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Shear-relative distributions of four types of precipitation/convection in tropical cyclones (TCs) are statistically analyzed using 14 years of Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. The dataset of 1139 TRMM PR overpasses of tropical storms through category-2 hurricanes over global TC-prone basins is divided by future 24-h intensity change. It is found that increased and widespread shallow precipitation (defined as where the 20-dBZradar echo height <6 km) around the storm center is a first sign of rapid intensification (RI) and could be used as a predictor of the onset of RI. The contribution to total volumetric rain and latent heating from shallow and moderate precipitation (20-dBZecho height between 6 and 10 km) in the inner core is greater in RI storms than in non-RI storms, while the opposite is true for moderately deep (20-dBZecho height between 10 and 14 km) and very deep precipitation (20-dBZecho height ≥14 km). The authors argue that RI is more likely triggered by the increase of shallow–moderate precipitation and the appearance of more moderately to very deep convection in the middle of RI is more likely a response or positive feedback to changes in the vortex. For RI storms, a cyclonic rotation of frequency peaks from shallow (downshear right) to moderate (downshear left) to moderately and very deep precipitation (upshear left) is found and may be an indicator of a rapidly strengthening vortex. A ring of almost 90% occurrence of total precipitation is found for storms in the middle of RI, consistent with the previous finding of the cyan and pink ring on the 37-GHz color product.

Electroreduction of carbon dioxide (CO 2 ) into multicarbon products provides possibility of large-scale chemicals production and is therefore of significant research and commercial interest. However, the production efficiency for ethanol (EtOH), a significant chemical feedstock, is impractically low because of limited selectivity, especially under high current operation. Here we report a new silver–modified copper–oxide catalyst (dCu 2 O/Ag 2.3% ) that exhibits a significant Faradaic efficiency of 40.8% and energy efficiency of 22.3% for boosted EtOH production. Importantly, it achieves CO 2 –to–ethanol conversion under high current operation with partial current density of 326.4 mA cm −2 at −0.87 V vs reversible hydrogen electrode to rank highly significantly amongst reported Cu–based catalysts. Based on in situ spectra studies we show that significantly boosted production results from tailored introduction of Ag to optimize the coordinated number and oxide state of surface Cu sites, in which the * CO adsorption is steered as both atop and bridge configuration to trigger asymmetric C–C coupling for stablization of EtOH intermediates. It is of high interest to convert CO 2 into valuable ethanol product. Here the authors demonstrate the asymmetric C-C coupling triggered on Ag-modified oxide-derived Cu sites can accelerate and steer the reaction pathway for ethanol production with high faradaic efficiency and current density.

Ever since hematopoietic stem cells (HSCs) were first identified half a century ago, their differentiation roadmap has been extensively studied. The classical model of hematopoiesis has long held as a dogma that HSCs reside at the top of a hierarchy in which HSCs possess self-renewal capacity and can progressively give rise to all blood lineage cells. However, over the past several years, with advances in single cell technologies, this developmental scheme has been challenged. In this review, we discuss the evidence supporting heterogeneity within HSC and progenitor populations as well as the hierarchical models revised by novel approaches mainly in mouse system. These evolving views provide further understanding of hematopoiesis and highlight the complexity of hematopoietic differentiation.

Enantiopure vicinal amino alcohols and derivatives are essential structural motifs in natural products and pharmaceutically active molecules, and serve as main chiral sources in asymmetric synthesis. Currently known asymmetric catalytic protocols for this class of compounds are still rare and often suffer from limited scope of substrates, relatively low regio- or stereoselectivities, thus prompting the development of more effective methodologies. Herein we report a dual catalytic strategy for the convergent enantioselective synthesis of vicinal amino alcohols. The method features a radical-type Zimmerman–Traxler transition state formed from a rare earth metal with a nitrone and an aromatic ketyl radical in the presence of chiral N , N ′-dioxide ligands. In addition to high level of enantio- and diastereoselectivities, our synthetic protocol affords advantages of simple operation, mild conditions, high-yielding, and a broad scope of substrates. Furthermore, this protocol has been successfully applied to the concise synthesis of pharmaceutically valuable compounds (e.g., ephedrine and selegiline). Chiral vicinal amino alcohols are found in many bioactive compounds and may serve as chiral ligands. Here, the authors report a photocatalytic enantioselective cross-coupling of nitrones with aromatic aldehydes with a chiral ligand-coordinated rare earth ion synergistically producing enantiopure vicinal amino alcohols.

