The ability of new neurons to promote repair of brain circuitry depends on their capacity to re-establish afferent and efferent connections with the host. In this review article, we give an overview of past and current efforts to restore damaged connectivity in the adult mammalian brain using implants of fetal neuroblasts or stem cell-derived neuronal precursors, with a focus on strategies aimed to repair damaged basal ganglia circuitry induced by lesions that mimic the pathology seen in humans affected by Parkinson’s or Huntington’s disease. Early work performed in rodents showed that neuroblasts obtained from striatal primordia or fetal ventral mesencephalon can become anatomically and functionally integrated into lesioned striatal and nigral circuitry, establish afferent and efferent connections with the lesioned host, and reverse the lesion-induced behavioral impairments. Recent progress in the generation of striatal and nigral progenitors from pluripotent stem cells have provided compelling evidence that they can survive and mature in the lesioned brain and re-establish afferent and efferent axonal connectivity with a remarkable degree of specificity. The studies of cell-based circuitry repair are now entering a new phase. The introduction of genetic and virus-based techniques for brain connectomics has opened entirely new possibilities for studies of graft-host integration and connectivity, and the access to more refined experimental techniques, such as chemo- and optogenetics, has provided new powerful tools to study the capacity of grafted neurons to impact the function of the host brain. Progress in this field will help to guide the efforts to develop therapeutic strategies for cell-based repair in Huntington’s and Parkinson’s disease and other neurodegenerative conditions involving damage to basal ganglia circuitry. The idea that new cells—neurons, neuroblasts, or immature neural precursors—can be used to repair damaged neural circuitry in the brain goes back to the late 1970s. These early studies were performed in rodents and made use of neuroblasts obtained from the developing CNS. The experiments pursued in our lab here in Lund were focused on the use of tissue dissected from the developing ventral mesencephalon or fetal striatal primordia  to replace the lost nigral or striatal neurons and restore axonal connectivity in rats with damage to the basal ganglia circuitry, similar to that seen in patients with Parkinson’s or Huntington’s disease.Neural transplantation is a classic approach in cold-blooded vertebrates, salamanders, fish, and frogs, that goes back to the early decades of the last century. Similar studies in mammals were initially unsuccessful due to shortcomings of the methods used at the time, and it was not until the 1970s, with the introduction of histochemical methods and modern tract-tracing techniques, that the effective tools for the study of survival and growth of neural tissue became available  or quinolinic acid (QUIN), and damage to the nigrostriatal dopamine (DA) system using the 6-hydroxydopamine (6-OHDA) neurotoxin—were also attractive in that they replicated some of the key pathology seen in patients with Huntington’s and Parkinson’s disease.During the last decade, the development of new genetic and viral techniques for the study of brain connectomics has opened new possibilities, and as a result, we are now entering a new phase in the study of cell-based brain repair, and at the same time, the approach using transplants of DA neurons derived from pluripotent stem cells (PSCs) is now entering the clinic in patients with Parkinson’s disease. The purpose of the present review is to summarize the progress made in this field, from the early days to the present, with a focus on the studies performed over the last four decades in our lab here in Lund, and discuss the possibilities and challenges for the development of cell-based therapies for brain circuitry repair.In rodents, striatal connectivity is organized in a way that is very well suited for the exploration of repair strategies. The striatum contains a well-defined mixture of short-axoned interneurons and long-axoned spiny projection neurons that link the striatum with three major downstream targets, the external and internal globus pallidus and the substantia nigra. As illustrated schematically in a dorsolateral part regulating habitual, automatic movements ; a dorsomedial part mediating associative and goal-directed behaviors ; and a ventromedial/nc accumbens part involved in motivational and emotional behavior  provide important modulatory control of striatal function. Importantly, the DA neurons are not themselves part of the motor execution pathway—i.e., the striatopallidal and striatonigral loops—meaning that removal of the DA input  will leave this circuitry intact, but in a dysfunctional state, resulting in a hypokinetic syndrome characterized by a difficulty to initiate and perform movements . The nigAblation of the striatal projection neurons disrupts major pathways for the execution of functions initiated at the level of the cerebral cortex and causes impairment of sensorimotor, associative, or limbic behaviors. These impairments are caused by the removal of inhibitory control of functionally related downstream targets, the substantia nigra, and the external and internal pallidum (see above). Conceived in this way, the impairments seen in striatum-lesioned animals can be viewed as a “disconnection syndrome” caused by a disruption of the cortico-striato-pallido/nigro-thalamic circuits that subserve these functions. The cell-based repair strategies pursued in this model seek to restore connectivity and function in these execution pathways.The excitotoxic lesion used in these studies causes a rapid neuron death followed by a gradual shrinkage of the striatal volume within the following weeks  are of the DARPP-32 positive medium spiny type, and the two major subtypes, the pre-proenkephalin (PPE) and pre-protachykinin (PPT) expressing neurons, which are the characteristic markers of the striatopallidal and striatonigral projection neurons, respectively, are present in similar proportions, around 50%, as in the host , and that this interaction can take place at the level of the globus pallidus where the outputs of these parallel circuits converge (see above).delayed alternation task  appears to be similar in animals receiving transplants of the whole GE  or the lThe nigrostriatal system has been in the focus of brain repair studies since the 1980s when the first exploratory intracerebral DA neuron grafting experiments were performed. It is experimentally attractive for several reasons: there are effective tools, the 6-OHDA and MPTP neurotoxins, that allow complete and selective lesions of the nigral DA projection, and there are accurate and relatively simple behavioral tests to monitor the effects of lesions and transplants. Moreover, the nigrostriatal pathway is strictly unilateral making it possible to use the non-lesioned side as an internal control. The motor impairments induced by lesions of the nigral DA system, which resemble the core motor symptoms in PD, are particularly interesting in the perspective of cell-based repair since they are due to loss of a component of the striatal circuitry that is regulatory, and thus not by itself part of the motor execution machinery. It is common to compare the role of the striatal DA input to the clutch in a car: it is needed to put the engine in gear, while the driving part of the motor is still intact. Thus, in DA lesioned animals as well as in PD patients, the cortico-striato-pallido/nigral circuitry that initiates and executes movements is intact, but its use is impaired or blocked due to the lack of the DA-mediated activating input, which regulates the threshold for the initiation of movement.This modulatory role of the nigral DA neurons suggests the possibility to reverse the DA-lesion induced motor impairments, at least to some extent, using relatively simple approaches that restore DA neurotransmission. This is supported by the fact that L-DOPA therapy works well in PD patients, showing that tonic activation of striatal DA receptors is sufficient to provide significant symptomatic improvement, at least in the early stages of the disease.The idea to use transplants of DA neurons to restore striatal DA neurotransmission goes back to the late 1970s. In these early studies, performed in 6-OHDA lesioned rats, fetal ventral mesencephalic (VM) tissue was transplanted either as a solid piece in direct contact with the striatum or as a cell suspension injected into the striatum. The grafted DA neuroblasts continued to develop in their new location and were seen to form an extensive functional axonal network in the surrounding host striatum. In their ectopic location, however, they were not in the correct position to reconstruct the entire nigrostriatal pathway and its full repertoire of afferent inputs  enzyme activity and GAD mRNA expression in the DA-denervated striatum. Similarly, the enkephalin neurons, which constitute a subset of the D2 expressing GABAergic projection neurons projecting to the globus pallidus, express increased peptide levels as well as increased proenkephalin mRNA. This is maintained over a long time indicating that enkephalin synthesis is permanently increased in the striatopallidal projection neurons after removal of the striatal DA afferents. These changes are completely reversed by intrastriatal nigral