bluuebunny/crossref_metadata_2025 huggingface dataset:
Lymphoma of the Uvula: Clinical, Morphological, Histopathological, and Genetic Characterization. A Nationwide Danish Study From 1980 to 2019
Authors: Iversen Lars , Eriksen Patrick Rene Gerhard , Andreasen Simon , Clasen-Linde Erik , Homøe Preben , Wessel Irene , von Buchwald Christian , Heegaard Steffen
Published: April 2021

Background: In the head and neck region the uvula is a rare site for extranodal lymphomas to develop. In this national study, we present six cases and provide an overview of the current literature, characterizing the clinical and histopathological features of lymphomas involving this location.

Materials and Methods: Clinical information was obtained retrospectively from patient records in a nationwide Danish study covering from 1980 through 2019. In order to validate the diagnoses, uvular tissue specimens were examined histologically and immunohistochemically and if relevant for subtyping, cytogenetic rearrangements were investigated.

Results: We present six cases of lymphomas involving the uvula, of which four of the cases were diagnosed with a B-cell lymphoma (two diffuse large B-cell lymphomas, one extranodal marginal zone B-cell lymphoma and one Mantle cell lymphoma), while two were diagnosed with a T-cell lymphoma (one peripheral T-cell lymphoma and one natural killer/T-cell lymphoma). Presenting symptoms included swelling, pain and ulceration of the uvula. Treatment was comprised of radiotherapy and/or chemotherapy, with T-cell lymphomas showing a poorer outcome than B-cell lymphomas.

Conclusion: Lymphoma of the uvula is rare, with few case reports being reported in the literature. The most frequent histological subtypes reported are extranodal marginal zone B-cell lymphoma and peripheral T-cell lymphoma. When encountering a swollen, painful and/or ulcerated uvula, the clinician should always consider malignancy as a possible cause. Lymphoma of the uvula is a possible diagnosis and if this is the case, there is a high risk of disseminated disease at the time of diagnosis.
Case Report: Clinical Remission in a Cat With Severe Bilateral Eosinophilic Keratitis Receiving Combined Immunosuppressive Therapy (Triamcinolone Acetonide and Tacrolimus)
Authors: Romaneck Amanda K. , Sebbag Lionel
Published: April 2021

A 2-year-old domestic shorthair cat was examined for severe keratitis of 10 months duration, non-responsive to 0.1% dexamethasone q8-12h. Patient and owner compliance were poor given ocular discomfort and hiding behavior. On presentation, both eyes (OU) had severe ulcerative keratitis that masked examination of deeper structures and resulted in absent menace response OU. Corneal cytology was consistent with eosinophilic keratitis (EK) OU. Initial therapy included subcutaneous triamcinolone acetonide injection (0.2 mg/kg), 0.3% ciprofloxacin OU q12h, and two compounded drugs started 5 days later upon receipt: 0.5% tacrolimus OU q6h, 0.5% cidofovir OU q12h. Visual behavior and ocular comfort were reportedly much improved within 24 h. At the first recheck (Day 11), patient and owner compliance were reported to be excellent, menace response was positive OU, and keratitis was dramatically reduced OU with absent fluorescein uptake. Subcutaneous triamcinolone acetonide was repeated (0.2 mg/kg), ciprofloxacin was discontinued, cidofovir was continued q12h, and tacrolimus was slowly tapered (q8h × 3 weeks then q12h). Keratitis was nearly 100% resolved at the second recheck (Day 38); cidofovir was discontinued and tacrolimus was slowly tapered (q12h × 1 week, q24h × 4 weeks, q48h × 4 weeks) then discontinued. A third recheck (Day 101) confirmed clinical remission OU, and disease did not reoccur within a 1 year follow up period (photographic documentation by owner). In sum, adjunct use of triamcinolone acetonide greatly improved therapeutic compliance and helped control severe EK in a rapid and effective manner.
Diversity of the Swine Leukocyte Antigen Class I and II in Commercial Pig Populations
Authors: Techakriengkrai Navapon , Nedumpun Teerawut , Golde William T. , Suradhat Sanipa
Published: April 2021

