Miracle-dz/UniFAR-FTUs huggingface dataset:
Predominance of clonal propagation conceals extinction risks of the highly endangered floodplain herb Cnidium dubium

Habitat loss and degradation due to human-induced landscape alterations are considered to be a major threat to biodiversity. The decline of biodiversity may occur with a time delay leading to a so called extinction debt. Therefore, determining extinction risks and conservation status is not always straightforward. Several life history traits might play a role for the accumulation of an extinction debt. Thus, perennial plant species capable of vegetative propagation might be able to persist temporarily in degraded habitats even though sexual and evolutionary processes are effectively halted. We studied Cnidium dubium, which occurs in scattered patches along river corridors in Central Europe and is critically endangered in Germany. It is a perennial species which is able to propagate clonally. Our aims were to reconstruct demographic processes regarding clonal propagation and gene flow along 400 km of river stretch and with respect to the position in the flooplain, i.e. before or behind dykes. We also wanted to determine whether there is evidence for an extinction debt in C. dubium and to use our insights for conservation recommendations. For this, we used nuclear microsatellites and maternally inherited chloroplast DNA markers and applied a systematic grid based sampling strategy for small scale geographic structures. We observed a high level of clonal propagation. In 935 analysed plants we observed only 121 different genotypes and of 50 studied patches of C. dubium the majority (31 patches) consisted of one single genotype each. Patch size and position were correlated with clonal diversity. Large patches and patches behind dykes exhibited higher clonal diversity. There was no evidence for a large scale genetic substructuring of the study area and no differences in overall genetic diversity between upstream and downstream patches as well as between patches before and behind the dykes. High levels of heterozygosity and a high number of 18 chloroplast DNA haplotypes togetherwith a slightly elevated inbreeding coefficient (Fis) point toward a high level of ancestral polymorphism in an out of equilibrium population due to high levels of clonal propagation and low levels of gene flow and recombination. Therefore, we assume that an extinction debt is present in C. dubium. As a management strategy, we propose to transplant ramets between multiple patches to increase the number of mating partners and therefore restore sexual reproduction.
A biogas‐steam combined cycle for sustainable development of industrial‐scale water‐power hybrid microgrids: design and optimal scheduling

Lack of sufficient pure water resources and energy crises are two important issues in summer, especially for remote communities located in tropical zones. Electricity is used for seawater desalination, and power generation units need freshwater for energy extraction, conversion, transportation, and distribution. The design and operation of renewable energy sources based on water‐energy hybrid grids therefore reduce on‐peak electricity demand, potable water scarcity, and pollutant emissions, significantly. This paper proposes a combined cycle for water and power production and investigates its performance under different ambient air temperatures and various load levels. First, biogas is employed to fuel a gas turbine electricity generation process. Then, its waste heat is utilized in a steam turbine heat‐and‐power extraction cycle. This heating flow is used in a water‐heater air‐cooler desalination process. A non‐linear optimization problem is developed under engineering equation solver (EES) and generalized algebraic modeling system (GAMS) with the aim of finding the near‐optimal operating points of the proposed industrial‐scale co‐generation system, considering its technical constraints and changes of fresh water and power demands during a 24 h operating period. This stand‐alone microgrid is able to produce 2.8 L of drinking water per second and 5.8 MW of electricity for large industrial sectors with a minimum efficiency of 37%. Replacing natural gas with biofuels in the water‐power nexus contributes most to emissions mitigation, peak electrical‐demand reduction, and sustainable water treatment. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd
Drift on holey landscapes as a dominant evolutionary process

An organism’s phenotype has been shaped by evolution but the specific processes have to be indirectly inferred for most species. For example, correlations among traits imply the historical action of correlated selection and, more generally, the expression and distribution of traits is expected to be reflective of the adaptive landscapes that have shaped a population. However, our expectations about how quantitative traits—like most behaviors, physiological processes, and life-history traits—should be distributed under different evolutionary processes is not clear. Here we show that genetic variation in quantitative traits is not distributed as would be expected under dominant evolutionary models. Instead, we found that genetic variation in quantitative traits across 6 phyla and 60 species (including both Plantae and Animalia) is consistent with evolution across high dimensional “holey landscapes”. This suggests that the leading conceptualizations and modeling of the evolution of trait integration fail to capture how phenotypes are shaped and that traits are integrated in a manner contrary to predictions of dominant evolutionary theory. Our results demonstrate that our understanding of how evolution has shaped phenotypes remains incomplete and these results provide a starting point for reassessing the relevance of existing evolutionary models. Significance Statement We found that empirical estimations of how quantitative genetic variation is distributed do not correspond to typical Gaussian representations of fitness landscapes. These Gaussian landscapes underpin major areas of evolutionary biology and how selection is estimated in natural populations. Rather than being consistent with evolution on Gaussian landscapes, empirical estimates of genetic variation are, instead, consistent with evolution on high-dimensional “holey” landscapes. These landscapes represent situations where specific combinations of trait values are either viable or not and populations randomly drift among the viable combinations. This finding suggests that we have substantially misunderstood how selection actually shapes populations and thus how evolution typically proceeds.
Characterization of the complete chloroplast genome of Hedysarum polybotrys var. alaschanicum (Fabaceae) and its phylogeny

