Fourier ptychographic microscopy with adaptive resolution strategy.

Fourier ptychographic microscopy (FPM) is a method capable of reconstructing a high-resolution, wide field-of-view (FOV) image, where dark-field images provide the high-frequency information required for the iterative process. Theoretically, using more dark-field images can lead to results with higher resolution. However, the resolution required to clearly detect samples with different microscales varies. For certain samples, the limit resolution of the imaging system may exceed the one required to resolve the details. This suggests that simply increasing the number of dark-field images will not improve the recognition capability for such samples and may instead significantly increase the computational cost. To address this issue, this Letter proposes an adaptive resolution strategy that automatically assigns the resolution required for the sample. Based on a Tenengrad approach, this strategy determines the number of images required for reconstruction by evaluating a series of differential images among the reconstructions for a certain subregion and then efficiently completes the full-FOV reconstruction according to the determined resolution. We conducted the full-FOV reconstruction utilizing feature-domain FPM for both the USAF resolution test chart and a human red blood cell sample. Employing the adaptive resolution strategy, the preservation of reconstruction resolution can be ensured while respectively economizing approximately 76% and 89% of the time.

Depth-of-field extended Fourier ptychographic microscopy without defocus distance priori.

Fourier ptychographic microscopy (FPM) provides a solution of high-throughput phase imaging. Thanks to its coherent imaging model, FPM has the capacity of depth-of-field (DOF) extension by simultaneously recovering the sample's transmittance function and pupil aberration, which contains a defocus term. However, existing phase retrieval algorithms (PRs) often struggle in the presence of a significant defocus. In this Letter, different PRs with embedded pupil recovery are compared, and the one based on the alternating direction multiplier method (ADMM-FPM) demonstrates promising potential for reconstructing highly defocused FPM images. Besides, we present a plug-and-play framework that integrates ADMM-FPM and total variation or Hessian denoiser for pupil function enhancement. Both simulations and experiments demonstrate that this framework enables robust reconstruction of defocused FPM images without any prior knowledge of defocus distance or sample characteristics. In experiments involving USAF 1951 targets and pathologic slides, ADMM-FPM combined with the Hessian denoiser successfully corrected the defocus up to approximately 200 µm, i.e., extending the DOF to 400 µm.

Computational modelling of articular joints with biphasic cartilage: recent advances, challenges and opportunities.

Biphasic models have been widely used to simulate the time-dependent biomechanical response of soft tissues. Modelling techniques of joints with biphasic weight-bearing soft tissues have been markedly improved over the last decade, enhancing our understanding of the function, degenerative mechanism and outcomes of interventions of joints. This paper reviews the recent advances, challenges and opportunities in computational models of joints with biphasic weight-bearing soft tissues. The review begins with an introduction of the function and degeneration of joints from a biomechanical aspect. Different constitutive models of articular cartilage, in particular biphasic materials, are illustrated in the context of the study of contact mechanics in joints. Approaches, advances and major findings of biphasic models of the hip and knee are presented, followed by a discussion of the challenges awaiting to be addressed, including the convergence issue, high computational cost and inadequate validation. Finally, opportunities and clinical insights in the areas of subject-specific modeling and tissue engineering are provided and discussed.

Genetic engineering of drought- and salt-tolerant tomato via Δ1-pyrroline-5-carboxylate reductase S-nitrosylation.

