Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time. Deep neural networks represent the state-of-the-art solution for virtually all relevant machine learning, computer vision, and speech recognition benchmarks , yielding an implementation of spiking DBNs called Minitaur  is a Dirac delta function which is zero except for the firing times tij of the ith input neuron. Once the membrane potential V crosses the threshold voltage Vthresh a spike is generated, the membrane potential is reset to Vreset and the neuron is not allowed to fire during the refractory period Tref. Default values of the parameters used in simulations are defined in Table Spiking DBNs use a training procedure that is very similar to conventional DBN training, discussed in Section 2.2, to yield suitable connection weights for a network of spiking neurons to solve the intended task. Once these weights ribed asτmdVdt=ELi and e be the vectors of inhibitory and excitatory input rates, and  be the corresponding weights. In order to compute the expected output rate of the LIF neuron, a number of auxiliary variables first needs to be computed. For completeness, we provide the full equations, but refer to previous work for the derivation and interpretation of each variable , and ζ is the Riemann zeta function. Then the average firing rate ρout of the neuron with reset potential Vreset, threshold voltage Vthresh, and refractory period Tref can be computed as  can be converted into a probability by normalizing with the maximum firing rate 1/Tref. This allows sampling the activation probabilities, as is done in standard contrastive divergence learning with with continuous-valued units. Specifically, the weight update in contrastive divergence for spiking networks computes the data- and model-driven activities of the visible and hidden layer using the Siegert approximation, and then computes the weight update as usual in RBM training. Let Vdata be the activity of the visible units driven by the input data (or activity of the hidden layer below). Then the data-driven activity of the hidden layer, given the full weight matrix W connecting the visible and hidden layer, isUsing this approximation of firing rates allows a direct translation between the analog activation probabilities required for CD training and the resulting firing rates of a spiking neuron with those weights. During training of the spiking DBN, the Siegert approximation is used as the nonlinearity of the neuron instead of a sigmoidal function. The predicted rate ρw isw by the weight matrix W, because later different weight matrices with different bit precisions will be used to calculate the activities of hidden and visible layers. After training, the parameters and weights are kept unchanged, but instead of sampling every time step, the units generate Poisson spike trains with rates computed by the Siegert formula (Equation 3). In O'Connor et al.  is needed for computations with floating-point numbers, which results in increased area of the hardware design and higher energy costs.WH are converted to lower-precision representation WL using the conversionHW are the original double floating-point weight values of the trained DBN, and 2f− is the resolution of the lower precision representation.The bit precision of the synaptic weights was set by keeping the number of the integer bits constant to a value that is capable of holding the maximum and minimum weight value of a particular DBN, while the number of bits in the fractional part is varied from eight bits down to one bit. The double precision floating-point weights In order to investigate the effect of mismatch when mapping the spiking DBN to mixed-mode analog/digital multi-neuron transistor circuits, we considered a particular synaptic circuit known as the digital-to-analog converter (DAC) synapse  per second while dissipating <1 W per chip . Finally, Section 3.5 presents a new method to avoid performance loss on lower-precision platforms by introducing a training algorithm that takes into account the lower bit precision of the hardware and produces weights which increase the overall performance of the network using the lower bit precision weights.f = 4 bits. Figure Reduction in bit precision will reduce the resources needed on a digital chip , even when the bit precision drops to f = 2. In order to summarize the noise robustness for different precisions and firing rates, the area under the curve in Figure f = 3 bits in all subplots. Even a synaptic weight in Q3.2 representation, which requires <10% of the memory resources for double precision weights gives a reasonable peak CA of 91% for low noise levels. In all subplots  of equally sized devices and comes from sources such as the random variations in the threshold, width and length of the transistor during fabrication  synapse used on various neural chip implementations  and a low-precision weight matrix (WL), which is stored in Qm.f numerical format. Learning proceeds as in  is thus parameterized as Δw(WL), and after the update both weight matrices are processed asm represents the largest possible value that can be stored in the Qm.f format. Importantly, note that the low-precision weight matrix WL is used to sample from the network, while the weight update is applied to the higher-precision representation WH, and WL is obtained via rounding. As in standard contrastive divergence, the weight update is calculated from the difference of pairwise correlations of the data-driven layers and the model-driven sample layers. Here, although the activations are calculated from the low-precision weights, the updates are accumulated in the high-precision weights. Then, the weights are checked to be within the maximum bounds of the given resolution (Equation 6) for the given fixed-point precision. Finally, the weights are copied over into the low-precision matrix (Equation 7). The learning can then proceed for another iteration, using the new updated low-precision weight matrix WL. The additional cost of dual-copy rounding is to store a second weight matrix in memory, which is typically not a limiting factor for off-chip learning.However, one challenge with this approach is that the gradient update may be too small to change the values of low-precision weights. To address this potential difficulty, this paper introduces a key, novel method called For qualitative differences, observe the weights shown in Figure For a quantitative analysis of the differences in performance, the classification accuracy in the MNIST task using different bit precisions and different rounding methods was measured. For performance reasons, the classification performance for this section unlike the other sections in this paper occurred in rate-based (non-spiking) conditions, but using the same training procedure for spiking neurons.As there was no performance loss in the Q3.12 representation compared to full double-precision, this was taken as the full-precision reference point. Figure Impressively, however, Figure After outperforming other machine learning approaches on typical benchmark tasks in vision and audition, transferring Deep Learning techniques into marketable applications has become the next big target, and is supported by large ongoing industrial efforts. One of the biggest challenges is making the classification results of deep networks available in real time, which is necessary to improve user experience for relevant applications such as speech recognition or visual object recognition. It has become clear that apart from cloud computing solutions, which require additional communication overheads, the development of special purpose hardware to support deep networks is one of the most promising routes, in particular for mobile platforms with limited resources. Spiking deep networks have demonstrated very favorable properties in terms of latency and scaling . This reduces to only 0.46 MByte, or <10% if weights are stored in Q3.3, i.e., six bit per weight precision. Even more, one of the effects of the reduced precision is that many of the weights, which typically are distributed around zero, actually become zero. For low precisions, this means that the performance can be maintained, although more than 50% of the weights become zero. Thus, these synapses are ineffective, and more than half of the weights can be ignored. This not only saves time during execution, because of the savings in the memory lookup time for the synaptic weights in the case of a digital platform implementation, but also means that larger networks can be realized on the same hardware, because only a smaller percentage of the weights actually need to be represented. A validation was achieved by running the DBN for MNIST classification on the biologically-inspired massively-parallel fixed-point SpiNNaker platform, which uses less precise weights than standard software implementations. We have shown that the resulting performance of the network implemented on SpiNNaker is very close to the results from the software simulation with only a 0.06% difference, despite the fact the SpiNNaker uses fixed-point arithmetic.For implementations on custom mixed-signal hardware systems one has to deal with the constraint that they can only offer reduced numerical precision in the synaptic weights , thus allowing a very fast and cheap hardware implementation of a DBN with reasonable performance levels. Because the dual-copy rounding method focuses on acceptable performance using very efficient and low-precision networks, it operates in regimes such as Q3.1 where methods such as iterative rounding entirely fail to train the network effectively. The method requires storage of a second weight matrix, and uses the two sets of weights in two different contexts—computing activations and learning. This is typically not a problem in the off-chip learning scenario that is the focus of this paper, but might be challenging for on-chip learning, in particular on hardware where synapses are physically emulated in silicon. Once trained, the single bit precision analog network can potentially be implemented on a digital neuron platform such as TrueNorth (Merolla et al., Although spiking DBNs are remarkably tolerant to porting their parameters to low-precision platforms, we have shown that their performance can even be improved if the constraints of the hardware are taken into account already during training. The novel Future work will investigate the scaling behavior of deeper DBN architectures and other types of deep architectures on custom analog/digital multi-neuron systems or digital platforms with limited precision. Since training remains the most computationally expensive task with DBNs, it will be interesting to study how event-based learning rules on neuromorphic platforms can contribute to speeding up this process. Online learning rules such as the recently proposed event-based Contrastive Divergence learning rule (Neftci et al., ES performed the software network simulations, data analysis, and the implementation on SpiNNaker. DN also performed the software network simulations, data analysis, and the experiments with the training networks. MP contributed to the design of the experiments, analysis and presentation of the work. FG contributed to the infrastructure on the SpiNNaker system for enabling the network implementation on SpiNNaker. SF contributed to the infrastructure of the SpiNNaker system. SL contributed to the conception and design of the experiments, data analysis, and presentation of the work.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.


