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Supporting seamless global coverage, satellite Internet is an important expansion and extension of terrestrial communication systems, plays a key role in the space–air–ground integrated network, and acts as an important information infrastructure for building China's strength in cyberspace. Developing satellite Internet is of great strategic significance, and corresponding research on its resource management and scheduling is urgently needed. This study illustrates the fundamental concepts and international development frontiers of satellite Internet as well as the basic elements and research progress of satellite Internet resource management and scheduling, identifies the challenges imposed by the new environment and new modes of satellite Internet, and proposes the development strategies. Moreover, it designs a technical route for satellite Internet resource management, including the primary stages of satellite – ground network interconnection, integration of heterogeneous resources, intelligent on-demand service, and native intelligence. In addition, this study proposes the model and architecture of satellite Internet resource management and scheduling, and constructs a key technical system involving high spatio-temporal dynamic resource characterization, multi-dimensional resource time-continuum deterministic analysis, multi-dimensional time-varying capacity pool indexing, and intelligent decision planning. To realize the intelligent and efficient management and scheduling of satellite Internet resources, we should focus on the network characteristics such as resource heterogeneity, dynamic change of network structure, and differentiated service requirements, implement satellite Internet resource management and scheduling according to the optimized technical route, achieve breakthroughs regarding corresponding key technologies, and integrate artificial intelligence technologies into the resource management and scheduling.

Laryngeal squamous cell carcinoma (LSCC) is a common malignancy among men in the anatomical position of head and neck. Hoarseness, pharyngalgia, and dyspnea are common symptoms. LSCC is a complex polygenic carcinoma that is caused by many factors involving polygenic alteration, environmental pollution, tobacco, and human papillomavirus. Classical protein tyrosine phosphatase nonreceptor type 12 (PTPN12) has been extensively studied to decipher its mechanism as a tumor suppressor gene in various human carcinomas; however, there is no comprehensive elucidation of the PTPN12 expression and its regulatory mechanisms in LSCC. As such, we expect to provide new insights for finding new biomarkers and effective therapeutic targets in LSCC. Immunohistochemical staining, western blot (WB), and quantitative real-time RT-PCR (qRT-PCR) were used for the messenger RNA (mRNA) and protein expression analyses of PTPN12, respectively. 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, clone formation, transwell migration, and transwell invasion assays were used to assess the proliferation, migration, and invasion ability of LSCC cells. Online prediction and design software tools (http://www.targetscan.org/ and http://www.microRNA.org) were used to predict associated miRNA. Studying the targeted regulatory relationship between miR-146b-3p and PTPN12 was based on dual luciferase reporter gene analysis. qRT-PCR was used to assess miR-146b-3p expression in LSCC. miR-146b-3p inhibitor and mimic were transfected, followed by qRT-PCR and WB assays to detect the expression of PTPN12. The gain and loss functional experiments were used to investigate the effects of miR-146b-3p transfection on the proliferation, migration, and invasion of tumor cells. Online bioinformatics prediction software (https://cn.string-db.org/ and https://www.genecards.org/) was used to determine potential downstream target genes of PTPN12. qRT-PCR and WB analyses were used to assess the mRNA and protein expression levels of target genes. Our study showed significantly decreased mRNA and protein expression levels of PTPN12 in LSCC compared with the adjacent normal tissues. The lower PTPN12 mRNA expression was correlated with pathological differentiation, and lower PTPN12 protein expression was correlated with the TNM stage in LSCC tissues. The subsequent in vitro functional analyses showed the inhibitory effect of PTPN12 over-expression on the proliferation, migration, and invasiveness abilities of LSCC cell line. Using online prediction and design software, miR-146b-3p was searched to target PTPN12. The miR-146b-3p was expressed at a high level in LSCC tissues and cell lines. Luciferase reporter assay exhibited that miR-146b-3p inhibited the luciferase activity of PTPN12 markedly. The functional analyses showed the tumor-promoting role of miR-146b-3p on the proliferation, migration, and invasiveness abilities of LSCC cell. Furthermore, co-transfection of cells with miR-146b-3p and PTPN12 significantly restored the inhibitory effect of PTPN12 on LSCC cell growth, migration, and invasiveness. This phenomenon unveiled that miR-146b-3p regulated the proliferation, migration, and invasion of LSCC cells by targeting PTPN12. EGFR and ERBB2 were selected as the downstream-regulation target genes. Up-regulation of PTPN12 significantly suppressed EGFR expression. Accordingly, the miR-146b-3p mimic significantly up-regulated the EGFR expression. However, up-regulation of PTPN12 and miR-146b-3p mimic suppressed ERBB2 protein expression but induced its gene expression. Down-regulation of PTPN12 is associated with up-regulation of miR-146b-3p in LSCC. Moreover, PTPN12 serves as a tumor suppressor gene through regulating the proliferation, migration, and invasion of LSCC cells. miR-146b-3p/PTPN12 axis is expected to be a novel therapeutic target in LSCC.

