Miracle-dz/UniFAR-FTUs huggingface dataset:
New Sources of Eastern Filbert Blight Resistance and Simple Sequence Repeat Markers on Linkage Group 6 in Hazelnut (Corylus avellana L.)

Commercial production of hazelnut (Corylus avellana) in Oregon’s Willamette Valley is threatened by eastern filbert blight (EFB), a serious canker disease caused by the pyrenomycete Anisogramma anomala (Peck) E. Müller. The fungus also prevents the establishment of hazelnut orchards in eastern North America. Genetic resistance is considered the most effective way to control the disease. A high level of EFB resistance was first discovered in ’Gasaway’. This resistance is conferred by a dominant allele at a single locus on linkage group 6 (LG6). Resistance from several additional sources has been assigned to the same chromosomal region. In this study, new simple sequence repeat (SSR) markers were developed for the resistance region on LG6 and new sources of resistance were investigated. Forty-two new SSR markers were developed from four contigs in the genome sequence of ‘Jefferson’ hazelnut, characterized, and nine of them were placed on LG6 of the genetic map. Accessions representing 12 new sources of EFB resistance were crossed with susceptible selections resulting in 18 seedling populations. Segregation ratios in the seedling populations fit the expected 1:1 ratio for 10 sources, while one source showed an excess of resistant seedlings and another showed an excess of susceptible seedlings. Based on correlation of disease response and scores of SSR markers in the ‘Gasaway’ resistance region in the seedlings, eight resistance sources were assigned to LG6. Linkage maps were constructed for each progeny using SSR markers. The LG6 resistance sources include two selections (#23 and #26) from the Russian Research Institute of Forestry and Mechanization near Moscow, four selections from southern Russia, one selection (OSU 1185.126) from Crimea, one selection (OSU 533.129) from Michigan, Corylus heterophylla ‘Ogyoo’ from the South Korea, and the interspecific hybrid ’Estrella #1’. These new LG6 resistance sources and SSR markers should be useful in breeding new cultivars, including the pyramiding of resistance genes. For the other four resistance sources (Moscow #37, hybrid selection OSU 401.014, C. americana ‘Winkler’ and C. americana OSU 366.060), SSR marker scores on linkage groups 6, 7 and 2 were not correlated with disease response and merit further investigation.
Automated Digital Quantification of Pulmonary Fibrosis in Human Histopathology Specimens

Pulmonary fibrosis is characterized by abnormal interstitial extracellular matrix and cellular accumulations. Methods quantifying fibrosis severity in lung histopathology samples are semi-quantitative, subjective, and analyze only portions of sections. We sought to determine whether automated computerized imaging analysis shown to continuously measure fibrosis in mice could also be applied in human samples. A pilot study was conducted to analyze a small number of specimens from patients with Hermansky-Pudlak syndrome pulmonary fibrosis (HPSPF) or idiopathic pulmonary fibrosis (IPF). Digital images of entire lung histological serial sections stained with picrosirius red and alcian blue or anti-CD68 antibody were analyzed using dedicated software to automatically quantify fibrosis, collagen, and macrophage content. Automated fibrosis quantification based on parenchymal tissue density and fibrosis score measurements was compared to pulmonary function values or Ashcroft score. Automated fibrosis quantification of HPSPF lung explants was significantly higher than that of IPF lung explants or biopsies and was also significantly higher in IPF lung explants than in IPF biopsies. A high correlation coefficient was found between some automated quantification measurements and lung function values for the three sample groups. Automated quantification of collagen content in lung sections used for digital image analyses was similar in the three groups. CD68 immunolabeled cell measurements were significantly higher in HPSPF explants than in IPF biopsies. In conclusion, computerized image analysis provides access to accurate, reader-independent pulmonary fibrosis quantification in human histopathology samples. Fibrosis, collagen content, and immunostained cells can be automatically and individually quantified from serial sections. Robust automated digital image analysis of human lung samples enhances the available tools to quantify and study fibrotic lung disease.
Nutritional Composition of Tissues of Wild and Bred Pheasants

