Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Impedance Monitoring During Electroporation Processes in Vegetal Tissue Using a High-Performance Generator

Classical application of electroporation is carried out by using fixed protocols that do not clearly assure the complete ablation of the desired tissue. Nowadays, new methods that pursue the control of the treatment by studying the change in impedance during the applied pulses as a function of the electric field are being developed. These types of control seek to carry out the treatment in the fastest way, decreasing undesired effects and treatment time while ensuring the proper tumour ablation. The objective of this research is to determine the state of the treatment by continuously monitoring the impedance by using a novel versatile high-voltage generator and sensor system. To study the impedance dynamics in real time, the use of pulses of reduced voltage, below the threshold of reversible electroporation, is tested to characterise the state-of-the-treatment without interfering with it. With this purpose, a generator that provides both low voltage for sense tissue changes and high voltage for irreversible electroporation (IRE) was developed. In conclusion, the characterisation of the effects of electroporation in vegetal tissue, combined with the real-time monitoring of the state-of-the-treatment, will enable the provision of safer and more effective treatments.
Bringing the Kok effect to light: A review on the integration of daytime respiration and net ecosystem exchange

Net ecosystem exchange (NEE) represents the difference between carbon assimilated through photosynthesis, or gross primary productivity (GPP), and carbon released via ecosystem respiration (ER). NEE, measured via eddy covariance and chamber techniques, must be partitioned into these fluxes to accurately describe and understand the carbon dynamics of an ecosystem. GPP and daytime ER may be significantly overestimated if the light inhibition of foliar mitochondrial respiration, or “Kok effect,” is not accurately estimated and further integrated into ecosystem measurements. The light inhibition of respiration, a composite effect of multiple cellular pathways, is reported to cause between 25-100% inhibition of foliar mitochondrial respiration, and for this reason needs to be considered when estimating larger carbon fluxes. Partitioning of respiration between autotrophic and heterotrophic respiration, and applying these scaled respiratory fluxes to the ecosystem-level proves to be difficult, and the integration of light inhibition into single and continuous measures of ecosystem respiration will require new interpretations and analysis of carbon exchange in terrestrial ecosystems.
Cloning and characterization of a gene coding for a class I chitin synthase from Fusarium graminearum

The full-length sequence of a gene coding for a class I chitin synthase (Chs) from Fusarium graminearum (Fg) was isolated by genome walking, using gene-specific primers and walking libraries constructed with a panel of eight restriction enzymes. Analysis of the gene (named FgChs1) and its corresponding cDNA revealed an open reading frame of 2703 bp interrupted by two introns of 120 and 55 bp, respectively. FgChs1 is a single-copy gene. Its deduced amino acid sequence shows 77% identity to Neurospora crassa Chs3 and 73% identity to Ophiostoma novo-ulmi ChsA in the conserved central region (residues 153 to 800). Quantitative reverse transcription – polymerase chain reaction was used to compare the levels of FgChs1 mRNA at different stages of development. This showed that FgChs1 was developmentally regulated, with peaks of expression during spore germination and mycelial growth. The first significant increase in FgChs1 mRNA levels was detected after 1 day of hyphal growth while the second peak was apparent after 5 days. This result suggests that FgChs1 may play an important role in fungal growth and development.
p27Kip1, a double-edged sword in Shh-mediated medulloblastoma

Medulloblastoma, a brain tumor arising in the cerebellum, is the most common solid childhood malignancy. The current standard of care for medulloblastoma leaves survivors with life-long side effects. Gaining insight into mechanisms regulating transformation of medulloblastoma cells-of-origin may lead to development of better treatments for these tumors. Cerebellar granule neuron precursors (CGNPs) are proposed cells-of-origin for certain classes of medulloblastoma, specifically those marked by aberrant Sonic hedgehog (Shh) signaling pathway activation. CGNPs require signaling by Shh for proliferation during brain development. In mitogen-stimulated cells, nuclear localized cyclin dependent kinase (cdk) inhibitor p27 (Kip1) functions as a checkpoint control at the G1- to S-phase transition by inhibiting cdk2. Recent studies have suggested cytoplasmically localized p27kip1 acquires oncogenic functions. Here, we show that p27Kip1 is cytoplasmically localized in CGNPs and mouse Shh-mediated medulloblastomas. Tranasgenic mice bearing an activating mutation in the Shh pathway and lacking one or both p27Kip1 alleles have accelerated tumor incidence compared to mice bearing both p27Kip1 alleles. Interestingly, mice heterozygous for p27Kip1 have decreased survival latency compared to p27Kip1-null animals. Our data indicate that this may reflect the requirement for at least one copy of p27Kip1 for recruiting cyclin D/cdk4/6 to promote cell cycle progression yet insufficient expression in the heterozygous or null state to inhibit cyclin E/cdk2. Finally, we find that mis-localized p27Kip1 may play a positive role in motility in medulloblastoma cells. Together, our data indicate that the dosage of p27Kip1 plays a role in cell cycle progression and tumor suppression in Shh-mediated medulloblastoma expansion.
The Suitability of Graph Databases for Big Data Analysis: A Benchmark