Drought and soil salinization substantially impact agriculture. While proline's role in enhancing stress tolerance is known, the exact molecular mechanism by which plants process stress signals and control proline synthesis under stress is still not fully understood. In tomato (Solanum lycopersicum L.), drought and salt stress stimulate nitric oxide (NO) production, which boosts proline synthesis by activating Δ1-pyrroline-5-carboxylate synthetase (SlP5CS) and Δ1-pyrroline-5-carboxylate reductase (SlP5CR) genes and the P5CR enzyme. The crucial factor is stress-triggered NO production, which regulates the S-nitrosylation of SlP5CR at Cys-5, thereby increasing its NAD(P)H affinity and enzymatic activity. S-nitrosylation of SlP5CR enables tomato plants to better adapt to changing NAD(P)H levels, boosting both SlP5CR activity and proline synthesis during stress. By comparing tomato lines genetically modified to express different forms of SlP5CR, including a variant mimicking S-nitrosylation (SlP5CRC5W), we found that SlP5CRC5W plants show superior growth and stress tolerance. This is attributed to better P5CR activity, proline production, water use efficiency, reactive oxygen species scavenging, and sodium excretion. Overall, this study demonstrates that tomato engineered to mimic S-nitrosylated SlP5CR exhibits enhanced growth and yield under drought and salt stress conditions, highlighting a promising approach for stress-tolerant tomato cultivation.

Exosomes derived from adipose tissues accelerate fibroblasts and keratinocytes proliferation and cutaneous wound healing via miR-92a/Hippo-YAP axis.

Delayed wound healing is an urgent clinical issue. Cellular communication involving exosome-borne cargo such as miRNA is a critical mechanism involved in wound healing. This study isolated and identified human adipose tissue-derived exosomes (Exo-ATs). The specific effects of Exo-ATs on keratinocytes and fibroblasts were examined. Enriched miRNAs in Exo-ATs were analyzed, and miR-92a-3p was selected. The transfer of Exo-ATs-derived miR-92a-3p to keratinocytes and fibroblasts was verified. miR-92a-3p binding to LATS2 was examined and the dynamic effects of the miR-92a-3p/LATS2 axis were investigated. In a dorsal skin wound model, the in vivo effects of Exo-ATs on wound healing were examined. Exo-AT incubation increased keratinocytes and fibroblast proliferation, migration, and extracellular matrix (ECM) accumulation. miR-92a-3p, enriched in Exo-ATs, could be transferred to keratinocytes and fibroblasts, resulting in enhanced proliferation, migration, and ECM accumulation. Large tumor suppressor kinase 2 (LATS2) was a direct target of miR-92a-3p. miR-92a-3p inhibitor effects on keratinocytes and fibroblasts could be partially reversed by LATS2 knockdown. In a dorsal skin wound model, Exo-ATs accelerated wound healing through enhanced cell proliferation, collagen deposition, re-epithelialization, and YAP/TAZ activation. In conclusion, Exo-ATs improve skin wound healing by promoting keratinocyte and fibroblast migration and proliferation and collagen production by fibroblast, which could be partially eliminated by miR-92a inhibition through its downstream target LATS2 and the YAP/TAZ signaling.

Cyclic Deformation Behavior of Additive-Manufactured IN738LC Superalloys from Virgin and Reused Powders.

In laser powder bed fusion (L-PBF), most powders are not melted in the chamber and collected after the printing process. Powder reuse is appreciable without sacrificing the mechanical properties of target components. To understand the influences of powder reuse on mechanical performance, a nickel-based superalloy, IN738LC, was investigated. Powder morphology, microstructure and chemical compositions of virgin and reused powders were characterized. An increase in oxygen content, generally metallic oxides, was located on the surface of powders. Monotonic tensile and cyclic fatigue were tested. Negligible deterioration in strength and tensile ductility were found, while scattered fatigue performance with regard to fatigue life was shown. Deformation and fatigue crack propagation mechanisms were discussed for describing the powder degradation effects.

Overexpression of VvASMT1 from grapevine enhanced salt and osmotic stress tolerance in Nicotiana benthamiana.