transplants, thus providing further support that the grafted nigral neurons can restore normal inhibitory control over the host striatopallidal projection neurons  the effect on substance P synthesis is partial and restricted to the area receiving a dense DA input, while the increased enkephalin synthesis is fully normalized also outside the graft-reinnervated area, indicating that DA released from the graft-derived terminal network can reach functional levels over areas that extend well beyond that covered by the outgrowing fibers  do not show the ability to reinnervate the striatum in a target-specific manner, a property that is necessary for behavioral recovery.The development of protocols for the generation of midbrain DA (mDA) neurons and striatal projection neurons from hPSCs have progressed in parallel. The first attempts to generate mDA neurons from hESCs were based on protocols developed for mouse ESCs . This is further supported by their ability to establish axonal connections with downstream striatal targets, including globus pallidus and substantia nigra, accompanied by a gradual improvement in measures of sensorimotor performance on the side opposite to the excitotoxic lesion and transplantation , after transplantation either to the striatum . The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The pollen tube in a flowering plant grows in a direction that is influenced by the mechanical properties of the stigma papillae and the organization of structures called cortical microtubules inside these cells. Related research article Riglet L, Rozier F, Kodera C, Bovio S, Sechet J, Isabelle F, Gaude T. 2020. KATANIN-dependent mechanical properties of the stigmatic cell wall mediate the pollen tube path in Arabidopsis. eLife9:e57282. doi: 10.7554/eLife.57282In a flowering plant, reproduction begins when grains of pollen stick to cells called stigma papillae that are located at the top of the pistil, which is the female part of the flower. A cell called a pollen tube then delivers the sperm cells contained in the pollen grains to the female gametes for fertilization. This is a long journey that involves the pollen tube travelling from the stigma papillae at the top of the pistil to the ovules that contain the female gametes, which are at the bottom of the pistil.So how does the plant ensure that the pollen tube – which is a single cell that grows longer over time – finds the ovules and does not get lost en route? Several molecules and nutrients secreted by the pistil direct the growth of the pollen tube . HoweverMost plant cells grow by increasing their surface area while remaining attached to neighboring cells: pollen tubes are different in that they are tip-growing cells that can grow through the walls of other cells to reach their target. When the pollen tube first enters the pistil, it remains within the cell wall of the stigma papillae ; left: cA number of studies have demonstrated how mechanical properties can influence a variety of cellular processes – including proliferation, differentiation, migration and cell signaling – in animal cells , and theArabidopsis thaliana that combine the power of microscopy, genetics, and chemical biology to provide new insights into the regulation of pollen tube growth , deep-tissue imaging and computational tools will help researchers to measure the mechanical forces operating on and in cells . The pol


Ormia ochracea’s coupling ears and harmonic nesting. First, we design a BCAVSA using several sets of cylindrical acoustic vector-sensor arrays (AVSAs), which are used as a guide to construct the constant beamwidth beamformer. Due to the mechanical coupling system of the Ormia ochracea’s two ears, the phase and amplitude differences of acoustic signals at the bilateral tympanal membranes are magnified. To obtain a virtual BCAVSA with larger interelement distances, we then extend the coupling magnified system into the BCAVSA by deriving the expression of the coupling magnified matrix for the BCAVSA and providing the selecting method of coupled parameters for fitting the underwater signal frequency. Finally, the frequency invariant beamforming method is developed to acquire the constant beamwidth pattern in the three-dimensional plane by deriving several sets of the frequency weighted coefficients for the different cylindrical AVSAs. Simulation results show that this method achieves a narrower mainlobe width compared to the original BCAVSA. This method has lower sidelobes and a narrower mainlobe width compared to the coupling magnified bi-cone pressure sensor array.In this work, we design a small-sized bi-cone acoustic vector-sensor array (BCAVSA) and propose a frequency invariant beamforming method for the BCAVSA, inspired by the  Interest in the measurement of the radiated noise has increased considerably in the last two decades, driven by a recognition of safeguarding the national marine safety. The early method used a single pressure sensor to measure the radiated noise . HoweverThe radiated noise produced by underwater targets usually consists of broadband and narrowband components. As the broadband signals carry more information of the underwater targets compared to the narrowband signals, the research concern has turned to the broadband signals. Beamforming is the most used spatial processing technique among the array signal processing techniques. However, the beamwidth of the traditional beamforming becomes narrower as the signal frequency increases. When the AVSA receives the broadband signal radiated from the volume element, except for the spindle direction of the mainlobe, the incident direction locating in the other directions will lead to the signal spectra distortion . In ordeIt is worth noting that the above-mentioned methods focus on the planar arrays, the volumetric array composed of linear arrays, or the linear AVSAs. Currently, there are few studies on the volumetric AVSA. On the other hand, the above-mentioned methods of designing frequency invariant beamformers work well under appropriate intersensor spacing. However, the frequency band of the radiated noise from underwater targets generally falls within dozens to one thousand Hertz. This indicates it is a requirement for the sensor array with half of the wavelength interelement spacing to guarantee the desired beamwidth of the beamforming, resulting in a large aperture sensor array. In practice, large aperture sensor arrays are not usually used due to the disadvantages of high cost, difficult deployment, a high requirement for the application environment, and the ancillary facilities. Compared to large aperture sensor arrays, the small-sized sensor arrays are more suitable for applying to sonar systems. Thus, this paper aims to design a frequency invariant beamformer with a narrow beamwidth and low sidelobes for a small-sized volumetric AVSA.Ormia ochracea gives us a hint to get out of this dilemma. To perpetuate the species, the female Ormia ochracea has to find a male cricket by using the cricket host’s call. The Ormia ochracea features an advantage of locating these crickets accurately by using its two-ear differences in intensity and arrival time from an incident acoustic wave. This phenomenon was surprising to the researchers as the distance between the Ormia ochracea’s two ears is only about 1.2 mm, and the wavelength of the cricket’s call is about 7 cm, which is a significant mismatch to the distance of the Ormia ochracea’s ears [Ormia ochracea creating this advantage was that the Ormia ochracea’s left and right acoustical-sensory organs were not physically separated, but connected by a mechanical coupling, which increased the two-ear time delay and amplitude difference. Recently, the mechanical system of the Ormia ochracea’s two ears has drawn sufficient attention and has been widely applied in the manufacture of microphones [Ormia ochracea’s two ears has not been studied for applications of AVSAs.The hearing system of the parasitoid fly a’s ears . Studiesa’s ears ,28 explarophones ,30,31, trophones ,33, the rophones ,35, etc.Ormia ocharcea and the harmonic nesting method. First, we design the BCAVSA using several groups of cylindrical AVSAs with different radii and heights, where the cylindrical AVSA is composed of two uniform circular AVSAs (UCAVSAs), which is used to guide the construction of the constant beamwidth beamformer. Specifically, we derive the coupling magnified matrix for the cylindrical AVSAs in order to increase the distance of the two adjacent AVSs located in the same UCAVSA, as well as that of the two UCAVSAs. Therefore, a virtual BCAVSA is obtained. Then, the idea of the harmonic nesting is extended to the BCAVSA, and the frequency invariant beamforming method for the virtual BCAVSA is developed. This method can form the frequency invariant beampattern in the three-dimensional space, and the beamwidth in the azimuth plane is invariant when the elevation angle is fixed. Owing to the coupling magnified system, the beamwidth of the beampattern based on the proposed method is narrower than that of using the original BCAVSA. Additionally, compared to the bi-cone pressure sensor array, the proposed method has lower sidelobes and a narrower width of the mainlobe.In this paper, we provide a frequency invariant beamforming method for the small-sized bi-cone AVSA (BCAVSA), which is inspired by the coupling magnification mechanism of the Ormia ocharea. In The remainder of this paper is organized as follows: In Incipiently, the wireless