The study and imitation of the biological and mechanical systems present in nature and living beings always have been sources of inspiration for improving existent technologies and establishing new ones. Pursuing this line of thought, we consider an artificial graft typical in the bone reconstruction surgery with the same microstructure of the bone living tissue and examine the interaction between these two phases, namely bone and the graft material. Specifically, a visco-poroelastic second gradient model is adopted for the bone-graft composite system to describe it at a macroscopic level of observation. The second gradient formulation is employed to consider possibly size effects and as a macroscopic source of interstitial fluid flow, which is usually regarded as a key factor in bone remodeling. With the help of the proposed formulation and via a simple example, we show that the model can be used as a graft design tool. As a matter of fact, an optimization of the characteristics of the implant can be carried out by numerical investigations. In this paper, we observe that the size of the graft considerably influences the interaction between bone tissue and artificial bio-resorbable material and the possibility that the bone tissue might substitute more or less partially the foreign graft for better bone healing.The success on the design of new oral nanocarriers greatly depends on the identification of the best physicochemical properties that would allow their diffusion across the mucus layer that protects the intestinal epithelium. In this context, particle tracking (PT) has arisen in the pharmaceutical field as an excellent tool to evaluate the diffusion of individual particles across the intestinal mucus. In PT, the trajectories of individual particles are characterized by the mean square displacement (MSD), which is used to calculate the coefficient of diffusion (D) and the anomalous diffusion parameter (α) as MSD=4Dτα. Unfortunately, there is no stablished criteria to evaluate the goodness-of-fit of the experimental data to the mathematical model. This work shows that the commonly used R2 parameter may lead to an overestimation of the diffusion capacity of oral nanocarriers. We propose a screening approach based on a combination of R2 with further statistical parameters. We have analyzed the effect of this approach to study the intestinal mucodiffusion of lipid oral nanocarriers, compared to the conventional screening approach. Last, we have developed software able to perform the whole PT analysis in a time-saving, user-friendly, and rational fashion.The AP2/ERF transcription factor family members play crucial roles in controlling plant growth and development, as well as responses to various abiotic stresses. Genome-wide identification and characterization of AP2/ERF genes has not yet been carried out in the oil palm genome. In the present work, we reported the occurrence of 172 EgAP2/ERFs (AP2, ERF, RAV & Soloist members) through genome-wide identification. Phylogenetic analysis was used to divide them into four groups, including 34 AP2, 131 ERF, 5 RAV, and 2 Soloist gene family members. All 172 AP2/ERF members were unevenly distributed across 16 chromosomes of oil palm. Gene duplication analysis elucidated the tandem duplication of AP2/ERFs on chromosome blocks of the oil palm genome during evolution. Gene structure as well as conserved motif analysis demonstrated the conserved nature of intron/exon organization and motifs among the AP2/ERF genes. Several cis-regulatory elements-related to hormone, stress, and defense responses-were identified in the promoter regions of AP2/ERFs. Tissue-specific expression of 172 AP2/ERFs in five different tissues of oil palm was also revealed by heatmap analysis using the available transcriptome data. Finally, abiotic stress (salinity, cold & drought)-responsive AP2/ERFs in the oil palm genome were validated through qPCR analysis. Our study provided valuable information on oil palm AP2/ERF superfamily members and dissected their role in abiotic stress conditions.Human prion and non-prion neurodegenerative diseases share pathogenic mechanisms and neuropathological features. The lesion profile of a particular entity results from specific involvement of vulnerable neuron populations and connectivity circuits by a pathogenic protein isoform with strain-like properties. The lesion profile of the medial temporal lobe (MTL) was studied in postmortem tissue of 143 patients with human prion disease (HPD) including sporadic, genetic, and acquired forms. Most cases (90%) were classified according to PrPres type and/or PRNP codon 129 status, in addition to a full neuropathological profile. Mixed histotypes represented 29.4% of total sporadic Creutzfeldt-Jakob disease (sCJD) cases. An intensity score of involvement including spongiosis and astrogliosis was determined for the amygdala, presubiculum, subiculum, entorhinal cortex, CA1 to CA4 sectors of the hippocampal cortex, and dentate gyrus. Connectivity hubs within the MTL presented the highest scores. Diverse lesion profiles were obtained for different types and subtypes of HPD. Impact of mixed PrPres types on the MTL lesion profile was higher for sCJDMV2K cases than in other histotypes. Differences between MTL profiles was globally consistent with current evidence on specific strains in HPD. These results may be relevant for the analysis of possible strain effects in focal non-prion neurodegenerative conditions limited to the MTL.Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. https://www.selleckchem.com/products/tpx-0005.html Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.