Laryngeal squamous cell carcinoma (LSCC) is an aggressive and lethal malignant neoplasm with extremely poor prognoses. Accumulating evidence has indicated that preferentially expressed antigen in melanoma (PRAME) is correlated with several kinds of cancers. However, there is little direct evidence to substantiate the biological function of PRAME in LSCC. The purpose of the current study is to explore the oncogenic role of PRAME in LSCC. PRAME expression was analyzed in 57 pairs of LSCC tumor tissue samples through quantitative real-time PCR, and the correlation between PRAME and clinicopathological features was analyzed. The result indicated that PRAME was overexpressed in the LSCC patients and correlated with the TNM staging and lymphatic metastasis. The biological functions and molecular mechanism of PRAME in LSCC progression were investigated through in vitro and in vivo assays. Functional studies confirmed that PRAME facilitated the proliferation, invasion, migration, and epithelial–mesenchymal transition of LSCC cells, and PRAME also promoted tumor growth in vivo. HDAC5 was identified as an upstream regulator that can affect the expression of PRAME. Moreover, PRAME played the role at least partially by activating PI3K/AKT/mTOR pathways. The above findings elucidate that PRAME may be a valuable oncogene target, contributing to the diagnosis and therapy of LSCC.

Unlike supramolecular self-assembly methods that can organize many distinct components into designer shapes in a homogeneous solution (e.g., DNA origami), only relatively simple, symmetric structures consisting of a few distinct components have been self-assembled at solid surfaces. As the self-assembly process is confined to the surface/interface by mostly nonspecific attractive interactions, an open question is how these interfacial interactions affect multicomponent self-assembly. To gain a mechanistic understanding of the roles of the surface environment in DNA origami self-assembly, here we studied the oligonucleotide-assisted folding of a long single-stranded DNA (ssDNA scaffold) that was end-tethered to a dynamic surface, which could actively regulate the DNA-surface interactions. The results showed that even weak surface attractions can lead to defective structures by inhibiting the merging of multiple domains into complete structures. A combination of surface anchoring and deliberate regulation of DNA-surface interactions allowed us to depart from the existing paradigm of surface confinement via nonspecific interactions and enabled DNA origami folding to proceed in a solution-like environment. Importantly, our strategy retains the key advantages of surface-mediated self-assembly. For example, surface-anchored oligonucleotides could sequence-specifically initiate the growth of DNA origamis of specific sizes and shapes. Our work enables information to be encoded into a surface and expressed into complex DNA surface architectures for potential nanoelectronic and nanophotonic applications. In addition, our approach to surface confinement may facilitate the 2D self-assembly of other molecular components, such as proteins, as maintaining conformational freedom may be a general challenge in the self-assembly of complex structures at surfaces.

The spatial arrangement of target and probe molecules on the biosensor is a key aspect of the biointerface structure that ultimately determines the properties of interfacial molecular recognition and the performance of the biosensor. However, the spatial patterns of single molecules on practical biosensors have been unknown, making it difficult to rationally engineer biosensors. Here, we have used high-resolution atomic force microscopy to map closely spaced individual probes as well as discrete hybridization events on a functioning electrochemical DNA sensor surface. We also applied spatial statistical methods to characterize the spatial patterns at the single molecule level. We observed the emergence of heterogeneous spatiotemporal patterns of surface hybridization of hairpin probes. The clustering of target capture suggests that hybridization may be enhanced by proximity of probes and targets that are about 10 nm away. The unexpected enhancement was rationalized by the complex interplay between the nanoscale spatial organization of probe molecules, the conformational changes of the probe molecules, and target binding. Such molecular level knowledge may allow one to tailor the spatial patterns of the biosensor surfaces to improve the sensitivity and reproducibility.