area proposed the bi-cone antenna array ,37,38, wstortion . Thus, tQ groups of cylindrical AVSAs. The qth cylindrical AVSA with the radius qth and qth UCAVSA on the z-axis, qth UCAVSA is constituted of N AVSs, where the mth AVS is located at x-axis, x-, y-, and z-axes, respectively. It is assumed that a planar wave impinges on the BCAVSA from qth UCAVSA at the signal frequency f can be expressed as   4) that Note that in practice the closely spaced AVSs in passive sonar systems can encounter the undesired electromagnetic coupling. In this work, we assume that the calibration for the closely spaced AVSs is accomplished beforehand, viz., the performance of the passive sonar system will not be affected, and hence we ignore the effect of the undesired electromagnetic coupling in this work.Ormia ochracea’s hearing organ is so small that its two-ear distance is only about 1.2 mm, but, magically, it can locate crickets quite accurately by using the cricket hosting’s calling (wavelength is about 7 cm). References [Ormia ochracea’s bilateral tympanal membranes are not physically separated but connected by a mechanical coupling which increases two-ears time delay and amplitude difference. This is the main reason that the Ormia ochracea can use their ears to find crickets. The mechanical coupling model of the Ormia ochracea’s hearing organ is given in [The female ferences ,28 show Ormia ochracea’s two ears, the effective mass In this system, the spring In order to solve Equation  and obta to Ref. , D1(f)=αLet Equation  can be fd between two ears is approximately equal to 1.2 mm. The incident plane wave impinges on the two ears from Ormia’s ears, Next, we analyse the performance of the response of the two ears. In , the disfound in . Taking Ormia’s ears into the small-sized BCAVSA to obtain a virtual BCAVSA with a larger aperture. Then, based on the principle of the harmonic nesting, we propose a frequency invariant beamforming for the virtual BCAVSA to form the beampattern with the constant beamwidth on the azimuth and elevation planes.In this section, we first introduce the mechanical coupling system of the qth cylindrical AVSA. Consequently, we first consider extending the magnification effect of the coupling system of the Ormia’s ears to a two-element pressure sensor array. The two pressure sensors are identical, and they have the same spring According to Equation , the steEquation , we can Ormia ochracea’s ears to the uniform circular pressure sensor array composed of N identical pressure sensors which own the same spring N circular pressure sensor array can form N coupling magnifications. According to the analysis, the coupling magnified matrix for the circular array can be expressed as follows:We next extend the magnification effect of the coupling system of the qth cylindrical AVSA composed of the qth UCAVSAs can be deduced as 10), the According to Equation , the couConsequently, we can apply the coupling magnified matrix Ormia ochracea are sensitive to the sound signal with Ormia ochracea’s two ears is approximately equal to f, the parameter qth UCAVSA, f, the parameters Ormia ochracea’s two ears, respectively.It can be seen from Equation  that theWe develop the harmonic nesting method to the virtual BCAVSA to achieve a desired frequency invariant beampattern in the azimuth and elevation planes. First, we take an easy example to understand the harmonic nesting method. We take two uniform linear arrays as the example. It is assumed that the high-frequency and the low-frequency uniform linear arrays have the same number of elements, and the interelement spacing of the high-frequency array is half of the low-frequency array. It is assumed that the desired broadband is equal to qth cylindrical AVSA of the virtual BCAVSA under the signal frequency f can be obtained by setting the weighted vector for the qth cylindrical AVSA equal to the normalized steering vector, and it can be written as:Then, we introduce the above-mentioned harmonic nesting method into the virtual BCAVSA to form the constant beamwidth beampattern. The directivity pattern of the qth cylindrical AVSA of the virtual BCAVSA, the directivity function associated with the signal frequency f can be written as:qth cylindrical AVSA can be treated as the product of three directivity functions which are respectively corresponding to the two-element pressure sensor array along the z-axis, the uniform circular sensor arrays, and a single acoustic vector sensor. The normalized directivity function of the qth cylindrical AVSA can be written as:For the itten as:P˘qPz,1To maintain a constant beamwidth of the high- and low-frequency cylindrical AVSAs under the bandwidth Firstly, to form the constant beampattern in the azimuth plane, the directivity function can be obtained by doing the following transformation, and it can be expressed as:V1We need two functions to solve the frequency weighted coefficients rding to , we can (1) when we set (2) when Due to quations  and 24)Pc,124), the olved by:|Rl,1(f)+According to Equations , 25), , , 27), a, a27), aobtained:VbQ cylindrical AVSAs, where the radii and heights of the 1st to the Qth cylindrical AVSAs are equal to qth and Q cylindrical AVSAs is shown in Although the above-described theory and method are designed for the two cylindrical AVSAs to form the constant beamwidth over the octave, this constant beamwidth beamforming method can be extended to the multiple cylindrical AVSAs to construct the frequency invariant beampattern over multiple octaves. Consider Equation ; similarEquation . Then, bquations  and 30)Q cylindrFinally, the array gain is discussed in this section. It is assumed that the signal is a unidirectional plane wave and the noise is isotropic. That is, the signal is perfectly coherent and the noise power in per unit solid angle is the same in all directions. At this time, the array gain reduces to the quantity called directivity index. In the case of a plane-wave signal in the isotropic noise impinging on the array and the BCAVSA steered in the direction of the signal, the array gain expression using the directivity function of the signal and noise can be written as :(31)AG=Dx-, y-, and z-axes.In this section, we provide some computational simulations to illustrate the frequency invariant property of the proposed method and the advantages of the proposed method compared to the original BCAVSA and the bi-cone pressure sensor array with the same configuration parameters. The BCAVSA is composed of several groups of cylindrical AVSAs. Each cylindrical AVSA consists of two UCAVSAs, and each UCAVSA is composed of eight AVSs. The directions of the acoustic particle velocity components of Each AVS is oriented along the f, We consider a BCAVSA composed of two cylindrical AVSAs. The desired bandwidth is equal to Ormia ochracea’s ears can be applied in the BCAVSA to increase the interelement distance. As a result, compared to the original BCAVSA, the coupling magnified BCAVSA has lower sidelobes and a narrower mainlobe. According to Equation  Compared to the original BCAVSA, the coupling magnified BCAVSA can provide a directivity pattern with a narrower beamwidth. This is because the coupling magnified system can convert the original BCAVSA into a virtual BCAVSA with larger interelement spacing.(2) Since the directivity function of the circular array is one part of the cylindrical AVSA, the frequency invariant beampattern has the same beamwidth over the 360 degrees in the azimuth plane when the elevation is fixed.(3) Since AVS can use more acoustic information than pressure sensors, the coupling magnified BCAVSA has lower sidelobes compared to the coupling magnified bi-cone pressure sensor array in both of the elevation and azimuth planes. In addition, the beampattern in azimuth plane of the coupling magnified BCAVSA has a narrower mainlobe compared to that of the coupling magnified bi-cone pressure sensor array. In the elevation plane, the coupling magnified BCAVSA has a narrower mainlobe compared to that of the coupling magnified bi-cone pressure sensor array when the mainpattern is not targeted at the elevation angle (4) By using multiple cylindrical AVSAs to constitute the BCAVSA, the proposed method can form the frequency invariant beampattern on the azimuth and elevation planes over multiple octaves.(5) As the proposed method is based on advantages of the coupling magnified system and AVSs, the coupling magnified BCAVSA has higher AG than the other methods.(6) In order to reduce the influence of the coupling magnified system to the noise, in this paper, we use the signal subspace and the eigenvalue corresponding the signal subspace to construct a new matrix before applying the coupling magnified system. It is found that the proposed beamforming method can still obtain a perfect constant beamwidth pattern when the SNR is low.(7) For the small steering vector error, the proposed method can hold a good constant beamwidth pattern. However, when the steering vector error is relatively large, the proposed method cannot maintain a good constant beamwidth pattern on the elevation plane, although it can keep a good constant beamwidth pattern on the azimuth plane.Finally, in our future work, we will discuss the influence of the electromagnetic coupling for the small sensor arrays and study the frequency invariant beamforming under the condition of the electromagnetic coupling. In addition, the method to improve the robustness of this method will also be studied in the future.


