Open-source High-precision Autonomous Suturing Framework With Visual Guidance

Autonomous surgery has attracted increasing attention for revolutionizing robotic patient care, yet remains a distant and challenging goal. In this paper, we propose an image-based framework for high-precision autonomous suturing operation. We first build an algebraic geometric algorithm to achieve accurate needle pose estimation, then design the corresponding keypoint-based calibration network for joint-offset compensation, and further plan and control suture trajectory. Our solution ranked first among all competitors in the AccelNet Surgical Robotics Challenge. The source code is opened here to accelerate future autonomous surgery research

E-hooks provide guidance and a soft landing for the microtubule binding domain of dynein

Macromolecular binding is a complex process that involves sensing and approaching the binding partner, adopting the proper orientation, and performing the physical binding. We computationally investigated the role of E-hooks, which are intrinsically disordered regions (IDRs) at the C-terminus of tubulin, on dynein microtubule binding domain (MTBD) binding to the microtubule as a function of the distance between the MTBD and its binding site on the microtubule. Our results demonstrated that the contacts between E-hooks and the MTBD are dynamical; multiple negatively charted patches of amino acids on the E-hooks grab and release the same positively charged patches on the MTBD as it approaches the microtubule. Even when the distance between the MTBD and the microtubule was greater than the E-hook length, the E-hooks sensed and guided MTBD via long-range electrostatic interactions in our simulations. Moreover, we found that E-hooks exerted electrostatic forces on the MTBD that were distance dependent; the force pulls the MTBD toward the microtubule at long distances but opposes binding at short distances. This mechanism provides a “soft-landing” for the MTBD as it binds to the microtubule. Finally, our analysis of the conformational states of E-hooks in presence and absence of the MTBD indicates that the binding process is a mixture of the induced-fit and lock-and-key macromolecular binding hypotheses. Overall, this novel binding mechanism is termed “guided-soft-binding” and could have broad-reaching impacts on the understanding of how IDRs dock to structured proteins

Remodeling and Estimation for Sparse Partially Linear Regression Models

When the dimension of covariates in the regression model is high, one usually uses a submodel as a working model that contains significant variables. But it may be highly biased and the resulting estimator of the parameter of interest may be very poor when the coefficients of removed variables are not exactly zero. In this paper, based on the selected submodel, we introduce a two-stage remodeling method to get the consistent estimator for the parameter of interest. More precisely, in the first stage, by a multistep adjustment, we reconstruct an unbiased model based on the correlation information between the covariates; in the second stage, we further reduce the adjusted model by a semiparametric variable selection method and get a new estimator of the parameter of interest simultaneously. Its convergence rate and asymptotic normality are also obtained. The simulation results further illustrate that the new estimator outperforms those obtained by the submodel and the full model in the sense of mean square errors of point estimation and mean square prediction errors of model prediction

Remodeling and Estimation for Sparse Partially Linear Regression Models

When the dimension of covariates in the regression model is high, one usually uses a submodel as a working model that contains signi�cant variables. �ut it may be highly biased and the resulting estimator of the parameter of interest may be very poor when the coefficients of removed variables are not exactly zero. In this paper, based on the selected submodel, we introduce a two-stage remodeling method to get the consistent estimator for the parameter of interest. More precisely, in the �rst stage, by a multistep adjustment, we reconstruct an unbiased model based on the correlation information between the covariates; in the second stage, we further reduce the adjusted model by a semiparametric variable selection method and get a new estimator of the parameter of interest simultaneously. Its convergence rate and asymptotic normality are also obtained. e simulation results further illustrate that the new estimator outperforms those obtained by the submodel and the full model in the sense of mean square errors of point estimation and mean square prediction errors of model prediction

Forces and Disease: Electrostatic force differences caused by mutations in kinesin motor domains can distinguish between disease-causing and non-disease-causing mutations

The ability to predict if a given mutation is disease-causing or not has enormous potential to impact human health. Typically, these predictions are made by assessing the effects of mutation on macromolecular stability and amino acid conservation. Here we report a novel feature: the electrostatic component of the force acting between a kinesin motor domain and tubulin. We demonstrate that changes in the electrostatic component of the binding force are able to discriminate between disease-causing and non-disease-causing mutations found in human kinesin motor domains using the receiver operating characteristic (ROC). Because diseases may originate from multiple effects not related to kinesin-microtubule binding, the prediction rate of 0.843 area under the ROC plot due to the change in magnitude of the electrostatic force alone is remarkable. These results reflect the dependence of kinesin’s function on motility along the microtubule, which suggests a precise balance of microtubule binding forces is required

Efficient C-C bond splitting on Pt monolayer and sub-monolayer catalysts during ethanol electro-oxidation: Pt layer strain and morphology effects