: Digitalization of our society brings various new digital ecosystems (e.g., Smart Cities, Smart Buildings, Smart Mobility), which rely on the collection, storage, and processing of Big Data. One of the recently popular advancements in Big Data storage and processing are the graph databases. A graph database is specialized to handle highly connected data, which can be, for instance, found in the cross-domain setting where various levels of data interconnection take place. Existing works suggest that for data with many relationships, the graph databases perform better than non-graph databases. However, it is not clear where are the borders for speciﬁc query types, for which it is still efﬁcient to use a graph database. In this paper, we design and perform tests that examine these borders. We perform the tests in a cluster of three machines so that we explore the database behavior in Big Data scenarios concerning the query. We speciﬁcally work with Neo4j as a representative of graph databases and PostgreSQL as a representative of non-graph databases.
Traffic Sign Recognition using CuDNN

– Object detection has been a prime area of focus for researchers across the world for several years now. With newer technology being developed at a very rapid rate, scientists have forever been on a quest to develop models with highest accuracy. This paper aims at providing a novel approach to the problem of detecting objects and comparing it with the existing methods. It has been observed that CuDNN (NVIDIA CUDA® Deep Neural Network library) can leverage the potential of GPU (Graphical Processor Units) over conventional CPU cores and can hence achieve significantly higher accuracy than other traditional neural network models. We use CLAHE (Contrast Limited Adaptive Histogram Equalization) for the preprocessing of the data and then take the help of CuDNN library to enhance the results even further. Moreover, computations done using CuDNN have been observed to run notably faster.
The Case of the Colombian Queen Conch Fishery

The rebuilding of collapsed fisheries is a major challenge for fisheries science and management, requiring multi-faceted evaluations to assess the current and potential performance of recovering fisheries. Single-dimensional analyses such as stock abundance assessments are only partially effective in determining the best course of action for fisheries in this condition. This study relied on the Fishery Performance Indicators (FPIs) – a rapid assessment tool for measuring economic, social and ecological outcomes from fishery management systems – to analyze performance and rebuilding challenges for the Colombian queen conch (Lobatus gigas) fishery. The ecological and economic indicators clearly revealed an underperforming fishery, even when compared to other fisheries in developing countries. Performance improved in the community (social) indicators, reflecting the socioeconomic and cultural importance of the fishery, even when operating at subsistence levels. Results also showed that rebuilding is constrained not only by ecological factors but also for managerial and economic reasons. In particular, bioeconomic modeling is strongly recommended for the fishery in order to add useful economic advice to the policymaking process, which has been traditionally guided by biological considerations such as maximization of sustainable yield.
Panel Data Analysis of International Trade in West African Sub Region

West African countries have suffered so much under poor economic growth rate; this issue is largely caused as a result of the poor performance in international trade, which is one of the most essential tools for economic growth. The study investigated the factors influencing international trade in West African Sub region. A Pooled OLS, Fixed Effect Model, and Random Effect Model were adopted to fit the panel regression model for the panel data sets. The study divided the models into three in other to have proper view of factors influencing international trade across West African Sub-region, each model contain the same independent variables and different dependent variables. The result shows that fixed effect model was accurate for the study. Also from the study it was observed that for the first model which use import as dependent variable, gross domestic production, foreign direct investment, and exchange rate are positively significant to import which implies that all the regressor variable influence import across west African sub region positively, while only GDP and FDI are positively significant to export and only FDI is positively significant to trade balance (TB). We therefore conclude that foreign direct investment is the key macro-economic variable that positively influences the policy of international trade across West African over the period of consideration.
Online Handwritten Character Recognition for Telugu Language Using Support Vector Machines

— A system for recognition of online handwritten telugu characters has been presented for Indian writing systems. A handwritten character is represented as a sequence of strokes whose features are extracted and classified. Support vector machines have been used for constructing the stroke recognition engine. The results have been presented after testing the system on Telugu scripts.
Diagnosis and Classification of Brain Tumor

