The stimulatory role of ICOS in the development of CD146+CCR5+ T cells co-expressing IFN-γ and IL-17 during graft-versus-host disease

Indiana University-Purdue University Indianapolis (IUPUI)Graft-versus-host disease (GVHD) remains the major complication after allogeneic hematopoietic stem cell transplantation (HSCT), resulting from immunological attack on target organs such as gastrointestinal (GI) tract, liver and skin from donor allogeneic T cells. The most common treatment for GVHD is immunosuppressive drugs such as corticosteroids, which may result in many side effects including the loss of the beneficial graft-versus-leukemia (GVL) effect and increased infection rates. However, GVHD-specific drugs have yet to be implemented. Here we show that by targeting on a novel pathogenic CD4+ T cell subpopulation that our lab previously found in patients with GI GVHD, we can develop new avenues to treat GVHD. This novel population is characterized as CD146+CCR5+ T cells, co-expressing IL-17A and IFN-γ. We found that the inducible T-cell costimulator (ICOS), which has been reported to be important for human Th17 differentiation in vitro, is critical for the development of this nonconventional T Helper 1 (Th1*)-polarized CD146+CCR5+ conventional T cells (Tconvs) population. Furthermore, we found that ICOS can induce the generation of Th1*-polarized CD146+CCR5+ regulatory T cells (Tregs) population, lowering the frequencies of phenotypic markers of functional Tregs. Our data also showed that inhibiting the major transcriptional factor of Th17, RAR-related orphan receptor gamma t (RORγt), could prevent the development of CD146+CCR5+ Tconvs in vitro. Our results demonstrate how pathogenic CD146+CCR5+ T cells are induced through ICOS or RORγt, suggesting new targets for GVHD treatment. We anticipate our assay to be a starting point for the development of novel GVHD-specific drugs. For example, the treatments that focus on inhibiting RORγ would have fewer side effects than general immunosuppressive drugs that GVHD patients use today and inhibit GVHD while sparing the GVL effect. Furthermore, we expect the CD146+CCR5+ Tconvs and/or Tregs can be used as GVHD biomarkers. These biomarkers may guide preemptive treatments such as RORγt inhibitor

A distributed anomaly detection system for in-vehicle network using HTM

With the development of 5G and Internet of Vehicles technology, the possibility of remote wireless attack on an in-vehicle network has been proven by security researchers. Anomaly detection technology can effectively alleviate the security threat, as the first line of security defense. Based on this, this paper proposes a distributed anomaly detection system using hierarchical temporal memory (HTM) to enhance the security of a vehicular controller area network bus. The HTM model can predict the flow data in real time, which depends on the state of the previous learning. In addition, we improved the abnormal score mechanism to evaluate the prediction. We manually synthesized field modification and replay attack in data field. Compared with recurrent neural networks and hidden Markov model detection models, the results show that the distributed anomaly detection system based on HTM networks achieves better performance in the area under receiver operating characteristic curve score, precision, and recall

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

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

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

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

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

Nonstandard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants