Transgenic Drosophila melanogaster Carrying a Human Full-Length DISC1 Construct (UAS-hflDISC1) Showing Effects on Social Interaction Networks.

Disrupted in Schizophrenia 1 (DISC1) is a scaffold protein implicated in major mental illnesses including schizophrenia, with a significant negative impact on social life. To investigate if DISC1 affects social interactions in Drosophila melanogaster, we created transgenic flies with second or third chromosome insertions of the human full-length DISC1 (hflDISC1) gene fused to a UAS promotor (UAS-hflDISC1). Initial characterization of the insertion lines showed unexpected endogenous expression of the DISC1 protein that led to various behavioral and neurochemical phenotypes. Social interaction network (SIN) analysis showed altered social dynamics and organizational structures. This was in agreement with the altered levels of the locomotor activity of individual flies monitored for 24 h. Together with a decreased ability to climb vertical surfaces, the observed phenotypes indicate altered motor functions that could be due to a change in the function of the motor neurons and/or central brain. The changes in social behavior and motor function suggest that the inserted hflDISC1 gene influences nervous system functioning that parallels symptoms of DISC1-related mental diseases in humans. Furthermore, neurochemical analyses of transgenic lines revealed increased levels of hydrogen peroxide and decreased levels of glutathione, indicating an impact of DISC1 on the dynamics of redox regulation, similar to that reported in transgenic mammals. Future studies are needed to address the localization of DISC1 expression and to address how the redox parameter changes correlate with the observed behavioral changes.

End-of-life walking ability in cancer patients with spinal metastases.

BACKGROUND: Even terminal cancer patients desire to walk to the toilet by themselves until the very last day. This study aimed to describe the walking ability of patients with spinal metastases at the end-of-life stage and identify the factors affecting this ability. METHODS: Among 527 patients who first visited our multidisciplinary team for bone metastasis between 2013 and 2016, 56 patients who had spinal metastases with a Spinal Instability Neoplastic Score ≥7 and died during follow-up were included. We collected general clinical data, performance status, Frankel classification, epidural spinal cord compression scale and Spinal Instability Neoplastic Score at the first consultation. Patients' last day of walking and date of death were also examined. Univariate analyses (chi-squared tests) were performed to identify the factors that impacted walking ability 30 and 14 days before patients' death. RESULTS: A total of 56 patients were extracted, and 57.1% (32/56) and 32.7% (16/49) of patients were ambulatory 30 and 14 days before death, respectively. Their performance status (P = 0.0007), Frankel grade (P = 0.012) and epidural spinal cord compression grade (P = 0.006) at the first examination, and administration of bone modifying agents during follow-up period (P = 0.029) were significantly related to walking ability 30 days before death. Among ambulatory patients 30 days before death, those with Spinal Instability Neoplastic Score ≥10 (P = 0.005), especially with high scores of collapse (P = 0.002) and alignment (P = 0.002), were less likely to walk 14 days before death. The walking period in the last month of their life was significantly longer in patients with total Spinal Instability Neoplastic Score 7-9 (P = 0.009) and in patients without collapse (P = 0.040) by the Wilcoxon test. CONCLUSION: The progression of spinal metastasis, especially neurological deficit, at the initial consultation were associated with walking ability 30 days before death, and spinal stability might be crucial for preserving walking ability during the last month. Early diagnosis and implementation of appropriate bone management might be important for the end-of-life walking ability.

The Spinal Instability Neoplastic Score correlates with epidural spinal cord compression -a retrospective cohort of 256 surgically treated patients with spinal metastases.

