Miracle-dz/UniFAR-FTUs huggingface dataset:
‘Santaclara’ Strawberry

Since 2000, the high significance of strawberry (Fragaria ·ananassa Duch.) production in Huelva, located on the southwest coast of Spain, has led public and private institutions to increase the releasing of new cultivars that are well adapted to the climate characteristics and growing conditions in Huelva and other mild areas. The Spanish public breeding program has contributed to this effort with cultivars such as ‘Andana’, ‘Carisma’, ‘Marina’, ‘Medina’, ‘Aguedilla’, and ‘Fuentepina’ (Bartual et al., 1997, 2002; López-Aranda et al., 2004, 2005a, 2005b; Soria et al., 2010). ‘Santaclara’ is a new short-day cultivar recently released by the Spanish public breeding program, participating in the Spanish National Agricultural Research Institute (INIA), the Andalusian Institute of Agriculture and Fishery Research and Training (IFAPA) as well as the private partners Fresas Nuevos Materiales S.A. (FNM), Freshuelva Viveristas (FV), and Nuevas Técnicas de Fresa S.L. (NTF). The main purpose of this program is to breed cultivars for high yield, large fruit size for the fresh market, and high fruit quality and which outperform currently grown shortday cultivars in mild climate areas (e.g., ‘Camarosa’). ‘Santaclara’ is noted for its mediumto high-balanced agronomic, postharvest, and fruit quality traits (including nutraceutic values). ‘Santaclara’ also showsexcellent comparative qualities for the fresh market and adapts well to mild agroenvironments. This new cultivar was named after an old monastery founded in 1337 and located at Moguer (Huelva), place of birth of the 1965 Nobel prize awarded poet Juan Ramón Jiménez, who spent his youth in the strawberry production area around this nice village.
D-SPACE4Cloud: Towards Quality-Aware Data Intensive Applications in the Cloud

—The last years witnessed a steep rise in data generation worldwide and, consequently, the widespread adoption of software solutions claiming to support data intensive applications. Competitiveness and innovation have strongly beneﬁted from these new platforms and methodologies, and there is a great deal of interest around the new possibilities that Big Data analytics promise to make reality. Many companies currently engage in data intensive processes as part of their core businesses; however, fully embracing the data-driven paradigm is still cumbersome, and establishing a production-ready, ﬁne-tuned deployment is time-consuming, expensive, and resource-intensive. This situation calls for novel models and techniques to streamline the process of deployment conﬁguration for Big Data applications. In particular, the focus in this paper is on the rightsizing of Cloud deployed clusters, which represent a cost-eﬀective alternative to installation on premises. We propose a novel tool, integrated in a wider DevOps-inspired approach, implementing a parallel and distributed simulation-optimization technique that eﬃciently and eﬀectively explores the space of alternative resource conﬁgurations, seeking the minimum cost deployment that satisﬁes predeﬁned quality of service constraints. The validity and relevance of the proposed solution has been thoroughly validated in a vast experimental campaign including diﬀerent applications and Big Data platforms.
Early magnesium modiﬁcations as a surrogate marker of efﬁcacy of cetuximab-based anticancer treatment in KRAS wild-type advanced colorectal cancer patients

Background: KRAS wild-type mutational status is necessary but not sufﬁcient to get beneﬁt from epidermal growth factor receptor inhibition. Predictive markers are currently being evaluated. In this study, we investigated early hypomagnesemia as a predictor of efﬁcacy and outcome in terms of time to progression (TtP) and overall survival (OS) in a cohort of patients affected by advanced colorectal adenocarcinoma KRAS wild-type cetuximab-treated. Patients and methods: One hundred and forty-three patients affected by stage IV colorectal adenocarcinoma KRAS wild type receiving cetuximab + irinotecan (CTX + IRI) as third-line anticancer treatment and resistant to oxaliplatin- and irinotecan-based chemotherapy were retrospectively included. Magnesium plasma levels were measured before the ﬁrst day and 7, 14, 21 and 28 days after CTX + IRI infusion. Results: The median magnesium basal value showed a statistically signiﬁcant decrease after the start of CTX + IRI treatment (at 28 days, P < 0.0001). Patients with an early decrease of magnesium levels > 50% compared with the basal level had a higher tumor response rate (55.8% versus 16.7%, P < 0.0001), a longer TtP (6.3 versus 3.6, P < 0.0001) and a longer median OS (11.0 versus 8.1, P = 0.002). Conclusions: We have shown that early hypomagnesemia could be a predictor of efﬁcacy and outcome in those patients. Magnesium circulating level is an easy and inexpensive biomarker to routinely be detected in patients treated with cetuximab.
A New Transfer Learning Ensemble Model with New Training Methods for Gear Wear Particle Recognition

