Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Polymorphism and Transplantation Success in the Mediterranean Seagrass Posidonia oceanica

We studied the role of genetic variability of donor beds in establishing transplantation criteria for the Mediterranean seagrass Posidonia oceanica. Horizontal rhizomes, collected from three geographically distinct populations, were transplanted into a common bed at a highly human‐impacted locality. The transplantation site was located near one of the donor populations. After three years, the shoots collected in the population adjacent to the transplanting site showed the lowest growth performance. Genetic variability, assessed through the analysis of hypervariable microsatellite regions, and growth performance followed a similar trend. The shoots growing and branching at the highest rate were those collected from populations with the highest heterozygosity values, despite greater geographic distance. No genetic differences were found between the transplanted shoots and shoots from donor meadows, as expected due to the low rate of sexual reproduction in P. oceanica and the short time that had passed since the transplants. The problem of affecting the local gene pool by the introduction of foreign genotypes could arise, but introduction of new alleles could balance the degradation of genetic variability caused by human impact. In general our study suggests that the genetic variability of source material is an important aspect to consider in the development of seagrass restoration strategies.
Investigation on the Interface Thermal Resistance of Copper-Titanium

The heat interface thermal resistance between the dielectric layer (Cu) and the barrier layer (Ti) in the Through-silicon via (TSV) are studied with molecular dynamics (MD) methods. The Cu/Ti interface thermal resistance is temperature dependent. Within the temperature from 293 K to 693 K, the resistance increases as the temperature rises. The increase in temperature causes an increase in atomic vacancy defects at the interface, and the interfacial voids degrade the heat transfer performance. However, when the temperature is higher than 693 K, the vacancies transfer from the interface to the second nearest or further neighbor on the adjacent atomic layers, which reduces the lattice mismatches at high temperatures. Besides, the single Ti atoms cross through the interface to match the Cu lattice vacancy when the temperature is higher than 693 K. As a result, interface thermal resistance decreases as temperature rises from 693 K to 1093 K.
Deep Learning and Computer Vision-based a Novel Framework for Himalayan Bear, Marco Polo Sheep and Snow Leopard Detection

Wildlife plays a vital role in balancing the environment. It also provides stability to different natural processes of nature. In recent year, there are many animals which are facing the danger of extinction. The reason for animal extinction is natural occurrences such as climatic heating, cooling, or changes in sea levels. In literate, many techniques are proposed to detect and classify animals, but each technique has a limitation. In this paper, we propose a novel framework using deep convolutional neural networks (D-CNN) and k Nearest Neighbors (kNN) to detect animals. The dataset contains four class snow leopard, Marco polo sheep, Himalayan bear, and other animals. Many D-CNN like AlexNet, ResNet-50, VGG-19, and inception v3 are used to extract features. The experimental results verify that inception v3 integrated with kNN outperforms other D-CNNs. It also has more accuracy of 98.3% with a classification error of 2%, which is quite negligible.
Analyses of Indexing Techniques on Uncertain Data With High Dimensionality

Deploying a solution for handling critical decision-based problem efficiently requires the processing of high-dimensional data. Over the years, due to modern technological advancement, unprecedented volume of uncertain data is been captured and this has necessitated the need to organize such data for better data access performance. To this effect, the use of indexing technique for supporting, organizing, and storing of uncertain data with high dimensionality has become pertinent. However, the choice of an indexing technique to improve search performance is highly influenced by the properties of the underlying data set, data construction methods employed by the indexing structure, and the query types it supports. This paper is motivated to conduct an extensive performance analysis among existing indexing techniques, namely: <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>-tree, <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>*-tree and <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>-tree, in order to realize the most efficient indexing structure for organizing, storing and ultimately improving search performance over uncertain data with high dimensionality. The results of the analyses with regard to CPU processing time and number of nodes visited clearly show the superiority of <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>-tree over <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>-tree and <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>*-tree, as its superiority holds for different data set sizes, data distributions, number of dimensions and even with varying selectivity ratio.
An Efficient Mining of Infrequent Weighted Itemset and Optimization using Transaction Mapping Technique

--- Mining infrequent Itemset is fundamental method for mining association rules as well as for many other frequent Itemset mining tasks. In existing methods for Mining frequent and infrequent has been implemented using FP growth or Apriori algorithm. Also numerous experimental results have demonstrated that these techniques are scalable to the mining process. In this paper, a novel Transaction Mapping Technique for filtering the association rules for infrequent Itemset in the transaction dataset is proposed. This Proposed technique produces improved performance for sparse data items. Furthermore, an open and closed Itemset extraction of rules using optimization techniques is presented. The experimental results prove that this proposed technique is highly scalable and consume less memory compared to the state of art techniques.
Low-Phase-Noise VCO using Cascode Q-enhancement Connection and Source-Damping- Resistance Technique

