The sentiment analysis has the key problem for the e-commerce product quality management, which customers know their people’s attitudes on interested products by e-commerce reviews. Meanwhile, manufacturers are able to learn the public sentiment on their products being sold in E-commerce platforms. This paper proposed the deep learning models of Bert-BiGRU-Softmax, which uses the input layer of Sentiment Bert model to extract multi-dimensional of E-commerce reviews, the hidden layer of Bidirectional GRU model to obtain semantic codes and calculate representing weights of reviews, the Softmax with attention mechanism as the output layer to classify the sentiment tendency. We conduct experiments on a large-scale dataset involving over 500 thousand reviews compared with different learning models. The experimental results show that the proposed models reach the high accuracy 95.5% on the E-commerce reviews, and the outperforms of RNN, BiGRU, Bert-BiLSTM in terms of accuracy and loss.

In this issue of JEM, Schuijs et al. (https://doi.org/10.1084/jem.20240103) highlight a novel role for basophils during allergic immune responses to house dust mites (HDM). They reveal that interleukin-33 (IL-33)-activated basophils facilitate the recruitment and extravasation of Th2 cells into the lungs during a specific time frame via their interactions with pulmonary endothelial cells.

A graph is a particular representation of a static network and labeling of a graph can be think of as automatic routing of data in a network topology. A visual representation of data in the form of a graph help us to look deep insight. The data science (e.g., Python Package: a computer programming language) uses graph theory concepts to study and analyze the networks. Analyzing these network is equivalent of finding a set of edges E′ for a graph G such that every vertex of G is incident with at least one edge in E′. Then E′ is called an edge-covering of G. A spanning tree of a connected graph is an example of edge-covering. A finite simple graph G is an (ad, d)-H-antimagic if the following three conditions are satisfied: G has an H-covering (H a subgraph of G), there exists a bijection α : V (G) ∪ E(G) → {1, 2, 3, . . . , |V (G) ∪ E(G)|} and the H-weights constitute an arithmetic progression with common difference d. The above said labeling is called super if α(V ) = {1, 2, . . . , |V |}. In this research article, we focused on studying super C3-antimagic labeling of line graph of a sun-let graph for several differences and C3-supermagic labeling of its disjoint union.

Augmented Lagrange multiplier method is an important method to solve constrained optimization problems. In recent years, it has become more important to study the application of augmented Lagrange multiplier method. This paper first introduces the augmented Lagrange multiplier method, which leads to the development of the application of the augmented Lagrange method to nonlinear mathematical equations, and summarizes the augmented Lagrange multiplier. The application of nonlinear mathematical equations. At the same time, the paper specifies the application of the augmented Lagrange method in urban and rural multi-objective programming, and proves the practical application of the augmented Lagrange multiplier nonlinear mathematical equations.

In the modern era, networks play a crucial role in connecting individuals, organizations, and devices, enabling efficient communication, collaboration, and information exchange across the globe. They serve as the backbone of the digital age, supporting the growth of technologies such as the internet, cloud computing, IoT, and social media. The examination of Quantitative Structure Activity/Property Relationships (QSPR) to analyse the network architecture uses topological indices as its main instrument. The butterfly network is a type of interconnection network that follows a specific geometric structure. It consists of multiple stages, with each stage connecting nodes in a binary pattern. The connections between nodes form a butterfly-like shape, hence the name. The butterfly network provides efficient routing and communication paths between nodes, making it suitable for parallel computing and distributed systems. With the aid of sixteen irregularity indices, we will examine and compare three butterfly networks in this article. We will then plot the findings to see how they depend on the various parameters.

