Miracle-dz/UniFAR-FTUs huggingface dataset:
Denoising and classification of urban ICESat-2 photon data fused with Sentinel-2 spectral images

ABSTRACT The ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) can collect earth surface elevation data with high precision on a global scale. However, the collected photon data contains a large amount of background noise due to the influence of sunlight, cloud reflection, and other factors. For photon data of different scenes, how to effectively denoise and achieve accurate classification of photon point clouds is crucial for subsequent applications. This study proposes a random forest based method for denoising and classifying ICESat-2 photon data in urban areas by fusing spectral features from Sentinel-2 images and spatial distribution features from photon data. The experimental results show that the method can effectively identify various types of photons. Compared with the reference data, the overall accuracy of photon denoising and classification is 95.97% on average, and the average kappa coefficient is 94.18%. Further analysis demonstrates that the addition of sentinel-2 spectral information can effectively improve the classification accuracy of photon point clouds in urban areas, and the photon classification method of combining photon lidar data and optical images can be a promising solution to improve classification accuracy.
Ionic Mechanisms of Propagated Repolarization in a One-Dimensional Strand of Human Ventricular Myocyte Model

Although repolarization has been suggested to propagate in cardiac tissue both theoretically and experimentally, it has been challenging to estimate how and to what extent the propagation of repolarization contributes to relaxation because repolarization only occurs in the course of membrane excitation in normal hearts. We established a mathematical model of a 1D strand of 600 myocytes stabilized at an equilibrium potential near the plateau potential level by introducing a sustained component of the late sodium current (INaL). By applying a hyperpolarizing stimulus to a small part of the strand, we succeeded in inducing repolarization which propagated along the strand at a velocity of 1~2 cm/s. The ionic mechanisms responsible for repolarization at the myocyte level, i.e., the deactivation of both the INaL and the L-type calcium current (ICaL), and the activation of the rapid component of delayed rectifier potassium current (IKr) and the inward rectifier potassium channel (IK1), were found to be important for the propagation of repolarization in the myocyte strand. Using an analogy with progressive activation of the sodium current (INa) in the propagation of excitation, regenerative activation of the predominant magnitude of IK1 makes the myocytes at the wave front start repolarization in succession through the electrical coupling via gap junction channels.
Methodological aspects and standardization of PET radiomics studies

Background : PET-based tumour delineation is an error prone and labor intensive part of image analysis. Especially for patients with advanced disease showing bulky tumour FDG load, segmentations are challenging. Reducing the amount of user-interaction in the segmentation might help to facilitate segmentation tasks in particular when labeling a large amount of images as needed for e.g. training of a convolutional neuronal network or a machine learning algorithm. Therefore, this study reports on the inter-observer variability of four segmentation methods for large tumours with complex shapes with different levels of user-interaction. Methods : Twenty PET images of bulky tumours were delineated independently by six observers using four approaches: (I) manual, (II) interactive threshold-based, (III) interactive threshold-based segmentation with the additional presentation of the PET-gradient image and (IV) the selection of the most reasonable result out of four established semi-automatic segmentation algorithms (select-the-best approach). The segmentations were compared using Jaccard coefficients (JC) and percentage volume differences. To obtain a reference standard, a majority vote (MV) segmentation was calculated including all segmentations of experienced observers. Performed and MV segmentations were compared regarding positive predictive value (PPV), sensitivity (SE), and percentage volume differences. Results : The results show that with decreasing user-interaction the inter-observer variability decreases. JC values and percentage volume differences of select-the-best and gradient approach were significantly better than the measurements of the other approaches (p-value<0.01). Threshold-based and manual segmentations also result in significant lower and more variable PPV/SE values when compared with the MV segmentation. Conclusions : FDG PET segmentations of bulky tumours with lower user-interaction showed less inter-observer variability. None of the methods led to good results in all cases, but use of either the gradient or the select-the-best method did outperform the other approaches tested and may be a good candidate for fast and reliable labeling of a large training set for machine learning purposes.
Treatments and Severe Outcomes for Patients Diagnosed With MIS-C at Four Children’s Hospitals in the United States, March 16, 2020–March 10, 2021