When analytical techniques are used to understand and analyse geographical events, adjustments to the datasets (e.g. aggregation, zoning, segmentation etc.) in both the spatial and temporal dimensions are often carried out for various reasons. The 'Modifiable Areal Unit Problem' (MAUP), which is a consequence of adjustments in the spatial dimension, has been widely researched. However, its temporal counterpart is generally ignored, especially in space-time analysis. In analogy to MAUP, the Modifiable Temporal Unit Problem (MTUP) is defined as consisting of three temporal effects (aggregation, segmentation and boundary). The effects of MTUP on the detection of space-time clusters of crime datasets of Central London are examined using Space-Time Scan Statistics (STSS). The case study reveals that MTUP has significant effects on the space-time clusters detected. The attributes of the clusters, i.e. temporal duration, spatial extent (size) and significance value (p-value), vary as the aggregation, segmentation and boundaries of the datasets change. Aggregation could be used to find the significant clusters much more quickly than at lower scales; segmentation could be used to understand the cyclic patterns of crime types. The consistencies of the clusters appearing at different temporal scales could help in identifying strong or 'true' clusters.

Modulating effector immune cells via monoclonal antibodies (mAbs) and facilitating the co-engagement of T cells and tumor cells via chimeric antigen receptor- T cells or bispecific T cell-engaging antibodies are two typical cancer immunotherapy approaches. We speculated that immobilizing two types of mAbs against effector cells and tumor cells on a single nanoparticle could integrate the functions of these two approaches, as the engineered formulation (immunomodulating nano-adaptor, imNA) could potentially associate with both cells and bridge them together like an ‘adaptor’ while maintaining the immunomodulatory properties of the parental mAbs. However, existing mAbs-immobilization strategies mainly rely on a chemical reaction, a process that is rough and difficult to control. Here, we build up a versatile antibody immobilization platform by conjugating anti-IgG (Fc specific) antibody (αFc) onto the nanoparticle surface (αFc-NP), and confirm that αFc-NP could conveniently and efficiently immobilize two types of mAbs through Fc-specific noncovalent interactions to form imNAs. Finally, we validate the superiority of imNAs over the mixture of parental mAbs in T cell-, natural killer cell- and macrophage-mediated antitumor immune responses in multiple murine tumor models. Current strategies to boost anti-tumor immune response include the use of immune checkpoint inhibitors and bispecific T cell-engaging antibodies. Here the authors describe a versatile antibody immobilization nanoplatform that can be used to deliver different combinations of immunotherapeutics, showing therapeutic superiority in pre-clinical models.

Blade icing seriously affects wind turbines’ aerodynamic performance and output power. Timely and accurately predicting blade icing status is crucial to improving the economy and safety of wind farms. However, existing blade icing prediction methods cannot effectively solve the problems of unbalanced icing/non-icing data and low prediction accuracy. In order to solve the above problems, this paper proposes a wind turbine blade icing prediction method based on the focal loss function and CNN-Attention-GRU. First, the recursive feature elimination method combined with the physical mechanism of icing is used to extract features highly correlated with blade icing, and a new feature subset is formed through a sliding window algorithm. Then, the focal loss function is utilized to assign more weight to the ice samples with a lower proportion, addressing the significant class imbalance between the ice and non-ice categories. Finally, based on the CNN-Attention-GRU algorithm, a blade icing prediction model is established using continuous 24-h historical data as the input and the icing status of the next 24 h as the output. The model is compared with advanced neural network models. The results show that the proposed method improves the prediction accuracy and F1 score by an average of 6.41% and 4.27%, respectively, demonstrating the accuracy and effectiveness of the proposed method.

