The combined diabetes and renal control trial (C-DIRECT) - a feasibility randomised controlled trial to evaluate outcomes in multi-morbid patients with diabetes and on dialysis using a mixed methods approach

Background: This cluster randomised controlled trial set out to investigate the feasibility and acceptability of the “Combined Diabetes and Renal Control Trial” (C-DIRECT) intervention, a nurse-led intervention based on motivational interviewing and self-management in patients with coexisting end stage renal diseases and diabetes mellitus (DM ESRD). Its efficacy to improve glycaemic control, as well as psychosocial and self-care outcomes were also evaluated as secondary outcomes. Methods: An assessor-blinded, clustered randomised-controlled trial was conducted with 44 haemodialysis patients with DM ESRD and ≥ 8% glycated haemoglobin (HbA1c), in dialysis centres across Singapore. Patients were randomised according to dialysis shifts. 20 patients were assigned to intervention and 24 were in usual care. The C-DIRECT intervention consisted of three weekly chair-side sessions delivered by diabetes specialist nurses. Data on recruitment, randomisation, and retention, and secondary outcomes such as clinical endpoints, emotional distress, adherence, and self-management skills measures were obtained at baseline and at 12 weeks follow-up. A qualitative evaluation using interviews was conducted at the end of the trial. Results: Of the 44 recruited at baseline, 42 patients were evaluated at follow-up. One patient died, and one discontinued the study due to deteriorating health. Recruitment, retention, and acceptability rates of C-DIRECT were generally satisfactory HbA1c levels decreased in both groups, but C-DIRECT had more participants with HbA1c < 8% at follow up compared to usual care. Significant improvements in role limitations due to physical health were noted for C-DIRECT whereas levels remained stable in usual care. No statistically significant differences between groups were observed for other clinical markers and other patient-reported outcomes. There were no adverse effects. Conclusions: The trial demonstrated satisfactory feasibility. A brief intervention delivered on bedside as part of routine dialysis care showed some benefits in glycaemic control and on QOL domain compared with usual care, although no effect was observed in other secondary outcomes. Further research is needed to design and assess interventions to promote diabetes self-management in socially vulnerable patients

Construction of S-Box based on chaotic map and algebraic structures

The Advanced Encryption Standard (AES) is widely used in different kinds of security applications. The substitution box (S-box) is the main component of many modern symmetric encryption ciphers that provides confusion between the secret key and ciphertext. The S-box component that is used in AES is fixed. If we construct this component dynamically, the encryption strength of AES would be greater than before. In this manuscript, we used chaotic logistic map, Mobius transformation and symmetric group S256 to construct S-box for AES. The idea behind the proposed work is to make supplementary safe S-box. The presented S-box is analyzed for the following analyses: linear approximation probability (LP), nonlinearity (NL), differential approximation probability (DP), strict avalanche criterion (SAC), and bit independence criterion (BIC). The analyses show that the proposed technique is useful in generating high resistance S-box to known attacksThe publication of this article was funded by the Qatar National Library

Patterns for Populus spp. stand biomass in gradients of winter temperature and precipitation of Eurasia

Based on a generated database of 413 sample plots, with definitions of stand biomass of the genus Populus spp. in Eurasia, from France to Japan and southern China, statistically significant changes in the structure of forest stand biomass were found, with shifts in winter temperatures and average annual precipitation. When analyzing the reaction of the structure of the biomass of the genus Populus to temperature and precipitation in their transcontinental gradients, a clearly expressed positive relationship of all components of the biomass with the temperature in January is visible. Their relationship with precipitation is less clear; in warm climate zones, when precipitation increases, the biomass of all wood components decreases intensively, and in cold climate zones, this decrease is less pronounced. The foliage biomass does not increase when precipitation decreases, as is typical for wood components, but decreases. This can be explained by the specifics of the functioning of the assimilation apparatus, namely its transpiration activity when warming, and the corresponding increase in transpiration, which requires an increase in the influx of assimilates into the foliage, and the desiccation of the climate that reduces this influx of assimilates. Comparison of the obtained patterns with previously published results for other species from Eurasia showed partial or complete discrepancies, the causes of which require special physiological studies. The results obtained can be useful in the management of biosphere functions of forests, which is important in the implementation of climate stabilization measures, as well as in the validation of the results of simulation experiments to assess the carbon-deposition capacity of forests. © 2020 by the authors

The principle of space-for-time substitution in predicting Betula spp. Biomass change related to climate shifts

Human society faces problems of a global scale today, as a result of which the priorities of environmental research are shifting to the macro level, and ecology has entered the era of big data. The authors have created a database of 1,717 model trees of Betula spp. with measured indicators of diameter at breast height (DBH), tree height, age, and aboveground biomass growing in the territory of Eurasia. Regression models for aboveground biomass components are calculated, including the dendrological indices mentioned, and two climate indicators as independent variables. Based on the theory of space-for time substitution, the obtained patterns of changes in aboveground biomass in the territorial climatic gradients of Eurasia are used to predict changes in biomass due to climate shifts. In accordance with the law of the limiting factor by Liebig, it is established that in sufficiently moisture-rich climatic zones, an increase in temperature by 1??C with a constant amount of precipitation causes an increase in biomass, and in water-deficient zones ??? its decrease. In warm climatic zones, a decrease in precipitation by 100 mm at a constant average January temperature causes a decrease in biomass, and in cold climatic zones ??? its increase

A Comparative Pattern for Populus spp. and Betula spp. Stand Biomass in Eurasian Climate Gradients

