Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in feeding selectivity of freshwater invertebrates across a natural thermal gradient

Abstract Environmental warming places physiological constraints on organisms, which may be mitigated by their feeding behavior. Theory predicts that consumers should increase their feeding selectivity for more energetically valuable resources in warmer environments to offset the disproportionate increase in metabolic demand relative to ingestion rate. This may also result in a change in feeding strategy or a shift towards a more specialist diet. This study used a natural warming experiment to investigate temperature effects on the feeding selectivity of three freshwater invertebrate grazers: the snail Radix balthica, the blackfly larva Simulium aureum, and the midgefly larva Eukiefferiella minor. Chesson’s Selectivity Index was used to compare the proportional abundance of diatom species in the guts of each invertebrate species with corresponding rock biofilms sampled from streams of different temperature. The snails became more selective in warmer streams, choosing high profile epilithic diatoms over other guilds and feeding on a lower diversity of diatom species. The blackfly larvae appeared to switch from active collector gathering of sessile high profile diatoms to more passive filter feeding of motile diatoms in warmer streams. No changes in selectivity were observed for the midgefly larvae, whose diet was representative of resource availability in the environment. These results suggest that key primary consumers in freshwater streams, which constitute a major portion of invertebrate biomass, can change their feeding behavior in warmer waters in a range of different ways. These patterns could potentially lead to fundamental changes in the flow of energy through freshwater food webs.
Effects of Glycine on Collagen, PDGF, and EGF Expression in Model of Oral Mucositis

Oral mucositis is frequently a toxic effect of chemotherapeutic and/or radiotherapeutic treatment, resulting from complex multifaceted biological events involving DNA damage. The clinical manifestations have a negative impact on the life quality of cancer patients. Preventive measures and curative treatment of mucositis are still not well established. The glycine has anti-inflammatory, immunomodulatory, and cytoprotective actions, being a potential therapeutic in mucositis. The objective was to evaluate the effects of glycine on the expression of collagen and growth factors, platelet and epidermal in a hamster model oral mucositis. The mucositis was induced by the protocol of Sonis. There were 40 hamsters used, divided into two groups: Group I-control; Group II-supplemented with 5% intraperitoneal glycine, 2.0 mg/g diluted in hepes. Histopathological sections were used to perform the immune-histochemical method, the evaluation of collagen expression, and the growth factors: Epidermal growth factor (EGF) and platelet (PDGF). It was observed that the group supplemented with glycine experienced higher amounts of collagen expression and predominance type of collagen I. The glycine group presented lower immunoexpression of the growth factors, EGF and PDGF. The group supplemented with glycine showed a marked healing process of the oral mucosite, demonstrated by the predominance of collagen type I and reduction of growth factors, EGF and PDGF.
Epidemiology of cutaneous adverse drug reactions

Epidemiologic investigation of cutaneous adverse drug reactions (cADRs) is important in order to evaluate their impact on dermatology and health care in general as well as their burden on affected patients. Few epidemiologic studies have been performed on frequent non-life-threatening cADR, including reactions of both delayed and immediate hypersensitivity, such as maculopapular exanthema (MPE), fixed drug eruption, and urticaria. Concerning rare but life-threatening severe cutaneous adverse reactions, e.g., toxic epidermal necrolysis (TEN), Stevens-Johnson syndrome (SJS), acute generalized exanthematous pustulosis (AGEP), and drug reaction with eosinophilia and systemic symptoms (DRESS), several epidemiologic studies have been performed to date, some of which are still ongoing. Such studies enable the calculation of reliable incidence rates and demographic data, and also allow researchers to perform risk estimation for drugs. The spectrum of drugs causing cADR differs substantially when separating the various clinical conditions. Whereas antibiotics are by far the most frequent inducers of milder cADRs, like MPE, they have a much lower risk of inducing SJS/TEN, for which “high-risk” drugs are anti-infective sulfonamides, allopurinol, certain anti-epileptic drugs, nevirapine, and non-steroidal anti-inflammatory drugs (NSAIDs) of the oxicam-type. In contrast, AGEP is predominantly caused by the antibiotics pristinamycin and aminopenicillins, followed by quinolones, (hydroxy-)chloroquine, and sulfonamides. DRESS can be induced by a number of drugs known to cause SJS/TEN, such as certain antiepileptics and allopurinol, but also other medications (e.g., minocyclin).
skedm: Empirical Dynamic Modeling

This python package implements nonlinear time series analysis techniques, also referred to as empirical dynamic modeling, based on many of the workflows and routines within TISEAN(Hegger and Schreiber 1999) and (Ye et al. 2017). The package provides a modern api, is written in pure python, and provides additional analysis routines not provided by TISEAN. skedm is capable of reconstructing state spaces from one, two, and even three-dimensional series. Additionally, it provides various methods for analyzing the evolution of nearby neighbors in the reconstructed state spaces. skedm also includes numerous one, two, and three-dimensional synthetic datasets for researchers to explore.
On the use of DNN Autoencoder for Robust Speaker Recognition

