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Prognostic value, DNA variation and immunologic features of a tertiary lymphoid structure-related chemokine signature in clear cell renal cell carcinoma

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CANCER IMMUNOLOGY IMMUNOTHERAPY 2022年第8期71卷 1923-1935页

作者： Xu, Wenhao Ma, Chunguang Liu, Wangrui Anwaier, Aihetaimujiang Tian, Xi Shi, Guohai Qu, Yuanyuan Wei, Shiyin Zhang, Hailiang Ye, Dingwei Fudan Univ Shanghai Canc Ctr Shanghai Med Coll Dept Urol Dongan Rd 270 Shanghai 200032 Peoples R China Youjiang Med Univ Nationalities Affiliated Hosp Zhongshan 2nd Rd 18 Baise 533000 Peoples R China

Background The tumor microenvironment (TME) and tertiary lymphoid structures (TLS) affect the occurrence and development of cancers. How the immune contexture interacts with the phenotype of clear cell renal cell carcinoma (ccRCC) remains unclear. Methods We identified and evaluated TLS clusters in ccRCC using machine learning algorithms and the 12-chemokine gene signature for TLS. Analyses for functional enrichment, DNA variation, immune cell distribution, association with independent clinicopathological features and predictive value of CXCL13 in ccRCC were performed. Results We found a prominently enrichment of the 12-chemokine gene signature for TLS in patients with ccRCC compared with other types of renal cell carcinoma. We identified a prognostic value of CCL4, CCL5, CCL8, CCL19 and CXCL13 expression in ccRCC. DNA deletion of the TLS gene signature significantly predicted poor outcome in ccRCC compared with amplification and wild-type gene signature. We established TLS clusters (C1-4) and observed distinct differences in survival, stem cell-like characteristics, immune cell distribution, response to immunotherapies and VEGF-targeted therapies among the clusters. We found that elevated CXCL13 expression significantly predicted aggressive progression and poor prognosis in 232 patients with ccRCC in a real-world validation cohort. Conclusion This study described a 12-chemokine gene signature for TLS in ccRCC and established TLS clusters that reflected different TME immune status and corresponded to prognosis of ccRCC. We confirmed the dense presence of TILs aggregation and TLS in ccRCC and demonstrated an oncogenic role of CXCL13 expression of ccRCC, which help develop immunotherapies and provide novel insights on the long-term management of ccRCC.

关键词： Clear cell renal cell carcinoma Tertiary lymphoid structures Tumor microenvironment Prognosis CXCL13 machine learning algorithm

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Condition-Based Monitoring for Marine Engine Maintenance by Analyzing Drain Cylinder Oil Sample

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TRIBOLOGY ONLINE 2022年第2期17卷 71-77页

作者： Famakinwa, Ayo Shibutani, Tadahiro Yokohama Natl Univ Hodogaya Ku 79-1 Tokiiwadai Yokohama Kanagawa 2408501 Japan

Improvement of condition-based monitoring for large bore 2-stroke marine engine cylinder liners and piston rings require close monitoring of anomaly wear elements in drain cylinder oil. This paper presents monitoring of seagoing ship engines because the environment of sailing affects the internal combustion conditions leading to mechanical failure of the cylinder liners. We presented condition-based monitoring of cylinder liners using X-ray to analyze drain cylinder oil samples. The approach for condition-based monitoring uses X-ray results to estimate wear amount and machine learning algorithm on elements in the drain cylinder oil samples. The wear amount is quantified for the purpose to estimate the degradation of cylinder liner materials through the drain cylinder oil samples, machine learning algorithm to evaluate the correlation of detected elements in drain oil samples. Finally, wear rate estimation to know the remaining useful life of the cylinder liner. Our results contributed to the improvement of condition-based monitoring of cylinder liners wear elements by quantification and machine learning to correlate elements in oil samples. Correlating wear elements facilitate quick decision-making on maintenance policies for slow-speed large bore 2-stroke engines.

