Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Pharmacokinetics and Vibrational Spectroscopy: MCR-ALS Hard-and-Soft Modelling of Drug Uptake In Vitro Using Tailored Kinetic Constraints

Raman microspectroscopy is a label-free technique which is very suited for the investigation of pharmacokinetics of cellular uptake, mechanisms of interaction, and efficacies of drugs in vitro. However, the complexity of the spectra makes the identification of spectral patterns associated with the drug and subsequent cellular responses difficult. Indeed, multivariate methods that relate spectral features to the inoculation time do not normally take into account the kinetics involved, and important theoretical information which could assist in the elucidation of the relevant spectral signatures is excluded. Here, we propose the integration of kinetic equations in the modelling of drug uptake and subsequent cellular responses using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and tailored kinetic constraints, based on a system of ordinary differential equations. Advantages of and challenges to the methodology were evaluated using simulated Raman spectral data sets and real Raman spectra acquired from A549 and Calu-1 human lung cells inoculated with doxorubicin, in vitro. The results suggest a dependency of the outcome on the system of equations used, and the importance of the temporal resolution of the data set to enable the use of complex equations. Nevertheless, the use of tailored kinetic constraints during MCR-ALS allowed a more comprehensive modelling of the system, enabling the elucidation of not only the time-dependent concentration profiles and spectral features of the drug binding and cellular responses, but also an accurate computation of the kinetic constants.
Effect of Critical Thinking-infused Paragraph Writing Instruction on University First-year Students’ Intellectual Traits

The purpose of this study was to investigate the effect of critical thinking-infused paragraph writing instruction on enhancing university first-year L2 students’ intellectual traits. The study involved two randomly selected first-year Social Science stream classes. The researchers employed the embedded design in the study because they mainly used quantitative data augmented by qualitative data. The researchers analyzed the questionnaire data and the interview data using the one-way between groups ANCOVA and thematic analysis method respectively. The finding of the questionnaire data analysis indicated that participants in the treatment group were better in their intellectual traits scores. The result of the interview data analysis also revealed that participants in the treatment group had a better interest in using elements of thought and intellectual traits after the intervention. Hence, integrating critical thinking instruction into APW lessons needs to be considered among all stakeholders to enhance university first-year EFL students’ intellectual traits. Keywords: Argumentative paragraph; critical thinking; instruction; intellectual traits; university first-year students.
Prolonged Exposure Therapy in the Treatment of Functional Neurological Disorder and Posttraumatic Stress Disorder

Functional neurological disorder (FND) symptoms resemble neurological symptoms but are unexplained by disease or injury to the body. Psychological trauma and posttraumatic stress disorder (PTSD) are prevalent among patients with FND. This is a case report of a 64-year-old female who began treatment 2 years after developing PTSD, generalised anxiety disorder, major depressive disorder and agoraphobia following a physical assault. She subsequently developed FND approximately 18 months later. Her FND symptomology was characterised by aphasia and paraplegia. These symptoms usually lasted for a period of hours to days and usually occurred on a weekly basis. She had not previously engaged in psychological therapy due to limited insight into the relationship between her psychological and physiological symptomology. She was treated with Prolonged Exposure for PTSD (PE), and by the last session, she no longer met the diagnostic criteria for either FND or PTSD disorder. PE successfully treated both PTSD and FND. Theoretical understandings of the mechanisms underpinning this improvement are discussed.
Automated defect detection and evaluation in X-ray CT images

X-ray computed tomography gains increasing popularity for industrial quality inspection tasks. It is a challenge however to use it in a fully automated assembly line, which requires robust algorithms for volumetric image analysis on noisy data. This work provides methods for the automatic detection of defects and their geometric description. The approach will be based on spatial statistical analysis of the voxel structure to determine the presence of a defect. Once detected, defect voxels will be clustered to determine shape and size of the associated material faults. The effectiveness of the method will be shown on pores and cracks in steel parts.
A Methodical Review on Carbon-Based Nanomaterials in Energy-Related Applications

Carbon nanomaterials are endowed with novel and magnificent optical, electrical, chemical, mechanical, and thermal properties, with a promising prospect in different advanced applications such as electronics, batteries, capacitors, wastewater treatment, membranes, heterogeneous catalysis, and medical sciences. However, macroscopic synthesis of carbon materials for industrial use has been a great challenge. Furthermore, structural nonhomogeneity and indefinite fabrication have hindered vigorous and consistent implementation of these materials in extensive technologies. Nevertheless, they offer exotic physics, and as a result, they have continued to attract great interest from the scientific community in an effort aimed to optimize their properties through innovative synthesis techniques, ensuring macroscopic production and discovering new applications. Hence, this study endeavours to provide a conscious review of these materials via the comprehensive discussion of the various allotropes of carbon (fullerenes, carbon nanotubes, and graphene), synthesis techniques (arc discharge, laser ablation, and chemical vapor deposition), and their applications in energy-related fields (batteries, capacitors, photocells, hydrogen storage, sensors, etc.) and their impending prospects.
Effects of social computing on students of agricultural sciences

