Care for older adults is facing a number of challenges: health problems are not consistently identified at a timely stage, older adults report a lack of autonomy in their care process, and care systems are often confronted with the need for better coordination between health care professionals. We aim to address these challenges by introducing the geriatric care model, based on the chronic care model, and to evaluate its effects on the quality of life of community-dwelling frail older adults.Participants: 1200 community-dwelling older adults aged 65 or over and their primary informal caregivers. Primary care physicians will identify frail individuals based on a composite definition of frailty and a polypharmacy criterion. Final inclusion criterion: scoring 3 or more on a disability case-finding tool. Intervention: Every 6 months patients will receive a geriatric in-home assessment by a practice nurse, followed by a tailored care plan. Expert teams will manage and train practice nurses. Patients with complex care needs will be reviewed in interdisciplinary consultations. Evaluation: We will perform an effect evaluation, an economic evaluation, and a process evaluation. Primary outcome is quality of life as measured with the Short Form-12 questionnaire. Effect analyses will be based on the “intention-to-treat” principle, using multilevel regression analysis. Cost measurements will be administered continually during the study period. A cost-effectiveness analysis and cost-utility analysis will be conducted comparing mean total costs to functional status, care needs and QALYs. We will investigate the level of implementation, barriers and facilitators to successful implementation and the extent to which the intervention manages to achieve the transition necessary to overcome challenges in elderly care.In a 2-year stepped-wedge cluster randomised clinical trial with 6-monthly measurements, the chronic care model will be compared with usual care. The trial will be carried out among 35 primary care practices in two regions in the Netherlands. Per region, practices will be randomly allocated to four allocation arms designating the starting point of the intervention. This is one of the first studies assessing the effectiveness, cost-effectiveness and implementation process of the chronic care model for frail community-dwelling older adults.The Netherlands National Trial Register NTR2160. As a result of aging of the population of industrialized countries, the group of community-dwelling older adults with multiple chronic conditions is vastly expanding ,2. The sIn Europe, three major barriers to high-quality care for frail community-dwelling older adults have repeatedly been identified over the past few years. First, our predominantly reactive care system fails to identify many older adults’ health risks and care needs at a timely stage, impeding the successful prevention of adverse outcomes. In addition, older adults experience a lack of autonomy in their own care process. Finally, care for frail older adults living at home is often fragmented, resulting in a lack of coordination and information exchange between health care professionals .Offering integrated chronic care services may be the appropriate approach to overcome the challenges observed in elderly care: Evidence suggests that integrated care models have the potential to successfully improve quality of elderly care and may have a positive effect on health-related outcomes . In addiThe chronic care model is a multidimensional framework for chronic illness management, designed to guide and enhance the comprehensive and interdisciplinary delivery of care. Previous research has demonstrated its potential to improve health outcomes of patients with a chronic condition, and to improve quality of care -19. DespTo overcome the aforementioned barriers to high-quality care for older adults, the frail older Adults: Care in Transition (ACT) - study introduces the geriatric care model, a multifaceted intervention based on the chronic care model. Corresponding with the chronic care model, the geriatric care model aims to enable productive interactions between activated, informed patients and proactive, prepared health care professionals by combining in-home geriatric assessments with strong management by expert geriatric teams. To our knowledge, we are the only European study so far to investigate the impact of a chronic care model approach on frail older adults, and to evaluate the effectiveness as well as the cost-effectiveness and implementation process of such an intervention in a stepped wedge cluster randomised clinical trial.Through implementation of the geriatric care model in a pragmatic trial, we seek to target the untimely recognition of health problems, the lack of autonomy perceived by older adults and the lack of coordination between health care professionals. In doing so, we hope to improve the quality of care for community-dwelling frail older adults, and subsequently improve their quality of life.We will implement the geriatric care model using a stepped-wedge cluster randomised clinical trial design. A stepped wedge design is a type of cluster randomized trial design involving sequential roll-out of an intervention to primary care practices (clusters) over a number of time periods . PrimaryPer region, primary care practices will be randomised using the computer-based ‘Random Allocation Software’ program. In both regions, primary care practices will be allocated to one of a total of four allocation arms by randomization. The allocation arm number designates the starting moment of the intervention with the geriatric care model at practice level . Figure We will include community-dwelling frail older adults aged 65 years and over, recruited in participating primary care practices involved in the project, and their primary informal caregivers.Frail older adults will be recruited in three steps.1. Primary care physicians of participating primary care practices will identify frail individuals based on a composite definition of frailty  in their population of patients age 65 years and over meeting a polypharmacy criterion: 5 or more drugs prescribed in the last 3 months . Additio2. Subsequently, all patients included by their primary care physician will receive an information letter and an informed consent form. Within two weeks, individuals selected during step one will be contacted by telephone by a project interviewer and asked to consider study participation.3. In case of verbal consent, eligibility for trial entry will be established with the Program on Research for Integrating Services for the Maintenance of Autonomy case-finding tool for disability (PRISMA-7) . EligiblPrimary informal caregivers will be recruited by inquiring with participating older adults. We define a primary informal caregiver as the caregiver carrying most of the care burden for the family member, relative or friend requiring care. If a primary informal caregiver is present, and the participating older adult does not oppose to their involvement in the study, eligible persons will be contacted by telephone and asked to enrol. Informal caregivers interested in participating will receive an information letter and an informed consent form.The geriatric care model aims to target three main challenges care for older adults is currently facing . We expect the geriatric care model to improve the quality of the organisation and delivery of care on structure, process and outcome levels. We expect the total impact of this process to improve patient outcomes, resulting in improved self-reported quality of life Figure .The geriatric care model aims to enable productive interactions between activated, informed patients and proactive, prepared health care professionals by combining tailored in-home geriatric assessments with strong management. In both the Amsterdam and West-Friesland region, the geriatric care model is integrated into routine practice by an expert geriatric team consisting of an experienced geriatric nurse and an elderly care physician.Every six months, a frail older adult will receive an assessment of health and care needs by a practice nurse, followed by a tailored care plan. This procedure will involve two home visits. During the first visit, a multidimensional assessment will be conducted using the web-based Community Health Assessment version 9.1 of the Resident Assessment Instrument (RAI-CHA) . RAI facDuring the intervention the geriatric expert teams will carry out the following three main tasks: (1)  management, 2) expert knowledge transfer and (3) building and maintaining local networks of care organisations  will be invited to join.Throughout the intervention, the two geriatric teams will set up and maintain regional networks of local organisations. In order to facilitate the coordination between providers of care services for older adults in the region, primary care professionals and representatives of various community-based care organisations will meet on a regular basis, with the aim to subsequently enhance the coordination of care on a patient level.Practice nurses carrying out the intervention will take part in a tailored training program. Prior to the start of the intervention, nurses will participate in a 3-day motivational interviewing course. Further, both the practice nurses and the geriatric team members will participate in a one day RAI workshop before carrying out the intervention. In order to prevent contamination bias between the allocation arms, nurses will start their training program shortly before they start working with the geriatric care model. Alongside the intervention a second training program will run, consisting of a ‘training on the job’ motivational interviewing session as well as clinical education on geriatric topics provided by the expert team staff. Expert team members will identify each session’s topic(s) by means of field observations and RAI-output reviews. Several months into the intervention, practice nurses and the geriatric team members will be offered a refresher course RAI.Implementation of the geriatric care model is predicated upon the idea of the professional self-regulation of expert teams and practice nurses. Before the intervention is carried out its main theoretical and practical content is outlined by the research team, where after the development of work routines and protocols is informed by the professionals’ experiences