关键词： condition-based monitoring machine learning algorithm environmental effects wear rate

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Identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndrome

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JOURNAL OF OVARIAN RESEARCH 2022年第1期15卷 1-13页

作者： Na, Zhijing Guo, Wen Song, Jiahui Feng, Di Fang, Yuanyuan Li, Da China Med Univ Ctr Reprod Med Shengjing Hosp 36 SanHao St Shenyang 110004 Peoples R China China Med Univ Dept Neurol First Hosp Shenyang 110002 Peoples R China China Med Univ Educ Ctr Clin Skills Practice Shenyang 110122 Peoples R China Key Lab Reprod Dysfunct Dis & Fertil Remodeling L Shenyang 110122 Peoples R China

Background In this study, we aimed to identify novel biomarkers for polycystic ovary syndrome (PCOS) and analyze their potential roles in immune infiltration during PCOS pathogenesis. Methods Five datasets, namely GSE137684, GSE80432, GSE114419, GSE138518, and GSE155489, were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were selected from the train datasets. The least absolute shrinkage and selection operator logistic regression model and support vector machine-recursive feature elimination algorithm were combined to screen potential biomarkers. The test datasets validated the expression levels of these biomarkers, and the area under the curve (AUC) was calculated to analyze their diagnostic value. Quantitative real-time PCR was conducted to verify biomarkers' expression in clinical samples. CIBERSORT was used to assess differential immune infiltration, and the correlations of biomarkers with infiltrating immune cells were evaluated. Results Herein, 1265 DEGs were identified between PCOS and control groups. The gene sets related to immune response and adaptive immune response were differentially activated in PCOS. The two diagnostic biomarkers of PCOS identified by us were HD domain containing 3 (HDDC3) and syndecan 2 (SDC2;AUC, 0.918 and 0.816, respectively). The validation of hub biomarkers in clinical samples using RT-qPCR was consistent with bioinformatics results. Immune infiltration analysis indicated that decreased activated mast cells (P = 0.033) and increased eosinophils (P = 0.040) may be a part of the pathogenesis of PCOS. HDDC3 was positively correlated with T regulatory cells (P = 0.0064), activated mast cells (P = 0.014), and monocytes (P = 0.024) but negatively correlated with activated memory CD4 T cells (P = 0.016) in PCOS. In addition, SDC2 was positively correlated with activated mast cells (P = 0.0021), plasma cells (P = 0.0051), and M2 macrophages (P = 0.038) but negatively correlated with eosinophils

关键词： Polycystic ovary syndrome Biomarkers Immune infiltration machine learning algorithm CIBERSORT

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Personal predictive model based on systemic inflammation markers for estimation of postoperative pancreatic fistula following pancreaticoduodenectomy

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World Journal of Gastrointestinal Surgery 2022年第9期14卷 963-975页

作者： Zhi-Da Long Chao Lu Xi-Gang Xia Bo Chen Zhi-Xiang Xing Lei Bie Peng Zhou Zhong-Lin Ma Rui Wang Department of Hepatobiliary and Pancreaticosplenic Surgery Jingzhou HospitalYangtze UniversityJingzhou 434020Hubei ProvinceChina Department of Hepatobiliary Surgery Lu’an Hospital of AnHui Medical UniversityHefei 237006Anhui ProvinceChina

BACKGROUND Postoperative pancreatic fistula(PF)is a serious life-threatening complication after pancreaticoduodenectomy(PD).Our research aimed to develop a machine learning(ML)-aided model for PF risk *** To develop an ML-aided model for PF risk *** We retrospectively collected 618 patients who underwent PD from two tertiary medical centers between January 2012 and August *** used an ML algorithm to build predictive models,and subject prediction index,that is,decision curve analysis,area under operating characteristic curve(AUC)and clinical impact curve to assess the predictive efficiency of each *** A total of 29 variables were used to build the ML predictive *** them,the best predictive model was random forest classifier(RFC),the AUC was[0.897,95%confidence interval(CI):0.370–1.424],while the AUC of the artificial neural network,eXtreme gradient boosting,support vector machine,and decision tree were between 0.726(95%CI:0.191–1.261)and 0.882(95%CI:0.321–1.443).CONCLUSION Fluctuating serological inflammatory markers and prognostic nutritional index can be used to predict postoperative PF.