Salt and drought stresses are major environmental conditions that severely limit grape growth and productivity, while exogenous melatonin can alleviate the drought and salt damage to grapevines. N-acetylserotonin methyltransferase (ASMT) is the key enzyme in melatonin synthesis, which plays a critical role in regulating stress responses. However, the roles of ASMTs from grapevine under drought and salt stresses responses remain largely unclear. In this study, the VvASMT1 gene was isolated from grapevine, and its physiological functions in salt and mimic drought stress tolerance were investigated. Expression pattern analysis revealed that VvASMT1 was significantly induced by different salt and osmotic stresses. Ectopic expression of VvASMT1 in Nicotiana benthamiana significantly enhanced melatonin production in transgenic plants. Compared with wild-type plants, the transgenic lines exhibited a higher germination ratio, longer root length, lower degree of leaf wilting and relative water content (RWC) under salt and osmotic stresses. In addition, under salt and osmotic stresses, overexpression of VvASMT1 improved proline and malondialdehyde (MDA) contents, increased the activity of antioxidant enzymes and decreased the accumulation of reactive oxygen species (ROS). Taken together, our results demonstrate the explicit role of VvASMT1 in salt and osmotic stress responses, which provides a theoretical foundation for the genetic engineering of grapevine.

Genome-wide analysis of the strigolactone biosynthetic and signaling genes in grapevine and their response to salt and drought stresses.

Strigolactones (SLs) are a novel class of plant hormones that play critical roles in regulating various developmental processes and stress tolerance. Although the SL biosynthetic and signaling genes were already determined in some plants such as Arabidopsis and rice, the information of SL-related genes in grapevine (Vitis vinifera L.) remains largely unknown. In this study, the SL-related genes were identified from the whole grapevine genome, and their expression patterns under salt and drought stresses were determined. The results indicated that the five genes that involved in the SL biosynthesis included one each of the D27, CCD7, CCD8, MAX1 and LBO genes, as well as the three genes that involved in the SL signaling included one each of the D14, MAX2, D53 genes. Phylogenetic analysis suggested that these SL-related proteins are highly conserved among different plant species. Promoter analysis showed that the prevalence of a variety of cis-acting elements associated with hormones and abiotic stress existed in the promoter regions of these SL-related genes. Furthermore, the transcription expression analysis demonstrated that most SL-related genes are involved in the salt and drought stresses response in grapevine. These findings provided valuable information for further investigation and functional analysis of SL biosynthetic and signaling genes in response to salt and drought stresses in grapevine.

Sub-pixel position estimation algorithm based on Gaussian fitting and sampling theorem interpolation for wafer alignment.

Wafer alignment is the core technique of lithographic tools. Image-processing-based wafer alignment techniques are commonly used in lithographic tools. An alignment algorithm is used to analyze the alignment mark image for obtaining the mark position. The accuracy and speed of the alignment algorithm are very important for guaranteeing the overlay and throughput of lithographic tools. The most commonly used algorithm in image-processing-based alignment techniques is the self-correlation method. This method has a high accuracy, but the calculation is complex, and the calculation speed is slow. In this paper, we propose a sub-pixel position estimation algorithm based on Gaussian fitting and sampling theorem interpolation. The algorithm first reconstructs the alignment signal by sampling theorem interpolation and then obtains the sub-pixel position of the mark by Gaussian fitting. The accuracy and robustness of the algorithm are verified by testing the simulated marks and experimentally captured marks. The repeat accuracy can reach 1/100 pixels, which is in the same level with the self-correlation method. The calculation speed is highly improved compared with the self-correlation method, which needs only about 1/3 of even short calculation time.

Measurement algorithm for wafer alignment based on principal component analysis.

We propose a novel measurement algorithm for wafer alignment technology based on principal component analysis (PCA) of a mark image. The waveform of the mark is extracted from the enlarged mark image, which is collected by CCD. The position of the mark center on the CCD can be calculated based on the extracted waveform. By applying PCA to the mark image, the first principal component containing position information of the mark can be obtained. Therefore PCA can be used to extract the waveform from the mark image. Compared with the typical waveform extraction method (the summed projection (SP) method), the proposed PCA method can use the position information contained in the mark image more effectively. Through simulation and experiment, it is proved that the proposed PCA method can improve the contrast of the normalized waveform, and then improve the alignment accuracy.

