We further propose the use of this platform as a unifying statistical framework to characterize brain disorders of known etiology in relation to idiopathic neurological disorders with similar phenotypic manifestations.There is a critical need for new analytics to personalize behavioral data analysis across different fields, including kinesiology, sports science, and behavioral neuroscience. Specifically, to better translate and integrate basic research into patient care, we need to radically transform the methods by which we describe and interpret movement data. Here, we show that hidden in the “noise,” smoothed out by averaging movement kinematics data, lies a wealth of information that selectively differentiates neurological and mental disorders such as Parkinson’s disease, deafferentation, autism spectrum disorders, and schizophrenia from typically developing and typically aging controls. In this report, we quantify the continuous forward-and-back pointing movements of participants from a large heterogeneous cohort comprising typical and pathological cases. We empirically estimate the statistical parameters of the probability distributions for each individual in the cohort and report the parameter ranges for each clinical group after characterization of healthy developing and aging groups. We coin this newly proposed platform for individualized behavioral analyses “ Precision medicine is a new approach to acquire and integrate knowledge from biomedical research and clinical practice . It is adimensions of functioning , as they are expressed across neuropsychological and neurological disorders on a spectrum. More explicitly, the Research Domain Criteria (RDoC) initiative , where various task forces have been created to achieve more objective science that unveils biological signatures of core itiative  is one oitiative . Here, wA simple experimental paradigm is presented, with a new statistical method for individualized behavioral analyses and a new kinematic data type (explained in Section “New Data Type and Different Assessment of Motor Variability”). Both form the basis of a unifying platform to help personalize research and patient care within the field of disorders of the central and peripheral nervous systems. More specifically, we address data analyses differently from traditional approaches Figure B and useTo empirically estimate the ranges of statistical parameters underlying kinematics data, we use data from various subject groups. All subjects provided written informed consent on forms approved by The Rutgers University Institutional Review Board (IRB) or Indiana University IRB. All protocols were approved by the IRB committees, in compliance with the Helsinki Act. Clinical records were obtained in compliance with the Health Insurance Portability and Accountability Act (HIPAA). Parents of the subjects with ASD signed the IRB approved consent on behalf of their child/adult participant with ASD. Table S1 in Supplementary Material summarizes the demographic characteristics of the 176 participants.The control subjects were subdivided into four broad groups including young children (CT1), young college students (CT2), middle-aged subjects (CT3) and the elderly subjects. In line with previous research indicating maturity of pointing kinematics after 4 years of age  who were ambulatory, independent, and with some visible resting tremor but without visible action tremor at the time of the visit, and 17 subjects (severe PD2) who had very impaired mobility, some of whom were ataxic and some with freezing of gait. The latter group generally needed assistance to walk and had visible action and resting tremor. Table S3 in Supplementary Material shows the demographics of both groups.A group of eight elderly individuals (ages 75–77 years old) with no formal diagnosis of a movement disorder was included as a control group for the PD groups comprising a broad range of ages (46–77 years old). These subjects were part of an earlier study  or were outpatients who only required biweekly or monthly visits to health-care providers. There were 10 patients enrolled in the PHP and 13 patients in the outpatient program. Table S4 in Supplementary Material reports demographics and Table S5 in Supplementary Material reports the Frontal System Behavior Scale (FrSBE) self-rating scores for executive dysfunction.Finally, a special subject (IW) without proprioception, secondary to deafferentation  was incla priori imposed clinical labels).Due to the individualized nature of this approach, whereby each participant is their own control , it is not necessary to match the number of participants in each clinical category to the exact number of controls of a given age/sex. However, the controls included a broad range of ages from both sexes, spanning the age and sex range of the participants with a clinical diagnosis. In this sense, the aim of the age- or disease-stage subdivisions was to use the new methods to show how to obtain the statistical summaries for the kinematic parameters of interest for representative cross-sections of the typical population. However, we emphasize that the individualized scatterplots that we provide can also be examined blindly ; pointing in the dark with a light-emitting-diode (LED) attached to the moving finger but no visual feedback from the target; and pointing in the dark with no LED on the finger but with continuous visual guidance from the target ON throughout the motion. We separated this subject’s performance according to visual feedback conditions: dark vs. vision.Participants sat in a chair facing the target location at a comfortable distance for reaching  distribution. Instead, we are interested in the accumulation (and the rate of change) of minute fluctuations in performance that occur from moment to moment in the parameters associated with the speed of motion. These include (among others) the fluctuations in maxima and those in the time to reach the velocity peak from the last pause or stop instance. As the motion of the arm-hand linkage is repeated, these minute fluctuations in speed accumulate and give rise to various frequency distributions. The shape and scale (dispersion – see Step 3 below) of these distributions can be estimated with high confidence to empirically approximate, along a continuum, the family of PDFs most likely describing the underlying random process. Once again, this is in contrast to assuming a theoretical PDF To experimentally measure fluctuations in the speed amplitude from moment to moment, we accumulate the changes in the speed maximum [termed here peak velocity (PV)]. Since the speed waveform localized between two minima within the time series of speed  and the denominator contains the sum of the PV and the average speed between two consecutive local speed minima. We term this the normalized PV index. This normalization process also avoids possible allometry effects due to differences in the sizes of the limbs of the subjects  . Larger The fluctuations in the overall profile, as determined by the changes in amplitude and timing of each peak, provide information about the individual rate of change of these variables as the nervous system of the person generates and then experiences them. Examples of the accumulation of variations in amplitude and timing in the time series of speed profiles are depicted in Figure Step 1) Acquire time-series data  from continuous trajectories of unconstrained target-directed pointing movements in three dimensions. Figure (Step 2) Plot the frequency histograms  Use maximum likelihood estimation empirically to obtain – from the data – the values and ranges of the shape (a) and scale (b) parameters of the continuous Gamma family of probability distributions. The Gamma PD F is given by:-Gamma parameter plane. Using this method, we localize the individual participant and can compare each subject’s location to those of the other subjects , the data follow the memoryless exponential probability distribution, a special case in the Gamma family. This is the most random distribution, coined as “memoryless” because events in the past do not accumulate information predictive of events in the future -plane tend toward the symmetric distributions, with a various skewed distributions in between the two extremes  Repeat this estimation procedure to characterize the rate of change of the Gamma parameters’ stochastic trajectory. This step can detect conditions and stimuli that accelerate the change in the parameters down and to the right  away from random regimes of the Gamma plane  and down along the scale axis, away from high noise-to-signal ratio values. Figure , whereby the older children were significantly faster on average. Figure The cross-sectional data under examination revealed that participants with no clinical diagnosis had different statistical signatures across ages, suggesting that even during typical development these signatures of fluctuations in motor performance may shift. Table S6 in Supplementary Material reports the ranges of the estimated summary statistics , for the normalized PV index corresponding to the control participants grouped by age. The results of the pairwise comparisons of the medians of each moment, using the nonparametric Wilcoxon rank sum test, are also reported in Table S6 in Supplementary Material. Here, the young children (CT1a) aged 3–4 years old showed the highest levels of noise-to-signal ratio, but this was not significantly different from those of children aged 5–10 years old (CT1b). The mean value of the normalized PV index did differ significantly between the two groups of children (p < 10−6). The summary statistics map for all control participants, along with their median values, are shown in Figure S3A in Supplementary Material accompanying Table S6 in Supplementary Material. Likewise, the probability plots comparing all control groups are shown in Figures S3B,C in Supplementary Material. Notice the overlap of the parents’ signatures with those of the elderly controls  = a × bx common to all groups (fit with 95% confidence bounds), where a = 0.794 ; b = −1.031  and goodness of fit: sum squared error: 9.433e−07; adjusted R-squared: 0.9982; root mean squared error: 9.57e−05.The lowest levels of noise-to-signal were registered in the young-to-mid age controls of the CT2 and CT3 groups. Figure All participants fall on this line with CT2–3 having the lowest noise-to-signal  levels and the largest shape values – indicating distributions tending toward the Gaussian shape. The average parameter values per group are shown in Figure Across all groups with neuropsychiatric/neurological disorders and the control groups, the ASD group generated the lowest values for the shape parameter and the highest values of the noise-to-signal ratio along the scale axis for the normalized PV index under examination.p-values from pairwise comparisons.Figure p < 0.01) across all pairwise comparisons of noise levels between both ASD groups and controls subgroups with overlapping ages. This was also the case for the estimated mean with the exception of ASD1 and the elderly participants, and ASD2 and their parents.Table S7 in Supplementary Material reveals significant statistical differences  according to the empirically estimated distributions. The estimated PDF’s for the severe and mild PD groups are shown in Figure Table S3 in Supplementary Material reports