To explore the safety and effectiveness of partial splenic embolization (PSE) in patients with hypersplenism and hepatocellular carcinoma (HCC) and to compare the efficacy of superselective and non-superselective embolization of splenic artery branches. We retrospectively analyzed 64 patients with HCC who underwent PSE between August 2020 and December 2022. The patients were categorized into two groups based on different treatment plans: Group A (n=33) underwent superselective embolization and Group B (n=31) underwent non-superselective embolization of the splenic artery branches. The safety and effectiveness of the two methods were evaluated along with changes in peripheral blood cells [mainly white blood cells (WBC) and red blood cells (RBC)] and platelet (PLT) counts at different time points after PSE. Postoperative adverse events were also compared between the two groups. The technical success rate was 100% for both procedures. The PLT and WBC counts of the two groups significantly increased one week after PSE (P<0.05), and there was no statistically significant difference in the RBC count changes. At follow-up (4, 16, and 24 weeks), the PLT and WBC counts remained consistent at levels which were significantly different from those before PSE (P<0.05). However, the RBC counts were not significantly different (P>0.05). An independent sample t-test was used to compare the differences in blood counts between the two groups at the same time point. There were no statistically significant differences in PLT, WBC, and RBC counts between Group A and Group B at any time point after PSE (P>0.05). The incidence of fever and pain in Group B was significantly higher than that in Group A (P<0.05). Partial splenic artery embolization is a safe and effective treatment option for hypersplenism. Both splenic artery branch superselective and non-superselective embolization strategies demonstrated comparable outcomes. However, superselective embolization exhibited a lower incidence of postprocedural complications than non-superselective embolization.

The full neutrophil heterogeneity and differentiation landscape remains incompletely characterized. Here, we profiled >25,000 differentiating and mature mouse neutrophils using single-cell RNA sequencing to provide a comprehensive transcriptional landscape of neutrophil maturation, function and fate decision in their steady state and during bacterial infection. Eight neutrophil populations were defined by distinct molecular signatures. The three mature peripheral blood neutrophil subsets arise from distinct maturing bone marrow neutrophil subsets. Driven by both known and uncharacterized transcription factors, neutrophils gradually acquire microbicidal capability as they traverse the transcriptional landscape, representing an evolved mechanism for fine-tuned regulation of an effective but balanced neutrophil response. Bacterial infection reprograms the genetic architecture of neutrophil populations, alters dynamic transitions between subpopulations and primes neutrophils for augmented functionality without affecting overall heterogeneity. In summary, these data establish a reference model and general framework for studying neutrophil-related disease mechanisms, biomarkers and therapeutic targets at single-cell resolution. Luo and colleagues use single-cell RNA sequencing to provide a comprehensive transcriptional landscape of neutrophil maturation, function and fate decision in their steady state and during bacterial infection.

Every day, around 400 million tweets are sent worldwide, which has become a rich source for detecting, monitoring and analysing news stories and special (disaster) events. Existing research within this field follows key words attributed to an event, monitoring temporal changes in word usage. However, this method requires prior knowledge of the event in order to know which words to follow, and does not guarantee that the words chosen will be the most appropriate to monitor. This paper suggests an alternative methodology for event detection using space-time scan statistics (STSS). This technique looks for clusters within the dataset across both space and time, regardless of tweet content. It is expected that clusters of tweets will emerge during spatio-temporally relevant events, as people will tweet more than expected in order to describe the event and spread information. The special event used as a case study is the 2013 London helicopter crash. A spatio-temporally significant cluster is found relating to the London helicopter crash. Although the cluster only remains significant for a relatively short time, it is rich in information, such as important key words and photographs. The method also detects other special events such as football matches, as well as train and flight delays from Twitter data. These findings demonstrate that STSS is an effective approach to analysing Twitter data for event detection.