Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards real time release testing of Shuxuening injection based on near infrared spectroscopy and accuracy profile

Real time release testing (RTRT) has been applied in the pharmaceutical process to ensure the high quality of finished products. Near infrared (NIR) spectroscopy is one of the primary analytical methods to implement RTRT. In this study, an NIR quantitative method was developed to determine the content of total flavonol glycosides in Shuxuening injection and validated by the accuracy profile approach. Combining the NIR validation with quality specification limits, a reliable RTRT method was constructed. Shuxuening injection samples of different concentrations were prepared and characterized by NIR spectroscopy. A first-order Savitzky–Golay derivative was used to pretreat the NIR spectra, and the competitive adaptive reweighted sampling method was used to select the feature variables. Partial least squares (PLS) regression was used to build the NIR quantitative model. The trueness, precision, and accuracy of the developed NIR models were validated by accuracy profile, and the measurement uncertainty was also estimated. Finally, the unreliability graph as a decision tool was established to avoid risk, enabling correct decision making to release of Shuxuening injection. The root mean square error of calibration, root mean square error of cross validation, root mean square error of prediction, and the ratio of prediction to deviation of the PLS model were 19.6 μg·mL−1, 20.9 μg·mL−1, 29.9 μg·mL−1, and 12.2, respectively, indicating the NIR quantitative model had good predictive performance. The validation results prove that the precision, trueness, and accuracy of the NIR quantitative model were within the acceptable limits. Based on the unreliability graph, the decision to release Shuxuening injection was satisfied, if the prediction of total flavonol glycosides fell into the range from 783 μg·mL−1 to 900 μg·mL−1. The RTRT method for Shuxuening injection based on NIR spectroscopy and accuracy profile can improve the efficiency and accuracy of quality control.
Synthesis of Au/ZnO nanocomposite and Au:ZnO core:shell via laser ablation for of photo-catalytic applications

ABSTRACT The pulsed laser ablation technique is a safe and eco-friendly method, this technique was used to prepare gold (Au) and zinc oxide (ZnO) in two forms, both as core:shell nanoparticles (CS NPs) and a nanocomposite (NC). The absorption behaviour for Au/ZnO NCs and Au:ZnO CS NPs was investigated the UV-visible of Au NPs peak at 525 nm whiles the peak of the ZnO NPs at 375 nm. Transmission electron microscope (TEM) images show the average size of Au NPs is 30–45 nm adhering to ZnO NPs with an average size of 55–60 nm. In Au/ZnO CSNPs, Au acts as a core while the ZnO nanoparticles produced a shell; in spherical gold nanoparticles, the average shell thickness of ZnO is 10–15 nm. As synthesised, the Au:ZnO CSNPs are endowed with high photocatalytic activity even after multiple cycles of photo-catalytic reactions when compared with Au/ZnO NCs.
The Use of Trichostatin A during Pluripotent Stem Cell Generation Does Not Affect MHC Expression Level

Pluripotent stem cells (PSCs) are considered as a potent tool for use in regenerative medicine. Highly efficient generation of PSCs through chromatin modulators such as trichostatin A (TSA) might change their MHC molecule expression profile. The efficiency of PSC generation and their immunogenicity is major obstacles for clinical use. Hence, we aim to investigate whether the use of TSA during PSC generation affects MHC expression level. Three PSC lines were generated by iPSCs, NT-ESCs, and IVF-ESCs' reprogramming methods from B6D2F1 mouse embryonic fibroblast cells. Established PSC lines were characterized by alkaline phosphatase assay (ALP) and immunocytochemistry. Their chromosome fidelity was checked by karyotyping. The expression level of pluripotent genes (oct4, nanog, sox2, klf4), HDACs (hdac1, hdac2, and hdac3), and immune-related genes (including Qa-1, Qa-2, H2kb, H2kd, H2db, H2db, CIITA, H2-IE-βb, H2-IE-βd) in iPSC and ESC lines were assessed by real-time PCR analysis. The presence of MHC molecules on the surface of pluripotent stem cells was also checked by flow cytometry technique. Significant increase of pluripotency markers, oct4, nanog, sox2, and klf4, was observed in 100 nM TSA-treated samples. 100 nM TSA induced significant upregulation of H2db in generated iPSCs. H2-IE-βd was remarkably downregulated in 50 and 100 nM TSA-treated iPSC lines. The expression level of other immune-related genes was not greatly affected by TSA in iPSC and NT-ESC lines. It is concluded that the use of short-term and low concentration of TSA during reprogramming in PSC generation procedure significantly increases PSC generation efficiency, but do not affect the MHC expression in established cell lines, which is in the benefit of cell transplantation in regenerative medicine.
Methods of Titanium Carbide-Based Coatings Laser Synthesis