The aim of this paper is to provide an outline on cerebrum (brain) tumor diagnoses for folks that are new to virtual medical image processing. The strange improvement of cells within the brain is brain Tumor. Detection of brain tumor at early stage is feasible with image processing and the development of device learning. For early detection of extraordinary adjustments in tumor cells, Computer tomography, Magnetic resonance imaging strategies are used. Early detection and identification of tumor is the handiest manner to get remedy. Brain tumor is classed into two kinds benign and malignant tumor. Various imaging processing strategies had been proposed in latest couple of years for detection and class of brain tumor. Automatic segmentation method the usage of clustering and convolution neural community gives nice consequences. The PCA has been used for reducing the features from the segmented area gives superb outcomes compared to other techniques. For classification of brain tumors diverse algorithms any such Support Vector Machine, Artificial Neural Network, K-Nearest Neighbor are reviewed. These strategies correctly paintings on CT and MRI images
Molecular identification of the extinct mountain goat, Oreamnos harringtoni (Bovidae)

Campos, P. F. , Willerslev, E. , Mead, J. I. , Hofreiter, M. & Gilbert, M. T. P. 2009: Molecular identification of the extinct mountain goat, Oreamnos harringtoni (Bovidae). Boreas, 10.1111/j.1502‐3885.2009. 00111.x. ISSN 0300‐9483.
Delay discounting and risky choice: Meta-analytic evidence regarding single-process theories

Preferences about delayed rewards and preferences about risk are central to the literature on decision making. Several proposals suggest that such preferences arise from a single process and thus predict strong associations between preferences about delay and risk. Although there is a wealth of data on this association, the evidence is inconclusive; some studies have reported significant associations but many have not. Consequently, it is unclear whether the association between delay preferences and risk preferences is strong enough to support single-process theories. To further explore this question, we took a meta-analytic approach surveying 26 studies totaling 32 effect sizes. Results reveal a small to moderate association between risk preferences and delay preferences. This result provides little support for existing proposals because the observed relationship is no stronger than associations observed between either delay preferences or risk preferences and other variables. Moderating variables provide some explanation for inconsistencies across studies. Implications, including the apparent discrepancy between this literature and the conventional construct of impulsivity, are also discussed.
Massive Mimo and Beamforming Techniques of 5G Networks

The need of wireless communication is increasing day to day life and it is mostly depends on spectral efficiency and bandwidth. The current operating wireless technologies are ranging between 300MHz to 3GHz band; consequently the 5G wireless network depends up on high frequency millimeter wave band ranging between 3GHz to 300GHz. The spectral efficiency can be improved by using Massive Multiple Input Multiple Output (MIMO) Technology. In this paper we are discussing MIMO along with some emerging technologies are present in 5G, they are Millimeter Wave, Beam Forming, and Beam Steering. By using these technologies the capacity is increased, higher data rates will be obtained, latency can be reduced and enhanced quality of service will occur.
Hybrid Bio-inspired Approach for Feature Subset Selection

In this paper a methodology for feature selection is proposed. We have used the Ant Colony, Artificial Bee and Firefly Algorithms to select the most relevant features in a dataset. A Genetic Algorithm can create a new population of chromosomes using as initial population the populations generated by the three algorithms used (ACO, ABC and FA) instead a random one. The main objective of this selection is to reduce the features number, by eliminating redundant and irrelevant attributes, while trying to maintain or improve the classifier performance using neural network algorithm. The goal of our study is to achieve a balance between the classification accuracy and the size of the feature subsets selected using new hybrid algorithm based on bio-inspired algorithms.
An Intrusion Detection Model Based on Deep Long Short Term Recurrent Neural Network

With the rapid increase of network based services and internet users on various platforms are becoming the major targets of attacks. Intrusion detection is the process of monitoring the attacks and analyzing their signs and violation of security policies which are occurring in the systems or networks. Intrusion Detection System is a prominent research area in security analysis and evaluation. In order to identify the attack type, we proposed Deep Long Short Term Memory-Recurrent Neural Network (DLSTM-RNN) method with seven optimizers and 500 epochs to train and test a dataset. Initially the data transformation, normalization are used to preprocess the data. The preprocessed train and test data is given input to the model. The bench mark NSL-KDD dataset used to train and test the model. The results are obtained for five-class classification (attack types). The model outperformed with adamax optimizer on NSL-KDD dataset. The metrics accuracy, detection rate, and false alarm rate areevaluated to ascertain the detection efficacy of the model. We compare the model to existing convolutional learning methods.
Rigid Payload Transport System with Loosely Coupled Robots

