Miracle-dz/UniFAR-FTUs huggingface dataset:
Patterns of low-complexity regions in human genes

Genome evolution stands as a paramount determinant for species survival and overall biodiversity on Earth. Among the myriad processes orchestrating genome evolution, the dynamic attributes of length and compositional polymorphism within low-complexity regions (LCR) are the fastest. Clusters of LCR hotspots serve as pivotal conduits connecting different modes of genome evolution, specifically arising through gene duplication events and harboring pivotal sites susceptible to point mutations. Thus, they offer a holistic perspective on the panorama of genome evolution. Furthermore, LCR actively participates in a multifaceted spectrum of neurological, developmental, and cognitive disorders. Despite the substantial body of knowledge concerning the roles of individual LCR-containing genes in the causation of diseases, a comprehensive framework remains conspicuously absent, failing to provide a unified portrayal of LCR-containing genes and their interactions. Furthermore, our understanding of the intricate interplay between paralogy and LCR remains notably deficient. Within this study, we have identified nine clusters of LCR hotspots within the human genome. These clusters are predominantly comprised of closely positioned paralogs, characterized by a significantly higher prevalence of shared LCR and a lower degree of differentiation (FST) across diverse human populations. Moreover, we have unveiled intricate networks of LCR-containing genes engaged in mutual interactions, sharing associations with a spectrum of diseases and disorders, with a particular emphasis on hereditary cancer-predisposing syndromes. Our discoveries shed light on the compelling potential of LCR-containing interacting genes to collectively engender identical diseases or disorders, thereby underscoring their pivotal role in the manifestation of pathological conditions. Significance Statement Among myriad genome evolution processes, low-complexity regions (LCR) are pivotal, being both the fastest and bridging other evolution modes like gene duplication and point mutations. Understanding LCR-containing paralogous genes is essential to comprehend genetic diseases. Here, we demonstrate that the human genome harbors clusters of LCR hotspots mainly composed of paralogous genes sharing LCR, indicating a role for segmental duplication. The degree of differentiation is significantly lower in clusters of LCR hotspots than in other regions. Moreover, we provide a detailed network of LCR-containing interacting genes associated with shared diseases. Instead of attributing a single disease to an LCR gene, a unified perspective on LCR-containing interacting genes causing the same disease enhances our understanding of LCR-induced disease mechanisms.
An improved chromosome-level genome assembly of perennial ryegrass (Lolium perenne L.)

This work is an update and extension of the previously published article “Ultralong Oxford Nanopore Reads Enable the Development of a Reference-Grade Perennial Ryegrass Genome Assembly”, by Frei et al. The published genome assembly of the doubled haploid perennial ryegrass (Lolium perenne L.) genotype Kyuss marked a milestone for forage grass research and breeding. However, order and orientation errors may exist in the pseudo-chromosomes of Kyuss, since barley (Hordeum vulgare L.), which diverged 30 million years ago from perennial ryegrass, was used as the reference to scaffold Kyuss. To correct for structural errors possibly present in the published Kyuss assembly, we de novo assembled the genome again and generated 50-fold coverage high-throughput chromosome conformation capture (Hi-C) data to assist pseudo-chromosome construction. The resulting new chromosome-level assembly showed improved quality with high contiguity (contig N50 = 120 Mb), high completeness (total BUSCO score = 99%), high base-level accuracy (QV = 50) and correct pseudo-chromosome structure (validated by Hi-C contact map). This new assembly will serve as a better reference genome for Lolium spp. and greatly benefit the forage and turf grass research community.
Research on process recommendation algorithm based on user intent perception

With the rapid development of the manufacturing industry, the design of manufacturing processes has become increasingly complex, and there is a growing demand from companies and users for efficient process recommendation systems. However, existing research on process recommendation often overlooks the importance of user intent. In reality, user intent often consists of multiple facets, each with different emphases, collectively driving users to choose different processes. For example, an intent to meet tight deadlines may favor less time-consuming processes, while an intent for safety requirements may prefer more mature processes. Disregarding the notion of multiple intents from users would confine the modeling of interactions between users and processes. Therefore, to enhance the efficiency and quality of process design, this paper proposes an intent-aware manufacturing process recommendation algorithm. This algorithm combines process knowledge activity analysis with intent awareness and establishes a mapping between process knowledge and process knowledge graph through graph representation learning and attention mechanisms, enabling perception of user intent and corresponding process knowledge recommendation. Through empirical validation and application, this paper demonstrates the effectiveness and practicality of the algorithm. Experimental results show that the intent-aware process knowledge recommendation algorithm significantly improves the efficiency and accuracy of process design, providing strong support for the development of the manufacturing industry.
Application of improved AlexNet for seismic data denoising

