Alternative normalization methods were proposed to solve the biased information of SPM in the study of neurodegenerative disease. The objective of this study was to determine the most suitable count normalization method for SPM analysis of a neurodegenerative disease based on the results of different count normalization methods applied on a prepared digital phantom similar to one obtained using fluorodeoxyglucose-positron emission tomography (FDG-PET) data of a brain with a known neurodegenerative condition. Digital brain phantoms, mimicking mild and intermediate neurodegenerative disease conditions, were prepared from the FDG-PET data of 11 healthy subjects. SPM analysis was performed on these simulations using different count normalization methods. In the slight-decrease phantom simulation, the Yakushev method correctly visualized wider areas of slightly decreased metabolism with the smallest artifacts of increased metabolism. Other count normalization methods were unable to identify this slightly decreases and produced more artifacts. The intermediate-decreased areas were well visualized by all methods. The areas surrounding the grey matter with the slight decreases were not visualized with the GM and VOI count normalization methods but with the Andersson. The Yakushev method well visualized these areas. Artifacts were present in all methods. When the number of reference area extraction was increased, the Andersson method better-captured the areas with decreased metabolism and reduced the artifacts of increased metabolism. In the Yakushev method, increasing the threshold for the reference area extraction reduced such artifacts.The Yakushev method is the most suitable count normalization method for the SPM analysis of neurodegenerative disease.  Statistical parametric mapping (SPM) analysis is used as a data analysis tool in functional neuroimaging studies –6. It isGlobal mean (GM) count normalization is the most commonly used count normalization , 12, 13 The objective of this study was to prepare a digital brain phantom similar to one obtained using fluorodeoxyglucose-positron emission tomography (FDG-PET) data of a brain with a known neurodegenerative condition. Then SPM analysis was performed on this phantom using each count normalization method to determine the most suitable one for SPM analysis of a neurodegenerative disease.Subjects In this study, we used FDG-PET data from 11 healthy subjects recruited from the local community through advertisements. The average age ± standard deviation was 62.6 ±4.5 years. They were six males and five females. All subjects  had a ClPET scanWe obtained PET scans of the healthy subjects within 4 weeks of neuropsychological testing. Subjects were told to fast 5 h before imaging and were administered an intravenous injection of FDG (185-218 MBq) 1 h prior to the scan. Subsequently, they were allowed to rest in a quiet room while wearing an eye mask until the time of imaging. A Biograph DUO PET/computed tomography (CT) scanner  was used for PET data collection. The imaging time lasted for 10 min, during which three-dimensional data were collected. Image reconstruction was performed with the ordered subset expectation maximization (OS-EM) method : 2.0 mm) and a reconstructed image with a matrix size of 256 × 256, a pixel size of 1.33×1.33 mm, and a slice thickness of 2.0 mm was prepared. FWHM for horizontal cross-section reconstruction was 3.38×3.38 mm. A CT scan was used for the collection of attenuation correction data.Preparation of the digital brain phantomSoftware We prepared a digital brain phantom using the following software:MATLAB:7.11.0 R2010bSPM8  Prominence Processor Ver.3.1 MRIcro  fMRIstat   Construction of digital brain phantomAccording to Borghammer et al. , 11, lowThree-dimensional extraction of brain dataUsing the digital brain phantom function of the Prominence Processor , we extrAnatomic normalization To prepare a digital brain phantom based on data from the healthy subjects and attenuation obtained using a mask, both sets of data must have the same form. However, the data from the cerebral grey matter and the grey matter of the left occipital lobe do not have the same form as data from the whole brain. As a result, accurate anatomic normalization was not possible. Therefore, data from the cerebral grey matter and the grey matter of the left occipital lobe were converted to the same form of data as the whole brain. This was done by adding the whole brain data to data from the cerebral grey matter and the grey matter of the left occipital lobe using MATLAB as a pretreatment to anatomic normalization. We performed anatomic normalization of the FDG-PET data collected from the 11 healthy subjects on whole brain data, combining whole brain and cerebral grey matter, and on combined data from the whole brain and the grey matter of the left occipital lobe. This was done by using an anatomical standard brain, which is an FDG-PET template built into SPM8.Preparation of the mask1) Slight-decrease mask: We obtained data for the cerebral grey matter only by subtracting the whole brain data from combined data from the whole brain and the cerebral grey matter. We prepared the slight-decrease mask by adding an 11% attenuation rate. 2) Intermediate-decrease mask: We obtained data for the grey matter of the left occipital lobe only by subtracting the whole brain data from combined data from the whole brain and the grey matter of the left occipital lobe. We obtained the area of slight decrease in the intermediate-decrease mask by removing the area of the grey matter of the left occipital lobe from the area of the cerebral grey matter. To create the intermediate-decrease mask phantom, attenuation rates of 11% and 23% were applied to the areas of the cerebral grey matter with slight decrease and the grey matter of the left occipital lobe, respectively.Preparation of slight-decrease and intermediate-decrease digital brain phantomsWe applied the slight-decrease and intermediate-decrease masks, to FDG-PET data collected from the 11 healthy subjects, which had undergone anatomic normalization using SPM8. We smoothed the data for a FWHM of 10 mm using SPM8. We then prepared 11 slight-decrease digital brain phantoms and 11 intermediate-decrease digital brain phantoms . Comparisons of each count normalization method using simulation data Count normalization methodsGM count normalizationUsing MATLAB and the fMRIstat program, we calculated the mean count for the whole brain  and performed GM count normalization. VOI count normalizationWe used the total sum for the cerebellum and the pons as the standard for the count normalization. Using MATLAB, we calculated the mean for the anatomical site in each slice. We then summed the means from the cerebellum and the pons for each slice and used this value as the standard for the count normalization.Andersson method1) Using the GM count normalization, the SPM analysis process up to the t-test step was performed on the data. 2) As a t-test was performed for each voxel, a t- map was prepared in which the results for each voxel were presented . 3) Using the prepared t-map, the areas with significant changes at any threshold were excluded as we prepared a mask only for the areas with small changes. The range of threshold processing was set to -2 < t < 2, as per the Andersson method. 4) To extract the reference area, the prepared mask was applied to all of the data . We calc5) Using the calculated standard value, we performed the count normalization on all the data.6) We processed the data subjected to the count normalization up to the t-test step in the SPM analysis process.7) We repeated steps  through Yakushev methodWe prepared a t-map in the same manner as in the Andersson method. The range of the extracted reference area for the t-map was t > 2 (p < 0.05), and the extraction was performed once. 1) We performed the SPM analysis on the data up to the t-test step using GM count normalization. 2) As the t-test was performed for each voxel, we prepared a t-map for the results of each voxel . 3) Using the prepared t-map, excluding the areas with significant changes at any threshold, we prepared a mask only for the areas with small changes. The range of threshold processing was set to t > 2 (p < 0.05).4) To extract the reference area, we applied the prepared mask to all of the data to be analyzed . To obta5) We performed count normalization using the standard value obtained in the extraction of the reference range. The result was considered as the final result of the SPM analysis using the Yakushev method.Examination of the impact of the number of extractions in the Andersson methodWhile using the Andersson method, we increased the number of reference area extractions to 20 in an increment manner and compared the results to those obtained by running the reference area extraction 3 times.Examination of the threshold for reference area extraction in the Yakushev method While using the Yakushev method, we increased the threshold of the reference area in steps and compared the resulting data.Statistical analysisWe used MATLAB and fMRIstat provided free of charge by Worsley et al. for our statistical analyses. fMRIstat allows for analyses nearly identical to those commonly performed using SPM. We used the mixed effects model proposed in chapter four of the paper by Worsley et al.  our analThe slight-decrease phantom simulation When SPM analysis was performed on the slight-decrease phantom simulation using the GM count normalization , the VOIThe intermediate-decrease phantom simulationThe areas with intermediate decreases were generally well visualized with all the count normalization methods . The areWe observed artifacts of hypermetabolism in all the count normalization methods . GM Fig and VOI Impact of the number of extractions in the Andersson methodWhen the number of extractions for the reference area was increased, the decreased metabolism area in the grey matter of the digital brain phantom was better captured compared to when three extractions were used. In addition, we were able to capture the artifacts of increased metabolism by increasing the number of reference area extractions. Examination of the threshold for reference area extraction in the Yakushev method Raising the threshold for the reference are extraction in the Yakushev method did not lead to any significant changes in the visualization of the area with decreased metabolism. However, increasing the threshold for the reference area extraction reduced the artifacts of increased metabolism. GM count normalization When performing SPM analysis using GM count normalization in neurodegenerative disease, it is important to consider the possibility of a wider area with decreased metabolism than that indicated by the results. It is also important to keep in mind that areas without increased metabolism may be visualized as areas of increases when analyzing the results. Here we report that performing an SPM analysis using GM count normalization when evaluating a neurodegenerative disease leads to the incomplete capture of the area with decreased metabolism. Furthermore, this method leads to the frequent visualization of areas of increased metabolism in the healthy areas, which are likely artifacts. This supports the report by Borghammer et al. . If the VOI count normalization The results of SPM analysis using VOI count normalization on the digital brain phantom were similar to that of GM count normalization. Therefore, caution must be taken to use this method in evaluating neurodegenerative disease.Andersson method Unlike GM count normalization, the Andersson method  could noYakushev method. Therefore, this method was considered to be the most favorable method to use for areas with both increased and decreased metabolism. However, it still led to artifacts of increased metabolism. As discussed for the Andersson method, the digital brain phantom prepared in this study had a wider area of decreased metabolism than that used in the report by Borghammer et al. . This leads to a lower value for the reference area used, lowering the standard value. Thus, the standard value for the reference area obtained in the extraction process decreases. Here, we