Gene expression and protein abundance data of cells or tissues belonging to healthy and diseased individuals can be integrated and mapped onto genome-scale metabolic networks to produce patient-derived models. As the number of available and newly developed genome-scale metabolic models increases, new methods are needed to objectively analyze large sets of models and to identify the determinants of metabolic heterogeneity. We developed a distance-based workflow that combines consensus machine learning and metabolic modeling techniques and used it to apply pattern recognition algorithms to collections of genome-scale metabolic models, both microbial and human. Model composition, network topology and flux distribution provide complementary aspects of metabolic heterogeneity in patient-specific genome-scale models of skeletal muscle. Using consensus clustering analysis we identified the metabolic processes involved in the individual responses to endurance training in older adults. •Distance metrics express heterogeneity between genome-scale metabolic models•Flux distribution and network topology are the main components of metabolic distance•Machine learning algorithms are applicable to sets of genome-scale metabolic models•We model individual responses to endurance training in older adults High-throughput techniques enable the analysis of complex biological systems at multiple levels, including genome, transcriptome, proteome, and metabolome. Integration of multi-omics data is often focused on dimensionality reduction and feature selection for classification tasks. Genome-scale metabolic models are extensive maps of the network of biochemical reactions taking place in a particular cell, tissue or organism. Each reaction is associated with the respective enzyme and gene, enabling the mapping of transcriptomics and proteomics data and providing a structure for the system-level interpretation of multi-omics datasets. The result of this process is a personalized model that gives a snapshot of the metabolic status of an individual. Analyzing these complex models, for example, to detect differences between individuals, is cumbersome. We applied consensus clustering to a set of data-driven models to monitor the progression of a lifestyle intervention in a cohort of older adults. Genome-scale metabolic models are maps of the metabolic network that function as structures for the integration of molecular data, such as transcriptomics and proteomics. We developed a method for the analysis of large sets of data-driven models, using different distance metrics to quantify model similarity. Consensus analysis is then used to reach a single metabolic distance. The method was applied to model the individual variability in the responses to endurance training in a cohort of older adults. Methods to identify clinically relevant subgroups of individuals who respond similarly to the same treatment and estimate their future outcome for a given treatment would be relevant both in research and for clinical practice, for example, to study the heterogeneous pace of aging observed in different individuals,,,,,,Genome-scale metabolic models (GSMMs) take a third, alternative approach: they mathematically reproduce the network of metabolic reactions happening inside an organism or tissue and can be used to simulate the response to specific environmental conditions, such as the availability of nutrients. The activity of each reaction in the model can be linked to experimental data, such as levels of gene expression and protein abundance, making such models a combination of data-driven and physiology-driven methods and interesting platforms for the system-level integration of multi-omics datasets.,Machine learning (ML) algorithms are able to identify patterns in very large datasets, relying on distance metrics to quantify similarity between different data points.We identified several different similarity metrics for model composition, network topology, and flux distribution and compared their properties by applying them to two large GSMMs sets: the AGORA set,GSMMs are complex data structures with three main features: they are databases of metabolic genes and the associated metabolites and biochemical reactions which were experimentally found or predicted to be present in a certain tissues or organisms; they are mathematical representations of the topology of the metabolic network; and they are used to simulate how the metabolism of a tissue or organism adapts to a particular environment or condition by limiting with upper and lower bounds the metabolic fluxes through exchange reactions with the external medium.Starting from the idea of Euclidean distance between data points as a similarity measure in ML, we asked how this concept could be expanded and applied to GSMMs. In this context, distance can be thought of as a proxy for functional similarities between the metabolisms of two organisms. As an example, let us consider Bacilli and Clostridia, two families of sporulating bacteria belonging to the same phylum (Firmicutes). What distinguishes them is the fact that the former is aerobic, and the latter is not.de facto standard to measure similarity between genome-scale models.,,One way to quantify their similarity is by measuring the proportion of biochemical reactions they have in common. This information is summarized by the Jaccard metric. We computed the pairwise Jaccard distance between hundreds of bacterial models belonging to the Bacilli (n = 197) and Clostridia (n = 131) families, which were included in the AGORA model set. The results are visualized in We hypothesized that the heterogeneity within each of these three complementary components contributed to the “total” model heterogeneity and defined a different distance metric for each of them: the Jaccard metric was used to compute the overlap between the reaction lists of a pair of models; A graph kernel, i.e., a function to compute similarity between network topologies, was used to obtain a similarity score between metabolic networks. Pearson's correlation coefficient was instead used as similarity score between normalized flux distribution vectors as a metric of similarity between constraint-based models. The flux distributions are obtained averaging 1,000 random samples of the solution space of each model, then the flux through each reaction is normalized by subtracting the mean and dividing by the standard deviation of each reaction. A more detailed discussion of these metrics can be found in the Experimental Procedures section. To compare the properties of these metrics, we computed the pairwise distances for the models in the AGORA and PD-GSM datasets for each of the three different distance metrics. To test our hypothesis, we then looked at the correlation between the resulting pairwise symmetric distance matrices. The clustered distance matrices for each of the three metrics are shown in The modest correlation between network structure, described by the first two metrics, and the flux correlation metric  can be eTo test if the observations we made regarding distance between microbial models are valid also for human metabolic models, we studied the distances in a subset of the PD-GSMM set, containing 100 patient-derived models of skin and liver metabolism.The correlation between the metrics  is lowerGiven the low correlation between flux correlation and the other two metrics, flux distribution similarity should be viewed as a separate and complementary component of metabolic heterogeneity. Nevertheless, when we applied this metric to the PD-GSMM set, the two classes overlapped significantly. From an ML perspective, the flux correlation metric could not discriminate between the two classes. A classifier algorithm trained using this similarity metric would have worse performance than one trained using the Jaccard metric. For these reasons we decided to use only the Jaccard metric as a measure of metabolic heterogeneity for the remainder of this study.The publication of large GSMM collections, such as AGORA for gut microbiota models and PD-GSM for individualized human cancer models, showed the need for new scalable methods of analysis that can be applied to large model sets. We were interested in the concept of distance between individualized models and whether it could be applied to obtain biologically relevant insights about a set of GSMMs. Once we established the properties of distance metrics in GSMMs, we developed a distance-based workflow for the characterization of heterogeneity in metabolic models sets that, through the combination of ML and constraint-based metabolic modeling techniques, such as FBA,n is the number of models in the set. Each of the three metrics gives a different view of the heterogeneity present in a given model set. A consensus clustering approach is used to reconcile these differences and give a more comprehensive and robust view of metabolic heterogeneity: agglomerative hierarchical clustering is applied separately to each of the three distance matrices, then a consensus algorithm is applied to find an agreement between the three label vectors. For this task, we considered three consensus algorithms: the cluster-based similarity partitioning algorithm, the hyper graph partitioning algorithm, and the meta-clustering algorithm.The distance between each pair of models is computed using three different metrics: Jaccard distance, graph kernel and flux correlation, resulting in three square distance matrices of size The label predicted by the consensus clustering algorithm is used to investigate the differences in the flux distributions of the models between the clusters. A flux distribution matrix is created, merging the flux distribution vectors obtained via averaging 1,000 random samples of the solution space of the models. The flux matrix is then normalized, by subtracting the mean for each flux and dividing by its standard deviation. Using the Kruskal-Wallis test,Skeletal muscle plays an important role during the aging process, not only in maintaining the ability to perform activities of daily living, but also because it increases resilience against stressors and adverse events, such as traumatic injuries and diseases.Nutritional and physical activity interventions are currently the only possible treatments to slow muscle wasting in older adults. Nevertheless, aging individuals express a large variation of health outcomes, due to causes ranging from genetic differences to socioeconomic and behavioral factors,,We developed an original set of 24 patient-derived GSMMs from longitudinal gene expression data of skeletal muscle of 12 older adults (84 ± 1 years old), before and after an intervention consisting of 12 weeks of resistance training. Although previous studies have investigated the metabolism of skeletal muscle using GSMMs,The observation that AT models are on average somewhat larger than UT models , suggestWe applied our distance-based workflow to visualize the relationships between the models in the skeletal muscle model set and to perform a cluster analysis. The first step was to compute the Jaccard distance between each pair of models. The pairwise distance matrix was clustered with hierarchical clustering, and presented as a heatmap in Health is not a single immutable state but rather can be defined as a set of dynamic processes at different points in time that together form a health trajectory.The ability to model the health trajectories of different individuals over time to understand the differences in their responses to the same interventions and how their present condition affects future health would have a large impact not only on basic biomedical research, but also in clinical practice. Previous examples include modeling the progression of biological treatment in a “health space,” whose axis represent biological processes, such as oxidative stress, inflammation, and metabolism.The different directions and magnitudes of these shifts in the metabolic space reveal the heterogeneity of the individual response to the training program. The results of consensus clustering are visualized as colored shapes. Three main responses can be observed: individuals 4, 6, and 10 show a limited shift and remain in cluster 1 (orange) with mostly UT models even after the training is finished. UT model no. 5 starts as an outlier, but clusters together with UT models at the end of the intervention and was included in this group. Individuals 1, 2, 3, and 12 remain in the blue cluster, which includes mostly AT individuals, but also show a limited shift during the training. Conversely, individuals 7, 8, 9, and 11, who were starting from the orange cluster on the right, show the largest shift during the training intervention, clustering with other AT models in the blue cluster after the end of the treatment.Since this study is based on publicly available data, we do not have any information about