Miracle-dz/UniFAR-FTUs huggingface dataset:
ABCB transporters in a leaf beetle respond to sequestered plant toxins

Phytophagous insects can tolerate and detoxify toxic compounds present in their host plants and have evolved intricate adaptations to this end. Some insects even sequester the toxins for their defence. This necessitates specific mechanisms, especially carrier proteins that regulate uptake and transport to specific storage sites or protect sensitive tissues from noxious compounds. We identified three ATP-binding cassette subfamily B (ABCB) transporters from the transcriptome of the cardenolide-sequestering leaf beetle Chrysochus auratus and analysed their functional role in the sequestration process. These were heterologously expressed and tested for their ability to interact with various potential substrates: verapamil (standard ABCB substrate), the cardenolides digoxin (commonly used), cymarin (present in the species's host plant) and calotropin (present in the ancestral host plants). Verapamil stimulated all three ABCBs and each was activated by at least one cardenolide, however, they differed as to which they were activated by. While the expression of the most versatile transporter fits with a protective role in the blood–brain barrier, the one specific for cymarin shows an extreme abundance in the elytra, coinciding with the location of the defensive glands. Our data thus suggest a key role of ABCBs in the transport network needed for cardenolide sequestration.
The impact of autophagy during the development and survival of glioblastoma

Glioblastoma is the most common and aggressive adult brain tumour, with poor median survival and limited treatment options. Following surgical resection and chemotherapy, recurrence of the disease is inevitable. Genomic studies have identified key drivers of glioblastoma development, including amplifications of receptor tyrosine kinases, which drive tumour growth. To improve treatment, it is crucial to understand survival response processes in glioblastoma that fuel cell proliferation and promote resistance to treatment. One such process is autophagy, a catabolic pathway that delivers cellular components sequestered into vesicles for lysosomal degradation. Autophagy plays an important role in maintaining cellular homeostasis and is upregulated during stress conditions, such as limited nutrient and oxygen availability, and in response to anti-cancer therapy. Autophagy can also regulate pro-growth signalling and metabolic rewiring of cancer cells in order to support tumour growth. In this review, we will discuss our current understanding of how autophagy is implicated in glioblastoma development and survival. When appropriate, we will refer to findings derived from the role of autophagy in other cancer models and predict the outcome of manipulating autophagy during glioblastoma treatment.
Nonparametric inference for copula density function under random censoring

This paper proposes a nonparametric estimation for the copula and copula density functions for right censored data. An empirical version for the bivariate distribution function is studied and its i.i. d. representation is provided. Based on this estimator an i.i. d. representation is deduced for the copula function. An estimator for the copula density, based on local-linear kernel smoothing, is proposed and its triangular i.i. d. approximation is derived. We distinguish the boundary and the interior cases and we provide an estimator for each case.
Rendering for Positional Tracking

Current virtual reality (VR) models rely heavily on head tracking to relay a users change in motion. However, to provide more realistic experiences or any augmented reality experience, VR environments must incorporate position tracking. In this paper, we implement rendering with position tracking to test the fused experience of using both position and orientation tracking. With IR beacons and pho-todiodes, we use the Levenberg-Marquardt algorithm to estimate user position and translate that into virtual space. Although there were several limitations with hardware and the IR beacons, users generally perceived the combined sys-tem to be more seamless to interact with.
The effects of long-term vigorous endurance exercise on the coronary arteries

Moderate endurance training is known to improve cardiovascular risk factors, and prolongs life expectancy. On the other hand, there has been some discussion whether “too much” exercise might have a contrarious effect by accelerating coronary atherosclerosis. The goal of this review was to evaluate the current literature on the effects of long-term vigorous endurance training on the coronary vasculature. In summary, data point to an increased calcium score, and a higher burden of atherosclerotic plaque in male athletes compared to sedentary controls. However, the plaques found in athletes were more prone to be calcified. The pathogenesis and clinical relevance of this athlete coronary artery disease phenotype remains incompletely understood and represents an area of important future work.
A concept-based sentiment analysis approach for arabic

Concept-Based Sentiment Analysis (CBSA) methods are considered to be more advanced and more accurate when it compared to ordinary Sentiment Analysis methods, because it has the ability of detecting the emotions that conveyed by multi-word expressions concepts in language. This paper presented a CBSA system for Arabic language which utilizes both of machine learning approaches and concept-based sentiment lexicon. For extracting concepts from Arabic, a rule-based concept extraction algorithm called semantic parser is proposed. Different types of feature extraction and representation techniques are experimented among the building prosses of the sentiment analysis model for the presented Arabic CBSA system. A comprehensive and comparative experiments using different types of classification methods and classifier fusion models, together with different combinations of our proposed feature sets, are used to evaluate and test the presented CBSA system. The experiment results showed that the best performance for the sentiment analysis model is achieved by combined Support Vector Machine-Logistic Regression (SVM-LR) model where it obtained a F-score value of 93.23% using the Concept-Based-Features+Lexicon-Based-Features+Word2vec-Features (CBF+LEX+W2V) features combinations
I Can't Be Myself

