Miracle-dz/UniFAR-FTUs huggingface dataset:
Bioanalytical methods for the detection of duloxetine and thioctic acid in plasma using ultra performance liquid chromatography with tandem mass spectrometry (UPLC-MS/MS)

Duloxetine and thioctic acid (TA) are standard drugs for treating diabetic neuropathy, a primary complication associated with diabetes. In this study, ultra performance liquid chromatography coupled with tandem mass spectrometry methods was successfully developed and validated for quantifying duloxetine and TA in biological samples. The protein precipitation method was used to extract duloxetine, TA and their internal standards from beagle dog plasma. A Hypersil Gold C18 column (150 × 2.1 mm, 1.9 μm) was used for the experiment. Isocratic elution with 0.1% formic acid in acetonitrile (A) and 0.1% formic acid (B) was used for duloxetine, whereas a gradient elution with 0.03% acetic acid (A) and acetonitrile (B) was used for TA. The validated parameters included linearity, sensitivity, accuracy, precision, selectivity, matrix effect, stability, and recovery under different conditions. The linear ranges of the calibration curves for duloxetine and TA were 5–800 ng/mL and 5–1,000 ng/mL, respectively. An intra- and inter-run precision of ± 15% can be observed in all quality control samples. These methods were successfully used for pharmacokinetics (PKs) studies in beagle dogs to compare PK differences in a fixed-dose combination including duloxetine and TA and co-administration of the 2 drugs.
The inverse kinematics of a 7R 6-degree-of-freedom robot with non-spherical wrist

The 7R 6-degree-of-freedom robots with hollow non-spherical wrist have been proven more suitable to spray painting. However, the inverse kinematics of this robot is still imperfect due to the coupling between position and orientation of the end-effector. In this article, a reliable numerical iterative algorithm for the inverse kinematics of a 7R 6-degree-of-freedom robot is proposed. Based on the geometry of the robot, the inverse kinematics is converted into a one-dimensional iterative research problem. Since the Jacobian matrix is not utilized, the proposed algorithm possesses good convergence, even for singular configurations. Moreover, the multiple-solution problem in the inverse kinematics is also discussed. By introducing three robot configuration indicators which are prespecified by a user, the correct solution could be chosen from all the possible solutions. In order to verify the accuracy and efficiency of the proposed algorithm, several simulations are implemented on a practical 7R 6-degree-of-freedom painting robot. The result shows that the proposed algorithm is more advantageous for a continuous trajectory.
Hide Secret Script Encryption in Video Frames Based on Magic Square

T he objective of this article is to hide mystery encryption texts in vidiums using cipher method, that work is conceal big datum in frame of video. The fast of increased developments communication nets technologies. Thus, it can be suggested modern manner for conceal mystery texts that big size into frame of vidium efficiently, powerful and height capacity. This system depends on of three main steps: the first step: partition texts and ciphering in 2 caser methods, the second step: partition vidium to a collection of frames, and the final step: hide mystery texts in frame of vidium uses mystery key is magic square. The outcomes of system are excellent in conceal big text without sensible by attacker, through
New Approaches for Mining Data on Web For Recommendations In More Accurate and Diverse Manner.

: R ecommender systems are being extensively used in the present generation. Recommender systems are gradually increasingly harder to find the relevant contents of information in the vast abundant current age of information overload. Thus, recommender systems are needed to help individual users find the most relevant items or products or data sets from an abundant number of choices, collection. Through this gradually increase sales by exposing users to what they might like. E.g. In real time or real world applications consider a product say laptop, the laptop present in numerous patterns with different applications in number depending upon different user’s requirements. Thus providing a user or the customer with relevant information about the product as per their requirements with the help of recommender systems would ease the work of an user. Hence we can conclude saying that the volume of information available in the current age is huge to individual users (for e.g., e-commerce sites applications such as Amazon, Netflix) and hence focusing in developing some recommendation techniques within both industry and academia. Most, research to date is focusing on improving the recommendation accuracy i.e. the accuracy with which the recommender system predicts users ratings for items that are yet to be rated. The diversity of recommendation also plays an important role to be considered, it is important to explore the relationship between the accuracy and diversity and also the recommendation quality. Empirical analysis consistently shows the diversity gains of different recommendation techniques which is being used in several real world rating applications or datasets and uses different rating prediction algorithms. Individual users and online content providers will also benefit from the proposed approaches, where in which each user can find more relevant and personalized items or products from accurate and diverse recommendations provided by these recommender systems. These approaches, ranking techniques and algorithms could potentially lead to increased loyalty and sales in e-commerce application sites, thus benefiting the providers as well.
Protein Quality Control at the Sarcomere: Titin Protection and Turnover and Implications for Disease Development