the demographic and clinical information concerning the PD patients. Table S8 in Supplementary Material reports the ranges for the estimated statistical summary for PD patients of severe and mild-to-moderate stages according to clinical scores in Table S3 in Supplementary Material. These subgroups differ significantly in noise levels, estimated mean, and estimated variance of the normalized PV index  from the ASD parents  but surprisingly, the severe PD cohort has comparable statistics in noise, variance, and skewness close to those of the much younger ASD parents and to the elderly controls between 75 and 77 years old. Note here again that the ASD parents are closer in age to the middle-aged CT3 group, much younger than these PD and elderly participants. Further differences between groups can be observed in the 4D plots of Figures The mild PD patients are closer in speed to the middle-aged CT3 group  and to the younger CT2 group . Thus these patients are not yet bradykinetic, but their noise levels are significantly higher than younger controls in CT2 and CT3 groups. The mild PD significantly differ . The separation between SZ statistics and those of the deafferented participant in both pointing conditions is also evidence in this plot. The difference between the subjects with SZ and the ASD parents is also shown . In summary, the statistics of SZ patients are atypical, highly heterogeneous, and different from those of other disorders.Patients with SZ did not significantly differ in stochastic signatures across ages. This is shown in matrix form in Figure Examination of the normalized PV index, as a function of the proportion of time to reach the PV for each group within the study, revealed differences between the forward and the backward segments of the pointing loop. These differences for the control groups CT1, CT2, CT3, elderly participants, and ASD parents can be appreciated in Figures S1A in Supplementary Material (forward) and Figure S1B in Supplementary Material (retraction with the shift from the forward case). As the parents of individuals with ASD demonstrate the largest shift, we divided the group by sex into mothers and fathers. The legend provides information on the group type.x-axis, we plot the proportion of time to reach PV. Along the y-axis, we plot the normalized PV index. Typical ranges along the horizontal axis are about 0.5, where the peak tends to occur . Lower values indicate reaching the speed maximum earlier, whereas higher values indicate reaching it later.In this graph, along the The graph in Figure S1A in Supplementary Material shows a separation between the younger CT1–3 groups and the remaining subjects, whereby the latter are slower on average . Along the time axis, these younger participants fall near 0.5, while the elderly participants and the parents vary. The elderly participants reach the peak earlier than half-way to the pause, and in the parents the mothers are similar to the younger controls but the fathers reach PV later. Indeed, the fathers of this cohort showed the largest difference in the proportion of time to reach the PV between the forward and the backwards reaches. The retractions peaked much earlier and the value of the normalized PV index dropped. This indicates a much faster retraction (as with the deafferented subject) whereby the end effector is retracted under less control. Specifically, the fathers were the slowest of the cohort to reach the velocity peak during the forward reach phase, but the retraction phase was almost a jerky, nonsmooth motion, suggesting poor motor control of these spontaneous, uninstructed reaches. The signatures of the deafferented participant are plotted for the cases of pointing in the dark and pointing when aided by visual feedback. In the dark, the deafferented participant reaches the PV earlier and moves faster on average than when guided by vision. This is expected and consistent with his description of feedforward strategies to initiate the motion and rely on visual feedback to compensate for the lack of proprioception. Evidence for feedforward control in IW was also shown in a mirror drawing task .y-axis. They perform more similar to controls in the retraction than in the forward segment. IW also shows changes in these patterns, with shifts in the opposite direction for the two conditions that he performed. Notice that the youngest group of children and the elderly participants show no discernible shift between forward and backwards reaches in these plots. Figure S1 in Supplementary Material also shows the distributions of these two parameters per group along each axis.In the parameter plane of Figure S1B in Supplementary Material, we also plot the shifts comparing the timing in forward and back movements of the participants. Here, the ASD parents have the largest shifts in the retraction movements, toward the 0.5 values of the proportion of time and toward faster average speed along the Figures S1C,D in Supplementary Material show similar plots for the patient groups under study in relation to the deafferented subject. All patients have slower average speeds than controls but the mild PD group shifts toward typical levels in the retractions. The severe PD group is, as expected, the slowest and tends to reach the PV earlier than the mild PD group. The distributions (color coded as in the legend) also show the differences across groups.x-axis of the time to reach the peak would prevent clarity if all graphs were set to the same scale. The Gamma plane summarizes the estimated Gamma parameters for each group and set of conditions under analysis (forward and retraction). The legend shows the color-coded data corresponding to the frequency histogram (each data set comprises 1000 data points randomly selected from the estimated Gamma parameters of the entire set). The ASD group is by far the most skewed, consistent with previous analyses of their temporal kinematics while performing other tasks  and neurodegenerative (PD) disorders. We also studied the performance patterns of patients with SZ, a syndrome with onset of its characteristic set of features  in early adulthood (in contrast to ASD and PD), and for which very little motor control research exists. Finally, we included a deafferented participant who lacks proprioception due to damage in afferent fibers conducting touch, pressure, and movement information from the periphery to the brain.The main purpose of this work was to estimate statistical ranges across the population so as to initiate a path of change in statistical analyses from the current “one-size-fits-all” model, to a more personalized approach in line with current NIMH initiatives. Here, we have shown that the typical patterns differ across different groups within the population, with ideal Gaussian-like shape and the lowest noise-to-signal ratio in the control subjects from college to middle age. The younger children and the elderly participants at the extreme of the bell curve of the human life span have different distribution shapes and dispersion that give rise to different summary statistics. In contrast to the ideal statistics from the young typical controls, the ASD, PD, SZ, and the elderly groups had marked differences along at least one of the moments’ axes that we empirically estimated here. Such differences were detected  above chance across the clinical groups.In addition to the grouped data analyses, we also showed that the summary statistics spaces localize each individual with respect to other individuals. Without color coding the scatter by disorder or age, we can see a gradient of differences with clear separation between ASD and controls. Likewise, we can see a separation between mild and severe PD that is quite unambiguous. However, the SZ patients were more heterogeneous than the other groups with brain disorders, as can be seen in Figures The velocity-dependent parameters used here could unambiguously detect differences in levels of severity in PD and unique levels of noise in ASD. Regardless of age, sex, or medication status, these patterns were distinguishable in this cohort when labeling the locations by clinical condition. Likewise, taken as a group, the SZ had radically different distributions from the groups with overlapping ages, CT2, and CT3. At the individual level though, the data from the SZ patients also emphasize the need for a personalized approach to this devastating yet heterogeneous disorder. In this sense, the case of PD is relevant as it shows that the clinical diagnosis already does a good job at characterizing the emergence of relatively homogeneous subgroups as the disease progresses. The utility of these analytical tools in ASD has yet to be clinically confirmed as at present the diagnosis does not include motor symptoms at all. Likewise in ASD and SZ, it will be important to ascertain the effects of medication intake on motor patterns, a task that is now possible with this statistical platform.A surprising result emerged from the data on ASD parents. Specifically, the signatures of ASD parents did not match those of typical controls their age. Instead, their signatures matched those of the elderly and the PD groups. There are many possible reasons and combinations of reasons for this result that we shall investigate in future work. One reason could be due to overall parental stress levels. Other reasons could include symptom-based medication intake  and/or genetic predisposition. Given their much younger ages than the elderly and severe PD groups, it may be useful to study the rate of change of these patterns in ASD parents. In particular, tracking the evolution of the motor signatures with age may illustrate whether the signatures of their motor patterns are in an atypical accelerated state of change.These data sets provide insights into the general motor statistics of these populations, and underscore the need for a personalized medicine approach to psychiatric and neurological disorders. In particular, the case of PD, a disorder that is diagnosed and tracked based on visible changes in motor patterns, beautifully illustrates the potential utility of motor noise-based biomarkers to characterize each person relative to the rest of the population. The two cohorts of PD, mild-to-moderate and severe, could serve as anchors to reference other more heterogeneous disorders such as SZ. Some of the SZ patients had patterns comparable to those of severe PD while others fell closer to those of the mild-to-moderate PD group, and yet others were overlapping with CT3 participants. Statistical distance metrics based on this type of sensory–motor noise may help us discern alterations in motor feedback as a function of anxiety, dopamine receptor-blocking medications, and other factors in these populations.One of the most striking features in the data from subjects with SZ, besides its heterogeneity in the motor domain, is the lack of similarity with the deafferented participant. Unlike the ASD and severe PD groups, who were close to the deafferented participant pointing in the dark, the SZ patients fell far from IW in the statistics parameter space. Subject IW lacks feedback from the fibers that transmit touch, pressure, and movement information but has temperature and pain channels spared. The result is noteworthy given the reports in the SZ literature of problems with thermoregulation –36 and hIn the