Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of noise in image restoration of multi-aperture telescope

Multi-aperture telescope is proposed to achieve high angular resolution without fabricating a large diameter monolithic primary mirror. Due to the array structure, the multi-aperture telescope has almost the same cut-off frequency as an equivalent diameter telescope, but decrease in the area of light collecting, which is the reason that the direct output image of multi-aperture telescope is blurred and low contrast. The additive noise level is another reason for low image quality. The Wiener filter is sensitive to noise because of the zero value out of the cut-off frequency in optical transfer function. An alternative image deblurring method is total variation (TV) blind deconvolution. The TV method is an iterative algorithm and preserves the edge information well. The most important characteristic of TV blind deconvolution is that the algorithm is still working with high noise level and produces reasonable result.
On the properties of young stellar populations: multiplicity and chemistry at the time of planet formation

Recent studies performed in the last two decades revealed that the dense cores where star formation takes place generally produce more than one star. It is essential to understand the formation of such multiple systems since they are, most likely, the natural outcome of the initial stages of the star formation process and may provide hints on the mechanisms of the pristine fragmentation of molecular cloud cores. One of the main aims of this thesis was to take advantage of the major advances in infrared spectroscopy and to go a step further in understanding how spectroscopic close stellar companions are formed and evolve dynamically since birth. Using high resolution near-IR spectroscopic observations we analysed a sample of 38 protostars to derive the close binary frequency of embedded sources where multiplicity had been previously studied by imaging techniques. With a new designed spectroscopic technique based in a two dimensional cross-correlation function we determined multi-epoch radial velocities of the Class I/II sources with unprecedented precision in the spectroscopic study of protostars. As a conclusion we have found strong evidence that early multiplicity at short distances scales from the central protostellar object is indeed possible in three targets analysed. Of major concern to this thesis was also to determine the chemical abundances of the new born comoving associations in the solar neighborhood in order to search for high metallicity stars that can be included in future exo-planet surveys. While it is still highly uncertain whether metallicity influences or not stellar multiplicity, it is nowadays a proven evidence that the metal content of the circumstellar disks play a central role in planetary formation. The more metal content the more likely is to find a giant planet around a star. Having this in mind we derived through an uniform and homogeneous method stellar atmospheric parameters and abundances for iron, and 13 other elements in 63 post-T Tauri stars from 11 nearby young associations and further compared the results with two different pre-main sequence (PMS) and main sequence (MS) star populations. Solar metallicity was found to be a common trend in all the nearby young associations studied. Two main conclusions and many future prospects can be drawn from this thesis. From the multiplicity project we set an entire new perspective on the formation of spectroscopic binaries. Our results show that multiplicity at sub-AU scale can be a natural outcome of the star formation process. Our preliminary binary fraction of ∼71% is also in line with the notion that multiplicity is very high at young ages and therefore it might be a product of star-formation. From the metallicity project we demonstrate that no metal overabundance can be found in 11 young associations around the sun. Our results suggest that the high-metallicity stars harboring planets were not formed in the solar neighborhood but were instead carried to their present position by Galactic dynamical forces.
DETERMINISTIC PREWHITENING TO IMPROVE SUBSPACE BASED PARAMETER ESTIMATION TECHNIQUES IN SEVERELY COLORED NOISE ENVIRONMENTS

Colored noise is encountered in a variety of signal processing applications. For such applications the prewhitening step becomes essential, since parameter estimation without prewhitening can be severely degraded. Traditionally stochastic prewhitening techniques transform the colored noise into white noise keeping the SNR constant. In this paper, we propose a deterministic approach for subspace prewhitening, where we remove the correlation, which increases the SNR. Consequently, in high noise correlation scenarios, where the subspace is prewhitened by our deterministic approach, there is a significant improvement in the parameter estimation accuracy. The proposed deterministic prewhitening requires knowledge of the noise correlation. Therefore, we also propose solutions to estimate the correlation coefficients.
Efficient reflection via four-wave mixing in a Doppler-free electromagnetically-induced-transparency gas system

