Breast cancer is one of the most dangerous diseases and the second largest cause of female cancer death. Breast cancer starts when malignant, cancerous lumps start to grow from the breast cells. Self-tests and Periodic clinical checks help to early diagnosis and thereby improve the survival chances significantly. The breast cancer classification is a medical method that provides researchers and scientists with a great challenge. Neural networks have recently become a popular tool in cancer data classification. In this paper, Deep Learning assisted Efficient Adaboost Algorithm (DLA-EABA) for breast cancer detection has been mathematically proposed with advanced computational techniques. In addition to traditional computer vision approaches, tumor classification methods using transfers are being actively developed through the use of deep convolutional neural networks (CNNs). This study starts with examining the CNNbased transfer learning to characterize breast masses for different diagnostic, predictive tasks or prognostic or in several imaging modalities, such as Magnetic Resonance Imaging (MRI), Ultrasound (US), digital breast tomosynthesis and mammography. The deep learning framework contains several convolutional layers, LSTM, Max-pooling layers. The classification and error estimation that has been included in a fully connected layer and a softmax layer. This paper focuses on combining these machine learning approaches with the methods of selecting features and extracting them through evaluating their output using classification and segmentation techniques to find the most appropriate approach. The experimental results show that the high accuracy level of 97.2%, Sensitivity 98.3%, and Specificity 96.5% has been compared to other existing systems. INDEX TERMS Breast cancer detection, deep learning, convolutional neural network, MRI, CT, US, long short-term memory. I. CIRCUMSTANTIAL REVIEW AND SIGNIFICANCE OF DETECTING BREAST CANCER

No abstract

Embryo-endosperm interaction is the dominant process controlling grain filling, thus being crucial for yield and quality formation of the three most important cereals worldwide, rice, wheat, and maize. Fundamental science of functional genomics has uncovered several key genetic programs for embryo and endosperm development, but the interaction or communication between the two tissues is largely elusive. Further, the significance of this interaction for grain filling remains open. This review starts with the morphological and developmental aspects of rice grain, providing a spatial and temporal context. Then, it offers a comprehensive and integrative view of this intercompartmental interaction, focusing on (i) apoplastic nutrient flow from endosperm to the developing embryo, (ii) dependence of embryo development on endosperm, (iii) regulation of endosperm development by embryo, and (iv) bidirectional dialogues between embryo and endosperm. From perspective of embryo-endosperm interaction, the mechanisms underlying the complex quality traits are explored, with grain chalkiness as an example. The review ends with three open questions with scientific and agronomic importance that should be addressed in the future. Notably, current knowledge and future prospects of this hot research topic are reviewed from a viewpoint of crop physiology, which should be helpful for bridging the knowledge gap between the fundamental plant sciences and the practical technologies.

No abstract

Although many members encoding different ammonium- and nitrate-transporters (AMTs, NRTs) were identified and functionally characterized from several plant species, little is known about molecular components for NH4+- and NO3- acquisition/transport in tobacco, which is often used as a plant model for biological studies besides its agricultural and industrial interest. We reported here the first molecular identification in tobacco (Nicotiana tabacum) of nine AMTs and four NRTs, which are respectively divided into four (AMT1/2/3/4) and two (NRT1/2) clusters and whose functionalities were preliminarily evidenced by heterologous functional-complementation in yeast or Arabidopsis. Tissue-specific transcriptional profiling by qPCR revealed that NtAMT1.1/NRT1.1 mRNA occurred widely in leaves, flower organs and roots; only NtAMT1.1/1.3/2.1NRT1.2/2.2 were strongly transcribed in the aged leaves, implying their dominant roles in N-remobilization from source/senescent tissues. N-dependent expression analysis showed a marked upregulation of NtAMT1.1 in the roots by N-starvation and resupply with N including NH4+, suggesting a predominant action of NtAMT1.1 in NH4+ uptake/transport whenever required. The obvious leaf-expression of other NtAMTs e.g., AMT1.2 responsive to N indicates a major place, where they may play transport roles associated with plant N-status and (NH4+-)N movement within aerial-parts. The preferentially root-specific transcription of NtNRT1.1/1.2/2.1 responsive to N argues their importance for root NO3- uptake and even sensing in root systems. Moreover, of all NtAMTs/NRTs, only NtAMT1.1/NRT1.1/1.2 showed their root-expression alteration in a typical diurnal-oscillation pattern, reflecting likely their significant roles in root N-acquisition regulated by internal N-demand influenced by diurnal-dependent assimilation and translocation of carbohydrates from shoots. This suggestion could be supported at least in part by sucrose- and MSX-affected transcriptional-regulation of NtNRT1.1/1.2. Thus, present data provide valuable molecular bases for the existence of AMTs/NRTs in tobacco, promoting a deeper understanding of their biological functions.

This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multisession, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the topperforming academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.

K326 and HD represent major tobacco cultivars in China, which required large N fertiliser input but at different application rates. To understand primary components affecting tobacco N use physiology, we adopted these two varieties as valuable genetic material to assess their growth response to N nutrition. We established a hydroponic culture system to grow plants supplied with different N regimes. Plant biomass, N, ammonium, nitrate, arginine, GS and NR activity, N transfer and use efficiency as well as root uptake were examined. Our data revealed the preference of K326 and HD to utilise nitrate or ammonium nitrate but not ammonium alone, with 2 mm N supply probably sufficient and economical to achieve good biomass production at the vegetative stage. Moreover, both varieties were very sensitive to ammonium, perhaps due to lack of or abnormal signalling related to nitrate and/or arginine rather than impairment of N acquisition and initial assimilation; this was supported by measurements of the plant content of N, ammonium and activities of GS and NR. Notably, short-term N root influx studies identified differential uptake kinetics of K326 and HD, with distinct affinities and transport rates for ammonium and nitrate. The data suggest that the growth adaptation of K326 or HD to higher or lower N may be ascribed to different competences for effective N uptake/translocation and assimilation. Thus, our work provides valuable information to prompt deeper investigation of the molecular basis controlling plant N use efficiency.

No abstract