Short-distance Connections Research Articles

PTFE-Based Circular Terahertz Dielectric Waveguides

This paper presents the transmission characteristics of flexible solid circular dielectric waveguides in the terahertz frequency band. In this paper, we measured the electrical properties of certain polymers within 325–500 GHz. Through simulation and measurement, the transmission loss, bending loss, and electric field distribution of solid-core polymer dielectric waveguides were analyzed and discussed. Additionally, we considered the surrounding cladding of the dielectric waveguide, the signal-feeding mode transmitter, and the interconnection of the dielectric waveguide. Ultimately, in the operating frequency range of 325–500 GHz, we selected PTFE rods with diameters of 0.5 mm and 1 mm as the dielectric waveguides, with measured transmission loss of less than 30 dB/m and 33 dB/m, respectively, and bending loss of less than 1 dB/m. The described dielectric waveguide has engineering significance for short-distance connections in complex geometric environments and provides a reference for subsequent research.

Exploring the service needs and the common-suited strengthening paths for bicycle-sharing services based on the AIA-NRM technique in consideration of various users

ABSTRACT Near metro stations in urban areas, a public bicycle rental service allows users to rent bicycles for leisure, short-distance connections, and daily commuting. This study aims to explore the driving factors of bicycle-sharing service systems (BSSSs) and different users’ preferences and service needs (leisure & recreation users, short-connection users, and daily commuting users). The four driving forces (primary services, primary facilities, ancillary services, and ancillary facilities) are applied to evaluate the BSSSs. The researcher used the attention and importance analysis (AIA) technique to assess service driving factors’ attention and importance status in the bicycle-sharing service systems (BSSSs). The DEMATEL technique was also employed to establish a network relation map (NRM) approach for different users. The researcher also combines the AIA and NRM techniques to provide the AIA-NRM technique for identifying common applicable paths and enhancement strategies for the BSSS. Furthermore, this study analyzes various institutions and BSSS service providers from multiple users’ perspectives, determines the development direction of BSSSs, evaluates related challenges, and finds sustainable strategies for strengthening BSSSs.

Spatial organisation of the mesoscale connectome: A feature influencing synchrony and metastability of network dynamics.

Significant research has investigated synchronisation in brain networks, but the bulk of this work has explored the contribution of brain networks at the macroscale. Here we explore the effects of changing network topology on functional dynamics in spatially constrained random networks representing mesoscale neocortex. We use the Kuramoto model to simulate network dynamics and explore synchronisation and critical dynamics of the system as a function of topology in randomly generated networks with a distance-related wiring probability and no preferential attachment term. We show networks which predominantly make short-distance connections smooth out the critical coupling point and show much greater metastability, resulting in a wider range of coupling strengths demonstrating critical dynamics and metastability. We show the emergence of cluster synchronisation in these geometrically-constrained networks with functional organisation occurring along structural connections that minimise the participation coefficient of the cluster. We show that these cohorts of internally synchronised nodes also behave en masse as weakly coupled nodes and show intra-cluster desynchronisation and resynchronisation events related to inter-cluster interaction. While cluster synchronisation appears crucial to healthy brain function, it may also be pathological if it leads to unbreakable local synchronisation which may happen at extreme topologies, with implications for epilepsy research, wider brain function and other domains such as social networks.

Networking of the Human Cerebellum: From Anatomo-Functional Development to Neurosurgical Implications.

In the past, the cerebellum was considered to be substantially involved in sensory-motor coordination. However, a growing number of neuroanatomical, neuroimaging, clinical and lesion studies have now provided converging evidence on the implication of the cerebellum in a variety of cognitive, affective, social, and behavioral processes as well. These findings suggest a complex anatomo-functional organization of the cerebellum, involving a dense network of cortical territories and reciprocal connections with many supra-tentorial association areas. The final architecture of cerebellar networks results from a complex, highly protracted, and continuous development from childhood to adulthood, leading to integration between short-distance connections and long-range extra-cerebellar circuits. In this review, we summarize the current evidence on the anatomo-functional organization of the cerebellar connectome. We will focus on the maturation process of afferent and efferent neuronal circuitry, and the involvement of these networks in different aspects of neurocognitive processing. The final section will be devoted to identifying possible implications of this knowledge in neurosurgical practice, especially in the case of posterior fossa tumor resection, and to discuss reliable strategies to improve the quality of approaches while reducing postsurgical morbidity.

