Study on Optimization Method for InSAR Baseline Considering Changes in Vegetation Coverage.

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR's monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR's monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from -73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR.

Roll angle autocollimator measurement method based on a cylindrical cube-corner reflector with a high resolution and large range.

A novel high-resolution and large-range autocollimator measurement system for roll angle is proposed. The system retains the basic internal structure of the traditional autocollimator (AC), which only uses a novel non-standard cylindrical cube-corner reflector (CCCR) instead of the planar reflector. In the article, the mathematical relationship between the structure of this special reflector and the spatial coordinate vector change of the reflected beam is deduced, and the measurement formula of the roll angle autocollimator (RAC) measurement system is established based on this mathematical relationship. The effectiveness of the measurement system and method is verified by experiments. Experimental results show that this method can effectively enhance the range to ±20°, and the whole measurement accuracy is 6.1", the measuring resolution is 1".

Autocollimation angle-measurement method with a large range based on spot deformation.

A novel hollow cylindrical cube-corner reflector (HCCCR) for the autocollimator (AC) is proposed. The angle measuring range of AC will be effectively increased by using the parallel propagation characteristics of the reflected light and the incident light in local area of this reflector. And the yaw and pitch angles of HCCCR will be measured through the morphological changes of the reflected beam. The experimental results show that the measuring range of the autocollimation angle measurement method is extended from ±30' to ±30°, and the dynamic measurement distance is 0.2∼5m, the measurement accuracy of pitch angle and yaw angle is better than 69" and 51", respectively.

Improved production of germacrene A, a direct precursor of ß-elemene, in engineered Saccharomyces cerevisiae by expressing a cyanobacterial germacrene A synthase.

BACKGROUND: The sesquiterpene germacrene A is a direct precursor of ß-elemene that is a major component of the Chinese medicinal herb Curcuma wenyujin with prominent antitumor activity. The microbial platform for germacrene A production was previously established in Saccharomyces cerevisiae using the germacrene A synthase (LTC2) of Lactuca sativa. RESULTS: We evaluated the performance of LTC2 (LsGAS) as well as nine other identified or putative germacrene A synthases from different sources for the production of germacrene A. AvGAS, a synthase of Anabaena variabilis, was found to be the most efficient in germacrene A production in yeast. AvGAS expression alone in S. cerevisiae CEN.PK2-1D already resulted in a substantial production of germacrene A while LTC2 expression did not. Further metabolic engineering the yeast using known strategies including overexpression of tHMGR1 and repression of squalene synthesis pathway led to an 11-fold increase in germacrene A production. Site-directed mutagenesis of AvGAS revealed that while changes of several residues located within the active site cavity severely compromised germacrene A production, substitution of Phe23 located on the lateral surface with tryptophan or valine led to a 35.2% and 21.8% increase in germacrene A production, respectively. Finally, the highest production titer of germacrene A reached 309.8 mg/L in shake-flask batch culture. CONCLUSIONS: Our study highlights the potential of applying bacterial sesquiterpene synthases with improved performance by mutagenesis engineering in producing germacrene A.

An AI-Application-Oriented In-Class Teaching Evaluation Model by Using Statistical Modeling and Ensemble Learning.

In-class teaching evaluation, which is utilized to assess the process and effect of both teachers' teaching and students' learning in a classroom environment, plays an increasingly crucial role in supervising and promoting education quality. With the rapid development of artificial intelligence (AI) technology, the concept of smart education has been constantly improved and gradually penetrated into all aspects of education application. Considering the dominant position of classroom teaching in elementary and undergraduate education, the introduction of AI technology into in-class teaching evaluation has become a research hotspot. In this paper, we propose a statistical modeling and ensemble learning-based comprehensive model, which is oriented towards in-class teaching evaluation by using AI technologies such as computer vision (CV) and intelligent speech recognition (ISR). Firstly, we present an index system including a set of teaching evaluation indicators combining traditional assessment scales with new values derived from CV and ISR-based AI analysis. Next, we design a comprehensive in-class teaching evaluation model by using both the analytic hierarchy process-entropy weight (AHP-EW) and AdaBoost-based ensemble learning (AdaBoost-EL) methods. Experiments not only demonstrate that the two modules in the model are respectively applicable to the calculation of indicators with different characteristics, but also verify the performance of the proposed model for AI-based in-class teaching evaluation. In this comprehensive in-class evaluation model, for students' concentration and participation, ensemble learning module is chosen with less root mean square error (RMSE) of 8.318 and 9.375. In addition, teachers' media usage and teachers' type evaluated by statistical modeling module approach higher accuracy with 0.905 and 0.815. Instead, the ensemble learning approaches the accuracy of 0.73 in evaluating teachers' style, which performs better than the statistical modeling module with the accuracy of 0.69.

