Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the bioactivity of plant tannins: developments in analysis methods and structure-activity studies

UNIVERSITY OF TURKU Department of Chemistry/Faculty of Mathematics and Natural Sciences ENGSTRÖM, MARICA THERESE: Understanding the bioactivity of plant tannins: developments in analysis methods and structure–activity studies Doctoral thesis, 185 p. Laboratory of Organic Chemistry and Chemical Biology/ Natural Compound Chemistry June 2016 Tannins, typically segregated into two major groups, the hydrolyzable tannins (HTs) and the proanthocyanidins (PAs), are plant polyphenolic secondary metabolites found throughout the plant kingdom. On one hand, tannins may cause harmful nutritional effects on herbivores, for example insects, and hence they work as plants’ defense against plant-eating animals. On the other hand, they may affect positively some herbivores, such as mammals, for example by their antioxidant, antimicrobial, antiinflammatory or anticarcinogenic activities. This thesis focuses on understanding the bioactivity of plant tannins, their anthelmintic properties and the tools used for the qualitative and quantitative analysis of this endless source of structural diversity. The first part of the experimental work focused on the development of ultra-high performance liquid chromatography−tandem mass spectrometry (UHPLC-MS/MS) based methods for the rapid fingerprint analysis of bioactive polyphenols, especially tannins. In the second part of the experimental work the in vitro activity of isolated and purified HTs and their hydrolysis product, gallic acid, was tested against egg hatching and larval motility of two larval developmental stages, L1 and L2, of a common ruminant gastrointestinal parasite, Haemonchus contortus. The results indicated clear relationships between the HT structure and the anthelmintic activity. The activity of the studied compounds depended on many structural features, including size, functional groups present in the structure, and the structural rigidness. To further understand tannin bioactivity on a molecular level, the interaction between bovine serum albumin (BSA), and seven HTs and epigallocatechin gallate was examined. The objective was to define the effect of pH on the formation on tannin–protein complexes and to evaluate the stability of the formed complexes by gel electrophoresis and MALDITOF-MS. The results indicated that more basic pH values had a stabilizing effect on the tannin–protein complexes and that the tannin oxidative activity was directly linked with their tendency to form covalently stabilized complexes with BSA at increased pH.
Farklı Kafes Yoğunluğunda Barındırılan ve Farklı Düzeyde Enerji İçeren Yemlerle Beslenen Yumurta Tavuklarının Rasyonlarına Karnitin Katılmasının Yumurta Kalitesi ve Bazı Biyokimyasal Parametrelere Etkisi

Bu calisma, farkli kafes yogunlugunda barindirilan ve farkli duzeyde enerji iceren yemlerle beslenen yumurtatavuklarinin rasyonlarina L-karnitin katilmasinin yumurta ic ve dis kalitesi ve serum enzim, lipid ve mineral duzeylerineetkisini arastirmak amaciyla gerceklestirildi. Arastirmada 32 haftalik 176 yumurta tavugu 4 tekrarli 8 gruba ayrildi. Ilk 4gruptaki hayvanlar her kafeste 4 tavuk (500 cm2/tavuk); diger 4 gruptakiler ise her kafeste 7 tavuk (285,7 cm2/tavuk)olacak sekilde yerlestirildi. Tavuklar, enerji duzeyi yuksek (2850 kcal/kg ME) veya dusuk (2650 kcal/kg ME) rasyonlara,0 veya 200 ppm L-karnitin (Carniking®) katilan yemlerle 70 gun beslendi. Yerlesim sikliginin artmasi, yumurta kabukoranini (P
Adaptation of olfactory receptor abundances for efficient coding

