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Economic growth target management is a government behavior that the authorities formulate at a specific time and direct the allocation of resources. Despite the importance of socio-economic development, economic growth targets have caused some potential hazards to the environment that cannot be ignored. Using a panel dataset of 278 prefecture-level cities in China over the period 2004-2019, the study employed a two-way fixed effects model to verify the inhibition effect of economic growth targets on green technology innovation (GTI). The results indicated that economic growth targets and hard constraints tended to have a significantly negative effect on GTI. More importantly, the results of the mediation effect test suggested a positive correlation between economic growth targets and the government fiscal expenditures and market segmentation, and both mediators played an intermediary role in the influence of economic growth target constraints restraining GTI. Other findings showed that the economic growth targets of prefecture-level cities had different impacts on GTI, which resulted from the different resource endowments, geographic locations, or periods. Overall, the results suggest that policymakers should lower the economic growth targets and use soft constraints to set them. The conclusions of this paper are applicable to policymakers not only in China but also other economies that regularly set economic growth targets., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This research paper investigates the intersection of the maker movement and educational innovation, using the China-U.S. Young Maker Competition as a foundational example. It examines how maker education, fueled by hands-on learning and a curiosity-driven approach, can evolve and influence. The study explores the roles and impacts of Human-Computer Interaction (HCI), Design-Driven Innovation (DDI), and Cross-Cultural Perspectives (CCP) within maker education. It highlights HCI's connection of technology with learning, DDI's focus on user-centered solutions, and the significance of CCP in enhancing cultural collaboration, vital for fostering an innovative and creative future. This paper offers a detailed perspective on the current state and future potential of maker education. It proposes a roadmap for the coming decade, emphasizing collaborative learning and creative endeavors, all set within the engaging environment of the competition.

In this paper, the microstructure and wear resistance of Zr-17Nb alloy treated by high current pulsed electron beam were studied in detail. A phase change occurs after pulse treatments using X-Ray Diffraction (XRD) analysis, showing β (Nb) phase and α (Zr) phase transformed by a part of β (Zr, Nb) phase. Also, narrowing and shifting of β (Zr, Nb) diffraction peaks were found. Scanning Electron Microscope (SEM) and metallographic analysis results reveal that the microstructure of alloy surface before high current pulsed electron beam (HCPEB) treatment is composed of equiaxed crystals. But, after 15 and 30 pulse treatments, crater structures are significantly reduced. Besides, it was also found that the alloy surface has undergone eutectoid transformation after 30 pulse treatments, and the reaction of β (Zr, Nb) → αZr + βNb had occurred. Microhardness test results show that microhardness value presents a downward trend as the number of pulses increases, which is mainly due to the coarsening of the grains and the formation of a softer β (Nb) phase after phase transformation. The wear resistance test results show that the friction coefficient increases first, then decreases and then increases with the increase of pulse number.

Supercritical hydrothermal synthesis of nanosized metal oxides is of great interest for scholars due to its one-pot and readily realizing commercial production process. Hydrothermal synthesis of nanosized TiO2 in subcritical and supercritical water was investigated with different reaction temperatures (250–450°C), time (3–10 min), precursor species (Ti(SO4)2, TiCl4), and the addition of a surfactant (CH3(CH2)5NH2). TiO2 synthesized under supercritical state has a higher degree of crystallinity when compared with TiO2 obtained from subcritical state, which is attributed to a higher reaction rate. Extending reaction time from 3 to 10 min leads to the growth of crystal and broader size distribution (36.66 ± 8.5 nm). More uniform products can be obtained when Ti(SO4)2 acting as the precursor compared with TiCl4 being the precursor. The application of surfactant CH3(CH2)5NH2 in the synthesis makes chemical bonding between the particle surface and organic ligand. It affects the growth direction of TiO2 crystal; as a result, rod-like TiO2 crystal is obtained.

Exocytosis is a critical factor for designing efficient nanocarriers and determining cytotoxicity. However, the research on the exocytosis mechanism of nanoparticles, especially the role of long non-coding RNAs (lncRNA), has not been reported. In this study, the exocytosis of AuNPs in the KYSE70 cells and the involved molecular pathways of exocytosis are analyzed. It demonstrates that nanoparticles underwent time-dependent release from the cells by exocytosis, and the release of β-hexosaminidase confirms that AuNPs are excreted through lysosomes. Mechanistic studies reveal that lncRNA ESCCAL-1 plays a vital role in controlling the exocytosis of AuNPs through activation of the MAPK pathway, including the phosphorylation of ERK and JNK. The study implies that the ESCCAL-1-mediated pathway plays an important role in the exocytosis of AuNPs in KYSE70 cells. This finding has implications for the role of ESCCAL-1 on the drug resistance of esophagus cancer by controlling lysosome-mediated exocytosis.

