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Abstract Micro/nanorobots have shown great potential to execute different tasks in microenvironments due to their small size, high controllability and environmental adaptability. However, it is still challenging to precisely control the deformation and navigation of soft micro/nanorobots to better adapt to unstructured and complex surroundings. Here, we report a photonic nanojet (PNJ)-regulated soft microalga robot (saBOT) based on Euglena gracilis with controlled deformation and precise navigation capability. The deformability of the saBOT was precisely controlled by the highly focused light energy from a microlens-based PNJ bound to a tapered optical fiber probe (TFP), which can precisely stimulate the channelrhodopsin-2 (ChR2) in the photoreceptor of the microalga. This saBOT can be further precisely navigated toward different positions in complex and unstructured microenvironments by combining the deformability with the phototaxis ability of the microalga via the flexible manipulation of TFP. Notably, due to the ability of controllable deformation and precision navigation, the saBOT can travel across cell clusters for precision drug delivery toward a target cell. This PNJ-regulated saBOT holds great promise in executing different biomedical tasks in complex and unstructured microenvironments that cannot be reached by conventional tools and rigid microrobots.

Abstract Spider silk exhibits an excellent combination of high strength and toughness, which originates from the hierarchical self-assembled structure of spidroin during fiber spinning. In this work, superfine nanofibrils are established in polyelectrolyte artificial spider silk by optimizing the flexibility of polymer chains, which exhibits combination of breaking strength and toughness ranging from 1.83 GPa and 238 MJ m−3 to 0.53 GPa and 700 MJ m−3, respectively. This is achieved by introducing ions to control the dissociation of polymer chains and evaporation-induced self-assembly under external stress. In addition, the artificial spider silk possesses thermally-driven supercontraction ability. This work provides inspiration for the design of high-performance fiber materials.

We propose an acoustic-mechanical metasurface with simultaneous sound absorption and vibration reduction functionalities. This acoustic-mechanical feature results from a nonlocal coupling effect in the designed composite meta-structure. A nonlocal meta-mesh is designed at the acoustic-mechanical interface and embedded in the metasurface array to connect adjacent unit cells, which can make a nonlocal coupling effect. This effect can improve the acoustic property, which strengthens sound absorption by 58.6 %, and the mechanical interface strengthens vibration acceleration reduction by 88.45 %. The resulting nonlocal acoustic-mechanical metasurface achieves an average absorption coefficient of higher than 0.9 within 267–638 Hz, and a vibration level difference of 18.75 dB within 490–920 Hz. The sample has an ultra-thin thickness of 8.5 cm (λ/14 for the lowest working frequency) and is lightweight (305.1 g), whose weight reduction reaches 42.8 % compared with conventional designs with similar functionalities. Our proposed design strategy will be enlightened for multi-functional acoustic-mechanical coupling meta-device.

A modified QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) -LC-MS/MS method was developed for the determination of hexachlorophene in fruits and vegetables. Samples were extracted by acetonitrile and then salted with an acetate buffer system. Extractants neutral alumina (Al-N), strong cation exchange silica gel bonded adsorbent (SCX) and graphitized carbon black (GCB) were used for sample purification. The method demonstrates excellent accuracy and reproducibility. Under optimized conditions, the correlation coefficients of hexachlorophene were higher than 0.995 in the range of 0.5-20 ng/mL. The limit of quantification (LOQ) was 2.0 μg/kg. The average recoveries, assessed at three spiked levels (2.0, 4.0, and 20.0μg/kg) across various matrices including cabbage, celery, tomato, eggplant, potato, radish, cowpea, chives, apple, peach, grape, citrus, bitter melon, banana and hami melon ranged from 72.0 to 100.5% with relative standard deviations from 3.2 to 9.8% (n = 6).

