Noise correction in differential phase contrast for improving phase sensitivity.

Differential phase contrast (DPC) imaging relies on computational analysis to extract quantitative phase information from phase gradient images. However, even modest noise level can introduce errors that propagate through the computational process, degrading the quality of the final phase result and further reducing phase sensitivity. Here, we introduce the noise-corrected DPC (ncDPC) to enhance phase sensitivity. This approach is based on a theoretical DPC model that effectively considers most relevant noise sources in the camera and non-uniform illumination in DPC. In particular, the dominating shot noise and readout noise variance can be jointly estimated using frequency analysis and further corrected by block-matching 3D (BM3D) method. Finally, the denoised images are used for phase retrieval based on the common Tikhonov inversion. Our results, based on both simulated and experimental data, demonstrate that ncDPC outperforms the traditional DPC (tDPC), enabling significant improvements in both phase reconstruction quality and phase sensitivity. Besides, we have demonstrated the broad applicability of ncDPC by showing its performance in various experimental datasets.

Establishment of transmission model for broad-spectrum artificial light in case 1 water.

A new simulation model for light transmission of broad-spectrum artificial light in case 1 water is introduced in this paper. The model simulates spectrum changes of fishing lamps due to absorption and scattering of seawater. According to underwater spectrum changes, this model restores the light field generated by fishing lamps and demonstrates the distribution of visual stimuli to marine organisms. The accuracy of the transmission model is verified by comparing it with experimental data. In addition, by comparing the simulation results of light transmission models of different fishing lamps in seawater of various fishing grounds, we investigate why current light-emitting diode (LED) lights are not as effective as metal halide (MH) lamps for light fishing. Lastly, suggestions for future optimization of LED fishing lamps in terms of light distribution design and spectrum configuration are provided.

Quantitative Evaluation System of Wrist Motor Function for Stroke Patients Based on Force Feedback.

Motor function evaluation is a significant part of post-stroke rehabilitation protocols, and the evaluation of wrist motor function helps provide patients with individualized rehabilitation training programs. However, traditional assessment is coarsely graded, lacks quantitative analysis, and relies heavily on clinical experience. In order to objectively quantify wrist motor dysfunction in stroke patients, a novel quantitative evaluation system based on force feedback and machine learning algorithm was proposed. Sensors embedded in the force-feedback robot record the kinematic and movement data of the subject, and the rehabilitation doctor used an evaluation scale to score the wrist function of the subject. The quantitative evaluation models of wrist motion function based on random forest (RF), support vector machine regression (SVR), k-nearest neighbor (KNN), and back propagation neural network (BPNN) were established, respectively. To verify the effectiveness of the proposed quantitative evaluation system, 25 stroke patients and 10 healthy volunteers were recruited in this study. Experimental results show that the evaluation accuracy of the four models is all above 88%. The accuracy of BPNN model is 94.26%, and the Pearson correlation coefficient between model prediction and clinician scores is 0.964, indicating that the BPNN model can accurately evaluate the wrist motor function for stroke patients. In addition, there was a significant correlation between the prediction score of the quantitative assessment system and the physician scale score (p < 0.05). The proposed system enables quantitative and refined assessment of wrist motor function in stroke patients and has the feasibility of helping rehabilitation physicians in evaluating patients' motor function clinically.

Quantitative Evaluation System of Upper Limb Motor Function of Stroke Patients Based on Desktop Rehabilitation Robot.

Rehabilitation training and movement evaluation after stroke have become a research hotspot as stroke has become a very common and harmful disease. However, traditional rehabilitation training and evaluation are mainly conducted under the guidance of rehabilitation doctors. The evaluation process is time-consuming and the evaluation results are greatly influenced by doctors. In this study, a desktop upper limb rehabilitation robot was designed and a quantitative evaluation system of upper limb motor function for stroke patients was proposed. The kinematics and dynamics data of stroke patients during active training were collected by sensors. Combined with the scores of patients' upper limb motor function by rehabilitation doctors using the Wolf Motor Function Test (WMFT) scale, three different quantitative evaluation models of upper limb motor function based on Back Propagation Neural Network (BPNN), K-Nearest Neighbors (KNN), and Support Vector Regression (SVR) algorithms were established. To verify the effectiveness of the quantitative evaluation system, 10 healthy subjects and 21 stroke patients were recruited for experiments. The experimental results show that the BPNN model has the best evaluation performance among the three quantitative evaluation models. The scoring accuracy of the BPNN model reached up to 87.1%. Moreover, there was a significant correlation between the models' scores and the doctors' scores. The proposed system can help doctors to quantitatively evaluate the upper limb motor function of stroke patients and accurately master the rehabilitation progress of patients.