We experimentally demonstrate the high-efficiency reflection of a probe field in {Lambda}-type three-level atoms of cesium vapor driven by two counterpropagating coupling fields. More than 60% of reflection efficiency is observed at the phase-matching angle. The underlying mechanism theoretically is investigated as the four-wave mixing is enhanced by the electromagnetically-induced transparency. Both of the two Doppler-free two-photon resonances (one for the probe and co-propagating fields, the other for the reflected and the counterpropagation fields) play an important role in satisfying the phase matching in the reflection direction. The phase compensation due to the anomalous dispersion and the decrease of effective absorption length in the atomic system allow the efficient reflection to be observed in a wide range of incident angles of the probe field and detunings of the coupling field.
Increasing reconstruction quality of diffractive optical elements displayed with LC SLM

Phase liquid crystal (LC) spatial light modulators (SLM) are actively used in various applications. However, majority of scientific applications require stable phase modulation which might be hard to achieve with commercially available SLM due to its consumer origin. The use of digital voltage addressing scheme leads to phase temporal fluctuations, which results in lower diffraction efficiency and reconstruction quality of displayed diffractive optical elements (DOE). Due to high periodicity of fluctuations it should be possible to use knowledge of these fluctuations during DOE synthesis to minimize negative effect. We synthesized DOE using accurately measured phase fluctuations of phase LC SLM “HoloEye PLUTO VIS” to minimize its negative impact on displayed DOE reconstruction. Synthesis was conducted with versatile direct search with random trajectory (DSRT) method in the following way. Before DOE synthesis begun, two-dimensional dependency of SLM phase shift on addressed signal level and time from frame start was obtained. Then synthesis begins. First, initial phase distribution is created. Second, random trajectory of consecutive processing of all DOE elements is generated. Then iterative process begins. Each DOE element sequentially has its value changed to one that provides better value of objective criterion, e.g. lower deviation of reconstructed image from original one. If current element value provides best objective criterion value then it left unchanged. After all elements are processed, iteration repeats until stagnation is reached. It is demonstrated that application of SLM phase fluctuations knowledge in DOE synthesis with DSRT method leads to noticeable increase of DOE reconstruction quality.
Carbon nanotube memory devices of high charge storage stability

Molecular memory devices with semiconducting single-walled carbon nanotubes constituting a channel of 150 nm in length are described. Data storage is achieved by sweeping gate voltages in the range of 3 V, associated with a storage stability of more than 12 days at room temperature. By annealing in air or controlled oxygen plasma exposure, efficient switching devices could be obtained from thin nanotube bundles that originally showed only a small gate dependence of conductance.
Estimation of Wind Speed Distribution Using Markov Chain Monte Carlo Techniques

The Weibull distribution is the most commonly used statistical distribution for describing wind speed data. Maximum likelihood has traditionally been the main method of estimation for Weibull parameters. In this paper, Markov chain Monte Carlo techniques are used to carry out a Bayesian estimation procedure using wind speed data obtained from the Observatory of Hong Kong. The method is extremely flexible. Inference for any quantity of interest is routinely available, and it can be adapted easily when data are truncated.
Early Wildfire Smoke Detection Using Different YOLO Models

Forest fires are a serious ecological concern, and smoke is an early warning indicator. Early smoke images barely capture a tiny portion of the total smoke. Because of the irregular nature of smoke’s dispersion and the dynamic nature of the surrounding environment, smoke identification is complicated by minor pixel-based traits. This study presents a new framework that decreases the sensitivity of various YOLO detection models. Additionally, we compare the detection performance and speed of different YOLO models such as YOLOv3, YOLOv5, and YOLOv7 with prior ones such as Fast R-CNN and Faster R-CNN. Moreover, we follow the use of a collected dataset that describes three distinct detection areas, namely close, medium, and far distance, to identify the detection model’s ability to recognize smoke targets correctly. Our model outperforms the gold-standard detection method on a multi-oriented dataset for detecting forest smoke by an mAP accuracy of 96.8% at an IoU of 0.5 using YOLOv5x. Additionally, the findings of the study show an extensive improvement in detection accuracy using several data-augmentation techniques. Moreover, YOLOv7 outperforms YOLOv3 with an mAP accuracy of 95%, compared to 94.8% using an SGD optimizer. Extensive research shows that the suggested method achieves significantly better results than the most advanced object-detection algorithms when used on smoke datasets from wildfires, while maintaining a satisfactory performance level in challenging environmental conditions.
Domestication Potential of Garcinia kola Heckel (Clusiaceae): Searching for Diversity in South Cameroon

