Machine learning-coupled tactile recognition with high spatiotemporal resolution based on cross-striped nanocarbon piezoresistive sensor array.

Flexible pressure sensor arrays have been playing important roles in various applications of human-machine interface, including robotic tactile sensing, electronic skin, prosthetics, and human-machine interaction. However, it remains challenging to simultaneously achieve high spatial and temporal resolution in developing pressure sensor arrays for tactile sensing with robust function to achieve precise signal recognition. This work presents the development of a flexible high spatiotemporal piezoresistive sensor array (PRSA) by coupling with machine learning algorithms to enhance tactile recognition. The sensor employs cross-striped nanocarbon-polymer composite as an active layer, though screen printing manufacture processes. A miniaturized signal readout circuit and transmission board is developed to achieve high-speed acquisition of distributed pressure signals from the PRSA. Test results indicate that the developed PRSA platform simultaneously possesses the characteristics of high spatial resolution up to 1.5 mm, fast temporal resolution of about 5 ms, and long-term durability with a variation of less than 2%. The PRSA platform also exhibits excellent performance in real-time visualization of multi-point touch, mapping embossed shapes, and tracking motion trajectory. To test the performance of PRSA in recognizing different shapes, we acquired pressure images by pressing the finger-type device coated with PRSA film on different embossed shapes and implementing the T-distributed Stochastic Neighbor Embedding model to visualize the distinction between images of different shapes. Then we adopted a one-layer neural network to quantify the discernibility between images of different shapes. The analysis results show that the PRSA could capture the embossed shapes clearly by one contact with high discernibility up to 98.9%. Collectively, the PRSA as a promising platform demonstrates its promising potential for robotic tactile sensing.

A ratiometric fluorescence imprinted sensor based on N-CDs and metal-organic frameworks for visual smart detection of malathion.

Sensitive and rapid detection of pesticide residues in food is essential for human safety. A ratiometric imprinted fluorescence sensor N-CDs@Eu-MOF@MIP (BR@MIP) was constructed to sensitively detect malathion (Mal). Europium-based metal organic frameworks (Eu-MOF) were used as supporters to improve the sensitivity of the BR@MIP. N-doped carbon dots (N-CDs) were used as fluorescent source to produce fluorescent signal. A linear relationship between the concentration of Mal and the fluorescence response of the sensor was found in the Mal concentration range of 1-10 µM with a limit of detection (LOD) of 0.05 µM. Furthermore, the sensor was successfully applied for the detection of Mal in lettuce, tap water, and soil samples, with recoveries in the range of 93.0 % - 99.3 %. Additionally, smartphone-based sensors were used to detect Mal in simulated real samples. Thus, the construction of ratiometric imprinted fluorescence sensor has provided a good strategy for the detection of Mal.

Copper-Catalyzed Carboxylation of Aryl Thianthrenium Salts with CO2.

Carboxyl group is one of the most widely used groups in organic synthesis. Herein, an efficient copper-catalyzed carboxylation of aryl thianthrenium salts with carbon dioxide (CO2 ) at room temperature has been developed. The reaction employs low loading of cuprous chloride catalyst under 1 atm CO2 and exhibits good functional group tolerance. In combination with C-H thianthrenation of aromatic hydrocarbons, this work provides an efficient method for the site-selective C-H carboxylation of aromatic hydrocarbons. It may serve as a significant late-stage carboxylation tool for the modification of drug molecules in the future.

Ruthenium(II)-Catalyzed Grignard-Type Nucleophilic Addition of C(sp2)-H Bonds to Unactivated Aldehydes.

The Grignard-type nucleophilic addition of C(sp2)-H bonds to aldehydes catalyzed by high-oxidation-state transition metal complexes is limited to activated aldehydes. Herein, we report the first example of Grignard-type nucleophilic addition of C(sp2)-H bonds to unactivated aldehydes catalyzed by high-oxidation-state ruthenium(II). The reaction has mild reaction conditions and good functional group tolerance. The corresponding alcohol products are obtained in good to excellent yields.

