A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise

The surge of interest towards Multi-modal Large Language Models (MLLMs), e.g., GPT-4V(ision) from OpenAI, has marked a significant trend in both academia and industry. They endow Large Language Models (LLMs) with powerful capabilities in visual understanding, enabling them to tackle diverse multi-modal tasks. Very recently, Google released Gemini, its newest and most capable MLLM built from the ground up for multi-modality. In light of the superior reasoning capabilities, can Gemini challenge GPT-4V's leading position in multi-modal learning? In this paper, we present a preliminary exploration of Gemini Pro's visual understanding proficiency, which comprehensively covers four domains: fundamental perception, advanced cognition, challenging vision tasks, and various expert capacities. We compare Gemini Pro with the state-of-the-art GPT-4V to evaluate its upper limits, along with the latest open-sourced MLLM, Sphinx, which reveals the gap between manual efforts and black-box systems. The qualitative samples indicate that, while GPT-4V and Gemini showcase different answering styles and preferences, they can exhibit comparable visual reasoning capabilities, and Sphinx still trails behind them concerning domain generalizability. Specifically, GPT-4V tends to elaborate detailed explanations and intermediate steps, and Gemini prefers to output a direct and concise answer. The quantitative evaluation on the popular MME benchmark also demonstrates the potential of Gemini to be a strong challenger to GPT-4V. Our early investigation of Gemini also observes some common issues of MLLMs, indicating that there still remains a considerable distance towards artificial general intelligence. Our project for tracking the progress of MLLM is released at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models.Comment: Total 120 pages. See our project at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Model

Legitimation Endeavor: A model of “Orchestrated Persuasive Communication” practiced by the Chinese Government in Public Crises

When a public crisis event occurs, “account pressure” stemmed from various stakeholders can challenge and threaten the government legitimacy and legitimate image. In order to control the situation and recover from the damage, the government will make great legitimation endeavors by applying diverse strategic communication. Taking four cases as examples, this paper aims at exploring and presenting a pattern of “orchestrated persuasive communication”, which identified four key variables in the government communication – crisis type, stakeholder, news themes and communicative tactics. By a content analysis of news coverage on the People’s net (n=539) as well as a multi-cases cross study, empirical findings show that, for different crisis type, the composition and priority of stakeholders is varied, that is, each crisis situation (type) is corresponding to key stakeholder(s). Thus, for each crisis type, the communicator should highlight different theme and apply matched tactic in the message design in order to influence the stakeholders’ perception about the crisis reality and emotion about the government in public crises. Only by such a pattern of “orchestrated persuasive communication” – recognize and reach the key stakeholder(s) by framing the messages with pertinent themes and appropriate tactics for particular crisis type can the government legitimation endeavor gain its effect

Computational screening of potential glioma-related genes and drugs based on analysis of GEO dataset and text mining.

BackgroundConsidering the high invasiveness and mortality of glioma as well as the unclear key genes and signaling pathways involved in the development of gliomas, there is a strong need to find potential gene biomarkers and available drugs.MethodsEight glioma samples and twelve control samples were analyzed on the GSE31095 datasets, and differentially expressed genes (DEGs) were obtained via the R software. The related glioma genes were further acquired from the text mining. Additionally, Venny program was used to screen out the common genes of the two gene sets and DAVID analysis was used to conduct the corresponding gene ontology analysis and cell signal pathway enrichment. We also constructed the protein interaction network of common genes through STRING, and selected the important modules for further drug-gene analysis. The existing antitumor drugs that targeted these module genes were screened to explore their efficacy in glioma treatment.ResultsThe gene set obtained from text mining was intersected with the previously obtained DEGs, and 128 common genes were obtained. Through the functional enrichment analysis of the identified 128 DEGs, a hub gene module containing 25 genes was obtained. Combined with the functional terms in GSE109857 dataset, some overlap of the enriched function terms are both in GSE31095 and GSE109857. Finally, 4 antitumor drugs were identified through drug-gene interaction analysis.ConclusionsIn this study, we identified that two potential genes and their corresponding four antitumor agents could be used as targets and drugs for glioma exploration