Background: Clinical management of multisystem inflammatory syndrome in children (MIS-C) has varied over time and by medical institution. Methods: Data on patients with MIS-C were collected from 4 children’s hospitals between March 16, 2020 and March 10, 2021. Relationships between MIS-C treatments and patient demographics, clinical characteristics, and outcomes were described. Propensity score matching was utilized to assess the relative risk of outcomes dependent on early treatment with intravenous immunoglobulin (IVIG) or low-dose steroids, controlling for potential confounding variables. Results: Of 233 patients diagnosed with MIS-C, the most commonly administered treatments were steroids (88.4%), aspirin (81.1%), IVIG (77.7%) and anticoagulants (71.2%). Compared with those patients without respiratory features, patients with respiratory features were less likely to receive IVIG and steroids on the same day (combination treatment) (44.1%). Controlling for confounding variables, patients receiving IVIG within 1 day of hospitalization were less likely to have hospital length of stay ≥8 days (RR = 0.53, 95% CI: 0.31–0.88). Patients receiving low-dose steroids within 1 day of hospitalization were less likely to develop ventricular dysfunction (RR = 0.45, 95% CI: 0.26–0.77), have increasingly elevated troponin levels (RR = 0.55, 95% CI: 0.40–0.75) or have hospital length of stay ≥8 days (RR = 0.46, 95% CI: 0.29–0.74). Conclusion: Treatments for MIS-C differed by hospital, patient characteristics and illness severity. When IVIG and low-dose steroids were administered in combination or low-dose steroids were administered alone within 1 day of hospitalization, the risk of subsequent severe outcomes was decreased.
Real-Time PCR Assay for detection and quantification of Leishmania: standardization, positive control, validation, and intra-laboratory assay

: his study aimed to develop a method to investigate PCR sensitivity for diagnosis and ensure reproducibility for parasite load quantification in tissues based on qPCR. In the first step, genes were selected to quantify the parasite load; then, a standard was developed to quantify the concentration of different Leishmania species. These tools were evaluated in intra-laboratory assays. The sensitivity was determined as 0.01 parasites/μL, and the method was reproducible with 100% concordance among human participants in the intra-laboratory validation study. Furthermore, the results demonstrated the specificity of the method in detecting the genus Leishmania without showing cross-reaction with
Multisource Soft Labeling and Hard Negative Sampling for Retrieval Distractor Ranking

Multiple-choice questions (MCQs) are a kind of widely adopted approaches in learning assessment. Recently, the automatic generation of MCQs has become a popular research area. In this task, distractor ranking (DR) is one of the most meaningful and challenging subtasks, where the DR models learn to select high-quality distractors from numerous candidates. Currently, some DR methods adopt a two-stage ranking strategy, which brings about a complex process and error propagation. Others directly use the single-encoder-based model to improve the overall performance, which, however, suffers from low efficiency. To tackle these problems, we propose a retrieval distractor ranking (ReDR) task to meet the requirements for practical distractor retrieval scenarios, in which the models should achieve relatively high performance within an acceptable time. In this research, we develop an end-to-end way based on the dual-encoder framework to solve the ReDR task. Besides, we propose multiple kinds of relevance scores, including context–context, context–distractor, and distractor–distractor, which have been employed in two strategies: 1) multisource soft labeling, which assigns each candidate an appropriate soft label from multiple kinds of relevance scores to better simulate the sample distribution of the ReDR task and 2) multisource hard negative sampling, which selects the hard negative samples according to multiple kinds of relevance scores and further distinguishes the difference between them and the positive samples. The extensive experiments on two well-known MCQ benchmarks have proven the effectiveness of our method.
INCREMENTAL PARALLEL CLASSIFIER FOR BIG DATA WITH CASE STUDY: NAÏVE BAYES USING MAPREDUCE PATTERNS

