Miracle-dz/UniFAR-FTUs huggingface dataset:
ArchaeoDAL: A Data Lake for Archaeological Data Management and Analytics

With new emerging technologies, such as satellites and drones, archaeologists collect data over large areas. However, it becomes difficult to process such data in time. Archaeological data also have many different formats (images, texts, sensor data) and can be structured, semi-structured and unstructured. Such variety makes data difficult to collect, store, manage, search and analyze effectively. A few approaches have been proposed, but none of them covers the full data lifecycle nor provides an efficient data management system. Hence, we propose the use of a data lake to provide centralized data stores to host heterogeneous data, as well as tools for data quality checking, cleaning, transformation and analysis. In this paper, we propose a generic, flexible and complete data lake architecture. Our metadata management system exploits goldMEDAL, which is the most generic metadata model currently available. Finally, we detail the concrete implementation of this architecture dedicated to an archaeological project.
Applying Evolutionary Algorithms Successfully: A Guide Gained from Real-world Applications

Metaheuristics (MHs) in general and Evolutionary Algorithms (EAs) in particular are well known tools for successful optimization of difficult problems. But when is their application meaningful and how does one approach such a project as a novice? How do you avoid beginner's mistakes or use the design possibilities of a metaheuristic search as efficiently as possible? This paper tries to give answers to these questions based on 30 years of research and application of the Evolutionary Algorithm GLEAM and its memetic extension HyGLEAM. Most of the experience gathered and discussed here can also be applied to the use of other metaheuristics such as ant algorithms or particle swarm optimization. This paper addresses users with basic knowledge of MHs in general and EAs in particular who want to apply them in an optimization project. For this purpose, a number of questions that arise in the course of such a project are addressed. At the end, some non-technical project management issues are discussed, whose importance for project success is often underestimated.
SuperCaustics: Real-time, open-source simulation of transparent objects for deep learning applications

Transparent objects are a very challenging problem in computer vision. They are hard to segment or classify due to their lack of precise boundaries, and there is limited data available for training deep neural networks. As such, current solutions for this problem employ rigid synthetic datasets, which lack flexibility and lead to severe performance degradation when deployed on real-world scenarios. In particular, these synthetic datasets omit features such as refraction, dispersion and caustics due to limitations in the rendering pipeline. To address this issue, we present SuperCaustics, a real-time, open-source simulation of transparent objects designed for deep learning applications. SuperCaustics features extensive modules for stochastic environment creation; uses hardware ray-tracing to support caustics, dispersion, and refraction; and enables generating massive datasets with multi-modal, pixel-perfect ground truth annotations. To validate our proposed system, we trained a deep neural network from scratch to segment transparent objects in difficult lighting scenarios. Our neural network achieved performance comparable to the state-of-the-art on a real-world dataset using only 10% of the training data and in a fraction of the training time. Further experiments show that a model trained with SuperCaustics can segment different types of caustics, even in images with multiple overlapping transparent objects. To the best of our knowledge, this is the first such result for a model trained on synthetic data. Both our open-source code and experimental data are freely available online.
Bio-inspired Rhythmic Locomotion in a Six-Legged Robot

Developing a framework for the locomotion of a six-legged robot or a hexapod is a complex task that has extensive hardware and computational requirements. In this paper, we present a bio-inspired framework for the locomotion of a hexapod. Our locomotion model draws inspiration from the structure of a cockroach, with its fairly simple central nervous system, and results in our model being computationally inexpensive with simpler control mechanisms. We consider the limb morphology for a hexapod, the corresponding central pattern generators for its limbs, and the inter-limb coordination required to generate appropriate patterns in its limbs. We also designed two experiments to validate our locomotion model. Our first experiment models the predator-prey dynamics between a cockroach and its predator. Our second experiment makes use of a reinforcement learning-based algorithm, putting forward a realization of our locomotion model. These experiments suggest that this model will help realize practical hexapod robot designs.
Composition of geranium (Pelargonium graveolens) essential oil from Tajikistan

