Chromaticity of a family of 5-partite graphs

AbstractLet P(G,λ) be the chromatic polynomial of a graph G. Two graphs G and H are said to be chromatically equivalent, denoted G∼H, if P(G,λ)=P(H,λ). We write [G]={H∣H∼G}. If [G]={G}, then G is said to be chromatically unique. In this paper, we first characterize certain complete 5-partite graphs G with 5n vertices according to the number of 6-independent partitions of G. Using these results, we investigate the chromaticity of G with certain stars or matching deleted parts . As a by-product, two new families of chromatically unique complete 5-partite graphs G with certain stars or matching deleted parts are obtained

Hydrometeorology and flood pulse dynamics drive diarrheal disease outbreaks and increase vulnerability to climate change in surface-water-dependent populations: A retrospective analysis

Background The impacts of climate change on surface water, waterborne disease, and human health remain a growing area of concern, particularly in Africa, where diarrheal disease is one of the most important health threats to children under 5 years of age. Little is known about the role of surface water and annual flood dynamics (flood pulse) on waterborne disease and human health nor about the expected impact of climate change on surface-water-dependent populations. Methods and findings Using the Chobe River in northern Botswana, a flood pulse river—floodplain system, we applied multimodel inference approaches assessing the influence of river height, water quality (bimonthly counts of Escherichia coli and total suspended solids [TSS], 2011–2017), and meteorological variability on weekly diarrheal case reports among children under 5 presenting to health facilities (n = 10 health facilities, January 2007–June 2017). We assessed diarrheal cases by clinical characteristics and season across age groups using monthly outpatient data (January 1998–June 2017). A strong seasonal pattern was identified, with 2 outbreaks occurring regularly in the wet and dry seasons. The timing of outbreaks diverged from that at the level of the country, where surface water is largely absent. Across age groups, the number of diarrheal cases was greater, on average, during the dry season. Demographic and clinical characteristics varied by season, underscoring the importance of environmental drivers. In the wet season, rainfall (8-week lag) had a significant influence on under-5 diarrhea, with a 10-mm increase in rainfall associated with an estimated 6.5% rise in the number of cases. Rainfall, minimum temperature, and river height were predictive of E. coli concentration, and increases in E. coli in the river were positively associated with diarrheal cases. In the dry season, river height (1-week lag) and maximum temperature (1- and 4-week lag) were significantly associated with diarrheal cases. During this period, a 1-meter drop in river height corresponded to an estimated 16.7% and 16.1% increase in reported diarrhea with a 1- and 4-week lag, respectively. In this region, as floodwaters receded from the surrounding floodplains, TSS levels increased and were positively associated with diarrheal cases (0- and 3-week lag). Populations living in this region utilized improved water sources, suggesting that hydrological variability and rapid water quality shifts in surface waters may compromise water treatment processes. Limitations include the potential influence of health beliefs and health seeking behaviors on data obtained through passive surveillance. Conclusions In flood pulse river—floodplain systems, hydrology and water quality dynamics can be highly variable, potentially impacting conventional water treatment facilities and the production of safe drinking water. In Southern Africa, climate change is predicted to intensify hydrological variability and the frequency of extreme weather events, amplifying the public health threat of waterborne disease in surface-water-dependent populations. Water sector development should be prioritized with urgency, incorporating technologies that are robust to local environmental conditions and expected climate-driven impacts. In populations with high HIV burdens, expansion of diarrheal disease surveillance and intervention strategies may also be needed. As annual flood pulse processes are predominantly influenced by climate controls in distant regions, country-level data may be inadequate to refine predictions of climate—health interactions in these systems

Intra-Articular Interleukin-1 Receptor Antagonist (IL1-ra) Microspheres for Posttraumatic Osteoarthritis: In Vitro Biological Activity and in Vivo Disease Modifying Effect