Néel-type magnetic skyrmions in perpendicularly magnetized systems have attracted considerable interest due to their potential in fundamental research on topological objects and spintronics applications. Various systems have been explored to study Néel-type magnetic skyrmions, including repeated magnetic multilayers, two-dimensional materials, and single magnetic thin-films. Among these, single magnetic thin-films, especially a CoFeB single layer, offers multiple benefits, such as reduced defect energy, high mobility, and easy integration with existing magnetoresistive random access memory technology. However, optimizing CoFeB-based skyrmion hosting materials remains challenging and requires further systematic and comprehensive investigation. In this study, we examine the effect of atomic-scale interface engineering by inserting a Ta layer between the CoFeB/MgO interface on perpendicular magnetic anisotropy, saturation magnetization, and Dzyaloshinskii-Moriya interaction. Moreover, we provide a guideline for engineering material parameters and demonstrate the validity of atomic-scale interface engineering. Our findings contribute to the development of optimized CoFeB-based skyrmion hosting materials.

Tracks classroom research on a technology-rich inquiry weather program with six urban science teachers. Investigates students' achievement of content and inquiry as well as technology's contribution to inquiry science and positive learning values. (Contains 30 references.) (Author/YDS)

Immuno-oncology (IO) and targeted therapies, such as small molecule inhibitors, have changed the landscape of cancer treatment and prognosis; however, durable responses have been difficult to achieve due to tumor heterogeneity, development of drug resistance, and adverse effects that limit dosing and prolonged drug use. To improve upon the current medicinal armamentarium, there is an urgent need for new ways to understand, reverse, and treat carcinogenesis. Clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein (Cas) 9 is a powerful and efficient tool for genome editing that has shown significant promise for developing new therapeutics. While CRISPR/Cas9 has been successfully used for pre-clinical cancer research, its use in the clinical setting is still in an early stage of development. The purpose of this review is to describe the CRISPR technology and to provide an overview of its current applications and future potential as cancer therapies.

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

Key messageThe CsGAI gene, identified by map-based, was involved in regulating seed germination in low temperature via the GA and ABA signaling pathways. Low temperature reduces the percentage of seeds germinating and delays seed germinating time, thus posing a threat to cucumber production. However, the molecular mechanism regulating low temperature germination in cucumber is unknown. We here dissected a major quantitative trait locus qLTG1.1 that controls seed germination at low temperature in cucumber. First, we fine-mapped qLTG1.1 to a 46.3-kb interval, containing three candidate genes. Sequence alignment and gene expression analysis identified Csa1G408720 as the gene of interest that was highly expressed in seeds, and encoded a highly conserved, low temperature-regulated DELLA family protein CsGAI. GUS expression analysis indicated that higher promoter activity underscored higher transcriptional expression of the CsGAI gene. Consistent with the known roles of GAI in ABA and GA signaling during germination, genes involved in the GA (CsGA2ox, CsGA3ox) and ABA biosynthetic pathways (CsABA1, CsABA2, CsAAO3 and CsNCED) were found to be differently regulated in the tolerant and sensitive genotypes under low temperatures, and this was reflected in differences in their ratio of GA-to-ABA. Based on these data, we proposed a working model explaining how CsGAI integrates the GA and ABA signaling pathways, to regulate cucumber seed germination at low temperature, thus providing new insights into this mechanism.

During the COVID-19 pandemic, genomics and bioinformatics have emerged as essential public health tools. The genomic data acquired using these methods have supported the global health response, facilitated development of testing methods, and allowed timely tracking of novel SARS-CoV-2 variants. Yet the virtually unlimited potential for rapid generation and analysis of genomic data is also coupled with unique technical, scientific, and organizational challenges. Here, we discuss the application of genomic and computational methods for the efficient data driven COVID-19 response, advantages of democratization of viral sequencing around the world, and challenges associated with viral genome data collection and processing.