reduced this artifact by increasing the threshold, which allowed us to accurately capture the area of decreased metabolism. These results indicate that the threshold must be set according to the severity of the disease, and that future examination of this point is necessary. The Yakushev method is the most suitable count normalization method for the SPM analysis of neurodegenerative diseases among all the count normalization methods examined in this study. Compared to other methods, the area of the slight decrease in metabolism could be visualized more accurately as a wide area. It resulted in the smallest area of the artifacts of increased metabolism in healthy areasr et al. , 19, 20.. If the area with decreased metabolism is highly localized, the initial GM count normalization will offset the decrease in metabolism, preventing the decrease in the standard value. Therefore, the threshold in the Yakushev method, which defines the area of increased metabolism as the reference area, may not be suitable. Thus, the Yakushev method is not suitable to analyze all neurodegenerative disease. Future studies are necessary to resolve this issue.Here we report that the Yakushev method is the most suitable count normalization method when metabolism is decreased in the whole brain. In other words, the Yakushev method is suitable to analyze patients with diseases such as neurodegenerative diseases. However, it may not be ideal if the decrease in metabolism is localized. The Yakushev method assumes that artifacts of increased metabolism area created the standard value decreases in the initial GM count normalizationFurther studies are needed in order to perceive the comparison between healthy subjects and patients with Alzheimer’s disease using SPM analysis with GM count normalization, VOI count normalization, the Yakushev method and the Andersson method. The two groups’ comparisons are now considering in our laboratory. We will report about the comparison of the normalization methods with the patients in a future work.When analyzing the neurodegenerative disease with SPM analysis, it is extremely difficult to fully describe the status of the brain. It is important to review the results after processing using different modalities. If there are differences in the obtained results, the most suitable count normalization may need to be selected based on the results from several different count normalization methods. The present study’s evaluations were based only on a digital brain phantom. However, we ultimately aim to select the ideal count normalization method to use with data from a real diseased brain to determine whether the similar results can be obtained with the actual patients. We compared count normalization methods for the analysis of digital brain phantoms meant to resemble brains with the neurodegenerative disease condition. The Yakushev method was shown to be the most desirable count normalization method for the SPM analysis of diseases, such as neurodegenerative disease in which metabolism is decreased throughout the brain. The GM count normalization may underestimate the areas with decreased metabolism and lead to artifacts of increased metabolism. In severe cases, the Yakushev method and the Andersson method may need to be altered to obtain more accurate analyses by raising the threshold for the reference area extraction process and increasing the number of the reference area extractions, respectively.


A hybrid particle swarm optimization (PSO), able to overcome the large-scale nonlinearity or heavily correlation in the data fusion model of multiple sensing information, is proposed in this paper. In recent smart convergence technology, multiple similar and/or dissimilar sensors are widely used to support precisely sensing information from different perspectives, and these are integrated with data fusion algorithms to get synergistic effects. However, the construction of the data fusion model is not trivial because of difficulties to meet under the restricted conditions of a multi-sensor system such as its limited options for deploying sensors and nonlinear characteristics, or correlation errors of multiple sensors. This paper presents a hybrid PSO to facilitate the construction of robust data fusion model based on neural network while ensuring the balance between exploration and exploitation. The performance of the proposed model was evaluated by benchmarks composed of representative datasets. The well-optimized data fusion model is expected to provide an enhancement in the synergistic accuracy. A moment of evolution is now emerging toward a new paradigm known as smart convergence, which is bringing together both heterogeneous and information communication technologies. These emerging phenomena have prompted researchers to explore new possibilities for sophisticated smart devices  to be emBecause a single sensor usually only recognizes a limited set of partial information about the environment, multiple similar and/or dissimilar sensors are needed to provide accurate sensing information from a variety of perspective in an integrated manner . Such muAs is well known, maintaining the balance between the exploration of new possibilities and the exploitation of old certainties ,13 has bThis paper proposes a hybrid PSO capable of overcoming large-scale nonlinearity or heavy correlation in the sensing data fusion and facilitates the construction of a robust data fusion model while maintaining the balance between the exploration of new possibilities and the exploitation of old certainties. The proposed algorithm was evaluated by benchmarks composed of representative datasets and the well-optimized data fusion model is expected to provide an enhancement in synergistic accuracy. In this paper, the neural network is used as a basic model and different backpropagation methods such as PSO, LM, and PSOpid are considered, thus the expression of neural network is simply omitted.Compared to a single sensor system, multiple sensor systems have the advantage of broadening the sensing range and improving the perception of environmental conditions . In addiIn , a neurai indexes units in the input layer and j indexes units in the hidden layer; j; and the dimension of input vector is d. In addition, the subscript k indexes units in the output layer, x, y) on the screen, respectively. The authors in [t and a are the target and actual output vectors of length c , the batch size is m and θ represents all weights and biases.Each output of the neural network as thors in  regardedFSRs show nonlinear issues or problems of correlation errors. It can be caused by instability of the glass or metal frame, response time of the sensor, errors in manufacturing, assembly processes, the sensor placement, and human errors. Therefore, Kim et al.  suggesteSince March  proposedn, including parameter tuning to optimize the performance of the neural network. In addition, a particle is used to determine the value of parameters in the neural network. The magnitude of vectors i-th particle after the n-th iteration are shown in Equations (4) and (5).The idea of PSO allows particles randomly placed on the search-space to explore new possibilities to the best global position. PSO is used to train a neural network with back propagation method considering a population based stochastic optimization technique . On the kZ (≈ 10 + 2 × (the dimension of the weights and biases)0.5) and the inertia weight is ω, which dynamically adjusts the velocity. Moreover, the cognitive component c1 and the social component c2 change the particles’ velocity toward the previous best and global best position, respectively. The PSO uses random numbers determined from a uniform distribution rand1 and rand2 to avoid unfortunate states in which all particles quickly settle into an unchanging direction. Consequently, the parameters are updated in accordance with the found global best position.The previous best position is selected using Equation (6), and the global best position is decided by Equation (7).This paper proposes a hybrid LM and PSO scheme, namely LM-PSO, based on the concept of two-phase evolutionary programming : First, In the first stage, normally a PSO has the initial parameters shown in In the second stage, the proposed LM-PSO is trained with the optimal initial weight and bias values derived during the PSO stage. Although PSO is implausible on its own as a solution for the convergence rate and solution accuracy as illustrated in Ordinary PSO alone has difficulty negotiating the tradeoff between global and local search because particles are initially deployed following a uniform random distribution in a hypercube big enough to contain a prospective global optimum. When the particle position range is not limited within the minimum and maximum positions or is too broad, the swarm can become unstable or explode, resulting in a slow convergence. Therefore, a novel approach is necessary for each particle to discover the global optimal solution securely, preventing the particles from becoming unstable or exploding.PK is the proportional term, IK is integral term, and DK is the derivative term, and they are selected through trial and error operations, as shown in The proportional-integral-derivative (PID) is widely applied in a feedback control loop technology . The majAdjustment of the velocity is calculated from the acceleration with respect to the distance error produced by both present and its previous best position compared to the global best solution. Furthermore, two random, uniformly distributed variables are utilized in preserving the diversity. The position of each particle is updated according to the multiplying the velocity with the three PID terms, which are described in The position of each particle is initialized to the range between maximum and minimum derived from the output of the precedent LM-PSO technique. The initial global best position is optimally configured in accordance with the LM-PSO method. Consequently, particles randomly distributed can better guarantee an accurate global optimal solution than they can through either method alone, as shown in The stabilized PSOpid, in which each particle is evaluated by using a fitness function and update, is located in an enhanced location to discover the best optimal solution without local minimum. The convergence rate of the proposed PSOpid is faster and it has much higher performance than previous methods. This technique enables efficient implementation because of its small number of parameters, which both shortens the training time and reduces overfitting compared to ordinary PSO. The proposed PSOpid-LM-PSO, as shown in In the case of PSO and PSOpid alone, the range of each particle position become unstable or explodes; therefore, the hybrid of three algorithms can be used to not only exploit old certainty optimum but also to explore new possibilities. Through all evaluative processes, this paper shows the enhanced performance by a hybrid scheme of ordinary PSO, LM, and PSOpid, namely PSOpid-LM-PSO. The results showed that the proposed PSOpid-LM-PSO provides faster convergence and better fitness than other algorithms within the range-optimized search space, as shown in We first evaluated the experimental dataset of the touch floor system in  and selePK, IK, and DK) which are fixed to 0.5, 0.4, and 0.3, respectively to verify the reliability of performance evaluation of the proposed methodology in different exemplar datasets. Other parameters were used in the same manner with the generic PSO and the PSOpid approach. In contrast, a performance analysis was performed using different exemplary datasets from MATLAB , which aThis study proposed a hybridization of enhanced particle swarm optimization (PSO) and a classic neural network to build a multi-sensor data fusion model. The results show that the proposed PSOpid-LM-PSO provides a faster convergence and better fitness than other algorithms within the range-optimized search space with the system accuracy improved by approximately 18.6% compared to the classic LM algorithm. The contributions of this paper will reduce human effort in training the data fusion model using an on-line adaptation approach based on small changes from a prior trained model, or by using support vector machines for the one-to-many mapping approximation ,28. Anot