Aiming at solving the acquisition problems of wear particle data of large-modulus gear teeth and few training datasets, an integrated model of LCNNE based on transfer learning is proposed in this paper. Firstly, the wear particles are diagnosed and classified by connecting a new joint loss function and two pretrained models VGG19 and GoogLeNet. Subsequently, the wear particles in gearbox lubricating oil are chosen as the experimental object to make a comparison. Compared with the other four models’ experimental results, the model superiority in wear particle identification and classification is verified. Taking five models as feature extractors and support vector machines as classifiers, the experimental results and comparative analysis reveal that the LCNNE model is better than the other four models because its feature expression ability is stronger than that of the other four models.
A New Look at the Statistical Pattern Recognition.

Abstract : During the last two decades, statistical pattern recognition was well developed in theory and applications with the peak activity in the late sixties. The paper outlines important but unsolved problem areas in statistical pattern recognition and then takes a new and close look at some problems which are related to the finite sample size constraint. In an effort to bridge the gap between theory and practice, constructive solutions are provided for the problems: finite sample distance and information measures, finite sample nearest neighbor decision rule, contextual analysis, decision rules based on discrete and continuous measurements, and finite sample stochastic syntax analysis. It is concluded that there are still many challenging problems to be solved in statistical pattern recognition and every effort should be made such that the theory works well in practice.
Mobile robot path planning based on fusion of improved A* algorithm and adaptive DWA algorithm

For mobile robot autonomous navigation, path planning algorithm matters. For the reason that traditional A* algorithm is unable to achieve the global path planning perfectly and avoid unknown obstacles at the same time, the mobile robot algorithm combining improved A* with adaptive DWA was proposed for path planning. Firstly, safe distance judgment is added in A* algorithm to avoid the problem that the path crosses the vertex of obstacle diagonally. Secondly, the dynamic tangential method is used to smooth the path of A* algorithm, which is more consistent with the actual motion curve of mobile robot. Then, the path key points explored by improved A* algorithm are extracted as intermediate target points of the velocity adaptive DWA algorithm. Unknown obstacles avoidance based on the optimal global path planning is realized. Finally, the proposed fusion algorithm’s feasibility is verified through mobile robot ROS simulation.
A review on generative based methods for MRI reconstruction

Magnetic resonance imaging (MRI) is one of the most important methods for clinical diagnosis. However, the main drawback of MRI is the long imaging time, which will cause the moving artifact by patient movements. With the rapid development of the computing power of computer, deep learning is widely used in computer vision, natural language processing, visual recognition and so on. Meanwhile, a large number of reconstruction methods based on deep learning have also emerged. Recently, many generative models have been proposed to solve the perception quality problem that existed in fast MRI images. In this paper, we manage to survey the motivations and reconstruction strategies of generative-based methods published in journals and conferences over the past five years. First, the background and theoretical basis of MRI reconstruction are introduced. Secondly, the application of generative-based methods in MRI reconstruction field is comprehensively summarized and analyzed, including Generative Adversarial Network (GAN), Variational Autoencoder (VAE) and VAE-GAN. Then the advantages and disadvantages of the existing generative-based MRI reconstruction methods are discussed. Finally, several publicly available MR image datasets and evaluation metrics are presented, which can provide a reference for researchers and practitioners working in related domains. The conclusions and challenges are also given.
Binding of albumin-fusions to FcRn

