Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

Modern object detectors usually suffer from low accuracy issues, as foregrounds always drown in tons of backgrounds and become hard examples during training. Compared with those proposal-based ones, real-time detectors are in far more serious trouble since they renounce the use of region-proposing stage which is used to filter a majority of backgrounds for achieving real-time rates. Though foregrounds as hard examples are in urgent need of being mined from tons of backgrounds, a considerable number of state-of-the-art real-time detectors, like YOLO series, have yet to profit from existing hard example mining methods, as using these methods need detectors fit series of prerequisites. In this paper, we propose a general hard example mining method named Loss Rank Mining (LRM) to fill the gap. LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples. By using LRM, some elements representing easy examples in final feature map are filtered and detectors are forced to concentrate on hard examples during training. Extensive experiments validate the effectiveness of our method. With our method, the improvements of YOLOv2 detector on auto-driving related dataset KITTI and more general dataset PASCAL VOC are over 5% and 2% mAP, respectively. In addition, LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.Comment: 8 pages, 6 figure

Metabolomics Analysis of Colostrum and Mature Milk from Saanen Goats

The differences in metabolites and related metabolism pathways in colostrum and mature milk from Saanen goats at different lactation stages were explored by untargeted metabolomics based on ultra-high performance liquid chromatography-quadrupole electrostatic field orbitrap mass spectrometry (UPLC-QE-orbitrap-MS). The results showed that a total of 118 differential metabolites were found between colostrum and mature milk, among which 62 had higher relative contents in colostrum than in mature milk and 56 had lower relative contents in colostrum than in mature milk. These metabolites were mainly lipids, amino acids, and nucleosides. Nine key metabolic pathways most associated with these metabolites were selected, which jointly regulated the lactation process of Saanen goats, and the citric acid cycle could act as a bridge connecting other metabolic pathways. The number of differential metabolites involved in these metabolic pathways was 12. The differential metabolites with relatively high contents in colostrum were taurine, hypotaurine, taurocholic acid, L-phenylalanine, L-tyrosine, succinic acid, isocitrate, D-maltose, α-lactose, 4-hydroxyphenylpyruvate and glycine. The differential metabolite with relatively high contents in mature milk was N1-methyl-4-pyridone-3-carboxamide. They could be used as potential marker metabolites in the colostrum and mature milk of Saanen goats. Metabolomics technology can also be used for identifying differential metabolites in milk from other dairy species at different lactation stages

A Phase Ib Study of the Simmitecan Single Agent and in Combination With 5-Fluorouracil/Leucovorin or Thalidomide in Patients With Advanced Solid Tumor

Background: Simmitecan is a potent inhibitor of topoisomerase I with anti-tumor activity. This phase Ib trial was conducted to investigate the safety and anti-tumor effect of simmitecan alone or in combination with other drugs.Methods: Eligible patients with advanced solid tumor had no further standard treatment options. Patients were allocated to receive simmitecan alone, simmitecan in combination with 5-fluorouracil (5-FU)/leucovorin (LV), or simmitecan in combination with thalidomide, 14 days a cycle, until disease progression or unacceptable toxicity occurred.Results: A total of 41 patients were enrolled, with a median age of 55 (range 29–69) years. Among them, 13 patients received simmitecan monotherapy, 10 received simmitecan + 5-FU/LV, and 18 received simmitecan + thalidomide. No dose-limiting toxicity occurred. Overall, the most common grade 3/4 adverse event (AE) was neutropenia (46.2, 70.0, and 88.9%, respectively, in simmitecan, simmitecan + 5-FU/LV, and simmitecan + thalidomide cohorts), and treatment-related severe AEs included anemia and febrile neutropenia (7.7% each in simmitecan cohort), diarrhea (10% in simmitecan +5-FU/LV cohort), and febrile neutropenia (5.6% in simmitecan + thalidomide cohort). The majority of patients (24/41, 58.3%) had progressed on prior irinotecan; nevertheless, partial response was achieved in one colorectal cancer patients treated with simmitecan + thalidomide. The disease control rates of simmitecan, simmitecan + 5-FU/LV, and simmitecan + thalidomide cohorts were 46.2, 80.0, and 61.1%, respectively.Conclusion: This study demonstrated a manageable safety profile of simmitecan as a single agent or as part of a combination therapy. There have not been any safety concerns with simmitecan in combination when compared to simmitecan alone. Simmitecan + 5-FU/LV regimen seemed to have a better efficacy. Nonetheless, the efficacy of this regimen needs to be further explored in the subsequent study

Characterization of microRNAs Identified in a Table Grapevine Cultivar with Validation of Computationally Predicted Grapevine miRNAs by miR-RACE

BACKGROUND: Alignment analysis of the Vv-miRNAs identified from various grapevine cultivars indicates that over 30% orthologous Vv-miRNAs exhibit a 1-3 nucleotide discrepancy only at their ends, suggesting that this sequence discrepancy is not a random event, but might mainly derive from divergence of cultivars. With advantages of miR-RACE technology in determining precise sequences of potential miRNAs from bioinformatics prediction, the precise sequences of vv-miRNAs predicted computationally can be verified with miR-RACE in a different grapevine cultivar. This presents itself as a new approach for large scale discovery of precise miRNAs in different grapevine varieties. METHODOLOGY/PRINCIPAL FINDINGS: Among 88 unique sequences of Vv-miRNAs from bioinformatics prediction, 83 (96.3%) were successfully validated with MiR-RACE in grapevine cv. 'Summer Black'. All the validated sequences were identical to their corresponding ones obtained from deep sequencing of the small RNA library of 'Summer Black'. Quantitative RT-PCR analysis of the expressions levels of 10 Vv-miRNA/target gene pairs in grapevine tissues showed some negative correlation trends. Finally, comparison of Vv-miRNA sequences with their orthologs in Arabidopsis and study on the influence of divergent bases of the orthologous miRNAs on their targeting patterns in grapevine were also done. CONCLUSION: The validation of precise sequences of potential Vv-miRNAs from computational prediction in a different grapevine cultivar can be a new way to identify the orthologous Vv-miRNAs. Nucleotide discrepancy of orthologous Vv-miRNAs from different grapevine cultivars normally does not change their target genes. However, sequence variations of some orthologous miRNAs in grapevine and Arabidopsis can change their targeting patterns. These precise Vv-miRNAs sequences validated in our study could benefit some further study on grapevine functional genomics