Seeds and bark of Garcinia kola Heckel (Clusiaceae) are popular products in West and Central Africa. Despite the tree’s economic and cultural importance, little is known about its phenotypic and genotypic variation. This study characterised the morphological and genetic diversity of G. kola in South Cameroon, searching for traits and populations that might be used for domestication. Morphological assessment and amplified fragment length polymorphism (AFLP) markers were applied to characterise diversity among geographic populations from Central and South regions, and between managed and wild trees. AFLP-SURV and analysis of molecular variance results indicated that a major part of genetic diversity is harboured within populations rather than between them. Bayesian analysis, principal component analysis and t-SNE identified three clusters where Ebolowa emerged as the transition population, combining features from both regions. Trees from the south had a higher prevalence of morphological domestication-related characteristics. Trees from the central region, on the other hand, demonstrated greater genetic diversity. No significant differences in phenotype and genotype were revealed between wild and managed populations, suggesting G. kola is still in the early stages of its domestication process.
HSIFoodIngr-64: A Dataset for Hyperspectral Food-Related Studies and a Benchmark Method on Food Ingredient Retrieval

Food-related issues have attracted increasing attention recently due to its various applications in our daily life, for example, restaurant service and dietary assessment, which are essential for human health management and understanding food characteristics. Many existing datasets are proposed for various food-relevant tasks, e.g., food detection and segmentation. However, most of the datasets are labelled with only dish-level annotations and lack detailed information for the corresponding ingredients. Hyperspectral imaging (HSI), which can explore the emissive characteristics of different objects with a long region of spectral bands, is of great potential for food and ingredient analysis. In the present work, a new food image dataset HSIFoodIngr-64 containing 3,389 pairs of HSI and RGB images with 21 dish classes and 64 ingredient categories was established. It is the first HSI-based food image dataset, of which all images are labelled with dish-level and ingredient-level annotations and corresponding pixel-wise ingredient masks. Therefore, our dataset can be applied to different food-centric tasks. Furthermore, this research proposed a benchmark method on the food ingredient retrieval task, which consists of two sub-networks called IPSN for ingredient prediction sub-network and ERSN for edge refinement sub-network. Extensive experiments were conducted on HSIFoodIngr-64 dataset and the effectiveness of our proposed method was verified. HSIFoodIngr-64 dataset is expected to provide a new perspective on food analysis and inspire more efforts on various food-related issues. We have made the HSIFoodIngr-64 dataset public at: https://doi.org/10.7910/DVN/E7WDNQ.
INTRODUCTION OF STATISTICAL DESIGN AND ANALYSIS BY THE QUEENSLAND BUREAU OF SUGAR EXPERIMENT STATIONS

summary The pioneering role of the Queensland Bureau of Sugar Experiment Stations in the introduction of Fisher's methods for the design and analysis of experiments to Australia is described. The paper is based on the recollections of the former director of the Bureau and the person responsible for the adoption of statistical methodology, Dr H.W. (Bill) Kerr. Details of the early experiments and their analysis are given based on published reports.
Selective Transcription Factor Blockade Reduces Human Retinal Endothelial Cell Expression of Intercellular Adhesion Molecule-1 and Leukocyte Binding

