Miracle-dz/UniFAR-FTUs huggingface dataset:
FIRST DESCRIPTION OF EPIZOIC CILIATES (SESSILIDA STEIN, 1933) ON BATHYPOREIA LINDSTROM, 1855 (PERACARIDA, AMPHIPODA) AND INFESTATION PATTERNS IN BRACKISH AND MARINE WATERS

During a re-examination of macrozoobenthic samples from a long-term monitoring programme in the Dutch Oosterschelde and Westerschelde, the abundant presence of peritrich ciliates on specimens of Bathyporeia Lindstrom, 1855, was discovered. Out of the more than 3500 Bathyporeia specimens investigated, 44% contained ciliates. Although Bathyporeia sarsi Watkin, 1938 was significantly more often infested than Bathyporeia pilosa Lindstrom, 1855, these differences in infestation rates were largely due to differences between water bodies with higher infestation rates in the polyhaline than in the mesohaline reach. Observation of additionally collected living specimens and freshly preserved material showed that at least two, and likely three, species of ciliates are present of which two might be undescribed so far. One of the observed species matches Zoothamnium nanum Kahl, 1933. A second species belongs to the genus Epistylis Ehrenberg, 1830, but does not seem to match a so far known species. This also accounts for a possible third species belonging to the genus Zoothamnium Bory de St. Vincent, 1826, deviating from Z. nanum amongst others in the habitus of the stalk. The front part of Bathyporeia spp. and the antennae in particular, significantly more often harboured ciliates than the remainder of the body, where additional ciliates were only found on the ventral side. This shows that the peritrich ciliates benefit from the water currents induced by the basibiont, providing food items, but might also indicate that Bathyporeia spp. benefits from the presence of the epibionts as they are most prevalent on the body parts that are easiest to clean. Analyses of densities and distributions of epibiont and basibiont species gave first indications of the ecological niche of the peritrich ciliate communities.
Protein design under competition for amino acids availability

Understanding the origin of the 20 letter alphabet of proteins is a long-lasting biophysical problem. In particular, studies focused extensively on the effect of a reduced alphabet size on the folding properties. However, the natural alphabet is a compromise between versatility and optimisation of the available resources. Here, for the first time, we include the additional impact of the relative availability of the amino acids. We present a protein design scheme that involves the competition for resources between a protein and a potential interaction partner that, additionally, gives us the chance to investigate the effect of the reduced alphabet on protein-protein interactions. We identify the optimal reduced set of letters for the design of the protein, and we observe that even alphabets reduced down to 4 letters allow for single protein folding. However, it is only with 6 letters that we achieve optimal folding, thus recovering experimental observations. Additionally, we notice that the binding between the protein and a potential interaction partner could not be avoided with the investigated reduced alphabets. Therefore, we suggest that aggregation could have been a driving force for the evolution of the large protein alphabet.
Associations of rumen volatile fatty acids with phenotypic and genetic variation in methane production traits in Angus cattle

Methane emissions from beef cattle contribute to greenhouse gas emissions in the atmosphere and waste gross feed energy consumed by cattle. Screening tests for methane emissions in cattle would be useful in genetic selection programs to reduce emissions. This paper reports results for 136 yearling-age Angus heifers and bulls tested for methane production in respiration chambers, and rumen fluid samples taken 3 hours post-feeding analysed for concentrations of volatile fatty acids (VFAs). A subsample of animals had repeat rumen samples taken 24 hours after feeding. The animals were fed a roughage ration offered at 1.2-times maintenance through testing. Concentrations of major VFAs (acetate, propionate and butyrate) and their proportions in the 3 hours post-feeding sample were strongly associated with methane production (g/d) (correlation coefficients up to 0.62), but less strongly with methane yield and residual methane production (correlation coefficients up to 0.17 and 0.28, respectively). Taking a rumen fluid sample during peak fermentation revealed stronger associations between methane emissions and VFA concentrations than previously reported for samples collected 24 hours after feeding. These relationships open the possibility of using VFA concentrations in rumen samples obtained at peak fermentation as indicator traits for methane emissions. For genetic selection, scrutiny of VFA as a screening test for methane emissions is still warranted.
Optimal feedback mechanisms for regulating cell numbers