Prediction of gold stage in patients hospitalized with COPD exacerbations using blood neutrophils and demographic parameters as risk factors.

BACKGROUND: Patients hospitalized with chronic obstructive pulmonary disease (COPD) exacerbations are unable to complete the pulmonary function test reliably due to their poor health conditions. Creating an easy-to-use instrument to identify the Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage will offer valuable information that assists clinicians to choose appropriate clinical care to decrease the mortality in these patients. The objective of this study was to develop a prediction model to identify the GOLD stage in the hospitalized exacerbation of chronic obstructive pulmonary disease (ECOPD) patients. METHODS: This prospective study involved 155 patients hospitalized for ECOPD. All participants completed lung function tests and the collection of blood neutrophils and demographic parameters. Receiver operating characteristic (ROC) curve was plotted based on the data of 155 patients, and was used to analyze the disease severity predictive capability of blood neutrophils and demographic parameters. A support vector regression (SVR) based GOLD stage prediction model was built using the training data set (75%), whose accuracy was then verified by the testing data set (25%). RESULTS: The percentage of blood neutrophils (denoted as NEU%) combined with the demographic parameters was associated with a higher risk to severe episode of ECOPD. The area under the ROC curve was 0.84. The SVR model managed to predict the GOLD stage with an accuracy of 90.24%. The root-mean-square error (RMSE) of the forced expiratory volume in one second as the percentage of the predicted value (denoted as FEV1%pred) was 8.84%. CONCLUSIONS: The NEU% and demographic parameters are associated with the pulmonary function of the hospitalized ECOPD patients. The established prediction model could assist clinicians in diagnosing GOLD stage and planning appropriate clinical care.

Two new sesquiterpenes from the stems of Tripterygium wilfordii.

Two new ß-dihydroagarofuran-type sesquiterpenes 1ß,2α,6α,8ß,15- pentaacetoxy- 9α-benzoyloxy- ß-dihydroagarofuran (1) and 1ß,2ß,6α,15-tetraacetoxy-9ß-benzoyloxy- ß-dihydroagarofuran (2), together with five known abietane diterpenoids (3-7) were isolated from ethyl acetate extract of stems of Tripterygium wilfordii. Their structures were elucidated on the basis of detailed spectroscopic and physico-chemical analyses. All the isolates were evaluated for in vitro inhibitory activity against A549, HOS and MCF-7. Among them, compounds 4 and 5 exhibited manifest inhibition on A549, HOS and MCF-7 cancer cell lines.

Superlensing plano-convex-microsphere (PCM) lens for direct laser nano-marking and beyond.

A high-performance all-dielectric lens, formed by integrating a conventional plano-convex lens with a high-index microsphere lens (PCM), was developed for far-field super-resolution applications. The PCM lens features a theoretical resolution of $\sim\lambda /{2.5}$∼λ/2.5 in air with a WD $\sim 2\;{\unicode{x00B5} \rm m}$∼2µm away from the lens. When combined with a femtosecond laser, the actual patterning resolution can reach $\sim\lambda /{3.5}$∼λ/3.5. The unusual focusing properties were theoretically and experimentally verified, and direct laser nano-writing of arbitrary patterns and nanostructures on various substrates was demonstrated. This Letter can be naturally extended to other super-resolution applications, including imaging, sensing, and trapping, with the potential of developing next-generation low-cost direct laser nano-marking machine and super-resolution imaging nanoscope.

Unibody microscope objective tipped with a microsphere: design, fabrication, and application in subwavelength imaging.

Microsphere-based subwavelength imaging technique was first demonstrated in 2011. After nearly a decade of efforts, such technique has spawned numerous interests in fields such as laser nano-machining, imaging, sensing, and biological detection. For wider industrial-scale application of the technique, a robust and low-cost objective lens incorporating a microsphere lens is highly desired and sought by many researchers. In this work, we demonstrate a unibody microscope objective lens formed by tipping a high-index microsphere onto a plano-convex lens and subsequently fitting them into a conventional objective lens. We call this the plano-convex-microsphere (PCM) objective, which resembles the appearance and operation of an ordinary microscope objective while providing super-resolving power in discerning subwavelength 100 nm features ($\lambda /{4}.{7}$λ/4.7) in air and far-field conditions. The imaging performance of the PCM objective, along with the working distance, has been systematically investigated. It has a calibrated resolution of $\lambda /{3}$λ/3 in the far field, a numerical aperture of 1.57, and a working distance of 3.5 µm. With the assistance of a scanning process, larger-area imaging is realized. The PCM objective can be easily adapted to existing microscope systems and is appealing for commercialization.