With the advance of internet and web technologies, the increasing accessibility of computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general. As such, social computing, a new paradigm of computing and technology development, has become a central theme across a number of information and communication technology (ICT) fields. It has become a hot topic attracting broad interest from not only researchers but also technologists, software and online game vendors, web entrepreneurs, business strategists, political analysis, and digital government practitioners, to name a few. This research study explores the aftereffects or consequences of using the Internet sources of information as perceived by the students in their academic as well as social life. To answer the question, ‘What are the aftereffects of social computing on the students? ’a study was conducted with the specific objective to analyze the effects of social computing as perceived by the students of agricultural sciences. The study was conducted in Varanasi district of Uttar Pradesh. One university, two colleges and 210 agricultural students, pursuing UG, PG and PhD, doing social computing formed the sample of the study. It was found that the students made new contacts, learnt new study techniques and had more reliance on self-study through social computing without having less contact with teachers or offline friends and less time for physical exercises.
Assessment of pH-shift drug interactions of palbociclib by in vitro micro-dissolution in bio relevant media: An analytical QbD-driven RP-HPLC method optimization

In the present study, pH-dependent solubility and dissolution of Palbociclib (PB), a weakly basic cyclin-dependent kinase 4/6 inhibitor was investigated by application of analytical quality by design (QbD) approach to an reverse phase high performance liquid chromatography (RP-HPLC) method. An integrated analytical QbD approach for quantification of PB using RP-HPLC was designed by three-level three factorial, Box–Behnken design with numerical and graphical optimization. In vitro micro-dissolution, pH-shift experiments in bio-relevant media were carried out to predict PB’s pH-dependent drug-drug interaction (DDI) behavior. RP-HPLC method developed utilizing a Box– Behnken three-stage three factorial design was shown to be specific towards PB. The optimized method leads to effective and faster chromatographic separation of PB with lower retention time value together with a satisfactory peak symmetry and low peak tailing. Based on in vitro micro-dissolution studies, it was observed that PB has a typical weak base pH-dependent solubility characteristic and dissolution behavior with its release ranging from 98.96% to 102.66% in simulated gastric fluid pH 1.2 before dropping to 43.43% by addition of fasted state simulated intestinal fluid pH 6.5. Overall, our findings demonstrated that in vitro micro-dissolution approaches may accurately predict the intensity of pH-dependent DDI and that the use of these techniques prior to clinical DDIs studies might allow for adequate prediction of pH-dependent drug absorption in vivo .
Mechanical properties of nanoparticles in the drug delivery kinetics

Nanoparticle formulation is a recently developed drug delivery technology with enhanced targeting potential. Nanoparticles encapsulate the drug of choice and delivers it to the target via a targeting molecules (ex. antigen) located on the nanoparticle surface. Nanoparticles can even be targeted to deeply penetrating tissue and can be modeled to deliver drugs through the blood brain barrier. These advancements are providing better disease targeting such as to cancer and Alzheimer’s. Various polymers can be manufactured into nanoparticles. The polymers examined in this paper are polycaprolactone (PCL), poly(lactic acid) (PLA), poly(lactic-co-glycolic acid) (PLGA), and poly(glycolic acid) (PGA). The purpose of this study is to analyze the mechanical properties of these polymers to establish drug delivery trends and model pharmacokinetics and biotransport. We found that, in general, as the melting point, elastic modulus and tensile strength increases, the degradation rate also increases. PLA composite material may be an ideal polymer for drug delivery due to its good control of degradation.
Efficiency of Trichoderma asperellum as a promoter of vegetable growth and soybean productivity