Among swine genetic markers, the highly polymorphic swine leukocyte antigen (SLA) is one of the key determinants, associated with not only immune responses but also reproductive performance and meat quality. The objective of this study was to characterize the SLA class I and II diversities in the commercial pig populations. In this study, a total number of 158 pigs (126 gilts and 32 boars) were randomly selected from different breeding herds of five major pig-producing companies, which covered ~70% of Thai swine production. The results indicate that a moderate level of SLA diversity was maintained in the Thai swine population, despite the performance-oriented breeding scheme. The highly common SLA class I alleles were SLA-1*08:XX, SLA-2*02:XX, and SLA-3*04:XX at a combined frequency of 30.1, 18.4, and 34.5%, respectively, whereas DRB1*04:XX, DQB1*02:XX and DQA*02:XX were the common class II alleles at 22.8, 33.3, and 38.6%, respectively. The haplotype Lr-32.0 (SLA-1*07:XX, SLA-2*02:XX, and SLA-3*04:XX) and Lr-0.23 (DRB1*10:XX, DQB1*06:XX, DQA* 01:XX) was the most common SLA class I and II haplotype, at 15.5 and 14.6%, respectively. Common class I and II haplotypes were also observed, which Lr-22.15 was the most predominant at 11.1%, followed by Lr-32.12 and Lr-4.2 at 10.8 and 7.9%, respectively. To our knowledge, this is the first report of SLA class I and II diversities in the commercial pigs in Southeast Asia. The information of the common SLA allele(s) in the population could facilitate swine genetic improvement and future vaccine design.
Clinical Features and Outcome of 79 Dogs With Digital Squamous Cell Carcinoma Undergoing Treatment: A SIONCOV Observational Study
Authors: Marconato Laura , Murgia Daniela , Finotello Riccardo , Meier Valeria , Morello Emanuela Maria , Pisoni Luciano , Foglia Armando , Guerra Dina , Chalfon Carmit , Aralla Marina , Mesto Paola , Annoni Maurizio , Albanese Francesco , Bettini Giuliano , Sabattini Silvia
Published: April 2021

In dogs, digit squamous cell carcinoma (SCC) is uncommon. Clinical signs are frequently underestimated, leading to a diagnostic delay. The purpose of this retrospective study was to report our experience regarding the clinical presentation, diagnostic work-up, treatment and outcome of 79 client-owned dogs with SCC of the digit. The greatest majority (84.8%) of dogs was dark-coated. Schnauzers represented approximately one third of the study population, and had a poorer outcome compared with other breeds. The majority of SCCs occurred in the front limbs (61%), and bone lysis was frequently observed (92.4%). Approximately 9% of dogs had involvement of multiple digits, and this was associated with a shorter time to progression (TTP;P= 0.047). Similarly, a duration of clinical signs >90 days was associated with a shorter TTP (P= 0.02). Regional lymph node metastases were documented in 17.7% of dogs at admission and were significantly associated with tumor-related death (P< 0.001). At presentation, none of the dogs had evidence of distant metastasis. Digit amputation achieved adequate local tumor control in the majority of cases. Adjuvant chemotherapy and radiation therapy were carried out in 21.5% of cases, with uncertain benefit. Due to the relatively non-aggressive clinical behavior of digit SCC, chemotherapy should only be offered in the case of metastatic disease. Approximately one fourth of dogs developedde novoSCCs during the follow-up. Careful examination of the digits should be encouraged in breeds considered at high risk and in dogs with a previous history of digital SCC.
Welfare Assessment of 30 Dairy Goat Farms in the Midwestern United States
Authors: Hempstead Melissa N. , Lindquist Taylor M. , Shearer Jan K. , Shearer Leslie C. , Cave Vanessa M. , Plummer Paul J.
Published: April 2021

Dairy goat animal welfare assessment protocols have been developed and conducted in Europe and the United Kingdom for dairy goats; however, there are no published reports of large-scale welfare assessment for dairy goats on farms in the Midwestern United States (US). Therefore, the objective of this study was to perform welfare assessment of lactating dairy goats and identify the most prevalent welfare issues on 30 farms across the Midwestern US. Thirty dairy goat farms (self-selected) were enrolled in the study if they shipped milk for human consumption (regardless of herd size). The number of lactating does on each farm ranged from 34 to 6,500 goats, with a median number of 158 lactating does (mean ± SD: 602 ± 1,708 lactating does). The protocol used was developed from available literature on goat welfare assessment but modified for use in the Midwestern US. Observations were made without handling the animals and included 22 animal-based indicators evaluated at the group- and individual-level. The observations were conducted during ~3–5 h during a milking session (either morning or afternoon) and time in the home pen. Principal components analysis (PCA) was carried out on the welfare assessment data from each farm. The first two dimensions of the PCA explained 34.8% of the variation. The PCA biplot indicated correlations between indicators. The most prevalent conditions observed across the 30 farms included any knee calluses (80.9%), any claw overgrowth (51.4%), poor hygiene (14.9%), skin lesions (8.9%), poor hair coat condition (8.3%) and any ear pathology (8.0%). These results are the first to provide the Midwestern US dairy goat industry with information to improve commercial dairy goat welfare.
Osteoclasts in Tumor Biology: Metastasis and Epithelial-Mesenchymal-Myeloid Transition
Authors: Behzatoglu Kemal
Published: April 2021

