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Abstract In this paper we study the problem of walk-specific information spread in directed complex social networks. Classical models usually analyze the “explosive” spread of information on social networks (e.g., Twitter) – a broadcast or epidemiological model focusing on the dynamics of a given source node “infecting” multiple targets. Less studied, but of equal importance is the case of single-track information flow, wherein the focus is on the node-by-node (and not necessarily a newly visited node) trajectory of information transfer. An important and motivating example is the sequence of physicians visited by a given patient over a presumed course of treatment or health event. This is the so-called a referral sequence which manifests as a path in a network of physicians. In this case the patient (and her health record) is a source of “information" from one physician to the next. With this motivation in mind we build a Bayesian Personalized Ranking (BPR) model to predict the next node on a walk of a given network navigator using network science features. The problem is related to but different from the well-investigated link prediction problem. We present experiments on a dataset of several million nodes derived from several years of U.S. patient referral records, showing that the application of network science measures in the BPR framework boosts hit-rate and mean percentile rank for the task of next-node prediction. We then move beyond the simple information walk to consider the derived network space of all information walks within a period, in which a node represents an information walk and two information walks are connected if have nodes in common from the original (social) network. To evaluate the utility of such a network of information walks, we simulate outliers of information walks and distinguish them with the other normal information walks, using five distance metrics for the derived feature vectors between two information walks. The experimental results of such a proof-of-concept application shows the utility of the derived information walk network for the outlier monitoring of information flow on an intelligent network.

Abstract In this paper, we analyze the millions of referral paths of patients’ interactions with the healthcare system for each year in the 2006-2011 time period and relate them to U.S. cardiovascular treatment records. For a patient, a “referral path” records the chronological sequence of physicians encountered by a patient (subject to certain constraints on the times between encounters). It provides a basic unit of analysis in a broader referral network that encodes the flow of patients and information between physicians in a healthcare system. We consider referral networks defined over a range of interactions as well as the characteristics of referral paths, producing a characterization of the various networks as well as the physicians they comprise. We further relate these metrics and findings to outcomes in the specific area of cardiovascular care. In particular, we match a referral path to occurrences of Acute Myocardial Infarction (AMI) and use the summary measures of the referral path to predict the treatment a patient receives and medical outcomes following treatment. Some referral path features are more significant with respect to their ability to boost a tree-based predictive model, and have stronger correlations with numerical treatment outcome variables. The patterns of referral paths and the derived informative features illustrate the potential for using network science to optimize patient referrals in healthcare systems for improved treatment outcomes and more efficient utilization of medical resources.

Autonomous path planning represents a cornerstone technology for enabling planetary rovers to carry out exploration missions on extraterrestrial planets. This technology facilitates planetary rovers in navigating safely and efficiently toward their mission goal. However, the special conditions of extraterrestrial planetary environments pose significant challenges for autonomous path planning compared with those faced by ground-based mobile robots. These challenges include communication delays and bandwidth limitations between the planetary rover and the ground control station, limited light, unknown and unpredictable terrain, distinct environmental features, and the difficulty of accurately modeling interactions between the rover wheels and the ground. Therefore, addressing how to generate a safe, efficient, and reasonable traversable path for a planetary rover under conditions of incomplete environmental data and uncertain positioning is a focal point of current research. This paper reviews advancements in perception map construction and autonomous path planning for planetary rovers, examining both research progress and engineering applications. First, this paper summarizes advances in perception map construction methods that rely on binocular vision. This method uses stereo vision to generate parallax maps, which in turn help construct a digital elevation model of the planet’s surface. Stereo matching algorithms are typically used to acquire parallax maps. The discussion includes an analysis of two main types of methods: region-based matching algorithms and feature-based matching algorithms. Second, the paper classifies existing autonomous path-planning methods for planetary rovers into two categories: those based on cost evaluation and those leveraging machine learning. As a typical representative of cost-evaluation–based path planning methods, the optional arc-based autonomous path planning method is spotlighted for its algorithmic principles and iterative applications in ongoing planetary rover missions. Furthermore, the potential of A* heuristic search algorithm, rapidly-exploring random tree star (RRT*) algorithm, and fast marching method(FMM) algorithms for enhancing planetary rover path planning is explored in a categorical manner. The application of machine learning in autonomous path planning for planetary rovers is also reviewed. Such methods are classified into two groups: end-to-end path planning methods and auxiliary approaches leveraging machine learning. Ultimately, based on the above analysis of the key technologies of planetary rover autonomous path planning, the paper identifies and discusses future research directions for improving autonomous path planning methods for planetary rovers. These include enhancing perception capabilities, improving optional arc paths, reducing slippage, integrating multiple path planning methods, and strengthening the use of machine learning.

