Small Noncoding RNAs in Knee Osteoarthritis: The Role of MicroRNAs and tRNA-Derived Fragments

Knee osteoarthritis (OA) is a degenerative knee joint disease that results from the breakdown of joint cartilage and underlying bone, affecting about 3.3% of the world’s population. As OA is a multifactorial disease, the underlying pathological process is closely associated with genetic changes in articular cartilage and bone. Many studies have focused on the role of small noncoding RNAs in OA and identified numbers of microRNAs that play important roles in regulating bone and cartilage homeostasis. The connection between other types of small noncoding RNAs, especially tRNA-derived fragments and knee osteoarthritis is still elusive. The observation that there is limited information about small RNAs different than miRNAs in knee OA was very surprising to us, especially given the fact that tRNA fragments are known to participate in a plethora of human diseases and a portion of them are even more abundant than miRNAs. Inspired by these findings, in this review we have summarized the possible involvement of microRNAs and tRNA-derived fragments in the pathology of knee osteoarthritis.     

The Causes and Potential Injurious Effects of Elevated Serum Leptin Levels in Chronic Kidney Disease Patients

Leptin is an adipokine that regulates appetite and body mass and has many other pleiotropic functions, including regulating kidney function. Increased evidence shows that chronic kidney disease (CKD) is associated with hyperleptinemia, but the reasons for this phenomenon are not fully understood. In this review, we focused on potential causes of hyperleptinemia in patients with CKD and the effects of elevated serum leptin levels on patient kidney function and cardiovascular risk. The available data indicate that the increased concentration of leptin in the blood of CKD patients may result from both decreased leptin elimination from the circulation by the kidneys (due to renal dysfunction) and increased leptin production by the adipose tissue. The overproduction of leptin by the adipose tissue could result from: (a) hyperinsulinemia; (b) chronic inflammation; and (c) significant lipid disturbances in CKD patients. Elevated leptin in CKD patients may further deteriorate kidney function and lead to increased cardiovascular risk.     

Identification of the Catalytic Residues in the Cyclase Domain of the Class IV Lanthipeptide Synthetase SgbL

Lanthipeptides belong to the family of ribosomally synthesized and post-translationally modified peptides (RiPPs) and are subdivided into different classes based on their processing enzymes. The three-domain class IV lanthipeptide synthetases (LanL enzymes) consist of N-terminal lyase, central kinase, and C-terminal cyclase domains. While the catalytic residues of the kinase domains (mediating ATP-dependent Ser/Thr phosphorylations) and the lyase domains (carrying out subsequent phosphoserine/phosphothreonine (pSer/pThr) eliminations to yield dehydroalanine/dehydrobutyrine (Dha/Dhb) residues) have been characterized previously, such studies are missing for LanL cyclase domains. To close this gap of knowledge, this study reports on the identification and validation of the catalytic residues in the cyclase domain of the class IV lanthipeptide synthetase SgbL, which facilitate the nucleophilic attacks by Cys thiols on Dha/Dhb residues for the formation of β-thioether crosslinks.     

Output controllability and optimal output control of state-dependent switched Boolean control networks

In the present paper, we investigate the output-controllability and optimal output control problems of a state-dependent switched Boolean control network. By using the semi-tensor product, the algebraic form of the system is obtained. Then, output-controllability problems of the system are discussed and some necessary and sufficient conditions are given. Next, the Mayer-type optimal output control issue is considered and an algorithm is provided to find out the control sequence. At last, an example is given to show the effectiveness of the main results.     

A survey of support for structured communication in concurrency control models

The two standard models used for communication in concurrent programs, shared memory and message passing, have been the focus of much debate for a long time. Still, we believe the main issue at stake should not be the choice between these models, but rather how to ensure that communication is structured, i.e., it occurs only in syntactically restricted code regions. In this survey, we explore concurrency control models and evaluate how their characteristics contribute positively or negatively to the support for structured communication. We focus the evaluation on three properties: reasonability, which is the main property we are interested in and determines how easily programmers can reason about a concurrent program’s execution; performance, which determines whether there are any distinct features which can prevent or facilitate efficient implementations; and composability, which determines whether a model offers constructs that can be used as building blocks for coarser-grained, or higher-level, concurrency abstractions.     