Sarcomeres are mainly composed of filament and signaling proteins and are the smallest molecular units of muscle contraction and relaxation. The sarcomere protein titin serves as a molecular spring whose stiffness mediates myofilament extensibility in skeletal and cardiac muscle. Due to the enormous size of titin and its tight integration into the sarcomere, the incorporation and degradation of the titin filament is a highly complex task. The details of the molecular processes involved in titin turnover are not fully understood, but the involvement of different intracellular degradation mechanisms has recently been described. This review summarizes the current state of research with particular emphasis on the relationship between titin and protein quality control. We highlight the involvement of the proteasome, autophagy, heat shock proteins, and proteases in the protection and degradation of titin in heart and skeletal muscle. Because the fine-tuned balance of degradation and protein expression can be disrupted under pathological conditions, the review also provides an overview of previously known perturbations in protein quality control and discusses how these affect sarcomeric proteins, and titin in particular, in various disease states.
One for All, All for One: A Close Look at In-Resin Fluorescence Protocols for CLEM

Sample preparation is the novel bottleneck for high throughput correlative light and electron microscopy (CLEM). Protocols suitable for both imaging methods must therefore balance the requirements of each technique. For fluorescence light microscopy, a structure of interest can be targeted using: 1) staining, which is often structure or tissue specific rather than protein specific, 2) dye-coupled proteins or antibodies, or 3) genetically encoded fluorescent proteins. Each of these three methods has its own advantages. For ultrastructural investigation by electron microscopy (EM) resin embedding remains a significant sample preparation approach, as it stabilizes the sample such that it withstands the vacuum conditions of the EM, and enables long-term storage. Traditionally, samples are treated with heavy metal salts prior to resin embedding, in order to increase imaging contrast for EM. This is particularly important for volume EM (vEM) techniques. Yet, commonly used contrasting agents (e.g., osmium tetroxide, uranyl acetate) tend to impair fluorescence. The discovery that fluorescence can be preserved in resin-embedded specimens after mild heavy metal staining was a game changer for CLEM. These so-called in-resin fluorescence protocols present a significant leap forward for CLEM approaches towards high precision localization of a fluorescent signal in (volume) EM data. Integrated microscopy approaches, combining LM and EM detection into a single instrument certainly require such an “all in one” sample preparation. Preserving, or adding, dedicated fluorescence prior to resin embedding requires a compromise, which often comes at the expense of EM imaging contrast and membrane visibility. Especially vEM can be strongly hampered by a lack of heavy metal contrasting. This review critically reflects upon the fundamental aspects of resin embedding with regard to 1) specimen fixation and the physics and chemistry underlying the preservation of protein structure with respect to fluorescence and antigenicity, 2) optimization of EM contrast for transmission or scanning EM, and 3) the choice of embedding resin. On this basis, various existing workflows employing in-resin fluorescence are described, highlighting their common features, discussing advantages and disadvantages of the respective approach, and finally concluding with promising future developments for in-resin CLEM.
Abundance and Habitat Associations of Winter and Spring Birds on a Reclaimed Surface Mine (The Wilds) in Ohio, USA

Several studies have examined how grassland birds use reclaimed surface mines during the breeding season, but few studies have documented how these birds use these areas during the winter and early spring months. Using point counts, birds occupying a reclaimed surface-mine site in southeastern Ohio were surveyed from late December 2020 to April 2021. The primary objective was to document bird abundance and species richness across a temporal gradient spanning from late December 2020 through April 2021. Additionally, bird species composition was examined in habitat patches dominated by cool-season plants and in a single warm-season plot dominated by switchgrass (Panicum virgatum). A total of 1,452 bird sightings were made consisting of 40 species in 7 orders. Songbirds (order Passeriformes) comprised 60% of all sightings, and most of these (53%) were Red-winged Blackbirds (Agelaius phoeniceus), Eastern Meadowlarks (Sturnella magna), and Song Sparrows (Melospiza melodia). Thirty-nine species were observed on cool-season sites versus 13 species in the switchgrass plot. Species including returning Eastern Meadowlarks, Henslow's Sparrows (Centronyx henslowii), and Field Sparrows (Spizella pusilla) were common on the cool-season plots, but uncommon or absent in the switchgrass plot. Conversely, the switchgrass plot had more wintering American Tree Sparrows (Spizelloides arborea) and returning Swamp Sparrows (Melospiza georgiana). Return rates of long-distance migratory species fell within the time frames for these species as obtained from previous studies in this region. The diverse habitat structure of the cool-season portions of this study site attracted some winter species, including birds of prey, as well as several returning migratory species.
CNN-based automated classification of SPECT bone scan images