Osteoclast is a specialized cell that originates from monocytic lineage, communicates closely with osteoblasts under physiological conditions, participates in bone modeling and re-modeling, contributes to calcium homeostasis and osteoimmunity. In pathological conditions, it is involved in many tumors such as giant cell bone tumor (osteoclastoma), aneurysmal bone cyst, osteosarcoma, and metastatic cancers, and it usually causes local spread and progression of the tumor, working against the host. Since osteoclasts play an active role in primary bone tumors and bone metastases, the use of anti-osteoclastic agents significantly reduces the mortality and morbidity rates of patients by preventing the progression and local spread of tumors. Osteoclasts also accompany undifferentiated carcinomas of many organs, especially pancreas, thyroid, bladder and ovary. Undifferentiated carcinomas rich in osteoclasts have osteoclastoma-like histology. In these organs, osteoclastoma-like histology may accompany epithelial carcinomas, and de novo, benign and borderline tumors. Mature and immature myeloid cells, including osteoclasts, play an active role in the tumor progression in primary and metastatic tumor microenvironment, in epithelial-mesenchymal transition (EMT), mesenchymal-epithelial-transition (MET), and cancer stem cell formation. Additionally, they are the most suitable candidates for cancer cells in cell fusion due to their evolutionary fusion capabilities. Myeloid features and markers (CD163, CD33, CD68 etc.) can be seen in metastatic cancer cells. Consequently, they provide metastatic cancer cells with motility, margination, transmigration, chemotaxis, phagocytosis, angiogenesis, matrix degradation, and resistance to chemotherapy. For these reasons, we think that the concept of Epithelial-Mesencyhmal-Myeloid-Transition (EMMT) will be more accurate than EMT for cancer cells with myeloid properties.
Overrelaxed Sinkhorn–Knopp Algorithm for Regularized Optimal Transport
Authors: Thibault Alexis , Chizat Lénaïc , Dossal Charles , Papadakis Nicolas
Published: April 2021

This article describes a set of methods for quickly computing the solution to the regularized optimal transport problem. It generalizes and improves upon the widely used iterative Bregman projections algorithm (or Sinkhorn–Knopp algorithm). We first proposed to rely on regularized nonlinear acceleration schemes. In practice, such approaches lead to fast algorithms, but their global convergence is not ensured. Hence, we next proposed a new algorithm with convergence guarantees. The idea is to overrelax the Bregman projection operators, allowing for faster convergence. We proposed a simple method for establishing global convergence by ensuring the decrease of a Lyapunov function at each step. An adaptive choice of the overrelaxation parameter based on the Lyapunov function was constructed. We also suggested a heuristic to choose a suitable asymptotic overrelaxation parameter, based on a local convergence analysis. Our numerical experiments showed a gain in convergence speed by an order of magnitude in certain regimes.
No-Wait Job Shop Scheduling Using a Population-Based Iterated Greedy Algorithm
Authors: Xu Mingming , Zhang Shuning , Deng Guanlong
Published: April 2021

When no-wait constraint holds in job shops, a job has to be processed with no waiting time from the first to the last operation, and the start time of a job is greatly restricted. Using key elements of the iterated greedy algorithm, this paper proposes a population-based iterated greedy (PBIG) algorithm for finding high-quality schedules in no-wait job shops. Firstly, the Nawaz–Enscore–Ham (NEH) heuristic used for flow shop is extended in no-wait job shops, and an initialization scheme based on the NEH heuristic is developed to generate start solutions with a certain quality and diversity. Secondly, the iterated greedy procedure is introduced based on the destruction and construction perturbator and the insert-based local search. Furthermore, a population-based co-evolutionary scheme is presented by imposing the iterated greedy procedure in parallel and hybridizing both the left timetabling and inverse left timetabling methods. Computational results based on well-known benchmark instances show that the proposed algorithm outperforms two existing metaheuristics by a significant margin.
The Effect of Sowing Date and Harvest Time on Leafy Greens of Quinoa (Chenopodium quinoa Willd.) Yield and Selected Nutritional Parameters
Authors: Adamczewska-Sowińska Katarzyna , Sowiński Józef , Jama-Rodzeńska Anna
Published: April 2021

In 2015–2017, field experiments were conducted, in two facilities of the Wroclaw University of Environmental and Life Sciences: at the Research and Didactic Station in Psary (51°19′08” N, 17°03′37” E) and in the plots of the Department of Crop Production in Pawlowice (51°17′32” N, 17°11′72” E). The research aimed to evaluate fresh biomass yield and selected quality parameters of quinoa grown as a green leafy vegetable. The study was conducted on two soils: medium (clay soil texture) and light (sand soil texture), sowing quinoa in spring and summer periods. On both soils, quinoa was harvested 5 times at each sowing date. The yields of fresh plant biomass, basic morphological characteristics and certain quality traits were compared. All experimental factors had a significant effect on quinoa fresh biomass yield. On light soil, quinoa yielded 4 times lower than on medium soil, and its cultivation posed a high risk, especially at the summer sowing date. The experimental factors used had the greatest effect on macronutrient content. Growing quinoa on medium soil yielded biomass with higher K, Mg, Ca and N-NO3 concentration, while spring sown plants had higher K and N-NO3 concentration. Harvesting date had the greatest effect on the change in nutritional values. Potassium content decreased as the harvest date was delayed, and N-NO3 content decreased gradually until the fourth harvest date. The results showed that quinoa should be harvested as a green leafy vegetable when it reaches a height of 20–30 cm; biomass production is then at 500–1000 g per m2, potassium content is less than 10 g per 100 g dry matter and N-NO3 content is less than 100 mg 100−1 f.m.
Physicochemical Characterization of Crithmum maritimum L. and Daucus carota subsp. gummifer (Syme) Hook.fil. and Their Antimicrobial Activity against Apple Tree and Grapevine Phytopathogens
Authors: Sánchez-Hernández Eva , Buzón-Durán Laura , Andrés-Juan Celia , Lorenzo-Vidal Belén , Martín-Gil Jesús , Martín-Ramos Pablo
Published: April 2021