Abstract The striatum is the main input structure of the basal ganglia, receiving information from the cortex and the thalamus and consisting of D1- and D2- medium spiny neurons (MSNs). D1-MSNs and D2-MSNs are essential for motor control and cognitive behaviors and have implications in Parkinson’s Disease. In the present study, we demonstrated that Sp9-positive progenitors produced both D1-MSNs and D2-MSNs and that Sp9 expression was rapidly downregulated in postmitotic D1-MSNs. Furthermore, we found that sustained Sp9 expression in lateral ganglionic eminence (LGE) progenitor cells and their descendants led to promoting D2-MSN identity and repressing D1-MSN identity during striatal development. As a result, sustained Sp9 expression resulted in an imbalance between D1-MSNs and D2-MSNs in the mouse striatum. In addition, the fate-changed D2-like MSNs survived normally in adulthood. Taken together, our findings supported that Sp9 was sufficient to promote D2-MSN identity and repress D1-MSN identity, and Sp9 was a negative regulator of D1-MSN fate.

Abstract The striatum is structurally highly diverse, and its organ functionality critically depends on normal embryonic development. Although several studies have been conducted on the gene functional changes that occur during striatal development, a system-wide analysis of the underlying molecular changes is lacking. Here, we present a comprehensive transcriptome profile that allows us to explore the trajectory of striatal development and identify the correlation between the striatal development and Huntington’s disease (HD). Furthermore, we applied an integrative transcriptomic profiling approach based on machine learning to systematically map a global landscape of 277 transcription factor (TF) networks. Most of these TF networks are linked to biological processes, and some unannotated genes provide information about the corresponding mechanisms. For example, we found that the Meis2 and Six3 were crucial for the survival of striatal neurons, which were verified using conditional knockout (CKO) mice. Finally, we used RNA-Seq to speculate their downstream targets.

The striatum is primarily composed of two types of medium spiny neurons (MSNs) expressing either D1- or D2-type dopamine receptors. However, the fate determination of these two types of neurons is not fully understood. Here, we found that D1 MSNs undergo fate switching to D2 MSNs in the absence of Zfp503. Furthermore, scRNA-seq revealed that the transcription factor Zfp503 affects the differentiation of these progenitor cells in the lateral ganglionic eminence (LGE). More importantly, we found that the transcription factors Sp8/9, which are required for the differentiation of D2 MSNs, are repressed by Zfp503. Finally, sustained Zfp503 expression in LGE progenitor cells promoted the D1 MSN identity and repressed the D2 MSN identity. Overall, our findings indicated that Zfp503 promotes the D1 MSN identity and represses the D2 MSN identity by regulating Sp8/9 expression during striatal MSN development.

Acoustic insulation in ventilated structures is an important problem in acoustic engineering with many potential practical applications, such as the noise control for ventilating ducts of buildings, vehicles, or air conditioners. Acoustic metamaterial is a good candidate for the design of acoustic insulation for ventilated channel (AIVC) because the structural design with hard boundary has longer lifetime than conventional sound-absorbing cotton. In this paper, an AIVC with an open region and narrow channels of different lengths is proposed. We numerically and experimentally demonstrate its acoustic insulation larger than 20 dB (T < 0.01) within approximately 500–1,200 Hz with a subwavelength channel length of λ/6. The parameter dependence and air flow effect are numerically studied. Our findings show an alternative design of AIVC that may have applications in noise control and architectural acoustics.

Working memory (WM) plays an important role in cognitive activity. The WM system is used to temporarily store information in learning and decision-making. WM always functions in many aspects of daily life, such as the short-term memory of words, cell phone verification codes, and cell phone numbers. In young adults, studies have shown that a central memory store is limited to three to five meaningful items. Little is known about how WM functions at the microscopic neural level, but appropriate neural network computational models can help us gain a better understanding of it. In this study, we attempt to design a microscopic neural network model to explain the internal mechanism of WM. The performance of existing positive feedback models depends on the parameters of a synapse. We use a negative-derivative feedback mechanism to counteract the drift in persistent activity, making the hybrid positive and negative-derivative feedback (HPNF) model more robust to common disturbances. To fulfill the mechanism of WM at the neural circuit level, we construct two main neural networks based on the HPNF model: a memory-storage sub-network (the memory-storage sub-network is composed of several sets of neurons, so we call it “SET network”, or “SET” for short) with positive feedback and negative-derivative feedback and a storage distribution network (SDN) designed by combining SET for memory item storage and memory updating. The SET network is a neural information self-sustaining mechanism, which is robust to common disturbances; the SDN constructs a storage distribution network at the neural circuit level; the experimental results show that our network can fulfill the storage, association, updating, and forgetting of information at the level of neural circuits, and it can work in different individuals with little change in parameters.