The Doha Development Agenda recognizes the central role that international trade can play in the promotion of economic development. In fact, the increase of exports from developing countries to developed nations' markets has been considered a key element for developing countries to realize the potential benefits of globalization. Over the last decades, developed countries have provided preferential access to their markets to developing countries through nonreciprocal trade agreements. Moreover, developing countries have also participated in reciprocal trade agreements. This paper re-examines comparatively the effect of both kinds of trade agreements on exports from developing countries but also from the developed world. In line with other studies, our results across specifications are unstable. However, the results of our preferred specification give additional support to the argument raised by critics of nonreciprocal preference regimes who consider that developing countries should abandon their reliance on one-way trade preferences in favor of reciprocal agreements. One of the most important challenges in the Doha’s round of trade negotiations is the promotion of development. The Doha Development Agenda recognizes the central role that international trade can play in the promotion of economic development. In fact, the rise of exports from developing countries to developed nations' markets has been for a long time viewed as a key element for developing countries to realize the potential benefits of globalization. The usual approach to accomplish this aim has implied that the developed world give support to the integration of developing countries into their economies through a series of unilateral concessional measures towards developing countries in the form of nonreciprocal trade preferences. In addition to all developed countries, since 2000s a number of emerging economies  also offer their own programs focused on Least Developed Countries (LDCs), although they are typically limited in scope.  providesThe main instrument for the so-called “special and differential treatment” for developing countries has been the Generalized System of Preferences (GSP), but there have been many other nonreciprocal preferential trade agreements (NRPTAs). Examples of these others nonreciprocal programs are several EU and USA initiatives , or the duty-free treatment granted by many other countries for least developed countries or for developing countries from a specific region. The intellectual underpinnings for the “special and differential treatment” arrangements in GATT for developing countries go back to the 1950s. They were based on balance of payments problems for developing countries, protection on infant-industry grounds and the Singer-Prebisch thesis about the secular decline in developing countries terms of trade .Mercado Común del Sur (MERCOSUR) are examples of South-South agreements. In contrast, the North American Free Trade Agreement (NAFTA), the agreement between Canada and Chile and that between the European Union and Mexico are examples of North-South agreements.As is well known, trade arrangements for developing countries have not been confined to one-way trade preferences. On the one hand, a large number of developing countries are members of the GATT/WTO system, which is based on the reciprocity and the most-favored nation principles. On the other hand, developing countries have also made a conscious effort to forge reciprocal preferential trade agreements, involving only developing countries (known as South-South agreements) or implicating both developing and developed countries (known as North-South agreements). The Common Market of Eastern and Southern Africa (COMESA), the Association of Southeast Asian Nations (ASEAN), and the Critics of nonreciprocal preference schemes have traditionally argued that developing countries should abandon their reliance on one-way trade preferences in favor of reciprocal agreements, since the latter implies a stronger, credible and lasting commitment  effects of economic integration agreements on aggregate bilateral trade flows. After accounting for multilateral resistance terms with time-varying fixed-effects and controlling for endogeneity bias using panel data techniques,   15] more that of  and 16]ex post t that of  emphasizAs noted by  a few paSince the late 2000s, empirical work on the determinants of trade flows has increasingly relied on a theoretically motivated gravity equation that controls for both multilateral resistance terms and unobserved bilateral heterogeneity with three sets of fixed effects: exporter-time, importer-time and country-pair  is a binary variable which is unity if i is a beneficiary (benefactor) of a nonreciprocal preferential trade agreement and j is the corresponding preference-giving (beneficiary) country, ηij are country-pair fixed effects, χit and λjt are exporter-year and importer-year fixed effects, respectively, and uijt is the standard classical error term.Our benchmark specification is the gravity Eq , which cPTAXben) or a developed country (PTAXdev). Secondly, we split the PTA dummy into two from the importer’s perspectives (denoted by PTAMben and PTAMdev). Finally, and most importantly, we split the PTA dummy into four dummies taking into account the group which each trading partner in the pair belong to: Exporter beneficiary and importer developed (PTAXbenMdev), exporter developed and importer beneficiary (PTAXdevMben), exporter and importer beneficiary (PTAXbenMben) and exporter and importer developed (PTAXdevMdev). For further clarification, let consider the first of these dummies as an example. PTAXbenMdev is a dummy that is unity if country i is a beneficiary country of a nonreciprocal trade agreement and country j is a developed country and they share membership in a reciprocal preferential trade agreement and zero otherwise. Comparing the estimated coefficient for this variable with that obtained for NRPTAXbenMdev allows us to test whether reciprocal or nonreciprocal trade agreements have been best to promote exports from developing countries. In a similar way, we can compare the export performance of developed countries participating in reciprocal (PTAXdevMben) and nonreciprocal (NRPTAXdevMben) trade agreements in trade with beneficiary countries.The benchmark specification includes only one dummy for all  preferential trade agreements (PTA). In order to investigate the issues addressed in this paper, we disaggregate the dummy variable PTA in three different ways by interacting the PTA dummy with dummies for whether the exporter and/or the importer countries are or not beneficiaries of nonreciprocal preferential trade agreements. Firstly, we split this dummy into two dummies depending on whether the exporter is a beneficiary country  come from the Direction of Trade dataset (IMF). The sample covers 182 countries and territories over 15 years of the period 1960–2016 at four-year intervals. Data for currency unions are taken from the CIA's World Factbook. The indicators of preferential trade agreements and GATT/WTO have been built using data from the World Trade Organization. In this study, we use the expression “preferential trade agreement” to refer also to other agreements involving a higher degree of economic integration. In fact, most economic integration agreements considered in the sample are free trade agreements. Data on the one-way trade preferences come from different sources. Data on the African Growth and Opportunity Act and Everything but Arms initiative come from the corresponding websites. The list of beneficiaries of the Caribbean Basin Initiative and the Andean Trade Preference Act come from the Office of the United States Trade Representative. The listing of beneficiaries of the Cotonou Agreement (ACP-EU Partnership Agreement) comes from its website and. The lisThe list of developed countries includes those countries that have never been beneficiaries of one-way trade preferences Australia, Austria, Belgium-Luxembourg, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, the Netherlands, New Zealand, Norway, Portugal, South Korea, Spain, Switzerland, Sweden, the United Kingdom and the United States, plus Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovak Republic and Slovenia (after their accession to the European Union in 2004), and Bulgaria and Romania (after their accession to the European Union in 2007).The estimation results for the structural gravity Eq  by OLS aColumn 2 and 3 report the results when we split the PTA dummy from the exporter and the importer perspectives, respectively. In the first case, there are no statistical differences in the impact of PTAs depending on whether the exporter is a beneficiary or a developed country (regardless the group that the trading partner belongs to). In particular, coefficient estimates are close (0.226 for PTAs in which the exporter is a beneficiary country and 0.251 for PTAs in which the exporter is a developed country). In contrast, when the PTA dummy is split from the importer perspective, the estimated coefficient is positive in both cases but significantly higher (at the 5 per cent level of significance) when the importer is a developed country (0.316) than when it is a beneficiary country (0.189).The next natural step is to split the PTA dummy taking into account the group to which each trading partner belong (column 4). The results suggest that preferential trade agreements have a positive effect in the four cases. The largest effect appears for trade flows between developed countries (0.3984) whereas the point estimates for the other three combinations goes in a range from 0.111 to 0.248.With regard to the comparison between reciprocal (PTAs) and nonreciprocal agreements, these results reveal that for exports from beneficiary countries to developed countries both dummies are positive and despite the point estimate is larger for reciprocal agreements than for nonreciprocal the difference between them is not statistically significant. However, for exports from developed countries to beneficiary countries the impact is higher for nonreciprocal agreements.Until now, the comparison between reciprocal and nonreciprocal trade agreements is based on PTAs. However, as noted before the multilateral trade liberalization under the auspices of the GATT/WTO is also reciprocal in nature. The empirical evidence about the impact of the GATT/WTO across groups of countries (developed versus developing) is varied .Finally, in column 8 we present the results when we split the NRPTA dummy for exports from beneficiary countries to developed countries into two: one for the years until 2008 and the other for data from 2008. This allows us to check whether the nonreciprocal preferences have been eroded in recent years, as a result of the proliferation of reciprocal PTAs between developed and developing countries over the last decade or the important revision of the European GSP that took place in 2014. The results confirm the erosion of preferences in the later years. The estimated coefficient for that variable from 2008 is very close to zero and not statistically significant at conventional levels. , when coAs noted before, Moreover, it is important to deal with the econometric problems resulting from both heteroskedasticity and the existence of zero values in bilateral trade flows. It allows us to avoid other potential sources of bias in the estimation that could be present in the log-linear specification. This paper benefits from the recent computational development implemented by , which m analyse This will be our preferred specification because, unlike OLS, it deals with econometric problems resulting from heteroscedasticity and zeros but gives less weight to large flows in levels, as OLS does, since it works with export shares for a given exporter. A similar solution was advanced by . As poinIn line with Mayer et al. (2018), the results for the model with export shares as the dependent variable are more proximate to those found with OLS, when we use country-pair clustered standard