The work is devoted to the problem of synthesis of coatings based on titanium carbide. Methods for obtaining coatings using femtosecond laser radiation and a CO2 laser are considered. The differences in mechanisms of titanium carbide synthesis, as well as the areas of application of technologies, are described. The Raman spectra of the obtained coatings are presented and analyzed. It can be judged that as a result of laser treatment, titanium carbide was formed.
FAM: Featuring Android Malware for Deep Learning-Based Familial Analysis

To handle relentlessly emerging Android malware, deep learning has been widely adopted in the research community. Prior work proposed deep learning-based approaches that use different features of malware, and reported a high accuracy in malware detection, i.e., classifying malware from benign applications. However, familial analysis of real-world Android malware has not been extensively studied yet. Familial analysis refers to the process of classifying a given malware into a family (or a set of families), which can greatly accelerate malware analysis as the analysis gives their fine-grained behavioral characteristics. In this work, we shed light on deep learning-based familial analysis by studying different features of Android malware and how effectively they can represent their (malicious) behaviors. We focus on string features of Android malware, namely the Abstract Syntax Trees (AST) of all functions extracted from each malware, which faithfully represent all string features of Android malware. We thoroughly study how different string features, such as how security-sensitive APIs are used in malware, affect the performance of our deep learning-based familial analysis model. A convolutional neural network was trained and tested in various configurations on 28,179 real-world malware dataset appeared in the wild from 2018 to 2020, where each malware has one or more labels assigned based on their behaviors. Our evaluation reveals how different features contribute to the performance of familial analysis. Notably, with all features combined, we were able to produce up to an accuracy of 98% and a micro F1-score of 0.82, a result on par with the state-of-the-art.
ENZYMATIC CHANGES IN SERUM AND BRAIN OF JAPANESE QUAIL EXPOSED TO CHLORPYRIFOS

The activity of cholinesterase (ChE) in serum, acetylcholinesterase (AChE) in brain,as well as acid phosphatase (ACP), alkaline phosphatase (ALP), aspartate amino transferase (ASAT), and alanin amino transferase (ALAT) in serum and brain of the quail after oral exposure to various single doses of chlorpyrifos (0.1, 0.25, 0.5 and 1.0 LD50) for 24 hours or daily low dose (0.1 LD50) for three months was studied. Results revealed that exposure to the oral single tested doses caused a significant inhibition of both ChE in serum and AChE in brain after 24 hours. The daily exposure to the low dose (0.1 LD50) for three months resulted also in an inhibition of the activity in both serum and brain. The percentage of the inhibition of ChE in the blood serum following the acute poisoning reached about 90% of the control value, while that of AChE in the brain reached about 46%. ACP activities in both serum and brain were also inhibited. Significant decrease in ALP activity was observed in serum while it increased in the brain after acute and subchronic toxicity. In general, ASAT activity was inhibited significantly in the brain. On the other hand, the activity of ASAT in serum after 24 hours of exposure is generally increased while it decreased significantly after daily exposure for one, two, and three months. In contrast, the activity of ALAT increased significantly in both serum and brain. These changes in enzymes activity may reflect liver and / or kidney dysfunction and tissues damage.
Risk Factors of Long-Term Care Insurance Certification in Japan: A Scoping Review

