Anodic Dissolution Characteristics of GH4169 Alloy in NaNO3 Solutions by Roll-Print Mask Electrochemical Machining Using the Linear Cathode.

GH4169 alloy/Inconel 718 is extensively utilized in aerospace manufacturing due to its excellent high temperature mechanical properties. Micro-structuring on the workpiece surface can enhance its properties further. Through-mask electrochemical micromachining (TMEMM) is a promising and potential processing method for nickel-based superalloys. It can effectively solve the problem that traditional processing methods are difficult to achieve large-scale, high-precision and efficiency processing of surface micro-structure. This study explores the feasibility of electrochemical machining (ECM) for GH4169 using roll-print mask electrochemical machining with a linear cathode. Electrochemical dissolution characteristics of GH4169 alloy were analyzed in various electrolyte solutions and concentrations. Key parameters including cathode sizes, applied voltage and corrosion time were studied in the roll-print mask electrochemical machining. A qualitative model for micro-pit formation on GH4169 was established. Optimal parameters were determined through experiments: 300 µm mask hole and cathode size, 10 wt% NaNO3 electrolyte, 12 V voltage, 6 s corrosion time. The results demonstrate that the micro-pits with a diameter of 402.3 µm, depth of 92.8 µm and etch factor (EF) of 1.81 show an excellent profile and localization.

An Investigation of the Efficient-Precise Continuous Electrochemical Grinding Process of Ti-6Al-4V.

Titanium alloys have many excellent characteristics, and they are widely used in aerospace, biomedicine, and precision engineering. Meanwhile, titanium alloys are difficult to machine and passivate readily. Electrochemical grinding (ECG) is an ideal technology for the efficient-precise machining of titanium alloys. In the ECG process of titanium alloys, the common approach of applying high voltage and active electrolytes to achieve high efficiency of material removal will lead to serious stray corrosion, and the time utilized for the subsequent finishing will be extended greatly. Therefore, the application of ECG in the field of high efficiency and precision machining of titanium alloys is limited. In order to address the aforementioned issues, the present study proposed an efficient-precise continuous ECG (E-P-C-ECG) process for Ti-6Al-4V applying high-pulsed voltage with an optimized duty cycle and low DC voltage in the efficient ECG stage and precise ECG stage, respectively, without changing the grinding wheel. According to the result of the passivation properties tests, the ideal electrolyte was selected. Optimization of the process parameters was implemented experimentally to improve the processing efficiency and precision of ECG of Ti-6Al-4V. Utilizing the process advantages of the proposed process, a thin-walled structure of Ti-6Al-4V was obtained with high efficiency and precision. Compared to the conventional mechanical grinding process, the compressive residual stress of the machined surface and the processing time were reduced by 90.5% and 63.3% respectively, and both the surface roughness and tool wear were obviously improved.

Fabricating Precise and Smooth Microgroove Structures on Zr-Based Metallic Glass Using Jet-ECM.

Zr-based metallic glasses (MGs) are promising materials for mold manufacturing due to their unique mechanical and chemical properties. However, the high hardness of metallic glasses and their tendency to crystallize at high temperatures make it challenging to fabricate precise and smooth microscale structures on metallic glasses. This limitation hampers the development of metallic glasses as molds. Jet electrochemical machining (jet-ECM) is a non-contact subtractive manufacturing technology that utilizes a high-speed electrolyte to partially remove material from workpieces, making it highly suitable for processing difficult-to-machine materials. Nevertheless, few studies have explored microgroove structures on Zr-based MGs using sodium nitrate electrolytes by jet-ECM. Therefore, this paper advocates the utilization of the jet-ECM technique to fabricate precise and smooth microgroove structures using a sodium nitrate electrolyte. The electrochemical characteristics were studied in sodium nitrate solution. Then, the effects of the applied voltages and nozzle travel rates on machining performance were investigated. Finally, micro-helical and micro-S structures with high geometric dimensional consistency and low surface roughness were successfully fabricated, with widths and depths measuring 433.7 ± 2.4 µm and 101.4 ± 1.6 µm, respectively. Their surface roughness was determined to be 0.118 ± 0.002 µm. Compared to non-aqueous-based methods for jet-ECM of Zr-based MGs, the depth of the microgrooves was increased from 20 µm to 101 µm. Furthermore, the processed microstructures had no uneven edges in the peripheral areas and no visible flow marks on the bottom.

