Miracle-dz/UniFAR-FTUs huggingface dataset:
Killing the competition: a theoretical framework for liver-stage malaria

The first stage of malaria infections takes place inside the host's hepatocytes. Remarkably, Plasmodium parasites do not infect hepatocytes immediately after reaching the liver. Instead, they migrate through several hepatocytes before infecting their definitive host cells, thus increasing their chances of immune destruction. Considering that malaria can proceed normally without cell traversal, this is indeed a puzzling behaviour. In fact, the role of hepatocyte traversal remains unknown to date, implying that the current understanding of malaria is incomplete. In this work, we hypothesize that the parasites traverse hepatocytes to actively trigger an immune response in the host. This behaviour would be part of a strategy of superinfection exclusion aimed to reduce intraspecific competition during the blood stage of the infection. Based on this hypothesis, we formulate a comprehensive theory of liver-stage malaria that integrates all the available knowledge about the infection. The interest of this new paradigm is not merely theoretical. It highlights major issues in the current empirical approach to the study of Plasmodium and suggests new strategies to fight malaria.
Facile synthesis of Cu2O nanorods in the presence of NaCl by successive ionic layer adsorption and reaction method and its characterizations

Cuprous oxide (Cu2O) nanorods have been deposited on soda-lime glass substrates by the modified successive ionic layer adsorption and reaction technique by varying the concentration of NaCl electrolyte into the precursor complex solution. The structural, electrical and optical properties of synthesized Cu2O nanorod films have been studied by a variety of characterization tools. Structural analyses by X-ray diffraction confirmed the polycrystalline Cu2O phase with (111) preferential growth. Raman scattering spectroscopic measurements conducted at room temperature also showed characteristic peaks of the pure Cu2O phase. The surface resistivity of the Cu2O nanorod films decreased from 15 142 to 685 Ω. cm with the addition of NaCl from 0 to 4 mmol and then exhibited an opposite trend with further addition of NaCl. The optical bandgap of the synthesized Cu2O nanorod films was observed as 1.88–2.36 eV, while the temperature-dependent activation energies of the Cu2O films were measured as about 0.14–0.21 eV. Scanning electron microscope morphologies demonstrated Cu2O nanorods as well as closely packed spherical grains with the alteration of NaCl concentration. The Cu2O phase of nanorods was found stable up to 230°C corroborating the optical bandgap results of the same. The film fabricated in presence of 4 mmol of NaCl showed the lowest resistivity and activation energy as well as comparatively uniform nanorod morphology. Our studies demonstrate that the nominal presence of NaCl electrolytes in the precursor solutions has a significant impact on the physical properties of Cu2O nanorod films which could be beneficial in optoelectronic research.
Comparative Assessment of Shell Properties in Eight Species of Cohabiting Unionid Bivalves

ABSTRACT Freshwater unionid mussels produce a bilayered shell with the mineral proportion comprising an outer prismatic and an inner nacreous layer. The shell is the animals' primary structural means of protection from predators and environmental challenges; therefore, variations in shell strength and properties may lead to differences in survival. Few studies have systematically assessed shell properties in unionids. A major challenge in such work is separating effects of environment from those of evolutionary history, because ultimately, both can affect shell properties. We collected eight species of unionids within a small area of the Allegheny River, Pennsylvania, that was relatively homogeneous in substratum type and other environmental characteristics. For each species, we quantified shell thickness, including thickness of the prismatic and nacreous layers, and shell micromechanical properties (microhardness and crack propagation, a measure of fracture resistance) in three regions of the shell. Shell thickness varied dramatically among species and was about five times greater in the thickest-shelled species, Pleurobema sintoxia, than in the thinnest-shelled species, Villosa iris. Because all species experienced similar environmental conditions, variation in shell thickness can be attributed largely to evolutionary history. In contrast, microhardness and crack propagation showed little variation among species. Given that micromechanical properties are similar among species, shell strength may be largely a function of thickness. These results have conservation implications, as differences in shell thickness could reflect relative vulnerability to predators and physical conditions.
An Estimation on Induced Voltage at Implantable Cardiac Pacemaker due to Magnetic Resonant Wireless Power Transfer Coils Using Finite Element Method

A numerical assessment methodology to understand EMI imposed by magnetic resonance type wireless power transfer systems on active implantable medical devices (implantable cardiac pacemaker / cardioverter defibrillator) is introduced. A numerical estimation model that consists of magnetic resonant coils and a human torso phantom with an implantable cardiac pacemaker is constructed. Numerical simulation based on the finite element method yields the interference voltage induced at the connector of the pacemaker inside the torso phantom. Our example assumes magnetic resonance coils operating in the frequency band of 10 MHz.
A call for revision of the World Health Organization classification system to include FLT3-ITD as a distinct entity