Wild and bred pheasants were divided into nine parts, i. e. , breast, wing, thigh, sasami, bone (bone soup), heart, liver, stomach, and intestine. For each part the amounts of moisture, protein, lipid, carbohydrate, ash, iron, calcium, magnesium, sodium, potassium, and phosphorus were analyzed. In all muscular parts, the moisture and lipid contents were higher and the protein and ash contents smaller in the bred pheasant. The carbohydrate content in the breast muscle was richer in the wild bird, while its contents in other muscular parts and bone soup were similar for both pheasants. The contents of iron, magnesium, potassium, and phosphorus in all of the muscular parts and bone soup, sodium in wing, thigh, and bone soup, and calcium in bone soup were clearly higher in the wild pheasant. The bred pheasant, however, had higher calcium contents in breast and sasami. The bred pheasant's intestine was richer in lipid, sodium, and potassium contents. Conversely, the wild heart was richer in magnesium and potassium contents. In the other parts of the viscera, the bred pheasant generally contained as high values of nutritive components and elements as the wild pheasant. (Received July 19, 1990)
Molecular Survey on Kobuviruses in Domestic and Wild Ungulates From Northwestern Italian Alps

Since the first identification in 1989 in humans, kobuviruses (KoVs) have been identified from a wide range of animal species including carnivores, rodents, birds, ungulates, rabbits, and bats. Several studies have described the identification of genetically related KoVs in the fecal virome of domestic and wild animals suggesting a mutual exchange of viruses. By screening a total of 231 fecal samples from wild and domestic ungulates, KoVs RNA was detected in wild boars (3.2%; 2/63), chamois (4.6%; 2/43), and goats (2.6%; 2/77). On phylogenetic analysis of the partial RdRp sequence, the wild boar strains clustered within the species Aichivirus C whilst the strains identified in domestic and wild ruminants grouped into the species Aichivirus B. The complete VP1 gene was obtained for chamois and goat KoVs. Interestingly, upon phylogenetic analysis the strains grouped together with a KoV of ovine origin within a distinct genetic type (B3) of the species Aichivirus B.
nZVI Impacts Substrate Conversion and Microbiome Composition in Chain Elongation From D- and L-Lactate Substrates

Medium-chain carboxylates (MCC) derived from biomass biorefining are attractive biochemicals to uncouple the production of a wide array of products from the use of non-renewable sources. Biological conversion of biomass-derived lactate during secondary fermentation can be steered to produce a variety of MCC through chain elongation. We explored the effects of zero-valent iron nanoparticles (nZVI) and lactate enantiomers on substrate consumption, product formation and microbiome composition in batch lactate-based chain elongation. In abiotic tests, nZVI supported chemical hydrolysis of lactate oligomers present in concentrated lactic acid. In fermentation experiments, nZVI created favorable conditions for either chain-elongating or propionate-producing microbiomes in a dose-dependent manner. Improved lactate conversion rates and n-caproate production were promoted at 0.5–2 g nZVI⋅L–1 while propionate formation became relevant at ≥ 3.5 g nZVI⋅L–1. Even-chain carboxylates (n-butyrate) were produced when using enantiopure and racemic lactate with lactate conversion rates increased in nZVI presence (1 g⋅L–1). Consumption of hydrogen and carbon dioxide was observed late in the incubations and correlated with acetate formation or substrate conversion to elongated products in the presence of nZVI. Lactate racemization was observed during chain elongation while isomerization to D-lactate was detected during propionate formation. Clostridium luticellarii, Caproiciproducens, and Ruminococcaceae related species were associated with n-valerate and n-caproate production while propionate was likely produced through the acrylate pathway by Clostridium novyi. The enrichment of different potential n-butyrate producers (Clostridium tyrobutyricum, Lachnospiraceae, Oscillibacter, Sedimentibacter) was affected by nZVI presence and concentrations. Possible theories and mechanisms underlying the effects of nZVI on substrate conversion and microbiome composition are discussed. An outlook is provided to integrate (bio)electrochemical systems to recycle (n)ZVI and provide an alternative reducing power agent as durable control method.
Comparison of Retention Rates Between Tumor Necrosis Factor-α Inhibitors in Patients With Ankylosing Spondylitis: Data From the Korean College of Rheumatology Biologics Registry