Crithmum maritimum and Daucus carota subsp. gummifer are two species of the Apiaceae family that share multiple characteristics: both are halophitic, live on cliffs in the same geographic habitats, and are edible. While C. maritimum is rich in essential oils and flavonoids, D. carota is rich in terpenes and a gum producer. In the work presented herein, the biomass of these two wild plants and the bioactive compounds present in their extracts have been studied by elemental and thermal analysis, infrared spectroscopy, and gas chromatography-mass spectroscopy. To explore their bioactivities, both their hydroalcoholic extracts and their major constituents (apiole in C. maritimum and geranyl acetate in D. carota), either alone or in combination with chitosan oligomers, were assayed in vitro against bacterial and fungal pathogens that affect apple trees (Malus domestica) and grapevine (Vitis vinifera). Remarkable inhibition was observed against Erwinia amylovora, the causal agent of fire blight in apple; Xylophilus ampelinus [syn. Erwinia vitivora], the causal agent of bacterial blight of grapevine; and Diplodia seriata, a virulent pathogen of grapevines that also causes canker, leaf spot and fruit rot of apple. In view of their effectiveness against these three phytopathogens, a potential application of these two medicinal plants in organic farming may be envisaged.
A Retrospective Literature Evaluation of the Integration of Stress Physiology Indices, Animal Welfare and Climate Change Assessment of Livestock
Authors: Narayan Edward , Barreto Michelle , Hantzopoulou Georgia-Constantina , Tilbrook Alan
Published: April 2021

In this retrospective study, we conducted a desktop-based analysis of published literature using the ScienceDirect™ search engine to determine the proportion of livestock research within the last 7 years (2015–2021) that have applied animal welfare assessment combining objective measures of physiological stress and evaluation of climate change factors in order to provide an account of livestock productivity. From the search results, 563 published articles were reviewed. We found that the majority of the literature had discussed animal production outcomes (n = 491) and animal welfare (n = 453) either individually or in conjunction with another topic. The most popular occurrence was the combination of animal welfare assessment, objective measures of stress physiology and production outcomes discussed collectively (n = 218). We found that only 125 articles had discussed the impact of climate change (22.20%) on livestock production and/or vice versa. Furthermore, only 9.4% (n = 53) of articles had discussed all four factors and published research was skewed towards the dairy sector. Overall, this retrospective paper highlights that although research into animal welfare assessment, objective measures of stress and climate change has been applied across livestock production systems (monogastrics and ruminants), there remains a shortfall of investigation on how these key factors interact to influence livestock production. Furthermore, emerging technologies that can boost the quantitative evaluation of animal welfare are needed for both intensive and extensive production systems.
Pig-Posture Recognition Based on Computer Vision: Dataset and Exploration
Authors: Shao Hongmin , Pu Jingyu , Mu Jiong
Published: April 2021

Posture changes in pigs during growth are often precursors of disease. Monitoring pigs’ behavioral activities can allow us to detect pathological changes in pigs earlier and identify the factors threatening the health of pigs in advance. Pigs tend to be farmed on a large scale, and manual observation by keepers is time consuming and laborious. Therefore, the use of computers to monitor the growth processes of pigs in real time, and to recognize the duration and frequency of pigs’ postural changes over time, can prevent outbreaks of porcine diseases. The contributions of this article are as follows: (1) The first human-annotated pig-posture-identification dataset in the world was established, including 800 pictures of each of the four pig postures: standing, lying on the stomach, lying on the side, and exploring. (2) When using a deep separable convolutional network to classify pig postures, the accuracy was 92.45%. The results show that the method proposed in this paper achieves adequate pig-posture recognition in a piggery environment and may be suitable for livestock farm applications.
Rapid and Non-Destructive Monitoring of Moisture Content in Livestock Feed Using a Global Hyperspectral Model
Authors: Uyeh Daniel Dooyum , Kim Juntae , Lohumi Santosh , Park Tusan , Cho Byoung-Kwan , Woo Seungmin , Lee Won Suk , Ha Yushin
Published: April 2021

