Miracle-dz/UniFAR-FTUs huggingface dataset:
Silicon Enhances Plant Vegetative Growth and Soil Water Retention of Soybean (Glycine max) Plants under Water-Limiting Conditions

Silicon has been implicated as a factor affecting the degree of resistance to abiotic stresses in several plant species. However, the role of silicon in soybean (Glycine max) under water-limiting conditions is not yet fully understood. This study was conducted to evaluate the effects of silicon application on the vegetative growth of two soybean cultivars (Asgrow 5332 and Progeny 5333) grown under water-limiting conditions. Silicon was provided by adding silicate to the soil. Water-limiting treatments were imposed on plants at two vegetative growth stages for 20 days by irrigating with a reduced amount of water (66% or 33% of the required water). Silicate application enhanced plant height, leaf area, and total dry weight of soybean plants. Significant increases in root volumes were observed in both the silicate-treated cultivars compared to the control plants under water-limiting conditions (33% irrigation). Net photosynthesis and stomatal conductance were decreased, but the quantum efficiency of photosystem II (Fv’/Fm’) did not change under the same irrigation condition, which indicates photosynthesis downregulation through stomatal limitation. Silicate-treated plants in both cultivars had higher water use efficiency as compared to control plants under water-limiting conditions (irrigated with 66% or 33% of required water). Under water-limiting conditions, the soil moisture content was significantly higher in pots containing silicate than in those without added silicate, suggesting that silicon application improves water holding capacity. Taken together, the results from this study indicate that silicon application can improve the vegetative growth of soybeans under low water conditions by increasing the water use efficiency of plants and enhancing the soil’s ability to retain moisture.
A Dramatic Response of a Thyroid Lymphoma to R-CHOP Chemotherapy Reversing Mechanical Airway Obstruction and Respiratory Failure

Primary thyroid lymphoma is an extremely rare thyroid malignancy that usually occurs in patients with preexisting Hashimoto thyroiditis and commonly presents in older women. The most common type is non-Hodgkin's lymphoma of B-cell origin, particularly diffuse large B-cell lymphoma (DLBCL), which is primarily treated with chemotherapy and radiotherapy. We reported an 83-year-old woman with a past medical history of hypothyroidism who suffered dysphagia and dyspnea secondary to a large thyroid mass. Her CT neck scan showed an enlarged thyroid mass with pathological cervical lymphadenopathy and marked tracheal narrowing. The ultrasound-guided biopsy confirmed the diagnosis of DLBCL of the thyroid. A few days later, she experienced respiratory distress and failure that required endotracheal intubation and mechanical ventilation. She was not considered for tracheostomy or surgical interventions, and after discussion with her family, the decision was made to start R-CHOP therapy (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) which resulted in a marked reduction of the thyroid size and reversal of the mechanical airway obstruction, enabling her extubation. This case report demonstrated the dramatic response of a large thyroid lymphoma to R-CHOP therapy, reducing the thyroid size and its fatal obstructive complications, including mechanical airway obstruction, within a few days of the initiation of R-CHOP therapy.
Assessment of Angiogenesis and Cell Survivability of an Inkjet Bioprinted Biological Implant in an Animal Model

The rapidly growing field of tissue engineering hopes to soon address the shortage of transplantable tissues, allowing for precise control and fabrication that could be made for each specific patient. The protocols currently in place to print large-scale tissues have yet to address the main challenge of nutritional deficiencies in the central areas of the engineered tissue, causing necrosis deep within and rendering it ineffective. Bioprinted microvasculature has been proposed to encourage angiogenesis and facilitate the mobility of oxygen and nutrients throughout the engineered tissue. An implant made via an inkjet printing process containing human microvascular endothelial cells was placed in both B17-SCID and NSG-SGM3 animal models to determine the rate of angiogenesis and degree of cell survival. The implantable tissues were made using a combination of alginate and gelatin type B; all implants were printed via previously published procedures using a modified HP inkjet printer. Histopathological results show a dramatic increase in the average microvasculature formation for mice that received the printed constructs within the implant area when compared to the manual and control implants, indicating inkjet bioprinting technology can be effectively used for vascularization of engineered tissues.
Studies of Single Hadron Calibration of the CMS Calorimeter

