Cyclin B1/CDK1-regulated mitochondrial bioenergetics in cell cycle progression and tumor resistance.

A mammalian cell houses two genomes located separately in the nucleus and mitochondria. During evolution, communications and adaptations between these two genomes occur extensively to achieve and sustain homeostasis for cellular functions and regeneration. Mitochondria provide the major cellular energy and contribute to gene regulation in the nucleus, whereas more than 98% of mitochondrial proteins are encoded by the nuclear genome. Such two-way signaling traffic presents an orchestrated dynamic between energy metabolism and consumption in cells. Recent reports have elucidated the way how mitochondrial bioenergetics synchronizes with the energy consumption for cell cycle progression mediated by cyclin B1/CDK1 as the communicator. This review is to recapitulate cyclin B1/CDK1 mediated mitochondrial activities in cell cycle progression and stress response as well as its potential link to reprogram energy metabolism in tumor adaptive resistance. Cyclin B1/CDK1-mediated mitochondrial bioenergetics is applied as an example to show how mitochondria could timely sense the cellular fuel demand and then coordinate ATP output. Such nucleus-mitochondria oscillation may play key roles in the flexible bioenergetics required for tumor cell survival and compromising the efficacy of anti-cancer therapy. Further deciphering the cyclin B1/CDK1-controlled mitochondrial metabolism may invent effect targets to treat resistant cancers

Distributed Cooperative Spatial Multiplexing System

Multiple-Input-Multiple-Output (MIMO) spatial multiplexing systems can increase the spectral efficiency manyfold, without extra bandwidth or transmit power, however these advantages are based on the assumption that channels between different transmit antenna and receive antenna are independent which requires the elements in antenna array be separated by several wavelengths. For small mobile devices, the requirement is difficult to implement in practice. Cooperative spatial multiplexing (C-SM) system provides a solution: it organizes antennas on distributed mobile stations to form a virtual antenna array (VAA) to support spatial multiplexing. In this thesis, we propose a novel C-SM system design which includes a transmitter with two antennas, a single antenna receiver and a relay group with two single antenna relays. In this design, we assume that the transmitter tries to transmit two coded independent messages to the receiver simultaneously but the transmitter-receiver link is too weak to support the transmission. Thus a relay group is introduced to help with the transmission. After relays receive the messages from the transmitter via a $2\times 2$ MIMO link, they first detect and quantize the received messages, then compress them independently according to the Slepian and Wolf theorem, the compressed messages are sent to the receiver simultaneously where de-compression and de-quantization are performed on the received messages. After that the resulting messages are combined to estimate the original coded messages. The estimated coded messages are decoded to produce the original messages. The basic system structure is studied and an analytical bit error rate expression is derived. Several transmission protocols are also introduced to enhance the system BER performance. The merit of this design is focus on the relay destination link. Because the Slepian and Wolf theorem is applied on the relay-destination link, messages at the relays can be compressed independently and de-compressed jointly at the receiver with arbitrarily small error probability but still achieve the same compression rate as a joint compression scheme does. The Slepian and Wolf theorem is implemented by a joint source-channel code in this thesis. Several schemes are introduced and tested, the testing results and performance analysis are given in this thesis. According to the chief executive officer (CEO) problem in the network information theory, we discover an error floor in this design. An analytical expression for this error floor is derived. A feedback link is also introduced from the receiver to the relays to allow the relays to cooperatively adapt their compression rates to the qualities of the received messages. Two combination schemes at the receiver are introduced, their performances are examined from the information theory point of view, the results and performance analysis are given in this thesis. As we assume that the relay destination link is a multiple access channel (MAC) suffers from block Rayleigh fading and white Gaussian noise, the relationship between the MAC channel capacity and the Slepian and Wolf compression rate region is studied to analyse the system performance

Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet mathematical constraints such as sparse coding and positivity both provide alternate biologically-plausible frameworks for generating brain networks. Non-negative Matrix Factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms ($L1$ Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks for different constraints are used as basis functions to encode the observed functional activity at a given time point. These encodings are decoded using machine learning to compare both the algorithms and their assumptions, using the time series weights to predict whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. For classifying cognitive activity, the sparse coding algorithm of $L1$ Regularized Learning consistently outperformed 4 variations of ICA across different numbers of networks and noise levels (p$<$0.001). The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy. Within each algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p$<$0.001). The success of sparse coding algorithms may suggest that algorithms which enforce sparse coding, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA