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Wireless sensor network (WSN) technology is becoming increasingly crucial in the context of the Fourth Industrial Revolution. This has led to the development of various protocols aimed at enhancing energy efficiency, with LEACH, PEGASIS, and EEACP being key among them. Despite these advancements, the EEACP protocol exhibits significant limitations in its current form, particularly regarding energy expenditure during data transmission phases. A primary inefficiency arises from suboptimal path configurations during data reception, which can severely impact the network’s longevity and effectiveness. To address these challenges, the newly proposed Advanced EEACP (O3EACP) protocol introduces strategic optimizations in data routing toward the sink node. This refinement aims to minimize energy drain during transmissions, thereby extending the operational lifespan of the network and bolstering its overall resilience. This enhancement not only promises a reduction in energy consumption but also a notable improvement in network sustainability, essential for supporting the robust, interconnected frameworks envisioned in the latest industrial revolution.

Asking and answering questions are common activities in both the workplace and everyday life. Knowledge-sharing websites have become a popular resource for obtaining instant and searchable answers. However, users of these sites may encounter challenges in acquiring timely and appropriate content from user-provided answers owing to factors such as limited expertise, spam, and time constraints. Identifying trustworthy experts who can provide relevant and reliable answers in knowledge-sharing communities is crucial to overcome this issue. In this study, we propose a solution to the problem of identifying credible experts on knowledge-sharing sites by introducing the CredibleExpertRank algorithm. Our algorithm calculates a CredibleExpert score based on two main factors: activity and credibility. The credibility score is determined by analyzing users’ interactions related to questioning, answering, recommending, and mining users’ opinions, while the activity score reflects the user’s level of participation on the platform. We conducted experiments to evaluate the performance of the CredibleExpertRank algorithm, using user satisfaction measures for answers to given questions. Our findings confirmed that the credible experts identified by our algorithm provided more relevant and timely answers compared to other ordinary users. The timely nature of the credible experts’ answers was due to the reflection of their activity factor, while the superior performance in relevance was attributed to the high recommendation rate of their answers and positive evaluations received from opinion mining results. Our study undertakes an extensive investigation focused on the identification and prioritization of credible experts, revealing their profound advantages in significantly enhancing the overall quality of knowledge-sharing platforms. We proposed the CredibleExpertRank algorithm as a powerful method for effectively identifying trustworthy experts and giving priority to their answers. Through a meticulous process of experimental evaluation, we provide compelling evidence that this approach leads to substantial improvements in both search efficiency and reliability on knowledge-sharing sites. By highlighting the potential benefits derived from the identification of credible experts, our study underscores their pivotal role in elevating the overall performance of knowledge-sharing platforms.

Abstract There is still an unmet need for development of safer antimelanogenic or melanin-degrading agents for skin hyperpigmentation, induced by intrinsic or extrinsic factors including aging or ultraviolet irradiation. Owing to the relatively low cytotoxicity compared with other chemical materials, several studies have explored the role of 2'-fucosyllactose (2'-FL), the most dominant component of human milk oligosaccharides. Here, we showed that 2'-FL reduced melanin levels in both melanocytic cells and a human skin equivalent three-dimensional in vitro model. Regarding the cellular and molecular mechanism, 2'-FL induced LC3I conversion into LC3II, an autophagy activation marker, followed by the formation of LC3II+/PMEL+ autophagosomes. Comparative transcriptome analysis provided a comprehensive understanding for the up- and downstream cellular processes and signaling pathways of the AMPK–ULK1 signaling axis triggered by 2'-FL treatment. Moreover, 2'-FL activated the phosphorylation of AMPK at Thr172 and of ULK1 at Ser555, which were readily reversed in the presence of dorsomorphin, a specific AMPK inhibitor, with consequent reduction of the 2'-FL-mediated hypopigmentation. Taken together, these findings demonstrate that 2'-FL promotes melanin degradation by inducing autophagy through the AMPK–ULK1 axis. Hence, 2'-FL may represent a new natural melanin-degrading agent for hyperpigmentation.

Abstract Gait, the style of human walking, has been studied as a behavioral characteristic of an individual. Several studies have utilized gait to identify individuals with the aid of machine learning and computer vision techniques. However, there is a lack of studies on the nature of gait, such as the identification power or the uniqueness. This study aims to quantify the uniqueness of gait in a cohort. Three-dimensional full-body joint kinematics were obtained during normal walking trials from 488 subjects using a motion capture system. The joint angles of the gait cycle were converted into gait vectors. Four gait vectors were obtained from each subject, and all the gait vectors were pooled together. Two gait vectors were randomly selected from the pool and tested if they could be accurately classified if they were from the same person or not. The gait from the cohort was classified with an accuracy of 99.71% using the support vector machine with a radial basis function kernel as a classifier. Gait of a person is as unique as his/her facial motion and finger impedance, but not as unique as fingerprints.

Cancer: Improving targeted therapy A new method for producing antibody-drug conjugates (ADCs), pairing anti-cancer drugs with antibodies that deliver them specifically to cancer cells, may help to develop therapies for hard-to-treat cancers. ADCs show promise for treating several cancers, but are difficult to produce. Junho Chung at the Seoul National University College of Medicine in South Korea and co-workers tested a new way of forming the conjugates, using cotinine, a non-toxic molecule related to nicotine, as a link. They bound cotinine to the anti-cancer drug duocarmycin, then formulated an antibody with two binding sites, one for cotinine, one for tumor cells. Combining the drug−cotinine complex with the antibody resulted in a drug–cotinine-antibody ADC that delivered duocarmycin exclusively to cancer cells and inhibited tumor growth in mice. This simplified method may help develop treatments for other drug-resistant cancers.

[This corrects the article DOI: 10.1371/journal.pbio.1002152.].

Epidermal growth factor receptor variant III (EGFRvIII) has been associated with glioma stemness, but the direct molecular mechanism linking the two is largely unknown. Here, we show that EGFRvIII induces the expression and secretion of pigment epithelium-derived factor (PEDF) via activation of signal transducer and activator of transcription 3 (STAT3), thereby promoting self-renewal and tumor progression of glioma stem cells (GSCs). Mechanistically, PEDF sustained GSC self-renewal by Notch1 cleavage, and the generated intracellular domain of Notch1 (NICD) induced the expression of Sox2 through interaction with its promoter region. Furthermore, a subpopulation with high levels of PEDF was capable of infiltration along corpus callosum. Inhibition of PEDF diminished GSC self-renewal and increased survival of orthotopic tumor-bearing mice. Together, these data indicate the novel role of PEDF as a key regulator of GSC and suggest clinical implications.