We propose an improved AlexNet network model, to address the problems of low denoising performance of traditional LeNet-5 neural networks in removing random noise from seismic data. The network retains the original eight-layer calculation depth and uses ReLU as the Activation function to reduce the convolution core and the number of nodes in the convolution layer, thus obtaining higher noise feature extraction accuracy. The network trains the network with 10000 seismic data, tests the network with 1000 data, and optimizes the network. Experiments were conducted using a wide range of Marousi2 seismic data, and the results showed that the proposed network has good denoising performance. Compared with traditional wavelet algorithms, SVD, and LeNet-5 networks, experimental results show that the proposed network can achieve higher PSNR and SNR values and has better seismic data denoising performance compared to the above networks.
A Robust and Efficient Boundary Point Detection Method by Measuring Local Direction Dispersion

Boundary points pose a significant challenge for machine learning tasks, including classification, clustering, and dimensionality reduction. Due to the similarity of features, boundary areas can result in mixed-up classes or clusters, leading to a crowding problem in dimensionality reduction. To address this challenge, numerous boundary point detection methods have been developed, but they are insufficiently to accurately and efficiently identify the boundary points in non-convex structures and high-dimensional manifolds. In this work, we propose a robust and efficient method for detecting boundary points using Local Direction Dispersion (LoDD). LoDD considers that internal points are surrounded by neighboring points in all directions, while neighboring points of a boundary point tend to be distributed only in a certain directional range. LoDD adopts a density-independent K-Nearest Neighbors (KNN) method to determine neighboring points, and defines a statistic-based metric using the eigenvalues of the covariance matrix of KNN coordinates to measure the centrality of a query point. We demonstrated the validity of LoDD on five synthetic datasets (2-D and 3-D) and ten real-world benchmarks, and tested its clustering performance by equipping with two typical clustering methods, K-means and Ncut. Our results show that LoDD achieves promising and robust detection accuracy in a time-efficient manner.
Unsupervised learning reveals landscape of local structural motifs across protein classes

Proteins are known to share similarities in local regions of 3D structure even across disparate global folds. Such correspondences can help to shed light on functional relationships between proteins and identify conserved local structural features that lead to function. Self-supervised deep learning on large protein structure datasets has produced high-fidelity representations of local structural microenvironments, enabling comparison of local structure and function at scale. In this work, we leverage these representations to cluster over 15 million environments in the Protein Data Bank, resulting in the creation of a “lexicon” of local 3D motifs which form the building blocks of all known protein structures. We characterize these motifs and demonstrate that they provide valuable information for modeling structure and function at all scales of protein analysis, from full protein chains to binding pockets to individual amino acids. We devise a new protein representation based solely on its constituent local motifs and show that this representation enables state-of-the-art performance on protein structure search and model quality assessment. We then show that this approach enables accurate prediction of drug off-target interactions by modeling the similarity between local binding pockets. Finally, we identify structural motifs associated with pathogenic variants in the human proteome by leveraging the predicted structures in the AlphaFold structure database.
Preliminary Estimation Of The Nitrogen Balance Between The Atmosphere And A Spruce Forest

The investigation was carried out in the Matra Mountains (Nyirjes station), in Hungary in a Norway Spruce forest. The average height of the stand is about 16 m, with a leaf area index of 3.3. During 1996 the measurement of the flux (nitric oxide, nitrogen dioxide, dinitrogen oxide) and concentration of nitrogen compounds (ammonia, nitric acid, ammonium and nitrate particles) as well as wet deposition, throughfall and stemflow measurement of the nitrate and ammonium have been carried out.
Injection of Creativity and Emotion-Elicitation in Poetry Generation

Poetry Generation, in the context of Natural Language Generation (NLG), involves teaching systems to automatically generate text that resembles poetic work. A system learns to recreate poetry through training on a corpus of poems and modeling the particular style of language. In this paper, we propose taking an approach of ﬁne-tuning GPT-2, a pre-trained language model, to our downstream task of poetry generation. Speciﬁcally, we attempt to create emotion-eliciting poetry and dream poetry. Our ﬁrst goal is to elicit emotions within the reader through the automatically generated text, so we believe a crowdsourced human-evaluation is the proper form of metric. Our model for the emotions of sadness and joy produced poems that correctly elicited emotions 87.5 and 85 percent of the time, respectively. Our second goal is to apply transfer learning to inject creativity and produce dreamlike poetry. Poems from this model are shown to capture elements of dream poetry with scores of no less than 3.2 on the Likert scale. For further quantitative evaluation, we make use of the Coh-Metrix tool, outlining certain metrics we use to gauge the quality of text generated.
Shifted PAUQ: Distribution shift in text-to-SQL

