Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-Wide Identification of Seven Polyamine Oxidase Genes in Camellia sinensis (L.) and Their Expression Patterns Under Various Abiotic Stresses

Polyamines (PAs) in plant play a critical role in growth and development and in response to environmental stress. Polyamine oxidase (PAO) is a flavin adenine dinucleotide dependent enzyme that plays a major role in PA catabolism. For the first time, PAO genes in tea plant were screened for the whole genome-wide and seven CsPAO genes were identified, which were named CsPAO1-7. Phylogenetic tree analysis revealed seven CsPAO protein sequences classed into three groups, including clade I, III, and IV. Compared with other plants, the tea plant lacked clade II members. Genetic structure and tissue specific expression analysis showed that there were significant differences among members of the CsPAO gene family. Among members of the CsPAOs family, CsPAO4 and CsPAO5 contain more introns and are highly expressed in various organizations. CsPAO1, CsPAO4, and CsPAO5 genes were cloned and expressed heterologously to verify theirs function. Heat map showed high response of CsPAO5 to drought stress, while CsPAO1 and CsPAO2 were sensitive to changes in nitrogen nutrition. Furthermore, exogenous abscisic acid (ABA) treatment indicated that the expression of most CsPAO genes in roots and leaves was significantly induced. In the root, Spm content increased significantly, while Put and Spd content decreased, suggesting that ABA has great influence on the biosynthesis of PAs. Anaerobic treatment of picked tea leaves showed that the decomposition of PAs was promoted to a certain extent. The above data help to clarify the role of CsPAO in response abiotic and nitrogen nutritional stresses in tea plants, and provide a reference perspective for the potential influence of PAs on the tea processing quality.
Breeding biology of Pacific white-fronted geese

Nidification; caracteristiques du nid; densite des nids et leur repartition; taille de la couvee; epoque d'eclosion; succes des couvees (etude menee en 1977-79, dans le delta du Yukon-Kuskokwim, Alaska)
Towards Systems Education for Artificial Intelligence: A Course Practice in Intelligent Computing Architectures

With the rapid development of artificial intelligence (AI) community, education in AI is receiving more and more attentions. There have been many AI related courses in the respects of algorithms and applications, while not many courses in system level are seriously taken into considerations. In order to bridge the gap between AI and computing systems, we are trying to explore how to conduct AI education from the perspective of computing systems. In this paper, a course practice in intelligent computing architectures are provided to demonstrate the system education in AI era. The motivation for this course practice is first introduced as well as the learning orientations. The main goal of this course aims to teach students for designing AI accelerators on FPGA platforms. The elaborated course contents include lecture notes and related technical materials. Especially several practical labs and projects are detailed illustrated. Finally, some teaching experiences and effects are discussed as well as some potential improvements in the future.
ScraTouch: Extending Touch Interaction Technique Using Fingernail on Capacitive Touch Surfaces

We present ScraTouch, an interaction technique using fingernails, as a new input modality for capacitive touch surfaces. We revealed the differences between the fingertip and the nail from the viewpoint of capacitance touch sensing. Differentiating between fingertip and fingernail touches requires only tens of milliseconds worth of shunt current data from the capacitive touch sensing mechanism, thus external sensors or other hardware are unnecessary. Owing to the small amount of friction generated when a nail touches the surface, not only tapping but also touch gestures by sliding a nail can be easily performed. In our initial investigation, we confirmed that setting a simple threshold on the measured shunt current for recognition works robustly.
Analyzing Genetic Testing Discourse on the Web Through the Lens of Twitter, Reddit, and 4chan

Recent progress in genomics has enabled the emergence of a flourishing market for direct-to-consumer (DTC) genetic testing. Companies like 23andMe and AncestryDNA provide affordable health, genealogy, and ancestry reports, and have already tested tens of millions of customers. Consequently, news, experiences, and views on genetic testing are increasingly shared and discussed on social media. At the same time, far-right groups have also taken an interest in genetic testing, using them to attack minorities and prove their genetic “purity. ” In this article, we set to study the genetic testing discourse on a number of mainstream and fringe Web communities. We do so in two steps. First, we conduct an exploratory, large-scale analysis of the genetic testing discourse on a mainstream social network such as Twitter. We find that the genetic testing discourse is fueled by accounts that appear to be interested in digital health and technology. However, we also identify tweets with highly racist connotations. This motivates us to explore the connection between genetic testing and racism on platforms with a reputation for toxicity, namely, Reddit and 4chan, where we find that discussions around genetic testing often include highly toxic language expressed through hateful and racist comments. In particular, on 4chan’s politically incorrect board (/pol/), content from genetic testing conversations involves several alt-right personalities and openly anti-semitic rhetoric, often conveyed through memes.
A Unified Tensor Framework for Clustering and Simultaneous Reconstruction of Incomplete Imaging Data