The CMS detector at the LHC accelerator is an experiment for the search of the missing Standard Model particle, the higgs boson, as well as for the search for new physics beyond the Standard Model. For a promising search, a precise understanding of the detector and its subcomponents as a basis for a precise calibration is essential. This thesis investigates, on the basis of Monte Carlo datasets, what the responses of the calorimeters for single pions are and how the showerenergy distributes radially around the shower center. Based on these measurements we will then investigate how such a dataset with isolated pions can be obtained for calibration purposes and what the neutral background in the investigated detector area looks like. Kurzfassung Der CMS Detektor am LHC-Beschleuniger ist ein Experiment zur Suche nach dem fehlenden Standardmodell-Teilchen, dem Higgs-Boson, sowie zur Suche nach möglicher neuer Physik jenseits des Standardmodells. Für eine erfolgsversprechende Suche ist ein genaues Verständnis des Detektors und seiner einzelnen Bauteile als Grundlage für eine möglichst genaue Kalibration unerlässlich. In dieser Arbeit wird anhand von Monte-Carlo-Datensätzen untersucht, wie die Responses der Kalorimeter für einzelne Pionen aussehen und wie die Schauerenergie radial um die Schauermitte verteilt ist. Anhand dieser Messungen wird im folgenden untersucht, wie sich ein Datensatz mit isolierten Pionen zu Kalibrationszwecken erstellen lässt und wie der neutrale Untergrund in der untersuchten Detektorregion aussieht.
Computational study on branch decomposition of planar graphs

The notions of branchwidth and branch-decomposition of graphs are introduced by Robertson and Seymour and have important algorithmic applications. It is known that many NP-hard problems in a graph G admit efficient dynamic programming algorithms if G has a small branchwidth. These algorithms usually run in exponential time in the width of a given branch-decomposition of G and polynomial time in the size of G. So it is very important to decide the branchwidth and compute an optimal branch-decomposition of a given graph. It is NP-hard to compute an optimal branch-decomposition for general graphs. For a planar graph G, it is known that whether the branchwidth of G is at most a given integer can be decided in O(n2) time and an optimal branch-decomposition of G can be computed in O(n3) time. It is shown that the branchwidth and optimal branch-decomposition of a planar graph G can be computed efficiently in practice. In this thesis, heuristics are proposed to improve the practical efficiency of the O(n3) algorithm for the optimal branch-decomposition. Computational studies are conducted for the proposed heuristics and the results show that the practical performance of the O(n3) time algorithm can be improved significantly. The heuristics implemented provide efficient tools for computing the branchwidth and optimal branch-decomposition of planar graphs.
Effects of a sport competition on acute affective response in wheelchair basketball players

The purpose of this study was to investigate the effects of sport game outcomes on acute affective responses in injured veterans. Twenty-three wheelchair basketball players (mean age, 38.39±11.78 years) participated in this study and were divided into two groups: game winner group (n=12) and game loser group (n=11). All participants completed the Physical Activity Affect Scale immediately before and after participation in the first game of a wheelchair basketball tournament. Non-parametric statistical tests were used to examine a significant difference in acute affective responses between the groups and over time within each group. The losers experienced significantly higher negative affect and lower positive affect at the end of the first game compared to the winners. Therefore, the results of the present study indicated that the experience of losing may diminish the positive effect that sport participation can have.
Optimization Algorithms on Homogeneous Spaces

