**Carolina Del-Valle-Soto 1,†, Leonardo J. Valdivia 1,\*,†, Ramiro Velázquez 2, Luis Rizo-Dominguez 3,† and Juan-Carlos López-Pimentel 1**

Received: 20 July 2019; Accepted: 11 August 2019; Published: 15 August 2019

**Abstract:** Presently, the Internet of Things (IoT) concept involves a scattered collection of different multipurpose sensor networks that capture information, which is further processed and used in applications such as smart cities. These networks can send large amounts of information in a fairly efficient but insecure wireless environment. Energy consumption is a key aspect of sensor networks since most of the time, they are battery powered and placed in not easily accessible locations. Therefore, and regardless of the final application, wireless sensor networks require a careful energy consumption analysis that allows selection of the best operating protocol and energy optimization scheme. In this paper, a set of performance metrics is defined to objectively compare different kinds of protocols. Four of the most popular IoT protocols are selected: Zigbee, LoRa, Bluethooth, and WiFi. To test and compare their performance, multiple sensors are placed at different points of a university campus to create a network that can accurately simulate a smart city. Finally, the network is analyzed in detail using two different schemes: collaborative and cooperative.

**Keywords:** energy consumption; wireless sensor network; cooperation; collaboration


*Edited by Aise Seda Artis*

Since the discovery of blood pressure by Stephen Hales in 1733, scientific interest in blood pressure regulation, particularly in hypertensive population, has not lost its popularity. The importance of the interactive effects of blood pressure shifts in different clinical conditions is well understood. We know many contributing factors regulate the pressure of the blood within the arteries. However, crucial blood pressure control and the exact mechanisms involved are still under debate. The present book aims to cover blood pressure from its measurement to various factors of its control with valuable contributions from different authors, in the light of contemporary data, from bench to bed.

ISBN 978-1-78984-271-5

Blood Pressure - From Bench to Bed

Published in London, UK © 2018 IntechOpen © igorr1 / iStock


**5. Nonlinear dimensionality reduction methods**

So far, we have investigated the use of linear dimensionality reduction methods (PCA and MDS) in extracting synergies. In this section we used nonlinear dimensionality reduction methods for the same purpose. The motivation to explore nonlinear methods was that physiologists who studied motor control have propounded that there were inherent nonlinearities in the human motor system. By using nonlinear methods we could probably achieve improved precision in reconstruction of natural movements. The nonlinear methods applied in this chapter are Isomap, local linear embedding (LLE), and kernel PCA (kPCA). The first two methods Isomap and LLE, are built on the framework of classical multidimensional scaling discussed in the previous section. kPCA is built on the framework of PCA.

### **5.1 Isomap**

Isomap is similar to PCA and MDS. Although Isomap does linear estimations in the data point neighborhoods, the synergies extracted are nonlinear because these small neighborhoods are stitched together without trying to maintain linearity.

**Angular**

**Samples Samples Samples**

0 10 20 30 40 -1

**Synergy 4 Synergy 5 Synergy 6**

**Synergy 1 Synergy 2 Synergy 3**

Application of Linear and Nonlinear Dimensionality Reduction Methods 119

> -1 0 1

> -1 0 1

> -1 0 1

> 0 1

> 0 0.1 0.2

> 0 0.1 0.2

> 0 0.5

0 10 20 30 40 -0.5

0 10 20 30 40 -1

> -1 0 1

> -1 0 1

> -1 0 1

> -1 0 1

> -1 0 1

> -1 0 1

> -1 0 1

> > 0 1

> 0 0.5

**T\_MCP**

**IͲMCP IͲPIP**

**MͲMCP MͲPIP RͲMCP RͲPIP**

**PͲMCP PͲPIP**

**T\_MCP**

**IͲMCP IͲPIP**

**MͲMCP MͲPIP RͲMCP RͲPIP**

**PͲMCP PͲPIP**

**TͲIP**

**TͲIP**

**Samples Samples Samples**

Fig. 5. Six kinematic synergies obtained for subject 1 using Isomap. Each synergy is about 0.45 s in duration (39 samples at 86 Hz). Abbreviations: T, thumb; I, index finger; M, middle finger; R, ring finger; P, pinky finger; MCP, metacarpophalangeal joint; IP, interphalangeal

0 10 20 30 40 -0.5

**(radians/sample)**

**Angular**

**velocities of ten joints**

**velocities of ten joints**

**(radians/sample)**

> -2 0 2

> -2 0 2

> -2 0 2

> -2 0 2

> -0.5 0 0.5

> -0.5 0 0.5

> -0.5 0 0.5

> -0.1 0 0.1

> > 0 0.5

0 10 20 30 40 -1

0 10 20 30 40 -0.5

joint; PIP, proximal IP joint.

The following were the steps involved in estimating nonlinear synergies using Isomap:

3. Find eigenvectors of *τ*(D), where *τ*(D) = −*HSH*/2, *Sij* = (*Dij*)2 and *Hij* = *dij* − 1/*N*, where *N* is number of data points and *d* is Kronecker delta function.

In PCA we estimated the eigen values and eigen vectors of covariance of the data. Similarly, here, we took a nonlinear approach to preserve inter-point distances on the manifold. The matrix D is similar to covariance matrix in PCA. D can actually be thought of as the covariance matrix in higher dimensions. Since in an N-dimensional space, the dimensions are the data points, the covariance for a particular pair of dimensions is the distance between the data points that define those dimensions.

Although this method looks linear like PCA, the source of nonlinearity is the method in which inter-point distances are calculated. For Isomap, we do not use the Euclidean distances between the points. If we use, it becomes classical MDS discussed in previous section. Rather, we use those distances only for points considered neighbors. The rest of inter-point distances are calculated by finding the shortest path through the graph on the manifold using Floyd's algorithm ( Tenenbaum et al. (2000)). The goal of the Isomap is to preserve the geodesic distances rather than the euclidian distances. Geodesic distances are calculated by moving along the approximate nonlinear manifold with given data point and interpolation between them.

We used drtoolbox in MATLAB by van der Maaten et al. (2009) to perform Isomap on the angular-velocity matrix to extract synergies. Fig. 5showed the top six synergies extracted using this method. Similar to PCA, all the nonlinear methods also yield the nonlinear synergies in descending order of their significance. The synergies extracted using this method had more submovements when compared to those in PCA.

#### **5.2 Local Linear Embedding**

Locally Linear Embedding (LLE) as the name suggests, tries to find a nonlinear manifold by stitching together small linear neighborhoods ( Roweis & Saul (2000)). This is very similar to Isomap. The difference between the two algorithms is in how they do the stitching. Isomap does this by doing a graph traversal by preserving geodesic distances while LLE does it by finding a set of weights that perform local linear interpolations that closely approximate the data.

The following were the steps involved in estimating nonlinear synergies using LLE:

3. Given those weights, find new data points that minimize interpolation error in lower dimensional space

We used drtoolbox in MATLAB by van der Maaten et al. (2009) to perform LLE on the angular-velocity matrix to extract synergies. Fig. 6showed the top six synergies extracted using this method. Similar to Isomap, in this method also we found more submovements in synergies than those from PCA.

12 Will-be-set-by-IN-TECH

3. Find eigenvectors of *τ*(D), where *τ*(D) = −*HSH*/2, *Sij* = (*Dij*)2 and *Hij* = *dij* − 1/*N*, where

In PCA we estimated the eigen values and eigen vectors of covariance of the data. Similarly, here, we took a nonlinear approach to preserve inter-point distances on the manifold. The matrix D is similar to covariance matrix in PCA. D can actually be thought of as the covariance matrix in higher dimensions. Since in an N-dimensional space, the dimensions are the data points, the covariance for a particular pair of dimensions is the distance between the data

Although this method looks linear like PCA, the source of nonlinearity is the method in which inter-point distances are calculated. For Isomap, we do not use the Euclidean distances between the points. If we use, it becomes classical MDS discussed in previous section. Rather, we use those distances only for points considered neighbors. The rest of inter-point distances are calculated by finding the shortest path through the graph on the manifold using Floyd's algorithm ( Tenenbaum et al. (2000)). The goal of the Isomap is to preserve the geodesic distances rather than the euclidian distances. Geodesic distances are calculated by moving along the approximate nonlinear manifold with given data point and interpolation between

We used drtoolbox in MATLAB by van der Maaten et al. (2009) to perform Isomap on the angular-velocity matrix to extract synergies. Fig. 5showed the top six synergies extracted using this method. Similar to PCA, all the nonlinear methods also yield the nonlinear synergies in descending order of their significance. The synergies extracted using this method

Locally Linear Embedding (LLE) as the name suggests, tries to find a nonlinear manifold by stitching together small linear neighborhoods ( Roweis & Saul (2000)). This is very similar to Isomap. The difference between the two algorithms is in how they do the stitching. Isomap does this by doing a graph traversal by preserving geodesic distances while LLE does it by finding a set of weights that perform local linear interpolations that closely approximate the

3. Given those weights, find new data points that minimize interpolation error in lower

We used drtoolbox in MATLAB by van der Maaten et al. (2009) to perform LLE on the angular-velocity matrix to extract synergies. Fig. 6showed the top six synergies extracted using this method. Similar to Isomap, in this method also we found more submovements in

The following were the steps involved in estimating nonlinear synergies using LLE:

2. Find weights that allow neighbors to interpolate original data accurately

The following were the steps involved in estimating nonlinear synergies using Isomap:

1. Define neighbors for each data point 2. Find D, a matrix of inter-point distances

points that define those dimensions.

**5.2 Local Linear Embedding**

1. Define neighbors for each data point

synergies than those from PCA.

them.

data.

dimensional space

*N* is number of data points and *d* is Kronecker delta function.

had more submovements when compared to those in PCA.

Fig. 5. Six kinematic synergies obtained for subject 1 using Isomap. Each synergy is about 0.45 s in duration (39 samples at 86 Hz). Abbreviations: T, thumb; I, index finger; M, middle finger; R, ring finger; P, pinky finger; MCP, metacarpophalangeal joint; IP, interphalangeal joint; PIP, proximal IP joint.

**5.3 Kernel PCA**

(2010a) for further details.

denoted **v**row:

Similarly, a synergy **s***j*

[*s j* 1(1), ...,*s*

samples, then we obtain the following row vector:

and all their possible shifts with 1 ≤ *tjk* ≤ *T* − *ts*.

