Peptide de Novo Sequencing Using 157 nm Photodissociation in a Tandem Time-of-Flight Mass Spectrometer

It has previously been shown that photodissociation of tryptic peptide ions with 157 nm light in a matrix-assisted laser desorption/ionization (MALDI) tandem time-of-flight (TOF) mass spectrometer generates an abundance of x-type ions. A peptide de novo sequencing algorithm has now been developed to interpret these data. By combination of photodissociation and postsource decay (PSD) spectra, the algorithm identifies x-type ions and derives peptide sequences. The confidence of amino acid assignments is evaluated by observing complementary y-, v-, and w-type ions that provide additional constraints to sequence identification. In the analysis of 31 tryptic peptides from 4 model proteins, the algorithm identified 322 (or 90.7%) of the 355 amino acids and made only 3 incorrect assignments. The other 30 amino acids were not identified because specific needed x-type ions were not detected. Based on the observation of v-and w-type ions, 45 of 50 detected leucine and isoleucine residues were successfully distinguished and there was only one mistake. The remaining four residues were not distinguished because the corresponding v-and w-type ions were not detected. These de novo sequencing results translated into successful identification of proteins through homology searches. To evaluate the robustness of the present sequencing approach, a collection of 266 tryptic peptides from 23 model proteins were analyzed and then sequenced. A total of 167 peptides yielded sequence tags of 5 or more residues. In 5 peptides, 1 or 2 residues were incorrectly assigned. Mass spectrometry (MS) is widely used to investigate biological systems following recent advances in both instrumentation and bioinformatics. 1-5 A number of MS-based techniques have been developed to characterize protein constituents in biological samples. 6-8 The two most common protein-identification methods involve tandem mass spectrometry (MS/MS) 9,10 and MALDI peptide mass mapping. [11][12][13] In either case, proteolytic peptides are analyzed by mass spectrometry and proteins are assigned by comparing mass spectrometric data with predicted peptide and fragment masses derived from a protein sequence database. Even though these methods have been successfully applied in numerous experiments, they have several fundamental limitations. Experimental data do not lead to correct protein identifications when there are database errors, genetic mutations, and modifications that occur post-translationally or during sample handling. In addition, some peptide fragmentation spectra contain limited sequence information. As a result, only about 10-20% of spectra typically lead to peptide identifications, although some high-quality experiments do yield as high as 50% identifications. 14,15 Furthermore, organisms without sequenced genomes cannot be studied by database-matching techniques. Finally, protein databases continue to grow in size, so the time it takes to search against them increases exponentially. In light of these limitations, methods that can identify peptides without protein databases are desirable. De novo sequencing methods have been developed to derive peptide sequences from tandem mass spectra without reference to a database. 16 Typically de novo sequencing algorithms identify amino acids using mass differentials between consecutive peaks in tandem mass spectra. Several of these algorithms have been developed to interpret low-energy collisionally induced dissociation (CID) spectra including Sherenga, Lutefisk, PEAKS, DACSIM, EigenMS, PepNovo, NovoHMM, and MSNovo. 1,[17][18][19][20][21][22][23] Most pro-* To whom the correspondence should be addressed. E-mail: [email protected].

An Image Enhancement Method for Improving Small Intestinal Villi Clarity

This paper presents, for the first time, an image enhancement methodology designed to enhance the clarity of small intestinal villi in Wireless Capsule Endoscopy (WCE) images. This method first separates the low-frequency and high-frequency components of small intestinal villi images using guided filtering. Subsequently, an adaptive light gain factor is generated based on the low-frequency component, and an adaptive gradient gain factor is derived from the convolution results of the Laplacian operator in different regions of small intestinal villi images. The obtained light gain factor and gradient gain factor are then combined to enhance the high-frequency components. Finally, the enhanced high-frequency component is fused with the original image to achieve adaptive sharpening of the edges of WCE small intestinal villi images. The experiments affirm that, compared to established WCE image enhancement methods, our approach not only accentuates the edge details of WCE small intestine villi images but also skillfully suppresses noise amplification, thereby preventing the occurrence of edge overshooting