关键词： Pancreatoduodenectomy Pancreatic fistula machine learning algorithm Systemic inflammatory biomarker Risk prediction

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Artificial intelligence in glomerular diseases

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PEDIATRIC NEPHROLOGY 2022年第11期37卷 2533-2545页

作者： Schena, Francesco P. Magistroni, Riccardo Narducci, Fedelucio Abbrescia, Daniela, I Anelli, Vito W. Di Noia, Tommaso Univ Bari Dept Emergency & Organ Transplantat Bari Italy Univ Modena Dept Nephrol Modena Italy Polytech Bari Dept Elect & Informat Engn Bari Italy Schena Fdn Bari Italy

In this narrative review, we focus on the application of artificial intelligence in the clinical history of patients with glomerular disease, digital pathology in kidney biopsy, renal ultrasonography imaging, and prediction of chronic kidney disease (CKD). With the development of natural language processing, the clinical history of a patient can be used to identify a computable phenotype. In kidney pathology, digital imaging has adopted innovative deep learning algorithms (DLAs) that can improve the predictive capability of the examined lesions. However, at this time, these applications can only be used in research because there is no recognized validation to replace the conventional diagnostic applications. Kidney ultrasonography, used in the clinical examination of patients, provides information about the progression of kidney damage. machine learning algorithms (MLAs) with promising results for the early detection of CKD have been proposed, but, still, they are not solid enough to be incorporated into the clinical practice. A few tools for glomerulonephritis, based on MLAs, are available in clinical practice. They can be downloaded on computers and cellular phones but can only be applied to uniracial cohorts of patients. To improve their performance, it is necessary to organize large consortia with multiracial cohorts. Finally, in many studies MLA development has been carried out using retrospective cohorts. The performance of the models might differ in retrospective cohorts compared to real-world data. Therefore, the models should be validated in prospective external large cohorts.

关键词： Glomerulonephritis Artificial Intelligence Natural Language Processing machine learning algorithm Deep learning algorithm Clinical Outcome

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Purchasing Intentions Analysis of Hybrid Cars Using Random Forest Classifier and Deep learning

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WORLD ELECTRIC VEHICLE JOURNAL 2023年第8期14卷 227页

作者： Ong, Ardvin Kester S. Cordova, Lara Nicole Z. Longanilla, Franscine Althea B. Caprecho, Neallo L. Javier, Rocksel Andry V. Borres, Rianina D. German, Josephine D. Mapua Univ Sch Ind Engn & Engn Management 658 Muralla St Manila 1002 Philippines Mapua Univ ET Yuchengco Sch Business 1191 Pablo Ocampo Sr Ext Makati 1205 Philippines Mapua Univ Young Innovators Res Ctr 658 Muralla St Manila 1002 Philippines Mapua Univ Sch Grad Studies 658 Muralla St Manila 1002 Philippines

In developed or first-world countries, hybrid cars are widely utilized and essential in technological development and reducing carbon emissions. Despite that, developing or third-world countries such as the Philippines have not yet fully adopted hybrid cars as a means of transportation. Hence, the Sustainability Theory of Planned Behavior (STPB) was developed and integrated with the UTAUT2 framework to predict the factors affecting the purchasing intentions of Filipino drivers toward hybrid cars. The study gathered 1048 valid responses using convenience and snowball sampling to holistically measure user acceptance through twelve latent variables. machine learning algorithm (MLA) tools such as the Decision Tree (DT), Random Forest Classifier (RFC), and Deep learning Neural Network (DLNN) were utilized to anticipate consumer behavior. The final results from RFC showed an accuracy of 94% and DLNN with an accuracy of 96.60%, which were able to prove the prediction of significant latent factors. Perceived Environmental Concerns (PENCs), Attitude (AT), Perceived Behavioral Control (PBC), and Performance Expectancy (PE) were observed to be the highest factors. This study is one of the first extensive studies utilizing the MLA approach to predict Filipino drivers' tendency to acquire hybrid vehicles. The study's results can be adapted by automakers or car companies for devising initiatives, tactics, and advertisements to promote the viability and utility of hybrid vehicles in the Philippines. Since all the factors were proven significant, future investigations can assess not only the behavioral component but also the sustainability aspect of an individual using the STPB framework.