the physiological status or lifestyle of these individuals , neither at baseline nor in response to the intervention. Thus we can only speculate whether the three different responses that we detected are correlated with the baseline fitness of the individual and to actual improvements in their condition after the intervention. For this reason, we will compare the models both according to the ground truth  and to the predicted classes , inspecting the reaction content and average flux distribution of the models in each of the two groups.In the last step of the workflow, we use a statistical test  to analyze the differences between flux distributions across groups of models and identify the metabolic processes associated with a certain class of models. We applied this step both to the ground truth label  and to the label predicted by the consensus clustering .www.vmh.life/#nutrition). The statistical test is used to screen for reactions whose activity differs between the groups, using a p value of 0.05 as threshold. In total, we identified 147 reactions whose activity significantly differed between AT and UT models, and 238 significant reactions when comparing models of consensus clusters 0 and 1. The full list of significant reactions and associated p values, for both cases, is available in The Kruskal-Wallis test is applied to a normalized flux distribution matrix obtained by random sampling of the solution space of each model in the set. All the exchange reactions of the models were constrained with input flux values that reproduce the nutritional intake of an average European diet , is organized and integrated, enabling the interpretation of the results in terms of the resulting system-level metabolic phenotype. The resulting individualized model is a snapshot of the status of the metabolic network of an individual in a particular condition that combines a metabolic knowledge base and experimental data. When ML algorithms are applied to these novel data structures and a pattern is detected, the relevant models can be further interrogated to generate and test hypotheses, going beyond mere correlations and toward mechanistic insights.To harmonize these two approaches, we used distance between GSMMs as a proxy for metabolic similarity. Our initial hypothesis considered three different aspects of a GSMM, and assigned to each of them a different metric, to describe different but complementary components of metabolic distance. When we compared these three metrics—Jaccard distance, graph kernel similarity, and correlation between flux distributions—we found that the first two were very highly correlated, while the flux similarity metric had a medium to low correlation with the other two metrics. This observation made us reject the initial hypothesis in favor of a two-components interpretation of metabolic heterogeneity: a static component, which is the structure of the metabolic network, described by the Jaccard metric, and a dynamic one, which is the flux distribution.Nevertheless, our proposed metric for flux distribution similarity, flux distribution correlation, was found to lack the resolution power to sufficiently separate the model classes and to be successfully used in ML applications. The problem of defining a suitable metric for flux similarity is still open. The solution space of the flux distribution is constrained by the topology of the metabolic network in combination with bounds set on particular reactions in the network. For example, lower and upper bounds on transport fluxes are used to incorporate information on medium composition and implement physiological conditions. We expect the metric based on flux correlation to be strongly dependent on these boundaries. Unknown variation and uncertainty in these boundaries can make the flux correlation metric less reliable. This could potentially explain why flux correlation performed worse than the other two metrics, when considering the separation of the classes, in both benchmark model sets.Comparing the normalized fluxes between AT and UT models, we were able to extract a metabolic signature consistent with the effects of resistance training A. Fatty The results of the comparison between models of the two groups identified via consensus clustering B shows tIn this study we investigated the concept of distance between patient-derived models comparing the properties of different distance metrics. We identified flux distribution and network topology/composition as the two complementary aspects of metabolic heterogeneity and developed a distance-based workflow that combines the ability of patient-derived metabolic models to provide previous knowledge of the structure of metabolic networks and a context for the integration and interpretation of omics data with the scalability and the pattern recognition capabilities of ML.By applying this workflow to a set of patient-derived genome-scale models of aging muscle metabolism before and after a 12-week endurance training program, we identified subgroups of individuals expressing heterogeneous responses to the intervention. The fatty acids oxidation, kynurenine, and beta-alanine pathways were identified as differentially active between the two groups. Increased activity of these pathways in response to training explains the benefits of physical activity on metabolic and mental health during aging. We believe that the application of ML algorithms to sets of physiology-based computational models parametrized with individual molecular data will contribute significantly to the advancement of personalized medicine in the near future.a.cabbia@tue.nl)Andrea Cabbia . For each reaction r in the parent model, if r is also included in the contextualized model, the rth position of the vector will be 1, else it will be 0.Graph kernel functions were used to compute similarity scores between pairs of networks. Kernels are non-linear functions that map the data samples to a different dimensional space, known as the Hilbert space, usually with a higher dimensionality than the starting one. In this new space, the distance of samples represents their similarity. The exact coordinates in the Hilbert space, but rather their distance, is found by simply computing their inner products, an operation that is often computationally cheaper than the explicit computation of the new coordinates.Kernel methods have been applied to heterogeneous types of data, such as biological sequence data,Two main approaches can be considered to compute the similarity between pairs of constraint-based metabolic models, defined as: the first consists in comparing the functionality of two models, for example, comparing their ability to utilize different carbon sources or the minimal nutrient combinations predicted from the structure of the metabolic network.We hypothesized that, since each of these three different aspects of a GSMM encapsulates different aspects of metabolic heterogeneity, they would need different definitions of distance. Two recently published large model sets of microbial and human cancer metabolism, the AGORA model set,GSE28422. Gene expression data were measured with microarray technology (platform HG U133 Plus 2.0). Older participants  included 6 men and 6 women. All subjects participated in 12 weeks of progressive resistance training consisting of bilateral knee extensions with 3 × 10 reps at 70% of 1-RM, and 3 days/week for a total of 36 training sessions. Vastus lateralis biopsies were obtained in conjunction with the first and last training session. We refer to the former as UT status, and to the latter as AT status. From this dataset we developed 24 patient-derived GSMMs, two for each subject, respectively, for the UT and AT status.The workflow described in the section above was applied to a set of metabolic models that was developed for this study. This model set describes the skeletal muscle metabolism of 12 older individuals, and was created from longitudinal gene expression data, collected before and after a resistance training program of 12 weeks. The gene expression dataset from which the skeletal muscle metabolism models were developed was collected during a study on the acute and long-term effects of resistance training on skeletal muscle gene expression in older adultsSeveral different methods for the integration of multi-omics data in GSMMs have been reviewed in Opdam et al.We used the CobraPy


Information production in both space and time has been highlighted as one of the elements that shapes the footprint of complexity in natural and socio-technical systems. However, information production in urban crime has barely been studied. This work copes with this problem by using multifractal analysis to characterize the spatial information scaling in urban crime reports and nonlinear processing tools to study the temporal behavior of this scaling. Our results suggest that information scaling in urban crime exhibits dynamics that evolve in low-dimensional chaotic attractors, and this can be observed in several spatio-temporal scales, although some of them are more favorable than others. This evidence has practical implications in terms of defining the characteristic scales to approach urban crime from available data and supporting theoretical perspectives about the complexity of urban crime. Crime is not what it looks like. Despite its apparent random configuration over time, space, and society, crime forms patterns . AccordiThe emergence of patterns in urban crime is related to the complexity of cities ,7. MoreoWith the development of geographical information systems, crime patterning has become in an intense research area . PatternSpatio-temporal patterns of crime have been studied from several perspectives to deal with the inherent uncertainty of this phenomenon. From probability and stochastic processes theories, crime patterning deals with fixing a probability distribution model over criminal data. Methods such as kernel density estimation (KDE) and self-exciting point processes (SPPs) among others have been considered in this context . HoweverStochastic approaches are sometimes used on the basis of a supposed similarity between crime and other phenomena. For example, SPP proposed to study crime more like a metaphor of seismic processes rather than a consequence of the nature of crime . In anotThe analysis and detection of criminal patterns by means of clustering techniques have also been considered . These sRecently, some artificial intelligence (AI) models for predicting crime patterns have been developed, with interesting results . These aSupported on crime pattern theory, risk terrain modeling (RTM) has been proposed as a promissory method to deal with the problem of detecting crime patterns ,7,21. InThe crime patterning problem relies on the supposition that patterns are represented as probability distributions or geometric prototypes that can be easily parametrized. Thus, classical geometry is privileged when searching spatio-temporal patterns in crime data. If crime is the result of complex phenomena that emerge from the entanglement of multiple relations in urban systems, then searching for simple geometric patterns would be in contradiction with this hypothesis. Therefore, the crime patterning problem should be directed in a different way, in which the study of the geometric properties of urban crime becomes necessary.Two new approaches that refocus the crime patterning problem were proposed in ,24. The The geometry of urban crime is conditioned by its support, which is the city itself—not only in the physical domain , but also in the social domain  ,26. The This geometric perspective can take advantage of several tools that have barely been explored in the understanding of crime dynamics. Among them, one can find fractal/multifractal geometry and chaos theory. As will be discussed later, multifractal analysis gives insight into the apparent random geometry of urban crime in different spatio-temporal scales, while information production in these scales is studied on the basis of dynamical systems theory. Information is relevant in this work because it has been highlighted as one of the elements that shapes the footprint of complexity in natural and socio-technical systems . In addiMultifractal analysis (MFA)  and analThe application of MFA for studying complex phenomena has been discussed in the literature e.g., ,31,32). ,32. 