Miracle-dz/UniFAR-FTUs huggingface dataset:
Bioprospecting of South African Plants as a Unique Resource for Bioactive Endophytic Microbes

South Africa has a long history and strong belief in traditional herbal medicines. Using ethnobotanical knowledge as a lead, a large number of South African medicinal plants have been discovered to possess a wide spectrum of pharmacological properties. In this review, bioprospecting of endophytes is highlighted by following the advantages of the ethnomedicinal approach together with identifying unique medicinal plants where biological activity may be due to endophytes. This review focuses on the current status of South African medicinal plants to motivate the research community to harness the benefits of ethnobotanical knowledge to investigate the presence of endophytic microbes from the most potent South African medicinal plants. The potential chemical diversity and subsequent putative medicinal value of endophytes is deserving of further research. A timely and comprehensive review of literature on recently isolated endophytes and their metabolites was conducted. Worldwide literature from the last 2 years demonstrating the importance of ethnobotanical knowledge as a useful approach to discover endophytic microbes was documented. Information was obtained from scientific databases such as Pubmed, Scopus, Scirus, Google Scholar, Dictionary of Natural Products, Chemical Abstracts Services, official websites, and scientific databases on ethnomedicines. Primary sources such as books, reports, dissertations, and thesises were accessed where available. Recently published information on isolated endophytes with promising bioactivity and their bioactive natural products worldwide (2015-2017) was summarized. The potential value of South African medicinal plants as sources of endophytes is discussed. The insights provided through this study indicate that medicinal plants in South Africa are highly under-investigated sources of potentially useful endophytic microbes. New approaches may be used by medicinal plant scientists for further exploration of natural products from endophytic fungi and bacteria in southern Africa.
A New Version of the Składnica Treebank of Polish Harmonised with the Walenty Valency Dictionary

This paper reports on developments in the Składnica treebank of Polish which were possible due to the switch to the Walenty valency dictionary. The change required several modiﬁcations in the Świgra parser, such as implementing unlike coordination, semantically motivated phrases, and non-standard case values. A procedure to upgrade manually disambiguated trees of Składnica was required as well. Modiﬁcations introduced in the treebank included systematic changes of notation and resolving ambiguity between semantically motivated phrases. The procedure of confronting Składnica treebank with the trees generated with the new version of the Świgra parser using Walenty dictionary allowed us to check the consistency of all the resources. This resulted in several corrections introduced in both the treebank and the valence dictionary.
Isolation of Listeria monocytogenes from the skin of slaughtered beef cattle.

We attempted to isolate Listeria monocytogenes from skin, contents of large intestines and carcasses of cattle introduced to a slaughterhouse in order to identify source of contamination for this pathogen. Sixty skin samples, 60 samples of the contents of large intestines and 30 carcass samples were colleted in June, August and November 2003 for use in this study. Listeria spp. and L. monocytogenes were isolated from 30 (50%) and 3 (5%) of the cattle skin samples, respectively. However, no Listeria spp. , including L. monocytogenes, were isolated from intestinal contents or carcasses. Seven isolates were obtained, of which five and two strains were serotypes 1/2a and 1/2b, respectively. Genetic analysis suggested that there was persistent inhabitation of the pathogen around the area investigated in this study.
Response of nutrients, biofilm, and benthic insects to salmon carcass addition

