GENERALIZED FORELLI-RUDIN TYPE OPERATORS BETWEEN SEVERAL FUNCTION SPACES ON THE UNIT BALL OF C^(N)

In this paper,we investigate sufficient and necessary conditions such that generalized Forelli-Rudin type operators T_(λ,τ,k),S_(λ,τ,k),Q_(λ,τ,k)and R_(λ,τ,k)are bounded between Lebesgue type spaces.In order to prove the main results,we first give some bidirectional estimates for several typical integrals.

Engineering vascularized organotypic tissues via module assembly

Adequate vascularization is a critical determinant for the successful construction and clinical implementation of complex organotypic tissue models. Currently, low cell and vessel density and insufficient vascular maturation make vascularized organotypic tissue construction difficult,greatly limiting its use in tissue engineering and regenerative medicine. To address these limitations, recent studies have adopted pre-vascularized microtissue assembly for the rapid generation of functional tissue analogs with dense vascular networks and high cell density. In this article, we summarize the development of module assembly-based vascularized organotypic tissue construction and its application in tissue repair and regeneration, organ-scale tissue biomanufacturing, as well as advanced tissue modeling.

Changepoint Detection with Outliers Based on RWPCA

Changepoint detection faces challenges when outlier data are present. This paper proposes a multivariate changepoint detection method which is based on the robust WPCA projection direction and the robust RFPOP method, RWPCA-RFPOP method. Our method is double robust which is suitable for detecting mean changepoints in multivariate normal data with high correlations between variables that include outliers. Simulation results demonstrate that our method provides strong guarantees on both the number and location of changepoints in the presence of outliers. Finally, our method is well applied in an ACGH dataset.

正规权一般函数空间到Bloch型空间的复合算子

该文讨论了正规权一般函数空间到一种特殊的正规权Bloch型空间之间复合算子的有界性和紧性,作者就所有情形给出了充要条件.

Bionic Hand Motion Control Method Based on Imitation of Human Hand Movements and Reinforcement Learning

Bionic hands are promising devices for assisting individuals with hand disabilities in rehabilitation robotics.Controlled primarily by bioelectrical signals such as myoelectricity and EEG,these hands can compensate for lost hand functions.However,developing model-based controllers for bionic hands is challenging and time-consuming due to varying control parameters and unknown application environments.To address these challenges,we propose a model-free approach using reinforcement learning(RL)for designing bionic hand controllers.Our method involves mimicking real human hand motion with the bionic hand and employing a human hand motion decomposition technique to learn complex motions from simpler ones.This approach significantly reduces the training time required.By utilizing real human hand motion data,we design a multidimensional sampling proximal policy optimization(PPO)algorithm that enables efficient motion control of the bionic hand.To validate the effectiveness of our approach,we compare it against advanced baseline methods.The results demonstrate the quick learning capabilities and high control success rate of our method.

Analysis of nonlinear multi-field coupling responses of piezoelectric semiconductor rods via machine learning

Piezoelectric semiconductors(PSs)have widespread applications in semiconductor devices due to the coexistence of piezoelec-tricity and semiconducting properties.It is very important to conduct a theoretical analysis of PS structures.However,the present of nonlinearity in the partial differential equations(PDEs)that describe those multi-feld coupling mechanical behaviors of PSs poses a significant mathematical challenge when studying these PS structures.In this paper,we present a novel approach based on machine learning for solving multi-field coupling problems in PS structures.A physics-informed neural networks(PINNs)is constructed for predicting the multi-field coupling behaviors of PS rods with extensional deforma-tion.By utilizing the proposed PINNs,we evaluate the multi-field coupling responses of a ZnO rod under static and dynamic axial forces.Numerical results demonstrate that the proposed PINNs exhibit high accuracy in solving both static and dynamic problems associated with Ps structures.It provides an effective approach to predicting the nonlinear multi-feld coupling phe-nomena in PS structures.

Mild and efficient recovery of lithium-ion battery cathode material by deep eutectic solvents with natural and cheap components

Dissolution of lithium cobalt oxide(LCO)is the key step for the recovery of valuable metals(e.g.,Co and Li)from spent LCO-based lithium-ion batteries(LIBs).However,the dissolution process of LCO either needs toxic solvents,and high temperature,or shows low efficiency.Deep eutectic solvents(DESs)are potential green solvents to dissolve LCO.Here,DESs with polyethylene glycol(PEG)as hydrogen bond acceptor and ascorbic acid(AA)as hydrogen bond donor are found to dissolve LCO with 84.2%Co leaching efficiency at 80℃ and 72 h,which is higher than that from the reported references by common DESs.Furthermore,both DESs components(i.e.,PEG and AA)are cheap,biodegradable,and biocompatible.AA could be easily and abundantly extracted from natural fruits or vegetables.It provides a new guide for the green,mild,and efficient dissolution of LCO aiming at sustainable recovery of spent LIBs.

