Miracle-dz/UniFAR-FTUs huggingface dataset:
WT1 Clone 6F-H2 Cytoplasmic Expression Differentiates Astrocytic Tumors from Astrogliosis and Associates with Tumor Grade, Histopathology, IDH1 Status, Apoptotic and Proliferative Indices: A Tissue Microarray Study.

OBJECTIVES This tissue microarray (TMA) immunohistochemical (IHC) study elucidates the role of Wilms' tumor 1 protein (WT1) in diagnosis and prognostication of astrocytic tumors. METHODS IHC was applied to 75 astrocytic lesions (18 astrogliosis and 57 astrocytic tumors) using antibodies directed against WT1 clone 6F-H2, isocitrate dehydrogenase 1(IDH1), Bcl2 and Ki67. WT1 IHC staining was evaluated and scored blindly by 2 pathologists. Bcl2 and Ki67 scores and labelling indices were assessed and IDH1 status determined. Statistical analysis was performed using the appropriate methodology. RESULTS WT1 cytoplasmic expression was detected in 89.5% of astrocytic tumors but not in astrogliosis. Positive WT1 differentiated astrocytic tumors (92.6% accuracy) and grade II diffuse astrocytomas (93.5% accuracy) from astrogliosis with high sensitivity, specificity and positive predictive values (p<0.001). Increased WT1 score significantly associated higher Bcl2 and Ki67 labelling indices, increasing WHO tumor grade and tumor's histopathologic type (p<0.05) showing staining pattern variability by tumor entity and cell type. Glioblastomas, gliosarcomas and subependymal giant cell astrocytomas were the most frequently WT1 expressing tumors with frequent +3 WT1 score. About 21.4% of pilocytic astrocytomas had +3WT1 score in association with increased Bcl2 and Ki67 indices. Low WT1 scores in grade II and III diffuse astrocytomas were linked to the high frequency of IDH1 positivity, and were associated with low Bcl2 and Ki67 labelling indices. In glioblastomas, WT1 significantly associated poor prognostic variables: older age, negative-IDH1 status, high Bcl2 and Ki67 labelling indices (p=0.04, <0.001, =0.001 and.
Study on microstructure and properties of resistance spot welding of Mg/Ti dissimilar materials

The dissimilar material of AZ31 Mg alloy and TA15 Ti alloy were welded by resistance spot welding. The influence of welding current on the interface of nuggets is studied. The microstructure, hardness, tensile strength and fracture surface of joints are analysed. The results indicate that when the electrode pressure and welding time are determined, the failure load of the joint can be improved by the increase in the welding current; however, excessively high welding current will cause cracks near the interface. Under the welding current of 14.5 kA, the electrode pressure of 0.22 MPa and the welding time of 200 ms, the maximum failure load of the joint can reach 4.79 kN, which is almost the same as the shear strength of AZ31B magnesium alloy base metal.
The effects of forest fragmentation on woodlot tree biotas in Southern Ontario

Certain predictions of island biogeographic theory were tested in Southern Ontario woodlots. The tree populations (> 4 cm DBH) of ten old growth woodlots were censused and each species in every woodlot assigned to one of three demographic classes (persisting, invading, going extinct). The domi- nant pattern of flux in all woodlots was that of species extinction, as predicted by island biogeographic theory. However, those species going extinct were mainly shade-intolerant, suggesting that succession, rather than forest fragmen- tation, may have been responsible for their disappearance. This was confirmed by multiple correlation analyses that showed no effects of area or isolation upon the numbers of species going extinct or upon the number of species persisting in woodlots. Instead, the results indicated that woodlots containing more species were losing more and that this loss was most severe in topographically diverse woodlots. The latter effect was interpreted to be the result of decreased illumi- nation in these woodlots, due to mutual shading of adjacent slopes by canopy trees. The results suggest that disturbance is an important species enrichment mechanism in these forest communities.
In vitro antioxidant activity of dewberry (Rubus caesius L. var. aquáticus Weihe. & Nees.) leaf extracts