A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning.

Lung cancer has one of the highest mortalities of all cancers. According to the National Lung Screening Trial, patients who underwent low-dose computed tomography (CT) scanning once a year for 3 years showed a 20% decline in lung cancer mortality. To further improve the survival rate of lung cancer patients, computer-aided diagnosis (CAD) technology shows great potential. In this paper, we summarize existing CAD approaches applying deep learning to CT scan data for pre-processing, lung segmentation, false positive reduction, lung nodule detection, segmentation, classification and retrieval. Selected papers are drawn from academic journals and conferences up to November 2020. We discuss the development of deep learning, describe several important aspects of lung nodule CAD systems and assess the performance of the selected studies on various datasets, which include LIDC-IDRI, LUNA16, LIDC, DSB2017, NLST, TianChi, and ELCAP. Overall, in the detection studies reviewed, the sensitivity of these techniques is found to range from 61.61% to 98.10%, and the value of the FPs per scan is between 0.125 and 32. In the selected classification studies, the accuracy ranges from 75.01% to 97.58%. The precision of the selected retrieval studies is between 71.43% and 87.29%. Based on performance, deep learning based CAD technologies for detection and classification of pulmonary nodules achieve satisfactory results. However, there are still many challenges and limitations remaining including over-fitting, lack of interpretability and insufficient annotated data. This review helps researchers and radiologists to better understand CAD technology for pulmonary nodule detection, segmentation, classification and retrieval. We summarize the performance of current techniques, consider the challenges, and propose directions for future high-impact research.

Endoscopy-assisted magnetic navigation of biohybrid soft microrobots with rapid endoluminal delivery and imaging.

High-precision delivery of microrobots at the whole-body scale is of considerable importance for efforts toward targeted therapeutic intervention. However, vision-based control of microrobots, to deep and narrow spaces inside the body, remains a challenge. Here, we report a soft and resilient magnetic cell microrobot with high biocompatibility that can interface with the human body and adapt to the complex surroundings while navigating inside the body. We achieve time-efficient delivery of soft microrobots using an integrated platform called endoscopy-assisted magnetic actuation with dual imaging system (EMADIS). EMADIS enables rapid deployment across multiple organ/tissue barriers at the whole-body scale and high-precision delivery of soft and biohybrid microrobots in real time to tiny regions with depth up to meter scale through natural orifice, which are commonly inaccessible and even invisible by conventional endoscope and medical robots. The precise delivery of magnetic stem cell spheroid microrobots (MSCSMs) by the EMADIS transesophageal into the bile duct with a total distance of about 100 centimeters can be completed within 8 minutes. The integration strategy offers a full clinical imaging technique-based therapeutic/intervention system, which broadens the accessibility of hitherto hard-to-access regions, by means of soft microrobots.

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography.

OBJECTIVE: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. METHODS: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. RESULTS: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. CONCLUSION: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

Real-Time Magnetic Navigation of a Rotating Colloidal Microswarm Under Ultrasound Guidance.

OBJECTIVE: Untethered microrobots hold great promise for applications in biomedical field including targeted delivery, biosensing, and microsurgery. A major challenge of using microrobots to perform in vivo tasks is the real-time localization and motion control using medical imaging technologies. Here we report real-time magnetic navigation of a paramagnetic nanoparticle-based microswarm under ultrasound guidance. METHODS: A three-axis Helmholtz electromagnetic coil system integrated with an ultrasound imaging system is developed for generation, actuation, and closed-loop control of the microswarm. The magnetite nanoparticle-based microswarm is generated and navigated using rotating magnetic fields. In order to localize the microswarm in real time, the dynamic imaging contrast has been analyzed and exploited in image process to increase the signal-to-noise ratio. Moreover, imaging of the microswarm at different depths are experimentally studied and analyzed, and the minimal dose of nanoparticles for localizing a microswarm at different depths is ex vivo investigated. For real-time navigating the microswarm in a confined environment, a PI control scheme is designed. RESULTS: Image differencing-based processing increases the signal-to-noise ratio, and the microswarm can be ex vivo localized at depth of 2.2-7.8 cm. Experimental results show that the microswarm is able to be real-time navigated along a planned path in a channel, and the average steady-state error is 0.27 mm (  âˆ¼ 33.7% of the body length). SIGNIFICANCE: The colloidal microswarm is real-time localized and navigated using ultrasound feedback, which shows great potential for biomedical applications that require real-time noninvasive tracking.

