Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Identification and Antifungal Properties of Four Thaumatin-like Proteins in Spruce (Picea likiangensis)

Thaumatin-like proteins (TLPs) are involved in the plant defense response against pathogens, and most of them exhibit antifungal activity. However, the role of TLPs in pathogen-induced defense responses in spruce is not fully understood. In this study, four TLP genes encoding thaumatin-like protein, designated as PlTLP1–4, were isolated and identified from Picea likiangensis needles. Sequence analysis showed that PlTLP1, PlTLP3, and PlTLP4 contained 16 conserved cysteine residues, while PlTLP2 had only 10 conserved cysteine residues. qPCR analysis showed that PlTLPs were expressed in all tissues tested, PlTLP1, PlTLP3, and PlTLP4 had the highest expression levels in young fruits, while PlTLP2 had the highest expression levels in roots. In addition, the expression levels of four PlTLPs were significantly upregulated during infection by Lophodermium piceae. Four recombinant PlTLPs expressed in Escherichia coli exhibited obvious β-1,3-glucanase activity. The antifungal activity assay showed that four recombinant PlTLPs had significant inhibitory effects on the mycelial growth of L. piceae, Fusarium proliferatum, Botrytis cinerea, and Roussoella doimaesalongensis. Microscopic observation revealed that the recombinant PlTLP1–4 induced the morphological changes of the mycelia of L. piceae, and the recombinant PlTLP2 and PlTLP3 induced the morphological changes of the mycelia of F. proliferatum and R. doimaesalongensis, while all the recombinant PlTLPs had no obvious negative effect on the morphology of B. cinerea mycelium. These results suggest that PlTLP genes may play an important role in the defense response of P. likiangensis against L. piceae invasion.
Surgery Theory and the Classification of Closed, Simply Connected 4-manifolds

Surgery theory and the classification of simply connected 4-manifolds comprise two key consequences of the disc embedding theorem. The chapter begins with an introduction to surgery theory from the perspective of 4-manifolds. In particular, the terms and maps in the surgery sequence are defined, and an explanation is given as to how the sphere embedding theorem, with the added ingredient of topological transversality, can be used to define the maps in the surgery sequence and show that it is exact. The surgery sequence is applied to classify simply connected closed 4-manifolds up to homeomorphism. The chapter closes with a survey of related classification results.
Flood routing via a copula-based approach

Floods are among the most common natural disasters that if not controlled may cause severe damage and high costs. Flood control and management can be done using structural measures that should be designed based on the flood design studies. The simulation of outflow hydrograph using inflow hydrograph can provide useful information. In this study, a copula-based approach was applied to simulate the outflow hydrograph of various floods, including the Wilson River flood, the River Wye flood and the Karun River flood. In this regard, two-dimensional copula functions and their conditional density were used. The results of evaluating the dependence structure of the studied variables (inflow and outflow hydrographs) using Kendall's tau confirmed the applicability of copula functions for bivariate modeling of inflow and outflow hydrographs. The simulation results were evaluated using the root-mean-square error, the sum of squared errors and the Nash–Sutcliffe efficiency coefficient (NSE). The results showed that the copula-based approach has high performance. In general, the copula-based approach has been able to simulate the peak flow and the rising and falling limbs of the outflow hydrographs well. Also, all simulated data are at the 95% confidence interval. The NSE values for the copula-based approach are 0.99 for all three case studies. According to NSE values and violin plots, it can be seen that the performance of the copula-based approach in simulating the outflow hydrograph in all three case studies is acceptable and shows a good performance.
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents

Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining the answer prediction by providing supporting evidences. In this paper, we propose an effective and interpretable Select, Answer and Explain (SAE) system to solve the multi-document RC problem. Our system first filters out answer-unrelated documents and thus reduce the amount of distraction information. This is achieved by a document classifier trained with a novel pairwise learning-to-rank loss. The selected answer-related documents are then input to a model to jointly predict the answer and supporting sentences. The model is optimized with a multi-task learning objective on both token level for answer prediction and sentence level for supporting sentences prediction, together with an attention-based interaction between these two tasks. Evaluated on HotpotQA, a challenging multi-hop RC data set, the proposed SAE system achieves top competitive performance in distractor setting compared to other existing systems on the leaderboard.
A Study of Data Pre-processing Techniques for Imbalanced Biomedical Data Classification

