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Abstract The transition‐metal‐catalyzed Suzuki‐Miyaura cross‐coupling (SMC) reaction of organoboron nucleophiles with aryl (pseudo)halide electrophiles is a reliable method for carbon‐carbon bond formation. This reaction generally requires the use of an exogenous base to promote transmetalation process, which limits the substrate scope of the reaction due to undesired protodeboronation and functional group incompatibilities. Here, we established a base‐free SMC reaction via a conceptually different electrophilic substitution transmetalation (EST). This transformation is applicable to a wide range of base‐sensitive and sterically hindered organoborons. Key to this advance is the formation of a stable cationic palladium(II) or nickel(II) intermediate via experimental and theoretical investigations. In a broader context, this research further expands the synthetic boundary of cross‐coupling chemistry. 

We present a novel topology-preserving 3D medial axis computation framework based on volumetric restricted power diagram (RPD), while preserving the medial features and geometric convergence simultaneously, for both 3D CAD and organic shapes. The volumetric RPD discretizes the input 3D volume into sub-regions given a set of medial spheres. With this intermediate structure, we convert the homotopy equivalency between the generated medial mesh and the input 3D shape into a localized contractibility checking for each restricted element (power cell, power face, power edge), by checking their connected components and Euler characteristics. We further propose a fractional Euler characteristic algorithm for efficient GPU-based computation of Euler characteristic for each restricted element on the fly while computing the volumetric RPD. Compared with existing voxel-based or point-cloud-based methods, our approach is the first to adaptively and directly revise the medial mesh without globally modifying the dependent structure, such as voxel size or sampling density, while preserving its topology and medial features. In comparison with the feature preservation method MATFP [Wang et al. 2022], our method provides geometrically comparable results with fewer spheres and more robustly captures the topology of the input 3D shape. 

It is an inside front cover to highlight the interaction between stem cells and oxygenated scaffold material. 

White spot syndrome virus (WSSV) is a very large dsDNA virus. The accepted shape of the WSSV virion has been as ellipsoidal, with a tail-like extension. However, due to the scarcity of reliable references, the pathogenesis and morphogenesis of WSSV are not well understood. Here, we used transmission electron microscopy (TEM) and cryogenic electron microscopy (Cryo-EM) to address some knowledge gaps. We concluded that mature WSSV virions with a stout oval-like shape do not have tail-like extensions. Furthermore, there were two distinct ends in WSSV nucleocapsids: a portal cap and a closed base. A C14 symmetric structure of the WSSV nucleocapsid was also proposed, according to our Cryo-EM map. Immunoelectron microscopy (IEM) revealed that VP664 proteins, the main components of the 14 assembly units, form a ring-like architecture. Moreover, WSSV nucleocapsids were also observed to undergo unique helical dissociation. Based on these new results, we propose a novel morphogenetic pathway of WSSV. 

Abstract We rigorously justify the mean-field limit of an N -particle system subject to Brownian motions and interacting through the Newtonian potential in $${\mathbb {R}}^3$$ R 3 . Our result leads to a derivation of the Vlasov–Poisson–Fokker–Planck (VPFP) equations from the regularized microscopic N -particle system. More precisely, we show that the maximal distance between the exact microscopic trajectories and the mean-field trajectories is bounded by $$N^{-\frac{1}{3}+\varepsilon }$$ N - 1 3 + ε ( $$\frac{1}{63}\le \varepsilon <\frac{1}{36}$$ 1 63 ≤ ε < 1 36 ) with a blob size of $$N^{-\delta }$$ N - δ ( $$\frac{1}{3}\le \delta <\frac{19}{54}-\frac{2\varepsilon }{3}$$ 1 3 ≤ δ < 19 54 - 2 ε 3 ) up to a probability of $$1-N^{-\alpha }$$ 1 - N - α for any $$\alpha >0$$ α > 0 . Moreover, we prove the convergence rate between the empirical measure associated to the regularized particle system and the solution of the VPFP equations. The technical novelty of this paper is that our estimates rely on the randomness coming from the initial data and from the Brownian motions. 

Diabetes is one of the leading causes of death globally. Currently, the donor pancreas is the only source of human islets, placing extreme constraints on supply. Hence, it is imperative to develop renewable islets for diabetes research and treatment. To date, extensive efforts have been made to derive insulin-secreting cells from human pluripotent stem cells with substantial success. However, the in vitro generation of functional islet organoids remains a challenge due in part to our poor understanding of the signaling molecules indispensable for controlling differentiation pathways towards the self-assembly of functional islets from stem cells. Since this process relies on a variety of signaling molecules to guide the differentiation pathways, as well as the culture microenvironments that mimic in vivo physiological conditions, this review highlights extracellular matrix proteins, growth factors, signaling molecules, and microenvironments facilitating the generation of biologically functional pancreatic endocrine cells from human pluripotent stem cells. Signaling pathways involved in stepwise differentiation that guide the progression of stem cells into the endocrine lineage are also discussed. The development of protocols enabling the generation of islet organoids with hormone release capacities equivalent to native adult islets for clinical applications, disease modeling, and diabetes research are anticipated. 

Electrocardiogram (ECG) sensing is an important application for the diagnosis of cardiovascular diseases. Recently, driven by the emerging technology of wearable electronics, massive wearable ECG sensors are developed, which however brings additional sources of noise contamination on ECG signals from these wearable ECG sensors. In this paper, we propose a new low-distortion adaptive Savitzky-Golay (LDASG) filtering method for ECG denoising based on discrete curvature estimation, which demonstrates better performance than the state of the art of ECG denoising. The standard Savitzky-Golay (SG) filter has a remarkable performance of data smoothing. However, it lacks adaptability to signal variations and thus often induces signal distortion for high-variation signals such as ECG. In our method, the discrete curvature estimation is adapted to represent the signal variation for the purpose of mitigating signal distortion. By adaptively designing the proper SG filter according to the discrete curvature for each data sample, the proposed method still retains the intrinsic advantage of SG filters of excellent data smoothing and further tackles the challenge of denoising high signal variations with low signal distortion. In our experiment, we compared our method with the EMD-wavelet based method and the non-local means (NLM) denoising method in the performance of both noise elimination and signal distortion reduction. Particularly, for the signal distortion reduction, our method decreases in MSE by 33.33% when compared to EMD-wavelet and by 50% when compared to NLM, and decreases in PRD by 18.25% when compared to EMD-wavelet and by 25.24% when compared to NLM. Our method shows high potential and feasibility in wide applications of ECG denoising for both clinical use and consumer electronics.