Vocal tract morphology is an important factor in voice production. Its analysis has potential implications for educational matters as well as medical issues like voice therapy. The knowledge of the complex adjustments in the spatial geometry of the vocal tract during phonation is still limited. For a major part, this is due to difficulties in acquiring geometry data of the vocal tract in the process of voice production. In this study, a centerline-based segmentation method using active contours was introduced to extract the geometry data of the vocal tract obtained with MRI during sustained vowel phonation. The applied semiautomatic algorithm was found to be time- and interaction-efficient and allowed performing various three-dimensional measurements on the resulting model. The method is suitable for an improved detailed analysis of the vocal tract morphology during speech or singing which might give some insights into the underlying mechanical processes. The process of human voice production involves a complex interaction of different components and mechanisms. It involves the generation of a pulsating transglottal airflow which is filtered by the vocal tract (VT) resonator. The shape of the VT, the aeroacoustic cavity between the vocal folds and the lips, defines the formant frequencies and the frequency response of the filter which, in turn, defines vowels, consonants, and essential parts of voice timbre .Magnetic resonance imaging (MRI) has become a promising technique for investigating the VT at a functional stage. MRI delivers images of high spatial resolution which can be analyzed in two dimensions within a single sagittal plane allowing for detailed analysis of dynamic VT adjustments during speech or even singing. For a detailed overview, see .The elongated structure of the VT along with its curved shape makes segmentation feasible with the use of centerline-based methods. In medical imaging, these approaches have been successfully used in vessel segmentation . The imaIn many cases, the estimation of the centerline is a problem in itself, as in the aforementioned vessel segmentation task. There exist a broad variety of segmentation methods which compute the centerline on runtime, based on segmentation results of previous cross sections. Some of them postulate a circular  or else With regard to segmentation, the geometrical structure of the VT poses similar challenges as vessel or cochlear segmentation. The potential of tomographic imaging techniques, especially MRI, to deliver three-dimensional (3D) image stacks of the VT has been exploited, for instance, to analyze area functions during sustained phonation , 11. So A few centerline-based methods have been applied to the VT. However, most studies focus solely on the extraction of the area function by employing 2D methods such as threshold segmentation , 15. A 3sinus piriformes) and vallecula. On the other hand, vocal tract cross-sectional area has attracted considerable interest within the research community T is defined asFrom these definitions, we could derive the following affine transform matrix for each Econt is the contour energy which controls the expansive behavior of the contour. It is defined by a first-order derivative of the active contour curve function v(tv):Ecurv is the curvature energy which models the bending stiffness of the contour via the second-order derivative of the curve function:Eext provides the contour with edge detection and is proportional to the negated square norm of the grayscale gradient:Edev provides cross links between contours on adjacent tomogram images:d is the distance to the nearest node of the contour on previous image. In the first contour, this energy is set to zero. The Greek letters in (The segmentation was performed with a greedy variant of active contours . A circun v(tv):Econt=α·∇m images:Edev=δ·d4n = 30). On the next image, a new contour was initialized with the end result of the previous image.A search for the local minimum of the energy sum over all nodes was performed. This caused the contour to expand and adapt to the cross section of the vocal tract iteratively. When the contour stopped moving, the finding of local energy minimum was stated and the algorithm moved to the next image. Alternatively, the processing of the contour on an image stopped when the number of iterations exceeded a predefined threshold .Using the abovementioned methods, we were able to segment cross sections along the entire vocal tract . Total tTo estimate algorithm objectivity, we performed the segmentation including centerline positioning twice. Out of the segmented data, we computed the area functions which are plotted against each other on 3. The volume of the oral cavity was 25965 mm3, the combined volume of oropharynx and hypopharynx (segments from uvula to arytenoids including the sinus piriformes) was 21900 mm3, and the volume of larynx (segments from arytenoids to glottis) was 2663 mm3.We calculated reference volumes by superposition of segment areas. The total volume of the VT was estimated at 50528 mmhttp://www.fon.hum.uva.nl/praat/). It is displayed in As a method of validation, we calculated the acoustic transfer function of the VT using PRAAT (Paul Boersma and David Weenink, With the obtained VT cross-section model, the geometrical analysis within all three spatial dimensions becomes feasible. The complete set of segmented cross sections, transformed from centerline-based coordinate system back to the global coordinate system , is showThe accuracy of the model is dependent on the used MRI tomography device and the stability of the test subject over time. A natural challenge to the stability is the requirement to the subject to keep a constant articulatory setting and to maintain phonation during the entire recording procedure. This is necessary to produce enough images to cover the whole VT. There are hints that the movement artifacts of the jaw during sustained phonation are in the submillimeter order . Yet thi2, depending on the area value.The technical accuracy of the segmentation algorithm is constrained by the obtained resolution of MRI images. Since active contours cannot perform segmentation to a higher precision than 1 pixel, the uncertainty in border estimation corresponds to the resolution value, that is, 1.04 mm, which is well in range of state-of-the-art publications , 18. HenThe amount of input image data calls for a time-efficient and at least semiautomatic algorithm in order to reduce man-hours spent on segmentation. Unlike vessels whose cross-section shape does not vary much along the centerline, the VT has highly variable cross-section geometry cf. . A heuriBy the presented approach for a complete MRI based 3D segmentation of the VT at a functional state, we were able to obtain a high-detailed model. The method could be used for answering questions regarding the physics and mechanical properties of the VT.


The well-known saying of “Seeing is believing” became even more apt in biology when stimulated emission depletion (STED) nanoscopy was introduced in 1994 by the Nobel laureate S. Hell and coworkers. We presently stand at a juncture. Nanoscopy represented a revolution in fluorescence microscopy but now is a mature technique applied to many branches of biology, physics, chemistry, and materials science. We are currently looking ahead to the next generation of optical nanoscopes, to the new key player that will arise in the forthcoming years. This article gives an overview of the various cutting-edge implementations of STED nanoscopy and tries to shine a light into the future: imaging everything faster with unprecedented sensitivity and label-free. As a result, the ability to quench fluorophores for a given STED laser power substantially improves. In the particular context of imaging, the longer the time-delay Tg between the excitation and the fluorescence collection, the more one ensures that the fluorescence is recorded mainly from fluorophores located in the doughnut center in which the STED beam intensity is “zero”, and thus, the higher is the effective spatial resolution. Unfortunately, the reduction of the effective collection volume comes together with a reduction of the overall signal that forms the image  lasers as the STED beam  microscopy is a well-known technique for the study of thick biological tissue. Although it relies on a non-linear quadratic dependence of the excitation power, 2PE does not improve the spatial resolution per se. As it was first demonstrated in 2009  represents an established method for studyng the motion of single molecules and its binding properties in solution, cells and tissues. Moreover, scanning microscopy imaging has been applied to add a spatial dimension to the classic FCS modality: spatiotemporal FCS (stFCS) measures the time and the routes that the diffusing particles or molecules follow to go from one place to another in the specimen , Abberior Instruments , and Picoquant  offer easy-to-use and flexible commercial STED nanoscopes. Moreover, many components of a STED nanoscope are now cheaper and easy to buy, even making simpler the achievement of custom-made STED nanoscopes. This will probably mean that STED nanoscopy will become a routinely used technique in many labs and microscopy facilities.The future challenges remain the usual ones: the attainment of new horizons in terms of speed, depth, and color multiplicity, while maintaining resolution at the molecular level in a living biological specimen.