The essential oil of Pelargonium graveolens from the aerial parts growing in Tajikistan was obtained by hydrodistillation and analyzed by gas chromatography – mass spectrometry. Seventy-nine components representing 95.1% of the total oil were identified. The main constituents of the essential oil were citronellol (37.5%), geraniol (6.0%), caryophyllene oxide (3.7%), menthone (3.1%), linalool (3.0%), β-bourbonene (2.7%), iso-menthone (2.1%) and geranyl formate (2.0%).
The decomposition of the higher-order homology embedding constructed from the k-Laplacian

The null space of the $k$-th order Laplacian $\mathbf{\mathcal L}_k$, known as the {\em $k$-th homology vector space}, encodes the non-trivial topology of a manifold or a network. Understanding the structure of the homology embedding can thus disclose geometric or topological information from the data. The study of the null space embedding of the graph Laplacian $\mathbf{\mathcal L}_0$ has spurred new research and applications, such as spectral clustering algorithms with theoretical guarantees and estimators of the Stochastic Block Model. In this work, we investigate the geometry of the $k$-th homology embedding and focus on cases reminiscent of spectral clustering. Namely, we analyze the {\em connected sum} of manifolds as a perturbation to the direct sum of their homology embeddings. We propose an algorithm to factorize the homology embedding into subspaces corresponding to a manifold's simplest topological components. The proposed framework is applied to the {\em shortest homologous loop detection} problem, a problem known to be NP-hard in general. Our spectral loop detection algorithm scales better than existing methods and is effective on diverse data such as point clouds and images.
Reverse Atom Transfer Radical Polymerization of Styrene in the Presence of Functionalized Silica Aerogel Nanoparticles

Abstract Pristine silica aerogel nanoparticles surface was functionalized with 3-(trimethoxysilyl)propyl methacrylate (MPS). Then, the resultant functionalized nanoparticles were used in grafting through polymerization of styrene by reverse initiation technique for atom transfer radical polymerization. Nitrogen adsorption/desorption isotherm is applied to examine surface area and structural characteristics of the synthesized nanoparticles. Evaluation of size distribution and morphological studies were also performed by scanning and transmission electron microscopy. Conversion and molecular weight determinations were carried out using gas and size exclusion chromatography respectively. Addition of MPS-functionalized nanoparticles by 3 wt % results in a decrease of conversion from 98 to 81%. Molecular weight (Mn) of the free polystyrene chains decreases by adding 3 wt % modified silica aerogel nanoparticles; however, polydispersity index (PDI) values increases from 1.31 to 1.92. Although PDI values of attached polystyrene chains are increased, Mn values reveal an increment by adding silica aerogel nanoparticles. A peak around 4.1 ppm which originates from hydrogen atom of terminal units of polystyrene chains in proton nuclear magnetic resonance spectra can demonstrate the living nature of the polymerization. Increasing thermal stability of the nanocomposites is demonstrated by TGA. Differential scanning calorimetry also shows a decrease in glass transition temperature by increasing modified silica aerogel nanoparticles.
Comprehending nulls

The Nested Relational Calculus (NRC) has been an influential high-level query language, providing power and flexibility while still allowing translation to standard SQL queries. It has also been used as a basis for language-integrated query in programming languages such as F#, Scala, and Links. However, SQL’s treatment of incomplete information, using nulls and three-valued logic, is not compatible with ‘standard’ NRC based on two-valued logic. Nulls are widely used in practice for incomplete data, but the question of how to accommodate SQL-style nulls and incomplete information in NRC, or integrate such queries into a typed programming language, appears not to have been studied thoroughly. In this paper we consider two approaches: an explicit approach in which option types are used to represent (possibly) nullable primitive types, and an implicit approach in which types are treated as possibly-null by default. We give translations relating the implicit and explicit approaches, discuss handling nulls in language integration, and sketch extensions of normalization and conservativity results.
FrameAxis: characterizing microframe bias and intensity with word embedding

