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It is well known that metal–organic framework (MOF) nanostructures have unique characteristics such as high porosity, large surface areas and adjustable functionalities, so they are ideal candidates for developing drug delivery systems (DDSs) as well as theranostic platforms in cancer treatment. Despite the large number of MOF nanostructures that have been discovered, conventional MOF-derived nanosystems only have a single biofunctional MOF source with poor colloidal stability. Accordingly, developing core–shell MOF nanostructures with good colloidal stability is a useful method for generating efficient drug delivery, multimodal imaging and synergistic therapeutic systems. The preparation of core–shell MOF nanostructures has been done with a variety of materials, but inorganic nanoparticles (NPs) are highly effective for drug delivery and imaging-guided tumor treatment. Herein, we aimed to overview the synthesis of core–shell inorganic NP@MOF nanostructures followed by the application of core–shell MOFs derived from magnetic, quantum dots (QDs), gold (Au), and gadolinium (Gd) NPs in drug delivery and imaging-guided tumor treatment. Afterward, we surveyed different factors affecting prolonged drug delivery and cancer therapy, cellular uptake, biocompatibility, biodegradability, and enhanced permeation and retention (EPR) effect of core–shell MOFs. Last but not least, we discussed the challenges and the prospects of the field. We envision this article may hold great promise in providing valuable insights regarding the application of hybrid nanostructures as promising and potential candidates for multimodal imaging-guided combination cancer therapy.

Nanoparticles (NPs) have been used in numerous applications as anticancer, antibacterial and antioxidant agents. Artificial engineering of protein interactions with NPs in biological systems is crucial to develop potential NPs for drug delivery and cancer nanotherapy. The protein corona (PC) on the NP surface, displays an interface between biomacromolecules and NPs, governing their pharmacokinetics and pharmacodynamics. Upon interaction of proteins with the NPs, their surface features are modified and they can easily be removed from the circulation by the mononuclear phagocytic system (MPS). PC properties heavily depend on the biological microenvironment and NP physicochemical parameters. Based on this context, we have surveyed different approaches that have been used for artificial engineering of the PC composition on NP surfaces. We discussed the effects of NP size, shape, surface modifications (PEGylation, self-peptide, other polymers), and protein pre-coating on the PC properties. Additionally, other factors including protein source and structure, intravenous injection and the subsequent shear flow, plasma protein gradients, temperature and local heat transfer, and washing media were considered in the context of their effects on the PC properties and overall target cellular effects. Moreover, the effects of NP–PC complexes on cancer cells based on cellular interactions, organization of intracellular PC (IPC), targeted drug delivery (TDD) and regulation of burst drug release profile of nanoplatforms, enhanced biocompatibility, and clinical applications were discussed followed by challenges and future perspective of the field. In conclusion, this paper can provide useful information to manipulate PC properties on the NP surface, thus trying to provide a literature survey to shorten their shipping from preclinical to clinical trials and to lay the basis for a personalized PC.

Tieli Dong,1,* Haichao Si,1,2,* Zhisong Li,1 Qian Bai,1 Feng Tao3 1Department of Anesthesiology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China; 2Department of Anesthesiology, Nanyang Central Hospital, Nanyang, People’s Republic of China; 3Department of Biomedical Sciences, Texas A&M University College of Dentistry, Dallas, TX, USA*These authors contributed equally to this workCorrespondence: Qian Bai; Feng Tao, Email baiqian@zzu.edu.cn; ftao81@tamu.eduBackground: Persistent facial pain heavily impacts the quality of life in patients with temporomandibular joint (TMJ) disorders. Previous studies have demonstrated that long non-coding ribonucleic acid (lncRNA) is an important regulator of pain. In this study, we aimed to analyze lncRNA expression in the whole transcriptome of trigeminal ganglia (TG) and spinal trigeminal nucleus caudalis (Sp5C) in a chronic inflammatory TMJ pain mouse model.Methods: Chronic inflammatory TMJ pain was induced by intra-TMJ injection of complete Freund’s adjuvant (CFA). Mouse TG and Sp5C tissues were harvested on day 4 after CFA injection. The lncRNA expression patterns in the whole transcriptome of TG and Sp5C were profiled with RNA sequencing.Results: We observed that 38 lncRNAs and 849 mRNAs were differentially expressed after CFA treatment. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis further revealed relationships among those differentially expressed lncRNAs and mRNAs and their potential functions. Specific categories of biological process, cellular processes, and molecular function of the differentially expressed transcripts were ascertained.Conclusion: Our results suggest that lncRNA expression in the whole transcriptome of trigeminal nociceptive system could contribute to the molecular mechanisms that underlie chronic inflammatory TMJ pain.Keywords: temporomandibular joint pain, lncRNAs, RNA sequencing, trigeminal ganglia, spina

