Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Supervised Cyber Threat Identification in Dark Net Markets: A Transductive and Deep Learning Approach

ABSTRACT Dark Net Marketplaces (DNMs), online selling platforms on the dark web, constitute a major component of the underground economy. Due to the anonymity and increasing accessibility of these platforms, they are rich sources of cyber threats such as hacking tools, data breaches, and personal account information. As the number of products offered on DNMs increases, researchers have begun to develop automated machine learning-based threat identification approaches. A major challenge in adopting such an approach is that the task typically requires manually labeled training data, which is expensive and impractical. We propose a novel semi-supervised labeling technique for leveraging unlabeled data based on the lexical and structural characteristics of DNMs using transductive learning. Empirical results show that the proposed approach leads to an approximately 3-5% increase in classification performance measured by F1-score, while increasing both precision and recall. To further improve the identification performance, we adopt Long Short-Term Memory (LSTM) as a deep learning structure on top of the proposed labeling method. The results are evaluated against a large collection of 79K product listings obtained from the most popular DNMs. Our method outperforms the state-of-the-art methods in threat identification and is considered as an important step toward lowering the human supervision cost in realizing automated threat detection within cyber threat intelligence organizations.
Synthesis and analysis of reactive nanocomposites prepared by arrested reactive milling

SYNTHESIS AND ANALYSIS OF REACTIVE NANOCOMPOSITES PREPARED BY ARRESTED REACTIVE MILLING by Swati Μ. Umbrajkar Different types of reactive nanocomposites have been synthesized by Arrested Reactive Milling (ARM). The technical approach was to increase the interface area available for heterogeneous reaction between solid fuel and oxidizer components. Using aluminum as the main fuel and different metal oxides as oxidizers, highly energetic reactive nanocomposites with different degrees of structural refinement were synthesized. Specifically, stoichiometric ΑΙ-ΜοΟ3, A1-CuO, and ΑΙ-ΝαΝΟ3 material systems were studied in detail. The correlation of heterogeneous exothermic reactions occurring in the nanocomposite powders upon their heating at low rates and ignition events observed for the same powders heated rapidly was of interest. Differential scanning calorimetry (DSC), X-ray diffraction (XRD) and heated filament ignition experiments were used to quantify the ignition kinetics and related reaction mechanisms. Fuel rich Al-Mo03 nanocomposites were also synthesized using ARM. Optimum composition and milling parameters were identified for fuel-rich compositions. Analysis of exothermic reactions in Al-Mo03 system showed that kinetics of such reactions could not be determined by isoconversion processing and respective activation energies could not be meaningfully found as functions of reaction progress. Instead, detailed DSC measurements at different heating rates are required to enable one in developing a multi-step kinetic model to describe such reactions adequately. SYNTHESIS AND ANALYSIS OF REACTIVE NANOCOMPOSITES PREPARED BY ARRESTED REACTIVE MILLING by Swati Μ. Umbrajkar A Dissertation Submitted to the Faculty of New Jersey Institute of Technology in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Mechanical Engineering Department of Mechanical Engineering
Evaluation of the MODIS LAI/FPAR Algorithm Based on 3D-RTM Simulations: A Case Study of Grassland

Uncertainty assessment of the moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) retrieval algorithm can provide a scientific basis for the usage and improvement of this widely-used product. Previous evaluations generally depended on the intercomparison with other datasets as well as direct validation using ground measurements, which mix the uncertainties from the model, inputs, and assessment method. In this study, we adopted the evaluation method based on three-dimensional radiative transfer model (3D RTM) simulations, which helps to separate model uncertainty and other factors. We used the well-validated 3D RTM LESS (large-scale remote sensing data and image simulation framework) for a grassland scene simulation and calculated bidirectional reflectance factors (BRFs) as inputs for the LAI/FPAR retrieval. The dependency between LAI/FPAR truth and model estimation serves as the algorithm uncertainty indicator. This paper analyzed the LAI/FPAR uncertainty caused by inherent model uncertainty, input uncertainty (BRF and biome classification), clumping effect, and scale dependency. We found that the uncertainties of different algorithm paths vary greatly (−6.61% and +84.85% bias for main and backup algorithm, respectively) and the “hotspot” geometry results in greatest retrieval uncertainty. For the input uncertainty, the BRF of the near-infrared (NIR) band has greater impacts than that of the red band, and the biome misclassification also leads to nonnegligible LAI/FPAR bias. Moreover, the clumping effect leads to a significant LAI underestimation (−0.846 and −0.525 LAI difference for two clumping types), but the scale dependency (pixel size ranges from 100 m to 1000 m) has little impact on LAI/FPAR uncertainty. Overall, this study provides a new perspective on the evaluation of LAI/FPAR retrieval algorithms.
Molecular Mechanism of the UDP-Glucuronosyltransferase 2B20-like Gene (AccUGT2B20-like) in Pesticide Resistance of Apis cerana cerana