This study aimed to investigate drug retention rates for various TNF inhibitors (TNFis) commonly prescribed to Korean patients with ankylosing spondylitis (AS) in the Korean College of Rheumatology Biologics registry (KOBIO; December 2012–June 2016). Discontinuation was defined as switching or stopping the biologic agent. Kaplan–Meier curves and Cox's proportional hazard models were used for further analysis. The reasons for discontinuation of TNFis were also assessed. Univariate and multivariate analyses were used to identify possible predictors of discontinuation. Data from 1,005 patients with AS were analyzed with a median follow-up period of 14 months. Seventy-six percent of patients were first-line biologic users. Discontinuation of TNFis occurred in 24.2% (switching in 9.6%) of patients during follow-up. An estimate of the drug failure showed that the adjusted hazard ratio (HR) for golimumab compared to etanercept was 0.441 (95% confidence interval: 0.277–0.703, p < 0.001). Reasons for discontinuation included lack of efficacy (32.6%), adverse events (23.6%), clinical improvement (11.2%), and others (32.6%). Predictors of discontinuation using a multivariate analysis were a shorter disease duration (HR: 0.973, p = 0.044) and being negative for HLA-B27 (HR: 1.623, p = 0.0093). In conclusion, few Korean patients with AS switched to other TNFis during their treatment. The drug retention rate for golimumab was higher than for other agents.
RNA N 6-Methyladenosine in Cancer Metastasis: Roles, Mechanisms, and Applications

Cancer metastasis is a symptom of adverse prognosis, a prime origin of therapy failure, and a lethal challenge for cancer patients. N 6-methyladenosine (m6A), the most prevailing modification in messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs) of higher eukaryotes, has attracted increasing attention. Growing studies have verified the pivotal roles of m6A methylation in controlling mRNAs and ncRNAs in diverse physiological processes. Remarkably, recent findings have showed that aberrant methylation of m6A-related RNAs could influence cancer metastasis. In this review, we illuminate how m6A modifiers act on mRNAs and ncRNAs and modulate metastasis in several cancers, and put forward the clinical application prospects of m6A methylation.
Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-Reinforcement Learning

There is considerable interest in designing meta-reinforcement learning (meta-RL) algorithms, which enable autonomous agents to adapt new tasks from small amount of experience. In meta-RL, the specification (such as reward function) of current task is hidden from the agent. In addition, states are hidden within each task owing to sensor noise or limitations in realistic environments. Therefore, the meta-RL agent faces the challenge of specifying both the hidden task and states based on small amount of experience. To address this, we propose estimating disentangled belief about task and states, leveraging an inductive bias that the task and states can be regarded as global and local features of each task. Specifically, we train a hierarchical state-space model (HSSM) parameterized by deep neural networks as an environment model, whose global and local latent variables correspond to task and states, respectively. Because the HSSM does not allow analytical computation of posterior distribution, i.e., belief, we employ amortized inference to approximate it. After the belief is obtained, we can augment observations of a model-free policy with the belief to efficiently train the policy. Moreover, because task and state information are factorized and interpretable, the downstream policy training is facilitated compared with the prior methods that did not consider the hierarchical nature. Empirical validations on a GridWorld environment confirm that the HSSM can separate the hidden task and states information. Then, we compare the meta-RL agent with the HSSM to prior meta-RL methods in MuJoCo environments, and confirm that our agent requires less training data and reaches higher final performance.
Social Media Analytics and Corporate Crises - a Case Study of Boeing's 737 Max Crashes