Biomedical data are widely accepted in developing prediction models for identifying a specific tumor, drug discovery and classification of human cancers. However, previous studies usually focused on different classifiers, and overlook the class imbalance problem in real-world biomedical datasets. There are a lack of studies on evaluation of data pre-processing techniques, such as resampling and feature selection, on imbalanced biomedical data learning. The relationship between data pre-processing techniques and the data distributions has never been analysed in previous studies. This article mainly focuses on reviewing and evaluating some popular and recently developed resampling and feature selection methods for class imbalance learning. We analyse the effectiveness of each technique from data distribution perspective. Extensive experiments have been done based on five classifiers, four performance measures, eight learning techniques across twenty real-world datasets. Experimental results show that: (1) resampling and feature selection techniques exhibit better performance using support vector machine (SVM) classifier. However, resampling and Feature Selection techniques perform poorly when using C4.5 decision tree and Linear discriminant analysis classifiers; (2) for datasets with different distributions, techniques such as Random undersampling and Feature Selection perform better than other data pre-processing methods with T Location-Scale distribution when using SVM and KNN (K-nearest neighbours) classifiers. Random oversampling outperforms other methods on Negative Binomial distribution using Random Forest classifier with lower level of imbalance ratio; (3) Feature Selection outperforms other data pre-processing methods in most cases, thus, Feature Selection with SVM classifier is the best choice for imbalanced biomedical data learning.
Ultra-precision single-point diamond turning of a complex sinusoidal mesh surface using machining accuracy active control

Single-point diamond turning (SPDT) assisted with slow tool servo (STS) is the most commonly utilized technique in the fabrication of optical modules. However, the tool path significantly affects the quality of the machined surface. In order to realize the determined machining accuracy effectively, a tool path generation (TPG) method based on machining accuracy active control (MAAC) is presented. The relationship between tool path and machining error is studied. Corner radius compensation (CRC) and the calculation of chord error and residual error are detailed. Finally, the effectiveness of the proposed approach is verified through a machining error simulation and a cutting experiment of a complex sinusoidal mesh surface fabrication.
TECHNOLOGY BASED OVERVIEW ON SOFTWARE TESTING TRENDS, TECHNIQUES, AND CHALLENGES

Software testing is a process of evaluating the developed software against the actual requirements from the customer. Software development methodology has transformed based on numerous driving factors over decades and so is the software testing methodologies. Software remains inevitable in rapid growth of technologies in various fields like academy, medicine, media, safety, agriculture, robotics, gaming, aviation, national defence etc., In this paper, the existing and emerging trends, techniques, and issues of software testing are discussed based on the technologies.
The prevalence of SARS-CoV-2 in sewage waste water: A review

Since one and half years, the pandemic of SARS-CoV-2 virus (disease caused is Covid-19) has ruined the entire humanity in unimaginable ways, whether it is economy or unemployment or children mental health disorder or large number of deaths. There is no country in the world which is not affected by this virus. In some countries, this pandemic is coming in the form of outbreak such as first wave of pandemic then after some gap period, second wave of pandemic. In country like India, the second wave of corona pandemic has crippled the economy, public health safety and at the same time put a big question on the health infrastructure of entire nation whether it is availability of oxygen cylinders, or testing facility, hospital bed or ventilators. The actual number of patients who can get affected had not been estimated correctly. This poses more problems due to asymptomatic nature of the expression of COVID-19 on individual basis. Tragically, for developing countries like India with high population density, the situation has been more complex. Additionally, more amount of waste from the Covid affected population goes to effluent water, waste water coming out of residential area, hospitals, isolation centres and so on. In this review article, we have focused on presence of corona virus and infection transmission through effluent water in country like India with huge number of population and also provide further scope in research to inform future studies.
A warning framework for avoiding vessel‐bridge and vessel‐vessel collisions based on generative adversarial and dual‐task networks

The vessel‐bridge and vessel‐vessel collisions are likely to occur on the river. For avoiding the two kinds of collisions, a video‐based framework about early warning is proposed in this paper, which mainly contains vessel positioning, vessel trajectory data augmentation, and trajectory anomaly detection and prediction. At first, a real‐time vessel positioning method is proposed based on homography. In this method, the buoys are used as the control points, whose instantaneous world coordinates are obtained based on aerial photography, for solving the homography. Based on the obtained homography, the pixel coordinates of the identified vessel center can be mapped to the corresponding world coordinates, which realizes the vessel positioning. Second, the trajectory generative adversarial networks with multiple critics (TGANs‐MC) is proposed to enrich the historical trajectories, especially the abnormal trajectories. TGANs‐MC contains a generator and multiple critics. The generator is based on a recurrent neural network (RNN) for generating the variable‐length trajectory sequence. The critic uses 1D convolution and 1D adaptive pooling to obtain the trajectory feature. Multiple critics with different structures are used in TGANs‐MC to guide the generator to generate diverse trajectories. Third, a dual‐task network is proposed to find the vessels with abnormal trajectories for warning vessel‐bridge collision and predict the trajectories of vessels for warning vessel‐vessel collision. The dual‐task network adopts the structure of an RNN‐based encoder‐decoder, and it has two branches so that it can jointly perform the dual tasks. The anomaly detection branch uses supervised binary classification and outputs the risk degree. The attention mechanism is adopted in the prediction branch. Finally, a real‐time collision warning system is developed, which is applied on a bridge.
Alginate Nanohydrogels as a Biocompatible Platform for the Controlled Release of a Hydrophilic Herbicide