context of internal models for action (IMA), it is possible that across these neurological disorders there are differentiable and selective disruptions in various components of forward planning linked to different levels motor noise. Among brain areas that are thought to be important for forward computations within the framework of IMA are the cerebellum , 42 and Velocity-dependent peripheral input signals from self-produced, goal-directed motions are an important source of guidance to the brain. They help compensate for synaptic transductions and transmission delays. In the context of visually guided reaching, areas in the PPC are known to be important for the planning and execution of such actions. Regions in the PPC receive eye position and velocity afferent inputs via ascending prepositothalamocortical pathways . ProprioAfferent sensory channels convey reafferent kinesthetic signals from mechanoreceptors involving touch, pressure, and ongoing self-produced movements. They also convey pain signals from nociceptors and temperature-related signals from thermoreceptors . The preThe new analyses reveal striking statistical differences between mild-to-moderate and severe stages of PD. In particular, these two cohorts have selectively overlapping features with the deafferented subject IW. In the case of the mild PD, the signatures overlap with those of IW under conditions of visual guidance. Interestingly, an overreliance on visual feedback has been reported in PD , 68–70, Traditional studies of motor control assume normality in the distributions of kinematic parameters. This work shows that there is a range of skewed distributions, from the memoryless exponential to the symmetric Gaussian in the velocity-dependent code of pointing behaviors. This result underscores the importance of providing an empirical characterization of the statistical properties underlying human movements. By assuming normality and smoothing out as noise the motor output fluctuations, we miss important information in the data from both typical and pathological conditions. This work also highlights the significance of individualized statistical assessments that may enable the discovery of self-emerging patterns inherent in the data. Analyzed as an ensemble using clinical labels, the data are very revealing when empirical statistical estimation is used, rather than theoretical assumptions and homogeneous treatment of the data. The clinical literature of motor control makes a number of assumptions that may blur the true features of the kinematics data from neuropsychiatric and neurological disorders. This work emphasizes the importance of reconsidering those traditional practices and researchers teaming up with clinicians to better inform data-driven approaches.We have demonstrated here the importance of providing empirical estimation of the statistical features underlying motor behaviors. Across different disorders of the nervous systems, we were able to characterize the ranges of statistical parameters that are traditionally treated as homogeneous under the assumption of normality in the movement data. The noise that is traditionally smoothed out through data averaging and the retracting movement segments that are often discarded as nuisances in the data revealed fundamental differences across neurological disorders that may be of use to genetics research. Specifically, classification of different types of sensory–motor noise may be possible and may aid in linking specific genetic factors that give rise to selectively different levels of synaptic noise with differentiable levels of sensory–motor noise. The specificity of these biometrics has yet to be tested, and better instrumentation discerning motor from sensory noise in electromyography combined with high dimensional kinematic signals will be required. We need to unveil the origins of synaptic noise in the first place before understanding different gradients of sensory–motor noise. Yet, the same personalized statistical platform presented here can be used to examine time series of other related signals. In disorders of known etiology, it should be possible to investigate these questions so as to build similar statistical maps to those presented here, whereby genetic factors and their resulting synaptic noise would be another data dimension. Such questions can be addressed using the present statistical platform.As presented here with the deafferented subject in different contexts, we could assess the patterns of sensory–motor noise from individuals that go on to receive a diagnosis of ASD, SZ, or PD but for whom a genetic history is available. One such a group is the Fragile X-related disorders, where premutation carriers may receive an ASD diagnosis at an early age or a PD misdiagnosis at a later stage in life, or a diagnosis of mood and other psychiatric disorders in the case of female premutation carriers . We suggGiven the diverse population and clinical expertise, the following breakdown of contributions is in order: ET conceived study, analyzed all data, and wrote paper. Autism: RI, ET, and CW collected data; RI, CW, JN, and JJ designed study/analyses; and JN performed clinical assessment. Parkinson: RI and ET collected data; JS performed clinical assessment. Schizophrenia: JN and TP collected data and designed study/analyses; SS performed clinical assessment. Deafferented subject: JC collected data and performed clinical assessment. All authors participated in the editing and approval of the final version of 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.


Biological energy conversion in mitochondria is carried out by the membrane protein complexes of the respiratory chain and the mitochondrial ATP synthase in the inner membrane cristae. Recent advances in electron cryomicroscopy have made possible new insights into the structural and functional arrangement of these complexes in the membrane, and how they change with age. This review places these advances in the context of what is already known, and discusses the fundamental questions that remain open but can now be approached.The online version of this article (doi:10.1186/s12915-015-0201-x) contains supplementary material, which is available to authorized users. Mitochondria are the powerhouses of the cell. In all eukaryotes that do not depend on photosynthesis, the mitochondria are the main source of adenosine triphosphate (ATP), the energy-rich compound that drives fundamental cell functions. These functions include force generation , the biosynthesis, folding and degradation of proteins, and the generation and maintenance of membrane potentials. ATP is produced on a massive scale in the human body, amounting to 50 kg per day in a healthy adult, but considerably more in a long-distance runner. ATP is generated by the mitochondrial ATP synthase from ADP and phosphate ions. These are the products of ATP hydrolysis at the sites where energy is needed in the cell. Apart from cellular respiration and ATP synthesis, mitochondria have numerous other essential functions, including the production of NADH and GTP in the citric acid cycle, the biosynthesis of amino acids, heme groups and iron-sulfur clusters or the synthesis of phospholipids for membrane biogenesis. They also act in calcium signaling , stress In many respects, mitochondria resemble α-proteobacteria, from which they are thought to have originated by endocytosis some 1.6 billion years ago. The most striking evidence of this evolutionary relationship is the close homology of bacterial and mitochondrial respiratory chain complexes. Mitochondria have their own genetic system, which uses a distinct DNA code that differs both from that of their bacterial ancestors and their eukaryotic hosts . They haMitochondria are highly dynamic . In the Mitochondria can be seen in the light microscope, but their detailed internal structure is only revealed by electron microscopy. In the 1990s, the structure of mitochondria was investigated by electron tomography of thin plastic sections . While tAs ubiquitous, semi-autonomous cellular organelles, mitochondria are separated from the cytoplasm by the outer and inner mitochondrial membrane Fig. . The outThe inner and outer membranes of mitochondria define three compartments within the organelle, each with its distinct role and corresponding protein components. The innermost compartment, surrounded by the inner membrane, is the mitochondrial matrix. It is the equivalent of the bacterial cytoplasm, from which it is distinguished by a pH of 7.9 to 8 , similarThe biosynthetic reactions that happen in the matrix include those of the citric acid cycle. As each reaction is catalyzed by its specific enzyme, the mitochondrial matrix has a high protein density of up to 500 mg/ml, close to that in a protein crystal. For cryo-ET of intact organelles, the high matrix density has the disadvantage of obscuring internal detail.The equivalent of the periplasm in the bacterial ancestors of mitochondria is the intermembrane space. This is the ~20 nm gap between the outer membrane and the part of the inner membrane that is known as the inner boundary membrane. All matrix proteins imported into the mitochondrion from the cytoplasm must pass through the outer and inner membrane and therefore also through the intermembrane space. Conventional EM of thin plastic sections suggested sites of direct contact between the lipid bilayers of the inner and outer membrane , 25, butc. The mitochondrial cristae are thus the main site of biological energy conversion in all non-photosynthetic eukaryotes.The inner membrane forms invaginations, called cristae, that extend deeply into the matrix. The cristae define the third mitochondrial compartment, the crista lumen. The crista membranes contain most, if not all, of the fully assembled complexes of the electron transport chain and the ATP synthase Fig. . The criCristae were first discovered by electron microscopy in thin sections of plastic-embedded cells and tissues , 29. TheIt is thought that the MICOS complex forms a diffusion barrier within the inner membrane at the crista junctions to account for the apparent lateral segregation of proteins between the cristae and boundary membranes. Evidence of such differences comes primarily from electron microscopy, because it has not been possible to separate cristae and boundary membranes biochemically. Immuno-staining of thin plastic sections has shown that respiratory chain complexes reside in the cristae rather than in the boundary membrane, whereas components of the protein translocases are found in the boundary and outer membranes . Cryo-ETIn tissues with a high energy demand, such as skeletal or heart muscle, the cristae are closely stacked disk-like lamellae that take up most of the mitochondrial volume Fig. . In anim1-Fo ATP synthase is the most conspicuous protein complex in the cristae. The ATP synthase is an ancient nanomachine that uses the electrochemical proton gradient across the inner mitochondrial membrane to produce ATP by rotatory catalysis [o complex in the membrane drive a rotor ring composed of 8  or 10  c-subunParamecium [Paramecium.For many years it was assumed that the ATP synthase and other energy-converting complexes are randomly distributed over the inner membrane. The first hint that this is not the case came from deep-etch freeze-fracture electron microscopy, which revealed