Contemporary corporate crises demand conscientious communication efforts to save stakeholders and firms from preventable loss. Social media, in this regard, have proven to create a climate of high emotional volatility. Consequentially, firms travail to leverage social media in a way that reliably moderates negative emotions towards the firm and its actions. Underpinned by the assumptions of Situational Crisis Communication Theory (SCCT), this study presents a social media analytics approach that is applied to a case study of the US-based airplane manufacturer Boeing, which suffers from a corporate crisis due to repeated fatal crashes of their 737 MAX 8 model. Based on a dataset of 417,583 Twitter postings, preliminary results suggest that prevalent negative emotions amend in distinct crisis phases that differ in responsibility attribution. This study informs theory and practice by exposing emotional climates on social media that might serve as a decision aid for corporate crisis response.
Generating sense inventories for ambiguous arabic words

The process of selecting the appropriate meaning of an ambigous word according to its context is known as word sense disambiguation. In this research, we generate a number of Arabic sense inventories based on an unsupervised approach and different pre-trained embeddings, such as Aravec, Fast text, and Arabic-News embeddings. The resulted inventories from the pre-trained embeddings are evaluated to investigate their efficiency in Arabic word sense disambiguation and sentence similarity. The sense inventories are generated using an unsupervised approach that is based on a graph-based word sense induction algorithm. Results show that the Aravec-Twitter inventory achieves the best accuracy of 0.47 for 50 neighbors and a close accuracy to the Fast text inventory for 200 neighbors while it provides similar accuracy to the Arabic-News inventory for 100neighbors. The experiment of replacing ambiguous words with their sense vectors is tested for sentence similarity using all sense inventories and the results show that using Aravec-Twitter sense inventory provides a better correlation value
Volleyball Data Analysis System and Method Based on Machine Learning

After the reform and the opening up, the economy of my country has grown rapidly and people’s lives have become better and better. As a result, there is a lot of time to pay attention to their health, which has promoted the rapid development of my country’s sports industry. Since the 2008 Beijing Olympics, the successful hosting of the Beijing Olympics has been further strengthened. With the rise of the development of sports in our country, the use of machine learning in a large amount of information can process this data and analyze it well. Based on this, this article is aimed at making volleyball players and coaches better understand the technical structure of hiking and the technique of hiking. The paper understands the characteristics of muscle activity over time and uses machine learning methods to analyze a large number of volleyball sports data. In this experiment, 12 volleyball players from a college of physical education were selected. According to the actual situation of the students’ physical fitness and skills, it is more reasonable to divide them into two arms with preswing technology (A type) group and two-arms without preswing technology (B type) group. Mainly study the volleyball spiking action, select the representative front-row 4th position strong attack and the back-row 6th position for comparison and analysis, and analyze the process from the take-off stage to the aerial shot stage in the four stages of the smash through the kinematics, dynamics, and surface electromyography parameters. Experiments have shown that for type A, the left gluteus maximus integral EMG sum value is significantly different between the front and rear rows ( P < 0.05 ). The discharge volume of the left gluteus maximus during the front-row spiking process is greater than that of the back-row spiking. This difference is mainly reflected in the kicking stage and the air attack stage. It shows that volleyball data analysis has a very broad prospect of exploration and application, which can create huge social and economic benefits. How to analyze kinematics is also a very demanding research project and is also part of the future analysis of sports data. Academic value and broad practical significance are important.
Sustainable MOOC Platforms - Searching for Business Models of the Future