Payload Transport is one of the newly explored areas, where a robot or a formation of robots are used to transport a payload from one point to another. Multiple approaches to payload transportation have been carried out using either ground robots or aerial vehicles like drones. A single robot or a drone has an advantage in terms of its low cost and less dependency on other robots but has a single point of failure. Hence various coordinated multi-robot systems came to existence to overcome the disadvantage. We focus on building a multi-robot system which moves in a formation carrying a load from one place to another. We support failure resiliency where if any robot fails, there is always a backup robot available to replace the failed robot. In this thesis, we present a mechanism to handle the failures in a multi-robot system by robot replacement. In our work, we use multiple non-holonomic differential drive robots which moves in a formation following a known trajectory. A formation here refers to a group of robots carrying a payload on them while maintaining the desired shape. A decentralized leader-follower based formation control law is used to move the robots in a formation. There are recharging stations present along the route taken by the robots. When the robots in formation reach a recharging station, a check is performed based on the remaining robots battery capacity. If any of the robots in the formation has low battery capacity, it therefore, cannot continue the task of transportation and is replaced by a charged robot present at the recharging station. In this work, we focus on building a custom robot that is capable of carrying a payload with a lifting mechanism and an accurate localization. The kinematics and dynamics are developed for a differential drive, non-holonomic robot. We also developed a friction and battery model and derived a relation to between the robot dynamics and the power capacity of the battery. Further, we presented a novel concept which ensures that the multi-robot system lasts much longer than the battery life of an individual robot. Each robot is considered having two operating states: Charging and Discharging. A robot in charging state means that the robot is present at the recharging station or is fully charged, waiting the replacement to happen and does not contribute to payload transport, whereas a robot in discharging state means a robot in formation transporting a payload and therefore is under continuous discharge. A system-level optimization is presented to decide upon these operational state (charging or discharging) of each robot in the system. The optimization provides the ids of the robots that need replacement at a particular recharge hub. We also present an algorithm for robot replacement to increase the running life of the Multi-Robot Payload Transport System. We also introduce the concept of robot battery recharge hubs which are
Minimizing Energy Consumption of SRAM Arrays by Optimally Determining the Data Retention Voltage

— This paper presents a new method to minimize the energy consumed by SRAM arrays by optimally measuring the data retention voltage (DRV) that would keep its data integrity during standby mode and in the presence of process variations. The proposed technique relies on implementing an on-chip test circuit to test the integrity of the SRAM array at different voltages. The on-chip test circuit consists of a built-in-self-test (BIST) unit, a DC-DC converter and a Test Control Unit (TCU). The proposed test circuit was implemented in 90nm TSMC technology and simulated using Spectre. Different DRVs were determined for each array depending on the variations present in it. Our circuit showed that the SRAM array under test could be operated at 80mV resulting in considerable energy saving.
Factores Determining the Large-Scale Seasonal Abundance of the Common Kestrel in Central Spain

Summary. Factores determining the large-scale seasonal abundance of the common kestrel in Central Spain. Details of landscape variables that determine the large-scale distribution of the common kestrel in Central Spain are provided. The most influential variables largely differ between breeding and wintering seasons, although purely geographical variables decide the overall trend in the species' distribution: the kestrel is more common in northwestern, moderately hilly locations below 900–1,000 m a.s. l. of the study area. Despite its association with farmland, agricultural use variables do not determine the year-round distribution of the kestrel. Moreover, breeding abundance is negatively correlated with farmland. These results exemplify the need to compare seasonal patterns of abundance/distribution in order to be able to state the habitat requirements of a species with precision.
The Uniform Variants of the Glivenko-Cantelli and Donsker Type Theorems for a Sequential Integral Process of Independence

In the analysis of statistical data in biomedical treatments, engineering, insurance, demography, and also in other areas of practical researches, the random variables of interest take their possible values depending on the implementation of certain events. So in tests of physical systems (or individuals) on duration of uptime values of operating systems depend on subsystems failures, in insurance business insurance company payments to its customers depend on insurance claims. In such experimental situations, naturally become problems of studying the dependence of random variables on the corresponding events. The main task of statistics of such incomplete observations is estimating the distribution function or what is the same, the survival function of the tested objects. To date, there are numerous estimates of these characteristics or their functionals in various models of incomplete observations. In this paper investigated the asymptotic properties of sequential processes of independence of the integral structure and uniform versions of the strong law of large numbers and the central limit theorem for integral processes of independence by indexed classes are established. The obtained results can be used to construct statistics of criteria for testing a hypothesis of independence of random variables on the corresponding events.
SUBMASSIVE: Resolving Subclass Cycles in Very Large Knowledge Graphs

Large knowledge graphs capture information of a large number of entities and their relations. Among the many relations they capture, class subsumption assertions are usually present and expressed using the rdfs:subClassOf construct. From our examination, publicly available knowledge graphs contain many potentially erroneous cyclic subclass relations, a problem that can be exacerbated when different knowledge graphs are integrated as Linked Open Data. In this paper, we present an automatic approach for resolving such cycles at scale using automated reasoning by encoding the problem of cycle-resolving to a MAXSAT solver. The approach is tested on the LOD-a-lot dataset, and compared against a semi-automatic version of our algorithm. We show how the number of removed triples is a trade-off against the efficiency of the algorithm. The code and the resulting cycle-free class hierarchy of the LOD-a-lot are published at www.submassive. cc.