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ObjectivesThe online behavior of online users has taken on complex and diverse characteristics, and posting product reviews on e-commerce platforms is no exception. In fact, reviews contain rich and multi-dimensional discrete emotional information, and whether there is a relationship between the expression of these different discrete emotions and the time interval between product purchase and review posting as well as their related characteristics are the issues that this study needs to analyze and solve in depth.MethodsBased on the OCC model (named after three proposers) of psychological emotional cognitive evaluation theory as the basis for emotion classification, the study used the massive amounts of Chinese reviews of mobile phones on the Chinese e-commerce platform Jingdong Mall as the research object, applied supervised machine learning methods to classify discrete emotions, and constructed a large corpus containing satisfaction, disappointment, admiration, reproach, love, and hate; then the study delved into the distribution and behavioral dynamics characteristics of consumers’ comments containing the different discrete emotions at different “purchase-comment” time intervals.ResultsThe results showed that the first peak of the distribution curves of the six discrete emotions at different “purchase-comment” time intervals occurs on the first day after purchase and then decreases gradually but at different rates. The three curves for satisfaction, love, and hate emotions also show a second peak on the eleventh day, which is more similar to the bimodal distribution, implying that the corresponding product reviews are more objective. In addition, the distribution of reviews containing the six discrete emotions at different “purchase-comment” time intervals follows a power-law distribution and has the temporal characteristics of human behavioral dynamics, that is, “strong paroxysms and weak memory“. However, the reviews containing the admiration and reproach emotions were most intensively written by consumers after the purchase, indicating that the service provided by the seller, logistics, and e-commerce platform stimulates more consumers to give quick responses and detailed reviews.ConclusionThis study is not only of great significance for exploring the internal mechanisms of consumer discrete emotional expression but also provides important decision-making references for potential consumer purchasing decisions, product updates for developers, marketing strategy formulation for marketing teams, and service improvement for sellers, logistics companies, and e-commerce platforms.

Pearl millet is a major cereal crop that feeds more than 90 million people worldwide in arid and semi-arid regions. The stalk phenotypes of Poaceous grasses are critical for their productivity and stress tolerance; however, the molecular mechanisms governing stalk development in pearl millet remain to be deciphered. In this study, we spatiotemporally measured 19 transcriptomes for stalk internodes of four different early developmental stages. Data analysis of the transcriptomes defined four developmental zones on the stalks and identified 12 specific gene sets with specific expression patterns across the zones. Using weighted gene co-expression network analysis (WGCNA), we found that two co-expression modules together with candidate genes were involved in stalk elongation and the thickening of pearl millet. Among the elongation-related candidate genes, we established by SELEX that a MYB-family transcription factor PMF7G02448 can bind to the promoters of three cell wall synthases genes (CesAs). In summary, these findings provide insights into stalk development and offer potential targets for future genetic improvement in pearl millet. [ABSTRACT FROM AUTHOR]

Background: Vessels encapsulating tumor cluster (VETC) and microvascular invasion (MVI) have a synergistic effect on prognosis assessment and treatment selection of hepatocellular carcinoma (HCC). Preoperative noninvasive evaluation of VETC and MVI is important. Purpose: To explore the diagnosis value of preoperative gadoxetic acid (GA)‐enhanced magnetic resonance imaging (MRI) features for MVI, VETC, and recurrence‐free survival (RFS) in HCC. Study Type: Retrospective. Population: 240 post‐surgery patients with 274 pathologically confirmed HCC (allocated to training and validation cohorts with a 7:3 ratio) and available tumor marker data from August 2014 to December 2021. Field Strength/Sequence: 3‐T, T1‐, T2‐, diffusion‐weighted imaging, in/out‐phase imaging, and dynamic contrast‐enhanced imaging. Assessment: Three radiologists subjectively reviewed preoperative MRI, evaluated clinical and conventional imaging features associated with MVI+, VETC+, and MVI+/VETC+ HCC. Regression‐based nomograms were developed for HCC in the training cohort. Based on the nomograms, the RFS prognostic stratification system was further. Follow‐up occurred every 3–6 months. Statistical Tests: Chi‐squared test or Fisher's exact test, Mann–Whitney U‐test or t‐test, least absolute shrinkage and selection operator–penalized, multivariable logistic regression analyses, receiver operating characteristic analysis, Harrell's concordance index (C‐index), Kaplan–Meier plots. Significance level: P < 0.05. Results: In the training group, 44 patients with MVI+ and 74 patients with VETC+ were histologically confirmed. Three nomograms showed good performance in the training (C‐indices: MVI+ vs. VETC+ vs. MVI+/VETC+, 0.892 vs. 0.848 vs. 0.910) and validation (C‐indices: MVI+ vs. VETC+ vs. MVI+/VETC+, 0.839 vs. 0.810 vs. 0.855) cohorts. The median follow‐up duration for the training cohort was 43.6 (95% CI, 35.0–52.2) months and 25.8 (95% CI, 16.1–35.6) months for the validation cohort. Patients with either pathologically confirmed or nomogram‐estimated MVI, VETC, and MVI+/VETC+ suffered higher risk of recurrence. Data Conclusion: GA‐enhanced MRI and clinical variables might assist in preoperative estimation of MVI, VETC, and MVI+/VETC+ in HCC. Evidence Level: 4 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Bird's nest fungi involve six different genera, but only one of these genera (i.e. Cyathus) have available mitochondrial genomes (mitogenomes) to date. In this study, we report the first mitogenome in the genus Nidula with Nidula shingbaensis K. Das & R.L. Zhao 2013 as a representative. The mitogenome is a circular molecule of 65,793 bp with a GC content of 26.2%. There are a total of 43 genes, including 14 typical protein-coding genes, 26 tRNA genes, two rRNA genes, and one free-standing intergenic open reading frame (ORF). Three introns (two in cox1 and one in cob) are present in the mitogenome, with each containing an ORF encoding for a LAGLIDADG endonuclease. Phylogenetic analysis based on mitochondrial amino acid sequences confirms the phylogenetic placement of N. shingbaensis in Nidulariaceae in Agaricales. This study serves as a springboard for future investigation on fungal evolution in Nidulariaceae. [ABSTRACT FROM AUTHOR]

