Miracle-dz/UniFAR-FTUs huggingface dataset:
Allometry of Escherichia coli surface area with volume: effect of size variability, filamentation and division dynamics

The cell surface area (SA) increase with volume (V) is determined by growth and regulation of size and shape. Most studies of the rod-shaped model bacterium Escherichia coli have focussed on the phenomenology or molecular mechanisms governing such scaling. Here, we proceed to examine the role of population statistics and cell division dynamics in such scaling by a combination of microscopy, image analysis and statistical simulations. We find that while the SA of cells sampled from mid-log cultures scales with V by a scaling exponent 2/3, i.e. the geometric law SA ∼V 2/3 , filamentous cells have higher exponent values. We modulate the growth rate to change the proportion of filamentous cells, and find SA-V scales with an exponent >2/3 , exceeding that predicted by the geometric scaling law. However, since increasing growth rates alter the mean and spread of population cell size distributions, we use statistical modeling to disambiguate between the effect of the mean size and variability. Simulating (i) increasing mean cell length with a constant standard deviation (s.d.), (ii) a constant mean length with increasing s.d. and (iii) varying both simultaneously, results in scaling exponents that exceed the 2/3 geometric law, when population variability is included, with the s.d. having a stronger effect. In order to overcome possible effects of statistical sampling of unsynchronized cell populations, we ‘virtually synchronized’ time-series of cells by using the frames between birth and division identified by the image-analysis pipeline and divided them into four equally spaced phases—B, C1, C2 and D. Phase-specific scaling exponents estimated from these time series and the cell length variability were both found to decrease with the successive stages of birth (B), C1, C2 and division (D). These results point to a need to consider population statistics and a role for cell growth and division when estimating SA-V scaling of bacterial cells.
Application of phytoaccumulation perspective of Monochoria hastate L. on fluoride contaminated water in hydroponic treatment: its statistical design and characterization studies

Abstract The present research work approaches the accumulation of fluoride ions from contaminated water using an aquatic plant Monochoria hastate L. in hydroponic culture. A design of experiment (DOE) has been adopted and an analysis of variance has been conducted to establish the statistical significance of various process parameters. The different experimental factors are root and shoot (Factor A), fluoride concentration (Factor B), and experimental days (Factor C) largely influence the output response. Plants treated with 5 mg/L of fluoride solutions accumulated the highest concentration in root biomass 1.23 mg/gm, and shoot biomass 0.820 mg/gm, dry weight after 21 days’ experimentation. The accumulation mechanism and potentiality of treated plants depend on root cells of the plasma membrane and energy-capturing molecules of adenosine triphosphate. Monochoria hastate L. root biomass was characterized to confirm the accumulation of fluoride ions in the experimented plants using scanning electron micrographs-energy dispersive spectrum (SEM-EDS), and Fourier transforms infrared analysis (FTIR) analysis. NOVELTY STATEMENT The novelty of this study is the high fluoride accumulation efficiency in hydroponic treatment by Monochoria hastate L an excellent choice for phytoremediation technique. The Design of Experiment (DOE) has a good approach for the optimization of fluoride in the accumulation process. The maximum absorption of fluoride ions in root biomass is 1.23 mg/gm, and shoot biomass is 0.820 mg/gm, dry weight after 21 days of treatment. To know the fluoride ions in shoot and root biomass are characterized using scanning electron micrographs-energy dispersive spectrum (SEM-EDS), and Fourier transforms infrared analysis (FTIR).
GQL inferences in linear mixed models with dynamic mean structure

In some panel data studies for continuous data, the expectation of the response variable of an individual (or individual firm) at a given point of time may depend on the covariate history up to the present time. Also, the response at a given point of time may be influenced by an individual random effect. This type of data are usually analyzed by fitting a linear mixed model with dynamic mean structure. When the distribution of the random effects and error components of the model are not known, the likelihood inferences cannot be used any longer. As a possible remedy, there exists some alternative estimation methods such as bias corrected least squares dummy variable (BCLSDV) and instrumental variables based generalized method of moments (IVGMM), which however may produce inefficient estimates. In this thesis, we develop a new GMM as well as a generalized quasi-likelihood (GQL) estimating approach and demonstrate that they perform well in estimating all parameters of the model, the GQL being in general more efficient than the GMM approach.
LTK and ALK promote neuronal polarity and cortical migration by inhibiting IGF1R activity