Given the year on year decrease of rural farmland and various forms of land degradation through the intrusion of non-farm land uses, the government of Bangladesh has drafted the agrarian reform strategies, primarily to protect the agricultural land from encroachment, conversion, and indiscriminate use. The draft Agricultural Land Protection and Land Use Bill since its inception in 2011 is facing serious uncertainties of implementation due to its borrowed nature from the developed contexts and inadequacy to recognize the local complexities. With a particular focus on the densification component of the draft bill, a semester-long design studio was conducted in consultation with the existing villagers to explore the practicability of the draft bill in the villages of Tetultala and Chhoygharia in the south-western coastal Bangladesh. The findings from the two villages hint that in Bangladesh, the unique and evolving nature of rural settlements dynamics that are disintegrating the rural society from farming practices and the farmland, thereby, unsettling the traditional village-morphology. The settlements dynamics vary from those of the western context; hence, there is an emerging need to build locally situated knowledge towards a feasible rural land reform. The rural farmland is in continuous decay as the unique and vernacular human-land interaction of Southeast Asia  is contested through urbanization and related development aggravations. Bangladesh is no exception with two-third of the populations lived in villages in 2014 FAO . 41.54 %Draft Agricultural Land Protection and Land Use Bill in 2011  and policy context of Bangladesh. Only the 1950 State Acquisition and Tenancy Act and the 2001 National Land Use Policy feebly touched upon the land reform strategies by acknowledging the restriction of agricultural land against non-farm purposes. However, there remained plenty of room for relaxation, the restrictions were largely ineffective LANDac . There wDraft Agricultural Land Protection and Land Use Bill of 2011 is a response towards the recent ineffective policy context. It aims to bring villages under prudent ‘zoning principle’ to stop any illegal captivation of agricultural land, as often the politically and financially powerful grabbers hold large ceiling of agricultural land for non-farm development. The bill demonstrates a strong determination to restrict the existing agricultural land only for cultivation under any circumstance. Under the resolution, all the ‘Khas’ lands must be retained for cultivation only. Existing landscape elements  are advised to retain intact and non-convertible to other usages under any circumstances. Apart from the protection measures, the bill principally encourages the compact development strategies for the existing non-farm lands to ensure maximum utilization and minimum wastage of spaces. Acquisition of land for infrastructure is advised to keep the minimum. Most importantly, ‘vertical extension’ of rural dwelling spaces has been advised as a priority measure for new built-forms conforming local development plan, rural characteristics, and the environment.The The proposal for a systematic compaction of the non-farm uses to the non-farm lands in order to keep the farmlands free from non-farm occupation indicates a major paradigm shift. The increase in development intensity through vertical extension of built-forms in non-farm land parcels implies that there would be increasing number of population in the homestead land. The intention to increase density in the homestead land would eventually demand alteration in the traditional rural resource management system. Within the limited resource based third world villages the proposal of a compact rural form seems apparently incompatible to the highly informal and relatively disperse rural settlements systems. In addition, while density design to achieve a compact and sustainable urban form is questioned for traffic congestion, air pollution, unhealthy living and psychological discomfort . Prevalence of these multiple socio-spatial settlements dynamics holds back in achieving any common consensus towards cooperative farming, utilizing shared farming technologies or protection of the agricultural land.Rural settlements present a spatial facade to the aspatial structures and processes . Commitment in implementing the detail regulations and enforcements should further suffice such social restoration process.Secondly, every measure taken for physical restructuring of the rural land should be sensitive enough to promote agricultural practices directly or indirectly. As revealed from the design exercise that the provision of efficient infrastructure services is a prerequisite towards compact development, however, cautions should be taken so that the non-farm land-economy is not stimulated. Non-farm land uses to the farmland must not be permitted. However, it is even more necessary to decide the compatibility of non-farm uses that are invited in villages. Non-farm uses  should conform to the nature and capacity of the agricultural production system of that particular rural setting so that together the protection of arable land and the better management of non-farm land uses—the densification strategy as a whole can render the villages as the engines of growth and harmonize with the regional setting.Thirdly, emphasis has to be placed on looking for an intelligent rural form that creates a renewed rural–urban interdependency. Therefore, a more agro-market responsive rural to regional linkage has to be established to redistribute the agrarian labor force. Density design has to incorporate selective mix of non-farm uses in villages with authentic non-agrarian demand and should complement to those in the nearest growth or urban centers. Due to the availability of no-farm jobs within an integrated agro-economic system, the rural society may avoid the depopulation syndrome prevalent among the rural marginal, thereby reducing excessive dependency on urban centers. Within such a reciprocal land use distribution, the rural and urban will not be rivalry geographic entities but sustain and grow together.Nevertheless, it is inevitable that the agrarian social structure will be constantly influenced by the globalization driven changes and urban expansion . The true prospect of densification will be relied on how the spatial structures of the rural system fill the gaps created in the social structure with explicit consideration of the carrying capacity of the given rural system to the extent that the characteristics of the ‘rurality’ is not jeopardized. The southern perspectives on the settlements dynamics would help to recognize those gaps within the degraded human-land relationship than blindfolded by globalization and its western progenies.The paper confirms that because of the ongoing structural and spatial changes of the present day rural settlements similar to many of the developing countries, more context specific strategiec opportunities should be sought after to cope with the evolving settlements dynamics. While there is no harm to employ the idea of rural densification, however to qualify itself as a feasible planning tool, it must have to satisfy the dynamics that influence the ongoing socio-spatial changes of villages in Bangladesh. No matter how innovative the proposed development control appears to be, it has to be realistic, hence adaptable for the rural communities not only to stop any indiscriminate use of land but also sustain growth.The 21st century villages in the developing context of Bangladesh are in the constant process of convergence with the urban. Increasing rural–urban linkages, accessibility to non-farm jobs and urban lifestyle reduce the differences in social-cultural practices thereby the spaces are reconfigured to fit the earlier ones. However, the majority idealization of the ‘rural’ is conveyed more by emotion than rationales and the changes are left unappreciated. Therefore, it is no surprise that, decision making for the villages is governed by the biased mental construction of the rural; thereby fails to correspond to the contextual dynamics allowing intrusion of conflicting strategies.Given the present-day unwanted changes in the rural land use and multiplicity in depletion of the rural resource base, government’s attempt towards the agricultural land protection and land use bill may have some prospect. But for that, the two-fold objectives of the densification component need to be adequately realized in relation to the recognition of the settlements dynamics that are imbuing a subtle separation between the villagers and their land. Densification as a land use tool takes control of the physical redistribution of the agricultural and non-farm spaces. However, its real prospect will depend on how well it re-cultivates the value of agricultural practices in the diminishing agrarian mindset among villagers.Contextual exploration of the rural settlements dynamics reveals that the dynamics are neither similar to those prevalent in the western context. Hence, there is an emerging need to build locally situated knowledge for any land regulation measure  to be successful. In fact, this the time to ‘think universally, see globally, behave regionally, act locally but insightfully’ Singh , p. 130 To conclude, given the year on year decreasing per capita land parcel in rural areas, the need for a land reform strategy cannot be ignored. With increasing contestation of rural settlements dynamics within the fast changing globalizing world, the paradox of a sustainable rural future will depend on the sensitivity to recognize the context-specific dynamics that drive the socio-spatial changes. The dynamics should be mutually inclusive towards the processes of densification or any other creative land reform strategy. Failure in doing so could render the villages victim of the bias created either by our romantic ideation about the ‘rural’ or the hegemony of knowledge we often borrowed from the West.


Flaviviridae and Bunyaviridae and helicases of superfamilies 1 and 2 that have homologs in cellular organisms. The second pattern can often be explained by horizontal gene transfer from the host or between viruses, an example being Orthomyxoviridae and Coronaviridae hemagglutinin esterases. Another facet of horizontal gene transfer comprises multiple independent introduction events of genes from cellular organisms into otherwise unrelated viruses.The origin and evolution of viruses is a subject of ongoing debate. In this study, we provide a full account of the evolutionary relationships between proteins of significant sequence and structural similarity found in viruses that belong to different classes according to the Baltimore classification. We show that such proteins can be found in viruses from all Baltimore classes. For protein families that include these proteins, we observe two patterns of the taxonomic spread. In the first pattern, they can be found in a large number of viruses from all implicated Baltimore classes. In the other pattern, the instances of the corresponding protein in species from each Baltimore class are restricted to a few compact clades. Proteins with the first pattern of distribution are products of so-called viral hallmark genes reported previously. Additionally, this pattern is displayed by the envelope glycoproteins from  Unlike cellular organisms, viruses do not constitute a monophyletic group, in which the phylogenetic history can be traced back to a common ancestor. The origin and relatedness of different virus families is currently a subject of active discussion. It is unclear whether viruses have evolved by reduction of genes from cellular species, whether they descended from mobile elements of other organisms or whether they precede cellular life and are ancient self-replicating units. Possibly, all of these hypotheses are true, for a subset of viral families . The recViruses are routinely classified according to the composition of their genome and the morphology of the viral particle, as defined by the International Committee on Virus Taxonomy , into faAnother approach to the inference of viral phylogenies, capable of reaching into the distant past, is based on the comparison of the three-dimensional structures of viral capsid proteins . It is wFinally, the evolution of viruses can be traced beyond the virus world itself. Virus evolution has been linked to selfish genetic elements ,16. By mHorizontal gene transfer (HGT) is one of the major forces that drive evolution. It is recognized to play a major role in the evolution of bacterial virulence and resistance ,18. ViruEmiliania huxleyi and its large dsDNA virus EhV-86 [Chordopoxvirinae, a subfamily of the Poxviridae family, have acquired many proteins from eukaryotes, which is evidenced by their higher similarity to the eukaryotic counterparts