Constrained optimization problems are commonplace in linear systems theory. In many cases the constraint set is a homogeneous space and the additional geometric insight provided by the Lie-group structure provides a framework in which to tackle the numerical optimization task. The fundamental advantage of this approach is that algorithms designed and implemented using the geometry of the homogeneous space explicitly preserve the constraint set. In this thesis the numerical solution of a number of optimization problems constrained to homogeneous spaces are considered. The first example studied is the task of determining the eigenvalues of a symmetric matrix (or the singular values of an arbitrary matrix) by interpolating known gradient flow solutions using matrix exponentials. Next the related problem of determining principal components of a symmetric matrix is discussed. A continuous-time gradient flow is derived that leads to a discrete exponential interpolation of the continuous-time flow which converges to the desired limit. A comparison to classical algorithms for the same task is given. The third example discussed, this time drawn from the field of linear systems theory, is the task of arbitrary pole placement using static feedback for a structured class of linear systems. The remainder of the thesis provides a review of the underlying theory relevant to the three examples considered and develops a mathematical framework in which the proposed numerical algorithms can be understood. This framework leads to a general form for a solution to any optimization problem on a homogeneous space. An important consequence of the theoretical review is that it develops the mathematical tools necessary to understand more sophisticated numerical algorithms. The thesis concludes by proposing a quadratically convergent numerical optimization method, based on the Newton-Raphson algorithm, which evolves explicitly on a Lie-group. v
Infectious Bronchitis Coronavirus: Genome Evolution in Vaccinated and Non-Vaccinated SPF Chickens

Infectious Bronchitis virus (IBV) continues to cause significant economic losses for the chicken industry despite the use of many live IBV vaccines around the world. Several authors have suggested that vaccine-induced partial protection may contribute to the emergence of new IBV strains. In order to study this hypothesis, three passages of a challenge IBV were made in SPF chickens sham inoculated or vaccinated at day of age using a live vaccine heterologous to the challenge virus. All birds that were challenged with vaccine heterologous virus were positive for viral RNA. NGS analysis of viral RNA in the unvaccinated group showed a rapid selection of seven genetic variants, finally modifying the consensus genome of the viral population. Among them, five were non-synonymous, modifying one position in NSP 8, one in NSP 13, and three in the Spike protein. In the vaccinated group, one genetic variant was selected over the three passages. This synonymous modification was absent from the unvaccinated group. Under these conditions, the genome population of an IBV challenge virus evolved rapidly in both heterologous vaccinated and non-vaccinated birds, while the genetic changes that were selected and the locations of these were very different between the two groups.
Arthropod Associations Show Naturalization with Non-Native Quercus Species in the Georgia Piedmont

Abstract Native plants may coevolve with native arthropods and may be associated with greater arthropod diversity than non-native plants. Thompson Mills Forest, a state arboretum owned by the University of Georgia and located in Braselton, GA, is home to a variety of oak (Quercus L., Fagacaeae) species, both native and non-native to Georgia. Arthropods were sampled from 20 trees belonging to 12 species, 8 native and 4 non-native, using beat sheets for 10 consecutive weeks in 2018. More than 500 arthropods were collected, with Coleoptera, Araneae, and Psocodea comprising more than 70% of the arthropods collected. Neither abundance nor Shannon index varied among trees of native or non-native origin or among tree species, although both variables peaked during the middle of the sampling period. Multivariate analyses showed similar arthropod communities were associated with native and non-native oaks. The results suggest that non-native plants may naturalize and, if so, may interact with arthropod communities in similar ways as native congeners. Further research into the long-term ecological interactions with non-native plants is recommended.
A Geometric Approach for Real-Time Forward Kinematics of the General Stewart Platform

This paper presents a geometric approach for real-time forward kinematics of the general Stewart platform, which consists of two rigid bodies connected by six general serial manipulators. By describing the rigid-body motion as exponential of twist, and taking advantage of the product of exponentials formula, a step-by-step derivation of the proposed algorithm is presented. As the algorithm naturally solves all passive joint displacements, the correctness is then verified by comparing the forward-kinematic solutions from all chains. The convergence ability and robustness of the proposed algorithm are demonstrated with large amounts of numerical simulations. In all test cases, the proposed algorithm terminates within four iterations, converging with near-quadratic speed. Finally, the proposed algorithm is also implemented on a mainstream embedded motion controller. Compared with the incremental method, the proposed algorithm is more robust, with an average execution time of 0.48 ms, meeting the requirements of most applications, such as kinematic calibration, motion simulation, and real-time control.
Self
 ‐supervised cross validation using data generation structure