BACKGROUND: Bone metastases can compromise the integrity of the spinal canal and cause epidural spinal cord compression (ESCC). The Spinal Instability Neoplastic Score (SINS) was developed in order to evaluate spinal instability due to a neoplastic process. The SINS has reached wide acceptance among clinicans but its prognostic value is still controversial. The aim was to investigate the correlation between the SINS and ESCC and the association between SINS and ambulation before and survival after surgery. METHODS: Correlations were assessed between SINS and grades of ESCC in patients who underwent spine surgery for spinal metastases. CT and MRI were used to calculate SINS and the grades of ESCC respectively. Correlations were analyzed with the Spearman's correlation test. Postoperative survival was estimated with Kaplan-Meier analysis and survival curves were compared with the log-rank test. The Cox proportional hazard model was used to assess the effect of prognostic variables including age, ambulation before surgery, SINS, and the Karnofsky Performance Status (KPS) as covariates. RESULTS: The study included 256 patients (196 men and 60 women) with a median age of 70 (24-88) years. The mean SINS was 10. One hundred fifty-two patients (59%) had lost ambulation before surgery. One hundred and one patients had grades 0-2 and 155 patients had grade 3 according to the ESCC-scale. SINS correlated with the grades of ESCC (p = 0.001). The SINS score was not associated with ambulation before surgery (p = 0.63). The median postoperative survival was 10 months, and there was no difference in postoperative survival between the SINS categories (p = 0.25). The ability to walk before surgery and a high KPS were associated with longer postoperative survival. CONCLUSION: SINS correlated with grades of ESCC, which implies that higher SINS may be considered as an indicator of risk for developing ESCC. The SINS was not associated with ambulation before or survival after surgery.

An Autocollimator Axial Measurement Method Based on the Strapdown Inertial Navigation System.

Autocollimators are widely used optical axis-measuring tools, but their measurement errors increase significantly when measuring under non-leveled conditions and they have a limited measurement range due to the limitations of the measurement principle. To realize axis measurement under non-leveled conditions, this paper proposes an autocollimator axis measurement method based on the strapdown inertial navigation system (SINS). First, the measurement model of the system was established. This model applies the SINS to measure the change in attitude of the autocollimator. The autocollimator was then applied to measure the angular relationship between the measured axis and its own axis, based on which the angular relationship of the axis was measured via computation through signal processing and data fusion in a multi-sensor system. After analyzing the measurement errors of the system model, the Monte Carlo method was applied to carry out a simulation analysis. This showed that the majority of the measurement errors were within ±0.002° and the overall measurement accuracy was within ±0.006°. Tests using equipment with the same parameters as those used in the simulation analysis showed that the majority of the measurement errors were within ±0.004° and the overall error was within ±0.006°, which is consistent with the simulation results. This analysis proves that this method solves the problem of the autocollimator being unable to measure the axis under non-leveled conditions and meets the needs of axis measurement with the application of autocollimators under a moving base.

A Method for Predicting Inertial Navigation System Positioning Errors Using a Back Propagation Neural Network Based on a Particle Swarm Optimization Algorithm.

In order to reduce the position errors of the Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) integrated navigation system during GPS denial, this paper proposes a method based on the Particle Swarm Optimization-Back Propagation Neural Network (PSO-BPNN) to replace the GPS for positioning. The model relates the position information, velocity information, attitude information output by the SINS, and the navigation time to the position errors between the position information output by the SINS and the actual position information. The performance of the model is compared with the BPNN through an actual ship experiment. The results show that the PSO-BPNN can obviously reduce the position errors in the case of GPS signal denial.

The Kinematic Models of the SINS and Its Errors on the SE(3) Group in the Earth-Centered Inertial Coordinate System.

In this paper, the kinematic models of the Strapdown Inertial Navigation System (SINS) and its errors on the SE(3) group in the Earth-Centered Inertial frame (ECI) are established. On the one hand, with the ECI frame being regarded as the reference, based on the joint representation of attitude and velocity on the SE(3) group, the dynamic of the local geographic coordinate system (n-frame) and the body coordinate system (b-frame) evolve on the differentiable manifold, respectively, and the high-order expansion of the Baker-Campbell-Haussdorff equation compensates for the non-commutative motion errors stimulated by strong maneuverability. On the other hand, the kinematics of the left- and right-invariant errors of the n-frame and the b-frame on the SE(3) group are separately derived, where the errors of the b-frame completely depend on inertial sensor errors, while the errors of the n-frame rely on position errors and velocity errors. In this way, the errors brought by the inconsistency of the reference coordinate system are tackled, and a novel attitude error definition is introduced to separate and decouple the factors affecting the dynamic of the n-frame errors and the b-frame errors for better attitude estimation. Through a turntable experiment and a car-mounted field experiment, the effectiveness of the proposed kinematic models in estimating attitude has been verified, with a remarkable improvement in yaw angle accuracy in the case of large initial misalignment angles, and the models developed have better robustness compared to the traditional SE(3) group-based model.

