Total Least-Squares Determination of Body Segment Attitude

To examine segment and joint attitudes when using image based motion capture it is necessary to determine the rigid body transformation parameters from an inertial reference frame to a reference frame fixed in a body segment. Determine the rigid body transformation parameters must account for errors in the coordinates measured in both reference frames, a total least-squares problem. This study presents a new derivation that shows that a singular value decomposition based method provides a total least-squares estimate of rigid body transformation parameters. The total least-squares method was compared with an algebraic method for determining rigid body attitude (TRIAD method). Two cases were examined: Case 1 where the positions of a marker cluster contained noise after the transformation, and Case 2 where the positions of a marker cluster contained noise both before and after the transformation. The white noise added to position data had a standard deviation from zero to 0.002 m, with 101 noise levels examined. For each noise level 10000 criterion attitude matrices were generated. Errors in estimating rigid body attitude were quantified by computing the angle, error angle, required to align the estimated rigid body attitude with the actual rigid body attitude. For both methods and cases as the noise level increased the error angle increased, with errors larger for Case 2 compared with Case 1. The SVD based method was superior to the TRIAD algorithm for all noise levels and both cases, and provided a total least-squares estimate of body attitude.

Immune Cell-Associated Protein Expression Helps to Predict Survival in Muscle-Invasive Urothelial Bladder Cancer Patients after Radical Cystectomy and Optional Adjuvant Chemotherapy

Bladder cancer (BCa) is the tenth most commonly diagnosed malignant cancer worldwide. Although adjuvant chemotherapy following radical cystectomy is a common therapy for muscle invasive bladder cancer patients, no applicable biomarkers exist to predict which patients will benefit from chemotherapy. In this study, we examined three immune cell markers, the chemokine CC motif ligand 2 (CCL2), the pan macrophage marker cluster of differentiation 68 (CD68) and the M2 macrophage marker cluster of differentiation 163 (CD163), using immunohistochemistry to determine their predictive value for the chemotherapy response in different nodal stage (pN0 vs. pN1 + 2) and tumor stage subgroups (pT2 vs. pT3 + 4). The prognosis was studied in terms of the overall survival (OS), disease-specific survival (DSS), and recurrence-free-survival (RFS) in 168 muscle invasive BCa patients. Chemotherapy was associated with a poorer prognosis in patients with a higher expression of the immune markers CCL2 (RFS), CD68 (DSS and RFS), and CD163 (DSS and RFS) in the N0 group and with poorer survival in patients with a higher expression of the immune markers CCL2 (OS, DSS, and RFS), CD68 (OS, DSS, and RFS), and CD163 (OS, DSS, and RFS) in the pT2 group when compared with treatments without chemotherapy. In contrast, chemotherapy was associated with a better prognosis in patients with a low expression of the immune markers CCL2 (DSS and RFS), CD68 (OS, DSS, and RFS), and CD163 (OS) in the N1 + 2 group. In addition, chemotherapy was associated with improved survival in patients with a low expression of the immune marker CD68 (OS and DSS) and there was a trend for a better prognosis in patients with a low expression of CD163 (OS) in the pT3 + 4 group compared to patients not treated with chemotherapy. Interestingly, CD68 appeared to be the most applicable immune marker to stratify patients by the outcome of chemotherapy in the nodal stage and tumor stage groups. Overall, we suggest that, in addition to the clinical factors of tumor stage and nodal stage, it is also meaningful to consider the abundance of immune cells, such as macrophages, to better predict the response to chemotherapy for BCa patients after radical treatment.

KERAGAMAN GENETIK 64 AKSESI KUNYIT ASAL INDONESIA BERDASARKAN MARKA P450-BASED ANALOGUE (PBA)

<em>Turmeric is a rhizome producing plant with many utilization such as for consumption, medicine, and colorant industries. The development of superior turmeric varieties in Indonesia needs to be supported by genetic diversity information availability. Despite its potential, genetic diversity information of Indonesian turmeric has not been widely observed. A molecular marker is used to address genetic diversity information with the accurate result due to minimum environmental influences. PBA can detect the P450 gene as a functional marker, which is related to the synthesis of secondary metabolites in a wide genome area. Thus, it can be used as an alternative marker to identify genetic diversity. This research aimed to obtain genetic diversity information of 64 turmeric accessions using eight primer sets of P450-Based Analogue (PBA). The study was conducted in the Central Laboratory of Padjadjaran University from June 2019 to January 2020. Results showed that the full 133 bands were detected with a range of allele number 8 - 45 bands and an average of 22.3 bands per allele. PIC analysis showed six primer sets of PBA had high polymorphisms ranged from 0.90 to 0.98, hence categorized </em><em>PBA as a highly informative marker. Cluster analysis divided 64 turmeric accessions into two main clusters based on a similarity index ranged from 0.01 to 0.83. The accession of CL-GTL01 origins from Gorontalo had a low similarity coefficient of 0.01 to the other 64 accessions cluster. On the other hand, CL-NTB01 dan CL-PPB01 had the highest similarity index of 0.83. Based on the PIC value, the total number of polymorphic bands, and genetic distance, it can be concluded that local Indonesian turmeric had wide diversity based on PBA marker.</em>

Genetic diversity and morphological characteristics of native seashore paspalum in Indonesia

