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Neutrophils in Atlantic salmon(Salmo salar L.)are MHC class II^(+)and secret IL-12p40 upon bacterial exposure

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Aquaculture and Fisheries 2024年第2期9卷 144-153页

作者： Gyri Teien Haugland Anita Rønneseth Lene Gundersen Harald SæbøLunde Kaja Nordland Heidrun Inger Wergeland Department of Biological Sciences Bergen High-Technology CentreUniversity of BergenBergen5020Norway

Antigen-presentation via major histocompatibility complex(MHC)to T cells is the key event to initiate adaptive immune responses.In teleosts,as in mammals,the main types of professional antigen-presenting cells(APCs)are dendritic cells(DCs),monocytes/macrophages,and B cells.In the current study,flow cytometry,immunostaining and qPCR have been used to show that neutrophils in the teleost fish Atlantic salmon(Salmo salar L.)have antigen-presenting properties.The neutrophils were positive for MHC class II,CD83 and CD80/86,and upon in vitro bacterial exposure,gene expression analysis of purified neutrophils showed that IL-12p40,which is essential for proliferation of naïve T cells,was highly upregulated at both 6 and 24 h post bacterial exposure.Based on presence of MHC class II and upregulation of molecules involved in antigen presentation and T cell activation,we suggest that neutrophils in Atlantic salmon have potential to function as professional APCs.This work makes an important basis for further exploring the potential of using neutrophils to develop new,targeted immunoprophylactic measures.

关键词： Granulocytes Antigen presenting cells Major histocompatibility complex T cell activation B cells CD83 CD80/86 MHC classⅡ

Anthropogenic Influence on Decadal Changes in Concurrent Hot and Dry Events over China around the Mid-1990s

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Advances in Atmospheric Sciences 2024年第2期41卷 233-246页

作者： Qin SU Buwen DONG Fangxing TIAN Nicholas P.KLINGAMAN Yunnan Key Laboratory of Meteorological Disasters and Climate Resources in the Great Mekong Subregion Yunnan UniversityKunming 650500China Department of Atmospheric Sciences Yunnan UniversityKunming 650500China National Centre for Atmospheric Science Department of MeteorologyUniversity of ReadingReadingRG66BBUK Geofysisk Institut University of BergenBergen 5007Norway Citadel Investment Group Moor House120 London WallLondonRG66BBUK

The frequency and duration of observed concurrent hot and dry events(HDEs) over China during the growing season(April–September) exhibit significant decadal changes across the mid-1990s. These changes are characterized by increases in HDE frequency and duration over most of China, with relatively large increases over southeastern China(SEC), northern China(NC), and northeastern China(NEC). The frequency of HDEs averaged over China in the present day(PD,1994–2011) is double that in the early period(EP, 1964–81);the duration of HDEs increases by 60%. Climate experiments with the Met Office Unified Model(MetUM-GOML2) are used to estimate the contributions of anthropogenic forcing to HDE decadal changes over China. Anthropogenic forcing changes can explain 60%–70% of the observed decadal changes,suggesting an important anthropogenic influence on HDE changes over China across the mid-1990s. Single-forcing experiments indicate that the increase in greenhouse gas(GHG) concentrations dominates the simulated decadal changes,increasing the frequency and duration of HDEs throughout China. The change in anthropogenic aerosol(AA) emissions significantly decreases the frequency and duration of HDEs over SEC and NC, but the magnitude of the decrease is much smaller than the increase induced by GHGs. The changes in HDEs in response to anthropogenic forcing are mainly due to the response of climatological mean surface air temperatures. The contributions from changes in variability and changes in climatological mean soil moisture and evapotranspiration are relatively small. The physical processes associated with the response of HDEs to GHG and AA changes are also revealed.

关键词： concurrent hot and dry events decadal variation greenhouse gases aerosol emissions

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Innovation in pathogenesis and management of aortic aneurysm

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World Journal of Experimental Medicine 2024年第2期14卷 35-45页

作者： Maryam Barkhordarian Hadrian Hoang-Vu Tran Aiswarya Menon Sai Priyanka Pulipaka Izage Kianifar Aguilar Axel Fuertes Shraboni Dey Angel Ann Chacko Tanni Sethi Ayrton Bangolo Simcha Weissman Department of Internal Medicine Palisades Medical CenterNorth BergenNJ 07047United States

Aortic aneurysm(AA)refers to the persistent dilatation of the aorta,exceeding three centimeters.Investigating the pathophysiology of this condition is important for its prevention and management,given its responsibility for more than 25000 deaths in the United States.AAs are classified based on their location or morphology.various pathophysiologic pathways including inflammation,the immune system and atherosclerosis have been implicated in its development.Inflammatory markers such as transforming growth factorβ,interleukin-1β,tumor necrosis factor-α,matrix metalloproteinase-2 and many more may contribute to this phenomenon.Several genetic disorders such as Marfan syndrome,Ehler-Danlos syndrome and Loeys-Dietz syndrome have also been associated with this disease.Recent years has seen the investigation of novel management of AA,exploring the implication of different immune suppressors,the role of radiation in shrinkage and prevention,as well as minimally invasive and newly hypothesized surgical methods.In this narrative review,we aim to present the new contributing factors involved in pathophysiology of AA.We also highlighted the novel management methods that have demonstrated promising benefits in clinical outcomes of the AA.