A Random Forest-Based Accuracy Prediction Model for Augmented Biofeedback in a Precision Shooting Training System.

In the military, police, security companies, and shooting sports, precision shooting training is of the outmost importance. In order to achieve high shooting accuracy, a lot of training is needed. As a result, trainees use a large number of cartridges and a considerable amount of time of professional trainers, which can cost a lot. Our motivation is to reduce costs and shorten training time by introducing an augmented biofeedback system based on machine learning techniques. We are designing a system that can detect and provide feedback on three types of errors that regularly occur during a precision shooting practice: excessive hand movement error, aiming error and triggering error. The system is designed to provide concurrent feedback on the hand movement error and terminal feedback on the other two errors. Machine learning techniques are used innovatively to identify hand movement errors; the other two errors are identified by the threshold approach. To correct the excessive hand movement error, a precision shot accuracy prediction model based on Random Forest has proven to be the most suitable. The experimental results show that: (1) the proposed Random Forest (RF) model achieves the prediction accuracy of 91.27%, higher than any of the other reference models, and (2) hand movement is strongly related to the accuracy of precision shooting. Appropriate use of the proposed augmented biofeedback system will result in a lower number of rounds used and shorten the precision shooting training process.

Cube-corner autocollimator with expanded measurement range.

A cube-corner is employed as a photoelectric autocollimator's reflector to extend the angle measurement range and the corresponding algorithms are deduced. Experimental investigations reveal that the measurement range is extended from 1.2° to 12° without enlarging the objective lens's aperture. The accuracy is better than 35" when the deflection is less than 8°.

Context Sensing System Analysis for Privacy Preservation Based on Game Theory.

In a context sensing system in which a sensor-equipped mobile phone runs an unreliable context-aware application, the application can infer the user's contexts, based on which it provides personalized services. However, the application may sell the user's contexts to some malicious adversaries to earn extra profits, which will hinder its widespread use. In the real world, the actions of the user, the application and the adversary in the context sensing system affect each other, so that their payoffs are constrained mutually. To figure out under which conditions they behave well (the user releases, the application does not leak and the adversary does not retrieve the context), we take advantage of game theory to analyze the context sensing system. We use the extensive form game and the repeated game, respectively, to analyze two typical scenarios, single interaction and multiple interaction among three players, from which Nash equilibriums and cooperation conditions are obtained. Our results show that the reputation mechanism for the context-sensing system in the former scenario is crucial to privacy preservation, so is the extent to which the participants are concerned about future payoffs in the latter one.

Generation of linearly polarized orbital angular momentum modes in a side-hole ring fiber with tunable topology numbers.

A refractive index (RI) tunable functional materials infiltrated side-hole ring fiber (SHRF) is proposed to generate 10 LP OAM states with 6 topology numbers. On the basis of perturbation theory, the basis of the SHRF is demonstrated to be the LP modes. After a fixed propagation distance of 0.03 m, 0.009 m and 0.012 m, the phase difference between the odd and even LP11x, LP21x,y, LP31x,y modes in the SHRF accumulate to ± π/2 respectively with na ranging from 1.412 to 1.44. Correspondingly, the output states are OAM ± 1x, OAM ± 2x,y, OAM ± 3x,y with a bandwidth of 380 nm, 100 nm and 80 nm respectively. The proposed fiber is easy to be fabricated with the mature fiber drawing technology and could facilitate the realization of all fiber based OAM system.

Smartphone-Based Patients' Activity Recognition by Using a Self-Learning Scheme for Medical Monitoring.