errors. In particular, PPML estimates reported in column 2 reveal positive and statistically significant effects for CUs, PTAs, GATT/WTO and NRPTAs (in the direction in which the trade preferences are given). However, using multi-way rather country-pair clustering has particularly relevant consequences in this case. Apart from the fall in the level of statistical significance for the PTA and GATT dummies  the effect for the nonreciprocal agreements vanishes.Columns 3 to 5 present the results when we disaggregate alternatively the PTA dummy, the GATT/WTO dummy or both according to the group which each trading partner belongs to. Two comments are in order. First, in all the cases and regardless the type of standard errors considered, the results show a positive and statistically significant effect (at least at the 10 per cent level) for the dummies of the reciprocal agreements that capture the impact on exports when the developed countries are the destination markets. Second, the five statistically significant coefficient for nonreciprocal agreements in columns 3 to 5 when standard errors are country-pair clustered loss the statistical significance when standard errors are multi-way clustered.Finally, columns 6 and 7 present the results when we again split the NRPTA dummy for exports from beneficiary countries to developed countries into two dummies in order to compare the results of the period before and after 2008. Curiously, the dummy leaving out the last years of the sample is positive and statistically significant (at least at the 5 per cent level) even when the standard errors are multi-way clustered. However, the estimated coefficient for the dummy from 2008 is never positive giving additional support to the argument of erosion of preferences in the last years.The increase of exports from developing countries has long been considered an essential element to promote economic development. Over the last five decades, developed countries have provided preferential access to their markets to developing countries through nonreciprocal trade agreements. Moreover, developing countries have also participated in reciprocal trade agreements . in this paper we re-examine comparatively the effect of reciprocal and nonreciprocal trade agreements on exports from developing countries to developed countries but also on exports from the opposite direction.The evidence across specifications is unstable. The results for the log-linear version of the gravity equation with OLS (including time-varying exporter and importer fixed effects as well as time-invariant country-pair fixed effects) suggest that both reciprocal and nonreciprocal trade agreements have had an economically and statistically significant impact regardless of the direction of the trade flows. However, this estimator may lead to biased results since it does not tackle the issues related to heteroskedasticity and zeros. Once we deal with these issues with the PPML estimator the picture that emerges is very different. We find the apparently implausible result that no economic integration agreement has had an effect on export flows. Given these discrepancies and following other authors we choose as our preferred specification a PPML estimation that uses export shares as the dependent variable and in which standard errors are multi-way clustered. According to this specification for the whole sample period only reciprocal agreements have had a positive effect in trade flows between developed and developing countries and only when the exporter is the developing country. For data up to 2008, there is evidence of a positive impact of the NRPTA on exports from beneficiary countries to the developed countries that vanishes in the last years. Finally, for exports from developed countries to beneficiary countries our preferred specification does not provide evidence of a significant effect in any case. In summary, the results of our preferred specification give additional support to the argument raised by critics of nonreciprocal preference regimes who consider that developing countries should abandon their reliance on one-way trade preferences in favor of reciprocal agreements.S1 Appendix(DOCX)Click here for additional data file.


Psychophysiological state monitoring provides a promising way to detect stress and accurately assess wellbeing. The purpose of the present work was to investigate the advantages of utilizing a new unobtrusive multi-transceiver system on the accuracy of remote psychophysiological state monitoring by means of a bioradar technique. The technique was tested in laboratory conditions with the participation of 35 practically healthy volunteers, who were asked to perform arithmetic and physical workload tests imitating different types of stressors. Information about any variation in vital signs, registered by a bioradar with two transceivers, was used to detect mental or physical stress. Processing of the experimental results showed that the designed two-channel bioradar can be used as a simple and relatively easy approach to implement a non-contact method for stress monitoring. However, individual specificity of physiological responses to mental and physical workloads makes the creation of a universal stress-detector classifier that is suitable for people with different levels of stress tolerance a challenging task. For non-athletes, the proposed method allows classification of calm state/mental workload and calm state/physical workload with an accuracy of 89% and 83% , respectively, without the usage of any additional a priori information on the subject. Stress is a normal organism response to changing environmental conditions, as defined by Selye . In 2],,2], SelyAt present, to detect stress and estimate its level, numerous psychological questionnaires are used. The main drawback of their usage is the necessity to interpret results by an expert . Moreover, there are stress detection techniques based on measuring physiological parameters, such as level of cortisol ,13, evenSome stress detection methods are based on analyzing information about pupil diameter , eye movFurthermore, there are mobile applications and wearables that claim to be able to monitor mental stress . For theOne of the methods that can be used for prolonged daily unobtrusive stress detection and monitoring is the bioradar technique . This meResearch activities dealing with the application of bioradars for the estimation of user’s psychophysiological states and detection of mental stress have been carried out at the Remote Sensing Laboratory of Bauman Moscow State Technical University (BMSTU) since 2011 ,28.It should be noted that, until now, the majority of work dealing with the monitoring of a human psychophysiological state by means of bioradars has been mainly focused on bioradar signal processing, which allows the registration of heartbeat and respiration patterns ,30,31. IThis paper deals with two main challenges that arise while applying the proposed technique in realistic conditions and suggests methods for their solution by using the experience of previous works along with new experimental data. The first one is caused by a high impact of the subject orientation toward radar on the level of the desired signal and the accuracy of the estimation of vital signs. The second challenge is determined by the variability of subjects’ reaction to a stressor, which depends on the level of individual stress tolerance.The purpose of the present study was to investigate the advantages of utilizing a multi-transceiver system on the accuracy of psychophysiological state monitoring by means of a bioradar technique. This is explicitly due to the capability of such bioradar architecture to overcome one of the challenges that arise while applying the proposed technique in realistic conditions, which is a high impact of the subject orientation toward radar antennae on the level of the desired signal and the accuracy of the estimation of vital signs from previous works . The novThe architecture of the bioradar used in the present work is shown in The bioradar was designed using two single-chip, high-sensitivity, dual-channel transceivers K-LC5 (RFbeam) , a photoAs an analog-to-digital converter (ADC), we used a higher-precision ADC ADS1115, which provides 16-bit precision at 860 samples/second over I2C and can be configured as four single-ended input channels. As a micro-controller (MC) board, an Arduino UNO board was used, which sent the data registered by the bioradar via Serial Port to the personal computer (PC) for further off-line analysis.The maximum power density radiated by the radar is less than 3 Experiments were conducted to determine whether the usage of a bioradar with two spaced transceivers increases the accuracy of detecting mental and physical stress in humans as compared to a single transceiver bioradar and to evaluate the corresponding accuracy gain. Using spaced transceivers should allow the observation of a biological object from different angles, which results in different amplitude levels of a received signal. Moreover, such architecture allows separating the vital signs patterns of two different, simultaneously observed humans if needed, which is described in .During the experiment, a subject sat in front of the bioradar at a distance of 0.5–1.0 m from the antennas. The distance to the subject varied depending on the individual anthropomorphic features of the subject and the way he/she sat in the chair during the experiment. Each transceiver was oriented to the surface of the examinee’s chest, and the distance between centers of the transceiver antennas was 0.3 m. The scheme of the experiment is given in Experiments were carried out at BMSTU from March to April 2018. For the experiments involving human participants, an ethical approval was obtained on 1 March 2018 from the ethics committee of BMSTU. The test population of 35 young healthy adults consisted of 14 males and 21 females in the age group of 19–22 years. All subjects gave their oral as well as written informed consent prior to the start of the experiments. In addition, each volunteer provided information about his/her individual fitness state, which may affect an organism individual reaction to stress. Four examinees turned out to be athletes.In the previous work , we founCalm breathing test: During this test, an examinee was asked to sit relaxed and breathe normally. If the subject was in a state of psycho-emotional agitation, then vital signs estimation should be performed only after the respiratory and heart rates dropped stationary levels, which corresponded to the calm state of the examinee. It took, in general, between 1 and 2 min for vital signs to stabilize after the beginning of the experiment as shown in . That isMental workload test: The volunteer was asked to perform a mental arithmetic task, which was more complex that the one from our previous papers dealing with mental stress monitoring ,28. The Exercise tolerance test: Each volunteer was asked to perform some physical exercises (30 bobs or plank exercise for 1 min). After that, the examinee’s vital signs were registered by a bioradar for 3 min.For each volunteer, experiments of three of the following types were carried out.The bioradar signal processing algorithm used in the present work was designed utilizing Matlab2018a. It consisted of pre-processing and classification algorithms. The former is required for accurate extraction of the features that are used by the latter for detection of stress.The scheme summarizing the steps of the signal pre-processing algorithm is depicted in The first stage consists of the baseline trend and movement algorithm suppression. These tasks are performed utilizing a highpass Butterworth filter with a cut-off frequency of 0.05 Hz for baseline trend filtering, and the algorithm proposed in  for moveThe second stage deals with the selection quadrature for further analysis over the I and Q channels for two transceivers. As is known, in realistic conditions, phase demodulation of two quadratures received by the radar does not always provide good results due to the clutter caused by reflections from surrounding objects and the walls of the room where the examination takes