Abstract. Acute Myeloid Leukemia (AML) is a heterogeneous malignancy driven by over 200 different cytogenetic abnormalities, giving rise to eight different subtypes, each identified as having a unique prognostic outcome and response to treatment. The World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia describes specific subtypes and is widely used to treat and diagnose AML. However, the current WHO classification system is based primarily on treatment outcomes for adult patients. In recent years, the incorporation of genetic and molecular testing has aided in the identification of several distinct subtypes that are not yet recognized by the WHO classification system. One key genomic driver in some cases of AML is the internal tandem duplication (ITD) within the gene that codes for the FMS-like tyrosine kinase receptor-3 (FLT3). Pediatric AML patients with FLT3-ITD consistently have high relapse rates and low overall survival rates, which may be improved by intensifying and specifying treatment regimens. Despite increasing research showing the prognostic significance of FLT3-ITD AML and development of FLT3 inhibitors, this cytogenetically unique form of AML with a distinctly poor prognosis in children is not mentioned in the current WHO classification. The authors suggest the need for inclusion of FLT3-ITD as a distinct entity in the WHO classification due to its prognostic effect in pediatric AML in particular. Though advancements have been made in the understanding and treatment of FLT3-ITD AML, the application of current and future research will be limited without the inclusion of FLT3-ITD as a distinct entity.
Revised Taxonomy of the Arctotis Annual Clade (Arctotideae, Asteraceae) from Southern Africa: Integration of Molecular Phylogenetic and Morphological Evidence

Abstract Previous phylogenetic analysis of ITS nrDNA sequence data for Arctotidinae species resolved a strongly supported clade containing all but one of the showy annual Arctotis species (informally designated the Arctotis Annual Clade). In the present study, phylogenetic relationships in the Arctotis Annual Clade were investigated by Bayesian inference and maximum parsimony analyses of cpDNA (trnT-trnL-trnF and trnH-psbA) and nrDNA (ITS) sequence data. The cpDNA and nrDNA phylogenies were notably incongruent. Arctotis venusta and a putative unnamed species (A. ‘sp. B’) were strongly supported as monophyletic by both data sets. The monophyly of A. leiocarpa was strongly supported by the ITS data set, whereas the remaining accessions formed a poorly resolved complex (the ‘A. fastuosa complex’). Within the A. fastuosa complex, A. hirsuta was monophyletic with strong support in the ITS phylogeny. A statistical parsimony-derived cpDNA haplotype network resolved five broad groups of haplotypes and showed no consistent geographical structure, but species-specific haplotype lineages for A. venusta and A. sp. B were resolved. Arctotis fastuosa accessions were distributed among four haplotype groups. Incongruence between the data sets and poor resolution within the A. fastuosa complex may reflect reticulate evolution, ancestral polymorphism, and incomplete lineage sorting, in tandem with the low information content of the data sets. The greatest phenotypic diversification in the clade is in cypsela morphology. Comparison of cypsela morphology with the phylogenies suggests a general trend for reduction in the sizes of the cypsela, abaxial wings, pappus scales, and loss of pubescence during diversification. A revised taxonomy, integrating currently available evidence, accompanied by full descriptive accounts and a key to the taxa are presented. Eight species are recognized, including the nomenclatural novelties Arctotis chrysantha (sp. nov.) and Arctotis namibiensis (sp. nov.). The names Arctotis karasmontana, Venidium fugax, and Venidium macrocephalum are lectotypified.
A Remarkable New Species of Euphorbia Section Helioscopia (Euphorbia Subgenus Esula, Euphorbiaceae) from Jiufeng Mountains in Fujian Province, Eastern China

Abstract Euphorbia xiangxiui, a new species of Euphorbiaceae from Eastern China, supported by morphological and molecular evidence, is illustrated and described here. It can be distinguished from its closest relatives by the combination of several characters, including taller stature (up to 1.6 m), leaves of 5–18 × 0.6–3 cm in size, color of primary involucral leaves and cyathophylls changing from pink to rose, fading to green after fruiting, and four to six reniform-rounded concave glands. The molecular phylogenetic analysis, based on combined nuclear ribosomal ITS and plastid ndhF sequences, further supports that E. xiangxiui should be placed in the East Asian Clade of Euphorbia subg. Esula sect. Helioscopia.
Smart energy meter based on hall effect current sensing techniques with IoT modules

Abstract The existing approach of an electrical board representative manually reading the energy metre and issuing invoices to the owner has been shown to be inefficient and prone to human mistakes and possibly misconduct. Indian energy users are also afflicted by a lack of current information and understanding. Using the IC ACS71020, this study presents a customized IoT-based Advanced Smart Energy Meter infrastructure with remote and local logging and monitoring of electrical parameters. The meter also has provisions for connections to both web and cellular internet using ESP8266 and GSMsim800a. The elimination of human interaction and the rapid updating of information makes the consumer’s life easier and more efficient. Data is also shown on an OLED screen and stored on an SD Card / Flash Memory, allowing access to a local server.
Feature space reduction method for classification based detection of Parkinson’s disease using vocal signals