Framing is a process of emphasizing a certain aspect of an issue over the others, nudging readers or listeners towards different positions on the issue even without making a biased argument. Here, we propose FrameAxis, a method for characterizing documents by identifying the most relevant semantic axes (“microframes”) that are overrepresented in the text using word embedding. Our unsupervised approach can be readily applied to large datasets because it does not require manual annotations. It can also provide nuanced insights by considering a rich set of semantic axes. FrameAxis is designed to quantitatively tease out two important dimensions of how microframes are used in the text. Microframe bias captures how biased the text is on a certain microframe, and microframe intensity shows how prominently a certain microframe is used. Together, they offer a detailed characterization of the text. We demonstrate that microframes with the highest bias and intensity align well with sentiment, topic, and partisan spectrum by applying FrameAxis to multiple datasets from restaurant reviews to political news. The existing domain knowledge can be incorporated into FrameAxis by using custom microframes and by using FrameAxis as an iterative exploratory analysis instrument. Additionally, we propose methods for explaining the results of FrameAxis at the level of individual words and documents. Our method may accelerate scalable and sophisticated computational analyses of framing across disciplines.
Children’s oral health of Roma, Ashkali and Egyptian Community

This study aimed to assess the oral health of Roma, Ashkali, and Egyptian (RAE) children in the Republic of Kosovo by assessing their decayed, missing, and filled teeth (DMFT) index and oral hygiene index (OHI) as well as salivary Streptococcus mutans levels. We included 202 RAE children aged 4–15 years. The DMFT and dmft indices were evaluated for permanent and deciduous teeth, respectively, in these children; furthermore, OHI (according to Greene and Vermillion) was assessed. Moreover, the presence of S. mutans was evaluated using the CRT® bacteria caries risk test (Ivoclar Vivadent, Liechtenstein) in 131 of the 202 children. The overall mean dmft index was 3.86 ± 3.38. The mean dmft index was higher in males than in females, without a significant difference (4.04 ± 3.38 vs 3.69 ± 3.38; Z = 0.70; P > 0.05 [P = 0.48]). The overall mean DMFT index was 2.07 ± 2.06. The mean DMFT index was higher in females than in males, without a significant difference (2.18 ± 2.25 vs 1.95 ± 1.83; Z = −0.38; P > 0.05 [P = 0.70]). The mean OHI (Greene and Vermillion) was 2.27 ± 0.74; the mean value of the plaque component of OHI was significantly higher in males than in females (2.38 ± 0.72 vs 2.18 ± 0.74; Z = 2.67; P < 0.01 [P = 0.008]). Furthermore, a significant difference was found in the number of S. mutans colonies in 1 mL saliva according to the sex of the children (Pearson χ2 = 8.24; P < 0.05 [P = 0.02]). The OHI index was high in all children, and S. mutans was present in the saliva of all children evaluated for it; this indicates poor oral hygiene status and high caries risk in RAE children. Clinical article (J Int Dent Med Res 2021; 14(1): 203-208)
Competitive Analysis Of Attacks On Social Media

- Today social media play very important role in the communication process. With the help of social media number of tasks is being accomplished. Normally social media is used to sell products, Career Consultancy, Communication, sharing resources etc. Along with the advantages there are number of disadvantages of the social media also. Social media uses number of mechanisms to create users. And increase their database. But they do not ensure validity of information provided bsy the user. This will cause the deception. Deception will cause number of problems. There are number of types of deceptions which exist over the internet. Deception model is prepared in order to analyze these problems. Some of the deceptions are difficult to detect than the others. Some of the challenges which social media must address are considered in this paper.
Ethanol Dehydration in a Fixed Bed Using Canola Meal

Ethanol dehydration was investigated in a fixed-bed packed with canola meal biosorbent. Breakthrough curves were used to investigate the effects of feed flow rate, column temperature, and ethanol feed concentration on water and ethanol adsorption capacity. Experimental results demonstrated that fuel grade ethanol (over 99 wt %) was achieved through water adsorption in the fixed bed with ethanol feed concentrations of 75 wt %, and 92 wt %, respectively, at a feed flow rate of 2.9 mL/min and column temperature of 85 °C. Both water and ethanol were adsorbed on canola meal. Water adsorption isotherm was described as type II isotherm. Peudo-equilibrium ethanol adsorption isotherm was also discussed in this work. Water selectivity over ethanol was higher at the breakthrough point defined at 99 wt % ethanol than that at water adsorption equilibrium. Separation factor can be optimized through controlling feed flow rate, ethanol feed concentration, and column temperature.
Advances in Human Norovirus Vaccine Research