Abnormal Brain Topological Structure of Mild Depression During Visual Search Processing Based on EEG Signals.

Studies have shown that attention bias can affect behavioral indicators in patients with depression, but it is still unclear how this bias affects the brain network topology of patients with mild depression (MD). Therefore, a novel functional brain network analysis and hierarchical clustering methods were used to explore the abnormal brain topology of MD patients based on EEG signals during the visual search paradigm. The behavior results showed that the reaction time of MD group was significantly higher than that of normal group. The results of functional brain network indicated significant differences in functional connections between the two groups, the amount of inter-hemispheric long-distance connections are much larger than intra-hemispheric short-distance connections. Patients with MD showed significantly lower local efficiency and clustering coefficient, destroyed community structure of frontal lobe and parietal-occipital lobe, frontal asymmetry, especially in beta band. In addition, the average value of long-distance connections between left frontal and right parietal-occipital lobes presented significant correlation with depressive symptoms. Our results suggested that MD patients achieved long-distance connections between the frontal and parietal-occipital regions by sacrificing the connections within the regions, which might provide new insights into the abnormal cognitive processing mechanism of depression.

Insights on the Relationship Between Hippocampal Connectivity and Memory Performances at the Early Stage of Multiple Sclerosis.

While memory impairment in multiple sclerosis (MS) is known to be associated with hippocampal alterations, whether hippocampal networks could dynamically reorganize as a compensation mechanism is still a matter of debate. In this context, our aim was to identify the patterns of structural and functional connectivity between the hippocampus and the rest of the brain and their possible relevance to memory performances in early MS. Thirty-two patients with a first episode suggestive of MS together with 10 matched healthy controls were prospectively explored at baseline, 1 and 5 years follow up. They were scanned with MRI and underwent a neuropsychological battery of tests that included the Selective Reminding Test and the Brief Visual Memory Test Revised to assess verbal and visuo-spatial memory, respectively. Hippocampal volume was computed together with four graph theory metrics to study the structural and functional connectivity of both hippocampi with the rest of the brain. Associations between network parameters and memory performances were assessed using linear mixed-effects (LME) models. Considering cognitive abilities, verbal memory performances of patients decreased over time while visuo-spatial memory performances were maintained. In parallel, hippocampal volumes decreased significantly while structural and functional connectivity metrics were modified, with an increase in hippocampal connections over time. More precisely, these modifications were indicating a reinforcement of hippocampal short-distance connections. LME models revealed that the drop in verbal memory performances was associated with hippocampal volume loss, while the preservation of visuo-spatial memory performances was linked to decreased hippocampal functional shortest path length. In conclusion, we demonstrated a differential impairment in memory performances in the early stages of MS and an important interplay between hippocampal-related structural and functional networks and those performances. As the structural damage increases, functional reorganization seems to be able to maintain visuo-spatial memory performances with strengthened short-distance connections.

Financial Risk Early Warning Based on Wireless Network Communication and the Optimal Fuzzy SVM Artificial Intelligence Model

Since the beginning of the new century, risk events such as the world economic crisis have occurred, which have greatly impacted the real economy of our country. A wireless network is a network implemented using wireless communication technology. It includes both global voice and data networks that allow users to establish long‐distance wireless connections, as well as infrared technology and radio frequency technology optimized for short‐distance wireless connections. These events have a great impact on many small‐ and medium‐sized listed companies, resulting to many small‐ and medium‐sized listed companies going bankrupt. Indeed, one of the important reasons for the frequent bankruptcy of small‐ and medium‐sized listed companies is the lack of awareness of risk prevention and effective financial risk early warning mechanism. The support vector machine is a machine learning method based on the VC dimension theory of statistical learning and the principle of structural risk minimization. This method shows many unique advantages when dealing with classification problems and has been widely used in many fields. The purpose of this article is to realize the financial risk analysis of listed companies through wireless network communication and the optimal fuzzy SVM artificial intelligence model, which help small‐ and medium‐sized listed companies find abnormalities in their business management activities in advance and deal with market risks in a timely manner. Taking 81 small‐ and medium‐sized listed companies as the research object, this paper chooses the small‐ and medium‐sized listed companies in every quarter of 2018 as the research sample. By using the financial and nonfinancial data of small‐ and medium‐sized listed companies and introducing the support vector machine (SVM) with the fuzzy method, the model of the fuzzy support vector machine (FSVM) is constructed. And the performance of the FSVM under four different kernel functions is compared and studied. At the same time, the performance of the FSVM is compared with other artificial intelligence models. The empirical results show that different kernel functions have different effects on the prediction performance of the FCM‐SVM model. Under the Gauss radial basis function, the prediction accuracy of the FCM‐SVM is over 86%. It can be seen that in predicting the financial crisis of small‐ and medium‐sized listed companies, the FCM‐SVM model with Gauss radial basis function has the best predictive performance. The FSVM model based on Gauss radial basis function not only has the advantages of linearity, being polynomial, and nonlinearity of neurons but also is significantly superior to the traditional artificial intelligence model.