in the field without research staff interference.To avoid contamination, nurses working with the geriatric care model will not be employed in primary care practices in the control group that have yet to start delivering the intervention.Aimed at providing integral care services, primary care in the Netherlands plays an important role in the organisation of community elderly care. Of people over 75, almost hundred percent initiate contact with a primary care physician at least once a year . OfferinWe will perform an effect evaluation, an economic evaluation, and a process evaluation. Throughout the 2-year data collection period older adults will advice researchers on the appropriateness and quality of the content of qualitative and quantitative research material, and will assist researchers with the interpretation of qualitative data derived from interviews with older adults.The primary outcome of the study is quality of life as measured by the 12-item Short Form questionnaire (SF-12) . The SF-Sample size calculations are based on the expected effects of the intervention on the primary outcome, quality of life, measured by the SF-12. Calculations are done using the equation for a longitudinal design  and adjuData will be collected among older adults at home by means of computer assisted personal interviewing. For each observation, interviewers will receive training and will be supervised by research staff. A random sample of the interviews will be tape recorded; In order to enhance the quality of the data, researchers will evaluate the quality of the recordings and report back their findings to the interviewers. We will use postal questionnaires to collect data among the primary informal caregivers.First, characteristics of the participants at baseline will be described and differences between the 4 allocation arms are tested using chi-square tests and ANOVA or non-parametric tests.Second, effect analyses will be done based on the “intention-to-treat” principle. For both older adults and their primary caregivers, all outcome measures will be compared between the group receiving the geriatric care model and the group receiving usual care using multilevel regression analysis. Multilevel regression analysis takes into account the non-independent nature of hierarchical data and does not require data for a fixed number of observations for all respondents . In the In addition to the investigation of the overall effect of the intervention, the stepped-wedge design allows us to study the effects of the duration of the intervention on outcome measures . TherefoWe will aim to maintain a blind status for as many people involved in the study as possible. Professionals carrying out the intervention will not be informed about a patient’s enrolment in the study until the start of the intervention. During data analysis, researchers will be blinded to the group assignment. Due to ethical considerations, it will not be feasible for interviewers collecting the data and participants to be blinded to group assignment. All participants will be informed about the starting time of the intervention, and will at all times be aware of their group status.The economic evaluation will be performed from a societal perspective. We will consider all relevant direct and indirect costs, such as costs of the care model, consultations with primary care physicians, medical specialists, home care, medication use, hospital and nursing home admissions, informal care time and cost of lost labour days of the informal caregiver. Care utilization data of both the client and the caregiver will be prospectively collected alongside the trial using six monthly cost diaries. Medication data of clients will be obtained from the centralized pharmacy files in the research regions. Dutch standard costs are used to value resource use ,41. MediMissing data on costs and outcomes will be imputed using multiple imputation according to the Multivariate Imputation by Chained Equations (MICE) algorithm . A cost-Alongside the intervention, we will conduct a mixed methods process evaluation. The process evaluation data will be used for summative purposes: Information will be used to investigate the extent to which the intervention was implemented as planned . By explProcess outcomes are level of implementation of the geriatric care model, barriers and facilitators to successful implementation, and the extent to which the intervention manages to achieve the transition necessary to target the three challenges in care for older adults, i.e. the untimely identification of health problems and care needs, the lack of client autonomy and the inadequate coordination between care professionals.First, we will aim to assess the level of implementation using process constructs fidelity, dose delivered and dose received . FurtherPer process outcome, results of the qualitative and quantitative data collection will be analysed conform the requirements of a convergent parallel design. In a convergent parallel design, quantitative and qualitative data are concurrently collected in the same phase of the research process, and outcomes of both qualitative and quantitative analysis are combined during the overall interpretation .The ACT-study aims to investigate the extent to which the geriatric care model has an effect on frail older adults’ quality of life. To our knowledge, it is one of the first European studies adopting a chronic care model approach to not only assess the effectiveness, but also the cost-effectiveness and the implementation process of an intervention for frail older adults living at home.Carrying out the study in a real life primary care setting will provide insight into the generalizability of the geriatric care model, and may facilitate future implementation into routine practice. Moreover, the stepped-wedge design used in this study will allow us to introduce the intervention to all primary care practices participating in the study, so all frail older adults in the four allocation arms will eventually receive the geriatric care model. This has a number of advantages. First, the phased rolling out of the intervention will give us an opportunity to avoid having to withhold the geriatric care model from people who might benefit from it. Not only is this ethical argument compelling in itself, it has also played an important role in motivating primary care physicians to participate in the study. In addition, introducing the intervention to all four allocation arms offers methodological advantages. As mentioned in the effect evaluation paragraph, it will present us with the opportunity to take into account the effects of the duration of the intervention on outcome measures, Thus allowing us to differentiate between changes in outcomes due to time and due to the intervention.In the years to come, the aging of the population will increasingly impose a strain on health care systems worldwide. It has become apparent that tackling emerging obstacles is essential in the securing of high-quality elderly care. By introducing the geriatric care model, we hope to contribute to the existing evidence on quality improvement and the effectiveness of integrated care models, and to present a solution for the many challenges facing care for older adults today.ACT = Frail older adults: care in transition; ADL = Activities of daily living; ANOVA = Analysis of variance; CE = Cost-effectiveness; CU = Cost-utility; MICE = Multivariate imputation by chained equations; PRISMA = Program on research for integrating services for the maintenance of autonomy; RAI = Resident assessment instrument; QALY = Quality adjusted life years.The authors report no competing interests.AJ, HvdH, HvH, and GN designed the initial study protocol and wrote the funding proposal. JT advised on the use of research methods. MM, EH and KvL worked out the initial study protocol and drafted the manuscript. MM wrote the background, the main part of the methods section and the discussion section. EH wrote the data collection and data analyses part of the effect evaluation section; KvL wrote the economic evaluation section. AJ supervised the writing process. All authors critically revised the manuscript for important intellectual content. All authors approved the final manuscript.


The primary goal of a Modular Neural Exploring Traveling Agent (MoNETA) project is to create an autonomous agent capable of object recognition and localization, navigation, and planning in virtual and real environments. Major components of the system perform sensory object recognition, motivation and rewards processing, goal selection, allocentric representation of the world, spatial planning, and motor execution. MoNETA is based on the real time, massively parallel, Cog Ex Machina environment co-developed by Hewlett-Packard Laboratories and the Neuromorphics Lab at Boston University. The agent is tested in virtual environments replicating neurophysiological and psychological experiments with real rats. The currently used environment replicates the Morris water maze .The motivational system represents the internal state of the agent that can be adjusted by sensory inputs. In the Morris water maze, only one drive can be satisfied (a desire to get out of the water) that persists as long as the animat is swimming and sharply decreases as soon as it is fully positioned on the platform. Another drive – curiosity – is constantly active and is never satisfied. It forces the agent to explore unfamiliar parts of the environment. Familiarity with environmental locations provides inhibition to the curiosity drive in a selective manner, so that recently explored locations are less appealing than either unexplored locations or locations that were explored long time ago. The main output of the motivational system is a goal selection map. It is based on competition between goals set by the curiosity system and goals learned by the animat. The goal selection map uses a winner-take-all selection of the most prominent input signal as a winning goal. Because curiosity-driven goals receive weaker inputs than well-learned reward locations, they can only win if there are no prominent inputs corresponding to the learned goals with an active motivational drive.The spatial planning system is built around a previously developed neural algorithm for goal-directed navigation . The oriThe complete implementation of MoNETA consists of 75,301 neurons and 1,362,705 synapses.