Analysis of the wear behavior and corrosion resistance of CoCrFeNiMn-2% CNTs laser cladding composite coating

Carbon nanotubes (CNTs), as a kind of nanopowder, can be used as reinforcing powder to improve various properties of coatings because of their good self-lubricating properties and three-dimensional structure. However, CNTs are difficult to mix uniformly with other powders due to their easily disrupted microstructure and extremely low density. Therefore, CoCrFeNiMn HEA and 2 wt% CNTs were mixed by a new mixing method—alcohol stirring and ball-less ball mill mixing methods. CoCrFeNiMn/2 wt% CNTs coatings prepared by laser cladding (LC). The phase composition, microstructure, wear resistance, and corrosion resistance of the HEA/CNTs coating were analyzed by various characterization techniques. The results showed that grain refinement occurred in the coatings after adding CNTs, generating fine carbides. It was calculated that the densification percentage of the HEA/CNTs coating increased and the porosity decreased. Compared with the HEA coating, the microhardness of the HEA/CNTs coating increased by 101.66%, and the friction coefficient and wear rate of the HEA/CNTs coating were only 63.30% and 6.22% of the HEA coating. The more positive Ecorr and smaller Icorr indicated that the HEA/CNTs coating has better corrosion resistance

Integrating variation in bacterial‐fungal co‐occurrence network with soil carbon dynamics

Bacteria and fungi are core microorganisms in diverse ecosystems, and their cross-kingdom interactions are considered key determinants of microbiome structure and ecosystem functioning. However, how bacterial-fungal interactions mediate soil organic carbon (SOC) dynamics remains largely unexplored in the context of artificial forest ecosystems. Here, we characterised soil bacterial and fungal communities in four successive planting of Eucalyptus and compared them to a neighbouring evergreen broadleaf forest. Carbon (C) mineralisation combined with five C-degrading enzymatic activities was investigated to determine the effects of successive planting of Eucalyptus on SOC dynamics. Our results indicated that successive planting of Eucalyptus significantly altered the diversity and structure of soil bacterial and fungal communities and increased the negative bacterial-fungal associations. The bacterial diversity significantly decreased in all Eucalyptus plantations compared to the evergreen forest, while the fungal diversity showed the opposite trend. The ratio of negative bacterial-fungal associations increased with successive planting of Eucalyptus due to the decrease in SOC, ammonia nitrogen (NH4+-N), nitrate nitrogen (NO3−-N) and available phosphorus (AP). Structural equation modelling indicated that the potential cross-kingdom competition, based on the ratio of negative bacterial-fungal correlations, was significantly negatively associated with the diversity of total bacteria and keystone bacteria, thereby increasing C-degrading enzymatic activities and C mineralisation. Synthesis and applications: Our results highlight the regulatory role of the negative bacterial-fungal association in enhancing the correlation between bacterial diversity and C mineralisation. This suggests that promoting short-term successive planting in the management of Eucalyptus plantations can mitigate the impact of this association on SOC decomposition. Taken together, our study advances the understanding of bacterial-fungal negative associations to mediate carbon mineralisation in Eucalyptus plantations, giving us a new insight into SOC cycling dynamics in artificial forests

Plasma campesterol and ABCG5/ABCG8 gene loci on the risk of cholelithiasis and cholecystitis: evidence from Mendelian randomization and colocalization analyses

Abstract The causal relationships between plasma metabolites and cholelithiasis/cholecystitis risks remain elusive. Using two-sample Mendelian randomization, we found that genetic proxied plasma campesterol level showed negative correlation with the risk of both cholelithiasis and cholecystitis. Furthermore, the increased risk of cholelithiasis is correlating with the increased level of plasma campesterol. Lastly, genetic colocalization study showed that the leading SNP, rs4299376, which residing at the ABCG5/ABCG8 gene loci, was shared by plasma campesterol level and cholelithiasis, indicating that the aberrant transportation of plant sterol/cholesterol from the blood stream to the bile duct/gut lumen might be the key in preventing cholesterol gallstone formation

