Study on the relationship between flavor components and quality of ice wine during freezing and brewing of 'beibinghong' grapes.

Ice wine has prominent fruity sweetness and unique, rich aroma compared to wine. The sweetness was accumulating, the acidity and astringency tended to soften of grape berry during the freezing period. The process gave the ice wine balanced taste, with prominent honey sweetness, accompanied by refreshing alcoholic taste, soft acidity and astringency. Eleven key aroma compounds were identified in ice wine through GC-MS and ROAV values. The key aroma compounds were analyzed with Pearson correlation coefficient and fragrance mechanism were speculated. Ethyl acetate and 1-octen-3-ol derived from the aroma of grape, are produced by anaerobic metabolism and lipoxygenase pathways of pyruvate and linoleic acid, respectively. Ester aromas, 2-phenylethanol and 2-methylbutanal were derived from the brewing process, were produced by octanoic acid, caproic acid, phenylalanine and isoleucine through lipid metabolism, Ehrlich pathway and Strecker pathway, respectively. Proposed corresponding control methods based on factors that affect the formation of ice wine aromas.

Neoadjuvant chemotherapy reduces the levels of HMGB1 and E-cadherin in patients with breast cancer.

This study investigated the changes in serum tumor marker levels in patients with breast cancer (BC) after neoadjuvant chemotherapy (NACT) and their potential as prognostic factors in NACT. A total of 134 consecutive patients with BC treated at our hospital between January 2019 and December 2021 were retrospectively analyzed. Patients were treated with NACT based on the docetaxel, epirubicin, and cyclophosphamide (TEC) regimen and assessed for marker levels, T cell subsets, and therapeutic outcomes. Receiver operating characteristic (ROC) curves were constructed to evaluate the predictive performance of the markers. Outcome assessments showed that NACT effectively reduced the tumor size, leading to increased complete remission, partial remission, stable disease, and significantly reduced disease progression. Improved immune function has also been observed after NACT. The levels of two (E-cadherin and HMGB1) out of five markers (CA153, CK19, CEA, E-cadherin, and HMGB1) were significantly reduced after NACT before surgery compared with those at admission, suggesting that NACT modulates the levels of biomarkers. ROC analysis revealed that the area under the curve (AUC) of HMGB1 and E-cadherin combination was 0.87 for discrimination of therapeutic response with a sensitivity and specificity of 91.3% and 88.4%, respectively. Serum tumor marker levels were reduced after NACT in patients with BC. The reduction was most prominent for HMGB1, followed by E-cadherin. These biomarkers can be used to predict the therapeutic response to NACT with an AUC of 0.87, thus offering a new tool to monitor treatment progress in NACT for patients with BC.

A Framework for Analyzing Fraud Risk Warning and Interference Effects by Fusing Multivariate Heterogeneous Data: A Bayesian Belief Network.

In the construction of a telecom-fraud risk warning and intervention-effect prediction model, how to apply multivariate heterogeneous data to the front-end prevention and management of telecommunication network fraud has become one of the focuses of this research. The Bayesian network-based fraud risk warning and intervention model was designed by taking into account existing data accumulation, the related literature, and expert knowledge. The initial structure of the model was improved by utilizing City S as an application example, and a telecom-fraud analysis and warning framework was proposed by incorporating telecom-fraud mapping. After the evaluation in this paper, the model shows that age has a maximum sensitivity of 13.5% to telecom-fraud losses; anti-fraud propaganda can reduce the probability of losses above 300,000 yuan by 2%; and the overall telecom-fraud losses show that more occur in the summer and less occur in the autumn, and that the Double 11 period and other special time points are prominent. The model in this paper has good application value in the real-world field, and the analysis of the early warning framework can provide decision support for the police and the community to identify the groups, locations, and spatial and temporal environments prone to fraud, to combat propaganda and provide a timely warning to stop losses.

Visual Diagnostics of Parallel Performance in Training Large-Scale DNN Models.

Diagnosing the cluster-based performance of large-scale deep neural network (DNN) models during training is essential for improving training efficiency and reducing resource consumption. However, it remains challenging due to the incomprehensibility of the parallelization strategy and the sheer volume of complex data generated in the training processes. Prior works visually analyze performance profiles and timeline traces to identify anomalies from the perspective of individual devices in the cluster, which is not amenable for studying the root cause of anomalies. In this paper, we present a visual analytics approach that empowers analysts to visually explore the parallel training process of a DNN model and interactively diagnose the root cause of a performance issue. A set of design requirements is gathered through discussions with domain experts. We propose an enhanced execution flow of model operators for illustrating parallelization strategies within the computational graph layout. We design and implement an enhanced Marey's graph representation, which introduces the concept of time-span and a banded visual metaphor to convey training dynamics and help experts identify inefficient training processes. We also propose a visual aggregation technique to improve visualization efficiency. We evaluate our approach using case studies, a user study and expert interviews on two large-scale models run in a cluster, namely, the PanGu- α 13B model (40 layers), and the Resnet model (50 layers).