Background: Albumin is utilized as carrier of biopharmaceuticals. FcRn binding regulates its long half-life. Results: ScFv-fusion to HSA only slightly reduces human FcRn binding, while HSA and scFv-HSA-fusions have very weak binding to rodent FcRn. Conclusion: Rodents have limitations for preclinical evaluation of HSA-fusions. Significance: We illuminate design of HSAfusions and highlight cross-species differences to consider prior to preclinical evaluation.
Mapping Quantitative Trait Loci for Salt Tolerance in Durum Wheat: Current Coping Strategy Adaptation to Climate Change

Durum wheat (Triticum turgidum L. var durum) is one of the most widely grown food grain crop in the world however, durum production and productivity is highly depending on many factors. Salinity is one of the major environmental factors limiting its growth. Improving salt tolerance in durum wheat is a big challenge in the world particularly in the consumer’s durum countries. The present study was conducted with the objective of investigating the genetic architecture of salt tolerance in F3 lines and their parents grown under salt stress to identify QTLs related to salt tolerance in durum wheat. A total of 163 F3 plants were subjected to salt stress at seedling stage. Fifty- seven polymorphic markers were used for genotyping F2 plants and their parents. Using composite interval mapping, two major QTLs for % DL were detected on chromosome 4B and 5B with 25% and 43% of the phenotypic variation respectively. of mapping QTLs controlling salt tolerance in F2:3 populations of durum wheat derived from a cross between Razzek (salt sensitive variety) and Saragolla (salt tolerant variety).
Myeloid-Derived Suppressor Cells and CCL5 Intragraft Regulatory T Cell Localization by Control of Transplant Tolerance and

Myeloid-derived suppressor cells (MDSC) are a heterogeneous population of immature cells that are believed to inhibit immune responses in the contexts of cancer and organ transplantation, in association with regulatory T cells (Treg). However, the way in which MDSC cooperate with Treg remains elusive. In this study, we used DNA microarrays to analyze gene expression in blood-derived MDSC from rat recipients of kidney allografts. We found CCL5 (Rantes), a chemotactic C-C motif 5 chemokine, to be strongly downregulated after treatment with a tolerizing regimen. The amount of CCL5 protein was also lower in the plasma of tolerant recipients, whereas intragraft CCL5 was unchanged. Because CCL5 is chemotactic for Treg, we hypothesized that a gradient of CCL5 between the graft and peripheral blood might contribute to the intragraft localization of Treg in tolerant animals. To test this hypothesis, we treated tolerant rat recipients of kidney allografts with recombinant rat CCL5 to restore normal plasma concentrations. This led to a strong reduction in intragraft Treg monitored by immunohistoﬂuorescence and by quantitative real-time PCR measurement of Foxp3 mRNA. Ultimately, this treatment led to an increase in serum creatinine concentrations and to kidney graft rejection after about a month. The kidney function of syngeneic grafts was not affected by a similar administration of CCL5. These data highlight the contribution of MDSC to the establishment of a graft-to-periphery CCL5 gradient in tolerant kidney allograft recipients, which controls recruitment of Treg to the graft where they likely contribute to maintaining tolerance. The Journal of Immunology , 2012, 188:
Modeling the SBC Tanzania Production-Distribution Logistics Network

The increase in customer expectation in terms of cost and services rendered, coupled with competitive business environment and uncertainty in cost of raw materials have posed challenges on effective supply chain engineering making it essential to do cost-benefit analysis before making final decisions on production-distribution logistics. This paper provides a conceptual model that provide guidance in supply chain decision making for business expansion. It presents a mathematical model for production-distribution of an integrated supply chain derived from current operations of SBC Tanzania Ltd which is a major supply chain that manages products' distribution in whole of Tanzania. In addition to finding the optimal cost, we also carried out a sensitivity analysis on the model so as to find ways in which the company can expand at optimal cost, while meeting customers' demands. Genetic algorithms is used to run the simulation for their efficient in solving combinatorial problems. Key words: Business environment, supply chain engineering, production-distribution, genetic algorithms, optimal cost.
Study of Electrical Properties of Poly (Vinyl Alcohol)/ Alumina (PVA/Al 2 O 3 )Nanocomposites