Abstract Hedysarum polybotrys var. alaschanicum is an important medicinal plant and is widely used in traditional Chinese medicine. The complete chloroplast genome of H. polybotrys var. alaschanicum was assembled from Illumina pair-end sequence reads. The whole chloroplast genome is 122,933 bp in length and encodes a total of 110 genes, including 76 protein-coding genes, 30 tRNA genes and 4 rRNA genes. The overall GC content of the cp genome is 35.3%. A maximum likelihood (ML) phylogenetic analysis revealed that H. polybotrys var. alaschanicum was close to Hedysarum semenovii.
Status of Symbiodiniaceae abundance in hard corals during the 2020 bleaching event along the Egyptian Red Sea coast

While coral reefs represent one of the most important marine ecosystems, their construction and persistence are primarily dependent upon a consolidate setup between the coral host and a wide array of unicellular mutualistic dinoflagellates from the family Symbiodiniaceae (formerly genus Symbiodinium) (LaJeunesse et al., 2018). In general, these endosymbiotic algae live in association with the coral animal and many prokaryotes as a single unit known as a holobiont in which all partners are mutually linked by physiological pathways in an integrative framework (Voolstra and Ziegler, 2020). ARTICLE INFO ABSTRACT Article History: Received: Aug. 30, 2021 Accepted: Sept. 19, 2021 Online: Sept. 29, 2021 _______________
Formal-semantic normative system for graphic-analytical modelling

A formal-semantic normative system (FSNS) is presented in this paper. The FSNS allows us to describe the links and functional units of typical information processes. The possibility of using the FSNS to automate graphical-analytical modelling and machine learning is shown. Properties of the FSNS are described. On its basis, a method is developed for specifying a formal alphabet of system connection nodes that has a specific subject content. The rules for manipulating the received alphabetic characters are also presented. The possibility and expediency of building a normative system with increased expressive capabilities is substantiated. This paper provides a brief description of the «Unit-Function-Object» system-object approach. Considered the possibility of using DL for the FSNS in an unambiguous, formalized form. An example of using the FSNS is presented.
Understanding the Evolutionary Track of Tau Sco

The τ Scorpii (τ Sco) star, was known as Al-Niyat in medieval Islamic period, is a B0V single star with mass of 15 solar mass (M⊙). It means that τ Sco is a massive star that has properties like B-type star such as has 31,440 K temperature effective and so on. The evolution simulation using MESA simulation software was conducted from pre-main sequence phase to red supergiant phase for τ Sco. We obtain that the age of τ Sco currently is 5.22 million years and it’s in main sequence phase. The τ Sco will reach the red supergiant phase at the age of 11.6 million years after formation.
Professional Marginalization and Isolation: A Study of Physical Education Teachers

In this study, it was aimed to determine the marginalization - isolation levels of physical education teachers and to examine these levels according to professional experience, gender and the school’s teacher population. A total of 339 physical education teachers participated in this research which were 114 female (33.6%) and 225 male (66.4%), working in 8 different cities of Turkey. Screening model was used in this research. As a data collection tool, personal data form and Physical Education Marginalization and Isolation Scale (PEMAIS) were used. In the analysis of the data collected in the study, in addition to descriptive statistics, a t-test of arithmetic means was conducted in independent groups in order to compare the values of marginalization and isolation sub-dimensions according to gender, professional experience and the number of teachers in the school. As a result, it has been observed that professional experience is an important variable in terms of marginalization and isolation, and that female teachers can cope with such situations more effectively.
A Review of Hyperbaric Oxygen Therapy for Inflammatory Bowel Disease