Microfabricating Mirror-like Surface Precision Micro-Sized Amorphous Alloy Structures Using Jet-ECM Process.

Amorphous alloy (AA) is a high-performance metal material generally with significantly excellent mechanical and corrosion resistance properties and thus is considered as a desirable material selection for micro-scale articles. However, the microfabrication of AA still faces a variety of technical challenges mainly because the materials are too hard to process and easily lose their original properties, although at moderately high temperatures. In this study, jet-electrolyte electrochemical machining (Jet-ECM) was proposed to microfabricate the Zr-based AA because it is a low-temperature material-removal process based on the anode dissolution mechanism. The electrochemical dissolution characteristics and material removal mechanism of AA were investigated, and then the optimal process parameters were achieved based on the evaluation of the surface morphologies, surface roughness, geometrical profile, and machining accuracy of the machined micro-dimples. Finally, the feasibility was further studied by using Jet-ECM to fabricate arrayed micro-dimples using the optimized parameters. It was found that Jet-ECM can successfully microfabricate mirror-like surface AA arrayed precision micro-dimples with significantly high dimensional accuracy and geometrical consistency. Jet-ECM is a promisingly advantageous microfabrication process for the hard-to-machine AA.

Jet Electrochemical Micromilling of Ti-6Al-4V Using NaCl-Ethylene Glycol Electrolyte.

Titanium alloys are widely used in aerospace and biomedicine because of their excellent mechanical characteristics, but these properties also make such alloys difficult to cut. Jet electrochemical micromilling (JEMM) is based on the principle of electrochemical anodic dissolution; it has some inherent advantages for the machining of titanium alloy microstructures. However, titanium oxidizes readily, forming an oxide film that impedes a uniform dissolution during electrochemical machining. Therefore, a high voltage and an aqueous NaCl electrolyte are usually used to break the oxide film, which can lead to severe stray corrosion. To overcome this problem, the present study investigated the JEMM of Ti-6Al-4V using a NaCl-ethylene glycol (NaCl-EG) electrolyte. Electrochemical testing showed that Ti-6Al-4V exhibits a better corrosion resistance in the NaCl-EG electrolyte compared to the aqueous NaCl electrolyte, thereby reducing stray corrosion. The localization and surface quality of the grooves were enhanced significantly when using JEMM with a NaCl-EG electrolyte. A multiple-pass strategy was adopted during JEMM to improve the aspect ratio, and the effects of the feed depth and number of passes on the multiple-pass machining performance were investigated. Ultimately, a square annular microstructure with a high geometric dimensional consistency and a smooth surface was obtained via JEMM with multiple passes using the optimal parameters.

Research Progress of Horizontal Cavity Surface-Emitting Laser.

The horizontal cavity surface emitting laser (HCSEL) boasts excellent properties, including high power, high beam quality, and ease of packaging and integration. It fundamentally resolves the problem of the large divergence angle in traditional edge-emitting semiconductor lasers, making it a feasible scheme for realizing high-power, small-divergence-angle, and high-beam-quality semiconductor lasers. Here, we introduce the technical scheme and review the development status of HCSELs. Firstly, we thoroughly analyze the structure, working principles, and performance characteristics of HCSELs according to different structures, such as the structural characteristics and key technologies. Additionally, we describe their optical properties. Finally, we analyze and discuss potential development prospects and challenges for HCSELs.

FXR1 regulates transcription and is required for growth of human cancer cells with TP53/FXR2 homozygous deletion.