How living cells employ counting mechanisms to regulate their numbers or density is a long-standing problem in developmental biology that ties directly with organism or tissue size. Diverse cells types have been shown to regulate their numbers via secretion of factors in the extracellular space. These factors act as a proxy for the number of cells and function to reduce cellular proliferation rates creating a negative feedback. It is desirable that the production rate of such factors be kept as low as possible to minimize energy costs and detection by predators. Here we formulate a stochastic model of cell proliferation with feedback control via a secreted extracellular factor. Our results show that while low levels of feedback minimizes random fluctuations in cell numbers around a given set point, high levels of feedback amplify Poisson fluctuations in secreted-factor copy numbers. This trade-off results in an optimal feedback strength, and sets a fundamental limit to noise suppression in cell numbers. Intriguingly, this fundamental limit depends additively on two variables: relative half-life of the secreted factor with respect to the cell proliferation rate, and the average number of factors secreted in a cell’s lifespan. We further expand the model to consider external disturbances in key physiological parameters, such as, proliferation and factor synthesis rates. Intriguingly, while negative feedback effectively mitigates disturbances in the proliferation rate, it amplifies disturbances in the synthesis rate. In summary, these results provide unique insights into the functioning of feedback-based counting mechanisms, and apply to organisms ranging from unicellular prokaryotes and eukaryotes to human cells.
Hypothalamic pituitary adrenal axis dysregulation in obese pregnancy

and Lay Summary 1 Presentations and Publications Relating to this Thesis 7 Abbreviations 12 List of Figures 16 List of Tables 18 List of Appendix Figures 19 List of Appendix Tables 20 Chapter 1: General Introduction 21 1.1 Obesity in Pregnancy 21 1.1.1 Global Incidence 21 1.1.2 Risks of Maternal Obesity in Pregnancy 22 1.1.3 Short-Term Offspring Outcomes 24 1.1. 4 Long-Term Offspring Outcomes 25 1.2 The Hypothalamic-Pituitary-Adrenal Axis 27 1.2.1 Glucocorticoids 27 1.2.1.1 Circadian Rhythm of Cortisol 30 1.2.1.2 Ultradian Rhythm of Cortisol 31 1.2.1.3 Cortisol Sampling Methods and Indices 37 1.2.2 The HPA-Axis in Obesity 39 1.3 The HPA-axis During Pregnancy 42 1.3. 1 Gestational Changes to the HPA Axis 42 1.3. 2 In Utero Cortisol Exposure 43 1.3. 2.1 Fetal Growth 43 1.3. 2.2 Length of Gestation 44
Temperature and humidity acclimation increase desiccation resistance in the butterfly Bicyclus anynana

Desiccation resistance, that is, the ability to reduce water loss, is an ecologically important trait relevant to all terrestrial organisms, which may constrain species distributions. Nevertheless, relatively few studies have investigated plastic capacities in desiccation resistance. We here investigate plastic responses in body mass change, used as a proxy of desiccation resistance, to variation in temperature and relative humidity in the tropical butterfly Bicyclus anynana (Butler) (Lepidoptera: Nymphalidae). Our results indicate that butterflies acclimated to a higher (27 °C) compared with a lower temperature (18 °C) and a lower (50%) compared with a higher (90%) relative humidity displayed a decreased loss of body mass, and therefore likely a loss of body water (27 °C: 11%, 18 °C: 15%; 50% r.h.: 14%, 90% r.h.: 18%). Thus, mass loss was reduced under conditions indicating increased desiccation risk, suggesting adaptive phenotypic plasticity. Effects were most pronounced during the first 24 h after acclimation, indicating quick and transient responses to environmental conditions. As anthropogenic climate change is predicted to increase the magnitude and frequency of heat and drought periods, we argue that more studies on plastic capacities in traits relating to desiccation resistance are needed to better understand species responses.
FT-MIR spectroscopy and multivariate analysis for determination of bioactive compounds and antioxidant capacity in Cabernet Sauvignon wines