Abstract Mel Frequency Cepstral is the most successful way to extract features from vocal signals for building speaker recognition software. Its application has now reached the domain of medical sciences and is now being actively used for building diagnostic instruments. Studies have shown that this method has been successful to capture discrepancies in the voice of patients, potentially showing biomarkers of Parkinson’s disease. MFCC features extracted are large in number, as it captures feature from high order energy components. However, it has a downfall. Due to large number of features created, and multiplied with the large amount of feature samples, the computational cost can increase by manifolds. This increases the run time and can potentially delay the diagnostic results. Therefore, it is important to build an efficient model which can ease the burden of large dataset and achieve faster results.
Multi-Class Object Learning with Application to Fabric Defects Detection

Deep convolutional neural networks (CNNs) have shown great success in single-class fabric image detection. However, real-world fabric defect images generally contain several types of defects in one image. Accurately recognizing and classifying multi-class fabric defect images is still an unsolved issue due to the complexity of intersected defects, as well as difficulty in distinguishing small-size defects. To address these challenges, this study develops a methodology based on the deep learning feature pyramid networks (FPN) approach to detect multi-class fabric defects. To evaluate the proposed detection model, we built a unique multi-class fabric defects database (DHU-MO1000), where multi-class defect images are generated by industrial monitors from a textile factory. We used the dataset as the benchmark for multi-class defects detection training and testing the FPN. Furthermore, we conducted extensive experimental validations for various design choices. The experimental results show that the model outperformed existing multi-class object detection methods.
Fabric Defect Detection Using Deep Convolution Neural Network

Abstract Fabric defect detection plays an increasingly important role in the industrial automation application for fabric production, but how to detect defects rapidly and accurately is still challenging. In this study, we propose a powerful fabric defect detection method using a hybrid of convolutional neural network (CNN) and variational autoencoder (VAE). The convolutional layers are used for extracting fabric image pattern features and the variational autoencoder is used for modeling the latent characteristics and inferring a reconstruction. The defect positions can be detected by the differences between the original image and the reconstruction image. The proposed method is validated on public patterned fabric datasets. The experimental results demonstrate that the proposed model can achieve outstanding performance in both image level and pixel level defect detection.
Abundance Causes Greed in Appropriation from Common Resources

Three experiments investigated the possibility that greed is not just a consequence of scarcity but may also result from abundance of common resources. It was predicted that abundance causes greed not through increasing competition but because it reduces social concerns for the outcomes of others, resulting in resource waste. Consistent with these predictions, we found that people take and waste more from common resources if these resources are more—rather than less—abundant. These findings were robust to different types of resources (chocolates and money). Implications of these findings for attempts at sustainable resource use are discussed.
An Improved Fabric Defect Detection Method Based on SSD

The fabric defect detection algorithm based on object detection has become a research hotspot. The method based on the Single Shot MultiBox Detector (SSD) model has a fast detection speed, but the detection accuracy is insufficient. To balance the detection speed and accuracy of the model and meet the actual needs of the industry, an improved fabric defect detection algorithm based on SSD is proposed in this study. The Fully Convolutional Squeeze-and-Excitation (FCSE) block is added into the traditional SSD to improve the detection accuracy of the model. The number of default boxes was adjusted to accommodate the detection of long strip defects on fabric surface. Experimental results on the TILDA and Xuelang dataset confirm that our detection method based on SSD efficiently detected various fabric defects.
A New Method Based on Deep Convolutional Neural Networks for Object Detection and Classification

Accurate object detection and classification has a broad application in industrial tasks, such as fabric defect and invoice detection. Previous state-of-the-art methods such as SSD and Faster-RCNN usually need to carefully adjust anchor box related hyper parameters and have poor performance in special fields with large object size/ratio variations and complex background texture. In this study, we proposed a new accurate, robust, and anchor-free method to handle automatic object detection and classification problems. First, we used the feature pyramid network (FPN), to merge the feature maps of different scales of features extracted from a convolutional neural network (CNN), which allowed easy and robust multi-scale feature fusion. Second, we built two subnets to generate candidate region proposals from the FPN outputs. followed by another CNN that determined the categories of the proposed regions from the two subnets.
Textile Fabric Defect Detection Based on Improved Faster R-CNN