This study aimed to review evidence on future long-term care associated with pre-existing factors among community-dwelling Japanese older adults. We systematically searched cohort and nested case–control studies published between 2000 and 2019 that assessed long-term care certification using the PubMed, CINAHL, and EMBASE databases. The relationship between long-term care insurance information and risk factors was investigated. The protocol was registered with the Open Science Framework. We extracted 91 studies for synthesis, including 84 prospective cohort studies, 1 retrospective cohort study, and 6 nested case–control studies. Certification for long-term care was classified into two endpoints: onset of functional disability and dementia. There were 72 studies that used long-term care certification as a proxy for functional disability, and 22 used long-term care information to indicate the onset of dementia. Common risk factors related to functional disability were physical function, frailty, and oral condition. Motor function and nutritional status were common risk factors for dementia. We found consistent associations between premorbid risk factors and functional disability and dementia. The accumulation of evidence on the incidence of long-term care and associated factors can aid the development of preventive measures. Future studies should aim to integrate this evidence.
Research on Spectrum Aggregation in Relay Cooperative Network based on Discrete Bands

With the rapid development of mobile communications,traditional continuous spectrum allocation cannot meet users' bandwidth needs.Through aggregating discrete spectrum pieces,spectrum aggregation,which is recommended in LTE-A,can satisfy users' bandwidth demands,meanwhile dynamically improve spectrum efficiency.In two-hop relay cooperative system,considering both spectrum efficiency and cooperative rate,a spectrum aggregation algorithm in relay cooperative network based on discrete bands has been put forward.Simulation results show that compared with continuous spectrum allocation strategy,this algorithm can enhance system throughput,improve spectrum efficiency,and consequently satisfies increasing bandwidth needs.
‘Superbugs’: raising public awareness of antimicrobial resistance through a pop-up science shop

‘Superbugs: A Pop-up Science Shop’ was a public engagement event in the school summer holidays of 2019, organised by members of Cardiff University’s School of Medicine. We transformed an empty retail unit in the centre of Wales’s largest shopping centre into an interactive and immersive microbiology experience. We facilitated two-way dialogue to impart positive impact on the awareness of antibiotic resistance, while concurrently evaluating the efficacy of an engagement strategy focused on the utilisation of public spaces to attract public demographics diverse to those who would normally engage with conventional science, technology, engineering and mathematics (STEM) outreach. Over the course of 14 days, we welcomed 6,566 visitors, with 67 per cent attending as part of the natural footfall of the shopping centre. We created 1,626 young Antibiotic Resistance Champions, located in over two hundred schools, across many of the most deprived areas in Wales. We imparted a positive impact to our stakeholders, with a significant increase in the knowledge and understanding of the subject of antimicrobial resistance (AMR); 91.7 per cent indicated that they had a better understanding after the event. In this article, we discuss the evolution of ‘Superbugs’ from concept, planning and design, to the logistics of delivering an engagement event of this scale. We focus in particular on the learning outcomes of the project, and on how this will shape the future of our ‘Superbugs’ project, and engagement events beyond.
Polymeric Coating of Silica Microspheres for Biological Applications: Suppression of Non-Specific Binding and Functionalization with Biomolecules

The use of micro- and nanoparticles in biological applications has dramatically grown during the last few decades due to the ease of protocols development and compatibility with microfluidics devices. Particles can be composed by different materials, i.e., polymers, inorganic dielectrics, and metals. Among them, silica is a suitable material for the development of biosensing applications. Depending on their final application, the surface properties of particles, including silica, are tailored by means of chemical modification or polymeric coating. The latter strategy represents a powerful tool to create a hydrophilic environment that enables the functionalization of particles with biomolecules and the further interaction with analytes. Here, the use of MCP-6, a dimethylacrylamide (DMA)-based ter-copolymer, to coat silica microspheres is presented. MCP-6 offers unprecedented ease of coating, imparting silica particles a hydrophilic coating with antifouling properties that is able to provide high-density immobilization of biological probes.
Fully integrated switched‐inductor switched‐capacitor dual‐path DC–DC converter

