Explore the vast range of titles available.

Cross-coupling reactions have developed into powerful approaches for carbon–carbon bond formation. In this work, a Ni-catalyzed migratory Suzuki–Miyaura cross-coupling featuring high benzylic or allylic selectivity has been developed. With this method, unactivated alkyl electrophiles and aryl or vinyl boronic acids can be efficiently transferred to diarylalkane or allylbenzene derivatives under mild conditions. Importantly, unactivated alkyl chlorides can also be successfully used as the coupling partners. To demonstrate the applicability of this method, we showcase that this strategy can serve as a platform for the synthesis of terminal, partially deuterium-labeled molecules from readily accessible starting materials. Experimental studies suggest that migratory cross-coupling products are generated from Ni(0/II) catalytic cycle. Theoretical calculations indicate that the chain-walking occurs at a neutral nickel complex rather than a cationic one. In addition, the original-site cross-coupling products can be obtained by alternating the ligand, wherein the formation of the products has been rationalized by a radical chain process. Migratory cross-coupling reactions are powerful tools to form bonds at predictable positions. Here the authors report a nickel-catalyzed migratory Suzuki–Miyaura cross-coupling of unactivated alkyl electrophiles with aryl and vinyl boron reagents and provide experimental and computational mechanistic evidence.

Pancreatic ductal adenocarcinoma (PDAC) is rich in dense fibrotic stroma that are composed of extracellular matrix (ECM) proteins. A disruption of the balance between ECM synthesis and secretion and the altered expression of matrix remodeling enzymes lead to abnormal ECM dynamics in PDAC. This pathological ECM promotes cancer growth, survival, invasion, and alters the behavior of fibroblasts and immune cells leading to metastasis formation and chemotherapy resistance, which contribute to the high lethality of PDAC. Additionally, recent evidence highlights that ECM, as a major structural component of the tumor microenvironment, is a highly dynamic structure in which ECM proteins establish a physical and biochemical niche for cancer stem cells (CSCs). CSCs are characterized by self-renewal, tumor initiation, and resistance to chemotherapeutics. In this review, we will discuss the effects of the ECM on tumor biological behavior and its molecular impact on the fundamental signaling pathways in PDAC. We will also provide an overview of how the different ECM components are able to modulate CSCs properties and finally discuss the current and ongoing therapeutic strategies targeting the ECM. Given the many challenges facing current targeted therapies for PDAC, a better understanding of molecular events involving the interplay of ECM and CSC will be key in identifying more effective therapeutic strategies to eliminate CSCs and ultimately to improve survival in patients that are suffering from this deadly disease.

Xylenes are important components of gasoline fuels, and their hydrogen abstraction reactions are crucial in the consumption pathways of combustion processes. In existing models, rate constants for these reactions are commonly derived by estimation, which can introduce large uncertainties into models and lead to prediction deviations. In this study, the hydrogen abstraction reactions of three xylene isomers (p-xylene, m-xylene, and o-xylene) with hydrogen and hydroxyl radicals were investigated using quantum chemical methods. The high-precision CBS-QB3 method was used to perform a series of calculations, including structure optimization, frequency analysis, and energy calculations. Rate constants for all reactions were obtained using transition state theory with tunneling corrections and fitted to the three-parameter Arrhenius expression. The kinetic parameters of these reactions were updated in existing models of xylene. The integration of the updated rate constants into combustion models generally improves predictive accuracy, particularly for ignition delay times, CO2 formation, and laminar flame speeds, although discrepancies remain for some species such as CO.

Net primary productivity (NPP) is a main contributor to ecosystem carbon pools. It is crucial to monitor the spatial and temporal dynamics of NPP, as well as to assess the impacts of climate change and human activities to cope with global change. The dynamic of the NPP in China’s Yellow River Basin (YRB) from 2000 to 2019 and its influencing factors were analyzed by using trend and persistence tests and the GeoDetector method. The results show that the NPP had strong spatial heterogeneity, with a low NPP in the west and north, and a high NPP in the east and south. From 2000 to 2019, the NPP showed a statistically significant increase (at a mean of 5.5 g C m−2 yr−1, for a cumulative increase of 94.5 Tg C). A Hurst analysis showed that for the NPP in 76.3% of the YRB, the time series was anti-persistent. The spatial heterogeneity of the NPP in the YRB was mainly explained by precipitation and relative humidity (q value ranged from 0.24 to 0.44). However, the strength of the precipitation explained the decreased variation over time (q value decreased from 0.40 in 2000 to 0.26 in 2019). Interactions between the climate factors and human activities affected the NPP more strongly than individual factors. The results emphasize the importance of strengthening future research on the interaction between climate change and human activities. The results reveal the risk and optimal ranges of the driving factors and provide a quantification of the impacts of those factors regarding NPP. These findings can provide a scientific basis for vegetation restoration in the YRB.

Anthropogenic nitrogen (N) addition has increased soil nutrient availability, thereby affecting ecosystem processes and functions in N-limited ecosystems. Long-term N addition decreases plant biodiversity, but the effects of short-term N addition on soil microbial community is poorly understood. The present study examined the impacts of short-term N addition (NH 4 NO 3 ) on these factors in a sandy grassland and semi-fixed sandy land in the Horqin Sandy Land. We measured the responses of soil microbial biomass C and N; on soil β-1,4-glucosidase (BG) and β-1,4-N-acetylglucosaminidase (NAG) activity; and soil microflora characteristics to N additions gradient with 0 (control), 5 (N5), 10 (N10), and 15 (N15) g N m −2 yr −1 . The soil microbial biomass indices, NAG activity, and soil microflora characteristics did not differ significantly among the N levels, and there was no difference at the two sites. The competition for N between plants and soil microbes was not eliminated by short-term N addition due to the low soil nutrient and moisture contents, and the relationships among the original soil microbes did not change. However, N addition increased BG activity in the N5 and N10 additions in the sandy grassland, and in the N5, N10, and N15 additions in the semi-fixed sandy land. This may be due to increased accumulation and fixation of plant litter into soils in response to N addition, leading to increased microbial demand for a C source and increased soil BG activity. Future research should explore the relationships between soil microbial community and N addition at the two sites.