Semantic parsing plays a pivotal role in advancing the accessibility of human-computer interaction on a large scale. Spider, a widely recognized dataset for text2SQL, contains a wide range of natural language (NL) questions in English and corresponding SQL queries. Original splits of Spider and its adapted to Russian language and improved version, PAUQ, assume independence and identical distribution of training and testing data (i.i.d split). In this work, we propose a target length split and multilingual i.i. d split to measure compositionality and cross-language generalization. We present experimental results of popular text2SQL models on original, multilingual, and target length splits. We also construct a context-free grammar for the evaluation of compositionality in text2SQL in an out-of-distribution setting. We make the splits publicly available on HuggingFace hub via https://huggingface.co/datasets/composite/pauq
Associations Between Patient-Reported Outcome Measures of Physical and Psychological Functioning and Willingness to Share Social Media Data for Research Among Adolescents With a Chronic Rheumatic Disease: Cross-Sectional Survey

Background Social media data may augment understanding of the disease and treatment experiences and quality of life of youth with chronic medical conditions. Little is known about the willingness to share social media data for health research among youth with chronic medical conditions and the differences in health status between sharing and nonsharing youth with chronic medical conditions. Objective We aimed to evaluate the associations between patient-reported measures of disease symptoms and functioning and the willingness to share social media data. Methods Between February 2018 and August 2019, during routine clinic visits, survey data about social media use and the willingness to share social media data (dependent variable) were collected from adolescents in a national rheumatic disease registry. Survey data were analyzed with patient-reported measures of disease symptoms and functioning and a clinical measure of disease activity, which were collected through a parent study. We used descriptive statistics and multivariate logistic regression to compare patient-reported outcomes between youth with chronic medical conditions who opted to share social media data and those who did not opt to share such data. Results Among 112 youths, (age: mean 16.1, SD 1.6 y; female: n=72, 64.3%), 83 (74.1%) agreed to share social media data. Female participants were more likely to share (P=.04). In all, 49 (43.8%) and 28 (25%) participants viewed and posted about rheumatic disease, respectively. Compared to nonsharers, sharers reported lower mobility (T-score: mean 49.0, SD 9.4 vs mean 53.9, SD 8.9; P=.02) and more pain interference (T-score: mean 45.7, SD 8.8 vs mean 40.4, SD 8.0; P=.005), fatigue (T-score: mean 49.1, SD 11.0 vs mean 39.7, SD 9.7; P<.001), depression (T-score: mean 48.1, SD 8.9 vs mean 42.2, SD 8.4; P=.003), and anxiety (T-score: mean 45.2, SD 9.3 vs mean 38.5, SD 7.0; P<.001). In regression analyses adjusted for age, sex, study site, and Physician Global Assessment score, each 1-unit increase in symptoms was associated with greater odds of willingness to share social media data, for measures of pain interference (Adjusted Odds Ratio [AOR] 1.07, 95% CI 1.001-1.14), fatigue (AOR 1.08, 95% CI 1.03-1.13), depression (AOR 1.07, 95% CI 1.01-1.13), and anxiety (AOR 1.10, 95% CI 1.03-1.18). Conclusions High percentages of youth with rheumatic diseases used and were willing to share their social media data for research. Sharers reported worse symptoms and functioning compared to those of nonsharers. Social media may offer a potent information source and engagement pathway for youth with rheumatic diseases, but differences between sharing and nonsharing youth merit consideration when designing studies and evaluating social media–derived findings.
Intelligent distribution technology of the edge computing task based on the competitive mechanism

In order to solve the problem that providers and consumers both meet their requirement in a maximum way, we need to match the best providers and consumers to each other, so as to achieve the maximum needs. For this problem, this paper proposes an intelligent distribution technology of edge computing task based on the competitive mechanism. Firstly, it models the problem, constructs the objective function and the constraint conditions, and uses the simulated annealing algorithm to realize the global optimization combination solution. The experiments show that this method can provide a better task assignment method for providers and consumers.
ELECTRON BEAM PROCESSING TO IMPROVE BIODEGRADABLE POLYMERS AND FOR INDUSTRIAL WASTEWATER TREATMENT AND RECYCLING

The aim of the two studies was to apply the electron beam radiation technology for controlling plastic pollution and environmental protection. (1) Mobile irradiation unit - The treatment of wastewater and industrial effluents by electron beam irradiation is a promising technique. The design and construction of a mobile unit by the Nuclear and Energy Research Institute, containing an electron beam accelerator of 0.7 MeV and 20 kW is innovative to demonstrate the effects and positive results of this technology. The mobile unit has as one of its main advantages the possibility of treating effluents in the place where the source is located, eliminating costs and bureaucratic problems associated with the transportation of waste, besides publicizing the technology in several places in the country. To implement the project, IPEN-CNEN has been consolidating partnerships with national and international companies. The resources for the development of the unit have been supplied by the Brazilian Innovation Agency (FINEP Process N. 01.18.0073.00 - Implementation of mobile facilities to make the technology generated available to the productive sector and society) and International Atomic Energy Agency, financing the “IAEA TC Project BRA1035 - Mobile electron beam accelerator to treat and recycle industrial effluents”. The Institute has associated with a specialized company (Truckvan Industry) in an innovation project for the unit design and development. (2) PBAT/PLA polymeric blend Ecovio® - The mechanical properties of the biodegradable polymer were evaluated. Products, such as injected packaging, films for tube production, plastic bags, packaging for cosmetics and food packaging, among others made with this biodegradable polymeric blend need to be resistant to cross sectional demands, impact and thermal stability and should have an average lifetime of 1 to 5 years. Then, it is recommended to use the PBAT/PLA polymeric blend Ecovio® irradiated by electron beam with adsorbed dose of 65 kGy.
A biologically plausible parser with Kleene star operation