In this paper, we present an analysis of a DNN-based autoencoder for speech enhancement, dereverberation and denoising. The target application is a robust speaker recognition system. We started with augmenting the Fisher database with artificially noised and reverberated data and we trained the autoencoder to map noisy and reverberated speech to its clean version. We use the autoencoder as a preprocessing step for a state-of-the-art text-independent speaker recognition system. We compare results achieved with pure autoencoder enhancement, multi-condition PLDA training and their simultaneous use. We present a detailed analysis with various conditions of NIST SRE 2010, PRISM and artificially corrupted NIST SRE 2010 telephone condition. We conclude that the proposed preprocessing significantly outperforms the baseline and that this technique can be used to build a robust speaker recognition system for reverberated and noisy data.
Automatic Annotation of Object Instances by Region-Based Recurrent Neural Networks

In recent years, a wide variety of automatic, semiautomatic and manual approaches to image annotation have been proposed. These prerequisites have been driven by continuous advances of deep learning algorithms that often encounter the problem of insufficient or inappropriate training data, as well as sub-par markings’ accuracy which can have a direct impact on the model’s performance regardless. The main contribution of this paper is the development of a complex annotation framework able to automatically generate high-quality markings. The annotation work-flow aims to be an iterative process allowing automatic labeling of object bounding boxes, while simultaneously predicting the polygon outlining the object instance inside the box. The markings’ format is fully compatible with COCO Detection & Panoptic APIs that provide open-source interfaces for loading, parsing, and visualizing annotations. Following the completion of the research project funding this research, the code will be publicly available.
Heterologous Expression of the Nybomycin Gene Cluster from the Marine Strain Streptomyces albus subsp. chlorinus NRRL B-24108

Streptomycetes represent an important reservoir of active secondary metabolites with potential applications in the pharmaceutical industry. The gene clusters responsible for their production are often cryptic under laboratory growth conditions. Characterization of these clusters is therefore essential for the discovery of new microbial pharmaceutical drugs. Here, we report the identification of the previously uncharacterized nybomycin gene cluster from the marine actinomycete Streptomyces albus subsp. chlorinus through its heterologous expression. Nybomycin has previously been reported to act against quinolone-resistant Staphylococcus aureus strains harboring a mutated gyrA gene but not against those with intact gyrA. The nybomycin-resistant mutants generated from quinolone-resistant mutants have been reported to be caused by a back-mutation in the gyrA gene that restores susceptibility to quinolones. On the basis of gene function assignment from bioinformatics analysis, we suggest a model for nybomycin biosynthesis.
Generative Adversarial Networks for Cross-Scene Classification in Remote Sensing Images

In this paper, we present a novel method for cross-scene classification in remote sensing images based on generative adversarial networks (GANs). To this end, we train in an adversarial manner an encoder-decoder network coupled with a discriminator network on labeled and unlabeled data coming from two different domains. The encoder-decoder network aims to reduce the discrepancy between the distributions of the two domains, while the discriminator tries to discriminate between them. At the end of the optimization process, we train an extra network on the obtained encoded labeled data and then classify the encoded unlabeled data. Experimental results on two datasets acquired over the cities of Potsdam and Vaihingen with spatial resolutions of 5cm and 9cm, respectively, confirm the promising capability of the proposed method.
Inversion of Deep Networks for Modelling Variations in Spatial Distributions of Land Cover Classes Across Scales

In this paper, we propose the use of network inversion for modeling the variation of class distributions with scale. Unlike the state of the art methods that predict the mapping between coarser and finer scale patches without considering the distributions at coarser scale, our approach uses coarser scale features for effective reconstruction. This is the pioneer work of using network inversion for the purpose. Analysis over the proposed framework reveals that both the computational performance and accuracy varies with the depth of the network as well as the size and number of filters in each layer. Also the performance of the approach has been found to improve with the increase in the number of input feature maps. Investigations over standard datasets indicate that the proposed approach performs much better than the recent sub-pixel classification as well as super resolution techniques.
Identity Regularized Sparse Representation for Automatic Target Recognition in Sar Images

An identity regularized sparse representation (IRSR) based SAR target recognition method is proposed in this paper. The method aims to find a transformation that can map the data to a transformed space, in which targets from the same class are close with each other, no matter the distance of them in the original space. This identity constraint can be formulated as a ℓ1 -norm minimization problem. By decoupling the problem into the sparse coding problem and the dictionary learning problem, the solution can be obtained iteratively. The solution is simply the weighted average of the sparse coding of all training data. Experimental results demonstrate that the proposed method is superior to several related methods.
Blind Nonlinear Hyperspectral Unmixing Using an $\ell_{q}$ Regularizer