A fully integrated switched‐inductor switched‐capacitor (SISC) DC–DC converter is proposed. This converter is designed in such a way that the input voltage can be twice the process allowable voltage without damaging the on‐chip transistors. To mitigate large series resistance of on‐chip inductors as one of the main challenges in the switched‐inductor power supply on chip, two solutions are proposed. By using analytical model of an on‐chip inductor, optimal physical dimensions are designed to achieve the desired inductance with minimum series resistance in a small area. Along with the optimization, the dual‐path structure of the proposed converter reduces series resistance losses of the on‐chip inductor and increases the effective quality factor up to 7 times in duty cycle of 0.8. The proposed converter is implemented in 0.18 μm standard CMOS process. The circuit converts input voltage of 3.6–0.9 V at the load current of 125 mA with the efficiency of 72.8%. Efficiency enhancement factor reaches 72% at 600 mV output voltage. The achieved current density of the proposed converter is 333 mA/mm2. By calculating small signal model of the proposed converter and designing a suitable feedback loop, an appropriate transient behavior was proved
Effects of agro-sensor time series approximation on plant stress detection: an experimental study

This paper describes an experimental study on the effect of reducing time series collected from IoT electrical agro-sensors through approximation techniques, in time series classification tasks, for plant stress detection. From large sets of real data, stored in time series format, experiments were carried out to analyze: (i) performance of mathematical methods to reduce the dimensionality of time series - PAA, SAX and MCB; and (ii) Whether the application of these techniques influences the performance of time series classification models for plant stress detection, using machine learning algorithms KNN, SVM and ANN. Both in terms of data volume reduction and time series classification, the experiment showed significant improvements in terms of compression rate and accuracy, with the best result found in the use of PAA+SAX techniques for reduction and SVM for classification.
Welded steel beam design using particle swarm analysis

The paper looks at the design of welded steel beams for a typical structure which contains both primary and secondary beams. The underlying theory is fully explained. Also the stress limits and other constraints are defined using practical limits which are defined in Eurocodes. The derived method is only approximate and suitable for early design. In order to give a means of assessing the suitability of PSO for solving the chosen problem, some exact solu-tions are first calculated using exhaustive search. These are then used to show that a simple PSO approach gives unsat-isfactory results and that it is necessary instead to use a layered algorithm.
Explorando técnicas de aprendizado de máquina para aprimoramento da previsão de geadas no sul e sudeste do Brasil

A previsão operacional de geadas do CPTEC/INPE é baseada em um índice estabelecido a partir da variação numérica de variáveis meteorológicas de um modelo de previsão de tempo, que é comparado aos valores previstos operacionalmente. Um constante aprimoramento dessas previsões visando mitigar danos econômicos e sociais decorrentes de geadas se faz necessária. O propósito deste trabalho é identificar uma metodologia baseada em aprendizado de máquina que supere o desempenho do sistema de previsão atual. Para tanto, esta pesquisa exploratória aplicou diferentes combinações entre três algoritmos, variando suas parametrizações e o conjunto de variáveis preditoras de dados de estações meteorológicas do sul e sudeste do Brasil. Testes preliminares em previsões de 24h mostram que o desempenho supera o método atual, sinalizando que o processo de busca da metodologia mais adequada deve ser continuada.
Noise-Shaping Cyclic ADC Architecture