积分估计和加权Lebesgue空间上广义Forelli-Rudin型算子的有界性

本文给出当p≥1时加权Lebesgue空间Lp(Bn,dvt)上广义Forelli-Rudin型算子Sλ,τ,c,k,k′有界的充要条件,部分推广了Zhao和Zhou(2022)的结果.同时,为了证明主要结果,本文给出球面和球体上几个积分的双向估计,推广了Rudin(1980)的相应结果.

Thoracic interstitial injection of drug-liposomes in mice for treating atherosclerosis

Intervaginal space injection(ISI)is a novel mode of administration investigated over the last decade.After injecting nanoparticles into the intervaginal space,they can be transported along low flow resistance channels into the interstitial space.This transport has a certain delivery direction,and site-specific injection can work on specific organs or tissues.In this study,the thorax,a new ISI site in the interstitial surrounding the internal thoracic artery named the thoracic interstitial injection(tISI)was investigated.To prove the targeting ability of the tISI,two sizes of gold nanoparticles(AuNPs)(47 and 87 nm)were administered to mice.After 1 h,the biodistribution of AuNPs in the tissues was measured via single particle inductively coupled plasma mass spectrometry(spICP-MS).The results showed that the concentration of AuNPs in the aorta after tISI injection was significantly higher than that after intravenous injection.Moreover,fewer nanoparticles with larger particle sizes were observed to have entered the blood and were better targeted to the aorta.Thereafter,tanshinone IIa sodium sulfonate liposomes were administered for the treatment of aortic atherosclerosis.The proportion of aortic plaques in atherosclerotic Apoe-/-mice administered via tISI was significantly lower than that in other model animals(P<0.001).Furthermore,the proteoglycan content and CD68-positive cell count in the plaques were significantly reduced.The vascular elastic fibers at the plaque site were thickened,and fractures were reduced.tISI was,therefore,determined to be an effective strategy for the treatment of atherosclerotic aortic plaques.

#ChronicPain:Automated Building of a Chronic Pain Cohort from Twitter Using Machine Learning

Importance:Drug-likeness of a compound is an overall assessment of its potential to succeed in clinical trials,and is essential for economizing research expenditures by filtering compounds with unfavorable properties and poor development potential.To this end,a robust drug-likeness prediction method is indispensable.Various approaches,including discriminative rules,statistical models,and machine learning models,have been developed to predict drug-likeness based on physiochemical properties and structural features.Notably,recent advancements in novel deep learning techniques have significantly advanced drug-likeness prediction,especially in classification performance.Highlights:In this review,we addressed the evolving landscape of drug-likeness prediction,with emphasis on methods employing novel deep learning techniques,and highlighted the current challenges in drug-likeness prediction,specifically regarding the aspects of generalization and interpretability.Moreover,we explored potential remedies and outlined promising avenues for future research.Conclusion:Despite the hurdles of generalization and interpretability,novel deep learning techniques have great potential in drug-likeness prediction and are worthy of further research efforts.

Reciprocal regulation between lunapark and atlastin facilitates ER three-way junction formation

Three-way junctions are characteristic structures of the tubular endoplasmic reticulum (ER) network. Junctions are formed through atlastin (ATL)-mediated membrane fusion and stabilized by lunapark (Lnp). However, how Lnp is preferentially enriched at three-way junctions remains elusiveHere, we showed that Lnp loses its junction localization when ATLs are deleted. Reintroduction of ATL1 R77A and ATL3, which have been shown to cluster at the junctions, but not wild-type ATL1, relocates Lnp to the junctions. Mutations in the Nmyristoylation site or hydrophobic residues in the coiled coil (CC1) of Lnp N-terminus (NT) cause mis-targeting of LnpConversely, deletion of the lunapark motif in the C-terminal zinc fin ger domain, which affects the homooligomerization of Lnp, does not alter its localizationPurified Lnp-NT attaches to the membrane in a myristoylation- dependent manner. The mutation of hydrophobic residues in CC1 does not affect membrane association, but compromises ATL interactionsIn addition, Lnp-NT inhibits ATL-mediated vesicle fusion in vitro. These results suggest that CC1 in Lnp-NT contacts junction-enriched ATLs for proper localization;subsequently, further ATL activity is limited by Lnp after the junction is formed. The proposed mechanism ensures coordinated actions of ATL and Lnp in generating and maintaining three-way junctions.