We add *T* − *ts* zeros after each *s*

with *tjk* zeros added before each *s*

*j*

*j i*

> *j i*

[0, ..., 0,*s j* 1(1), ...,*s*

reconstruct the velocity profiles as in the following equation.

synergies were similar to those obtained from PCA.

**6. Reconstruction of natural and ASL movements**

Kernel PCA (kPCA) is an extension of PCA in a high-dimensional space ( Scholkopf et al. (1998)). A high-dimensional space is first constructed by using a kernel function. Instead of directly doing a PCA on the data, the kernel based high dimensional feature space is used as input. In this chapter, we have used a gaussian kernel function. Kernel PCA computes the principal eigenvectors of the kernel matrix, rather than those of the covariance matrix. A kernel matrix is similar to the inner product of the data points in the high dimensional space that is constructed using the kernel function. The application of PCA in the kernel space provides Kernel PCA the property of constructing nonlinear mappings. We used drtoolbox in MATLAB by van der Maaten et al. (2009) to perform kPCA on the angular-velocity matrix to extract synergies. Fig. 7showed the top six synergies extracted using this method. These

Application of Linear and Nonlinear Dimensionality Reduction Methods 121

The synergies extracted from linear and nonlinear dimensionality reduction methods were used in reconstruction of natural movements. *l*1-norm minimization was used to reconstruct natural and ASL movements from the extracted synergies. This method with illustrations was already presented in Vinjamuri et al. (2010a). We have included a brief explanation here for readability and for the sake of completeness. We ask the readers to refer Vinjamuri et al.

Briefly, these were the steps involved in *l*1-norm minimization algorithm that was used for reconstruction of natural and ASL movements. Let us assume for a subject *m* synergies were obtained. The duration of the synergies is *ts* samples (*ts* = 39 in this study). Consider an angular-velocity profile of the subject, {**v**(*t*),*t* = 1, ..., *T*}, where *T* (*T* = 82 in this study) represents the movement duration (in samples). This profile can be rewritten as a row vector,

**v**row = [*v*1(1), ..., *v*1(*T*), ..., *v*10(1), ..., *v*10(*T*)].

(·) can be rewritten as the following row vector:

length of the vector the same as that of **v**row. If the synergy is shifted in time by *tjk* (*tjk* ≤ *T* − *ts*)

Then we construct a matrix as shown in Fig. 8 consisting of the row vectors of the synergies

With the above notation, we are trying to achieve a linear combination of synergies that can

1(*ts*), 0, ..., 0, ...,

*j*

(1) and *T* − *ts* − *tjk* zeros added after each *s*

*j*

0, ..., 0,*s j* 10(1), ...,*s*

*j* 10(1), ...,*s*

*j*

10(*ts*), 0, ..., 0].

(*ts*) (*i* = 1, ..., 10) in the above vector in order to make the

10(*ts*), 0, ..., 0]

**v**row = **c***B* (8)

*j i* (*ts*).

1(*ts*), 0, ..., 0, ...,*s*

Fig. 6. Six kinematic synergies obtained for subject 1 using LLE. Each synergy is about 0.45 s in duration (39 samples at 86 Hz). Abbreviations: T, thumb; I, index finger; M, middle finger; R, ring finger; P, pinky finger; MCP, metacarpophalangeal joint; IP, interphalangeal joint; PIP, proximal IP joint.

#### **5.3 Kernel PCA**

14 Will-be-set-by-IN-TECH

> 0 0.1

> -0.2 0 0.2

> -0.1 0 0.1

> -0.1 0 0.1

> -0.1 0 0.1

> 0 0.2

0 10 20 30 40 -0.1

0 10 20 30 40 -0.2

**T\_MCP**

**IͲMCP IͲPIP**

**MͲMCP MͲPIP RͲMCP RͲPIP**

**PͲMCP PͲPIP**

**T\_MCP**

**IͲMCP IͲPIP**

**MͲMCP MͲPIP RͲMCP RͲPIP**

**PͲMCP PͲPIP**

**TͲIP**

**TͲIP**

**Synergy 1 Synergy 2 Synergy 3**

0.01 0.02 0.03

> -0.1 0 0.1

0 0.02 0.04

> -0.1 0 0.1

0 0.02 0.04

> -0.1 0 0.1

0.01 0.02 0.03

> 0 0.2

> -0.1 0 0.1

> -0.1 0 0.1

> -0.1 0 0.1

> -0.1 0 0.1

> > 0 0.1

**Samples Samples Samples**

0 10 20 30 40 -0.2

**Synergy 4 Synergy 5 Synergy 6**

**Samples Samples Samples**

Fig. 6. Six kinematic synergies obtained for subject 1 using LLE. Each synergy is about 0.45 s in duration (39 samples at 86 Hz). Abbreviations: T, thumb; I, index finger; M, middle finger; R, ring finger; P, pinky finger; MCP, metacarpophalangeal joint; IP, interphalangeal joint; PIP,

0 10 20 30 40 -0.1

**Angular**

**(radians/sample)**

**Angular**

**velocities of ten joints**

**velocities of ten joints**

**(radians/sample)**

> -0.1 0 0.1

> -0.1 0 0.1

> -0.1 0 0.1

> -0.1 0 0.1

> -0.1 0 0.1

> -0.2 0 0.2

> -0.1 0 0.1

> -0.2 0 0.2

> -0.1 0 0.1

> -0.2 0 0.2

> -0.2 0 0.2

0 0.05

proximal IP joint.

0 10 20 30 40 -0.1

0 10 20 30 40 -0.05

Kernel PCA (kPCA) is an extension of PCA in a high-dimensional space ( Scholkopf et al. (1998)). A high-dimensional space is first constructed by using a kernel function. Instead of directly doing a PCA on the data, the kernel based high dimensional feature space is used as input. In this chapter, we have used a gaussian kernel function. Kernel PCA computes the principal eigenvectors of the kernel matrix, rather than those of the covariance matrix. A kernel matrix is similar to the inner product of the data points in the high dimensional space that is constructed using the kernel function. The application of PCA in the kernel space provides Kernel PCA the property of constructing nonlinear mappings. We used drtoolbox in MATLAB by van der Maaten et al. (2009) to perform kPCA on the angular-velocity matrix to extract synergies. Fig. 7showed the top six synergies extracted using this method. These synergies were similar to those obtained from PCA.

#### **6. Reconstruction of natural and ASL movements**

The synergies extracted from linear and nonlinear dimensionality reduction methods were used in reconstruction of natural movements. *l*1-norm minimization was used to reconstruct natural and ASL movements from the extracted synergies. This method with illustrations was already presented in Vinjamuri et al. (2010a). We have included a brief explanation here for readability and for the sake of completeness. We ask the readers to refer Vinjamuri et al. (2010a) for further details.

Briefly, these were the steps involved in *l*1-norm minimization algorithm that was used for reconstruction of natural and ASL movements. Let us assume for a subject *m* synergies were obtained. The duration of the synergies is *ts* samples (*ts* = 39 in this study). Consider an angular-velocity profile of the subject, {**v**(*t*),*t* = 1, ..., *T*}, where *T* (*T* = 82 in this study) represents the movement duration (in samples). This profile can be rewritten as a row vector, denoted **v**row:

$$\mathbf{v}\_{\text{row}} = [v\_1(1), \dots, v\_1(T), \dots, v\_{10}(1), \dots, v\_{10}(T)].$$

Similarly, a synergy **s***j* (·) can be rewritten as the following row vector:

$$[s\_1^j(1), \ldots, s\_1^j(t\_s), 0, \ldots, 0, \ldots, s\_{10}^j(1), \ldots, s\_{10}^j(t\_s), 0, \ldots, 0].$$

We add *T* − *ts* zeros after each *s j i* (*ts*) (*i* = 1, ..., 10) in the above vector in order to make the length of the vector the same as that of **v**row. If the synergy is shifted in time by *tjk* (*tjk* ≤ *T* − *ts*) samples, then we obtain the following row vector:

$$\begin{aligned} [0, \ldots, 0, s\_1^{\dot{j}}(1), \ldots, s\_1^{\dot{j}}(t\_\mathbf{s}), 0, \ldots, 0, \ldots \\ 0, \ldots, 0, s\_{10}^{\dot{j}}(1), \ldots, s\_{10}^{\dot{j}}(t\_\mathbf{s}), 0, \ldots, 0] \end{aligned}$$

with *tjk* zeros added before each *s j i* (1) and *T* − *ts* − *tjk* zeros added after each *s j i* (*ts*).

Then we construct a matrix as shown in Fig. 8 consisting of the row vectors of the synergies and all their possible shifts with 1 ≤ *tjk* ≤ *T* − *ts*.

With the above notation, we are trying to achieve a linear combination of synergies that can reconstruct the velocity profiles as in the following equation.

$$\mathbf{v}\_{\text{row}} = \mathbf{c}B \tag{8}$$

…

… …

… …

… … … …

Fig. 8. (a) Synergies were rearranged as row vectors (b) A template matrix was formed by adding the time-shifted versions of synergies along the rows. Adapted from Vinjamuri et al.

[0, ..., *c*11, ..., 0, ..., *c*1*K*1 , ..., 0, ..., *cm*1, ..., 0, ..., *cmKm* , ..., 0] with nonzero values *cjk* appearing at the (*T* − *ts* + 1)(*j* − 1) + *tjk*-th elements of **c**. The matrix *B* (shown in Fig. 8(b)) can be viewed as a bank or library of template functions with each row of *B* as a template. This bank can be overcomplete and contain linearly dependent subsets. Therefore, for a given movement profile **v**row and an overcomplete bank of template functions

We hypothesize that the strategy of central nervous system for dimensionality reduction in movement control is to use a small number of synergies and a small number of recruitments of these synergies for movement generation. Therefore, the coefficient vector **c** in (8) should be sparse, i.e., having a lot of zeros and only a small number of nonzero elements. Therefore,

The following was optimization problem that was used in selection of synergies in

where �·�2 represents the *l*2 norm or Euclidean norm of a vector and *λ* is a regulation

1

*λ* � **c***B* − **v**row �2

2 (9)

…

Joint 1 Joint 2 Joint 10

… Joint 1 Joint 2 Joint 10

Application of Linear and Nonlinear Dimensionality Reduction Methods 123

…

(a)

(b)

Time-shifted versions of synergy 1

Time-shifted versions of synergy 2

*B*, there exists an infinite number of **c** satisfying (8).

reconstruction of a particular movement.

we seek the sparsest coefficient vector **c** such that **c***B* = **v**row.