In this paper a cascode CMOS VCO with a source-damping-resistance technique is proposed to reach low phase noise. A cascode cross-coupling connection is able to enhance the quality factor (Q) of the tank. Furthermore, a source-damping-resistance technique is adopted to improve phase noise originating from different mechanism. An optimum damping resistance is studied for different offset of phase noise. Measured oscillation frequency is from 2.26 GHz to 2.14 GHz. The measured phase noise at 1 MHz offset is −125.9 dBc/Hz at 2.14 GHz, and the core power consumption is 3.0mW from a supply voltage of 1.5V. A FOM of −187.6 at 1MHz offset is obtained. Two different phase noise improvement techniques merged simultaneously are demonstrated.
LLC Resonant Converter Utilizing Parallel-Series Transformers Connection

This paper presents a single-phase full bridge LLC resonant converter which utilizes parallel-series connected transformers. This converter can be used in onboard battery charger of electric vehicle. This circuit achieves zero turn on voltage switching (ZVS) of power switches as well as zero turn off current switching (ZCS) of rectifier diodes. In order to have a better heat dissipation, the large single transformer of conventional converter have been substituted with two equal transformers. Furthermore, a more compact structure is resulted. Additionally, parallel connection of transformers in the primary side reduces the turns of primary windings which leads to decrease the Joule loss of coils in high power applications. The principle of operation, steady state analysis and the design procedure of proposed converter have been described in detail. Finally, simulation results have been presented to verify the effectiveness of the proposed converter. The results show higher efficiency, lower output voltage ripple and better space utilization.
A Structure and Circuit Coupling Modeling Method for Flexible Interconnection Points and Transmission Performance of Gold Belt in Microwave Modules

Microwave circuits and devices are widely used in the fields of communication, detection and chip components, etc. With the increasing demand of high speed, high reliability, miniaturization and integration of microwave electronic devices, the change of interconnection configuration in microwave components will have a significant impact on signal transmission performance. In this paper, a parametric representation model of flexible interconnection point configuration of gold belt in microwave module is established. Based on the parametric representation model, a coupling model is established to correlate the interconnection point configuration with signal transmission performance. This will provide theoretical guidance for engineers in interconnection design and transmission performance control, and ensure the design and service quality of high-performance electronic equipment.
Pancreatic beta cell selective deletion of mitofusins 1 and 2 (Mfn1 and Mfn2) disrupts mitochondrial architecture and abrogates glucose-stimulated insulin secretion in vivo

Background and aimsMitochondria are highly dynamic organelles, fundamental to cellular energy homeostasis. Mitochondrial metabolism of glucose is essential for the initiation of insulin release from pancreatic beta cells. Whether mitochondrial ultra-structure, and the proteins controlling fission and fusion, are important for glucose recognition are unclear. Mitochondrial fusion is supported by proteins including mitofusin 1 (MFN1), mitofusin 2 (MFN2) and optic atrophy (OPA1), and fission by dynamin-related protein 1 (DRP1). Here, we generated mice with beta cell-selective, adult-restricted deletion of Mfn1 and Mfn2 ({beta}Mfn1/2-KO), and explored the impact on insulin secretion and glucose homeostasis in vivo and in vitro. Materials and methodsC57BL/6J mice bearing Mfn1 and Mfn2 alleles with loxP sites, were crossed to animals carrying an inducible Cre recombinase at the Pdx1 locus (PdxCreERT). Isolated islets were used for live beta cell fluorescence imaging of cytosolic (Cal-520) or mitochondrial (Pericam) free Ca2+ concentration and membrane potential (TMRE). Mitochondrial network characteristics were quantified using super resolution fluorescence and transmission electron microscopy. Beta cell-beta cell connectivity was assessed using the Pearson (R) analysis and Monte Carlo simulation in intact mouse islets. Intravital imaging was performed in mice injected with an adeno-associated virus to express the cytosolic Ca2+ sensor GCaMP6s selectively in beta cells and TMRM to visualise mitochondria using multiphoton microscopy. Results{beta}Mfn1/2-KO mice displayed higher fasting glycaemia than control littermates at 14 weeks (8.6 vs 6.4 mmol/L, p>0.05) and a >five-fold decrease in plasma insulin post-intraperitoneal glucose injection (5-15 min, p<0.0001). Mitochondrial length, and glucose-induced Ca2+ accumulation, mitochondrial hyperpolarisation and beta cell connectivity were all significantly reduced in {beta}Mfn1/2-KO mouse islets. Examined by intravital imaging of the exteriorised pancreas, antiparallel changes in cytosolic Ca2+ and mitochondrial membrane potential, observed in control animals in vivo, were suppressed after Mfn1/2 deletion. ConclusionMitochondrial fusion and fission cycles are essential in the beta cell to maintain normal mitochondrial bioenergetics and glucose sensing both in vitro and in the living mouse. Such cycles may be disrupted in some forms of diabetes to impair mitochondrial function and, consequently, insulin secretion.
Spintronic Memories: From Memory to Computing-in-Memory