In this study, the antioxidant properties and total phenolic and flavonoid contents of Rubus caesius L. var. aquaticus Weihe & Nees leaf extracts were examined. The radical scavenging capacity of four leaf extracts (methanol, ethanol, acetone and aqueous) was estimated against 2,2-diphenyl-1-picrylhydrazil (DPPH) and 2,2-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) assays. Total antioxidant capacity was tested by ferric reducing ability of plasma (FRAP) and total reducing capacity (TRC) procedures. Total contents of phenols and flavonoids were also determined. Total phenol content in the extracts was determined using Folin-Ciocalteu reagent and amounts ranged between 263.74 for ethanol to 366.27 mg gallic acid (GA)/g for acetone extracts. The amounts of flavonoids varied from 10.73 mg quercetin equivalent (QE)/g for aqueous to 75.83 mg QE/g for acetone extract. The highest antioxidant activity against DPPH and ABTS●+ radicals was that of the acetone extract. Values for FRAP varied between 3.00 μmol Fe+2/mg for the ethanol extract and 5.31 μmol Fe+2/mg for the methanol extract. The results obtained for total reducing capacity indicate that the acetone extract has the highest antioxidant potential. [Projekat Ministarstva nauke Republike Srbije, br. 173029]
Document Classification Method based on Graphs and Concepts of Non-rigid 3D Models Approach

Text document classification is an important re-search topic in the field of information retrieval, and so it is how we represent the information extracted from the documents to be classified. There exists document classification methods and tech-niques based on the vector space model, which doesn’t capture the relation between words, which is considered of importance to make a better comparison and therefore classification. For this reason, two significant contributions were made, the first one is the way to create the feature vector for document comparison, which uses adapted concepts of non-rigid 3D models comparison and graphs as a data structure to represent such documents. The second contribution is the classification method itself, which uses the representative feature vectors of each category to classify new documents.
Automatic Support for Multi-Domain Model Management

The process of developing complex systems often involves knowledge of engineers from multiple domains: e.g., to develop a robot one needs to combine expertise about mechanics, electronics, and software. Such domain-specific knowledge is often represented in a form of interdependent models, consequently a change in a model of one domain might impact a model from a different domain. Thus, identifying which models are affected due to a change is an important problem, which is further exacerbated due to heterogeneity of modeling notations used.The aim of this PhD research project is to facilitate model management in a multi-domain setting. In the earlier stage of this study, we investigated the available approaches used to manage models from different domains. We concluded that the available approaches are tool-dependent, and do not fully support co-evolution of the models. Additionally, previous research recommends to explicitly indicate the dependency between models in order to support the co-evolution of models from different domains. Since these models are created using different modeling notations we believe that it is not reasonable to develop a tool to parse every notation. Furthermore, it is possible that the source code of the model is missing, but engineers still have an image of the model. Thus, to ensure the maintenance of multi-domain systems we investigated the suitability of optical character recognition (OCR) as a uniform approach. We observed that even though OCR has shortcomings, it produces satisfactory results, and once the identified shortcomings are addressed, OCR can become a crucial technology to support the evolution of multi-domain systems. To this end we envision the development of an infrastructure where we can use OCR to identify relationships between models from different domains, store them in a structured manner making it easier to maintain the consistency of the entire system.
Abandoned pastures in eastern Amazonia. II. Nutrient stocks in the soil and vegetation