Smartphone based activity recognition has recently received remarkable attention in various applications of mobile health such as safety monitoring, fitness tracking, and disease prediction. To achieve more accurate and simplified medical monitoring, this paper proposes a self-learning scheme for patients' activity recognition, in which a patient only needs to carry an ordinary smartphone that contains common motion sensors. After the real-time data collection though this smartphone, we preprocess the data using coordinate system transformation to eliminate phone orientation influence. A set of robust and effective features are then extracted from the preprocessed data. Because a patient may inevitably perform various unpredictable activities that have no apriori knowledge in the training dataset, we propose a self-learning activity recognition scheme. The scheme determines whether there are apriori training samples and labeled categories in training pools that well match with unpredictable activity data. If not, it automatically assembles these unpredictable samples into different clusters and gives them new category labels. These clustered samples combined with the acquired new category labels are then merged into the training dataset to reinforce recognition ability of the self-learning model. In experiments, we evaluate our scheme using the data collected from two postoperative patient volunteers, including six labeled daily activities as the initial apriori categories in the training pool. Experimental results demonstrate that the proposed self-learning scheme for activity recognition works very well for most cases. When there exist several types of unseen activities without any apriori information, the accuracy reaches above 80 % after the self-learning process converges.

Generation and excitation of different orbital angular momentum states in a tunable microstructure optical fiber.

A tunable microstructure optical fiber for different orbital angular momentum states generation is proposed and investigated by simulation. The microstructure optical fiber is composed of a high refractive index ring and a hollow core surrounded by four small air holes. The background material of the microstructure fiber is pure silica. The hollow core and the surrounded four small air holes are infiltrated by optical functional material whose refractive index can be modulated via physical parameters, leading to the conversion between circular polarized fundamental mode and different orbital angular momentum states at tunable operating wavelengths. A theoretical model is established and the coupling mechanism is systematically analyzed and investigated based on coupled mode theory. The fiber length can be designed specifically to reach the maximum coupling efficiency for every OAM mode respectively, and can also be fixed at a certain value for several OAM modes generation under tunable refractive index conditions. The proposed fiber coupler is flexible and compact, making it a good candidate for tunable OAM generation and sensing systems.

Intermodal interferometer with low insertion loss and high extinction ratio composed of a slight offset point and a matching long period grating in two-mode photonic crystal fiber.

An all-fiber modal interferometer based on a long period grating (LPG) inscribed in a two-mode photonic crystal fiber (PCF) and a slight core-offset spliced end is proposed and demonstrated. The LPG is fabricated to realize energy coupling from LP01 core mode to LP11 core mode, and the two core modes will interfere at the slight core-offset spliced end. We analyze the impact of energy coupling efficiencies of the LPG and the output spliced end on the extinction ratio of the interference fringes. With an appropriate energy coupling efficiency matching condition, our modal interferometer can realize lower insertion loss and high extinction ratio. Moreover, the sensitivities of our interferometer to strain and temperature are investigated, and the good stability of this device to external refractive index change is also demonstrated. As an all-fiber interferometer made of pure silica, this device has great potential applications in high temperature sensing fields, especially in harsh conditions.

Tunable fiber polarizing filter based on a single-hole-infiltrated polarization maintaining photonic crystal fiber.

A tunable fiber polarizing filter based on selectively filling a single hole of a solid-core polarization maintaining photonic crystal fiber with high index liquid are proposed and demonstrated. Two groups of polarization-dependent resonance dips in the transmission spectrum of the single-hole-infiltrated photonic crystal fiber are observed. Theoretical and experimental investigations reveal that these resonant dips result from the couplings between the silica core fundamental mode at x or y polarization and high order modes (TM(01), TE(01) and HE(11)) in the liquid core. Especially, a distinctive characteristic near the strongest resonant point (SRP) is demonstrated and revealed. The transmission loss and spectral shape at the SRP wavelength are extremely sensitive to the filling length and temperature (or Refractive Index, RI), which permits a fiber bandpass or bandstop polarizing filter with a good performance on tunability and controllability. Furthermore, the narrowband dips on both sides of the SRP wavelength have wavelength-dependent tuning velocities, providing a method to achieve flexible and controllable filters as well as two- or multi-parameter sensors with a compact structure.