The dry matter (DM) content of feed is vital in cattle nutrition and is inversely correlated with moisture content. The established ranges of moisture content serve as a marker for factors such as safe storage limit and DM intake. Rapid changes in moisture content necessitate rapid measurements. A rapid and non-destructive global model for the measurement of moisture content in total mixed ration feed and feed materials was developed. To achieve this, we varied and measured the moisture content in the feed and feed materials using standard methods and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 1000–2500 nm. The spectral data from the samples were extracted and preprocessed using seven techniques and were used to develop a global model using partial least squares regression (PLSR) analysis. The range preprocessing technique had the best prediction accuracy (R2 = 0.98) and standard error of prediction (2.59%). Furthermore, the visual assessment of distribution in moisture content made possible by the generated PLSR-based moisture content mapped images could facilitate precise formulation. These applications of HSI, when used in commercial feed production, could help prevent feed spoilage and resultant health complications as well as underperformance of the animals from improper DM intake.
A Metaheuristic Harris Hawk Optimization Approach for Coordinated Control of Energy Management in Distributed Generation Based Microgrids
Authors: Abdelsalam Mahmoud , Diab Hatem Y. , El-Bary A. A.
Published: April 2021

Cost management of microgrids represents a real challenge since the power generation of microgrids is usually composed of different renewable and non-renewable sources. Additionally, it is always desired to make a connection between the microgrid and national grid to secure the load demand and to fit the regulations of liberated energy markets. Because of all these reasons, it is essential to develop a smart energy management unit to control different energy resources within the microgrid to achieve minimum operation costs. This paper presents a proposal for a smart unit for the cost management and operation of multi-source based microgrids. The proposed unit utilizes the Harris hawk optimization (HHO) algorithm which is used to optimize the cost of operation based on current load demand, energy prices and generation capacities. The proposed unit is tested on a microgrid with different energy resources using MATLAB while applying different operation scenarios. All simulation results show that the proposed unit succeeds in operating the microgrid at minimum cost. Obtained results are compared with other optimization algorithms and the proposed Harris hawk algorithm gives superior performance.
Stochastic Model Driven Performance and Availability Planning for a Mobile Edge Computing System
Authors: Brito Carlos , Rodrigues Laécio , Santos Brena , Fé Iure , Nguyen Tuan-Anh , Min Dugki , Lee Jae-Woo , Silva Francisco Airton
Published: April 2021

Mobile Edge Computing (MEC) has emerged as a promising network computing paradigm associated with mobile devices at local areas to diminish network latency under the employment and utilization of cloud/edge computing resources. In that context, MEC solutions are required to dynamically allocate mobile requests as close as possible to their computing resources. Moreover, the computing power and resource capacity of MEC server machines can directly impact the performance and operational availability of mobile apps and services. The systems practitioners must understand the trade off between performance and availability in systems design stages. The analytical models are suited to such an objective. Therefore, this paper proposes Stochastic Petri Net (SPN) models to evaluate both performance and availability of MEC environments. Different to previous work, our proposal includes unique metrics such as discard probability and a sensitivity analysis that guides the evaluation decisions. The models are highly flexible by considering fourteen transitions at the base model and twenty-five transitions at the extended model. The performance model was validated with a real experiment, the result of which indicated equality between experiment and model with p-value equal to 0.684 by t-Test. Regarding availability, the results of the extended model, different from the base model, always remain above 99%, since it presents redundancy in the components that were impacting availability in the base model. A numerical analysis is performed in a comprehensive manner, and the output results of this study can serve as a practical guide in designing MEC computing system architectures by making it possible to evaluate the trade-off between Mean Response Time (MRT) and resource utilization.
A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification
Authors: Diniz Débora N. , Rezende Mariana T. , Bianchi Andrea G. C. , Carneiro Claudia M. , Ushizima Daniela M. , de Medeiros Fátima N. S. , Souza Marcone J. F.
Published: April 2021

Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.
Implementation of Cognitive Digital Twins in Connected and Agile Supply Networks—An Operational Model
Authors: Kalaboukas Kostas , Rožanec Joze , Košmerlj Aljaž , Kiritsis Dimitris , Arampatzis George
Published: April 2021

Supply chain agility and resilience are key factors for the success of manufacturing companies in their attempt to respond to dynamic changes. The circular economy, the need for optimized material flows, ad-hoc responses and personalization are some of the trends that require supply chains to become “cognitive”, i.e., able to predict trends and flexible enough in dynamic environments, ensuring optimized operational performance. Digital twins (DTs) is a promising technology, and a lot of work is done on the factory level. In this paper, the concept of cognitive digital twins (CDTs) and how they can be deployed in connected and agile supply chains is elaborated. The need for CDTs in the supply chain as well as the main CDT enablers and how they can be deployed under an operational model in agile networks is described. More emphasis is given on the modelling, cognition and governance aspects as well as on how a supply chain can be configured as a network of connected CDTs. Finally, a deployment methodology of the developed model into an example of a circular supply chain is proposed.
Submerged Cultivation of Inonotus obliquus Mycelium Using Statistical Design of Experiments and Mathematical Modeling to Increase Biomass Yield
Authors: Petre Alexandru , Ene Mihaela , Vamanu Emanuel
Published: April 2021

