The chemical, rheological and sensory properties of the final produced yogurt were evaluated during storage periods up to 9 days at 5 °C. In conclusion, L. paracasei MK852178 β-galactosidase is a promising additive in manufacturing low lactose yogurt for lactose-intolerant people since it reduces the lactose content and doesn't influence the chemical and sensory properties.Amount of H2S and SO2 were generated during the SCWG of sewage sludge. It is essential to reduce the sulfur concentration in syngas for depth utilization of syngas from SCWG of sewage sludge. The syngas desulfurization ability of five additives (KOH, K2CO3, NaOH, Na2CO3, AC) were tested and the result indicated that K2CO3 had the best syngas desulfurization effect while KOH could significantly promote the yield of syngas at 450 °C and 4% loading. Increasing KOH and K2CO3 loading to 12% could reduce around 90% of sulfur in syngas comparing to no additives. The XPS analysis results indicated that alkali additives promoted the cyclization and oxidation of unstable sulfur compounds in raw sludge, which can convert it into stable sulfur compounds such as thiophene, sulfone and sulfate. The sulfur in liquid was mainly in the forms of sulfate, and the effect of alkali and AC additive on sulfur in liquid was relatively weak.
  An efficient readily employable risk prognostication method is desirable for MM in settings where genomics tests cannot be performed owing to geographical/economical constraints. In this work, a new Modified Risk Staging (MRS) has been proposed for newly diagnosed Multiple Myeloma (NDMM) that exploits six easy-to-acquire clinical parameters i.e. age, albumin, β2-microglobulin (β2M), calcium, estimated glomerular filtration rate (eGFR) and hemoglobin.
 
  MRS was designed using a training cohort of 716 NDMM patients of our inhouse MM Indian (MMIn) cohort and validated on MMIn (n=354) cohort and MMRF (n=900) cohort. K-adaptive partitioning (KAP) was used to find new thresholds for the parameters. Risk staging rules, obtained via training a J48 classifier, were used to build MRS.
 
  New thresholds were identified for albumin (3.6g/dL), β2M (4.8mg/L), calcium (11.13mg/dL), eGFR (48.1mL/min), and hemoglobin (12.3g/dL) using KAP on the MMIn dataset. On the MMIn dataset, MRS outperformed ISS for OS prediction in terms of C-index, hazard ratios, and its corresponding p-values, but performs comparable in prediction of PFS. On both MMIn and MMRF datasets, MRS performed better than RISS in terms of C-index and p-values. A simple online tool was also designed to allow automated calculation of MRS based on the values of the parameters.
 
  Our proposed ML-derived yet simple staging system, MRS, although does not employ genetic features, outperforms RISS as confirmed by better separability in KM survival curves and higher values of C-index on both MMIn and MMRF datasets.
 
  Grant BT/MED/30/SP11006/2015 (Department of Biotechnology, Govt. of India), Grant DST/ICPS/CPS-Individual/2018/279(G) (Department of Science and Technology, Govt. of India), UGC-Senior Research Fellowship.
 Grant BT/MED/30/SP11006/2015 (Department of Biotechnology, Govt. of India), Grant DST/ICPS/CPS-Individual/2018/279(G) (Department of Science and Technology, Govt. of India), UGC-Senior Research Fellowship.The main challenges for the automatic detection of the coronavirus disease (COVID-19) from computed tomography (CT) scans of an individual are a lack of large datasets, ambiguity in the characteristics of COVID-19 and the detection techniques having low sensitivity (or recall). Hence, developing diagnostic techniques with high recall and automatic feature extraction using the available data are crucial for controlling the spread of COVID-19. This paper proposes a novel stacked ensemble capable of detecting COVID-19 from a patient's chest CT scans with high recall and accuracy. A systematic approach for designing a stacked ensemble from pre-trained computer vision models using transfer learning (TL) is presented. A novel diversity measure that results in the stacked ensemble with high recall and accuracy is proposed. The stacked ensemble proposed in this paper considers four pre-trained computer vision models the visual geometry group (VGG)-19, residual network (ResNet)-101, densely connected convolutional network (DenseNet)-169 and wide residual network (WideResNet)-50-2. The proposed model was trained and evaluated with three different chest CT scans. As recall is more important than precision, the trade-offs between recall and precision were explored in relevance to COVID-19. The optimal recommended threshold values were found for each dataset.
  The impact of diet on COVID-19 patients has been a global concern since the pandemic began. Choosing different types of food affects peoples' mental and physical health and, with persistent consumption of certain types of food and frequent eating, there may be an increased likelihood of death. In this paper, a regression system is employed to evaluate the prediction of death status based on food categories.
 
  A Healthy Artificial Nutrition Analysis (HANA) model is proposed. The proposed model is used to generate a food recommendation system and track individual habits during the COVID-19 pandemic to ensure healthy foods are recommended. To collect information about the different types of foods that most of the world's population eat, the COVID-19 Healthy Diet Dataset was used. This dataset includes different types of foods from 170 countries around the world as well as obesity, undernutrition, death, and COVID-19 data as percentages of the total population.  https://www.selleckchem.com/products/odq.html  The dataset was used to predict the status of deat products, animal fats, meat, milk, sugar and sweetened foods, sugar crops, were associated with a higher number of deaths and fewer patient recoveries. The outcome of sugar consumption was important and the rates of death and recovery were influenced by obesity.
 
  Based on evaluation metrics, the proposed HANA model may outperform other algorithms used to predict death status. The results of this study may direct patients to eat particular types of food to reduce the possibility of becoming infected with the COVID-19 virus.
 Based on evaluation metrics, the proposed HANA model may outperform other algorithms used to predict death status. The results of this study may direct patients to eat particular types of food to reduce the possibility of becoming infected with the COVID-19 virus.