The frontal eye fields (FEF) in rhesus monkeys have been implicated in visual short-term memory (VSTM) as well as control of visual attention. Here we examined the importance of the area in the VSTM capacity and the relationship between VSTM and attention, using the chemical inactivation technique and multi-target saccade tasks with or without the need of target-location memory. During FEF inactivation, serial saccades to targets defined by color contrast were unaffected, but saccades relying on short-term memory were impaired when the target count was at the capacity limit of VSTM. The memory impairment was specific to the FEF-coded retinotopic locations, and subject to competition among targets distributed across visual fields. These results together suggest that the FEF plays a crucial role during the entry of information into VSTM, by enabling attention deployment on targets to be remembered. In this view, the memory capacity results from the limited availability of attentional resources provided by FEF: The FEF can concurrently maintain only a limited number of activations to register the targets into memory. When lesions render part of the area unavailable for activation, the number would decrease, further reducing the capacity of VSTM. Primates process visual information with remarkable efficiency, yet can hold only a limited number of discrete locations or objects in memory at one time The frontal eye fields (FEF) seems to play an important role in VSTM First, we asked whether or not FEF inactivation would affect the VSTM capacity. Recent works demonstrated the bilateral advantage in visual tracking Second, numerous studies have implicated the FEF in attention shift All experimental procedures were approved by the Seoul National University Hospital Animal Care and Use Committee , and followed the US Public Health Service Policy on the humane care and use of laboratory animals. All animals used in this study were cared for at a temperature- and humidity-controlled room in the Primate Center of Seoul National University Hospital. While they were housed in individual cages, social contacts were encouraged by regularly opening a retractable door between cages. Environmental enrichment was also provided with a variety of toys. The animals were provided with a regular chow for monkeys, supplemented by fresh fruits. The health status of the animals was monitored daily by care-givers and by regular physical examinations and blood tests by the staff veterinarian. At the end of experiments, each animal was euthanized by deep anesthesia with zoletil chloride (10 mg/kg IM) and sodium pentobarbital . After confirming total lack of a corneal reflex as an indication of adequate level of anesthesia, the animal was perfused with a liter of 0.1% phosphate buffered saline followed by several liters of fixative solution . This procedure is consistent with the recommendations of the Panel on Euthanasia of the American Veterinary Medical Association Macaca mulatta, M9 and M10) weighing between 4 and 5 kg were prepared for chemical inactivation experiments. They were the same animals previously used in another study Two adult female rhesus monkeys (a) Survival surgery. The purpose of the surgical procedures was to implant recording chambers and a head restraint device for neurophysiological experiments. All surgeries were carried out in the animal surgical suite at the Primate Center of Seoul National University Hospital. Animals were prepared with sterile, anesthetic surgical procedures. A licensed veterinarian was present throughout the surgical procedures and the recovery period for anesthetic induction and for monitoring and recording all measured physiological variables. Animals were allowed free access to water but no food the night prior to scheduled surgery. One hour before the surgery the animal was given atropine sulfate  to prevent excessive salivation during the surgery. One-half hour later it was sedated with zoletil chloride , intubated, and placed under isoflurane anesthesia. A saline drip was maintained through an intravenous catheter placed into a leg vein. Throughout the surgery, core body temperature, heart rate, blood pressure, oxygen saturation and respiratory rate was continuously monitored. The animal was returned to its home cage after waking from the anesthesia and allowed to recover fully from the effects of surgery before behavioral training started. During the period of post-surgical recovery the animal was monitored closely and given injections of an analgesic agent (meloxicam 0.4 mg/kg IM) and antibiotics  in consultation with the veterinarian for 3 days post-op. b) Restraint for handling or routine testing. Restraint for certain procedures, such as physical examination or blood sampling for health check, was accomplished with zoletil chloride (10 mg/kg IM). c) Training and experimental recording sessions. After recovery from the surgical procedure the animal was trained to be held by the arms and moved into a large plastic primate chair. This was done by supplying the animal with rewards of fruit and juice. The chair had a perch with an adjustable height for each animal’s comfort. Wastes fell into a collection pan below the animal, and thus, did not cause the animal discomfort. The animals were trained by the delivery of water or fruit juice rewards in daily sessions during which time they received their entire liquid intake in the experimental apparatus. When the animal was fully trained the experiments began. During the experimental sessions the animal’s head was painlessly restrained through the use of the implanted head post which mated to a vertical rod attached to the primate chair. The animals did not show any sign of discomfort by the head restraint device: They continued to train steadily for the period of time that they were in restraint and often fell asleep as they sat in the darkened room between blocks of trials.Three situations existed in which a monkey might experience discomfort, distress and/or pain in our experimental protocols: a) survival surgery; b) restraint for handling or routine testing and c) training and experimental recording sessions. The following steps were taken to ameliorate animal suffering in each situation. A trial began with the appearance of a white 1.0-degree square at the center of visual field. Four hundred milliseconds after the animal started fixation at the square, a four-by-three matrix of circular discs was presented. The color of discs distinguished targets (red) from non-targets (green), and the animal was trained to make saccades to the targets. Now, as soon as the first saccade was initiated, the targets were rendered green and indistinguishable from non-targets. The animal therefore had to rely on memory in order to make subsequent saccades to the second target and onwards. A drop of water reward was given after all targets were visited by at least one saccade.The location of targets varied randomly across trials, but the target number, ranging from one to four, remained constant in a block of 40 trials. Blocks were randomly ordered and counter-balanced in terms of the target number, so that blocks of the same target number were run twice in a session.2 with the black background of 1.70 cd/m2 . The rows and columns of discs were separated by 15 degrees, such that the matrix covered a visual field of 45 degrees horizontally and 30 degrees vertically.Each disc was 1.0 degree in diameter with luminance of 124 cd/mThe purpose of using the matrix-form arrangement of target locations, instead of a more traditional circular array, was to explore a wider range of visual fields with targets evenly spaced. In the circular array, the vector relationship between the first and the second saccades is biased to roughly opposite directions. In contrast, the matrix formation has the advantage that the target array remains similar for successive saccades in a series. For instance, after the first saccade to the nearest left or right target from the central fixation, a similar target array becomes available for the second saccade. This will result in a more even mixture of contra- and ipsilesional saccades in series. Thus, the matrix array would be better in general for studying saccade sequences such as in saccade remapping. By the same token, it would also be useful in examining the eye-position effect on visual or oculomotor functions, for instance, with respect to eye- versus head-centered coordinates.The task for the animal was to make at least one saccade to each target, and water reward was delivered as soon as all targets had been visited. The order of visits was left up to the animal. Repeated saccades to the same target was permitted, as was saccades to non-targets. Constraints were set on the response period such that a trial would be terminated if the total number of saccades exceeded two times that of targets  or the total elapsed time after the matrix onset surpassed 400 ms times the target number . However, once trained, the animals rarely reached these constraints, and the saccades formed an efficient and specific trajectory, stopping only on targets and once at each target.Given these behavioral requirements, the chance levels in the memory task were calculated using a bootstrap method: Serial saccades to twelve target locations were numerically simulated using MATLAB . Ten thousand blocks of forty sample-trials each were generated and trials were counted where the random series of saccades met the behavioral constraints for reward described above. According to this simulation, the chance levels of trial success rates are 16.6, 7.9, 5.7, and 5.1% for the target number of one through four, respectively, when the saccades were sequenced with the possibility of revisit to targets. When inhibition of return was imposed, the chance levels were higher at 16.6, 9.1, 9.0, and 14.1% for one to four targets, respectively.Events in this task were the same as in the MEM task above, except one feature that the red targets remained visible throughout the trial. Therefore, the animals did not need to rely on short-term