Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral texture classification of high-resolution satellite images for the state forest inventory in Russia

State forest inventory (SFI) program has been adopted for obtaining and updating forest inventory data in the territory of the Russian Federation. In the course of SFI, circular permanent sample plots (PSP) of constant radii are laid out. The number of PSP depends on the representation of plantations in the work object. Currently, high resolution satellite images are used within the framework of SFI mainly for updating the cartographic basis of forest inventory. We propose a method for joint processing of multispectral and panchromatic satellite images of high spatial resolution in order to retrieve the species composition and age classes of mixed forest stands. The method consists of several steps. The preprocessing step includes calibration, correction, matching satellite and ground data. The next step is obtaining regional specific training data from PSP measurements. For the retrieval of forest parameters, we propose the recognition method based on the modified ECOC (Error Correcting Output Codes) classifier and regularized stepwise forward feature selection. This allows us to combine spectral and texture features more effectively. The last postprocessing step is the correction of classification results using the methods of mathematical morphology. The proposed method contributes to the automation of updating data on species composition and age classes of forest stands and allows improving the efficiency of SFI works. The accuracy of the retrieval of the species composition of mixed forests using high-resolution satellite images is comparable with the accuracy of the standard archival ground inventory data.
A three dimensional point cloud registration method based on backpropagation neural network and random sphere cover set

Due to the large number of points in the point cloud, the complexity of registration is quite high. To solve this problem, a registration method based on backpropagation (BP) neural network and random sphere cover set (RSCS) is proposed in this study. For the two point clouds to be registered, each is simplified based on the BP neural network. In order to avoid losing a large number of key points in the simplification process, a fixed RSCS algorithm is used for each point cloud to replace the key points with the super-point (SP) sets, and then the SP sets are combined with the simplified data. The iterative closest point (ICP) algorithm is used for fine registration. The point cloud is simplified by BP neural network and fixed RSCS, which reduce the number of points for the subsequent fine registration. Therefore, the time and space complexity can be effectively reduced. Experimental results show that the proposed method effectively improves the computational efficiency while maintaining almost the same precision details, which is of great significance for the registration of point clouds with a large number of points.
Evaluation of the synergistic effect of tamoxifen and HSP70 inhibitor on breast cancer cells based on optical metabolic imaging

Nicotinamide adenine dinucleotide (NADH) and Flavin adenine dinucleotide (FAD) are the endogenous fluorophores in cells. Optical redox ratio determined by the ratio of NADH and FAD was an important oxygenic indicator to reflect cell metabolism. Heat shock protein 70 (HSP70) has been recognized as a target for antitumor therapy. In this study, the optical redox ratio was used to evaluate the metabolic changes after the synergistic treatment of tamoxifen and HSP70 inhibitor in MCF-7 breast cancer cells based on optical metabolic imaging. In addition, the cell viability was also measured after the treatments of tamoxifen and HSP70 inhibitor by MTT assay. We observed that changes of the redox ratio and the cell viability with synergistic treatment of tamoxifen and HSP70 inhibitor were more obvious than the results of the sole treatment of tamoxifen or HSP70 inhibitor. These results indicated that the inhibition of HSP70 improve drug response of MCF-7 breast cancer cells.
Analysis of the normal mode effect in resonator integrated optic gyro

Resonantor Integrated Optic Gyro (RIOG) is a type of high-accuracy gyroscope based on the Sagnac effect. The shape of the resonance curve determines the limited sensitivity of RIOG. Any asymmetry of the resonance curve will not only induce a bias error into the gyro output, but also decrease the frequency discrimination coefficient of the demodulation curve. The differential nomal mode loss is suspected to be the major noise induces the resonance curve asymmetry. In this paper, the normal mode effect is fully investigated and the conclusions are verified by Finite-Difference Time-Domain (FDTD) simulation software. Two kinds of asymmetry resonance line shape were found in experiment. Analysis shows that the bending direction of the resonance curve is related to the sidewall roughness between coupler inside and outside, and the resonance asymmetry ratio is linearly proportional to the two nomal modes throughput difference.
High-speed secure optical communication with physical random temporal encryption

