Miracle-dz/UniFAR-FTUs huggingface dataset:
THE EFFECT OF MATURITY ON NDF DIGESTIBILITY

Recently forage testing laboratories have begun to evaluate forages for neutral detergent fiber (NDF) digestibility. Evaluation of forage for NDF digestibility is being conducted to improve laboratory prediction of animal performance. One of the factors that influences NDF digestibility is the maturity stage at which the forage is harvested. This Focus on Forage article will highlight the effects of maturity on forage NDF digestibility. With advancing maturity, all plants, including grasses, legumes, corn, and small grains, under-go physiological changes. With advancing maturity, plants develop xylem tissue for water transport, accumulate cellulose and other complex carbohydrates, and these tissues become bound together by a process known as lignification. The combined effect of physiological changes results in plant cell walls (NDF), which are more difficult for rumen bacteria to attach to and to digest. How does maturity affect forage NDF digestibility? One simple, practical aspect of maturity effects on NDF digestibility in forages is related to changes in leaf-to-stem ratio. The NDF in legume or grass leaves is significantly more digestible than NDF in stem tissue. As forage matures, leaf-to-stem ratio declines (more stem, fewer leaves) and as a result NDF digestibility declines because a greater portion of the total NDF is NDF associated with stem tissue. Corn silage is unique in defining maturity effects on NDF digestibility because leaf-to-stem ratio is not greatly altered across normal harvest maturity stages. It is actually common to observe a decline in total NDF content in corn silage as the corn plant matures. This is because the corn ear is filling with grain, which dilutes the total forage NDF content. Despite this illusionary maturity effect, the NDF digestibility of the corn plant still declines with advancing maturity (Figure 3) mainly due to advancing lignification of cell walls (NDF). As forage matures, NDF digestibility can decline over 40 percentage units (% of NDF). Figures 1 to 4 of this article feature the classic relationship of increasing NDF content of grasses, legumes, corn silage, and small grain forages with advancing maturity. Each figure also details the decline in NDF digestibility with advancing maturity. The rate of decline in NDF digestibility is, however, different between forage types. The decline in NDF digestibility in grasses and small grain silages is particularly dramatic (Figures 1 and 4) with advancing maturity. While grasses and small grain forages are in the vegetative state, NDF digestibility is very high (> 70% of NDF). When stem elongation occurs in grasses and small grain forage, NDF digestibility declines at a relatively fast rate. In legumes (Figure 2), NDF digestibility is less as compared to grasses during vegetative stages of growth but declines slower and in a more linear fashion with advancing maturity. Finally, NDF digestibility in corn silage declines approximately 10.0 percentage units between the 1⁄2 milk-line to advanced black layer stages of maturity. How can forage NDF digestibility be best managed?
Learning-Based Safety-Stability-Driven Control for Safety-Critical Systems under Model Uncertainties

Safety and tracking stability are crucial for safety-critical systems such as self-driving cars, autonomous mobile robots, and industrial manipulators. To efficiently control safety-critical systems to ensure their safety and achieve tracking stability, accurate system dynamic models are usually required. However, accurate system models are not always available in practice. In this paper, a learning-based safety-stability-driven control (LBSC) algorithm is presented to guarantee the safety and tracking stability for nonlinear safety-critical systems subject to control input constraints under model uncertainties. Gaussian Processes (GPs) are employed to learn the model error between the nominal model and the actual system dynamics, and the estimated mean and variance of the model error are used to quantify a high-confidence uncertainty bound. Using this estimated uncertainty bound, a safety barrier constraint is devised to ensure safety, and a stability constraint is developed to achieve rapid and accurate tracking. Then the proposed LBSC method is formulated as a quadratic program incorporating the safety barrier, the stability constraint, and the control constraints. The effectiveness of the LBSC method is illustrated on the safety-critical connected cruise control (CCC) system simulator under model uncertainties.
Community models for partially observed networks from surveys

Communities are a common and widely studied structure in networks, typically under the assumption that the network is fully and correctly observed. In practice, network data are often collected through sampling schemes such as surveys. These sampling mechanisms introduce noise and bias which can obscure the community structure and invalidate assumptions underlying standard community detection methods. We propose a general model for a class of network sampling mechanisms based on survey designs, designed to enable more accurate community detection for network data collected in this fashion. We model edge sampling probabilities as a function of both individual preferences and community parameters, and show community detection can be done by spectral clustering under this general class of models. We also propose, as a special case of the general framework, a parametric model for directed networks we call the nomination stochastic block model, which allows for meaningful parameter interpretations and can be fitted by the method of moments. Both spectral clustering and the method of moments in this case are computationally efficient and come with theoretical guarantees of consistency. We evaluate the proposed model in simulation studies on both unweighted and weighted networks and apply it to a faculty hiring dataset, discovering a meaningful hierarchy of communities among US business schools.
An Iterative Machine-Learning Framework for Turbulence Modeling in RANS