Olfactory receptor usage is highly non-uniform, with some receptor types being orders of magnitude more abundant than others. We propose an explanation for this striking fact: the receptor distribution is tuned to maximally represent information about the olfactory environment in a little-studied regime of efficient coding that is sensitive to the global context of correlated sensor responses. This model predicts further that in mammals, where olfactory sensory neurons are replaced regularly, receptor abundances should continuously adapt to odor statistics. Indeed, experiments have found such changes in response to olfactory experience. These changes are mysteriously context-dependent, with increased exposure to odorants leading variously to increased, decreased or unchanged abundances of activated receptors. We show that efficient coding theory predicts precisely such a context-dependence when sensors are correlated. Finally, we demonstrate simple dynamical rules for neural birth and death processes that might provide a mechanism for receptor abundances to adapt optimally.
Real-world Multi-object, Multi-grasp Detection

A deep learning architecture is proposed to predict graspable locations for robotic manipulation. It considers situations where no, one, or multiple object(s) are seen. By defining the learning problem to be classification with null hypothesis competition instead of regression, the deep neural network with RGB-D image input predicts multiple grasp candidates for a single object or multiple objects, in a single shot. The method outperforms state-of-the-art approaches on the Cornell dataset with 96.0% and 96.1% accuracy on image-wise and object- wise splits, respectively. Evaluation on a multi-object dataset illustrates the generalization capability of the architecture. Grasping experiments achieve 96.0% grasp localization and 88.0% grasping success rates on a test set of household objects. The real-time process takes less than . 25 s from image to plan.
The Complexity of Piroplasms Life Cycles

Although apicomplexan parasites of the group Piroplasmida represent commonly identified global risks to both animals and humans, detailed knowledge of their life cycles is surprisingly limited. Such a discrepancy results from incomplete literature reports, nomenclature disunity and recently, from large numbers of newly described species. This review intends to collate and summarize current knowledge with respect to piroplasm phylogeny. Moreover, it provides a comprehensive view of developmental events of Babesia, Theileria, and Cytauxzoon representative species, focusing on uniform consensus of three consecutive phases: (i) schizogony and merogony, asexual multiplication in blood cells of the vertebrate host; (ii) gamogony, sexual reproduction inside the tick midgut, later followed by invasion of kinetes into the tick internal tissues; and (iii) sporogony, asexual proliferation in tick salivary glands resulting in the formation of sporozoites. However, many fundamental differences in this general consensus occur and this review identifies variables that should be analyzed prior to further development of specific anti-piroplasm strategies, including the attractive targeting of life cycle stages of Babesia or Theileria tick vectors.
Microbial Community Changes in a Chlorinated Solvents Polluted Aquifer Over the Field Scale Treatment With Poly-3-Hydroxybutyrate as Amendment

This study investigated the organohalide-respiring bacteria (OHRB) and the supporting microbial populations operating in a pilot scale plant employing poly-3-hydroxybutyrate (PHB), a biodegradable polymer produced by bacteria from waste streams, for the in situ bioremediation of groundwater contaminated by chlorinated solvents. The bioremediation was performed in ground treatment units, including PHB reactors as slow release source of electron donors, where groundwater extracted from the wells flows through before the re-infiltration to the low permeability zones of the aquifer. The coupling of the biological treatment with groundwater recirculation allowed to drastically reducing the contamination level and the remediation time by efficiently stimulating the growth of autochthonous OHRB and enhancing the mobilization of the pollutants. Quantitative PCR performed along the external treatment unit showed that the PHB reactor may efficiently act as an external incubator to growing Dehalococcoides mccartyi, known to be capable of fully converting chlorinated ethenes to innocuous end-products. The slow release source of electron donors for the bioremediation process allowed the establishment of a stable population of D. mccartyi, mainly carrying bvcA and vcrA genes which are implicated in the metabolic conversion of vinyl chloride to harmless ethene. Next generation sequencing was performed to analyze the phylogenetic diversity of the groundwater microbiome before and after the bioremediation treatment and allowed the identification of the microorganisms working closely with organohalide-respiring bacteria.
The Effect of Dose Adjustments in a Subsequent Cycle of Women With Suboptimal Response Following Conventional Ovarian Stimulation