Minimize � **c** �1 +

(2010a).

parameter.

where **c** denotes

Synergy 1 Synergy 2

Fig. 7. Six kinematic synergies obtained for subject 1 using kPCA. Each synergy is about 0.45 s in duration (39 samples at 86 Hz). Abbreviations: T, thumb; I, index finger; M, middle finger; R, ring finger; P, pinky finger; MCP, metacarpophalangeal joint; IP, interphalangeal joint; PIP, proximal IP joint.

Fig. 8. (a) Synergies were rearranged as row vectors (b) A template matrix was formed by adding the time-shifted versions of synergies along the rows. Adapted from Vinjamuri et al. (2010a).

where **c** denotes

16 Will-be-set-by-IN-TECH

> -0.2 0 0.2

> -0.2 0 0.2

> -0.2 0 0.2

> -0.2 0 0.2

> -0.2 0 0.2

> -0.2 0 0.2

> -0.1 0 0.1

> -0.2 0 0.2

> > 0 0.2

> -0.2 0 0.2

> -0.1 0 0.1

> -0.1 0 0.1

> -0.1 0 0.1

> -0.1 0 0.1

> -0.1 0 0.1

> > 0 0.1

0 10 20 30 40 -0.2

0 10 20 30 40 -0.1

**T\_MCP**

**IͲMCP IͲPIP**

**MͲMCP MͲPIP RͲMCP RͲPIP**

**PͲMCP PͲPIP**

**T\_MCP**

**IͲMCP IͲPIP**

**MͲMCP MͲPIP RͲMCP RͲPIP**

**PͲMCP PͲPIP**

**TͲIP**

**TͲIP**

**Synergy 1 Synergy 2 Synergy 3**

> -0.1 0 0.1

> -0.2 0 0.2

> -0.5 0 0.5

> -0.5 0 0.5

> -0.5 0 0.5

> -0.2 0 0.2

> -0.2 0 0.2

> -0.2 0 0.2

> > 0 0.5

> > > -0.1 0 0.1

> -0.1 0 0.1

> -0.2 0 0.2

> -0.1 0 0.1

> -0.2 0 0.2

> -0.1 0 0.1

> -0.1 0 0.1

> -0.2 0 0.2

> > 0 0.2

**Samples Samples Samples**

0 10 20 30 40 -0.5

**Synergy 4 Synergy 5 Synergy 6**

**Samples Samples Samples**

0 10 20 30 40 -0.2

Fig. 7. Six kinematic synergies obtained for subject 1 using kPCA. Each synergy is about 0.45 s in duration (39 samples at 86 Hz). Abbreviations: T, thumb; I, index finger; M, middle finger; R, ring finger; P, pinky finger; MCP, metacarpophalangeal joint; IP, interphalangeal

**Angular**

**(radians/sample)**

**Angular**

**velocities of ten joints**

**velocities of ten joints**

**(radians/sample)**

0 0.1 0.2

> 0 1

0 0.05 0.1

> -0.2 0 0.2

> -0.1 0 0.1

> -0.2 0 0.2

> -0.1 0 0.1

> -0.1 0 0.1

> -0.1 0 0.1

> -0.1 0 0.1

> -0.1 0 0.1

> > 0 0.2

0 10 20 30 40 -1

0 10 20 30 40 -0.2

joint; PIP, proximal IP joint.

$$[0, \dots, c\_{11}, \dots, 0, \dots, c\_{1K\_1}, \dots, 0, \dots, c\_{m1}, \dots, 0, \dots, c\_{mK\_{m'}}, \dots, 0]$$

with nonzero values *cjk* appearing at the (*T* − *ts* + 1)(*j* − 1) + *tjk*-th elements of **c**. The matrix *B* (shown in Fig. 8(b)) can be viewed as a bank or library of template functions with each row of *B* as a template. This bank can be overcomplete and contain linearly dependent subsets. Therefore, for a given movement profile **v**row and an overcomplete bank of template functions *B*, there exists an infinite number of **c** satisfying (8).

We hypothesize that the strategy of central nervous system for dimensionality reduction in movement control is to use a small number of synergies and a small number of recruitments of these synergies for movement generation. Therefore, the coefficient vector **c** in (8) should be sparse, i.e., having a lot of zeros and only a small number of nonzero elements. Therefore, we seek the sparsest coefficient vector **c** such that **c***B* = **v**row.

The following was optimization problem that was used in selection of synergies in reconstruction of a particular movement.

$$\text{Minimize} \quad \|\mathbf{c}\|\_1 + \frac{1}{\lambda} \left\| \|\mathbf{c}\mathbf{B} - \mathbf{v}\_{\text{row}}\|\_2^2 \tag{9}$$

where �·�2 represents the *l*2 norm or Euclidean norm of a vector and *λ* is a regulation parameter.

these synergies to reconstruct natural movements that were similar to activities of daily living. To broaden the applicability of synergies we also tested them on ASL postural movements which are different movements when compared to natural reach and grasp movements. We employed four types of dimensionality reduction methods: (1) PCA and MDS (2) Isomap (3) LLE (4) kPCA. PCA is a well known linear dimensionality reduction method. Two widely used PCA implementations (Covariance and SVD) were presented and relevant MATLAB codes were provided. Classical MDS is very similar to PCA but operates on a distance matrix. This was introduced as Isomap and LLE both work on a similar framework. Isomap and LLE are both neighborhood graph based nonlinear dimensionality reduction methods. The difference between Isomap and LLE is that the former is a global dimensionality reduction technique where as the former as the name suggests is a local linear technique. kPCA is similar to Isomap as it is a global dimensionality reduction method, but the uniqueness of the method is in using kernel tricks to transform the input data to higher dimensional space.

Application of Linear and Nonlinear Dimensionality Reduction Methods 125

Subject PCA Isomap LLE kPCA

Table 1. Mean reconstruction errors (± standard deviation) in natural movements

Subject PCA Isomap LLE kPCA

Table 2. Mean reconstruction errors (± standard deviation) in ASL postural movements

employed in this chapter.

Thus the reader was given the opportunity to sample different varieties of dimensionality reduction methods. Quantitative and qualitative comparison of the results obtained from reconstruction follows but the verdict is that PCA outperformed the nonlinear methods

The results from the reconstructions reveal that nonlinear techniques do not outperform the traditional PCA for both natural movements as well as ASL postural movements. The

1 0.2449 ± 0.0986 0.3480 ± 0.0988 0.3867±0.1052 0.3654 ± 0.0865 2 0.2782 ± 0.0915 0.3280 ± 0.0891 0.3661±0.0963 0.3291 ± 0.0745 3 0.2677 ± 0.0866 0.3110 ± 0.0764 0.3527±0.0881 0.3321 ± 0.0666 4 0.2321 ± 0.0645 0.3450 ± 0.0662 0.3667±0.1123 0.3540 ± 0.0831 5 0.2899 ± 0.0713 0.3780 ± 0.0843 0.3889±0.0792 0.3624 ± 0.0934 6 0.2569 ± 0.0456 0.3200 ± 0.0894 0.3457±0.0856 0.3654 ± 0.0678 7 0.2319 ± 0.0521 0.3890 ± 0.0663 0.3999±0.0996 0.3889 ± 0.0610 8 0.2789 ± 0.0486 0.3671 ± 0.0713 0.3897±0.1152 0.3764 ± 0.0885 9 0.2999 ± 0.0816 0.3335 ± 0.0465 0.3867±0.1672 0.3478 ± 0.0585 10 0.2569 ± 0.0936 0.3146 ± 0.0548 0.3547±0.0082 0.3354 ± 0.0635

1 0.0797 ± 0.0596 0.3240 ± 0.0769 0.3544±0.0954 0.2954 ± 0.0851 2 0.0845 ± 0.0781 0.3780 ± 0.0519 0.3998±0.0765 0.3444 ± 0.0658 3 0.0678 ± 0.0396 0.3180 ± 0.0554 0.3234±0.0834 0.2654 ± 0.0767 4 0.0590 ± 0.0432 0.3680 ± 0.0663 0.3879±0.0645 0.3214 ± 0.0873 5 0.0997 ± 0.0831 0.2640 ± 0.0734 0.2950±0.0790 0.2230 ± 0.0667 6 0.0661 ± 0.0976 0.3240 ± 0.0545 0.3754±0.0531 0.2875 ± 0.0931 7 0.0598 ± 0.0542 0.4140 ± 0.0787 0.4344±0.0632 0.3895 ± 0.0696 8 0.0732 ± 0.0348 0.3540 ± 0.0989 0.3954±0.0854 0.3123 ± 0.0555 9 0.0814 ± 0.0212 0.2290 ± 0.0823 0.3100±0.0991 0.2074 ± 0.0651 10 0.0883 ± 0.0443 0.2490 ± 0.0665 0.2780±0.0799 0.2189 ± 0.0799

Using the above optimization algorithm, the synergies extracted from four methods (PCA, Isomap, LLE, and kPCA) were used in reconstruction of natural movements and ASL postural movements. The reconstruction errors were calculated using the methods in Vinjamuri et al. (2010a). Figures 9 and 10 showed the comparison between the four dimensionality reduction methods. Fig. 9 showed the reconstruction errors for 100 natural movements and Fig. 10 showed the reconstruction errors for 36 ASL postural movements for all four methods. It is observed that PCA still has the best overall performance when compared with the novel nonlinear methods. Fig. 11 showed one of the best reconstructions by all four methods. Tables 1 and 2 summarize the reconstruction results obtained for all ten subjects.

Fig. 9. The reconstruction errors for 100 natural movements with four dimensionality reduction methods PCA, Isomap, LLE, and kPCA. PCA outperformed other three nonlinear methods.

Fig. 10. The reconstruction errors for 36 ASL postural movements with four dimensionality reduction methods PCA, Isomap, LLE, and kPCA. All methods performed poorly in reconstruction of ASL movements when compared to natural movements. PCA performed better than other three nonlinear methods.