miR-214-3p Regulates Multi-Drug Resistance and Apoptosis in Retinoblastoma Cells by Targeting ABCB1 and XIAP.

BACKGROUND: MicroRNAs (miRNAs) have been shown to contribute to the initiation and progression of human cancer, including retinoblastoma. However, expression levels and potential roles of miRNAs in retinoblastoma remain largely unknown. In this study, we aimed to identify dysregulated miRNAs and explore their functional roles in the development of retinoblastoma. MATERIAL AND METHODS: First, miRNA expression profiling in retinoblastoma tissues was performed via microarray analysis. To evaluate the involvement of miR-214-3p in multi-drug resistance, gain-of-function experiments were employed in vitro and in vivo. Bioinformatics analysis, luciferase reporter assay, qRT-PCR and Western blot were used to investigate the underlying mechanisms. RESULTS: Here, we identified 57 up-regulated and 34 down-regulated miRNAs. Among them, miR-214-3p was the most significantly decreased. We found that miR-214-3p level was positively correlated with clinical outcome and chemotherapy response. Overexpression of miR-214-3p significantly sensitized retinoblastoma cells to multiple chemodrugs and promoted cell apoptosis in vitro and in vivo. Further investigations revealed that miR-214-3p directly regulated ABCB1 and XIAP expression through interacting with the 3' untranslated regions (3'UTRs). Pearson correlation analysis showed that miR-214-3p expression in retinoblastoma tissues was negatively correlated with ABCB1 and XIAP expression. We also observed that overexpression of ABCB1 or XIAP partly reversed the chemoresistance inhibition-induced by miR-214-3p overexpression. CONCLUSION: Our data demonstrate that miR-214-3p functions as a tumor suppressor to inhibit the chemoresistance in retinoblastoma, suggesting that miR-214-3p might be potential diagnostic and therapeutic targets for retinoblastoma treatment.

An Automated Microrobotic Platform for Rapid Detection of C. diff Toxins.

OBJECTIVE: Clostridium difficile (C. diff) infection leads to hundreds of nosocomial infections, and early diagnosis of this toxin-mediated disease is important. This paper aims to develop a microrobotic system and related methods that enable the automated and rapid detection of toxins secreted by C. diff that exist in patient's stool. METHODS: We utilize the fluorescent magnetic spore-based microrobot (FMSM), a microscale mobile sensing tool, to efficiently detect C. diff toxins by utilizing its property of selective fluorescence responses to C. diff toxins. A plug-and-play (PnP) electromagnetic coil system integrated with fluorescence microscopy is developed for actuation, control and observation of FMSMs. In order to track in real time and accurately obtain the fluorescence parameters of a FMSM under varied background noise in fluorescence signal, an image gradient-based method is proposed. For accelerating the FMSM-toxin interaction in different samples, an automated navigation control scheme for the FMSM is proposed and implemented. Moreover, data post-processing methods that can optimally extract the fluorescence decay trend from the dense and fluctuated fluorescence data are developed. RESULTS: This automated mobile detection process finishes within only 20 minutes, and the toxin detection result is immediately given by adopting the proposed system and methods. Experimental results on different biological samples confirm the qualitative detection capability. And, C. diff toxins are automatically detected from the clinical stool of infectious patients and the relationship between the fluorescence decay and the toxin concentration is calibrated for semi-quantitative detection purpose. SIGNIFICANCE: The proposed automated microrobotic platform provides a rapid and low-cost detection technique for C. diff toxins, and it has good competency for future clinical use.

lncRNA UCA1 Increases Proliferation and Multidrug Resistance of Retinoblastoma Cells Through Downregulating miR-513a-5p.