Incomplete observations in the data are always troublesome to data clustering algorithms. In fact, most of the well-received techniques are not designed to encounter such imperative scenarios. Hence, clustering of images under incomplete samples is an inquisitive yet unaddressed area of research. Therefore, the aim of this article is to design a single-stage optimization procedure for clustering as well as simultaneous reconstruction of images without breaking the intrinsic spatial structure. The method employs the self-expressiveness property of submodules, and images are stacked as the lateral slices of a three-dimensional tensor. The proposed optimization method is designed to extract a sparse t-linear combination tensor with low multirank constraint, consisting of a unique set of linear coefficients in the form of mode-3 fibers and the spectral clustering is performed on these fibers. Simultaneously, the recovery of lost samples is accomplished by twisting the entire lateral slices of the data tensor and applying a low-rank approximation on each slice. The prominence of the proposed method lies in the simultaneous execution of data clustering and reconstruction of incomplete observations in a single step. Experimental results reveal the excellence of the proposed method over state-of-the-art clustering algorithms in the context of incomplete imaging data.
Digging deeper: towards a better understanding of transfer learning for human activity recognition

Transfer Learning is becoming increasingly important to the Human Activity Recognition community, as it enables algorithms to reuse what has already been learned from models. It promises shortened training times and increased classification results for new datasets and activity classes. However, the question of what exactly is transferred is not dealt with in detail in many of the recent publications, and it is furthermore often difficult to reproduce the presented results. Therefore we would like to contribute with this paper to the understanding of transfer learning for sensor-based human activity recognition. In our experiment use weight transfer to transfer models between two datasets, as well as between sensors from the same dataset. As source- and target- datasets PAMAP2 and Skoda Mini Checkpoint are used. The utilized network architecture is based on a DeepConvLSTM. The result of our investigation shows that transfer learning has to be considered in a very differentiated way, since the desired positive effects by applying the method depend very much on the data and also on the architecture used.
Architecture-Accuracy Co-optimization of ReRAM-based Low-cost Neural Network Processor

Resistive RAM (ReRAM) is a promising technology with such advantages as small device size and in-memory-computing capability. However, designing optimal AI processors based on ReRAMs is challenging due to the limited precision, and the complex interplay between quality of result and hardware efficiency. In this paper we present a study targeting a low-power low-cost image classification application. We discover that the trade-off between accuracy and hardware efficiency in ReRAM-based hardware is not obvious and even surprising, and our solution developed for a recently fabricated ReRAM device achieves both the state-of-the-art efficiency and empirical assurance on the high quality of result.
On exploiting Gamification for the Crowdsensing of Air Pollution: a Case Study on a Bicycle-based System

Cities all over the world struggle with air pollution. With the ever-increasing concentration of people in urban areas, more and more people suffer from the negative effects of air pollutants. Crowdsensing systems are a unique chance to increase the users' awareness of this problem and to provide more fine-grained data to policymakers so that they can adopt appropriate strategies. In this paper, we present a crowdsensing system to collect air pollution in urban and suburban environments through the use of bicycles. It consists of a Web application, enriched with gamification elements, that communicates with a portable low-cost sensor. The user interface of such a system, as well as the adopted gamification mechanisms, has been designed by involving a group of target users, with the aim of better meeting users' preferences and needs, and, then, better engaging them.
Accelerating RRT Motion Planning Using TCAM

Real-time motion planning is important for robot movement. In motion planning, path search and collision detection are two performance bottlenecks. In this paper, we adopt a range-based matching scheme with ternary content-addressable memories (TCAMs) to accelerate the processes of both nearest neighbor search and collision detection. In our approach, the nearest node search and collision detection can be both processed in a few TCAM lookup cycles. The evaluation shows that the TCAM-based accelerator is 236× faster than CPU for motion planning tasks. It is 5.4× faster and at least 8.8× more energy-efficient than a state-of-the-art dedicated ASIC-based accelerator.
Resource partitioning in three syntopic forest-dwelling European bat species (Chiroptera: Vespertilionidae)

Abstract Resource partitioning in syntopic bats is mainly studied through comparative ecomorphology and behavioural ecology. We used simultaneous radio-tracking of syntopic forest-dwelling bats, the western barbastelle (Barbastella barbastellus), Bechstein’s bat (Myotis bechsteinii) and the brown long-eared bat (Plecotus auritus) to analyse individual and species-specific foraging habitat and roost site selection. We estimated niche partitioning through Euclidean distance analyses to landscape elements and through Pianka’s niche overlap index for roost types. We analysed a total of 2278 fixes and 54 roosts of 15 bats. Western barbastelles and Bechstein’s bats foraged significantly closer to the forest than brown long-eared bats. Female barbastelles also foraged significantly closer to forest tracks, which were avoided by female brown long-eared and Bechstein’s bats. Female and male brown long-eared bats were found close to open habitats, which were avoided by the other two species. We found a high niche overlap in terms of roost height, but low overlap in terms of roost type and roost tree species among the three bat species. Individual niche variation may allow these specialised forest-dwelling bat species to minimise competition when foraging and roosting in syntopy.
Fast ECO Leakage Optimization Using Graph Convolutional Network