A Robust Error-Resistant View Selection Method for 3D Reconstruction

To address the issue of increased triangulation uncertainty caused by selecting views with small camera baselines in Structure from Motion (SFM) view selection, this paper proposes a robust error-resistant view selection method. The method utilizes a triangulation-based computation to obtain an error-resistant model, which is then used to construct an error-resistant matrix. The sorting results of each row in the error-resistant matrix determine the candidate view set for each view. By traversing the candidate view sets of all views and completing the missing views based on the error-resistant matrix, the integrity of 3D reconstruction is ensured. Experimental comparisons between this method and the exhaustive method with the highest accuracy in the COLMAP program are conducted in terms of average reprojection error and absolute trajectory error in the reconstruction results. The proposed method demonstrates an average reduction of 29.40% in reprojection error accuracy and 5.07% in absolute trajectory error on the TUM dataset and DTU dataset

Accurate Sybil attack detection based on fine-grained physical channel information

With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless network

A Highlight Removal Method for Capsule Endoscopy Images

The images captured by Wireless Capsule Endoscopy (WCE) always exhibit specular reflections, and removing highlights while preserving the color and texture in the region remains a challenge. To address this issue, this paper proposes a highlight removal method for capsule endoscopy images. Firstly, the confidence and feature terms of the highlight region's edges are computed, where confidence is obtained by the ratio of known pixels in the RGB space's R channel to the B channel within a window centered on the highlight region's edge pixel, and feature terms are acquired by multiplying the gradient vector of the highlight region's edge pixel with the iso-intensity line. Subsequently, the confidence and feature terms are assigned different weights and summed to obtain the priority of all highlight region's edge pixels, and the pixel with the highest priority is identified. Then, the variance of the highlight region's edge pixels is used to adjust the size of the sample block window, and the best-matching block is searched in the known region based on the RGB color similarity and distance between the sample block and the window centered on the pixel with the highest priority. Finally, the pixels in the best-matching block are copied to the highest priority highlight removal region to achieve the goal of removing the highlight region. Experimental results demonstrate that the proposed method effectively removes highlights from WCE images, with a lower coefficient of variation in the highlight removal region compared to the Crinimisi algorithm and DeepGin method. Additionally, the color and texture in the highlight removal region are similar to those in the surrounding areas, and the texture is continuous

Region Feature Descriptor Adapted to High Affine Transformations

To address the issue of feature descriptors being ineffective in representing grayscale feature information when images undergo high affine transformations, leading to a rapid decline in feature matching accuracy, this paper proposes a region feature descriptor based on simulating affine transformations using classification. The proposed method initially categorizes images with different affine degrees to simulate affine transformations and generate a new set of images. Subsequently, it calculates neighborhood information for feature points on this new image set. Finally, the descriptor is generated by combining the grayscale histogram of the maximum stable extremal region to which the feature point belongs and the normalized position relative to the grayscale centroid of the feature point's region. Experimental results, comparing feature matching metrics under affine transformation scenarios, demonstrate that the proposed descriptor exhibits higher precision and robustness compared to existing classical descriptors. Additionally, it shows robustness when integrated with other descriptors

Slower-decaying tropical cyclones produce heavier precipitation over China

The post-landfall decay of tropical cyclones (TC) is often closely linked to the magnitude of damage to the environment, properties, and the loss of human lives. Despite growing interest in how climate change affects TC decay, data uncertainties still prevent a consensus on changes in TC decay rates and related precipitation. Here, after strict data-quality control, we show that the rate of decay of TCs after making landfall in China has significantly slowed down by 45% from 1967 to 2018. We find that, except the warmer sea surface temperature, the eastward shift of TC landfall locations also contributes to the slowdown of TC decay over China. That is TCs making landfall in eastern mainland China (EC) decay slower than that in southern mainland China (SC), and the eastward shift of TCs landfall locations causes more TCs landfalling in EC with slower decay rate. TCs making landfall in EC last longer at sea, carry more moisture upon landfall, and have more favorable dynamic and thermodynamic conditions sustaining them after landfall. Observational evidence shows that the decay of TC-induced precipitation amount and intensity within 48 h of landfall is positively related to the decay rate of landfalling TCs. The significant increase in TC-induced precipitation over the long term, due to the slower decay of landfalling TCs, increases flood risks in China’s coastal areas. Our results highlight evidence of a slowdown in TC decay rates at the regional scale. These findings provide scientific support for the need for better flood management and adaptation strategies in coastal areas under the threat of greater TC-induced precipitation