In the present work, nanocomposites of polyvinyl alcohol and alumina nanoparticles, have been prepared by casting technique. The alumina nanoparticles were added to polyvinyl alcohol with different concentrations are(0, 4, 8, 12 and16) wt. % . The electrical properties of nanocomposites were studied. The experimental results showed that the D.C electrical conductivity increases with increase the alumina nanoparticles concentrations and temperature. The activation energy of D.C electrical conductivity decreases with increase of alumina nanoparticle concentrations," and by find the samples by the microscopic photographs.
Scalable Optimization using Weighted Similarity Join With Map-Reduce

— Now a days wide range of data is incrementing very expeditious . and Due to immensely colossal development of Web applications and Web accommodations which deployed on the Internet , some quandaries are found regarding similarity in the data . So to find the similar data between the sets of data, similarity attribute join has received conciderable attention. Similarity couple is the campaign which finds the dyads of records, which having the carrying a lot of weight same old thing surrounded by the data. same old thing tie performs the literally importent nature in the dirty clothes of data, duplication of web page, clustering, plagiarism checking, malfeasance detection, idea of entity, combining data , etc . As the data is preferably it is problematic for homogeneous attribute join to evaluate the astronomical ammount of data. However, firm techniques of uniformity suffers from dominant performance degradation when the data is free . The earth mover's distance (EMD) is a absolutely important quantification which measures similarity surrounded by two scattered data around a place of ‘D’. As the EMD have computationaly high cost . already stated we are utilizing MapReduce which characterize hadoop technology to moderate the system. The feasible indexing of the Earth Mover’s Distance require the computational worth its weight in gold operators and thereby, the distance spaces based on the Earth Mover’s Distance regularly suffer from steep dimensionality. In term to use the issue of steep issues, weighted-based distance measurement can be developed. Here, an factual mapping-based data dividation context is furthermore proposed to ensure alike histogram tuples in proviso of EMD are supposing to the much the comparable minimized onus for besides verification
Death-Ligand 1 Protein−Protein Interaction

Here, we report a novel class of potent PD-L1/PD-1 inhibitors based on the rigidified biphenyl-inspired structure – terphenyls. Using in-silico docking, we designed and later experimentally shown the efficacy of terphenyl-based scaffolds to inhibit the PD-1/PD-L1 complex formation using various biophysical and biochemical techniques. We also report a high-resolution structure of the PD-L1 complex with our most potent inhibitors allowing the identification of key interactions with PD-L1 at the molecular level. Moreover, we show the efficacy of our most potent inhibitors at activating the antitumor response using primary T-cells derived from healthy donors. This effect was not observed even for the therapeutic antibodies. This makes our compounds prominent candidates for further optimization for anti-PD-L1 cancer treatments. Ranges of the activity of tested compounds in stage II optimization: “very good” (++++) – below 1 n M; “good” (+++) 1-10 n M, “moderate” (++) – 10-50 n M and “bad” (+) 50 n M – 1 µ M. Exact values are given for full IC 50 determination experiments. peak. Thin layer chromatography was performed on SigmaAldrich/Fluka precoated silica gel plates (0.20 mm thick, particle size 25 mm). Flash chromatography was performed on a Reveleris® X2 Flash Chromatography, using Grace® Reveleris Silica flash cartridges. The LC-MS measurements were performed on the UPLC-MS/MS system consisted of a Waters ACQUITY ® UPLC ® (Waters Corporation, Milford, MA, USA) coupled to a Waters TQD mass spectrometer (electrospray ionization mode ESI-tandem quadrupole). Chromatographic separations were carried out using the Acquity UPLC BEH (bridged ethyl hybrid) C 18 column; 2.1 × 100 mm, and 1.7 µm particle size, equipped with Acquity UPLC BEH C18 VanGuard pre-column; 2.1 × 5 mm, and 1.7 µm particle size. The column was maintained at 40°C, and eluted under gradient conditions using from 95% to 0% of eluent A over 10 min, at a flow rate of 0.3 mL min -1 . Eluent A: water/formic acid (0.1%, v/v); eluent B: acetonitrile/formic acid (0.1%, v/v). The purity of all final compounds determined using chromatographic LC-MS was >95%. HRMS were carried out by the Laboratory for Forensic Chemistry Faculty of Chemistry, Jagiellonian University with the microOTOF-QII (Bruker) spectrometer using ESI ionization technique.
Exploiting depth features for picking objects in cluttered environments