UDP-glucuronosyltransferases (UGTs), being multifunctional detoxification enzymes, play a major role in the process of resistance to various pesticides in insects. However, the mechanism underlying the molecular regulation of pesticide resistance remains unclear, especially in Apis cerana cerana. In this study, all of the UGTs in Apis cerana cerana (AccUGT) have been identified through the multiple alignment and phylogenetic analysis. Expression of AccUGT genes under different pesticides, and antioxidant genes after silencing of AccUGT2B20-like, were detected by qRT-PCR. The resistance of overexpressed AccUGT2B20-like to oxidative stress was investigated by an Escherichia coli overexpression system. Also, antioxidant-related enzyme activity was detected after silencing of the AccUGT2B20-like gene. Expression pattern analysis showed that almost all UGT genes were upregulated under different pesticide treatments. This result indicated that AccUGTs participate in the detoxification process of pesticides. AccUGT2B20-like was the major gene because it was more highly induced than the others. Overexpression of AccUGT2B20-like in E. coli could effectively improve oxidative stress resistance. Specifically, silencing the AccUGT2B20-like gene increased oxidative stress by repressing the expression of oxidation-related genes, decreasing antioxidant-related enzyme activity, and increasing malondialdehyde concentration. Taken together, our results indicate that AccUGTs are involved in pesticide resistance, among which, AccUGT2B20-like contributes to the detoxification of pesticides by eliminating oxidative stress in Apis cerana cerana. This study explains the molecular basis for the resistance of bees to pesticides and provides an important safeguard for maintaining ecological balance.
Improved de novo Assembly of the Achlorophyllous Orchid Gastrodia elata

Achlorophyllous plants are full mycoheterotrophic plants with no chlorophyll and they obtain their nutrients from soil fungi. Gastrodia elata is a perennial, achlorophyllous orchid that displays distinctive evolutionary strategy of adaptation to the non-photosynthetic lifestyle. Here in this study, the genome of G. elata was assembled to 1.12 Gb with a contig N50 size of 110 kb and a scaffold N50 size of 1.64 Mb so that it helped unveil the genetic basics of those adaptive changes. Based on the genomic data, key genes related to photosynthesis, leaf development, and plastid division pathways were found to be lost or under relaxed selection during the course of evolution. Thus, the genome sequence of G. elata provides a good resource for future investigations of the evolution of orchids and other achlorophyllous plants.
Anti-Obesity Effect of Dictyophora indusiata Mushroom Polysaccharide (DIP) in High Fat Diet-Induced Obesity via Regulating Inflammatory Cascades and Intestinal Microbiome

