Miracle-dz/UniFAR-FTUs huggingface dataset:
Fucoidan and Alginate from the Brown Algae Colpomenia sinuosa and Their Combination with Vitamin C Trigger Apoptosis in Colon Cancer

Brown seaweeds are producers of bioactive molecules which are known to inhibit oncogenic growth. Here, we investigated the antioxidant, cytotoxic, and apoptotic effects of two polysaccharides from the brown algae Colpomenia sinuosa, namely fucoidan and alginate, in a panel of cancer cell lines and evaluated their effects when combined with vitamin C. Fucoidan and alginate were isolated from brown algae and characterized by HPLC, FTIR, and NMR spectroscopy. The results indicated that highly sulfated fucoidans had higher antioxidant and cytotoxic effects than alginate. Human colon cancer cells were the most sensitive to the algal treatments, with fucoidan having an IC50 value (618.9 µg/mL−1) lower than that of alginate (690 µg/mL−1). The production of reactive oxygen species was increased upon treatment of HCT-116 cells with fucoidan and alginate, which suggest that these compounds may trigger cell death via oxidative damage. The combination of fucoidan with vitamin C showed enhanced effects compared to treatment with fucoidan alone, as evidenced by the significant inhibitory effects on HCT-116 colon cancer cell viability. The combination of the algal polysaccharides with vitamin C caused enhanced degeneration in the nuclei of cells, as evidenced by DAPI staining and increased the subG1 population, suggesting the induction of cell death. Together, these results suggest that fucoidan and alginate from the brown algae C. sinuosa are promising anticancer compounds, particularly when used in combination with vitamin C.
Spike sorting: new trends and challenges of the era of high-density probes

Recording from a large neuronal population of neurons is a crucial challenge to unravel how information is processed by the brain. In this review, we highlight the recent advances made in the field of ‘spike sorting’, which is arguably a very essential processing step to extract neuronal activity from extracellular recordings. More specifically, we target the challenges faced by newly manufactured high-density multi-electrode array devices (HD-MEA), e.g. Neuropixels probes. Among them, we cover in depth the prominent problem of drifts (movements of the neurons with respect to the recording devices) and the current solutions to circumscribe it. In addition, we also review recent contributions making use of deep learning approaches for spike sorting, highlighting their advantages and disadvantages. Next, we highlight efforts and advances in unifying, validating, and benchmarking spike sorting tools. Finally, we discuss the spike sorting field in terms of its open and unsolved challenges, specifically regarding scalability and reproducibility. We conclude by providing our personal view on the future of spike sorting, calling for a community-based development and validation of spike sorting algorithms and fully automated, cloud-based spike sorting solutions for the neuroscience community.
Role of the P2X7 Receptor in the Osteogenesis of Heterotopic Ossification of the Achilles Tendon

Background Heterotopic ossification (HO) refers to a painful and complex disease. HO occurs in the setting of persistent systemic inflammation and appears in flare-ups during inflammation, following injury. In the recent research, the P2X7 receptor (P2X7R) is tightly involved in the osteogenesis of periodontal ligament stem cells under the inflammatory conditions. The ionotropic P2X7 receptor (P2X7R) is an ATP-gated ion channel expressed in the majority of stem cells. However, the function of P2X7R in the pathological formation of HO is unclear. Here, this paper hypothesizes that in the model of Achilles tendon ectopic ossification, P2X7R is overexpressed in tendon-derived stem cells and promote osteogenesis of tendon-derived stem cells under inflammatory conditions. Methods The tenotomy puncture and burn injury-induced HO model was constructed. The qPCR and immunofluorescence were used to detect the expression of P2X7R at the site of injured Achilles tendon where HO occurs. Achilles tendon stem cells (SCs) from control group and experimental group sources were cultivated separately under inflammatory conditions. The cells from the two groups were cultured for osteogenic analysis. In addition, a specific antagonist of P2X7R, BBG was used to detect whether reversed the above process. At last, BBG was used to intervene in animal models of heterotopic ossification. Results Under inflammatory conditions, P2X7R expression of the Achilles tendon and osteogenic capability of SCs is higher in heterotopic ossification group (HOG) than in other two groups. The P2X7R expression was positive correlated with the capacity of osteogenesis of SCs. BBG can inhibit osteogenic differentiation and subsequent bone formation in the P2X7R overexpress of SCs. BBG impeded the heterotopic bone formation in animal model. Conclusions P2X7R is one of the crucial mediators in the formation of the HO, blocking which may represent a potential therapeutic target for HO.
The Relationship between Managerial Commitment towards Manufacturing Capabilities: Investigating in an Electrical and Electronic Industries in Malaysia