memory to reach the second target and onwards. When there was only one target, VIS was identical to MEM.VIS served as a baseline condition in comparison with MEM. In both tasks, saccade targets were selected based on salient color contrast. While VIS did not require memory of the targets, the planning and execution of sequential saccades was comparable with MEM. Other details, such as constraints on the response period and behavioral measures were the same in both.Eye movements were monitored by infrared video-oculography with a sampling rate of 500 Hz . Events in the tasks were controlled and saccade behavior measured on-line by custom-made applications written in MATLAB . Markers were set for all experimental events for off-line analysis. The onset and offset of saccades were determined by velocity criteria . This was performed on-line to control the events in the behavioral tasks, such as detecting the first saccade and hiding targets in MEM. Correct detection of saccade onsets was confirmed during off-line data analysis.Given the matrix arrangement of the visual stimuli, saccades were judged as directed to the nearest disc, regardless of the absolute distance from the disc to the saccade end-point. In other words, a saccade was judged as directed to a disc if it ended within a square window of 15-degree width and height centered at the disc.To identify and map the FEF , tungsteAn injectrode constructed using a 33-gauge hypodermic cannula was inserted at the same site as the electrode, and lowered slowly until its tip was located at the depth marked as having the minimal ES threshold. Muscimol  was injected using a minipump . The muscimol concentration was five mg/ml, and the injected volume was one microliter over about two minutes. Following the injection, the cannula was left in place for about five minutes before it was withdrawn. Data collection began immediately before the injection and continued up to three hours. Data were also collected in the following day, and full recovery was always noted. The injection experiments including the saline control were separated by at least two days.In order to obtain homogenous behavioral effects, muscimol injections were made at FEF sites where ES-evoked saccades were directed to the left either horizontally or with an upward component and the current threshold was lower than or equal to 50 microampere  the trial success rate, or the proportion of trials rewarded, reflecting the overall performance in the tasks, and (2) the saccade proportion rate to each target location, i.e., the proportion of trials where at least one saccade was made to the location over trials in which a target was shown at that location. This measure was used to assess the spatial distribution of effects by FEF inactivation.Two-way analysis-of-variance (ANOVA) was used to test statistical significance of the main effects of time after an injection and task type  or target number. Interactions between post-injection time and task type or between post-injection time and target number were also tested. The Kruskal-Wallis test (a non-parametric one-way ANOVA) was used for comparisons over categorical variables. A threshold of p<0.05 was regarded as evidence of statistical significance, and the p values reported. All statistical analyses were conducted using MATLAB .Macaca mulatta, M9 and M10) reached an asymptote. For both animals, the trial success rate (TSR) in four-target MEM was consistently lower than 50%. Since the animals often got frustrated with the low yield of reward and refused to work, MEM was tested only up to three targets in inactivation experiments. To use the reward sparingly, the VIS task was administered only with one or three targets, skipping the two-target condition. Mean TSR before FEF inactivation were: in VIS with one target and three targets, >99% for both animals; in MEM with two targets, 89% by M9 and 93% by M10; and in MEM with three targets, 67% by M9 and 79% by M10 .During reversible inactivation of the right FEF .A direct comparison between the three-target MEM and VIS tasks confirmed the task effect , such that, if a total of 120 trials were administered, for each location, ten trials were expected to have a target at that location in the one-target VIS or MEM task, 20 trials in the two-target task, and so on. Therefore, the denominator of the saccade proportion varied depending on the target number in the tasks. On the other hand, the numerator in the saccade proportion was trials in which at least one saccade was made to a location on each trial. For this calculation, even if multiple saccades was made to a location on a single trial, the trial was counted only once. In this way, the saccade proportion was designed to quantify the saccade responses as a function of target location, normalizing the saccade behavior with respect to the target appearance in multi-target search and memory.To evaluate the effect of FEF inactivation, the saccade proportion was compared before versus during FEF inactivation by a ratio . Now, SPR close to one indicated no change in the saccade proportion, while zero meant complete disruption of memory saccades to a specific target location by the inactivation. .In the grayscale images of In contrast, in the two- and three-target MEM  During the inactivation, the capacity was reduced to less than three in both animals: The performance in the three-target memory condition dropped to the chance level in M9, and to a significantly lower level in M10.At the same time, the memory impairment was visual-field specific: it occurred only when a target was presented in the upper left visual fields, which matched with the direction of saccades electrically evoked at the inactivated FEF sites. Therefore, the FEF inactivation effect was conditional on both requirements, that is, high memory load and location of targets in the visual fields. Note in this regard that, while depicted in spatial coordinates of target locations, the memory deficit was in fact retinotopic since the animal fixated at the center when the targets were shown in the MEM task. The conjunction of visual-field specificity and memory-load dependence points to specific behavioral situations where the FEF are crucially required. This indicates that this cortical area is the neural structure where visual attention and VSTM interact with each other.Only minor abnormalities in visually-guided saccades were observed during FEF inactivation previously  [13]–[16Current findings have a number of implications on the role played by the FEF in visual attention and VSTM Second, our findings implicate the FEF as a neural substrate for the VSTM capacity. The inactivation effect on the MEM performance was load-dependent, which in behavioral terms amounted to a reduction in the VSTM capacity. Location memory has long been modeled as consisting of storage slots that are discrete in the sense that the entry into the slots is in an approximately all-or-none manner and does not critically depend on attentional effort during encoding The importance of the interaction between FEF and visual cortices for the VSTM capacity has been emphasized by a recent study where VSTM of location and object identity were investigated with functional MRI. FEF as well as parietal regions including the intraparietal sulcus exhibited activity related to the location VSTM Third, the current results suggest that there is a dynamic competition during memory encoding of target locations, consistent with recent neurophysiological investigations using human These considerations are consistent with the following neural model on how the FEF normally functions in visual attention and VSTM: Being spatially coded and provided with visual signals from posterior cortices, FEF neurons may determine the entry of visual information into storage. As targets and non-targets are distinguished by color contrast, ensembles of FEF neurons get activated by neurons at more posterior parts of the brain, and a competition will start among the ensembles. The entry into VSTM storage will then depend on whether an ensemble grows beyond a size large enough to establish a self-sustaining activation. Important insights from our study are 1) that the size of FEF matters in this process: That is, the cortical area can concurrently support only a limited number of such large ensembles, and the entry into VSTM is allowed only up to this number. 2) Our data also suggest that the neuronal ensembles encoding saccade targets compete to recruit from the limited population of FEF neurons. The extent and the activity level of the competing ensembles of active neurons might represent the averaged sum of discrete resources assigned to slots in the slots-plus-averaging model per se or on updating the memory after saccades Our results alone could not determine whether the effect of FEF inactivation was on the memory trace Neither was determinable by our experiments whether mnemonic representations of the saccade target were rendered weaker in strength or fewer in number by FEF inactivation. Behaviorally, either situation would result in the same TSR decline, because we probed the memory trace by saccade responses and consequently the probing was stretched over time. With more targets to remember, the time taken by the saccade series also lengthened. Therefore, we cannot tell whether FEF inactivation have affected the memory by limiting the number of targets encoded, or by accelerating the memory decay over time.An issue may be raised regarding the short and variable retention times in our memory task: Saccades were made immediately after visual encoding of the targets and the retention time for each target was variable because the saccade responses were made in sequence. Thus, our task might have tapped on the iconic memory