Nelder-Mead Simplex Optimization Routine for Large-Scale Problems: A Distributed Memory Implementation

The Nelder-Mead simplex method is an optimization routine that works well with irregular objective functions. For a function of $n$ parameters, it compares the objective function at the $n+1$ vertices of a simplex and updates the worst vertex through simplex search steps. However, a standard serial implementation can be prohibitively expensive for optimizations over a large number of parameters. We describe an implementation of the Nelder-Mead method in parallel using a distributed memory. For $p$ processors, each processor is assigned $(n+1)/p$ vertices at each iteration. Each processor then updates its worst local vertices, communicates the results, and a new simplex is formed with the vertices from all processors. We also describe how the algorithm can be implemented with only two MPI commands. In simulations, our implementation exhibits large speedups and is scalable to large problem sizes.     

The application of ionizing radiation in reversible addition-fragmentation chain transfer (RAFT) polymerization: Renaissance of a key synthetic and kinetic tool

Ionizing radiation, such as γ, ultraviolet, microwave and X-ray radiation, has long been used in polymer chemistry as a means of initiating polymerization, crosslinking gels and decomposing particular polymer components. More recently, ionizing radiation has found application in tandem with living radical polymerization to form novel polymeric materials with defined molecular weight and narrow molecular weight distribution. In particular, γ-rays and ultraviolet light both have shown promise as sources of initiation in reversible addition-fragmentation chain transfer (RAFT) polymerization. The ability to apply these sources of initiation at low temperatures is useful in applications where elevated temperature is likely to be detrimental to the system, for instance, in preparing protein-polymer conjugates. Similarly, the use of these initiating sources at low temperature is particularly suitable for some monomers, such as allyl compounds, which have not been synthesized using any other living radical approach. The current review examines the development of ionizing radiation as a tool in RAFT polymerization, with particular reference to the elucidation of the polymerization mechanism, the synthesis of high functionality polymers and probing the kinetic parameters of the RAFT process.     

Direct Ink-Jet Printing of Vertical Walls

Direct jet printing can assemble ceramic powder into a three dimensional shape by firing droplets of ink through a nozzle to build a multiple layered structure. As with stereolithography and selective laser sintering, the surface texture is expected to witness the layered assembly. The ability to create vertical walls by direct ink-jet printing was explored using a test piece based on a maze. The structure and topography are discussed in terms of droplet spreading and drying.     

Novel image fusion scheme based on dependency measure for robust multispectral palmprint recognition

Multispectral palmprint is considered as an effective biometric modality to accurately recognize a subject with high confidence. This paper presents a novel multispectral palmprint recognition system consisting of three functional blocks namely: (1) novel technique to extract Region of Interest (ROI) from the hand images acquired using a contact less sensor (2) novel image fusion scheme based on dependency measure (3) new scheme for feature extraction and classification. The proposed ROI extraction scheme is based on locating the valley regions between fingers irrespective of the hand pose. We then propose a novel image fusion scheme that combines information from different spectral bands using a Wavelet transform from various sub-bands. We then perform the statistical dependency analysis between these sub-bands to perform fusion either by selection or by weighted fusion. To effectively process the information from the fused image, we perform feature extraction using Log-Gabor transform whose feature dimension is reduced using Kernel Discriminant Analysis (KDA) before performing the classification by employing a Sparse Representation Classifier (SRC). Extensive experiments are carried out on a CASIA multispectral palmprint database that shows the strong superiority of our proposed fusion scheme when benchmarked with contemporary state-of-the-art image fusion schemes.     

End-to-end scene text recognition using tree-structured models

Detecting and recognizing text in natural images are quite challenging and have received much attention from the computer vision community in recent years. In this paper, we propose a robust end-to-end scene text recognition method, which utilizes tree-structured character models and normalized pictorial structured word models. For each category of characters, we build a part-based tree-structured model (TSM) so as to make use of the character-specific structure information as well as the local appearance information. The TSM could detect each part of the character and recognize the unique structure as well, seamlessly combining character detection and recognition together. As the TSMs could accurately detect characters from complex background, for text localization, we apply TSMs for all the characters on the coarse text detection regions to eliminate the false positives and search the possible missing characters as well. While for word recognition, we propose a normalized pictorial structure (PS) framework to deal with the bias caused by words of different lengths. Experimental results on a range of challenging public datasets (ICDAR 2003, ICDAR 2011, SVT) demonstrate that the proposed method outperforms state-of-the-art methods both for text localization and word recognition.