Generally, manufacturing capabilities are the important factor in running and developing business. Hence, this research is aimed to identifying the relationships of critical factors and make sure the companies can survive in the northern region of Malaysia esspecially in Electrical and Electronic area. Those identified factors are managerial commitment and manufacturing capabilities in term of agility. Through the survey, a total of 119 companies representing various industries in Northern Region of Malaysia had responded. The hypotheses involved were tested using correlation and regression techniques. The results of the study support all the hypotheses. The multiple regression analysis indicates that there are significant relationships among the factors on each criterion to manufacturing capabilities. It is believed that results of this study will be beneficial for shareholders and directors of electrical and electronic companies to apply these manufacturing capabilities.
Radical Scavenging Activity and Pharmacokinetic Properties of Coumarin–Hydroxybenzohydrazide Hybrids

Free radicals often interact with vital proteins, violating their structure and inhibiting their activity. In previous studies, synthesis, characterisation, and the antioxidative properties of the five different coumarin derivatives have been investigated. In the tests of potential toxicity, all compounds exhibited low toxicity with significant antioxidative potential at the same time. In this paper, the radical scavenging activity of the abovementioned coumarin derivatives towards ten different radical species was investigated. It was found that all investigated compounds show good radical scavenging ability, with results that are in correlation with the results published in the previous study. Three additional mechanisms of radical scavenging activity were investigated. It was found that all three mechanisms are thermodynamically plausible and in competition. Interestingly, it was found that products of the Double Hydrogen Atom Transfer (DHAT) mechanism, a biradical species in triplet spin state, are in some cases more stable than singlet spin state analogues. This unexpected trend can be explained by spin delocalisation over the hydrazide bridge and phenolic part of the molecule with a low probability of spin pairing. Besides radical-scavenging activity, the pharmacokinetic and drug-likeness of the coumarin hybrids were investigated. It was found that they exhibit good membrane and skin permeability and potential interactions with P-450 enzymes. Furthermore, it was found that investigated compounds satisfy all criteria of the drug-likeness tests, suggesting they possess a good preference for being used as potential drugs.
Smartic: A smart tool for Big Data analytics and IoT

The Internet of Things (IoT) is leading the physical and digital world of technology to converge. Real-time and massive scale connections produce a large amount of versatile data, where Big Data comes into the picture. Big Data refers to large, diverse sets of information with dimensions that go beyond the capabilities of widely used database management systems, or standard data processing software tools to manage within a given limit. Almost every big dataset is dirty and may contain missing data, mistyping, inaccuracies, and many more issues that impact Big Data analytics performances. One of the biggest challenges in Big Data analytics is to discover and repair dirty data; failure to do this can lead to inaccurate analytics results and unpredictable conclusions. Different imputation methods were employed in the experimentation with various missing value imputation techniques, and the performances of machine learning (ML) models were compared. A hybrid model that integrates ML and sample-based statistical techniques for missing value imputation is being proposed. Furthermore, the continuation involved the dataset with the best missing value imputation, chosen based on ML model performance for subsequent feature engineering and hyperparameter tuning. K-means clustering and principal component analysis were applied in our study. Accuracy, the evaluated outcome, improved dramatically and proved that the XGBoost model gives very high accuracy at around 0.125 root mean squared logarithmic error (RMSLE). To overcome overfitting, K-fold cross-validation was implemented.
Control of multiple DOFs robots using motor imagery EEG combined with Huffman coding