MOOCs went through a typical hype. The promising concept attracted many companies and educational institutions, leading to a massive rush of enrollments. This trend of massive growth lasted for about three years. MOOCs turned out to exhibit several shortcomings, including high production costs and massive drop­out rates. As the world is confronted with pandemic­induced lockdowns, MOOCs are considered vi­ able options to keep up education systems, with traditional methods becoming unavailable. To sustainably unfold MOOCs’ inherent advantages and extend their range and acceptance, a deeper understanding of the underlying business models is needed. Using the well­known Business Model Canvas combined with a classical web­content analysis, we set forth to chart existing MOOC platform business models. Based on the identified concepts, we seek to identify problematic and promising patterns as a basis for optimization. We present preliminary results and formulate propositions that will guide our future research.
Precision Measurement for Industry 4.0 Standards towards Solid Waste Classification through Enhanced Imaging Sensors and Deep Learning Model

Achievement of precision measurement is highly desired in a current industrial revolution where a significant increase in living standards increased municipal solid waste. The current industry 4.0 standards require accurate and efficient edge computing sensors towards solid waste classification. Thus, if waste is not managed properly, it would bring about an adverse impact on health, the economy, and the global environment. All stakeholders need to realize their roles and responsibilities for solid waste generation and recycling. To ensure recycling can be successful, the waste should be correctly and efficiently separated. The performance of edge computing devices is directly proportional to computational complexity in the context of nonorganic waste classification. Existing research on waste classification was done using CNN architecture, e.g., AlexNet, which contains about 62,378,344 parameters, and over 729 million floating operations (FLOPs) are required to classify a single image. As a result, it is too heavy and not suitable for computing applications that require inexpensive computational complexities. This research proposes an enhanced lightweight deep learning model for solid waste classification developed using MobileNetV2, efficient for lightweight applications including edge computing devices and other mobile applications. The proposed model outperforms the existing similar models achieving an accuracy of 82.48% and 83.46% with Softmax and support vector machine (SVM) classifiers, respectively. Although MobileNetV2 may provide a lower accuracy if compared to CNN architecture which is larger and heavier, the accuracy is still comparable, and it is more practical for edge computing devices and mobile applications.
Probabilistic Graphical Models with Neural Networks in InferPy

InferPy is an open-source Python package for variational inference in probabilistic models containing neural networks. Other similar libraries are often difﬁcult for non-expert users. InferPy provides a much more compact and simple way to code such models, at the expense of slightly reducing expressibility and ﬂexibility. The main objective of this package is to permit its use without having a strong theoretical background or thorough knowledge of the deep learning frameworks.
Text Indexing for Regular Expression Matching

Finding substrings of a text T that match a regular expression p is a fundamental problem. Despite being the subject of extensive research, no solution with a time complexity significantly better than O(|T||p|) has been found. Backurs and Indyk in FOCS 2016 established conditional lower bounds for the algorithmic problem based on the Strong Exponential Time Hypothesis that helps explain this difficulty. A natural question is whether we can improve the time complexity for matching the regular expression by preprocessing the text T? We show that conditioned on the Online Matrix–Vector Multiplication (OMv) conjecture, even with arbitrary polynomial preprocessing time, a regular expression query on a text cannot be answered in strongly sublinear time, i.e., O(|T|1−ε) for any ε>0. Furthermore, if we extend the OMv conjecture to a plausible conjecture regarding Boolean matrix multiplication with polynomial preprocessing time, which we call Online Matrix–Matrix Multiplication (OMM), we can strengthen this hardness result to there being no solution with a query time that is O(|T|3/2−ε). These results hold for alphabet sizes three or greater. We then provide data structures that answer queries in O(|T||p|τ) time where τ∈[1,|T|] is fixed at construction. These include a solution that works for all regular expressions with Expτ·|T| preprocessing time and space. For patterns containing only ‘concatenation’ and ‘or’ operators (the same type used in the hardness result), we provide (1) a deterministic solution which requires Expτ·|T|log2|T| preprocessing time and space, and (2) when |p|≤|T|z for z=2o(log|T|), a randomized solution with amortized query time which answers queries correctly with high probability, requiring Expτ·|T|2Ωlog|T| preprocessing time and space.