place. Thus, in the present work, we did not use phase demodulation. Instead, we picked one quadrature with a higher peak-to-peak variation for each transceiver. Selected quadratures were used for further analysis.After that, respiration and heartbeat patterns were extracted from chosen quadratures by sixth-order bandpass Butterworth filters with bandwidth  Hz and  Hz, respectively.After filtration, peaks and troughs were detected in extracted respiration and heartbeat patterns by a search of local maximums and minimums using the function findpeak from Signal Processing Toolbox in Matlab. Peaks were detected as turning points in the signal with the minimum distance of 0.5 s and 1.5 s for heartbeat and respiratory patterns, respectively. In Time and frequency domain features of respiration and heartbeat patterns filtered from the bioradar signal were extracted for further classification.Time Features: The number of positive peaks was computed in a time window of 30 s and 10 s to estimate respiration and heartbeat rates, respectively. For these variables, the average, median, Inter Quartile Range (IQR), median-IQR rate, variance and skewness were computed. In addition, we estimated the same parameters for respiration circles , exhaling (time between peaks and troughs) and inhaling (time between troughs and peaks) periods. For more details, see Frequency Features: The average respiration and heartbeat frequencies were detected by frequency spectrum analysis. A standard Matlab function providing Discrete Fourier Transform was used for this purpose. Estimation of respiratory and heartbeat frequencies was done in a time window of 30 s and 10 s, respectively, by detecting a global maximum in a frequency spectrum in corresponding filtered signals. Such widths of time windows allow considering the analyzed fragments as being quasi-stationary.To discriminate between the calm breathing test and workload of different types , we used a support vector machine (SVM) classifier with a linear kernel realized in MATLAB. We chose this classifier because it showed the best performance using the cross-validation k-folds technique with The classifier was trained to distinguish between the calm state of the examinee and his/her state under mental or physical stress. Firstly, training was carried out using features extracted from data recorded by transceivers No. 1 and 2 (FThe classifiers’ relatively low accuracies (less than 80% ) were caused by nine ”outliers”; persons whose reactions to the mental stress was completely different from the other 26 subjects. Their cardiorespiratory system reacted by increasing frequencies of respiration and heartbeat.Four outliers were experienced swimmers, which is why their cardiorespiratory system responded to the mental workload by increasing amplitudes of respiratory and heart muscles contractions, while the frequencies of these processes remained mostly unchanged, which is typical for trained persons.Five other outliers had tachypnea. Their respiration rate during the calm breathing test was higher than 0.5 Hz, which is known to be too high for normal calm breathing. These examinees’ respiration and heartbeat systems react to the mental workload by decreasing the analyzed vital signs frequencies. Moreover, eight out of nine outliers had BMI > 25, which may cause less accurate detection of respiration and heartbeat patterns, and thus influence the classifier accuracy.The re-trained classifier for experimental dataset without nine outliers showed much better performance  than thePsychophysiological state monitoring provides a promising way for detecting stress and accurate assessment of wellbeing. The major advantage of the proposed technique, compared to other stress detection methods, is its unobtrusive nature that does not require any direct contact between the device and the person. The technique was tested in laboratory conditions with the participation of 35 young, healthy volunteers who were asked to perform arithmetic and physical workload tests that imitated different types of stressors. The information about variations of vital signs registered by a bioradar with two transceivers was used to detect mental or physical workload. The usage of two transceivers provides the benefit of observing a subject from different angles, which results in increasing classification accuracy as compared to using a bioradar with a single transceiver. A drawback of the proposed approach might be given by the increasing complexity of the device architecture.The analysis of the experimental results showed that the physiological responses to mental and physical workload differ for trained and untrained persons as well as for persons with tachypnea. This individual specificity of physiological responses to mental and physical workload makes the creation of a universal stress detector suitable for people with different level of stress tolerance a challenging task. One of the possible solutions of this issue may be training different classifiers for athletes and non-athletes without tachypnea. In the present paper, using such an approach allows increasing accuracy for classification of the calm state/mental workload from 78% to 89% as well as increasing AUC values .The achieved results should be accepted with caution because the experimental data used for the classifier training are only for young, practically healthy examinees. The relatively low number of volunteers who were declared to be athletes or having tachypnea does not allow the training of the classifier for these groups; however, in the future, we are planning to enrich the experimental dataset and add heuristics to make the classifier consider individual information of the person , which should increase the accuracy of psychophysiological monitoring.The work might contribute to the development of a noncontact system for evaluating individual reactions of a user to mental stress factors in everyday life.In future work, it is planned to extend the research to the evaluation of different stress levels using standard stress-inducing procedures. This activity will be carried out in cooperation with psychologists and medical researchers from Lomonosov Moscow State University .


One of the main obstacles for the implementation of deep convolutional neural networks (DCNNs) in the clinical pathology workflow is their low capability to overcome variability in slide preparation and scanner configuration, that leads to changes in tissue appearance. Some of these variations may not be not included in the training data, which means that the models have a risk to not generalize well. Addressing such variations and evaluating them in reproducible scenarios allows understanding of when the models generalize better, which is crucial for performance improvements and better DCNN models. Staining normalization techniques (often based on color deconvolution and deep learning) and color augmentation approaches have shown improvements in the generalization of the classification tasks for several tissue types. Domain-invariant training of DCNN's is also a promising technique to address the problem of training a single model for different domains, since it includes the source domain information to guide the training toward domain-invariant features, achieving state-of-the-art results in classification tasks. In this article, deep domain adaptation in convolutional networks (DANN) is applied to computational pathology and compared with widely used staining normalization and color augmentation methods in two challenging classification tasks. The classification tasks rely on two openly accessible datasets, targeting Gleason grading in prostate cancer, and mitosis classification in breast tissue. The benchmark of the different techniques and their combination in two DCNN architectures allows us to assess the generalization abilities and advantages of each method in the considered classification tasks. The code for reproducing our experiments and preprocessing the data is publicly available Since its start, one of the main goals of computational pathology (CP) is to find precise and reproducible methods to quantify the content of tissue slides and the relationships of this with the disease stage and patient outcome  and the cytoplasm with a light pink one (Eosin). One of the most important factors preventing the application of machine learning methods to clinical practice is related to the heterogeneity of Hematoxylin and Eosin (H&E) images due to tissue preparation and several parameters involved in the tissue preparation and digital scanning process . Several image processing and machine learning techniques reported in the literature deal with color heterogeneity improving classification and segmentation performance for various tissue types  result in specific color characteristics in the resulting digital image, for example, varying in the image sensor and also the stitching techniques. These parameters are also a part of the digital pathology pipeline on which pathologists have limited control. In the work of Leo et al.  the authImage processing and machine learning approaches that deal with color heterogeneity show generalization improvements in classification and segmentation performance  space, or deconvolving the Hematoxylin-Eosin-DAB (HED) channels and slightly modifying them . We thoroughly evaluate the adversarial neural network training approach first proposed by Lafarge et al.  to learnFor comparing the generalization capabilities of the approaches, we used two highly heterogeneous data sets that target classification tasks. The data sets account for the staining variability by including images with differing preparation parameters from several centers. Such parameters result in strong differences in the visual characteristics of the images.Most frequently, for the evaluation of algorithms, subsets of images from the same center are used as a training set and then validation and testing is done with images of different patients but with similar image preparation characteristics from the same center.In our evaluation, we extend the classical evaluation by separating internal test data: different patients with similar preparation parameters, from external test data: different patients and preparation characteristics. Such an evaluation provide us with a better approximation of the performance of the algorithms on images acquired from different centers, i.e., the generalizability of the networks to such changes.The first dataset is the Tumor Proliferation Assessment Challenge (TUPAC), which is built to evaluate algorithmic performance for mitotic figure detection in breast cancer tissue . It contains image patches from diagnostic slides of the cancer genome atlas prostate adenocarcinoma dataset (TCGA-PRAD) and also from the manually annotated prostate tissue microarray images used in the study of Arvaniti et al. , we refeSince in the TCGA-PRAD dataset there are no Gleason pattern annotations but only the global labels of the two most prominent Gleason patterns, we selected only those diagnostic images with the same primary and secondary Gleason patterns, i.e., GS = 3 + 3 and GS = 4 + 4. With this consideration, we are more likely to extract patches from relevant regions. Furthermore, we employ the heuristic first presented by Rousson et al. , to builh we selected a fixed amount of random patches in the WSI from positive locations in the thresholded image. Example locations of extracted patches from two WSIs of the train and internal test sets are illustrated in By fixing a threshold (0.65) on the resulting heatmap In contrast to the TUPAC dataset, in this case, we have more than 3 centers, the total number of source centers for the TCGA-TMAZ dataset is 34, which makes it challenging to train the algorithms with a limited amount of images for each center. The total number of images per partition is reported in We do not have a larger public data set with information on the different centers, which is a current challenge for evaluation of the generalization of CP algorithms. In the literature there are interesting approaches to solve CP classification tasks that use more than 100 million annotated patches as in Nagpal