A brain-computer interface (BCI) can help establish a bridge between humans and external devices, like assisting stroke patients with several daily tasks. Motor Imagery (MI) electroencephalogram plays a critical role in BCI applications. To address the problem of lack of control instruction sets for brain-computer interfaces based on motor imagery, this paper proposes the use of a variable-length coding strategy to control the movement of a KUKA robotic arm with multiple degrees of freedom (DOF). The motor imagery EEG signals are classified into four categories by using common spatial pattern (CSP) for feature extraction and support vector machine (SVM) for classification. The classification results of the left-hand and right-hand motor imagery are encoded with binary Huffman Coding, and the codewords are mapped to 7 degrees of freedom of the robotic arm. The number of occurrences of each Huffman codeword is used to control the rotation angle of the corresponding degree of freedom. The classification results of tongue and feet motor imagery are used to enter and exit the selection of the degrees of freedom of the robotic arm, respectively. In the designed object grasping and placing scene, the instruction control using Huffman variable-length coding strategy reduces the time by 19 and 18 times, and the efficiency increases by 23.4% and 20.2%, respectively, compared with the use of binary fixed-length coding. Experimental results demonstrate that the use of Huffman coding enables the control of multiple degrees of freedom of a robotic arm using finite motor imagery classification categories. The method can improve the information transfer efficiency and expand the set of control instructions for brain-computer interfaces, which provides new ideas for the application scenarios of motor imagery-based brain-computer interfaces.
Surface Design of 3D-printed PEEK by Controlling Slicing Parameters

Additive manufacturing of high-performance materials such as polyether ether ketone (PEEK) is constantly gaining attention because of its applications in diverse fields. PEEK is a semicrystalline thermoplastic that exhibits superior mechanical properties, biocompatibility, and wear resistance that makes it suitable for biomedical and other industrial applications. Most of these applications call for surface designs where roughness and porosity are a major consideration. In this study, PEEK samples with different surface designs were prepared by modifying slicing parameters such as wall line, infill density, and raster angle. The samples were printed using fused deposition modelling and were characterized using a non-destructive method, Xray micro computed tomography (X-ray micro-CT). X-ray tomograms and void content analysis show that voids usually occur at the junction between the walls and the infill for all three designs. Reducing the infill travel path by adding inner walls resulted to higher defect volume ratio. Defect volume ratio increased from 0.06% to 0.36% after the addition of inner walls. Reduction in infill density further increased the defect volume ratio. These results show that different surface and internal designs can be prepared by modifying slicing parameters and its defects/void content can be readily evaluated by X-ray micro-CT. 
Module Integral Method for the Calculation of Frequency-Dependent Leakage Inductance of High-Frequency Transformers

The accurate calculation of frequency-dependent leakage inductance of high-frequency transformers is critical for the optimized design of power electronic transformers. The leakage inductance can be obtained through the magnetic energy, usually calculated by the phasor integral of the magnetic energy density in the literature. This article analyzes the difference between the module and the phasor integral based on frequency-domain energy and proposes to take the module integral of magnetic energy density as the periodic averaged magnetic energy. Furthermore, the closed-form formula of the leakage inductance is given accordingly. The modules under different frequencies correspond to the intrinsic constant magnetic densities. Thus, the proposed method has a better physical interpretation compared with the previous phasor integral method. The finite element method simulation and experimental results of five transformers are taken as benchmarks to compare the proposed method with the phasor integral method. The comparison shows that the proposed module integral method has satisfying effectiveness and accuracy in the whole frequency band for all the cases involving both noninterleaved and interleaved windings.
Molecular Genetic Understanding of Photoperiodic Regulation of Flowering Time in Arabidopsis and Soybean