Giant reed (Arundo donax) is widely distributed across the globe and is considered an important energy crop. This study presents the first comprehensive analysis of the chloroplast genome of giant reed, revealing detailed characteristics of this species' chloroplast genome. The chloroplast genome has a total length of 137,153 bp, containing 84 protein-coding genes, 38 tRNA genes, and 8 rRNA genes, with a GC content of 39%. Functional analysis indicates that a total of 45 photosynthesis-related genes and 78 self-replication-related genes were identified, which may be closely associated with its adaptability and growth characteristics. Phylogenetic analysis confirmed that Arundo donax cv. Lvzhou No.1 belongs to the Arundionideae clade and occupies a distinct evolutionary position compared to other Arundo species. The findings of this study not only enhance our understanding of the giant reed genome but also provide valuable genetic resources for its application in biotechnology, bioenergy crop development, and ecological restoration. [ABSTRACT FROM AUTHOR]

Using linear low-density polyethylene (LLDPE) and polyolefin elastomer (POE) as polyolefin materials and boron nitride (BN) as thermal conductivity filler, the LLDPE/POE/BN thermal conductivity composites was prepared by melt blending hot pressing method. The influence of the process on the dispersion structure of BN was studied, and the relationship between the structure and thermal conductivity of the composites was discussed. The thermal conductivity and temperature field of the composites were predicted by finite element simulation. The results show that the composites with different BN dispersion structures are obtained by three processes. The cocontinuous structure composites formed by BN dispersed in LLDPE phase has excellent thermal conductivity. When the mass fraction of BN is 40% and 50%, the thermal conductivity of the composites is 0.72 W/(m·K) and 0.91 W/(m·K), respectively. Based on the dispersion structure of BN, the structure model of the composites was constructed, the thermal conductivity of the composites was predicted successfully, and the better heat transfer behavior of the composites was revealed. [ABSTRACT FROM AUTHOR]

With the development of port automation and artificial intelligence, coordination with multi-geographic data centers (Geo-DCs) has become a viable solution to address the issue of limited port computing resources. This study proposes a distributed energy dispatch method for the port microgrid coordinated with Geo-DCs (Geo-DCPM), aimed at reducing port carbon emissions and operational costs. Consider the single point of failure problem and high construction costs of centralized data centers. Geo-DCs are first introduced to solve the problem of insufficient computing resources in ports. An energy consumption calculation model for Geo-DCs is established, considering the data load delay constraint and the data space transfer constraint caused by specific delay-sensitive loads in the port microgrid. Then, an energy dispatch model (EDM) is constructed for the Geo-DCPM, taking into account carbon capture costs. Moreover, based on mixed-integer linear programming, a distributed algorithm is proposed to solve the EDM problem. Finally, the simulation results verify the effectiveness of the proposed method. Compared with the centralized algorithm, the packet loss rate of the distributed algorithm combined with Geo-DCs is significantly lower, reduced by about 70%. [ABSTRACT FROM AUTHOR]