Obesity is a multifactorial metabolic disorder characterized by low-grade chronic inflammation, hyper-permeability of the gut epithelium, and perturbation of the intestinal microbiome. Despite the numerous therapeutic efficacies of Dictyophora indusiata mushroom, its biological activity in alleviating obesity through regulation of the gut microbiota and inflammatory cascades remain obscure. Henceforth, we determined the modulatory impact of D. indusiata polysaccharide (DIP) in the high-fat diet (HFD)-induced obesity mice model. The experimental subjects (BALB/C mice) were supplemented with chow diet (Control group), high-fat diet (HFD group), or HFD along with DIP at a low dose [HFD + DIP(L)] and high dose [HFD + DIP(H)]. Obesity-related parameters, including body weight gain, epididymal adipocyte size, fat accumulation, adipogenic markers, lipogenic markers, inflammatory associated markers, intestinal integrity, and intestinal microbiome, were elucidated. Our findings demonstrated that the oral administration of DIP at low dose partially and at high dose significantly reversed HFD-induced obesity parameters. Furthermore, the body weight, fat accumulation, adipocyte size, adipogenic and liver associated markers, glucose levels, inflammatory cytokines, and endotoxin (Lipopolysaccharide, LPS) levels were reduced considerably. Moreover, the study revealed that DIP treatment reversed the dynamic alterations of the gut microbiome community by decreasing the Firmicutes to Bacteroidetes ratio. These findings led us to infer the therapeutic potential of DIP in alleviating HFD-induced obesity via regulating inflammatory cascades, modulating intestinal integrity and intestinal microbiome community.
Recombination-line intensities for hydrogenic ions. I - Case B calculations for H I and He II. [in astronomical objects

Assuming Case B of Baker and Menzel (1938), the relative intensities of H I and He II recombination lines are determined for a larger range of temperature and density than previously considered and with the upper principle quantum number up to 50 and the lower number up to 29. Full collisional effects are included, and new collision strengths for the n = 1, 2, and 3 states of He(+) are presented. Case B theory is found to be invalidated for collisional excitation of the n = 3 levels from both n = 1 and n = 2 states under certain conditions which depend upon electron density and the Lyman-alpha escape probability, and the regimes of temperature and density for which Case B is valid are also considered.
Development of bio-analytical methods for the quantitative and qualitative analysis of labelled peptides and proteins via hyphenation of chromatography and mass spectrometry

This PhD thesis was a Cotutelle between the Universite de Pau et des Pays de l’Adour (UPPA) in Pau, France and the Christian-Albrechts University (CAU) in Kiel, Germany. In the course of this international collaboration, bio-analytical methods for the quantitative and qualitative analysis of labelled peptides and proteins were developed, which were based on the hyphenation of chromatography with mass spectrometry. Peptides and protein digests were lanthanide labelled using DOTA-based compounds according to an optimised protocol. Separation on the peptide level was performed using IP-RP-nanoHPLC. Complementary data sets were acquired using MALDI-MS for identification and ICP-MS for quantification. In this context, an online precleaning step was developed and implemented in the nanoHPLC separation routine, which allowed for effective removal of excess reagents. This lead to lowered metal backgrounds during ICP-MS measurements and thus better data interpretability, while guarding peptide recovery at a maximum level. An alternative offline purification using solid phase extraction (SPE) resulted in important peptide losses and can be considered unsuitable for quantitative analysis. Additives to the nanoHPLC eluents, such as HFBA and EDTA were tested and not deemed beneficial for the analysis of normal peptide samples. HFBA can be reconsidered for special application on very hydrophilic peptide species. A set of labelled peptides was developed, which due to application of known quantities could be employed for quick and simple quantification of a low complexity digest sample. In addition this peptide set allowed for the reliable superposition of chromatograms, enabling sample comparability especially for complementary ICP-MS and MALDI-MS data. Experiments for application of fsLA-ICP-MS on MALDI-MS target plates were conducted and showed very promising results. For this purpose, samples that were already identified using MALDI-MS were supposed to be remeasured using fsLA-ICP-MS. First quantification attempts on the modified steel target plate were successful and in the range of expectance. Adjusted parameters for MALDI-MS allowed for proper peptide identifications.
Reasoning about software security via synthesized behavioral substitutes