At the very late design stage, engineering change order (ECO) leakage optimization is often performed to swap some cells for the ones with lower leakage, e.g. the cells with higher threshold voltage (Vth) or with longer gate length. It is very effective but time consuming due to iterative nature of swap and timing check with correction. We introduce a graph convolutional network (GCN) for quick ECO leakage optimization. GCN receives a number of input parameters that model the current timing information of a netlist as well as the connectivity of the cells in a form of a weighted connectivity matrix. Once it is trained, GCN predicts exact Vth (with Vth given by commercial ECO leakage optimization as a reference) of 83% of cells, on average of test circuits. The remaining 17% of cells are responsible for some negative timing slack. To correct such timing as well as to remove any minimum implant width (MIW) violations, we propose a heuristic Vth reassignment. The combined GCN and heuristic achieve 52% reduction of leakage, which can be compared to 61% reduction from commercial ECO, but with less than half of runtime.
Analog Circuit Implementation of LIF and STDP Models for Spiking Neural Networks

Spiking Neural Networks (SNN) is one special implementation of Artificial Neural Networks (ANN), where the input signals are encoded in the temporal relationship between consecutive spikes (spike trains) instead of real-numbered values. Nevertheless, SNN is believed to be a closer representation of the biological neural system, because it imitates the current spikes that are transmitted between neurons in real biological systems. Practical and simplified SNN models include the Leaky Integrate-and-Fire (LIF) function of the neurons and the Spiking Timing Dependent Plasticity (STDP) function of the synapses. While most ANN architectures can be implemented with digital logic gates, SNN is more suitable to be implemented in analog circuits. In this paper, we propose revised analog circuit implementations of SNN neurons with LIF and STDP functions. Compared with previous works by other researchers, our proposed analog designs use fewer components and can be cascaded to form a complete neural system. The circuits are designed and simulated with SMIC 55nm CMOS LP process. The simulated results demonstrate that the analog neural system can work under a very small current (less than 10 µA) and voltage supply (1.0 Volts), and consumes less power consumption than digital implementations.
Relationships between the number of integrated pressure sensors and step count accuracy using smart insoles

Several studies have demonstrated that instrumented insoles enable the quantification of steps taken in people with or without walking limitations. However, the number and location of embedded pressure sensors in an insole vary considerably in the literature, resulting in variable step count accuracies. The objective of this study was to determine optimum locations to consider with a minimum pressure sensors (fewer than 5), without altering the accuracy of step detection. An insole was equipped with five pressure sensors (FSRs) located under the heel (FSRH), the first (FSRM1), the third (FSRM3) and the fifth (FSRM5) metatarsal heads, and the great toe (FSRT). Step detection was based on single or combined pressure signals using a step count algorithm, in twelve healthy people who walked during six-minutes at self-selected comfortable speeds. Results showed that there was a statistically significant difference (F=17.8, p<0.05) between the average step count accuracy of single and combinations from two-to-five pressure signals. The best accuracies per sensor combinations were as follows: FSRM1-FSRM5 (99.0±0.9%), FSRM3-FSRM5-FSRT (99.3±0.7%), FSRH-FSRM3-FSRM5-FSRT (99.5±0.4%) and all five FSRs (99.5±0.4%). For a single pressure signal, the best accuracy was 98.0±2.3% with FSRH. These results suggest that the combination of at least two pressure sensors in the insole can yield an accuracy of 99% for step counting in adult people
Field study of a tactile sound awareness device for deaf users

Sound can provide important information about the environment, human activity, and situational cues but can be inaccessible to deaf or hard of hearing (DHH) people. In this paper, we explore a wearable tactile technology to provide sound feedback to DHH people. After implementing a wrist-worn tactile prototype, we performed a four-week field study with 12 DHH people. Participants reported that our device increased awareness of sounds by conveying actionable cues (e.g., appliance alerts) and 'experiential' sound information (e.g., bird chirp patterns).
Class I histone deacetylases in lung development and regeneration