(1) Soil nutrient content was studied in relation to vegetation development on thirteen abandoned pastures differing in age from two to eight years and in intensity of pasture use from light to heavy. The surface soils of these sites had higher cation concentration and pH than those of undisturbed mature forest of the area, but similar phosphorus, organic matter and total nitrogen concentrations. (2) Soil nutrient concentration generally was independent of age or prior intensity of use in the pastures. Pasture use has a significant effect on soil nutrient concentration when the sites are first abandoned, but this does not persist. Nutrient uptake by successional vegetation may substantially reduce soil nutrient stocks. (3) Soil nutrient concentration was not significantly correlated with vegetation biomass, primary tree biomass or species richness, either among or within sites. (4) Nutrient depletion of pastures abandoned eight years previously relative to total nutrient stocks in mature forest depended on pasture disturbance. Sites previously subjected to low use were only depleted of N, moderate-use sites were depleted of N and K, and heavy-use sites were depleted of N, K, Mg and possibly Ca and P. There was less nutrient depletion than the relatively small biomass of the successional sites would indicate. This was due to a higher proportion of nutrient-rich leaves and fine branches, enriched soil nutrient stocks relative to mature forest and, on the lightly disturbed sites, a large pool of nutrients in unburned woody residue. (5) Decomposition of unburned woody residue may provide one-half of the nutrients taken up by vegetation during eight years of recovery following light pasture use, but only 15% following moderate use, and none following the most intense use. (6) General schemes of succession following deforestation for pasture development are presented. Recovery after disturbance is complex, and follows different patterns depending on the intensity of pasture use.
Accessory spleen after splenectomy mimicking adrenal tumor: a case report

Accessory spleen is a congenital anomaly, those asymptomatic generally do not need surgical intervention. Retroperitoneal heterotopic accessory spleens are often misdiagnosed, especially in cases that have undergone splenectomy for various reasons. In these cases, most patients are received unnecessary resection for misdiagnosed as adrenal gland tumors, ganglioblastomas or paragangliomas preoperatively. We report on a case of accessory spleen mimicking a left adrenal tumor. A 47-year-old man who had undergone splenectomy 25 years ago was referred to our department because of hypertension, CT scan revealed a mass about 4 cm in the left adrenal gland. The mass was confirmed nonfunctional through hormonal evaluation of the adrenal gland. We found the mass was not originated from the adrenal gland intraoperation even they were close together. Histopathologic examination of the surgical specimen revealed an accessory spleen. The differences between the case and other accessory spleen patients include a history of hypertension, the location of the mass and a history of splenectomy, and these also are the main reasons for our misdiagnosis. This case remind us that an accessory spleen should be considered for the biochemically inactive mass in left adrenal area, even the splenectomy has been performed before.
Deep Learning Driven Networking Applications

: Deep learning architectures are inspired from the architecture of human brains since brains also have a deep architecture. With the progress and advancement of the Internet, networking research has become one of the most important fields. Since the networking deals with difficult problems that require effectual results, deep learning algorithms work very well in network domain by leveraging their capabilities for advanced network performance. Though, many researchers have exploited deep learning in networking but have been found to be dispersed in the literature. The present research has been focused on to contribute to the limited literature on applications of deep learning in computer networking. This research has focused on deep learning researches in the network related areas such as network trafﬁc classiﬁcation, WSN and social networks, network ﬂow prediction and mobility prediction. The present research will facilitate the readers to ﬁnd some motivating and stimulating research areas to pursue in the significant ﬁeld of deep learning in networking domain and move ahead in the forward direction.
Towards Mitigating Spreading of Coronavirus via Mobile Devices

—Recently, impact of coronavirus has been witnessed by almost every country around the world. To mitigate spreading of coronavirus, a fundamental strategy would be reducing chance of healthy people from being exposed to it. Having observed the fact that most viruses come from coughing/sneezing/runny nose of infected people, in this work we propose to detect such symptom events via mobile devices (e.g., smartphones, smart watches, and other IoT devices) possessed by most people in modern world and, to instantly broadcast locations where the symptoms have been observed to other people. This would be able to signiﬁcantly reduce risk that healthy people get exposed to the viruses. The mobile devices today are usually equipped with various sensors including microphone, accelerometer, and GPS, as well as network connection (4G, LTE, Wi-Fi), which makes our proposal feasible. Further experimental evaluation shows that coronavirus-like symptoms (coughing/sneezing/runny nose) can be detected with an accuracy around 90%; in addition, the dry cough (more likely happening to COVID-19 patients) and wet cough can also be differentiated with a high accuracy.
An Assessment of MJO Circulation Influence on Air–Sea Interactions for Improved Seasonal Rainfall Predictions over East Africa

