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•We identified 7 new genetic regions for factor VIII levels, 1 for von Willebrand factor levels, and 3 in a combined analysis.•Silencing B3GNT2and CD36reduced factor VIII release in vitro.Silencing B3GNT2, CD36, and PDIA3reduced von Willebrand factor release.

Bi2SeO5has garnered considerable attention as a van der Waals (vdW) layered dielectric material featuring excellent electrical insulation properties. However, the related theoretical understanding of the dielectric properties of atomically thin films is still lacking. Here, we conducted the first-principles calculations to determine the dielectric performance of Bi2SeO5, showing a high average dielectric constant (ε) of >20 ranging from bulk to bilayer and monolayer. Besides, the conduction and valance band offsets between the monolayer Bi2SeO5and bilayer Bi2O2Se were calculated to be greater than 1 eV, suggesting that monolayer Bi2SeO5works well as the dielectric for atomically thin Bi2O2Se. Unlike h-BN or other two-dimensional (2D) vdW insulators, εof Bi2SeO5is dominated by its ionic component and remains nearly constant as the thickness decreases, demonstrating an ultralow equivalent oxide thickness (EOT) of 0.3 nm for its monolayer form. Moreover, the high εof monolayer Bi2SeO5survives under tensile or compressive strains up to 6%, which greatly facilitates its integration with various 2D semiconductors. Our work suggests that Bi2SeO5ultrathin films can serve as excellent atomically flat encapsulation and dielectric layers for high-performance 2D electronic devices.

Objective: To leverage machine learning (ML) for fast selection of optimal regularization parameter in constrained image reconstruction. Methods: Constrained image reconstruction is often formulated as a regularization problem and selecting a good regularization parameter value is an essential step. We solved this problem using an ML-based approach by leveraging the finding that for a specific constrained reconstruction problem defined for a fixed class of image functions, the optimal regularization parameter value is weakly subject-dependent and the dependence can be captured using few experimental data. The proposed method has four key steps: a) solution of a given constrained reconstruction problem for a few (say, 3) pre-selected regularization parameter values, b) extraction of multiple approximated quality metrics from the initial reconstructions, c) predicting the true quality metrics values from the approximated values using pre-trained neural networks, and d) determination of the optimal regularization parameter by fusing the predicted quality metrics. Results: The effectiveness of the proposed method was demonstrated in two constrained reconstruction problems. Compared with L-curve-based method, the proposed method determined the regularization parameters much faster and produced substantially improved reconstructions. Our method also outperformed state-of-the-art learning-based methods when trained with limited experimental data. Conclusion: This paper demonstrates the feasibility and improved reconstruction quality by using machine learning to determine the regularization parameter in constrained reconstruction. Significance: The proposed method substantially reduces the computational burden of the traditional methods (e.g., L-curve) or relaxes the requirement of large training data by modern learning-based methods, thus enhancing the practical utility of constrained reconstruction.

Network pruning can reduce the computation cost of deep neural network (DNN) models. However, sparse models often produce randomly-distributed weights to maintain accuracy, leading to irregular computations. Consequently, unstructured sparse models cannot achieve meaningful speedup on commodity hardware built for dense matrix computations. Accelerators are usually modified or designed with structured sparsity-optimized architectures for exploiting sparsity. For example, the Ampere architecture introduces a sparse tensor core, which adopts the 2:4 sparsity pattern. We propose a pruning method that builds upon the insight that matrix multiplication generally breaks the large matrix into multiple smaller tiles for parallel execution. We present the “tile-wise” sparsity pattern, which maintains a structured sparsity pattern at the tile level for efficient execution but allows for irregular pruning at the global scale to maintain high accuracy. In addition, the tile-wise sparsity is implemented at the global memory level, and the 2:4 sparsity executes at the register level inside the sparse tensor core. We can combine these two patterns into a “tile-vector-wise” (TVW) sparsity pattern to explore more fine-grained sparsity and further accelerate the sparse DNN models. We evaluate the TVW on the GPU, achieving averages of 1.85$\boldsymbol{\times}$×, 2.75$\boldsymbol{\times}$×, and 22.18$\boldsymbol{\times}$× speedups over the dense model, block sparsity, and unstructured sparsity.

Image reconstruction from limited and/or sparse data is known to be an ill-posed problem and a priori information/constraints have played an important role in solving the problem. Early constrained image reconstruction methods utilize image priors based on general image properties such as sparsity, low-rank structures, spatial support bound, etc. Recent deep learning-based reconstruction methods promise to produce even higher quality reconstructions by utilizing more specific image priors learned from training data. However, learning high-dimensional image priors requires huge amounts of training data that are currently not available in medical imaging applications. As a result, deep learning-based reconstructions often suffer from two known practical issues: a) sensitivity to data perturbations (e.g., changes in data sampling scheme), and b) limited generalization capability (e.g., biased reconstruction of lesions). This paper proposes a new method to address these issues. The proposed method synergistically integrates model-based and data-driven learning in three key components. The first component uses the linear vector space framework to capture global dependence of image features; the second exploits a deep network to learn the mapping from a linear vector space to a nonlinear manifold; the third is an unrolling-based deep network that captures local residual features with the aid of a sparsity model. The proposed method has been evaluated with magnetic resonance imaging data, demonstrating improved reconstruction in the presence of data perturbation and/or novel image features. The method may enhance the practical utility of deep learning-based image reconstruction.