Salmon carcass addition to streams is expected to increase stream productivity at multiple trophic levels. This study examined stream nutrient (nitrogen, phosphorus, and carbon), epilithic biofilm (ash-free dry mass and chlo- rophyll a), leaf-litter decomposition, and macroinvertebrate (density and biomass) responses to carcass addition in three headwater streams of southwestern Washington State, USA. We used stable isotopes (δ 13 C and δ 15 N) to trace incorpora- tion of salmon-derived (SD) nutrients into stream food webs. SD nutrients were assimilated by biofilm, benthic insects (Perlidae and Limnephilidae spp.), and age-1 steelhead (Oncorhynchus mykiss gairdneri). SD nutrients peaked ~2 weeks after carcass addition for insects and fish feeding on carcasses, but indirect uptake of SD nutrients by biofilm and insects was delayed by ~2 months. A strong stable isotope signal did not always correspond with measurable bio- logical change. At reaches 10-50 m downstream from carcasses, ammonium concentration, leaf-litter decomposition, and benthic insect density all increased relative to upstream control sites. The strongest responses and greatest SD- nutrient uptake were observed 10 m from decomposing carcasses, with effects generally decreasing to undetectable lev- els 250 m downstream. Carcass addition to headwater streams can have a transient effect on primary and secondary trophic levels, but responses may be limited to specific taxa near carcass locations. Resume : L'addition de carcasses de saumons dans les cours d'eau doit augmenter, croit-on, la productivite du milieu a plusieurs niveaux trophiques. Notre etude examine les reactions des nutriments du cours d'eau (azote, phosphore et
A Strong Tracking SLAM Algorithm Based on the Suboptimal Fading Factor

This paper proposes an innovative simultaneous localization and mapping (SLAM) algorithm which combines a strong tracking filter (STF), an unscented Kalman filter (UKF), and a particle filter (PF) to deal with the low accuracy of unscented FastSLAM (UFastSLAM). UFastSLAM lacks the capacity for online self-adaptive adjustment, and it is easily influenced by uncertain noise. The new algorithm updates each Sigma point in UFastSLAM by an adaptive algorithm and obtains optimized filter gain by the STF adjustment factor. It restrains the influence of uncertain noise and initial selection. Therefore, the state estimation would converge to the true value rapidly and the accuracy of system state estimation would be improved eventually. The results of simulations and practical tests show that strong tracking unscented FastSLAM (STUFastSLAM) has a significant improvement in accuracy and robustness.
Modeling and Three-Layer Adaptive Diving Control of a Cable-Driven Underwater Parallel Platform

This paper focuses on the modeling, diving controller design, and experiment of a special cable-driven underwater parallel platform with eight-cable coupling drive structure. Kinematic and dynamic models of the platform are established utilizing a simplified approach, and the hydraulic driven control model is derived based on joint-space method. To improve the diving control precision and system robustness despite the complex dynamic behaviors and manifold unknown disturbances, a three-layer adaptive diving control strategy is proposed. Among the three control layers, layer 1 is responsible for dive planning and online monitoring, layer 2 places emphasis on synchronous control by employing an improved relative coupling strategy, layer 3 utilizes an adaptive radial basis function neural network-based backstepping sliding mode control algorithm (ARBFNN-BSMC) to achieve high precision speed control of the single driving branch. Hardware-in-the-loop simulations and experimental results illustrate that the proposed three-layer adaptive diving control strategy can asymptotically drive the cable-driven underwater parallel platform onto a predefined diving trajectory with favorable precision, robustness, and stability.
PyRATA, Python Rule-based feAture sTructure Analysis

In this paper, we present a new Python 3 module named PyRATA , which stands for " P ython R ule-based fe A ture s T ructure A nalysis". The module is released under the Apache V2 license. It aims at supporting rules-based analysis on structured data. PyRATA offers a language expressiveness which covers the functionalities of all the concurrent modules and more. Designed to be intuitive, the pattern syntax and the engine API follow existing standard deﬁnitions; Respectively Perl regular expression syntax and Python re module API. Based on simple native Python data structures (i.e. sequence of feature sets), PyRATA is able to deal with various kinds of data (textual or not) at various levels, such as a list of words, a list of sentences, a list of posts in a forum thread, a list of events in a calendar. This speciﬁcity makes it free from any (linguistic) process.
Risk factors for vitamin D deficiency in sickle cell disease