Hyperspectral unmixing consists of estimating pure material spectra (endmembers) and their corresponding abundances in hyperspectral images. In this paper, a blind nonlinear hyperspectral unmixing algorithm is presented. The algorithm promotes sparse abundance maps using an $\ell_{q}$ regularizer and assumes that the spectra are mixed according to an extension to generalized bilinear model, called the Fan model. The algorithm is evaluated using both simulated and real hyperspectral data.
Plant Calcium Signaling in Response to Potassium Deficiency

Potassium (K+) is an essential macronutrient of living cells and is the most abundant cation in the cytosol. K+ plays a role in several physiological processes that support plant growth and development. However, soil K+ availability is very low and variable, which leads to severe reductions in plant growth and yield. Various K+ shortage-activated signaling cascades exist. Among these, calcium signaling is the most important signaling system within plant cells. This review is focused on the possible roles of calcium signaling in plant responses to low-K+ stress. In plants, intracellular calcium levels are first altered in response to K+ deficiency, resulting in calcium signatures that exhibit temporal and spatial features. In addition, calcium channels located within the root epidermis and root hair zone can then be activated by hyperpolarization of plasma membrane (PM) in response to low-K+ stress. Afterward, calcium sensors, including calmodulin (CaM), CaM-like protein (CML), calcium-dependent protein kinase (CDPK), and calcineurin B-like protein (CBL), can act in the sensing of K+ deprivation. In particular, the important components regarding CBL/CBL-interacting protein kinase (CBL/CIPK) complexes-involved in plant responses to K+ deficiency are also discussed.
Smart Charities? Analyses of IT-enabled Charitable Fundraising

Crowdfunding has transformed charitable fundraising by enabling fundraisers, even inexperienced ones, to reach a wider set of potential donors. Inspired by this development, my research agenda aims to gain a deeper understanding of the dynamics of fundraising on charitable crowdfunding platforms. My thesis examines how campaign-specific factors, other campaigns on the platform, and factors from outside of the platform influence the success of charitable crowdfunding campaigns. First, I examine how quality signals influence the donations received by charitable crowdfunding campaigns. Second, I examine how competition among campaigns with an identical cause affects the amount of donations received by each campaign. Lastly, I plan to examine how announcements of governmental grants to help victims of a particular natural disaster event influence the total donations received by campaigns associated with that event.
A Tight Analysis of Bethe Approximation for Permanent

We prove that the permanent of nonnegative matrices can be deterministically approximated within a factor of square root 2 n in polynomial time, improving upon the previous deterministic approximations. We show this by proving that the Bethe approximation of the permanent, a quantity computable in polynomial time, is at least as large as the permanent divided by square root 2 n. This resolves a conjecture of Gurvits [21]. Our bound is tight, and when combined with previously known inequalities lower bounding the permanent, fully resolves the quality of Bethe approximation for permanent.
Opposing Morphogenetic Defects on Dendrites and Mossy Fibers of Dentate Granular Neurons in CRMP3-Deficient Mice

Collapsin response mediator proteins (CRMPs) are highly expressed in the brain during early postnatal development and continue to be present in specific regions into adulthood, especially in areas with extensive neuronal plasticity including the hippocampus. They are found in the axons and dendrites of neurons wherein they contribute to specific signaling mechanisms involved in the regulation of axonal and dendritic development/maintenance. We previously identified CRMP3’s role on the morphology of hippocampal CA1 pyramidal dendrites and hippocampus-dependent functions. Our focus here was to further analyze its role in the dentate gyrus where it is highly expressed during development and in adults. On the basis of our new findings, it appears that CRMP3 has critical roles both in axonal and dendritic morphogenesis of dentate granular neurons. In CRMP3-deficient mice, the dendrites become dystrophic while the infrapyramidal bundle of the mossy fiber shows aberrant extension into the stratum oriens of CA3. This axonal misguided projection of granular neurons suggests that the mossy fiber-CA3 synaptic transmission, important for the evoked propagation of the activity of the hippocampal trisynaptic circuitry, may be altered, whereas the dystrophic dendrites may impair the dynamic interactions with the entorhinal cortex, both expected to affect hippocampal function.
Engaging Turn-Based Combat in the Children of the Galaxy Videogame

In this paper we tackle a problem of tile-based combat in the turn-based strategy (space 4X) video game Children of the Galaxy (CotG). We propose an improved version of Monte Carlo tree search (MCTS) called MCTS considering hit points (MCTS_HP). We show MCTS_HP is superior to Portfolio greedy search (PGS), MCTS and NOKAV reactive agent in small to medium combat scenarios. MCTS_HP performance is shown to be stable when compared to PGS, while it is also more time-efficient than regular MCTS. In smaller scenarios, the performance of MCTS_HP with 100 millisecond time limit is comparable to MCTS with 2 seconds time limit. This fact is crucial for CotG as the combat outcome assessment is precursor to many strategical decisions in CotG game. Finally, if we fix the amount of search time given to the combat agent, we show that different techniques dominate different scales of combat situations. As the result, if search-based techniques are to be deployed in commercial products, a combat agent will need to be implemented with portfolio of techniques it can choose from given the complexity of situation it is dealing with to smooth gameplay experience for human players.
Finding and Following of Honeycombing Regions in Computed Tomography Lung Images by Deep Learning