#### **7. Summary**

In this chapter we applied linear and nonlinear dimensionality reduction methods to extract movement primitives or synergies from rapid reach and grasp movements. We then used 18 Will-be-set-by-IN-TECH

Using the above optimization algorithm, the synergies extracted from four methods (PCA, Isomap, LLE, and kPCA) were used in reconstruction of natural movements and ASL postural movements. The reconstruction errors were calculated using the methods in Vinjamuri et al. (2010a). Figures 9 and 10 showed the comparison between the four dimensionality reduction methods. Fig. 9 showed the reconstruction errors for 100 natural movements and Fig. 10 showed the reconstruction errors for 36 ASL postural movements for all four methods. It is observed that PCA still has the best overall performance when compared with the novel nonlinear methods. Fig. 11 showed one of the best reconstructions by all four methods. Tables

10 20 30 40 50 60 70 80 90 100

5 10 15 20 25 30 35

Fig. 10. The reconstruction errors for 36 ASL postural movements with four dimensionality reduction methods PCA, Isomap, LLE, and kPCA. All methods performed poorly in reconstruction of ASL movements when compared to natural movements. PCA performed

In this chapter we applied linear and nonlinear dimensionality reduction methods to extract movement primitives or synergies from rapid reach and grasp movements. We then used

Fig. 9. The reconstruction errors for 100 natural movements with four dimensionality reduction methods PCA, Isomap, LLE, and kPCA. PCA outperformed other three nonlinear

1 and 2 summarize the reconstruction results obtained for all ten subjects.

0 0.5 1

PCA

LLE

kPCA

PCA

0 0.5 1

0 0.5 1

0 0.5 1

0 0.5 1

better than other three nonlinear methods.

LLE

kPCA

**7. Summary**

 Isomap

methods.

 Isomap

0 0.5 1

0 0.5 1

0 0.5 1 these synergies to reconstruct natural movements that were similar to activities of daily living. To broaden the applicability of synergies we also tested them on ASL postural movements which are different movements when compared to natural reach and grasp movements. We employed four types of dimensionality reduction methods: (1) PCA and MDS (2) Isomap (3) LLE (4) kPCA. PCA is a well known linear dimensionality reduction method. Two widely used PCA implementations (Covariance and SVD) were presented and relevant MATLAB codes were provided. Classical MDS is very similar to PCA but operates on a distance matrix. This was introduced as Isomap and LLE both work on a similar framework. Isomap and LLE are both neighborhood graph based nonlinear dimensionality reduction methods. The difference between Isomap and LLE is that the former is a global dimensionality reduction technique where as the former as the name suggests is a local linear technique. kPCA is similar to Isomap as it is a global dimensionality reduction method, but the uniqueness of the method is in using kernel tricks to transform the input data to higher dimensional space.

Table 1. Mean reconstruction errors (± standard deviation) in natural movements

Thus the reader was given the opportunity to sample different varieties of dimensionality reduction methods. Quantitative and qualitative comparison of the results obtained from reconstruction follows but the verdict is that PCA outperformed the nonlinear methods employed in this chapter.

The results from the reconstructions reveal that nonlinear techniques do not outperform the traditional PCA for both natural movements as well as ASL postural movements. The

reconstruction errors were more for ASL postural movements when compared to those of natural movements for all methods. The reconstruction errors were in general larger for Isomap and LLE when compared with PCA and kPCA and of course PCA had outstanding performance for more than 90% of the tasks. van der Maaten et al. ( van der Maaten et al. (2009)) also found that nonlinear methods performed well on specific data sets but could not perform better than PCA for real world tasks. For example, for the Swiss roll data set that contains points that lie on a spiral like two dimensional manifold within a three dimensional space, several nonlinear techniques such as Isomap, LLE were able to find the two dimensional planar embedding, but linear techniques like PCA failed to find so. The reasons for two nonlinear methods Isomap and LLE to perform poorly in this study, might be that they relied neighborhood graphs. Moreover LLE might have been biased to local properties that do not necessarily follow the global properties of high dimensional data. It was surprising to see that Isomap, being a global dimensionality reduction technique performed poorly when compared to LLE for natural movements. kPCA performed better than Isomap and LLE, but kPCA does suffer from the limitation of selection of ideal kernel. The selection of gaussian kernel in this study might not be favorable in extracting the kinematic synergies in this study. In conclusion, although there are numerical advantages and disadvantages with both linear and nonlinear dimensionality reduction methods, PCA seemed to generalize and

Application of Linear and Nonlinear Dimensionality Reduction Methods 127

This work was supported by the NSF grant CMMI-0953449, NIDRR grant H133F100001. Special thanks to Laurens van der Maaten for guidance with the dimensionality reduction toolbox, and Prof. Dan Ventura (Brigham Young University) for helpful notes on comparison of LLE and Isomap. Thanks to Stephen Foldes for his suggestions with formatting. Thanks to Mr. Oliver Kurelic for his guidance and help through the preparation of the manuscript.

Bernabucci, I., Conforto, S., Capozza, M., Accornero, N., Schmid, M. & D'Alessio, T. (2007). A

Bernstein, N. (1967). *The Co-ordination and Regulation of Movements*, Pergamon Press, Oxford,

Braido, P. & Zhang, X. (2004). Quantitative analysis of finger motion coordination in hand

Cole, K. J. & Abbs, J. H. (1986). Coordination of three-joint digit movements for rapid

d'Avella, A., Portone, A., Fernandez, L. & Lacquaniti, F. (2006). Control of

d'Avella, A., Saltiel, P. & Bizzi, E. (2003). Combinations of muscle synergies in the contruction

Flash, T. & Hochner, B. (2005). Motor primitives in vertebrates and invertebrates, *Current*

Grinyagin, I. V., Biryukova, E. V. & Maier, M. A. (2005). Kinematic and dynamic synergies of human precision-grip movements, *Journal of Neurophysiology* 94(4): 2284–2294.

manipulative and gestic acts, *Human Movement Science* 22: 661–678.

finger-thumb grasp, *Journal of Neurophysiology* 55(6): 1407–1423.

of a natural motor behavior, *Nature Neuroscience* 6(3): 300–308.

*of NeuroEngineering and Rehabilitation* 4(33): 1–17.

*Opinion in Neurobiology* 15(6): 660–666.

biologically inspired neural network controller for ballistic arm movements, *Journal*

fast-reaching movements by muscle synergy combinations, *Journal of Neuroscience*

perform well on the real world data.

**8. Acknowledgments**

**9. References**

UK.

26(30): 7791–7810.

Fig. 11. An example reconstruction (in black) of a natural movement (in red) for task 24 when subject 1 was grasping an object.

reconstruction errors were more for ASL postural movements when compared to those of natural movements for all methods. The reconstruction errors were in general larger for Isomap and LLE when compared with PCA and kPCA and of course PCA had outstanding performance for more than 90% of the tasks. van der Maaten et al. ( van der Maaten et al. (2009)) also found that nonlinear methods performed well on specific data sets but could not perform better than PCA for real world tasks. For example, for the Swiss roll data set that contains points that lie on a spiral like two dimensional manifold within a three dimensional space, several nonlinear techniques such as Isomap, LLE were able to find the two dimensional planar embedding, but linear techniques like PCA failed to find so. The reasons for two nonlinear methods Isomap and LLE to perform poorly in this study, might be that they relied neighborhood graphs. Moreover LLE might have been biased to local properties that do not necessarily follow the global properties of high dimensional data. It was surprising to see that Isomap, being a global dimensionality reduction technique performed poorly when compared to LLE for natural movements. kPCA performed better than Isomap and LLE, but kPCA does suffer from the limitation of selection of ideal kernel. The selection of gaussian kernel in this study might not be favorable in extracting the kinematic synergies in this study. In conclusion, although there are numerical advantages and disadvantages with both linear and nonlinear dimensionality reduction methods, PCA seemed to generalize and perform well on the real world data.

#### **8. Acknowledgments**

20 Will-be-set-by-IN-TECH

0 0.02

0 0.05

0 0.05

0 0.05

0 0.05

0 0.05

0 0.05

0 0.05

0 0.05

> 0 0.1

0 0.01

0 0.05

0 0.05

0 0.05

0 0.05

0 0.05

0 0.05

0 0.05

0 0.05

> 0 0.1

Fig. 11.  Special thanks to Laurens van der Maaten for guidance with the dimensionality reduction toolbox, and Prof. Dan Ventura (Brigham Young University) for helpful notes on comparison of LLE and Isomap. Thanks to Stephen Foldes for his suggestions with formatting. Thanks to Mr. Oliver Kurelic for his guidance and help through the preparation of the manuscript.

#### **9. References**

**0**

**7**

**Components Analysis**

1*Okayama University of Science*

2*Okayama University*

*Japan*

**Acceleration of Convergence of the Alternating**

**Least Squares Algorithm for Nonlinear Principal**

Masahiro Kuroda1, Yuichi Mori1, Masaya Iizuka2 and Michio Sakakihara1

Principal components analysis (PCA) is a popular descriptive multivariate method for handling quantitative data. In PCA of a mixture of quantitative and qualitative data, it requires quantification of qualitative data to obtain optimal scaling data and use ordinary PCA. The extended PCA including such quantification is called *nonlinear PCA*, see Gifi [Gifi, 1990]. The existing algorithms for nonlinear PCA are PRINCIPALS of Young et al. [Young et al., 1978] and PRINCALS of Gifi [Gifi, 1990] in which the alternating least squares (ALS) algorithm is utilized. The algorithm alternates between quantification of qualitative data and

In the application of nonlinear PCA for very large data sets and variable selection problems, many iterations and much computation time may be required for convergence of the ALS algorithm, because its speed of convergence is linear. Kuroda et al. [Kuroda et al., 2011] proposed an acceleration algorithm for speeding up the convergence of the ALS algorithm using the vector *ε* (v*ε*) algorithm of Wynn [Wynn, 1962]. During iterations of the v*ε* accelerated ALS algorithm, the v*ε* algorithm generates an accelerated sequence of optimal scaling data estimated by the ALS algorithm. Then the v*ε* accelerated sequence converges faster than the original sequence of the estimated optimal scaling data. In this paper, we use PRINCIPALS as the ALS algorithm for nonlinear PCA and provide the v*ε* acceleration for PRINCIPALS (v*ε*-PRINCIPALS). The computation steps of PRINCALS are given in Appendix A. As shown

in Kuroda et al. [Kuroda et al., 2011], the v*ε* acceleration is applicable to PRINCALS.

v*ε*-PRINCIPALS. In Section 6, we present our concluding remarks.

