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Metastatic breast cancer is an intractable disease that responds poorly to immunotherapy. We show that p38MAPKα inhibition (p38i) limits tumor growth by reprogramming the metastatic tumor microenvironment in a CD4+ T cell-, IFNγ-, and macrophage-dependent manner. To identify targets that further increased p38i efficacy, we utilized a stromal labeling approach and single-cell RNA sequencing. Thus, we combined p38i and an OX40 agonist that synergistically reduced metastatic growth and increased overall survival. Intriguingly, patients with a p38i metastatic stromal signature had better overall survival that was further improved by the presence of an increased mutational load, leading us to ask if our approach would be effective in antigenic breast cancer. The combination of p38i, anti-OX40, and cytotoxic T-cell engagement cured mice of metastatic disease and produced long-term immunologic memory. Our findings demonstrate that a detailed understanding of the stromal compartment can be used to design effective antimetastatic therapies. Significance: Immunotherapy is rarely effective in breast cancer. We dissected the metastatic tumor stroma, which revealed a novel therapeutic approach that targets the stromal p38MAPK pathway and creates an opportunity to unleash an immunologic response. Our work underscores the importance of understanding the tumor stromal compartment in therapeutic design.

The continuous emergence of new variants of concern for SARS-CoV-2 has created a challenge for existing therapies. To address this, we developed a series of single-stranded DNA aptamers that not only bind specifically to the trimer S protein of SARS-CoV-2 but also block the interaction between the trimer S protein and ACE2 receptors. The systematic evolution of ligands by exponential enrichment (SELEX) was performed to select the aptamers for SARS-CoV-2 trimer S protein. ELISA-based assay and flow cytometry were performed to test the apatmers’ binding and inhibition of trimer S protein in vitro. Binding affinity was measured using surface plasmon resonance. Significance was determined in Prism 9.0 using the one-way ANOVA test (Dunnett’s multiple comparisons test) or two-way ANOVA test (Tukey’s multiple comparisons test) for comparisons. The p values < 0.05 were considered statistically significant. After 12 rounds of SELEX, eight highly enriched aptamers were able to bind to the trimer S protein of the SARS-CoV-2 Wuhan original strain as well as the trimer S proteins of the Delta, Delta plus, Alpha, Lambda, Mu, and Omicron variants, with affinities in the nM range, while also inhibiting their interaction with ACE2 receptors in Vero E6 cells. Modifications to our best aptamer were made by adding forward and reverse primer sequences and truncation. The modified aptamers AYA2012004_L and AYA2012004_L-M1 showed up to 70% inhibition of the binding of virus-like particles (VLPs) expressing S protein to the ACE2 receptor expressed in HEK293T cells. Our findings imply that the selected aptamers can prevent SARS-CoV-2 from entering host cells and hence suppress the viral infection. In addition, the findings suggest that the selected aptamers might be an innovative therapy for the treatment of COVID-19.

We study a multi-user multiple-input single-output downlink system aided by a reconfigurable intelligent surface (RIS). Users' sum rate and transmit power are two important performance indicators in such systems. However, most existing works only optimize one of them, resulting in severe performance degradation of the other. Motivated by this, in this paper, we formulate a multi-objective optimization problem to maximize the sum rate of users and minimize the transmit power simultaneously. According to our early work on fitness landscape analysis of sum rate maximization problems, the proposed problem is inferred to be multi-modal. To solve this non-convex and multi-modal problem, we propose a novel multi-objective evolutionary hybrid beamforming (MEHB) framework to find different trade-off solutions between the two conflicting objectives. In particular, we employ different kinds of multi-objective evolutionary algorithms and multi-modal multi-objective evolutionary algorithms as the baseline of MEHB framework, so as to design the passive beamforming. And the active beamforming at the base station is optimized by the classical zero-forcing method. The simulation results have verified the effectiveness of the dominance-based evolutionary algorithms in handling hybrid beamforming problems.

El interés superior del niño es el principio reconocido por la Convención sobre los Derechos del Niño (CDN), siendo la norma y consideración primordial para resolver los casos que involucren infantes. Históricamente, el concepto de familia ha pasado por tres etapas: orientado a la familia, orientado a los padres y orientado al niño como individuo. En este proceso, el estatus del menor ha sufrido tres transformaciones pasando de ser un bien adscrito al padre; un individuo dependiente adscrito a los padres, y; un sujeto de derechos independientes. Actualmente, la configuración de familia no se limita a una sola opción. Y la función emocional de la familia se fortalece continuamente. Es una elección inevitable construir un sistema moderno de matrimonio y familia vinculado por una relación estable entre padres e hijos. Para defenderlo, se utilizan análisis teóricos y jurisprudencias comparativas entre el derecho de China, de España y de Argentina, con el objetivo de determinar el contenido del propio principio, a fin de familiarizar a los lectores con el problema de aplicarlo. Finalmente, se recomienda asegurar una participación substancial de los niños en todos los asuntos infantiles, con el fin de proteger su intereses y derechos.

Intelligent answering technology, which enables computers to solve problems automatically, is often used to develop tutorial systems, and has a wide range of application prospects. However, due to the lack of linguistic analysis and understanding methods, there are few researches on intelligent algorithms for solving kinematics problems. Developing such an algorithm is challenging, because solving kinematics problems is a complex task that includes text understanding, problem analysis, and automatic solution. To understand all these complexities involved in kinematics problems requires background knowledge. And only when an automatic solver contains a powerful internal knowledge representation system can it perform these tasks. We, thus, develop KinRob, an tutorial system for solving kinematics problems by combining neural network and ontology. Firstly, we propose an ontology for KinRob, which defines the knowledge of kinematics, and can help the robot understand a kinematics problem. Secondly, to match the text in natural language with the ontology, we propose a novel tagging scheme based on the kinematic problem understanding model in named entity recognition (NER). Finally, extensive experiments are conducted, and the experimental results show that the performance of the proposed method on a dataset of kinematic problems from authoritative sources better than the baseline algorithms.