In conventional optical communication systems, the transmission signals are regular digital signals. Due to the openness of optical fiber networks, it is rather easy for eavesdroppers to obtain the transmission signals from the public fiber-link and then intercept the message directly. To address this crucial security challenge, in this work we propose a high-speed secure optical communication system in virtue of physical random temporal encryption based on private physical random phase modulation and phase-to-intensity conversion. The proposed physical random temporal encryption is performed by a module composed of a phase modulator (PM) and a chirped fiber Bragg grating (CFBG), and the corresponding decryption is achieved with a similar module composed of an inverse-phase driven PM and an oppositedispersion CFBG. By distributing a constant-amplitude random-phase signal to the local semiconductor lasers deployed in the encryption module and decryption module, a pair of synchronized physical random PM driving signals that are not exchanged on public link can be independently generated, which guarantees the receiver end can correctly decrypt the original transmission message. Our numerical results demonstrate that with the proposed encryption scheme, the regular transmission signal is encrypted as a noise-like signal that can greatly enhance the security of message, and moreover, based on the private synchronized physical random phase modulation, the privacy of encryption and decryption are guaranteed, which prevents the eavesdroppers from intercepting transmission message. This work provides a promising strategy for the implement of high-speed high-security physical-layer optical communication.
A comparison of image recognition algorithms for cell phenotyping in optofluidic time-stretch microscopy

Optofluidic time-stretch imaging system has enabled high-throughput phenotyping of cells with unprecedented high speed and resolution. However, significant amount of raw image data is produced, which requires recognition algorithm with not only high accuracy but also high speed to analyze image data efficiently. In this paper, we compare the performance of popular feature extraction methods and learning-based classification algorithms on time-stretch microscopy image recognition. The applied image recognition system comprises an outlier detection step, feature extraction method and classification. The main concept of outlier detection uses DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to eliminate error images. Gabor wavelet, HOG (Histograms of Oriented Gradients), LBP (Local Binary Pattern) and PCA (Principal Components Analysis) are applied and compared as the feature extraction methods. Finally, with a set of extracted features, the computing time and accuracy of SVM (Support Vector Machines), LR (Logistics Regression), ResNet (Residual Neural Network) and XGBoost (Extreme Gradient Boosting) classification algorithms are evaluated. The tested cell image datasets are acquired from high-throughput imaging of numerous drug-treated and untreated cells (N = ~21,000) with an optofluidic time-stretch microscope. Results show that PCA feature extraction and XGBoost classification proves to be the fastest algorithms with the highest level of accuracy. DBSCAN outlier detection helps to improve the recognition accuracy by 2% approximately. Therefore, we propose a recognition algorithm consisting of DBSCAN outlier detection, PCA feature extraction and XGBoost classification as a promising solution to process the image data of high-throughput optofluidic time-stretch microscopy accurately and rapidly.
Modelling the ground-LAI to satellite-NDVI (Sentinel-2) relationship considering variability sources due to crop type (Triticum durum L., Zea mays L., and Medicago sativa L.) and farm management

Plant Leaf Area Index (LAI) is a key variable for several land surface processes related to vegetation dynamics. Satellite systems offer the opportunity to estimate LAI by remote, allowing to reduce costs for crop monitoring over large areas. Indeed, LAI, is a process variable of several models used for the prediction of crop biomass accumulation, yield and canopy coverage over space and time. Uncertainty of LAI estimation by means of Vegetation Indices extrapolated from multispectral satellite images is related to several factors: (i) crop type (due to the variability of plant architectures, the light extinction coefficient highly variable among crop species), (ii) the effect of environmental covariates, which affects the crop development together with the farm management. This study aimed at evaluating how the ground-LAI (Leaf Area Index) measurements and satellite vegetation indexes from Sentinel 2 reflectance were affected by the variety of crop type and the uncertainties related to different farm managements. We used Sentinel-2 imagery spanning across the growth seasons, over three agricultural sites located in the Mediterranean agroecosystem (Central Italy) and we produced satellite NDVI index to estimate LAI by remote over the sites. In the same period, with a monthly stratified random ground sampling design, the three agricultural farms with prevalent herbaceous crop cover were monitored in terms of ground- LAI variations. The novelty and the added value extracted from the results was that the uncertainty induced by farm management (FARM) variability can be relevant as much as the one caused by crop type (CROP) and seasonality (TIME) variations when ground-LAI measurements and satellite NDVI were analyzed by a set of non-liner regression models.
Role of miR-221/222 in Tumor Development and the Underlying Mechanism

MicroRNA-221/222 (miRNA-221/222, miR-221/222) is a noncoding microRNA which is widely distributed in eukaryotic organisms and deeply involved in the posttranscriptional regulation of gene expressions. According to recent studies, abnormal expressions of miR-221/222 are closely related to the occurrence and development of various kinds of malignant tumors. The role of miR-221/222 in tumor development and their potential molecular mechanism in various cancers, including liver cancer, colorectal cancer, cervical cancer, ovarian cancer, and endometrial carcinoma, are summarized and reviewed in this paper. Moreover, the potential translational biomarker role of abnormal miR-221/222 level in tumor or blood circulation for tumor diagnosis is also discussed.
Understanding of ROS-Inducing Strategy in Anticancer Therapy