ABSTRACT Thirty samples of Cabernet Sauvignon Mexican wines from 5 wine regions, 19 wineries and vintage between 2004 and 2012 were characterised regarding total phenolic compounds (TPC), anthocyanins (AC), tannins (TC), flavonoids (FC) and antioxidant capacity (ABTS and DPPH assays). Samples were processed using Fourier transform mid-infrared spectroscopy combined with partial least squares regression (PLS1 and PLS2) and principal components regression algorithms to obtain one optimised chemometric model for each algorithm. The spectral region selected for each model was between 1550 and 824 cm−1. The PLS1 model showed the best predictive capacity for simultaneous prediction of the six parameters. Excellent values of the coefficient of determination in validation (Rv2; 0.93–0.95) and residual predictive deviation (RPD; 4.28–6.95) were obtained for TPC, FC, ABTS and DPPH. Suitable values of Rv2 (0.91–0.92) and RPD (3.75–3.84) were obtained for TC and AC.
Inducible alpha-synuclein overexpression affects human Neural Stem Cells behavior

Converging evidence suggest that levels of alpha-Synuclein (aSyn) expression play a critical role in Parkinson’s disease (PD). Several mutations of the SNCA gene, encoding for aSyn have been associated to either the familial or the sporadic forms of PD. Nonetheless, the mechanism underlying wild type aSyn-mediated neurotoxicity in neuronal cells as well as its specific driving role in PD pathogenesis has yet to be fully clarified. In this view, the development of proper in vitro cellular systems is a crucial step. Here we present a novel human Tet-on hNSC cell line, in which aSyn timing and level of expression can be tightly experimentally tuned. Induction of aSyn in self-renewing hNSCs leads to progressive formation of aSyn aggregates and impairs their proliferation and cell survival. Furthermore, aSyn induction during the neuronal differentiation process results in impaired neurogenic potential due to enhanced refractoriness to exit self-renewal and to increase of gliogenic vs neurogenic competence. Finally, acute aSyn induction in hNSC-derived dopaminergic neuronal cultures results in cell toxicity. This novel conditional in vitro cell model system may be a valuable tool for dissecting of aSyn pathogenic effects in hNSCs and neurons and in developing new potential therapeutic strategies.
Cephaloziella konstantinovae (Cephaloziellaceae, Marchantiophyta), A New Leafy Liverwort Species from Russia and Mongolia Identified by Integrative Taxonomy

In the course of a taxonomic study of the genus Cephaloziella (Spruce) Schiffn. (Cephaloziellaceae, Marchantiophyta) in Asia, the new species Cephaloziella konstantinovae Mamontov & Vilnet, sp. nov . , from the eastern regions of Russia and from the Republic of Mongolia was discovered. The new species is formally described and illustrated here. Morphologically it is similar to C. divaricata var . asperifolia (Taylor) Damsh. , but differs in its leaf shape and thin-walled, inflated stem and leaf cells. The new species can be distinguished from other Cephaloziella taxa by the following characters: ( i ) female bracts entirely free from each other and from bracteole, ( ii ) perianth campanulate, ( iii ) cells of perianth mouth subquadrate, ( iv ) capsule spherical, ( v ) seta with 8–10 + 4–6-seriate morphology, and ( vi ) elaters with 1–2 spiral bands. Molecular phylogenetic analyses of nrITS1-5.8S-ITS2 and chloroplast trn L-F sequences from 63 samples (34 species, 23 genera) confirm the taxonomical status of the new species. Five specimens of C. konstantinovae form a clade placed sister to a clade of C. elachista (J. B. Jack) Schiffn. and C. rubella (Nees) Warnst.
Price Forecast with High-Frequency Finance Data: An Autoregressive Recurrent Neural Network Model with Technical Indicators