The large-scale application of volatile and highly water-soluble pesticides to guarantee crop production can often have negative impacts on the environment. The main loss pathways are vapor drift, direct volatilization, or leaching of the active substances. Consequently, the pesticide can either accumulate and/or undergo physicochemical transformations in the soil. In this scenario, we synthesized alginate nanoparticles using an inverse miniemulsion template in sunflower oil and successfully used them to encapsulate a hydrophilic herbicide, i.e., dicamba. The formulation and process conditions were adjusted to obtain a unimodal size distribution of nanohydrogels of about 20 nm. The loading of the nanoparticles with dicamba did not affect the nanohydrogel size nor the particle stability. The release of dicamba from the nanohydrogels was also tested: the alginate nanoparticles promoted the sustained and prolonged release of dicamba over ten days, demonstrating the potential of our preparation method to be employed for field application. The encapsulation of hydrophilic compounds inside our alginate nanoparticles could enable a more efficient use of pesticides, minimizing losses and thus environmental spreading. The use of biocompatible materials (alginate, sunflower oil) also guarantees the absence of toxic additives in the formulation.
Boost Your CotS IEEE 802.15.4 Network with Inter-Slot Interference Cancellation for Industrial IoT

The current cellular standardization for 5G is working towards wireless advances to enable further productivity for industrial automation. However, this development will take several years. Meanwhile, the capabilities of the currently available standards should be pushed to their limits. To this end, in this work, we present results from the first inter-slot successive interference cancellation testbed using commercial off the shelf IEEE 802.15. 4 sensors. Through our implementation, we have measured a throughput of 0.72 packets per slot which doubles the currently used contention-based access, Slotted ALOHA, with a limit of 0.36 packets per slot. The hardware effects of the boards, which degrade the successive interference cancellation performance from the theoretical limit of 1 packet per slot, are modeled and validated through measurements. We also propose a model that can be used to calculate the expected successive interference cancellation throughput with the specific hardware available in a factory. Furthermore, our proposed model should replace the perfect physical layer assumptions for researchers to design new MAC algorithms taking practical limitations into account.
Second-Order Group Influence Functions for Black-Box Predictions

With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable. Often we want to identify an influential group of training samples in a particular test prediction. Existing influence functions tackle this problem by using first-order approximations of the effect of removing a sample from the training set on model parameters. To compute the influence of a group of training samples (rather than an individual point) in model predictions, the change in optimal model parameters after removing that group from the training set can be large. Thus, in such cases, the first-order approximation can be loose. In this paper, we address this issue and propose second-order influence functions for identifying influential groups in test-time predictions. For linear models and across different sizes of groups, we show that using the proposed second-order influence function improves the correlation between the computed influence values and the ground truth ones. For nonlinear models based on neural networks, we empirically show that none of the existing first-order and the proposed second-order influence functions provide proper estimates of the ground-truth influences over all training samples. We empirically study this phenomenon by decomposing the influence values over contributions from different eigenvectors of the Hessian of the trained model.
Barrier to Gene Flow of Grey Mangrove Avicennia marina Populations in the Malay Peninsula as Revealed From Nuclear Microsatellites and Chloroplast Haplotypes