Based on the generated database of 413 and 490 plots of biomass of Populus spp. and Betula spp. in Eurasia, statistically significant changes in the structure of forest stand biomass were found with shifts in January temperatures and average annual precipitation. When analyzing harvest data, the propeller-shaped biomass patterns in the gradients of average annual precipitation and average January temperatures are obtained, which are common for both deciduous species. Correspondingly, Populus and Betula forests show a regularity common to the biomass components: In the cold zones the precipitation increase leads to the increase of biomass, and in the warm ones to their decrease. In wet areas, the increase of temperature causes the decrease of biomass, and in dry areas, it causes their increase. In accordance with the law of the limiting factor by Liebig-Shelford, it is shown that both an decrease in temperature in dry conditions and a increase in precipitation in a warm climate lead to a decrease in the biomass of trees. © 2022 by the authors

Interaction of atopy and smoking on respiratory effects of occupational dust exposure: a general population-based study

BACKGROUND: For individual exposures, effect modification by atopy or smoking has been reported on the occurrence of occupational airway disease. It is unclear if effect modification can be studied in a general population by an aggregated exposure measure. Assess relationship between airway obstruction and occupational exposure using a job-exposure-matrix (JEM) classifying jobs into 3 broad types of exposure, and test for effect modification by atopy, and smoking. METHODS: Data from 1,906 subjects were analyzed, all participants of the European Community Respiratory Health Survey. Job titles were categorized by an a priori constructed job exposure matrix into three classes of exposure to respectively organic dust, mineral dust, and gases/ fumes. Relationships were assessed for 'current wheeze', bronchial hyperresponsiveness (BHR), 'current asthma' (wheeze+BHR), and 'chronic bronchitis' (morning phlegm or morning cough), and lung function. RESULTS: Subjects with organic dust exposure in their work environment more frequently had 'current asthma' (OR 1.48, 95% C.I. 0.95;2.30), and a lower FEV(1 )(-59 mL, 95% C.I. -114;-4). The relationship was only present in asthmatic workers, and their risk was four-fold greater than in subjects with either atopy or exposure alone. Mineral dust exposure was associated with 'chronic bronchitis' (OR 2.22, 95% C.I. 1.16;4.23) and a lower FEV(1)/FVC ratio (-1.1%, 95% C.I. -1.8;-0.3). We observed an excess risk in smokers, greater than the separate effects of smoking or mineral dust exposure together. CONCLUSION: Occupational exposure to organic dust is associated with an increased risk of asthma, particularly in atopics. Chronic bronchitis occurs more frequently among individuals exposed to mineral dust, and smoking doubles this risk

Spatial estimation of average daily precipitation using multiple linear regression by using topographic and wind speed variables in tropical climate

Complex topography and wind characteristics play important roles in rising air masses and in daily spatial distribution of the precipitations in complex region. As a result, its spatial discontinuity and behaviour in complex areas can affect the spatial distribution of precipitation. In this work, a two-fold concept was used to consider both spatial discontinuity and topographic and wind speed in average daily spatial precipitation estimation using Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR) in tropical climates. First, wet and dry days were identified by the two methods. Then the two models based on MLR (Model 1 and Model 2) were applied on wet days to estimate the precipitation using selected predictor variables. The models were applied for month wise, season wise and year wise daily averages separately during the study period. The study reveals that, Model 1 has been found to be the best in terms of categorical statistics, R2 values, bias and special distribution patterns. However, it was found that sets of different predictor variables dominates in different months, seasons and years. Furthermore, necessities of other data for further enhancement of the results were suggested

Redefining diagnostic lesional status in temporal lobe epilepsy with artificial intelligence.

Despite decades of advancements in diagnostic MRI, 30-50% of temporal lobe epilepsy (TLE) patients remain categorized as "non-lesional" (i.e., MRI negative or MRI-) based on visual assessment by human experts. MRI- patients face diagnostic uncertainty and significant delays in treatment planning. Quantitative MRI studies have demonstrated that MRI- patients often exhibit a TLE-specific pattern of temporal and limbic atrophy that may be too subtle for the human eye to detect. This signature pattern could be successfully translated into clinical use via artificial intelligence (AI) advances in computer-aided MRI interpretation, thereby improving the detection of brain "lesional" patterns associated with TLE. Here, we tested this hypothesis by employing a three-dimensional convolutional neural network (3D CNN) applied to a dataset of 1,178 scans from 12 different centers. 3D CNN was able to differentiate TLE from healthy controls with high accuracy (85.9% ± 2.8), significantly outperforming support vector machines based on hippocampal (74.4% ± 2.6) and whole-brain (78.3% ± 3.3) volumes. Our analysis subsequently focused on a subset of patients who achieved sustained seizure freedom post-surgery as a gold standard for confirming TLE. Importantly, MRI- patients from this cohort were accurately identified as TLE 82.7% ± 0.9 of the time, an encouraging finding since clinically these were all patients considered to be MRI- (i.e., not radiographically different than controls). The saliency maps from the CNN revealed that limbic structures, particularly medial temporal, cingulate, and orbitofrontal areas, were most influential in classification, confirming the importance of the well-established TLE signature atrophy pattern for diagnosis. Indeed, the saliency maps were similar in MRI+ and MRI- TLE groups, suggesting that even when humans cannot distinguish more subtle levels of atrophy, these MRI- patients are on the same continuum common across all TLE patients. As such, AI can identify TLE lesional patterns and AI-aided diagnosis has the potential to greatly enhance the neuroimaging diagnosis of TLE and redefine the concept of "lesional" TLE