The availability of high-frequency trade data has made it possible for the intraday forecast of price patterns. With the help of technical indicators, recent studies have shown that LSTM based deep learning models are able to predict price directions (a binary classification problem) with performance better than a random guess. However, only naive recurrent networks were adopted, and these works did not compare with the tools used by finance practitioners. Our experiments show that GARCH beats their LSTM models by a large margin. We propose to adopt an autoregressive recurrent network instead so that the loss of the prediction at every time step contributes to the model training; we also treat a rich set of technical indicators at each time step as covariates to enhance the model input. Finally, we treat the problem of price pattern forecast as a regression problem on the price itself; even for price direction prediction, we show that our performance is much better than if we model the problem as binary classification. We show that only when all these designs are adopted, an LSTM model can beat GARCH (and by a large margin). This work corrects the poor use of LSTM networks in recent studies, and provides "the" baseline that is able to fully unleash the power of LSTM for future work to compare with. Moreover, since our model is a price regressor with very good prediction performance, it can serve as a valuable tool for designing trading strategies (including day trading). Our model has been used by quantitative analysts in Freddie Mac for over one quarter, and is found to be more effective than traditional GARCH variants in market prediction.
Seed-free Graph De-anonymiztiation with Adversarial Learning

The huge amount of graph data are published and shared for research and business purposes, which brings great benefit for our society. However, user privacy is badly undermined even though user identity can be anonymized. Graph de-anonymization to identify nodes from an anonymized graph is widely adopted to evaluate users' privacy risks. Most existing de-anonymization methods which are heavily reliant on side information (e.g., seeds, user profiles, community labels) are unrealistic due to the difficulty of collecting this side information. A few graph de-anonymization methods only using structural information, called seed-free methods, have been proposed recently, which mainly take advantage of the local and manual features of nodes while overlooking the global structural information of the graph for de-anonymization. In this paper, a seed-free graph de-anonymization method is proposed, where a deep neural network is adopted to learn features and an adversarial framework is employed for node matching. To be specific, the latent representation of each node is obtained by graph autoencoder. Furthermore, an adversarial learning model is proposed to transform the embedding of the anonymized graph to the latent space of auxiliary graph embedding such that a linear mapping can be derived from a global perspective. Finally, the most similar node pairs in the latent space as the anchor nodes are utilized to launch propagation to de-anonymize all the remaining nodes. The extensive experiments on some real datasets demonstrate that our method is comparative with the seed-based approaches and outperforms the start-of-the-art seed-free method significantly.
Empirical Analysis of Impact of Query-Specific Customization of nDCG: A Case-Study with Learning-to-Rank Methods

In most existing works, nDCG is computed for a fixed cutoff k, i.e., nDCG@k and some fixed discounting coefficient. Such a conventional query-independent way to compute nDCG does not accurately reflect the utility of search results perceived by an individual user and is thus non-optimal. In this paper, we conduct a case study of the impact of using query-specific nDCG on the choice of the optimal Learning-to-Rank (LETOR) methods, particularly to see whether using a query-specific nDCG would lead to a different conclusion about the relative performance of multiple LETOR methods than using the conventional query-independent nDCG would otherwise. Our initial results show that the relative ranking of LETOR methods using query-specific nDCG can be dramatically different from those using the query-independent nDCG at the individual query level, suggesting that query-specific nDCG may be useful in order to obtain more reliable conclusions in retrieval experiments.
Feature Fusion Based Subgraph Classification for Link Prediction

Link prediction, which centers on whether or not a pair of nodes is likely to be connected, is a fundamental problem in complex network analysis. Network-embedding-based link prediction has shown strong performance and robustness in previous studies on complex networks, recommendation systems, and knowledge graphs. This approach has certain drawbacks, however; namely, the hierarchical structure of a subgraph is ignored and the importance of different nodes is not distinguished. In this study, we established the Subgraph Hierarchy Feature Fusion (SHFF) model for link prediction. To probe the existence of links between node pairs, the SHFF first extracts a subgraph around the two nodes and learns a function to map the subgraph to a vector for subsequent classification. This reveals any link between the two target nodes. The SHFF learns a function to obtain a representation of the extracted subgraph by hierarchically aggregating the features of nodes in that subgraph, which is accomplished by grouping nodes with similar structures and assigning different importance to the nodes during the feature fusion process. We compared the proposed model against other state-of-the-art link-prediction methods on a wide range of data sets to find that it consistently outperforms them.
A Graph Matching Attack on Privacy-Preserving Record Linkage