Modern statistics and machine learning typically involve large amounts of data coupled with computationally intensive methods. In a predictive modeling context, one seeks models that achieve high predictive accuracy on new datasets. This is typically implemented by partitioning the data into training and hold-out data sets. The allocation is often conducted randomly, at the row level of the data matrix. In this work, we discuss an overlooked gap in machine learning and predictive modeling, the role of data structure and data generation process in the partitioning of observational data into training and hold-out datasets. Ignoring such structures can lead to deficiencies in model generalizability and operationalization. We highlight that explicitly embracing the data generation structure to partition the data for validating predictive model is essential to the success of data science projects. The proposed approach is called befitting cross validation (BCV). It relies on an information quality perspective of analytics. This requires an assessment with inputs from domain experts, in contrast to automated approaches that are purely data driven. BCV is motivated by the objective of generating information quality with data and modeling. Two case studies are illustrating the proposed approach. One is based on a 96-h burn-in process applied to electro-mechanical devices, implemented in order to reduce early failures at the customer site. The goal was to shorten the burn-in process with a predictive model applied at 20 h. The other case study is combining tablet dissolution profiles and designed mixture experiments. The goal there was to match the tablet under test dissolution profiles with a brand tablet reference profile. These case studies demonstrate the methodological points made with BCV, which are generic in nature. We suggest that BCV principles should be always considered in the development of data-driven predictive models.
Malaysian Intention to Use e-Wallet: Forthcoming Expectation in Cashless Transactions

This research intended to explore the intention to use of e-wallets among Malaysian. The influential factors consist of perceived usefulness, perceived ease of use, perceived trust, social influence, facilitating conditions, and lifestyle compatibility using the technology accepted model (TAM) and the unified theory of acceptance and use of technology (UTAUT). Population of this research are general consumers who used e-wallet in Malaysia. The research acquires 146 respondents and data collection was established using the application of Google Form. Data analysis were facilitated by SPSS application to measure the relationship between variables. Result portrays that there is strong correlation between facilitating conditions, lifestyle compatibility, perceived usefulness, perceived trust, perceived ease of use, social influence towards intention to use e-wallet. The highest correlate value is facilitating condition while social influence yield the lowest correlation value.
Pharmacological Activities of Safflower Yellow and Its Clinical Applications

Background Safflower is an annual herb used in traditional Chinese herbal medicine. It consists of the dried flowers of the Compositae plant safflower. It is found in the central inland areas of Asia and is widely cultivated throughout the country. Its resistance to cold weather and droughts and its tolerance and adaptability to salts and alkalis are strong. Safflower has the effect of activating blood circulation, dispersing blood stasis, and relieving pain. A natural pigment named safflower yellow (SY) can be extracted from safflower petals. Chemically, SY is a water-soluble flavonoid and the main active ingredient of safflower. The main chemical constituents, pharmacological properties, and clinical applications of SY are reviewed in this paper, thereby providing a reference for the use of safflower in preventing and treating human diseases. Methods The literature published in recent years was reviewed, and the main chemical components of SY were identified based on chemical formula and structure. The pharmacological properties of hydroxysafflor yellow A (HSYA), SYA, SYB, and anhydrosafflor yellow B (AHSYB) were reviewed. Results The main chemical constituents of SY included HSYA, SYA, SYB, and AHSYB. These ingredients have a wide range of pharmacological activities. SY has protective effects on the heart, kidneys, liver, nerves, lungs, and brain. Moreover, its effects include, but are not limited to, improving cardiovascular and cerebrovascular diseases, abirritation, regulating lipids, and treating cancer and diabetic complications. HSYA is widely recognised as an effective ingredient to treat cardiovascular and cerebrovascular diseases. Conclusion SY has a wide range of pharmacological activities, among which improving cardiovascular and cerebrovascular diseases are the most significant.
Multiplex Immunofluorescence and the Digital Image Analysis Workflow for Evaluation of the Tumor Immune Environment in Translational Research

