Saint Louis University (SLU) is a private Jesuit research university with campuses in St. Louis, Missouri, United States, and Madrid, Spain. Founded in 1818 by Louis William Valentine DuBourg, it is the oldest university west of the Mississippi River and the second-oldest Jesuit university in the United States. It is one of 27 member institutions of the Association of Jesuit Colleges and Universities. The university is accredited by the North Central Association of Colleges and Secondary Schools.In the 2021-2022 academic year, SLU had an enrollment of 12,883 students. The student body included 8,138 undergraduate students and 4,745 graduate students that represents all 50 states and 82 countries. The University is classified as a Research II university by the Carnegie Classification of Institutions of Higher Education.For more than 50 years, the university has maintained a campus in Madrid, Spain. The Madrid campus was the first freestanding campus operated by an American university in Europe and the first American institution to be recognized by Spain's higher education authority as an official foreign university. The campus has 850 students, a faculty of 110, an average class size of 17 and a student-faculty ratio of 12:1. Of the 12,883 students, 788 study at the Madrid Campus which also represents students from 45 of the 82 total countries students are from.SLU's athletic teams compete in the National Collegiate Athletic Association's Division I and are a member of the Atlantic 10 Conference.
Network traffic telemetry plays a crucial role in the management of modern networks. Estimation of the network traffic matrix is a widely recognized problem whose solutions can span a diverse set of applications. Current approaches to traffic matrix inference through statistical methods often rely on assumptions about the matrix structure, which may be invalid in certain scenarios. Data-driven methods, instead, often use detailed information about the network topology that may be unavailable or impractical to collect. To overcome these challenges, we propose a super-resolution technique for traffic matrix inference that leverages coarser measurements to predict fine-grained network traffic. Furthermore, we devise a distributed learning procedure and adapt our model to scenarios of partial network visibility. Our experiments on real network traces demonstrate that the proposed approach can infer fine-grained network traffic with high precision. Moreover, we prove that our distributed approach improves the inference accuracy with respect to its centralized counterpart, significantly lowering the training time, even in scenarios with partial network knowledge.
The P53-destabilizing TBC1D15-NOTCH protein interaction promotes self-renewal of tumor-initiating stem-like cells (TICs); however, the mechanisms governing the regulation of this pathway have not been fully elucidated. Here, we show that TBC1D15 stabilizes NOTCH and c-JUN through blockade of E3 ligase and CDK8 recruitment to phosphodegron sequences. Chromatin immunoprecipitation (ChIP-seq) analysis was performed to determine whether TBC1D15-dependent NOTCH1 binding occurs in TICs or non-TICs. The TIC population was isolated to evaluate TBC1D15-dependent NOTCH1 stabilization mechanisms. The tumor incidence in hepatocyte-specific triple knockout (Alb::CreERT2;Tbc1d15Flox/Flox;Notch1Flox/Flox;Notch2Flox/Flox;HCV-NS5A) Transgenic (Tg) mice and wild-type mice was compared after being fed an alcohol-containing Western diet (WD) for 12 months. The NOTCH1-TBC1D15-FIS1 interaction resulted in recruitment of mitochondria to the perinuclear region. TBC1D15 bound to full-length NUMB and to NUMB isoform 5, which lacks three Ser phosphorylation sites, and relocalized NUMB5 to mitochondria. TBC1D15 binding to NOTCH1 blocked CDK8- and CDK19-mediated phosphorylation of the NOTCH1 PEST phosphodegron to block FBW7 recruitment to Thr-2512 of NOTCH1. ChIP-seq analysis revealed that TBC1D15 and NOTCH1 regulated the expression of genes involved in mitochondrial metabolism-related pathways required for the maintenance of TICs. TBC1D15 inhibited CDK8-mediated phosphorylation to stabilize NOTCH1 and protect it from degradation The NUMB-binding oncoprotein TBC1D15 rescued NOTCH1 from NUMB-mediated ubiquitin-dependent degradation and recruited NOTCH1 to the mitochondrial outer membrane for the generation and expansion of liver TICs. A NOTCH-TBC1D15 inhibitor was found to inhibit NOTCH-dependent pathways and exhibited potent therapeutic effects in PDX mouse models. This unique targeting of the NOTCH-TBC1D15 interaction not only normalized the perinuclear localization of mitochondria but also promoted potent cytotoxic effects against TICs to eradicate patient-derived xenografts through NOTCH-dependent pathways.