Submerged culturing of mycelium is an efficient technique used to increase biomass yields, more so when employed with naturally slow-growing species of mushrooms. This paper is concerned with optimizing nutrient broth components used in Inonotus obliquus cultures for achieving high biomass yields. We modeled the effect of seven biotechnological parameters (six broth ingredients and the initial pH of nutritive broth) on mycelial biomass and predicted an optimum broth formula using response surface methodology. An analysis of variance showed that the elaborated model is significant (F-value of 2.76 and p-value of 0.0316). We used bioreactor cultures to confirm the model’s optimum prediction and to compare these results with a general-purpose mycology medium, namely potato dextrose broth (PDB). The optimized bioreactor culture yielded 4.37 g/L (93.36% of the dry weight prediction), while the PDB bioreactor culture yielded 2.084 g/L, after 15 days of cultivation. The optimized formula was: 2.15299 g malt extract, 3.99296 g yeast extract, 11.0041 g fructose, 17.4 g soluble starch, 0.1 g MgSO4, and 0.05 g CaCl2 per liter of broth.
Model Comparisons of Flow and Chemical Kinetic Mechanisms for Methane–Air Combustion for Engineering Applications
Authors: He Di , Yu Yusong , Kuang Yucheng , Wang Chaojun
Published: April 2021

The reasonably accurate numerical simulation of methane–air combustion is important for engineering purposes. In the present work, the validations of sub-models were carried out on a laboratory-scale turbulent jet flame, Sandia Flame D, in comparison with experimental data. The eddy dissipation concept (EDC), which assumes that the molecular mixing and subsequent combustion occur in the fine structures, was used for the turbulence–chemistry interaction. The standard k-ε model (SKE) with the standard or the changed model constant C1ε, the realizable k-ε model (RKE), the shear-stress transport k-ω model (SST), and the Reynolds stress model (RSM) were compared with the detailed chemical kinetic mechanism of GRI-Mech 3.0. Different reaction treatments for the methane–air combustion were also validated with the available experimental data from the literature. In general, there were good agreements between predictions and measurements, which gave a good indication of the adequacy and accuracy of the method and its further applications for industry-scale turbulent combustion simulations. The differences between predictions and measured data might have come from the simplifications of the boundary settings, the turbulence model, the turbulence–reaction interaction, and the radiation heat transfer model. For engineering predictions of methane–air combustion, the mixture fraction probability density function (PDF) model for the partially premixed combustion with RSM is recommended due to its relatively low simulation expenses, acceptable accuracy predictions, and quantitatively good agreement with the experiments.
Modifications of Wound Dressings with Bioactive Agents to Achieve Improved Pro-Healing Properties
Authors: Vivcharenko Vladyslav , Przekora Agata
Published: April 2021

The great variety of wounds and the lack of an effective universal treatment method has resulted in high demand for modern treatment strategies. Traditional approaches are often ineffective on a variety of chronic wounds, such as venous ulcers or the diabetic foot ulcer. There is strong evidence that naturally derived bioactive compounds have pro-healing properties, raising a great interest in their potential use for wound healing. Plant-derived compounds, such as curcumin and essential oils, are widely used to modify materials applied as wound dressings. Moreover, dressing materials are more often enriched with vitamins (e.g., L-ascorbic acid, tocopherol) and drugs (e.g., antibiotics, inhibitors of proteases) to improve the skin healing rate. Biomaterials loaded with the above-mentioned molecules show better biocompatibility and are basically characterized by better biological properties, ensuring faster tissue repair process. The main emphasis of the presented review is put on the novel findings concerning modern pro-healing wound dressings that have contributed to the development of regenerative medicine. The article briefly describes the synthesis and modifications of biomaterials with bioactive compounds (including curcumin, essential oils, vitamins) to improve their pro-healing properties. The paper also summarizes biological effects of the novel wound dressings on the enhancement of skin regeneration. The current review was prepared based on the scientific contributions in the PubMed database (supported with Google Scholar searching) over the past 5 years using relevant keywords. Scientific reports on the modification of biomaterials using curcumin, vitamins, and essential oils were mainly considered.
Linguistic Patterns and Linguistic Styles for Requirements Specification: Focus on Data Entities
Authors: da Silva Alberto Rodrigues , Savić Dušan
Published: April 2021

Requirements specification includes technical concerns of an information system and is used throughout its life cycle. It allows for sharing the vision of the system among stakeholders and facilitates its development and operation processes. Natural languages are the most common form of requirements representation, however, they also exhibit characteristics that often introduce quality problems, such as inconsistency, incompleteness, and ambiguousness. This paper adopts the notions of linguistic pattern and linguistic style and discusses their relevance to produce better technical documentation. It focuses on the textual specification of data entities, which are elements commonly referred to throughout different types of requirements, like use cases, user stories, or functional requirements. This paper discusses how to textually represent the following elements: data entity, attribute, data type, data entity constraint, attribute constraint, and even cluster of data entities. This paper shows concrete examples and supports the discussion with three linguistic styles, represented by a rigorous requirements specification language and two informal controlled natural languages, one with a more compact and another with a more verbose, expressive, and complete representation. We analyzed how other languages cope with the representation of these data entity elements and complemented that analysis and comparison based on the PENS classification scheme. We conducted a pilot evaluation session with nineteen professional subjects who participated and provided encouraging feedback, with positive scores in all the analyzed dimensions. From this feedback, we preliminarily conclude that the adoption of these linguistic patterns would help to produce better requirements specifications written more systematically and consistently.
Cooperative Control Mechanism of Long Flexible Bolts and Blasting Pressure Relief in Hard Roof Roadways of Extra-Thick Coal Seams: A Case Study
Authors: Xiang Zhe , Zhang Nong , Xie Zhengzheng , Guo Feng , Zhang Chenghao
Published: April 2021