Data_Sheet_6_Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning.CSV

ObjectiveA clinical prediction model for postoperative combined Acute kidney injury (AKI) in patients with Type A acute aortic dissection (TAAAD) and Type B acute aortic dissection (TBAAD) was constructed by using Machine Learning (ML).MethodsBaseline data was collected from Acute aortic division (AAD) patients admitted to First Affiliated Hospital of Xinjiang Medical University between January 1, 2019 and December 31, 2021. (1) We identified baseline Serum creatinine (SCR) estimation methods and used them as a basis for diagnosis of AKI. (2) Divide their total datasets randomly into Training set (70%) and Test set (30%), Bootstrap modeling and validation of features using multiple ML methods in the training set, and select models corresponding to the largest Area Under Curve (AUC) for follow-up studies. (3) Screening of the best ML model variables through the model visualization tools Shapley Addictive Explanations (SHAP) and Recursive feature reduction (REF). (4) Finally, the pre-screened prediction models were evaluated using test set data from three aspects: discrimination, Calibration, and clinical benefit.ResultsThe final incidence of AKI was 69.4% (120/173) in 173 patients with TAAAD and 28.6% (81/283) in 283 patients with TBAAD. For TAAAD-AKI, the Random Forest (RF) model showed the best prediction performance in the training set (AUC = 0.760, 95% CI:0.630–0.881); while for TBAAD-AKI, the Light Gradient Boosting Machine (LightGBM) model worked best (AUC = 0.734, 95% CI:0.623–0.847). Screening of the characteristic variables revealed that the common predictors among the two final prediction models for postoperative AKI due to AAD were baseline SCR, Blood urea nitrogen (BUN) and Uric acid (UA) at admission, Mechanical ventilation time (MVT). The specific predictors in the TAAAD-AKI model are: White blood cell (WBC), Platelet (PLT) and D dimer at admission, Plasma The specific predictors in the TBAAD-AKI model were N-terminal pro B-type natriuretic peptide (BNP), Serum kalium, Activated partial thromboplastin time (APTT) and Systolic blood pressure (SBP) at admission, Combined renal arteriography in surgery. Finally, we used in terms of Discrimination, the ROC value of the RF model for TAAAD was 0.81 and the ROC value of the LightGBM model for TBAAD was 0.74, both with good accuracy. In terms of calibration, the calibration curve of TAAAD-AKI's RF fits the ideal curve the best and has the lowest and smallest Brier score (0.16). Similarly, the calibration curve of TBAAD-AKI's LightGBM model fits the ideal curve the best and has the smallest Brier score (0.15). In terms of Clinical benefit, the best ML models for both types of AAD have good Net benefit as shown by Decision Curve Analysis (DCA).ConclusionWe successfully constructed and validated clinical prediction models for the occurrence of AKI after surgery in TAAAD and TBAAD patients using different ML algorithms. The main predictors of the two types of AAD-AKI are somewhat different, and the strategies for early prevention and control of AKI are also different and need more external data for validation.</p

Data from: Integrating variation in bacterial-fungal co-occurrence network with soil carbon dynamics