than to other viral proteins [There is anecdotal evidence that some viruses have experienced horizontal gene transfer. It has been proposed that viruses of the nucleocytoplasmic large DNA virus (NCLDV) group have acquired many of their genes via HGT . For exas EhV-86 . Howeverproteins . The Spuproteins . On the In this work, we have used proteins with detectable sequence or structural similarity from distant viruses, such as those with different genome types, as a starting point to investigate the evolutionary relationships among viral and cellular proteins. The major goal of the study was to detect the unlikely event of horizontal gene transfer between seemingly unrelated viruses. Through comparison of protein sequences, we have identified proteins from several protein families that appear in very distant viruses, with a conserved function, but with an origin that cannot be traced back to a single viral class, possibly pointing to several HGT events from the host. The sequence similarity in viral proteins quickly becomes undetectable with the increase of evolutionary distance. Here, we report an all-to-all comparison of the three-dimensional structures of viral proteins from different function classes that reveals many more relationships between proteins of viruses in different Baltimore classes, both structural and enzymatic. Overall, we detect two patterns in the evolution of viral proteins. Some protein families are populated by many viral proteins originating from species in different Baltimore classes and probably date back to ancient evolutionary events that led to a wide spread of certain folds in viruses. In other cases, only a small set of viruses harbor proteins similar to those from an unrelated family, in which case horizontal gene transfer events can be usually pinpointed, both between viruses and from host to virus.Methodologically, the study consists of two parts: a comparison of protein sequences and structures. For the sequence comparison, all complete viral proteomes (as of June 2015) were downloaded from UniProt , and allFor the structural analysis, known three-dimensional structures of viral proteins that are the major components of the virion particle  and structures of viral enzymes were collected from the Protein Data Bank  in Januaame fold , so onlyame fold  structurame fold .All complete viral proteomes available in the UniProt database , comprisFor each hit pair, we searched the Pfam database  for the The Pfam families are unevenly populated with pairs of similar proteins from distant viruses, the dUTPase family having the highest number of hits. We analyzed the conservation profile in all families and in the identified viral proteins. Where a three-dimensional structure of one of the proteins was available, we have mapped sequence differences onto it and analyzed their distribution with respect to active sites and other important protein regions. Next, we collected all viral sequences significantly similar to the HMM profile of the corresponding Pfam family and constructed maximum likelihood phylogenetic trees with bootstraps. The analysis of these trees reveals two distinct patterns of the distribution of viral sequences: either sequences from viruses from two Baltimore classes are present in comparable and large numbers, and the bootstrap support values do not allow for distinguishing whether the parts of the tree covered by each Baltimore class are monophyletic ; or one or very few sequences from one Baltimore class cluster together on a single branch of the phylogeny with a good bootstrap support, and the rest of the tree is interspersed with sequences from viruses from the other Baltimore class . We will consider now each of these scenarios in detail and discuss the possible evolutionary implications.Several protein families exhibit a balanced distribution of the virus species from the two Baltimore classes in question. Examples include the RNA-directed RNA polymerase family 1 , RNA helicase , the parvo replication initiator NS1 family  and the helicase C family  .Circoviridae, where a critical lysine in motif A can be replaced by an arginine or a glutamine [RNA-directed RNA polymerases (RdRP) and RNA helicases of superfamily 3 (S3H) are established hallmark viral proteins and were proposed to be present already in the ancient virus world . RNA hellutamine . In the lutamine . Howeverlutamine .Parvoviridae, 62% of sequences identified as members of the parvo NS1 family in our analysis are E1 proteins from Papillomaviridae, and three and six proteins come from Betaherpesvirinae and Adenoviridae, respectively. Each of these groups is located on a separate branch with a high bootstrap support. For all of these proteins, their annotation suggests their involvement in DNA replication, and the P-loop motif characteristic for ATP/GTP binding is well conserved in the whole family , the catalytic domain of the serine/threonine and tyrosine kinase family , the phage integrase family , the Hema esterase family , the OrfB IS605 domain family that is found in transposases  and the MMTV SAg family (dsDNA and retroviruses) .Herpesvirales, Poxviridae and various bacteriophages. Each of the viral families resides on a separate branch of the phylogenetic tree; however, the bootstrap support of these branches is low. In certain cases, there is significant sequence identity between the viral protein and the dUTPase from its host, e.g., Orf virus dUTPase is 72% identical to the human dUTPase and has a GC content that is closer to that of human than of the virus itself , suggesting recent HGT from human to virus. The bifunctional nucleocapsid-dUTPase protein from Mason-Pfizer monkey Betaretrovirus (MPMV) and protease from simian retrovirus exhibit significant sequence similarity to this family; and the MPMV protein has been proven to be an active dUTPase [dUTPases are ubiquitous in cellular organisms and catalyze the hydrolysis of dUTP to dUMP to prevent the incorporation of dUTP into DNA . These p dUTPase . Phyloge dUTPase  are wellAlpharetrovirus) protein Src. The proteins closest to Src are the eukaryotic kinases Fyn and Fgr  and is significantly similar to hemagglutinin-esterases from Torovirus and Betacoronavirus (positive-strand ssRNA), and all three viral species form separate clades with high bootstrap support D. The keersa see .Escherichia coli transposase, and its GC content is more similar to that of E. coli than to that of the phage itself . Thus, we can reasonably assume that this domain was introduced into Enterobacteria phage If1 from its host.The C-terminal transposase domain OrfB IS605 is found in bacteria and in a variety of dsDNA bacteriophages, as well as in a single ssDNA Enterobacteria phage If1 E. It is Herpesrvirus genus: immediate-early protein IE-G from saimiriine herpesvirus 2 and mitogen from ateline herpesvirus 3, which are quite distant and separated with a good bootstrap support from the rest of the family  and the structure of the capsid protein V20, belonging to the unclassified virophage Sputnik  Table 2B. The SpBunyaviridae and Flaviviridae [C. elegans, although sequence homology is not detectable , negative-strand ssRNA viruses  and positive-strand ssRNA viruses  D. All ofAs noted above, the superfamily 3 helicase family is one of the viral hallmark protein families , and simFor other helicase superfamilies, we detected a group of structurally-similar superfamily 1 helicases (S1H) containing proteins from viruses with positive-strand ssRNA genome (tomato mosaic virus strain tomato/L and equine arteritis virus strain Bucyrus) and with dsDNA genome (Enterobacteria phage T4) and of structures of the superfamily 2 helicases (S2H) containing proteins from viruses with dsDNA genome (Enterobacteria phage T4) and two structures of HCV NS3 protease/helicase proteins belonging to positive-strand ssRNA viruses (hepatitis C virus subtype 1b and Dengue virus 4). All of these structures can be readily superposed using their C-terminal RecA-like domain F, a motiBoth groups of helicases also have structural homologs outside the viral world. Viral helicases of superfamily 3 have significant similarity to several human proteins, in particular the human AAA+ ATPase Tip49b , the papain-like protease 2 of equine arteritis virus and the papain-like cysteine protease of porcine reproductive and respiratory syndrome virus. Sequence similarity searches deliver no clues about the evolutionary history of these proteases. Indeed, homologs of the protease of the SARS coronavirus can be found in a variety of beta- and gamma-coronaviruses, while the homologs of leader protease of the foot and mouth disease virus can be found only within the genus of Aphthovirus and of the ubiquitin-specific protease of murine herpesvirus 1 in a small set of cytomegaloviruses. Despite the lack of sequence similarity, these proteins share the same papain-like fold. A search for structural similarities with DALI [In the set of 36 protease structures, we detect similarity between cysteine proteases with a papain-like fold belonging to viruses with positive-strand ssRNA and dsDNA viruses A. In parith DALI  revealedE. coli with 65% sequence identity and an e-value of 1e-138, and viral homologs are detected in numerous coronaviruses. The homologs of methyltransferase from Vaccinia virus can be found in several other poxviruses, while the homologs of the Wesselsbron virus are restricted to flaviviruses. However, no sequence homology between these three groups is detectable. On the other hand, structural similarities can be observed between these enzymes and methyltransferases of prokaryotic and eukaryotic organisms and may suggest gene transfer events  and positive-strand ssRNA genome (SARS coronavirus and Wesselsbron virus) . The supr events . Again, Coronaviridae), whose structure is unknown, is similar to these two with sequence identity of about 30%. Structural similarities were previously reported between hemagglutinins of influenza A, B and C in the HA2/HEF2 chain. Similarity between the hemagglutinin esterases of coronaviruses and orthomixoviruses has been reported to be a result of lateral gene transfer events [Coronaviridae and subsequent loss in all other genera is unlikely.The second group of structurally-similar envelope proteins comprises glycoproteins belonging to negative-strand ssRNA viruses  and to betacoronavirus of the Coronaviridae family with positive-strand ssRNA genome C. The Asr events . Howeverr events , either We have performed a comprehensive analysis of similarities between viral proteins from different Baltimore classes, both at the level of sequence and of structural similarity. It is generally assumed that viruses are polyphyletic. Nevertheless, some key viral proteins, such as RNA-dependent RNA polymerases, appear to be related in very large groups of viruses, possibly all RNA viruses . It has Our procedure involves both sequence and structural comparison. Both parts have their limitations: on the one hand, only a fraction of viral proteins have a resolved three-dimensional structure; on the other hand, comparing protein structures allows for discovering remote homologies unrecognizable by sequence comparison. Naturally, the two parts complement each other in the analysis of certain proteins . A sequeFunctionally, a unifying theme is that almost all of the proteins, the similarity of which can be detected from sequence comparison, are related to nucleic acid processing or modification: dUTPase, transposase, helicase and RNA polymerase. Comparison on the structural level adds viral structural proteins  and some protein- and RNA-modifying enzymes  to this pool. Thus, we observe a network of evolutionary links between the proteins that perform very basic biochemical and structural functions in different virus families.Studying relationships among proteins found in viruses of different Baltimore classes that nevertheless exhibit significant sequence or structural similarity, we observe two distinct patterns. Either both virus classes are represented by a large number of proteins, and the evolutionary relationships between the viral proteins  are ancient and difficult to resolve; or one of the two or both groups of proteins are present only in a few genera, often one group being underrepresented relative to the other. In the latter case, HGT can often be proposed. When comparing protein sequences, we call the first pattern “balanced sequence distribution” and the second pattern “unbalanced sequence distribution”. Since the sequence homology is in all cases evident, we can be sure that we deal with a monophyletic protein family and, hence, use phylogenetic analysis and discuss the evolutionary events that happened in that family. In the comparison of protein structures, we set the algorithm parameters such that we discover proteins that have the same fold according to the