A Novel γ'-Strengthened Nickel-Based Superalloy for Laser Powder Bed Fusion.

An experimental printable γ'-strengthened nickel-based superalloy, MAD542, is proposed. By process optimization, a crack-free component with less than 0.06% defect was achieved by laser powder bed fusion (LPBF). After post-processing by solution heat treatment, a recrystallized structure was revealed, which was also associated with the formation of annealing twins. After the aging treatment, 60-65% γ' precipitates were obtained with a cuboidal morphology. The success of printing and post-processing the new MAD542 superalloy may give new insights into alloy design approaches for additive manufacturing.

Dual-fiber point diffraction interferometer to measure the wavefront aberration of an imaging system.

A dual-fiber point diffraction interferometer is built to measure the wavefront aberration of an imaging system with high precision. The optical intensity of the test light and reference light are controlled independently, so that high interference contrast can be obtained. The interferometer has several advantages: high precision, a flexible structure, and a quasi-common optical path. System errors of the interferometer that influence the measurement accuracy are considered and calibrated. In the experiments, a projection lens with numerical aperture of 0.3 is measured. The measured wavefront error is 1.5 nm rms, and the measurement repeatability is 35 pm rms.

Briaviolides K-N, New Briarane-Type Diterpenoids from Cultured Octocoral Briareum violaceum.

Four new briarane diterpenoids, briaviolides K-N (1-4), have been obtained from the cultured-type octocoral Briareum violaceum. Using a spectroscopic approach, the structures of briaranes 1-4 were identified. This study employed an in vitro model of lipopolysaccharide (LPS)-induced inflammation in the murine macrophage RAW 264.7 cell line, and found that among the four briaranes, briarane 2 possessed anti-inflammatory activity against inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) protein expressions in cells. In addition, principal component analysis using the chemical global positioning system (ChemGPS) for natural products (ChemGPS-NP) was employed in order to analyze the structure-activity relationship (SAR), and the results indicated that the ring conformation of the compound has a leading role in suppressing the expressions of pro-inflammatory iNOS and COX-2 proteins in macrophages.

PseDNA-Pro: DNA-Binding Protein Identification by Combining Chou's PseAAC and Physicochemical Distance Transformation.

Identification of DNA-binding proteins is an important problem in biomedical research as DNA-binding proteins are crucial for various cellular processes. Currently, the machine learning methods achieve the-state-of-the-art performance with different features. A key step to improve the performance of these methods is to find a suitable representation of proteins. In this study, we proposed a feature vector composed of three kinds of sequence-based features, including overall amino acid composition, pseudo amino acid composition (PseAAC) proposed by Chou and physicochemical distance transformation. These features not only consider the sequence composition of proteins, but also incorporate the sequence-order information of amino acids in proteins. The feature vectors were fed into Support Vector Machine (SVM) for DNA-binding protein identification. The proposed method is called PseDNA-Pro. Experiments on stringent benchmark datasets and independent test datasets by using the Jackknife test showed that PseDNA-Pro can achieve an accuracy of higher than 80 %, outperforming several state-of-the-art methods, including DNAbinder, DNA-Prot, and iDNA-Prot. These results indicate that the combination of various features for DNA-binding protein prediction is a suitable approach, and the sequence-order information among residues in proteins is relative for discrimination. For practical applications, a web-server of PseDNA-Pro was established, which is available from http://bioinformatics.hitsz.edu.cn/PseDNA-Pro/.

iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition.