Vitamin D deficiency (VDD), 25‐OHD levels <20 ng/ml, is prevalent among patients with sickle cell disease (SCD) and is linked to acute and chronic pain and bone fracture in this population. There is limited literature regarding VDD‐associated risk factors for SCD. We examined potential clinical and genomic parameters associated with VDD in 335 adults with SCD in a cross‐sectional study. VDD was present in 65% of adult SCD patients, and 25‐OHD levels independently and positively correlated with older age (P < 0·001) and vitamin D supplementation (P < 0·001). 25‐OHD levels were higher in SCD patients over 40 years of age compared to the general African‐American population. Both lower 25‐OHD levels and increased pain frequency were associated with increased expression of SLC6A5 encoding glycine transporter‐2 (GlyT2), a protein involved in neuronal pain pathways. Lower 25‐OHD levels were also associated with increased expression of CYP3A4, and with decreased expression of GC (also termed DBP) and VDR, three genes involved in vitamin D metabolism. We conclude that vitamin D supplementation should be an almost universal feature of the care of young adults with SCD, and that further research is warranted into genomic factors that regulate vitamin D metabolism in SCD.
Kakutani equivalence of unipotent flows

We study Kakutani equivalence in the class of unipotent flows acting on compact quotients of semisimple Lie groups. For every such flow we compute the Kakutani invariant of M. Ratner, the value of which being explicitly given by the Jordan block structure of the unipotent element generating the flow. This, in particular, answers a question of M. Ratner. Moreover, it follows that the only standard unipotent flows are given by ( 1 t 0 1 ) × id acting on (SL(2,R)×G′)/Γ′, where Γ is an irreducible lattice in SL(2,R)×G′ (with the possibility that G = {e}).
The family variable in the wine sector: an Italian perspective

The main goal of this study is to analyse the impact of the family variable on performance in the Italian wine sector. We referred to several studies in which the family could be considered a missing variable in management research. The analysis was conducted in both family firms (FFs) and non-family firms (NFFs) to determine the similarities, differences and impact on performance. The research population was composed of all the 369 medium and large-sized Italian companies operating in the Italian wine sector existing in October 2014. Data were extracted from the Amadeus database. The analysis considered three years (2011-2012-2013). From this paper it emerged that FFs outperform NFFs in terms of economic performance in Italian wine firms (with the exception of the Earnings before interest and taxes - EBIT - margin). However, in terms of financial performance, NFFs outperform FFs (with the exception of the solvency ratio). So, this study highlights that the family variable is partially relevant to achieving good performance and has different significance to the firms.
Improving the DVB-T2 BICM performance by newly optimized Two-Dimensional Non-Uniform Constellations

This paper focuses on the potential enhancements of the DVB-T2 standard by the use of newly designed Two-Dimensional Non-Uniform Constellations (2D-NUQAM). In this work, a complete analysis of the DVB-T2 system performance when using the proposed 2D-NUQAM constellations against regular uniform (M-QAM) is presented. Based on the optimization of the Bit-interleaved Coded Modulation (BICM) capacity, the designed constellations provide a potential performance improvement of 1.05 dB for the overall DVB-T2 system performance in high order constellations. All carried out analyses are based on a complete DVB-T2 system specification simulation where equivalent 64-QAM and 256-QAM constellation and all baseline code rates have been evaluated.
Experimental Design under the Bradley-Terry Model

Labels generated by human experts via comparisons exhibit smaller variance compared to traditional sample labels. Collecting comparison labels is challenging over large datasets, as the number of comparisons grows quadratically with the dataset size. We study the following experimental design problem: given a budget of expert comparisons, and a set of existing sample labels, we determine the comparison labels to collect that lead to the highest classification improvement. We study several experimental design objectives motivated by the Bradley-Terry model. The resulting optimization problems amount to maximizing submodular functions. We experimentally evaluate the performance of these methods over synthetic and real-life datasets.
Parsivar: A Language Processing Toolkit for Persian

