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Additional file 18. Review history.

Additional file 5: Table S1. The sequences of the primers. Table S2. The association of HNRNPA2B1 expression with clinicopathological characteristics of LAC patients. Table S3. Cox regression analysis of HNRNPA2B1 expression as survival predictor. Table S4. The correlation of MEG3 expression with clinicopathologic characteristics of LAC patients. Table S5. The association of miR-21-5p expression with clinicopathological characteristics of LAC patients. Table S6. Cox regression analysis of miR-21-5p expression as survival predictor.

Additional file 2: Supplementary figures. Fig. S1. Additional QC data for BZR1-YFP and HASCh-seq. Fig. S2. Multiple sequence alignment of BZR1 DNA-binding domain. Fig. S3. IP-Input allelic bias control for HASCh-seq. Fig. S4. ASBs of ZmBZR1 overrepresentation at enhancer sites. Fig. S5. Differential NBL resistance of B73 and Mo17 in response to BR inhibitor PPZ treatment. Fig. S6. ASBs overlapping with complex differences in ZmBZR1 binding pattern. Fig. S7. ASBs and bgSNPs share similar minor allele frequency.

A GaInAsP/GaAs/GaAsP Al-free laser with asymmetric potential barriers is designed theoretically to prevent carrier leakage. The band alignment demonstrates that a high height of the potential barrier decreases the leakage current. The internal quantum efficiency increases by increasing the injection efficiency, which is attributed to the decreasing electron potential barrier heights. Moreover, the threshold current and operating voltage decrease by adopting a novel barrier so that the output power and power conversion efficiency (PCE) increase. When the injection current is 5 kA/cm

BackgroundSpinocerebellar ataxia type 3 (SCA3) is one of the most common hereditary neurodegenerative diseases. Postural control dysfunction is the main symptom of SCA3, and the proprioceptive system is a critical sensory component of postural control. Accordingly, proprioception quantification assessment is necessary in monitoring the progression of SCA3.ObjectiveWe aimed to quantitatively assess lower limb proprioception and investigate the relationship between proprioception and clinical characteristics in patients with SCA3.MethodsA total of 80 patients with SCA3 and 62 health controls were recruited, and their lower limb proprioception was measured using the Pro-kin system. Clinical characteristics of the SCA3 patients were collected. Multivariable linear regression was used to investigate potential affected factors for lower limb proprioception.ResultsWe found that the patients with SCA3 experience poorer lower limb proprioception characterized by significant impairment in the average trace error (ATE) and time to carry out the test time execution (TTE) compared to controls (P < 0.05). Moreover, there were significant differences in TTE between the right and left lower limbs (P < 0.05) of the patients. Regression analyses revealed that increasing age at onset (AAO) predicts poorer lower limb proprioception for both ATE (β = 2.006, P = 0.027) and TTE (β = 1.712, P = 0.043) and increasing disease duration predicts poorer lower limb proprioception for ATE (β = 0.874, P = 0.044). AAO (β = 0.328, P = 0.019) along with the expanded alleles (β = 0.565, P = 0.000) could affect the severity of ataxia. By contrast, ATE (β = 0.036, P = 0.800) and TTE (β = −0.025, P = 0.862) showed no significant predictors.ConclusionsLower limb proprioception in patients with SCA3 is significantly impaired when compared to healthy controls. Increasing AAO and disease duration are related to impaired lower limb proprioception.

To improve pattern representation capabilities and robustness in traditional finger-vein recognition algorithms. In this paper, we propose FFV-MBC, a novel fused finger-vein recognition method based on monogenic binary coding (MBC). First of all, the amplitude, orientation, and phase information of the finger-vein images are filtered by a multi-scale monogenic log-Gabor filter and encoded by the binary coding theory. Three local features, MBC-A, MBC-P, and MBC-O, are achieved from different combinations of local image intensity and variation coding. After obtaining the features, we utilize the block-based Fisher Linear Discriminant method to reduce the dimension. Finally, the similarity components are calculated by the cosine distance and fused for the final finger-vein recognition results. We evaluate our proposed method on two publicly available datasets and one self-built dataset, i.e., Malaysian Polytechnic University (FV-USM), the Group of Machine Learning and Applications of Shandong University (SDUMLA-HMT), and our team, Signal and Information Processing Laboratory (FV-SIPL). On average, the proposed method achieved high recognition accuracy, i.e., 99.30%, and 1.10% equal error rates (EER). Overall, the proposed method performs better than most classical and state-of-the-art finger-vein recognition methods. &nbsp