The paper is organized as follows. We briefly describe nonlinear PCA of a mixture of quantitative and qualitative data in Section 2, and describe PRINCIPALS for finding least squares estimates of the model and optimal scaling parameters in Section 3. Section 4 presents the procedure of v*ε*-PRINCIPALS that adds the v*ε* algorithm to PRINCIPALS for speeding up convergence and demonstrate the performance of the v*ε* acceleration using numerical experiments. In Section 5, we apply v*ε*-PRINCIPALS to variable selection in nonlinear PCA. Then we utilize modified PCA (M.PCA) approach of Tanaka and Mori [Tanaka and Mori, 1997] for variable selection problems and give the variable selection procedures in M.PCA of qualitative data. Numerical experiments examine the the performance and properties of

computation of ordinary PCA of optimal scaling data.

**1. Introduction**


 If pulmonary embolism is still considered to be likely, the next step is usually to obtain a ventilationperfusion lung scan. If the lung scan is likely to be indeterminate (because of underlying lung disease), spiral chest CT scans may be useful. Finding deep vein thrombosis with ultrasonography indicates the need for anticoagulation and usually eliminates the need for further testing for pulmonary emboli. The gold standard for diagnosing pulmonary embolism is pulmonary angiography.

**Chest x-rays:** Results of chest x-rays may be normal or may show nonspecific abnormalities, eg, atelectasis, an elevated hemidiaphragm, pleural effusion, or an infiltrate.

**ECG:** ECG findings are usually nonspecific; 33% of patients with pulmonary embolism have a normal ECG.

BNP (brain natriuretic peptid) and echocardiography may be also useful determinants of the short-term outcome for patiens with PE (Sanchez et al., 2010).

**d-Dimer:** Levels of d-dimer, a fibrin-specific product, are increased in patients with acute thrombosis (Douma et al., 2010; Kabrhel et al. 2010). About 60% of patients < 50 who are suspected of having a pulmonary embolus have an abnormal d-dimer result. In contrast, 92% of patients > 70 have abnormal d-dimer levels, probably due to comorbid conditions (Douketis

Pulmonary Embolism in the Elderly – Significance and Particularities 59

knee or hip replacement and in abdominal surgery (Bottaro et al., 2008). For total hip replacement, some investigators find that 4 to 6 weeks of LMWH postoperatively may be

The approach to older patients should be consistently individualised. New diagnostic methods and therapeutic algorithms used in acute geriatric wards together enable us to treat successfully also multi-morbid patients in advanced age admitted by hospital's doctors. Modern iatrotechniques make possible also the treatment (including recovery) and protect self-sufficiency and preserve quality of life in the elderly being

Physicians committed to the care of elderly patients, are challenged with the diagnosis of venous thrombembolism due to a higher incidence, co-morbidities masking signs and

We would like to emphasize the need to permanently think of the possibility of PE in elderly persons with present risk factors and in suspected cases the use of pretest probability scale as Wells or Geneva score as soon as possible (Carrier et al., 2009` Pasha et al., 2010). The requirement of correctly assessed diagnosis and starting of therapeutic procedures is crucial and essential proceeding for giving the hope to patient and generally, from the professional

Alikhan R, Peters F, Wilmott R, Cohen AT. (2006). *Fatal pulmonary embolism in hospitalised*

Applegate WB, Blass JP, Williams TF. (1990). *Instruments for the functional assessment of older*

Asplund K, Gustafson Y, Jacobsson C. (2000). *Geriatric- Based Versus General Wards for Older*

Barba R, Martinez-Espinosa S, Rodriguez-Garcia E. (2000). *Poststroke dementia: clinical features*

Barba R, Zapatero A, Losa JE, Valdaos V, Todola JA, Di Micco P, Monreal M, Riete

Beckman JA, Creager MA, Libby P. (2002). *Diabetes and Atherosclerosis. Epidemiology, Pathophysiology, and Management,.* JAMA, Vol. 287, pp. 2570-81, ISSN: 00987484 Beers MH, Thomas VJ & Jones TV (eds.) (2006) *The Merck Manual of Geriatrics- 7th edition,*

Bertoletti L, Le Gal G, Aujesky D, Roy PM, Sanchez O, Verschuren F, Bounameaux H,

*patients,* N Engl J Med, Vol.322, pp.1207-14, ISSN 0028-4793.

*and risk factors,.* Stroke, Vol. 31, pp. 1494-501, ISSN 00392499

Merck & Co, ISBN-13:<PHONE_NUMBER>667, New York, USA

JAGS, Vol.48, pp.1381-88, ISSN 0002-8614

pp. 595-600, ISSN: 1538-7933

*patients: a necropsy review*, J Clin Pathol, Vol.57, No.12, pp.1254-7, ISSN 0021-

*Acute Medical Patients: A Randomized Comparison of Outcomes and Use of Resources,*

Investigators (2008) *Body mass index and mortality in patients with acute venous thromboembolism: findings from the RIETE registry,* J Thromb Haemost, Vol.6, No.4,

Perrier A, Righini M. (2011). *Prognostic value of the Geneva prediction rule in patients in*

more effective (Kanaan et al., 2007).

symptoms and burdening referrals (Siccama et al., 2011).

viewpoint, improvement of the prognosis.

**13. Conclusion**

acutely ill.

**14. References**

9746

et al., 2010). Therefore, if d-dimer test results are negative, deep vein thrombosis or pulmonary embolism is unlikely to be present, but positive test results are not useful in patients > 70.

The use of d-dimers as a secondary strategy to exclude the diagnosis of VTE has been recommended because the test has a high sensitivity, although a low specificity. False positives may occur in patients with recent trauma or surgery, malignancy, pregnancy, severe infections, and liver disease.


*Article* **Analysis of Antimicrobial Use and the Presence of Antimicrobial-Resistant Bacteria on Austrian Dairy Farms— A Pilot Study**

**Clair L. Firth 1,\* , Annemarie Käsbohrer 1 , Peter Pless 2 , Sandra Koeberl-Jelovcan 3 and Walter Obritzhauser 1,4**

**Abstract:** The assumed link between high levels of antimicrobial use on farms and selection for antimicrobial-resistant (AMR) bacteria on that farm remains difficult to prove. In the pilot study presented here, we analysed total antimicrobial use on 50 dairy farms in Austria and also collected environmental samples to ascertain whether specific AMR bacteria were present. Antimicrobial use (AMU) analysis was based on electronic veterinary treatment records over a one-year period. Faecal samples for the assessment of extended-spectrum beta-lactamase (ESBL)-producing *E. coli* were collected from cowsheds, calf pens, and youngstock housing areas, as well as dust samples from barns, to isolate methicillin-resistant *Staphylococcus aureus* (MRSA). Bacteriological cultures were carried out on selective agar. Farms were split into groups of 25 of the highest antimicrobial users and 25 of the lowest users. Overall, samples from 13/50 (26.0%) farms were found to be positive for the presence of ESBL-producing *E. coli*. Of these, eight farms were in the low user group and five were in the high user group. Only one farm was confirmed to harbour MRSA. Statistical analyses demonstrated that there was no significant difference in this study population between high or low antimicrobial use with respect to the presence of ESBL-producing *E. coli* on farms (*p* = 0.33). In conclusion, the presence of specific AMR bacteria on farms in this study population was not found to have a statistically proven relationship with their level of antimicrobial use.

**Keywords:** antimicrobial resistance; antibiotics; dairy; ESBL; MRSA; farms; veterinary

#### **1. Introduction**

Globally, the excessive use of antimicrobials and the increasing level of antimicrobialresistant bacterial infections in both humans and animals are continuing to cause concern among veterinarians, medics, and the general public [1]. Veterinary antimicrobial use worldwide is expected to increase by an estimated 11.5% by 2030 (based on 2017 figures), and so the problem will persist [2]. While many countries (such as China, Brazil, the USA, and Australia) continue to misuse antimicrobials for non-therapeutic growth promotion, such use has been banned in the European Union (EU) since 2006 [3,4]. In Austria, antimicrobials for use in animals are only available from veterinarians and never over the counter. Injectable antimicrobials are further restricted and can only be dispensed to farmers who are members of the Animal Health Service (*Tiergesundheitsdienst*) and have completed a specific training course in medication administration and documentation. Since 2015, all antimicrobials dispensed for use in food-producing animals by veterinarians must also be reported to the relevant authorities [5]. Based on the latest national reports (for 2020), the vast majority of dispensed antimicrobials (73.4%) are provided for use in Austrian pig

**Citation:** Firth, C.L.; Käsbohrer, A.; Pless, P.; Koeberl-Jelovcan, S.; Obritzhauser, W. Analysis of Antimicrobial Use and the Presence of Antimicrobial-Resistant Bacteria on Austrian Dairy Farms—A Pilot Study. *Antibiotics* **2022**, *11*, 124. https://doi.org/10.3390/ antibiotics11020124

Academic Editors: Carlos M. Franco and David Rodríguez-Lázaro

Received: 23 December 2021 Accepted: 15 January 2022 Published: 18 January 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

production, while 19.7% are for use in cattle (of which, around 7% are for dairy cows and 9% for beef cattle) and the remaining 6.7% for use in poultry [6].

Whilst a proven relationship between antimicrobial use (AMU) and the selection for antimicrobial-resistant (AMR) bacteria remains a contentious issue, a number of studies have assessed the possibility of such a correlation. In 2014, a report on seven European countries (including Austria) determined a strong association between total antimicrobial use in livestock in each country and the reported prevalence of AMR in *E. coli* in those countries [7]. Similarly, a study of outpatient antimicrobial consumption and AMR in human patients determined a strong linear relationship between macrolide use and resistant *S. pneumoniae* (MRSP) in 16 countries (including Austria) [8]. Meanwhile, a study from the Netherlands determined no association between total AMU on farms and the presence of ESBL/AmpC-producing *E. coli*, although a correlation between increasing third- and fourth-generation cephalosporin use and increasing levels of these cephalosporin-resistant bacteria was detected [9]. More recently, a Swedish study of antimicrobial use on dairy farms analysed the presence of resistant phenotypes among *E. coli* and found no link between overall AMU and *E. coli* resistance [10]. However, the study's authors noted that AMU in Sweden is generally much lower than in other EU countries [10].