The developmental switch from a vegetative phase to reproduction (flowering) is essential for reproduction success in flowering plants, and the timing of the floral transition is regulated by various environmental factors, among which seasonal day-length changes play a critical role to induce flowering at a season favorable for seed production. The photoperiod pathways are well known to regulate flowering time in diverse plants. Here, we summarize recent progresses on molecular mechanisms underlying the photoperiod control of flowering in the long-day plant Arabidopsis as well as the short-day plant soybean; furthermore, the conservation and diversification of photoperiodic regulation of flowering in these two species are discussed.
OClO as observed by TROPOMI: a comparison with meteorological parameters and polar stratospheric cloud observations

Abstract. Chlorine dioxide (OClO) is a by-product of the ozone-depleting halogen chemistry in the stratosphere. Although it is rapidly photolysed at low solar zenith angles (SZAs), it plays an important role as an indicator of the chlorine activation in polar regions during polar winter and spring at twilight conditions because of the nearly linear dependence of its formation on chlorine oxide (ClO). Here, we compare slant column densities (SCDs) of chlorine dioxide (OClO) retrieved by means of differential optical absorption spectroscopy (DOAS) from spectra measured by the TROPOspheric Monitoring Instrument (TROPOMI) with meteorological data for both Antarctic and Arctic regions for the first three winters in each of the hemispheres (November 2017–October 2020). TROPOMI, a UV–Vis–NIR–SWIR instrument on board of the Sentinel-5P satellite, monitors the Earth's atmosphere in a near-polar orbit at an unprecedented spatial resolution and signal-to-noise ratio and provides daily global coverage at the Equator and thus even more frequent observations at polar regions. The observed OClO SCDs are generally well correlated with the meteorological conditions in the polar winter stratosphere; for example, the chlorine activation signal appears as a sharp gradient in the time series of the OClO SCDs once the temperature drops to values well below the nitric acid trihydrate (NAT) existence temperature (TNAT). Also a relation of enhanced OClO values at lee sides of mountains can be observed at the beginning of the winters, indicating a possible effect of lee waves on chlorine activation. The dataset is also compared with CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) polar stratospheric cloud (PSC) observations. In general, OClO SCDs coincide well with CALIOP measurements for which PSCs are detected. Very high OClO levels are observed for the northern hemispheric winter 2019/20, with an extraordinarily long period with a stable polar vortex being even close to the values found for southern hemispheric winters. An extraordinary winter in the Southern Hemisphere was also observed in 2019, with a minor sudden stratospheric warming at the beginning of September. In this winter, similar OClO values were measured in comparison to the previous (usual) winter till that event but with a OClO deactivation that was 1–2 weeks earlier.
Analyzing communication dependencies in product development using the Design Structure Matrix

For the design and development of complex technical products, communication between staff who work across functional and organizational boundaries plays an important role. However, the quality of communication is hard to measure. One method to assess communication is the Communication Grid Method (CGM). The CGM is a maturity-based form of self-assessment to reflect on different factors influencing communication in product development. The factors belong to manifold areas influencing communication, such as the organizational structure, the company culture, teamwork, different aspects of information transfer and availability as well as several aspects of product representations. In a number of CGM-based studies, designers and managers from a number of industry sectors gave insight into their communication situation by scoring the current state of 24 factors influencing communication. To do so, the participants chose between four maturity levels, adapted from the learning types by Argyris and Schon, for each factor influencing communication. This data were used in the presented study to explore interrelations between the different factors describing communication. Within the correlations, a number of structural patterns were ascertained.
Modeling mRNA-based vaccine YFV.E1988 against yellow fever virus E-protein using immuno-informatics and reverse vaccinology approach