Abstract IBD (inflammatory bowel disease) characterized by chronic inflammation of the gastrointestinal (GI) tract is one of the chronic diseases the prevalence of which has been increasing globally. In the past few decades, significant improvements were made in the treatments relating to IBD which have reduced the hospitalization and mortality rates; however, there is still room for improvement with other alternative therapies. HBOT (hyperbaric oxygen treatment) is one of the effective therapies used in treating various conditions including wounds, decompression sickness, stroke, etc. Several studies have reported the role of HBOT as an adjunctive treatment to IBD, while adopting conventional treatment procedures. There is an increasing trend of research in this particular area. Studies have shown that HBOT reflects its therapeutic effect by controlling inflammation, reducing oxidation stress, improving the process of cleaning damaged cells and recruiting the cells involved in repair; thereby improving the immunity response system. In this context, the purpose of this review is to summarize past clinical and experimental studies and to understand the role of hyperbaric treatment for IBD. The findings from the review have suggested that hyperbaric therapy can be an effective adjunctive approach for IBD, based on which some ideas for future clinical and research work are provided.
Design optimization of exhaust manifold’s length for Spark Ignition (SI) engine through CFD analysis on low-end rpm using Taguchi’s Method

The exhaust system especially the exhaust manifold is an important factor that affects the performance of any SI engine. The most influential boundary condition in the exhaust manifold is backpressure where it is defined as the difference between maximum pressure in the exhaust system and the atmospheric pressure. Higher backpressure was documented to reduce the overall performance of an IC engine and increases its fuel consumption based on previous studies. Even though backpressure could not be removed entirely from the exhaust system, it could be reduced the maximize the engine’s performance. This study aimed to reduce the backpressure in the exhaust manifold of the 115cc SI engine by optimizing its lengths (by taking consideration of the impact of bending angles) through Computational Fluid Dynamic (CFD) analysis and Taguchi’s method. From the results, it was found that the bending angles are more dominant in reducing the backpressure even after the lengths are optimized. It was found that the optimal length configuration reduces the backpressure by 13.56%. Therefore, the outcome of this study shows that the optimal length configuration offers lower backpressure which significantly reduces the harmful impacts on the engine’s performance.
Minimizing Warpage and Shrinkage of Plastic Car Rear Bumper Fabrication via Simulation Based Optimisation

Car bumpers are designed to absorb small impacts, such as accidentally bumping into another car while backing off a parking spot, not major road accidents. As to make it lighter, plastic is the best available material. To fabricate it by injection molding, thin-wall and complex parts such as car bumper might create a lot of defects such as shrinkage and warpage. So, the stronger plastic material and correct optimized parameters should be applied. Butadiene Styrene (ABS) is selected in this study. This project aims to optimize the injection moulding process parameters of plastic car bumper by using Central Composite Design (CCD) in Response Surface Methodology. Mould temperature, melt temperature, injection time are selected as factors to be studied. As the results, the optimum values suggested by the software were mould temperature of 25°C, 226.36°C melt temperature and 1.88s injection time. The best material suggested is ABS. With small differences in error value between solution and simulation, 0.895% of shrinkage and 10.3% for warpage, the results were acceptable.
Reconstruction of groundwater level at Kumamoto, Japan by means of deep learning to evaluate its increase by the 2016 earthquake

Groundwater is a very important water resource at Kumamoto City. Kumamoto City is the capital city of Kumamoto Prefecture, which is located in the Kyushu region, Japan. All domestic water is obtained from groundwater in Kumamoto City. Modeling groundwater is a difficult issue. Conditions under the ground are complex, and difficult to be obtained. Even the delineation of a groundwater basin is frequently unknown. Nowadays, deep learning is a hot topic in many research fields including geoscience. A recurrent neural network (RNN) is a type of deep learning that is suitable for time series modeling. Then, it has been successfully applied for groundwater modeling. Therefore, this study utilized a new type of RNN, Long and Short-Term Memory (LSTM) network, to model groundwater level at a monitoring well within Kumamoto City. The results in this study showed good agreement with the observed groundwater. In addition, it is known that severe earthquakes in April 2016 affected the groundwater level around Kumamoto City. The groundwater level model by LSTM was also utilized to estimate the effects of the severe earthquakes on the groundwater level. The results indicated that the earthquakes may have increased the groundwater level at Kumamoto City by more than 3 m.
To the development of intelligent adaptive learning systems