Several infertile patients, who may even represent around 40% of the infertile cohort, may respond “suboptimally” (4–9 oocytes retrieved) following IVF, despite being predicted as normal responders. The aim of our longitudinal study was to evaluate the ovarian response of suboptimal responders in terms of the number of oocytes retrieved, following their second IVF cycle, evaluating exclusively patients who had the same stimulation protocol and used the same or higher initial dose of the same type of gonadotropin compared to their previous failed IVF attempt. Overall, our analysis included 160 patients treated with a fixed antagonist protocol in their second cycle with the same [53 (33.1%)] or higher [107 (66.9%)] starting dose of rFSH. The number of oocytes retrieved was significantly higher in the second IVF cycle [6 (5–8) vs. 9 (6–12), p < 0.001]. According to our results, a dose increment of rFSH remained the only significant predictor of the number of oocytes retrieved in the subsequent IVF cycle (coefficient 0.02, p-value = 0.007) after conducting GEE multivariate regression, while adjusting for relevant confounders. A regression coefficient of 0.02 for the starting dose implies that an increase of 50 IU of the initial rFSH dose would lead to 1 more oocyte.
Screening of Barley Resistance Against Powdery Mildew by Simultaneous High-Throughput Enzyme Activity Signature Profiling and Multispectral Imaging

Molecular marker analysis allow for a rapid and advanced pre-selection and resistance screenings in plant breeding processes. During the phenotyping process, optical sensors have proved their potential to determine and assess the function of the genotype of the breeding material. Thereby, biomarkers for specific disease resistance traits provide valuable information for calibrating optical sensor approaches during early plant-pathogen interactions. In this context, the combination of physiological, metabolic phenotyping and phenomic profiles could establish efficient identification and quantification of relevant genotypes within breeding processes. Experiments were conducted with near-isogenic lines of H. vulgare (susceptible, mildew locus o (mlo) and Mildew locus a (Mla) resistant). Multispectral imaging of barley plants was daily conducted 0–8 days after inoculation (dai) in a high-throughput facility with 10 wavelength bands from 400 to 1,000 nm. In parallel, the temporal dynamics of the activities of invertase isoenzymes, as key sink specific enzymes that irreversibly cleave the transport sugar sucrose into the hexose monomers, were profiled in a semi high-throughput approach. The activities of cell wall, cytosolic and vacuole invertase revealed specific dynamics of the activity signatures for susceptible genotypes and genotypes with mlo and Mla based resistances 0–120 hours after inoculation (hai). These patterns could be used to differentiate between interaction types and revealed an early influence of Blumeria graminis f.sp. hordei (Bgh) conidia on the specific invertase activity already 0.5 hai. During this early powdery mildew pathogenesis, the reflectance intensity increased in the blue bands and at 690 nm. The Mla resistant plants showed an increased reflectance at 680 and 710 nm and a decreased reflectance in the near infrared bands from 3 dai. Applying a Support Vector Machine classification as a supervised machine learning approach, the pixelwise identification and quantification of powdery mildew diseased barley tissue and hypersensitive response spots were established. This enables an automatic identification of the barley-powdery mildew interaction. The study established a proof-of-concept for plant resistance phenotyping with multispectral imaging in high-throughput. The combination of invertase analysis and multispectral imaging showed to be a complementing validation system. This will provide a deeper understanding of optical data and its implementation into disease resistance screening.
Generalization Bounds for Unsupervised Cross-Domain Mapping with WGANs

The recent empirical success of unsupervised cross-domain mapping algorithms, between two domains that share common characteristics, is not well-supported by theoretical justifications. This lacuna is especially troubling, given the clear ambiguity in such mappings. We work with the adversarial training method called the Wasserstein GAN and derive a novel generalization bound, which limits the risk between the learned mapping $h$ and the target mapping $y$, by a sum of two terms: (i) the risk between $h$ and the most distant alternative mapping that was learned by the same cross-domain mapping algorithm, and (ii) the minimal Wasserstein GAN divergence between the target domain and the domain obtained by applying a hypothesis $h^*$ on the samples of the source domain, where $h^*$ is a hypothesis selected by the same algorithm. The bound is directly related to Occam's razor and encourages the selection of the minimal architecture that supports a small Wasserstein GAN divergence. The bound leads to multiple algorithmic consequences, including a method for hyperparameters selection and for an early stopping in cross-domain mapping GANs. We also demonstrate a novel capability for unsupervised learning of estimating confidence in the mapping of every specific sample. Lastly, we show how non-minimal architectures can be effectively trained by an inverted knowledge distillation, in which a minimal architecture is used to train a larger one, leading to higher quality outputs.
Phosphatidylinositol 3-kinase p110δ expression in Merkel cell carcinoma