double rows of macromolecular complexes in the tubular cristae of the single-cell ciliate ramecium . The douramecium , 38, whiramecium  Fig. 4)Parameciu1 head  and IV (cytochrome c oxidase)  transfers electrons from succinate directly to quinol and does not contribute to the proton gradient.The proton gradient across the cristae membrane is generated by three large membrane protein complexes of the respiratory chain in the cristae, known as complex I (NADH/ubiquinone oxidoreductase), III (cytochrome se) Fig. . ComplexYarrowia lipolytica has been obtained [Thermus thermophilus [The respiratory chain complexes have been studied in great detail for decades. High-resolution X-ray structures are available for mitochondrial complex III  and IV . At a moobtained . Comparimophilus  indicatemophilus . HoweverSaccharomyces cerevisiae, which lacks complex I, nevertheless has a respiratory chain supercomplex consisting of complex III and IV [Not unlike the ATP synthase, which forms dimer rows in the cristae, the proton pumps of the electron transport chain assemble into supercomplexes or ‘respirasomes’. Respiratory chain supercomplexes were first postulated on the basis of blue-native gels of yeast and bovine heart mitochondria solubilized in the mild detergent digitonin . NegativI and IV . Far froI and IV . Alternac that shuttles electrons from complex III to complex IV. If released into the cytoplasm, cytochrome c triggers apoptosis [c does not leak from the cristae and that the outer membrane remains tightly sealed during mitochondrial fission and fusion.A main protein component of the crista lumen is the small soluble electron carrier protein cytochrome poptosis . It is iPodospora anserina revealed profound age-dependent changes in the inner membrane architecture [c, into the cytoplasm. Cytochrome c activates a cascade of proteolytic caspases, which degrade cellular proteins [Ageing is a fundamental yet poorly understood biological process that affects all eukaryotic life. Deterioration in mitochondria is clearly seen in ageing, but details of the underlying molecular events are largely unknown. Cryo-ET of mitochondria from the short-lived (~18 days) model organism itecture . In normitecture  and on titecture . With inproteins . The celP. anserina mitochondria may be a direct or indirect consequence of cumulative oxidative damage, which is known to promote cellular and organismal senescence [The observed morphological changes during ageing in nescence . The elenescence , which cnescence . During nescence . This prEven though mitochondria and their membrane protein complexes have been studied intensely for more than five decades, they remain a constant source of fascinating and unexpected new insights. Open questions abound, many of them of a fundamental nature and of direct relevance to human health .Concerning macromolecular structure and function, we do not yet understand the precise role of the highly conserved feature of ATP synthase dimers and dimer rows in the cristae and the interplay between the MICOS complex and the dimer rows in cristae formation. Are there other factors involved in determining crista size and shape?We still do not know how complex I works, especially how electron transfer is coupled to proton translocation. What is the role of respiratory chain supercomplexes? Do they help to prevent oxidative damage to mitochondria, and if so, how? And how does this affect ageing and senescence?We also do not know how the TIM and TOM protein translocases work, and what they look like at high resolution. The same is true for the structure of the MICOS complex at the crista junctions. How does it anchor the cristae to the outer membrane, and how does it separate the cristae form the contiguous boundary membrane? Similarly, the mechanisms of mitochondrial fission and fusion and the precise involvement and coordination of the various protein complexes in this intricate process is a fascinating area of discovery.The biogenesis and assembly of large membrane protein complexes in mitochondria is largely unexplored. Where and exactly how do the respiratory chain complexes and the ATP synthase assemble? How is their assembly from mitochondrial and nuclear gene products coordinated? Does this involve feedback from the mitochondrion to the cytoplasm or the nucleus, and what is it?And finally, how exactly are mitochondria implicated in ageing? Why do some cells and organisms live only for days, while others have lifespans of years or decades? Is this genetically programmed or simply a consequence of different levels of oxidative damage? How is this damage prevented or controlled, and how does it affect the function of mitochondrial complexes? Is the breakdown of ATP synthase dimers also an effect of oxidative damage, and is it a cause of ageing?It will be challenging to find answers to these questions because many of the protein complexes involved are sparse, fragile and dynamic, and they do not lend themselves easily to well established methods, such as protein crystallography. Cryo-EM, which is currently undergoing rapid development in terms of high-resolution detail, will have a major impact but is limited to molecules above about 100 kDa . Even be


Aim: Interprofessional education (IPE) is a central feature of modern education in the health care professions. Despite this, empirically founded and systematically structured IPE courses are absent from many curricula. To answer the WHO’s call for improved interprofessional collaboration in the health care system, a seminar was designed, implemented and evaluated. The target group consisted of students beginning nursing and medical studies (first and second semesters) and physiotherapy students (first year of training).The aim was to develop a basic IPE module focusing not only on the demands placed by academia and politics, but also the interests of the target group. This module was evaluated on the basis of the modified four-level Kirkpatrick approach.Method: Based on focus group interviews analyzed qualitatively using Mayring’s content analysis, it was possible to define five learning objectives and develop four practice-oriented modules. The seminar was then implemented and evaluated using written pre- and post-seminar evaluations and group discussions.Results: Analysis confirmed the success of the IPE concept in that the seminar was positively rated by attendees not only in terms of their immediate reactions, but also attitude, knowledge and skills according to Kirkpatrick.Conclusion: In the future, it is intended to offer the IPE module on a permanent basis and assess the competencies acquired in the seminar using observation. Courses to ensure sustained learning outcomes would also be desirable. An international overview study published by the WHO  illustrahttp://www.nklm.de] has restructured medical study with clearer objectives. In particular, the NKLM emphasizes the relevance of collaborative interprofessional teams. Regulations governing other health professions have for some time now specified that interprofessional cooperation is an important aspect of professional practice. For instance, physiotherapists commit themselves in Section 5 of their professional rules and regulations to collaboration with interprofessional teams [https://www.physio-deutschland.de/fileadmin/data/bund/Dateien_oeffentlich/Beruf_und_Bildung/Ausbildung/ZVK-Verband-Berufsordnung-BroschA5-2012-RZ.pdf], and the Deutsche Pflegerat (German Nursing Council) sets down in Section 2 of its Code of Professional Conduct that professional nurses work together in an interdisciplinary manner with other professional groups [http://www.deutscher-pflegerat.de/Downloads/DPR%20Dokumente/Rahmenberufsordnung.pdf].Given that teamwork is a central factor for improved efficiency in the provision of health care  and is dSo that this can be realized and IP anchored in the professional identities of the different health care professions, educational programs and institutions must foster a positive attitude toward interprofessional collaboration (http://www.careum.ch/web/guest/lancet-report) and combat stereotyping . In addiTeam-centered interprofessional training and education in medicine, nursing and physiotherapy (TIPAS), intended to encourage interprofessional knowledge and skills among the three professions in general and, more specifically, enable the three professions to become acquainted with each other.The aim of this project was to design, implement and evaluate an IPE course at the University of Freiburg. The project, To design empirically based courses, the first step should be to create module content, and following its implementation, have the target group evaluate it. The study’s aim gives rise to the following research questions:Issue 1: How can an IPE course be designed to meet the needs of potential attendees?Issue 2: What influence does the IPE course have on the attitudes and skills of the attendees in regard to interprofessional collaboration? In this section the method of seminar development is elaborated on, followed by a description of the seminar concept as a result of the developmental phase. The teaching of the seminar and the evaluation are also covered.To gather information on needs based on the preferences of the target group concerning IPE  and thusThe questions followed a guideline and referred to IPE  and roles and stereotypes . To facilitate transcription, audio recordings were made of the interviews. The focus group interviews yielded a total of 55 pages of transcribed material .The subsequent qualitative content analysis based on Mayring  was carrBased on the content analysis, five seminar objectives emerged that are central to the target group in respect to IPE and which should be mastered in the unit addressing knowledge: the concepts of interprofessionalism and multiprofessionalism, advantages of IP for patients and medical team, individual and institutional possibilities for fostering IP, spheres of responsibility and roles of different health care professions, andthe motives for choosing a particular career. Table 1 The learning objectives were then transformed into four modules to structure the sequence of the course: introduction, stereotypes, role profiles, and wrap-up  and scheduling the seminar early on in the curriculum (nine statements for early inclusion vs. six for later inclusion). This corresponds with the recommendations of Anderson & Thorpe  who advoIn keeping with the recommendations of the Institute of Medicine , the evaThe level-1 reaction (Did they enjoy it?) was assessed by holding a group discussion at the end of the class session. In addition, the relevance of IPE was assessed by the item in the questionnaire asking how important students considered interprofessional courses to be on an eleven-point Likert scale . Satisfaction was measured after the seminar using seven eleven-point items . An example being the item, “I would recommend this seminar to others.” For all of the items reported here, refer to the post-seminar evaluation questionnaire in attachment 2 .To gather insights on levels 2a) attitude and reaction and 2b) knowledge and skill, a survey instrument was developed and filled out by the attendees both before and after the seminar session . The pre-seminar version consisted of six sociodemographic items  and 22 items representing knowledge and attitude regarding