Mathematical modeling of signaling and gene regulatory networks has provided unique insights about systems behaviors for many cell biological problems of medical importance. Quantitative single cell monitoring has a crucial role in advancing systems modeling of molecular networks. However, due to the multidisciplinary techniques that are necessary for adaptation of such systems biology approaches, dissemination to a wide research community has been relatively slow. In this essay, I focus on some technical aspects that are often under-appreciated, yet critical in harnessing live cell imaging methods to achieve single-cell-level understanding and quantitative modeling of molecular networks. The importance of these technical considerations will be elaborated with examples of successes and shortcomings. Future efforts will benefit by avoiding some pitfalls and by utilizing the lessons collectively learned from recent applications of imaging in systems biology. Here I will primarily focus on practical aspects of conducting live cell microscopy studies. For conceptual advantages of real time live cell monitoring over snapshot imaging of single cells or cell population studies, readers are referred to ,2. This Although many tools are borrowed from techniques commonly used in molecular cell biology, special needs arise due to specific requirements of systems biology. Moreover, routinely used options in cell biology are sometimes deemed inadequate. Below I present a list of technical issues in imaging strategies during the course of a systems biology project, from the initial design to data analysis and interpretation. The particular choices made for each stage can significantly affect whether the monitoring results represent physiological behaviors of the molecular network of interest.All imaging studies rely on the assumption that the visualized protein is a reasonably accurate surrogate of the endogenous protein. For most live cell imaging approaches, the measured signal comes from fluorescent proteins, typically expressed from a transgene encoding the fluorescent protein. Even in the more tempered situation where the transgene is stably integrated in the genome, the expression pattern of the fluorescent protein varies depending on the copy number and the genomic contexts. Single cell cloning allows sampling and selection of cells with a most desirable expression pattern which recapitulates that of the endogenous gene. The following are some measures that help guide the design of the expression construct and subsequent selection of single cell clones. These precautions would avoid unintended re-wiring of the regulatory network and ensure visualization of the natural activity of the molecular system.Tools for expressing DNA constructs have traditionally been geared toward high expression levels. In many applications, a protein is ectopically over-expressed or expressed at a sufficiently high level that permits high intensity signal from the fluorescent proteins  included in the construct, the fluorescent protein may be either constitutively expressed (more common) or induced/repressed by stimuli. In systems biology studies of stimulus-responsive signaling dynamics, the protein of interest (to be fluorescently labeled) may be direct targets of relevant signaling. For example, the protein may be a component of a transcriptional feedback or feed-forward network. Since real-time dynamic activity can greatly depend on feedback loops, it is important to capture stimulus-dependent regulation of the expression. Hence, when the gene is known to be regulated by the stimuli under study, it is recommended that the sequence upstream of the fluorescent protein-coding DNA contain a promoter from the native locus including a minimal set of sites that confer appropriate responsiveness . Howeveretc.). Since the visualized molecule is often a fusion of the protein of interest and a fluorescent protein attached at the N- or the C-terminus, it may not only have a larger size but also have altered molecular function. Each important property needs to be confirmed by comparing to that of the endogenous protein. For example, certain fluorophores tend to form dimers or tetramers in vivo, inducing improper behaviors of the fusion protein. Using structural knowledge if available, the position of the fluorophore can be placed far from important functional domains in order to preserve protein-protein interaction or substrate recognition.The final product from the construct must behave as the (unlabeled) native protein in terms of subcellular localization, oligomerization, degradation, interactions with other biomolecules  that still allows subsequent quantitative analysis . Such an optimal setting may be achieved far from what is conventionally used in routine cell biological applications, again requiring special attention  of moving the cells from the incubator to the microscope stage, by having the incubator close to the microscope room or by allowing some recovery/settling time after mounting the cell dish on the microscope before the start of imaging. For a prolonged duration of imaging, it is crucial to keep a stable culture condition with constant temperature , CO2 level, and humidity, which usually requires a special inner chamber holding the cell dish and a larger temperature-stabilizing box surrounding the microscope stage. Other substitute devices  are generally inadequate for maintaining stable temperature due to a steep temperature gradient from cells to the microscope components in room temperature.A unique requirement for live cell imaging is to maintain uncompromised incubation conditions for the cells during microscopy. There should be little or no unintended perturbation of cell microenvironment, from the moment of taking the dish or chamber of cells from the incubator until the end of imaging. To this end, it is preferable to minimize the physical effects . In the absence of an objective label, segmentation of the compartment relies on imaging data itself and becomes a much harder computational task, necessitating extensive, if not entirely, manual segmentation [If the project calls for quantification of signal from a subcellular compartment , further segmentation is necessary to identify the compartment from each cell. If a compartment-specific label  is used and imaged, the analysis pipeline can be nearly automated with only occasional human interventions for spotting errors . Howeverentation . TherefoSingle cell observations typically have cell-to-cell variability. Therefore, it is important to determine what outliers are and what are frequently observed behaviors or whether there are subpopulations of cells with distinct behaviors that have functional relevance . Some stIf essential components of the molecular network and their interactions are known, a mathematical model can be constructed to represent the individual reactions. Computer simulations can be performed to generate useful hypotheses prior to live cell observations. The initial modeling effort may guide experimental design by suggesting non-intuitive behaviors to look for. The experimental observation may confirm such hypotheses or reveal different results which motivate subsequent model revisions. Such an iterative process of modeling and experimentation is a hallmark of systems biology investigations and accelerates knowledge discovery.Successful applications of live cell imaging in systems cell biology projects are still technically demanding , despite


Coastal UHF radar provides a unique capability to measure the sea surface dynamic parameters and detect small moving targets, by exploiting the low energy loss of electromagnetic waves propagating along the salty and good conducting ocean surface. It could compensate the blind zone of HF surface wave radar at close range and reach further distance than microwave radars. However, its performance is susceptible to wind turbines which are usually installed on the shore. The size of a wind turbine is much larger than the wavelength of radio waves at UHF band, which results in large radar cross section. Furthermore, the rotation of blades adds time-varying Doppler frequency to the clutter and makes the suppression difficult. This paper proposes a mitigation method which is based on the specific periodicity of wind turbine clutter and performed mainly in the time-frequency domain. Field experimental data of a newly developed UHF radar are used to verify this method, and the results prove its effectiveness. Coastal area is closely related to the production and living of people. Improvement of capacity in sea state measurement and target detection would enhance the efficiency and safety of marine fishery and engineering, maritime search and rescue, disaster forecasting, and ecological conservation. Compared to conventional measurement devices which directly contact the tested water such as buoys, drifters, and ADCPs (acoustic Doppler current profiler), remote sensing devices are more suitable for the complex and volatile sea environment due to their larger coverage, higher safety, and lower cost in operation. Similar to the HF (high-frequency) surface wave radar which has been widely installed along coastline in many countries, coastal UHF (ultrahigh frequency) radar is based on the electromagnetic wave propagation along the salty and good conducting ocean surface and could monitor areas much larger than the conventional microwave radar coverage. Besides, it could compensate the blind zone of HF radar at close range and provide higher precision data. Several UHF radar hardware systems have been developed and the research of underlying theory is always in progress –7.However, a problem that should not be ignored in the development of coastal UHF radar is the interference from wind turbines that is often called WTC (wind turbine clutter). The interest in renewable energy has grown continuously in recent years due to the climate change and fossil fuel diminishment. As the most mature technology currently available, more and more wind turbines are installed, especially in coastal areas where the wind power is usually rich. These wind turbines cause serious interference to neighbouring radio systems. On the one hand, in order to reach higher altitudes where the winds are more constant and faster, the wind turbine tower and blades are usually tens of meters long, which results