Tumor suppressor p53 prevents cell transformation by inducing apoptosis and other responses. Homozygous TP53 deletion occurs in various types of human cancers for which no therapeutic strategies have yet been reported. TCGA database analysis shows that the TP53 homozygous deletion locus mostly exhibits co-deletion of the neighboring gene FXR2, which belongs to the Fragile X gene family. Here, we demonstrate that inhibition of the remaining family member FXR1 selectively blocks cell proliferation in human cancer cells containing homozygous deletion of both TP53 and FXR2 in a collateral lethality manner. Mechanistically, in addition to its RNA-binding function, FXR1 recruits transcription factor STAT1 or STAT3 to gene promoters at the chromatin interface and regulates transcription thus, at least partially, mediating cell proliferation. Our study anticipates that inhibition of FXR1 is a potential therapeutic approach to targeting human cancers harboring TP53 homozygous deletion.

Investigation of material removal in inner-jet electrochemical grinding of GH4169 alloy.

Electrochemical grinding (ECG) is a low-cost and highly efficient process for application to difficult-to-machine materials. In this process, the electrolyte supply mode directly affects machining stability and efficiency. This paper proposes a flow channel structure for an abrasive tool to be used for inner-jet ECG of GH4169 alloy. The tool is based on a dead-end tube with electrolyte outlet holes located in the sidewall. The diameter and number of outlet holes are determined through numerical simulation with the aim of achieving uniform electrolyte flow in the inter-electrode gap. Experiments show that the maximum feed rate and material removal rate are both improved by increasing the diamond grain size, applied voltage, electrolyte temperature and pressure. For a machining depth of 3 mm in a single pass, a feed rate of 2.4 mm min-1 is achieved experimentally. At this feed rate and machining depth, a sample is produced along a feed path under computer numerical control, with the feed direction changing four times. Inner-jet ECG with the proposed abrasive tool shows good efficiency and flexibility for processing hard-to-cut metals with a large removal depth.

Prediction of protein amidation sites by feature selection and analysis.

Carboxy-terminal α-amidation is a widespread post-translational modification of proteins found widely in vertebrates and invertebrates. The α-amide group is required for full biological activity, since it may render a peptide more hydrophobic and thus better be able to bind to other proteins, preventing ionization of the C-terminus. However, in particular, the C-terminal amidation is very difficult to detect because experimental methods are often labor-intensive, time-consuming and expensive. Therefore, in silico methods may complement due to their high efficiency. In this study, a computational method was developed to predict protein amidation sites, by incorporating the maximum relevance minimum redundancy method and the incremental feature selection method based on the nearest neighbor algorithm. From a total of 735 features, 41 optimal features were selected and were utilized to construct the final predictor. As a result, the predictor achieved an overall Matthews correlation coefficient of 0.8308. Feature analysis showed that PSSM conservation scores and amino acid factors played the most important roles in the α-amidation site prediction. Site-specific feature analyses showed that features derived from the amidation site itself and adjacent sites were most significant. This method presented could be used as an efficient tool to theoretically predict amidated peptides. And the selected features from our study could shed some light on the in-depth understanding of the mechanisms of the amidation modification, providing guidelines for experimental validation.

Inter- and intra-chain disulfide bond prediction based on optimal feature selection.

Protein disulfide bond is formed during post-translational modifications, and has been implicated in various physiological and pathological processes. Proper localization of disulfide bonds also facilitates the prediction of protein three-dimensional (3D) structure. However, it is both time-consuming and labor-intensive using conventional experimental approaches to determine disulfide bonds, especially for large-scale data sets. Since there are also some limitations for disulfide bond prediction based on 3D structure features, developing sequence-based, convenient and fast-speed computational methods for both inter- and intra-chain disulfide bond prediction is necessary. In this study, we developed a computational method for both types of disulfide bond prediction based on maximum relevance and minimum redundancy (mRMR) method followed by incremental feature selection (IFS), with nearest neighbor algorithm as its prediction model. Features of sequence conservation, residual disorder, and amino acid factor are used for inter-chain disulfide bond prediction. And in addition to these features, sequential distance between a pair of cysteines is also used for intra-chain disulfide bond prediction. Our approach achieves a prediction accuracy of 0.8702 for inter-chain disulfide bond prediction using 128 features and 0.9219 for intra-chain disulfide bond prediction using 261 features. Analysis of optimal feature set indicated key features and key sites for the disulfide bond formation. Interestingly, comparison of top features between interand intra-chain disulfide bonds revealed the similarities and differences of the mechanisms of forming these two types of disulfide bonds, which might help understand more of the mechanisms and provide clues to further experimental studies in this research field.