Wearable sensors can provide reliable, automated measures of health behaviors in free-living populations. However, validation of these measures is impossible without observable confirmation of behaviors. Participants have expressed discomfort during the use of ego-centric wearable cameras with first-person view. We argue that mounting the camera on different body locations with a different lens orientation, gives a device recording affordance that has the effect of reducing surveillance and social discomfort compared to ego-centric cameras. We call these types of cameras "activity-oriented" because they are designed to capture a particular activity, rather than the field of view of the wearer. We conducted an experiment of three camera designs with 24 participants, collecting qualitative data on participants' experience while wearing these devices in the wild. We provide a model explaining factors that lead to an increase in social presence and social stigma, which, therefore, create social and surveillance discomfort for the wearer. Wearers' attempts to reduce this discomfort by modifying their behavior or abandoning the device threatens the validity of observations of authentic behaviors. We discuss design implications and provide recommendations to help reduce social presence and stigma in order to improve the validity of observations with cameras in the wild.
Saliency cuts: salient region extraction based on local adaptive thresholding for image information recognition of the visually impaired

In recent years, there has been an increased scope for assistive software and technologies, which help the visually impaired to perceive and recognize natural scene images. In this article, we propose a novel saliency cuts approach using local adaptive thresholding to obtain four regions from a given saliency map. The saliency cuts approach is an effective tool for salient object detection. First, we produce four regions for image segmentation using a saliency map as an input image and applying an automatic threshold operation. Second, the four regions are used to initialize an iterative version of the Grab Cut algorithm and to produce a robust and high-quality binary mask with a full resolution. Lastly, based on the binary mask and extracted salient object, outer boundaries and internal edges are detected by Canny edge detection method. Extensive experiments demonstrate that the proposed method correctly detects and extracts the main contents of the image sequences for delivering visually salient information to the visually impaired people compared to the results of existing salient object segmentation algorithms
Enhanced latent semantic indexing using cosine similarity measures for medical application

The Vector Space Model (VSM) is widely used in data mining and Information Retrieval (IR) systems as a common document representation model. However, there are some challenges to this technique such as high dimensional space and semantic looseness of the representation. Consequently, the Latent Semantic Indexing (LSI) was suggested to reduce the feature dimensions and to generate semantic rich features that can represent conceptual term-document associations. In fact, LSI has been effectively employed in search engines and many other Natural Language Processing (NLP) applications. Researchers thereby promote endless effort seeking for better performance. In this paper, we propose an innovative method that can be used in search engines to find better matched contents of the retrieving documents. The proposed method introduces a new extension for the LSI technique based on the cosine similarity measures. The performance evaluation was carried out using an Arabic language data collection that contains 800 medical related documents, with more than 47,222 unique words. The proposed method was assessed using a small testing set that contains five medical keywords. The results show that the performance of the proposed method is superior when compared to the standard LSI
EFFICIENT VECTOR PARTITIONING ALGORITHMS FOR MODULARITY-BASED GRAPH CLUSTERING

This paper addressed the problem of ﬁnding clusters included in graph-structured data such as Web graphs, social networks, and others. Graph clustering is one of the fundamental techniques for understanding structures present in the complex graphs such as Web pages, social networks, and others. In the Web and data mining communities, the modularity-based graph clustering algorithm is successfully used in many applications. However, it is diﬃcult for the modularity-based methods to ﬁnd ﬁne-grained clusters hidden in large-scale graphs; the methods fail to reproduce the ground truth. In this paper, we present a novel modularity-based algorithm, CAV , that shows better clustering results than the traditional algorithm. The proposed algorithm employs a cohesiveness-aware vector partitioning into the graph spectral analysis to improve the clustering accuracy. Additionally, this paper also presents a novel eﬃcient algorithm P-CAV for further improving the clustering speed of CAV; P-CAV is an extension of CAV that utilizes the thread-based parallelization on a many-core CPU. Our extensive experiments on synthetic and public datasets demonstrate the performance superiority of our approaches over the state-of-the-art approaches.
The Seasonal Cycle of Swarming in Honeybees

SummaryA six-year study of natural swarming in Ithaca, NY, USA, showed a bimodal distribution for date of swarm emergence, with a peak during the first two weeks in June and a lesser peak during the last week in August and the first week in September. The mean swarm size for 126 swarms was 1·53 kg (11 800 bees). The mean weight of 116 swarm queens was 195·9 mg; of mated queens 203·4 mg, and of virgin queens 185·0 mg. Data from 1976 suggest that a virgin or a young mated queen may accompany a prime swarm.
Engineered heme peroxidases as genetically encoded probes with diverse applications in cell biology