Rahayu, Fatimah, Bae EJ, Mo YG, Choi JS. 2020. Genetic diversity and morphological characteristics of native seashore paspalum in Indonesia. Biodiversitas 21: 4981-4989. Seashore paspalum (Paspalum vaginatum) is a warm-season turfgrass indigenous to tropical and coastal areas worldwide. The objectives of this study were to measure the genetic diversity and genetic variation of Indonesian seashore paspalum germplasm. Three turf quality, six morphological characters, and ten SSR (microsatellite) markers were used to assess genetic relationships and genetic variation among 22 germplasm resources from Indonesia and one commercial variety (Salam) from United States of America. The results showed significant variation for five morphological characters among 23 tested seashore paspalum accessions. The cluster analysis of morphological characters of 23 seashore paspalum accessions using 0,6 cut off divided into three morphological types: tall high-density, intermediate, and dwarf low-density ecotype. The genetic variation revealed 22 alleles with average number of alleles per locus was 2 and polymorphism information content (PIC) values average was 0.33. The microsatellite marker cluster analysis showed that 23 seashore paspalum accessions were grouped into two major groups, with a genetic similarity coefficient was 0,72. The low level of genetic diversity occurred among Indonesia natural grass germplasm and the genetic distance was relatively low between Indonesian germplasm and Salam variety. The genetic diversity and morphological characteristics will be useful for further study and utilization of Indonesian seashore paspalum germplasm.

Force quantification and simulation of pedicle screw tract palpation using direct visuo-haptic volume rendering

Purpose We present a feasibility study for the visuo-haptic simulation of pedicle screw tract palpation in virtual reality, using an approach that requires no manual processing or segmentation of the volumetric medical data set. Methods In a first experiment, we quantified the forces and torques present during the palpation of a pedicle screw tract in a real boar vertebra. We equipped a ball-tipped pedicle probe with a 6-axis force/torque sensor and a motion capture marker cluster. We simultaneously recorded the pose of the probe relative to the vertebra and measured the generated forces and torques during palpation. This allowed us replaying the recorded palpation movements in our simulator and to fine-tune the haptic rendering to approximate the measured forces and torques. In a second experiment, we asked two neurosurgeons to palpate a virtual version of the same vertebra in our simulator, while we logged the forces and torques sent to the haptic device. Results In the experiments with the real vertebra, the maximum measured force along the longitudinal axis of the probe was 7.78 N and the maximum measured bending torque was 0.13 Nm. In an offline simulation of the motion of the pedicle probe recorded during the palpation of a real pedicle screw tract, our approach generated forces and torques that were similar in magnitude and progression to the measured ones. When surgeons tested our simulator, the distributions of the computed forces and torques were similar to the measured ones; however, higher forces and torques occurred more frequently. Conclusions We demonstrated the suitability of direct visual and haptic volume rendering to simulate a specific surgical procedure. Our approach of fine-tuning the simulation by measuring the forces and torques that are prevalent while palpating a real vertebra produced promising results.

A method for tracking centre of mass displacement during non-seated cycling using an inertial sensor

Instantaneous crank power does not equal total joint power if a rider's centre of mass (CoM) gains and loses mechanical energy. Thus, estimating CoM motion and the associated energy changes can provide valuable information about cycling performance. To date, an accurate and precise method for tracking CoM motion during outdoor cycling has not been validated. \textbf{Purpose:} To assess the suitability of an inertial measurement unit (IMU) for tracking CoM motion during non-seated cycling by comparing vertical displacement derived from an inertial sensor mounted to the lower back of the rider to an attached marker cluster and to a kinematic estimate of vertical CoM displacement from a full-body musculoskeletal model (Model). \textbf{Methods:} IMU and motion capture data were collected synchronously for 10 seconds while participants ($n=7$) cycled on an ergometer in a non-seated posture at three power outputs and two cadences. A limits of agreement analysis, corrected for repeated measures, was performed on the range of vertical displacement between the IMU and the two other measures. A total of 303 crank cycles were analysed. \textbf{Results:} The IMU measured vertical displacement of the marker cluster with high accuracy (1.6 mm) and precision (3.5 mm) but substantially overestimated the kinematic estimate of rider CoM displacement. \textbf{Conclusion:} We interpret these findings as evidence that a single IMU placed on the lower back is unsuitable for tracking rider CoM displacement during non-seated cycling if the linearly increasing overestimation is unaccounted for.

A method for tracking centre of mass displacement during non-seated cycling using an inertial sensor

Instantaneous crank power does not equal total joint power if a rider's centre of mass (CoM) gains and loses mechanical energy. Thus, estimating CoM motion and the associated energy changes can provide valuable information about cycling performance. To date, an accurate and precise method for tracking CoM motion during outdoor cycling has not been validated. Purpose: To assess the suitability of an inertial measurement unit (IMU) for tracking CoM motion during non-seated cycling by comparing vertical displacement derived from an inertial sensor mounted to the lower back of the rider to an attached marker cluster and to a kinematic estimate of vertical CoM displacement from a full-body musculoskeletal model (Model). Methods: IMU and motion capture data were collected synchronously for 10 seconds while participants (n = 7) cycled on an ergometer in a non-seated posture at three power outputs and two cadences. A limits of agreement analysis, corrected for repeated measures, was performed on the range of vertical displacement between the IMU and the two other measures. A total of 303 crank cycles were analysed. Results: The IMU measured vertical displacement of the marker cluster with high accuracy (1.6 mm) and precision (3.5 mm) but substantially overestimated the kinematic estimate of rider CoM displacement. Conclusion: We interpret these findings as evidence that a single IMU placed on the lower back is unsuitable for tracking rider CoM displacement during non-seated cycling if the linearly increasing overestimation is unaccounted for.