Since software is an integral part of our daily life, it is of great importance to ensure its safety and security. Still, we frequently observe attackers exploiting security vulnerabilities to steal secret customer information, manipulate essential data and take control over critical infrastructure. On the other hand, security researchers make efforts to find and eliminate security flaws, which is often a non-trivial task. Even more, it is proven to be undecidable. As a consequence, security analysis is often goal-oriented, effectively limiting the analysis scope by focusing only on certain types of bugs or proving the presence of specific program characteristics. Therefore, analysis techniques are based on assumptions that may only hold in artificial scenarios. In practice, such methods are either too broad and suffer from false positives or too narrow and miss many cases. Still, they often are very effective for their designed use case and alleviate tedious and time-consuming work of a human analyst. Some techniques are based on abstraction in which we transform parts of a program into an abstract domain that is explicitly constructed to facilitate reasoning about specific characteristics. In this domain, a so-called behavioral substitute represents only the desired characteristics of a given program. Often, the transformation process relies on labor-intensive manually implemented rules, resulting in behavioral substitutes that are too generic or incomplete in some cases. In this thesis, we propose problem-specific analysis techniques based on synthesized behavioral substitutes to advance research on topics related to code deobfuscation, fuzzing and root cause analysis. In each case, we design a domain-specific representation that allows generic reasoning in its associated area. We apply stochastic program synthesis techniques to automatically learn behavioral substitutes. For this, we use the target program as a black-box, basically using the program’s behavior as feedback. This combination of crafting target-specific representations in a problem-specific domain allows us to reason about more generic instances of the problem while staying close to the target. As a consequence, our methods are generic regarding the problem and geared to the target, allowing us to operate on a wide range of problem instances without implementing a target-specific analysis. In our empirical evaluation, we show for various real-world targets that we either outperform state-of-the-art approaches or that our techniques are orthogonal to existing approaches and perform in scenarios where others do not.
Uncertainty and Overfitting in Fluvial Landform Classification Using Laser Scanned Data and Machine Learning: A Comparison of Pixel and Object-Based Approaches

Floodplains are valuable scenes of water management and nature conservation. A better understanding of their geomorphological characteristic helps to understand the main processes involved. We performed a classification of floodplain forms in a naturally developed area in Hungary using a Digital Terrain Model (DTM) of aerial laser scanning. We derived 60 geomorphometric variables from the DTM and prepared a geomorphological map of 265 forms (crevasse channels, point bars, swales, levees). Random Forest classification was conducted with Recursive Feature Elimination (RFE) on the objects (mean pixel values by forms) and on the pixels of the variables. We also evaluated the classification probabilities (CP), the spatial uncertainties (SU), and the overfitting in the function of the number of the variables. We found that the object-based method had a better performance (95%) than the pixel-based method (78%). RFE helped to identify the most important 13–20 variables, maintaining the high model performance and reducing the overfitting. However, CP and SU were not efficient measures of classification accuracy as they were not in accordance with the class level accuracy metric. Our results help to understand classification results and the specific limits of laser scanned DTMs. This methodology can be useful in geomorphologic mapping.
Experimental Fire Measurement with UAV Multimodal Stereovision

In wildfire research, systems that are able to estimate the geometric characteristics of fire, in order to understand and model the behavior of this spreading and dangerous phenomenon, are required. Over the past decade, there has been a growing interest in the use of computer vision and image processing technologies. The majority of these works have considered multiple mono-camera systems, merging the information obtained from each camera. Recent studies have introduced the use of stereovision in this field; for example, a framework with multiple ground stereo pairs of cameras has been developed to measure fires spreading for about 10 meters. This work proposes an unmanned aerial vehicle multimodal stereovision framework which allows for estimation of the geometric characteristics of fires propagating over long distances. The vision system is composed of two cameras operating simultaneously in the visible and infrared spectral bands. The main result of this work is the development of a portable drone system which is able to obtain georeferenced stereoscopic multimodal images associated with a method for the estimation of fire geometric characteristics. The performance of the proposed system is tested through various experiments, which reveal its efficiency and potential for use in monitoring wildfires.
Brain Delivery of Nanomedicines: Trojan Horse Liposomes for Plasmid DNA Gene Therapy of the Brain