Abstract To surmount constraints of live-attenuated vaccines we have in silico designed mRNA vaccine using envelope protein as a target antigen. From the alignment of 216 envelope proteins, a consensus sequence was obtained which was used for codon optimization. The secondary structure was predicted using Mfold and RNAfold tool. IEDB server was used to predict T-cell and B-cell epitopes, epitope conservancy, immunogenicity, and population coverage. Antigenicity, allergenicity, and toxicity were predicted using Vaxijen, AllerTOP, and ToxinPred tools, respectively. Interactions between MHC and identified epitopes were confirmed by docking and molecular dynamics simulation. In silico immune simulation was done using the C-ImmSim server. Vaccine peptide 3D structure was predicted and validated based on the Ramachandran plot. Finally, we designed the vaccine construct for simulating restriction cloning using the SnapGene tool. Our optimization of consensus E protein is highly immunogenic, conserved, has immune-dominance characteristics, and suggests high translational efficiency in the host cell. We validated the presence of T and B cell epitopes and interestingly we found one CD4+ and four CD8+ T-cell epitopes that satisfied all the criteria of an effective vaccine candidate. We found high-affinity interactions between epitope and HLA alleles that can stimulate the T-cell response. The immune simulation verified the immune cell response to eliminate the antigen. To ensure effective expression of the vaccine, a circular plasmid has been designed using in silico cloning approach for the in vitro transcription process. Obtained results suggest that the vaccine YFV.E1988 will elicit specific immune responses against YFV and it is a potential model ready for laboratory testing. Highlights The envelope (E) protein was found to be highly conserved and it has the potential to protect individuals against YFV infection. YFV.E1988 vaccine has been capable to stimulate both the CD8+ and CD4+ T cell, solving the major limitations of the current live-attenuated vaccines against YFV. Presence of T- and B-cell epitopes across the antigen have been validated using several computational tools. Molecular docking ensured the epitope-allele binding and protein-TLR/MR interaction. The vaccine was found to be immune-stimulatory, safe, and stable. The codons were optimized for efficient translation and increased stability into the human host. The UTR regions and poly (A) tail used for the development of YFV.E1988 showed immune stimulatory potential in several experiments. Communicated by Ramaswamy H. Sarma
AUTOMATIC PARENTAL GUIDE SCENE CLASSIFICATION MENGGUNAKAN METODE DEEP CONVOLUTIONAL NEURAL NETWORK DAN LSTM

Menonton film merupakan salah satu hobi yang paling digemari oleh berbagai kalangan. Seiring dengan semakin bertambahnya film yang beredar di pasaran, semakin banyak pula konten tidak pantas pada film-film tersebutu. Oleh karena itu, dibutuhkan sebuah metode untuk mengklasifikasikan film agar konten yang ditonton sesuai dengan usia penonton. Konten film yang kurang cocok untuk pengguna di bawah umur yang akan diklasifikasikan pada penelitian ini antara lain: kekerasan, pronografi, kata-kata kasar, minuman keras, penggunaan obat-obatan terlarang, merokok, adegan mengerikan (horror) dan intens. Metode klasifikasi yang digunakan berupa modifikasi dari convolutional neural network dan LSTM. Gabungan kedua metode ini dapat mengakomodasi data training dalam jumlah yang kecil, serta dapat melakukan multi klasifikasi berdasarkan video, audio, dan subtitle film. Penggunaan multi klasifikasi ini dikarenakan sebuah film selalu memiliki lebih dari satu klasifikasi. Dalam proses training dan testing pada penelitian ini digunakan sebanyak 1000 data untuk klasifikasi video, 600 data klasifikasi audio, dan 400 data klasifikasi subtitle yang didapatkan dari internet. Dari hasil percobaan dihasilkan tingkat akurasi yang diukur dengan menggunakan F1-Score sebesar 0.922 untuk klasifikasi video, 0.741 untuk klasifikasi audio, dan 0.844 untuk klasifikasi subtitle dengan rata-rata akurasi sebesar 0.835. Pada penelitian berikutnya akan dicoba dengan menggunakan metode Deep Convolutional Neural Network yang lain serta dengan memperbanyak jumlah dan variasi dari data testing.