CATH  classifiUsing the comparison of protein three-dimensional structures, we detected similarities between viral capsids of the jelly-roll fold that are characteristic to a variety of species and also were reported among viral hallmark proteins . The douFlaviviridae and Poxviridae, the available data are insufficient to draw any concrete conclusions, but their structural similarity to proteases and methyltransferases of cellular organisms suggests that genetic transfer events happened very anciently. However, for both proteases and methyltransferases, we cannot rule out that the similarities observed with proteins of cellular organisms are the result of convergent evolution processes that caused the same fold to be present in viral and cellular proteins. Further investigations will be needed in order to elucidate the origin and evolution of these two classes of viral proteins.We have found several cases in which the structural similarity is limited to a few species and sheds light on the evolutionary relationships that otherwise are difficult to interpret due to the lack of traceable sequence similarity. In the case of hemagglutinin esterases of coronaviruses and orthomixoviruses, lateral gene transfer, but not its direction, has been previously reported . We can MMTV superantigen (SAg) family proteins are specific to very particular viruses. We observe these proteins in unrelated virus families, where the proteins exhibit significant sequence similarity and conserved structure and function and, hence, very likely share the same evolutionary origin. Gene transfer from a common (possibly past) host is not likely, since these protein families are not represented in cellular organisms. Options for how these genes could have been acquired include gene exchange between viruses, e.g., through co-infection of the same host and recombination, or convergent evolution.An interesting pattern of genetic exchange arises in bacteriophages. Bacteria have already been observed to donate their genes to phages , which lHGT can be detected in protein families with a variety of methods, which can be divided into two groups. The first group of methods is based on the analysis of a reliable phylogenetic tree of the protein family: any event in this tree that contradicts the species tree is a candidate for HGT . The secThe elevated rates of evolution, especially in RNA viruses, could be a reason why HGT events are so difficult to detect in viruses. In the absence of a common evolutionary tree for viruses beyond their immediate families, phylogeny-based methods for HGT detection are inapplicable. Sequence statistics also does not offer a reliable tool, since the transferred segments can adjust very quickly to their new background. In fact, the only case, when the GC content analysis reliably supports HGT, involves DNA phages (OrfB IS 605 family) or dsDNA poxviruses (dUTPase of the Orf virus). Given the indications that HGT in viruses is quite common, at least in some clades, such as NCLDV ,83, therCoronaviridae. These few examples hint at the potential multitude and scale of these events, which can be only fully appreciated if more data on three-dimensional structure of viral proteins become available, which may paint a much more complex picture of viral evolution than previously expected.The extension of sequence comparison analysis at the structural level elucidates the same pattern of genetic exchange as those revealed by sequence analysis: probable acquisition from a common ancestor, potential lateral transfer from other viruses and multiple introductions from the host. Additionally, structural analysis led to new findings, e.g., it allowed us to identify an interesting example of envelope glycoproteins that are related to a cell-cell fusion protein and also resolve the evolutionary history of hemagglutinin-esterase introduction into


Limitations of the currently used standard DTI protocols were exacerbated by large scanner-to-scanner variability of the connectivity-based targets.Functional stereotactic neurosurgery by means of deep brain stimulation or ablation provides an effective treatment for movement disorders, but the outcome of surgical interventions depends on the accuracy by which the target structures are reached. The purpose of this pilot study was to evaluate the feasibility of diffusion tensor imaging (DTI) based probabilistic tractography of deep brain structures that are commonly used for pre- and perioperative targeting for functional neurosurgery. Three targets were reconstructed based on their significance as intervention sites or as a no-go area to avoid adverse side effects: the connections propagating from the thalamus to (1) primary and supplementary motor areas, (2) to somatosensory areas and the cerebello-thalamic tract (CTT). We evaluated the overlap of the reconstructed connectivity based targets with corresponding atlas based data, and tested the inter-subject and inter-scanner variability by acquiring repeated DTI from four volunteers, and on three MRI scanners with similar sequence parameters. Compared to a 3D histological atlas of the human thalamus, moderate overlaps of 35-50% were measured between connectivity- and atlas based volumes, while the minimal distance between the centerpoints of atlas and connectivity targets was 2.5 mm. The variability caused by the MRI scanner was similar to the inter-subject variability, except for connections with the postcentral gyrus where it was higher. While CTT resolved the anatomically correct trajectory of the tract individually, high volumetric variability was found across subjects and between scanners. DTI can be applied in the clinical, preoperative setting to reconstruct the CTT and to localize subdivisions within the lateral thalamus. In our pilot study, such subdivisions moderately matched the borders of the ventrolateral-posteroventral (VL Functional stereotactic neurosurgery by means of deep brain stimulation or ablation provides an effective treatment for movement disorders including Parkinson’s disease , essentiLocalization of the target structures can be performed either directly from MRI or indirectly from atlas coordinates and predefined anatomical landmarks . Given tIn the clinical setting, while the relaxation properties depicted by T1- or T2-weighted MRI offer limited insight into the anatomy of the thalamus, recently, diffusion MRI based techniques have been suggested as a way to visualize various subdivisions representing possible target locations within the thalamus . DiffusiThe direct mapping of the thalamic nuclei, such as the Vim through diffusion tractography is not possible, since the lateral thalamus acts as a relay station with specific and somatotopically organized cortico-thalamic connections . HoweverIn addition to the thalamic nuclei, other structures including the dentato-rubro-thalamic or CTT encompass fibers that act to coordinate the initiation, planning and timing of movement, and accordingly have also been proposed as a putative target for the alleviation of symptoms in various motor disorders . Diffusipv nucleus), or as a putative white matter target for ablative therapy , and lastly, a possible “no-go” area to avoid adverse effects during deep brain stimulation or ablative therapy (VPL nucleus with somatosensory connections). We aimed to test the feasibility of tractography using the following criteria. First, to demonstrate the correspondence to the histologically defined anatomical structures for the targets that have digitized 3D atlas data  in common neuroimaging space. Secondly, since various aspects of reproducibility represent a predictor of the expected accuracy of targeting during neurosurgery, we set out to quantify the reproducibility of tractography from the observed variability in target position across subjects and the corresponding reproducibility within a subject but on different MRI scanners.The purpose of our pilot study was to evaluate the technical feasibility of DTI based probabilistic tractography for localizing target areas that are commonly used during functional neurosurgery. Three targets were selected for this evaluation based either on their importance as a primary location targeted for the alleviation of the symptoms of movement disorders  repeated scans were acquired on clinical MRI scanners of different manufacturers resulting in 10 different acquisitions. Diffusion weighted magnetic resonance imaging (DWI) sessions were carried out on three different 3.0T MRI systems: Siemens Skyra , GE Discovery MR750  and Phillips Ingenia , using spin echo echo-planar imaging sequences. The DWI acquisition details are summarized in The following image processing steps were performed on the DWI data: (1) estimating symmetric tensors in each voxel of the diffusion data and using the tensor’s eigenvalues to calculate secondary to generate DTI data, parametric maps, such as the fractional anisotropy images, (2) spatial standardization of DWI data to a standard neuroimaging template space, (3) estimation of intra-voxel distribution of multiple fiber populations. We carried out the DTI processing steps with the FMRIB Diffusion Toolbox in the FSL software package . FractioFollowing the methods reported in the literature , thalamiin vivo studies on the cortico-thalamic system  in the relevant standard atlases , VPLp: ventral-posterolateral nucleus posterior part and the whole-thalamus volume for visualization purposes. The MNI-transformed thalamus atlas and the atlas based targets used in our study are illustrated in Supplementary Figure S1. For illustration purposes, we used the histology based reconstructions provided by the Big Brain Atlas. This representation was based on the optically balanced non-linear matching of histological sections to a standard neuroimaging space  were extracted from probabilistic maps, their borders must be defined using arbitrary probability thresholds. The threshold choice will inherently affect the size of the target volume and its overlap percentage with the atlas based nuclei, e.g., a lower connectivity threshold will result in higher overlap between subjects, but the volume will be less specific to the targeted anatomical location. To achieve identical criteria for the threshold ranges across the study subjects, we used a range of percentile values of all non-zero voxel intensities to cut-off the connectivity values from the 50th to the 100th percentile, thereby selecting the voxels with the highest probability of connection to the cortical target regions. In practice, 50th percentile threshold meant that a large volume of the CBT is kept including regions of low probability of being interconnected to the relevant cortical region, while a 100th percentile threshold corresponded to the point which had the largest probability of connection to its target. All overlap and distance metrics were iteratively determined over this entire range of values. The dependency of the overlap with anatomical landmarks and the reproducibility was tested by using the following quantitative metrics:1.fslstats tool in the FSL software package.Euclidian distance (mm) between the center-of-gravity (mid-point) point of the CBT and the relevant atlas targets. The coordinates for the center-of-gravity points of the thresholded connectivity maps and the atlas targets were calculated using the 2.Volumetric overlap between the CBT and atlas targets, determined by the Dice overlap (0–1), The Dice overlap coefficient D was defined as:where X and Y are the two volume sets to be compared.The first metric assumes that we use point-like targets, either determined by connectivity or a 3D atlas model, the latter assumes that the targets consist of volumetric label maps relevant for surgical localization. Quantitative evaluations of the connectivity maps and 3D voxelised atlas volumes and the visualizations were carried out with Matlab R2010a for Windows  using in-house developed algorithms.pv nucleus to two CBTs: the CBT using the precentral gyrus as target  and the CBT using a more extended a priori target list of the VLpv nucleus (“VLpv connections”).The center-of-gravity points of the connectivity maps were located inside the atlas based targets in all cases and all scans. Center-of-gravity points are organized according to somatotopy along the lateral border of the thalamus, such that the connections to precentral gyrus are more anterior than the postcentral gyrus connections. In the present study, we described the correspondence of the VLpv or VPL nucleus, and only partially overlap with more medially located thalamic nuclei with known motor connectivity, such as the centro-median (CM) nucleus. In the midline, the VLpv was oriented more anteriorly, while the CBT defined this nucleus differently: the connectivity-based segment appeared wedge-shaped , with the tip oriented toward the center