A robust understanding of the tumor immune environment has important implications for cancer diagnosis, prognosis, research, and immunotherapy. Traditionally, immunohistochemistry (IHC) has been regarded as the standard method for detecting proteins in situ, but this technique allows for the evaluation of only one cell marker per tissue sample at a time. However, multiplexed imaging technologies enable the multiparametric analysis of a tissue section at the same time. Also, through the curation of specific antibody panels, these technologies enable researchers to study the cell subpopulations within a single immunological cell group. Thus, multiplexed imaging gives investigators the opportunity to better understand tumor cells, immune cells, and the interactions between them. In the multiplexed imaging technology workflow, once the protocol for a tumor immune micro environment study has been defined, histological slides are digitized to produce high-resolution images in which regions of interest are selected for the interrogation of simultaneously expressed immunomarkers (including those co-expressed by the same cell) by using an image analysis software and algorithm. Most currently available image analysis software packages use similar machine learning approaches in which tissue segmentation first defines the different components that make up the regions of interest and cell segmentation, then defines the different parameters, such as the nucleus and cytoplasm, that the software must utilize to segment single cells. Image analysis tools have driven dramatic evolution in the field of digital pathology over the past several decades and provided the data necessary for translational research and the discovery of new therapeutic targets. The next step in the growth of digital pathology is optimization and standardization of the different tasks in cancer research, including image analysis algorithm creation, to increase the amount of data generated and their accuracy in a short time as described herein. The aim of this review is to describe this process, including an image analysis algorithm creation for multiplex immunofluorescence analysis, as an essential part of the optimization and standardization of the different processes in cancer research, to increase the amount of data generated and their accuracy in a short time.
Case Report: Challenging Treatment of an AorticParavalvular Leak: How We Avoided Interference With Mechanical Valve Function?

Background Aortic paravalvular leak (APVL) after surgical valve replacement (AVR) is an ominous complication with a high risk of morbidity and mortality. Approximately 1–5% of PVLs can lead to serious clinical consequences, including congestive heart failure and/or hemolytic anemia. Case Summary A 69-year-old man with multiple comorbidities underwent surgical replacement of the aortic valve with a mechanical tilting disc prosthetic valve (Medtronic Starlight 27 mm). Several years later, recurrent episodes of congestive heart failure and hemolytic anemia developed due to a large crescent-shaped aortic PVL located at non coronary cusp (NCC) 9–12 o'clock, with moderate-to-severe regurgitation. The patient was deemed at prohibitive surgical risk due to significant multiple comorbidities and a transcatheter PVL closure (TPVLc) was planned. The huge PVL was partially closed by a first specifically designed paravalvular leak device (PLD). The procedure was complicated by transient interference of the second PLD with mechanical prosthetic valve function. This issue has however been solved with correct manipulation, orientation and downsizing of the second device implanted. At 3-month and 13-month follow-up, the patient showed a relevant clinical improvement and good quality of life. 2D TTE color Doppler confirmed the stable position of the two PLDs with trace residual leak. Discussion Surgical redo has been considered the treatment of choice for symptomatic patients with PVLs. Notwithstanding, TPVLc is a less invasive alternative, particularly in patients at high surgical risk in whom early diagnosis and prompt interventional treatment are crucial for improving expectancy and quality of life. Dedicated devices, appropriate procedural techniques, and the close interaction between imaging modalities, allowed to deal successfully with a challenging case of severe symptomatic aortic PVL.