X-ray tomography has been widely used in various research fields thanks to its capability of observing 3D structures with high resolution non-destructively. However, due to the nonlinearity and inconsistency of detector pixels, ring artifacts usually appear in tomographic reconstruction, which may compromise image quality and cause nonuniform bias. This study proposes a new ring artifact correction method based on the residual neural network (ResNet) for X-ray tomography. The artifact correction network uses complementary information of each wavelet coefficient and a residual mechanism of the residual block to obtain high-precision artifacts through low operation costs. In addition, a regularization term is used to accurately extract stripe artifacts in sinograms, so that the network can better preserve image details while accurately separating artifacts. When applied to simulation and experimental data, the proposed method shows a good suppression of ring artifacts. To solve the problem of insufficient training data, ResNet is trained through the transfer learning strategy, which brings advantages of robustness, versatility and low computing cost.

Wall-resolved large-eddy simulations (WR-LES) are conducted to study transitional and turbulent boundary-layer properties and flow structures downstream of a zigzag trip on the low-Reynolds-number SD7032 airfoil at [Formula: see text] and [Formula: see text] and angle of attack [Formula: see text]. Without a zigzag trip, the airfoil features a prominent laminar separation bubble on the suction side. First, a verification and validation study of the LES method is conducted for the zigzag trip at a roughness-height-based Reynolds number [Formula: see text]. The boundary-layer velocity profiles and the integral parameters are compared with experimental data acquired using a long-distance [Formula: see text]-particle image velocimetry setup in a wind tunnel. In the LES, if the zigzag tape is thick enough to trigger transition just behind the tape, the transition to turbulence is generally quicker than what was observed in the experiment. Although the general phenomenology and the development of the turbulent boundary layer after transition are consistent with the experimental data, there remain some differences in the exact length of the transitional region and also in the associated airfoil drag. A zigzag tape with [Formula: see text] or thicker induces transition quickly behind the trip through a shedding process of the shear layer created by the tape. The boundary layer behind the zigzag tape features prominent three-dimensional vortical structures emanating from the zigzags that persist over long chordwise distances and well into the turbulent boundary layer. Nevertheless, in a spanwise-averaged sense, the integral thicknesses of the newly created turbulent boundary layer are actually quite similar to a two-dimensional boundary-layer theory solution, given that the tape has a suitable thickness. In contrast, an overly thick tape will generate a thicker turbulent boundary layer, where the increased displacement and momentum loss are sustained and also contribute to total drag.

Although point and nonpoint sources contribute roughly equal nutrient loads to lakes, their relative role in supporting algae growth has not been clarified. In this research, we have established a quantitative relationship between algae-available phosphorus (P) and P chemical fractions in sediments; the latter indicates the relative contribution of point versus nonpoint sources. Surface sediments from three large shallow lakes in eastern China, namely, the Chaohu, Taihu and Hongzehu Lakes, were sampled to assess their algae-available P and chemically extracted P fractions. The algae-available P primarily comes from iron/aluminium (hydr)oxide-bound P (Fe/Al-bound P), 45% of which is algae-available P. The ratio of Fe/Al-bound P to calcium compound-bound P (Ca-bound P) indicated the relative contribution of point to nonpoint sources, with the point sources contributing the majority of increased Fe/Al-bound P in sediments. Therefore, the reduction of point sources from urbanized areas, rather than nonpoint sources from agricultural areas that primarily contribute to the Ca-bound P fraction, should be prioritized to alleviate cyanobacterial algal blooms (CyanoHABs) in shallow lakes with sediment P as a potential source to support algae growth. With these important results, we proposed a conceptual model for "P-pumping suction" from sediments to algae to aid in the development of the criteria for sediment P concentrations in shallow lakes.

In recent years, environmental pollution and resource waste have become the focus of heated discussion around the world. In order to realize green and sustainable development, the development of green supply chain (GSC) has attracted the attention of many scholars. The research object of this paper is the dual-channel green supply chain (DGSC) composed of online channels and offline channels. The supplier is the leader of the entire DGSC, and it needs to optimize the wholesale price of the product and the level of green investment to maximize its own benefit. In addition, the supplier also needs to establish contracts with network sales platform (NSP) and store sales platform (SSP) to increase the benefit of NSP and SSP, and ultimately increase the benefit level of the entire DGSC. Among them, we constructed a centralized decision (CD) model and a decentralized decision (DD) model, and obtained the optimal pricing, optimal greenness and optimal benefit of the supplier, NSP and SSP under the two models. We found that the benefits of SC members under the DD model are generally lower than those of the CC model. Therefore, we built contracts between the supplier, NSP and SSP to coordinate. Finally, we substituted specific parameters to verify the model. The following conclusions are drawn: (i) When consumers prefer online sales channels, it will positively affect the online sales price, online sales volume and greenness level of DGSC. At the same time, it negatively affects offline sales price and sales volume. (ii) The benefit of DGSC and the benefit of NSP, SSP, and the supplier show a phenomenon of decline first and then increase with respect to consumers’ preference for online sales channels. (iii) Under the coordination contract, the subsidy factor positively affects the benefits of the supplier and online and offline wholesale prices, and negatively affects the benefits of online platforms and physical stores.