Optical design of a compact multispectral LED light source with high irradiance and uniformity.

As light-emitting diodes (LEDs) become dominating solutions in general lighting, their applications are also penetrating into biomedical engineering, especially light therapies. These new applications usually require much higher light power density at a shorter working distance than general lighting. Besides the high power, uniformity in power distribution is another important factor in such applications to illuminate samples with equal irradiance. These factors require designing a compact optical system to transmit light from a highly integrated high-power LED light source. While existing designs mainly focus on providing the desired illuminance in a much larger target space, little work has been devoted to the optical design to achieve a high irradiance that is uniformly distributed onto a target area at a short distance away from the light source. This work proposes a design method to solve such a problem, based on a highly integrated LED module, a mixing rod, and a pair of aspheric lenses. Both numerical simulations and experiments with a prototype are performed, which have verified the effectiveness of the proposed method.

[Quantitative assessment of motor function in patients with Parkinson's disease using wearable sensors].

Motor dysfunction is the main clinical symptom and diagnosis basis of patients with Parkinson's disease (PD). A total of 30 subjects were recruited in this study, including 15 PD patients (PD group) and 15 healthy subjects (control group). Then 5 wearable inertial sensor nodes were worn on the bilateral upper limbs, lower limbs and waist of subjects. When completing the 6 paradigm tasks, the acceleration and angular velocity signals from different parts of the body were acquired and analyzed to obtain 20 quantitative parameters which contain information about the amplitude, frequency, and fatigue degree of movements to assess the motor function. The clinical data of the two groups were statistically analyzed and compared, and then Back Propagation (BP) Neural Network was used to classify the two groups and predict the clinical score. The final results showed that most of the parameters had significant difference between the two groups, ten times of 5-fold cross validation showed that the classification accuracy of the BP Neural Network for the two groups was 90%, and the predictive accuracy of Hoehn-Yahr (H-Y) staging and unified PD rating scale (UPDRS) Ⅲ score of the patients were 72.80% and 68.64%, respectively. This study shows the feasibility of quantitative assessment of motor function in PD patients using wearable sensors, and the quantitative parameters obtained in this paper may have reference value for future related research.

DFT coupled with NEGF study of ultra-sensitive HCN and HNC gases detection and distinct I-V response based on phosphorene.

The sensing performances of pristine and X-doped phosphorene substrates (X = Al, Si, and S atoms) toward the adsorption of the toxic gases HCN and HNC were systematically investigated by first-principles simulations. The numerical results show that the pristine phosphorene is sensitive to HCN and HNC molecules with moderate adsorption energy, excellent charge transfer, high sensitivity and selectivity, implying its potential applications as excellent HCN and HNC sensors. In addition, the Al-doped phosphorene exhibits extremely high reactive activity toward HCN and HNC gases; thus, it has potential for use as a metal-free catalyst for activating or catalyzing HCN or HNC adsorbates. Moreover, the transport properties, i.e., current-voltage (I-V) characteristics, were calculated by the non-equilibrium Green's function (NEGF) method within the framework of the density functional theory (DFT). The obtained results reveal that the adsorbed HCN or HNC gas molecules have a remarkable impact on the electronic conductivity of phosphorene, and the zigzag direction of phosphorene is more sensitive to gas molecules than the armchair direction. The combination of the high sensitivity, superior selectivity, and moderate adsorption energy of pristine phosphorene toward HCN or HNC gas molecules adsorption, makes phosphorene an excellent candidate for HCN and HNC sensors.

A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients.

Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information.

Photobiomodulation for knee osteoarthritis: a model-based dosimetry study.

LED-based photobiomodulation (LED-PBM) for the treatment of knee osteoarthritis (KOA) is a promising technology. However, the light dose at the targeted tissue, which dominates the phototherapy effectiveness, is difficult to measure. This paper studied the dosimetric issues in the phototherapy of KOA by developing an optical model of the knee and performing Monte Carlo (MC) simulation. The model was validated by the tissue phantom and knee experiments. In the study, we investigated the effect of luminous characteristics of the light source, such as divergence angle, wavelength and irradiation position, on the treatment doses for PBM. The result showed that the divergence angle and the wavelength of the light source have a significant impact on the treatment doses. The optimal irradiation location was on both sides of the patella, where the largest dose could reach the articular cartilage. This optical model can be used to determine the key parameters in phototherapy and help the phototherapy of KOA patients.