Abstract Background Patients with lung adenocarcinoma (LUAD) receiving drug treatment often have an unpredictive response and there is a lack of effective methods to predict treatment outcome for patients. Dendritic cells (DCs) play a significant role in the tumor microenvironment and the DCs-related gene signature may be used to predict treatment outcome. Here, we screened for DC-related genes to construct a prognostic signature to predict prognosis and response to immunotherapy in LUAD patients. Methods DC-related biological functions and genes were identified using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing. DCs-related gene signature (DCRGS) was constructed using integrated machine learning algorithms. Expression of key genes in clinical samples was examined by real-time q-PCR. Performance of the prognostic model, DCRGS, for the prognostic evaluation, was assessed using a multiple time-dependent receiver operating characteristic (ROC) curve, the R package, “timeROC”, and validated using GEO datasets. Results Analysis of scRNA-seq data showed that there is a significant upregulation of LGALS9 expression in DCs isolated from malignant pleural effusion samples. Leveraging the Coxboost and random survival forest combination algorithm, we filtered out six DC-related genes on which a prognostic prediction model, DCRGS, was established. A high predictive capability nomogram was constructed by combining DCRGS with clinical features. We found that patients with a high-DCRGS score had immunosuppression, activated tumor-associated pathways, and elevated somatic mutational load and copy number variant load. In contrast, patients in the low-DCRGS subgroup were resistant to chemotherapy but sensitive to the CTLA-4 immune checkpoint inhibitor and targeted therapy. Conclusion We have innovatively established a deep learning-based prediction model, DCRGS, for the prediction of the prognosis of patients with LUAD. The model possesses a strong prognostic prediction performance with high accuracy and sensitivity and could be clinically useful to guide the management of LUAD. Furthermore, the findings of this study could provide an important reference for individualized clinical treatment and prognostic prediction of patients with LUAD.

Urban form, which refers to the land use patterns and the structure of transport network within a city, indirectly influences carbon dioxide emissions in cities by impacting fossil fuel consumption in the transportation and residential sectors. Previous studies on the relationships between urban form and carbon emissions have mainly focused on general analysis at city level, usually considering urban built-up areas as homogenous units, while ignoring the spatial heterogeneity of functions within urban built-up areas. To fill the gap, this study aims to examine the impact of urban functional form on carbon emissions in 31 major cities in China by introducing urban functional zones data to capture the spatial heterogeneity of functions within cities. Firstly, ODIAC (Open-source Data Inventory for Anthropogenic Carbon dioxide) was preprocessed to obtain gridded carbon dioxide emission data as dependent variables. Secondly, a comprehensive indicator system was established to quantify the functional form of a city in terms of both land use patterns and transport networks structure as independent variables. Thirdly, a scale sensitivity analysis was then conducted to determine the optimal scale for analyzing the impacts. Finally, lasso regression was utilized to investigate the impact of urban functional form on carbon emissions. Experimental results indicate that 1) the grid size of 6 km is the optimal scale for studying the relationships between urban functional form and carbon emissions; 2) the topology of road networks is more important than its size for influencing carbon emissions; 3) specific relationships between land use patterns and carbon emissions has been revealed, highlighting the importance of considering functional heterogeneity when studying the relationship between urban form and carbon emissions, while simultaneously providing significant guidance for the construction of low-carbon cities. These findings not only provide an important reference for future research (e.g., optimal scale), but also further highlight the theoretical and practical significance of urban functional form adjustment in low-carbon and sustainable urban development. The study offers solid theoretical basis for urban planning, construction and management, demonstrating that urban greenhouse gas emissions can be effectively reduced by optimizing urban form planning, and provides reliable decision support for the development of specific optimization schemes.