Genome sequences of wild and domestic bactrian camels.

Bactrian camels serve as an important means of transportation in the cold desert regions of China and Mongolia. Here we present a 2.01 Gb draft genome sequence from both a wild and a domestic bactrian camel. We estimate the camel genome to be 2.38 Gb, containing 20,821 protein-coding genes. Our phylogenomics analysis reveals that camels shared common ancestors with other even-toed ungulates about 55-60 million years ago. Rapidly evolving genes in the camel lineage are significantly enriched in metabolic pathways, and these changes may underlie the insulin resistance typically observed in these animals. We estimate the genome-wide heterozygosity rates in both wild and domestic camels to be 1.0 × 10(-3). However, genomic regions with significantly lower heterozygosity are found in the domestic camel, and olfactory receptors are enriched in these regions. Our comparative genomics analyses may also shed light on the genetic basis of the camel's remarkable salt tolerance and unusual immune system.

Prediction of functional phosphorylation sites by incorporating evolutionary information.

Protein phosphorylation is a ubiquitous protein post-translational modification, which plays an important role in cellular signaling systems underlying various physiological and pathological processes. Current in silico methods mainly focused on the prediction of phosphorylation sites, but rare methods considered whether a phosphorylation site is functional or not. Since functional phosphorylation sites are more valuable for further experimental research and a proportion of phosphorylation sites have no direct functional effects, the prediction of functional phosphorylation sites is quite necessary for this research area. Previous studies have shown that functional phosphorylation sites are more conserved than non-functional phosphorylation sites in evolution. Thus, in our method, we developed a web server by integrating existing phosphorylation site prediction methods, as well as both absolute and relative evolutionary conservation scores to predict the most likely functional phosphorylation sites. Using our method, we predicted the most likely functional sites of the human, rat and mouse proteomes and built a database for the predicted sites. By the analysis of overall prediction results, we demonstrated that protein phosphorylation plays an important role in all the enriched KEGG pathways. By the analysis of protein-specific prediction results, we demonstrated the usefulness of our method for individual protein studies. Our method would help to characterize the most likely functional phosphorylation sites for further studies in this research area.

Predicting protein oxidation sites with feature selection and analysis approach.

Protein oxidation is a ubiquitous post-translational modification that plays important roles in various physiological and pathological processes. Owing to the fact that protein oxidation can also take place as an experimental artifact or caused by oxygen in the air during the process of sample collection and analysis, and that it is both time-consuming and expensive to determine the protein oxidation sites purely by biochemical experiments, it would be of great benefit to develop in silico methods for rapidly and effectively identifying protein oxidation sites. In this study, we developed a computational method to address this problem. Our method was based on the nearest neighbor algorithm in which, however, the maximum relevance minimum redundancy and incremental feature selection approaches were incorporated. From the initial 735 features, 16 features were selected as the optimal feature set. Of such 16 optimized features, 10 features were associated with the position-specific scoring matrix conservation scores, three with the amino acid factors, one with the propensity of conservation of residues on protein surface, one with the side chain count of carbon atom deviation from mean, and one with the solvent accessibility. It was observed that our prediction model achieved an overall success rate of 75.82%, indicating that it is quite encouraging and promising for practical applications. Also, the 16 optimal features obtained through this study may provide useful clues and insights for in-depth understanding the action mechanism of protein oxidation.