et al. . HoweverSince all the pixels of the digital H&E images are represented in RGB space, ideally each pixel should contain a composition of the color representation of Hematoxylin, Eosin, and background. Images acquired from the same center and using the same preparation parameters should share the stain absorbance coefficients, which can be written as the linear transformation (omitting background that should be close to 255 for the three channels):S and quantify the absorbed light C by the tissue when it was scanned, which is the value in the H&E space of each pixel. The Beer-Lambert law provides a way to estimate them in the optical density space, given the original pixel content for the c-channel Ic:Where the first-row vector corresponds to the RGB components of hematoxylin and the second one to the components of Eosin. In staining normalization methods, the aim is to estimate the individual staining absorbance coefficients of the images c ranges in the RGB channels, S ∈ 3×2 is the matrix of absorbance coefficients, C ∈ 2 is the vector of the two staining concentration coefficients and I0 is the background value. The widely used method of Macenko et al.  image. An optional step is to preprocess the image with brightness standardization making the obtained coefficients less dependent on the brightness values. A schematic view of the normalization approach, used in our experiments, is displayed in Where o et al.  providesIc (or estimated staining concentrations in the H&E space) of the original image by a random small constant ac that will scale the original intensity value and then add a second constant bc that shifts the color toward higher/lower intensity values:One common strategy used to create new images with color variations consists of multiplying each of the color channels ac and bc are drawn determines how much variation is allowed in the generation of the new images. Examples of color augmented patches from the TUPAC dataset using this strategy in the RGB space are shown in The range from where the random constants While staining normalization methods aim at homogenizing the appearance of the images, they might fall short at inter-center generalization because of a non-optimal normalization or overfitting in the training set due to the small amount of variation between the samples. Recent studies have shown how by creating images for training with an expansion of the training set with duplicate images from a broader color range have a significant impact at generalization in the inter-center evaluation of machine learning methods. Particularly with deep learning architectures, studies have shown how color augmentation techniques usually outperform staining normalization methods . It is an active research area in machine learning where many novel frameworks have been proposed  and (6) optimize the parameters for the task and domain loss functions, respectively, as in a classical multi-output DCNN scenario. The loss for the task classifier is minimized while the loss for the domain classifier is maximized. This is done in Equation (4) that is optimized in an adversarial manner by going in the opposite gradient direction that minimizes task loss and in the positive direction of the gradient for the domain features, maximizing the domain classifier loss.A simplified architecture scheme is shown in The main characteristic of DANN models is that they do not require external models to perform staining normalization or color augmentation but are the task labels and the indicators from the scanners or domains that are used to drive the training process and solve the task without depending explicitly on such models. This feature is also a potential drawback if one is interested in a staining quantification scenario, because DANN models do not output an explicit normalized image but the normalization is done in the feature space instead. This group samples from the same class regardless of the domain from where they were generated. Our experiments are inspired by the work of Lafarge et al.  where thIt was noted by Engilberge et al.  that in We designed a baseline DCNN model for both datasets. For the TUPAC dataset, we designed a relatively compact DCNN architecture due to the small input size of 96 × 96 × 3 patches. The architecture is composed of two convolutional layers followed by two blocks of convolution, batch normalization, and max-pooling. Then, a dropout layer is included to regularize the model followed by another block of convolution, batch normalization, and max-pooling. At the end of the network, two branches of dense layers are connected to the mitosis and domain class predictions. For the domain branch, the gradient is reversed in order to optimize (Equation 4).For the TCGA-TMAZ dataset, we used as base model the MobilNet architecture used in the experiments by Arvaniti et al. . The lasWe implemented the DCNN architectures and performed model training using the Keras deep learning framework with the Tensorflow backend. For the staining normalization method, we used the public implementation of the Macenko method from the StainTools libraryA warmup of 100 batch iterations for the task branch (λ = 0) in the DANN model was observed to lead to a more stable training as compared to starting with random weights for both branches. We provide the code of our experiments for further implementation detailsFour DCNN models were trained for each experiment combination, to minimize performance variations in the test sets due to the random-weight initializations of the DCNN models. Average and standard deviation in performance is the final result reported. In total 56 DCNN models were trained.The classification results are shown in In the TUPAC dataset experiments, color augmentation shows a good performance on the internal test set, while the performance on the external test set is slightly above the baseline . This reFor this dataset the best results involve color augmentation and DANN. DANN alone obtained the best AUC performance on the internal test set and DANN combined with color augmentation obtained the best AUC performance for the external test. Staining normalization and color augmentation together obtained the best F1 scores, both for the internal and external datasets.p-value of 0. In the external test set, the difference was significant (p < 0.05) in two out of four runs: p < 1.7232 * 10−7, 0.0723, 0.1808, and 1.8721 * 10−9, respectively.Comparing the decisions of the best vs. second-best performing methods using the Wilcoxon signed-rank test yield the following results: For the TUPAC dataset, the difference between the color augmentation and DANN, were compared only for the external dataset, since the number of patches in the internal test set always led to a p < 0.05) in three out of four runs for the internal test dataset , and significant in the four runs for the external test dataset .For the TCGA-TMA dataset, the difference between the combination of color augmentation and staining normalization vs. the combination of DANN and color augmentation was significant  can deal with the problem of having a limited set of centers for training deep learning models.In the TCGA-TMAZ dataset, the performance gain of DANN+CA with respect to the baseline is considerable and shows how naively training DCNN models without any strategy to overcome staining variety is suboptimal.The combination of color augmentation, staining normalization, and DANN training of DCNN models did not improve the results and, in some cases, obtained results below the baseline. Such behavior might be due to overfitting to the train centers because color augmentation over the normalized images accounts only for a limited range of variation (those in the training centers) and leading to domain adversarial training to not learn enough staining invariances. Similar behavior occurs when training DANN with stain-normalized images, for which the results are not reported here in the table, but were close and in some cases worse than the baseline.winner or combination strategy. Statistical significance tests of the results suggest that the use of color augmentation can alleviate color heterogeneity problems up to some extent and that DANN training of DCNN models alone or in combination with color augmentation can lead to even better results.To conclude, the experimental results show that staining normalization, color augmentation, and DANN methods improve DCNN generalization for classification tasks using digital pathology images. Results did not show a clear Designing deep learning experiments for computational pathology with images from different centers can provide meaningful insights about the performance of the classification algorithms in realistic scenarios (for instance by predicting class labels for data with the same pathology but scanned under different staining conditions). In future work, we devise a training of DANN with staining augmentation done in an end-to-end architecture, also evaluating DANN performance thoroughly, including the external set images in the fine-tunning of the model.https://portal.gdc.cancer.gov/projects/TCGA-PRAD) and the Replication Data for Automated Gleason grading of prostate cancer tissue microarrays via deep learning (doi: 10.7910/DVN/OCYCMP).The datasets analyzed for this study can be found in the TCGA-PRAD repository (SO, MA, VA, AK, and HM conceived the study and wrote the manuscript. SO carried out the experiments.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Neural control of standing balance has been extensively studied. However, most falls occur during walking rather than standing, and findings from standing balance research do not necessarily carry over to walking. This is primarily due to the constraints of the gait cycle: Body configuration changes dramatically over the gait cycle, necessitating different responses as this configuration changes. Notably, certain responses can only be initiated at specific points in the gait cycle, leading to onset times ranging from 350 to 600 ms, much longer than what is observed during standing (50–200 ms). Here, we investigated the neural control of upright balance during walking. Specifically, how the brain transforms sensory information related to upright balance into corrective motor responses. We used visual disturbances of 20 healthy young subjects walking in a virtual reality cave to induce the perception of a fall to the side and analyzed the muscular responses, changes in ground reaction forces and body kinematics. Our results showed changes in swing leg foot placement and stance leg ankle roll that accelerate the body in the direction opposite of the visually induced fall stimulus, consistent with previous results. Surprisingly, ankle musculature activity changed rapidly in response to the stimulus, suggesting the presence of a direct reflexive pathway from the visual system to the spinal cord, similar to the vestibulospinal pathway. We also observed systematic modulation of the ankle push-off, indicating the discovery of a previously unobserved balance mechanism. Such modulation has implications not only for balance but plays a role in modulation of step width and length as well as cadence. These results indicated a temporally-coordinated series of balance responses over the gait cycle that insures flexible control of upright balance during walking. Balancing our body while walking is an activity that humans perform seemingly effortlessly. What makes walking so critical to everyday life is that it serves as a platform for functional behavior. Navigation through complicated environments while performing functional tasks such as obstacle avoidance and object manipulation require a dynamically stable base of support. When such upright stability is compromised, the nervous system must devote cognitive resources just to maintain upright balance, severely limiting other functional behavior Horak, . MobilitFailure of balance control during walking is a public health problem of enormous importance, with fall-related injuries such as hip fracture costing the US health care system $20–30B per year , between 18 and 37 years of age (22.8 ± 4.1), weighing 75.2 ± 17.9 kg, volunteered for