in large scattering cross section for radio signals with comparable or shorter wavelength. On the other hand, the rotation of blades adds Doppler frequency to the scattering signals and makes the contamination hard to eliminate. These factors render the installation of wind turbines receiving a lot of objections from managers and operators of radio systems.The conflict between wind turbines and radio systems has aroused wide attention and several approaches have been proposed to address the problem. From the wind turbine aspect, stealth turbine technology has been investigated, including shaping the wind turbines to avoid specular reflections  and partFrom the radar aspect, several signal processing-based mitigation methods have also been proposed. For the air surveillance radar, techniques in development include inhibiting track initiation in the vicinity of wind farms , using iPeriodicity is a unique feature of WTC which yet has never been exploited in mitigation algorithms. Method proposed in this paper is based on the periodicity of WTC and performed mainly in time-frequency domain. Field experimental data is used for algorithm verification, which shows that SCR  is substantially increased and the masked ocean wave and vessel echoes could be identified clearly after the mitigation algorithm is performed.The newly developed coastal UHF radar is a coherent Doppler radar with one transmitting channel and eight receiving channels. Different from conventional superheterodyne receivers, the coastal UHF radar receiver directly samples the echoes in RF (radio frequency) stage and both the frequency downconversion and signal demodulation are performed in digital domain and thus the receiver is called “full-digital receiver” .The coastal UHF radar transmits LFMICW (linear frequency modulated interrupted continuous wave) signal which is formed by modulating a LFMCW signal with gating pulses. The transmitting gating pulses are used to isolate the transmitting and receiving phases, which in turn prevent the transmitting signal from jamming the receiver. In the receiver, the dechirp and downconversion processing of receiving signals are performed through CORDIC  algorithm in FPGA (field programmable gates array), and then the resulting data is decimated by CIC (cascade integrator comb) filter to reduce the data rate. After that, the data is sent to PC , and the information of range and velocity is extracted through double-FFT (fast Fourier transform) algorithm . The firDuring the field experimental period, the radar was deployed in LiuAo Peninsula of Fujian Province of China. The main purpose of the experiment was to test the performance of sea state measurement and ship detection. A directional eight-element Yagi antenna was used as the transmitting antenna, and eight identical Yagi antennas formed a 8.4 m-long ULA (uniform linear array) for receiving. The main lobe width of each antenna is 40° in vertical plane and 44° in horizontal plane. As All the turbines in the wind farm are 3-blade horizontal-axis wind turbines with 34 m maximum rotor length and 185.64 Hz maximum Doppler frequency. On the one hand, the blades are longer than the radar range resolution. On the other hand, the maximum Doppler frequency is bigger than the maximum unambiguous Doppler frequency of the UHF radar. Due to the delay-Doppler coupling effect of LFM signal, too high Doppler frequency makes the range measurement error bigger than the range resolution. Thus the WTC may appear in its neighbouring range bins and results in the range aliasing phenomenon.Figures The time-varying radar signature of WTC depends on many factors including wind turbine blade position, yaw motion, and rotation speed. The unstable position and motion parameters as well as the complex aerodynamic shape of blades make it difficult to model WTC with a simple formulation and effectively suppress it. However, the periodicity signature of WTC nearly always exists within a relatively short time, as shown from The mitigation algorithm is performed on each single range bin that has been detected contaminated. The detection method which needs a specific study is not the focus of this paper. (1)s(k) is the signal samples with L length. The time interval between each two adjacent samples equates the radar frequency-sweeping period which is denoted by  T. η(k) is the smoothing window with Lwin length. F is the interval between frequency bins, which equates 1/NFFT, where NFFT is the number of frequency bins.Calculate the time-frequency spectrogram of the selected range bin using STFT according to(2)Delete the frequency bins where the ocean wave echoes may locate from the spectrogram and calculate the cyclic ACF (autocorrelation function) of amplitude of each remaining frequency bin.(3)Average all the cyclic ACFs and a curve like (4)P is regarded as the WTC period TWTC divided by T. In P = 93, which means that the dominant period of WTC equates 93T.Find the highest peak with positive time bin number, whose corresponding time bin number This step is performed in the time-frequency domain, which enables both the frequency-based isolation of ocean wave echoes and time-based period estimation. The process is listed as follows.P. After the WTC period is known, several snapshots of time-frequency spectrogram could be obtained. The spectrogram snapshot means the spectrogram of a section of signal that is shorter than the total signal length L. The time interval between snapshots is set as the WTC period P, and the time duration of each snapshot is set as a value Lsnap smaller than L. Then the number of snapshots Nsnap is obtained according toA) means the maximum integer that is not bigger than A.The correlation function reflects the periodicity strength of each frequency bin at the period Lwin and NFFT.Calculate the time-frequency spectrogram of each snapshot using STFT. Both the window length and FFT length should be the same as those of the last step, Nsnap snapshots generate Nsnap × (Nsnap − 1)/2 correlation coefficients for each frequency bin.For each frequency bin, calculate the amplitude correlation coefficient between each two spectrogram snapshots. Thus Average all the correlation coefficients corresponding to each frequency bin and then the correlation function like Process of this step is listed as the following.WTC possesses strong periodicity, while echoes from vessels and ocean waves do not. The echo of a vessel only exists in a short duration in one range-frequency cell and the ocean wave echo bears stochasticity due to the random motion of ocean waves. Using WTC period as the snapshot interval, the snapshots of spectrogram should be highly correlated with each other if only WTC exists or WTC is predominant in the frequency bin. The vessel and ocean wave echoes could reduce the correlation, and thus the frequency bins where they predominate should have relatively low correlation. Besides, the ambient noise is also random and hence the noise-dominated frequency bins also bear low correlation.S′(f) is the PSD after modulation, S(f) is the original signal PSD, a is a positive constant, and C(f) is the correlation function as shown in aC(f)− is a decreasing function, the sinks in the correlation function caused by ocean wave and vessel echoes will increase the PSD amplitude at corresponding positions and hence the wanted signals will be highlighted after modulation. The noise-derived sinks will also raise the PSD, but, since the PSD of the original signal is lower at these positions, it will not stand out after modulation. The constant a is chosen mainly based on observations and is set as 5 in this paper.According to the observation result,  is propoMultiple times processing means to repeat the above three steps several times. On the one hand, there may be more than one wind turbine covering a range bin; on the other hand, WTC could spread to multiple range bins due to the delay-Doppler coupling effect of LFM signal as explained in Data used for the three experiments in this section is from real measured data but with artificial adjustment. The adjustment is to add the signals of two range bins. One is WTC contaminated, while the other is not and the vessel echo is clearly seen in its PSD. The sum signal is then used for algorithm verification. By doing this, the frequency of the vessel echo masked by WTC is known a priori and hence could be used for comparison.L equates 512, the smoothing window is Hamming window with length 64, FFT length NFFT is set as 512, and the spectrogram snapshot length Lsnap equates 128.In the three experiments, the signal length By comparing In the above two experiments, the mitigation algorithm is performed only once. While in this experiment, the algorithm is done twice. Both the Bragg peaks and the vessel echoes are very weak and totally buried in WTC in the original signal. But after the first time mitigation, they become prominent. However, there are also significant peaks at around −2.5 and 2 Bragg-normalized frequency. They are probably caused by the remaining noise since, as shown in Since the Bragg peaks of the original sum signal are hardly identified, the verification is only performed on the vessel echo, which is proven to be standing out at the right frequency through comparing Figures The complexity of wind turbine motion and blade shape makes the WTC mitigation difficult. The algorithm proposed in this paper utilizes the specific periodicity of WTC which is not possessed neither by the ocean wave and vessel echoes nor by the ambient noise. Three measured data-based experiments are conducted to verify the algorithm performance, which proves that the SNR of the wanted signals, that is, ocean wave and vessel echoes, is significantly increased which could then be easily identified.The algorithm is not influenced by the range aliasing effect and is capable of addressing the problem that multiple WTC with different periods coexist in one range bin. The optimization of the selection of several parameters in the algorithm, such as the constant in the PSD modulation formulation, still needs further study.