In recent years, besides the medical treatment methods in medical field, Computer Aided Diagnosis (CAD) systems which can facilitate the decision making phase of the physician and can detect the disease at an early stage have started to be used frequently. The diagnosis of Idiopathic Pulmonary Fibrosis (IPF) disease by using CAD systems is very important in that it can be followed by doctors and radiologists. It has become possible to diagnose and follow up the disease with the help of CAD systems by the development of high resolution computed imaging scanners and increasing size of computation power. The purpose of this project is to design a tool that will help specialists diagnose and follow up the IPF disease by identifying areas of honeycombing and ground glass patterns in High Resolution Computed Tomography (HRCT) lung images. Creating a program module that segments the lung pair and creating a self-learner deep learning model from given Computed Tomography (CT) images for the specific diseased regions thanks to doctors are the main purposes of this work. Through the created model, program module will be able to find special regions in given new CT images. In this study, the performance of lung segmentation was tested by the S{\o}rensen-Dice coefficient method and the mean performance was measured as 90.7%, testing of the created model was performed with data not used in the training stage of the CNN network, and the average performance was measured as 87.8% for healthy regions, 73.3% for ground-glass areas and 69.1% for honeycombing zones.
Learning Deep Relationship for Image Change Detection

Very high resolution image change detection is difficult due to the low interclass variability and the resulting high overlap between the changed class and the unchanged class. To address the above difficulties, the concept “relationship” is proposed to represent the intraclass similarity and interclass difference, which is established by interclass couples and intraclass couples. By relationship representation and relationship learning, intraclass couples can be compressed into a compact cluster, and the distances between interclass couples are enlarged. To better discover the complex relationship hidden in change features, relationship learning is integrated into a deep learning framework, where the relationship is learned progressively. In consequence, the final change detection performance is improved with the reduced overlap between the changed class and the unchanged class. Experiments demonstrate the effectiveness of the proposed approach.
Evaluation, Gap Analysis, and Potential Expansion of the Finnish Marine Protected Area Network

Marine Protected Areas (MPAs) are considered to be an essential tool for safeguarding marine biodiversity. Sustainable use of the oceans and seas relies on the benefits of MPAs, now even more than ever, due to environmental degradation and anthropogenic impacts on marine ecosystems. Various international and regional agreements require that nations designate sufficiently marine areas under protection. MPAs are powerful tools, if coherent and ecologically efficient. Assessing the functionality of MPA networks is challenging, unless extensive data on underwater species and habitats is available. We evaluated the efficiency of the Finnish MPA network by utilizing a unique new dataset of ~140 000 samples, collected by the Finnish Inventory Programme for the Underwater Marine Environment VELMU. For the evaluation of MPAs, we used comprehensive data on: species distribution and abundance models for over 100 taxa, IUCN Red List of Ecosystems, fish reproduction areas, EU Habitats Directive Annex I Habitats and human pressures. Using the quantitative conservation planning and spatial prioritization method Zonation, we identified sites of high biodiversity and developed a balanced ranking of underwater conservation values. Only 27 % of the ecologically most valuable features were covered by the current MPA network. Based on the analyses a set of expansion sites were identified, complementing the ecological and geographical gaps in the current MPA network. Increasing the protected sea area by just one percent with the selection of the most valuable areas indicated by the analysis, the protection level of biological features could be significantly increased. We also discovered that the EU Directive habitats are not in their present form functional proxies for marine benthic species. This suggests that MPA networks based on habitats are not sufficient for safeguarding marine biodiversity in the northern Baltic Sea. Furthermore, the produced rankings are essentially environmental value maps, they can be used in ecosystem-based marine spatial planning and impact avoidance, including, e.g., siting of wind energy or aquaculture. Our approach and analytical procedure can be replicated elsewhere in the Baltic Sea and in other marine areas around the world, provided sufficient data exists.
mcMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem

The single-objective minimum spanning tree (MST) problem is a combinatorial optimization problem known to be polynomial-time solvable, e.g., using the algorithm of Prim (Prim 1957). However, in real-world applications one is frequently confronted with multiple objectives which need to be minimized simultaneously. Since the objectives are usually conflicting, there is no single optimal solution to this problem. Instead the goal is the approximate the so-called Pareto-front, i.e., the set of nondominated solutions (see Deb (2001)).