Multi-component-intermodal-interference mechanism and characteristics of a long period grating assistant fluid-filled photonic crystal fiber interferometer.

A compact in-line modal interferometer based on a long period grating (LPG) inscribed in water-filled photonic crystal fiber (PCF) is proposed and demonstrated. The interferometer works from the interference between fundamental core mode and different vector components of LP(11) core mode. The LPG is especially inscribed to realize the energy exchange between the fundamental core mode and different vector components of LP(11) core mode in the PCF. We build a complete theoretical model and systematically analyze the multi-component-intermodal-interference mechanism of the interferometer based on coupled-mode theory. Due to the asymmetric index distribution over the cross section of the PCF caused by CO(2)-laser side illumination, the dispersion curves and temperature sensitivities referring to different vector components of LP(11) core mode are quite different. Thus the interferometer is polarization-dependent and the adjacent interference fringes according to different components of LP(11) mode show greatly discrimination in sensitivities of temperature and strain, making it a good candidate for multiple physics parameters measurements.

Microfluidic assistant beat-frequency interferometer based on a single-hole-infiltrated dual-mode microstructured optical fiber.

A microfluidic assistant beat-frequency interferometer based on a single-hole-infiltrated dual-mode microstructured optical fiber (DM-MOF) is proposed and demonstrated. The interferometer is constructed by inserting a piece of fluid-filled DM-MOF into two sections of single-mode fiber with slight core-offset. The mode-mismatch induced interferences with high-frequency dips and low-frequency envelope are observed in the transmission spectrum. Theoretical and experimental investigations reveal that the beat-frequency spectrum works from the interferences between LP(01) core mode and the components of LP(11) core mode with close but different frequencies. The deliberately infiltrated liquid rod in the air hole located at the second ring near the core of the DM-MOF enhances the sensitivity of the envelope to temperature or axial force. However, it shows little impact on the high-frequency dips. The distinguishing sensitivities of -959.22 pm/°C (-70.59 pm/°C) and 24.26 nm/N (-3.14 nm/N) for the envelope (dips) are simultaneously achieved in experiment, allowing for dual parameter measurement in such a compact structure.

Control and design of fiber birefringence characteristics based on selective-filled hybrid photonic crystal fibers.

We demonstrated a kind of birefringence-controllable hybrid photonic crystal fibers (HPCFs) by selectively infiltrating air holes of PCFs with index-tunable liquids processing higher index than silica background. Detailed theoretical investigations on mode couplings from fundamental core mode to high-index-liquid-rod modes and birefringence properties of several HPCFs were presented. Strong wavelength dependence of phase and group birefringence was found, and HPCFs with different arrangements of high index liquid rods possess distinct birefringence characteristics. Then, the Sagnac interferometers (SIs) based on two typical HPCFs with different liquid-rod arrangements were theoretically and experimentally studied. The results indicated the SIs exhibit different transmission spectra and temperature responses due to the distinct birefringence features of HPCFs. A temperature sensitivity of -45.8 nm/°C at 56.5 °C was achieved using one HPCF, and a sensitivity of -11.6 nm/°C from 65 °C to 85 °C was achieved using the other HPCF. The thermal tunable HPCFs with birefringence-controllable properties will provide great potential for a variety of tunable optical devices and sensors.

Protective effects of mesenchymal stromal cells on adriamycin-induced minimal change nephrotic syndrome in rats and possible mechanisms.