Abstract Epidermal growth factor receptor wild type lung adenocarcinoma (EGFRWT LUAD) still has limited treatment options and unsatisfactory clinical outcomes. Ferroptosis, as a form of cell death, has been reported to play a dual role in regulating tumor cell survival. In this study, we constructed a 3-ferroptosis-gene signature, FeSig, and verified its accuracy and efficacy in predicting EGFRWT LUAD prognosis at both the RNA and protein levels. Patients with higher FeSig scores were found to have worse clinical outcomes. Additionally, we explored the relationship between FeSig and tumor microenvironment, revealing that enhanced interactions between fibroblasts and tumor cells in FeSighigh patients causing tumor resistance to ferroptosis. To address this challenge, we screened potential drugs from NCI-60 (The US National Cancer Institute 60 human tumour cell line anticancer drug screen) and Connectivity map database, ultimately identifying 6-mercatopurine (6-MP) as a promising candidate. Both in vitro and in vivo experiments demonstrated its efficacy in treating FeSighigh EGFRWT LUAD tumor models. In summary, we develop a novel FeSig for predicting prognosis and guiding drug application.

Highlights The lithium-ion conduction mechanism of organic-inorganic composite solid electrolytes (OICSEs) is thoroughly conducted and concluded from the microscopic perspective based on filler content, type, and system. The classic inorganic filler types, including inert and active fillers, are categorized with special emphasis on the relationship between inorganic filler structure design and the electrochemical performance of OICSEs. Advanced characterization techniques for OICSEs are discussed, and the challenges and prospects for developing superior all-solid-state lithium batteries are highlighted.

Abstract The genes of Wnt/β-catenin pathway may have potential roles in fat accumulation of Non-traumatic osteonecrosis of the femoral head (ONFH), but the effects of their variants in the pathway on ONFH development have been remained unclear. To explore the potential roles of the variants in the development of ONFH, we completed the investigation of the paired interactions as well as their related biological functions of 17 variants of GSK3β, LRP5, and FRP4 genes etc. in the pathway. The genotyping of the 17 variants were finished by MASS ARRAY PLATFORM in a 560 ONFH case–control system. The association of variants interactions with ONFH risk and clinical traits was evaluated by logistic regression analysis etc. and bioinformatics technology. The results showed that the genotype, allele frequency, and genetic models of Gsk3β rs334558 (G/A), SFRP4 rs1052981 (A/G), and LRP5 rs312778 (T/C) were significantly associated with the increased and decreased ONFH risk and clinical traits, respectively (P

Abstract Yutu-2 rover conducted an exciting expedition on the 41st lunar day to investigate a fin-shaped rock at Longji site (45.44°S, 177.56°E) by extending its locomotion margin on perilous peaks. The varied locomotion encountered, especially multi-form wheel slippage, during the journey to the target rock, established unique conditions for a fin-grained lunar regolith analysis regarding bearing, shear and lateral properties based on terramechanics. Here, we show a tri-aspect characterization of lunar regolith and infer the rock’s origin using a digital twin. We estimate internal friction angle within 21.5°−42.0° and associated cohesion of 520-3154 Pa in the Chang’E-4 operational site. These findings suggest shear characteristics similar to Apollo 12 mission samples but notably higher cohesion compared to regolith investigated on most nearside lunar missions. We estimate external friction angle in lateral properties to be within 8.3°−16.5°, which fills the gaps of the lateral property estimation of the lunar farside regolith and serves as a foundational parameter for subsequent engineering verifications. Our in-situ spectral investigations of the target rock unveil its composition of iron/magnesium-rich low-calcium pyroxene, linking it to the Zhinyu crater (45.34°S, 176.15°E) ejecta. Our results indicate that the combination of in-situ measurements with robotics technology in planetary exploration reveal the possibility of additional source regions contributing to the local materials at the Chang’E-4 site, implying a more complicated geological history in the vicinity.