31,3This work follows this line of research to analyze the dynamic informational dimension of urban crime, which is connected at the root of the decisional and complex aspects of this phenomenon. The decisional process behind crime can be described by means of scripts that depict how agents rely on experience, environmental clues, and opportunities to refine their behavior ,9. OffenThe rest of this paper is organized as follows: Crime pattern theory understands crime as a variety of complex phenomena that do not occur randomly in space, time, or society . This thRoutines are the mechanisms behind the formation of crime patterns. Actors perform daily routines that interact over the urban support, generating crime opportunities . SometimTwo perspectives that complement crime pattern theory have been proposed in order to explain some details about the decisional process behind the execution of a crime event. The rational choice perspective suggests that crime is a decision-making process under uncertainty . CriminaThe routine activity approach explains how crime rates emerge . This apL. The local density i-th counting box:Consider an object that is covered with counting boxes of longitude i-th region or spatial location. These exponents show the local behavior of L. Most of the time, similar values for In heterogeneous objects,  vary as :(2)Pi(L)The number of boxes q-th moments of the density function Multifractal measures appear as scalings of the on Pi(L) :(4)∑i=1Mrding to :(5)Dq=1qq-th moment of order The exponent in Equation  is callemultifractal spectrum, which is a convex function with a maximum The curve l masses .L. This also restricts the range of moments q that can be used. Therefore, the multifractal spectrum is computed from:L.Practically speaking, the multifractal spectrum cannot be computed in the infinity, so its estimation is limited to the set of local scales that can be expressed as powers of Thus, the computation of  follows :(9)f(q)=q as the slope of the linear regression of numerators in Equations f(q) and Note the singularity Equation . Therefoown that :(11)log−D1linformational entropy E of Considering a probabilistic interpretation of the mass densities Equation  is the iE is given straight forward by L.Chaos is a phenomenon that appears in some signals. Its footprint is characterized by several attributes, such as complex dynamical traces in time, broadband power density spectra, nonperiodic motion, and exponential sensitivity to reduced perturbations in the orbit of the phase plane, among others . AlthougTaken’s embedding theorem ,51 givesEquation . Since xConsidering a general scalar function T between delayed versions of Regarding Equation , it is nT, its identification should guarantee that this delay is large enough so that T can be established in terms of the information among measurements, which is expressed as the average mutual information  . In the The proximity between When the dimension is increased to If d in which false neighbors become minimum is selected as the embedding dimension of the dynamics According to practical observations, the number of false neighbors remains constant when tic data ,50. If dtimation ,54,55,56A criminal complaint is defined as a tuple vailable .R is the set of all criminal complaints between the time interval A criminal report R:Q is the amount of registered complaints in the interval A criminal subreport in a time window Two criminal subreports M is the total number of criminal subreports.A criminal report is a collection of disjunctive criminal subreports:An ordered criminal report (OCR) is a criminal report with:R produces a sequence of multifractal spectra n and The application of multifractal analysis to each subreport posed in ). From tDefinition. An MF time series To evaluate the quality in the generation of The cumulative concavity index A concavity test is proposed to accept or reject the generation of Generate the OCR: Given the record of urban crime complaints in a time window T disjunctive subreports n reveals the order in occurrence of the subreports over the OCR and will refer to the day, week, or month of the subreport Multifractal analysis and concavity test: Given a minimum spatial scale M. Then, the CCI is obtained and the concavity test is verified, and if negative a new Equation , until cSynthesis of MF time series: The signals c values .Linear processing: Linear statistics are computed over produced MF time series, such as: autocorrelation function, power spectrum, mean estimation, variance estimation, and coefficient of variation, among others. It is recommended to complement this analysis with the calculation of the signal histogram. The autocorrelation and the power spectrum make it possible to determine if there are any periodic behaviors within the signal detectable in a linear sense. These two statistics have a special link through the Wiener–Khinchin [Khinchin  theorem.Khinchin ,60.Nonlinear processing: In this stage, a battery of nonlinear statistics is applied to explore the structure of the time series to reveal details of its behavior that escape the linear analysis [analysis ,50. Someanalysis ,55,56. Oanalysis . This ananalysis ,29,37.Characteristic scales: In addition to the results produced from previous stages, spatio-temporal scales are suggested to approximate the understanding of the phenomenon. The This method is proposed to study the temporal structure of MF time series by means of the analysis of observed chaotic data (AOCD). The MF-A2-OCD method is depicted in the flow diagram of Informational entropy is a measure of the average information content of a set’s density  distribution. The occurrence of rare events increases this content, whereas common events produce just a small increase of it. Therefore, according to the routine activity approach, if crime offenses are rare events that emerge from the interaction of routine events, densities may produce a significant average content of information. On the contrary, if crime events populate in certain locations, corresponding densities may reduce the informational entropy of the distribution.The maximum informational entropy is achieved when a probability distribution is uninformative. Typically, this situation appears when there is no prior knowledge about the phenomenon so that the best distribution that supports any decision is the one with the highest informational entropy. For example, if no constraints are given, the uniform distribution is the best choice. In the framework of crime pattern theory, crime does not distribute uniformly, so the crime decisional processes would modulate crime distributions, reducing their informational entropy. Therefore, an observer would note that crime distributions over space or time would become more informative as the learning processes of offenders improve. However, there are other elements that may contribute to shape the distribution, since crime is connected to the urban backcloth.L. This makes the same set of criminal events to configure different spatial patterns depending on L, but some similarity can be noted between several scales, as shown in The quantification of crime densities L  sho sho13) sEquation  applied The slope of the fitted linear regression corresponds to the informational dimension According to Equation , informaInformational entropy is related to information scaling in a multifractal object. The MF-A2-OCD method looks to obtain a consistent multifractal behavior from an OCR in order to guarantee the integrity of information scaling over the sequence of disjunctive crime subreports. Thus, the The understanding of crime dynamics through informational patterns in time may help to detect correlations or seasonalities between disjunctive crime subreports. This approach would provide a general look at the memory structure of crime dynamics captured through an OCR considering a set of temporal scales. The absence of informational patterns may suggest that crime dynamics corresponds to a truly stochastic process. On the contrary, detecting these patterns would be a confirmation that crime dynamics exhibits a temporal structure that can be studied. Hence, the temporal non-randomness of the crime hypothesis at the core of crime pattern theory can be tested. In addition, this perspective can be used to contrast the information patterns in different temporal instances such as weekdays/weekends, night/day, or seasons, among other possibilities ,64,65 inFive cases of urban crime report in cities of America were considered, as follows: Los Angeles (USA), Chicago (USA), Philadelphia (USA), San Francisco (USA), and Bogota (Colombia). The choice of these cases was mainly due to the availability of open criminal databases. In these five cities, criminal reports cover 1237 days , extending from January 2012 to May 2015. The length of the reports was standardized with respect to the Bogota case, which is the shortest. The minimum time scale of analysis was daily, given that not all reports recorded information on an hourly scale.Records focus solely on property crimes, which could involve violence but not weapons ,66. In pComputation of Equation  10) was cThe multifractal spectra spanned over an interesting set of local scales, indicating the strong mutlifractality characteristic of the objects in both temporal scales. The temporary aggregation allowed the levels of informational entropy to increase for the lowest spatial scales. However, information loss between spatial scales became more noticeable given the increase in Results from the concavity test expressed in the The CCI clearly improved for the weekly scale. On average, the concavity test became positive for MF time series Several statistics were computed over The basic statistical analysis of The autocorrelation function revealed a fast decay of linear memory in daily The spectral centroids  levels Equation  to studyT and d in Equation (The s method  with AMIEquation , respectFinally, the Hurst exponent estimation result was greater than 0.5 for all cases in both scales, indicating that these From the urban crime reports that were studied in this work, similar CCIs were computed for the weekly and daily generations of dynamic multifractal spectra. However, this sole criterion is not enough to make a decision about the convenience of an initial The evidence supported in the previous results suggests that the spatial information of selected urban crime cases, studied through MF The spatial information dynamics of urban crime exhibits a chaotic behavior in time. Although a deterministic production of information lies behind the core of urban crime, the low predictability of this phenomenon in space, time, and society is related to its chaotic informational dynamics. This consideration invites us to think that the complexity of crime emerges as a result of the interaction between the rational choice of agents and their interactions  with theEven though dynamic properties of One of the variables to which information production of crime may be related in dynamical terms is risk. There is evidence from the RTM practice that risk related to certain features of the urban backcloth is also a dynamic variable . At the The presence of temporal structure in the informational signals of crime dynamics also invites one to think about making inferences supported on cross-correlation measures with informational signals of risk computed from the MF-A2-OCD method. From the literature, it can be seen that RTM methods are supported on the ground of linear statistics when trying to find independent variables. It is known that nonlinear correlations can fool traditional statistics . Hence, In this paper, we carried out a data-driven investigation of the information dynamics in reported urban crime. This dynamic was explored by means of a novel conjugation of multifractal analysis and some processing tools related to chaotic time series analysis. Our results suggest that information dynamics in crime evolve in a low-dimensional chaotic attractor. This can be observed in available crime reports in different spatio-temporal scales. However, certain scales are more favorable than others regarding the temporal properties of spatial information scaling, which is captured through the dynamic multifractal spectrum in its information dimension This work suggested the use of an information-based method to identify the set of spatio-temporal scales in which urban crime dynamics should be studied given a report of crime data. The key point of this method is to identify the scales in which the multifractal characteristic of urban crime report becomes evident. The identification is possible by looking at the integrity of the dynamic multifractal spectrum and the properties of The findings presented in this paper support some theoretical perspectives that intend to explain urban crime as a phenomenon that emerges from complex urban systems. Information production can be considered as one of the elements that characterize the footprint of complexity in natural and socio-technical systems. However, the theoretical background that connects information production and nonlinear dynamics should be developed in an attempt to approach the complexity of this kind of system. Moreover, multifractal and nonlinear approaches combined through the MF-A2-OCD method can be considered as complementary tools to the practice of the risk terrain modeling of crime.