Aiming to achieve greater economic returns by increasing the biomass and productivity of strategic crops, such as soybeans, this article aimed to evaluate the efficiency of TrichoPlus (Trichoderma asperellum), as a promoter of plant growth in soybeans. Four independent experiments were carried out in the municipalities of Porto Nacional and Gurupi, Tocantins, Brazil, in the 2017/2018 and 2018/2019 harvests. Four experiments were conducted, each with four treatments with different doses of TrichoPlus (2, 3, 4 and 5 g kg-1 per seeds), plus two control treatments, one positive control with commercial product based on Trichoderma asperellum and one absolute control (without inoculation). For the treatment with the TrichoPlus product, the powder formulation was used, with active ingredient based on Trichoderma asperellum 201, formulated with a minimum concentration of 2 x 108 CFU g-1. The positive results for the biomass characteristics, stand maintenance and productivity were evidenced in the different doses of TrichoPlus, with emphasis on doses close to 5 g kg-1 of seeds, with gains in productivity above 23% for the 2017/2018 harvest and above 9% for the 2018/2019 harvest in Gurupi. In Porto Nacional the productivity gain for the 2017/2018 harvest was over 21% and for the 2018/2019 harvest it was over 27% in relation to the absolute control. Considering the different doses of TrichoPlus used in the experiments, there were differences between the doses with the best results for treatments with doses of 4 and 5 g kg-1 of seeds.
An Optimized Hybrid Fuzzy Weighted k-Nearest Neighbor with the Presence of Data Imbalance

—We present an optimized hybrid fuzzy Weighted k-Nearest Neighbor classification model in the presence of imbalanced data. More attention is placed on data points in the boundary area between two classes. Finding greater results in the general classification of imbalanced data for both the minority and the majority classes. The fuzzy weighted approach assigns large weights to small classes and small weights to large classes. It improves the classification performance for the minority class. Experimental results show a higher average performance than other relevant algorithms, e.g., the variants of kNN with SMOTE such as Weighted kNN alone and Fuzzy kNN alone. The results also signify that the proposed approach makes the overall solution more robust. At the same time, the overall classification performance on the complete dataset is also increased, thereby improving the overall solution.
Bone Segmentation of the Human Body in Computerized Tomographies using Deep Learning

: The segmentation of human body organs in medical imaging is a widely used process to detect and diagnose diseases in medicine and to help students learn human anatomy in education. Despite its significance, segmentation is time consuming and costly because it requires experts in the field, time, and the requisite tools. Following the advances in artificial intelligence, deep learning networks were employed in this study to segment computerized tomography images of the full human body, made available by the Visible Human Project (VHP), which included among 19 classes (18 types of bones and background): cranium, mandible, clavicle, scapula, humerus, radius, ulna, hands, ribs, sternum, vertebrae, sacrum, hips, femur, patella, tibia, fibula, and feet. For the proposed methodology, a VHP male body tomographic base containing 1865 images in addition to the 20 IRCAD tomographic bases containing 2823 samples were used to train deep learning networks of various architectures. Segmentation was tested on the VHP female body base containing 1730 images. Our quantitative evaluation of the results with respect to the overall average Dice coefficient was 0.5673 among the selected network topologies. Subsequent statistical tests demonstrated the superiority of the U-Net network over the other architectures, with an average Dice of 0.6854.
Uncovering the Motives to Adopt Voluntary
Environmental Management: An Organizational
Legitimacy Perspective

In recent years, voluntary environmental management has gradually attracted the attention of entrepreneurs and researchers. As a typical form of environmental management, it clearly demonstrates the growth of corporate environmental awareness. Based on the perspective of organizational legitimacy, this study explored firms’ motivations to adopt voluntary environmental management with the data of 104 Chinese manufacturers. The results from fuzzy set qualitative comparative analysis (fsQCA) showed that both internal and external legitimacy could lead to the implementation of voluntary environmental management. However, the stimulating effect of internal legitimacy on voluntary environmental management was greater than external legitimacy. In addition, there was a certain complementarity between internal legitimacy and external legitimacy. This study provided theoretical guidance for cultivating corporate environmental awareness and promoting the popularization of voluntary environmental management.
Sonochemical Combined Synthesis of Nickel Ferrite and Cobalt Ferrite Magnetic Nanoparticles and Their Application in Glycan Analysis

The combination of the sonochemical activation of Ni(NO3)2 and Co(NO3)2 in the presence of Fe(NO3)3 and polyethylene glycol and consecutive heat treatment of the formed metal hydroxides offers a cheap and efficient method for the preparation of nickel ferrite and cobalt ferrite magnetic nanoparticles, which can be successfully applied in the selective capture of fluorescently derivatized N-glycans from human serum. XRD measurement revealed that, besides the ferrite phase, nickel and cobalt oxides also form during heat treatment. The amount of simple metal oxides can be well controlled by the temperature of the heat treatment, since increasing temperature yielded higher spinel content. For both nickel and cobalt, the best heat treatment temperature was found to be 673 K, where the samples contained 84.1% nickel ferrite, and in the case of cobalt, almost pure (99.6%) cobalt ferrite could be prepared. FT-IR and zeta potential measurements indicated the presence of surface OH groups, which aided in the dispersion of the particles in water and, in addition, can promote the adsorption of polar compounds. The practical applicability of the magnetic nanopowders was demonstrated in the purification of fluorescently derivatized N-glycans (from human serum). Cobalt ferrite was found to be the most effective. Owing to the easy preparation and the simplicity of the magnetic separation the pure cobalt ferrite, magnetic nanoparticles could be efficient tools for the selective enrichment of serum N-glycans in HPLC measurements.
Driving Change: Guidelines for Developing Graduate Programs to Meet High International Standards