An Improved Initial Alignment Method Based on SE2(3)/EKF for SINS/GNSS Integrated Navigation System with Large Misalignment Angles.

This paper proposes an improved initial alignment method for a strap-down inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation system with large misalignment angles. Its methodology is based on the three-dimensional special Euclidean group and extended Kalman filter (SE2(3)/EKF) and aims to overcome the challenges of achieving fast alignment under large misalignment angles using traditional methods. To accurately characterize the state errors of attitude, velocity, and position, these elements are constructed as elements of a Lie group. The nonlinear error on the Lie group can then be well quantified. Additionally, a group vector mixed error model is developed, taking into account the zero bias errors of gyroscopes and accelerometers. Using this new error definition, a GNSS-assisted SINS dynamic initial alignment algorithm is derived, which is based on the invariance of velocity and position measurements. Simulation experiments demonstrate that the alignment method based on SE2(3)/EKF can achieve a higher accuracy in various scenarios with large misalignment angles, while the attitude error can be rapidly reduced to a lower level.

Scyllo-inositol: Transverse relaxation time constant at 3 T and concentration changes associated with aging and alcohol use.

The goals of this study were to measure the apparent transverse relaxation time constant, T2 , of scyllo-inositol (sIns) in young and older healthy adults' brains and to investigate the effect of alcohol usage on sIns in young and older healthy adults' brains, using proton magnetic resonance spectroscopy (MRS) at 3 T. Twenty-nine young adults (age 21 ± 1 years) and 24 older adults (age 74 ± 3 years) participated in this study. MRS data were acquired from two brain regions (the occipital cortex and posterior cingulate cortex) at 3 T. The T2 of sIns was measured using a localization by adiabatic selective refocusing (LASER) sequence at various echo times, while the sIns concentrations were measured using a short-echo-time stimulated echo acquisition mode (STEAM) sequence. A trend towards lower T2 relaxation values of sIns in older adults was observed, although these were not significant. sIns concentration was higher with age in both brain regions and was significantly higher in the young when considering alcohol consumption of more than two drinks per week. This study shows that differences in sIns can be found in two distinct regions of the brain across two age groups, potentially reflecting normal aging. In addition, it is important to take into account alcohol consumption when reporting the sIns level in the brain.

Spine Instability Neoplastic Score (SINS) as a Predictive Tool in Conventional Radiotherapy: A Narrative Review.

AIMS: This narrative review seeks to identify the SINS score application in the radiation oncology field. METHODS: This literature review was performed searching papers on MEDLINE published from January 2010 to August 2022. RESULTS: In terms of vertebral painful lesions and RT symptomatic responses, the SINS score could be an interesting aid in order to choose the right therapeutic approach. Lesions with higher level of instability, and therefore higher SINS score, could did not find any significant benefit from radiation therapy which is more effective on the tumor-related pain component. For SINS as a predictor of adverse event after RT or its changes after RT, we obtained contrasting results. CONCLUSIONS: The reported few experiences showed ambiguous conclusions. Further prospective studies are needed.

Simulation Optimization and Application of Shearer Strapdown Inertial Navigation System Modulation Scheme.

The operating attitude of a shearer based on a three-dimensional (3D) space scale is the necessary basic information for realizing intelligent mining. Aiming to address the problem of the insufficient perception accuracy of shearers, in this paper, the rotation model of the actual turning mechanism of the strapdown inertial navigation system (SINS) of shearers is established, and the error propagation characteristics of different single-axis rotation modulation schemes are revealed. Through theory and simulation, the optimal rotation modulation scheme is determined to be the improved four-position turn-stop modulation with a rotation of <360°. The experiment shows that the 24 h positioning error of this scheme is 3.7 nmile, and the heading angle changes by 0.06°, which proves that this scheme can effectively improve the attitude perception accuracy of the inertial navigation system (INS). The field application of the shearer operating attitude perception based on this scheme shows that the positioning error after error compensation is 17% of that before compensation, and the heading angle error is 75% of that before compensation, which verifies that this scheme can significantly improve the accuracy of shearer operating attitude perception in field applications. This scheme can achieve higher precision perception accuracy based on SINS and has broad application prospects in the field of high-precision pose perception of coal mining machines, roadheaders, and other equipment.

