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Bacillus licheniformis 9945a alpha-amylase is known as a potent enzyme for raw starch hydrolysis. In this paper, a mixed mode Nuvia cPrime (TM) resin is examined with the aim to improve the downstream processing of raw starch digesting amylases and exploit the hydrophobic patches on their surface. This resin combines hydrophobic interactions with cation exchange groups and as such the presence of salt facilitates hydrophobic interactions while the ion-exchange groups enable proper selectivity. alpha-Amylase was produced using an optimized fed-batch approach in a defined media and significant overexpression of 1.2 g L-1 was achieved. This single step procedure enables simultaneous concentration, pigment removal as well as purification of amylase with yields of 96% directly from the fermentation broth.

Bacillus licheniformis 9945a alpha-amylase is known as a potent enzyme for raw starch hydrolysis. In this paper, a mixed mode Nuvia cPrime (TM) resin is examined with the aim to improve the downstream processing of raw starch digesting amylases and exploit the hydrophobic patches on their surface. This resin combines hydrophobic interactions with cation exchange groups and as such the presence of salt facilitates hydrophobic interactions while the ion-exchange groups enable proper selectivity. alpha-Amylase was produced using an optimized fed-batch approach in a defined media and significant overexpression of 1.2 g L-1 was achieved. This single step procedure enables simultaneous concentration, pigment removal as well as purification of amylase with yields of 96% directly from the fermentation broth.

Bacillus licheniformis 9945a alpha-amylase is known as a potent enzyme for raw starch hydrolysis. In this paper, a mixed mode Nuvia cPrime (TM) resin is examined with the aim to improve the downstream processing of raw starch digesting amylases and exploit the hydrophobic patches on their surface. This resin combines hydrophobic interactions with cation exchange groups and as such the presence of salt facilitates hydrophobic interactions while the ion-exchange groups enable proper selectivity. alpha-Amylase was produced using an optimized fed-batch approach in a defined media and significant overexpression of 1.2 g L-1 was achieved. This single step procedure enables simultaneous concentration, pigment removal as well as purification of amylase with yields of 96% directly from the fermentation broth.

Bacillus licheniformis 9945a alpha-amylase is known as a potent enzyme for raw starch hydrolysis. In this paper, a mixed mode Nuvia cPrime (TM) resin is examined with the aim to improve the downstream processing of raw starch digesting amylases and exploit the hydrophobic patches on their surface. This resin combines hydrophobic interactions with cation exchange groups and as such the presence of salt facilitates hydrophobic interactions while the ion-exchange groups enable proper selectivity. alpha-Amylase was produced using an optimized fed-batch approach in a defined media and significant overexpression of 1.2 g L-1 was achieved. This single step procedure enables simultaneous concentration, pigment removal as well as purification of amylase with yields of 96% directly from the fermentation broth.

Bacillus licheniformis 9945a alpha-amylase is known as a potent enzyme for raw starch hydrolysis. In this paper, a mixed mode Nuvia cPrime (TM) resin is examined with the aim to improve the downstream processing of raw starch digesting amylases and exploit the hydrophobic patches on their surface. This resin combines hydrophobic interactions with cation exchange groups and as such the presence of salt facilitates hydrophobic interactions while the ion-exchange groups enable proper selectivity. alpha-Amylase was produced using an optimized fed-batch approach in a defined media and significant overexpression of 1.2 g L-1 was achieved. This single step procedure enables simultaneous concentration, pigment removal as well as purification of amylase with yields of 96% directly from the fermentation broth.

Bacillus licheniformis 9945a alpha-amylase is known as a potent enzyme for raw starch hydrolysis. In this paper, a mixed mode Nuvia cPrime (TM) resin is examined with the aim to improve the downstream processing of raw starch digesting amylases and exploit the hydrophobic patches on their surface. This resin combines hydrophobic interactions with cation exchange groups and as such the presence of salt facilitates hydrophobic interactions while the ion-exchange groups enable proper selectivity. alpha-Amylase was produced using an optimized fed-batch approach in a defined media and significant overexpression of 1.2 g L-1 was achieved. This single step procedure enables simultaneous concentration, pigment removal as well as purification of amylase with yields of 96% directly from the fermentation broth.