The interaction between leukocytes and cytokine-activated retinal endothelium is an initiating step in non-infectious uveitis involving the posterior eye, mediated by cell adhesion molecules. However, because cell adhesion molecules are required for immune surveillance, therapeutic interventions would ideally be employed indirectly. Using 28 primary human retinal endothelial cell isolates, this study sought to identify transcription factor targets for reducing levels of the key retinal endothelial cell adhesion molecule, intercellular adhesion molecule (ICAM)-1, and limiting leukocyte binding to the retinal endothelium. Five candidate transcription factors—C2CD4B, EGR3, FOSB, IRF1, and JUNB—were identified by differential expression analysis of a transcriptome generated from IL-1β- or TNF-α-stimulated human retinal endothelial cells, interpreted in the context of the published literature. Further filtering involved molecular studies: of the five candidates, C2CD4B and IRF1 consistently demonstrated extended induction in IL-1β- or TNF-α-activated retinal endothelial cells and demonstrated a significant decrease in both ICAM-1 transcript and ICAM-1 membrane-bound protein expression by cytokine-activated retinal endothelial cells following treatment with small interfering RNA. RNA interference of C2CD4B or IRF1 significantly reduced leukocyte binding in a majority of human retinal endothelial cell isolates stimulated by IL-1β or TNF-α. Our observations suggest that the transcription factors C2CD4B and IRF1 may be potential drug targets for limiting leukocyte–retinal endothelial cell interactions in non-infectious uveitis involving the posterior eye.
High-Precision Detection of Cellular Drug Response Based on SERS Spectrum and Multivariate Statistical Analysis

The rapid development of personalized medicine places high demands on the control of drug dose and cellular drug response to provide patients with better curative effects and low side effects. To solve the problem of low detection accuracies of the cell-counting kit-8 (CCK8) method, a detection method based on surface-enhanced Raman spectroscopy (SERS) of cell-secreted proteins was adopted to evaluate the concentration of the anticancer drug cisplatin and the cellular drug response of nasopharyngeal carcinoma. CNE1 and NP69 cell lines were used to evaluate cisplatin response. The results showed that the combination of the SERS spectrum with principal component analysis–linear discriminant analysis could detect the difference in the response of cisplatin with a concentration difference of 1 μg/mL, which considerably exceeded that of CCK8. In addition, the SERS spectral peak intensity of the cell-secreted proteins strongly correlated with the cisplatin concentration. Furthermore, the mass spectrum of the secreted proteins of the nasopharyngeal carcinoma cells was analyzed to verify the results obtained using the SERS spectrum. The results demonstrated that SERS of secreted proteins has great potential for high-precision detection of chemotherapeutic drug response.
Multidrug-Loaded Lipid Nanoemulsions for the Combinatorial Treatment of Cerebral Cavernous Malformation Disease

Cerebral cavernous malformation (CCM) or cavernoma is a major vascular disease of genetic origin, whose main phenotypes occur in the central nervous system, and is currently devoid of pharmacological therapeutic strategies. Cavernomas can remain asymptomatic during a lifetime or manifest with a wide range of symptoms, including recurrent headaches, seizures, strokes, and intracerebral hemorrhages. Loss-of-function mutations in KRIT1/CCM1 are responsible for more than 50% of all familial cases, and have been clearly shown to affect cellular junctions, redox homeostasis, inflammatory responses, and angiogenesis. In this study, we investigated the therapeutic effects of multidrug-loaded lipid nanoemulsions in rescuing the pathological phenotype of CCM disease. The pro-autophagic rapamycin, antioxidant avenanthramide, and antiangiogenic bevacizumab were loaded into nanoemulsions, with the aim of reducing the major molecular dysfunctions associated with cavernomas. Through Western blot analysis of biomarkers in an in vitro CCM model, we demonstrated that drug-loaded lipid nanoemulsions rescue antioxidant responses, reactivate autophagy, and reduce the effect of pro-angiogenic factors better than the free drugs. Our results show the importance of developing a combinatorial preventive and therapeutic approach to reduce the risk of lesion formation and inhibit or completely revert the multiple hallmarks that characterize the pathogenesis and progression of cavernomas.
ENDOGENOUS HORMONES FROM THE YOUNG LEAVES OF OIL PALM (Elaeis guineensis Jacq.): A RESPONSE TO In Vitro CALLUS AND EMBRYOID INDUCTION