ABSTRACTA steam generator heat transfer tube is important for energy exchange between the primary circuit and secondary circuit . The changes in friction and wear properties of Inconel 690 alloy and 403 stainless-steel anti-vibration strips in different media (air, deionized water, and boric acid solution) were compared and analyzed. Friction and wear tests were conducted in different media environments and under different loads, and finite element simulation was performed in different media environments.Results show that the main wear mechanisms in the air are delamination wear and oxidation friction. The oxygen content in the water is reduced, and the oxide formed is reduced. Because the solution replaces the abrasive particles to form the third body, which can reduce friction and lubricate, the abrasive particles and abrasive particles can be easily removed or dissolved. The wear mechanism in a water environment is delamination wear and abrasive wear. In boric acid, a passive film is formed on the contact wear surface of the Inconel 690 alloy after the previous fretting wear. The passivation film is worn first in the next fretting wear, which slows down wearing. The wear mechanism in a boric acid environment primarily includes abrasive wear and corrosion wear.

After three decades of collective leadership, strongman rule has returned to China. But do the Chinese people prefer this political system, characterized by a leader with unchecked power? Using four waves of data from the World Values Survey, this study finds, first, that from 2001 to 2018, public preference for strongman rule in China was moderate compared to other countries. Second, the Chinese people expressed a growing desire for a strongman from 2001 to 2007 and again in 2012, but this desire did not increase further between 2012 and 2018. Third, in the 2000s, trust in the central government was negatively associated with strongman preference, while in the 2010s, perceptions of national crisis and political socialization became increasingly relevant. This study evaluates the degree, dynamics, and possible drivers of public preference for strongman rule in China and contributes to the understanding of political strongmen and their popular bases worldwide.

Images of anatomical regions and neuron type distribution, as well as their related literature are valuable assets for neuroscience research. They are vital evidence and vehicles in discovering new phenomena and knowledge refinement through image and text big data. The knowledge acquired from image data generally echoes with the literature accumulated over the years. The knowledge within the literature can provide a comprehensive context for a deeper understanding of the image data. However, it is quite a challenge to manually identify the related literature and summarize the neuroscience knowledge in the large‐scale corpus. Thus, neuroscientists are in dire need of an automated method to extract neuroscience knowledge from large‐scale literature. A proposed deep learning model named BioBERT‐CRF extracts brain region entities from the WhiteText dataset. This model takes advantage of BioBERT and CRF to predict entity labels while training. The proposed deep learning model demonstrated comparable performance against or even outperforms the previous models on the WhiteText dataset. The BioBERT‐CRF model has achieved the best average precision, recall, and F1 score of 81.3%, 84.0%, and 82.6%, respectively. We used the BioBERT‐CRF model to predict brain region entities in a large‐scale PubMed abstract dataset and used a rule‐based method to normalize all brain region entities to three neuroscience dictionaries. Our work shows that the BioBERT‐CRF model can be well‐suited for brain region entity extraction. The rankings of different brain region entities by their appearance in the large‐scale corpus indicate the anatomical regions that researchers are most concerned about. Author summary:In this study, the BioBERT‐CRF model was used to extract brain region entities from a large‐scale PubMed abstract dataset and a normalization pipeline was created for normalizing all the labeled brain region entities extracted to three neuroscience dictionaries. Prior to entity prediction, the performance of the BioBERT‐CRF model was evaluated using the WhiteText dataset. Compared to other deep learning models, the BioBERT‐CRF model achieved the best average precision, recall, and F1 score of 81.3%, 84.0%, and 82.6%, respectively. Our work demonstrates how the BioBERT‐CRF model can be well‐suited for neuroscience brain region entity extraction. The rankings of different brain region entities by their appearance in the large‐scale corpus reflect the anatomical regions that researchers are most concerned with.

Non-metallic inclusions are critical for the fatigue failure of clean steels in service; especially, the large and hard inclusions are detrimental. Since it is not possible to measure all the inclusions in the large-volume clean steels, statistical models have been developed to evaluate inclusions, aiming at predicting the maximum inclusion size in the large volume from the data of inclusions, which are derived from the limited observations on small-volume specimens. Different statistical models were reviewed together with their supporting theories. In particular, the block maxima and the threshold types of models were discussed through a thorough comparison as they are both widely used and based on the extreme value theory. The predicted results not only are used to distinguish the different cleanliness levels of steels, but also help to estimate fatigue strength. Finally, future research is proposed to focus on tackling the present difficulties encountered by statistical models, including the sufficient credibility of obtained results and the robustness of models for applications.