of the thalamus.While there was a good agreement of the volumes along the antero-posterior axis of the most lateral parts of the thalamus, the connectivity patterns did not overlap with the medial parts of the VLpv nucleus were lowest on the population level when the connectivity maps were thresholded at the 65–70th percentile (left side) and between the 80th and 90th percentile (right side), and remained below 2.5 mm if the threshold was set to lower than the 85th percentile on the left side and 92nd percentile on the right side . The VLpv nucleus overlapped modestly with the CBT, demonstrating greater overlap in the lateral thalamus regions . While the lateral segments were correctly matched visually to the VLpv nucleus, the maximum value of the Dice coefficient was modest, median D = 0.32  and median D = 0.30 . While the calculations were carried out in MNI152 standard and symmetrical template space, and the thalamic seed volumes were also symmetric, we noted an apparent left–right difference between the CBT volumes . Regarding other nuclei that showed minimal overlap with the CBT of the VLpv: the CM nucleus showed no consistent overlap with the CBT volume , and only moderate overlap was observed in the most medial regions of the CBTs.The mid-point distances between CBT and the anatomical target for the VLpv volume : the CBT calculated using a single precentral target region overlapped slightly more with the atlas target compared to the CBT defined with additional  targets, and no interhemispheric asymmetry was observed . Using a threshold at the 89th percentile (left side) and 88th percentile (right side), a median Dice overlap of 0.34 and 0.31 was measured on the left and right sides, respectively . The pointwise distances from the atlas based targets were in the range of 2.2–3 mm in the connectivity range between the 70th and 86th percentiles .The precentral connections also showed moderate overlap with the atlas based VLp (and VPLa) was larger . The mid-point distances between CBT and anatomical target for the VPL nucleus were lowest on the population level when the connectivity maps were thresholded at the 90th percentile, with median distances of 2.1 mm for the left and 2.4 mm for the right side . The volumetric overlap between the CBT and the ABT for the VPL nucleus was generally higher than in the other examined thalamic nuclei. Consistent with results from the other nuclei, the maximum overlap was achieved by thresholding around the 90th percentile of the connectivity values .In comparison with the previous two targets, the overlap between the postcentral gyrus connections and the atlas based VPLFigure 3C at the 90th percentile threshold), the inter-subject reproducibility was decreased at larger connectivity thresholds. Therefore, it was necessary to determine a consensual cut-off value which represents a trade-off between the two feasibility metrics. First, we investigated the effect of connectivity probability thresholding on the expected reproducibility of CBT maps. The lower the connectivity threshold, the higher the inter-subject overlap . This means, however, that the CBTs become less specific for the given neurosurgical target: as the connectivity threshold values converge to zero, almost the entire thalamus is included as a potential target volume, which is anatomically incorrect. In order to find the optimal connectivity-based threshold, representing a compromise between larger inter-subject reproducibility and larger overlap with the atlas target, we multiplied the two overlap metrics and located the maximum value of this combined metric . To provide a consensual value, we multiplied the median values of population-overlaps and the median values of the correspondence to the anatomical atlas. In a similar manner as for the overlap with the anatomical target , the consensus metric also had a maximum, which was used to determine the optimal cut-off value .Our results suggest that while the agreement with the atlas targets is higher if the connectivity threshold is increased  was found to be more strongly connected to its pathway targets. Compared to the intra-thalamic targets, the CTT reconstruction showed significantly larger variability across the population. In some cases, the pathway was split and alternative routes were taken, possibly due to errors in the estimation of crossing fibers. In some cases, zero or a very low inter-subject overlap was reported using the 95th percentile of connectivity values, possibly due to splitting of the pathway into alternative routes, or due to the diminished sensitivity of locating the fibers at a single connectivity threshold. As observed for the intra-thalamic CBTs, the inter-scanner variability was similar to the inter-subject variability (Table 2).The CTT was reconstructed for each of the 10 datasets of the four participants. The typical trajectory of the DTI based reconstruction was the following. The tract emerged from the ipsilateral dentate nucleus, crossed to the opposite hemisphere at the level of superior cerebellar peduncle, and then passes medially and across the red nucleus. Cross-sectional images with annotations of the thalamic nuclei and major anatomical landmarks are shown in Figures 6A,B,E,F), the VLpv CBT showed considerable variability , as well as a larger volume and a modest interhemispheric asymmetry. We investigated the inter-scanner reproducibility of the CBT structures by comparing both the volumetric overlaps and the center-point distances. The maximum of the volumetric overlap was observed at similar connectivity percentiles as in the previous experiments; however, the center-point distances measured showed considerable differences between scanners of different vendors. The results obtained from Scanner 1 were found to match better the center-points of the ABTs, and the minimum Euclidean distances were below 2 mm when the CBT was thresholded at the 75th percentile. A similar effect was not observed using the data from the other two scanners. The volumetric overlaps for each of the 3 CBTs showed similar curves with maximum peaks at 90–95th percentiles. As summarized in Table 2, the overlap between CBTs across all subjects and scans was between 58 and 69% for the thalamic targets. When decoupling the variability into inter-subject within-scanner and intra-subject inter-scanner variability, we observed that for most of the structures, the inter-subject overlap was similar to the inter-scanner variability. Only in the case of the postcentral gyrus connections were the inter-scanner variability significantly higher (Table 2), meaning that the vendor type (Scanners 1–3) caused more variability than the anatomical differences between individuals after non-linear standardization.While the pre- and postcentral gyrus CBT were largely reproducible across different scanners (Table 2). The CTT was significantly more reproducible across subjects on the right side (D = 37.4%) than on the left (20.7%), and the left CTT showed a similar phenomenon to the intrathalamic targets in that the inter-scanner variability exceeded the inter-subject variability.While the thalamic CBTs were defined by setting a cut-off value of connection probability based on the optimization step described above, we were not able to perform this optimization step for the CTT due to its specific shape and configuration. Since the CTT showed less variability in the sub-thalamic region, we decided to calculate the variability measures after restricting the volume of the tract to a 40 mm subvolume symmetrically around the AC-PC plane, which is most commonly used for neurosurgical targeting . After limiting to this volume and setting the connectivity value to the top 90th percentile of connections, a low overlap was observed for the CTT , but did not increase the overall reproducibility of the CBT. Furthermore, we observed interhemispheric asymmetry in the VLpv CBT reconstruction, which was not the case when using only the precentral gyrus as remote connection target.In most previous studies, the CBTs were defined using a single target region of interest within the cortex, although there is experimental support from imaging studies that the Vim or VLTable 2). The non-linear matching used to normalize images to MNI space should reduce the inter-subject variability, but the uncertainty of the registration algorithm cannot be separated easily from the other apparent sources of variability, and significant inter-scanner variability remained even after normalization. The exact source of this inter-scanner variability is unknown, but may arise from a number of different hardware and software factors. While some vendors use a spectrospatial (water excitation) pulse for fat suppression, others use fat saturation methods, resulting in different limitations on the minimum slice thickness allowed. Differences in these excitation pulses, coupled with differences in gradient design, geometric distortion, and the use of wide vs. narrow bore systems may contribute to the observed inter-scanner variability, but future studies would be needed to elucidate further the sources of this observed variability. This inter-scanner variability further underscores the sensitivity of DTI and connectivity derived measures to sequence parameters and hardware parameters  using current standard clinical DTI protocols. The trajectory is similar to what we know about the anatomy of this tract, especially in the subthalamic region: after crossing to the contralateral hemisphere, the tract projects into the thalamus by passing near the red nucleus and sending collaterals to it, and the tract is generally located posterior to the mid-commissural line around the AC-PC plane (Using the seed and target list known from the literature , we werePC plane . This obPC plane ,b, and iPC plane . In theiOur study is limited by the small number of volunteers for whom we were able to acquire repeated DTI scans. This particularly limits our capability to test inter-subject variability and to decouple the variability caused by anatomical differences from the repeatability of imaging, uncertainty of fiber tracking and errors of image co-registration.A major limitation in our study is the lack of ground truth information on thalamic fibers and the lack of population-wide histology-based atlases of white matter structures. This limits the generalisability of our validation: although we used a shape model that incorporated variability , it remaWhile our study aimed to evaluate clinically feasible imaging protocols, such as the widely used 32-direction DTI, we assume that higher angular resolution diffusion datasets will reduce the variability associated with imaging, and will also allow us to resolve the challenging fiber anatomy of the thalamic and subthalamic region more accurately. For this, more sophisticated diffusion models and the resolution of multiple crossing fiber populations are necessary .Our study is further limited by its exploratory nature, and specifically the small participant group size. The widespread usage of probabilistic diffusion tractography for defining intra-thalamic and cerebello-thalamic targets must be preceded by large, preferably multi-centric validation efforts that test the technique during interventions and compare the results to those of the standard target localization methods. Furthermore, the inter-surgeon variability should also be evaluated, which can be a critical point when applying localization approaches that rely heavily on image processing with complex parameter settings.pv nucleus and the postcentral connections emerging from the VPL nucleus. The applicability of connectivity-based segmentation methods with the current clinical sequence parameters is further limited by large scanner-to-scanner variability of the CBTs. Further investigations using higher angular resolution DTI are needed to elucidate the sources of this variability and to assess the precise contribution of subject-to-subject anatomical variability.In this pilot study, we demonstrated the reproducibility of DTI based tractography to map targets with relevance to preoperative planning. The accuracy with regard to the center-points of atlas-based targets was around 2.5 mm, and a relatively low volumetric overlap of 35–40% was observed. We conclude that the connectivity based subdivisions can only predict the lateral and ventral segments of the Vim or VLAJ, BW, EM, JT, TY, GS, and RT designed the study. AJ has written the manuscript, performed the analysis and created figures. KK has performed the analysis and contributed to the manuscript. RT has acquired data and contributed to the manuscript. EM has written the manuscript and provided expert consultation. MP has acquired data and provided expert consultation. All authors have read and edited 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.