With the growth of Internet usage, a massive amount of textual data is generated on social media and the Web. As the text on the Web are generated by different authors with various types of writing styles and different encodings, a preprocessing step is required before applying any NLP task. The goal of preprocessing is to convert text into a standard format that makes it easy to extract information from documents and sentences. Moreover, the problem is more acute when we deal with Arabic script-based languages, in which there are some different kinds of encoding schemes, different kinds of writing styles and the spaces between or within the words. This paper introduces a preprocessing toolkit named as Parsivar, which is a comprehensive set of tools for Persian text preprocessing tasks. This toolkit performs various kinds of activities comprised of normalization, space correction, tokenization, stemming, parts of speech tagging and shallow parsing. To evaluate the performance of the proposed toolkit, both intrinsic and extrinsic approaches for evaluation have been applied. A Persian plagiarism detection system has been exploited as a downstream task for extrinsic evaluation of the proposed toolkit. The results have revealed that our toolkit outperforms the available Persian preprocessing toolkits by about 8 percent in terms of F1.
MIsA: Multilingual “IsA” Extraction from Corpora

In this paper we present Multilingual IsA (MIsA), which is a collection of hypernymy relations in ﬁve languages (i.e., English, Spanish, French, Italian and Dutch) extracted from the corresponding full Wikipedia corpus. For each language, we ﬁrst established a set of existing (viz. found in literature) or newly deﬁned lexico-syntactic patterns. Similarly to WebIsADb, the resulting resource contains hypernymy relations represented as "tuples", as well as additional information such as provenance (Gil and Groth, 2011), context of the extraction, etc. To measure the precision of the patterns, we performed a manual assessment of the quality of the extracted relations and an error analysis. In addition, we release the software developed for the extraction of the hypernym tuples.
New fusional framework combining sparse selection and clustering for key frame extraction

Key frame extraction can facilitate rapid browsing and efficient video indexing in many applications. However, to be effective, key frames must preserve sufficient video content while also being compact and representative. This study proposes a syncretic key frame extraction framework that combines sparse selection (SS) and mutual information-based agglomerative hierarchical clustering (MIAHC) to generate effective video summaries. In the proposed framework, the SS algorithm is first applied to the original video sequences to obtain optimal key frames. Then, using content-loss minimisation and representativeness ranking, several candidate key frames are efficiently selected and grouped as initial clusters. A post-processor – an improved MIAHC – subsequently performs further processing to eliminate redundant images and generate the final key frames. The proposed framework overcomes issues such as information redundancy and computational complexity that afflict conventional SS methods by first obtaining candidate key frames instead of accurate key frames. Subsequently, application of the improved MIAHC to these candidate key frames rather than the original video not only results in the generation of accurate key frames, but also reduces the computation time for clustering large videos. The results of comparative experiments conducted on two benchmark datasets verify that the performance of the proposed SS–MIAHC framework is superior to that of conventional methods.
The other white‐nose syndrome transcriptome: Tolerant and susceptible hosts respond differently to the pathogen Pseudogymnoascus destructans

Abstract Mitigation of emerging infectious diseases that threaten global biodiversity requires an understanding of critical host and pathogen responses to infection. For multihost pathogens where pathogen virulence or host susceptibility is variable, host–pathogen interactions in tolerant species may identify potential avenues for adaptive evolution in recently exposed, susceptible hosts. For example, the fungus Pseudogymnoascus destructans causes white‐nose syndrome (WNS) in hibernating bats and is responsible for catastrophic declines in some species in North America, where it was recently introduced. Bats in Europe and Asia, where the pathogen is endemic, are only mildly affected. Different environmental conditions among Nearctic and Palearctic hibernacula have been proposed as an explanation for variable disease outcomes, but this hypothesis has not been experimentally tested. We report the first controlled, experimental investigation of response to P. destructans in a tolerant, European species of bat (the greater mouse‐eared bat, Myotis myotis). We compared body condition, disease outcomes and gene expression in control (sham‐exposed) and exposed M. myotis that hibernated under controlled environmental conditions following treatment. Tolerant M. myotis experienced extremely limited fungal growth and did not exhibit symptoms of WNS. However, we detected no differential expression of genes associated with immune response in exposed bats, indicating that immune response does not drive tolerance of P. destructans in late hibernation. Variable responses to P. destructans among bat species cannot be attributed solely to environmental or ecological factors. Instead, our results implicate coevolution with the pathogen, and highlight the dynamic nature of the “white‐nose syndrome transcriptome. ” Interspecific variation in response to exposure by the host (and possibly pathogen) emphasizes the importance of context in studies of the bat‐WNS system, and robust characterization of genetic responses to exposure in various hosts and the pathogen should precede any attempts to use particular bat species as generalizable “model hosts. ”
Do deep reinforcement learning agents model intentions?