The higher strength of a hard roof leads to higher coal pressure during coal mining, especially under extra-thick coal seam conditions. This study addresses the hard roof control problem for extra-thick coal seams using the air return roadway 4106 (AR 4106) of the Wenjiapo Coal Mine as a case study. A new surrounding rock control strategy is proposed, which mainly includes 44 m deep-hole pre-splitting blasting for stress releasing and flexible 4-m-long bolt for roof supporting. Based on the new support scheme, field tests were performed. The results show that roadway support failure in traditional scenarios is caused by insufficient bolt length and extensive rotary subsidence of the long cantilever beam of the hard roof. In the new proposed scheme, flexible 4-m-long bolts are shown to effectively restrain the initial expansion deformation of the top coal. The deflection of the rock beam anchored by the roof foundation are improved. Deep-hole pre-splitting blasting effectively reduces the cantilever distance of the “block B” of the voussoir beam structure. The stress environment of the roadway surrounding rock is optimized and anchorage structure damage is inhibited. The results provide insights regarding the safe control of roadway roofs under extra-thick coal seam conditions.
A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of Hurricanes
Authors: Devaraj Jayanthi , Ganesan Sumathi , Elavarasan Rajvikram , Subramaniam Umashankar
Published: April 2021

The prediction of severe weather events such as hurricanes is always a challenging task in the history of climate research, and many deep learning models have been developed for predicting the severity of weather events. When a disastrous hurricane strikes a coastal region, it causes serious hazards to human life and habitats and also reflects a prodigious amount of economic losses. Therefore, it is necessary to build models to improve the prediction accuracy and to avoid such significant losses in all aspects. However, it is impractical to predict or monitor every storm formation in real time. Though various techniques exist for diagnosing the tropical cyclone intensity such as convolutional neural networks (CNN), convolutional auto-encoders, recurrent neural network (RNN), etc., there are some challenges involved in estimating the tropical cyclone intensity. This study emphasizes estimating the tropical cyclone intensity to identify the different categories of hurricanes and to perform post-disaster management. An improved deep convolutional neural network (CNN) model is used for predicting the weakest to strongest hurricanes with the intensity values using infrared satellite imagery data and wind speed data from HURDAT2 database. The model achieves a lower Root mean squared error (RMSE) value of 7.6 knots and a Mean squared error (MSE) value of 6.68 knots by adding the batch normalization and dropout layers in the CNN model. Further, it is crucial to predict and evaluate the post-disaster damage for implementing advance measures and planning for the resources. The fine-tuning of the pre-trained visual geometry group (VGG 19) model is accomplished to predict the extent of damage and to perform automatic annotation for the image using the satellite imagery data of Greater Houston. VGG 19 is also trained using video datasets for classifying various types of severe weather events and to annotate the weather event automatically. An accuracy of 98% is achieved for hurricane damage prediction and 97% accuracy for classifying severe weather events. The results proved that the proposed models for hurricane intensity estimation and its damage prediction enhances the learning ability, which can ultimately help scientists and meteorologists to comprehend the formation of storm events. Finally, the mitigation steps in reducing the hurricane risks are addressed.
Extension of the Equivalent Material Concept to Compressive Loading: Combination with LEFM Criteria for Fracture Prediction of Keyhole Notched Polymeric Samples
Authors: Torabi Ali Reza , Hamidi Kazem , Shahbazian Behnam , Cicero Sergio , Berto Filippo
Published: April 2021

This work analyzes, both theoretically and experimentally, the fracture process of square specimens weakened by keyhole notches and subjected to compressive stresses. Two materials are covered: general-purpose polystyrene (GPPS) and poly(methyl methacrylate) (PMMA). Firstly, the load-carrying capacity (LCC) of the specimens is determined experimentally. Then, by using the equivalent material concept (EMC) for compressive conditions coupled with the maximum tangential stress (MTS) and the mean stress (MS) criteria, the LCC of the notched specimens is predicted. The results show that by using the approach proposed in the present investigation, not only can the critical loads in the keyhole notched polymeric specimens be precisely predicted, but also the corresponding compressive critical stress of the two mentioned polymers can be successfully estimated.
Deep Neural Network Analysis for Environmental Study of Coral Reefs in the Gulf of Eilat (Aqaba)
Authors: Raphael Alina , Dubinsky Zvy , Netanyahu Nathan S. , Iluz David
Published: April 2021