Human norovirus (HuNoV) is the leading cause of acute gastroenteritis (AGE) worldwide, which is highly stable and contagious, with a few virus particles being sufficient to establish infection. Although the World Health Organization in 2016 stated that it should be an absolute priority to develop a HuNoV vaccine, unfortunately, there is currently no licensed HuNoV vaccine available. The major barrier to the development of an effective HuNoV vaccine is the lack of a robust and reproducible in vitro cultivation system. To develop a HuNoV vaccine, HuNoV immunogen alone or in combination with other viral immunogens have been designed to assess whether they can simultaneously induce protective immune responses against different viruses. Additionally, monovalent and multivalent vaccines from different HuNoV genotypes, including GI and GII HuNoV virus-like particles (VLPs), have been assessed in order to induce broad protection. Although there are several HuNoV vaccine candidates based on VLPs that are being tested in clinical trials, the challenges to develop effective HuNoV vaccines remain largely unresolved. In this review, we summarize the advances of the HuNoV cultivation system and HuNoV vaccine research and discuss current challenges and future perspectives in HuNoV vaccine development.
Recapture Lysosomal Enzyme Deficiency via Targeted Gene Disruption in the Human Near-Haploid Cell Line HAP1

Background: Advancement in genome engineering enables rapid and targeted disruption of any coding sequences to study gene functions or establish human disease models. We explored whether this approach can be used to study Gaucher disease, one of the most common types of lysosomal storage diseases (LSDs) in a near-haploid human cell line (HAP1). Results: CRISPR-Cas9 targeting to coding sequences of β-glucocerebrosidase (GBA), the causative gene of Gaucher disease, resulted in an insertional mutation and premature termination of GBA. We confirmed the GBA knockout at both the gene and enzyme levels by genotyping and GBA enzymatic assay. Characterization of the knockout line showed no significant changes in cell morphology and growth. Lysosomal staining revealed more granular lysosomes in the cytosol of the GBA-knockout line compared to its parental control. Flow cytometry analysis further confirmed that more lysosomes accumulated in the cytosol of the knockout line, recapturing the disease phenotype. Finally, we showed that this knockout cell line could be used to evaluate a replacement therapy by recombinant human GBA. Conclusions: Targeted gene disruption in human HAP1 cells enables rapid establishment of the Gaucher model to capture the key pathology and to test replacement therapy. We expect that this streamlined method can be used to generate human disease models of other LSDs, most of which are still lacking both appropriate human disease models and specific treatments to date.
The Neural Gut–Brain Axis of Pathological Protein Aggregation in Parkinson’s Disease and Its Counterpart in Peroral Prion Infections

A neuropathological hallmark of Parkinson’s disease (PD) is the cerebral deposition of abnormally aggregated α-synuclein (αSyn). PD-associated αSyn (αSynPD) aggregates are assumed to act, in a prion-like manner, as proteinaceous nuclei (“seeds”) capable of self-templated propagation. Braak and colleagues put forward the idea of a neural gut-brain axis mediating the centripetal spread of αSynPD pathology from the enteric nervous system (ENS) to the brain in PD. This has sparked great interest and initiated passionate discussions both in support of and opposing the suggested hypothesis. A precedent for the spread of protein seeds or seeding from the gastro-intestinal (GI) tract to the central nervous system (CNS) had been previously revealed for pathological prion protein in peroral prion infections. This article scrutinizes the similarities and dissimilarities between the pathophysiological spread of disease-associated protein aggregation along the neural gut–brain axis in peroral prion infections and PD. On this basis, evidence supporting the proposed neural gut–brain axis in PD is concluded to be not as robust as that established for peroral prion infections. New tools for the ultrasensitive detection of αSynPD-associated seeding activity in archived or fresh human tissue samples such as real-time quaking induced conversion (RT-QuIC) or protein misfolding cyclic amplification (PMCA) assays can possibly help to address this deficit in the future.
Proteomic Analysis Unveils Expressional Changes in Cytoskeleton- and Synaptic Plasticity-Associated Proteins in Rat Brain Six Months after Withdrawal from Morphine