—This work presents a commercial collaborative robot that integrates a classical two-ﬁnger gripper and a custom-designed Universal Jamming Gripper (UJG). To exploit at best the potentialities of the UJG 3D perception technologies are employed for selecting suitable geometries for picking objects of different shapes. The proposed system is competitive in terms of grasping success rate with respect to two state-of-the-art systems and outperforms them in the searching time of the grasping point.
ASSESSING THE PROPERTIES OF LASER USAGE IN STRAIGHT PULP CAPPING COVERING THROUGH THE META-ANALYSIS

: Background: The writers of the current research assessed properties of lasers on consequence of straight pulp covering through the meta-analysis. The writers accomplished the works exploration on PubMed, Cochrane Library, Embassy, in addition China Nationwide Information Substructure, also the physical exploration of orientation leans of altogether recognized researches since outline of lasers in endodontics in 1973 finished June 2017 to September 2018 at Lahore General Hospital Lahore Pakistan. The writers methodically assessed researches that encountered presence standards also achieved the meta-analysis. Results: The writers designated 6 researches around 5 lasers schemes from 515 trainings to remain comprised in the current meta-analysis study. By means of the secure-belongings perfect, they originate not any substantial heterogeneity amongst those researches (c3 ¼ 0.85, P ¼ .98, I3 ¼ 0%). Its outcomes displayed that achievement proportion (90.2%) of laser sets remained advanced than that of 68.3% of controller sets, also variance remained statistically substantial (danger proportion, 2.36; 96% confidence interval, 2.24-2.50; P < .00002). Conclusions : On foundation of incomplete indication, usage of lasers efficiently enhanced prediction of straight pulp capping cure for enduring teeth.
SE-CapsNet: Automated evaluation of plant disease severity based on feature extraction through Squeeze and Excitation (SE) networks and Capsule networks

Diseases in plants harm the quantity of the overall food production as well as the quality of the yield. Early detection, diagnosis and treatment can greatly reduce losses, both economic and ecological. Intuitively, reduction in the use of agrochemicals due to timely detection of the disease, would greatly help in mitigating the environmental impact. In this paper, the authors have proposed an improved feature computation approach based on Squeeze and Excitation (SE) Networks, before processing by the original Capsule networks (CapsNet) for classification, for estimating the disease severity in plants. Two SE networks, one based on AlexNet and another on ResNet have been combined with Capsule networks. Leaf images for the devastating Late Blight disease occurring in the Tomato crop have been utilized from the PlantVillage dataset. The images, divided into four severity stages i.e. healthy, early, middle and end, are downscaled, enhanced and given as input to the SE networks. The feature maps generated from the two networks are separately given as input to the Capsule Network for classification and their performances are compared with the original CapsNet, on two image sizes 32X32 and 64X64. SE-Alex-CapsNet achieves the highest accuracy of 92.1% and SE-Res CapsNet achieves the highest accuracy of 93.75% with 64X64 image size, as compared to CapsNet that results in 85.53% accuracy. The classification accuracies of six state-of-the-art CNN models namely AlexNet, SqueezeNet, ResNet50, VGG16, VGG19 and Inception V3 are also presented for comparison purposes. Accuracy as well as precision, recall, F1-score, validation loss etc. measures have been recorded and compared. The findings have been validated by implementing the proposed approaches with another dataset, achieving similar resultant accuracy measures. The implementation was also accomplished with datasets after noise addition in six different variations, to verify the robustness of the proposed model. Based on the performances, the proposed techniques can be exploited for disease severity assessment in other crops as well and can be extended to other areas of applications such as plant species classification, weed identification etc. In addition to improved performance, with reduced image size, the proposed methodology can be utilized to create a mobile application requiring low processing capabilities, to be installed on reasonably priced smartphones for practical usage by farmers.