With respect to the risk to humans from antimicrobial-resistant bacteria in food, a number of studies have found evidence of AMR bacteria [11–13], including an Austrian study in 2014 which reported the presence of ESBL-producing *E. coli* in 20% of minced pork/beef samples tested, as well as MRSA in 9% of these samples [14]. As part of the EU AMR monitoring system, the European Food Standards Agency (EFSA) and the European Centre for Disease Control and Prevention (ECDC) publish joint annual summary reports on AMR in zoonotic and indicator bacteria from humans, animals, and food. The Austrian health authorities provide data for these reports and the most recent national monitoring results in 2019 reported the isolation of ESBL/AmpC-producing *E. coli* in 4 of 340 (1.2%) beef samples, as well as MRSA in 6 of 228 (2.8%) beef samples [15–17]. A recent pan-European study of 11 countries, including Austria, failed to detect a statistically significant association between ESBL and/or AmpC-producing *E. coli* (i.e., those resistant to thirdgeneration cephalosporins) in humans and calves under one year of age [18]. Nevertheless, a source attribution model of ESBL-producing *E. coli* in the Netherlands highlighted that transmission to and from non-human sources (including cattle) is necessary to continue the intra-community spread of ESBL-producing *E. coli* and plasmid-mediated AmpCproducing *E. coli* [19].

The study presented here represents initial data from a pilot study of a small group of dairy farms, where extensive data on antimicrobial use over a one-year period, as well as farm management practices, were available. These data, as well as whether farms were considered "high" or "low" users of antimicrobials relative to the study population, were compared with the prevalence of ESBL-producing *E. coli* and MRSA obtained from samples collected on farms.

#### **2. Results**

In total, 138 voided faecal samples were collected from 50 farms (25 classified as "high" antimicrobial users and 25 as "low" users). Samples from the areas of the barn where cows were kept were available from all 50 farms. Calves were present on 46/50 farms (92.0%) and pooled samples (1–5 animals per pool) were taken from calf holding areas/hutches. Youngstock (>six months of age) were present on 32/50 farms (64.0%) and pooled samples (1–5 animals per pool) were taken from their pens. An additional ten pooled samples were taken from calves and youngstock on four farms, which reared more than 50 head of cattle. Dust samples were taken from all 50 farms.

#### *2.1. Farm Population*

In the "high" AMU group, the mean herd size was 47.3 head of cattle (median 38; range 14–128), including 22.6 dairy cows (median 17; range 8–56); whereas in the "low" AMU group, the mean herd size was 57.1 head of cattle (median 52; range 11–157), including 29.6 dairy cows (median 24; range 5–77). In the high use group, 17/25 (68.0%) farms were run conventionally, while just 2/25 (8.0%) farms were organic producers. No production type was reported for the remaining six farms (24.0%) as the farmers did not complete the farm management survey (as previously described elsewhere [20]). In the low use group, 7/25 (28.0%) farms were run organically, 14/25 (56.0%) conventionally, and production type was not known for the remaining 4 farms. Of the 40 farmers in the overall study group who completed the farm management survey, 27 (67.5%) reported that they routinely fed waste/discard milk containing antimicrobial residues (from the treatment of cows) to calves on their farms. For further details, see Table 1.

**Table 1.** Demographics of study farms, total AMU in DDDvet/cow/year, and results of ESBLproducing *E. coli* screening.

\* Waste milk is defined here as non-saleable milk, usually containing antimicrobial residues, or within the milk withholding period. # Farmer did not complete farm management survey; therefore this information is not available for this farm.

#### *2.2. Bacteriology of Farm Samples*

ESBL-producing *E. coli* were isolated in faecal samples from 13 (26%) of the 50 participating farms. Of these 11 pooled calf samples, seven pooled cowshed boot swab samples and four pooled youngstock samples were positive (for details, see Table 1). Three "high use" farms were found to be positive in all types of bovine faecal samples (i.e., cows, calves, and youngstock); while two of the "low use" farms were positive in both the cow and calf sample types and no youngstock were present at the time of sampling. One "high use" farm kept enough calves and youngstock (>5 head in each group) to require two pooled samples to be taken; however, these additional samples were negative, as were the other samples from this farm. In total, additional pooled samples were taken from three "low use" farms with sufficient animals. On two of these farms, all samples were negative and on one farm, the samples from cows and calves were positive for ESBL-producing *E. coli* in both the standard and additional samples.

Dust samples from only one farm were found to be positive for MRSA, this isolate was found to contain the *mecC* gene.

#### *2.3. Antimicrobial Use Data*

The total Defined Daily Doses per cow and year (DDDvet/cow/year) for all disease indications on each farm was calculated according to the standardised Defined Daily Dose (DDDvet) values published by the European Medicines Agency [21]. Total antimicrobial use (AMU) for all disease indications ranged from 2.47 to 8.04 DDDvet/cow/year in the high use group (mean 4.35; median 3.82 DDDvet/cow/year), compared to 0.01 to 0.63 DDDvet/cow/year in the low use group (mean 0.29; median 0.31 DDDvet/cow/year). As would be expected, the two groups ("high" and "low" AMU in DDDvet/cow/year) of antimicrobial use showed a highly statistically significant difference in the Mann–Whitney U-test (U score 0, Z score 4.88, *p* < 0.00001).

Among high antimicrobial users on ESBL-producing *E. coli* positive farms, the median AMU was 3.38 DDDvet/cow/year (mean 3.77, *n* = 5); compared to the negative farms where the median AMU was 4.14 DDDvet/cow/year (mean 4.50, *n* = 20). On farms classed as low users, the median AMU was 0.40 DDDvet/cow/year (mean 0.31, *n* = 8) on farms where at least one sample tested positive, compared to a median of 0.29 DDDvet/cow/year (mean 0.28, *n* = 17) on negative farms.

The comparison of antimicrobial classes used on both ESBL-producing *E. coli* positive and negative farms is shown in Figure 1. On ESBL-producing *E. coli* positive farms, betalactamase sensitive penicillins (such as benzylpenicillin) had the highest DDDvet/cow/year (mean 0.49, median 0.12, maximum 1.95 DDD/cow/year); while the DDDvet/cow/year for third/fourth-generation cephalosporins was higher on the ESBL-producing *E. coli*-negative farms (mean 1.13, median 0.19, maximum 6.39 DDD/cow/year) (for details, see Figure 1). As would be expected on dairy farms, the majority of antimicrobial treatment on all farms was for udder diseases (53.8% as calculated as a total of the DDDvet/cow/year), followed by a much lower proportion of treatments for reproductive disorders (17.8% of the total DDDvet/cow/year) (for details, see Table 2 and Figure 2).

\* HPCIA—highest priority critically important antimicrobials as defined by the World Health Organization [22]. For this study, HPCIA included third and fourth generation cephalosporins, macrolides, and fluoroquinolones. # DCT—dry cow therapy.

**Figure 1.** Antimicrobial treatments by antimicrobial class (and ATCVet code), divided into those farms where faecal samples tested positive for ESBL-producing *E. coli* (**A**) and those where samples tested negative for ESBL-producing *E. coli* (**B**). **Figure 1.** Antimicrobial treatments by antimicrobial class (and ATCVet code), divided into those farms where faecal samples tested positive for ESBL-producing *E. coli* (**A**) and those where samples tested negative for ESBL-producing *E. coli* (**B**).

(**B**)

**Figure 2.** (**A**): Comparison of antimicrobial use in DDDvet/cow/year by disease indication on farms which tested POSITIVE for the presence of ESBL-producing *E. coli* (N = 13). (**B**): Comparison of antimicrobial use in DDDvet/cow/year by disease indication on farms which tested NEGATIVE for the presence of ESBL-producing *E. coli* (N = 37). X—mean; horizontal line—median; box—range between 1st and 3rd quartile; dots—outliers. **Figure 2.** (**A**): Comparison of antimicrobial use in DDDvet/cow/year by disease indication on farms which tested POSITIVE for the presence of ESBL-producing *E. coli* (N = 13). (**B**): Comparison of antimicrobial use in DDDvet/cow/year by disease indication on farms which tested NEGATIVE for the presence of ESBL-producing *E. coli* (N = 37). X—mean; horizontal line—median; box—range between 1st and 3rd quartile; dots—outliers.

Dry cow therapy was analysed by Defined Course Dose (DCDvet) per cow and year and is shown in Figure 3. Among farms where at least one sample tested positive for the presence of ESBL-producing *E. coli*, the median was 0.53 DCDvet/cow/year (mean 0.71), whereas on negative farms the median was 0.50 DCDvet/cow/year (mean 0.77).

**Figure 3.** Comparison of antimicrobial dry cow therapy in Defined Course Dose per cow and year (DCDvet/cow/year). **Figure 3.** Comparison of antimicrobial dry cow therapy in Defined Course Dose per cow and year (DCDvet/cow/year).

#### *2.4. Statistical Analysis 2.4. Statistical Analysis*

Using the Mann–Whitney U-test for independent samples, no statistically significant difference was determined between AMU in DDDvet/cow/year on each of the farms which were classified as ESBL-producing *E. coli* positive or negative (Mann–Whitney U score 187.0, Z score 1.17, *p* = 0.24). With respect to the use of third and fourth generation cephalosporins, the Mann–Whitney U-test similarly showed no statistically significant difference between the levels of use of this antimicrobial group regardless of whether ESBL-producing *E. coli* were isolated on the farm or not (Mann–Whitney U score 206.5, Z score 0.74, *p* = 0.46). Using the Mann–Whitney U-test for independent samples, no statistically significant difference was determined between AMU in DDDvet/cow/year on each of the farms which were classified as ESBL-producing *E. coli* positive or negative (Mann–Whitney U score 187.0, Z score 1.17, *p* = 0.24). With respect to the use of third and fourth generation cephalosporins, the Mann–Whitney U-test similarly showed no statistically significant difference between the levels of use of this antimicrobial group regardless of whether ESBL-producing *E. coli* were isolated on the farm or not (Mann–Whitney U score 206.5, Z score 0.74, *p* = 0.46).

Using the Chi-squared test, no statistical significance was found at the 5% level between ESBL-positive *E. coli* or negative farms with respect to either "high" (≥2.47 DDDvet/cow/year) or "low" (≤0.63 DDDvet/cow/year) use groups (*p* = 0.33), or the number of dairy cows (*p* = 0.42) (Tables 3 and 4). Using the Chi-squared test, no statistical significance was found at the 5% level between ESBL-positive *E. coli* or negative farms with respect to either "high" (≥2.47 DDDvet/cow/year) or "low" (≤0.63 DDDvet/cow/year) use groups (*p* = 0.33), or the number of dairy cows (*p* = 0.42) (Tables 3 and 4).

With respect to feeding calves waste milk, no statistically significant difference was determined for the presence of ESBL-producing *E. coli* between those farms routinely feeding calves waste milk containing antimicrobial residues and those which did not **Table 3.** 2 × 2 contingency table with respect to high and low AMU. Observed frequencies of ESBL-producing *E. coli* positive or negative farms (expected frequencies in brackets).

