Effect of reducing portion size at a compulsory meal on later energy intake, gut hormones, and appetite in overweight adults.

OBJECTIVE: Larger portion sizes (PS) are associated with greater energy intake (EI), but little evidence exists on the appetitive effects of PS reduction. This study investigated the impact of reducing breakfast PS on subsequent EI, postprandial gastrointestinal hormone responses, and appetite ratings. METHODS: In a randomized crossover design (n = 33 adults; mean BMI 29 kg/m(2) ), a compulsory breakfast was based on 25% of gender-specific estimated daily energy requirements; PS was reduced by 20% and 40%. EI was measured at an ad libitum lunch (240 min) and snack (360 min) and by weighed diet diaries until bed. Blood was sampled until lunch in 20 participants. Appetite ratings were measured using visual analogue scales. RESULTS: EI at lunch (control: 2,930 ± 203; 20% reduction: 2,853 ± 198; 40% reduction: 2,911 ± 179 kJ) and over the whole day except breakfast (control: 7,374 ± 361; 20% reduction: 7,566 ± 468; 40% reduction: 7,413 ± 417 kJ) did not differ. Postprandial PYY, GLP-1, GIP, insulin, and fullness profiles were lower and hunger, desire to eat, and prospective consumption higher following 40% reduction compared to control. Appetite ratings profiles, but not hormone concentrations, were associated with subsequent EI. CONCLUSIONS: Smaller portions at breakfast led to reductions in gastrointestinal hormone secretion but did not affect subsequent energy intake, suggesting small reductions in portion size may be a useful strategy to constrain EI

Secure Bluetooth Communication in Smart Healthcare Systems: A Novel Community Dataset and Intrusion Detection System †

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97–99.5% based on the F1 scores.Peer reviewe

Chapter 4 Geographic information system spatial data structures, models, and case studies

This chapter provides a basic overview of geographic information systems (GISs) as well as a summary of basic concepts encountered with GISs. Specifically, it touches on the various spatial data structures and models used by GISs to represent geographical information. First, general concepts related to information organization and data structure are briefly described and related to the different ways of representing real-world geographical data and information in GISs. Second, different perspectives on information organization are discussed, including different types of spatial relationships processed by GISs as well as the underlying information organization structure within GISs. Finally, the concept of data is investigated, as well as the purpose of databases with respect to GISs, including the various methods of modeling real-world data, relationships, and processes into databases

B cell-specific conditional expression of Myd88(p.L252P) leads to the development of diffuse large B cell lymphoma in mice

The adaptor protein MYD88 is critical to relay activation of Toll-like receptor signaling to NF-{kappa}B activation.MYD88 mutations, particularly the p.L265P mutation, have been described in numerous distinct B cell malignancies, including diffuse large B cell lymphoma (DLBCL). 29% of activated B cell (ABC)-type DLBCL, which is characterized by constitutive activation of the NF-{kappa}B pathway, carry the p.L265P mutation. In addition, ABC-DLBCL frequently displays focal copy number gains affecting BCL2. Here, we generated a novel mouse model, in which Cre-mediated recombination, specifically in B cells, leads to the conditional expression of Myd88(p.L252P)(the orthologous position of the human MYD88(p.L265P) mutation) from the endogenous locus. These animals develop a lympho-proliferative disease, and occasional transformation into clonal lymphomas. The clonal disease displays morphological and immunophenotypical characteristics of ABC-DLBCL. Lymphomagenesis can be accelerated by crossing in a further novel allele, which mediates conditional overexpression ofBCL2 Cross-validation experiments in human DLBCL samples revealed that bothMYD88andCD79Bmutations are substantially enriched in ABC-DLBCL, compared to germinal center B cell DLBCL. Furthermore, analyses of human DLBCL genome sequencing data confirmed that BCL2 amplifications frequently co-occur with MYD88 mutations, further validating our approach. Lastly,in silicoexperiments revealed that particularly MYD88-mutant ABC-DLBCL cells display an actionable addiction to BCL2. Altogether, we generated a novel autochthonous mouse model of ABC-DLBCL, which could be used as a preclinical platform for the development and validation of novel therapeutic approaches for the treatment of ABC-DLBCL

Modeling flow cytometry data for cancer vaccine immune monitoring

Flow cytometry (FCM) is widely used in cancer research for diagnosis, detection of minimal residual disease, as well as immune monitoring and profiling following immunotherapy. In all these applications, the challenge is to detect extremely rare cell subsets while avoiding spurious positive events. To achieve this objective, it helps to be able to analyze FCM data using multiple markers simultaneously, since the additional information provided often helps to minimize the number of false positive and false negative events, hence increasing both sensitivity and specificity. However, with manual gating, at most two markers can be examined in a single dot plot, and a sequential strategy is often used. As the sequential strategy discards events that fall outside preceding gates at each stage, the effectiveness of the strategy is difficult to evaluate without laborious and painstaking back-gating. Model-based analysis is a promising computational technique that works using information from all marker dimensions simultaneously, and offers an alternative approach to flow analysis that can usefully complement manual gating in the design of optimal gating strategies. Results from model-based analysis will be illustrated with examples from FCM assays commonly used in cancer immunotherapy laboratories

A Kir6.2 mutation causing severe functional effects in vitro produces neonatal diabetes without the expected neurological complications

AIMS/HYPOTHESIS: Heterozygous activating mutations in the pancreatic ATP-sensitive K+ channel cause permanent neonatal diabetes mellitus (PNDM). This results from a decrease in the ability of ATP to close the channel, which thereby suppresses insulin secretion. PNDM mutations that cause a severe reduction in ATP inhibition may produce additional symptoms such as developmental delay and epilepsy. We identified a heterozygous mutation (L164P) in the pore-forming (Kir6.2) subunit of the channel in three unrelated patients and examined its functional effects. METHODS: The patients (currently aged 2, 8 and 20 years) developed diabetes shortly after birth. The two younger patients attempted transfer to sulfonylurea therapy but were unsuccessful (up to 1.1 mg kg(-1) day(-1)). They remain insulin dependent. None of the patients displayed neurological symptoms. Functional properties of wild-type and mutant channels were examined by electrophysiology in Xenopus oocytes. RESULTS: Heterozygous (het) and homozygous L164P K(ATP) channels showed a marked reduction in channel inhibition by ATP. Consistent with its predicted location within the pore, L164P enhanced the channel open state, which explains the reduction in ATP sensitivity. HetL164P currents exhibited greatly increased whole-cell currents that were unaffected by sulfonylureas. This explains the inability of sulfonylureas to ameliorate the diabetes of affected patients. CONCLUSIONS/INTERPRETATION: Our results provide the first demonstration that mutations such as L164P, which produce a severe reduction in ATP sensitivity, do not inevitably cause developmental delay or neurological problems. However, the neonatal diabetes of these patients is unresponsive to sulfonylurea therapy. Functional analysis of PNDM mutations can predict the sulfonylurea response