In this work, we apply the relativistic chiral nuclear force to describe the state-of-the-art lattice QCD simulations. In particular, we focus on the $^3S_1$-$^3D_1$ coupled channel for a pion mass of 469 MeV. We show that at leading order the relativistic chiral nuclear force can only describe $\delta_{3S1}$ and $\varepsilon_1$ up to $T_\mathrm{lab.}\approx10$ MeV, while at the next-to-leading order it can do much better up to $T_\mathrm{lab}=200$ MeV. However, at the next-to-next-to-leading order, the description deteriorates, which can be attributed to the fact that the pion-mass dependence of the pion-nucleon couplings $c_{1,2,3,4}$ may not be negligible. Furthermore, all the studies consistently yield negative $\delta_{3D1}$, contrary to the lattice QCD results which are positive but consistent with zero. The present study is relevant to a better understanding of the lattice QCD nucleon-nucleon force and more general baryon-baryon interactions., Comment: 6 pages, 2 figures, studies performed up to the next-to-next-to-leading order, paper rewritten

Roads in modern cities facilitate different types of users, including car drivers, cyclists, and pedestrians. These different users often have a designated section of the road to operate on. Road management, e.g., by municipalities, needs to take this sectioning into account, preferably in an efficient way. Mobile laser scanning (MLS) point clouds provide accurate and dense three-dimensional (3D) measurements of road scenes, showing strong mapping capabilities, although their massive data volume and lack of structure still bring difficulties in automatic processing. Methods for the automatic classification of road surface types are still largely lacking, and the existing methodology did not consider the potential of MLS point clouds yet. In recent years, point cloud understanding through deep neural networks has achieved breakthroughs. However, perceiving large-scale point clouds by deep learning depends on aggregating local features and progressive downsampling, to extract rich contextual information. As a consequence, low-level features that reveal details in point clouds may not be well preserved, possibly resulting in ambiguous delineation in point cloud classification. For road mapping, inaccurate classification of points on road boundaries hinders the generation of high-quality map products. Some existing deep learning methods propose to mitigate the fuzzy classification near boundaries, either by utilizing refinement for network predictions, or by indirectly modifying neighboring weights when summarizing local information. Approaches to achieving a satisfying overall performance, while maintaining accurate delineation, still need investigation. In this study, we propose a novel approach for road type classification of MLS point clouds in dense urban areas based on a deep neural network. We follow the main architecture of RandLA-Net, a point-wise neural network designed for large-scale point cloud processing. To alleviate the ambiguous delineation of point clo, Geoscience and Remote Sensing

Objective: To retrospectively investigate the comparative efficacy, safety and cost-benefits of three nutritional treatment schemes including short peptide jejunal nutrition (SPJN), whole protein jejunal nutrition (WPJN), and partial parenteral nutrition (PPN) in patients underwent esophagectomy for esophageal cancer in our hospital. This study was carried out in accordance with the conceptual framework of nutritional therapy in fast-track rehabilitation surgery. Methods: We retrospectively reviewed 305 patients who were assigned to receive esophagectomy for esophageal cancer. Eligible patients was naturally divided into SPJN group [n = 98 (32.1%)], WPJN group [n = 95 (31.1%)], and PPN group [n = 112 (36.7%)] according to the type of nutritional scheme which was actually prescribed to patients by the attendingphysician in clinical practice. The differences of the serum total protein (TP), albumin (ALB), pre-albumin (PA), hemoglobin (HGB), white blood cells (WBCs), red blood cells (RBCs) and neutrophils were compared among 3 nutritional schemes groups. We also investigated the relationship of the fluid intake, urine output, gastric juice drainage volume and thoracic drainage volume among 3 nutritional groups at 3 days after surgery. Moreover, the differences of cost-benefit indexes, complications, length of hospitalization and hospital expenditure were also compared. Results: The serum TP, ALB, and PA in the SPJN group were all higher than those in the WPJN and PPN groups (p < 0.05). The gastric juice volume of gastrointestinal decompression drainage and fluid volume of thoracic drainage in the SPJN group were all less than that in the WPJN group (p < 0.05). The overall hospital stay and post-operative hospital stay in the SPJN group were all shorter than that in WPJN gr

Semantic segmentation of aerial point clouds with high accuracy is significant for many geographical applications, but is not trivial since the data is massive and unstructured. In the past few years, deep learning approaches designed for 3D point cloud data have made great progress. Pointwise neural networks, such as PointNet and its extensions, show their ability to process 3D point clouds, especially in classification and semantic segmentation. In this work, we implement DGCNN (Dynamic Graph CNN), which combines PointNet with Graph CNN, and extend its semantic segmentation application from indoor scenes to an aerial point cloud dataset: The Current Elevation File Netherlands (AHN), which was produced by airborne laser scanners for the whole Netherlands. Point clouds from the iteration AHN3 are classified into four classes: ground, building, water and others (including vegetation, railways, etc). Moreover, DGCNN splits the input point cloud into regular blocks before operating on it and processes each block independently, which limits the effective range (receptive field) of the network to some extent. Thus, the second aim of this work is to investigate the impact of the effective range on the performance of DGCNN by adjusting two crucial parameters: the block size and the neighborhood size k in k-NN graphs. It turns out that enlarging the block size or k helps to improve the overall accuracy of DGCNN, but cannot ensure better segmentation results from each individual class. With the block size 50 m and k=20, the most balanced F1 scores for all classes and an overall accuracy of 93.28% are achieved. Based on the evaluation for each setting with a certain block size and k, we also manage to further improve the overall accuracy to 93.51% by combining smaller-scale (with block size 30 m) and larger-scale (with block size 50 m) segmentation results, with k=20., Geoscience and Remote Sensing