An Improved In-Motion Coarse Alignment Method for SINS/GPS Integration with Initial Velocity Error Suppression.

The integrated system with the strapdown inertial navigation system (SINS) and the global positioning system (GPS) is the most popular navigation mode. It has been used in many navigation fields. Before the integrated system works properly, it must determine the initial attitude for SINS. In SINS/GPS-integrated systems, the navigational velocity can be used to carry out the initial alignment when the system is installed in the in-motion vehicle. However, the initial velocity errors are not considered in the current popular in-motion alignment methods for SINS/GPS integration. It is well-known that the initial velocity errors must exist when the initial velocity is obtained from the GPS outputs. In this paper, an improved method was proposed to solve this problem. By analyzing the original observation vectors in the in-motion coarse alignment method, an average operation was used to construct the intermediate vectors, and the new observation vector can be calculated by subtracting the intermediate vector from the original observation vector. Then, the initial velocity errors can be eliminated from the newly constructed observation vector. Thus, the interferences of the initial velocity errors for the initial alignment process can be suppressed. The simulation and field tests are designed to verify the performance of the proposed method. The tests results showed that the proposed method can obtain the higher accurate results than the current methods when the initial velocity is considered. Additionally, the results of the proposed method were similar to the current methods when the initial velocity errors were not considered. This shows that the initial velocity errors were eliminated effectively by the proposed method, and the alignment accuracy were not decreased.

Application of Adaptive Robust Kalman Filter Base on MCC for SINS/GPS Integrated Navigation.

In this paper, an adaptive and robust Kalman filter algorithm based on the maximum correntropy criterion (MCC) is proposed to solve the problem of integrated navigation accuracy reduction, which is caused by the non-Gaussian noise and time-varying noise of GPS measurement in complex environment. Firstly, the Grubbs criterion was used to remove outliers, which are contained in the GPS measurement. Then, a fixed-length sliding window was used to estimate the decay factor adaptively. Based on the fixed-length sliding window method, the time-varying noises, which are considered in integrated navigation system, are addressed. Moreover, a MCC method is used to suppress the non-Gaussian noises, which are generated with external corruption. Finally, the method, which is proposed in this paper, is verified by the designed simulation and field tests. The results show that the influence of the non-Gaussian noise and time-varying noise of the GPS measurement is detected and isolated by the proposed algorithm, effectively. The navigation accuracy and stability are improved.

A Vision Aided Initial Alignment Method of Strapdown Inertial Navigation Systems in Polar Regions.

The failure of the traditional initial alignment algorithm for the strapdown inertial navigation system (SINS) in high latitude is a significant challenge due to the rapid convergence of polar longitude. This paper presents a novel vision aided initial alignment method for the SINS of autonomous underwater vehicles (AUV) in polar regions. In this paper, we redesign the initial alignment model by combining inertial navigation mechanization equations in a transverse coordinate system (TCS) and visual measurement information obtained from a camera fixed on the vehicle. The observability of the proposed method is analyzed under different swing models, while the extended Kalman filter is chosen as an information fusion algorithm. Simulation results show that: the proposed method can improve the accuracy of the initial alignment for SINS in polar regions, and the deviation angle has a similar estimation accuracy in the case of uniaxial, biaxial, and triaxial swing modes, which is consistent with the results of the observable analysis.

One-Step Initial Alignment Algorithm for SINS in the ECI Frame Based on the Inertial Attitude Measurement of the CNS.