Evaluating the impacts of HSR stations on the creation of firms

Justified on territorial equity criteria, Spain has developed one of the largest high-speed rail (HSR) network in the world. This research evaluates how the opening of a new station affects the number of firms created at provincial level. We estimate differential impacts on firm creation across four different types of activities: service, tourism, knowledge intensive and manufacturing. The use of panel data makes it possible both to test for the dynamic structure in the data and to provide new insights on the territorial impact of HSR effects. Moreover, we develop a methodological procedure to improve the accuracy of small but positive effects. The results point out that being connected to a HSR network may promote firm creation, but this is not always the case. It has a larger effect in the service, tourism and knowledge intensive activities, whereas in the manufacturing sector the impact is almost null. Regarding the territorial impact, we find that the HSR can reinforce the concentration of firm creation in the biggest cities. Additionally, improving short-distance connections can strengthen agglomeration benefits by enlarging metropolitan areas. Finally, HSR would contribute to increasing the level of divergence in terms of firm creation across provinces. Therefore, our work calls for a rethinking of the strategy of basing investment decisions on territorial equity criteria.

Localization of Epileptogenic Zone Based on Cortico-Cortical Evoked Potential (CCEP): A Feature Extraction and Graph Theory Approach.

ObjectiveEpilepsy is a chronic brain disease, which is prone to relapse and affects individuals of all ages worldwide, particularly the very young and elderly. Up to one-third of these patients are medically intractable and require resection surgery. However, the outcomes of epilepsy surgery rely upon the clear identification of epileptogenic zone (EZ). The combination of cortico-cortical evoked potential (CCEP) and electrocorticography (ECoG) provides an opportunity to observe the connectivity of human brain network and more comprehensive information that may help the clinicians localize the epileptogenic focus more precisely. However, there is no standard analysis method in the clinical application of CCEPs, especially for the quantitative analysis of abnormal connectivity of epileptic networks. The aim of this paper was to present an approach on the batch processing of CCEPs and provide information relating to the localization of EZ for clinical study.MethodsEight medically intractable epilepsy patients were included in this study. Each patient was implanted with subdural grid electrodes and electrical stimulations were applied directly to their cortex to induce CCEPs. After signal preprocessing, we constructed three effective brain networks at different spatial scales for each patient, regarding the amplitudes of CCEPs as the connection weights. Graph theory was then applied to analyze the brain network topology of epileptic patients, and the topological metrics of EZ and non-EZ (NEZ) were compared.ResultsThe effective connectivity network reconstructed from CCEPs was asymmetric, both the number and the amplitudes of effective CCEPs decreased with increasing distance between stimulating and recording sites. Besides, the distribution of CCEP responses was associated with the locations of EZ which tended to have higher degree centrality (DC) and nodal shortest path length (NLP) than NEZ.ConclusionOur results indicated that the brain networks of epileptics were asymmetric and mainly composed of short-distance connections. The DC and NLP were highly consistent to the distribution of the EZ, and these topological parameters have great potential to be readily applied to the clinical localization of the EZ.