Pinus tabuliformis Carr.), planted larch (Larix principis-rupprechtii Mayr.), and natural secondary deciduous broadleaf forests in a mountainous region of Beijing, China. Additive diversity partition analysis showed that, compared to natural forests, the planted pine forests had a different woody species diversity partitioning pattern at multi-scales (except the Simpson diversity in the regeneration layer), while the larch plantations did not show multi-scale diversity partitioning patterns that were obviously different from those in the natural secondary broadleaf forest. Compare to the natural secondary broadleaf forests, the effects of planted pine forests on woody species diversity are dependent on the sampling scale and layers selected for analysis. Diversity in the planted larch forest, however, was not significantly different from that in the natural forest for all diversity components at all sampling levels. Our work demonstrated that the species selected for afforestation and the sampling scales selected for data analysis alter the conclusions on the levels of diversity supported by plantations. We suggest that a wide range of scales should be considered in the evaluation of the role of forest plantations on biodiversity conservation.The role of forest plantations in biodiversity conservation has gained more attention in recent years. However, most work on evaluating the diversity of forest plantations focuses only on one spatial scale; thus, we examined the effects of sampling scale on diversity in forest plantations. We designed a hierarchical sampling strategy to collect data on woody species diversity in planted pine ( Forests are vital to the conservation of terrestrial biodiversity, as more than a quarter of the earth's land surface is covered by forests, and more than half of the earth's terrestrial species dwell in or depend on these forests The value of forest plantations in biodiversity conservation has been found to be context dependent The rationale for such assessments is that biodiversity distribution in heterogeneous landscapes is the result of different processes operating at different scales γ) is the sum of within-community diversity  and between-community diversity , i.e., γ = α+βIn the additive diversity partitioning approach, the total species diversity  forest, 3 were planted larch (Larix principis-rupprechtii Mayr.) forest, and 3 were natural secondary broadleaf forest. We hypothesized that the effects of forest plantations on woody species diversity  would vary with sampling scales. We compared the multi-scale partitioning patterns of woody species diversity using the additive diversity partitioning approach. We tested and compared the difference of all species diversity components  among forest types using mixed-effect models and multiple comparisons.In our study, to partition patterns of woody species diversity, we designed a hierarchical sampling system including four levels: plot, slope position, slope, and watershed . The watershed is a basic geohydrological unit in a mountainous region that is used for effective ecological management practices to sustain and enhance ecosystem functions No specific permits were required for the described field studies. The location was not privately-owned or protected in any way, and the study did not involve endangered or protected species.The study was conducted on Donglingshan Mountain, an extension of the Xiaowutaishan Mountains belonging to the broader Taihangshan Mountains, 100 km northwest of Beijing City, China. The study area, within Beijing Xiaolongmen National Forest Park, is located at 39°57'53" N and 115°26'05" E. The soil type in the area is brown soil, classified as Eutriccambisol Quercus spp.), mixed species , birches (Betula spp.) and poplar (Populus davidiana). There are also pine (P. tabuliformis Carr.) and larch (L. principis-rupprechtii Mayr.) forest plantations that were established during the late1950s. During this period, most natural broadleaf forests were clear-cut for the steel industry. After deforestation, some of the small watersheds in the area were planted with pines (P. tabuliformis Carr.), while others were planted with larch (L. principis-rupprechtii Mayr.); still others are secondary broadleaf forest that recovered naturally. The primary purpose of the plantations has been for wood production. Now, since the establishment of the nature reserve in 1985, which then became a forest park in 1994, all of the forests in this area—both natural and planted—serve roles in biodiversity conservation, soil and water conservation, and carbon sequestration The zonal vegetation is highly heterogeneous, warm temperate zone deciduous broadleaf forest P. tabuliformis Carr.) plantations, 3 were larch (L. principis-rupprechtii Mayr.) plantations, and 3 were natural secondary broadleaf forests . The natural secondary broadleaf forest selected in this work was ideal as the control for the comparison of the effects of plantations on woody species diversity. In each watershed, two slopes (eastern and western exposures) were nested; in each slope, three slope positions  were nested; and in each slope position, 5 10 m×10 m plots were selected for data collection . The highest level was the watershed. In this region, most watersheds have only two main slope exposures, with areas ranging in size from 20–30 ha. We selected 9 such watersheds, with primarily eastern and western slope exposures: 3 were pine  is the important value of species i, which is used primarily for the calculation of the diversity measure in plant communities ih, ic, and ia are the sum of the height, coverage and number of individuals for species i; th, tc, and ta is the total value of the height, coverage, and number of all individuals for all species, respectively.The variability in different diversity components  are shown in The results of the mixed-effects models for between-scale diversity components  for the tree layer; the species richness on the eastern exposure of a slope in the planted pine forests was significantly lower than in the planted larch forest , while the species richness on the eastern exposure of a slope in the planted larch forest was significantly higher than in the natural secondary broadleaf forest  for the tree layer; the Simpson diversity on the western exposure of a slope in the planted pine forest was significantly lower than in the natural secondary broadleaf forest  and the planted larch forest  for the tree layer; the Simpson diversity on the eastern exposure of a slope in the planted pine forest was significantly lower than in the planted larch forest  for the shrub layer. For beta diversity, the species richness on the eastern exposure of a slope in the pine forest plantations was significantly lower than in the larch forest plantations for all three layers ; the species richness in the pine forest plantations was significantly lower than in the natural secondary broadleaf forest on both slope exposures  in the regeneration layer; the Simpson diversity on the eastern exposure of a slope in the pine forest plantations was significantly lower than in the larch forest plantations ; the Simpson's diversity on the western exposure of a slope in the planted pine forest was significantly lower than in the natural secondary broadleaf forest  and planted larch forest .The multiple comparisons of first-order interactions for forest type and slope at the plot sampling scale  revealed 13 comparable pairs that were significantly different. For alpha diversity , the species richness on the western exposure of a slope in the pine plantations was significantly lower than in the natural secondary broadleaf forest ; the species richness at the upper slope position on the western exposure of a slope in the planted pine forest was significantly lower than in the natural secondary broadleaf forest ; the Simpson diversity at the upper slope position on the western exposure of a slope in the planted pine forest was significantly lower than in the planted larch forest  and natural secondary broadleaf forest  in the tree layer; in the shrub layer, however, the Simpson diversity at the lower slope position on the eastern exposure of a slope in the planted pine forest was significantly higher than in the natural secondary broadleaf forest . For beta diversity, in the regeneration layer, the species richness at the middle slope position on the eastern exposure of a slope in the pine forest plantations was significantly lower than in the larch forest plantations  and natural secondary broadleaf forest ; the species richness at the middle slope position on the western exposure of a slope in the pine forest plantations was significantly lower than in the natural secondary broadleaf forest ; the species richness at the upper slope position on the eastern exposure of a slope in the pine forest plantations was significantly lower than in the larch forest plantations ; the Simpson diversity at the middle slope position on the western exposure of a slope was significantly lower than in the larch forest plantations  and natural secondary broadleaf forest .The multiple comparisons of first-order interactions for forest type, slope, and slope position at the plot sampling scale  showed 11 comparable pairs that were significantly different. For alpha diversity, species richness at the middle slope position on the eastern exposure of a slope in the planted pine forest was significantly lower than in the planted larch forest  changed the multi-scale diversity partitioning pattern of woody species diversity (except the Simpson diversity in the regeneration layer) compared to natural secondary broadleaf forest, while the larch plantations (L. principis-rupprechti) did not show multi-scale diversity partitioning patterns that were obviously different from those in the natural secondary broadleaf forest , the significance of the difference among forest types was dependent on the sampling scale selected in the shrub and regeneration layers, although not in the tree layer which showed significant differences among forest types for all within-diversity components  and 5. FL. principis-rupprechtii) did not have significant difference from the natural secondary broadleaf forest, while the pine plantations (P. tabuliformis) had different effects on woody species richness at different sampling scales compare to natural secondary broadleaf forest (L. principis-rupprechtii) and broadleaf deciduous species, pine (P. tabuliformis) is an evergreen, which changes seasonal light schemes, especially during the non-growing season, ultimately increasing the homogeneity of the light environment for woody species. As biotic homogenization has been suggested to be a threat to biodiversity Previous work has shown that the effects of forest plantations are also dependent on the species selected f forest –7. This In most regions, forest plantations were designed for degraded ecosystem recovery and biodiversity conservation There is increasing recognition that ecosystems and their services need to be managed in the face of landscape change S1 AppendixData used in this study.(XLSX)Click here for additional data file.