&lt;p&gt;Bacteria and fungi are core microorganisms in diverse ecosystems, and their cross-kingdom interactions are considered key determinants of microbiome structure and ecosystem functioning. However, how bacterial-fungal interactions mediate soil organic carbon (SOC) dynamics remains largely unexplored in the context of artificial forest ecosystems. Here, we characterized soil bacterial and fungal communities in four successive planting of Eucalyptus and compared them to a neighboring evergreen broadleaf forest. Carbon (C) mineralization combined with five C-degrading enzymatic activities was investigated to determine the effects of successive planting of Eucalyptus on SOC dynamics. Our results indicated that successive planting of Eucalyptus significantly altered the diversity and structure of soil bacterial and fungal communities and increased the negative bacterial-fungal associations. The bacterial diversity significantly decreased in all Eucalyptus plantations compared to the evergreen forest, while the fungal diversity showed the opposite trend. The ratio of negative bacterial-fungal associations increased with successive planting of Eucalyptus due to the decrease in SOC, ammonia nitrogen (NH4+−N), nitrate nitrogen (NO3−−N), and available phosphorus (AP). Structural equation modeling indicated that the potential cross-kingdom competition, based on the ratio of negative bacterial-fungal correlations, was significantly negatively associated with the diversity of total bacteria and keystone bacteria, thereby increasing C-degrading enzymatic activities and C mineralization.&lt;/p&gt; &lt;p&gt;Synthesis and applications: Our results highlight the regulatory role of the negative bacterial-fungal association in enhancing the correlation between bacterial diversity and C mineralization. This suggests that promoting short-term successive planting in the management of Eucalyptus plantations can mitigate the impact of this association on SOC decomposition. Taken together, our study advances the understanding of bacterial-fungal negative associations to mediate carbon mineralization in Eucalyptus plantations, giving us a new insight into SOC cycling dynamics in artificial forests.&lt;/p&gt;&lt;p&gt;Funding provided by: National Natural Science Foundation of China&lt;br&gt;Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809&lt;br&gt;Award Number: 41922048&lt;/p&gt;&lt;p&gt;Funding provided by: China Postdoctoral Science Foundation&lt;br&gt;Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100002858&lt;br&gt;Award Number: 2021M693577&lt;/p&gt;&lt;p&gt;Funding provided by: Distinguished Youth Scholar Program of Hunan Education Department*&lt;br&gt;Crossref Funder Registry ID: &lt;br&gt;Award Number: 20B605&lt;/p&gt;&lt;p&gt;Funding provided by: Youth Innovation Promotion Association of the Chinese Academy of Sciences&lt;br&gt;Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100004739&lt;br&gt;Award Number: Y2021084&lt;/p&gt;&lt;p&gt;Funding provided by: National Natural Science Foundation of China&lt;br&gt;Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809&lt;br&gt;Award Number: 42177298&lt;/p&gt;&lt;p&gt;Funding provided by: National Natural Science Foundation of China&lt;br&gt;Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809&lt;br&gt;Award Number: 32301568&lt;/p&gt

Data_Sheet_1_Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning.CSV

ObjectiveA clinical prediction model for postoperative combined Acute kidney injury (AKI) in patients with Type A acute aortic dissection (TAAAD) and Type B acute aortic dissection (TBAAD) was constructed by using Machine Learning (ML).MethodsBaseline data was collected from Acute aortic division (AAD) patients admitted to First Affiliated Hospital of Xinjiang Medical University between January 1, 2019 and December 31, 2021. (1) We identified baseline Serum creatinine (SCR) estimation methods and used them as a basis for diagnosis of AKI. (2) Divide their total datasets randomly into Training set (70%) and Test set (30%), Bootstrap modeling and validation of features using multiple ML methods in the training set, and select models corresponding to the largest Area Under Curve (AUC) for follow-up studies. (3) Screening of the best ML model variables through the model visualization tools Shapley Addictive Explanations (SHAP) and Recursive feature reduction (REF). (4) Finally, the pre-screened prediction models were evaluated using test set data from three aspects: discrimination, Calibration, and clinical benefit.ResultsThe final incidence of AKI was 69.4% (120/173) in 173 patients with TAAAD and 28.6% (81/283) in 283 patients with TBAAD. For TAAAD-AKI, the Random Forest (RF) model showed the best prediction performance in the training set (AUC = 0.760, 95% CI:0.630–0.881); while for TBAAD-AKI, the Light Gradient Boosting Machine (LightGBM) model worked best (AUC = 0.734, 95% CI:0.623–0.847). Screening of the characteristic variables revealed that the common predictors among the two final prediction models for postoperative AKI due to AAD were baseline SCR, Blood urea nitrogen (BUN) and Uric acid (UA) at admission, Mechanical ventilation time (MVT). The specific predictors in the TAAAD-AKI model are: White blood cell (WBC), Platelet (PLT) and D dimer at admission, Plasma The specific predictors in the TBAAD-AKI model were N-terminal pro B-type natriuretic peptide (BNP), Serum kalium, Activated partial thromboplastin time (APTT) and Systolic blood pressure (SBP) at admission, Combined renal arteriography in surgery. Finally, we used in terms of Discrimination, the ROC value of the RF model for TAAAD was 0.81 and the ROC value of the LightGBM model for TBAAD was 0.74, both with good accuracy. In terms of calibration, the calibration curve of TAAAD-AKI's RF fits the ideal curve the best and has the lowest and smallest Brier score (0.16). Similarly, the calibration curve of TBAAD-AKI's LightGBM model fits the ideal curve the best and has the smallest Brier score (0.15). In terms of Clinical benefit, the best ML models for both types of AAD have good Net benefit as shown by Decision Curve Analysis (DCA).ConclusionWe successfully constructed and validated clinical prediction models for the occurrence of AKI after surgery in TAAAD and TBAAD patients using different ML algorithms. The main predictors of the two types of AAD-AKI are somewhat different, and the strategies for early prevention and control of AKI are also different and need more external data for validation.</p