Redox homeostasis is essential for the maintenance of diverse cellular processes. Cancer cells have higher levels of reactive oxygen species (ROS) than normal cells as a result of hypermetabolism, but the redox balance is maintained in cancer cells due to their marked antioxidant capacity. Recently, anticancer therapies that induce oxidative stress by increasing ROS and/or inhibiting antioxidant processes have received significant attention. The acceleration of accumulative ROS disrupts redox homeostasis and causes severe damage in cancer cells. In this review, we describe ROS-inducing cancer therapy and the anticancer mechanism employed by prooxidative agents. To understand the comprehensive biological response to certain prooxidative anticancer drugs such as 2-methoxyestradiol, buthionine sulfoximine, cisplatin, doxorubicin, imexon, and motexafin gadolinium, we propose and visualize the drug-gene, drug-cell process, and drug-disease interactions involved in oxidative stress induction and antioxidant process inhibition as well as specific side effects of these drugs using pathway analysis with a big data-based text-mining approach. Our review will be helpful to improve the therapeutic effects of anticancer drugs by providing information about biological changes that occur in response to prooxidants. For future directions, there is still a need for pharmacogenomic studies on prooxidative agents as well as the molecular mechanisms underlying the effects of the prooxidants and/or antioxidant-inhibitor agents for effective anticancer therapy through selective killing of cancer cells.
Determinants of Change in Fertility among Women in Rural Areas of Uganda

Fertility among rural women in Uganda continues to decline. Studies on fertility in Uganda have focused on the overall fertility in the country. In this study, we focus on determinants of change in fertility among rural women in Uganda using a multivariate Poisson decomposition technique to quantify the contribution of changes in the socioeconomic and demographic composition of women which we also refer to as the characteristic effects and changes in their fertility behavior (the coefficients' effects or risk of childbearing) to the overall reduction in fertility among women in rural areas during the 2006–2016 period. The “characteristics effects” are used to mean the effect of changing composition of women by the socioeconomic and demographic characteristics between 2006 and 2016. On the other hand, fertility behavior also presented as coefficients' effects mean changes in the risk or likelihood of giving birth to children by the rural women between the two survey years. Our findings indicate that the mean number of children ever born (MCEB) reduced from 4.5 to 3.9 in 2006 and this reduction was associated with both the changes in composition of women and fertility behavior. The composition of women contributed to 42% while the fertility behavior contributed to 58% of the observed reduction. The education level attained and the age at first sex showed significant contributions on both components of the decomposition. The observed decline in fertility is largely associated with the variation in the risk of childbearing among the rural women. The variation in the risk of childbearing by education and age at first sex of the rural women showed to be the biggest contribution to the observed change in fertility. Continued improvements in access, attendance, and completion of secondary schools by women in rural areas will be the key drivers to Uganda's overall transition to low fertility. Furthermore, with improved access to mass media in the rural areas, there can be changes in attitudes and large family size preferences which can create a conducive environment for the utilization of family planning services in the rural communities. Efforts should therefore focus on applying appropriate methods to deliver packaged family planning messages to these communities.
Extract of Cornus officinalis Protects Keratinocytes from Particulate Matter-induced Oxidative Stress

The skin is one of the large organs in the human body and the most exposed to outdoor contaminants such as particulate matter < 2.5 µm (PM2.5). Recently, we reported that PM2.5 induced cellular macromolecule disruption of lipids, proteins, and DNA, via reactive oxygen species, eventually causing cellular apoptosis of human keratinocytes. In this study, the ethanol extract of Cornus officinalis fruit (EECF) showed anti-oxidant effect against PM2.5-induced cellular oxidative stress. EECF protected cells against PM2.5-induced DNA damage, lipid peroxidation, and protein carbonylation. PM2.5 up-regulated intracellular and mitochondrial Ca2+ levels excessively, which led to mitochondrial depolarization and cellular apoptosis. However, EECF suppressed the PM2.5-induced excessive Ca2+ accumulation and inhibited apoptosis. The data confirmed that EECF greatly protected human HaCaT keratinocytes from PM2.5-induced oxidative stress.
Progress in Carnot and Chambadal Modeling of Thermomechanical Engine by Considering Entropy Production and Heat Transfer Entropy

Nowadays the importance of thermomechanical engines in recognized worldwide. Since the industrial revolution, physicists and engineers have sought to maximize the efficiency of these machines, but also the mechanical energy or the power output of the engine, as we have recently found. The optimization procedure applied in many works in the literature focuses on considering new objective functions including economic and environmental criteria (i.e., ECOP ecological coefficient of performance). The debate here is oriented more towards fundamental aspects. It is known that the maximum of the power output is not obtained under the same conditions as the maximum of efficiency. This is shown, among other things, by the so-called nice radical that accounts for efficiency at maximum power, most often for the endoreversible configuration. We propose here to enrich the model and the debate by emphasizing the fundamental role of the heat transfer entropy together with the production of entropy, accounting for the external or internal irreversibilities of the converter. This original modeling to our knowledge, leads to new and more general results that are reported here. The main consequences of the approach are emphasized, and new limits of the efficiency at maximum energy or power output are obtained.
Vegetation expansion in the subnival Hindu Kush Himalaya

