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Societal Impact Statement: Winter wheat is one of the most important crops in the world. Microplastics, as an emerging pollutant, are widespread in agricultural soils due to various modern agricultural practices and can have adverse impacts on agricultural soils and plant growth. Herein, we investigated the effects of 10 types of microplastics on the properties of three agricultural soil types and the growth of winter wheat. This study contributes insights toward the conservation of agricultural soils and potential wheat yield responses to microplastic. Understanding the mechanisms that underpin the differences in responses to this pollutant class is of great importance for management recommendations. Summary: Microplastics (MPs) (size < 5 mm) are increasingly recognized as anthropogenic contaminants that severely affect terrestrial ecosystems. These particles are always detected as a mixture of various polymer types and shapes. However, we have limited knowledge of the effect of combined MPs on plant–soil systems.To address this, we selected 10 types of MP, applied to three soil types singly and in combination along an increasing gradient of 1, 2, 5, 8, and 10 MP types at a content of 0.4% (w/v). After 8 weeks of pre‐incubation, winter wheat (TOBAK) was grown in each pot for another 8 weeks. Shoot and root biomass, soil aggregation, and carbon and nitrogen content were measured.The effects of the same MP on both soil and plant properties were drastically different (in size and effect direction) in the different soil types. However, no clear patterns were observed along an increasing number of microplastic types, suggesting that knowing the number of microplastic types in a sample, at equity of overall concentration, does not help predict effects.In contrast, our findings reveal the complex effects of multiple MPs on the soil–plant system and highlight that soil properties need to be taken into consideration when studying MP effects on terrestrial systems. [ABSTRACT FROM AUTHOR]

Study Design: Human Cadaveric Biomechanical Study Objectives: Lumbar Lateral Interbody Fusion (LLIF) utilizing a wide cage has been reported as having favorable biomechanical characteristics. We examine the biomechanical stability of unilateral pedicle screw and rod fixation after multilevel LLIF utilizing 26 mm wide cages compared to bilateral fixation. Methods: Eight human cadaveric specimens of L1-L5 were included. Specimens were attached to a universal testing machine (MTS 30/G). Three-dimensional specimen range of motion (ROM) was recorded using an optical motion-tracking device. Specimens were tested in 3 conditions: 1) intact, 2) L1-L5 LLIF (4 levels) with unilateral rod, 3) L1-L5 LLIF with bilateral rods. Results: From the intact condition, LLIF with unilateral rod decreased flexion-extension by 77%, lateral bending by 53%, and axial rotation by 26%. In LLIF with bilateral rods, flexion-extension decreased by 83%, lateral bending by 64%, and axial rotation by 34%. Comparing unilateral and bilateral fixation, LLIF with bilateral rods reduced ROM by a further 23% in flexion-extension, 25% in lateral bending, and 11% in axial rotation. The difference was statistically significant in flexion-extension and lateral bending (P <.005). Conclusions: Considerable decreases in ROM were observed after multilevel (4-level) LLIF utilizing 26 mm cages supplemented with both unilateral and bilateral pedicle screws and rods. The addition of bilateral fixation provides a 10-25% additional decrease in ROM. These results can inform surgeons of the incremental biomechanical benefit when considering unilateral or bilateral posterior fixation after multilevel LLIF. [ABSTRACT FROM AUTHOR]

The characteristics of fresh lumbar spondylolysis at multiple levels were developed and compared with single-level spondylolysis. Fresh lumbar spondylolysis was diagnosed in 530 patients between March 2015 and March 2022. The subjects were divided into two groups: those with multiple-level spondylolysis (multiple group) and those with single-level spondylolysis (single group). The survey items included age, gender, time from pain awareness to consultation (disease duration), time from pain awareness to checkup (checkup duration), sports events, side of lamina, vertebral level, disease stage, and bone union rate. The number of patients in the multiple group was 44 (8.5%), and that of the single group was 486 (91.5%). Age was significantly higher in the multiple group (p<0.01). No significant differences were observed between the two groups in terms of gender or disease duration or checkup duration. The rate of patients in athletics was 4.9%, the lowest of all sports events, in the multiple group. However, that of jumping event was high. The rate of bone union was 84.5% in the multiple group and 86.8% in the single group. The rate of bone union was not significantly different between the two groups. Multiple level spondylolysis was not a factor in inhibiting bone union. [ABSTRACT FROM AUTHOR]

The objective of the research is to estimate the transmission rate of an infection (β) in the SI epidemical model, using Bayesian statistical methods from observed data in Peru. After studying the SI mathematical model and Bayesian statistical inference metho'ds, a Bayesian estimator is proposed to estimate the transmisión rate of an infection in this model and a procedure is proposed to estimate this rate using Montecarlo simulation based on Markov chains - MCMC.

