NUTRITIONAL APPROACHES TOWARDS PREVENTION AND TREATMENT OF DCLD

Decompensated chronic liver disease (DCLD) is also known as decompensating cirrhosis. Cirrhosis is a chronic liver disease that is commonly resulting of hepatitis or alcohol use disorder. It is the severe scarring of liver seen at the terminal stages of chronic liver disease. The diet of patients with chronic liver disease is based on a standard diet with supplements addition as necessary. Restrictions may be harmful and should be individualized. In this study we detailed a patient having decompensated chronic liver disease and observed all require parameter in dietary management. The patient undertook a dietary counselling for 16 days and dietary modification was done according to the patient condition. The HB level was 9.1g/dl, so beetroot juice in the mid-morning and soybean and 2 egg whites were suggested to increase the protein level. Later it was seen that Hb level was increased to 9.9g/dl and protein level was increased to 6g/dl. Also, the potassium level was below normal, so coconut water suggested. what to avoid and what to include and a sample menu and a diet chat was given to the patient at the time of discharge. Malnutrition is a potentially reversible condition that, when identified and treated appropriately, can lead to improvement of the outcomes of patients with DCLD.

Influence of Changes of Catchment Permeability and Frequency of Rainfall on Critical Storm Duration in an Urbanized Catchment—A Case Study, Cracow, Poland

The increase of impermeable areas in a catchment is known to elevate flood risk. To adequately understand and plan for these risks, changes in the basin water cycle must be quantified as imperviousness increases, requiring the use of hydrological modeling to obtain design runoff volumes and peak flow rates. A key stage of modeling is adopting the structure of the model and estimating its parameters. Due to the fact that most impervious basins are uncontrolled, hydrological models that do not require parameter calibration are advantageous. At the same time, it should be remembered that these models are sensitive to the values of assumed parameters. The purpose of this work is to determine the effect of catchment impermeability on the flow variability in the Sudół Dominikański stream in Cracow, Poland, and assess the influence of the frequency of rainfall on values of time of concentration (here it is meant as critical storm duration). The major finding in this work is that the critical storm duration for all different scenarios of catchment imperviousness depends on the rainfall exceedance probability. In the case of rainfall probability lower than 5.0%, the critical storm duration was equal to 2 h, for higher probabilities (p ≥ 50%) it was equal to 24 h. Simulations showed that the increase of impermeable areas caused peak time abbreviation. In the case of rainfall with exceedance probability p = 1.0% and critical storm duration Dkr = 2 h, the peak time decreased about 12.5% and for impermeable areas increased from 22.01 to 44.95%.

manta - a clustering algorithm for weighted ecological networks

Microbial network inference and analysis has become a successful approach to generate biological hypotheses from microbial sequencing data. Network clustering is a crucial step in this analysis. Here, we present a novel heuristic flow-based network clustering algorithm, which equals or outperforms existing algorithms on noise-free synthetic data. manta comes with unique strengths such as the ability to identify nodes that represent an intermediate between clusters, to exploit negative edges and to assess the robustness of cluster membership. manta does not require parameter tuning, is straightforward to install and run, and can easily be combined with existing microbial network inference tools.

A technique for detecting concealed objects in terahertz images based on information measure

In this paper, a new technique for detecting concealed objects in the images acquired by a passive THz imaging system is proposed. The technique is based on a method for mutual information maximization successfully used for image matching. For reducing computational expenses, we propose to analyze the mutual information at local maxima of the crosscorrelation function computed in the Fourier domain. The proposed technique does not require parameter tuning. A computing experiment approved the efficiency of the proposed technique and the possibility of its implementation in security systems.

Machine Learning Approaches to Reduce Electrical Waste and Improve Power Grid Stability

My research contributions are summarized as follows: In electricity disaggregation, I introduced the first label correction approach for supervised training samples. For unsupervised disaggregation, I introduced event detection that does not require parameter tuning and appliance discovery that makes no assumptions on appliance types. These improvements produce better accuracy, faster computation, and more scalability than any previously introduced method and can be applied to natural gas disaggregation, water disaggregation, and other source separation domains. My current work challenges long-held assumptions in time series shapelets, a classification tool with applicability in electrical time series and dozens of additional domains.