Nucleon-nucleon interactions, both bare and effective, play an important role in our understanding of the non-perturbative strong interaction, as well as nuclear structure and reactions. In recent years, tremendous efforts have been seen in the lattice QCD community to derive nucleon-nucleon interactions from first principles. Because of the daunting computing resources needed, most of such simulations were still performed with larger than physical light quark masses. In the present work, employing the recently proposed covariant chiral effective field theory (ChEFT), we study the light quark mass dependence of the nucleon-nucleon interaction extracted by the HALQCD group. It is shown that the pion-full version of the ChEFT can describe the lattice QCD data with $m_\pi=469$ MeV and their experimental counterpart reasonably well, while the pion-less version can describe the lattice QCD data with $m_\pi=672, 837, 1015, 1171$ MeV, for both the $^1S_0$ and $^3S_1$-$^3D_1$ channels. The slightly better description of the single channel than the triplet channel indicates that higher order studies are necessary for the latter. Our results confirmed previous studies that the nucleon-nucleon interaction becomes more attractive for both the singlet and triplet channels as the pion mass decreases towards its physical value. It is shown that the virtual bound state in the $^1S_0$ channel remains virtual down to the chiral limit, while the deuteron only appears for a pion mass smaller than about 400 MeV. It seems that proper chiral extrapolations of nucleon-nucleon interaction are possible for pion masses smaller than 500 MeV, similar to the mesonic and one-baryon sectors., Comment: 7 pages, 5 figures, uncertainties generated by cutoff variations are added, and matched to the published version

Qian Bai,1 Jing Cao,2 Tieli Dong,1 Feng Tao3 1Department of Anesthesiology, The Second Affiliated Hospital of Zhengzhou University, Henan, People’s Republic of China; 2Department of Anatomy, School of Basic Medical Sciences, Zhengzhou University, Henan, People’s Republic of China; 3Department of Biomedical Sciences, Texas A&M University College of Dentistry, Dallas, Texas, USACorrespondence: Tieli DongThe Second Affiliated Hospital of Zhengzhou University, Henan, People’s Republic of ChinaEmail tlddtl@126.comFeng TaoTexas A&M University College of Dentistry, Dallas, Texas, USAEmail ftao81@tamu.eduBackground: Rheumatoid arthritis (RA) leads to pain through alteration of gene expression. Although gene expression alteration in knee cartilage or peripheral blood from RA patients has been identified using microarray, it remains unclear whether long non-coding RNA (lncRNA)-mediated gene regulation occurs in primary sensory neurons of dorsal root ganglia (DRG) during RA-like joint inflammation. In the present study, we aimed to analyze lncRNA and related mRNA profiles in the DRG in a knee joint inflammation rat model.Methods: Complete Freund’s adjuvant (CFA) was injected in the rat knee joint for preparing the joint inflammation model. A lncRNA-mRNA microarray of rat DRG was employed for transcriptome analysis. Functional roles of differentially expressed lncRNAs and their related mRNAs in the injured DRG were delineated by bioinformatic analysis.Results: We observed that expression levels of 9000 lncRNAs were altered on day 7 post-CFA, of which 45.17% were up-regulated and 54.83% were down-regulated. Specifically, 69 lncRNAs (42 up and 27 down) were significantly regulated. We also observed that expression levels of 13,744 mRNAs were altered on day 7 post-CFA, of which 49.67% were up-regulated and 50.33% were down-regulated. Specifically, 102 mRNAs (51 up and 51 down) were significantly regulated. Using quantitative

Transportation agencies strive to maintain their systems in good condition and also to provide, acceptable levels of service to users. However, funding is often inadequate to meet the needs, of system preservation and expansion, and thus performance- and budget-constrained, optimization continues to be an issue. Adding complexity to this issue is the increasing visibility, of different stakeholders who advocate for consideration of a multiplicity of diverse, perspectives in the highway decision-making process. Thus agencies are grappling with the issue, of how best to incorporate multiple performance objectives in their decision-making processes., Some of these objectives conflict with each other, and therefore a need arises for decisionmakers, to find optimal solutions that examine the tradeoffs and provide a reasonable balance, between the different objectives. Furthermore, there is the issue of uncertainty: outcomes of, projects are never exactly what the decision-makers envisage; if such inevitable uncertainties, are not duly accounted for, the final decision that may seem optimal may actually be associated, with high risk. Finally, at most agencies, the management of highway assets is divided into, several sub-areas such as pavements and safety assets. In this management structure, optimal, management decisions are carried out separately for specific types of highway assets or, management systems but do not always guarantee a global optimal strategy for all the, management systems combined. Thus, a decision-making framework that integrates all asset, types is needed to enhance decision-making and to ensure more efficient use of scarce funds., Clearly, a need exists for a multi-objective decision-making problem that integrates the various, management systems, duly incorporates uncertainty, and helps decision-makers assess the, tradeoffs between the performance measures. This study addresses that need.