this study. Subjects provided informed verbal and written consent to participate. Subjects with self-reported history of neurological disorder or surgical procedures involving the legs, spine or head were excluded. Experiments were performed at Temple University and the design was approved by the Temple University Institutional Review Board.s−2 for 600 ms in a randomized direction. The resulting rotation of 10.8° was then held constant for 2,000 ms, before being reset to neutral rotation with uniform speed over 1,000 ms. After resetting to neutral rotation, a randomized interval of 10–13 steps elapsed before the next stimulus was triggered. Heelstrikes were defined as downward threshold crossings of the vertical heel-marker position. The threshold was set to the vertical heel-marker position of each foot during quiet standing, plus 3 mm.Subjects walked on a split-belt treadmill in a virtual environment projected onto a curved dome that covered almost their entire field of vision . The treadmill was self-paced, using a nonlinear PD-controller in Labview  to keep the markers on the posterior superior iliac spine on the mid-line of the treadmill. The same speed command was sent to each belt of the treadmill. The virtual environment consisted of a tiled marble floor with floating cubes randomly distributed in a volume 0–10 m above the floor, 2–17 m to each side from the midline, and infinitely into the distance, forming a 4 m wide corridor for the subjects to walk through . Forces and moments were transformed into a common coordinate frame and then used to calculate the whole-body CoP  and 38 degrees of freedom (DoF). We estimated the hip joint centers based pelvis landmarks following Tylkowski  and perturbation direction (toward/away from triggering foot), and a possible interaction between these two. As random effects, we used individual intercepts for subjects.To test our hypotheses about whether humans use the foot placement and the lateral ankle mechanisms to control balance in response to visually induced fall sensations during locomotion, we used R  foot placement is defined as the medial-lateral position of the swing leg relative to the stance leg at heelstrike, and foot placement change is the difference between a stimulus foot placement and the average of the control foot placements. (ii) stimulus-induced foot placement change is defined as the difference between the measured foot placement value and the foot placement value predicted based on the position and velocity of the CoM at mid-stance using the linear model (see above). (iii) hip abduction change is defined as the abduction/adduction angle of the swing leg hip at the first post-stimulus heelstrike, with the average over the control steps subtracted. (iv) integrated gluteus medius EMG change is defined as the gluteus medius EMG of the swing leg, with the average over the control steps subtracted, then integrated over the first post-stimulus swing phase.To test our hypothesis about the foot placement mechanism, we analyzed the following four variables related to the swing leg: (v) integrated relative CoP change is defined as the medial-lateral position of the CoP relative to the CoM, with the average over the control steps subtracted, then integrated over the first post-stimulus swing phase. (vi) ankle eversion change is defined as the eversion/inversion angle of the stance leg ankle at the first post-stimulus heelstrike, with the average over the control steps subtracted. (vii) integrated peroneus longus EMG change is defined as the peroneus longus EMG of the stance leg, with the average over the control steps subtracted, then integrated over the first post-stimulus swing phase.To test our hypothesis about the lateral ankle roll mechanism, we analyzed the following three variables related to the stance leg: foot placement change, stimulus-induced foot placement change,and integrated relative CoP change were inverted for perturbation direction away and the other, body-relative outcome variables for perturbation direction toward. All outcome variables used in the statistical analysis pertained to step ONE after the stimulus. We confirmed the assumptions of normality and homoscedasticity by visual inspection of the residual plots.To allow testing whether the magnitude of the motor response depends upon the direction of the stimulus relative to the stance leg, we inverted the outcome variables depending on perturbation direction. The spatial outcome variables of foot trigger foot and perturbation direction, using Satterthwaite's method  had no statistically significant effect on any outcome measure, but the effect of perturbation direction (toward/away from the trigger foot) was statistically significant for two of the seven outcome measures  leg, which is the new stance leg in step TWO, is increased during double stance for both stimulus directions, then decreases again during single stance for toward stimuli, and further increases for away-stimuli  leg shows an increase of activity during its stance period of step TWO for both stimulus directions, which tends to be larger for stimulus toward the triggering leg (see Figure The in-/external rotation angles of the swing leg hip and stance leg knee showed a similar pattern to the first step as well, with internal rotation of the stance leg knee and external rotation of the swing leg hip for  Figures . The anktoward stimuli and decreasing for away stimuli, as in the previous step (Figures Changes in step time and length were systematic depending on stimulus for step TWO, with both variables increasing for  Figures . While tWe studied the role of optical flow for the control of balance during walking by manipulating a virtual reality environment to give visual sensations that are indistinguishable from the optical flow experienced during an actual lateral fall. Subjects responded by accelerating their body in the opposite direction, as would be the appropriate response if the perceived fall was real. We observed the changes in muscle activation, body kinematics and ground reaction force to understand how the CNS reacts to a perceived threat to balance. Using model-based prediction allowed us to estimate the continuously ongoing control that generates corrective motor action based on the current mechanical state of the body as perceived by multiple sensory modalities, and use this estimate to isolate the motor response to the experimentally induced sensory stimulus, providing a direct estimate of the feedback loop mapping visual information to balance-related motor output. Furthermore, we studied the detailed body kinematics to determine whether humans use other, previously unknown mechanisms of balance control during walking.peroneus longus muscle (see Figure The first response to the visual stimulus was the lateral ankle strategy. Early in single stance, the center of pressure under the stance leg begins to shift in the direction of the perceived fall (see Figure Toward the end of the first step, the swing foot shifts laterally in the direction of the perceived fall. Contrary to our expectations and how this mechanism is commonly explained in the literature Kuo, , we did We also observed a previously unobserved mechanism for the control of medial-lateral balance during walking. Toward the end of the first step, the stance foot ankle plantar-/dorsiflexion angle was systematically modulated depending on the visual stimulus. When subjects perceived themselves falling toward the stance foot, they increased plantarflexion in their trailing leg, and increased dorsiflexion for perceived falls away from the stance foot, generating a change in push-off force. Assuming that the direction of this push-off force difference is toward the center of mass, its effect would accelerate the body in a direction that is mostly anterior-posterior, but also partly medial-lateral. This coupling between directions is unique to the push-off mechanism, since for both the ankle strategy and the foot placement strategy, these two directions are in principle completely decoupled (Collins and Kuo, not in the stimulus-induced foot placement change, which has the estimated component depending on the mechanical state of the body already removed.Most of the effects we discussed are immediate responses to the visual stimulus occurring in the first post-stimulus step. Our experimental paradigm did not completely open up the control loop, however, but only altered it temporarily, which means that the changes resulting from the motor response generated by the neural controller are also sensed and fed back into the system, which leads to second-order changes. Concretely, the visual stimulus conveys the sensation of falling to one side, and the neural controller reacts invoking different mechanisms that accelerate the body to the other side. Since the originally perceived fall was illusory, this acceleration now generates an actual fall, in the form of lateral movement of the CoM (see Figure Another secondary effect is the general widening of the gait observed in steps THREE and FOUR. During these steps, the direction of the foot placement changes is in the direction toward the swing foot, meaning the steps are wider. This is in contrast to the first two steps, where the direction of the foot placement is determined by the visual stimulus. We hypothesize that this is a strategy to increase the general stability of the walking body. While the three mechanisms of balance control described above, lateral ankle strategy, foot placement shift and push-off strategy, are control laws that map sensory information about movement of the body in space to directed motor responses, this step widening is a non-specific response that increases the passive stability characteristics of the walking body. This general stability increase would come at the cost of increased metabolic energy consumption (Donelan et al., Our data provided an understanding of how the central nervous system controls the upright body during walking at an unprecedented level of detail. While it was known that vision does play a role in balance control during walking (Salinas et al., It is worth to point out that estimating onset times of these very early balance responses during walking is inherently difficult, because the movement patterns of a walking human body are highly variable. The neural controller constantly monitors the state of this system and makes adjustements. Most of these adjustments are small, but over time they add up to large variability in the state space describing the whole body. The initial, small balance responses are hidden within this host of other small balance responses occurring naturally. We provided a rough estimate of onset time here using a linear fit to the population reponse, following (Oostwoud Wijdenes et al., tibialis anterior and gastrocnemius medialis muscles. This was puzzling, since these two muscles act mainly in the sagittal plane, but the push-off modulation provides a clear explanation for this phenomenon. Modulating the push-off during double stance to control medial-lateral balance would adversely affect balance in the anterior-posterior direction, requiring additional control action in this direction. The small but systematic modulation of step length that we observed in response to the stimulus during the later steps might be such a secondary control action.Our second major finding is the discovery of the push-off response. Previous research in balance control during locomotion has mostly focused on the foot placement mechanism (Townsend, In conclusion, the results presented here establish a baseline of how healthy humans maintain their balance during locomotion. This is a necessary and important step in understanding how the control system breaks down in different situations. The presented results will allow the formulation of specific questions regarding the deterioration of these three balance mechanisms with advancing age and neural disorders affecting movement, such as Parkinson's disease or cerebral palsy.10.5061/dryad.bn960cq.The datasets generated for this study can be found at datadryad.org, doi: HR, TF, and JJ designed the experiment. HR, TF, and ET performed the experiment and