Clinical Study of a Wearable Remote Rehabilitation Training System for Patients With Stroke: Randomized Controlled Pilot Trial.

BACKGROUND: In contrast to the large and increasing number of patients with stroke, clinical rehabilitation resources cannot meet their rehabilitation needs. Especially for those discharged, ways to carry out effective rehabilitation training without the supervision of physicians and receive guidance from physicians remain urgent problems to be solved in clinical rehabilitation and have become a research hot spot at home and abroad. At present, there are many studies on home rehabilitation training based on wearable devices, Kinect, among others, but these have disadvantages (eg, complex systems, high price, and unsatisfactory rehabilitation effects). OBJECTIVE: This study aims to design a remote intelligent rehabilitation training system based on wearable devices and human-computer interaction training tasks, and to evaluate the effectiveness and safety of the remote rehabilitation training system for nonphysician-supervised motor rehabilitation training of patients with stroke through a clinical trial study. METHODS: A total of 120 inpatients with stroke having limb motor dysfunction were enrolled via a randomized, parallel-controlled method in the rehabilitation institutions, and a 3-week clinical trial was conducted in the rehabilitation hall with 60 patients in the experimental group and 60 in the control group. The patients in the experimental group used the remote rehabilitation training system for rehabilitation training and routine clinical physical therapy (PT) training and received routine drug treatment every day. The patients in the control group received routine clinical occupational therapy (OT) training and routine clinical PT training and routine drug treatment every day. At the beginning of the training (baseline) and after 3 weeks, the Fugl-Meyer Motor Function Rating scale was scored by rehabilitation physicians, and the results were compared and analyzed. RESULTS: Statistics were performed using SAS software (version 9.4). The total mean Fugl-Meyer score improved by 11.98 (SD 8.46; 95% CI 9.69-14.27) in the control group and 17.56 (SD 11.65; 95% CI 14.37-20.74) in the experimental group, and the difference between the 2 groups was statistically significant (P=.005). Among them, the mean Fugl-Meyer upper extremity score improved by 7.45 (SD 7.24; 95% CI 5.50-9.41) in the control group and 11.28 (SD 8.59; 95% CI 8.93-13.62) in the experimental group, and the difference between the 2 groups was statistically significant (P=.01). The mean Fugl-Meyer lower extremity score improved by 4.53 (SD 4.42; 95% CI 3.33-5.72) in the control group and 6.28 (SD 5.28; 95% CI 4.84-7.72) in the experimental group, and there was no significant difference between the 2 groups (P=.06). The test results showed that the experimental group was better than the control group, and that the patients' motor ability was improved. CONCLUSIONS: The remote rehabilitation training system designed based on wearable devices and human-computer interaction training tasks can replace routine clinical OT training. In the future, through medical device registration certification, the system will be used without the participation of physicians or therapists, such as in rehabilitation training halls, and in remote environments, such as communities and homes. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2200061310; https://tinyurl.com/34ka2725.

Wearable Intelligent Machine Learning Rehabilitation Assessment for Stroke Patients Compared with Clinician Assessment.

In order to solve the shortcomings of the current clinical scale assessment for stroke patients, such as excessive time consumption, strong subjectivity, and coarse grading, this study designed an intelligent rehabilitation assessment system based on wearable devices and a machine learning algorithm and explored the effectiveness of the system in assessing patients' rehabilitation outcomes. The accuracy and effectiveness of the intelligent rehabilitation assessment system were verified by comparing the consistency and time between the designed intelligent rehabilitation assessment system scores and the clinical Fugl−Meyer assessment (FMA) scores. A total of 120 stroke patients from two hospitals participated as volunteers in the trial study, and statistical analyses of the two assessment methods were performed. The results showed that the R2 of the total score regression analysis for both methods was 0.9667, 95% CI 0.92−0.98, p < 0.001, and the mean of the deviation was 0.30, 95% CI 0.57−1.17. The percentages of deviations/relative deviations falling within the mean ± 1.96 SD of deviations/relative deviations were 92.50% and 95.83%, respectively. The mean time for system assessment was 35.00% less than that for clinician assessment, p < 0.05. Therefore, wearable intelligent machine learning rehabilitation assessment has a strong and significant correlation with clinician assessment, and the time spent is significantly reduced, which provides an accurate, objective, and effective solution for clinical rehabilitation assessment and remote rehabilitation without the presence of physicians.