Bacillus licheniformis 9945a alpha-amylase is known as a potent enzyme for raw starch hydrolysis. In this paper, a mixed mode Nuvia cPrime (TM) resin is examined with the aim to improve the downstream processing of raw starch digesting amylases and exploit the hydrophobic patches on their surface. This resin combines hydrophobic interactions with cation exchange groups and as such the presence of salt facilitates hydrophobic interactions while the ion-exchange groups enable proper selectivity. alpha-Amylase was produced using an optimized fed-batch approach in a defined media and significant overexpression of 1.2 g L-1 was achieved. This single step procedure enables simultaneous concentration, pigment removal as well as purification of amylase with yields of 96% directly from the fermentation broth.

The objective of this paper is to elucidate the variables that govern coffee extraction from single serve coffee capsules. The study was conducted on 43 Nespresso and Nespresso-compatible capsules of the same geometry, from all of which the coffee was extracted on the same machine. This allowed the link between a range of coffee and capsule (input) parameters with coffee brew (output) variables to be studied. It was demonstrated that the most efficient way to increase total dissolved solids in the brew is to use more coffee for extraction, and/or to grind the coffee more finely. However, grinding too finely can lead to excessive flow restriction. The most significant new insight from this study is the importance of the proportion of fines (particles smaller than 100 µm) regarding the capsule extraction dynamics. Capsules with a higher share of fines, for similar median particle size of the ground coffee, led to longer extraction times. General rules applicable for capsule coffee product development were established, although fine-tuning of parameters for successful capsule coffee extraction remains specific to production line and type of coffee.

The objective of this paper is to elucidate the variables that govern coffee extraction from single serve coffee capsules. The study was conducted on 43 Nespresso and Nespresso-compatible capsules of the same geometry, from all of which the coffee was extracted on the same machine. This allowed the link between a range of coffee and capsule (input) parameters with coffee brew (output) variables to be studied. It was demonstrated that the most efficient way to increase total dissolved solids in the brew is to use more coffee for extraction, and/or to grind the coffee more finely. However, grinding too finely can lead to excessive flow restriction. The most significant new insight from this study is the importance of the proportion of fines (particles smaller than 100 µm) regarding the capsule extraction dynamics. Capsules with a higher share of fines, for similar median particle size of the ground coffee, led to longer extraction times. General rules applicable for capsule coffee product development were established, although fine-tuning of parameters for successful capsule coffee extraction remains specific to production line and type of coffee.

Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples (N≈150,000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous ap

Single-cell sequencing provides a new level of granularity in studying the heterogeneous nature of cancer cells. For some cancers, this heterogeneity is the result of copy number changes of genes within the cellular genomes. The ability to accurately determine such copy number changes is critical in tracing and understanding tumorigenesis. Current single-cell genome sequencing methodologies infer copy numbers based on statistical approaches followed by rounding decimal numbers to integer values. Such methodologies are sample dependent, have varying calling sensitivities which heavily depend on the sample’s ploidy and are sensitive to noise in sequencing data. In this paper we have demonstrated the concept of integer-counting by using a novel bioinformatic algorithm built on our library construction chemistry in order to detect the discrete nature of the genome.

This paper examines whether compliance with COVID-19 mitigation measures is motivated by wanting to save lives or save the economy (or both), and which implications this carries to fight the pandemic. National representative samples were collected from 24 countries (N = 25,435). The main predictors were (1) perceived risk to contract coronavirus, (2) perceived risk to suffer economic losses due to coronavirus, and (3) their interaction effect. Individual and country-level variables were added as covariates in multilevel regression models. We examined compliance with various preventive health behaviors and support for strict containment policies. Results show that perceived economic risk consistently predicted mitigation behavior and policy support—and its effects were positive. Perceived health risk had mixed effects. Only two significant interactions between health and economic risk were identified—both positive.

Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples (N≈150,000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous ap

Single-cell sequencing provides a new level of granularity in studying the heterogeneous nature of cancer cells. For some cancers, this heterogeneity is the result of copy number changes of genes within the cellular genomes. The ability to accurately determine such copy number changes is critical in tracing and understanding tumorigenesis. Current single-cell genome sequencing methodologies infer copy numbers based on statistical approaches followed by rounding decimal numbers to integer values. Such methodologies are sample dependent, have varying calling sensitivities which heavily depend on the sample’s ploidy and are sensitive to noise in sequencing data. In this paper we have demonstrated the concept of integer-counting by using a novel bioinformatic algorithm built on our library construction chemistry in order to detect the discrete nature of the genome.

This paper examines whether compliance with COVID-19 mitigation measures is motivated by wanting to save lives or save the economy (or both), and which implications this carries to fight the pandemic. National representative samples were collected from 24 countries (N = 25,435). The main predictors were (1) perceived risk to contract coronavirus, (2) perceived risk to suffer economic losses due to coronavirus, and (3) their interaction effect. Individual and country-level variables were added as covariates in multilevel regression models. We examined compliance with various preventive health behaviors and support for strict containment policies. Results show that perceived economic risk consistently predicted mitigation behavior and policy support—and its effects were positive. Perceived health risk had mixed effects. Only two significant interactions between health and economic risk were identified—both positive.

Single-cell sequencing provides a new level of granularity in studying the heterogeneous nature of cancer cells. For some cancers, this heterogeneity is the result of copy number changes of genes within the cellular genomes. The ability to accurately determine such copy number changes is critical in tracing and understanding tumorigenesis. Current single-cell genome sequencing methodologies infer copy numbers based on statistical approaches followed by rounding decimal numbers to integer values. Such methodologies are sample dependent, have varying calling sensitivities which heavily depend on the sample’s ploidy and are sensitive to noise in sequencing data. In this paper we have demonstrated the concept of integer-counting by using a novel bioinformatic algorithm built on our library construction chemistry in order to detect the discrete nature of the genome.

Due to the increased demand for sunfower production, its breeding assignment is the ntensifcation of the development of highly productive oil seed hybrids to satisfy the edible oil industry. Sunfower Oil Yield Prediction (SOYP) can help breeders to identify desirable new hybrids with high oil yield and their characteristics using machine learning (ML) algorithms. In this study, we developed ML models to predict oil yield using two sets of features. Moreover, we evaluated the most relevant features for accurate SOYP. ML algorithms that were used and compared were Artifcial Neural Network (ANN), Support Vector Regression, K-Nearest Neighbour, and Random Forest Regressor (RFR). The dataset consisted of samples for 1250 hybrids of which 70% were randomly selected and were used to train the model and 30% were used to test the model and assess its performance. Employing MAE, MSE, RMSE and R2 evaluation metrics, RFR consistently outperformed in all datasets, achieving a peak of 0.92 for R2 in 2019. In contrast, ANN recorded the lowest MAE, reaching 65 in 2018 The paper revealed that in addition to seed yield, the following characteristics of hybrids were important for SOYP: resistance to broomrape (Or) and downy mildew (Pl) and maturity. It was also disclosed that the locality feature could be used for the estimation of sunfower oil yield but it is highly dependable on weather conditions that afect the oil content and seed yield. Up to our knowledge, this is the frst study in which ML was used for sunfower oil yield prediction. The obtained results indicate that ML has great potential for application in oil yield prediction, but also selection of parental lines for hybrid production, RFR algorithm was found to be the most efective and along with locality feature is going to be further evaluated as an alternative method for genotypic selection.