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

Changes in the geological environment and track wear, and deterioration of train bogies may lead to the looseness of subway fasteners. Identifying loose fasteners randomly distributed along the subway line is of great significance to avoid train derailment. This paper presents a convolutional autoencoder (CAE) network-based method for identifying fastener loosening features from the distributed vibration responses of track beds detected by an ultra-weak fiber Bragg grating sensing array. For an actual subway tunnel monitoring system, a field experiment used to collect the samples of fastener looseness was designed and implemented, where a crowbar was used to loosen or tighten three pairs of fasteners symmetrical on both sides of the track within the common track bed area and the moving load of a rail inspection vehicle was employed to generate 12 groups of distributed vibration signals of the track bed. The original vibration signals obtained from the on-site test were converted into two-dimensional images through the pseudo-Hilbert scan to facilitate the proposed two-stage CAE network with acceptable capabilities in feature extraction and recognition. The performance of the proposed methodology was quantified by accuracy, precision, recall, and F1-score, and displayed intuitively by t-distributed stochastic neighbor embedding (t-SNE). The raster scan and the Hilbert scan were selected to compare with the pseudo-Hilbert scan under a similar CAE network architecture. The identification performance results represented by the four quantification indicators (accuracy, precision, recall, and F1-score) based on the scan strategy in this paper were at least 23.8%, 9.5%, 20.0%, and 21.1% higher than those of the two common scan methods. As well as that, the clustering visualization by t-SNE further verified that the proposed approach had a stronger ability in distinguishing the feature of fastener looseness.

Understanding complex biological systems requires visualizing structures and processes deep within living organisms. We developed a compact adaptive optics module and incorporated it into two- and three-photon fluorescence microscopes, to measure and correct tissue-induced aberrations. We resolved synaptic structures in deep cortical and subcortical areas of the mouse brain, and demonstrated high-resolution imaging of neuronal structures and somatosensory-evoked calcium responses in the mouse spinal cord at great depths in vivo.

Stargardt disease (STGD) is an autosomal recessive retinal disorder caused by a monogenic ABCA4 mutation. Currently, there is no effective therapy to cure Stargardt disease. The replacement of mutated ABCA4 with a functional gene remains an attractive strategy. In this study, we have developed a non-viral gene therapy using nanoparticles self-assembled by a multifunctional pH-sensitive amino lipid ECO and a therapeutic ABCA4 plasmid. The nanoparticles mediated efficient intracellular gene transduction in wild-type (WT) and Abca4-/- mice. Specific ABCA4 expression in the outer segment of photoreceptors was achieved by incorporating a rhodopsin promoter into the plasmids. The ECO/pRHO-ABCA4 nanoparticles induced substantial and specific ABCA4 expression for at least 8 months, 35% reduction in A2E accumulation on average, and a delayed Stargardt disease progression for at least 6 months in Abca4-/- mice. ECO/plasmid nanoparticles constitute a promising non-viral gene therapy platform for Stargardt disease and other visual dystrophies.

The development of DNA-barcoded antibodies to tag cell surface molecules has enabled the use of droplet-based single-cell sequencing (dsc-seq) to profile protein abundances from thousands of cells simultaneously. As compared to flow and mass cytometry, the high per cell cost of current dsc-seq-based workflows precludes their use in clinical applications and large-scale pooled screens. Here, we introduce SCITO-seq, a workflow that uses splint oligonucleotides (oligos) to enable combinatorially indexed dsc-seq of DNA-barcoded antibodies from over 105 cells per reaction using commercial microfluidics. By encoding sample barcodes into splint oligos, we demonstrate that multiplexed SCITO-seq produces reproducible estimates of cellular composition and surface protein expression comparable to those from mass cytometry. We further demonstrate two modified splint oligo designs that extend SCITO-seq to achieve compatibility with commercial DNA-barcoded antibodies and simultaneous expression profiling of the transcriptome and surface proteins from the same cell. These results demonstrate SCITO-seq as a flexible and ultra-high-throughput platform for sequencing-based single-cell protein and multimodal profiling.

Accurate microbial identification and abundance estimation are crucial for metagenomics analysis. Various methods for classification of metagenomic data and estimation of taxonomic profiles, broadly referred to as metagenomic profilers, have been developed. Nevertheless, benchmarking of metagenomic profilers remains challenging because some tools are designed to report relative sequence abundance while others report relative taxonomic abundance. Here we show how misleading conclusions can be drawn by neglecting this distinction between relative abundance types when benchmarking metagenomic profilers. Moreover, we show compelling evidence that interchanging sequence abundance and taxonomic abundance will influence both per-sample summary statistics and cross-sample comparisons. We suggest that the microbiome research community pay attention to potentially misleading biological conclusions arising from this issue when benchmarking metagenomic profilers, by carefully considering the type of abundance data that were analyzed and interpreted and clearly stating the strategy used for metagenomic profiling.