Drug withdrawal is associated with abstinence symptoms including deficits in cognitive functions that may persist even after prolonged discontinuation of drug intake. Cognitive deficits are, at least partially, caused by alterations in synaptic plasticity but the precise molecular mechanisms have not yet been fully identified. In the present study, changes in proteomic and phosphoproteomic profiles of selected brain regions (cortex, hippocampus, striatum, and cerebellum) from rats abstaining for six months after cessation of chronic treatment with morphine were determined by label-free quantitative (LFQ) proteomic analysis. Interestingly, prolonged morphine withdrawal was found to be associated especially with alterations in protein phosphorylation and to a lesser extent in protein expression. Gene ontology (GO) term analysis revealed enrichment in biological processes related to synaptic plasticity, cytoskeleton organization, and GTPase activity. More specifically, significant changes were observed in proteins localized in synaptic vesicles (e.g., synapsin-1, SV2a, Rab3a), in the active zone of the presynaptic nerve terminal (e.g., Bassoon, Piccolo, Rims1), and in the postsynaptic density (e.g., cadherin 13, catenins, Arhgap35, Shank3, Arhgef7). Other differentially phosphorylated proteins were associated with microtubule dynamics (microtubule-associated proteins, Tppp, collapsin response mediator proteins) and the actin–spectrin network (e.g., spectrins, adducins, band 4.1-like protein 1). Taken together, a six-month morphine withdrawal was manifested by significant alterations in the phosphorylation of synaptic proteins. The altered phosphorylation patterns modulating the function of synaptic proteins may contribute to long-term neuroadaptations induced by drug use and withdrawal.
Preparation, Characterization, and Anti-Cancer Activity of Nanostructured Lipid Carriers Containing Imatinib

Breast cancer is the most widespread malignancy in women worldwide. Nanostructured lipid carriers (NLCs) have proven effective in the treatment of cancer. NLCs loaded with imatinib (IMA) (NANIMA) were prepared and evaluated for their in vitro efficacy in MCF-7 breast cancer cells. The hot homogenization method was used for the preparation of NANIMAs. An aqueous solution of surfactants (hot) was mixed with a molten mixture of stearic acid and sesame oil (hot) under homogenization. The prepared NANIMAs were characterized and evaluated for size, polydispersity index, zeta potential, encapsulation efficiency, release studies, stability studies, and MTT assay (cytotoxicity studies). The optimized NANIMAs revealed a particle size of 104.63 ± 9.55 d.nm, PdI of 0.227 ± 0.06, and EE of 99.79 ± 0.03. All of the NANIMAs revealed slow and sustained release behavior. The surfactants used in the preparation of the NANIMAs exhibited their effects on particle size, zeta potential, encapsulation efficiency, stability studies, and release studies. The cytotoxicity studies unveiled an 8.75 times increase in cytotoxicity for the optimized NANIMAs (IC50 = 6 µM) when compared to IMA alone (IC50 = 52.5 µM) on MCF-7 breast cancer cells. In the future, NLCs containing IMA will possibly be employed to cure breast cancer. A small amount of IMA loaded into the NLCs will be better than IMA alone for the treatment of breast cancer. Moreover, patients will likely exhibit less adverse effects than in the case of IMA alone. Consequently, NANIMAs could prove to be useful for effective breast cancer treatment.
Modelling Nuclear Morphology and Shape Transformation: A Review

As one of the most important cellular compartments, the nucleus contains genetic materials and separates them from the cytoplasm with the nuclear envelope (NE), a thin membrane that is susceptible to deformations caused by intracellular forces. Interestingly, accumulating evidence has also indicated that the morphology change of NE is tightly related to nuclear mechanotransduction and the pathogenesis of diseases such as cancer and Hutchinson–Gilford Progeria Syndrome. Theoretically, with the help of well-designed experiments, significant progress has been made in understanding the physical mechanisms behind nuclear shape transformation in different cellular processes as well as its biological implications. Here, we review different continuum-level (i.e., energy minimization, boundary integral and finite element-based) approaches that have been developed to predict the morphology and shape change of the cell nucleus. Essential gradients, relative advantages and limitations of each model will be discussed in detail, with the hope of sparking a greater research interest in this important topic in the future.