**>2.47 DDDvet/Cow/Year**  Chi-squared test statistic 0.93, *p* = 0.33.

ESBL-producing *E. coli—*positive 5 (6.5) 8 (6.5) 13 ESBL-producing *E. coli—*negative 20 (18.5) 17 (18.5) 37 **Table 4.** 2 × 2 contingency table with respect to numbers of dairy cows. Observed frequencies of ESBL-producing *E. coli* positive or negative farms (expected frequencies in brackets).

**DDDvet/Cow/Year**

Chi-squared test statistic 0.64, *p* = 0.42.

With respect to feeding calves waste milk, no statistically significant difference was determined for the presence of ESBL-producing *E. coli* between those farms routinely feeding calves waste milk containing antimicrobial residues and those which did not (Fisher's exact test, *p* = 0.44) (Table 5).

**Table 5.** 2 × 2 contingency table with respect to whether calves were fed waste milk containing antimicrobial residues on the farm. Observed frequencies of ESBL-producing *E. coli* positive (at least one positive sample) or negative farms.

Fisher's exact test, *p* = 0.44. \* Details on which farms fed waste milk were obtained via a questionnaire, which was completed by 40/50 of the farmers included in this study.

#### **3. Discussion**

To the authors' knowledge, this is the first study to analyse the presence of antimicrobial-resistant bacteria in relation to antimicrobial use on Austrian dairy farms. The current study represents the first steps in data collection on 50 dairy farms participating in a larger study of 250 farms. Analyses of faecal samples from the farm environment (boot swabs from alleyways of the cowshed, pooled faecal samples from calves and youngstock) determined a low prevalence of ESBL-producing *E. coli*, with approximately one quarter (26.0%) of the 50 farms testing positive in at least one sample. This is relatively low in comparison to neighbouring Bavaria (Germany) where a previous study of dairy and beef farms reported at least one positive ESBL-producing *E. coli* sample was found on 86.7% of the 45 farms tested [23]. Similarly, a study in the Netherlands collected ESBL/AmpC-positive samples on 41% of 100 conventionally run dairy farms [9]. A follow-up to this Dutch study carried out two years later after a change in local legislation to restrict the use of thirdand fourth-generation cephalosporins demonstrated that the herd prevalence between matching herds fell from 32.7% ESBL/AmpC *E. coli*-positive in 2011 to 18.0% in 2013 [24]. However, the results determined here in Austria are not surprising as they correspond to the most recent official statistics published by the European Food Safety Authority and the European Centre for Disease Prevention and Control in 2020, where 20.5% of slaughtered calves (<1 year of age) in Austria were found to have presumptive ESBL-producing *E. coli* in their caeca, compared to a similar prevalence in Switzerland (19.1%), slightly higher in the Netherlands (36.4%), and much higher prevalences in Germany (66.8%), Belgium (64.8%), and Italy (86.8%) [25].

The statistical analysis presented here demonstrated that there was no statistically significant correlation between farms classified as "high" (≥2.47 DDDvet/cow/year) antimicrobial users or "low" (≤0.63 DDDvet/cow/year) antimicrobial users in this study population and the presence of ESBL-producing *E. coli* on these farms. This trend has also been reported in the Netherlands, where researchers found that the total annual animaldefined daily dose (DDDA) did not significantly differ between ESBL/AmpC-positive and negative farms [9,26]. Furthermore, the results presented here did not show a relationship between the presence of ESBL-producing *E. coli* and the use of third and fourth generation cephalosporins, contrasting with a Dutch study which found the use of these highest priority critically important antimicrobials (HPCIAs) led to an 8.05-fold increase in the odds of testing positive for ESBL/AmpC-producing *E. coli* [9]. A German study comparing dairy farms, which either did or did not use antimicrobials for a one-year period prior to sampling, found that 30% of the 10 control farms (no antimicrobial use) were ESBLproducing *E. coli* positive, while 39/45 (86.7%) of the dairy farms using antimicrobials tested positive [23]. A recent pan-European analysis on the prevalence of ESBL and/or AmpC-producing *E. coli* in slaughtered veal calves aged under one year (as well as broilers, turkeys, and fattening pigs) has also shown a statistically significant association between the national consumption of third and fourth generation cephalosporins in food-producing animals and the presence of these AMR bacteria (logistic regression based on national data from 31 countries, including Austria, in 2017–2018, odds ratio 1.29 (95% CI: 1.14; 1.46), *p* < 0.001) [18].

A study in France demonstrated that young calves (<7 weeks of age) on dairy farms were harbouring a variety of AMR bacteria in their intestinal microbiome [27]. In particular, this study demonstrated that the proportion of ESBL-producing *E. coli* as determined by selective media, fell slightly from 22% at 15 days of age to 19% at 7 weeks [27]; a trend which has been observed in many other studies of the intestinal flora of dairy calves [28–31]. While the feeding of waste milk containing antimicrobial residues to calves did not appear to have a statistically significant effect on the farms sampled in the current study, a number of other studies have shown that feeding such milk to calves leads to a transient increase in the presence of AMR bacteria in their faeces [31–34]. It is important to note that, similar to a Canadian study investigating extended-spectrum cephalosporin-resistant *E. coli* in calf faeces [34], the survey of farm management practices in the current study only provided "herd level" information and did not confirm that the calves sampled here had actually received waste milk containing antimicrobial residues, only that it was a routine practice on that farm. Furthermore, we did not investigate the impact of the prevalence of AMR bacteria on this practice.

Although data on MRSA on dairy farms are limited, other European studies in dairy cattle have reported a much higher prevalence of MRSA than that determined in the present study, where only one farm tested positive. A German study of three dairy herds more than a decade ago found that 46.7% (7/15) of cows tested were MRSA-positive and at a similar time, a Belgian study reported 9.3% of the 118 dairy farms tested were MRSApositive [35,36]. The results determined for MRSA on these 50 Austrian farms are, however, comparable to the extremely low proportion of cattle testing positive for MRSA in a study of patients at the University of Veterinary Medicine in Vienna's ruminant clinic, where only 0.45% (95% CI: 0.01; 2.90%) of 221 cattle were MRSA-positive [37].

A previous study of human patients in the federal state of Upper Austria reported that 16/21 (76.2%) patients testing positive for MRSA CC398 were either pig or poultry farmers or had relatives with direct animal contact [38]. The Austrian researchers authoring that study suggested that the high level of doxycycline resistance (19/21, 90.5%) of MRSA isolates determined among these MRSA-positive patients could be connected to the relatively high proportion of tetracycline use on pig and poultry farms in Austria at this time [38]. However, a more recent Austrian study reported that human patients were much more likely to become infected with resistant *S. aureus* just through living in rural areas rather than working directly in animal production (OR 1.53 (95% CI: 1.02; 2.30) vs. OR 0.54 (95% CI: 0.19; 1.55)), although this may have been due to the relatively small number of livestock farmers (96/3309; 2.9%) included in that study population [39].

A study from the United States reported that, of 18 dairy farms sampled, 50% were positive for antimicrobial-resistant *Enterobacteriaceae* in environmental sampling and that on three (17%) of these farms such strains were isolated from 75.1 to 100% of all tested surfaces [40]. This study found that over 60% of "shared human and animal contact surfaces" were positive for AmpC and over 20% were positive for ESBL-producing *Enterobacteriaceae*, a finding which is extremely relevant for the possible transfer of antimicrobial resistance from animals to humans [40].

A meta-analysis of 181 studies to assess possible associations between antimicrobial use in food-producing animals and antimicrobial resistance in such animals and humans was published in 2017 [41]. A total of 81 studies on animals and 13 studies on humans were included in the final meta-analysis. In this study, the pooled absolute risk differences when interventions were introduced into livestock farming systems to reduce antimicrobial use led to a 10 to 15% (range 1–39%) lower proportion of resistant isolates in these systems compared to the control group, where no interventions were implemented [41]. The authors of this meta-analysis reported that there is sufficient evidence to demonstrate that a transfer of AMR bacteria can occur between livestock and farm workers and that this appears to occur less frequently when AMU in animal populations is reduced. However, they also pointed out that farmworkers are also the most commonly investigated group and that the evidence is, therefore, weaker for the general human population [41].

While the milk produced in this study population was not specifically tested for ESBL-producing *E. coli*, other European studies have investigated the presence of these AMR bacteria in the milk. A German study of bulk tank milk detected ESBL-producing *Enterobacteriaceae* in 9.5% of the 866 dairy farms tested [42], while a Swiss study of 100 dairy farms did not determine any ESBL-producing *E. coli* [43] in saleable milk.

In the present study, no statistically significant relationship could be proven between AMU and AMR on farms. It is important to note, however, that AMU on these Austrian dairy farms was relatively low compared to the dairy industry in other European countries [44–47], as well as the pig and poultry sector as a whole, and it is also unlikely that definitive conclusions can be made based on the small number of samples isolated which tested positive for ESBL-producing *E. coli*.

The main limitation of the current study was that the study population was not randomised and the data on antimicrobial use in this population of dairy cows cannot, therefore, be extrapolated to the rest of the Austrian dairy cow population. However, given the potentially sensitive nature of collecting diagnosis and antimicrobial treatment data directly from herd veterinarians, as well as the high level of commitment required by both veterinarian and farmer to complete all required study tasks, the decision was made to invite veterinarians to participate (convenience sampling) and that they should, in turn, suggest farmers from their client base to join the study (respondent-driven sampling). The subpopulation of 50 farms included here to investigate AMR bacteria was actually part of a much larger study containing around 250 dairy farms. Over a one-year period, the following data were collated from the 250 farms: AMU, bacteriological milk culturing results, veterinary diagnoses, milk recording results, animal movement data, and many other farm management factors [20,44]. While it is true that the enrolment of the 50 farms in the current study does indeed introduce a certain level of bias into the study population, we are of the opinion that this bias was limited as neither the farmers nor their herd vets knew which farms would be sampled for AMR bacteria at enrolment. The larger observational study commenced in October 2015, the faecal/dust samples included here were taken in July 2017, and the farms included in the current study were based on their AMU in 2015–2016. We do not believe that the herd veterinarians were able to influence the inclusion or exclusion of specific farms with antimicrobial resistance problems as the initial enrolment of farmers took place in winter 2014/2015, i.e., more than two years prior to AMR samples being collected.