To solve the problem of high-precision and fast initial alignment for the Strapdown Inertial Navigation System (SINS) under both dynamic and static conditions, the high-precision attitude measured by the celestial navigation system (CNS) is used as the reference information for the initial alignment. The alignment algorithm is derived in the Earth-centered inertial (ECI) frame. Compared with the alignment algorithm in the navigation frame, it is independent of position parameters and avoids the influence of the approximate error caused by the dynamic deflection angle. In addition, hull deformation is considered in attitude optimal estimation, which can realize initial the alignment of the SINS installed in various parts of the carrier. On this basis, the velocity measurement information is added to the alignment process, which further improves the accuracy and speed of the initial alignment under static conditions. The experimental results show that the algorithms proposed in this paper have better performance in alignment accuracy, speed, and stability. The attitude and velocity matching algorithm in the ECI frame can achieve alignment accuracy better than 0.6'. The attitude matching algorithm in the ECI frame has better robustness and can be used for both dynamic and static conditions, which can achieve alignment accuracy better than 1.3'.

An Anti-Turbulence Self-Alignment Method for SINS under Unknown Latitude Conditions.

For the alignment problem of strapdown inertial navigation system (SINS) under the complex environment of unknown latitude, angular oscillation interference, and line interference, the ant colony simulated annealing algorithm of gravity vector optimization is proposed to obtain the gravity apparent motion vector optimization equation, and the polynomial fitting method is proposed to simultaneously perform latitude estimation and self-alignment in combination with the alignment principle of SINS. Simulations and experiments show that the proposed method has more robust anti-interference capability than the traditional interference-based alignment method, the latitude estimation accuracy is improved by six times, the self-alignment yaw angle error RMSE value after obtaining the latitude is within 0.7°, and the roll angle and pitch angle error values are within 0.1°.

A Polar Robust Kalman Filter Algorithm for DVL-Aided SINSs Based on the Ellipsoidal Earth Model.

Autonomous underwater vehicles (AUVs) play an increasingly essential role in the field of polar ocean exploration, and the Doppler velocity log (DVL)-aided strapdown inertial navigation system (SINS) is widely used for it. Due to the rapid convergence of the meridians, traditional inertial navigation mechanisms fail in the polar region. To tackle this problem, a transverse inertial navigation mechanism based on the earth ellipsoidal model is designed in this paper. Influenced by the harsh environment of the polar regions, unknown and time-varying outlier noise appears in the output of DVL, which makes the performance of the standard Kalman filter degrade. To address this issue, a robust Kalman filter algorithm based on Mahalanobis distance is used to adaptively estimate measurement noise covariance; thus, the Kalman filter gain can be modified to weight the measurement. A trial ship experiment and semi-physical simulation experiment were carried out to verify the effectiveness of the proposed algorithm. The results demonstrate that the proposed algorithm can effectively resist the influence of DVL outliers and improve positioning accuracy.

An Improved Online Fast Self-Calibration Method for Dual-Axis RINS Based on Backtracking Scheme.

In the field of high accuracy dual-axis rotational inertial navigation system (RINS), the calibration accuracy of the gyroscopes and accelerometers is of great importance. Although rotation modulation can suppress the navigation error caused by scale factor error and bias error in a static condition, it cannot suppress the scale factor errors thoroughly during the maneuvering process of the vehicle due to the two degrees of rotation freedom. The self-calibration method has been studied by many researchers. However, traditional calibration methods need several hours to converge, which is unable to meet the demand for quick response to positioning and orientation. To solve the above problems, we do the following work in this study: (1) we propose a 39-dimensional online calibration Kalman filtering (KF) model to estimate all calibration parameters; (2) Error relationship between calibration parameters error and navigation error are derived; (3) A backtracking filtering scheme is proposed to shorten the calibration process. Experimental results indicate that the proposed method can shorten the calibration process and improve the calibration accuracy simultaneously.

A New Robust Adaptive Filter Aided by Machine Learning Method for SINS/DVL Integrated Navigation System.

As an important means of underwater navigation and positioning, the accuracy of SINS/DVL integrated navigation system greatly affects the efficiency of underwater work. Considering the complexity and change of the underwater environment, it is necessary to enhance the robustness and adaptability of the SINS/DVL integrated navigation system. Therefore, this paper proposes a new adaptive filter based on support vector regression. The method abandons the elimination of outliers generated by Doppler Velocity Logger (DVL) in the measurement process from the inside of the filter in the form of probability density function modeling. Instead, outliers are eliminated from the perspective of external sensors, which effectively improves the robustness of the filter. At the same time, a new Variational Bayesian (VB) strategy is adopted to reduce the influence of inaccurate process noise and measurement noise, and improve the adaptiveness of the filter. Their advantages complement each other, effectively improve the stability of filter. Simulation and ship-borne tests are carried out. The test results show that the method proposed in this paper has higher navigation accuracy.