Visual Reasoning for Uncertainty in Spatio-Temporal Events of Historical Figures.

The development of digitized humanity information provides a new perspective on data-oriented studies of history. Many previous studies have ignored uncertainty in the exploration of historical figures and events, which has limited the capability of researchers to capture complex processes associated with historical phenomena. We propose a visual reasoning system to support visual reasoning of uncertainty associated with spatio-temporal events of historical figures based on data from the China Biographical Database Project. We build a knowledge graph of entities extracted from a historical database to capture uncertainty generated by missing data and error. The proposed system uses an overview of chronology, a map view, and an interpersonal relation matrix to describe and analyse heterogeneous information of events. The system also includes uncertainty visualization to identify uncertain events with missing or imprecise spatio-temporal information. Results from case studies and expert evaluations suggest that the visual reasoning system is able to quantify and reduce uncertainty generated by the data.

CohortVA: A Visual Analytic System for Interactive Exploration of Cohorts based on Historical Data.

In history research, cohort analysis seeks to identify social structures and figure mobilities by studying the group-based behavior of historical figures. Prior works mainly employ automatic data mining approaches, lacking effective visual explanation. In this paper, we present CohortVA, an interactive visual analytic approach that enables historians to incorporate expertise and insight into the iterative exploration process. The kernel of CohortVA is a novel identification model that generates candidate cohorts and constructs cohort features by means of pre-built knowledge graphs constructed from large-scale history databases. We propose a set of coordinated views to illustrate identified cohorts and features coupled with historical events and figure profiles. Two case studies and interviews with historians demonstrate that CohortVA can greatly enhance the capabilities of cohort identifications, figure authentications, and hypothesis generation.

HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity in Horizontal Federated Learning.

Horizontal federated learning (HFL) enables distributed clients to train a shared model and keep their data privacy. In training high-quality HFL models, the data heterogeneity among clients is one of the major concerns. However, due to the security issue and the complexity of deep learning models, it is challenging to investigate data heterogeneity across different clients. To address this issue, based on a requirement analysis we developed a visual analytics tool, HetVis, for participating clients to explore data heterogeneity. We identify data heterogeneity through comparing prediction behaviors of the global federated model and the stand-alone model trained with local data. Then, a context-aware clustering of the inconsistent records is done, to provide a summary of data heterogeneity. Combining with the proposed comparison techniques, we develop a novel set of visualizations to identify heterogeneity issues in HFL. We designed three case studies to introduce how HetVis can assist client analysts in understanding different types of heterogeneity issues. Expert reviews and a comparative study demonstrate the effectiveness of HetVis.

Modeling and Risk Analysis of Chemical Terrorist Attacks: A Bayesian Network Method.

The chemical terrorist attack is an unconventional form of terrorism with vast scope of influence, strong concealment, high technical means and severe consequences. Chemical terrorism risk refers to the uncertainty of the effects of terrorist organisations using toxic industrial chemicals/drugs and classic chemical weapons to attack the population. There are multiple risk factors infecting chemical terrorism risk, such as the threat degree of terrorist organisations, attraction of targets, city emergency response capabilities, and police defense capabilities. We have constructed a Bayesian network of chemical terrorist attacks to conduct risk analysis. The scenario analysis and sensitivity analysis are applied to validate the model and analyse the impact of the vital factor on the risk of chemical terrorist attacks. The results show that the model can be used for simulation and risk analysis of chemical terrorist attacks. In terms of controlling the risk of chemical terrorist attack, patrol and surveillance are less critical than security checks and police investigations. Security check is the most effective approach to decrease the probability of successful attacks. Different terrorist organisations have different degrees of threat, but the impacts of which are limited to the success of the attack. Weapon types and doses are sensitive to casualties, but it is the level of emergency response capabilities that dominates the changes in casualties. Due to the limited number of defensive resources, to get the best consequence, the priority of the deployment of defensive sources should be firstly given to governmental buildings, followed by commercial areas. These findings may provide the theoretical basis and method support for the combat of the public security department and the safety prevention decision of the risk management department.