The establishment of axon‐dendrite polarity is fundamental for radial migration of neurons, cortical patterning, and formation of neuronal circuits. Here, we show that the receptor tyrosine kinases, Ltk and Alk, are required for proper neuronal polarization. In isolated primary mouse embryonic neurons, the loss of Ltk and/or Alk causes a multiple axon phenotype. In mouse embryos and newborn pups, the absence of Ltk and Alk delays neuronal migration and subsequent cortical patterning. In adult cortices, neurons with aberrant neuronal projections are evident and axon tracts in the corpus callosum are disrupted. Mechanistically, we show that the loss of Alk and Ltk increases the cell‐surface expression and activity of the insulin‐like growth factor 1 receptor (Igf‐1r), which activates downstream PI3 kinase signaling to drive the excess axon phenotype. Our data reveal Ltk and Alk as new regulators of neuronal polarity and migration whose disruption results in behavioral abnormalities.
Assembly-style making: How structured making serves as an on-ramp to creativity and engineering design

Makerspaces, workspaces where families can explore materials and tools collaboratively, can provide an opportunity for creative expression and early engineering learning in community spaces. The present study examined a cardboard-focused museum makerspace that included an assembly-style activity. Assembly-style making uses instructions to support makers. Such activities have been critiqued as limiting creativity and engineering thinking. However, makers who are less comfortable in makerspaces may benefit from assembly-style activities helping to scaffold their entry into the space. We explored these criticisms and potential benefits of assembly-style making through developing case studies of video data taken by families in a makerspace. Visitors made creative and personally meaningful creations when engaged in assembly style making. Moreover, assembly-style making mediated a family less comfortable with making to get started in the space alongside ample evidence of families following engineering design processes. Contrary to popular belief, assembly-style making offers an important support to novice makers, without eliminating creativity and engineering design processes, and should be considered in the mix of activities available in makerspaces to support makers of all levels of comfort in making.
Mapping marine ecosystem services in a multifunctional seascape: the case of Grande Vasière (Bay of Biscay)

Introduction Coastal and continental shelf ecosystems are among the most productive, yet exploited, ecosystems. The Grande Vasière (GV) covers most part of the French Northern part of the Bay of Biscay, a crucial fishing ground for metropolitan France. It is the place of numerous uses, especially fishing, and will soon shelter sand extraction activities and offshore windfarms. All these activities may compete for space and resources and put pressure on habitats, biodiversity and subsequent ecosystem services (ES). Current management strategies integrate these activities and biodiversity conservation schemes but no ES. Methods To fill that gap, we quantified and mapped nine indicators of ecosystem processes (EP), used as proxies of four ES (i.e. sea food provisioning, biological control, regulation of environmental conditions and life cycle maintenance). Due to the complexity of ES, we investigated several EP by ES. Then we identified hotspots and coldspots of supply and spatial overlap among EP and other uses (conservation, fishing, offshore windfarms and sand extraction). Results EP mapping suggests a higher capacity of supply of the chosen ES in the Northern part of the GV. We found a strong spatial heterogeneity among the EP related to the same ES, underlining a point of vigilance when designing management measures to sustain ES supply. Northern EP hotspots overlap with high amounts of bottom trawl fishing effort. Higher levels of commercial species diversity and sole spawning grounds in the South overlap with higher amounts of gillnet fishing effort. Areas of sand extraction prospection and offshore windfarms under construction should not overlap with hotspots of EP, at the exception of the sole spawning ground and the commercial species diversity hotspots. Finally, we highlight an overlap of more than 20% of four EP hotspots with Natura 2000 areas, while the Marine Natural Park in the South covers more EP coldspots (i.e. hake nursery, encounter rate, trophic links diversity) than hotspots. Discussion Incorporating such a multifunctional spatial approach with hotspots and coldspots opens new perspective for marine spatial planning, pointing out the potential strengths and weaknesses of areas currently defined or prioritized for future uses like conservation, sand extraction or emerging activities like offshore windfarms.
DMU-Net: a dual-channel multi-scale medical image segmentation model

Automatic segmentation of the liver from the patients CT is of great significance for the diagnosis of liver diseases. Because the use of bottom-up feature fusion in U-Net ignores the importance of low-level features, resulting in poor network segmentation performance, and the similarity of gray values between liver and adjacent organs and tissues, making some small detail features difficult to pay attention to. In this work, we propose a network called DMU-Net. DMU-Net replaces the original convolution module with dual-channel multi-scale convolution to obtain richer semantic information. In addition, the attention mechanism is added between the encoder and decoder to increase the feature extraction ability of the model for small targets. The results on LITS dataset show that the DICE coefficient and IOU of the proposed model get 97.46% and 93.27% separately. Compared with the classic U-Net network and MultiResUNet, the model can segment more accurate liver regions and achieve better segmentation results.
Detection of L2 Mandarin vowel pronunciation errors based on multi-task learning and articulatory features

