Search

Additional file 1: Supplemental Table 1a. Moderately carbohydrate restricted diet meals during crossover phase. Supplemental Table 1b. The goal and the actual values of macronutrients and salt in crossover meals of Moderately carbohydrate restricted diet, per day. Supplemental Table 2a. Healthy Nordic Diet meals during crossover phase. Supplemental Table 2b. The goal and the actual values of macronutrients and salt in crossover meals of Healthy Nordic diet, per day.

In this paper, some inequalities between the Wiener, hyper-Wiener, first Zagreb, second Zagreb, first reformulated Zagreb, second reformulated Zagreb and the general Zagreb indices of a simple graph are given. Our results improve some earlier bonds between these graph invariants.

Estimating the effect of a treatment on a given outcome, conditioned on a vector of covariates, is central in many applications. However, learning the impact of a treatment on a continuous temporal response, when the covariates suffer extensively from measurement error and even the timing of the treatments is uncertain, has not been addressed. We introduce a novel data-driven method that can estimate treatment-response trajectories in this challenging scenario. We model personalized treatment-response curves as a combination of parametric response functions, hierarchically sharing information across individuals, and a sparse Gaussian process for the baseline trend. Importantly, our model considers measurement error not only in treatment covariates, but also in treatment times, a problem which arises in practice for example when treatment information is based on self-reporting. In a challenging and timely problem of estimating the impact of diet on continuous blood glucose measurements, our model leads to significant improvements in estimation accuracy and prediction.

Suppose G is a finite group and C(G) denotes the set of all conjugacy classes of G. The normal graph of G, N(G), is a finite simple graph such that V(N(G)) = C(G). Two conjugacy classes A and B in C(G) are adjacent if and only if there is a proper normal subgroup N such that A U B ? N. The aim of this paper is to study the normal graph of a finite group G. It is proved, among other things, that the groups with identical character table have isomorphic normal graphs and so this new graph associated to a group has good relationship by its group structure. The normal graphs of some classes of finite groups are also obtained and some open questions are posed.

Let G be a finite group. The power graph P(G) and its main supergraph S(G) are two simple graphs with the same vertex set G. Two elements x,y ? G are adjacent in the power graph if and only if one is a power of the other. They are joined in S(G) if and only if o(x)|o(y) or o(y)|o(x). The aim of this paper is to compute the characteristic polynomial of these graph for certain finite groups. As a consequence, the spectrum and Laplacian spectrum of these graphs for dihedral, semi-dihedral, cyclic and dicyclic groups were computed.

Low implementation complexity, low delay and close-to-optimal performance over a wide variety of channels are some of the advantages of spatially-coupled low-density parity-check (LDPC) codes. However, the error performance of the sliding window decoding scheme that is used to decode these codes is considerably degraded over channels with memory, such as the correlated erasure channel. Employing a block interleaver to encounter this situation is not always a viable option, since it introduces a large amount of delay and cancels out the low-delay property of the sliding window decoder. Another way to reduce the effects of erasure bursts is to construct a more robust code ensemble by presenting additional code design rules. However, this approach results in additional constraints on the already complicated code construction process. The authors propose a novel communication system that combats the effects of the erasure bursts through the use of a convolutional interleaver. The proposed system combines the inherent convolutional nature of the spatially-coupled LDPC codes with that of a convolutional interleaver to achieve very low overall delay. The performance of the proposed approach is analysed using the density evolution technique and the performance improvement is demonstrated as a function of the interleaving delay via computer simulations.

Infective endocarditis is one of three common cardiac infections in the United Kingdom, in addition to myocarditis and pericarditis, with a reported incidence of 1.7 to 6.2 cases per 100,000 patient years. Infective endocarditis can often have serious consequences and a wide variety of organisms may be the causative pathogen. There are little published data regarding the exact spectrum of organisms that cause endocarditis in the United Kingdom and whether organisms such as streptococci still dominate. In the present study, all cases of endocarditis at the authors' institution, representing a typical nontertiary centre, were retrospectively examined and audited to provide a snapshot of the organism spectrum in these patients. The cases of more than 120 patients who were coded as having endocarditis by the institution's clinical coding department during the period between December 2000 and January 2011 were examined. Microbiological tests and clinical case notes of all patients were reviewed. Of the 101 patients diagnosed with and treated for endocarditis, 64 were male, with a mean age of 60.57 years. The most common organisms identified were Streptococcus species (31%), Staphylococcus aureus (27%) and Enterococcus faecalis (21%). The organisms with the highest associated mortality rate were S aureus and the 'other organism' group, which included non-HACEK group (Haemophilus species, Actinobacillus actinomycetemcomitans, Cardiobacterium hominis, Eikenella corrodens and Kingella species) pathogens such as Candida albicans. Streptococcus species and S aureus remain the main cause of endocarditis in a typical hospital setting in the United Kingdom, although in a smaller proportion of cases than historical data suggests. Overall, mortality remains high, and the clinician should remain vigilant to endocarditis in any patient with a positive blood culture because the number of cases of endocarditis caused by less typical organisms are increasing.

Background. We present the case of a 35-year-old gentleman who presented with an aggressive cardiomyopathy with normal coronary arteries. He was later diagnosed with systemic lupus-related cardiomyopathy. Methods. We undertook an extensive review of the literature regarding cardiac manifestations of lupus and used over 100 journals to identify the key points in pathology, diagnosis, and treatment. Results. We have shown that cardiac lupus can be rapidly progressive and, unless treated early, can have severe consequences. The predominant pathologies are immune complex and accelerated atherosclerosis drive. Treatment comprised of high-level immunosuppression.