The basal ganglia and cortex are strongly implicated in the control of motor preparation and execution. Re-entrant loops between these two brain areas are thought to determine the selection of motor repertoires for instrumental action. The nature of neural encoding and processing in the motor cortex as well as the way in which selection by the basal ganglia acts on them is currently debated. The classic view of the motor cortex implementing a direct mapping of information from perception to muscular responses is challenged by proposals viewing it as a set of dynamical systems controlling muscles. Consequently, the common idea that a competition between relatively segregated cortico-striato-nigro-thalamo-cortical channels selects patterns of activity in the motor cortex is no more sufficient to explain how action selection works. Here, we contribute to develop the dynamical view of the basal ganglia–cortical system by proposing a computational model in which a thalamo-cortical dynamical neural reservoir is modulated by disinhibitory selection of the basal ganglia guided by top-down information, so that it responds with different dynamics to the same bottom-up input. The model shows how different motor trajectories can so be produced by controlling the same set of joint actuators. Furthermore, the model shows how the basal ganglia might modulate cortical dynamics by preserving coarse-grained spatiotemporal information throughout cortico-cortical pathways.The online version of this article (doi:10.1007/s00422-015-0662-6) contains supplementary material, which is available to authorized users. Preparation and execution of intentional movements requires the activity of the motor cortex. This cortical region forms re-entrant parallel loops with both the dorsolateral basal ganglia and the cerebellum  choose between different cortical assemblies, but rather between different y et al. . The modliquid state machines . Projections from the Str to the GPe, and from there to the GPi/SNpr complex, form the indirect pathway. They are GABAergic and segregated in parallel substantially segregated channels similarly to those of the direct pathway. Str spiny neurons whose projections form the direct and indirect pathways are mainly distinguishable for two reasons. First, they tend to express two different families of dopamine receptors in different proportions. Neurons in the direct pathway tend to express more D1-like low-affinity dopamine receptors, while those in the indirect pathway tend to express more D2-like high-affinity dopamine receptors. Second, the direct pathway has a feed-forward organization. Instead, the indirect pathway consists in a multi-synaptic pathway involving a negative feedback circuit. Indeed, the GPe is reached by STN projections that are similar to those reaching the GPi/SNpr complex, with the difference that the former also sends back inhibitory projections to the STN itself  would suffice for a reservoir network to be modulated in a similar way as done here by the basal ganglia  represents the primary motor loop and the read-out units of its cortical component directly control movements. The other CSNTC module  represents a higher-level motor loop whose cortical output projects both to the striatum and to the cortex of the previous module. The cortical module at the right of Fig. The second model  to simplify the visualization of the results. However, the model is able to scale to a higher number of tasks as shown in figure 1 in Online Resource 4.This section illustrates the computational details of the various components of the model and the learning algorithms used to train the cortical read-out and the cortico-striatal connection weights.th is the threshold of the function. This output function differs from the simple tanh function used in classical echo state models  algorithm described by Steil  see als as the ots Steil , and redIn the simulations implementing SYSTEM_MODEL learning of the cortico-striatal connections was also simulated  movements but also discrete (fixed-point attractor) movements. All settings were as those of the first simulation with the only difference consisting in the use of three fixed-point target trajectories instead of the ones described in Fig. To test how the model scaled to real scenarios in motor control, we further tested it as a controller of a 20-DoF dynamic hand in a 3D physics simulator. The task was similar to the ones just described and consisted in moving the hand so to reach one of three different postures based on the task information. The model could easily learn all the three desired postures and reproduce them based on the related input. Learning of the read-out units was implemented via the online learning rule described in Sect. The third set of simulations implemented SYSTEM_MODEL described in Sect. Learning happened at two levels. One learning process involved the connections going from the high-level motor cortical module to the striatum of the primary motor CSNTC module. A second learning process involved the connections going from the primary motor cortical module to its read-out units (the top-centre of the figure). Learning the connections between external input  and the high-level motor striatum was instead abstracted by implementing hardwired connections, as in this case unsupervised learning is not sufficient. Indeed, information about the desired categories to be acquired is not contained in the prefrontal information, and further motivational information would be needed. Thus in this case a reward-based learning process, the study of which was out of the scope of this work, should have been implemented.First training phase Cortico-striatal weights from the high-level motor cortical module to the primary motor striatum were updated using the unsupervised learning rule described in Sect. Second training phase The read-out weights from the primary motor cortical module to the subcortical actuators were updated in a supervised manner, as described in Sect. Test phase The behaviour was tested in two different conditions. In one condition (SAME), the temporal pattern of binary vector signals reaching the higher-level motor module was the same as in the training phases. In the other condition (DIFF), the last bit within each group was switched on to encode the desired trajectory, instead of the first bit of the same group as done in the previous phases , the resulting behaviour was a correct reproduction of the requested behaviour Fig. .TESTLESIONIn the case of the PARTIAL_LESION simulation, the resulting behaviour during the test phase was partially impaired Fig. . Indeed,TESTLESIONIn the case of the FULL_LESION simulations, the resulting behaviour during the test phase was destructively altered Fig. . A two-wA video showing the SAME test conditions for the three simulation types is given in Online Resource 3.The simulations presented here show that the disinhibitory process implemented by the basal ganglia can be a reliable mechanism for the selection of the internal dynamics within a single cortical module. In particular, the simulation in Sect. The simulation in Sect. amplifies the sparsification processes of the cortical module. Based on this strengthened sparsification, the cortical module can learn multiple radically different mappings between input and output signals while limiting possible interference effects. The third set of simulations illustrates this enhancing effect of basal ganglia disinhibition. The reproduction error measured in the SAME test condition of the PARTIAL_LESION simulation is significantly higher than the error in the SAME test condition of the BASELINE simulation. This is a clear evidence that the primary motor cortical module without the disinhibition by the primary motor basal ganglia fails to sufficiently sparsify the input so as to suitably map it to the target movement trajectory. Instead, the motor cortex with the enhancement of the basal ganglia can learn all the target trajectories without interference in correspondence to the coarse information related to the different movements.Simulations also show that basal ganglia disinhibition can play several roles. First, basal ganglia disinhibition potentiates how cortical modules sparsify the input in time and space. Sparsification in time and space is a fundamental property of reservoirs. Thanks to this property reservoir, networks allow a linear solution of problems that are originally nonlinearly separable. In the model, the striatum filters the coarse-grained information of the input. Based on this coarse information, basal ganglia disinhibition persistently enhances the dynamics of differential cortical sub-populations Fig. . This fothroughout cortico-corticalpathways. This property derives from the same enhancement effect discussed above. When a cortical subpopulation is enhanced a strong mark is impressed to the dynamics of the cortical module. This mark can be easily exploited by both cortico-cortical and cortico-striatal learning processes when information traverses multiple CSNTC modules. The third set of simulations shows the importance of this property. The reproduction error measured in the DIFF test condition of the FULL_LESION simulation is dramatically higher than the error in the other simulations, resulting in a completely disrupted behaviour. This dramatic effect is due to the impairment of the striatal activity of the high-level motor module that prevents coarse-grained information about the task to reach the primary motor cortex.Second, basal ganglia disinhibition preserves coarse-grained information The model presented here can be compared with the main computational hypotheses proposed in the literature to describe the functional interaction between cortex and basal ganglia by appealing to dynamical concepts as here. The models described by Wickens et al. , Houk anA different model was proposed by Dominey  in whichThe model presented here is coherent with the described works because it implements both context detection by the basal ganglia, here in a biological plausible way, and maintenance of cortical states through the release of thalamo-cortical loops. However, our model has also important novelties with respect to the described models. First, it merges the property of maintaining a memory of the current state , or by motor efferent copies, during movement performance. Modelling the effects of this modality of interaction with the environment is very important as it produces relevant effects on the nature of motor control and performance. Some solutions have been developed with the aim to manage online feedback in reservoirs networks while updating the read-out weights Steil . SimulatAn interesting issue that can be investigated with the model is the relation between learning equilibrium-points and learning complex motor trajectories. Are these two different possible modalities of motor learning or are they interrelated? Do they share the same neural substrates? Section Supplementary material 1 (mpg 8196 KB)Supplementary material 2 (mpg 28118 KB)Supplementary material 3 (mpg 15874 KB)Supplementary material 4 (pdf 282 KB)