Computer-assisted pronunciation training (CAPT) can meet the needs of second language learners for pronunciation training. Since articulatory features can reflect the intrinsic logic of the pronunciation process and show the specific process of pronunciation, it is of great significance to incorporate them into the CAPT system. To address the complexity of vowels in continuous Mandarin speech, we established a multi-task pronunciation feature recognition model that includes four pronunciation channels. Multiple acoustic features were used to train the multitask articulatory feature recognition model on the standard Chinese corpus, obtained the correlation between multiple pronunciation features, and the trained model was then applied to the detection of pronounciation errors for L2 learners. The experimental results of Biaobei corpus show that the combination of CNN-BLSTM and multi-task training can improve the recognition accuracy of various articulatory features by 0.1% to 3.19%. The model for detecting Mandarin vowel pronunciation errors and deviations was tested on samples of second language Mandarin speakers, and it was proven to be effective in detecting errors and deviations in Mandarin vowel pronunciation.
Deep learning for segmentation of optic disc and retinal layers in peripapillary optical coherence tomography images

Optical coherence tomography (OCT) is a non-invasive technique that allows the retina to be studied with precision, the analysis of the features of its layers and other structures such as the macula or the optic nerve. This is why it is used in the diagnosis and monitoring of eye diseases such as glaucoma and optic neuritis. A crucial step in this process is the segmentation of the different layers, which is a great challenge due to its complexity. In this work, a methodology based on deep learning and transfer learning will be developed to automatically segment nine retinal layers in OCT images centred on the optic disc. In addition, the thickness of each retinal layer will be measured along each B-scan. For this purpose, OCT images from a public dataset and a dataset collected from depth-enhanced images will be used. The proposed method achieves a Dice score of 83.6%, similar to that obtained in the state of the art, segmenting the nine retinal layers and the optic disc in both sets of images. In addition, the different layers are represented in three different graphical formats.
Evaluating High-Resolution Model Forecasts of European Winter Storms by Use of Satellite and Radar Observations

Abstract The forecasting performance of the operational Lokal Modell (LM) of the German Weather Service (DWD) is evaluated for two severe winter storms that crossed central Europe in December 1999. Synthetic satellite images constructed from model output fields are compared with observed imagery from a Meteosat satellite. Also, synthetic radar images constructed from forecast precipitation fields are taken for validation against observed precipitation as represented by the European radar composite of the DWD. Comparisons are performed by visual inspection of satellite and radar imagery and by calculating statistical measures such as frequency histograms of observed and synthetic brightness temperature. Whereas the visual inspection allows detection of even finescale details in both synthetic satellite and radar imagery, for example, the position of fronts and rainbands, the statistical analysis reveals model deficiencies with respect to the representation of upper-level cloudiness. The operational LM did .
Detection and correction of image distortions induced by parasitic light sensitivity using deep learning methods

Images captured by mobile camera systems are subject to distortions that can be irreversible. Sources of these distortions vary and can be attributed to sensor imperfections, lens defects, or shutter inefficiency. One form of image distortion is associated with high Parasitic-Light-Sensitivity (PLS) in CMOS Image Sensors when combined with Global Shutters (GS-CIS) in a moving camera system. The resulting distortion appears as widespread semi-transparent purple artifacts, or a complex purple fringe, covering a large area in the scene around high-intensity regions. Most of the earlier approaches addressing the purple fringing problems have been directed towards the simplest forms of this distortion and rely on heuristic image processing algorithms. Recently, machine learning methods have shown remarkable success in many image restoration and object detection problems. Nevertheless, they have not been applied for the complex purple fringing detection or correction. In this paper, we present our exploration and deployment of deep learning algorithms in a pipeline for the detection and correction of the purple fringing induced by high-PLS GS-CIS sensors. Experiments show that the proposed methods outperform state-of-the-art approaches for both problems of detection and color restoration. We achieve a final MS-SSIM of 0.966 on synthetic data, and a distortion classification accuracy of 96.97%. We further discuss the limitations and possible improvements over the proposed methods.
Image segmentation algorithm based on cumulative residual Tsallis entropy

An algorithm which applies Cumulative Residual Tsallis Entropy (CRTE) to threshold image segmentation is proposed. Cumulative residual Tsallis entropy is an uncertainty measure generalized from Shannon entropy. The approach utilizes the cumulative residual Tsallis entropy function as the objective function, specifies the threshold segmentation criterion, and finds the optimal cumulative residual Tsallis entropy function solution. This method is applied to Berkeley Segmentation Dataset and compared with several classical segmentation methods on the same dataset. Experiment findings suggest that this algorithm is capable of producing improved segmentation results.
A deep neural network supported by ground verification data for active fire detection in MODIS

At present, the use of MODIS to detect fires mainly uses a series of thresholds to identify fire. However, in areas with high heterogeneity, the division of thresholds is more difficult, resulting in many false detections of fire, and cold fire are easily missed. The emergence of deep learning technology has improved the classification accuracy of images, but the surface information of hotspots cannot be determined only by visual interpretation of MODIS images, and the acquisition of highquality fire samples further limits the detection performance of deep learning methods. In order to solve the above problems, this paper proposes a deep neural network fire detection method based on ground truth data. First, the data structure of the US ground wildfire dataset is studied, a spatiotemporal matching strategy between ground data and remote sensing images is designed, and a priori sample is constructed. library. Second, robust classification features are selected combining the fire physical properties and the spectral distribution of the dataset. Finally, a DNN (Deep Neural Network) for MODIS fire detection is constructed according to the image characteristics. Application experiments were carried out in three typical scenarios and compared with MODIS fire products. The results show that the improved method improves the temporal and spatial adaptability of fire detection, and the extraction of small fire in farming areas is better than MODIS fire products; especially in the suburbs Significant reduction of spurious fire in region extraction,false positives decreased by 19.8%. This method provides a new reference for coarse resolution satellite data to detect fires, and effectively improves the efficiency of fire detection.
MTP-Net: road extraction from remote sensing images based on multi-type pooling network

As a popular task in remote sensing, road extraction has been widely concerned and applied by researchers, especially by using deep learning methods. However, many methods ignore the properties of roads in remote sensing images, which have long-range structures or discrete distributions. Therefore, this paper designs a network(MTP-Net) based on strip pooling and multi-scale spatial pooling. This network uses the ResNet50 network as the encoder to achieve feature extraction and ensures the overall connectivity and edge details of roads by the multi-type pooling module including strip pooling and multi-scale spatial pooling. The MTP-Net was tested on the Massachusetts Roads Dataset, the F1-score and IoU(intersection ratio) reached 72.24% and 56.54%, respectively. Compared with the mainstream methods such as UNet and deeplabV3+, the experiment shows that the MTP-Net is superior to the comparison model and has good results in road extraction.
Medical image segmentation of systemic lupus erythematosus related cerebral small vessel disease

Image segmentation is an important and difficult task in many medical applications. The segmentation results can be used to help doctors diagnose diseases, observe the lesion areas, and make surgical plans. Traditional medical image segmentation methods rely on experienced radiologists, which makes medical image segmentation time-consuming and costly. With the help of computer technology, the use of deep learning neural network for medical image segmentation has been applied in multiple disease lesions or organ segmentation tasks. Cerebral small vessel disease is a manifestation of systemic lupus erythematosus. In this paper, we apply Unet, Nested Unet, and Recurrent Residual Unet to segment the collected medical images of systemic lupus erythematosus patients. The results of Unet and Nested Unet is better than Recurrent Residual Unet. All of the three networks can segment parts of lesion areas, but small areas are missed. The results of our experiments showed the performance of the existing segmentation neural networks on our dataset still need to be further improved to meet the requirements of practical applications.
Research on intelligent monitoring method of external damage around transmission line based on millimeter wave radar and camera

In recent years, due to the shortage of transmission line channel resources, power companies have begun to expand the capacity of existing important transmission lines. The safe and stable operation of important transmission lines is critical to the power supply status of users. Timely monitoring of potential hazards of construction machinery near the transmission lines has become a hot topic of transmission line protection against external damage. Aiming at the technical bottleneck of high false alarm rate of the current transmission line using online monitoring camera, this paper proposes a construction vehicle detection method based on the fusion of radar and visual features, which uses the physical features and geometric features of the target. The physical features such as velocity and acceleration are selected from radar. After the fusion of the radar data and camera data, the region of interest (ROI) of the radar target on the image is obtained, and the gradient direction histogram feature is extracted on the ROI. The visual features are calculated by the statistical features of gradient direction histogram, including standard deviation, median and average. This paper constructs a neural network R-V-DenseNet whose input is the fusion feature of radar and vision. Then a data set is made to train the network. The experimental results on the test set prove that the accuracy of R-V-DenseNet is improved compared with the traditional HOG-SVM method and the single sensor based detection method, which means the proposed method gains more accurate detection.
Improved hybrid convolutional neural network combined with attention mechanism for hyperspectral image classification

To address the problems of high dimensionality of hyperspectral images, small training samples and overfitting and too many parameters caused by model training, a hyperspectral image classification model (CBAM-HybridSN) with improved hybrid neural network combined with convolutional attention mechanism is proposed. The model firstly uses principal component analysis to remove the redundancy of spectral dimensional data, extracts the null spectral features by the hybrid neural network model, and introduces the convolutional attention module to rescale the extracted features and highlight the important features, thus improving the classification accuracy. In the experiments, the Pavia University dataset was divided into samples with 1:9, and the OA reached 99.32%, achieving accurate classification of hyperspectral images with small samples.