Application of Affinity-Capture Self-Interaction Nanoparticle Spectroscopy in Predicting Protein Stability, Especially for Co-Formulated Antibodies.

PURPOSE: From traditional monoclonal antibodies to more and more complex mAb-based formulations, biopharmaceutical faces one challenge after another. To avoid these issues, identification of therapeutic proteins in the initial discovery process that has high stability and low self-interaction would simplify the development of safe and effective antibody therapeutics. METHOD: Affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) is a new prediction method capable of identifying mAbs with different self-association propensity. In this study, 10 formulated monoclonal antibody (mAb) therapeutics include different mAb isotypes and co-formulated antibodies were measured by AC-SINS and some biophysical methods to predict protein stability. The prediction results of all 10 mAbs were compared to their stability data (Δ%monomer and Δ%HMWs) at accelerated (25°C and 40°C) and long-term storage conditions (4°C) as measured by size exclusion chromatography. RESULT: AC-SINS method has a good predictive correlation with each mAbs and co-formulated antibodies. There were no physicochemical, intermolecular, or biological interactions that occurred between the two components of co-formulated antibodies which confirmed by Analytical ultracentrifugation (AUC). CONCLUSION: Here we discuss the correlation between each method and protein stability, and also use AC-SINS assay to predict the stability of co-formulated antibodies for the first time. This may be an effective way to predict the stability of these complex mAb-based formulations such as co-formulated mAbs.

An analysis of tumor-related potential spinal column instability (Spine Instability Neoplastic Scores 7-12) eventually requiring surgery with a 1-year follow-up.

OBJECTIVE: Within the Spine Instability Neoplastic Score (SINS) classification, tumor-related potential spinal instability (SINS 7-12) may not have a clear treatment approach. The authors aimed to examine the proportion of patients in this indeterminate zone who later required surgical stabilization after initial nonoperative management. By studying this patient population, they sought to determine if a clear SINS cutoff existed whereby the spine is potentially unstable due to a lesion and would be more likely to require stabilization. METHODS: Records from patients treated at the University of California, San Francisco, for metastatic spine disease from 2005 to 2019 were retrospectively reviewed. Seventy-five patients with tumor-related potential spinal instability (SINS 7-12) who were initially treated nonoperatively were included. All patients had at least a 1-year follow-up with complete medical records. A univariate chi-square test and Student t-test were used to compare categorical and continuous outcomes, respectively, between patients who ultimately underwent surgery and those who did not. A backward likelihood multivariate binary logistic regression model was used to investigate the relationship between clinical characteristics and surgical intervention. Recursive partitioning analysis (RPA) and single-variable logistic regression were performed as a function of SINS. RESULTS: Seventy-five patients with a total of 292 spinal metastatic sites were included in this study; 26 (34.7%) patients underwent surgical intervention, and 49 (65.3%) did not. There was no difference in age, sex, comorbidities, or lesion location between the groups. However, there were more patients with a SINS of 12 in the surgery group (55.2%) than in the no surgery group (44.8%) (p = 0.003). On multivariate analysis, SINS > 11 (OR 8.09, CI 1.96-33.4, p = 0.004) and Karnofsky Performance Scale (KPS) score < 60 (OR 0.94, CI 0.89-0.98, p = 0.008) were associated with an increased risk of surgery. KPS score was not correlated with SINS (p = 0.4). RPA by each spinal lesion identified an optimal cutoff value of SINS > 10, which were associated with an increased risk of surgical intervention. Patients with a surgical intervention had a higher incidence of complications on multivariable analysis (OR 2.96, CI 1.01-8.71, p = 0.048). CONCLUSIONS: Patients with a mean SINS of 11 or greater may be at increased risk of mechanical instability requiring surgery after initial nonoperative management. RPA showed that patients with a KPS score of 60 or lower and a SINS of greater than 10 had increased surgery rates.