Abstract Background The new-onset postoperative atrial fibrillation (NOPAF) following pulmonary resection is a common clinical concern. The aim of this study was to construct a nomogram to intuitively predict the risk of NOPAF and offered protective treatments. Methods Patients who underwent pulmonary resection between January 2018 and December 2020 were consecutively enrolled. Forward stepwise multivariable logistic regression analyses were used to screen independent predictors, and a derived nomogram model was built. The model performance was evaluated in terms of calibration, discrimination and clinical utility and validated with bootstrap resampling. Results A total of 3583 patients who met the research criteria were recruited for this study. The incidence of NOPAF was 1.507% (54/3583). A nomogram, composed of five independent predictors, namely age, admission heart rate, extent of resection, laterality, percent maximum ventilation volume per minute (%MVV), was constructed. The concordance index (C-index) was 0.811. The nomogram showed substantial discriminative ability, with an area under the receiver operating characteristic curve of 0.811 (95% CI 0.758-0.864). Moreover, the model shows prominent calibration performance and higher net clinical benefits. Conclusion We developed a novel nomogram that can predict the risk of NOPAF following pulmonary resection, which may assist clinicians predict the individual probability of NOPAF and perform available prophylaxis. By using bootstrap resampling for validation, the optimal discrimination and calibration were demonstrated, indicating that the nomogram may have clinical practicality.

The aim of this study was to evaluate the effect of conjugated linoleic acid (CLA) on milk fat globule (MFG) size and the ruminal microbiome of goats. Twenty-four mid-lactation Saanen dairy goats weighing 49 ± 4.5 kg (168 ± 27 d in milk, 1.2 ± 0.1 kg milk/d, 2–3 years old) were randomly divided into four groups—a control (CON) group, which was fed a basal diet, and three CLA supplementation groups, in which 30 g CLA (low-dose group, L-CLA), 60 g CLA (medium-dose group, M-CLA), or 90 g CLA (high-dose group, H-CLA) was added to the basal diet daily. The experiment lasted for 21 days, during which time goat milk was collected for composition and MFG size analysis. On day 21 of feeding, ruminal fluid was collected from the CON and H-CLA groups for analysis of the changes in microorganismal abundance. The results showed that CLA supplementation did not affect milk production, milk protein, or lactose content in the dairy goats (p > 0.05), but significantly reduced the milk fat content (p < 0.01) compared with the CON group. The CLA supplementation significantly decreased the D[3,2] and D[4,3] of the MFGs in a dose-dependent manner (p < 0.01). Moreover, dietary CLA inclusion increased the proportion of small-sized MFGs and decreased that of large-sized ones. The results of 16S rRNA gene sequencing showed that CLA-induced milk fat depression in dairy goats was accompanied by significant changes in the relative abundance of ruminal bacterial populations, most of which belonged to the Firmicutes and Bacteroidetes phyla. The relative abundance of Rikenellaceae_RC9_gut_group and Prevolellaceae_UCG-003 in Bacteroidetes and UCG-002, Succiniclasticum, and norank_f__norank_o__Clostridia_vadinBB60_group in Firmicutes was significantly higher in the CON group than in the H-CLA group. In contrast, the relative abundance of norank_f__UCG-011, norank_f_Eubacterium_coprostanoligenes_group, unclassified_f__Lachnospiraceae, and UCG-001 in Firmicutes and norank_f__Muribaculaceae in Bacteroidetes was significantly higher in the H-CLA group than in the CON group. Correlation analysis showed that the milk fat content was negatively correlated with the relative abundance of some bacteria, including members of Firmicutes and Bacteroidetes. Similarly, MFG size (D[3,2] and D[4,3]) was negatively correlated with several members of Firmicutes and Bacteroidetes, including Lachnospiraceae, norank_f__UCG-011, UCG-001, norank_f__Eubacterium_coprostanoligenes_group (Firmicutes), and norank_f__Muribaculaceae (Bacteroidetes), while positively correlated with the relative abundance of some members of Firmicutes and Bacteroidetes, including Mycoplasma, Succiniclasticum, norank_f__norank_o__Clostridia_vadinBB60_group, UCG-002 (Firmicutes), and Rikenellaceae_RC9_gut_group (Bacteroidetes). Overall, our data indicated that CLA treatment affected milk fat content and MFG size in dairy goats, and these effects were correlated with the relative abundance of ruminal bacterial populations. These results provide the first evidence to explain the mechanism underlying diet-induced MFG from the perspective of the ruminal microbiome in dairy goats.