Oil palm is a plant that produces vegetable oil. Quality oil palm seedlings with superior characteristics can be produced clonally using tissue culture techniques. The selection of explant sources is very important, and one of the explant sources that can be used is the young oil palm leaves. The ability of callogenesis and embryogenesis remains low, and this is thought to be related to endogenous hormones. This study is aimed to identify the types of endogenous hormones in explants of young clonal oil palm leaves and their relationship to callus and embryoid induction. The leaf midribs of six young oil palms were selected as explant sources. Explants were extracted and analysed for endogenous hormone content using high-performance liquid chromatography. The results of the analysis obtained various types of endogenous hormones in four oil palm clones. Zeatin hormones, ABA and IBA were found in two clones. Nine hormones were found in the other two clones, namely zeatin, kinetin, gibberellic acid, IAA, BAP, ABA, IBA, 2,4-dichlorophenoxyacetic acid, and naphthalene acetic acid with various concentrations. Young oil palm leaves with a more complete content of endogenous hormones have the potential to produce embryogenic callus in greater numbers and can produce embryoids to ensure that they can support the production of plantlets using tissue culture technology.
AICAR Ameliorates Non-Alcoholic Fatty Liver Disease via Modulation of the HGF/NF-κB/SNARK Signaling Pathway and Restores Mitochondrial and Endoplasmic Reticular Impairments in High-Fat Diet-Fed Rats

Non-alcoholic fatty liver disease (NAFLD) is a global health problem characterized by altered lipid and redox homeostasis, mitochondrial dysfunction, and endoplasmic reticulum (ER) stress. The AMP-dependent kinase (AMPK) agonist 5-aminoimidazole-4-carboxamide ribonucleoside (AICAR) has been shown to improve the outcome of NAFLD in the context of AMPK activation, yet the underlying molecular mechanism remains obscure. This study investigated the potential mechanism(s) of AICAR to attenuate NAFLD by exploring AICAR’s effects on the HGF/NF-κB/SNARK axis and downstream effectors as well as mitochondrial and ER derangements. High-fat diet (HFD)-fed male Wistar rats were given intraperitoneal AICAR at 0.7 mg/g body weight or left untreated for 8 weeks. In vitro steatosis was also examined. ELISA, Western blotting, immunohistochemistry and RT-PCR were used to explore AICAR’s effects. NAFLD was confirmed by steatosis score, dyslipidemia, altered glycemic, and redox status. HGF/NF-κB/SNARK was downregulated in HFD-fed rats receiving AICAR with improved hepatic steatosis and reduced inflammatory cytokines and oxidative stress. Aside from AMPK dominance, AICAR improved hepatic fatty acid oxidation and alleviated the ER stress response. In addition, it restored mitochondrial homeostasis by modulating Sirtuin 2 and mitochondrial quality gene expression. Our results provide a new mechanistic insight into the prophylactic role of AICAR in the prevention of NAFLD and its complications.
Neural Networks Application for Accurate Retina Vessel Segmentation from OCT Fundus Reconstruction

The use of neural networks for retinal vessel segmentation has gained significant attention in recent years. Most of the research related to the segmentation of retinal blood vessels is based on fundus images. In this study, we examine five neural network architectures to accurately segment vessels in fundus images reconstructed from 3D OCT scan data. OCT-based fundus reconstructions are of much lower quality compared to color fundus photographs due to noise and lower and disproportionate resolutions. The fundus image reconstruction process was performed based on the segmentation of the retinal layers in B-scans. Three reconstruction variants were proposed, which were then used in the process of detecting blood vessels using neural networks. We evaluated performance using a custom dataset of 24 3D OCT scans (with manual annotations performed by an ophthalmologist) using 6-fold cross-validation and demonstrated segmentation accuracy up to 98%. Our results indicate that the use of neural networks is a promising approach to segmenting the retinal vessel from a properly reconstructed fundus.