This paper describes a new biologically plausible parser based on the Assembly Calculus, a recently proposed computational framework implemented through the spiking of neurons. We demonstrate this parser can support Kleene star, an essential operator of regular expression. While the original parser proposed by Mitropolsky doesn’t support Kleene star. Hence our parser can deal with much more regular languages than the original parser. Our discussion suggests that the parser can be extended to parse some center-embedded sentences.
Kronen-Wurzelfrakturen lateraler Inzisivi – Erhaltung von vitalen und avitalen permanenten Zähnen durch kombinierte kieferorthopädisch-endodontische Therapie

Zusammenfassung In der Therapie dentaler Traumata ist die definitive Behandlungsmethode unter Berücksichtigung restaurativer, endodontischer und parodontaler Aspekte zu wählen. Kronen-Wurzelfrakturen sind zwar vergleichsweise selten, stellen dabei aufgrund des charakteristischen Frakturverlaufs jedoch eine besondere Herausforderung dar. In der Trauma-Versorgung sollte der Zahnerhalt einen hohen Stellenwert einnehmen, da häufig bleibende Frontzähne junger Patientinnen und Patienten vor Abschluss des knöchernen Wachstums betroffen sind. Im Rahmen der Primärversorgung sollten Maßnahmen eingeleitet werden, um die definitive Therapie unter entzündungsfreien Parodontal-Verhältnissen durchführen zu können. In den vorliegenden Falldarstellungen wird die Zahnerhaltung durch kieferorthopädische Extrusion mit geringen orthodontischen Kräften bei vitalen und avitalen permanenten lateralen Inzisivi diskutiert.
Vestige garden production and deagrarianization in three villages in the Eastern Cape, South Africa

Abstract Set in one of the most highly degarianized zones in South Africa, this paper focuses on garden production in three regions of the Eastern Cape Province. Rather than concentrating on commercial agriculture, we aim to develop a better understanding of vestige garden production, particularly as practised by women. As we indicate, although the use of maize fields has declined, the farming of homestead gardens is still popular and has actually expanded in certain regions. The expansion of these gardens largely depends on whether women can access adequate water storage and irrigation solutions, as well as fencing, both of which save time and lessen dependence on labour. Household gardens are particularly prevalent in areas of the Eastern Cape which have not been affected by villagization or forced removal and where household clusters and communal land systems are relatively intact. The paper argues that gardens are particularly important for defining the cultural position of women as owners and producers of domestic crops, and that this maintains household integrity and the predilection towards ‘building the homestead’.
X-Ray Constraints on the Hot Gaseous Corona of Edge-on Late-type Galaxies in Virgo

We present a systematic study of the putative hot gas corona around late-type galaxies (LTGs) residing in the Virgo cluster, based on archival Chandra observations. Our sample consists of 21 nearly edge-on galaxies representing a range of star formation rate (SFR) of 0.2–3 M ⊙ yr−1 and a range of stellar mass (M *) of (0.2–10) × 1010 M ⊙, the majority of which have not been explored with high-sensitivity X-ray observations so far. Significant extraplanar diffuse X-ray (0.5–2 keV) emission is detected in only three LTGs, which are also the three galaxies with the highest SFR. A stacking analysis is performed for the remaining galaxies without individual detection, dividing the whole sample into two subsets based on SFR, stellar mass, or specific SFR. Only the high-SFR bin yields a significant detection, which has a value of L X ∼ 3 × 1038 erg s−1 per galaxy. The stacked extraplanar X-ray signals of the Virgo LTGs are consistent with the empirical L X–SFR and L X–M * relations found among highly inclined disk galaxies in the field, but appear to be systematically lower than those of a comparison sample of simulated star-forming galaxies in clusters identified from the Illustris-TNG100 simulation. The apparent paucity of hot gas coronae in the sampled Virgo LTGs might be understood as the net outcome of the long-lasting effect of ram pressure stripping exerted by the hot intracluster medium and in-disk star-forming activity acting on shorter timescales. A better understanding of the roles of environmental effects in regulating the hot gas content of cluster galaxies invites sensitive X-ray observations for a large sample of galaxies.