Crustal thickening associated with orogenic growth elevates topography, causing orographic enhancement of precipitation, which in turn facilitates local erosion and possibly intensifies localization of deformation. How these three processes—deformation, precipitation, and erosion—coordinate during orogenic growth remains unknown. Here, we present a numerical model where tectonics, surface processes, and orographic precipitation are tightly coupled, and explore the impact on low, intermediate, and high erodibility orogens. We show that, for intermediate erodibility models, rock uplift rates and precipitation rates correlate well with erosion rates during the formation of orogenic plateaus with high correlation coefficients of ~0.9 between rock uplift and erosion rates, and ~0.8 between precipitation and erosion rates. We demonstrate a cyclicity of correlation evolution among uplift, precipitation, and erosion rates through the development of new faults propagating outward. These results shed insights into the relative tectonic or climatic control on erosion in active orogens (e.g., Himalayas, Central Andes, and Southern Alps of New Zealand), and provide a plausible explanation for several conflicting data and interpretations in the Himalayas, which depend on the stage of maturity of the newest fault and the relative locations to old faults.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a low survival rate and limited responsiveness to current therapies. The role of hypoxia in the tumor microenvironment is critical, influencing tumor progression and therapy resistance. The aim of this study was to implement the complex dynamics of the hypoxic tumor microenvironment in PDAC in a hypoxia-related prognosis model. We utilized single-cell RNA sequencing (scRNA-seq) data and integrated it with TCGA-PAAD database to identify hypoxia-responsive macrophage subsets and related genes. Kaplan-Meier survival analysis, Cox regression, and Lasso regression methods were employed to construct and validate a hypoxia-related prognostic model. The model's effectiveness was evaluated through its predictive capabilities regarding chemotherapy sensitivity and overall survival. Our research integrated data from scRNA-seq and the TCGA-PAAD database to construct a hypoxia-related prognostic model that encompassed 13 critical genes. This hypoxia model independently predicted chemotherapy response and poor outcomes, outperforming traditional clinicopathologic features. Additionally, a pan-cancer analysis affirmed the relevance of our hypoxia-related genes across multiple malignancies, particularly highlighting KRTCAP2 as a pivotal biomarker associated with worse prognosis and reduced immune infiltration. Our findings underscored the prognostic potential of hypoxia-related model and offered a novel avenue for therapeutic targeting, aiming to ameliorate outcomes in pancreatic cancer.

Vegetation restoration is an effective measure to improve the ecosystem service of degraded sandy land ecosystem. However, it is unclear how vegetation restoration on severely desertified land affect soil organic carbon (SOC) sequestration and nutrients balance. Therefore, this study was designed to clarify the response of SOC, total nitrogen (TN), total phosphorus (TP), and the resulting stoichiometric ratios (C:N:P) to afforestation and grazing exclusion, and to quantify their dynamics over time. We conducted vegetation community investigation and soil sampling in natural sparse-forest grassland (the climax community stage), afforestation ( var. (40-year, 48-year), (20-year, 40-year)), and grazing exclusion (20-year, 40-year) in China's Horqin Sandy Land. Soil C:N:P stoichiometry and its driving factors under different restoration measures were then studied. Afforestation and grazing exclusion significantly ( < 0.05) increased SOC, TN, and TP concentrations. Vegetation restoration significantly increased C:N, C:P, and N:P ratios, indicating that nutrient limitations may occur in the later stages of restoration. The C:N, C:P, and N:P ratios after a 40-year grazing exclusion were closest to those of natural sparse-forest grassland. The N:P under grazing exclusion increased from 3.1 to 4.1 with increasing restoration age (from 20 to 40 years), which was close to the national mean values (4.2). Moreover, afforestation may lead to water deficit in the surface soil. Vegetation restoration is the main factor leading to changes in soil C:N:P stoichiometry, and indirectly affects soil C:N:P stoichiometry by altering soil structure and chemical properties. In terms of ecological stoichiometry, grazing exclusion was more conducive to restore SOC and nutrient balance than afforestation on severely desertified land. Due to the poor soil nutrients, attentions should be paid to the soil nutrients and water conditions in the later stages of vegetation restoration. Those findings can provide valuable information for the restoration of degraded sandy land in semi-arid areas.

Machine learning interatomic potentials (MLIPs) enable more efficient molecular dynamics (MD) simulations with ab initio accuracy, which have been used in various domains of physical science. However, distribution shift between training and test data causes deterioration of the test performance of MLIPs, and even leads to collapse of MD simulations. In this work, we propose an online Test-time Adaptation Interatomic Potential (TAIP) framework to improve the generalization on test data. Specifically, we design a dual-level self-supervised learning approach that leverages global structure and atomic local environment information to align the model with the test data. Extensive experiments demonstrate TAIP’s capability to bridge the domain gap between training and test dataset without additional data. TAIP enhances the test performance on various benchmarks, from small molecule datasets to complex periodic molecular systems with various types of elements. TAIP also enables stable MD simulations where the corresponding baseline models collapse. Molecular dynamics simulations using machine learning interatomic potentials often face stability issues due to distribution shifts. Here, the authors develop an online test-time adaptation framework to improve generalization, allowing for more stable simulations without the need for additional training data.