Analogous adaptations in speed, impulse and endpoint stiffness when learning a real and virtual insertion task with haptic feedback

Humans have the ability to use a diverse range of handheld tools. Owing to its versatility, a virtual environment with haptic feedback of the force is ideally suited to investigating motor learning during tool use. However, few simulators exist to recreate the dynamic interactions during real tool use, and no study has compared the correlates of motor learning between a real and virtual tooling task. To this end, we compared two groups of participants who either learned to insert a real or virtual tool into a fixture. The trial duration, the movement speed, the force impulse after insertion and the endpoint stiffness magnitude decreased as a function of trials, but they changed at comparable rates in both environments. A ballistic insertion strategy observed in both environments suggests some interdependence when controlling motion and controlling interaction, contradicting a prominent theory of these two control modalities being independent of one another. Our results suggest that the brain learns real and virtual insertion in a comparable manner, thereby supporting the use of a virtual tooling task with haptic feedback to investigate motor learning during tool use.

Muscle Synergy and Musculoskeletal Model-Based Continuous Multi-Dimensional Estimation of Wrist and Hand Motions

In this study, seven-channel electromyography signal-based two-dimensional wrist joint movement estimation with and without handgrip motions was carried out. Electromyography signals were analyzed using the synergy-based linear regression model and musculoskeletal model; they were subsequently compared with respect to single and combined wrist joint movements and handgrip. Using each one of wrist motion and grip trial as a training set, the synergy-based linear regression model exhibited a statistically significant performance with 0.7891 ± 0.0844 Pearson correlation coefficient (r) value in two-dimensional wrist motion estimation compared with 0.7608 ±  0.1037 r value of the musculoskeletal model. Estimates on the grip force produced 0.8463 ± 0.0503 r value with 0.2559 ± 0.1397 normalized root-mean-square error of the wrist motion range. This continuous wrist and handgrip estimation can be considered when electromyography-based multi-dimensional input signals in the prosthesis, virtual interface, and rehabilitation are needed.

Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate

Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studies, there still remain solving works for the purpose of realizing an ECoG-based prosthesis. We suggest a neuromuscular interface to control robot using decoded muscle activities and joint angles. We used sparse linear regression to find the best fit between band-passed ECoGs and electromyograms (EMG) or joint angles. The best coefficient of determination for 100 s continuous prediction was 0.6333 ± 0.0033 (muscle activations) and 0.6359 ± 0.0929 (joint angles), respectively. We also controlled a 4 degree of freedom (DOF) robot arm using only decoded 4 DOF angles from the ECoGs in this study. Consequently, this study shows the possibility of contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

Reduced Effort Does Not Imply Slacking: Responsiveness to Error Increases With Robotic Assistance

In both neurorehabilitation and functional augmentation, the patient or the user's muscular effort diminishes when the movement of their limb is supported by a robot. Is this relaxation a result of "slacking" by letting the robot take-over the movement, resulting in less responsiveness in the task? To address this question, we tested subjects who controlled a virtual cursor isometrically to track a moving target without and with different assistants. We measured the force applied by the subject as a metric for effort and estimated their control gain as the metric for responsiveness in the task. Although subjects applied less force with position assistance, the norm of the control gain increased with all assistants, i.e., they applied proportionately larger forces for the same difference between the cursor and the target states. Furthermore, assisting velocity errors improved baseline performance without reducing effort. Though all assistants improved task performance, the control gain adapted differently to position and velocity assistance. Position assistance was exploited to accurately track the target, whereas velocity assistance was treated as a disturbance, and was effectively nullified as it prevented submovements that minimized positional error. Our results show that robotic assistance increases task responsiveness in healthy individuals and that assisting velocity errors could boost patient performance without reducing their motor effort.

Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods

The classification of ankle movements from non-invasive brain recordings can be applied to a brain-computer interface (BCI) to control exoskeletons, prosthesis, and functional electrical stimulators for the benefit of patients with walking impairments. In this research, ankle flexion and extension tasks at two force levels in both legs, were classified from cortical current sources estimated by a hierarchical variational Bayesian method, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The hierarchical prior for the current source estimation from EEG was obtained from activated brain areas and their intensities from an fMRI group (second-level) analysis. The fMRI group analysis was performed on regions of interest defined over the primary motor cortex, the supplementary motor area, and the somatosensory area, which are well-known to contribute to movement control. A sparse logistic regression method was applied for a nine-class classification (eight active tasks and a resting control task) obtaining a mean accuracy of 65.64% for time series of current sources, estimated from the EEG and the fMRI signals using a variational Bayesian method, and a mean accuracy of 22.19% for the classification of the pre-processed of EEG sensor signals, with a chance level of 11.11%. The higher classification accuracy of current sources, when compared to EEG classification accuracy, was attributed to the high number of sources and the different signal patterns obtained in the same vertex for different motor tasks. Since the inverse filter estimation for current sources can be done offline with the present method, the present method is applicable to real-time BCIs. Finally, due to the highly enhanced spatial distribution of current sources over the brain cortex, this method has the potential to identify activation patterns to design BCIs for the control of an affected limb in patients with stroke, or BCIs from motor imagery in patients with spinal cord injury.

Mapping ECoG channel contributions to trajectory and muscle activity prediction in human sensorimotor cortex

Studies on brain-machine interface techniques have shown that electrocorticography (ECoG) is an effective modality for predicting limb trajectories and muscle activity in humans. Motor control studies have also identified distributions of “extrinsic-like” and “intrinsic-like” neurons in the premotor (PM) and primary motor (M1) cortices. Here, we investigated whether trajectories and muscle activity predicted from ECoG were obtained based on signals derived from extrinsic-like or intrinsic-like neurons. Three participants carried objects of three different masses along the same counterclockwise path on a table. Trajectories of the object and upper arm muscle activity were predicted using a sparse linear regression. Weight matrices for the predictors were then compared to determine if the ECoG channels contributed more information about trajectory or muscle activity. We found that channels over both PM and M1 contributed highly to trajectory prediction, while a channel over M1 was the highest contributor for muscle activity prediction.

Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents

With the goal of providing assistive technology for the communication impaired, we proposed electroencephalography (EEG) cortical currents as a new approach for EEG-based brain-computer interface spellers. EEG cortical currents were estimated with a variational Bayesian method that uses functional magnetic resonance imaging (fMRI) data as a hierarchical prior. EEG and fMRI data were recorded from ten healthy participants during covert articulation of Japanese vowels /a/ and /i/, as well as during a no-imagery control task. Applying a sparse logistic regression (SLR) method to classify the three tasks, mean classification accuracy using EEG cortical currents was significantly higher than that using EEG sensor signals and was also comparable to accuracies in previous studies using electrocorticography. SLR weight analysis revealed vertices of EEG cortical currents that were highly contributive to classification for each participant, and the vertices showed discriminative time series signals according to the three tasks. Furthermore, functional connectivity analysis focusing on the highly contributive vertices revealed positive and negative correlations among areas related to speech processing. As the same findings were not observed using EEG sensor signals, our results demonstrate the potential utility of EEG cortical currents not only for engineering purposes such as brain-computer interfaces but also for neuroscientific purposes such as the identification of neural signaling related to language processing.

Decoding fingertip trajectory from electrocorticographic signals in humans

Seeking to apply brain-machine interface technology in neuroprosthetics, a number of methods for predicting trajectory of the elbow and wrist have been proposed and have shown remarkable results. Recently, the prediction of hand trajectory and classification of hand gestures or grasping types have attracted considerable attention. However, trajectory prediction for precise finger motion has remained a challenge. We proposed a method for the prediction of fingertip motions from electrocorticographic signals in human cortex. A patient performed extension/flexion tasks with three fingers. Average Pearson's correlation coefficients and normalized root-mean-square errors between decoded and actual trajectories were 0.83-0.90 and 0.24-0.48, respectively. To confirm generalizability to other users, we applied our method to the BCI Competition IV open data sets. Our method showed that the prediction accuracy of fingertip trajectory could be equivalent to that of other results in the competition.