Machine-learning (ML) techniques provide a new perspective for constructing turbulence models for Reynolds-averaged Navier-Stokes (RANS) simulations. Designed with advanced fitting ability, they can increase the accuracy of the turbulence models given enough information from high-fidelity datasets is provided. In this study, an iterative ML-RANS computational framework is proposed, that combines the ML algorithm and transport equations of a conventional turbulence model built on empirical knowledge. This framework could maintain a consistent procedure to obtain the input features for ML models in both the training and predicting stages. The effective form of the closure term is discussed to explain how to determine the target variables for the ML algorithm, which ensures a mean flow solution of RANS equations free of amplified error. The inherent multi-valued problem of the existing constitutive theory is studied to establish a proper regression system for ML algorithms. From the same input features, an accurate closure can be obtained through ML model, and a flow field similar to the high-fidelity datasets can be obtained based on such closure so that a built-in reproducibility for the training cases can be achieved. It is demonstrated that the framework can deal with a cross-case training strategy with data from turbulent channel flows at different Reynolds numbers. A posteriori simulations of channel flows show that the framework is able to predict both the mean flow field and turbulent variables accurately. Applied to the flow over periodic hills a better result than for a conventional first order turbulence model is obtained, indicating a promising prediction capability of the developed ML-RANS model for a recirculating flow even though the model is trained with planar channel flow data
Road Segmentation for Remote Sensing Images Using Adversarial Spatial Pyramid Networks

Road extraction in remote sensing images is of great importance for a wide range of applications. Because of the complex background, and high density, most of the existing methods fail to accurately extract a road network that appears correct and complete. Moreover, they suffer from either insufficient training data or high costs of manual annotation. To address these problems, we introduce a new model to apply structured domain adaption for synthetic image generation and road segmentation. We incorporate a feature pyramid (FP) network into generative adversarial networks to minimize the difference between the source and target domains. A generator is learned to produce quality synthetic images, and the discriminator attempts to distinguish them. We also propose a FP network that improves the performance of the proposed model by extracting effective features from all the layers of the network for describing different scales’ objects. Indeed, a novel scale-wise architecture is introduced to learn from the multilevel feature maps and improve the semantics of the features. For optimization, the model is trained by a joint reconstruction loss function, which minimizes the difference between the fake images and the real ones. A wide range of experiments on three data sets prove the superior performance of the proposed approach in terms of accuracy and efficiency. In particular, our model achieves state-of-the-art 78.86 IOU on the Massachusetts data set with 14.89M parameters and 86.78B FLOPs, with $4\times $ fewer FLOPs but higher accuracy (+3.47% IOU) than the top performer among state-of-the-art approaches used in the evaluation.
Adubação, cultivar e controle de plantas daninhas na cultura do gergelim

Objetivando verificar os efeitos isolados e conjuntos dos fatores adubacao, cultivar e metodos de controle de plantas daninhas na cultura do gergelim ( Sesamum indicum L.), um experimento foi conduzido em Patos, PB, Zona fisiografica do Serido paraibano, no ano agricola de 1989, em solo Bruno Nao-Calcico associado. Foram testados oito tratamentos envolvendo os fatores adubacao, cultivar e metodo de controle de plantas daninhas. Verificou-se, com relacao ao rendimento de sementes, que as cultivares apresentaram comportamento semelhante, media de 672 kg/ha. A adubacao (30-30-0) elevou o rendimento de sementes em 45%. O herbicida diuron foi tao eficiente quanto o metodo mecânico, apresentando boa seletividade e excelente controle das plantas daninhas. Observou-se, ainda, que com a adubacao ocorreu reducao do controle das plantas daninhas quando foi utilizado o metodo mecânico fato nao verificado quando do uso do metodo quimico.
Modification of quantum many-body relaxation by perturbations exhibiting a banded matrix structure

We investigate how the observable relaxation behavior of an isolated quantum many-body system is modified in response to weak-to-moderate perturbations within a nonperturbative typicality framework. A key role is played by the so-called perturbation profile, which characterizes the dependence of the perturbation matrix elements in the eigenbasis of the unperturbed Hamiltonian on the difference of the corresponding energy eigenvalues. In particular, a banded matrix structure is quantitatively captured by a perturbation profile which approaches zero for large energy differences. The temporal modification of the relaxation is linked to the perturbation profile via a nonlinear integral equation, which admits approximate analytical solutions for sufficiently weak and strong perturbations, and for which we work out a numerical solution scheme in the general case. As an example, we consider a spin lattice model with a pronounced banded matrix structure, and we find very good agreement of the numerics with our analytical predictions without any free fit parameter.
The ectoparasites Dermanyssus gallinae and Megninia ginglymura: bioecology and natural enemies in commercial egg-laying hens