Spintronic memory has been considered as one of the most promising nonvolatile memory candidates to address the leakage power consumption in the post-Moore’s era. To date, the spintronic magnetic random access memory (MRAM) family has mainly evolved in four-generation technology advancement, from toggle-MRAM (product in 2006), to STT-MRAM (product in 2012), to SOT-MRAM (intensive R&D today), and to VCMA- MRAM (intensive R&D today). In addition, another spintronic memory, named racetrack memory (RM), proposed in 2008, has also evolved in two generations from domain wall (DW) based RM to skyrmion-based RM. On the other hand, from the architectural perspective, data transfer bandwidth and the related power consumption has become the most critical bottleneck in vonNeumann computing architecture, owing to the separation of the processor and the memory units and the performance mismatch between the two. Realization of the unity of computing and memory in the same place has opened up a promising research direction of computing-in-memory (CIM). Spintronic memory could be a promising technology to implement the CIM paradigm, owing to its intrinsic processing capability. Lots of interests have been attracted and a number of attempts have been made in this field, within both MRAM and RM. In this paper, we perform a mini review on the R&D evolution of spintronic memories: from memory to computing-in-memory. Particularly, we will introduce our recent work on advanced spintronic memories as well as CIM paradigms implemented within spintronic memories.
Voice Keyword Recognition Based on Spiking Convolutional Neural Network for Human-Machine Interface

In this paper, a spiking convolutional neural network (SCNN) model for voice keyword recognition is presented. The model consists of an input pre-processing layer, a spiking neural network (SNN) layer with build-in filter bank and the convolutional neural network (CNN) layers. A 16-channel infinite impulse response (IIR) filter bank with energy detector extracts power from the voice signal band and converts it to spikes via the SNN layer. The spiking rate in a defined time window is used as the inputs to the following CNN layers for classification. The network is trained using a voice digit dataset, while the weights of the convolutional layers are adjusted through the training of spike-integration results obtained from the spiking layer. This model has been implemented for voice keyword recognition and achieved 96.0 % accuracy. The combination of SNN and CNN reduces the overall number of layer and neuron in the system without compromise in classification accuracy. It is suitable for low-power hardware implementation in edge devices for human machine interface (HMI) applications.
KnetMiner: a comprehensive approach for supporting evidence‐based gene discovery and complex trait analysis across species

Abstract The generation of new ideas and scientific hypotheses is often the result of extensive literature and database searches, but, with the growing wealth of public and private knowledge, the process of searching diverse and interconnected data to generate new insights into genes, gene networks, traits and diseases is becoming both more complex and more time‐consuming. To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open‐source and containerized for easy use. KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists explore and understand the biological stories of complex traits and diseases across species. It features fast algorithms for generating rich interactive gene networks and prioritizing candidate genes based on knowledge mining approaches. KnetMiner is used in many plant science institutions and has been adopted by several plant breeding organizations to accelerate gene discovery. The software is generic and customizable and can therefore be readily applied to new species and data types; for example, it has been applied to pest insects and fungal pathogens; and most recently repurposed to support COVID‐19 research. Here, we give an overview of the main approaches behind KnetMiner and we report plant‐centric case studies for identifying genes, gene networks and trait relationships in Triticum aestivum (bread wheat), as well as, an evidence‐based approach to rank candidate genes under a large Arabidopsis thaliana QTL. KnetMiner is available at: https://knetminer.org.
3D Synaptic Organization of the Rat CA1 and Alterations Induced by Cocaine Self-Administration