关键词： hybrid cars machine learning algorithm purchasing intentions sustainability theory of planned behavior UTAUT2

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Improvement of blueberry freshness prediction based on machine learning and multi-source sensing in the cold chain logistics

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FOOD CONTROL 2023年 145卷

作者： Huang, Wentao Wang, Xuepei Zhang, Junchang Xia, Jie Zhang, Xiaoshuan China Agr Univ Coll Engn Beijing Lab Food Qual & Safety Beijing 100083 Peoples R China Shanghai Jiao Tong Univ Sch Informat & Elect Engn Shanghai 200240 Peoples R China China Agr Univ Sch Informat & Elect Engn Beijing 100083 Peoples R China China Agr Univ Beijing 100083 Peoples R China

Traditional fruit freshness prediction and modeling heavily rely on various physicochemical indicators (such as water loss rate, pH, and VC content), which is facing predicaments of time-consuming, laborious, destructive, and low prediction accuracy. To this end, this paper proposes a new method for fruit freshness prediction based on multi-sensing technology and machine learning algorithm, thereby improving the automation, intelligentialize, and high accuracy of fruit freshness prediction. The critical control points of blueberry cold chain logistics were analyzed firstly based on the HACCP method, identifying the key gas parameters (O2, CO2, and C2H4) and interaction mechanisms of gas and blueberry freshness. Then the blueberry cold chain microenvironment monitoring platform (BCCMMP) was developed for critical gas content monitoring at different temperatures (0 degrees C, 5 degrees C, and 22 degrees C). It was demonstrated that gas information can replace quality information to characterize blueberry freshness, and further emerging machine learning (ML) algorithms (BP, RBF, SVM, and ELM) were constructed for blueberry freshness prediction using critical gas information, and the results showed prediction accuracies of 90.87% (BP), 92.24% (RBF), 94.01% (SVM), and 91.31% (ELM). By contrast, the 85.10% prediction accuracy was achieved by the traditional Arrhenius equation method based on temperature and quality parameters. Through the automatic non-destructive acquisition of sensing data and emerging machine learning algorithms, this paper provides a new approach to improving the freshness prediction accuracy and food quality management level during fruit cold chain logistics.

关键词： Wireless sensor network (WSN) Blueberry Freshness prediction machine learning algorithm HACCP methods

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INTRAGRO: A machine learning approach to predict future growth of trees under climate change

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ECOLOGY AND EVOLUTION 2023年第10期13卷 e10626页

作者： Aryal, Sugam Griessinger, Jussi Dyola, Nita Gaire, Narayan Prasad Bhattarai, Tribikram Braeuning, Achim Friedrich Alexander Univ Erlangen Nurnberg Inst Geog Wetterkreuz 15 D-91058 Erlangen Bayern Germany Chinese Acad Sci Inst Tibetan Plateau Res State Key Lab Tibetan Plateau Earth Syst Resources Beijing Peoples R China Univ Quebec Chicoutimi Dept Sci Fondamentales Lab Ecosyst Terr Boreaux Chicoutimi PQ Canada Tribhuvan Univ Dept Environm Sci Patan Multiple Campus Lalitpur Nepal Tribhuvan Univ Cent Dept Biotechnol Kathmandu Nepal

The escalating impact of climate change on global terrestrial ecosystems demands a robust prediction of the trees' growth patterns and physiological adaptation for sustainable forestry and successful conservation efforts. Understanding these dynamics at an intra-annual resolution can offer deeper insights into tree responses under various future climate scenarios. However, the existing approaches to infer cambial or leaf phenological change are mainly focused on certain climatic zones (such as higher latitudes) or species with foliage discolouration during the fall season. In this study, we demonstrated a novel approach (INTRAGRO) to combine intra-annual circumference records generated by dendrometers coupled to the output of climate models to predict future tree growth at intra-annual resolution using a series of supervised and unsupervised machine learning algorithms. INTRAGRO performed well using our dataset, that is dendrometer data of P. roxburghii Sarg. from the subtropical mid-elevation belt of Nepal, with robust test statistics. Our growth prediction shows enhanced tree growth at our study site for the middle and end of the 21st century. This result is remarkable since the predicted growing season by INTRAGRO is expected to shorten due to changes in seasonal precipitation. INTRAGRO's key advantage is the opportunity to analyse changes in trees' intra-annual growth dynamics on a global scale, regardless of the investigated tree species, regional climate and geographical conditions. Such information is important to assess tree species' growth performance and physiological adaptation to growing season change under different climate scenarios.

关键词： dendrometer growing-season change intra-annual growth machine learning algorithm Pinus roxburghii sub-tropical Nepal

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Balancing phosphorus fertilization for sustainable maize yield and soil test phosphorus management: A long-term study using machine learning

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FIELD CROPS RESEARCH 2023年 304卷

作者： Gupta, Naveen Punjab Agr Univ Dept Soil Sci Ludhiana 141004 Punjab India

Context Excessive phosphorus (P) applications to ensure enhanced crop growth and increased profitability are commonly used, leading to declined P use efficiency and high P accumulation in most cultivated soils across north-western India. However, the effects of consistently applying excessive P fertilizers on soil test P (STP) levels, P availability, and maize production outcomes are poorly *** The study aims to find the long-term impacts of different fertilizer P application rates on maize yield, agronomic P efficiency, optimal P rates for yield maximization, and predictive models for sustainable crop production and environmental protection across soils with different STP *** The current study collected and analysed maize plant and soil data spanning from 2011 to 2021 from an ongoing long-term field experiment conducted on three different STP status soils i.e., medium P (MPS), high P (HPS), and very high P (VHPS) soils. The experiment involves five different fertilizer P application rates No-P, 13, 26, 39, and 52 kg P ha(-1).Results The results indicated a significant increase in maize grain yield and P bioavailability with the application of 13 kg ha(-1) of fertilizer P in VHPS (>50.0 kg P ha(-1)) and 26 kg ha(-1) in both HPS (22.5-0.0 kg P ha(-1)) and MPS ((1)2.5-22.5 kg P ha(-1)). Improved agronomic efficiency reducing P rate to half of the recommended 26 kg P ha(-1) was estimated in VHPS. The economically optimal P rate (EOPR) model consistently sustains crop yield with lower P rates. Among the different machine learning algorithms, multiple linear regression (MLR) was found more reliable in predictive accuracy. The decrease in predicted grain yield and increase in predicted soil P accumulation at P rates of 26 kg P ha(-1) and above in varying P status soils underscores the need for judicious P fertilization practices to avoid negative impacts on *** The P application required to obtain an average maize yield of 6000 k

关键词： Long-term fertilization Soil residual P Agronomic efficiency P uptake machine learning algorithm

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Using machine learning algorithms to analyse the scute structure and sex identification of sterlet Acipenser ruthenus (Acipenseridae)

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AQUACULTURE RESEARCH 2019年第10期50卷 2810-2825页

作者： Barulin, Nikolai V. Belarusian State Agr Acad Dept Ichthyol & Pisciculture Michurina 5 Gorki 213407 Mogilev Region BELARUS

Sturgeons are valued as specialty black caviar, which is very expensive. Only females are used in the technology of caviar aquaculture. Universal method of sex determination has not yet been developed. Most of known methods are not sufficiently accurate, or used at a relatively late age, or difficult to use. Perfect early determination of sex is considered to be impossible. Because of the dark colour of most sturgeons and important morphological differences, which fish of almost all ages have, were overlooked. We first found that the scute structure of sterlet sturgeon depends on the sex. The found dependencies with the help of machine learning algorithms open a possibility for creation of sex determination equipment using the artificial intelligence. Our results open a perspective for creation of sex determination methods for other 23 sturgeon species, which can increase the efficiency of caviar aquaculture and restoration of sturgeons in natural waters.

关键词： Acipenser ruthenus machine learning algorithm scute sex sterlet sturgeons

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