Inferring other agents’ mental states, such as their knowledge, beliefs and intentions, is thought to be essential for effective interactions with other agents. Recently, multi-agent systems trained via deep reinforcement learning have been shown to succeed in solving various tasks. Still, how each agent models or represents other agents in their environment remains unclear. In this work, we test whether deep reinforcement learning agents trained with the multi-agent deep deterministic policy gradient (MADDPG) algorithm explicitly represent other agents’ intentions (their specific aims or plans) during a task in which the agents have to coordinate the covering of different spots in a 2D environment. In particular, we tracked over time the performance of a linear decoder trained to predict the final targets of all agents from the hidden-layer activations of each agent’s neural network controller. We observed that the hidden layers of agents represented explicit information about other agents’ intentions, i.e., the target landmark the other agent ended up covering. We also performed a series of experiments in which some agents were replaced by others with fixed targets to test the levels of generalization of the trained agents. We noticed that during the training phase, the agents developed a preference for each landmark, which hindered generalization. To alleviate the above problem, we evaluated simple changes to the MADDPG training algorithm which lead to better generalization against unseen agents. Our method for confirming intention modeling in deep learning agents is simple to implement and can be used to improve the generalization of multi-agent systems in fields such as robotics, autonomous vehicles and smart cities.
Oncological Considerations for Breast Reconstruction

Fifteen percent of women treated for breast cancer with total mastectomy receive immediate or early breast reconstruction [1, 2]. The percentage is higher in young women and those treated in tertiary care medical centers. Immediate breast reconstruction (IBR) has several advantages [3, 4]. It can prevent some of the negative psychological and emotional sequelae seen with mastectomy. The aesthetic results of immediate reconstruction are superior to those seen after delayed reconstruction. IBR also reduces hospital costs by reducing the number of procedures and length of hospitalization. Immediate breast reconstruction has the potential to impact the treatment of breast cancer. It could affect the delivery of adjuvant therapy and the detection and treatment of recurrent disease. Chemotherapy and radiation therapy could also impact the complication rates of reconstruction. The oncological considerations of breast reconstruction are outlined in this chapter.
Risk assessment of choledocholithiasis prior to laparoscopic cholecystectomy and its management options

Background Accurate risk evaluation of choledocholithiasis prior to laparoscopic cholecystectomy is essential to determine optimal management strategy. Objective Our study aimed to evaluate the accuracy of separate predictors and Vilnius University Hospital Index (VUHI = A/30 + 0.4 × B; A = total bilirubin concentration (µmol/l), B = common bile duct (CBD) diameter (mm) measured by ultrasound) diagnosing choledocholithiasis and to assess different management strategies (cholecystectomy with intraoperative cholangiography and endoscopic retrograde cholangiopancreatography (ERCP)). Methods The retrospective study included 350 patients admitted to a tertiary care centre for laparoscopic cholecystectomy for cholecystolithiasis who were investigated for concomitant choledocholithiasis. Results Choledocholithiasis was diagnosed in 182 (76.2%) cases in the high-risk group (VUHI value ≥4.7) and 44 (39.6%) in the low, odds ratio is 4.86 (95% CI: 3.00–7.88). Its sensitivity was 80.5%, specificity 54.0%, accuracy 71.1%. Dilated CBD had the highest sensitivity (92.5%) of predictors. ERCP showed better diagnostic performance than intraoperative cholangiography. Complications of ERCP were more frequent for patients without stones. There was no significant difference of outcomes between the two management strategies. Conclusion The prognostic index has good diagnostic accuracy but dividing patients into two risk groups is insufficient. The suggested model allows determining an intermediate-risk group, which requires additional investigation. Both management approaches are appropriate.