Functional medicine imaging has been successfully applied to capture functional changes in pathological tissues of the body in recent years. SPECT nuclear medicine functional imaging has the potential to acquire information about areas of concern (e.g., lesions and organs) in a non-invasive manner, enabling semi-automated or automated decision-making for the purposes of disease diagnosis, treatment, evaluation, and prediction. To reliably identify that whether or not at least one hotspot or lesion presents in a whole-body SPECT image, in this work, we develop a group of CNN-based classifiers. Specifically, we first propose a preprocessing method that transforms each original SPECT file into the required form by deep learning model, including normalization, 3-channel construction, rotation and scaling, size standardization, and size adapting. Second, six different classifiers are constructed by fine-tuning parameters of the standard VGG-16 model. Last, a group of real-world SPECT whole-body bone scan files were utilized to evaluate the developed classifiers. Experiment results shows that our classifiers are workable for the 2-class classification of SPECT images, achieving a best value of 0.7641, 0.6678, 1.000, and 0.6574 for defined evaluation metrics Acc, Pre, Rec, and AUC, respectively.
Pupils’ Adaptability at School, a Balance Between Demands and Resources?

School burnout is affecting more and more adolescents. In order to develop appropriate preventative measures, the problem more than ever requires a detailed understanding of the mechanisms operating on students’ capacity to adapt to circumstances rendered particularly difficult by the SARS-CoV-2 pandemic (COVID-19). In the absence of an explanatory framework in the field of school health, the JD-R model of work-related stress constitutes an original prism for reading the quality of pupils’ adaptation to their school environment. The aim of this research is on the one hand to observe the predictive links between the different variables operationalized in the school JD-R model and on the other, to test the adequacy of this model against the data in schools acutely affected by the COVID situation. A total of 470 middle school, high school and 1st year BTS students agreed to participate in the longitudinal study. They were administered six scales spread over three measurement periods. The results of the predictive path analyses reveal that the hypotheses assumed are to a good degree verified. However, even if the model as a whole does not fit the data well, in their essence, the results point to the importance of strengthening students’ own resources as well as those present within the school.
Dams and Ditches: Cultural landscapes of Colonial water management in the central highlands of Victoria

Gold miners captured and diverted large volumes of water to work their claims in the nineteenth century. This extensive manipulation of water sources, however, has not been widely researched or understood, despite the profound effect it had on the transformation of landscapes and waterways, its role in the commodification of water, and its influence on the development of colonial water law. The alluvial goldfields around Creswick in central Victoria, where extensive evidence of water management is preserved in the landscape today, provides an important case study of water use in alluvial mining. The Humbug Hill Sluicing Company was one of many groups in the district to engage in water engineering on a large scale, and the remains of their activities shed light on changes in the use and perception of water and the role of miners with Californian experience in developing water resources on the goldfields.
Failure Mechanism of Bolts and Countermeasures in Swelling Soft Rock Support

The effect of conventional bolt support is not ideal due to the large deformation character of soft rock. As an innovative bolt, constant resistance large deformation (CRLD) bolt has been successfully applied to swelling soft rock engineering, but the reinforcement mechanism is not yet clear. To investigate the interaction mechanism between bolt and surrounding rock, Nanshan Coal Mine of China was selected as the engineering background. The plastic zone of surrounding rock mass and the axial force of three bolts were obtained by theoretical analysis and FLAC3D numerical simulation. Failure processes of conventional pretension bolts in soft rock were reproduced, and the interaction between CRLD bolt and soft rock was investigated in comparison. The results indicate that: (1) The fracture zone of surrounding rock exceeds the anchorage range of the low pretension bolt, the bolt slides with surrounding rock and finally fails. (2) The fracture zone of surrounding rock does not exceed the anchorage range of the high pretension bolt. However, with the accumulation of deformation energy, stress concentration makes the bolt break. (3) CRLD bolt can effectively absorb the deformation energy released by soft rock and maintain constant support resistance. The conclusions obtained in this study provide significant references in the selection of bolts in soft rock engineering.
The relationship of the formulas for the number of connected vertices labeled graphs with order five and order six without loops

Given a graph with n points and m lines. If each vertex is labeled, then it can be constructed many graphs, connected, or disconnected graphs. A graph G is called a connected graph if there is at least one path that connects a pair of vertices in G. In addition, the graph formed may be simple or not simple. A simple graph is a graph that does not contain loops or parallel lines. A loop is a line that connects a point to itself, and a parallel line is two or more lines that connect the same pair of points. This paper will discuss the relationship between the formula patterns for calculating the number of connected graphs labeled with vertices of order five and six without loops.
Modelling Electric Vehicle Charge Demand: Implementation for the Greek Power System