Playing crucial roles in various cellular processes, such as recognition of specific nucleotide sequences, regulation of transcription, and regulation of gene expression, DNA-binding proteins are essential ingredients for both eukaryotic and prokaryotic proteomes. With the avalanche of protein sequences generated in the postgenomic age, it is a critical challenge to develop automated methods for accurate and rapidly identifying DNA-binding proteins based on their sequence information alone. Here, a novel predictor, called "iDNA-Prot|dis", was established by incorporating the amino acid distance-pair coupling information and the amino acid reduced alphabet profile into the general pseudo amino acid composition (PseAAC) vector. The former can capture the characteristics of DNA-binding proteins so as to enhance its prediction quality, while the latter can reduce the dimension of PseAAC vector so as to speed up its prediction process. It was observed by the rigorous jackknife and independent dataset tests that the new predictor outperformed the existing predictors for the same purpose. As a user-friendly web-server, iDNA-Prot|dis is accessible to the public at http://bioinformatics.hitsz.edu.cn/iDNA-Prot_dis/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step protocol guide is provided on how to use the web-server to get their desired results without the need to follow the complicated mathematic equations that are presented in this paper just for the integrity of its developing process. It is anticipated that the iDNA-Prot|dis predictor may become a useful high throughput tool for large-scale analysis of DNA-binding proteins, or at the very least, play a complementary role to the existing predictors in this regard.

Using distances between Top-n-gram and residue pairs for protein remote homology detection.

BACKGROUND: Protein remote homology detection is one of the central problems in bioinformatics, which is important for both basic research and practical application. Currently, discriminative methods based on Support Vector Machines (SVMs) achieve the state-of-the-art performance. Exploring feature vectors incorporating the position information of amino acids or other protein building blocks is a key step to improve the performance of the SVM-based methods. RESULTS: Two new methods for protein remote homology detection were proposed, called SVM-DR and SVM-DT. SVM-DR is a sequence-based method, in which the feature vector representation for protein is based on the distances between residue pairs. SVM-DT is a profile-based method, which considers the distances between Top-n-gram pairs. Top-n-gram can be viewed as a profile-based building block of proteins, which is calculated from the frequency profiles. These two methods are position dependent approaches incorporating the sequence-order information of protein sequences. Various experiments were conducted on a benchmark dataset containing 54 families and 23 superfamilies. Experimental results showed that these two new methods are very promising. Compared with the position independent methods, the performance improvement is obvious. Furthermore, the proposed methods can also provide useful insights for studying the features of protein families. CONCLUSION: The better performance of the proposed methods demonstrates that the position dependant approaches are efficient for protein remote homology detection. Another advantage of our methods arises from the explicit feature space representation, which can be used to analyze the characteristic features of protein families. The source code of SVM-DT and SVM-DR is available at http://bioinformatics.hitsz.edu.cn/DistanceSVM/index.jsp.

Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection.

MOTIVATION: Owing to its importance in both basic research (such as molecular evolution and protein attribute prediction) and practical application (such as timely modeling the 3D structures of proteins targeted for drug development), protein remote homology detection has attracted a great deal of interest. It is intriguing to note that the profile-based approach is promising and holds high potential in this regard. To further improve protein remote homology detection, a key step is how to find an optimal means to extract the evolutionary information into the profiles. RESULTS: Here, we propose a novel approach, the so-called profile-based protein representation, to extract the evolutionary information via the frequency profiles. The latter can be calculated from the multiple sequence alignments generated by PSI-BLAST. Three top performing sequence-based kernels (SVM-Ngram, SVM-pairwise and SVM-LA) were combined with the profile-based protein representation. Various tests were conducted on a SCOP benchmark dataset that contains 54 families and 23 superfamilies. The results showed that the new approach is promising, and can obviously improve the performance of the three kernels. Furthermore, our approach can also provide useful insights for studying the features of proteins in various families. It has not escaped our notice that the current approach can be easily combined with the existing sequence-based methods so as to improve their performance as well. AVAILABILITY AND IMPLEMENTATION: For users' convenience, the source code of generating the profile-based proteins and the multiple kernel learning was also provided at http://bioinformatics.hitsz.edu.cn/main/~binliu/remote/