Effect of Dairy, Season, and Sampling Position on Physical Properties of Trentingrana Cheese: Application of an LMM-ASCA Model

Trentingrana hard cheese is a geographic specification of the PDO Grana Padano. It is produced according to an internal regulation by many cooperative dairy factories in the Trentino region (northern Italy), using a semi-artisanal process (the only allowed ingredients are milk, salt, and rennet). Within the PSR project TRENTINGRANA, colorimetric and textural measurements have been collected from 317 cheese wheels, which were sampled bi-monthly from all the consortium dairies (n = 15) within the timeframe of two years, to estimate the effect on physical properties related to the season of the year and the dairy factory implant. To estimate the effect of the dairy and the time of the year, considering the internal variability of each cheese wheel, a linear mixed-effect model combined with a simultaneous component analysis (LMM-ASCA) is proposed. Results show that all the factors have a significant effect on the colorimetric and textural properties of the cheese. There are five clusters of dairies producing cheese with similar properties, three different couples of months of the year when the cheese produced is significantly different from all the others, and the effect of the geometry of the cheese wheel is reported as well.

A Semiautomated Deep Learning Approach for Pancreas Segmentation

Accurate pancreas segmentation from 3D CT volumes is important for pancreas diseases therapy. It is challenging to accurately delineate the pancreas due to the poor intensity contrast and intrinsic large variations in volume, shape, and location. In this paper, we propose a semiautomated deformable U-Net, i.e., DUNet for the pancreas segmentation. The key innovation of our proposed method is a deformable convolution module, which adaptively adds learned offsets to each sampling position of 2D convolutional kernel to enhance feature representation. Combining deformable convolution module with U-Net enables our DUNet to flexibly capture pancreatic features and improve the geometric modeling capability of U-Net. Moreover, a nonlinear Dice-based loss function is designed to tackle the class-imbalanced problem in the pancreas segmentation. Experimental results show that our proposed method outperforms all comparison methods on the same NIH dataset.

Inter-Signal Timing Skew Compensation Of Source-Synchronized Parallel Links With Incremental Signaling

This thesis deals with inter-signal timing skew compensation of source-synchronized multi-Gbytes/s parallel links with both voltage-mode and current-mode incremental signaling schemes. To compensate for the inter-signal timing skew of parallel links with voltage-mode incremental signaling, an early/late block that detects the rising and falling edges of the pulses generated by inter-signal timing skews at the far end of the channels, and subsequently allocates the optimal sampling point of the sampler of each data bit to maximize the timing margins. Two cascaded delay-locked loops are employed to place the sampling clock to the optimal sampling position of each data bit. To compensate for the inter-signal timing skew of parallel links with current-mode incremental signaling, each current-mode receiver maps the direction of its channel current representing the logic state of the incoming data to two voltages of different values. The feedback at the front-end of the receiver minimizes the dependence of the input imedance of the receiver on the channel current so that data dependent impedance mismatch is minimized. Inter-signal timing skews are compensated by inserting a delay line in each channel.

Inter-Signal Timing Skew Compensation Of Source-Synchronized Parallel Links With Incremental Signaling

This thesis deals with inter-signal timing skew compensation of source-synchronized multi-Gbytes/s parallel links with both voltage-mode and current-mode incremental signaling schemes. To compensate for the inter-signal timing skew of parallel links with voltage-mode incremental signaling, an early/late block that detects the rising and falling edges of the pulses generated by inter-signal timing skews at the far end of the channels, and subsequently allocates the optimal sampling point of the sampler of each data bit to maximize the timing margins. Two cascaded delay-locked loops are employed to place the sampling clock to the optimal sampling position of each data bit. To compensate for the inter-signal timing skew of parallel links with current-mode incremental signaling, each current-mode receiver maps the direction of its channel current representing the logic state of the incoming data to two voltages of different values. The feedback at the front-end of the receiver minimizes the dependence of the input imedance of the receiver on the channel current so that data dependent impedance mismatch is minimized. Inter-signal timing skews are compensated by inserting a delay line in each channel.

Analysis of the Heterogeneous Distribution of Amiloride and Propranolol in Dried Blood Spot by UHPLC-FLD and MALDI-IMS

Dried blood spot (DBS) has lately experienced an increase in its use in bioanalysis due to its several advantages compared with traditional blood sampling methods. Nevertheless, the use of DBS with quantitative purposes is hindered by the heterogeneous distribution of some compounds in the supporting matrix and the dependence of the response on different factors, such as the hematocrit, blood volume, and sampling position. In this study the effect of those factors in the analytical response was investigated by ultra high performance liquid chromatography coupled to fluorescence detection, using amiloride and propranolol as model compounds. The results showed a heterogeneous and drug-dependent distribution of the compounds in the blood spot. While amiloride concentration was higher in the center, propranolol concentration was higher in the periphery of the spot. Besides, the influence of the hematocrit on the quantitative results was observed. MALDI mass spectrometry imaging (MALDI-IMS) has allowed study of the distribution of the two cardiovascular drugs when they were placed in the DBS card using water:methanol solutions, demonstrating that they followed a similar distribution pattern as in blood. This work has showed the potentiality of the MALDI-IMS technique to predict the distribution of the drugs in the DBS card.

Predicting N Status in Maize with Clip Sensors: Choosing Sensor, Leaf Sampling Point, and Timing

Nitrogen (N) losses from agricultural systems increase air and water pollution, and these losses are highly correlated with the excessive fertilization. An adjusted N fertilization is then a key factor in increasing the N fertilizer efficiency, and leaf clip sensors can help to improve it. This study (combining five different field experiments in Central Spain) tried to identify the ability of the clip sensors in maize N status identification and yield prediction, comparing two different devices (SPAD-502® and Dualex®) and identifying the best protocol for maize leaf sampling. As a result, the study demonstrated that different leaf clip chlorophyll sensors presented similar results, although some differences appeared at larger N concentrations. Complementary polyphenol information (as flavonol) can improve the maize N deficiency prediction. Moreover, valuable information for a proper sampling protocol was obtained with this study. It proved that the sampling position (in the leaf and in the plant) and sampling time were crucial for a better estimation of the maize N status. Proper fertilization recommendations could be achieved based on clip chlorophyll sensor measurements.

Use of Gradient-Based Shadow Detection for Estimating Environmental Illumination Distribution

Environmental illumination information is necessary to achieve a consistent integration of virtual objects in a given image. In this paper, we present a gradient-based shadow detection method for estimating the environmental illumination distribution of a given scene, in which a three-dimensional (3-D) augmented reality (AR) marker, a cubic reference object of a known size, is employed. The geometric elements (the corners and sides) of the AR marker constitute the candidate’s shadow boundary; they are obtained on a flat surface according to the relationship between the camera and the candidate’s light sources. We can then extract the shadow regions by collecting the local features that support the candidate’s shadow boundary in the image. To further verify the shadows passed by the local features-based matching, we examine whether significant brightness changes occurred in the intersection region between the shadows. Our proposed method can reduce the unwanted effects caused by the threshold values during edge-based shadow detection, as well as those caused by the sampling position during point-based illumination estimation.