Rainfall variability affects agriculture planning and water resource management. In extreme flood and drought events, lives and property are destroyed. This study aims to improve East Africa’s seasonal rainfall prediction by determining the impact of the standard eight Real-time Multivariate Madden–Julian Oscillation (MJO) (RMM) phases on rainfall and using sea surface temperature (SST) response to test the predictability of theMarch–May (MAM) andOctober–December (OND)main rainfall seasons over a period of 33 years (1981–2013). Pearson correlation patterns, composite maps, and regression analyses were applied, and the Brier skill score (BSS) and correlation coefficients (CC) were utilized as validation metrics. Low correspondence of rainfall to MJO 1 and MJO 2 was observed except for the months of November and December. Seasonally, MAM andOND correlation patterns withMJO 2 revealed enhanced rainfall over the highlands and insignificant correspondence over coastal areas. Conversely, enhancedMJO 8 corresponded to suppressed rainfall during the June–August season over the coast and the eastern highlands. MAM rainfall was shown to be predictable using Maritime Continent SST indices, with a BSS of 0.41, while OND rainfall was shown to be predictable using Atlantic and Maritime Continent SSTs with a BSS of 0.62. Positive and negativeMJO 2 corresponded, respectively, to enhanced and suppressed rainfall during the OND season and was confirmed to be related to, respectively, a positive and negative IndianOcean dipole (IOD). An IODyear could possibly be triggered by changes in MJO 2 amplitudes observed as early peaks between February and June.
Anti-fatigue activity of extract form the submerged fermentation of Ganoderma Lucidum using Radix astragali as substrate.

1 SUMMARY The objective of this study was to use Radix astragali as an alternative growth medium for cultivation of Ganoderma lucidum and to investigate anti-fatigue activity of its fermentation product. The results suggested that the growth of mycelial of G. lucidum can be inhibited when using Radix astragali as a substrate, but the extracellular polysaccharide in the fermentation production of G. lucidum increased significantly compared to that of general medium. Different doses of its product lengthened significantly the exhaustive swimming time, increased the contents of hepatic glycogen, and reduced significantly the levels of serum urea nitrogen (SUN) in blood compared to the control. However, the accumulation of blood lactic acid (BLA) or accelerate the clearance of BLA in the group which was administrated with the extract of submerged fermentation of G. lucidum using Radix astragali as substrate (GR) were significantly inhibited compared to the control. These results suggested that the extract of submerged fermentation of G. lucidum using Radix astragali as substrate might significantly alleviate physical fatigue of the mice in vivo.
Efficient Convex-Lifting-Based Robust Control of a Chemical Reactor

Efficient operation of chemical reactors is a very challenging task. Continuous stirred-tank reactors (CSTRs) are important devices of the process industry. CSTRs have complex non-linear behaviour. CSTRs operation is influenced by various uncertain parameters. The industrial operation of CSTRs still offers lots of opportunities to become more energy efficient. The optimisation-based robust control design evaluates optimal control action in the presence of the uncertain parameters subject to the constraints on manipulated variables and controlled variables, taking into account economic criteria. The main contributions of this project are designing and tuning of the advanced control strategy for a CSTR. Particularly, the robust control method based on the convex lifting is designed for a laboratory CSTR. The novel convex-lifting-based robust control strategy considering the improved control law is developed to optimise the control performance in real-time control. The presented case study is the first analysis investigating the application of robust convex-lifting based control on a CSTR. The reference tracking problem is investigated under various working conditions. In the case study, the controllers are designed with (i) single tunable robust positive invariant (RPI) set and (ii) multiple tunable RPI sets. The designed offset-free convex-lifting-based robust controllers are compared with respect to their control performance and the computational complexity.
Distributed Minimum Error Entropy Algorithms