The emerging popularity of Plug-in Electric Vehicles (PEVs) is creating new connections between the transportation and electric sectors, and PEV charging will bring new opportunities and challenges to a system of growing complexity. The electrification of transport will increase energy security, reduce carbon emissions, and improve local air quality. The actual expansion of electric vehicles (EVs) will depend on several factors: the evolution of autonomy, the acquisition price, the charging process and infrastructure, etc. This paper provides a guide for simulating the accumulative load profile for EV charging on a national level. The importance of all the parameters and variables involved (deterministic or stochastic) is investigated. Detailed tables and references concerning the distribution of values and the composition of the EV fleet are provided. A multivariate probabilistic model is developed considering the EV classes, weekly and seasonal driving patterns, charging strategies, battery capacities, consumption per EV, etc., leading to an accurate estimation of aggregated EV charging demand. Finally, a net-metering scheme is proposed, in which a photovoltaic (PV) system of a certain size will be able to provide the annual energy needs of the first 10,000 EVs in the Greek market.
A deep learning-based system for IoT intrusion detection

The Internet of Things devices has rapidly increased and been widely used in recent years. The era of the Internet of Everything is quietly coming, which puts forward higher requirements for the research on network traffic classification in the Internet of Things environment. However, traffic in the network layer and link layer is often ignored. This paper proposes a network traffic classification and feature extraction tool that covers multiple layers of network protocols to convert the original network traffic into digital features. With the features, two deep neural network models constructed were trained, and evaluation of their multiple indicators proved the effectiveness and superiority of our proposed intrusion detection system for IoT. It can achieve a classification accuracy of 98% and 97% of detection rate.
Extracellular Heat Shock Protein-90 (eHsp90): Everything You Need to Know

“Extracellular” Heat Shock Protein-90 (Hsp90) was initially reported in the 1970s but was not formally recognized until 2008 at the 4th International Conference on The Hsp90 Chaperone Machine (Monastery Seeon, Germany). Studies presented under the topic of “extracellular Hsp90 (eHsp90)” at the conference provided direct evidence for eHsp90’s involvement in cancer invasion and skin wound healing. Over the past 15 years, studies have focused on the secretion, action, biological function, therapeutic targeting, preclinical evaluations, and clinical utility of eHsp90 using wound healing, tissue fibrosis, and tumour models both in vitro and in vivo. eHsp90 has emerged as a critical stress-responding molecule targeting each of the pathophysiological conditions. Despite the studies, our current understanding of several fundamental questions remains little beyond speculation. Does eHsp90 indeed originate from purposeful live cell secretion or rather from accidental dead cell leakage? Why did evolution create an intracellular chaperone that also functions as a secreted factor with reported extracellular duties that might be (easily) fulfilled by conventional secreted molecules? Is eHsp90 a safer and more optimal drug target than intracellular Hsp90 chaperone? In this review, we summarize how much we have learned about eHsp90, provide our conceptual views of the findings, and make recommendations on the future studies of eHsp90 for clinical relevance.
Tools for Trustworthy Autonomy: Robust Predictions, Intuitive Control, and Optimized Interaction

Author(s): Driggs Campbell, Katherine Rose | Advisor(s): Bajcsy, Ruzena | Abstract: In the near future, robotics will impact nearly every aspect of life. Yet for technology to smoothly integrate into society, we need interactive systems to be well modeled and predictable; have robust decision making and control; and be trustworthy to improve cooperation and interaction. To achieve these goals, we propose taking a human-centered approach to ease the transition into human-dominated fields. In this work, our modeling methods and control schemes are validated through user studies in a realistic motion simulator and demonstrate improved interaction, predictability, and trustworthiness. Autonomous vehicles are a great motivating example, due to the wealth of interesting problems that arise with human-in-the-loop control and multi-agent interaction and cooperation. While autonomous vehicles will likely be publicly available soon, it can be assumed that the transition will not be instantaneous, suggesting that: (1) levels of autonomy will be introduced incrementally, and (2) autonomous vehicles will have to be capable of driving in a mixed environment, with both humans and autonomy. In both cases, the human drivers must be modeled in an accurate and precise manner that easily integrates into control frameworks. We present a data-driven approach to hybrid system tools, that approximates the forward reachable set of a coupled human-robot system. This empirical reachable set is an alternative look at a classic control theoretic safety metric and allows us to predict driver behavior over long time horizons in a robust, yet informative, manner. This method is compared to an extension of traditional reachability, in which the optimal disturbances are uncovered using empirical metrics with probabilistic guarantees. Applications of this work include the design of minimally invasive intervention schemes for semi-autonomous vehicles and of planning nuanced interactions between humans and autonomy in interactive maneuvers. We also consider concerns that arise with shared control. Given a fixed semi-autonomous framework, we model the communication between the human and automation using information theory metrics. By controlling information flow, we observe an information/performance trade-off that follows a strongly concave relationship. This is formulated as an optimization paradigm, giving a model-based approach to interface design for optimizing interaction.