The hippocampus plays a key role in contextual conditioning and has been proposed as an important component of the cocaine addiction brain circuit. To gain knowledge about cocaine-induced alterations in this circuit, we used Focused Ion Beam milling/Scanning Electron Microscopy (FIB/SEM) to reveal and quantify the 3D synaptic organization of the stratum radiatum of rat CA1, under normal circumstances and after cocaine-self administration (SA). Most synapses are asymmetric (excitatory), macular-shaped, and in contact with spine heads. After cocaine-SA, the size and complexity of both asymmetric and symmetric (inhibitory) synapses increased but no changes were observed in the synaptic density. This work constitutes the first detailed report on the 3D synaptic organization in the stratum radiatum of the CA1 field of cocaine-SA rats. Our data contribute to the elucidation of the normal and altered synaptic organization of the hippocampus, which is crucial for better understanding the neurobiological mechanisms underlying cocaine addiction.
Electricity Demand Prediction Using Fractal Dimension of Load Sequence

Electricity demand prediction is an important task in smart power grid. The measured load values along time compose the load time series. The electrical load time series is a sequence that not only is crumple in many parts but also has self-similarity characteristic. So, a load sequence can be considered as a fractal. The fractal dimension shows how the fractal shape is complex and snuggle. Therefore, the fractal dimension is a good property of the load sequence that can be used beside the original load history for load forecasting. A short-term load forecasting method is proposed in this work that benefits the fractal features for characterizing the consumed electricity. The extracted features are fed into two different predictors: support vector regression and feed forward neural network. The experiments on two electricity datasets of Ireland and Canada show decreasing of the load prediction error using fractal features.
Asparagine accumulation in chicory storage roots is controlled by translocation and feedback regulation of asparagine biosynthesis in leaves

The presence of acrylamide (AA), a potentially carcinogenic and neurotoxic compound, in food has become a major concern for public health. AA in plant-derived food mainly arises from the reaction of the amino acid asparagine (Asn) and reducing sugars during processing of foodstuffs at high temperature. Using a selection of genotypes from the chicory germplasm we performed Asn measurements in storage roots and leaves to identify genotypes contrasting for Asn accumulation. We combined molecular analysis and grafting experiments to show that leaf to root translocation controls asparagine biosynthesis and accumulation in chicory storage roots. We could demonstrate that Asn accumulation in storage roots depends on Asn biosynthesis and transport from the leaf, and that a negative feedback loop by Asn on CiASN1 expression impacts Asn biosynthesis in leaves. Our results provide a new model for asparagine biosynthesis in root crop species and highlight the importance of characterizing and manipulating asparagine transport to reduce AA content in processed plant-based foodstuffs.
Circuits for integrating learnt and innate valences in the fly brain

Animal behavior is shaped both by evolution and by individual experience. In many species parallel brain pathways are thought to encode innate and learnt behavior drives and as a result may link the same sensory cue to different actions if innate and learnt drives are in opposition. How these opposing drives are integrated into a single coherent action is not well understood. In insects, the Mushroom Body Output Neurons (MBONs) and the Lateral Horn Neurons (LHNs) are thought to provide the learnt and innate drives, respectively. However their patterns of convergence and the mechanisms by which their outputs are used to select actions are not well understood. We used electron microscopy reconstruction to comprehensively map the downstream targets of all MBONs in Drosophila larva and characterise their patterns of convergence with LHNs. We discovered convergence neurons that receive direct input from MBONs and LHNs and compare opposite behaviour drives. Functional imaging and optogenetic manipulation suggest these convergence neurons compute the overall predicted value of approaching or avoiding an odor and mediate action selection. Our study describes the circuit mechanisms allowing integration of opposing drives from parallel olfactory pathways.
Within-population sperm competition intensity does not predict asymmetry in conpopulation sperm precedence

Postcopulatory sexual selection can generate coevolutionary arms races between the sexes resulting in the rapid coevolution of reproductive phenotypes. As traits affecting fertilisation success diverge between populations postmating prezygotic barriers to gene flow may evolve. Conspecific sperm precedence is a form of such isolation thought to evolve early during speciation yet has mostly been studied between species. Here we show conpopulation sperm precedence between Drosophila montana populations. Using genomic data to estimate divergence times and patterns of gene flow between populations, we show gene flow has played a considerable role during divergence. We find conpopulation sperm precedence is asymmetric and is concordant with asymmetry in non-competitive postmating prezygotic reproductive isolation. These results suggest these phenomena have a shared mechanism, but we show that this asymmetry is unrelated to the strength of postcopulatory sexual selection acting within populations. We tested whether overlapping foreign and coevolved ejaculates within the female reproductive tract altered fertilisation success but found no effect. Our results show that neither time since divergence nor sperm competitiveness predicts the strength of postmating prezygotic reproductive isolation. We suggest that divergence of postcopulatory phenotypes resulting in postmating prezygotic isolation is potentially driven by cryptic female choice, or mutation order divergence.