IPE. Nine of these items referred to the seminar objectives aimed for and are evaluated in the following. These internally developed items are formulated as statements, for instance, “I know the advantages of interprofessional collaboration,” and each statement is rated on a five-point Likert scale . To gather information on previous experience with interprofessional collaboration, space was left for open-ended responses with instructions to “please describe the characteristics of successful interprofessional teamwork in health care.” The post-seminar version of the questionnaire was reviewed and adapted after the first TIPAS seminar during the winter semester 2014/15, a process that led to the deletion of six items.After designing the seminar, it was offered for two consecutive semesters (winter 2014/15 and summer 2015), both times as a seven-hour seminar block. To establish a shared knowledge base , the regEach of the professions was represented by one experienced practitioner; additionally an instructor with a specialty in interprofessionalism and teamwork was on the teaching team, as required by the Institute of Medicine . Thirty-n=15) and second (n=20) seminar cycles. Due to the small sample size and absence of a normal distribution, the Wilcoxon rank-sum test was applied to determine significant changes in knowledge and attitudes concerning interprofessionalism as a result of the seminar.Quantitative data analysis was carried out on the entire data set of 35 attendees from the first (M=8.68) and significantly higher after the seminar (M=9.35), z=-2.99, p=.003, r=-.51. In the post-seminar evaluation there was a high level of satisfaction among attendees with mean values between 8.26 (SD=2.21) for the item “My attitude toward interprofessional teamwork was positively affected by the seminar” and 9.30 (SD=1.07) for the item “I would recommend the seminar to others” , the physiotherapy students commented that the seminar helps to raise awareness of stereotyping. Nursing attendees agreed with this assessment and added that stereotyping should be avoided, while several medical students stated that stereotypes help make it easier to identify and assign the tasks to the professional groups. In respect to the advantages of IP for patients and medical teams , the nursing students said that they felt encouraged to bring up problems when working in interprofessional teams and to actively request assistance when they needed it. Medical students added that the advantages of interprofessional collaboration for patients became clear, even if this required some effort. They drew this insight primarily from the experience with the case vignette, in which it was the nursing students in particular who were able to share detailed knowledge since they had prior practical experience in providing patient care. This is why the course participants discussed whether or not it would make more sense if the seminar were offered at a later point in the curriculum when all of the participants would have acquired some prior practical experience. Knowledge about the roles and responsibilities of the different health care professions  was viewed as being improved after the seminar. In this context, grappling with self-image and the images of the other professional groups in the group exercises was responded to positively.SD=0.91) and 3.97 (SD=0.76). After the seminar these values lay between 4.09 (SD=0.57) and 4.76 (SD=0.44). In respect to the seminar objectives there was a significant improvement in the rating of these items in terms of the concepts of interprofessionalism and multiprofessionalism , , , 16]. F. F16]. FThe results of the evaluation and the reactions to the seminar were very positive (Issue 2). Knowledge was acquired on all five seminar goals, while at the same time the attitude toward the relevancy of IPE was generally strengthened. The positive ratings for the seminar indicate a purposeful and interest-oriented seminar concept, as well as effective implementation. Significant improvements for the knowledge items on the questionnaire, however, do not allow any assertions about an actual improvement in skills at the behavior level, but do reflect subjective competency expectations of the seminar attendees. Bandura  argues iOffering the seminar at the beginning of the curriculum was favored by the majority of the focus group participants and follows the recommendations of Areskog  to combaThe seminar and the evaluations were included in the regular semester course load for the three professions and were subject to organizational constraints. The field study nature of this investigation also leads to limitations regarding quality criteria. That being said, the feasibility of this approach for future years can still be evaluated positively.Due to the small sample size and the short duration of the course, one day in each case, the validity of the study results must be viewed as limited. The effort of teaching is high with four instructors and a maximum of 18 student participants per course. On the other hand, demonstrating in person the principles which the course seeks to impart is indispensible for the success of IPE . The smaThe small sample size was further reduced by the drop out of physiotherapy students: during the first course offering only three of the six registered students showed up. In response, an announcement was made prior to holding the seminar for the second time that attendance certificates would be issued, leading to a reduction in the drop-out rate.Furthermore, a selection bias must be assumed since the seminar participants attended it voluntarily for the most part: a student interested in IPE would need to show a great degree of motivation to attend the seminar. For this reason, the reported results can be applied only generally to the total university and vocational student populations in the health care professions.Due to the small sample size so far, psychometric verification of the measuring instrument has not yet been possible, the reason why the questionnaire does not represent a valid evaluation of achieving the seminar goals. Still, the high values for satisfaction (see table 3 The strategy presented here has been confirmed in practice, and the seminar will continue to be offered at least once a year. As a topical supplement, an advanced module on the subject of interprofessional communication will take place. In order to make any claims about the acquisition of skills, an instructor guideline on making observations is being planned so that it will be possible to evaluate the work done on the interprofessional case vignettes. In general, the benefits of counting the course toward study credit should be regulated in the same manner for all the groups to combat any systematic drop outs. Courses or events to ensure sustained learning outcomes would also be desirable . In addiThe authors declare that they have no competing interests.


Molecular recognition is a process that brings together several biological macromolecules to form a complex and one of the most important characteristics of the process is the binding free energy. Various approaches exist to model the binding free energy, provided the knowledge of the 3D structures of bound and unbound molecules. Among them, continuum approaches are quite appealing due to their computational efficiency while at the same time providing predictions with reasonable accuracy. Here we review recent developments in the field emphasizing on the importance of adopting adequate description of physical processes taking place upon the binding. In particular, we focus on the efforts aiming at capturing some of the atomistic details of the binding phenomena into the continuum framework. When possible, the energy components are reviewed independently of each other. However, it is pointed out that rigorous approaches should consider all energy contributions on the same footage. The two major schemes for utilizing the individual energy components to predict binding affinity are outlined as well. Many proteins carry their functions by interacting with other molecules, such as other proteins, DNA/RNA, peptides or small molecules. Having in mind that human cell is estimated to have roughly half million different proteins and on average each protein is involved in four interactions, one can appreciate the complexity of protein-protein interaction (PPI) networks. The picture becomes even more complex if one considers protein-DNA and protein-RNA interactions as well. It was demonstrated that protein-DNA recognition is a complex process utilizing base- and shape-readout mechanisms  non-bonded interactions, i.e., interactions that do not involve chemical bonds; (b) bonded interactions, typically referred as internal energies; (c) the energy term resulting from proton uptake/release upon binding and (d) the entropy change. The relative weights of these energy terms for the total binding free energy depends on the interactions being studied and the model being applied. For example, a model considering rigid binding will result in no change of the internal energy and internal entropy and this may be adequate approach for studying binding involving lock-and-key recognition  and the effects of water phase  only. The modeling can be significantly improved, in terms of mimicking the physical phenomena occurring at the binding, by considering that electrostatic interactions across molecules are not in homogeneous medium, but rather in a media which dielectric properties reflect the difference between surface bound and bulk water, between rigid and flexible regions of the molecules and presence of partially occupied water sites (at the interfaces and inside macromolecules)  phase is considered as a high dielectric constant medium  should be introduced in the GB model. To address the problem, the CHA effect is added to the GB equation via an analytical correction. The correction quantifies the specific propensity of CHA by the charge distribution of the water model    for example), and to integrate over the volume from molecular density ρ = ρ0 to ρ = 0 of biomolecule. This problem bears a lot of similarity with modeling Zeta-potential in colloidal science, since it is not clear exactly where is the border between molecule attached and free water molecules.Significant efforts were invested to optimize the parameters for the nonpolar solvation energy calculations. Different groups have suggested different Protein-ligand bindings often accompany the changes in protonation states of the receptor and the ligand  vdW energy and the new interactions across the interface Figure .ab-initio docking may apply softened vdW function to tolerate small structural imperfections  protein to completely folded one should result in large loss of entropy, which should be overcompensated by the enthalpy gain upon complex formation , or small set of representative structures for bound and unbound molecules, typically use linear interaction energy (LIE) formalism or some kind of scoring function with optimized weights   Aaqvist, , thermodThe Review Editor Feng Ding declares that, despite being affiliated with the same institute as the authors Lin Li, Lin Wang and Emil Alexov and having collaborated with the authors Lin Li and Emil Alexov, the review process was handled objectively. 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.