Graph theory is widely used to analyze the structure models in chemistry, biology, computer science, operations research and sociology. Molecular bonds, species movement between regions, development of computer algorithms, shortest spanning tree in a weighted graphs, aircraft scheduling and exploration of diffusion mechanism are some of these structure models. A computing system C can execute an algorithm A if A is graph-isomorphic to a subgraph of CG. This is equivalent of defining a graph isomorphism between A and subgraph of CG. This research article gives a small description of graph theory applications and one of graph model (zig-zag triangle) to define a suitable computer algorithm. Let Γ be a connected, simple graph with finite vertices v and edges e. A family {Γ1, Γ2, . . . , Γp} ⊂ Γ of subgraphs such that for all e ∈ E, e ∈ Γl, for some l, l = 1, 2, . . . , p is an edge-covering of Γ. If Γl ∼= Γ′, ∀l, then Γ has an Γ′-covering. Graph Γ with Γ′-covering is an (ad, d)-Γ′-antimagic if f : V ∪ E → {1, 2, . . . , |V | + |E|} a bijection exists and the sum over all vertex-weights and edge-weights of Γ′ form a set {ad, ad + d, . . . , ad + (p − 1)d}. The labeling ξ is super for ξ(VΓ) = {1, 2, 3, . . . , |VΓ|} and graph Γ is Γ′-supermagic for d = 0. This manuscript investigates super (ad, d)-Γ′-antimagic labelings of zig-zag triangles for differences d = 1, 2, . . . , 8.

Most eukaryotic genes encode polypeptides that are either obligate members of hetero-stoichiometric complexes or clients of organelle-targeting pathways. Proteins in these classes can be released from the ribosome as "orphans"-newly synthesized proteins not associated with their stoichiometric binding partner(s) and/or not targeted to their destination organelle. Here we integrate recent findings suggesting that although cells selectively degrade orphan proteins under homeostatic conditions, they can preserve them in chaperone-regulated biomolecular condensates during stress. These orphan protein condensates activate the heat shock response (HSR) and represent subcellular sites where the chaperones induced by the HSR execute their functions. Reversible condensation of orphan proteins may broadly safeguard labile precursors during stress.

We identify BEACH domain-containing proteins (BDCPs) as novel membrane coat proteins involved in the sorting of transmembrane proteins (TMPs) on the trans-Golgi network and tubular sorting endosomes. The seven typical mammalian BDCPs share a predicted alpha-solenoid-beta propeller structure, suggesting they have a protocoatomer origin and function. We map the subcellular localization of seven BDCPs based on their dynamic colocalization with RAB and ARF small GTPases and identify five typical BDCPs on subdomains of dynamic tubular-vesicular compartments on the intersection of endocytic recycling and post-Golgi secretory pathways. We demonstrate that BDCPs interact directly with the cytosolic tails of selected TMPs and identify a subset of TMPs, whose trafficking to the plasma membrane is affected in cells lacking BDCP. We propose that the competitive binding of BDCPs and clathrin coat adaptors to the cytosolic tails of TMPs, followed by their clustering to distinct subdomains of secretory/recycling tubules function as a mechanism for sorting of TMPs in pleomorphic tubular-vesicular compartments that lack a clathrin coat.

Our current understanding of postpartum people’s functioning while managing substance use disorder (SUD) recovery is limited. This study explored whether having a SUD impacts maternal functioning and examined the reliability of the Barkin Index of Maternal Functioning (BIMF) in postpartum people with SUDs. We conducted a prospective, cross-sectional survey (n = 141) by administering the BIMF and a demographic questionnaire with a sample of postpartum people with SUD (n = 48) and a comparative sample of postpartum people without SUD (n = 93). The internal reliability (Cronbach’s alpha = .86) was consistent with previous studies. SUD diagnosis was significantly associated with lower functioning, with individuals in the SUD group having an 8-point lower BIMF score compared to persons without SUDs, although both groups reported moderately high BIMF scores (>80). While not statistically significant, participants with SUDs had reduced functioning scores over time. Considering the increasing rate of overdose deaths in postpartum persons, further examination of functioning in postpartum persons with SUDs is warranted. Integration of the BIMF into SUD screening and treatment can provide a holistic view of postpartum experiences in this high-risk population and serve as a unique tool to understand the needs of new mothers navigating SUD recovery.