Contemporary mangrove forest areas took shape historically and their genetic connectivity depends on sea-faring propagules, subsequent settlement, and persistence in suitable environments. Mangrove species world-wide may experience genetic breaks caused by major land barriers or opposing ocean currents influencing their population genetic structure. For Malay Peninsula, several aquatic species showed strong genetic differentiation between East and West coast regions due to the Sunda shelf flooding since the Last Glacial Maximum. In this study genetic diversity and structure of Avicennia marina populations in Malay Peninsula were assessed using nuclear microsatellite markers and chloroplast sequences. Even though all populations showed identical morphological features of A. marina, three evolutionary significant units were obtained with nuclear and cytoplasmic markers. Avicennia marina along a 586 km stretch of the West coast differed strongly from populations along an 80 km stretch of the East coast featuring chloroplast capture of Avicennia alba in an introgressive A. marina. Over and above this expected East-West division, an intra-regional subdivision was detected among A. marina populations in the narrowest region of the Strait of Malacca. The latter genetic break was supported by an amova, structure, and barrier analysis whereas RST > FST indicated an evolutionary signal of long-lasting divergence. Two different haplotypes along the Western coast showed phylogeographic relationship with either a northern or a putative southern lineage, thereby assuming two Avicennia sources facing each other during Holocene occupation with prolonged separation in the Strait of Malacca. Migrate-n model testing supported a northward unidirectional stepping-stone migration route, although with an unclear directionality at the genetic break position, most likely due to weak oceanic currents. Low levels of genetic diversity and southward connectivity was detected for East coast Avicennia populations. We compared the fine-scale spatial genetic structure (FSGS) of Avicennia populations along the exposed coast in the East vs. the sheltered coast in the West. A majority of transects from both coastlines revealed no within-site kinship-based FSGS, although the remoteness of the open sea is important for Avicennia patches to maintain a neighborhood. The results provide new insights for mangrove researchers and managers for future in-depth ecological-genetic-based species conservation efforts in Malay Peninsula.
Abstract 3-Rigidity and Bivariate $C_2^1$-Splines II: Combinatorial Characterization.

We showed in the first paper of this series that the generic $C_2^1$-cofactor matroid is the unique maximal abstract $3$-rigidity matroid. In this paper we obtain a combinatorial characterization of independence in this matroid. This solves the cofactor counterpart of the combinatorial characterization problem for the rigidity of generic 3-dimensional bar-joint frameworks. We use our characterization to verify that the counterparts of conjectures of Dress (on the rank function) and Lov\'{a}sz and Yemini (which suggested a sufficient connectivity condition for rigidity) hold for this matroid.
Using participatory design to inform cyber/bullying prevention and intervention practices: Evidence-Informed insights and strategies

Abstract Bullying and cyberbullying are global phenomena negatively impacting on children’s and young people’s (CYP’s) mental health and wellbeing and affecting their school social experiences and learning outcomes. Many interventions and prevention approaches have been employed over the decades, most impacting differentially, with some success in certain contexts and situations but not universally, suggesting the need for more contextualised, nuanced approaches at the whole school, community, individual and peer-group levels. The recognition of the importance of student and teacher voice in recent years has heralded interest in co-design practices to deliver more context-relevant interventions and prevention strategies. This article considers how participatory design and co-design practices can form part of the prevention and intervention repertoire for schools, teachers, counsellors and psychologists in their quest to understand and reduce cyberbullying and/or bullying (C/B) behaviours. Two case-study exemplars are provided that reflect the importance of context and student-centred relevancy to inform practice.
Optical phase mining by adjustable spatial differentiator

Abstract. Phase is a fundamental resource for optical imaging but cannot be directly observed with intensity measurements. The existing methods to quantify a phase distribution rely on complex devices and structures and lead to difficulties of optical alignment and adjustment. We experimentally demonstrate a phase mining method based on the so-called adjustable spatial differentiation, by analyzing the polarization of light reflection from a single planar dielectric interface. Introducing an adjustable bias, we create a virtual light source to render the measured images with a shadow-cast effect. From the virtual shadowed images, we can further recover the phase distribution of a transparent object with the accuracy of 0. 05λ RMS. Without any dependence on wavelength or material dispersion, this method directly stems from the intrinsic properties of light and can be generally extended to a broad frequency range.
Deep Learning for MIMO Channel Estimation: Interpretation, Performance, and Comparison

Deep learning (DL) has emerged as an effective tool for channel estimation in wireless communication systems, especially under some imperfect environments. However, even with such unprecedented success, DL methods are often regarded as black boxes and are lack of explanations on their internal mechanisms, which severely limits further improvement and extension. In this paper, we present a preliminary theoretical analysis on DL based channel estimation for multiple-antenna systems to understand and interpret its internal mechanism. Deep neural network (DNN) with rectified linear unit (ReLU) activation function is mathematically equivalent to a piecewise linear function. Hence, the corresponding DL estimator can achieve universal approximation to a large family of functions by making efficient use of piecewise linearity. We demonstrate that DL based channel estimation does not restrict to any specific signal model and approaches to the minimum mean-squared error (MMSE) estimation in various scenarios without requiring any prior knowledge of channel statistics. Therefore, DL based channel estimation outperforms or is at least comparable with traditional channel estimation, depending on the types of channels. Simulation results confirm the accuracy of the proposed interpretation and demonstrate the effectiveness of DL based channel estimation under both linear and nonlinear signal models.