This study has contributed to the changes in Chulalongkorn University’s graduate program management policy. It investigated the external and internal educational environments of Chulalongkorn University. The findings were used to help Chulalongkorn University’s Graduate School in formulating policies and guidelines to develop graduate programs that meet high international standards and balance revenue and student publications. Information was obtained from the official websites of the world’s 15 leading universities, and 8 graduates or current students were interviewed. Chulalongkorn University’s external educational environment was related to the following aspects: (1) program structures, (2) graduation requirements, and (3) program strategies. The internal factors hindering the development of Chulalongkorn University’s graduate programs were as follows: (1) seniority system and faculty possessing long-held stature, (2) relatively limited collaboration with other agencies, (3) failure of the content and nature of master’s programs to address business and industry requirements, and (4) emphasis on the scale of a project but failure to focus on the critical thinking process. Four recommendations for the executive board of the Graduate School were suggested. Received: 24 February 2022 / Accepted: 6 April 2022 / Published: 5 May 2022
Low-Dimensional Signal Models in Compressive Sensing

In today's world, we often face an explosion of data that can be difficult to handle. Signal models help make this data tractable, and thus play an important role in designing efficient algorithms for acquiring, storing, and analyzing signals. However, choosing the right model is critical. Poorly chosen models may fail to capture the underlying structure of signals, making it hard to achieve satisfactory results in signal processing tasks. Thus, the most accurate and concise signal models must be used. Many signals can be expressed as a linear combination of a few elements of some dictionary, and this is the motivation behind the emerging field of compressive sensing. Compressive sensing leverages this signal model to enable us to perform signal processing tasks without full knowledge of the data. However, this is only one possible model for signals, and many signals could in fact be more accurately and concisely described by other models. In particular, in this thesis, we will look at two such models, and show how these other two models can be used to allow signal reconstruction and analysis from partial knowledge of the data. First, we consider signals that belong to low-dimensional nonlinear manifolds, i.e. that can be represented as a continuous nonlinear function of few parameters. We show how to apply the kernel trick, popular in machine learning, to adapt compressive sensing to this type of sparsity. Our approach provides computationally-efficient, improved signal reconstruction from partial measurements when the signal is accurately described by such a manifold model. We then consider collections of signals that together have strong principal components, so that each individual signal may be modeled as a linear combination of these few shared principal components. We focus on the problem of finding the center and principal components of these high-dimensional signals using only their measurements. We show experimentally and theoretically that our approach will generally return the correct center and principal components for a large enough collection of signals. The recovered principal components also allow performance gains in other signal processing tasks.
The CANOVA Study Real-World Evidence of Biologic Treatments in Moderate-Severe Psoriasis in Italy: A Gender Perspective

Background: In psoriasis, several studies have indicated sex differences in clinical characteristics, type of treatment, and outcomes. A higher impact of psoriasis on quality of life (QoL) and a lower treatment satisfaction have been reported in women by different authors. Objectives: This article reports the results of a post hoc gender analysis of CANOVA study, aimed at assessing 16/24/52-week effectiveness of biologics in patients with moderate-severe plaque psoriasis. Materials and Methods: CANOVA was a real-world, multicenter, noninterventional, retro-prospective study conducted in 17 Italian hospital dermatology clinics. Results: Of the 669 eligible patients, 63.8% were men. Demographic and baseline characteristics and duration of disease were rather homogeneous between sexes. Slightly more women had been treated with biologics (50.4% vs. 46.5%) and had received ≥2 biologic treatment lines (17.2% vs. 12.4%) before study treatment. The most frequently used biologics were secukinumab, ustekinumab, adalimumab, and ixekizumab in both sexes. At 6 months, Psoriasis Area Severity Index (PASI) 75/90/100 responders were 90.8%/72.3%/45.3% of men and 89.2%/76.6%/48.2% of women. Sustained PASI responders were 79.5% of men and 75.9% of women. Treatment satisfaction was significantly lower in women at enrolment for all subscales, and was still lower at 6 months, no longer significantly. Gender distribution in Dermatology Life Quality Index total score classes showed a significantly greater effect of psoriasis on QoL in women, both at enrolment and at the 6-month follow-up. Conclusions: In conclusion, this gender analysis confirms in both genders the efficacy of biologics in psoriasis. However, women reported a greater impact of the disease on QoL and lower treatment satisfaction.