Predict and analyze S-nitrosylation modification sites with the mRMR and IFS approaches.

S-nitrosylation (SNO) is one of the most important and universal post-translational modifications (PTMs) which regulates various cellular functions and signaling events. Identification of the exact S-nitrosylation sites in proteins may facilitate the understanding of the molecular mechanisms and biological function of S-nitrosylation. Unfortunately, traditional experimental approaches used for detecting S-nitrosylation sites are often laborious and time-consuming. However, computational methods could overcome this demerit. In this work, we developed a novel predictor based on nearest neighbor algorithm (NNA) with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). The features of physicochemical/biochemical properties, sequence conservation, residual disorder, amino acid occurrence frequency, second structure and the solvent accessibility were utilized to represent the peptides concerned. Feature analysis showed that the features except residual disorder affected identification of the S-nitrosylation sites. It was also shown via the site-specific feature analysis that the features of sites away from the central cysteine might contribute to the S-nitrosylation site determination through a subtle manner. It is anticipated that our prediction method may become a useful tool for identifying the protein S-nitrosylation sites and that the features analysis described in this paper may provide useful insights for in-depth investigation into the mechanism of S-nitrosylation.

Prediction of protein modification sites of pyrrolidone carboxylic acid using mRMR feature selection and analysis.

Pyrrolidone carboxylic acid (PCA) is formed during a common post-translational modification (PTM) of extracellular and multi-pass membrane proteins. In this study, we developed a new predictor to predict the modification sites of PCA based on maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS). We incorporated 727 features that belonged to 7 kinds of protein properties to predict the modification sites, including sequence conservation, residual disorder, amino acid factor, secondary structure and solvent accessibility, gain/loss of amino acid during evolution, propensity of amino acid to be conserved at protein-protein interface and protein surface, and deviation of side chain carbon atom number. Among these 727 features, 244 features were selected by mRMR and IFS as the optimized features for the prediction, with which the prediction model achieved a maximum of MCC of 0.7812. Feature analysis showed that all feature types contributed to the modification process. Further site-specific feature analysis showed that the features derived from PCA's surrounding sites contributed more to the determination of PCA sites than other sites. The detailed feature analysis in this paper might provide important clues for understanding the mechanism of the PCA formation and guide relevant experimental validations.

Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.

As an important tumor suppressor protein, reactivate mutated p53 was found in many kinds of human cancers and that restoring active p53 would lead to tumor regression. In this work, we developed a new computational method to predict the transcriptional activity for one-, two-, three- and four-site p53 mutants, respectively. With the approach from the general form of pseudo amino acid composition, we used eight types of features to represent the mutation and then selected the optimal prediction features based on the maximum relevance, minimum redundancy, and incremental feature selection methods. The Mathew's correlation coefficients (MCC) obtained by using nearest neighbor algorithm and jackknife cross validation for one-, two-, three- and four-site p53 mutants were 0.678, 0.314, 0.705, and 0.907, respectively. It was revealed by the further optimal feature set analysis that the 2D (two-dimensional) structure features composed the largest part of the optimal feature set and maybe played the most important roles in all four types of p53 mutant active status prediction. It was also demonstrated by the optimal feature sets, especially those at the top level, that the 3D structure features, conservation, physicochemical and biochemical properties of amino acid near the mutation site, also played quite important roles for p53 mutant active status prediction. Our study has provided a new and promising approach for finding functionally important sites and the relevant features for in-depth study of p53 protein and its action mechanism.

Prediction and analysis of protein methylarginine and methyllysine based on Multisequence features.