To facilitate advanced analytics, data science projects increasingly require records about individuals to be linked across databases. Generally no unique entity identifiers are available in the databases to be linked, and therefore quasi-identifiers such as names, addresses, and dates of birth are used to link records. The process of linking records without revealing any sensitive or confidential information about the entities represented by these records is known as privacy-preserving record linkage (PPRL). Various encoding and encryption based PPRL methods have been developed in the past two decades. Most existing PPRL methods calculate approximate similarities between records because errors and variations can occur in quasi-identifying attribute values. Even though being used in real-world linkage applications, certain PPRL methods, such as popular Bloom filter encoding, have shown to be vulnerable to cryptanalysis attacks. In this paper we present a novel attack on PPRL methods that exploits the approximate similarities calculated between encoded records. Our attack matches nodes in a similarity graph generated from an encoded database with a corresponding similarity graph generated from a plain-text database to re-identify sensitive values. Our attack is not limited to any specific PPRL method, and in an experimental evaluation we apply it on three PPRL encoding methods using three different databases. This evaluation shows that our attack can successfully re-identify sensitive values from these encodings with high accuracy where no previous attack on PPRL would have been successful.
Challenges and Solutions to the Student Dropout Prediction Problem in Online Courses

Online courses and e-degrees, although present since the mid-1990, have received enormous attention only in the last decade. Moreover, the new Coronavirus disease (COVID-19) outbreak forced many nations (e.g. Italy, the US, and other countries) to massively push their education system towards an online environment. Academics now are also looking at the crisis as an opportunity for universities to adopt digital technologies for teaching more broadly. But they will have to understand what possible ways of evaluating and effectively teaching will be in this new scenario. The depicted overview, in conjunction with the utility and ubiquitous access to the educational platforms of online courses, entails a vast amount of enrolments. Nevertheless, a high enrolment rate usually translates into a significant dropout (or withdrawal) rate of students (40-80% of online students drop out). Student dropout prediction (SDP) consists of modelling and fore-casting student behaviour when interacting with e-learning platforms. It is a significant phenomenon that has repercussions on online institutions, the involved students and professors. Early approaches tended to perform manual analytic examinations to devise retention strategies. Recent research has adopted automated policies to thoroughly exploit the advantages of student activities(hereafter e-tivities) in the e-platforms and identify at-risk students. These approaches include machine learning and deep learning techniques to predict the student dropout status. Therefore, being able to cope with the trend shifting of student interactions with the course platforms in real-time has become of paramount importance. In this tutorial, we comprehensively overview the SDP problem in the literature. We provide mathematical formalisation to the different definitions proposed, and we introduce simple and complex predictive methods adhering to the following: Student dropout definition, Input modelling, Underlying machine and deep learning techniques, Evaluation measures, Datasets, and privacy concerns.
Pardon the interruption: saccadic inhibition enables the rapid deployment of alternate oculomotor plans

Diverse psychophysical and neurophysiological results show that oculomotor networks are continuously active, such that plans for making the next eye movement are always ongoing. So, when new visual information arrives unexpectedly, how are those plans affected? At what point can the new information start guiding an eye movement, and how? Here, based on modeling and simulation results, we make two observations that are relevant to these questions. First, we note that many experiments, including those investigating the phenomenon known as “saccadic inhibition,” are consistent with the idea that sudden-onset stimuli briefly interrupt the gradual rise in neural activity associated with the preparation of an impending saccade. And second, we show that this stimulus-driven interruption is functionally adaptive, but only if perception is fast. In that case, putting on hold an ongoing saccade plan toward location A allows the oculomotor system to initiate a concurrent, alternative plan toward location B (where a stimulus just appeared), deliberate (briefly) on the priority of each target, and determine which plan should continue. Based on physiological data, we estimate that the actual advantage of this strategy, relative to one in which any plan once initiated must be completed, is of several tens of milliseconds.