Understanding the negative effects of climate change and changes to land use/land cover on natural hazards is an important feature of sustainable development worldwide, as these phenomena are inextricably linked with natural hazards such as landslides. The contribution of this study is an attempt to develop a state-of-the-art method to assess the effects of climate change and changes in land use/land cover on landslide susceptibility in the Tra Khuc river basin in Vietnam. The method is based on machine learning and remote sensing algorithms, namely radial basis function neural networks–search and rescue optimization (RBFNN–SARO), radial basis function neural network–queuing search algorithm (RBFNN–QSA), radial basis function neural network–life choice-based optimizer (RBFNN–LCBO), radial basis function neural network–dragonfly optimization (RBFNN–DO). All proposed models performed well, with AUC value of >0.9. The RBFNN–QSA model performed best, with an AUC value of 0.98, followed by RBFNN–SARO (AUC = 0.97), RBFNN–LCBO (AUC = 0.95), RBFNN–DO (AUC = 0.93), and support vector machine (SVM; AUC = 0.92). The results show that both climate and land use/land cover change greatly in the future: Precipitation increases 18% by 2030 and 25.1% by 2050; the total production forest, protected forest and built-up area change considerably between 2010 and 2050. These changes influence landslide susceptibility: The area of high and very high landslide susceptibility decrease by approximately 100 and 300 km2 respectively in the study area from 2010 to 2050. The findings of this study can support decision-makers in formulating appropriate strategies to reduce damage from landslides, such as limiting construction in areas where future landslides are predicted. Although this study applies to a particular region of Vietnam, the findings can be applied in other mountainous regions around the world.

Aptamers are chemically synthesized oligonucleotides or peptides with molecular recognition capabilities. We investigated recognition of substrate-tethered small-molecule targets, using neurotransmitters as examples, and fluorescently labeled DNA aptamers. Substrate regions patterned via microfluidic channels with dopamine or   l-tryptophan were selectively recognized by previously identified dopamine or l-tryptophan aptamers, respectively. The on-substrate dissociation constant determined for the dopamine aptamer was comparable to, though, slightly greater than the previously determined solution dissociation constant. Using prefunctionalized neurotransmitter-conjugated oligo(ethylene glycol) alkanethiols and microfluidics patterning, we produced multiplexed substrates to capture and to sort aptamers. Substrates patterned with l-3,4-dihydroxyphenylalanine, l- threo-dihydroxyphenylserine, and l-5-hydroxytryptophan enabled comparison of the selectivity of the dopamine aptamer for different targets via simultaneous determination of in situ binding constants. Thus, beyond our previous demonstrations of recognition by protein binding partners (i.e., antibodies and G-protein-coupled receptors), strategically optimized small-molecule-functionalized substrates show selective recognition of nucleic acid binding partners. These substrates are useful for side-by-side target comparisons and future identification and characterization of novel aptamers targeting neurotransmitters or other important small molecules.

Interactions between small molecules and biomolecules are important physiologically and for biosensing, diagnostic, and therapeutic applications. To investigate these interactions, small molecules can be tethered to substrates through standard coupling chemistries. While convenient, these approaches co-opt one or more of the few small-molecule functional groups needed for biorecognition. Moreover, for multiplexing, individual probes require different surface functionalization chemistries, conditions, and/or protection/deprotection strategies. Thus, when placing multiple small-molecules on surfaces, orthogonal chemistries are needed that preserve all functional groups and are sequentially compatible. Here, we approach high-fidelity small-molecule patterning by coupling small-molecule neurotransmitter precursors, as examples, to monodisperse asymmetric oligo(ethylene glycol)alkanethiols during synthesis and prior to self-assembly on Au substrates. We use chemical lift-off lithography to singly and doubly pattern substrates. Selective antibody recognition of pre-functionalized thiols was comparable to or better than recognition of small molecules functionalized to alkanethiols after surface assembly. These findings demonstrate that synthesis and patterning approaches that circumvent sequential surface conjugation chemistries enable biomolecule recognition and afford gateways to multiplexed small-molecule functionalized substrates.