Protein methylation, one of the most important post-translational modifications, typically takes place on arginine or lysine residue. The reversible modification involves a series of basic cellular processes. Identification of methyl proteins with their sites will facilitate the understanding of the molecular mechanism of methylation. Besides the experimental methods, computational predictions of methylated sites are much more desirable for their convenience and fast speed. Here, we propose a method dedicated to predicting methylated sites of proteins. Feature selection was made on sequence conservation, physicochemical/biochemical properties, and structural disorder by applying maximum relevance minimum redundancy and incremental feature selection methods. The prediction models were built according to nearest the neighbor algorithm and evaluated by the jackknife cross-validation. We built 11 and 9 predictors for methylarginine and methyllysine, respectively, and integrated them to predict methylated sites. As a result, the average prediction accuracies are 74.25%, 77.02% for methylarginine and methyllysine training sets, respectively. Feature analysis suggested evolutionary information, and physicochemical/biochemical properties play important roles in the recognition of methylated sites. These findings may provide valuable information for exploiting the mechanisms of methylation. Our method may serve as a useful tool for biologists to find the potential methylated sites of proteins.

Prediction and analysis of protein palmitoylation sites.

Palmitoylation is a universal and important lipid modification, involving a series of basic cellular processes, such as membrane trafficking, protein stability and protein aggregation. With the avalanche of new protein sequences generated in the post genomic era, it is highly desirable to develop computational methods for rapidly and effectively identifying the potential palmitoylation sites of uncharacterized proteins so as to timely provide useful information for revealing the mechanism of protein palmitoylation. By using the Incremental Feature Selection approach based on amino acid factors, conservation, disorder feature, and specific features of palmitoylation site, a new predictor named IFS-Palm was developed in this regard. The overall success rate thus achieved by jackknife test on a newly constructed benchmark dataset was 90.65%. It was shown via an in-depth analysis that palmitoylation was intimately correlated with the feature of the upstream residue directly adjacent to cysteine site as well as the conservation of amino acid cysteine. Meanwhile, the protein disorder region might also play an import role in the post-translational modification. These findings may provide useful insights for revealing the mechanisms of palmitoylation.

Analyses of copy number variation of GK rat reveal new putative type 2 diabetes susceptibility loci.

Large efforts have been taken to search for genes responsible for type 2 diabetes (T2D), but have resulted in only about 20 in humans due to its complexity and heterogeneity. The GK rat, a spontanous T2D model, offers us a superior opportunity to search for more diabetic genes. Utilizing array comparative genome hybridization (aCGH) technology, we identifed 137 non-redundant copy number variation (CNV) regions from the GK rats when using normal Wistar rats as control. These CNV regions (CNVRs) covered approximately 36 Mb nucleotides, accounting for about 1% of the whole genome. By integrating information from gene annotations and disease knowledge, we investigated the CNVRs comprehensively for mining new T2D genes. As a result, we prioritized 16 putative protein-coding genes and two microRNA genes (rno-mir-30b and rno-mir-30d) as good candidates. The catalogue of CNVRs between GK and Wistar rats identified in this work served as a repository for mining genes that might play roles in the pathogenesis of T2D. Moreover, our efforts in utilizing bioinformatics methods to prioritize good candidate genes provided a more specific set of putative candidates. These findings would contribute to the research into the genetic basis of T2D, and thus shed light on its pathogenesis.

Prediction of tyrosine sulfation with mRMR feature selection and analysis.

Protein tyrosine sulfation is a ubiquitous post-translational modification (PTM) of secreted and transmembrane proteins that pass through the Golgi apparatus. In this study, we developed a new method for protein tyrosine sulfation prediction based on a nearest neighbor algorithm with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). We incorporated features of sequence conservation, residual disorder, and amino acid factor, 229 features in total, to predict tyrosine sulfation sites. From these 229 features, 145 features were selected and deemed as the optimized features for the prediction. The prediction model achieved a prediction accuracy of 90.01% using the optimal 145-feature set. Feature analysis showed that conservation, disorder, and physicochemical/biochemical properties of amino acids all contributed to the sulfation process. Site-specific feature analysis showed that the features derived from its surrounding sites contributed profoundly to sulfation site determination in addition to features derived from the sulfation site itself. The detailed feature analysis in this paper might help understand more of the sulfation mechanism and guide the related experimental validation.