Efficient catalytic C–C bond splitting coupled with complete 12-electron oxidation of the ethanol molecule to CO2is reported on nanoscale electrocatalysts comprised of a Pt monolayer (ML) and sub-monolayer (sML) deposited on Au nanoparticles (Au@Pt ML/sML). The Au@Pt electrocatalysts were synthesized using surface limited redox replacement (SLRR) of an underpotentially deposited (UPD) Cu monolayer in an electrochemical cell reactor. Au@Pt ML showed improved catalytic activity for ethanol oxidation reaction (EOR) and, unlike their Pt bulk and Pt sML counterparts, was able to generate CO2at very low electrode potentials owing to efficient C–C bond splitting. To explain this, we explore the hypothesis that competing strain effects due to the Pt layer coverage/morphology (compressive) and the Pt–Au lattice mismatch (tensile) control surface chemisorption and overall activity. Control experiments on well-defined model Pt monolayer systems are carried out involving a wide array of methods such as high-energy X-ray diffraction, pair-distribution function (PDF) analysis,in situelectrochemical FTIR spectroscopy, andin situscanning tunneling microscopy. The vibrational fingerprints of adsorbed CO provide compelling evidence on the relation between surface bond strength, layer strain and morphology, and catalytic activity.BMBF, 16N11929, Netzwerk TU9/CN Elektromobilität - Teilvorhaben: Brennstoffzellen Range-Extende

Study on rheological, adsorption and hydration properties of cement slurries incorporated with EPEG-based polycarboxylate superplasticizers

A series of polycarboxylate superplasticizers (PCEs) with different side-chain densities, main chain polymerization degrees, and side-chain lengths were designed and synthesized using a novel highly active ethylene glycol mono vinyl ether polyethylene glycol as the ether monomer. The influence of polycarboxylate ether on the rheological properties, interface adsorption, and hydration characteristics in cement paste was investigated through characterization of charge density, rheological properties, adsorption behavior, and hydration heat. The results indicate that the adsorption process of PCE on cement particles is spontaneous physical adsorption, and the hydration kinetics fitting reveals that the geometric crystal growth exponent n is in the range of 1–2, with needle-like and lamellar hydration products formed. With a decrease in side-chain density, the broadening of molecular weight distribution and the increase of charge density accelerate the flow of slurry, reduces saturation adsorption, and delays cement hydration. A decrease in main chain polymerization degree results in a downward trend in molecular weight and charge density, leading to larger molecular conformations, reduced slurry flow, decreased saturation adsorption, and delayed cement hydration. As the side-chain length of PCE (molecular weight) increases, the charge density decreases, and the molecular conformation exhibits a compact structure with reduced slurry flow, decreased saturation adsorption, and delayed cement hydration. In cases of low side-chain density, short side chains, and low molecular weight, enhanced adsorption capacity and faster adsorption rates are observed, resulting in the lower viscosity and a delay in the cement hydration process

Visual Atlas Analysis of Acceleration Sensor Research Literature Based on CiteSpace Bibliometrics

This paper uses CiteSpace information visualization software to visualize the acceleration sensor research literature based on more than 1100 literatures in the field of acceleration sensor research and application from 2010 to 2018. From the point of view of bibliometrics, the paper analyzes the visualization map of hot spot distribution, such as the country, discipline, research institution and funded status, the co-citation literature and the research frontier. Moreover, this paper compares and analyzes literature information on research and application fields of acceleration sensors at home and abroad in recent years. Information is used to evaluate the research progress and development trend of acceleration sensors, in order to provide literature reference for the relevant personnel engaged in the research of acceleration sensors

Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor

The human hand has multiple degrees of freedom (DOF) for achieving high-dexterity motions. Identifying and replicating human hand motions are necessary to perform precise and delicate operations in many applications, such as haptic applications. Surface electromyography (sEMG) sensors are a low-cost method for identifying hand motions, in addition to the conventional methods that use data gloves and vision detection. The identification of multiple hand motions is challenging because the error rate typically increases significantly with the addition of more hand motions. Thus, the current study proposes two new methods for feature extraction to solve the problem above. The first method is the extraction of the energy ratio features in the time-domain, which are robust and invariant to motion forces and speeds for the same gesture. The second method is the extraction of the concordance correlation features that describe the relationship between every two channels of the multi-channel sEMG sensor system. The concordance correlation features of a multi-channel sEMG sensor system were shown to provide a vast amount of useful information for identification. Furthermore, a new cascaded-structure classifier is also proposed, in which 11 types of hand gestures can be identified accurately using the newly defined features. Experimental results show that the success rate for the identification of the 11 gestures is significantly high