Current treatments for antibody-mediated autoimmunity are associated with lack of specificity, leading to immunosuppressive effects. To overcome this limitation, we have developed a class of antibody-based therapeutics for the treatment of autoimmunity involving antibodies that recognize the autoantigen, myelin oligodendrocyte glycoprotein (MOG). These agents ("Seldegs," for selective degradation) selectively eliminate antigen (MOG)-specific antibodies without affecting the levels of antibodies of other specificities. Seldeg treatment of mice during antibody-mediated exacerbation of experimental autoimmune encephalomyelitis by patient-derived MOG-specific antibodies results in disease amelioration. Consistent with their therapeutic effects, Seldegs deliver their targeted antibodies to Kupffer and liver sinusoidal endothelial cells that are known to have tolerogenic effects. Our results show that Seldegs can ameliorate disease mediated by MOG-specific antibodies and indicate that this approach also has the potential to treat other autoimmune diseases where the specific clearance of antibodies is required.

Automatic Feature Recognition (AFR) is considered as the key connection technique of the integration of Computer Aided Design (CAD) and Computer Aided Process Planning (CAPP). At present, there is a lack of a systematic method to identify and evaluate the local features of 3D CAD models. The process information such as topological structure, shape and size, tolerance and surface roughness should be considered. Therefore, a novel Model Based Definition (MBD) based on 3D CAD model AFR and similarity evaluation are proposed in this paper. A Multi-Dimensional Attributed Adjacency Matrix (MDAAM) based on MBD is established based on the fully consideration of the topological structure, shape and size, surface roughness, tolerance and other process information of the B-rep model. Based on the MDAAM, a two-stage model local feature similarity evaluation method is proposed, which combines the methods of optimal matching and adjacency judgment. First, the faces of source feature and target model are used as independent sets to construct a bipartite graph. Secondly, supplement the vertices in the independent set of source feature to make the number of vertices in two independent sets equal. Thirdly, based on MDAAM data, the weighted complete bipartite graph is constructed with the face similarity between two independent sets as the weight. Fourthly, Kuhn-Munkres algorithm is used to calculate the optimal matching between the faces of source feature and target model. Fifthly, the adjacency between matching faces in target model is judged. Finally, the similarity between matching faces of the two models is calculated, which is used as the similarity evaluation result. The effectiveness of this method is verified by three applications.

Symbionts can regulate animal reproduction in multiple ways, but the underlying physiological and biochemical mechanisms remain largely unknown. The presence of multiple lineages of maternally inherited, intracellular symbionts (the primary and secondary symbionts) in terrestrial arthropods is widespread in nature. However, the biological, metabolic, and evolutionary role of co-resident secondary symbionts for hosts is poorly understood. The bacterial symbionts Hamiltonella and Arsenophonus have very high prevalence in two globally important pests, the whiteflies Bemisia tabaci and Trialeurodes vaporariorum, respectively. Both symbionts coexist with the primary symbiont Portiera in the same host cell (bacteriocyte) and are maternally transmitted. We found that elimination of both Hamiltonella and Arsenophonous by antibiotic treatment reduced the percentage of female offspring in whiteflies. Microsatellite genotyping and cytogenetic analysis revealed that symbiont deficiency inhibited fertilization in whiteflies, leading to more haploid males with one maternal allele, which is consistent with distorted sex ratio in whiteflies. Quantification of essential amino acids and B vitamins in whiteflies indicated that symbiont deficiency reduced B vitamin levels, and dietary B vitamin supplementation rescued fitness of whiteflies. This study, for the first time, conclusively demonstrates that these two intracellular symbionts affect sex ratios in their whitefly hosts by regulating fertilization and supplying B vitamins. Our results reveal that both symbionts have the convergent function of regulating reproduction in phylogenetically-distant whitefly species. The 100% frequency, the inability of whiteflies to develop normally without their symbiont, and rescue with B vitamins suggests that both symbionts may be better considered co-primary symbionts.

Genetically encoded dopamine sensors based on green fluorescent protein (GFP) enable high-resolution imaging of dopamine dynamics in behaving animals. However, these GFP-based variants cannot be readily combined with commonly used optical sensors and actuators, due to spectral overlap. We therefore engineered red-shifted variants of dopamine sensors called RdLight1, based on mApple. RdLight1 can be combined with GFP-based sensors with minimal interference and shows high photostability, permitting prolonged continuous imaging. We demonstrate the utility of RdLight1 for receptor-specific pharmacological analysis in cell culture, simultaneous assessment of dopamine release and cell-type-specific neuronal activity and simultaneous subsecond monitoring of multiple neurotransmitters in freely behaving rats. Dual-color photometry revealed that dopamine release in the nucleus accumbens evoked by reward-predictive cues is accompanied by a rapid suppression of glutamate release. By enabling multiplexed imaging of dopamine with other circuit components in vivo, RdLight1 opens avenues for understanding many aspects of dopamine biology.