The mountain systems of the Hindu Kush Himalaya (HKH) are changing rapidly due to climatic change, but an overlooked component is the subnival ecosystem (between the treeline and snow line), characterized by short‐stature plants and seasonal snow. Basic information about subnival vegetation distribution and rates of ecosystem change are not known, yet such information is needed to understand relationships between subnival ecology and water/carbon cycles. We show that HKH subnival ecosystems cover five to 15 times the area of permanent glaciers and snow, highlighting their eco‐hydrological importance. Using satellite data from the Landsat 5, 7 and 8 missions, we measured change in the spatial extent of subnival vegetation from 1993 to 2018. The Landsat surface reflectance‐derived Normalized Difference Vegetation Index product was thresholded at 0.1 to indicate the presence/absence of vegetation. Using this product, the strength and direction of time‐series trends in the green pixel fraction were measured within three regions of interest. We controlled for cloud cover, snow cover and evaluated the impact of sensor radiometric differences between Landsat 7 and Landsat 8. Using Google Earth Engine to expedite data processing tasks, we show that there has been a weakly positive increase in the extent of subnival vegetation since 1993. Strongest and most significant trends were found in the height region of 5,000–5,500 m a.s. l. across the HKH extent: R2 = . 302, Kendall's τ = 0.424, p < . 05, but this varied regionally, with height, and according to the sensors included in the time series. Positive trends at lower elevations occurred on steeper slopes whilst at higher elevations, flatter areas exhibited stronger trends. We validated our findings using online photographs. Subnival ecological changes have likely impacted HKH carbon and water cycles with impacts on millions of people living downstream, but the strength and direction of impacts of vegetation expansion remain unknown.
A Phase II Trial of Albumin-Bound Paclitaxel and Gemcitabine in Patients with Newly Diagnosed Stage IV Squamous Cell Lung Cancers

Purpose: Gemcitabine and albumin-bound paclitaxel (ABP) exhibit synergistic antitumor efficacy, with ABP serving to increase the intratumoral gemcitabine concentration. Both drugs are active in squamous cell lung cancers (SQCLC) and are conventional partners for carboplatin. We hypothesized that combining gemcitabine and ABP would enhance the antitumor activity in patients with advanced SQCLCs. Patients and Methods: This was a Simon two-stage, open-label, single-arm, multicenter phase II study that enrolled patients between August 1, 2015 and June 1, 2018. We enrolled 37 patients with chemotherapy-naïve, PD-L1 low/unknown advanced stage IV SQCLC. Patients were administered weekly intravenous gemcitabine (1,000 mg/m2) plus ABP (100 mg/m2) in a 3-week on, 1-week off schedule during stage I and a 2-week on, 1-week off schedule in stage II. The primary endpoint was best objective response rate (ORR). Next-generation sequencing by MSK-IMPACT was used to calculate tumor mutation burden and genome doubling and assess somatic variants for correlations with efficacy. Results: Thirty-two patients were evaluable for response assessment. The study satisfied its primary endpoint, with confirmed partial responses in 18 of 32 patients and a complete response in 1 patient [ORR 59%; 95% confidence interval (CI), 42%–74%]. Median progression-free survival (PFS), a secondary endpoint, was 7.5 (95% CI, 6.7–10.5) months. There were no unexpected toxicities. Conclusions: Gemcitabine plus ABP was a safe, tolerable, and effective first-line therapy for patients with chemotherapy-naïve SQCLCs, with an ORR and median PFS substantially higher than carboplatin doublet regimens and efficacy comparable with carboplatin plus taxane plus pembrolizumab.
Inverse design of porous materials using artificial neural networks

We have developed a user-desired generative adversarial network and used them to generate 121 zeolite materials. Generating optimal nanomaterials using artificial neural networks can potentially lead to a notable revolution in future materials design. Although progress has been made in creating small and simple molecules, complex materials such as crystalline porous materials have yet to be generated using any of the neural networks. Here, we have implemented a generative adversarial network that uses a training set of 31,713 known zeolites to produce 121 crystalline porous materials. Our neural network takes in inputs in the form of energy and material dimensions, and we show that zeolites with a user-desired range of 4 kJ/mol methane heat of adsorption can be reliably produced using our neural network. The fine-tuning of user-desired capability can potentially accelerate materials development as it demonstrates a successful case of inverse design of porous materials.