Classification methods can be used to derive values from big data in the form of models, which then can be utilized to predict new cases. Several parallel classification methods for big data have been developed based on Hadoop MapReduce as well as for Spark system. As big data keeps on coming, the models must be updated from time to time to represent the old as well as the new data. The computations must be efficient and scalable for handling big data. This research aims to enhance the existing parallel classifiers such that they will perform as incremental classifier handling batches of big data. The research results are presented as follows. First, the architecture and main concept of the enhancement is presented. Secondly, the proposed incremental parallel Naïve Bayes classifier (NBC) based on MapReduce that handles dataset with discrete attributes is discussed in detailed. Two series of experiment were performed on Hadoop clusters with 5 and 10 nodes. The results show that the incremental parallel NBC has acceptable accuracy, is efficient and scalable.
Persistent ciprofloxacin exposure induced the transformation of Klebsiella pneumoniae small colony variant into mucous phenotype

Introduction Small colony variant (SCV) is a bacterial phenotype closely related to persistent and recurrent infections. SCVs are mutations that occur within bacterial populations, resulting in a change in bacterial morphology and the formation of small colonies. This morphological change may enhance bacterial resistance to antibiotics and contribute to persistent and recurrent infections. Methods We isolated Klebsiella pneumoniae (KPN) and its SCV from a child with recurrent respiratory tract infections. KPN and SCV were treated with subinhibitory concentrations of antibiotics. growth curves, serum resistance experiments, macrophage phagocytosis experiments and whole genome sequencing were used to characterize KPN and SCV. Results After treating KPN and SCV with subinhibitory concentrations of antibiotics, we found that ciprofloxacin induced the SCV transition to the mucoid phenotype. We found that the growth of mucoid Klebsiella pneumoniae was significantly slower than maternal strain and SCV though growth curves. Serum resistance experiments showed that mucoid strains had significantly higher serum resistance compared to maternal strain and SCV. Macrophage phagocytosis experiments revealed that SCV had significantly higher intracellular survival rates compared to maternal strain and mucoid strains. Differential gene analysis of three strains revealed that the mucoid strain contained DNA polymerase V subunit UmuC gene on the plasmid, while the SCV strain had an additional IcmK family IV secretion protein on its plasmid. Discussion Our study showed the SCV of KPN changed to a mucoid colony when exposed to subinhibitory concentrations of ciprofloxacin. The higher resistance of serum of mucoid colonies was possibly related to the UmuC gene, while the increased intracellular survival of SCV may be related to the IcmK family type IV secretion proteins.
Overview of Cooperative Fault-Tolerant Control Driven by the Full Information Chain of Intelligent Connected Vehicle Platoons Under the Zero-Trust Framework: Opportunities and Challenges

The zero-trust framework is a potential solution to address complex dynamic behaviors, information interactions, complex network topologies, and environmental security threats to groups, such as the safety of the intelligent connected vehicle (ICV) platoon. In addition, it addresses the challenges faced due to increased traffic of intelligent vehicles, communication in critical infrastructure, and malicious attacks between vehicles. Motivated by these observations, we have investigated the zero-trust theory and framework based on the authorization and confirm processes among connected nodes in a group. Furthermore, we have analyzed and summarized the information perception, full information chain communication, and fault-tolerant control mechanism of ICV systems under a zero-trust framework, in addition to the methods of trust value calculation and heterogeneous vehicle platoons. The results indicated that zero-trust theory has a strong potential in group reliability research.
A Study of Aerosol Distribution over Indian Region based on Satellite Retrieved Data