The Cramér–Rao lower bound (CRLB) indicates the inherent power of stochastic optical localization nanoscopy in spatially resolving emitters. The CRLB for a known and identical intensity of emitters was obtained previously. In practice, the intensities of emitters at times are unknown and must be estimated along with emitter localization. To study effect of unknown emitter intensities on localization accuracy, we analyze Fisher information and CRLB and obtain their formulas in three extended cases: emitter intensities are (i) known and arbitrary, (ii) known to be identical with an unknown value, and (iii) all unknown. The effect of unknown emitter intensities on CRLB is then numerically investigated in three scenarios: 2D emitters on a line with an Airy point spread function (PSF), 2D randomly distributed emitters with a Gaussian PSF, and 3D randomly distributed emitters with an astigmatic PSF. In all three scenarios it is shown that in comparison with the case of a known and identical emitter intensity, cases (i) and (ii) slightly increase CRLB; however, case (iii) significantly increases CRLB no matter whether the emitter intensities are identical or not. These results imply that, in practice, the emitter intensities, which are known a priori to have an identical value, incur little penalty on localization accuracy. In contrast, the emitter intensities, which are all unknown, significantly lower localization accuracy unless the emitter density is so low as to have little chance of overlapped PSFs. The analytical and numerical results can be extensively applied to set up an experiment, develop localization algorithms, and benchmark localization algorithms.

Hydrodenitrogenation (HDN) is still a challenging process that many refineries need to overcome. The influence of catalysts on HDN efficiency cannot be ignored. In this study, Qilu atmospheric residue was subjected to a pilot-scale continuous hydrotreater, and liquid products were obtained from four outlets under two typical catalyst gradations. Basic nitrogen (BN) and non-basic nitrogen (NBN) compounds in the feedstock and liquid products were investigated by positive and negative (±) electrospray ionization (ESI) Fourier transform ion cyclotron resonance mass spectrometry. It shows that during hydrotreating, compared to BN, NBN compounds were more refractory. The removal of multi-heteroatom compounds occurred mainly in BN compounds, and the conversion of multi-heteroatom compounds and the hydrodearomatization of N1class species happened simultaneously in NBN compounds. By comparing the effects of two typical catalyst gradations on the products, it was clear that in the removal of N1class species, catalyst gradations had subtle differences. The significant difference in N removal efficiency was reflected in removal and transformation of N-containing compounds containing multiple heteroatoms in both BN and NBN compounds during the refining section. This observation provides a new contribution to understanding the use of different catalyst gradations in the beds and the effect of each catalyst on the resulting liquid products.

Biothiol assays offer vital insights into health assessment and facilitate the early detection of potential health issues, thereby enabling timely and effective interventions. In this study, we developed ultrasmall CuMn-Histidine (His) nanozymes with multiple enzymatic activities. CuMn-His enhanced peroxidase (POD)-like activity at neutral pH was achieved through hydrogen bonding and electrostatic effects. In addition, CuMn-His possesses laccase (LAC)-like and superoxide dismutase (SOD)-like activities at neutral pH. Based on three different enzyme mimetic activities of CuMn-His at neutral pH, the colorimetric sensing array without changing the buffer solution was successfully constructed. The array was successfully used for the identification of three biothiols, glutathione (GSH), cysteine (Cys), and homocysteine (Hcy). Subsequently, excellent application results were shown in complex serum and cellular level analyses. This study provides an innovative strategy for the development of ultrasmall bimetallic nanozymes with multiple enzymatic activities and the construction of colorimetric sensing arrays.

Photodynamic therapy (PDT) is an auspicious strategy for cancer therapy by yielding reactive oxygen species (ROS) under light irradiation. Here, we have developed near-infrared (NIR) triggered polymer encapsulated upconversion nanoparticles (UCNPs) based on aggregation-induced emission (AIE) characteristics and mitochondria target ability for PDT. The coated AIE polymer as a photosensitizer can be photoactivated by the up-converted energy of UCNPs upon 980 nm laser irradiation, which could generate ROS efficiently in mitochondria and induce cell apoptosis. Moreover, a “sheddable” poly(ethylene glycol) (PEG) layer was easily conjugated at the surface of NPs. The pH-responsive PEG layer shields the surface positive charges and shows stronger protein-resistance ability. In the acidic tumor environment, PEGylated NPs lose the PEG layer and show the mitochondria-targeting ability by responding to tumor acidity. A cytotoxicity study indicated that these NPs have good biocompatibility in the dark but exert severe cytotoxicity to cancer cells, with only 10% cell viability, upon being irradiated with an NIR laser. The AIE nanoparticles are a good candidate for effective mitochondria targeting photosensitizer for PDT.