Abstract Egg-laying hens and free and wild birds may introduce pathogenic organisms and ectoparasites to farms linked to commercial poultry farm systems. Two mite species are reported as an economic problem for the poultry industry worldwide: Dermanyssus gallinae (Dermanyssidae) and Megninia ginglymura (Analgidae). The present study aimed to evaluate the bioecology of mite fauna especially D. gallinae and M. ginglymuraassociated with commercial egg-laying hens, and recognize which are infestation periods, favorable environments and efficient predators of these ectoparasites. Megninia ginglymura was the most abundant in feathers and more frequent in the battery cage than in free-range chickens, representing 98% of the species. Both of these husbandry systems showed high infestations at different times. On the other hand, D. gallinae was the most abundant species in traps (98.9%), being found mostly in the winter from free-range chickens, with more than twice the mites when compared with battery cage chickens. This work revealed a possible new tool for biological control of ectoparasites: Cheyletus malaccensis could be considered the main natural enemy with the best potential for controlling these ectoparasites associated with laying hens and should be taken into consideration for future biological control studies, becoming an additional strategy for controlling D. gallinae principally.
Lane Detection Model Based on Spatio-Temporal Network with Double ConvGRUs

Lane detection is one of the indispensable and key elements of self-driving environmental perception. Many lane detection models have been proposed, solving lane detection under challenging conditions, including intersection merging and splitting, curves, boundaries, occlusions and combinations of scene types. Nevertheless, lane detection will remain an open problem for some time to come. The ability to cope well with those challenging scenes impacts greatly the applications of lane detection on advanced driver assistance systems (ADASs). In this paper, a spatio-temporal network with double Convolutional Gated Recurrent Units (ConvGRUs) is proposed to address lane detection in challenging scenes. Both of ConvGRUs have the same structures, but different locations and functions in our network. One is used to extract the information of the most likely low-level features of lane markings. The extracted features are input into the next layer of the end-to-end network after concatenating them with the outputs of some blocks. The other one takes some continuous frames as its input to process the spatio-temporal driving information. Extensive experiments on the large-scale Tusimple lane marking challenge dataset and Unsupervised LLAMAS dataset demonstrate that the proposed model can effectively detect lanes in the challenging driving scenes. Our model can outperform the state-of-the-art lane detection models.
Lenvatinib prolongs the progression-free survival time of patients with intermediate-stage hepatocellular carcinoma refractory to transarterial chemoembolization: A multicenter cohort study using data mining analysis

Tyrosine kinase inhibitors are considered for use in patients with hepatocellular carcinoma (HCC) refractory to transarterial chemoembolization (TACE). The aim of the present retrospective study was to identify factors associated with progression-free survival (PFS) and to evaluate the indications for lenvatinib treatment in patients with intermediate-stage HCC refractory to TACE using a data-mining analysis. A total of 171 patients with intermediate-stage HCC refractory to TACE were included. All patients were classified into three groups according to their HCC treatment: Lenvatinib (n=45), sorafenib (n=53) and TACE (n=73) groups. PFS time was calculated using the Kaplan-Meier method and analyzed using a log-rank test. Factors associated with PFS time were evaluated using multivariate and decision-tree analyses. The median PFS time was 5.8, 3.2 and 2.4 months in the lenvatinib, sorafenib and TACE groups, respectively (P<0.001). In the Cox regression analysis, lenvatinib treatment and being within the up-to-seven criteria were identified as independent factors for PFS (lenvatinib, P<0.0001; within up-to-seven, P=0.001). The decision-tree analysis revealed that patients beyond the up-to-seven criteria, treated with lenvatinib and with albumin-bilirubin (ALBI) grade 1 had a longer PFS time (245.2±107.9 days) than patients beyond the up-to-seven criteria, treated with lenvatinib and with ALBI grade 2 (147.1±78.6 days). Additionally, lenvatinib was independently associated with longer PFS time in patients with intermediate-stage HCC refractory to TACE. Therefore, lenvatinib may be recommended for patients who have intermediate-stage HCC refractory to TACE, ALBI grade 1 and who are within the up-to-seven criteria.
Biologic Therapies in Sarcoidosis and Uveitis: A Review

Sarcoidosis and uveitis are chronic inflammatory conditions with potentially debilitating effects on quality of life. Steroids form the mainstay standard therapy in both conditions. Biologic agents are considered to be appropriate alternatives for treatment in steroid-refractory sarcoidosis and uveitis due to the role of tumor necrosis factor (TNF) in mediating the inflammatory cascade seen in both conditions. We performed a thorough literature search using PubMed to compare the extent of use, efficacy, and safety profile of individual anti-TNF agents in the management of these conditions. Our review consists of two systematic reviews with meta-analysis, thirteen observational studies, and fifteen case series/reports. Infliximab had the widest range of organ-system usage in extra-pulmonary sarcoidosis but is equivalent to adalimumab in terms of efficacy. In uveitis, adalimumab was found to be the most efficacious agent for maintaining disease remission in adults and children with chronic non-infectious uveitis. Etanercept was neither used widely, nor was it efficacious in the management of either condition. In terms of safety profile, biologic agents were found to be well tolerated and have a similar safety profile. More randomized clinical trials are needed to inform evidence-based use of biologic agents in these conditions.
Phylogenetic relationships and a new sectional classification of Croton (Euphorbiaceae) in Australia