Chemoresistance is one of the major obstacles for cancer therapy. Abnormal expression of long noncoding RNAs (lncRNAs) was broadly implicated in chemoresistance of multiple cancers. This study was aimed to investigate the function of urothelial cancer associated 1 (UCA1) in multidrug resistance of retinoblastoma and its potential molecular mechanism. In this study, we observed that UCA1 was significantly upregulated in chemoresistant retinoblastoma tissues and multidrug resistant retinoblastoma cell lines and predicted an unfavorable overall survival. Functionally, knockdown of UCA1 remarkably inhibited proliferation and sensitized retinoblastoma cells to multiple chemotherapy drugs, including vincristine (VCR), carboplatin (CBP), cisplatin (DDP), VP-16 (etoposide), and 5-fluorouracil (5-Fu). Mechanistic studies demonstrated that UCA1 functioned as a miRNA sponge to increase stathmin 1 (STMN1) expression through sponging miR-513a-5p. In addition, silence of miR-513a-5p or STMN1 overexpression could partly reverse UCA1 knockdown-induced inhibitory effects on proliferation and multidrug resistance of retinoblastoma cells. Overall, this study is the first to demonstrate that UCA1 plays a critical role in retinoblastoma chemoresistance, and UCA1 may serve as a potential diagnostic biomarker and therapeutic target of retinoblastoma.

Long Noncoding RNA SNHG16 Sponges miR-182-5p And miR-128-3p To Promote Retinoblastoma Cell Migration And Invasion By Targeting LASP1.

BACKGROUND: Long noncoding RNAs (lncRNAs) are dysregulated and play an important role in the tumorigenesis and progression of cancers. However, the potential roles of SNHG16 in retinoblastoma progression still remain largely unclear. MATERIALS AND METHODS: The expression levels of SNHG16, miR-182-5p, miR-128-3p and LASP1 in retinoblastoma tissues and cell lines were detected using qRT-PCR. The migratory and invasive abilities of retinoblastoma cells were assessed using Transwell assay. The regulatory relationships among SNHG16, miR-182-5p, miR-128-3p and LASP1 were analyzed through bioinformatics prediction and validated by luciferase reporter assay and Western blot. RESULTS: Here, we demonstrated that SNHG16 was frequently up-regulated in retinoblastoma tissue samples and cell lines. Clinicopathological features showed that high levels of SNHG16 were significantly associated with retinoblastoma metastasis and predicted poor overall survival. Functional studies demonstrated that knockdown of SNHG16 suppressed retinoblastoma cell migration and invasion. Mechanistic investigation revealed that SNHG16 exerted its oncogenic activity through increasing LASP1 expression and sponging miR-182-5p and miR-128-3p. CONCLUSION: Taken together, our findings suggest SNHG16 as a novel biomarker and therapeutic target against retinoblastoma.

Bubble-Assisted Three-Dimensional Ensemble of Nanomotors for Improved Catalytic Performance.

Combining catalysts with active colloidal matter could keep catalysts from aggregating, a major problem in chemical reactions. We report a kind of ensemble of bubble-cross-linked magnetic colloidal swarming nanomotors (B-MCS) with enhanced catalytic activity because of the local increase of the nanocatalyst concentration and three-dimensional (3D) fluid convection. Compared with the two-dimensional swarming collective without bubbles, the integral rotation was boosted because of the dynamic dewetting and increased slip length caused by the continuously ejected tiny bubbles. The bubbles cross-link the nanocatalysts and form stack along the vertical axis, generating the 3D network-like B-MCS ensemble with high dynamic stability and low drag resistance. The generated B-MCS ensemble exhibits controllable locomotion performance when applying a rotating magnetic field. Benefiting from locally increased catalyst concentration, good mobility, and 3D fluidic convection, the B-MCS ensemble offers a promising approach to heterogeneous catalysis.