In the last few years, the increasing use of LEDs in illumination systems has been conducted due to the emergence of Visible Light Communication (VLC) technologies, in which data communication is performed by transmitting through the visible band of the electromagnetic spectrum. In 2011, the Institute of Electrical and Electronics Engineers (IEEE) published the IEEE 802.15.7 standard for Wireless Personal Area Networks based on VLC. Due to limitations in the coverage of the transmitted signal, wireless networks can suffer from the hidden node problems, when there are nodes in the network whose transmissions are not detected by other nodes. This problem can cause an important degradation in communications when they are made by means of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) access control method, which is used in IEEE 802.15.7 This research work evaluates the effects of the hidden node problem in the performance of the IEEE 802.15.7 standard We implement a simulator and analyze VLC performance in terms of parameters like end-to-end goodput and message loss rate. As part of this research work, a solution to the hidden node problem is proposed, based on the use of idle patterns defined in the standard. Idle patterns are sent by the network coordinator node to communicate to the other nodes that there is an ongoing transmission. The validity of the proposed solution is demonstrated with simulation results. Optical wireless communications based on visible light , named VThe hidden node problem is a well known problem in wireless networks based on radio frequency using CSMA/CA channel access mechanisms ,6. This In order to improve efficiency in energy consumption, the use of directional antennas has been proposed for wireless sensor networks . DirectiIn VLC, the hidden node problem can be considered as a key issue because of the directional characteristics of both optical transmitters (LED) and receivers (photo detectors).Since the IEEE 802.15.7 standard was published in 2011, we are unaware of other studies or published works to evaluate the effects of the hidden node problem when using CSMA/CA mechanisms in a VLC network. Several research papers have been published to evaluate the IEEE 802.15.7 uplink  performance based on CSMA/CA. Papers  presenteIn this paper, we present an evaluation of the effects of the hidden node problem in the performance of uplink communications when using the slotted CSMA/CA random access procedure defined in the IEEE 802.15.7 standard. Furthermore, in order to improve network performance in the presence of hidden nodes, one solution is presented, which has been chosen not only according to effectiveness and ease of implementation criteria, but also taking into account its ease of integration in IEEE 802.15.7 standard, since it requires no modifications. Our proposed solution is based on selecting the type of idle patterns to be sent by the network coordinator, depending on whether or not it is receiving transmissions coming from network devices. According to IEEE 802.15.7 standard specification, sending idle patterns is a mandatory requirement for infrastructure nodes while idle or receiving operations, to ensure continuous illumination and avoid flickering.The rest of this paper is organized as follows: Optical wireless links for indoor environments can be classified according to two criteria. The first criterion is the degree of directionality of the optical transmitter and receiver. The second one relates to whether the link relies upon the existence of an uninterrupted line of sight (LOS) path between the transmitter and receiver .Directed links resort to directional transmitters and receivers, which must be aimed in order to establish a link. Directed links maximize power efficiency since path loss and reception of ambient light noise is minimized. They have the disadvantage of limiting mobility in user terminals because they need to be aligned. On the other hand, non directed links may be more convenient to use for mobile terminals, since they do not require precise alignment. However, non directed links present some disadvantages—such as multipath distortion and lower power efficiency—that make it difficult to achieve high transmission rates.LOS links require a direct path between the transmitter and receiver that is without obstacles. They allow for maximizing received power and minimizing multipath distortion, but the need for a line of sight diminishes flexibility of use. Non-LOS (NLOS) links are more robust because they can operate even in the presence of obstacles between the transmitter and receiver. NLOS non directed links are referred to as diffuse links. VLCs can use several link configurations, like directed LOS, non directed LOS and diffuse links, see The optical channel components are the optical transmitter (LED), the photo detector (PD) and the transmission medium. For VLC links, intensity modulation (IM) is used, in which the waveform of the signal to be transmitted is modulated onto the instantaneous power of the optical carrier. The technique used in reception is direct detection (DD), in which a photodetector produces an electrical current proportional to the received optical instantaneous power. Usually, optical wireless systems based on IM/DD are modeled as a base band linear, time-invariant system , see FigR is the receiver responsivity. This base band channel model can be expressed by Equation , so the most important quantity for characterizing this channel is the DC gain H(0) , which rθ. Where m is the mode number of the radiation lobe, which specifies the directionality of the source, and is related to the transmitter half power angle A, and field of view (FOV). The angle formed between the optical incident signal and the orientation vector ψ. The maximum incident angle defines the receiver FOV.In VLC, the received power can be expressed as the sum of LOS and NLOS components . In direAccording to , when cod is the distance between transmitter and receiver. The Equation  isEquation  for chanSilicon photo-diodes are usually employed in optical receivers to convert the optical signal into an electrical signal, which has an added noise. In VLC systems, there are two main noise sources: thermal noise, rding to . Since bThen, the signal to noise ratio (SNR) can be expressed as:R is the receiver responsivity, and In Equation , Per is butions, ,24,25,26The hidden node problem is a well known problem in radio wireless networks using channel access mechanisms based on contention techniques as CSMA/CA . CSMA/CAIn elopment  and in selopment  with proelopment , there aStar: communication is made between several devices and one central controller, called the coordinator, which is also used as an illumination infrastructure. The rest of devices are typically mobile terminals powered by batteries.Peer-to-peer: allows two devices to communicate, provided they are within reach of each other. One of the devices acts as the communication coordinatorBroadcast: one coordinator sends data to one or several devices in a uni-directional way.This standard defines a PHY and MAC layer for short range optical wireless communications using visible light, employing free space as propagation medium. Three network topologies are defined: star, peer-to-peer and broadcast. These topologies are shown in The IEEE 802.15.7 standard PHY layer is responsible for several functions, such as transmission and reception via the optical medium, activation and deactivation of the optical transceiver, and detection of the state of the transmission channel (idle/busy).PHY I: intended for outdoor usage with low data rates (from 11.6 Kbps to 266.6 Kbps).PHY II: intended for indoor usage with moderate data rates (from 1.25 Mbps to 96 Mbps).PHY III: for applications with multiple optical transmitters and receivers working on different frequencies (colors). It uses the technique of modulation Color-Shift Keying (CSK) [ng (CSK) , with trThe PHY layer supports three operation modes, which allow the use of different transmission rates and include support for different types of optical devices and operating environments:Each operation mode of the PHY layer supports the use of different combinations of transmission rates, optical clock frequencies, modulation schemes, line codes, and error correction coding techniques. Each one of the aforementioned parameters settings is designated with an operation mode identifier or Modulation Coding Scheme Identifier (MCS-ID).i.e., according to the spectrum of the optical detector output signal. In-band idle patterns do not require changes in the frequency of the optical clock, and remain within the frequency range the receiver circuits can accept. Out-of-band idle patterns maintain visibility using lower clock frequencies (can even use direct current) and do not lie in the receiver’s modulation-domain.The operation of IEEE 802.15.7 devices of infrastructure type—such as the coordinator—must be compatible with its use as a lighting source. According to the standard, infrastructure devices must transmit idle patterns during the time intervals they receive data or do not have transmission activity, in order to maintain visibility, ensure brightness control, and avoid visible flicker to the human eye. The standard allows idle patterns to be in-band or out-of-band, in relation to the frequency spectrum of the optical carrier modulating signal, Among the functions of the IEEE 802.15.7 MAC, the task of regulating optical medium access is the most important, allowing either scheduled or random access transmissions. Four random access methods are defined: unslotted random access, slotted random access, unslotted CSMA/CA and slotted CSMA/CA.macBeaconOrder, BO, describes the interval used by the coordinator to transmit beacon frames. BO and aBaseSuperframeDuration attributes define the time interval between beacon frames, BI  according to the following expression:aBaseSuperframeDuration is defined as the number of clock cycles that form the superframe when the attribute macSuperframeOrder (see below) is equal to 0.The attribute macSuperframeOrder, SO, defines the length of the active part of the superframe, including the beacon frame. The relationship between the value of SO and the duration of the active part of the superframe, SD  is given by:The attribute The MAC layer allows the use of a time based structure called superframe, whose characteristics are defined by the coordinator. The superframe is a timing pattern that is delimited by beacon frames. The coordinator transmits beacon frames at the beginning of each superframe to synchronize devices, identify the network and define the characteristics of the superframe. The superframe can have an active part and an inactive part. The active part is divided in 16 equally spaced intervals or time slots, during which all communications are performed. The inactive part allows devices to pass to an energy saving state. The active part is divided into a Contention Access Period (CAP), where devices compete with each other to access the channel using a random access procedure, and a Contention Free Period (CFP), intended for applications with latency and bandwidth requirements. In the CFP channel, access is centrally managed by the coordinator through the use of time slots called Guaranteed Time Slot (GTS). The structure of the superframe is defined by the values of several parameters, described below:The beacon frame is transmitted at the beginning of the first slot of the superframe without the use of any random access method. The CAP begins immediately after the beacon frame, and the CFP—if it exists—begins following the CAP and extends to the end of the active part of the superframe.aUnitBackoffPeriod clock cycles:When transmitting during the CAP of the superframe, the MAC layer uses the slotted version of the random access algorithm defined in the standard. This algorithm is implemented using units of time called backoff periods, where one backoff period is equal to NB (Number of Backoffs): counts the number of times the medium access algorithm has made a backoff—random delay—during the current transmission attempt. This variable is initialized to zero at the beginning of each new frame transmission attempt.BE (Backoff Exponent): related to the number of backoff periods, a device shall wait before attempting to access/assess a channel. This variable is initialized to the value of macMinBE attribute.NB and BE, ensuring that BE does not exceed the value of macMaxBE attribute. If NB is less than or equal to the value of the macMaxCSMABackoffs attribute, a new backoff delay will begin. If NB is greater than macMaxCSMABackoffs, the algorithm will terminate with a channel access failure status. If the PHY layer evaluates that the channel is available—or if the CCA is not active—the frame will be transmitted. After transmitting the frame, if enabled the use of acknowledgement (ACK), the MAC layer will wait for at most macAckWaitDuration optical clocks for the corresponding ACK. In case the ACK frame is received, the procedure will finish successfully. In case the ACK frame is not received, NB and BE variables will be increased to perform a new backoff, provided that NB is not greater than macMaxCSMABackoffs.Access procedure begins with the initialization of NB and BE variables. Next, backoff periods in the device are aligned with the beginning of the superframe. Then, the MAC layer shall delay for a random number of complete backoff periods in the range macMaxFrameRetries retransmissions is not exceeded (marked in blue color in the flow chart).In our developed simulation model, we have added the necessary steps to the algorithm in order to verify that the maximum number of frames retransmissions limit is not exceeded, in accordance with clause number 5.1.7.5 of the standard. This clause specifies that when the ACK frame is not received, the data frame should be retransmitted, repeating this sequence up to a maximum of macMaxFrameRetries times. If a data transfer attempt fails a total of We built our simulator using OMNET++ simulation framework  from theWe have chosen IEEE 802.15.7 star topology, due to its importance and wide range of applications.For the MAC layer, we opted to use the superframe, since it allows the use of both contention (CAP) and no contention (CFP) access methods. In addition, the use of the superframe enables devices to enter the energy save state during the idle period.Our developed simulation model has been designed with the following premises:N, that communicate with the coordinator. We have defined scenarios with Our developed simulation model has been designed to evaluate the performance of uplink transmissions during the CAP of the superframe. For this purpose, we have defined four basic scenarios that differ in the number of devices, Next, we present the most important features of our simulation model, which are needed to adequately interpret the results obtained from the simulations performed.The transmission medium is modeled as free space without obstacles. We chose the directed LOS link configuration to model the optical signal propagation, requiring a line of sight between each device and the coordinator. We have considered the direct component of the received signal to calculate the power of the received optical signal, not considering the existence of reflections. According to the results presented in , at leasN devices employing an application message size of L bits and average time between messages of In order to evaluate the performance of uplink communications in our simulation model, the application layer on each device generates data messages destined to the coordinator. The time intervals between generated messages follow an exponential probability distribution. Each message is encapsulated in the MAC Service Data Unit (MSDU) of one data frame. All devices in all scenarios use the same application layer settings. This way, in a scenario with In this section, we present and analyze the results obtained from simulations made with our implemented model. The effects of the hidden node problem are evaluated by comparing simulation results when there are not hidden nodes with results when there are hidden nodes in the network. To ensure the reliability of the results, several independent simulations have been replicated for the same set of input parameters, averaging output result values. Simulation time was at least 400 s per replica.This section presents the main configuration parameters used in the developed simulation model.rding to , both paThe values selected to define the rest of characteristics of optical transmitters and receivers are commonly used values to characterize VLC transmitters and receivers, similar to those used in ,38,39. LRegarding the configuration of the superframe, both BO (macBeaconOrder) and SO (macSuperframeOrder) parameters are set with the same value, so that there is no inactive period in the superframe. Since this work is intended to evaluate the performance of uplink communications during the CAP, we decided not to assign any slot of the superframe to the CFP. Thus, the entire superframe is available to perform transmissions using the CSMA/CA based slotted random algorithm. The value assigned to both the BO and SO parameters is:BO and SO parameters, up to the maximum limit allowed by the standard when the superframe is used (SO = BO = 14). We checked that network performance tends to improve as the value used for both BO and BO increases. The rest of the MAC layer configuration parameters were chosen according to the default values defined in the standard. We performed several simulations using larger values for both Application message sizes of 512 bytes, 1024 bytes and 1500 bytes were used for simulations . The largest message size—1500 bytes—was selected according to the maximum transmission unit of the Ethernet standard. Simulation results did not vary significantly depending on application message size, since variation margin was below 5%. Hence, we decided to present here only the results obtained with the intermediate message size of 1024 bytes.The IEEE802.15.7 standard limits the frame size (PHY layer SDU) to 65,535 bytes when PHY II and PHY III is used. When PHY I is used, the maximum frame size is 1023 bytes.End-to-end goodput: calculated as the average number of bits of application layer messages received per unit time in the coordinator. This value is normalized and expressed as a percentage of the transmission rate used in the PHY layer.macMaxCSMABackoff limit is exceeded), or due to frame transmission failures (when macMaxFrameRetries limit is exceeded).End-to-end message loss rate: percentage of application messages generated in devices not received by the coordinator. Packets are not received by the coordinator due to the following reasons: because there is an overflow in the queue storing the messages to be transmitted by the MAC layer, due to channel access failures , the percentage of channel access failures and the percentage of frame transmission failures.Energy consumption: calculated from consumed current by the optical transceiver in each device. Consumption depends on the states through which the transceiver passes along simulation time.Network performance was evaluated by means of the following metrics:In order to exclude the existence of hidden nodes in the network due to the directivity of the optical transmitters and receivers, the simulated channel model was modified to remove all directivity characteristics in them. Additionally, devices were configured with an optical transmission power of 150 mW to prevent the occurrence of hidden nodes due to the limitation on the optical signal coverage. Using the described channel model, we verified that each and every device in the network detects all transmissions from all other devices in all of the simulation scenarios. Hence, the existence of hidden nodes is discarded. Although the employed channel model does not match any link configuration used in VLC networks, it allows establishing the simulation conditions that guarantee the non-existence of hidden nodes in the network. The simulation results achieved with these conditions will be compared with the results of simulations performed employing the LOS channel model, in order to assess the impact of hidden nodes on network performance.Successfully transmitted frames: percentage of data frames which have been transmitted by the device and acknowledged by the coordinator. This percentage refers to the total number of data frames that MAC layers in the device attempts to transmit.macMaxCSMABackoffs) being exceeded. This percentage refers to the total number of data frames that MAC layers in the device attempts to transmit.Channel access failures: percentage of dropped frames by MAC layers in the device because the CSMA/CA algorithm has failed due to the maximum number of backoffs (macMaxFrameRetries) being exceeded. This percentage refers to the total number of data frames that MAC layers in the device attempts to transmit.Frame transmission failures: percentage of dropped frames by MAC layer in device because the CSMA/CA algorithm has failed due to the maximum number of frame transmission retries  being exceeded.Values shown in Energy consumption results have been calculated from average current consumption by transceivers in devices during the entire simulation time. Due to present unavailability of VLC transceiver consumption specification data, simulations have been carried out with consumption values corresponding to the IRMT6452  optical This section presents simulation results obtained by using the optical channel model based on directed LOS link configuration. Directed LOS link configuration results in the occurrence of hidden nodes in the network, due to the directivity of transceivers and limited optical transmission power of mobile devices (30 mW). It was found that, in all simulation scenarios, devices can only communicate with the coordinator. None of the devices in the network can detect the transmissions of any other device. Hence, in VLC networks with star topology employing directed LOS link configuration, all devices are hidden nodes to each other.Goodput results are validated with message loss ratio values presented in macMaxFrameRetries) is exceeded. This makes a lot of difference with the statistics obtained without hidden nodes, in which all CSMA/CA failures are accounted in the column “Channel access failures”, produced when the limit of macMaxCSMABackoffs is exceeded.Due to the high percentage of collisions produced with hidden nodes, the whole failures produced in the CSMA/CA algorithm are accounted in the column “Frame transmission failures”, produced when the maximum retransmission number  have been obtained by applying our proposed solution. The coordinator sends a signaling pattern when it detects a transmission coming from one of nodes in the network. Thus, the rest of nodes in the network know that the channel is not free. In this case, goodput results are better than the results obtained in experiment (1). In addition, message loss rate is lower. The performance of the VLC network improves when our mechanism is used to avoid the hidden node problem. Results obtained in (2) are similar to those obtained from simulations performed without hidden nodes, see Since it was published in 2011, the IEEE 802.15.7 standard has become one of the most important initiatives to normalize and deploy the use of VLC in WPAN. Though the implantation of IEEE 802.15.7 based networks must face several technical challenges, among which are the difficulties in uplink transmissions (from devices to coordinators), including limitations in mobile device transmission power imposed by LED power consumption, and mobility restrictions caused by the necessity of keeping a proper orientation in devices.Additionally, uplink transmissions made by means of the CSMA/CA method in the IEEE 802.15.7 standard are affected by the hidden node problem, whose effects have been evaluated and analyzed in this research work. We have evaluated the hidden node problem by simulating a star network employing the directed LOS channel model. Under these conditions, we verified that all devices are hidden nodes in all of our modeled scenarios, since none of the devices are capable of detecting the signal transmitted by the rest of them. Our simulation results make evident a severe degradation in network performance produced as a consequence of the hidden node problem. Performance degradation is proven by obtained values for the analyzed metrics, like end-to-end goodput, message loss ratio, percentage of collisions, and energy consumption.Actual performance degradation may depend on specific characteristics of optical transmitters and receivers used in devices. The use of less directive transmitters and receivers could partially mitigate the effects of the hidden node problem in networks employing a diffuse link configuration. In any case, limitations in optical signal coverage—due to restrictions in transmission power in devices—demand not ignoring the effects of the hidden node problem, regardless of the link configuration employed.In conclusion, the hidden node problem has to be included as one of the difficulties to be solved in uplink transmissions made by using the CSMA/CA method in IEEE 802.15.7 networks. Therefore, as part of this research work, we elaborated on a solution proposal to the hidden node problem, which has the advantage of not requiring modifications in the specifications defined in the standard for PHY and MAC layers in devices and coordinators.Our proposed solution is based on the use of the idle patterns sent by the coordinator, during idle or receive operation. Appropriate selection of idle pattern type—in-band or out-of-band—sent by the coordinator can indicate if it is receiving transmissions from devices in the network. Simulation results obtained with our solution demonstrate its high degree of effectiveness, since it allows obtaining identical performance values to those obtained from the simulations performed without hidden nodes in the network.