GCN2 deficiency protects mice from denervation-induced skeletal muscle atrophy via inhibiting FoxO3a nuclear translocation

Dear Editor Several recent clinical studies have indicated that dietary supplementation with branched-chain amino acids （BCAA）, particularly with leucine, is an effective anti-atrophic therapy （Bauer et al., 2015; Tsien et al., 2015; English et al., 2016）. In animal models, BCAA can prevent denervation （Ribeiro et al., 2015）, hindlimb suspension （Maki et al., 2012; Jang et al., 2015） or dexamethasone-induced （Yamamoto et al., 2010） muscle atrophy. General control nonderepressible 2 kinase （GCN2） is a well-known amino-acid sensor. Under conditions of amino-acid deprivation, the increased level of uncharged transfer RNA （tRNA） activates GCN2 through binding to the histadyl-tRNA synthetase-like domain （Wek et al., 1995）. Upon activation, GCN2 phosphorylates eukaryotic initiation factor 2 alpha at Ser51, which leads to translational arrest and restoration of amino acid home- ostasis （Wek et al., 1995; Sood et al., 2000）. As amino acids are potent modulators of protein turnover in skeletal muscle, we proposed that GCN2 may affect denervation-induced muscle atrol0hv, but the detail mechanism remains unclear.

Spatiotemporally-regulated multienzymatic polymerization endows hydrogel continuous gradient and spontaneous actuation

There are several natural materials which have evolved functional gradients,ingeniously attaining maximal efficacy from limited components.Herein,we utilized the spatiotemporal distribution of initiator acetylacetone to regulate the multienzyme polymerization and fabricate a chitosan-polymer hydrogel.The temporal priority order of acetylacetone was higher than phenolmodified chitosan by density functional theory calculation.The acetylacetone within the gelatin could gradually diffuse spatially into the chitosan hydrogel to fabricate the composite hydrogel with gradient network structure.The gradient hydrogel possessed a transferring topography from the two-dimensional pattern.A continuously decreased modulus along with acetylacetone diffusion was confirmed by atomic force microscope-based force mapping experiment.The water-retaining ability of various regions was confirmed by low-field nuclear magnetic resonance(NMR)and thermogravimetric analysis(TG)analysis,which led to the spontaneous actuation of gradient hydrogel with maximum 1821°/h curling speed and 227°curling angle.Consequently,the promising gradient hydrogels could be applied as intelligent actuators and flexible robots.

Monitoring stratification of anode biofilms in bioelectrochemical laminar flow reactors using flow cytometry

A laminar flow bioelectrochemical systems(BES)was designed and benchmarked using microbial anodes dominated with Geobacter spp.The reactor architecture was based on modeled flow fields,the resulting structure was 3D printed and used for BES manufacturing.Stratification of the substrate availability within the reactor channels led to heterogeneous biomass distribution,with the maximum biomass found mainly in the initial/middle channels.The anode performance was assessed for different hydraulic retention times while coulombic efficiencies of up to 100%(including also hydrogen recycling from the cathode)and current densities of up to 75 μA cm^(-2) at an anode surface to volume ratio of 1770 cm^(2) L^(-1) after 35 days were achieved.This low current density can be clearly attributed to the heterogeneous distributions of biomass and the stratification of the microbial community structure.Further,it was shown that time and space resolved analysis of the reactor microbiomes per channel is feasible using flow cytometry.

Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use

Background.The behaviors and emotions associated with and reasons for nonmedical prescription drug use(NMPDU)are notwell-captured through traditional instruments such as surveys and insurance claims.Publicly available NMPDU-related postson social media can potentially be leveraged to study these aspects unobtrusively and at scale.Methods.We applied a machinelearning classifier to detect self-reports of NMPDU on Twitter and extracted all public posts of the associated users.Weanalyzed approximately 137 million posts from 87,718 Twitter users in terms of expressed emotions,sentiments,concerns,andpossible reasons for NMPDU via natural language processing.Results.Users in the NMPDU group express more negativeemotions and less positive emotions,more concerns about family,the past,and body,and less concerns related to work,leisure,home,money,religion,health,and achievement compared to a control group(i.e.,users who never reported NMPDU).NMPDU posts tend to be highly polarized,indicating potential emotional triggers.Gender-specific analyses show that femaleusers in the NMPDU group express more content related to positive emotions,anticipation,sadness,joy,concerns aboutfamily,friends,home,health,and the past,and less about anger than males.The findings are consistent across distinctprescription drug categories(opioids,benzodiazepines,stimulants,and polysubstance).Conclusion.Our analyses of large-scaledata show that substantial differences exist between the texts of the posts from users who self-report NMPDU on Twitter andthose who do not,and between males and females who report NMPDU.Our findings can enrich our understanding ofNMPDU and the population involved.