Non-viral gene therapy of the brain is enabled by the development of plasmid DNA brain delivery technology, which requires the engineering and manufacturing of nanomedicines that cross the blood-brain barrier (BBB). The development of such nanomedicines is a multi-faceted problem that requires progress at multiple levels. First, the type of nanocontainer, e.g., nanoparticle or liposome, which encapsulates the plasmid DNA, must be developed. Second, the type of molecular Trojan horse, e.g., peptide or receptor-specific monoclonal antibody (MAb), must be selected for incorporation on the surface of the nanomedicine, as this Trojan horse engages specific receptors expressed on the BBB, and the brain cell membrane, to trigger transport of the nanomedicine from blood into brain cells beyond the BBB. Third, the plasmid DNA must be engineered without bacterial elements, such as antibiotic resistance genes, to enable administration to humans; the plasmid DNA must also be engineered with tissue-specific gene promoters upstream of the therapeutic gene, to insure gene expression in the target organ with minimal off-target expression. Fourth, upstream manufacturing of the nanomedicine must be developed and scalable so as to meet market demand for the target disease, e.g., annual long-term treatment of 1,000 patients with an orphan disease, short term treatment of 10,000 patients with malignant glioma, or 100,000 patients with new onset Parkinson's disease. Fifth, downstream manufacturing problems, such as nanomedicine lyophilization, must be solved to ensure the nanomedicine has a commercially viable shelf-life for treatment of CNS disease in humans.
The Role of G protein-coupled estrogen receptor in vascular function and hypertension

Regulation of arterial tone relies heavily on molecular crosstalk between endothelial and smooth muscle cells. Although it has been demonstrated that estrogen (E2) exerts protective effects against cardiovascular disease (CVD), the exact mechanisms and balance of E2 signaling in endothelial and smooth muscle cells remain unresolved. Here we characterize the G protein-coupled estrogen receptor (GPER) in the vasculature by elucidating its pathway in endothelial nitric oxide (NO) production and vascular smooth muscle maintenance of reactive oxygen species (ROS) by NADPH oxidases (NOX). NO is thought of as a vasoprotective entity, while the overproduction of ROS by NOX can lead to vascular dysfunction. These two forces counteract each other to maintain arterial tone, and understanding their mediation by GPER will provide significant insights in sex differences in vascular function. In endothelial cells, we demonstrated that GPER activation induces phosphorylation of eNOS at ser1179, and contributes to ER-dependent NO production. Inhibition of GPER through pharmacologic antagonism (G36) or genomic deletion (GPER KO) reduced NO production and vasorelaxation to
Study on Tim3 Regulation of Multiple Myeloma Cell Proliferation via NF-κB Signal Pathways

Objective As an important negative regulatory factor of immunological cells, Tim3 plays a regulating role in tumor immune microenvironment. The purpose of this study was to investigate the expression of Tim3 on MM cells and its effect on the proliferation and apoptosis of MM cells, as well as its potential mechanism. Methods In this study, the expression of Tim3 was detected on myeloma cells (CD38+CD138+ cells) of bone marrow by flow cytometry (FCM) from 167 patients with MM and 51 healthy donors as controls and making correlation analysis with related clinical indexes. In vitro, MM cell lines (RPMI-8226 and U266) were treated with Tim3 knock-down alone, bortezomib alone and combination of Tim3 knock-down and bortezomib, then cell proliferation, cell apoptosis and downstream signaling pathway were detected by CCK-8, FCM, RT-PCR and western blot. Results The expression of Tim3 on myeloma cells in MM patients was significantly higher than normal control group and positively correlated with β2 microglobulin, creatine, and plasma cells of bone marrow, negatively correlated with hemoglobin and red blood cells. In vitro, we validated the high expression of Tim3 in RPMI-8226 and U266 cell lines. After Tim3 knock-down, the cell proliferation was inhibited and cell apoptosis was induced, the relative mRNA and protein expression of Tim3 and NF-κB signal pathway (PI3K, AKT, mTOR, NF-κB) were significantly decreased. Also, the cell proliferation was inhibited, cell apoptosis was increased, the relative mRNA and protein expression of NF-κB were decreased significantly in combination group than bortezomib or Tim3 knock-down group. Conclusions The high expression of Tim3 on MM cells is associated with progression of MM patients. Tim3 maybe regulate the proliferation of MM cells via NF-κB signal pathway. Down-regulation of Tim3 expression can inhibit proliferation and induce apoptosis of MM cells, also has an additive inhibitory effect of bortezomib on NF-κB signaling pathway, then inhibit proliferation and induce apoptosis. Therefore, Tim3 may be a potential target for the treatment of MM.