Verticillin A is a diketopiperazine compound which was previously isolated from Amanita flavorubescensAlk (containing parasitic fungi Hypomyces hyalines(Schw.) Tul.). Here, we initially found, by wound healing assay and Transwell assay in vitro, that verticillin A possesses an inhibitory effect against the migration and invasion of the human colon cancer cell. Subsequently, c-mesenchymal-epithelial transition factor (c-Met) was identified as a molecular target of verticillin A by screening key genes related to cell migration. Verticillin A-mediated c-Metsuppression is at the transcriptional level. Further study demonstrated that verticillin A suppressed c-MET phosphorylation and decreased c-MET protein level. In addition, verticillin A inhibited the phosphorylation of c-MET downstream molecules including rat sarcoma (Ras)-associated factor (Raf), extracellular signal-regulated kinase (ERK), and protein kinase B (AKT). Overexpression of Erkpartially reversed the verticillin A-mediated anti-metastasis action in the human colon cancer cell. More importantly, verticillin A also inhibited cancer cell metastasis in vivo. Thus, verticillin A can significantly inhibit the migration and invasion of colon cancer cells by targeting c-Metand inhibiting Ras/Raf/mitogen-activated extracellular signal-regulated kinase (MEK)/ERK signaling pathways. Therefore, we determined that verticillin A is a natural compound that can be further developed as an anti-metastatic drug in human cancers.

Background and aims: Endoscopic submucosal dissection (ESD) was developed as a feasible and reliable treatment for gastrointestinal mucosal or submucosal lesions. Bacteremia may occur after ESD. Currently, the use of prophylactic antibiotics after gastric ESD is controversial. This study was designed to explore the value of prophylactic antibiotics in the treatment of gastric mucosal and submucosal lesions after ESD. Methods: In this prospective study, in total, 103 patients who underwent gastric ESD were included, and the frequency of bacteremia and changes in inflammation-related indicators before and after the procedure were investigated. The patients did not receive prophylactic antibiotics. Results: The body temperature (T), white blood cell count (WBC), and procalcitonin (PCT) and C-reactive protein (CRP) levels did not significantly differ between the perforation group (N= 40) and nonperforation group before gastric ESD (N= 63) (all P > 0.05). The T, WBC, and CRP levels (all P < 0.05) in the patients with perforation were significantly higher than those in the patients without perforation 24 h after gastric ESD. The incidence of bacteremia did not significantly differ between the two groups (all P > 0.05). In total, 6 of 309 blood cultures were cultured with bacteria. The total frequency of bacteremia in the perforation and nonperforation groups was 2.5% and 3.2%, respectively. No sepsis occurred in any patients. Conclusion: Regardless of whether perforation occurred after gastric ESD, the incidence of bacteremia was transient and low. Therefore, based on our single-center study in China, prophylactic antibiotics may not be necessary in patients with intraoperative perforation during gastric ESD. Trial registration: A single-center prospective study at chictr.org.cn (ChiCTR1800019668).

Asthma is a common respiratory disorder with a highly heterogeneous nature that remains poorly understood. The objective was to use whole genome sequencing (WGS) data to identify regions of common genetic variation contributing to lung function in individuals with a diagnosis of asthma.

Shortcut nitrification-denitrification (SCND) is widely concerned because of its low energy consumption and high nitrogen removal efficiency. However, the current difficulty lies in the stable maintenance of SCND performance, which leads to the challenge of large-scale application of this new denitrification technology. In this study, the nitrogen removal pathway from complete nitrification-denitrification (CND) to SCND was rapidly realized under high free ammonia (FA), high pH and low dissolved oxygen (DO) conditions. The variations of specific oxygen uptake rate (SOUR) of activated sludge in both processes were investigated by an online SOUR monitoring device. Different curves of SOUR from CND to SCND process were observed, and the ammonia peak obtained based on SOUR monitoring could be used to control aeration time accurately in SCND process. Accordingly, the SOUR ratio of ammonia oxidizing bacteria (AOB) to nitrite oxidizing bacteria (NOB) (SOURAOB/SOURNOB) was increased from 1.40 to 2.93. 16S rRNA Miseq high throughput sequencing revealed the dynamics of AOB and NOB, and the ratio of relative abundance (AOB/NOB) was increased from 1.03 to 3.12. Besides, SOURAOB/SOURNOBdisplayed significant correlations to ammonia removal rate (P < 0.05), ammonia oxidation rate / nitrite oxidation rate (P < 0.05), nitrite accumulation rate (P < 0.05) and the relative abundance of AOB/NOB (P < 0.05). Thus, a strategy for evaluation the SCND process stability based on online SOUR monitoring is proposed, which provides a theoretical basis for optimizing the SCND performance.