Prioritizing restoration of fragmented landscapes for wildlife conservation: A graph-theoretic approach

Anthropogenic disturbances fragmenting wildlife habitat greatly contribute to extinction risk for many species. In western Canada, four decades of oil and gas exploration have created a network of seismic lines, which are linear disturbances where seismic equipment operates. Seismic lines cause habitat fragmentation and increase predator access to intact forest, leading to declines of some wildlife populations, particularly the threatened woodland caribou, Rangifer tarandus caribou. Restoration of forests within seismic lines is an important activity to reduce habitat fragmentation and recovery caribou. We present an optimization model with the objective of guiding landscape restoration strategies that maximize the area of connected habitat for a caribou population in a fragmented landscape. We use our model to find optimal strategies for seismic line restoration in the Cold Lake Area of Alberta, Canada, a 6726-km2 expanse of boreal forest that represents prime caribou habitat. We formulate mixed integer programming models that depict the landscape as a network of interconnected habitat patches. We develop and compare formulations that emphasize the population's local or long-distance access to habitat. Optimal restoration involves a mix of two strategies: the first establishes short-distance connections between forest patches with large areas of intact habitat and the second establishes corridors between areas with known species locations and large amounts of suitable habitat. Our approach reveals the trade-offs between these strategies and finds the optimal restoration solutions under a limited budget. The approach is generalizable and applicable to other regions and species sensitive to changes in landscape-level habitat connectivity.

Distinct Interactions between Fronto-Parietal and Default Mode Networks in Impaired Consciousness.

Existing evidence suggests that the default-mode network (DMN) and fronto-pariatal network (FPN) play an important role in altered states of consciousness. However, the brain mechanisms underlying impaired consciousness and the specific network interactions involved are not well understood. We studied the topological properties of brain functional networks using resting-state functional MRI data acquired from 18 patients (11 vegetative state/unresponsive wakefulness syndrome, VS/UWS, and 7 minimally conscious state, MCS) and compared these properties with those of healthy controls. We identified that the topological properties in DMN and FPN are anti-correlated which comes, in part, from the contribution of interactions between dorsolateral prefrontal cortex of the FPN and precuneus of the DMN. Notably, altered nodal connectivity strength was distance-dependent, with most disruptions appearing in long-distance connections within the FPN but in short-distance connections within the DMN. A multivariate pattern-classification analysis revealed that combination of topological patterns between the FPN and DMN could predict conscious state more effectively than connectivity within either network. Taken together, our results imply distinct interactions between the FPN and DMN, which may mediate conscious state.

Aberrant topographical organization of the default mode network underlying the cognitive impairment of remitted late-onset depression

To investigate the alteration of resting-state functional connectivity (FC) and topological organization of the default mode network (DMN), and their contribution to the cognitive impairment in remitted late-onset depression (rLOD) patients. Thirty-three rLOD patients and thirty-one healthy controls underwent clinical and cognitive evaluations as well as resting-state functional magnetic resonance imaging (R-fMRI) scans. The FC networks were constructed by thresholding Pearson correlation metrics of the DMN regions, and their topological properties were analyzed using graph theory-based approaches. Nonparametric permutation tests were further used for group comparisons of topological metrics. Finally, multiple linear regression analyses were performed to examine the relationships between the network measures and cognitive performances. Patients displayed universally decreased FC of DMN and abnormal global topology of the DMN (i.e., increased characteristic path length Lp and reduced global efficiency Eglob) compared with healthy controls. According to the distance-dependent FC results, the long-distance connections were mainly involved in the connectivity between anterior and posterior hubs, and the short-distance connections were primarily located in the frontal lobe. There were significant correlations between the global topology and the episodic memory performance in rLOD patients. In conclusion, the present study indicated that the disrupted topological organization of the DMN might be considered as a potential biomarker of the episodic memory deficits in rLOD patients.

Functional Maps at the Onset of Auditory Inputs in Very Early Preterm Human Neonates.

During the last trimester of human gestation, neurons reach their final destination and establish long- and short-distance connections. Due to the difficulties obtaining functional data at this age, the characteristics of the functional architecture at the onset of sensory thalamocortical connectivity in humans remain largely unknown. In particular, it is unknown to what extent responses evoked by an external stimulus are general or already sensitive to certain stimuli. In the present study, we recorded high-density event-related potentials (ERPs) in 19 neonates, tested ten weeks before term (28-32 weeks gestational age (wGA), that is, at an average age of 30 wGA) by means of a syllable discrimination task (i.e., a phonetic change: ba vs. ga; and a voice change: male vs. female voice). We first observed that the syllables elicited 4 peaks with distinct topographies implying a progression of the sensory input along a processing hierarchy; second, repetition induced a decrease in the amplitude (repetition suppression) of these peaks, but their latencies and topographies remained stable; and third, a change of stimulus generated mismatch responses, which were more precisely time-locked to event onset in the case of a phonetic change than in the case of a voice change. A hierarchical and parallel functional architecture is therefore able to process environmental sounds in a timely precise fashion, well before term birth. This elaborate functional architecture at the onset of extrinsic neural activity suggests that specialized areas weakly dependent on the environment are present in the perisylvian region as part of the genetic endowment of the human species.