关键词： Aortic aneurysm Abdominal aneurysm Thoracic aneurysm Immunotherapy Surgical management Pathophysiology Inflammation and molecular pathways

Impact of artificial intelligence in the management of esophageal,gastric and colorectal malignancies

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Artificial Intelligence in Gastrointestinal Endoscopy 2024年第2期5卷 1-14页

作者： Ayrton Bangolo Nikita Wadhwani Vignesh K Nagesh Shraboni Dey Hadrian Hoang-Vu Tran Izage Kianifar Aguilar Auda Auda Aman Sidiqui Aiswarya Menon Deborah Daoud James Liu Sai Priyanka Pulipaka Blessy George Flor Furman Nareeman Khan Adewale Plumptre Imranjot Sekhon Abraham Lo Simcha Weissman Department of Internal Medicine Palisades Medical CenterNorth BergenNJ 07047United States Department of Medicine Palisades Medical CenterNorth BergenNJ 07047United States

The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate.Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality.Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes.Artificial intelligence(AI)-assisted diagnostic,prognostic,and therapeutic tools can assist in expeditious diagnosis,treatment planning/response prediction,and post-surgical prognostication.AI can intercept neoplastic lesions in their primordial stages,accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic,histopathological,and/or endoscopic analyses,and eliminate over-dependence on clinicians.AI-based models have shown to be on par,and sometimes even outperformed experienced gastroenterologists and radiologists.Convolutional neural networks(state-of-the-art deep learning models)are powerful computational models,invaluable to the field of precision oncology.These models not only reliably classify images,but also accurately predict response to chemotherapy,tumor recurrence,metastasis,and survival rates post-treatment.In this systematic review,we analyze the available evidence about the diagnostic,prognostic,and therapeutic utility of artificial intelligence in gastrointestinal oncology.

关键词： Artificial intelligence Gastrointestinal malignancies Machine learning Helicobacter pylori State-of-the-art deep learning models

Recent Ventures in Interdisciplinary Arctic Research:The ARCPATH Project

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Advances in Atmospheric Sciences 2024年第8期41卷 1559-1568页

作者： Astrid E.J.OGILVIE Leslie A.KING Noel KEENLYSIDE Fran?ois COUNILLON Brynhildur DAVIDSDóTTIR Níels EINARSSON Sergey GULEV Ke FAN Torben KOENIGK James R.MCGOODWIN Marianne H.RASMUSSON Shuting YANG Swedish Meteorological and Hydrological Institute Danish Meteorological Institute Institute of Arctic and Alpine Research (INSTAAR) University of Colorado Stefansson Arctic Institute Royal Roads University University of bergen Nansen Environmental and Remote Sensing Centre University of Iceland Shirshov Institute of Oceanology Moscow State University Chinese Academy of Sciences

This paper celebrates Professor Yongqi GAO's significant achievement in the field of interdisciplinary studies within the context of his final research project Arctic Climate Predictions: Pathways to Resilient Sustainable Societies-ARCPATH(https://www.***.is/en/projects/finished-projects/arcpath). The disciplines represented in the project are related to climatology, anthropology, marine biology, economics, and the broad spectrum of social-ecological studies. Team members were drawn from the Nordic countries, Russia, China, the United States, and Canada. The project was transdisciplinary as well as interdisciplinary as it included collaboration with local knowledge holders. ARCPATH made significant contributions to Arctic research through an improved understanding of the mechanisms that drive climate variability in the Arctic. In tandem with this research, a combination of historical investigations and social, economic, and marine biological fieldwork was carried out for the project study areas of Iceland, Greenland, Norway, and the surrounding seas, with a focus on the joint use of ocean and sea-ice data as well as social-ecological drivers. ARCPATH was able to provide an improved framework for predicting the near-term variation of Arctic climate on spatial scales relevant to society, as well as evaluating possible related changes in socioeconomic realms. In summary, through the integration of information from several different disciplines and research approaches, ARCPATH served to create new and valuable knowledge on crucial issues, thus providing new pathways to action for Arctic communities.