Atmospheric aerosols play a major role in climate change. However, aerosol measurements, particularly their vertical distribution, are sparse over Indian region. Data from Vertical distribution of aerosols obtained from space-borne Lidars such as CALIPSO (Cloud-Aerosol and Infrared Pathfinder Satellite Observation) has been used to study the properties of pollution aerosols, dust aerosols and biomass burning aerosols on randomly selected days over Indian region in the pre-monsoon and monsoon seasons of 2009 The first case study describes the presence of dust aerosols as retrieved by CALIPSO over central India on 18 March 2009. It is observed that thick layer of aerosols is present up to a height of 5km across India extending from south to north. It is also interesting to observe that the aerosols are being lifted along the slopes of Himalayas. In the second study on a monsoon day, the strong westerly winds with patches of upward and downward vertical winds give a good insight to understand the weather conditions on 22 June 2009. A significant variation in the vertical distribution of aerosols is seen from day to night. During day maximum total attenuated backscatter is at 2-3 km and during night the maximum is at 4 km. Dust and polluted dust aerosols are dominating during both day and night. The year 2009 being a bad monsoon year, the presence of high aerosol levels up to a height of ~ 4 km is clearly seen. MODIS derived Aerosol Optical Depth (AOD) also confirms high aerosol loading ranging from 0.4 to 1 µm.
Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration

Background: The prognosis of diffuse midline glioma (DMG) patients with H3K27M (H3K27M-DMG) alterations is poor; however, a model that encourages accurate prediction of prognosis for such lesions on an individual basis remains elusive. We aimed to construct an H3K27M-DMG survival model based on DeepSurv to predict patient prognosis. Methods: Patients recruited from a single center were used for model training, and patients recruited from another center were used for external validation. Univariate and multivariate Cox regression analyses were used to select features. Four machine learning models were constructed, and the consistency index (C-index) and integrated Brier score (IBS) were calculated. We used the receiver operating characteristic curve (ROC) and area under the receiver operating characteristic (AUC) curve to assess the accuracy of predicting 6-month, 12-month, 18-month and 24-month survival rates. A heatmap of feature importance was used to explain the results of the four models. Results: We recruited 113 patients in the training set and 23 patients in the test set. We included tumor size, tumor location, Karnofsky Performance Scale (KPS) score, enhancement, radiotherapy, and chemotherapy for model training. The accuracy of DeepSurv prediction is highest among the four models, with C-indexes of 0.862 and 0.811 in the training and external test sets, respectively. The DeepSurv model had the highest AUC values at 6 months, 12 months, 18 months and 24 months, which were 0.970 (0.919–1), 0.950 (0.877–1), 0.939 (0.845–1), and 0.875 (0.690–1), respectively. We designed an interactive interface to more intuitively display the survival probability prediction results provided by the DeepSurv model. Conclusion: The DeepSurv model outperforms traditional machine learning models in terms of prediction accuracy and robustness, and it can also provide personalized treatment recommendations for patients. The DeepSurv model may provide decision-making assistance for patients in formulating treatment plans in the future.
Low-Cost Hyperspectral Imaging Device for Portable Remote Sensing

Hyperspectral imaging has revolutionized various scientific fields by enabling a detailed analysis of objects and materials based on their spectral signatures. However, the high cost and complexity of commercial hyperspectral camera systems limit their accessibility to researchers and professionals. In this paper, a do-it-yourself (DIY) hyperspectral camera device that offers a cost-effective and user-friendly alternative to hyperspectral imaging is presented. The proposed device leverages off-the-shelf components, commercially available hardware parts, open-source software, and novel calibration techniques to capture and process hyperspectral imaging data. The design considerations, hardware components, and construction process are discussed, providing a comprehensive guide for building the device. Furthermore, the performance of the DIY hyperspectral camera is investigated through experimental evaluations with a multi-color 3D-printed box in order to validate its sensitivities to red, green, blue, orange and white colors.
Remembering Sets: Capacity Limit and Time Limit of Ensemble Representations in Working Memory