The dissimilar weld joint of HR3C steel and T92 steel is widely used in the ultra supercritical (USC) boiler due to the different service conditions of HR3C steel and T92 steel. It is important to study how serviced influenced the HR3C/T92 dissimilar joint. The HR3C/T92 dissimilar joint had already served 51020 h under 605 °C and 26.25 MPa. The microstructure and mechanical properties of the long term serviced HR3C/T92 dissimilar joint were tested by XRD, SEM, EPMA,TEM, the impact testing machine and the creep and stress rupture testing machine. The XRD results show that the weld is mainly γ-Fe, which is similar to the HR3C base metal, while the T92 base metal is mainly α-Fe. The SEM results indicate that serviced HR3C’s precipitation behavior is complicated, cause multiple precipitated phases precipitated during the service. Chain-liked M23C6 precipitated along the grain boundary. M23C6 phase cause the chromium depletion zone along the grain boundary. T92 base metal precipitated lots of carbide particles during the service. Those carbide particles are around 1 μm. The impact fracture of HR3C base metal is brittle fracture while the impact fracture of T92 base metal is ductile fracture. Moreover, the impact fracture of weld is mixed fracture. Chain-liked M23C6 along the grain boundary is the main cause of the brittleness of serviced HR3C steel.The constant strain creep tests were proceeded under 650 °C, 700 °C and 750 °C. The high-temperature creep resistance of HR3C base metal is better than that of T92 base metal after service. The resistant to high-temperature creep of served joint is similar to that of serviced T92 base metal. With the increase of temperature, the creep resistance of the T92 base metal decreases the most. On the contrary, the creep resistance of the HR3C base metal shows the minimum reduction.

The profession of genetic counseling (also called genetic counselling in many countries) began nearly 50 years ago in the United States, and has grown internationally in the past 30 years. While there have been many papers describing the profession of genetic counseling in individual countries or regions, data remains incomplete and has been published in diverse journals with limited access. As a result of the 2016 Transnational Alliance of Genetic Counseling (TAGC) conference in Barcelona, Spain, and the 2017 World Congress of Genetic Counselling in the UK, we endeavor to describe as fully as possible the global state of genetic counseling as a profession. We estimate that in 2018 there are nearly 7000 genetic counselors with the profession established or developing in no less than 28 countries.

The rapid growth of grain yield in China accelerates a discussion on whether the grain system in China is sustainable. To answer the question, a comprehensive assessment from economic and environmental points is necessary. This study jointly used economic analysis (ECA), emergy evaluation (EME) and carbon footprint (CF) to analyze the environmental and economic sustainability of the grain production system in China based on the national statistical data during 2000–2015. Results showed that the costs of maize, wheat, rice and soybean had increased by 252–346% from 2000 to 2015, causing the lower profit of grain system in recent years. The situation resulted in a serious problem on economic sustainability of grain system in China. Meanwhile, the emergy sustainability index (ESI) of maize, wheat, rice and soybean systems were increasing during 2000–2015, and the CF on unit yield of the crops had been reduced by 10–30% in the study period. The results reflected the improved environmental sustainability of grain system in China during 2000–2015. Nevertheless, the emergy flow of industrial inputs for the crops were increased by 4–22% in the study period, and the CF from the inputs presented a growth rate of 16–23% as well during the same period. The results implied that the grain system in China was relying more on fossil-based inputs. Finally, according to the key points of cost, emergy and CF, we suggest that improving labor efficiency, advanced agricultural practices and optimizing cropping pattern will be effective ways to further improve the economic and environmental sustainability of grain system in China.

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Current loose nanofiltration membrane materials are not suitable for harsh dye/salt separation environments because they are not resistant to high temperatures, acids and bases, and organic solvents. In this work, a polybenzimidazole (PBI) hollow fiber loose nanofiltration membrane (HFLNM) was prepared by dry-wet spinning and used for dye and salt separation. The membrane showed good heat resistance (25–70 ℃), acid resistance (immerse in10 mol/L HCl solution for 100 h) and solvent resistance (immerse in EtOH, MeOH and IPA for 120 h, respectively). The effects of air gap distance on the membrane structure and properties as well as the effect of cross-linking using K2S2O8on the solvent resistance of the membrane were investigated. By adjusting the air gap distance to 6 cm, the dense layer of the optimized membrane got thinner. With the help of double layer finger-like pore and strong negative charge, the membrane exhibited outstanding water flux (64.5 L·m−2·h−1), low NaCl salt rejection (∼ 7%), and excellent Congo Red (CR) dye rejection (100%) at the dye/salt mixture solution (100 mg/L Congo red, 1 g/L NaCl) and operating pressure of 0.1 MPa. Moreover, the crosslinked PBI membrane showed good stability after 72 h immersion in polar aprotic solvents (NMP, DMF, and DMAc). Hence, PBI HFLNM has a good application prospect in the field of dye/salt separation in harsh environments.