Many structural properties such as solvent accessibility, dihedral angles and helix-helix contacts can be assigned to each residue in a membrane protein. Independent studies exist on the analysis and sequence-based prediction of some of these so-called one-dimensional features. However, there is little explanation of why certain residues are predicted in a wrong structural class or with large errors in the absolute values of these features. On the other hand, membrane proteins undergo conformational changes to allow transport as well as ligand binding. These conformational changes often occur via residues that are inherently flexible and hence, predicting fluctuations in residue positions is of great significance.http://tardis.nibio.go.jp/netasa/htmone/) for predicting the one-dimensional ESFs from sequence information and analyzing the differences between the predicted and observed values of the ESFs.We performed a statistical analysis of common patterns among selected one-dimensional equilibrium structural features (ESFs) and developed a method for simultaneously predicting all of these features using an integrated system. Our results show that the prediction performance can be improved if multiple structural features are trained in an integrated model, compared to the current practice of developing individual models. In particular, the performance of the solvent accessibility and bend-angle prediction improved in this way. The well-performing bend-angle prediction can be used to predict helical positions with severe kinks at a modest success rate. Further, we showed that single-chain conformational dynamics, measured by B-factors derived from normal mode analysis, could be predicted from observed and predicted ESFs with good accuracy. A web server was developed (The prediction performance of the integrated model is significantly better than that of the models performing the task separately for each feature for the solvent accessibility and bend-angle predictions. The predictability of the features also plays a role in determining flexible positions. Although the dynamics studied here concerns local atomic fluctuations, a similar analysis in terms of global structural features will be helpful in predicting large-scale conformational changes, for which work is in progress. Due to the difficulties in protein production and crystallization, complete structures of very few of these proteins have been determined[Membrane proteins are essential to intercellular transport and communication and they constitute among the most important drug targets. Since the majority of membrane proteins adopt helical conformations, this type of membrane proteins has been studied extensively and in this work, the term etermined. To overetermined.As an alternative and complementary approach, it has been found useful to try to estimate lower resolution structural features, rather than the whole structure, from amino acid sequence information alone. This has led to methods for predicting transmembrane (TM) regions,4, solveThese methods have been successful in predicting solvent accessibility, TM regions and helical contacts with fair degrees of accuracy. For example, the prediction of solvent accessibility has been performed with a mean absolute error of ~19%, HHC witIt has been reported in the case of globular proteins that adding predicted values of one structural feature (secondary structure) improves the performance of predicting another feature (solvent accessibility). This obWhile most of the existing prediction methods deal with static structural properties, membrane proteins have dynamic structures, required for their channeling and gating type functions. DerivinIn this paper, we wish to discuss both static and dynamic structural features of membrane proteins within the framework of the prediction of residue-wise properties from amino acid sequence information alone. As we mentioned above, many prediction methods have been developed but so far, there has been no attempt to integrate predictions of multiple one-dimensional features, which are expected to share some common patterns and even influence one another. The first component of this study addresses this issue and develops an integrated system to predict multiple one-dimensional structural properties from amino acid sequence and evolutionary information using a well-established technique for sequence-based predictions. Mutual dependencies of errors in these features are also explored. Due to the relatively static nature of these one-dimensional features within a structure, we term them one-dimensional equilibrium structural features (ESFs), even though they may not represent equilibrium in the strict sense.In the second component of this study, we try to determine whether the observed or predicted ESFs may be used to estimate the dynamic nature of structures. We derive a data set of atomic fluctuations estimated from a normal modes analysis (NMA) and represented by a theoretical B-factor and try to predict these B-factor values from sequence and structural features directly. We show that the B-factor values derived from the NMA can be predicted from the observed and predicted values of ESFs. A small data set of MD-trajectories of proteins suggests that these predictabilities extend to the local dynamics at a larger time scale.A non-redundant data set of helical membrane proteins (selected at a chain level) was developed as follows. First, protein chains from PDBTM  were dowUnlike earlier studies predicting the residue-wise solvent accessibility and helix-contacting status separately, we study, in this paper, eight one-dimensional structural features in an integrated manner. Since all these structural features are calculated from crystal structures, which represent a good estimate of the equilibrium state of at least one of the available conformations, we call these features equilibrium structural features (ESF). In the strict sense these features may not represent equilibrium, but none of the results presented in this work are likely to be affected by this approximation. The first three features represent the solvent accessible surface area (ASA), followed by the HHC status and four different conformational angles. These eight features are calculated/defined as follows:ASA values were computed using the program NACCESS, with thWe assigned each TM residue a binary value of 1 or 0, depending on whether it is in contact with another TM helix. A contact between an inter-helical pair of residues was defined if any atom from one residue was within a cut-off distance from any atom from the other residue. The distance threshold for this study was chosen to be 3.5Å after analyzing the distribution of the closest inter-helical distances (see Results). Only intra-chain contacts were included, as the atomic coordinates of isolated chains were used throughout.α Catoms of residues i-2, i (the target) and i+2. This angle provides an estimate of the local bend in the peptide chain of the protein. Alpha refers to the helical torsion angle defined by the four α Catoms of residues i-1, i, i+1 and i+2 and approximates the chirality, whereas phi and psi are well known dihedral angles.Four conformational angles, kappa (κ), alpha (α), phi (φ) and psi (ψ), were considered, as defined and calculated by the dictionary of secondary structure of proteins (DSSP). In brieThe bend angle defined above is a property of peptide chain conformation and has the advantage that each position can be assigned a finite and unambiguous value. In some studies, the bend angle is also referred to as the angle between the helical axes over the residues in preceding and following parts of a helix. There is no unique definition of helical axes when a bend is present, but we used the definition of Bansal and Kumar, and performed this analysis using their software HELANAL ,28. To dα Catoms, estimated by two techniques, NMA and MD simulations.In contrast to the residue-wise ESFs considered above, dynamic structural features (DSFs) are defined by real-time displacements from the equilibrium state and cannot be calculated directly from the atomic coordinate data of crystal structures. As DSFs, we considered the root mean square fluctuations (RMSF) of the In NMA, the energy surface is assumed to be harmonic and atomic displacements are expressed as a superposition of linearly independent collective motions called normal modes. We carried out the normal mode analysis of all the proteins in our data set using the El Nemo server,30. ThisAlthough the normal modes and displacements were calculated for an entire protein chain, all the analysis of relationships between ESFs and B-factors was performed for the membrane-spanning regions only. Residues close to the membrane boundaries are expected to be more mobile than those inside and thus a baseline performance needed to be estimated. To do so, all the residues were assigned a helical z-axis position taken from OPM and a correlation between the values of the z-coordinate and the NMA-derived B-factors (BNMA) was computed. This correlation serves as the baseline indicating the extent to which fluctuations can be determined from the knowledge of TM topology alone.Unlike NMA, MD simulations make no assumption about the energy surface and therefore, are expected to provide more accurate estimates of DSFs. In addition, they represent a larger conformational space compared to normal modes or static crystal structures. However, all-atom MD simulations are time consuming and it is not possible to obtain the trajectory data for all the proteins in our data set. We, therefore, used a smaller data set of proteins, for which MD data became available during the course of this work. Due to the small number of examples available, this part of the work serves as an example application of the current methodology rather than providing a statistical rule.Thermus thermophilus [The proteins used in this study are the protein-conducting channel SecYE from ode: 2ZJS and SecYde: 1RH5),34; ttSede: 1RH5),34. The We used observed and predicted ESFs, as well as their combination, to estimate the MD-derived RMSF values, i.e., the fluctuations that take place over a longer time scale, much in the same way as we did for BNMA predictions.The prediction of multiple features can be achieved either by training models for each feature separately or in a single integrated system. The integrated predictions allow for information flow from the prediction of one feature to another and hence may be beneficial for performance reasons. In addition, the number of training/validation cycles in an integrated system is substantially fewer than that of models for individual features. However, at the outset, it is not clear if the integration results in an improved performance or various components of the target features in the integrated model interfere with each other and cause a performance loss. To find a definite answer to this question, we trained separate models for each of the eight features considered and compared the performance between the individually trained models and the integrated model.In general, the prediction models were similar to that of our recent work on predicting multidimensional properties corresponding to the bound dinucleotide identities in DNA-binding proteins. To summESF predictions for the proteins with MD trajectory data were performed by using one of the 76 neural networks trained above, where the left-out protein showed the highest sequence similarity with the query protein. This procedure ensures that the prediction of ESFs was made by a model trained on truly dissimilar (< 25%) sequences, eliminating a bias causing exaggerated prediction performance.In this work, we do not intend to develop a method for predicting TM regions, as a number of methods for doing so are available,12,36-38To evaluate the relationships between ESFs and DSFs, we calculated the coefficients of correlation between each of the eight ESFs and the BNMA for the individual proteins. To determine the role of cooperativity between various features, neural networks were trained, using various combinations of feature sets as inputs, to predict the BNMA values of all the residues in the membrane-spanning regions.Further, the observed and predicted ESFs were used in various combinations to predict the MD-derived RMSF values. Since the amount of data is small for an LOO validation here, all the samples were pooled to train the neural network. To avoid