To identify and locate industrial textile defects accurately, this study proposes a textile detection model based on a convolution neural network (CNN) known as Faster R-CNN. First, a textile defect feature map was extracted by ResNet-101 deep convolution network. Faster R-CNN only extracts features from the last layer of the feature map, which leads to a loss of low-level location information. The proposed method adds the feature pyramid network (FPN) to the network architecture to make an independent prediction for each level in the feature extraction stage. The extracted feature map is input into the regional proposal network, among which the overlapping regional proposals are suppressed. The proposed improved Faster R-CNN model with Region Proposal Network (RPN), Soft Non-Maximum Suppression (NMS), and Region of Interest (ROI) Align can achieve a detection accuracy of 98% and an mean of Average Precision (mAP) of 85%, which is more competitive than the state-of-the-art deep learning-based object detection algorithms.
Making email actionable: the identification and use of obligation acts in workplace email / by Andrew Lampert

Email is a key communication medium in business environments, where it is often used to assign and delegate tasks. Existing research has established that task-oriented communication is built upon the exchange of request and commitment speech acts— collectively, obligation acts—between interlocutors, but email software has so far ignored this insight; it has not adapted to support task management, despite its popularity as a medium for such workflows. The lack of task awareness in email software has been repeatedly highlighted as a key factor in the ‘information overload’ that burdens many email users. In particular, the difficulty of distilling tasks from the ever-increasing email flow leads to obligations that remain unfulfilled. This thesis explores how to address this problem by making email more actionable. We begin by analysing data from a series of annotation experiments through which we gathered independent human judgements about requests and commitments across a collection of more than 2000 real-world email messages. These annotated messages provide insight into how obligation acts are realised and interpreted. We identify and analyse a range of complex phenomena involved in these speech acts, and provide definitions for identifying them in email. Building on this analysis, we then present effective computational techniques for detecting obligation acts at three levels of granularity within email messages: the message, paragraph and sentence levels. Message-level identification determines whether or not an email message contains obligation acts, and aims to assist users to triage their messages by focusing on those containing actionable content. Paragraph-level identification builds on this to classify each paragraph in the same manner; this enables, for example, the production of extractive summaries of messages. Finally, sentence-level identification classifies each sentence in an email message, and allows requests and commitments to be extracted to external task lists. We use our annotated email data to train supervised machine learning algorithms for each of these classification tasks. These classifiers also exploit a novel classification system that segments the text of email messages into different functional zones, identifying material such as signatures, advertising, and quoted reply content. This enables our obligation classifiers to focus on only relevant email text when identifying requests and commitments. In sum, this thesis provides both theoretical and practical foundations for managing obligation acts in real email data. Our empirically-grounded analysis and the software tools developed on the basis of this analysis demonstrate how it is possible to make email actionable today, as well as providing a platform for future task-related email research.
Towards Robust Gesture Recognition by Characterizing the Sensing Quality of WiFi Signals

WiFi-based gesture recognition emerges in recent years and attracts extensive attention from researchers. Recognizing gestures via WiFi signal is feasible because a human gesture introduces a time series of variations to the received raw signal. The major challenge for building a ubiquitous gesture recognition system is that the mapping between each gesture and the series of signal variations is not unique, exact the same gesture but performed at different locations or with different orientations towards the transceivers generates entirely different gesture signals (variations). To remove the location dependency, prior work proposes to use gesture-level location-independent features to characterize the gesture instead of directly matching the signal variation pattern. We observe that gesture-level features cannot fully remove the location dependency since the signal qualities inside each gesture are different and also depends on the location. Therefore, we divide the signal time series of each gesture into segments according to their qualities and propose customized signal processing techniques to handle them separately. To realize this goal, we characterize signal's sensing quality by building a mathematical model that links the gesture signal with the ambient noise, from which we further derive a unique metric i.e., error of dynamic phase index (EDP-index) to quantitatively describe the sensing quality of signal segments of each gesture. We then propose a quality-oriented signal processing framework that maximizes the contribution of the high-quality signal segments and minimizes the impact of low-quality signal segments to improve the performance of gesture recognition applications. We develop a prototype on COTS WiFi devices. The extensive experimental results demonstrate that our system can recognize gestures with an accuracy of more than 94% on average, and significant improvements compared with state-of-arts.
Deep learning on microcontrollers: a study on deployment costs and challenges

Microcontrollers are an attractive deployment target due to their low cost, modest power usage and abundance in the wild. However, deploying models to such hardware is non-trivial due to a small amount of on-chip RAM (often < 512KB) and limited compute capabilities. In this work, we delve into the requirements and challenges of fast DNN inference on MCUs: we describe how the memory hierarchy influences the architecture of the model, expose often under-reported costs of compression and quantization techniques, and highlight issues that become critical when deploying to MCUs compared to mobiles. Our findings and experiences are also distilled into a set of guidelines that should ease the future deployment of DNN-based applications on microcontrollers.