BACKGROUND AIMS: Minimal change nephrotic syndrome is the most frequent cause of nephrotic syndrome in childhood. Current treatment regimes, which include glucocorticoid hormones and immunosuppressive therapy, are effective and have fast response. However, because of the side effects, long treatment course, poor patient compliance and relapse, novel approaches for the disease are highly desired. METHODS: The adriamycin-induced nephrotic rat model was established. Rats were allocated to a model group, a prednisone group or mesenchymal stromal cell (MSC) group. Clinical parameters in each treatment group were determined at 2 weeks, 4 weeks and 8 weeks. The messenger RNA (mRNA) levels of synaptopodin, p21 and monocyte chemoattractant protein-1 were determined through the use of quantitative real-time-polymerase chain reaction. Protein levels were determined by means of Western blot or enzyme-linked immunosorbent assay. Podocytes were isolated and apoptotic rate after adriamycin with or without MSC treatment was analyzed by means of flow cytometry. RESULTS: MSC intervention improved renal function as assessed by urinary protein, blood creatinine and triglyceride levels. MSC intervention reduced adriamycin-induced renal tissue damage visualized by immunohistochemistry and light and electron microscopic analysis and reduced adriamycin-induced podocyte apoptosis. After MSC intervention, mRNA and protein levels of synaptopodin and p21 in renal cortex were significantly increased. MSCs also restored synaptopodin mRNA and protein expression in isolated podocytes. In addition, monocyte chemoattractant protein-1 mRNA in renal cortex and protein level in serum of the MSC treatment group were significantly decreased compared with that in the adriamycin-induced nephropathy model group. CONCLUSIONS: Our data indicate that MSCs could protect rats from adriamycin-induced minimal change nephrotic syndrome, and the protective effects of MSCs are mediated through multiple actions.

The role and future prospects of artificial intelligence algorithms in peptide drug development.

Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to treat a variety of illnesses. Most of these peptide medications are developed on the basis of endogenous peptides or natural peptides, which frequently required expensive, time-consuming, and extensive tests to confirm. As artificial intelligence advances quickly, it is now possible to build machine learning or deep learning models that screen a large number of candidate sequences for therapeutic peptides. Therapeutic peptides, such as those with antibacterial or anticancer properties, have been developed by the application of artificial intelligence algorithms.The process of finding and developing peptide drugs is outlined in this review, along with a few related cases that were helped by AI and conventional methods. These resources will open up new avenues for peptide drug development and discovery, helping to meet the pressing needs of clinical patients for disease treatment. Although peptide drugs are a new class of biopharmaceuticals that distinguish them from chemical and small molecule drugs, their clinical purpose and value cannot be ignored. However, the traditional peptide drug research and development has a long development cycle and high investment, and the creation of peptide medications will be substantially hastened by the AI-assisted (AI+) mode, offering a new boost for combating diseases.

Coleps shanghaiensis n. sp. challenges the validity of the genus Levicoleps (Ciliophora, Prostomatida).

Coleps is a common genus of pelagic ciliates in freshwater and brackish water habitats. Classification and phylogeny of Coleps species are, however, still full of confusion. In this study, we investigated Coleps shanghaiensis n. sp., collected from a river in Shanghai, China, by living observation, protargol staining, and molecular methods. Coleps shanghaiensis is about 70-90 µm × 35-55 µm in size, has a barrel-shaped body with three posterior spines, and possesses 21-24 ciliary rows, each composed of two perioral dikinetids and 19-22 monokinetids, and six caudal cilia. In SSU rRNA gene phylogenies, C. shanghaiensis fell within the clade of subspecies of Levicoleps biwae, which questions the validity of the genus Levicoleps. Furthermore, the biogeography of the genus Coleps is discussed.

[Corrigendum] Induction of microRNA­let­7a inhibits lung adeno-carcinoma cell growth by regulating cyclin D1.

After the publication of the article, an interested reader drew to the authors' attention that, in the western blots shown in Fig. 5C and D, a pair of data panels were inadvertently duplicated comparing between panels (C) and (D); in addition, the cell migration data shown in Fig. 7F on p. 1852 were selected incorrectly. The authors have examined their original data, and realize that these errors arose inadvertently as a consequence of their mishandling of their data. The revised versions of Figs. 5 and 7, featuring the corrected data for the caspase-8 experiment in Fig. 5C and alternative data for the cell migration assay experiments in Fig. 7F, are shown on the next two pages. The revised data shown for these Figures do not affect the overall conclusions reported in the paper. All the authors agree to the publication of this corrigendum, and are grateful to the Editor of Oncology Reports for allowing them the opportunity to publish this. Furthermore, the authors apologize to the readership for any inconvenience caused. [Oncology Reports 40: 1843-1854, 2018; DOI: 10.3892/or.2018.6593].