Sparse short-distance connections enhance calcium wave propagation in a 3D model of astrocyte networks.

Traditionally, astrocytes have been considered to couple via gap-junctions into a syncytium with only rudimentary spatial organization. However, this view is challenged by growing experimental evidence that astrocytes organize as a proper gap-junction mediated network with more complex region-dependent properties. On the other hand, the propagation range of intercellular calcium waves (ICW) within astrocyte populations is as well highly variable, depending on the brain region considered. This suggests that the variability of the topology of gap-junction couplings could play a role in the variability of the ICW propagation range. Since this hypothesis is very difficult to investigate with current experimental approaches, we explore it here using a biophysically realistic model of three-dimensional astrocyte networks in which we varied the topology of the astrocyte network, while keeping intracellular properties and spatial cell distribution and density constant. Computer simulations of the model suggest that changing the topology of the network is indeed sufficient to reproduce the distinct ranges of ICW propagation reported experimentally. Unexpectedly, our simulations also predict that sparse connectivity and restriction of gap-junction couplings to short distances should favor propagation while long–distance or dense connectivity should impair it. Altogether, our results provide support to recent experimental findings that point toward a significant functional role of the organization of gap-junction couplings into proper astroglial networks. Dynamic control of this topology by neurons and signaling molecules could thus constitute a new type of regulation of neuron-glia and glia-glia interactions.

The convergence of maturational change and structural covariance in human cortical networks.

Large-scale covariance of cortical thickness or volume in distributed brain regions has been consistently reported by human neuroimaging studies. The mechanism of this population covariance of regional cortical anatomy has been hypothetically related to synchronized maturational changes in anatomically connected neuronal populations. Brain regions that grow together, i.e., increase or decrease in volume at the same rate over the course of years in the same individual, are thus expected to demonstrate strong structural covariance or anatomical connectivity across individuals. To test this prediction, we used a structural MRI dataset on healthy young people (N = 108; aged 9-22 years at enrollment), comprising 3-6 longitudinal scans on each participant over 6-12 years of follow-up. At each of 360 regional nodes, and for each participant, we estimated the following: (1) the cortical thickness in the median scan and (2) the linear rate of change in cortical thickness over years of serial scanning. We constructed structural and maturational association matrices and networks from these measurements. Both structural and maturational networks shared similar global and nodal topological properties, as well as mesoscopic features including a modular community structure, a relatively small number of highly connected hub regions, and a bias toward short distance connections. Using resting-state functional magnetic resonance imaging data on a subset of the sample (N = 32), we also demonstrated that functional connectivity and network organization was somewhat predictable by structural/maturational networks but demonstrated a stronger bias toward short distance connections and greater topological segregation. Brain structural covariance networks are likely to reflect synchronized developmental change in distributed cortical regions.

Modelling human connectome development: precursors to neural circuits

There are several brain disorders like schizophrenia, autism and certain kinds of epilepsy, which have their origins in early brain development. In vivo studies can be extremely complex and riddled with technical and practical hurdles, heralding the need for in silico models for testing hypotheses. To fully understand brain network diseases, it is essential to find out which developmental factors lead to altered network topologies and resulting functional changes such as waves or large-scale activations (as for epileptic seizures). Recent results on local neuronal development have shown how preferences for short-distance connections [1] as well as how long-distance connections can arise [2]. Here, we present a model that simulates the layered development of the human neocortex, from the division of neural precursors to the establishment of layers and connections. Results from the model focus on the emerging network characteristics of cortical networks as synaptic connections are established. We test how quantitative and qualitative network features relate to changes in developmental factors, such as time-windows, spatial positions and chemical gradients. Results from the model include identification of developmental factors influential in the formation of cortical networks, highlighting those yielding aberrations. In conclusion, the model might inform future clinical and experimental work on the developmental origins of brain network disorders.