Microbial coculture to mimic the ecological habitat has been suggested as an approach to elucidate the effect of microbial interaction on secondary metabolite biosynthesis of Streptomyces. However, because of chemical complexity during coculture, underlying mechanisms are largely unknown. Here, we found that iron competition triggered antibiotic biosynthesis in Streptomyces coelicolor during coculture with Myxococcus xanthus. During coculture, M. xanthus enhanced the production of a siderophore, myxochelin, leading M. xanthus to dominate iron scavenging and S. coelicolor to experience iron-restricted conditions. This chemical competition, but not physical contact, activated the actinorhodin biosynthetic gene cluster and the branched-chain amino acid degradation pathway which imply the potential to produce precursors, along with activation of a novel actinorhodin export system. Furthermore, we found that iron restriction increased the expression of 21 secondary metabolite biosynthetic gene clusters (smBGCs) in other Streptomyces species. These findings suggested that the availability for key ions stimulates specific smBGCs, which had the potential to enhance secondary metabolite biosynthesis in Streptomyces.

Although biodiversity and ecosystem functions are strongly shaped by contemporary environments, such as climate and local biotic and abiotic attributes, relatively little is known about how they depend on long-term geological processes. Here, along a 3000-m elevational gradient with tectonic faults on the Tibetan Plateau (that is, Galongla Mountain in Medog County, China), we study the joint effects of geological and contemporary environments on biological communities, such as the diversity and community composition of plants and soil bacteria, and ecosystem functions. We find that these biological communities and ecosystem functions generally show consistent elevational breakpoints at 2000-2800 m, which coincide with Indus-Yalu suture zone fault and are similar to the elevational breakpoints of soil bacteria on another mountain range 1000 km away. Mean annual temperature, soil pH and moisture are the primary contemporary determinants of biodiversity and ecosystem functions, which support previous findings. However, compared with the models excluding geological processes, inclusion of geological effects, such as parent rock and weathering, increases 67.9 and 35.9% of the explained variations in plant and bacterial communities, respectively. Such inclusion increases 27.6% of the explained variations in ecosystem functions. The geological processes thus provide additional links to ecosystem properties, which are prominent but show divergent effects on biodiversity and ecosystem functions: parent rock and weathering exert considerable direct effects on biodiversity, whereas indirectly influence ecosystem functions via interactions with biodiversity and contemporary environments. Thus, the integration of geological processes with environmental gradients could enhance our understanding of biodiversity and, ultimately, ecosystem functioning across different climatic zones.

Genetically encoded fluorescent voltage indicators, such as ArcLight, have been used to report action potentials (APs) in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). However, the ArcLight expression, in all cases, relied on a high number of lentiviral vector-mediated random genome integrations (8-12 copy/cell), raising concerns such as gene disruption and alteration of global and local gene expression, as well as loss or silencing of reporter genes after differentiation. Here, we report the use of clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 nuclease technique to develop a hiPSC line stably expressing ArcLight from the AAVS1 safe harbor locus. The hiPSC line retained proliferative ability with a growth rate similar to its parental strain. Optical recording with conventional epifluorescence microscopy allowed the detection of APs as early as 21 days postdifferentiation, and could be repeatedly monitored for at least 5 months. Moreover, quantification and analysis of the APs of ArcLight-CMs identified two distinctive subtypes: a group with high frequency of spontaneous APs of small amplitudes that were pacemaker-like CMs and a group with low frequency of automaticity and large amplitudes that resembled the working CMs. Compared with FluoVolt voltage-sensitive dye, although dimmer, the ArcLight reporter exhibited better optical performance in terms of phototoxicity and photostability with comparable sensitivities and signal-to-noise ratios. The hiPSC line with targeted ArcLight engineering design represents a useful tool for studying cardiac development or hiPSC-derived cardiac disease models and drug testing.

In the past years, data-driven approaches such as deep learning have been widely applied on machinery signal processing to develop intelligent fault diagnosis systems. In real-world applications, domain shift problem usually occurs where the distribution of the labeled training data, denoted as source domain, is different from that of the unlabeled testing data, known as target domain. That results in serious diagnosis performance degradation. This paper proposes a novel domain adaptation method for rolling bearing fault diagnosis based on deep learning techniques. A deep convolutional neural network is used as the main architecture. The multi-kernel maximum mean discrepancies (MMD) between the two domains in multiple layers are minimized to adapt the learned representations from supervised learning in the source domain to be applied in the target domain. The domain-invariant features can be efficiently extracted in this way, and the cross-domain testing performance can be significantly improved. Experiments on two rolling bearing datasets are carried out to validate the effectiveness of the domain adaptation approach. Comparisons with other approaches and related works demonstrate the superiority of the proposed method. The experimental results of this study suggest the proposed domain adaptation method offers a new and promising tool for intelligent fault diagnosis.