This paper presents an ADC architecture comprising a pipelined cyclic ADC and continuous-time delta-sigma ADC; it provides high resolution at medium speed, with small power requirements. It is also reconfigurable for different combinations of speed, precision, and power consumption. The cyclic ADC produces a residue after the final cycle, and the following delta-sigma ADC converts it to a digital value (the residue is then noise-shaped). The ADC output combines the digital outputs of the cyclic ADC and the delta-sigma ADC so as to achieve high resolution. The delta-sigma ADC can be implemented simply with continuous-time analog circuitry. We describe the overall ADC architecture and operation, show simulation results, as well as features such as its potential for reconfiguration and one of ADC architecture candidates with good tradeoff for high resolution, medium speed and small power.
Development of methylation-based biomarkers for breast cancer detection by model training and validation in synthetic cell-free DNA

Circulating tumour-derived DNA (ctDNA) carries the genetic and epigenetic characteristics of the tumour from which it is derived and can give information about the biology and tissue origins of the underlying tumour. DNA methylation is an epigenetic mark that is specific to individual tissues and, as methylation profiles are disrupted in tumours, they can indicate the tissue of origin and cancer type of ctDNA. We have developed a set of methylation biomarkers for detecting breast cancer in plasma cell-free DNA (cfDNA). First, we mined publicly available methylation datasets to create synthetic methylation profiles that were modelled to reflect cfDNA from healthy subjects and cancer patients. These profiles were restricted to the most differentially methylated CpGs between breast tumour samples and haematopoietic cells. Regularised logistic regression models were trained using 10-fold cross-validation on synthetic cfDNA datasets with distinct fractions of breast tumour DNA spiked in silico into healthy cfDNA with the addition of 10% of a mix of different tissues. Initial validation with synthetic cfDNA permitted detection of breast cancer-derived DNA with as little as 0.25% tumour DNA spiked in silico into healthy subject cfDNA with an area under ROC curve (AUC) of 0.63. Performances of classifiers increased with increased fractions of spike-in tumour DNA (AUCs 0.77 and 0.93 at tumour DNA fractions 0.5% and 1% respectively). We then combined the most discriminative CpG markers from our models with methylation markers of breast cancer that had already been published to obtain a single marker set. In vitro testing of MCF-7 breast cancer cell line DNA spiked into leukocyte DNA showed highly significant correlation for individual markers between laboratory-measured and published methylation data for MCF-7 and leukocytes (R > 0.89, P < 2.2 × 10−16). These preliminary data indicate promising results for detection of breast cancer cell line DNA using this methylation marker set, which now require testing in cfDNA from breast cancer patients and healthy controls.
TECHNOLOGICAL ACQUISITION AND ABSORPTION VIA MULTINATIONAL COMPANIES: THE MALAYSIAN EXPERIENCE

~ritten infonnation but al so person­ embodied skill s, know-how arid the adaptation of manufacrufing processes to lo cal operating conditions and mark ets. Therefore, the acquisition of technology by domesti c industrie s can manifest it self in many ways. The more important ones being: I. exchange of infonnation and personnel through technical cooperation program, 2. direct employment of foreign experts, 3. imports of machinery and equipment and related literature, 4. contractual purchase of a given technology or know-how through li cences and agreements for th e right or access or use of the particular technology, that is protected by trade-marks, patents, licence s, S. internali zed technology transfer from parent companies to subsidiaries overseas as may be embodied in direct foreign investme nt , and 6. books, journals, and other forms of published information, either in the public domain (for example library) or through private infonnation services companies. In th eir quest for technology to establi sh new industrie s or to improve existin g production processes, domes ti c industries have resorted to most of the above methods, either sin gly or in combination. However, there is virtually no data captured in any systematic manner on most of the above forms of technology transfer, not only in Malaysia, but also in most developing countries in general. Technological development within an economy with a small and
Process mining of train describer event data and automatic conflict identification

Data records from train describer systems are a valuable source of information for analysing railway operations performance and assessing railway timetable quality. This paper presents a process mining tool based on event data records from the Dutch train describer system TROTS, including algorithms developed for the automatic identification of route conflicts with conflicting trains, arrival and departure times/delays at stations, and train paths on track section and blocking time level. Visualisations of the time-distance diagrams and blocking time diagrams support the analysis of incidents, track obstructions, disruptions, and structural errors in the timetable design.