Study Design: Retrospective magnetic resonance imaging grading with comparison between experts and deep convolutional neural networks (CNNs)., Objective: The application of deep learning to clinical diagnosis has gained popularity. This approach can accelerate image interpretation and serve as a screening tool to help doctors., Methods: A comparison was conducted between retrospective magnetic resonance imaging (MRI) grading performed by experts and grading obtained using CNN classifiers. Data were collected from the lumbar axial dataset in the DICOM format. Two experts labeled the sampled images using the same diagnostic tools: localization of patches near the spinal canal, rootlet leveling, and stenosis grading. Comprehensive comparisons were presented for both rootlet cord classification and stenosis grading., Results: Rootlet-cord classification for the two analyzers was 90.3% and the F1 score was 86.6%. The agreement of Analyzers-Classifiers was 92.7% and 96.8% for data with 90.6% and 95.6% F1 scores, respectively. For stenosis grading, there was an agreement of 89.2% between the two analyzers, resulting in an F1 score of 76.5%. The grades of the Analyzers-Classifiers agreed on 91.5/89.4% of the data, with an F1 score of 78.4/75.7%. Analyzer1 and Analyzer2 classified >74% as grade A (78.8% and 74.4%, respectively), 15.4% and 18.6% as grade B, 4.2% and 6.0% as grade C, and 1.6% and 2.0% as grade D, respectively., Conclusions: The fully automated deep learning model showed competitive results in stenosis grade diagnosis and rootlet cord classification under similar anatomical conditions. However, abrupt anatomical changes can lead to a puzzle diagnosis based only on images., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Natural language inference (NLI) task requires an agent to determine the semantic relation between a premise sentence (${p}$) and a hypothesis sentence (${h}$), which demands sufficient understanding about sentences semantic. Due to the issues, such as polysemy, ambiguity, as well as fuzziness of sentences, intense sentence understanding is very challenging. To this end, in this article, we introduce the corresponding image of sentences as reference information for enhancing sentence semantic understanding and representing. Specifically, we first propose an image-enhanced multilevel sentence representation net (IEMLRN), that utilizes the image features from pretrained models for enhancing the sentence semantic understanding at different scales, i.e., lexical-level, phrase-level, and sentence-level. The proposed model advances the performance on NLI tasks by leveraging the pretrained global features of images. However, as these pretrained image features are optimized on specific image classification datasets, they may not show the best performance on NLI tasks. Therefore, we further propose to design an adaptive image feature generator that extracts fine-grained image labels from the corresponding sentences. After that, we extend the IEMLRN to multilevel image-enhanced sentence representation net (MIESR) by utilizing not only the coarse-grained pretrained features of images, but also the fine-grained adaptive features of images. Therefore, sentence semantic can be evaluated and enhanced more comprehensively and precisely. Extensive experiments on two benchmark datasets (SNLI, SICK) clearly show our proposed IEMLRN significantly outperform the state-of-the-art baselines, and our proposed MIESR model achieves the best performance by considering not only the text but also images in an adaptive multigranularities way. [ABSTRACT FROM AUTHOR]

Object: Microsurgical decompression of the lumbar spine is a beneficial approach for selected patients of lumbar spinal stenosis (LSS). The purpose of this prospective study was to describe the clinical results of surgical treatment for microsurgical bilateral decompression via unilateral approach on patients with LSS with multiple-level involvement. Materials and methods: Seventy-three consecutive patients who had microsurgical bilateral decompression via unilateral approach of more than two spinal levels were included in this study. Seventy-seven patients who had surgery at a single level over the same time periods were compared as a single-level LSS group. Results: The preoperative Japanese Orthopaedic Association scores averaged 11.7 points, and the postoperative scores averaged 21.2 points with an average recovery rate (RR) of 56 % in the multiple-level LSS group. There was no significant difference in the RR between the groups. There were no major complications related to the surgery in the both groups. Conclusion: Microsurgical bilateral decompression via unilateral approach was a useful and safe operative procedure for LSS patients. Sufficient clinical results that were comparable to single-level LSS patients were obtained even in the patients with multiple-level LSS, if the patient were correctly selected and the microsurgical decompression surgery were carefully performed. [ABSTRACT FROM AUTHOR]

Estimation of the number or proportion of true null hypotheses in multiple-testing problems has become an interesting area of research. The first important work in this field was performed by Schweder and Spjøtvoll. Among others, they proposed to use plug-in estimates for the proportion of true null hypotheses in multiple-test procedures to improve the power. We investigate the problem of controlling the familywise error rate FWER when such estimators are used as plug-in estimators in single-step or step-down multiple-test procedures. First we investigate the case of independent p-values under the null hypotheses and show that a suitable choice of plug-in estimates leads to control of FWER in single-step procedures. We also investigate the power and study the asymptotic behaviour of the number of false rejections. Although step-down procedures are more difficult to handle we briefly consider a possible solution to this problem. Anyhow, plug-in step-down procedures are not recommended here. For dependent p-values we derive a condition for asymptotic control of FWER and provide some simulations with respect to FWER and power for various models and hypotheses. [ABSTRACT FROM AUTHOR]

Abstract: In a recent paper, Finner and Straßburger [2002. The partitioning principle: a powerful tool in multiple decision theory. Ann. Statist. 30, 1194–1213.] introduced a general, weak and strong partitioning principle (SPP), respectively, for the construction of multiple decision procedures, e.g., multiple testing or selection procedures. Partitioning principles can be viewed as natural extensions of the well-known closure principle and may yield more powerful decision procedures. In this paper, we are concerned with the construction of a step-down procedure for selecting a subset of treatments containing all good treatments by rigorously applying the weak partitioning principle (WPP). This results in some new least favourable parameter configuration (LFC) problems and an improved set of critical values. The new step-down procedure improves the step-down procedure based on the closure principle considerably and decreases the required sample size with respect to a pre-specified power criterion. Various procedures including a Newman–Keuls-type procedure which is related to the SPP and probably the best possible step-down procedure will be compared with respect to power and sample sizes. Finally, we reanalyse an experiment with 13 treatment means. [Copyright &y& Elsevier]