In the past years, data-driven approaches such as deep learning have been widely applied on machinery signal processing to develop intelligent fault diagnosis systems. In real-world applications, domain shift problem usually occurs where the distribution of the labeled training data, denoted as source domain, is different from that of the unlabeled testing data, known as target domain. That results in serious diagnosis performance degradation. This paper proposes a novel domain adaptation method for rolling bearing fault diagnosis based on deep learning techniques. A deep convolutional neural network is used as the main architecture. The multi-kernel maximum mean discrepancies (MMD) between the two domains in multiple layers are minimized to adapt the learned representations from supervised learning in the source domain to be applied in the target domain. The domain-invariant features can be efficiently extracted in this way, and the cross-domain testing performance can be significantly improved. Experiments on two rolling bearing datasets are carried out to validate the effectiveness of the domain adaptation approach. Comparisons with other approaches and related works demonstrate the superiority of the proposed method. The experimental results of this study suggest the proposed domain adaptation method offers a new and promising tool for intelligent fault diagnosis.

Elastography is of great interest in biomechanics and medical imaging due to its nondestructive capability of mapping elasticity of tissues. The elastography framework relies on external excitations which actuate deformation inside an object. The internal response is then acquired and analyzed to map the distribution of elastic moduli. In this paper, with no need of measuring any internal responses, an integrated elastography method is developed which only requires the transmitted responses of applied sound waves. During the process, the tomography image (e.g., CT or MRI) and the applied waves are integrated into a computational model. Following a procedure of inverse analysis, the elasticity of all phases in the object is reconstructed when the computational transmission of waves matches with the measured transmission. The numerical simulation on brain tissues and a demonstration on silicon rubber phantom are conducted to validate the proposed method. Both cases demonstrate that the integrated method successfully predicts the real elasticity of samples. The verification measurements on the phantom further show that the predicted elastic moduli agree well with the experimental results of uniaxial compression testing.

Elastography is of great interest in biomechanics and medical imaging due to its nondestructive capability of mapping elasticity of tissues. The elastography framework relies on external excitations which actuate deformation inside an object. The internal response is then acquired and analyzed to map the distribution of elastic moduli. In this paper, with no need of measuring any internal responses, an integrated elastography method is developed which only requires the transmitted responses of applied sound waves. During the process, the tomography image (e.g., CT or MRI) and the applied waves are integrated into a computational model. Following a procedure of inverse analysis, the elasticity of all phases in the object is reconstructed when the computational transmission of waves matches with the measured transmission. The numerical simulation on brain tissues and a demonstration on silicon rubber phantom are conducted to validate the proposed method. Both cases demonstrate that the integrated method successfully predicts the real elasticity of samples. The verification measurements on the phantom further show that the predicted elastic moduli agree well with the experimental results of uniaxial compression testing.

We propose a chelation-assisted assembly of multidentate CNs into metal-organic nanoparticles (MONs). Multidentate CNs functionalized with coordination sites participate equally as organic linkers in MON construction, which is driven by chelation between metal ions and coordination sites. MONs assembled from Au nanoparticles display particle number- and size-dependent optical properties. In addition, the resulting CN-assembled MONs give evidence that assembly was dictated by the multidentate surface ligand rather than the size, shape or material of CNs. With this chelation-assisted strategy, it is possible to control the number of assembled CNs and build the connections between them.

BackgroundSwitchgrass (Panicum virgatum L.) is a promising bioenergy feedstock because it can be grown on marginal land and produces abundant biomass. Recalcitrance of the lignocellulosic components of the switchgrass cell wall to enzymatic degradation into simple sugars impedes efficient biofuel production. We previously demonstrated that overexpression of OsAT10, a BAHD acyltransferase gene, enhances saccharification efficiency in rice.ResultsHere we show that overexpression of the rice OsAT10 gene in switchgrass decreased the levels of cell wall-bound ferulic acid (FA) in green leaf tissues and to a lesser extent in senesced tissues, and significantly increased levels of cell wall-bound p-coumaric acid (p-CA) in green leaves but decreased its level in senesced tissues of the T0 plants under greenhouse conditions. The engineered switchgrass lines exhibit an approximate 40% increase in saccharification efficiency in green tissues and a 30% increase in senesced tissues.ConclusionOur study demonstrates that overexpression of OsAT10, a rice BAHD acyltransferase gene, enhances saccharification of lignocellulosic biomass in switchgrass.