Real-time tracking of fluorescent magnetic spore-based microrobots for remote detection of C. diff toxins.

A rapid, direct, and low-cost method for detecting bacterial toxins associated with common gastrointestinal diseases remains a great challenge despite numerous studies and clinical assays. Motion-based detection through tracking the emerging micro- and nanorobots has shown great potential in chemo- and biosensing due to accelerated "chemistry on the move". Here, we described the use of fluorescent magnetic spore-based microrobots (FMSMs) as a highly efficient mobile sensing platform for the detection of toxins secreted by Clostridium difficile (C. diff) that were present in patients' stool. These microrobots were synthesized rapidly and inexpensively by the direct deposition of magnetic nanoparticles and the subsequent encapsulation of sensing probes on the porous natural spores. Because of the cooperation effect of natural spore, magnetic Fe3O4 nanoparticles, and functionalized carbon nanodots, selective fluorescence detection of the prepared FMSMs is demonstrated in C. diff bacterial supernatant and even in actual clinical stool samples from infectious patients within tens of minutes, suggesting rapid response and good selectivity and sensitivity of FMSMs toward C. diff toxins.

Collective Behavior of Reconfigurable Magnetic Droplets via Dynamic Self-Assembly.

Dynamic self-assembly represents an effective approach to form energy-dissipative structures by introducing interactions among multiple building blocks with a continuous energy supply. Time-dependent magnetic fields are treated as convenient energy inputs to construct such a dynamic self-assembled system. The induced interactions can be further tuned by modulating the input field, resulting in a diversity of assembled patterns. However, formation of a functional dynamic pattern with controllability remains a challenge. Herein, we report the formation and pattern control of dynamically self-assembled magnetic droplets at an air-liquid interface, energized by a precessing magnetic field. The formation process involves the assembly of magnetic microparticles into particle chains inside droplets, and then highly ordered patterns are generated by balancing the induced interactions among droplets. By modulating the input field, the interactions and collective behaviors are adjusted and the pattern can be reversibly tuned, i.e., expand and shrink, in a controlled manner. Furthermore, the assembled droplets are able to be steered in two-dimensional as an entity by applying a magnetic field gradient. Utilizing dynamic pattern control and steerability of the assembled structure, cargoes are successfully trapped, transported, and released in a noncontact fashion, indicating that the dynamically assembled droplets can act as a reconfigurable untethered robotic end-effector for manipulation.

Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs.

OBJECTIVE: A novel computer-aided detection (CAD) scheme for lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy is proposed to assist radiologists by providing a second opinion on accurate lung nodule detection, which is a crucial step in early diagnosis of lung cancer. METHOD: A 3D deep convolutional neural network (CNN) with multi-scale prediction was used to detect lung nodules after the lungs were segmented from chest CT scans, with a comprehensive method utilized. Compared with a 2D CNN, a 3D CNN can utilize richer spatial 3D contextual information and generate more discriminative features after being trained with 3D samples to fully represent lung nodules. Furthermore, a multi-scale lung nodule prediction strategy, including multi-scale cube prediction and cube clustering, is also proposed to detect extremely small nodules. RESULT: The proposed method was evaluated on 888 thin-slice scans with 1186 nodules in the LUNA16 database. All results were obtained via 10-fold cross-validation. Three options of the proposed scheme are provided for selection according to the actual needs. The sensitivity of the proposed scheme with the primary option reached 87.94% and 92.93% at one and four false positives per scan, respectively. Meanwhile, the competition performance metric (CPM) score is very satisfying (0.7967). CONCLUSION: The experimental results demonstrate the outstanding detection performance of the proposed nodule detection scheme. In addition, the proposed scheme can be extended to other medical image recognition fields.