Prediction models for pulmonary function during acute exacerbation of chronic obstructive pulmonary disease.

OBJECTIVE: The pulmonary function test is an effort-dependent test; however, during acute exacerbation of chronic obstructive pulmonary disease (AECOPD), patients are unable to effectively cooperate due to poor health. The present study aimed to establish prediction models that only require demographic and inflammatory parameters to predict pulmonary function indexes: forced expiratory volume in one second (FEV1) and forced vital capacity (FVC). APPROACH: The goal was to establish prediction models based on multi-output support vector regression. A total of 143 subjects received a peripheral blood examination and pulmonary function test. The demographic and inflammatory parameters were used as input features, and FEV1 and FVC were used as the target features in prediction models. Three models (mixed model, severe model and nonsevere model) were established with FEV1 < 1 l as the threshold of severe episodes of AECOPD. The values of FEV1 and FVC from the pulmonary function tests were compared with the prediction models to validate the performances of the developed prediction models. MAIN RESULTS: The severe and nonsevere models' prediction performances were better than that of the mixed model. The mean squared errors were lower than 0.05 l2, and the decision coefficients (R 2) were higher than 0.40. The two-tailed t-test results showed that for both severe and nonsevere models, the absolute percentage errors of FEV1 and FVC were within 10%. SIGNIFICANCE: Our study shows the feasibility of predicting the pulmonary function indexes FEV1 and FVC with demographic and inflammatory parameters when the pulmonary function test fails to be implemented, which is beneficial for the treatment of AECOPD.

Diacylglycerol acyltransferase inhibitory new meroterpenes from the seeds of Psoralea corylifolia, and their structure-activity relationship study.

Five new meroterpenes, 12α-Psoracorylifol F (1), 7ß,8α-hydroxy-12ß-Psoracorylifol F (2), 8-ketone-Cyclobakuchiol C (3), 7α,8ß-hydroxy-12ß-Cyclobakuchiol C (4) and 8α-hydroxy-Cyclobakuchiol C (5) together with six known compounds (6-11) were isolated from seeds of Psoralea corylifolia, and their structures were elucidated on the basis of spectroscopic and physicochemical analyses. All the isolates were evaluated for in vitro inhibitory activity against DGAT1/2. Among them, compounds 1-6 were found to exhibit selective inhibitory activity on DGAT1 with IC50 values ranging from 61.5 ± 1.1 to 89.1 ± 1.2 µM.

Establishing the quantitative relationship between diffuse speckle contrast analysis signals with absolute blood flow.

Diffuse speckle contrast analysis (DSCA) measures blood flow in deep tissues by taking advantage of the sensitivity of the speckle contrast signal to red blood cells (RBCs) motions. However, there has yet to be presented a clearly defined relationship between the absolute blood flow BFabs and the measured speckle contrast signal. Here, we derive an expression of linear approximation function for speckle contrast, taking into account both shear-induced diffusive and correlated advective RBCs motions in the vessels. We provide a linear relationship between the slope k slope of this linear function and BFabs. The feasibility of this relationship is validated by Monte Carlo simulations of heterogeneous tissue with varying vessel radii. Furthermore, based on this quantitative relationship, we can determine the relative contributions of diffusive RBCs motion on the reduction of speckle contrast, considering different vascular morphology and flow profiles.

Two-dimensional penta-Sn3H2 monolayer for nanoelectronics and photocatalytic water splitting: a first-principles study.

Exploring two-dimensional materials with novel properties is becoming particularly important due to their potential applications in future electronics and optoelectronics. In the current work, the electronic and optical properties of penta-Sn3H2 are investigated by density-functional theory. By assessing the phonon spectrum, we find that penta-Sn3H2 monolayer is energetically more favorable compared with pristine penta-stanene due to hydrogenation transforming the sp2-sp3 hybrid orbitals into sp3 hybridization. Our calculations revealed that penta-Sn3H2 is a semiconductor with indirect band gaps of 1.48 eV according to the GGA functional (2.44 eV according to the HSE06 functional). Moreover, the electronic structures of penta-Sn3H2 can be effectively modulated by biaxial tensile strain. Meanwhile, our calculations reveal that the indirect to direct band gap transition can be achieved in this monolayer sheet by >4% biaxial strain. On the other hand, the well-located band edge and visible light absorption make penta-Sn3H2 a potentially promising optoelectronic material for photocatalytic water splitting.