DNA mimic proteins are relatively obscure control factors that resemble DNA by mimicking its negatively charged distribution. They achieve this using negatively charged amino acids like aspartic acid (ASP/D) and glutamic acid (GLU/E). Known DNA mimic proteins control various cellular mechanisms, such as transcription, DNA repair, and gene regulation, by intervening in the binding of DNA to effector proteins. In addition to their biological functions, DNA mimic proteins may also be applicable in biotechnology, for example, by regulating CRISPR-Cas9 activity to enhance gene editing precision. Therefore, DNA mimic proteins warrant further research. However, most DNA mimic proteins cannot be identified using traditional bioinformatics methods owing to their unique amino acid sequences and structural features. We developed a new protein fingerprint, called relative distance protein fingerprint (RD-PFP), that can be used to analyze the distribution of amino acids on a protein surface. We optimized our RD-PFP by using machine learning and the characteristic feature of DNA mimic proteins (namely, their DNA-like negatively charged distribution) to more accurately predict DNA mimicry from protein structures. Our pioneering study contributes to the development of machine learning-based bioinformatics methods for screening DNA mimic proteins.

In this paper, we consider Switched-Delay-Lines (SDL) constructions of optical priority queues by using optical (bufferless) crossbar switches and optical fiber delay lines. In a priority queue, each packet is associated with a priority upon its arrival, the highest-priority packet is sent out from the queue whenever there is a departure request, and the lowest-priority packet is dropped from the queue whenever there is a buffer overflow. Given any system for SDL constructions of optical priority queues, the main research problem is twofold: (i) the design of the routing policy performed by the optical crossbar switches; (ii) the choice of the delays of the optical fiber delay lines. Sarwate and Anantharam are the first to propose a feedback system consisting of an optical$(M+2)\times (M+2)$crossbar switch and M optical fiber delay lines (seeFigure 1inSection I) for SDL constructions of optical priority queues, and they have shown that the largest buffer size that can possibly be achieved by using such a feedback system is$2^{M}$. However, whether this theoretical buffer size$2^{M}$can be achieved or not remains an open research problem. Currently, the best result in the literature was obtained by Cheng et al. and the achieved buffer size is$2^{O(\sqrt {\alpha M})}$, where$\alpha $is a constant that depends on the parameters used in their constructions. In this paper, we consider a discrete-time setting and use a feedback system consisting of an optical crossbar switch and multiple groups of optical first-in first-out (FIFO) multiplexers with delay one (FM1’s) for SDL constructions of optical priority queues under apriority-based routing policy (seeFigure 2inSection I). Our contributions are as follows: (i) We extend and generalize an important class of constructions that contains the optimal constructions in the work of Cheng et al. As a result, we achieve larger buffer sizes and less construction complexities/costs than those by Cheng et al. (ii) We obtain a closed-form expression for the maximum buffer size that is achieved by the optimal construction for the scenario that each group of FM1’s has the same number of FM1’s. (iii) Our constructions possess a salient feature, namely, fault-tolerant capability, that can tolerate the malfunctioning of some FM1’s by using the generalized results obtained in this paper. (iv) We show that our constructions can be implemented by using an optical$(M+2)\times (M+2)$crossbar switch andMoptical fiber delay lines, and achieve a buffer size$2^{O(\sqrt {\alpha M})}$, where$\alpha $is a constant that depends on the parameters used in our constructions and is better, i.e., larger, than that in the work of Cheng et al. in a very broad regime.

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models.

The most popular tools for predicting pathogenicity of single amino acid variants (SAVs) were developed based on sequence-based techniques. SAVs may change protein structure and function. In the context of van der Waals force and disulfide bridge calculations, no method directly predicts the impact of mutations on the energies of the protein structure. Here, we combined machine learning methods and energy scores of protein structures calculated by Rosetta Energy Function 2015 to predict SAV pathogenicity. The accuracy level of our model (0.76) is higher than that of six prediction tools. Further analyses revealed that the differential reference energies, attractive energies, and solvation of polar atoms between wildtype and mutant side-chains played essential roles in distinguishing benign from pathogenic variants. These features indicated the physicochemical properties of amino acids, which were observed in 3D structures instead of sequences. We added 16 features to Rhapsody (the prediction tool we used for our data set) and consequently improved its performance. The results indicated that these energy scores were more appropriate and more detailed representations of the pathogenicity of SAVs.