The timing and dynamics of many diverse behaviors of mammals, e.g., patterns of animal foraging or human communication in social networks exhibit complex self-similar properties reproducible over multiple time scales. In this paper, we analyze spontaneous locomotor activity of healthy individuals recorded in two different conditions: during a week of regular sleep and a week of chronic partial sleep deprivation. After separating activity from rest with a pre-defined activity threshold, we have detected distinct statistical features of duration times of these two states. The cumulative distributions of activity periods follow a stretched exponential shape, and remain similar for both control and sleep deprived individuals. In contrast, rest periods, which follow power-law statistics over two orders of magnitude, have significantly distinct distributions for these two groups and the difference emerges already after the first night of shortened sleep. We have found steeper distributions for sleep deprived individuals, which indicates fewer long rest periods and more turbulent behavior. This separation of power-law exponents is the main result of our investigations, and might constitute an objective measure demonstrating the severity of sleep deprivation and the effects of sleep disorders. Although good sleep  is considered a basic contributor to human health and well-being, its chronic deprivation seems symptomatic of modern societies. In the last century, the average sleep duration has shortened from approximately nine hours to no more than seven hours in many countries, e.g., USA Daytime drowsiness is characterized by an urge to sleep, a lack of energy, and decreased ability to complete tasks, often resembling the state of alcohol intoxication. Slowed movements, reduced facial expressions and muscle tone are typical physical symptoms of being sleepy. Thus, the spontaneous locomotor activity may be considered as an index of alertness and sleepiness.Power laws are ubiquitous in nature and have been repeatedly detected not only in physics, biology, earth and planetary sciences, but also in economics and finance, demography, epidemiology, and social sciences This paper continues along this line of research, seeking to determine standards for measurable criteria by discriminating human behavioral organization as impaired by sleep deficiency. In particular, the aim of the present study is to find whether the same individuals show measurable differences when undertaking two different styles of everyday life. The chosen experimental setup and subsequent statistical analysis allow us to observe such disparities in locomotor activity between subjects sleeping regularly and those undergoing chronic sleep deprivation. As discussed further, those distinct variations in patterns of activity can be already detected in the first day of sleep deficit, leading to significantly different scaling exponents for fluctuations of actigraphy recordings in both groups. When interpreting the findings we also point to their potential application in identifying patients with sleep disorders.Data analysis subsection. The resulting time series were statistically analyzed and compared to former studies Actigraphy measurements were performed on healthy individuals over one week of their normal life [rested wakefulness (RW)] and one week of partial sleep deprivation (SD)  participated in the study. They were all healthy, non-smokers, and drug-free. They were asked to limit alcohol and caffeine intake during the experimental weeks. They reported regular sleep patterns and no sleep-related problems, controlled with Pittsburgh Sleep Quality Index Among the 24 sets of data collected 17 were selected for further analysis , the other 7 were corrupted by either removing a recording device during the experiment, or not following the sleep schedule.The data acquisition comprised of one week of unrestricted sleep according to individual needs, i.e., rested wakefulness (RW), and one week of daily partial sleep deprivation (SD), with a two-week gap in between the two measurements. Half of the subjects began with the RW phase followed by the SD phase, while the other half had the order reversed. During the sleep deficit week, the participants were asked to shorten their sleep by 33% of their ‘ideal sleep’ by delaying bed-time and using an alarm clock in the morning. The precise length of the restricted sleep was calculated individually for each participant, where the individual sleep need was determined on the basis of the questionnaire administered before entering the experiment . In half of cases the self-reported length of sleep was verified with actigraphy before entering SD phase.For the 17 selected subjects the average unrestricted sleep, as measured by actigraphy, was 8 h 16 m Movement tracking was recorded with Micro Motionlogger SleepWatch , worn on the participant's non-dominant wrist. The data were collected in 1-minute epochs in the Zero-Crossing Method (ZCM) mode, which counts the number of times per epoch that the activity signal level crosses zero (or a threshold very close to zero). The working limitation of the ZCM mode is the difficulty in registering the acceleration of movements, which may potentially cause high frequency artifacts to be counted as a considerable movement.The raw actigraph data collected in the ZCM mode represent the number of counts per epoch as a function of time. The first step in the analysis is the localization of the ‘day’ 5+16) mode. For comparison, we also analyze the reversed combination: 16 hours of wakefulness followed by 5 hours of sleep, denoted as (16+5).Next, one has to avoid artifacts connected with forced, different lengths of sleep/wakefulness in the two week period under study. Whereas the RW individuals sleep on average 8 hours and are alert 16 hours per day, the SD ones sleep approx. 3 hours less and stay alert 3 hours longer. To make the two samples comparable, out of the total 24 hour period we take 5 hours of sleep, which are common to all participants, and 16 hours of consecutive wakefulness, again common to both groups. We call it the - or (16+5)-hour period] from all the 17 participants in RW or SD condition. The statistics for individual subjects is insufficient to construct reliable cumulative distributions. Consequently, for each day we obtained a single data point characterizing the distribution of the whole group in RW condition and a single point for SD condition.Each cumulative distribution was constructed from rest/activity periods collected over a single day [i.e., a single .Throughout the paper the parameters characterizing a single cumulative distribution of rest/activity periods are given together with the standard error of the fit  exponential for states of activity. It is thus clear that there must be an optimal value that best distinguishes the two. The criterion we used to find it was the best fit of the resting-state cumulative distribution Data analysis). With the choice All the results discussed above were obtained with the single threshold definition –7. In or of rest  and acti of rest  time int of rest . In the The evidence of motion recorded by actigraphs is broadly used in medical practice aimed at understanding typical circadian-rhythm patterns of healthy subjects, and at detecting possible rhythm disorders in humans. Although the method is not indicated for the routine diagnosis, the actigraphy recordings may be useful in quantitative evaluation of the variety of neuropsychiatric diseases. Specific analytical methods applied to actigraphy records can reflect various features of physiological activities and serve as an outcome measure in characterizing disturbances of the circadian-rhythm patterns and alterations in locomotor activity in specific populations like children (apnea testing) or individuals suffering from depression In the former studies by Nakamura et al. In analogy to those investigations, we have performed the experiment and applied statistical evaluation of the results, keeping in mind that our RW and their healthy control groups should reach similar conclusions. Therefore, e.g., the choice of activity threshold which separates state of activity from rest has been validated by the use of a criterion similar to Evaluation of residence-time distributions and calculation of a mean waiting time to an activity event are broadly studied problems in biological physics and have been addressed in a number of studies Lévy-stable distributions are a class of self-similar infinitely-divisible probability laws Higher values of the exponent It seems likely that some aspects of both the waking period  and the sleep period (sleep length and continuity) may explain performance deficits and the restoration of function. Accordingly, deeper theoretical understanding of different forms of sleep loss would be possible by comparing them together, in a single experiment Finally, it is important to recognize that the analysis of activity patterns of chronically sleep deprived subjects will not necessarily reveal their decreased capabilities. Compensatory efforts and recruitment of additional brain resources could intervene to make the activity level appear unaffected. It could be that unraveling these disturbances requires more sophisticated analysis, or that the subtle neurocognitive effects of sleep loss can be observed only with the use of refined neuroimaging techniques, possibilities that deserve to be explored in future work.We have studied the statistics of resting (inactivity) times and confirmed that the derived distributions are of power-law type The pattern of time-duration  is related to the rate function of a point process featuring events following spontaneous discharge (escape) from the inactivity  state. The spectrum of times between subsequent events can be described mathematically by a probability density function (PDF) This leads to a simple relation between the rate function Note that cumulative probability that a state change happens by time Accordingly, for In particular, for a uniform rate The above formula The survival function Note that as The non-exponential form ate  cf. .To clarify this point, let us rewrite the survival function as a conditional probabilityIn fact, it is easy to check that, if the rate PDF takes the Dirac-delta formAltogether, the above analysis classifies stretched exponential and Pareto distributions as probability laws stemming from the same origin, i.e.: either from the randomization of an individual rate File S1Details on data processing and fitting. The file includes analysis of goodness of fit of cumulative distributions for one and two activity thresholds; it contains 5 figures.(PDF)Click here for additional data file.