Proteins perform myriad functions inside cells. In vitro studies of purified proteins yield important information about their function, but it is an important pursuit in cell biology to study proteins within the native context of intact cells, since their physiological functions might not be recapitulated in any other environment. The most basic question, the location of a protein within the cell, is difficult to answer, since most proteins do not possess a spectroscopic handle that causes them to stand out above all the other proteins and biomolecules in the cell. Fluorescent proteins such as GFP have been revolutionary in allowing specific proteins to be tracked within living cells, but the resolution of fluorescence microscopy (~200 nm) is often inadequate for determining precise subcellular localization. Electron microscopy (EM), on the other hand, offers far superior resolution (<10 nm), but no genetic tag equivalent to GFP has been developed for EM. Horseradish peroxidase (HRP), a multifunctional reporter enzyme widely used in bioanalytical assays, has been utilized for EM because of its ability to generate electron-dense deposits. In addition, HRP produces extremely sensitive chemiluminescent, colorimetric, and fluorescent readouts and has applications for promiscuous protein labeling and detection of hydrogen peroxide. However, the failure of HRP to become active in most cellular compartments has hampered its adoption as a genetic tag. This thesis describes our engineering of genetically encoded peroxidase probes with diverse applications in cell biology. We describe the development of enhanced ascorbate peroxidase (APEX), a HRP-like tag that works in all cellular compartments. We present our applications of this tag for EM imaging, live-cell proteomic mapping, and intracellular detection of hydrogen peroxide. We also discuss the discovery and characterization of APEX2, a greatly improved variant of APEX. Finally, we describe the development of a split HRP protein complementation assay using a combination of rational design and directed evolution, followed by our application of split HRP for detection of neurological synapses. Thesis Supervisor: Alice Y. Ting Title: Ellen Swallow Richards Professor of Chemistry
Development of eggs and larvae of Stolephorus commersonnii and taxonomic key to fish eggs of the Clupeidae and Engraulidae off China

ABSTRACT Fish eggs of successive stages of embryonic development, which were identified as belonging to specimens of Commerson's anchovy Stolephorus commersonnii through comparative molecular techniques, were collected from Leqing Bay, Zhejiang, China. Some eggs were reared artificially to obtain samples of successive developmental stages of larvae. The fertilized eggs of S. commersonnii are ellipsoidal and non-adhesive. The surface of the egg membrane is smooth and the perivitelline space is narrow. There is a single oil globule in the irregularly segmented yolk. Newly hatched larvae are transparent and devoid of pigments. Development of the larvae occurs in the following sequence: 8 h after hatching, the anus unfolds; 12 h after hatching, the pectoral fins emerge; 24 h after hatching, the liver and branchial arches emerge, and the alimentary canal differentiates into the oesophagus and intestinal canal; 30 h after hatching, the eyes become pigmented; 36 h after hatching, the upper and lower jaws become distinct; 42 h after hatching, the stellate melanophores emerge; 72 h after hatching, the postlarval developmental stage begins with the emergence of the dorsal fin. Based on the morphology of eggs and larvae of S. commersonnii, a taxonomic key to fish eggs of the Clupeidae and Engraulidae off China is established to provide an efficient and convenient way to identify the egg specimens. The fish eggs of the Clupeidae and Engraulidae off China could be identified to some extent by the buoyancy, shape and diameter of eggs, the number of layers in the egg membrane, the size of the perivitelline space, the number and diameters of oil globules, and the distribution of pigments. Meanwhile, their prelarvae could be identified by the number and diameters of oil globules, the distribution of pigments, the location of the anus and the number of myomeres.
Interactive comment on “Extreme heat in India and anthropogenic climate change” by Geert Jan van Oldenborgh et al

dence of the real impact I recommend to use variations on the mortality rate occurred during the two extreme heat events analysed. This rate could support sentences as ‘The previous year, a widely-reported very lethal heat wave occurred in the southeast, in Andhra Pradesh and Telangana, killing thousands ofpeople’ (page1,l2-3). ‘From a health impact point of view, the severity of heat waves has increased in India’.
Hints

The systematic use of hints in the learning-from-examples paradigm is the subject of this review. Hints are the properties of the target function that are known to us independently of the training examples. The use of hints is tantamount to combining rules and data in learning, and is compatible with different learning models, optimization techniques, and regularization techniques. The hints are represented to the learning process by virtual examples, and the training examples of the target function are treated on equal footing with the rest of the hints. A balance is achieved between the information provided by the different hints through the choice of objective functions and learning schedules. The Adaptive Minimization algorithm achieves this balance by relating the performance on each hint to the overall performance. The application of hints in forecasting the very noisy foreign-exchange markets is illustrated. On the theoretical side, the information value of hints is contrasted to the complexity value and related to the VC dimension.
The ability of the green peach aphid (Myzus persicae) to penetrate mesh crop covers used to protect potato crops against tomato potato psyllid (Bactericera cockerelli)