China's Chang'e‐5 (CE‐5) mission successfully returned a total of 1.731 kg of lunar material from the north‐eastern Oceanus Procellarum region. Young (∼2.0 Ga) ages were reported from the CE‐5 basalts. However, there are controversies on whether they are low‐Ti or high‐Ti mare basalt type. Here, we report the results of a comprehensive petrographic, elemental, and Pb isotopic investigation on a 17.6 mg CE‐5 basalt clast (CE5C0000YJYX048). By combining high‐resolution X‐ray tomography of the clast and microbeam (scanning electron microscope SEM, electron probe microanalyzer EPMA) analyses of mineral grains peeled off the clast surface, we derived the bulk clast chemical composition with a very small sample consumption (i.e., <3 mg). The poikilitic clast has a bulk TiO2content of 3.78 ± 1.01 wt%, which is representative of a low‐Ti basalt end‐member in the lunar mare basalts samples. Furthermore, the pyroxene in the studied clast is dominantly pigeonite, in contrast to other reported CE‐5 samples where augite is the dominant pyroxene mineralogy. Despite these compositional peculiarities, in situ secondary ion mass spectrometer dating yielded a Pb‐Pb age of 2,040 ± 22 Ma for the fragment, which is consistent with ages reported from other CE‐5. Based on the chemical and age data analyzed in this study, it can be concluded that the lunar basalts obtained from the CE‐5 mission originated from a low‐Ti mare basalt‐type source. The mineral composition of the mare basalt clast studied, which is dominated by pigeonite, fayalite, and anorthite, likely resulted from late‐stage magmatic crystallization when the magma was enriched in Fe. Studying lunar meteorites and samples returned from missions are key ways to conduct scientific research and increase our knowledge of the moon. These methods allow us to move beyond simple observations and make direct measurements of lunar materials. After 45 years from the Apollo and Luna missions, in December 2020, Chang'E‐5 (CE‐5), China's first lunar mission, returned with a total of 1.731 kg of lunar soil. The basalt clasts in the soil are dated at ∼2 billion years ago, which represents the youngest lunar basalt ever studied. The origin of the CE‐5 basalts is still debated because it is composed of heterogeneous materials. The CE‐5 basaltic clast studied here has a unique mineral composition compared to previous data of other CE‐5 basaltic clasts and Apollo basalt samples. This discrepancy is ascribed to the different stages of magma formation in which the occurrence of different minerals subtracted elements and changed the magma's composition. As a result, the heterogeneity of the CE‐5 returned lunar basalts relates to different stages of magma evolution. The present project adds new knowledge to understanding the formation of the lunar basalts around the CE‐5 landing site. A new processing method of lunar rock clast is given to characterize the composition and age at milligram‐level sample consumptionAn independent high‐precision in situ secondary ion mass spectrometer Pb‐Pb age of 2,040 ± 22 Ma is determined for a basaltic clast from the Chang'e‐5 lunar missionThe basaltic clasts returned by Chang'e‐5 mission have remarkable compositional variability and are likely composed of low‐Ti mare basalt A new processing method of lunar rock clast is given to characterize the composition and age at milligram‐level sample consumption An independent high‐precision in situ secondary ion mass spectrometer Pb‐Pb age of 2,040 ± 22 Ma is determined for a basaltic clast from the Chang'e‐5 lunar mission The basaltic clasts returned by Chang'e‐5 mission have remarkable compositional variability and are likely composed of low‐Ti mare basalt

Supplemental digital content is available in the text.

PurposeImplementation research provides a structure for evaluating the clinical integration of genomic medicine interventions. This paper describes the Implementing Genomics in Practice (IGNITE) Network’s efforts to promote (i) a broader understanding of genomic medicine implementation research and (ii) the sharing of knowledge generated in the network.MethodsTo facilitate this goal, the IGNITE Network Common Measures Working Group (CMG) members adopted the Consolidated Framework for Implementation Research (CFIR) to guide its approach to identifying constructs and measures relevant to evaluating genomic medicine as a whole, standardizing data collection across projects, and combining data in a centralized resource for cross-network analyses.ResultsCMG identified 10 high-priority CFIR constructs as important for genomic medicine. Of those, eight did not have standardized measurement instruments. Therefore, we developed four survey tools to address this gap. In addition, we identified seven high-priority constructs related to patients, families, and communities that did not map to CFIR constructs. Both sets of constructs were combined to create a draft genomic medicine implementation model.ConclusionWe developed processes to identify constructs deemed valuable for genomic medicine implementation and codified them in a model. These resources are freely available to facilitate knowledge generation and sharing across the field.