Our aim was to investigate in a real-life setting the use of machine learning for modelling the postprandial glucose concentrations in morbidly obese patients undergoing Roux-en-Y gastric bypass (RYGB) or one-anastomosis gastric bypass (OAGB). As part of the prospective randomized open-label trial (RYSA), data from obese (BMI ≥35 kg/m2) non-diabetic adult participants were included. Glucose concentrations, measured with FreeStyle Libre, were recorded over 14 preoperative and 14 postoperative days. During these periods, 3-day food intake was self-reported. A machine learning model was applied to estimate glycaemic responses to the reported carbohydrate intakes before and after the bariatric surgeries. Altogether, 10 participants underwent RYGB and 7 participants OAGB surgeries. The glucose concentrations and carbohydrate intakes were reduced postoperatively in both groups. The relative time spent in hypoglycaemia increased regardless of the operation (RYGB, from 9.2 to 28.2%; OAGB, from 1.8 to 37.7%). Postoperatively, we observed an increase in the height of the fitted response curve and a reduction in its width, suggesting that the same amount of carbohydrates caused a larger increase in the postprandial glucose response and that the clearance of the meal-derived blood glucose was faster, with no clinically meaningful differences between the surgeries. A detailed analysis of the glycaemic responses using food diaries has previously been difficult because of the noisy meal data. The utilized machine learning model resolved this by modelling the uncertainty in meal times. Such an approach is likely also applicable in other applications involving dietary data. A marked reduction in overall glycaemia, increase in postprandial glucose response, and rapid glucose clearance from the circulation immediately after surgery are evident after both RYGB and OAGB. Whether nondiabetic individuals would benefit from monitoring the post-surgery hypoglycaemias and the potential to prevent them by dietary means should be investigated.KEY MESSAGESThe use of a novel machine learning model was applicable for combining patient-reported data and time-series data in this clinical study.Marked increase in postprandial glucose concentrations and rapid glucose clearance were observed after both Roux-en-Y gastric bypass and one-anastomosis gastric bypass surgeries.Whether nondiabetic individuals would benefit from monitoring the post-surgery hypoglycaemias and the potential to prevent them by dietary means should be investigated. The use of a novel machine learning model was applicable for combining patient-reported data and time-series data in this clinical study. Marked increase in postprandial glucose concentrations and rapid glucose clearance were observed after both Roux-en-Y gastric bypass and one-anastomosis gastric bypass surgeries. Whether nondiabetic individuals would benefit from monitoring the post-surgery hypoglycaemias and the potential to prevent them by dietary means should be investigated.

Our aim was to investigate in a real-life setting the use of machine learning for modelling the postprandial glucose concentrations in morbidly obese patients undergoing Roux-en-Y gastric bypass (RYGB) or one-anastomosis gastric bypass (OAGB). As part of the prospective randomized open-label trial (RYSA), data from obese (BMI ≥35 kg/m2) non-diabetic adult participants were included. Glucose concentrations, measured with FreeStyle Libre, were recorded over 14 preoperative and 14 postoperative days. During these periods, 3-day food intake was self-reported. A machine learning model was applied to estimate glycaemic responses to the reported carbohydrate intakes before and after the bariatric surgeries. Altogether, 10 participants underwent RYGB and 7 participants OAGB surgeries. The glucose concentrations and carbohydrate intakes were reduced postoperatively in both groups. The relative time spent in hypoglycaemia increased regardless of the operation (RYGB, from 9.2 to 28.2%; OAGB, from 1.8 to 37.7%). Postoperatively, we observed an increase in the height of the fitted response curve and a reduction in its width, suggesting that the same amount of carbohydrates caused a larger increase in the postprandial glucose response and that the clearance of the meal-derived blood glucose was faster, with no clinically meaningful differences between the surgeries. A detailed analysis of the glycaemic responses using food diaries has previously been difficult because of the noisy meal data. The utilized machine learning model resolved this by modelling the uncertainty in meal times. Such an approach is likely also applicable in other applications involving dietary data. A marked reduction in overall glycaemia, increase in postprandial glucose response, and rapid glucose clearance from the circulation immediately after surgery are evident after both RYGB and OAGB. Whether nondiabetic individuals would benefit from monitoring the post-surgery hypoglycaemias and the potential to prevent them by dietary means should be investigated.KEY MESSAGESThe use of a novel machine learning model was applicable for combining patient-reported data and time-series data in this clinical study.Marked increase in postprandial glucose concentrations and rapid glucose clearance were observed after both Roux-en-Y gastric bypass and one-anastomosis gastric bypass surgeries.Whether nondiabetic individuals would benefit from monitoring the post-surgery hypoglycaemias and the potential to prevent them by dietary means should be investigated. The use of a novel machine learning model was applicable for combining patient-reported data and time-series data in this clinical study. Marked increase in postprandial glucose concentrations and rapid glucose clearance were observed after both Roux-en-Y gastric bypass and one-anastomosis gastric bypass surgeries. Whether nondiabetic individuals would benefit from monitoring the post-surgery hypoglycaemias and the potential to prevent them by dietary means should be investigated.