Type I interferons (IFN) are key mediators of innate immune activation, promoting upregulation of costimulatory molecules and Major Histocompatibility Complex (MHC) I/II on antigen-presenting cells (APCs). However, IFN also suppress endogenous translation to restrict viral replication. Critically, IFN-stimulated APCs lose the capacity to acquire new antigens, making the timing of IFN signaling a crucial determinant of vaccine efficacy. Here, we show that both DC-specific loss of IFNα/β receptor (IFNαR) and transient blockade of IFNαR before vaccination enhances vaccine uptake and expression within DCs, improves CD8⁺ T cell priming, and leads to superior tumor control. We also demonstrate that IFN signaling before vaccination, triggered by prior infection or administration of a different vaccine, impairs dendritic cell uptake of mRNA-LNP vaccines and reduces the magnitude of vaccine-specific CD8⁺ T cell responses. These findings highlight the dual-edged nature of IFN signaling and offer a potential strategy for enhancing vaccine-induced immunity.
BACKGROUND AND AIMS:Dyslipidemia is common in patients with MASLD, but the frequency and significance of inherited disorders of dyslipidemia are unclear. We investigated the prevalence and significance of pathogenic variants associated with selected monogenic disorders of dyslipidemia in 3358 patients with well-characterised MASLD. APPROACH:We identified clinically relevant variants in APOB, MTTP, PCSK9, ANGPTL3, LDLR and LDLRAP1 genes which can cause hypobetalipoproteinemia (HBL) and familial hypercholesterolemia (FH). Using ClinVar annotations as initial variant selection, we identified 2027 variants in those 6 genes which are reported as 'pathogenic' or 'likely pathogenic' (P/LP). We first assessed for the presence of P/LP variants in the study cohort and then investigated the effect of carrying P/LP variants on liver histology, by comparing ~4 matched controls for each APOB and LDLR carrier. As interpretative analyses, we also looked at the difference between liver enzymes, lipid measures and outcomes between the carriers and matched controls. RESULTS:Twenty-two variants among these 2027 P/LP variants were present in 24 out of 3358 patients (12 ApoB, 10 LDLR, 1 ANGPTL3 and 1 MTTP variant carriers). Compared to controls, APOB carriers had higher steatosis grade (2.4 vs. 1.7, p-value 0.0028), higher NAFLD activity score (NAS) (4.9 vs. 3.8, p-value 0.04), and numerically higher but statistically not significant fibrosis stage (1.2 vs. 1.1, p-value 0.75) and ALT (87.4 vs. 58.1 U/L, p-value 0.06). Their LDL-c (51 vs. 147.8 mg/dL, p-value 6.1E-09) and triglycerides (91.5 vs. 160.6 mg/dL, p-value 2.8E-03) were significantly lower. Compared to controls, LDLR carriers had numerically higher steatosis grade, NAS, fibrosis stage and LDL-c levels, but these were not statistically different. CONCLUSIONS:Monogenic disorders of dyslipidemia are rarely present in patients with MASLD and are sometimes associated with worse liver histology. Testing for these conditions may be considered on a case-by-case basis.
Smart cities face increasing challenges in managing crowd dynamics while ensuring public safety and maintaining quality of life. The development of artificial intelligence and robotics technology has opened new avenues for addressing these challenges. This paper presents a behavioral data-driven approach to human-robot collaborative navigation that enhances crowd management in smart urban environments. The proposed approach leverages real-time behavioral data from pedestrian movements and crowd patterns to enable intelligent human-robot collaboration. The system dynamically adjusts robot guidance through a rule-based state transition mechanism that ensures decision transparency and predictability, and employs a multi-robot collaborative scheduling algorithm to control crowd density in key areas. Additionally, it utilizes a dynamic instruction system to provide pedestrians with directional guidance and behavioral recommendations, thereby enhancing the effectiveness of human-robot collaborative navigation. Through empirical validation in a virtual reality environment, we demonstrate significant improvements in navigation efficiency, crowd flow control, pedestrian safety, and user satisfaction. The results indicate that integrating real-time behavioral data with human-robot collaboration offers a promising pathway for addressing complex crowd management challenges in smart cities, demonstrating how rule-based systems can balance automated efficiency with human-centered design principles.