IntroductionLittle is known about factors affecting motivation and satisfaction of participants in dementia prevention trials.MethodsA Research Satisfaction Survey was administered to 422 nondemented older adults who participated in the Home-Based Assessment trial.ResultsOverall satisfaction was high, with means of all individual items near to above a value of 3 on a scale from 1 (worst) to 4 (best). Greater satisfaction was associated with staff-administered interviews versus automated technologies. The most liked aspects of research participation were volunteerism, opportunity to challenge and improve mental function, and positive interactions with staff. The least liked aspect was repetitiveness of the assessments. Participants requested more contact with staff and other older adults and more feedback on performance.DiscussionOlder adults' participation in research was primarily motivated by altruism. Methodologies that facilitate human contact, encourage feedback and novelty of tasks should be incorporated into future trial design.

Androgen signaling is essential for prostate development, morphogenesis, and regeneration. Emerging evidence indicates that Wnt/β-catenin signaling also contributes to prostate development specifically through regulation of cell fate determination. Prostatic Axin2-expressing cells are able to respond to Wnt signals and possess the progenitor properties to regenerate prostatic epithelium. Despite critical roles of both signaling pathways, the biological significance of androgen receptor (AR) in Axin2-expressing/Wnt-responsive cells remains largely unexplored. In this study, we investigated this important question using a series of newly generated mouse models. Deletion of Ar in embryonic Axin2-expressing cells impaired early prostate development in both ex vivo and tissue implantation experiments. When Ar expression was deleted in prostatic Axin2-expressing cells at pre-puberty stages, it results in smaller and underdeveloped prostates. A subpopulation of Axin2 expressing cells in prostate epithelium is resistant to castration and, following androgen supplementation, is capable to expand to prostatic luminal cells. Deletion of Ar in these Axin2-expressing cells reduces their regenerative ability. These lines of evidence demonstrate an indispensable role for the Ar in Wnt-responsive cells during the course of prostate development, morphogenesis, and regeneration, which also imply an underlying interaction between the androgen and Wnt signaling pathways in the mouse prostate. Stem Cells 2018;36:891-902.

Robust and predictably performing synthetic circuits rely on the use of well-characterized regulatory parts across different genetic backgrounds and environmental contexts. Here we report the large-scale metagenomic mining of thousands of natural 5' regulatory sequences from diverse bacteria, and their multiplexed gene expression characterization in industrially relevant microbes. We identified sequences with broad and host-specific expression properties that are robust in various growth conditions. We also observed substantial differences between species in terms of their capacity to utilize exogenous regulatory sequences. Finally, we demonstrate programmable species-selective gene expression that produces distinct and diverse output patterns in different microbes. Together, these findings provide a rich resource of characterized natural regulatory sequences and a framework that can be used to engineer synthetic gene circuits with unique and tunable cross-species functionality and properties, and also suggest the prospect of ultimately engineering complex behaviors at the community level.

The regulation of human pluripotent stem cell (hPSC) behaviors has been mainly studied through exploration of biochemical factors. However, the current directed differentiation protocols for hPSCs that completely rely on biochemical factors remain suboptimal. It has recently become evident that coexisting biophysical signals in the stem cell microenvironment, including nanotopographic cues, can provide potent regulatory signals to mediate adult stem cell behaviors, including self-renewal and differentiation. Herein, we utilized a recently developed, large-scale nanofabrication technique based on reactive-ion etching (RIE) to generate random nanoscale structures on glass surfaces with high precision and reproducibility. We report here that hPSCs are sensitive to nanotopographic cues and such nanotopographic sensitivity can be leveraged for improving directed neuronal differentiation of hPSCs. We demonstrate early neuroepithelial conversion and motor neuron (MN) progenitor differentiation of hPSCs can be promoted using nanoengineered topographic substrates. We further explore how hPSCs sense the substrate nanotopography and relay this biophysical signal through a regulatory signaling network involving cell adhesion, the actomyosin cytoskeleton, and Hippo/YAP signaling to mediate the neuroepithelial induction of hPSCs. Our study provides an efficient method for large-scale production of MNs from hPSCs, useful for regenerative medicine and cell-based therapies.