关键词： SEA-ICE INITIALIZATION PREDICTION SKILL CLIMATE SCIENCE

Characteristics and distinct prognostic determinants of individuals with hepatosplenic T-cell lymphoma over the past two decades

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World Journal of Clinical Oncology 2024年第6期15卷 745-754页

作者： Ayrton Bangolo Pierre Fwelo Shraboni Dey Tanni Sethi Sowmya Sagireddy Jawaria Chatta Ashish Goel Sneha Nagpaul Eric Pin-Shiuan Chen Chiranjeeve Saravanan Sheeja Gangan Joel Thomas Sarah Potiguara Vignesh K Nagesh Daniel Elias Charlene Mansour Prajakta H Ratnaparkhi Priyanshu Jain Midhun Mathew Taylor Porter Shadiya Sultan Shailaja Abbisetty Linh Tran Megha Chawla Abraham Lo Simcha Weissman Christina Cho Department of Internal Medicine Palisades Medical CenterNorth BergenNJ 07047United States Department of Epidemiology Human Geneticsand Environmental SciencesUTHealth School of Public HealthHoustonTX 77030United States Department of Medicine Palisades Medical CenterNorth BergenNJ 07047United States Stem Cell Transplantation and Cellular Therapy John Theurer Cancer CenterHackensackNJ 07601United States

BACKGROUND Hepatosplenic T-cell lymphoma(HSTCL)is a rare and aggressive peripheral T-cell lymphoma with historically dismal outcomes,representing less than one percent of non-Hodgkin lymphomas.Given its rarity,the true incidence of HSTCL is unknown and most data have been extrapolated through case reports.To the best of our knowledge,the largest and most up to date study addressing the epidemiology and outcomes of patients with HSTCL in the United States covered a period from 1996 to 2014,with a sample size of 122 patients.AIM To paint the most updated epidemiological picture of HSTCL.METHODS A total of 186 patients diagnosed with HSTCL,between 2000 and 2017,were ultimately enrolled in our study by retrieving data from the Surveillance,Epidemiology,and End Results database.We analyzed demographics,clinical characteristics,and overall mortality(OM)as well as cancer-specific mortality(CSM)of HSTCL.Variables with a P value<0.01 in the univariate Cox regression were incorporated into the multivariate Cox model to determine the independent prognostic factors,with a hazard ratio of greater than 1 representing adverse prog-nostic factors.CONCLUSION Overall,the outlook for this rare malignancy is very grim.In this retrospective cohort study of the United States population,non-Hispanic blacks and the elderly had a higher CSM.This data highlights the need for larger prospective studies to investigate factors associated with worse prognosis in one ethnic group,such as treatment delays,which have been shown to increase mortality in this racial/ethnic group for other cancers.

关键词： Extra nodal lymphoma Mortality Survival Racial disparity Age

Preface to the Special Topic on Ocean, Sea Ice and Northern Hemisphere Climate:In Remembrance of Professor Yongqi GAO's Key Contributions

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Advances in Atmospheric Sciences 2024年第8期41卷 1463-1464页

作者： Noel KEENLYSIDE Shengping HE Fei LI Geophysical Institute University of Bergen and Bjerkenes Centre for Climate Research Nansen Environmental and Remote Sensing Center Nansen-Zhu International Research Center Institute of Atmospheric Physics Chinese Academy of Sciences

This special issue commemorates the life work of Prof. Yongqi GAO who passed away in July 2021, his time cut short by illness. He had many great achievements, but still much more to contribute. The seven articles in this special issue are from research areas where he contributed, and they illustrate how his close colleagues are continuing his work.

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Arctic Sea Ice Variations in the First Half of the 20th Century:A New Reconstruction Based on Hydrometeorological Data

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Advances in Atmospheric Sciences 2024年第8期41卷 1483-1495+1686-1693页

作者： Vladimir A.SEMENOV Tatiana A.ALDONINA Fei LI Noel Sebastian KEENLYSIDE Lin WANG A.M.Obukhov Institute of Atmospheric Physics RAS Institute of Geography RAS Geophysical Institute University of Bergen and Bjerknes Centre for Climate Research Nansen Environmental and Remote Sensing Center Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of Sciences

The shrinking Arctic sea-ice area（SIA） in recent decades is a striking manifestation of the ongoing climate change.Variations of the Arctic sea ice have been continuously observed by satellites since 1979, relatively well monitored since the 1950s, but are highly uncertain in the earlier period due to a lack of observations. Several reconstructions of the historical gridded sea-ice concentration（SIC） data were recently presented based on synthesized regional sea-ice observations or by applying a hybrid model–empirical approach. Here, we present an SIC reconstruction for the period1901–2019 based on established co-variability between SIC and surface air temperature, sea surface temperature, and sea level pressure patterns. The reconstructed sea-ice data for March and September are compared to the frequently used Had ISST1.1 and SIBT1850 datasets. Our reconstruction shows a large decrease in SIA from the 1920 to 1940 concurrent with the Early 20th Century Warming event in the Arctic. Such a negative SIA anomaly is absent in Had ISST1.1 data. The amplitude of the SIA anomaly reaches about 0.8 mln km2in March and 1.5 mln km2in September. The anomaly is about three times stronger than that in the SIBT1850 dataset. The larger decrease in SIA in September is largely due to the stronger SIC reduction in the western sector of the Arctic Ocean in the 70°–80°N latitudinal zone. Our reconstruction provides gridded monthly data that can be used as boundary conditions for atmospheric reanalyses and model experiments to study the Arctic climate for the first half of the 20th century.

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IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Mat Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Intermeural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI Cybersecurity