One of the main challenges in all-optical packet switching is to design optical buffers for packet conflict resolution. In this paper, we consider a very general type of buffering schemes, namely, optical N-to-K priority queues with time-varying service capacity, where each packet is associated with a unique priority upon its arrival, at time slot t at most c(t) highest-priority packets are sent out from the queue if there are packets in the queue and the service capacity c(t) (0 ≤ c(t) ≤ K) of the queue is not zero, and up to N lowest-priority packets are dumped from the queue if there is a buffer overflow. We extend and generalize our previous constructions [8] of optical priority queues under a priority-based routing policy from single input/output to multiple inputs/outputs. The main contributions of this paper are as follows: (i) The priority queues considered in this paper subsume those considered in all previous works as special cases. (ii) Our queueing model with time-varying service capacity is not only more general but also more realistic than that with fixed service capacity previously studied in the literature. (iii) For the special case that N = K = 1, our constructions in this paper subsume those in [8] as special cases. (iv) For the special case that N = K, we show that an optical N-to-N priority queue with buffer size ${2^{O(\sqrt {M/N} )}}$ (exponential in $\sqrt {M/N} $) can be constructed by using an optical (M+2N)×(M+2N) (bufferless) crossbar switch and M fiber delay lines, which substantially improves the best result O(M3/N2) (polynomial in M/N) in the literature.

Considerable sequence data are produced in genome annotation projects that relate to molecular levels, structural similarities, and molecular and biological functions. In structural genomics, the most essential task involves resolving protein structures efficiently with hardware or software, understanding these structures, and assigning their biological functions. Understanding the characteristics and functions of proteins enables the exploration of the molecular mechanisms of life. In this paper, we examine the problems of protein classification. Because they perform similar biological functions, proteins in the same family usually share similar structural characteristics. We employed this premise in designing a classification algorithm. In this algorithm, auxiliary graphs are used to represent proteins, with every amino acid in a protein to a vertex in a graph. Moreover, the links between amino acids correspond to the edges between the vertices. The proposed algorithm classifies proteins according to the similarities in their graphical structures. The proposed algorithm is efficient and accurate in distinguishing proteins from different families and outperformed related algorithms experimentally.

The protein folding problem (PFP) is an important issue in bioinformatics and biochemical physics. One of the most widely studied models of protein folding is the hydrophobic-polar (HP) model introduced by Dill. The PFP in the three-dimensional (3D) lattice HP model has been shown to be NP-complete; the proposed algorithms for solving the problem can therefore only find near-optimal energy structures for most long benchmark sequences within acceptable time periods. In this paper, we propose a novel algorithm based on the branch-and-bound approach to solve the PFP in the 3D lattice HP model. For 10 48-monomer benchmark sequences, our proposed algorithm finds the lowest energies so far within comparable computation times than previous methods.

Recently, constructing optical queues by using optical crossbar Switches and fiber Delay Lines (SDL) has been recognized as a key research issue for all-optical packet switching. In this paper, we focus on SDL constructions of optical FIFO queues. We consider a network element consisting of a 1 × 2 optical crossbar switch, 2k + 1 2 × 2 optical crossbar switches, and 2k + 1 fiber delay lines of lengths ℓ0, ℓ1, ..., ℓ2k. The main contribution of this paper is to provide an explicit control scheme that explicitly specifies the connection patterns of the optical crossbar switches, and obtain a necessary and sufficient condition on the lengths ℓ0, ℓ1, ..., ℓ2k(specifically, the condition in (A1) in Section I) for such a network element to be operated as an optical FIFO queue with buffer equation under our proposed control scheme. The key idea in our proposed control scheme is to operate the network element such that packets stored in the network element satisfy an ordered property and a circularly contiguous property, which lead to the properties required of a FIFO queue.

In this paper, we propose an algorithm that detects overlapping communities in networks (graphs) based on a simple node behavior model. The key idea in the proposed algorithm is to find communities in an agglomerative manner such that every detected community S has the following property: For each node i ∈ S, we have (i) the fraction of nodes in S \ {i} that are neighbors of node i is greater than a given threshold, or (ii) the fraction of neighbors of node i that are in S \ {i} is greater than another given threshold. Through computer simulations of random graphs with built-in overlapping community structure, including LFR benchmark random graphs and Erdös-Rényi type random graphs, we show that our algorithm has excellent performance. Furthermore, we apply our algorithm to several real-world networks and show that the overlapping communities detected by our algorithm are very close to the known communities in these networks.