Data_Sheet_3_Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning.CSV

ObjectiveA clinical prediction model for postoperative combined Acute kidney injury (AKI) in patients with Type A acute aortic dissection (TAAAD) and Type B acute aortic dissection (TBAAD) was constructed by using Machine Learning (ML).MethodsBaseline data was collected from Acute aortic division (AAD) patients admitted to First Affiliated Hospital of Xinjiang Medical University between January 1, 2019 and December 31, 2021. (1) We identified baseline Serum creatinine (SCR) estimation methods and used them as a basis for diagnosis of AKI. (2) Divide their total datasets randomly into Training set (70%) and Test set (30%), Bootstrap modeling and validation of features using multiple ML methods in the training set, and select models corresponding to the largest Area Under Curve (AUC) for follow-up studies. (3) Screening of the best ML model variables through the model visualization tools Shapley Addictive Explanations (SHAP) and Recursive feature reduction (REF). (4) Finally, the pre-screened prediction models were evaluated using test set data from three aspects: discrimination, Calibration, and clinical benefit.ResultsThe final incidence of AKI was 69.4% (120/173) in 173 patients with TAAAD and 28.6% (81/283) in 283 patients with TBAAD. For TAAAD-AKI, the Random Forest (RF) model showed the best prediction performance in the training set (AUC = 0.760, 95% CI:0.630–0.881); while for TBAAD-AKI, the Light Gradient Boosting Machine (LightGBM) model worked best (AUC = 0.734, 95% CI:0.623–0.847). Screening of the characteristic variables revealed that the common predictors among the two final prediction models for postoperative AKI due to AAD were baseline SCR, Blood urea nitrogen (BUN) and Uric acid (UA) at admission, Mechanical ventilation time (MVT). The specific predictors in the TAAAD-AKI model are: White blood cell (WBC), Platelet (PLT) and D dimer at admission, Plasma The specific predictors in the TBAAD-AKI model were N-terminal pro B-type natriuretic peptide (BNP), Serum kalium, Activated partial thromboplastin time (APTT) and Systolic blood pressure (SBP) at admission, Combined renal arteriography in surgery. Finally, we used in terms of Discrimination, the ROC value of the RF model for TAAAD was 0.81 and the ROC value of the LightGBM model for TBAAD was 0.74, both with good accuracy. In terms of calibration, the calibration curve of TAAAD-AKI's RF fits the ideal curve the best and has the lowest and smallest Brier score (0.16). Similarly, the calibration curve of TBAAD-AKI's LightGBM model fits the ideal curve the best and has the smallest Brier score (0.15). In terms of Clinical benefit, the best ML models for both types of AAD have good Net benefit as shown by Decision Curve Analysis (DCA).ConclusionWe successfully constructed and validated clinical prediction models for the occurrence of AKI after surgery in TAAAD and TBAAD patients using different ML algorithms. The main predictors of the two types of AAD-AKI are somewhat different, and the strategies for early prevention and control of AKI are also different and need more external data for validation.</p