In this paper, we propose a novel graph-based semisupervised learning framework, called joint sparse representation and embedding propagation learning (JSREPL). The idea of JSREPL is to join EPL with sparse representation to perform label propagation. Like most of graph-based semisupervised propagation learning algorithms, JSREPL also constructs weights graph matrix from given data. Different from classical approaches which build weights graph matrix and estimate the labels of unlabeled data in sequence, JSREPL simultaneously builds weights graph matrix and estimates the labels of unlabeled data. We also propose an efficient algorithm to solve the proposed problem. The proposed method is applied to the problem of semisupervised image clustering using the ORL, Yale, PIE, and YaleB data sets. Our experiments demonstrate the effectiveness of our proposed algorithm.

Purpose: To minimize the mismatch error between patient surface and immobilization system for tumor location by a noninvasive patient setup method.Materials and Methods: The method, based on a point set registration, proposes a shift for patient positioning by integrating information of the computed tomography scans and that of optical surface landmarks. An evaluation of the method included 3 areas: (1) a validation on a phantom by estimating 100 known mismatch errors between patient surface and immobilization system. (2) Five patients with pelvic tumors were considered. The tumor location errors of the method were measured using the difference between the proposal shift of cone-beam computed tomography and that of our method. (3) The collected setup data from the evaluation of patients were compared with the published performance data of other 2 similar systems.Results: The phantom verification results showed that the method was capable of estimating mismatch error between patient surface and immobilization system in a precision of <0.22 mm. For the pelvic tumor, the method had an average tumor location error of 1.303, 2.602, and 1.684 mm in left–right, anterior–posterior, and superior–inferior directions, respectively. The performance comparison with other 2 similar systems suggested that the method had a better positioning accuracy for pelvic tumor location.Conclusion: By effectively decreasing an interfraction uncertainty source (mismatch error between patient surface and immobilization system) in radiotherapy, the method can improve patient positioning precision for pelvic tumor.

Reactive sulfur species (RSS) are emerging as a potential key gasotransmitter in diverse physiological processes linking two signaling molecules H2S and SO2. However, the exact roles of H2S and SO2remain unclear. A major hurdle is the shortage of accurate and robust approaches for sensing of H2S and SO2in biological systems. Herein, we report a reaction-mediated dual-recognition strategy-based nanosensor, silver nanoparticles (AgNPs)-loaded MIL-101 (Fe) (ALM) hybrids, for the simultaneous detection of H2S and SO2in a living cell. Upon exposure to H2S, AgNPs can be oxidized to form Ag2S, causing a decrease of surface enhanced Raman spectroscopy (SERS) signals of p,p′-dimercaptoazobenzene. Moreover, SO2reacts with the amino moiety of MIL-101 to form charge-transfer complexes, resulting in an increment of fluorescent (FL) intensity. The ALM with dual-modal signals can simultaneously analyze H2S and SO2at a concentration as low as 2.8 × 10–6and 0.003 μM, respectively. Most importantly, the ALM sensing platform enables targeting mitochondria and detection multiple RSS simultaneously in living cells under external stimulation, as well as displays indiscernible crosstalk between SERS and FL signals, which is very beneficial for the comprehension of physiological issues related with RSS.

Tuning the ferroelectric domain structure by a combination of elastic and electrostatic engineering provides an effective route for enhanced piezoelectricity. However, for epitaxial thin films, the clamping effect imposed by the substrate does not allow aftergrowth tuning and also limits the electromechanical response. In contrast, freestanding membranes, which are free of substrate constraints, enable the tuning of a subtle balance between elastic and electrostatic energies, giving new platforms for enhanced and tunable functionalities. Here, highly tunable piezoelectricity is demonstrated in freestanding PbTiO3membranes, by varying the ferroelectric domain structures from c-dominated to c/aand adomains via aftergrowth thermal treatment. Significantly, the piezoelectric coefficient of the c/adomain structure is enhanced by a factor of 2.5 compared with typical cdomain PbTiO3. This work presents a new strategy to manipulate the piezoelectricity in ferroelectric membranes, highlighting their great potential for nano actuators, transducers, sensors and other NEMS device applications.

Lamotrigine is an antiepileptic drug that can be used to control many types of seizures as a single-agent or an add-on therapy in patients over 2 years of age. In addition to common adverse reactions, this current case report describes a paediatric male patient with a rare side-effect of persistent penile erectile due to lamotrigine. Previous studies have shown that it can improve sexual function in adult male patients. This patient suffered from refractory epilepsy and pneumonia. He had taken a variety of antiepileptic drugs for a long time and developed priapism after the dosage of lamotrigine had been increased. The priapism improved after drug withdrawal and sedation. Further research is needed to elucidate the mechanism of this rare side-effect.