Small-World Effect in Epidemics Using Cellular Automata

The spread of an infectious disease in a population involves interactions leading to an epidemic outbreak through a network of contacts. Extending on Watts and Strogatz (1998) who showed that short-distance connections create a small-world effect, a model combining short- and long-distance probabilistic and regularly updated contacts helps considering spatial heterogeneity. The method is based on cellular automata. The presence of long-distance connections accelerates the small-world effect, as if the world shrank in proportion of their total number.

A Simple Rule for Axon Outgrowth and Synaptic Competition Generates Realistic Connection Lengths and Filling Fractions

Neural connectivity at the cellular and mesoscopic level appears very specific and is presumed to arise from highly specific developmental mechanisms. However, there are general shared features of connectivity in systems as different as the networks formed by individual neurons in Caenorhabditis elegans or in rat visual cortex and the mesoscopic circuitry of cortical areas in the mouse, macaque, and human brain. In all these systems, connection length distributions have very similar shapes, with an initial large peak and a long flat tail representing the admixture of long-distance connections to mostly short-distance connections. Furthermore, not all potentially possible synapses are formed, and only a fraction of axons (called filling fraction) establish synapses with spatially neighboring neurons. We explored what aspects of these connectivity patterns can be explained simply by random axonal outgrowth. We found that random axonal growth away from the soma can already reproduce the known distance distribution of connections. We also observed that experimentally observed filling fractions can be generated by competition for available space at the target neurons--a model markedly different from previous explanations. These findings may serve as a baseline model for the development of connectivity that can be further refined by more specific mechanisms.

Simulating Neural Network Development: Competition in Space and Time Influences the Formation of Neural Circuits

Event Abstract Back to Event Simulating Neural Network Development: Competition in Space and Time Influences the Formation of Neural Circuits Marcus Kaiser1* 1 Newcastle University, School of Computing Science, United Kingdom Introduction: The set of neural connections in the brain, or the connectome, is influenced by several factors including genetic information, self-organisation, physical constraints, and neural activity (learning and plasticity). Recent studies in Caenorhabditis elegans indicate that only 60% of the wiring organisation can be explained by gene expression patters indicating a major role of external factors. We use computer simulations to observe the effect of such developmental factors on the final network organisation (cf. http://www.biological-networks.org). On various spatial scales, from connectivity between individual neurons in C. elegans and rat visual cortex to connectivity between cortical areas in the mouse, macaque [1] and human brain, connection length distributions have very similar shapes, with a long flat tail representing the admixture of long-distance connections to mostly short-distance connections. Furthermore, not all potentially possible synapses are formed, and only a fraction of axons (called filling fraction, [2]) establish synapses with spatially neighbouring neurons. Results: Investigating local connectivity between individual neurons, we show that simple, random outgrowth of axons can reproduce distance-dependent connectivity as found in many neural systems [3]. Experimentally observed filling fractions can also be generated by competition for free space at the dendritic tree; a model markedly different from previous explanations. The filling fraction can be determined by the ratio between axon collaterals and free target sites which we call competition factor. The modeled filling fraction decays exponentially with the competition factor. Additional topological features, such as small-world organisation and modular systems can be derived by using multiple time windows [4]. Conclusion: Simple models that assume a random axonal outgrowth and competition for target space can account for the experimentally found exponential decay in the connection length distribution and the filling fraction. Acknowledgements: We thank the EPSRC (EP/E002331/1) and the Royal Society (RG/2006/R2) for financial support. INCF-09-88

Multiple sensor stabilization system for local probe microscopes

We introduce the concept of a multiple sensor stabilization system (MSS) applicable to all local probe microscopes. With this it is possible to separate distance control and sensor-sample-interaction signals with unprecedented stability. The MSS uses two or more sensors of an array of local probe sensors, which are coupled via rigid low-mass short-distance connections. At least one of these sensors is employed to always provide a continuous and independent feedback signal. Using this, the distance between any other sensor of the array and the sample surface can be controlled, under ambient and in situ conditions, with the resolution and range of the designated sensors on time scales of up to hours. The concept of MSS is applicable to the whole range and any conceivable combination of local probe techniques, especially all other scanning near field probes. MSS offers particularly large advantages for spectroscopic applications. We demonstrate its utility by the example of an atomic force microscope using a commercially available array of cantilevers. By using two cantilever sensors for position control it is possible to eliminate all drift between the sample and the tip’s position. The high potential of the MSS is illustrated by two applications: a thermal noise-reduction based approach with minimal contact forces and the first pN-“force clamp” for single molecule force spectroscopy.