A further limitation may have been the decision to include only the highest and lowest AMU farms in one geographical region in the subpopulation. In theory, the low use farms would have had either no or a low level of selection pressure on the bacteria present leading to a lower probability of resistant bacteria being favoured in the farm environment. In contrast, the high use farms could have had such a broad use of antimicrobials such that even ESBL-producing *E. coli* were not able to thrive. Nevertheless, we do not believe either of these cases to be true, as we determined no statistically significant difference between the prevalence of ESBL-producing *E. coli* on high or low AMU farms in this study, and the overall AMU on Austrian dairy farms is known to be relatively low (even the maximum use of antimicrobials on the highest using farm was <8.10 DDD/cow/year (median 3.82 DDD/cow/year), compared to other countries [44,45,47–49]).

#### **4. Materials and Methods**

#### *4.1. Study Population*

The sample of farms included in this pilot study was taken from a larger nationwide study of 248 Austrian dairy farms, as described elsewhere [20,44]. Overall antimicrobial use (AMU) was analysed as part of the previous study [44] and a subset of 50 farms in the federal state of Styria were chosen to participate in this pilot study based on their total AMU over a one-year period. These farms were selected to include the 25 highest users of all AMU and the 25 lowest users in the federal state who were participating in the larger study. The analysis of AMU was based on the Defined Daily Dose metric (DDDvet) as assigned for each antimicrobial active ingredient by the European Medicines Agency [21].

Farms were selected solely on their AMU calculated in DDDvet per cow per year in the previous year, no adjustments were made for housing system, production system (organic or conventional), herd size, or any other farm-related factors.

Bacteriological culture results from milk samples taken from these farms over the one-year study period were available, as described in detail for all 248 farms elsewhere [50]. The most common mastitis pathogens in the larger study population were *Staphylococcus* spp. (40.1%), *Streptococcus* spp. (24.1%), and *Enterobacteriaceae* (13.3%) [50]. A total of 3020 quarter samples from 647 cows on 166 farms were analysed as part of the wider study and it was determined that multiresistant (MDR, i.e., resistant to at least 3 antimicrobials from different classes [51]) strains were most common among *Enterobacteriaceae* spp., with 14.3% (19/133) of isolates, followed by *Staphylococcus* spp. with 5.5% (22/402) MDR isolates [50].

#### *4.2. Background Information on Udder Health and Herd Management*

Detailed information on the farms included in this pilot study is available in the form of questionnaire responses (see Supplementary Materials), as previously described with respect to mastitis risk factors and overall farm management [20].

#### *4.3. Total Antimicrobial Use in Defined Daily Dose (DDDvet/Cow/Year)*

Total AMU was calculated from veterinary treatment records covering 1 October 2015 to 30 September 2016, as detailed elsewhere [44,52]. A total of 11 veterinary practices provided data for this pilot study of 50 farms. Briefly, the number of DDDvet were calculated for each antimicrobial substance by dividing the total amount of active substance in milligrams by the European Medicines Agency's predefined DDDvet values [21].

Intramammary treatments were calculated slightly differently, as the European Medicines Agency classes each udder tube as 1 DDDvet, regardless of milligrams of active substance or standardised cow live weight (for details, see [21,44]).

DDDvet values have not been assigned by the EMA for dry cow therapy (DCT) intramammary tubes, only Defined Course Doses (DCDvet). For this reason, DCT was excluded from the majority of the AMU analyses included here. However, as dry cow therapy is an important part of the antimicrobial use statistics of dairy farms, a comparison of the corrected DCDvet/cow/year was carried out between those farms testing positive for the presence of ESBL-producing *E. coli* and those testing negative. The DCD/cow/year value was corrected by the replacement rate and calving interval for each of the farms included here, as described elsewhere [53].

No antimicrobial treatments were excluded in the present study (with the exception of unquantifiable sprays), regardless of diagnosis.

#### *4.4. Farm Sampling and Bacteriological Culture*

Each farm was sampled by one of two authors (CLF and WO). Personal protective equipment (PPE) in the form of disposable overalls, gloves, and boot covers were used to ensure that each site was not contaminated. As many Austrian dairy farms keep dualpurpose Simmental cows (Austrian *Fleckvieh*) for milk production, they frequently rear a number of female replacement heifers, as well as some male animals for beef production. For this reason, cows, calves, and youngstock are often found in the same building or on the same farm.

Each farm was visited in July 2017 and the following samples were collected:

Additionally, one dust sample was collected on dry gauze from 3 to 5 faeces-free areas of the main cowshed (for the isolation of MRSA).

Samples were immediately stored in an insulated cool box until they could be refrigerated. Faecal samples were transported to the laboratory of the Styrian Provincial Government Veterinary Authorities in Graz. Approximately 2 g faeces from each sample were pre-enriched in 200 mL buffered peptone water (BPW) at 37 ◦C for 18–24 h and then subsequent selective plated on ChromID-ESBL agar (bioMerieux, Marcy-L'Etoile, France). Plates were incubated at 37 ◦C for 24–28 h. Broth microdilution of the presumptive ESBL-producing *E. coli* was performed using the Sensititre™ EU Surveillance ESBL (EUVSEC2) plate (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer's instructions (briefly, each well was filled with 50 µL inoculum, the plate was then sealed and incubated at 34–36 ◦C for 18 h in a non-CO2 incubator). The Sensititre™ EU Surveillance ESBL (EUVSEC2) plate includes wells containing cefotaxime and ceftazidime with or without clavulanic acid, as well as cefoxitin, ertapenem, imipenem, meropenem, cefipime, temocillin, and positive control wells. An ESBL phenotype was categorised according to the EFSA/ECDC standard EU surveillance method, namely a minimal inhibitory concentration (MIC) >1 mg/L for cefotaxime and/or ceftazidime, a positive synergy test for these antimicrobials with clavulanic acid, an MIC ≤8 mg/L for cefoxitin, and an MIC ≤ 0.12 mg/L for meropenem [54].

Dust samples for MRSA isolation were transported to the National Reference Laboratory for Antimicrobial Resistance at the Institute for Medical Microbiology & Hygiene, Graz (part of the Austrian Agency for Health and Food Safety (AGES)). Swabs were inoculated in 100 mL Mueller–Hinton broth supplemented with 6.5% sodium chloride (NaCl) and incubated for 16–20 h at 37 ◦C. A 10 µL loopful of pre-enrichment broth was spread on chromID MRSA agar (bioMerieux, Marcy-L'Etoile, France) and incubated for 24–48 h at 37 ◦C. In addition, 1 mL of pre-enrichment broth was added to 9 mL tryptone soya broth (TSB) containing 3.5 mg/L cefoxitin and 75 mg/L aztreonam and incubated for another 16–20 h at 37 ◦C. A total of 10 µL of the selective enrichment broth were spread on chromID MRSA agar (bioMerieux) and incubated for 24–48 h at 37 ◦C. Presumptive MRSA colonies were confirmed by Time of Flight Mass Spectrometry (MALDI-TOF MS) and subsequent PCR targeting mecA and mecC according to methods described elsewhere [55,56], with minor deviations (for details, see the Supplementary Materials).

#### *4.5. Statistical Analysis*

Statistical analyses were carried out using the SPSS (IBM, Armonk, NY, USA) statistical software and Microsoft Excel (Microsoft Corporation, WA, USA). A non-parametric test of independent samples, the Mann–Whitney U-test, was done using the total DDDvet/cow/ year for each of the 50 farms and whether at least one sample from the farm tested positive for the presence of ESBL-producing *E. coli*. This test was then repeated using the DDDvet/cow/year for third and fourth generation cephalosporins for each farm. Furthermore, using 2 × 2 contingency tables, the Chi-squared test was carried out on group sizes larger than five, while contingency tables where one or more category was smaller than five were assessed using Fisher's *t*-test. Statistical significance was set at *p* < 0.05 in all cases.

#### **5. Conclusions**

The level of antimicrobial resistance on the study farms was assessed by means of environmental samples collected from 50 dairy farms in the Austrian federal state of Styria chosen for either their high or low levels of overall AMU over a one-year period. The most frequent indication for antimicrobial use was the treatment of udder disease. No statistically significant relationships between high AMU in DDDvet/cow/year and the presence of these AMR bacteria were determined. While the low prevalence of ESBL-producing *E. coli* in cattle determined here correspond with national AMR monitoring of young calves, our findings may also have been due to the generally low level of antimicrobial use and subsequent lack of selection pressure for resistance genes on the study farms and, therefore, further investigation is needed with a larger sample size.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/antibiotics11020124/s1, S1: English summary of the farm management questionnaire; S2: Description of PCR methods.

**Author Contributions:** Conceptualisation, C.L.F. and W.O.; methodology, C.L.F., A.K., P.P., S.K.-J. and W.O.; formal analysis, C.L.F. and W.O.; investigation, C.L.F., P.P., S.K.-J. and W.O.; resources, A.K.; data curation, C.L.F. and W.O.; writing—original draft preparation, C.L.F.; writing—review and editing, C.L.F., A.K., P.P., S.K.-J. and W.O.; supervision, W.O. and A.K.; project administration, W.O. and A.K.; funding acquisition, W.O. All authors have read and agreed to the published version of the manuscript.

**Funding:** The K-Projekt ADDA—Advancement of Dairying in Austria was supported by the Austrian Ministry for Transport, Innovation and Technology (BMVIT), the Federal Ministry of Science, Research and Economy (BMWFJ), the province of Lower Austria and the city of Vienna within the framework of COMET—Competence Centers for Excellent Technologies. The COMET program is handled by the Austrian Research Promotion Agency (FFG). C.L.F. is partly funded by the K-Project "D4Dairy" which is supported by BMVIT, BMDW, the province of Lower Austria and the city of Vienna.

**Institutional Review Board Statement:** In accordance with national legislation and good scientific practice (GSP) guidelines, the study was registered with the institutional ethics and animal welfare committee of the University of Veterinary Medicine, Vienna (Ref No. ETK-13/11/2015). No invasive procedures were performed as part of this study.

**Informed Consent Statement:** Written informed consent was obtained from all farmers and veterinarians involved in the study.

**Data Availability Statement:** The authors do not own the antimicrobial use data analysed here. These data were collected from the veterinarians who were treating the animals included in the study. Under the data privacy agreement signed by the farmers and veterinarians, these data are not available to be published.

**Acknowledgments:** The authors would like to thank all the veterinarians and farmers who participated in this study, and Stefanie Klatovsky for help with PCR amplification of mecA/mecC.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**

*Article*