Introduction:Colorectal cancer is the third most common malignancy and the second most deadly cancer worldwide. In this present study, the effects of eupafolin on DMH-induced colon cancer in rats were assessed.Methods:The acute and sub-acute oral toxicity study in the balb/c mice was performed to evaluate the LD50 dose and the chemotherapeutic doses of eupafolin. The colon cancer was induced in the animals through a single intraperitoneal injection (i.p) of 30 mg/kg of dimethylhydrazine followed by 2% DSS for 7 days in the drinking water in male Wistar rats. The rats were treated with eupafolin (50, 100, and 200 mg/kg) through oral route for 18 weeks. The animals were sacrificed and colon tissues were subsequently investigated for aberrant crypt foci (ACF), in vivo antioxidant studies, histology and immunohistochemical analysis, and apoptosis by TUNNEL technique after 18 weeks of eupafolin therapy.Results:The acute oral toxicity data represented the LD50 dose of eupafolin which was found to be 500 mg/kg body weight. Along with that, the sub-acute toxicity study suggested the chemotherapeutic doses of eupafolin, that is, 50, 100, and 200 mg/kg body weight. Eupafolin therapy inhibits ACF development in rat colon mucosa efficiently. Additionally, eupafolin has improved the colonic lesions and the structural integrity of the colonic mucosa. Eupafolin therapy causes anti-oxidant enzymes such as superoxide dismutase, catalase, and glutathione to increase as well. Increased levels of P53, BAX, and PCNA and a simultaneous decrease in Bcl2 and IL-6 expressions show eupafolin therapy successfully regulated these biological markers in colorectal cancer. Eupafolin also induced apoptosis efficiently in the rat colon mucous membrane.Conclusion:These results show that eupafolin can improve colon cancer by modulating the p53, Bcl2, BAX, and IL-6 pathways in rats.

Meshless methods for in silico modeling and simulation of cardiac electrophysiology are gaining more and more popularity. These methods do not require a mesh and are more suitable than the Finite Element Method (FEM) to simulate the activity of complex geometrical structures like the human heart. However, challenges such as numerical integration accuracy and time efficiency remain, which limit their applicability. Recently, the Fragile Points Method (FPM) has been introduced in the meshless methods family. It uses local, simple, polynomial, discontinuous functions to construct trial and test functions in the Galerkin weak form. This allows for accurate integration and improved efficiency while enabling the imposition of essential and natural boundary conditions as in FEM. In this work, we consider the application of FPM for cardiac electrophysiology simulation. We derive the cardiac monodomain model using the FPM formulation and we solve several benchmark problems in 2D and 3D. We show that FPM leads to solutions of accuracy and efficiency similar to FEM while alleviating the need for a mesh. Additionally, FPM demonstrates comparable convergence to FEM in the considered benchmarks. • The novel meshless FPM is employed to derive the cardiac monodomain model. • Atrial and ventricular tissue benchmark models are solved both in 2D and 3D. • Simulation results are found in good agreement with FEM for all benchmark models. • Electrical activation of a 3D infarction model is simulated with high accuracy. [ABSTRACT FROM AUTHOR]

This article is concerned with the weak convergence of invariant measures associated with multivalued stochastic differential equations in the finite dimensional space.

In this article, the generalised nonlinear Schrödinger–Maxwell–Bloch system is investigated, which describes the propagation of the optical solitons in an optical fibre doped with two-level resonant impurities like erbium with the fourth-order dispersion taken into account. Bilinear forms are derived via the Hirota method, symbolic computation, and the auxiliary function. Bright solitons can be obtained for the complex envelope of the field and the measure of the polarisation for the resonant medium, while the dark ones have been deduced for the extant population inversion. Propagation of the one and two solitons is analysed with the results that the solitons keep their shapes unchanged after the interaction, except for the phase shifts, which means that the interaction is elastic. Velocities of the solitons decrease when the effect of discreteness and higher-order dispersion increases. For the bound-state solitons, which can be formed among the solitons at the same velocity, the period decreases when the effect of discreteness and higher-order dispersion increases.

Clinical observations of spontaneous disease regression in some renal cell carcinoma (RCC) patients implicate a role for tumor immunity in controlling this disease. Puzzling, however, are findings that high levels of tumor infiltrating lymphocytes (TIL) are common to RCC. Despite expression of activation markers by TILs, functional impairment of innate and adaptive immune cells has been consistently demonstrated contributing to the failure of the immune system to control RCC. Immunotherapy can overcome the immunosuppressive effects of the tumor and provide an opportunity for long-term disease free survival. Unfortunately, complete response rates remain sub-optimal indicating the effectiveness of immunotherapy remains limited by tumor-specific factors and/or cell types that inhibit antitumor immune responses. Here we discuss immunotherapies and the function of multiple immune system components to achieve an effective response. Understanding these complex interactions is essential to rationally develop novel therapies capable of renewing the immune system's ability to respond to these tumors.