The genetic background of bovine milk infrared spectra

Milk infrared (IR) spectroscopy is a cheap, quick and high-throughput technique that has been widely used to determine milk components. It has been used as the standard method for routine quantification of fat, protein and lactose content of milk, and it is a promising technique to obtain information about milk composition. The aim of this thesis was to explore the genetic background of bovine milk IR spectra, identify the environmental factors affecting milk IR spectra, and combined use genotypic information and milk IR spectra in predicting dairy cattle phenotypes. Two studies were conducted to explore the genetic background of milk IR spectra of Holstein Friesian dairy cows in the Netherlands. Studies were focused on individual IR wavenumbers, and results showed that for many of them 20 to 60% of variation can be attributed to genetic factors. Polymorphisms of individual gene diacylglycerol O-acyltransferase 1 (DGAT1), k-casein (CSN3) and b-lactoglobulin (LGB), as well as lactation stage of dairy cows and the different dates of IR analysis have significant effect on the values of milk IR spectra. Genome wide association study (GWAS) identified the associated genomic regions. In addition to the regions that related to milk fat, protein and lactose content, this thesis detected 3 new regions related to phosphorus, orotic acid and citric acid content in milk. Knowledge of the genetic background of milk IR spectra could enhance the prediction for dairy cattle phenotypes. This thesis investigated if combined use of genotypes of dairy cows can improve the prediction for milk fat composition. Results suggest that prediction accuracy of unsaturated fatty acids can be considerably improved by adding stearoyl-CoA desaturase (SCD1) genotypes of dairy cows. Predicting methane (CH4) emission based on milk IR spectra is of great interest due the environmental impact of dairy production. This thesis showed the importance of validation strategy in interpreting the results of predicting CH4 emission. This result has general value in milk IR prediction for dairy cattle phenotypes that a block cross validation with farms as block could reflect the true predicative ability for independent observations. This thesis also suggested to predict based on IR wavenumbers from water absorption regions of the spectra as a negative control, to detect potential problem due to dependency structure in the data. </p

Developing Strategies for Anatomical Characterization of Locus Coeruleus - Cortical Projections

The locus coeruleus (LC) is a small noradrenergic nucleus located in the midbrain that releases the neurotransmitter norepinephrine to diverse brain regions. Through release of norepinephrine, the LC plays a central role in modulating numerous physiological functions including attention, arousal, and mood and behavior. Although the LC projects to many brain region, there is limited information about the organization and the afferent projections to the LC that modulates its activity. The goal of this study was to characterize the anatomical projections between LC and cortical areas using a variety of different experimental techniques, including survival brain surgery, stereotaxic injections of fluorescent dyes, trans-cardiac perfusion, and immunohistochemistry. To determine cortical projections from different brain region to the locus coeruleus, we injected the retrograde fluorescent tracer Fast Blue into the LC. Immunolabeling technique using dopamine-b-hydroxylase antibody allowed for detection of norepinephrine neurons and their extensive projections. The results from the experiment after microscopic imaging of the histology slices do not reveal a direct projection from the visual cortex to the locus coeruleus

Becoming Sustainable Together: ESG Data Commons for Fintech Startups

Environmental, social, and corporate governance (ESG) reporting has become an important instrument for the sustainable transition of the next generation of business-startup. Nonetheless, poor ESG data quality impedes effective reporting, especially in domains such as Fintech where top-down ESG metrics may overlook pertinent material issues. This action research study applies a design probe in the form of the notion of an ESG data commons to explore possible strategies to improve ESG data quality in Fintech startup. By reporting on the initial results of an ongoing study of a Danish Fintech startup cluster, we develop a practice-based approach that highlights the changing processes, teleoaffective structures, and sociomaterial dynamics of ESG data commons. We contribute to information systems (IS) research in two areas. First, we contribute to the call for a data-driven approach to ESG reporting. Second, the study extends the IS design literature by applying data commons as a design probe

Multiphase lattice Boltzmann modeling of cyclic water retention behavior in unsaturated sand based on X-ray Computed Tomography

The water retention curve (WRC) defines the relationship between matric suction and saturation and is a key function for determining the hydro-mechanical behavior of unsaturated soils. We investigate possible microscopic origins of the water retention behavior of granular soils using both Computed Tomography (CT) experiment and multiphase lattice Boltzmann Method (LBM). We conduct a CT experiment on Hamburg sand to obtain its WRC and then run LBM simulations based on the CT grain skeleton. The multiphase LBM simulations capture the hysteresis and pore-scale behaviors of WRC observed in the CT experiment. Using LBM, we observe that the spatial distribution and morphology of gas clusters varies between drainage and imbibition paths and is the underlying source of the hysteresis. During drainage, gas clusters congregate at the grain surface; the local suction increases when gas clusters enter through small pore openings and decreases when gas clusters enter through large pore openings. Whereas, during imbibition, gas clusters disperse in the liquid; the local suction decreases uniformly. Large pores empty first during drainage and small pores fill first during imbibition. The pore-based WRC shows that an increase in pore size causes a decrease in suction during drainage and imbibition, and an increase in hysteresis

Effect of Yuanbao Maple Tea Powder with High Chlorogenic Acid Content on Bread Quality

Using Yuanbao maple leaves as raw materials, the extraction process of chlorogenic acid in leaves was optimized, and single-factor and orthogonal experiments were carried out on ultrasonic temperature, time, and solid-liquid ratio through ultrasonic extraction. The results showed that the optimal level of the experiment was when the ratio of solid to liquid was 16:1, the concentration of ethanol was 60%, and the ultrasonic time was 15 min, and the extraction amount was 6.86% (mass fraction). Under the optimal extraction process conditions, the dynamic content of chlorogenic acid in the growth cycle of Yuanbaofeng in 2020 was analyzed. The results showed that the content of chlorogenic acid in the leaves of Yuanbaofeng in June was the highest, and the content in September was the least. In order to further explore the effect of Yuanbao maple tea powder on bread quality, different proportions of Yuanbao maple tea powder were added to bread to study its sensory effects on bread. The effects of scores, moisture content, texture, polyphenol content, antioxidant activity and other qualities. The results show that the water holding capacity, elasticity and anti-oxidation of bread are the best when the addition amount of GTB is 0.5%. Less elastic, more difficult to chew, and gradually unstable antioxidant properties

Multi-Modal 3D Object Detection in Autonomous Driving: a Survey

In the past few years, we have witnessed rapid development of autonomous driving. However, achieving full autonomy remains a daunting task due to the complex and dynamic driving environment. As a result, self-driving cars are equipped with a suite of sensors to conduct robust and accurate environment perception. As the number and type of sensors keep increasing, combining them for better perception is becoming a natural trend. So far, there has been no indepth review that focuses on multi-sensor fusion based perception. To bridge this gap and motivate future research, this survey devotes to review recent fusion-based 3D detection deep learning models that leverage multiple sensor data sources, especially cameras and LiDARs. In this survey, we first introduce the background of popular sensors for autonomous cars, including their common data representations as well as object detection networks developed for each type of sensor data. Next, we discuss some popular datasets for multi-modal 3D object detection, with a special focus on the sensor data included in each dataset. Then we present in-depth reviews of recent multi-modal 3D detection networks by considering the following three aspects of the fusion: fusion location, fusion data representation, and fusion granularity. After a detailed review, we discuss open challenges and point out possible solutions. We hope that our detailed review can help researchers to embark investigations in the area of multi-modal 3D object detection