Minimum Error Entropy (MEE) principle is an important approach in Information Theoretical Learning (ITL). It is widely applied and studied in various fields for its robustness to noise. In this paper, we study a reproducing kernel-based distributed MEE algorithm, DMEE, which is designed to work with both fully supervised data and semi-supervised data. The divide-and-conquer approach is employed, so there is no inter-node communication overhead. Similar as other distributed algorithms, DMEE significantly reduces the computational complexity and memory requirement on single computing nodes. With fully supervised data, our proved learning rates equal the minimax optimal learning rates of the classical pointwise kernel-based regressions. Under the semi-supervised learning scenarios, we show that DMEE exploits unlabeled data effectively, in the sense that first, under the settings with weak regularity assumptions, additional unlabeled data significantly improves the learning rates of DMEE. Second, with sufficient unlabeled data, labeled data can be distributed to many more computing nodes, that each node takes only O(1) labels, without spoiling the learning rates in terms of the number of labels. This conclusion overcomes the saturation phenomenon in unlabeled data size. It parallels a recent results for regularized least squares (Lin and Zhou, 2018), and suggests that an inflation of unlabeled data is a solution to the MEE learning problems with decentralized data source for the concerns of privacy protection. Our work refers to pairwise learning and non-convex loss. The theoretical analysis is achieved by distributed U-statistics and error decomposition techniques in integral operators.
Evaluating Agronomic Performance and Yield Stability of Improved Bread Wheat Varieties Across Low Moisture Stress Areas of Guji Zone, Southern Oromia

In Ethiopia, many low moisture stress tolerant bread wheat varieties that consistently have high yield in a variety of environments had been developed by different research centers. However, none of these cultivars are tested and selected for low moisture stressed areas of Guji Zone. As a result, the study was conducted at two mid land districts of Guji Zone (Adola and Wadera) at six testing sites (Dufa, Gobicha, Kiltu Sorsa, Chalo Sigida, Tulam and Andoya Haro) to evaluate the performance and yield stability of eight released low moisture stress tolerant bread wheat varieties in different low land areas of Guji Zone prior to varietal recommendation. Seven low moisture stress tolerant improved bread wheat varieties (Adel Werer, Amibara, Fantale, Gambo, Lucy, Mekele 01 and Mekele 02) were used as testing materials with local check (farmers variety. The analysis of variance (ANOVA) indicated presence of highly significant differences at (P≤0.01) among the evaluated bread wheat varieties, the testing sites and GEI for most of the characters studied. Different parametric and non-parametric stability models were used to assess stability and yield performance of the evaluated varities across the testing environment. The highest grain yield (25.66qt/ha) was recorded for Mekele 02 followed by Mekele 01 (24.13qt/ha). But, low yield of 10.0qt/ha was obtained from local (control) variety. Therefore, based on the agronomic performance and yield stability analysis, variety Mekele 02 followed Mekele 01 were high yielding and comparably stable across the tested environments and then recommended for production to the study areas.
Thailand’s Low-Cost Carrier Airline Industry: Is the Services Marketing Mix the Elixir for Economic Growth and Prosperity?

In 2017, low-cost carrier (LCC) airlines consistently grew at a faster pace compared to the world’s average growth. In 2017, LCCs carried an estimated 1.2 billion passengers and accounted for approximately 30% of the world’s total scheduled passengers. In Southeast Asia, passenger traffic grew by approximately 10% in 2017, where six of the region’s 10 countries recorded double-digit growth. Southeast Asian airlines now have 1,600 airplanes on order, in addition to an active fleet of close to 2,000 airplanes, with LCCs currently accounting for approximately 70% of Thailand’s domestic seat capacity. Competition, however, amongst the LCCs has become fierce, with fare reductions being a common tactic to fill seats. Therefore, the researchers sought out to determine which aspects play the greatest roles in an LCC’s competitiveness, and used multi-stage random sampling to obtain 320 passenger respondents’ opinions who were departing on either Thai AirAsia, Thai Lion Air, Thai Smile, or Nok Air from one of Bangkok’s two main airports. Results determined that the service marketing mix of promotion, place, price, and services play the most important role in LCC competitiveness.