Background: Interleukin-1 receptor antagonist (IL-1 ra) can be disease-modifying in posttraumatic osteoarthritis (PTOA). One limitation is its short joint residence time. We hypothesized that IL-1 ra encapsulation in poly (lactide-co-glycolide) (PLGA) microspheres reduces IL-1 ra systemic absorption and provides an enhanced anti-PTOA effect. Methods: IL-1 ra release kinetics and biological activity: IL-1 ra encapsulation into PLGA microsphere was performed using double emulsion solvent extraction. Lyophilized PLGA IL-1 ra microspheres were resuspended in PBS and supernatant IL-1 ra concentrations were assayed. The biological activity of IL-1 ra from PLGA IL-1 ra microspheres was performed using IL-1 induced lymphocyte proliferation and bovine articular cartilage degradation assays. Systemic absorption of IL-1 ra following intra-articular (IA) injection of PLGA IL-1 ra or IL-1 ra: At 1, 3, 6, 12 and 24 h following injection of 50 μl PLGA IL-1 ra (n = 6) or IL-1 ra (n = 6), serum samples were collected and IL-1 ra concentrations were determined. Anterior cruciate ligament transection (ACLT) and IA dosing: ACLT was performed in 8–10 week old male Lewis rats (n = 42). PBS (50 μl; n = 9), IL-1 ra (50 μl; 5 mg/ml; n=13), PLGA IL-1 ra (50 μl; equivalent to 5 mg/ml IL-1 ra; n = 14) or PLGA particles (50 μl; n = 6) treatments were performed on days 7, 14, 21 and 28 following ACLT. Cartilage and synovial histopathology: On day 35, animal ACLT joints were harvested and tibial cartilage and synovial histopathology scoring was performed. Results: Percent IL-1 ra content in the supernatant at 6 h was 13.44 ± 9.27 % compared to 34.16 ± 12.04 %, 47.89 ± 12. 71 %, 57.14 ± 11.71 %, and 93.90 ± 8.50 % at 12, 24, 48 and 72 h, respectively. PLGA IL-1 ra inhibited lymphocyte proliferation and cartilage degradation similar to IL-1 ra. Serum IL-1 ra levels were significantly lower at 1, 3, and 6 h following PLGA IL-1 ra injection compared to IL-1 ra. Cartilage and synovial histopathology scores were significantly lower in the PLGA IL-1 ra group compared to PBS and PLGA groups (p \u3c 0.001). Conclusions: IL-1 ra encapsulation in PLGA microspheres is feasible with no alteration to IL-1 ra biological activity. PLGA IL-1 ra exhibited an enhanced disease-modifying effect in a PTOA model compared to similarly dosed IL-1 ra

User Profiling Based on Application-Level Using Network Metadata.

There is an increasing interest to identify users and behaviour profiling from network traffic metadata for traffic engineering and security monitoring. Network security administrators and internet service providers need to create the user behaviour traffic profile to make an informed decision about policing, traffic management, and investigate the different network security perspectives. Additionally, the analysis of network traffic metadata and extraction of feature sets to understand trends in application usage can be significant in terms of identifying and profiling the user by representing the user's activity. However, user identification and behaviour profiling in real-time network management remains a challenge, as the behaviour and underline interaction of network applications are permanently changing. In parallel, user behaviour is also changing and adapting, as the online interaction environment changes. Also, the challenge is how to adequately describe the user activity among generic network traffic in terms of identifying the user and his changing behaviour over time. In this paper, we propose a novel mechanism for user identification and behaviour profiling and analysing individual usage per application. The research considered the application-level flow sessions identified based on Domain Name System filtering criteria and timing resolution bins (24-hour timing bins) leading to an extended set of features. Validation of the module was conducted by collecting Net Flow records for a 60 days from 23 users. A gradient boosting supervised machine learning algorithm was leveraged for modelling user identification based upon the selected features. The proposed method yields an accuracy for identifying a user based on the proposed features up to 74

Predicting the Epidemic Sizes of Influenza A/H1N1, A/H3N2, and B: A Statistical Method

Using weekly influenza surveillance data from the US CDC, Edward Goldstein and colleagues develop a statistical method to predict the sizes of epidemics caused by seasonal influenza strains. This method could inform decisions about the most appropriate vaccines or drugs needed early in the influenza season

Environmental Predictors of Seasonal Influenza Epidemics across Temperate and Tropical Climates

Human influenza infections exhibit a strong seasonal cycle in temperate regions. Recent laboratory and epidemiological evidence suggests that low specific humidity conditions facilitate the airborne survival and transmission of the influenza virus in temperate regions, resulting in annual winter epidemics. However, this relationship is unlikely to account for the epidemiology of influenza in tropical and subtropical regions where epidemics often occur during the rainy season or transmit year-round without a well-defined season. We assessed the role of specific humidity and other local climatic variables on influenza virus seasonality by modeling epidemiological and climatic information from 78 study sites sampled globally. We substantiated that there are two types of environmental conditions associated with seasonal influenza epidemics: “cold-dry” and “humid-rainy”. For sites where monthly average specific humidity or temperature decreases below thresholds of approximately 11–12 g/kg and 18–21°C during the year, influenza activity peaks during the cold-dry season (i.e., winter) when specific humidity and temperature are at minimal levels. For sites where specific humidity and temperature do not decrease below these thresholds, seasonal influenza activity is more likely to peak in months when average precipitation totals are maximal and greater than 150 mm per month. These findings provide a simple climate-based model rooted in empirical data that accounts for the diversity of seasonal influenza patterns observed across temperate, subtropical and tropical climates