Identification and functional characterization of two Secretogranin II genes in orange-spotted grouper (Epinephelus coioides).

Secretoneurin (SN) is an important stimulator of pituitary luteinizing hormone (LH) synthesis and secretion in goldfish. It is unknown whether this neuropeptide performs the same role in other fish species. In this study, the full-length cDNAs encoding Secretogranin IIa (SgIIa) and b (SgIIb) were cloned from the brain of orange-spotted grouper. Sequence analysis showed that a 34-amino acid SN peptide (SNa) is present in SgIIa proprotein, and a 33-amino acid SN peptide (SNb) is present in SgIIb proprotein. The two SN peptides share a low degree of similarity but contain the signature YTPQ-X-LA-X7-EL sequence. Real-time PCR showed that two SgII genes are mainly expressed in the brain and pituitary. During ovarian development, the expression levels of two SgII genes in the hypothalamus and pituitary were significantly reduced at the stage when the ovary contained full-grown oocytes. The biological functions of the two SN peptides were further investigated in vitro and in vivo. Both SN peptides could significantly elevate the mRNA levels of Gonadotropin-Releasing Hormone 1 (GnRH1) and 3 (GnRH3) in the hypothalamic fragments and upregulated the expression of Follicle-Stimulating Hormone beta (FSHb) and Luteinizing Hormone beta (LHb) in the pituitary cells. The stimulatory effects on the expression of GnRHs and Gonadotropins were also observed after intraperitoneal injection of SN peptides. Our study indicated that the SgII/SN system has stimulatory effects on the reproductive axis of orange-spotted grouper.

Characterizing dynamic behaviors of three-particle paramagnetic microswimmer near a solid surface.

Particle-based magnetically actuated microswimmers have the potential to act as microrobotic tools for biomedical applications. In this paper, we report the dynamic behaviors of a three-particle paramagnetic microswimmer. Actuated by a rotating magnetic field with different frequencies, the microswimmer exhibits simple rotation and propulsion. When the input frequency is below 8 Hz, it exhibits simple rotation on the substrate, whereas it shows propulsion with varied poses when subjected to a frequency between 8 and 15 Hz. Furthermore, a solid surface that enhances swimming velocity was observed as the microswimmer is actuated near a solid surface. Our simulation results testify that the surface-enhanced swimming near a solid surface is because of the induced pressure difference in the surrounding fluid of the microagent.

An auto-adaptive background subtraction method for Raman spectra.

Background subtraction is a crucial step in the preprocessing of Raman spectrum. Usually, parameter manipulating of the background subtraction method is necessary for the efficient removal of the background, which makes the quality of the spectrum empirically dependent. In order to avoid artificial bias, we proposed an auto-adaptive background subtraction method without parameter adjustment. The main procedure is: (1) select the local minima of spectrum while preserving major peaks, (2) apply an interpolation scheme to estimate background, (3) and design an iteration scheme to improve the adaptability of background subtraction. Both simulated data and Raman spectra have been used to evaluate the proposed method. By comparing the backgrounds obtained from three widely applied methods: the polynomial, the Baek's and the airPLS, the auto-adaptive method meets the demand of practical applications in terms of efficiency and accuracy.

The complete mitochondrial genome of the Megalobrama skolkovii (Cyprinidae: Cultrinae).

The complete mitochondrial DNA (mtDNA) sequence of Megalobrama skolkovii was first presented in this study. The mitochondrial genome is 16,620 bp in length, including 13 protein-coding genes, 2 ribosomal RNA gens (12S rRNA and 16S rRNA), 22 transfer RNA genes (tRNA) and a control region (D-loop), with the gene identical to that of typical vertebrates. The overall base composition of the light strand are 31.23% A, 24.73% T, 16.16% G and 27.88% C. Two copies of tandem repeat sequence was found in the control region.