analyzed the data. HR, TF, and JJ wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To release the strong dependence of the conventional inertial navigation mechanization on the a priori low-cost inertial measurement unit (IMU) error model, this research applies an unconventional multi-sensor integration strategy to integrate multiple low-cost IMUs and a global positioning system (GPS) for mass-market automotive applications. The unconventional integration strategy utilizes a basic three-dimensional (3D) kinematic trajectory model as the system model to directly estimate navigational parameters, and it allows the measurements from all of the sensors independently participating in measurement updates. However, the less complex kinematic model cannot realize smooth transitions between different motion statuses for the road vehicle with acceleration maneuvers. In this manuscript, we establish a more practical 3D kinematic trajectory model based on a “current” statistical Singer acceleration model to realize smooth transitions for the maneuvering vehicle. In addition, taking advantage of the unconventional strategy, we individually model the systematic errors of each IMU and the measurements of all sensors, in contrast to most existing approaches that adopt the common-mode errors for different sensors of the same design. A real dataset involving a GPS and multiple IMUs is processed to validate the success of the proposed algorithm model under the unconventional integration strategy. With the popularity of intelligent vehicles, positioning and navigation technology is particularly important, because the foremost requirement for all of these safety and assistance systems is an accurate knowledge of the vehicular states  at all times . High-prThe global positioning system (GPS) is by far the most widely used high-precision localization method. However, it is further constrained by the non-availability of location during signal outage in dense urban areas ,11. AddiThe optimal filter design is the key to combining multiple sensors for accuracy improvement, and the adoption of the Kalman filter (KF) is widespread. Conventionally, the Kalman filter based on integration mechanization usually adopts the inertial navigation mechanization to estimate the error states and sensor systematic errors through error measurements on the basis of the aiding sensors ,13,14. IThis research applies the unconventional integration strategy for multiple low-cost IMUs and a GPS-integrated system. There are some characteristics and some corresponding defects from previous 3D kinematic trajectory models. Inevitably, a road vehicle is certain to experience severe or light irregular changes of velocity from time to time, and the changing velocity indicates the presence of acceleration. However, the jerk vector in the kinematic trajectory model is usually treated as process noise. In most cases, the a priori knowledge of vehicle maneuvers is very little. Because the maneuvering process is controlled by human forces, it is difficult to be accurately described with mathematical formulas and can only be artificially approximated under various assumptions. Therefore, a less complex kinematic model cannot realize smooth transitions between different motion statuses for the road vehicle, which leads to an intolerable positioning and navigation solution. One should avoid employing the most complex kinematic model as the core system model throughout . An apprWe found that the key problem is how to model the unknown target accelerations. Some mature maneuvering target models were proposed on this issue. So far, the most influential maneuvering target model is the Singer model proposed by Singer in 1970, which attempts to model large-scale maneuvers by assuming a time-correlated input process and incorporating the statistics into the subsequent filter design . As a glIn addition, the significant approach to how the low-cost IMU arrays are fused in this research involves enabling the direct use of measurements for individual IMUs and separately modeling their systematic errors in Kalman filtering, in contrast to most existing approaches working under the supposition of “the common-mode errors of different sensors of the same design” , which mAs a matter of fact, given the integration strategy and the system model, various nonlinear filtering candidates , unscented Kalman filter (UKF), particle filter (PF), etc.) are available. Nevertheless, this manuscript uses the EKF instead of other nonlinear filtering algorithms because (1) the research does not focus on the filtering techniques, (2) the system model does not present strong nonlinearity, and (3) the EKF algorithm is simple and easy to use in practical engineering.A more practical system motion model is described in The general system motion model contains three parts: (1) the 3D kinematic trajectory , (2) theThe instantaneous motion of a rigid body can be described using kinematics without considering the forces that cause different types of motion. For a mechanical system, suitable coordinate systems are necessary to give a mathematical expression of the position, velocity, and acceleration. Two reference frames move relative to each other, as shown in In In components, the motion of the moving point at a time instant With respect to a rigid body in motion, the basic trajectory parameters are considered in the local navigation frame  model adopts a non-zero mean and modified Rayleigh distribution to characterize the maneuvering acceleration. Specifically, the modified Rayleigh distribution is used to describe the “current” probability density of maneuvering acceleration, and the mean value is the “current” acceleration prediction. The random acceleration still conforms to the first-order time-correlated process. According to the Singer model and the CS model, the body acceleration can be given asThe differential equation of body acceleration is acquired by consolidating Equation (11), as shown below.Typically, Euler angles  of For a normally running vehicle with a smooth steering within a small time interval, three components of of ωnbzb ,27,28.In a general way, the state and measurement vector ought to be arranged for the system Kalman filter. In this research, the whole value state which describes the kinetic characteristic of the vehicle is one part of the state vector. The systematic errors of each IMU, e.g., the biases, scale factor errors, and so forth, are also modeled individually in the Kalman filter, instead of assuming “the common-mode errors of different sensors of the same design”. Therefore, the systematic error is the other part. As for the measurement vector, the raw observables from the GPS and multiple IMUs are considered. In this manuscript, the state vector of the constructed multi-sensor integration Kalman filter contains 51 components as follows:Firstly, the local coordinate increment Next, Finally, we obtain the position vector On the basis of the differential model in Furthermore, there is a particular discussion for the discretization of the body acceleration model. The discrete state of Equation (13) can be derived asThe discrete acceleration is obtained from Equation (18) as follows:Since the CS model assumes that the maneuvering acceleration at an arbitrary time obeys the modified Rayleigh distribution, the variance of body acceleration can be obtained as follows:As can be seen from Equations (13) and (18), the position vector The discrete system equations of the attitude and body angular rate are directly given as follows:The discrete system equations of the systematic errors of three different low-cost IMUs are individually modeled as follows:Generally, the IMU raw outputs include specific forces from three orthogonal accelerometers and angular rates from three orthogonal gyroscopes. We should derive three groups of measurement equations because there are three IMUs in the integration system. The measurement equations for low-cost IMUs could be simplified depending on specific needs ,19. ConsIn this study, not only do the raw observables of IMUs participate in measurement updates, but so do those of the GPS. The GPS, as another sensor distinct from the IMU but with equal status in the system, offers two types of observables (carrier phase and pseudo-range). However, only the pseudo-range is adopted to complete the specific navigation task. The pseudo-range observation equation is generally given asOn the basis of the system model proposed above, the KF is structured straightforwardly. The state Equations (22)–(32) and measurement Equations (33)–(39) are separately generalized by the discrete nonlinear system and measurement models as shown below.i-th system equation; j-th component of state vector As is well known, the Kalman filter performs the prediction of the state vector through two information update processes: time update and measurement update. Specifically, the one-step prediction and the variance propagation of the state vector proceed from epoch i-th measurement equation; j-th component of state vector Subsequently, the measurement update is performed as follows when the measurements at epoch The proposed algorithm model under the unconventional integration strategy was adopted to process the navigation data from multiple low-cost IMUs and a GPS-integrated system on a land vehicle. Several road tests were performed by our ground-based vehicle navigation system with a Harxon Mini Survey Antenna GPS500 and three IMUs , as shown in In order to obtain the ground truth as the reference, we equipped a high-grade Fiber-Optic Gyroscope (FOG) inertial navigation system (INS)  and the unconventional counterpart (right side) are shown in The acceleration vector and angular rate vector are only regarded as inputs of the mechanization in the conventional error state-based Kalman filter, while the realistic measurements of the IMU directly participate in the measurement update process in the unconventional Kalman filter. In principle, the Kalman filter is equivalent to a sequential least square with a time-variant state vector and the process noises . That isGenerally speaking, the most significant feature of the unconventional integration mechanism lies in the improvement of the overall measurement redundancy of the system through the system model based on the 3D kinematic trajectory model.As discussed above, it is expected that the accuracy of the navigation solutions shall be improved by using the rigorous trajectory model as the system model in the unconventional KF. As the components of the state vector, the acceleration and angular rate vectors in the body frame (As can be seen from The comparison results from Apparently, the navigation parameters during the 8-min vehicle motion were estimated within acceptable ranges by adopting the proposed algorithm model under the unconventional integration strategy. Furthermore, the reason why the 3D position error using the basic trajectory model did not diverge is that the output of the central accelerometer with real-time calibration was utilized to regulate the acceleration estimation error when significant acceleration maneuvers occurred.Since the systematic errors of these IMU arrays, i.e., biases and scale factor errors of gyroscopes and accelerometers, are individually modeled in Kalman filtering, the systematic error estimations of each IMU can be obtained separately, as illustrated in As can be seen from This research fused the information from one GPS receiver and three low-cost IMUs by applying an unconventional multi-sensor integration strategy. The enhanced and improved parts involved establishing a more practical 3D kinematic trajectory model based on the “current” statistical Singer acceleration model as the core of the system model, and individually modeling the measurements and systematic errors of these IMU arrays in Kalman filtering. The processing results of the experimental data demonstrated the success of the proposed algorithm model under the unconventional integration strategy with satisfactory solution performance and reliability. Future work will involve developing a more precise fusion algorithm by using the carrier phase information from the GPS.