To answer to the question raised by the author as: "why do people not pay attention to the major causes of death as much as they do to COVID-19?” we grasp the opportunity to reshape the question as:" Why do health policymakers not pay attention to the major causes of death?".We reviewed the editorial entitled: "Neglected Major Causes of Death Much Deadlier Than COVID-19", by Prof. Jalal Poorolajal 2. One example is the critical role of policy makers in nutrition, a significant contributor to many major chronic diseases3. Uncoordinated, uninformed policymaking has caused serious consequences to the pattern of food preparation and distribution, leaving insurmountable barriers to make a healthy diet for many people worldwide. These barriers, which are responsible for health inequities, can be overcome thoughtfully by governments to provide evidence-informed policies 4. Another example is the governmental contribution in regulating national policies to reduce the amount of sodium intake, another common and well-known risk factor for chronic diseases in most parts of the world6. Low-cost and highly effective solutions for the prevention of chronic diseases are readily available; the failure to respond is now a political, rather than a technical issue 6.Indeed, the burden of non-communicable diseases must be taken into account. We acknowledge the critical role of communities in prevention and control of diseases, especially non-communicable ones. Meanwhile, we shall not forget that a large portion of prevention and control efforts relies on the governments as a political obligation. There has been a growing awareness of the serious burden of major non-communicable diseases including cardiovascular diseases, diabetes, malignancies, and chronic obstructive pulmonary disease worldwide. In many parts of the world; however, these diseases are being overlooked in prevention measures. Global and national policies have failed to stop, and in many cases have contributed to, the chronic disease pandemic7. As for the COVID-19 epidemic in Iran, despite a few community-based endeavors by the government to tackle the disease8, major challenges to combat the epidemic in the country are insufficient whole-government, whole-society approach in managing the outbreak, inadequate lifesaving and protective equipment, and delayed decisive governance9. What we have learned from the COVID-19 pandemic shows that successful prevention and control measures relies heavily on the community involvement. Conditional on being asked by policy makers, people are appreciable collaborators in their own health.Regarding the COVID-19 pandemic, we agree with the author on the important role of communities to stop spreading the pandemic. Nevertheless, the same premise concerning non-communicable diseases exists. Prevention and control of widespread epidemics such as COVID-19 is a political priority and commitment rather than a social responsibility. We disagree with the author on the points that more deaths will occur by the COVID-19, simply because communities will fed up with restrictions. Along with WHO, we truly believe that control of an epidemic or any other health crisis is the foremost priority for governmentsIn conclusion, we believe that prevention and control of the major causes of death worldwide are indeed health policy priorities and require professional advocacy attempts through appropriate inter-sectoral collaboration and whole-government coalitions. Meanwhile, we should not forget the significant role of media on a thriving community. The premise of a united society, which has a substantial responsibility on its health, is not achievable without governmental efforts to educate, aware, and mobilize it.The authors declare that there is no conflict of interests. I thank very grateful to Prof. Koroush Holakouie-Naieni and his colleague for their kind comments on my recent editorial in the Journal of Research in Health Sciences.1. On the other hand, COVID-19 has become the major concern of the general population for several months because a huge propaganda and international campaign was launched to curb the disease. The media has a remarkable impact on public beliefs and awareness 2. They can manage and shape public beliefs and attitudes 4. So people pay attention to the same things on which policymakers and media focus.I appreciate their concern and emphasize that their question “Why do health policymakers not pay attention to the major causes of death?” and mine “Why do people not pay attention to the major causes of death as much as they do to COVID-19?” are the both sides of the same coin. Indeed, people's lack of attention to the major causes of death and dangerous diseases is the result of negligence of the health policy makers and media. As I emphasized in the last paragraph of the paper, people do not pay enough attention to the importance and catastrophic consequences of common causes of death because they hear little about these realities in media


Advanced driver assistance system such as adaptive cruise control, traffic jam assistance, and collision warning has been developed to reduce the driving burden and increase driving comfort in the car-following situation. These systems provide automated longitudinal driving to ensure safety and driving performance to satisfy unspecified individuals. However, drivers can feel a sense of heterogeneity when autonomous longitudinal control is performed by a general speed planning algorithm. In order to solve heterogeneity, a speed planning algorithm that reflects individual driving behavior is required to guarantee harmony with the intention of the driver. In this paper, we proposed a personalized longitudinal driving system in a car-following situation, which mimics personal driving behavior. The system is structured by a multi-layer framework composed of a speed planner and driver parameter manager. The speed planner generates an optimal speed profile by parametric cost function and constraints that imply driver characteristics. Furthermore, driver parameters are determined by the driver parameter manager according to individual driving behavior based on real driving data. The proposed algorithm was validated through driving simulation. The results show that the proposed algorithm mimics the driving style of an actual driver while maintaining safety against collisions with the preceding vehicle. Automated vehicle control technologies such as advanced driver assistance system (ADAS) and autonomous vehicle (AV) have been researched over the years and have become the core of the future automotive industry ,2. The aIn this paper, we focus on the personalization of ACC that intervenes in longitudinal driving by controlling ego-vehicle speed and distance to a preceding vehicle in car-following situations. Car-following situations have become significantly frequent with the marked increase in traffic over the past several decades . The ACCThe importance of personalized ACC has long been recognized in the automotive industry, and researchers have conducted studies to reflect driver characteristics in conventional ACC to improve user acceptance. In several studies, heterogeneity of conventional ACC was confirmed through traffic simulation based on car-following models . Car-folMost previous studies for practical application of personalization proposed a set of predefined values by categorizing drivers into several groups ,13,14. FTherefore, several studies have proposed driver models that map vehicle states directly through deterministic equations into control inputs such as speed, acceleration, and pedal operation based on an individual’s driving style ,16,17,18In recent years, with the growth of computing devices and the rise of big data research, many researchers apply data-driven methods on driver behavior prediction. The Markov models stochastically predict driving states to inform the uncertainties of real-driving situations ,20. BesiThe online update of personalization parameters is required to accommodate continuous changes in driver preferences . In ordeIn order to expand the previous research, we propose personalized ACC that is called Driver Characteristics Oriented ACC (DCO-ACC). The DCO-ACC is structured by a multi-layer framework composed of driver parameter manager, speed planner, and speed controller. The multi-layer framework is derived from the architecture of an autonomous driving system to ensure scalability and compatibility to AV or Connected Vehicle (CV) . The driTo clarify the contribution of this study, the two featured aspects of this study are summarized as follows. The first aspect is that the proposed driving system is structured based on the motion planning and control system of AVs. Since the previously proposed ACC personalization was based on conventional ACC structure, the controller components are dependent on each other, and compatibility for other control systems is not guaranteed. Therefore, the DCO-ACC is designed by combining the individual driver parameter management algorithm to a framework of longitudinal planning and control system of AVs. This multi-layer framework ensures compatibility with new additional functions and AV’s software structure. The second aspect is that the proposed driver parameter manager can learn online and have small-sized models. Previous driver models had bulky models that required large memories. Moreover, such models also should be updated offline with pre-logged data. However, since the proposed algorithm manages a driver parameter as a small-sized vector, it has advantages in terms of learning or memories.The rest of this paper is arranged as follows. In order to design the longitudinal driving system reflecting individual driving styles, personal drivers’ driving data have been analyzed. The driving data for modeling of individual drivers were collected through real-driving tests with various in-vehicle sensors and radar, which has 150 m of maximum range, as shown in A two-lane urban straight road in Incheon, Korea, was selected as a driving environment, as shown in Driver features in car-following situations are related to the ego-vehicle and the behavior of the preceding vehicle. Driver features related to the ego-vehicle include ego-vehicle velocity, acceleration, and jerk, i.e., a derivative of acceleration. Features related to the vehicle in front are spacing gap and relative velocity. Furthermore, THW and