DNA double strand breaks but not interstrand crosslinks prevent progress through meiosis in fully grown mouse oocytes

There is some interest in how mammalian oocytes respond to different types of DNA damage because of the increasing expectation of fertility preservation in women undergoing chemotherapy. Double strand breaks (DSBs) induced by ionizing radiation and agents such as neocarzinostatin (NCS), and interstrand crosslinks (ICLs) induced by alkylating agents such as mitomycin C (MMC), are toxic DNA lesions that need to be repaired for cell survival. Here we examined the effects of NCS and MMC treatment on oocytes collected from antral follicles in mice, because potentially such oocytes are readily collected from ovaries and do not need to be in vitro grown to achieve meiotic competency. We found that oocytes were sensitive to NCS, such that this ionizing radiation mimetic blocked meiosis I and caused fragmented DNA. In contrast, MMC had no impact on the completion of either meiosis I or II, even at extremely high doses. However, oocytes treated with MMC did show ?-H2AX foci and following their in vitro maturation and parthenogenetic activation the development of the subsequent embryos was severely compromised. Addition of MMC to 1-cell embryos caused a similarly poor level of development, demonstrating oocytes have eventual sensitivity to this ICL-inducing agent but this does not occur during their meiotic division. In oocytes, the association of Fanconi Anemia protein, FANCD2, with sites of ICL lesions was not apparent until entry into the embryonic cell cycle. In conclusion, meiotic maturation of oocytes is sensitive to DSBs but not ICLs. The ability of oocytes to tolerate severe ICL damage and yet complete meiosis, means that this type of DNA lesion goes unrepaired in oocytes but impacts on subsequent embryo quality

Topology design and performance analysis of an integrated communication network

A research study on the topology design and performance analysis for the Space Station Information System (SSIS) network is conducted. It is begun with a survey of existing research efforts in network topology design. Then a new approach for topology design is presented. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. The algorithm for generating subsets is described in detail, and various aspects of the overall design procedure are discussed. Two more efficient versions of this algorithm (applicable in specific situations) are also given. Next, two important aspects of network performance analysis: network reliability and message delays are discussed. A new model is introduced to study the reliability of a network with dependent failures. For message delays, a collection of formulas from existing research results is given to compute or estimate the delays of messages in a communication network without making the independence assumption. The design algorithm coded in PASCAL is included as an appendix

School-based curriculum development as reflective practice: a case study in Hong Kong

This paper examines a school-based curriculum development (SBCD) experience in Hong Kong. Traditionally, curriculum change in Hong Kong has normally been bureaucratic with teachers’ actions monitored. This qualitative case study investigates the lived experience of an SBCD practice. Semi-structured interviews were utilized to examine teachers’ perceptions of the reflective SBCD experience in their school and what adaptations they had made when delivering the school-based materials. The findings identified that all teachers held a positive attitude towards this reflective approach to SBCD and emphasized artistry in their teaching practice. Teachers also exercised discretion in response to their students’ level and interests when implementing the school-based curriculum at the classroom level. This research concludes that a reflective approach to curriculum planning with a bottom-up implementation can empower teachers reflecting their creativity, artistry, knowledge of the subject and related pedagogy, and knowledge of their students. The findings of this case study thus contrast sharply with previous research relating to Hong Kong government-led SBCD programs which focus more on meeting the requirements of the intended curriculum than on personalizing the curriculum to meet to learners’ needs

Endmember learning with k-means through SCD model in hyperspectral scene reconstructions

This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS is the technique which allows sparse signals to be decomposed into a sparse representation “a” of a dictionary Du" role="presentation" style="max-height: none; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">Du . The goodness of the learnt dictionary has direct impacts on the quality of the end results, e.g., in the HSI scene reconstructions. This paper proposes the construction of a concise and comprehensive dictionary by using the cluster centres of the input dataset, and then a greedy approach is adopted to learn all elements within this dictionary. The proposed method consists of an unsupervised clustering algorithm (K-Means), and it is then coupled with an advanced sparse coding dictionary (SCD) method such as the basis pursuit algorithm (orthogonal matching pursuit, OMP) for the dictionary learning. The effectiveness of the proposed K-Means Sparse Coding Dictionary (KMSCD) is illustrated through the reconstructions of several publicly available HSI scenes. The results have shown that the proposed KMSCD achieves ~40% greater accuracy, 5 times faster convergence and is twice as robust as that of the classic Spare Coding Dictionary (C-SCD) method that adopts random sampling of data for the dictionary learning. Over the five data sets that have been employed in this study, it is seen that the proposed KMSCD is capable of reconstructing these scenes with mean accuracies of approximately 20–500% better than all competing algorithms adopted in this work. Furthermore, the reconstruction efficiency of trace materials in the scene has been assessed: it is shown that the KMSCD is capable of recovering ~12% better than that of the C-SCD. These results suggest that the proposed DL using a simple clustering method for the construction of the dictionary has been shown to enhance the scene reconstruction substantially. When the proposed KMSCD is incorporated with the Fast non-negative orthogonal matching pursuit (FNNOMP) to constrain the maximum number of materials to coexist in a pixel to four, experiments have shown that it achieves approximately ten times better than that constrained by using the widely employed TMM algorithm. This may suggest that the proposed DL method using KMSCD and together with the FNNOMP will be more suitable to be the material allocation module of HSI scene simulators like the CameoSim packag

Image dehazing based on partitioning reconstruction and entropy-based alternating fast-weighted guided filters

A robust image dehazing algorithm based on the first-order scattering of the image degradation model is proposed. In this work, there are three contributions toward image dehazing: (i) a robust method for assessing the global irradiance from the most hazy-opaque regions of the imagery is proposed; (ii) more detailed depth information of the scene can be recovered through the enhancement of the transmission map using scene partitions and entropy-based alternating fast-weighted guided filters; and (iii) crucial model parameters are extracted from in-scene information. This paper briefly outlines the principle of the proposed technique and compares the dehazed results with four other dehazing algorithms using a variety of different types of imageries. The dehazed images have been assessed through a quality figure-of-merit, and experiments have shown that the proposed algorithm effectively removes haze and has achieved a much better quality of dehazed images than all other state-of-the-art dehazing methods employed in this work

Sample selection with SOMP for robust basis recovery in sparse coding dictionary learning

Sparse Coding Dictionary (SCD) learning is to decompose a given hyperspectral image into a linear combination of a few bases. In a natural scene, because there is an imbalance in the abundance of materials, the problem of learning a given material well is directly proportional to its abundance in the training scene. By a random selection of pixels to train a given dictionary, the probability of bases learning a given material is proportional to its distribution in the scene. We propose to use SOMP residue for sample selection with each iteration for a more robust or ‘more complete’ learning. Experiments show that the proposed method learns from both background and trace materials accurately with over 0.95 in Pearson correlation coefficient. Furthermore, the proposed implementation has resulted in considerable improvements in Target Detection with Adaptive Cosine Estimator (ACE)

Scheme for teleportation of quantum states onto a mechanical resonator

We propose an experimentally feasible scheme to teleport an unkown quantum state onto the vibrational degree of freedom of a macroscopic mirror. The quantum channel between the two parties is established by exploiting radiation pressure effects.Comment: 5 pages, 2 figures, in press on PR