Region-based image segmentation is well-addressed by the ChanVese (CV) model. However, this approach fails when images are affected by artifacts (outliers) and illumination bias that outweigh the actual image contrast. Here, we introduce a model for segmenting such images. In a single energy functional, we introduce 1) a dynamic artifact class preventing intensity outliers from skewing the segmentation, and 2), in Retinex-fashion, we decompose the image into a piecewise-constant structural part and a smooth bias part. The CV-segmentation terms then only act on the structure, and only in regions not identified as artifacts. The segmentation is parameterized using a phase-field, and efficiently minimized using threshold dynamics. We demonstrate the proposed model on a series of sample images from diverse modalities exhibiting artifacts and/or bias. Our algorithm typically converges within 10-50 iterations and takes fractions of a second on standard equipment to produce meaningful results. We expect our method to be useful for damaged images, and anticipate use in applications where artifacts and bias are actual features of interest, such as lesion detection and bias field correction in medical imaging, e.g., in magnetic resonance imaging (MRI).

The supported monolayer of Au that accompanies alkanethiolate molecules removed by polymer stamps during chemical lift-off lithography is a scarcely studied hybrid material. We show that these Au-alkanethiolate layers on poly(dimethylsiloxane) (PDMS) are transparent, functional, hybrid interfaces that can be patterned over nanometer, micrometer, and millimeter length scales. Unlike other ultrathin Au films and nanoparticles, lifted-off Au-alkanethiolate thin films lack a measurable optical signature. We therefore devised fabrication, characterization, and simulation strategies by which to interrogate the nanoscale structure, chemical functionality, stoichiometry, and spectral signature of the supported Au-thiolate layers. The patterning of these layers laterally encodes their functionality, as demonstrated by a fluorescence-based approach that relies on dye-labeled complementary DNA hybridization. Supported thin Au films can be patterned via features on PDMS stamps (controlled contact), using patterned Au substrates prior to lift-off (e.g., selective wet etching), or by patterning alkanethiols on Au substrates to be reactive in selected regions but not others (controlled reactivity). In all cases, the regions containing Au-alkanethiolate layers have a sub-nanometer apparent height, which was found to be consistent with molecular dynamics simulations that predicted the removal of no more than 1.5 Au atoms per thiol, thus presenting a monolayer-like structure.

Problem definition: Intraproject learning in project scheduling involves the use of learning among the similar tasks in a project to improve the overall performance of the project schedule. Under intraproject learning, knowledge gained from completing some tasks in a project is used to execute similar later tasks in the same project more efficiently. We provide the first model and solution algorithms to address this intraproject learning problem. Academic/practical relevance: Intraproject learning is possible when, for example, the difficulty of the tasks becomes better understood, or the efficiency of the resources used becomes better known. Hence, it is necessary to explore the potential of intraproject learning to further improve the performance of project scheduling. Because learning consumes time, firms may underinvest in intraproject learning if they do not recognize its value. Although the project scheduling literature discusses the potential value of using obtained information from learning within the same project, we formally model and optimize the use of intraproject learning in project scheduling. Methodology/results: We model the tradeoff between investing time in learning from completed tasks and achieving reduced durations for subsequent tasks to minimize the total project cost. We show that this problem is intractable. We develop a heuristic that finds near optimal solutions and a strong relaxation that allows some learning from partially completed tasks. Our computational study identifies project characteristics where intraproject learning is most worthwhile. In doing so, it motivates project managers to understand and apply intraproject learning to improve the performance of their projects. A real case is provided by a problem of the Consumer Business Group of Huawei Corporation, for which our model and algorithm provide a greater than 20% improvement in project duration. Managerial implications: We find consistent evidence that projects in general can benefit substantially from intraproject learning, and larger projects benefit more. Our computational studies provide the following insights. First, the benefit from learning varies with the features of the project network, and projects with more complex networks possess greater potential benefit from intraproject learning and deserve more attention to learning opportunities; second, noncritical tasks at an earlier project stage should be learned more extensively; and third, tasks that are more similar (or have more similar processes) to later tasks also deserve more investment in learning. Learning should also be invested more in tasks that have more successors, where knowledge gained can be used repetitively. Funding: This work was supported by the National Natural Science Foundation of China [Grant 71732003 to N. G. Hall and Grants 72131010 and 72232001 to W. Zhao], the Shanghai Subject Chief Scientist Program [Grant 16XD1401700 to G. Wan], and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning [Grant TP2022019 to W. Zhao]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0159. [ABSTRACT FROM AUTHOR]