TTCi have been considered significant parameters to represent driver features. They are defined as in Equations (1) and (2), respectively:Statistical analysis such as comparing the means and the standard deviations of these driver features to distinguish the driving styles of individual drivers in car-following situations has been used in several studies ,14,27. FIn this paper, Kernel Density Estimation (KDE) was utilized to analyze driving features differed by drivers. The KDE is a statistical approach to estimate the probability density of datasets difficult to be expressed by parametric density functions. In fact, driving datasets show irregular patterns caused by protean real-driving situations. As shown in In addition, jerk is the index that best represents the feeling of deceleration felt by the driver, and the maximum and minimum values of this jerk show different distributions for each driver. Therefore, if you design a longitudinal controller that follows the vehicle ahead by reflecting the distance to the vehicle in front, the jerk’s maximum and minimum values, this can satisfy individual drivers.Furthermore, each of the analyzed driver features not only appears differently for each driver but also appears differently depending on the current driving condition of the vehicle. In other words, the driver feature is not defined as one value for one driver but should be modeled differently for one driver for each situation. The proposed longitudinal driving system calculates the desired acceleration according to driving conditions and individual driver’s characteristics. As shown in The driver parameter manager decides the driver’s preferred spacing gap that has been modeled by individual driving data in a car-following situation. According to the driving data, the spacing gap is a representative feature that varies depending on the driver. This is because the driver maintains a safe distance to the preceding vehicle to avoid a collision, and the distance each driver feels safe is different. In addition, the proposed parameter manager calculates maximum and minimum values of acceleration and jerk. These values indicate the feeling of acceleration that drivers can endure. For example, some drivers may be afraid of excessive acceleration. Conversely, other drivers may be bored with too low acceleration. The driver parameter manager determines parameter values based on real driving data to reflect such different preferences of each driver.In order to manage the driver parameters, parameter values are defined as the vector array value according to the index parameter. The index parameter represents a driving state related to the driver parameter. For example, the spacing gap, one of the driver parameters, is highly correlated to the ego-vehicle velocity for all drivers, as shown in The parameter vector is derived from the driving data analyzed in Based on the parameter vectors and an effective probability vector, the parameter activation algorithm calculates the driver parameters. The speed planner produces the optimal speed profile to control the spacing gap between ego-vehicle and preceding vehicle because the spacing gap should be controlled according to the driver’s preferred value to reflect the driver’s personality. Additionally, the speed profile must be able to drive safely in a real vehicle.Therefore, the generation of the speed profile is defined as an optimization problem that is composed of vehicle dynamics and system constraints, as shown in In addition, the vehicle dynamics model of Equation (8) was applied to estimate driving states and calculate the cost function in every control period. The state vector contains the spacing gap, relative velocity, and ego-vehicle speed. Ego-vehicle acceleration takes the role of control input. Furthermore, the proposed speed planner considers the system constraints, as shown in a(t) serves as a control input.The speed tracking controller calculates the target acceleration required for the vehicle to follow the planned speed profile. Since the lower-level controller of an autonomous driving system of EV generally calculates the desired motor torque to follow the target acceleration, the target acceleration must be calculated. The speed tracking controller was designed by Model Predictive Controller (MPC). The optimal speed profile is used as a reference input. A simple velocity model is used as the dynamics model, and the acceleration The controller is only aiming at tracking performance for the planned speed profile. This is because the speed planner considered both driver propensity and vehicle dynamics. Therefore, a cost function consists of only the error between the target speed profile and the estimated speed profile. The MPC searches for the acceleration value that minimizes the cost function of speed error, which means that speed tracking performance has the highest priority. Besides, only the minimum limiting conditions are applied to the acceleration value. The acceleration value is constrained based on the range that the vehicle can achieve within its own capabilities. In order to validate the proposed DCO-ACC, a simulation environment including the test track, preceding vehicle, and radar sensor was developed with vehicle simulation software, IPG CarMaker; this provides the various sensor models required for ADAS and is a simulation software that can freely configure the shape of test track and the behavior of the surrounding vehicles. In addition, it has detailed fidelity at the vehicle powertrain level and has been used extensively in the research of AVs and CVs, as well as ADAS. We modeled the Hyundai KONA EV to build a simulation environment similar to the data acquisition environment. Furthermore, the speed tracking controller was implemented to control the vehicle in IPG CarMaker to generate output from the proposed speed planner. It takes the role of a higher-level controller as a Model Predictive Control (MPC)-based controller that can systematically handle the nonlinearities and constraints of the vehicle system. Based on the target acceleration from the speed tracking controller, a lower-level controller as a simple Proportional-Integral-Derivative (PID)controller outputs the normalized pedal position.Besides, the proposed algorithm and IPG CarMaker were integrated through “generic.mdl”, which is the Simulink model supported by IPG CarMaker to verify the proposed planning algorithm designed with MATLAB/Simulink. In the generic model, based on the IPG CarMaker for Simulink (CM4SL) library, internal signals such as the accelerator and brake pedal position could be modified, and the control output of the velocity tracking controller such as the accelerator and brake pedal positions were connected to the internal variables of the virtual vehicle. The entire simulation environment was constructed. First of all, in the MATLAB/Simulink, the proposed planning algorithm and velocity tracking controller were implemented, and the normalized pedal position was output, as described previously. Secondly, the CM4SL library was used to convert the control output to the CarMaker variables in the generic model. Third, the ego-vehicle was controlled via the input pedal position in the IPG CarMaker. Finally, the CarMaker variables, including the ego-vehicle states and the preceding vehicle states, were fed back into the proposed algorithm and speed tracking controller.The vehicle in car-following situations was simulated with the proposed driving system using the different driver parameters for the identical preceding vehicle behavior of premeasured driving data in Furthermore, algorithms for each driver and actual driving results were compared based on Root Mean Square Error (RMSE) for vehicle speed and distance between vehicles in In addition, The simulation results show that for the same preceding vehicle, only the ego-vehicle behavior differed according to the driver model used in the proposed algorithm, as shown in The K-S distance and the K-L divergence were used to objectively evaluate the similarity between the driving style of the proposed planning algorithm and the human manual driving style. The K-S distance and the K-L divergence are indicators that can measure the similarity between two distributions and are respectively defined as in the following equations :(10)DKS=In this paper, we proposed the driver characteristics oriented autonomous longitudinal driving system in the car-following situation. The proposed driving system was constructed by three subsystems: driver parameter manager, speed planner, and speed tracking controller. First, the driver parameter manager calculates driving parameters that are defined through driving feature analysis based on real-driving data. The driver parameters represent the personal driver behaviors in the car-following situation. Then, the speed planner generates the target speed profile through the optimization process. In order to make the speed planner special, the optimization problem contains the driver parameter to reflect individual driver characteristics. Finally, the speed tracking controller determines to follow the target speed profile. Since the structure of the proposed driving system is based on a general autonomous driving system, it can be easily expanded to autonomous driving. The proposed algorithm was validated through simulations. Since the optimization parameters are determined based on individual driving behaviors, the proposed driving system reduces the sense of heterogeneity for a specific driver. In future research, the proposed algorithm will be applied to the autonomous driving system in real-driving situations. The operating condition should be extended from car-following situations to various driving situations such as traffic jam or cut-in situations. Accordingly, the driver parameter manager will be modified to handle additional driving parameters practically. Besides, the target vehicle will be equipped with a motor torque control interface. The motor torque control interface is necessary to access the motor control unit and intervene control process. Furthermore, the speed tracking controller of the proposed driving system can be advanced to reflect the EVs’ powertrain characteristics. The MPC of the tracking controller will be modified to consider motor-to-wheel dynamics to compensate for the influence of vehicle inertia and improve speed tracking performance.