Attribution of the record-breaking extreme precipitation events in July 2021 over central and eastern China to anthropogenic climate change

In July 2021, Typhoon In-Fa produced record-breaking extreme precipitation events (hereafter referred to as the 2021 EPEs) in central and eastern China, and caused serious socioeconomic losses and casualties. However, it is still unknown whether the 2021 EPEs can be attributed to anthropogenic climate change (ACC) and how the occurrence probabilities of precipitation events of a similar magnitude might evolve in the future. The 2021 EPEs in central (eastern) China occurred in the context of no linear trend (a significantly increasing trend at a rate of 4.44%/decade) in the region-averaged Rx5day (summer maximum 5-day accumulated precipitation) percentage precipitation anomaly (PPA), indicating that global warming might have no impact on the 2021 EPE in central China but might have impacted the 2021 EPE in eastern China by increasing the long-term trend of EPEs. Using the scaled generalized extreme value distribution, we detected a slightly negative (significantly positive) association of the Rx5day PPA time series in central (eastern) China with the global mean temperature anomaly, suggesting that global warming might have no (a detectable) contribution to the changes in occurrence probability of precipitation extremes like the 2021 EPEs in central (eastern) China. Historical attributions (1961–2020) showed that the likelihood of the 2021 EPE in central/eastern China decreased/increased by approximately +47% (−23% to +89%)/+55% (−45% to +201%) due to ACC. By the end of the 21st century, the likelihood of precipitation extremes similar to the 2021 EPE in central/eastern China under SSP585 is 14 (9–19)/15 (9–20) times higher than under historical climate conditions

Proteomics analysis reveals a Th17-prone cell population in presymptomatic graft-versus-host disease

Gastrointestinal graft-versus-host-disease (GI-GVHD) is a life-threatening complication occurring after allogeneic hematopoietic cell transplantation (HCT), and a blood biomarker that permits stratification of HCT patients according to their risk of developing GI-GVHD would greatly aid treatment planning. Through in-depth, large-scale proteomic profiling of presymptomatic samples, we identified a T cell population expressing both CD146, a cell adhesion molecule, and CCR5, a chemokine receptor that is upregulated as early as 14 days after transplantation in patients who develop GI-GVHD. The CD4+CD146+CCR5+ T cell population is Th17 prone and increased by ICOS stimulation. shRNA knockdown of CD146 in T cells reduced their transmigration through endothelial cells, and maraviroc, a CCR5 inhibitor, reduced chemotaxis of the CD4+CD146+CCR5+ T cell population toward CCL14. Mice that received CD146 shRNA-transduced human T cells did not lose weight, showed better survival, and had fewer CD4+CD146+CCR5+ T cells and less pathogenic Th17 infiltration in the intestine, even compared with mice receiving maraviroc with control shRNA- transduced human T cells. Furthermore, the frequency of CD4+CD146+CCR5+ Tregs was increased in GI-GVHD patients, and these cells showed increased plasticity toward Th17 upon ICOS stimulation. Our findings can be applied to early risk stratification, as well as specific preventative therapeutic strategies following HCT

AI of Brain and Cognitive Sciences: From the Perspective of First Principles

Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation. Despite these powerful applications, there are still many tasks in our daily life that are rather simple to humans but pose great challenges to AI. These include image and language understanding, few-shot learning, abstract concepts, and low-energy cost computing. Thus, learning from the brain is still a promising way that can shed light on the development of next-generation AI. The brain is arguably the only known intelligent machine in the universe, which is the product of evolution for animals surviving in the natural environment. At the behavior level, psychology and cognitive sciences have demonstrated that human and animal brains can execute very intelligent high-level cognitive functions. At the structure level, cognitive and computational neurosciences have unveiled that the brain has extremely complicated but elegant network forms to support its functions. Over years, people are gathering knowledge about the structure and functions of the brain, and this process is accelerating recently along with the initiation of giant brain projects worldwide. Here, we argue that the general principles of brain functions are the most valuable things to inspire the development of AI. These general principles are the standard rules of the brain extracting, representing, manipulating, and retrieving information, and here we call them the first principles of the brain. This paper collects six such first principles. They are attractor network, criticality, random network, sparse coding, relational memory, and perceptual learning. On each topic, we review its biological background, fundamental property, potential application to AI, and future development.Comment: 59 pages, 5 figures, review articl