Coral reefs are undergoing a severe decline due to ocean acidification, seawater warming and anthropogenic eutrophication. We demonstrate the applicability of Deep Learning (DL) for following these changes. We examined the distribution and frequency appearance of the eleven most common coral species at four sites in the Gulf of Eilat. We compared deep learning with conventional census methods. The methods used in this research were natural sampling units via photographing the coral reef, line transects for estimating the cover percentage at the four test sites and deep convolutional neural networks, which proved to be an efficient sparse classification for coral species using the supervised deep learning method. The main research goal was to identify the common coral species at four test sites in the Gulf of Eilat, using DL to detect differences in coral cover and species composition among the sites, and relate these to ecological characteristics, such as depth and anthropogenic disturbance. The use of this method will produce a vital database to follow changes over time in coral reefs, identify trend lines and recommend remediation measures accordingly. We outline future monitoring needs and the corresponding system developments required to meet these.
lncRNA PVT1 in the Pathogenesis and Clinical Management of Renal Cell Carcinoma
Authors: Bohosova Julia , Kubickova Adela , Slaby Ondrej
Published: April 2021

LncRNA PVT1 (plasmacytoma variant translocation 1) has become a staple of the lncRNA profile in patients with renal cell carcinoma (RCC). Common dysregulation in renal tumors outlines the essential role of PVT1 in the development of RCC. There is already a plethora of publications trying to uncover the cellular mechanisms of PVT1-mediated regulation and its potential exploitation in management of RCC. In this review, we summarize the literature focused on PVT1 in RCC and aim to synthesize the current knowledge on its role in the cells of the kidney. Further, we provide an overview of the lncRNA profiling studies that have identified a more or less significant association of PVT1 with the clinical behavior of RCC. Based on our search, we analyzed the 17 scientific papers discussed in this review that provide robust support for the indispensable role of PVT1 in RCC development and future personalized therapy.
Comparison of Anticancer Activity of Dorycnium pentaphyllum Extract on MCF-7 and MCF-12A Cell Line: Correlation with Invasion and Adhesion
Authors: Koygun Gözde , Arslan Emine , Zengin Gökhan , Orlando Giustino , Ferrante Claudio
Published: April 2021

Dorycnium pentaphyllum subsp. haussknechtii is an important medicinal plant in several countries, including Turkey. This study aimed to evaluate the cytotoxicity of a crude extract of D. pentaphyllum subsp. haussknechtii against different breast cell lines to determine invasion, adhesion, and lipid peroxidation. The cytotoxic effects on MCF-7 breast cancer and MCF-12A as the immortalized cell line were examined by the XTT assay. Invasion and adhesion studies were performed according to the manufacturer’s kit procedure to IC50 values for 48 h. Lipid peroxidation was measured in the MCF-7 cell. A bioinformatics analysis was conducted to unravel the mechanism of action underlying antiproliferative effects, as well. According to XTT results, the tested extract showed a time- and a concentration-dependent cytotoxic effect. The most effective concentration was 100.5 µg/mL (48 h), which was selected for biological activities, such as apoptotic activity, invasion, adhesion, and lipid peroxidation assays. The extract caused tumoral cell death, and it did not have a cytotoxic effect on healthy human breast cells. Duplication times and measurement of CI analyses of cells were performed using the real-time cell analysis system xCELLigence. Finally, the bioinformatics analysis indicated the prominent role of quercetin as an extract component exerting a key role in the observed antiproliferative effects. This was supported by the micromolar/submicromolar affinity of quercetin towards proto-oncogene serine/threonine–protein kinase (PIM-1) and hematopoietic cell kinase (HCK), both involved in breast cancer. Altogether, our findings proposed that the extraction of the plant can be an effective strategy to isolate biomolecules with promising cytotoxic effects against breast cancer cells.
Isopeptidase Kinetics Determination by a Real Time and Sensitive qFRET Approach
Authors: Liu Yan , Shen Yali , Song Yang , Xu Lei , P. Perry J. Jefferson P. , Liao Jiayu
Published: April 2021

Isopeptidase activity of proteases plays critical roles in physiological and pathological processes in living organisms, such as protein stability in cancers and protein activity in infectious diseases. However, the kinetics of protease isopeptidase activity has not been explored before due to a lack of methodology. Here, we report the development of novel qFRET-based protease assay for characterizing the isopeptidase kinetics of SENP1. The reversible process of SUMOylation in vivo requires an enzymatic cascade that includes E1, E2, and E3 enzymes and Sentrin/SUMO-specific proteases (SENPs), which can act either as endopeptidases that process the pre-SUMO before its conjugation, or as isopeptidases to deconjugate SUMO from its target substrate. We first produced the isopeptidase substrate of CyPet-SUMO1/YPet-RanGAP1c by SUMOylation reaction in the presence of SUMO E1 and E2 enzymes. Then a qFRET analyses of real-time FRET signal reduction of the conjugated substrate of CyPet-SUMO1/YPet-RanGAP1c to free CyPet-SUMO1 and YPet-RanGAP1c by the SENP1 were able to obtain the kinetic parameters, Kcat, KM, and catalytic efficiency (Kcat/KM) of SENP1. This represents a pioneer effort in isopeptidase kinetics determination. Importantly, the general methodology of qFRET-based protease isopeptidase kinetic determination can also be applied to other proteases.