The article describes the prospects for creating an informational adaptive automated learning system (based on the English Language) using advanced artificial intelligence technologies. In particular, the work shows that the quality of education largely depends on the form of educational content and the parameters of the learning object (a student) associated with its perception of information. The formulated hypothesis of this work indicates that according to the written speech of the learning object, it is possible to classify it (the learning object) by the type of perception of educational content. For the first time, the paper proposes the use of the apparatus of artificial neural networks to solve the problem of automatic classification of learning objects according to the parameter indicated above, while analyzing the text written by this object. For the formation of educational content in the required form, it is proposed to use the system-object method of knowledge representation. The logical architecture of an adaptive teaching automated system is being considered.
Network Embedding via Motifs

Network embedding has emerged as an effective way to deal with downstream tasks, such as node classification [16, 31, 42]. Most existing methods leverage multi-similarities between nodes such as connectivity, which considers vertices that are closely connected to be similar and structural similarity, which is measured by assessing their relations to neighbors; while these methods only focus on static graphs. In this work, we bridge connectivity and structural similarity in a uniform representation via motifs, and consequently present an algorithm for Learning Embeddings by leveraging Motifs Of Networks (LEMON), which aims to learn embeddings for vertices and various motifs. Moreover, LEMON is inherently capable of dealing with inductive learning tasks for dynamic graphs. To validate the effectiveness and efficiency, we conduct various experiments on two real-world datasets and five public datasets from diverse domains. Through comparison with state-of-the-art baseline models, we find that LEMON achieves significant improvements in downstream tasks. We release our code on Github at https://github.com/larry2020626/LEMON.
Cerebrospinal fluid purinomics as a biomarker approach to predict outcome after severe traumatic brain injury

Severe traumatic brain injury (TBI) is associated with high rates of mortality and long‐term disability linked to neurochemical abnormalities. Although purine derivatives play important roles in TBI pathogenesis in preclinical models, little is known about potential changes in purine levels and their implications in human TBI. We assessed cerebrospinal fluid (CSF) levels of purines in severe TBI patients as potential biomarkers that predict mortality and long‐term dysfunction. This was a cross‐sectional study performed in 17 severe TBI patients (Glasgow Coma Scale <8) and 51 controls. Two to 4 h after admission to ICU, patients were submitted to ventricular drainage and CSF collection for quantification of adenine and guanine purine derivatives by HPLC. TBI patients’ survival was followed up to 3 days from admission. A neurofunctional assessment was performed through the modified Rankin Scale (mRS) 2 years after ICU admission. Purine levels were compared between control and TBI patients, and between surviving and non‐surviving patients. Relative to controls, TBI patients presented increased CSF levels of GDP, guanosine, adenosine, inosine, hypoxanthine, and xanthine. Further, GTP, GDP, IMP, and xanthine levels were different between surviving and non‐surviving patients. Among the purines, guanosine was associated with improved mRS (p = 0.042; r = −0.506). Remarkably, GTP displayed predictive value (AUC = 0.841, p = 0.024) for discriminating survival versus non‐survival patients up to 3 days from admission. These results support TBI‐specific purine signatures, suggesting GTP as a promising biomarker of mortality and guanosine as an indicator of long‐term functional disability.
Metacognitive insight into cognitive performance in pre and early-stage Huntington's disease

ABSTRACT Objectives Insight is an important predictor of quality of life in Huntington's disease and other neurodegenerative conditions. However, estimating insight with traditional methods such as questionnaires is challenging and subject to limitations. This study experimentally quantified metacognitive insight into cognitive performance in Huntington's disease gene-carriers. Methods We dissociated perceptual decision-making performance and metacognitive insight into performance in healthy controls (n=29), premanifest (n=19) and early-manifest (n=10) Huntington's disease gene-carriers. Insight was operationalised as the degree to which a participant's confidence in their performance was informative of their actual performance (metacognitive efficiency) and estimated using a computational model (HMeta-d). Results We found that pre and early-manifest Huntington's disease gene-carriers were impaired in making perceptual decisions compared to controls. Gene-carriers required more evidence in favour of the correct choice to achieve similar performance and perceptual impairments were increased in those with manifest disease. Surprisingly, despite marked perceptual impairments, Huntington's disease gene-carriers retained metacognitive insight into their perceptual performance. This was the case after controlling for confounding variables and regardless of disease stage. Conclusion We report for the first time a dissociation between impaired cognition and intact metacognition (trial-by-trial insight) in the early-stages of a neurodegenerative disease. This unexpected finding contrasts with the prevailing assumption that cognitive deficits are associated with impaired insight. Future studies should investigate how intact metacognitive insight could be used by some early Huntington's disease gene-carriers to positively impact their quality of life. Key words: Huntington's disease, decision-making, cognition, insight, metacognition