In a constantly changing visual environment, the ability to extract and store ensemble representations plays a crucial role in efficiently processing and remembering complex visual information. However, how working memory maintains these ensemble representations remains unclear. Therefore, the present study aimed to investigate the limits and characteristics of ensemble representations in working memory using a change detection paradigm. Participants were presented with multiple sets of circles grouped by spatial proximity and were asked to memorize the mean diameter of the circles in each set. Results showed that working memory could stably maintain mean sizes of approximately two sets for at least four seconds. Moreover, the memory performance of ensembles was not affected by the number of circles within a set, suggesting that individual details were not stored in working memory. These results suggest that the visual system can effectively store ensembles in working memory without preserving detailed individual information.
Caffeic Acid in Spent Coffee Grounds as a Dual Inhibitor for MMP-9 and DPP-4 Enzymes

Type 2 diabetes mellitus and diabetic foot ulcers remain serious worldwide health problems. Caffeic acid is one of the natural products that has been experimentally proven to have diverse pharmacological properties. This study aimed to assess the inhibitory activity of caffeic acid and ethanolic extract of spent coffee grounds targeting DPP-4 and MMP-9 enzymes and evaluate the molecular interactions through 50-ns molecular dynamics simulations. This study also introduced our new version of PyPLIF HIPPOS, PyPLIF HIPPOS 0.2. 0, which allowed us to identify protein–ligand interaction fingerprints and interaction hotspots resulting from molecular dynamics simulations. Our findings revealed that caffeic acid inhibited the DPP-4 and MMP-9 activity with an IC50 of 158.19 ± 11.30 µM and 88.99 ± 3.35 µM while ethanolic extract of spent coffee grounds exhibited an IC50 of 227.87 ± 23.80 µg/100 µL and 81.24 ± 6.46 µg/100 µL, respectively. Molecular dynamics simulations showed that caffeic acid interacted in the plausible allosteric sites of DPP-4 and in the active site of MMP-9. PyPLIF HIPPOS 0.2. 0 identified amino acid residues interacting more than 10% throughout the simulation, which were Lys463 and Trp62 in the plausible allosteric site of DPP-4 and His226 in the active site of MMP-9.
Whole Genome Resequencing Reveals Selection Signals Related to Wool Color in Sheep

Simple Summary The color of wool is an essential trait in sheep which plays a significant role in the textile industry. The color of wool is determined by the presence of various pigments, which can range from white to various shades of brown, gray, black, etc. Understanding the genetics behind wool color is crucial for selective breeding and producing desirable colors for different textile products. By studying the genetic basis of wool color, researchers can identify genes related to pigmentation and develop strategies to enhance or modify wool color. This knowledge contributes to the improvement of wool quality, diversification of textile options, and economic development in the wool industry. Abstract Wool color is controlled by a variety of genes. Although the gene regulation of some wool colors has been studied in relative depth, there may still be unknown genetic variants and control genes for some colors or different breeds of wool that need to be identified and recognized by whole genome resequencing. Therefore, we used whole genome resequencing data to compare and analyze sheep populations of different breeds by population differentiation index and nucleotide diversity ratios (Fst and θπ ratio) as well as extended haplotype purity between populations (XP-EHH) to reveal selection signals related to wool coloration in sheep. Screening in the non-white wool color group (G1 vs. G2) yielded 365 candidate genes, among which PDE4B, GMDS, GATA1, RCOR1, MAPK4, SLC36A1, and PPP3CA were associated with the formation of non-white wool; an enrichment analysis of the candidate genes yielded 21 significant GO terms and 49 significant KEGG pathways (p < 0.05), among which 17 GO terms and 21 KEGG pathways were associated with the formation of non-white wool. Screening in the white wool color group (G2 vs. G1) yielded 214 candidate genes, including ABCD4, VSX2, ITCH, NNT, POLA1, IGF1R, HOXA10, and DAO, which were associated with the formation of white wool; an enrichment analysis of the candidate genes revealed 9 significant GO-enriched pathways and 19 significant KEGG pathways (p < 0.05), including 5 GO terms and 12 KEGG pathways associated with the formation of white wool. In addition to furthering our understanding of wool color genetics, this research is important for breeding purposes.