In Central and North America, Australia and New Zealand, potato (Solanum tuberosum) crops are attacked by Bactericera cockerelli, the tomato potato psyllid (TPP). ‘Mesh crop covers’ which are used in Europe and Israel to protect crops from insect pests, have been used experimentally in New Zealand for TPP control. While the covers have been effective for TPP management, the green peach aphid (GPA, Myzus persicae) has been found in large numbers under the mesh crop covers. This study investigated the ability of the GPA to penetrate different mesh hole sizes. Experiments using four sizes (0.15 × 0.15, 0.15 × 0.35, 0.3 × 0.3 and 0.6 × 0.6 mm) were carried out under laboratory conditions to investigate: (i) which mesh hole size provided the most effective barrier to GPA; (ii) which morph of adult aphids (apterous or alate) and/or their progeny could breach the mesh crop cover; (iii) would leaves touching the underside of the cover, as opposed to having a gap between leaf and the mesh, increase the number of aphids breaching the mesh; and (iv) could adults feed on leaves touching the cover by putting only their heads and/or stylets through it? No adult aphids, either alate or apterous, penetrated the mesh crop cover; only nymphs did this, the majority being the progeny of alate adults. Nymphs of the smaller alatae aphids penetrated the three coarsest mesh sizes; nymphs of the larger apterae penetrated the two coarsest sizes, but no nymphs penetrated the smallest mesh size. There was no statistical difference in the number of aphids breaching the mesh crop cover when the leaflets touched its underside compared to when there was a gap between leaf and mesh crop cover. Adults did not feed through the mesh crop cover, though they may have been able to sense the potato leaflet using visual and/or olfactory cues and produce nymphs as a result. As these covers are highly effective for managing TPP on field potatoes, modifications of this protocol are required to make it effective against aphids as well as TPP.
Description of a New Species of the Genus Aselliscus (Chiroptera, Hipposideridae) from Vietnam

Trident bats found in mainland Southeast Asia are currently subsumed into a single species, Aselliscus stoliczkanus. In this study, we examined morphological and genetic data from different populations from Southeast Asia, with a special focus on specimens from Vietnam. Our analyses support the existence of a further species of Aselliscus in northeastern Vietnam that separated from A. stoliczkanus sensu lato (s.l.) during the late Miocene. Within the latter taxon, we identified five geographic lineages that diverged from each other during the Plio-Pleistocene epoch. Some of them may also correspond to further separate taxa, but additional molecular and morphological data are needed to test this hypothesis. Herewith, based on the combined evidences we describe the northeastern Vietnamese population as a separate species.
Physicochemical, morphological, thermal, pasting, and textural properties of starch acetates

ABSTRACT Acetylated starches are a commercially important group of modified starch known for their reduced retrogradation tendency, high clarity, low viscosity, high swelling power, and lower gelatinization temperature and time. Due to these remarkable properties, acetylation is a commonly used chemical modification for preparing stabilized starches. This review summarizes the methods used for preparation of starch acetates and corresponding effects of acetylation on morphological, thermal, pasting, digestibility, textural, and other functional characteristics of starches isolated from various botanical sources. Starch acetates are also useful as packaging material, as wall material for encapsulating active ingredients, and to improve the storage stability of different products.
of Convolutional Neural Network in the Classification of Vitiligo

---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In recent years, machine learning has been successfully applied in the field of medical sciences to diagnose the various diseases by image classification and has achieved enormous success for diagnosing skin diseases like psoriasis, chronic dermatitis, vitiligo, hives, rosacea, etc. In particular, vitiligo is a complex pigmentary disorder of the skin, characterized by the destruction of melanocytes, apparently marked by white patches on the skin. In the present study, classification based on the deep learning-based approach to solving the problem of classifying Vitiligo lesions using convolutional neural networks (CNNs) was employed. We have used the dataset of 696 images having 368 vitiligo infected images and 328 normal images to carry out the whole problem. At the technical facet, we have used four pre-trained models for feature extraction namely, Inception-V3, VGG-16, VGG-19, and SqueezeNet. Using these pre-trained models, we have trained four classification models, kNN (k-nearest neighbors), SVM (Support Vector Machine), Convolutional Neural Network and Logistic Regression. Our findings revealed that Inception-V3 provides maximum accuracy of 98.0% on Logistic Regression and 96.5% on Neural Network.