Directed evolution continues to expand the capabilities of complex biomolecules for a range of applications, such as adeno-associated virus vectors for gene therapy; however, advances in library design and selection strategies are key to develop variants that overcome barriers to clinical translation. To address this need, we applied structure-guided SCHEMA recombination of the multimeric adeno-associated virus (AAV) capsid to generate a highly diversified chimeric library with minimal structural disruption. A stringent in vivo Cre-dependent selection strategy was implemented to identify variants that transduce adult neural stem cells (NSCs) in the subventricular zone. A novel variant, SCH9, infected 60% of NSCs and mediated 24-fold higher GFP expression and a 12-fold greater transduction volume than AAV9. SCH9 utilizes both galactose and heparan sulfate as cell surface receptors and exhibits increased resistance to neutralizing antibodies. These results establish the SCHEMA library as a valuable tool for directed evolution and SCH9 as an effective gene delivery vector to investigate subventricular NSCs.

By utilizing the interaction between inorganic species and organic surfactants, the ordered layered mesostructures were generated for the synthesis of the one-dimensional oxide nanomaterials. The oxide nanomaterial products which evolved from the above layered structures were demonstrated as ultrathin (less than 2 nm) one-dimensional structures with superior catalytic performance. The synthetic method based on layered structures can be extended to prepare other one-dimensional oxide nanomaterials with the same ultrathin structures.

Extensive research on engineered nanomaterials (ENMs) has led to the development of numerous nano-based formulations for theranostic purposes. Although some nano-based drug delivery systems already exist on the market, growing numbers of newly designed ENMs exhibit improved physicochemical properties and are being assessed in preclinical stages. While these ENMs are designed to improve the efficacy of current nano-based therapeutic or imaging systems, it is necessary to thoroughly determine their safety profiles for successful clinical applications. As such, our aim in this mini-review is to discuss the current knowledge on predictive safety and structure-activity relationship (SAR) analysis of major ENMs at the developing stage, as well as the necessity of additional long-term toxicological analysis that would help to facilitate their transition into clinical practices. We focus on how the interaction of these nanomaterials with cells would trigger signaling pathways as molecular initiating events that lead to adverse outcomes. These mechanistic understandings would help to design safer ENMs with improved therapeutic efficacy in clinical settings.

Li2Ni1-xCuxO2 solid solutions were prepared by a solid-state method to study the correlation between composition and electrochemical performance. Cu incorporation improved the phase purity of Li2Ni1-xCuxO2 with orthorhombic Immm structure, resulting in enhanced capacity. However, the electrochemical profiles suggested Cu incorporation did not prevent irreversible phase transformation during the electrochemical process, instead, it likely influenced the phase transformation upon lithium removal. By combining ex situ X-ray diffraction (XRD), X-ray absorption spectroscopy (XAS), and differential electrochemical mass spectrometry (DEMS) measurements, this study elucidates the relevant phase transformation (e.g., crystal structure, local environment, and charge compensation) and participation of electrons from lattice oxygen during the first cycle in these complex oxides.

Traditionally, crystallographic analysis of macromolecules has depended on large, well-ordered crystals, which often require significant effort to obtain. Even sizable crystals sometimes suffer from pathologies that render them inappropriate for high-resolution structure determination. Here we show that fragmentation of large, imperfect crystals into microcrystals or nanocrystals can provide a simple path for high-resolution structure determination by the cryoEM method MicroED and potentially by serial femtosecond crystallography.

Printed p-type single walled carbon nanotube (SWCNT) based circuits exhibit high power dissipation owing to their thick printed dielectric layers (>2 μm) and long channels (>100 μm). In order to reduce the static power dissipation of printed SWCNT-base circuits while maintaining the same printing conditions and channel lengths, complementary metal-oxide-semiconductor (CMOS) based circuits are more ideal. These circuits, however, have not been successfully implemented in a scalable printing platform due to unstable threshold voltages of n-doped SWCNT based thin film transistors (TFTs). In this work, a thermally curable epoxy-imine-based n-doping ink is presented for achieving uniform doping and sealing of SWCNT layers by gravure printing. After printing the n-doping ink, the ink is cured to initiate a cross-linking reaction to seal the n-doped SWCNT-TFTs so that the threshold voltage of the n-doped SWCNT-TFTs is stabilized. Flexible CMOS ring oscillators using such n-doped SWCNT-TFTs combined with the intrinsically p-type SWCNT-TFTs can generate a 0.2 Hz clock signal with significantly lower power consumption compared to similarly printed p-type only TFT based ring oscillators. Moving forward, this CMOS flexible ring oscillator can be practically used to develop fully printed inexpensive wireless sensor tags.