The generation and development of the mammalian lung requires an elegantly regulated molecular program to control cell number, lineage specification, as well as morphogenetic remodeling. Histone deacetylases (HDACs) are a group of critical epigenetic factors that can mediate genome-wide transcriptional repression. However, the functional roles of HDACs in lung development and regeneration have not been previously characterized. In my dissertation, I utilized a series of mouse genetic models, ex vivo and in vitro assays to determine the functions of three members of class I HDAC family, HDAC1, HDAC2 and HDAC3 in lung epithelial development and regeneration. These studies reveal that HDAC1 and HDAC2 are redundantly required for the development and regeneration of Sox2+ proximal lung endoderm progenitors via regulation of Bmp4 and cell cycle inhibitors, while HDAC3 is critical for the alveolar epithelial remodeling and spreading during lung sacculation and alveologenesis. These findings demonstrate strong evidence for the crucial contributions of HDACs to lung development and regeneration, and provide novel insights into potential therapeutic directions for human lung diseases. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Cell & Molecular Biology First Advisor Edward E. Morrisey
Virtual Paving

Tactile pavings are public works for visually impaired people, designed to indicate a particular path to follow by providing haptic cues underfoot. However, they face many limitations such as installation errors, obstructions, degradation, and limited coverage. To address these issues, we propose Virtual Paving, which aims to assist independent navigation by rendering a smooth path to visually impaired people through multi-modal feedback. This work assumes that a path has been planned to avoid obstacles and focuses on the feedback design to guide users along the path safely, smoothly, and efficiently. Firstly, we extracted the design guidelines of Virtual Paving based on an investigation into visually impaired people's current practices and issues with tactile pavings. Next, we developed a multi-modal solution through co-design and evaluation with visually impaired users. This solution included (1) vibrotactile feedback on the shoulders and waist to give readily-perceivable directional cues and (2) audio feedback to describe road conditions ahead of the user. Finally, we evaluated the proposed solution through user tests. Guided by the designed feedback, 16 visually impaired participants successfully completed 127 out of 128 trials with 2.1m-wide basic paths, including straight and curved paths. Subjective feedback indicated that our solution to render Virtual Paving was easy for users to learn, and it also enabled them to walk smoothly. The feasibility and potential limitations for Virtual Paving to support independent navigation in real environments are discussed.
Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs)

Classification of histopathological images of cancer is challenging even for well-trained professionals, due to the fine-grained variability of the disease. Deep Convolutional Neural Networks (CNNs) showed great potential for classification of a number of the highly variable fine-grained objects. In this study, we introduce a Bilinear Convolutional Neural Networks (BCNNs) based deep learning method for fine-grained classification of breast cancer histopathological images. We evaluated our model by comparison with several deep learning algorithms for fine-grained classification. We used bilinear pooling to aggregate a large number of orderless features without taking into consideration the disease location. The experimental results on BreaKHis, a publicly available breast cancer dataset, showed that our method is highly accurate with 99.24% and 95.95% accuracy in binary and in fine-grained classification, respectively.
Construction of High-Quality Rice Ribosome Footprint Library

High-throughput sequencing of ribosome footprints precisely maps and quantifies in vivo mRNA translation. The ribosome footprint sequencing has undergone continuing development since its original report. Here we provide a detailed protocol for construction of high-quality ribosome footprint library of rice. Rice total polysomes are isolated with a modified low ionic polysome extraction buffer. After nuclease digestion, rice ribosome footprints are extracted using SDS method followed by column purification. High-quality rice ribosome footprint library with peak reads of approximately 28-nucleotide (nt) length and strong 3-nt periodicity is constructed via key steps including rRNA depletion, end repair, 3’ adapter ligation, reverse transcription, circularization, PCR enrichment and several rounds of purification. Biological significance of rice ribosome footprint library is further revealed by the comparison of transcriptomic and translatomic responses to salt stress and the utilization for novel open reading frame (ORF) identification. This improved protocol for rice ribosome footprint library construction will facilitate the global comprehension and quantitative measurement of dynamic translation in rice.
International Journal of Food Engineering Study on Chemical Constituents and Antioxidant Activity of Anthocyanins from Purple Sweet Potato ( Ipomoea batatas L . )

The radical scavenging effects by α,α-Diphenyl-β-picrylhydrazyl (DPPH) and superoxide anions of anthocyanin extract from purple sweet potato were investigated. The antioxidation experiments showed that the reducing power of the anthocyanin extract reduced 0.572 at 0.5 mg/ ml, while those of L-ascorbic acid (L-AA) and butylated hydroxytoluene (BHT) reduced 0.460 and 0.121, respectively. They also displayed potent antioxidant effects against the DPPH radical and superoxide anions radical, showing the IC50 values of 6.94 and 3.68 μg/ml, respectively. Moreover, this anthocyanin extract also could significantly inhibit the formation of lipid peroxidation compound. Sixteen kinds of anthocyanins in purple sweet potato were detected by HPLC-DAD, most of the anthocyanins were acylated.