The prognosis of stage III/IV Merkel cell carcinoma (MCC) is very poor. The Phosphatidylinositol 3-kinase p110δ specific inhibitor idelalisib has recently been reported to induce complete clinical remission in a stage IV MCC patient. Here we assessed the expression of p110δ in primary MCC and MCC cell lines including its functionality. Immunofluorescence microscopy revealed a specific cytoplasmic p110δ expression in 71.4% of the tested MCCs and in all tested MCC cell lines. Compared to the B cell leukemia cell line REH all MCC cell lines, except MKL-1, revealed a lower response towards the treatment with idelalisib. MKL-1 showed a 10-fold higher IC50 compared to REH which was accompanied by a significant decrease of Akt phosphorylation. However, treating the MCC cells with the specific PI3K p110α subunit inhibitor BYL719 led to a more effective decrease of the cell viability compared to idelalisib: WaGa cells 30-fold, PeTa cells 15-fold and all other MCC cell lines 3-fold. Although PI3K p110δ is expressed in the majority of MCCs and cell lines its inhibition by idelalisib alone does not suffice to effectively affect MCC cells viability.
Immersive VR and Education: Embodied Design Principles That Include Gesture and Hand Controls

This article explores relevant applications of educational theory for the design of immersive virtual reality (VR). Two unique attributes associated with VR position the technology to positively affect education: (1) the sense of presence, and (2) the embodied affordances of gesture and manipulation in the 3rd dimension. These are referred to as the two profound affordances of VR. The primary focus of this article is on the embodiment afforded by gesture in 3D for learning. The new generation of hand controllers induces embodiment and agency via meaningful and congruent movements with the content to be learned. Several examples of gesture-rich lessons are presented. The final section includes an extensive set of design principles for immersive VR in education, and finishes with the Necessary Nine which are hypothesized to optimize the pedagogy within a lesson.
Hepatoprotective Effect of Steroidal Glycosides From Dioscorea villosa on Hydrogen Peroxide-Induced Hepatotoxicity in HepG2 Cells

Dioscorea villosa, commonly known as “Wild Yam” and native to North America, is well documented for its pharmacological properties due to the presence of steroidal glycosides. However, the hepatoprotective potential of these compounds has not been studied so far. The present investigation was aimed to study the hepatoprotective effect of the steroidal glycosides from D. villosa against H2O2, a known hepatotoxin, in human liver cell line (HepG2). Cytotoxicity assessment was carried out in cells exposed to various concentrations (10–50 μM) of compounds for 24 h using MTT assay and morphological changes. All tested compounds were known and among them, spirostans (zingiberensis saponin I, dioscin, deltonin and progenin III) were found to be cytotoxic whereas, furostans (huangjiangsu A, pseudoprotodioscin, methyl protobioside, protodioscin, and protodeltonin) were non-cytotoxic. Further, HepG2 cells were pretreated with biologically safe concentrations (10, 30, and 50 μM) of non-cytotoxic compounds and then cytotoxic (0.25 mM) concentration of H2O2. After 24 h, cell viability was assessed by MTT and NRU assays, while morphological changes were observed under the microscope. The results showed that treatment of HepG2 cells with compounds prior to H2O2 exposure effectively increased cell viability in a concentration-dependent manner. Furthermore, huangjiangsu A, pseudoprotodioscin, methyl protobioside, protodioscin, and protodeltonin at 50 μM increased GSH level and decreased intracellular ROS generation against H2O2-induced damages. The results from this study revealed that compounds isolated from D. villosa have hepatoprotective potential against H2O2-induced cytotoxicity and ROS generation and could be promising as potential therapeutic agents for liver diseases.
Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy

Deep convolution neural network has achieved great success in many artificial intelligence applications. However, its enormous model size and massive computation cost have become the main obstacle for deployment of such powerful algorithm in the low power and resource limited embedded systems. As the countermeasure to this problem, in this work, we propose statistical weight scaling and residual expansion methods to reduce the bit-width of the whole network weight parameters to ternary values (i.e. −1, 0, +1), with the objectives to greatly reduce model size, computation cost and accuracy degradation caused by the model compression. With about 16X model compression rate, our ternarized ResNet-32/44/56 could outperforms full-precision counterparts by 0.12%, 0.24% and 0.18% on CIFAR-10 dataset. We also test our ternarization method with AlexNet and ResNet-18 on ImageNet dataset, which both achieve the best top-1 accuracy compared to recent similar works, with the same 16X compression rate. If further incorporating our residual expansion method, compared to the full-precision counterpart, our ternarized ResNet-18 even improves the top-5 accuracy by 0.61% and merely degrades the top-1 accuracy only by 0.42% for ImageNet dataset, with 8X model compression rate. It outperforms the recent ABC-Net by 1.03% in top-1 accuracy and 1.78% in top-5 accuracy, with around 1.25X higher compression rate and more than 6X computation reduction due to the weight sparsity.
Git Loss for Deep Face Recognition

Convolutional Neural Networks (CNNs) have been widely used in computer vision tasks, such as face recognition and verification, and have achieved state-of-the-art results due to their ability to capture discriminative deep features. Conventionally, CNNs have been trained with softmax as supervision signal to penalize the classification loss. In order to further enhance the discriminative capability of deep features, we introduce a joint supervision signal, Git loss, which leverages on softmax and center loss functions. The aim of our loss function is to minimize the intra-class variations as well as maximize the inter-class distances. Such minimization and maximization of deep features are considered ideal for face recognition task. We perform experiments on two popular face recognition benchmarks datasets and show that our proposed loss function achieves maximum separability between deep face features of different identities and achieves state-of-the-art accuracy on two major face recognition benchmark datasets: Labeled Faces in the Wild (LFW) and YouTube Faces (YTF). However, it should be noted that the major objective of Git loss is to achieve maximum separability between deep features of divergent identities.
AceKG: A Large-scale Knowledge Graph for Academic Data Mining

Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine processing. In this paper, we present AceKG, a new large-scale KG in academic domain. AceKG not only provides clean academic information, but also offers a large-scale benchmark dataset for researchers to conduct challenging data mining projects including link prediction, community detection and scholar classification. Specifically, AceKG describes 3.13 billion triples of academic facts based on a consistent ontology, including necessary properties of papers, authors, fields of study, venues and institutes, as well as the relations among them. To enrich the proposed knowledge graph, we also perform entity alignment with existing databases and rule-based inference. Based on AceKG, we conduct experiments of three typical academic data mining tasks and evaluate several state-of-the-art knowledge embedding and network representation learning approaches on the benchmark datasets built from AceKG. Finally, we discuss promising research directions that benefit from AceKG.
Fault Localization for Declarative Models in Alloy

Fault localization is a popular research topic and many techniques have been proposed to locate faults in imperative code, e.g. C and Java. In this paper, we focus on the problem of fault localization for declarative models in Alloy – a first order relational logic with transitive closure. We introduce AlloyFLhy, the first fault localization technique for faulty Alloy models which leverages multiple test formulas. AlloyFLhy brings the traditional spectrum-based and mutation-based fault localization techniques to Alloy and combines both techniques to locate faults. To measure the effectiveness of AlloyFLhy, we define three distance metrics and use both distance-based and top-k metrics to measure the effectiveness of AlloyFLhy on 90 real faulty models. The results show that AlloyFLhy is substantially more effective than Alloy’s built-in unsat core.