overfitting on the training samples, the background performance of a random data was computed in the following way. The RMSF values were randomly redistributed amongst all the residues and an attempt was made to predict these randomized values from the same feature set, as in the real data. These randomized correlations serve as a background, subtracting which from the trained correlation in the real data ensures that the performance in each case is not the result of simply an increased number of features and allows us to compare performance levels between models using different feature sets.Also, due to the limited amount of MD data, we analyzed the entire protein including the residues outside the membrane (see Results). Since the membrane spanning regions are likely to be less flexible, a method trying to predict the RMSF over the entire protein may end up separating only the TM and non-TM regions. To estimate this effect, we labeled all the residues with a binary number (0 for membrane-spanning and 1 for non-membrane-spanning) and computed the correlation between this class label and the MD-derived RMSF. This gives background values, against which the ESF-based predictions can be compared.Both the analogue  and binary (HHC) features were predicted by defining a multidimensional output function represented by the optimized neural network. Following the standard convention, our neural networks were trained to optimize a cumulative mean squared error (MSE) defined byres is the total number of amino acid residues in the training data set, Nf the total number of features (eight) and Oij and Pij are the observed and predicted values of th jfeature of th iresidue, respectively. The summation was over all the residues in all the proteins in the training data set.where NMSE was used only for optimizing the neural networks. To compare performances, we used the following more widely reported measures.th jfeature of a single residue is defined asAbsolute error in th jfeature in kth protein was given by:Mean absolute error (MAE), which is the mean of absolute errors over all the residues, was calculated for each protein, such that MAE for the k is the total number of amino acid residues in kth protein and kij Oand kij Pare, respectively, the observed and predicted values of th jfeature of th iresidue in kth protein.where NWhen referring to the MAE of the entire data, we used the average of all the protein-wise MAE values, which may be slightly different from the MAE computed by pooling together all the residues from all the proteins and computing their average error.m and for every protein k as:For the binary valued feature of HHC, the expected (or target) values of the feature are binary  but the predicted values are analogue between 0 and 1. These analogue values can be translated into binary class labels by using a continuous cutoff, leading to different values of sensitivity and specificity, defined for every selected cutoff where k is the protein identifier, TP represents the number of true positive cases , TN is true negative (non-contacting residues predicted correctly), FP is false positive (the number of non-contacting residues predicted wrongly as contacting) and FN is false negative (the number of contacting residues wrongly predicted as non-contacting). By changing the cutoff (m) systematically, a set of sensitivity versus specificity values may be obtained.An ROC curve was plotted between sensitivity and (1-specificity) and the area under this curve (AUC) was used as a measure of prediction performance in the selected protein, over the entire range of cutoffs. The overall performance was measured by averaging the AUC values from the cross-validated prediction for all the proteins.The prediction performance for the DSFs was measured by calculating the correlation coefficient (C) between all (n) predicted (X) and observed (Y) values of the corresponding feature as follows:Performance of two models  was compared by calculating the performance measure  for each protein providing a pair of sets of performance scores corresponding to the two models. Distributions of the performance scores from the two models were compared by using a t-test implemented in the R-programming language. For all the analyses paired two-tailed t-test was used returning a p-value for the comparison, which is a measure of the evidence against the null hypothesis that the two means are equal.Overall, we considered 76 protein chains for this analysis. Figure S1  and the bend angle (kappa), both of which represent deviations from an ideal helix. The moderate level of correlation indicates that these structural features are significantly independent of each other.Table To make a comparison with other publicly available methods of prediction, we identified three web servers, two of which (MPRAP and ASAP) successfully returned ASA-predictions ,40, wherThe overall average MAE-values for MPRAP and ASAP are 15% and 21%, respectively. On the other hand, our LOO cross-validated integrated model has an MAE of 17%, whereas a self-consistently trained integrated model (used in the HTM One web server described below) has the lowest MAE of 14.2%. Since web servers are likely to be based on a model that is trained with all the proteins available at the time of the study and hence, the servers' performance will be exaggerated for the proteins used in the training. Our own web server performs about 3 percentage point better than the cross-validated performance on the considered proteins. ASAP was one of the first servers of this kind and is likely to have used fewer proteins. MPRAP has been developed recently and is based on more data, explaining why the latter performs better on the selected proteins. The cross-validated performance of our method is slightly lower than the result from the MPRAP server, but is more likely to represent a performance on totally new proteins.In summary, we conclude that the performance of our integrated approach, which was shown to outperform the individually trained models under identical conditions, is also likely to be better than currently available web servers.Tables Since the prediction errors are found to be correlated with the observed ASA states in the data, we explored a relationship between the actually observed values of a feature and the prediction error therein. The results of such an analysis for ASA  are consistent with the previous observation that surface residues are more difficult to predict than buried ones -44. The To examine relationships between the predictability of two features, we calculated correlation coefficients between their prediction errors (MAE), shown in Table Second and the most striking observation is that the prediction performance (MAE) of all four conformational angles show a strong positive correlation (R = 0.35 to 0.76) with each other. This observation indicates that the occurrence of residues with far-from-the-average bend angles is also accompanied by far-from-the-average conformational angles alpha, phi and psi, thus all being poorly predicted at the same time.Helical kinks have been found to be important for various functions of membrane proteins. To estimate if the bend angle prediction in this work can be used for predicting kink angles, we computed kink angles for all the residues (at least 4 residues inside the membrane from both sides) and converted them to a binary value of kinked versus non-kinked at a threshold. We then calculated the AUC of prediction using the bend angle predictions as a score, from which the kinked positions are labeled. Using different thresholds of kink angles, we find the best performance at a 30 degree kink angle, at which the AUC was a modest 57%. There are 141 kinked positions at this cutoff. Thus, at least some critically kinked positions can be identified using this crude approach. For a more accurate prediction of kinked positions, more work is required.We now explore whether the prediction of the ESFs described above can provide information about the dynamic behavior of TM proteins. We first compared the BNMA (see Methods) with those provided in the PDB files. Table S3 . This correlation came out to be 0.48 and it is the minimum correlation for the predictions to be useful.Table S4  aims to make an integrated prediction of one-dimensional structural features of helical membrane proteins using only an amino acid sequence as the input. It consists of two modules.In this module, the users are required to submit only an amino acid sequence and the server computes a PSSM and pattern data and makes a prediction of the eight one-dimensional structural features of all the residues.In this module, the users upload a PDB formatted file or enter a PDB ID and a chain identifier to be analyzed. The server automatically extracts sequence information and compares the observed structural features of the given protein chain with what would be expected from a purely sequence-based prediction (as in the prediction module). The server returns the observed and predicted values of these features and calculates the prediction errors, which could be used for further analysis, e.g., to estimate atomic displacements of each residue.In both modules, a local implementation of the neural network based prediction of TM helical residues is carried out to help the users identify which regions to concentrate on (see Methods). Predictions are provided for all the residues, so if the users have their own prior TM segment assignments, they may extract the relevant data from the server output.This work addressed two key questions about the sequence-based modeling of membrane proteins, i.e., the prediction of one-dimensional structural features and their relationships with conformational flexibility. We were able to demonstrate clearly that a computational model, which predicts multiple structural features in an integrated fashion, works significantly better than the ones trained individually. This is the first effort of its kind and is likely to be helpful in developing even more accurate methods of predicting membrane protein structures.As a byproduct of this work, we were able to show that kinked positions in membrane proteins are related to the peptide bend angle, even though the latter is not strictly based on the secondary structure definition. Severely kinked positions in helical membrane proteins are significant from a functional perspective and a quick estimate of these positions gives us good insights into the nature of membrane protein dynamics.The second part of the work aimed at predicting B-factors and here, we clearly showed that within the membrane-spanning regions, predicted and observed one-dimensional structural features led to an effective way of determining flexible helical residue positions. The one-dimensional structural features improved the prediction of flexibility based simply on the residue position with respect to the membrane boundaries. Although the available MD data was insufficient to develop strictly general models, an application of the ESFs-based prediction model showed a good promise for estimating MD-derived dynamics. Hopefully, in future this aspect of the work can be improved. Also, the analysis of local atomic fluctuations undertaken in this work can be extended to large-scale conformational changes, for which work is in progress.An integrated system to predict eight one-dimensional structural features of helical membrane proteins was developed. The performance of the integrated system was significantly better than that of the models trained on individual features. The analysis of prediction errors from this system led to a method for estimating NMA-derived fluctuations in proteins. The relationships between the prediction errors and atomic displacements were analyzed by comparing the prediction results with data from NMA and MD simulations. Although sequence information alone has a limited power to estimate atomic displacements, it was shown to improve the structure-based estimates of this property.Project name: HTM Onehttp://tardis.nibio.go.jp/netasa/htmoneProject home page: Operating system: Platform independentProgramming language: C, Perl and bash scriptingLicense: Free online usage with no restriction or warrantySA designed and implemented this study in consultation with KM. YHS provided data sets and benchmark results. YS and TM provided MD data. SA and KM prepared manuscript and all authors read and approved of it.additional-data.pdf .Click here for file