other regions in the protein sequence causing pathogenic missense mutations likely to arise in the rapidly evolving IDR and affect molecular networks, leading to disease. The results also provide novel insights into the genetic basis for RTT and the evolution of the neocortex in higher vertebrates.

For evaluating probabilities of arbitrary random events with respect to a given multivariate probability distribution, specific techniques are of great interest. An important two-dimensional high risk limit law is the Gauss-exponential distribution whose probabilities can be dealt with based upon the Gauss-Laplace law. The latter will be considered here as an element of the newly introduced family of (p,q)-spherical distributions. Based upon a suitably defined non-Euclidean arc-length measure on (p,q)-circles we prove geometric and stochastic representations of these distributions and correspondingly distributed random vectors, respectively. These representations allow dealing with the new probability measures similarly like with elliptically contoured distributions and more general homogeneous star-shaped ones. This is demonstrated at hand of a generalization of the Box-Muller simulation method. En passant, we prove an extension of the sector and circle number functions.

We assume that poetic devices have an implicit goal: producing an overall sound scheme that will induce the reader to associate intended and expressed meaning to the sound of the poem. Sounds may be organized into categories and assigned presumed meaning as suggested by traditional literary studies. In my work, I have extracted automatically the sound grids of all the sonnets by William Shakespeare and have combined them with the themes expressed by their contents. In a first experiment I have computed lexically and semantically based sentiment analysis obtaining an 80% of agreement. In a second experiment sentiment analysis has been substituted by Appraisal Theory thus obtaining a more fine-grained interpretation which in some cases contradicts the first one. The computation for the second poet - regarded by many critics the best of last century - includes both vowels and consonants. In addition, it combines automatic semantically and lexically based sentiment analysis with sound grids. The results produce visual maps that clearly separate poems into three clusters: negative harmony, positive harmony and disharmony where the latter instantiates the need by the poet to encompass the opposites in a desperate attempt to reconcile them.