Abstract. A molecular phylogeny, morphological descriptions, species lists and a key to the sections of Croton L. (Euphorbiaceae) recognised for Australia are presented. The molecular phylogenetic results supported the recognition of six sections, to account for the 29 native Australian species. The monophyly of each of these sections was highly supported in the Bayesian and maximum-likelihood analyses of nuclear ITS and plastid trnL–F DNA sequences, whereas their relationships to each other and to other groups were less well resolved. Croton may represent one, two or three separate arrivals to Australia, with support for hypotheses of subsequent dispersals from Australia to Pacific islands and to Asia. Croton sections Argyrati, Arnhemici, Caudati, Dockrilliorum and Insulares are newly described. Croton section Gymnocroton Baill. , previously placed in synonymy, is again recognised. Croton armstrongii S.Moore is excluded from the Australian flora.
Out of Sight but Not Out of Mind: Caring for Patients Receiving OPAT During the SARS-CoV-2 Pandemic

Abstract In an effort to “flatten the curve” of SARS-CoV-2, society is urged to practice social distancing. However, the management of other disease states may require some patients to undergo outpatient parenteral antimicrobial therapy (OPAT). Practical considerations for OPAT management during an infectious pandemic are reviewed. These include shortening durations of therapy, utilizing the oral route, avoiding medications requiring therapeutic drug monitoring, selecting antimicrobials with long durations, decreasing frequency of laboratory blood draws, and performing tele-health visits.
Effect of Trochlear Dysplasia on Commonly Used Radiographic Parameters to Assess Patellar Instability

Background: Trochlear dysplasia (TD) is an important anatomic risk factor for patellar instability (PI). Several imaging-based risk factors for PI have been established, but it is unclear what effect TD has on these measurements, and the Dejour method of categorizing TD has been shown to be unreliable. The lateral trochlear inclination (LTI) is a quantifiable measurement of proximal trochlear morphology. Recently, a modified technique for measuring LTI referencing the posterior condylar angle has demonstrated near-perfect reliability and may serve as a new standard for quantifying TD. Purpose/Hypothesis: The purpose of this study was to evaluate how TD, expressed in terms of LTI, affects historically used measures of PI, including the sulcus angle (SA), tibial tuberosity to trochlear groove (TT-TG) distance, lateral patellar inclination (LPI), and Caton-Deschamps (CD) ratio. We hypothesized that lower LTI (ie, more dysplastic trochlea) will correlate with higher SA and TT-TG distances, increased patellar tilt, and more patella alta. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Patients aged 9 to 18 years treated for PI between January 2014 and August 2017 were queried. On magnetic resonance imaging (MRI), LTI, SA, LPI, and the TT-TG measured at the most proximal (pTT-TG) and distal (dTT-TG) aspects of the fully formed trochlear groove were measured on axial images. CD ratio was measured using sagittal MRI as well as lateral radiographs. All measurements were performed by 2 independent observers. Patients were included if there was a diagnosis of PI that was confirmed with a chart review and if there were appropriate imaging studies to analyze. Results: A total of 65 patients met inclusion criteria for this study. Average pTT-TG distance (15.7 ± 4.5 mm) was not significantly different from dTT-TG distance (15.7 ± 4.2 mm) (P = .94). LTI had a weak but significant inverse correlation with pTT-TG (r = –0.40; β = –0.15 mm; P < .001) and SA (r = –0.43; β = –0.478°; P < .001). There was a moderate and significant inverse correlation of LTI with dTT-TG (r = –0.593; β = –0.21 mm; P < .001) and with LPI (r = –0.69; β = –0.54°; P < .001). There was a weak but significant correlation between LTI and the MRI-based CD ratio (r = 0.279; β = 0.149; P = .005). Radiograph-based CD ratio did not show a significant correlation with LTI (r = –0.189; P = .135). Conclusion: Smaller (ie, more dysplastic) LTI values correlated significantly with larger TT-TG, SA, LPI, and MRI-based CD ratio measurements. The relationship between SA and LTI is intuitive, but the relationship between LTI and the other analyzed variables suggests that they are dependent variables to TD and may not be consistent independent risk factors for PI. Together, these suggest that TD alters the radiographic interpretation of TT-TG and patellar tilt. The correlation between TD and patellar height is unclear and warrants further investigation.