Decoding grasp force profile from electrocorticography signals in non-human primate sensorimotor cortex

The relatively low invasiveness of electrocorticography (ECoG) has made it a promising candidate for the development of practical, high-performance neural prosthetics. Recent ECoG-based studies have shown success in decoding hand and finger movements and muscle activity in reaching and grasping tasks. However, decoding of force profiles is still lacking. Here, we demonstrate that lateral grasp force profile can be decoded using a sparse linear regression from 15 and 16 channel ECoG signals recorded from sensorimotor cortex in two non-human primates. The best average correlation coefficients of prediction after 10-fold cross validation were 0.82±0.09 and 0.79±0.15 for our monkeys A and B, respectively. These results show that grasp force profile was successfully decoded from ECoG signals in reaching and grasping tasks and may potentially contribute to the development of more natural control methods for grasping in neural prosthetics.

Prediction of hand trajectory from electrocorticography signals in primary motor cortex

Due to their potential as a control modality in brain-machine interfaces, electrocorticography (ECoG) has received much focus in recent years. Studies using ECoG have come out with success in such endeavors as classification of arm movements and natural grasp types, regression of arm trajectories in two and three dimensions, estimation of muscle activity time series and so on. However, there still remains considerable work to be done before a high performance ECoG-based neural prosthetic can be realized. In this study, we proposed an algorithm to decode hand trajectory from 15 and 32 channel ECoG signals recorded from primary motor cortex (M1) in two primates. To determine the most effective areas for prediction, we applied two electrode selection methods, one based on position relative to the central sulcus (CS) and another based on the electrodes' individual prediction performance. The best coefficients of determination for decoding hand trajectory in the two monkeys were 0.4815 ± 0.0167 and 0.7780 ± 0.0164. Performance results from individual ECoG electrodes showed that those with higher performance were concentrated at the lateral areas and areas close to the CS. The results of prediction according with different numbers of electrodes based on proposed methods were also shown and discussed. These results also suggest that superior decoding performance can be achieved from a group of effective ECoG signals rather than an entire ECoG array.

Prediction of three-dimensional arm trajectories based on ECoG signals recorded from human sensorimotor cortex

Brain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography (ECoG) has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes. Although several studies have already succeeded in the inference of computer cursor trajectories and finger flexions using human ECoG signals, precise three-dimensional (3D) trajectory reconstruction for a human limb from ECoG has not yet been achieved. In this study, we predicted 3D arm trajectories in time series from ECoG signals in humans using a novel preprocessing method and a sparse linear regression. Average Pearson’s correlation coefficients and normalized root-mean-square errors between predicted and actual trajectories were 0.44∼0.73 and 0.18∼0.42, respectively, confirming the feasibility of predicting 3D arm trajectories from ECoG. We foresee this method contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

The effect of temporal perception on weight perception

A successful catch of a falling ball requires an accurate estimation of the timing for when the ball hits the hand. In a previous experiment in which participants performed ball-catching task in virtual reality environment, we accidentally found that the weight of a falling ball was perceived differently when the timing of ball load force to the hand was shifted from the timing expected from visual information. Although it is well known that spatial information of an object, such as size, can easily deceive our perception of its heaviness, the relationship between temporal information and perceived heaviness is still not clear. In this study, we investigated the effect of temporal factors on weight perception. We conducted ball-catching experiments in a virtual environment where the timing of load force exertion was shifted away from the visual contact timing (i.e., time when the ball hit the hand in the display). We found that the ball was perceived heavier when force was applied earlier than visual contact and lighter when force was applied after visual contact. We also conducted additional experiments in which participants were conditioned to one of two constant time offsets prior to testing weight perception. After performing ball-catching trials with 60 ms advanced or delayed load force exertion, participants’ subjective judgment on the simultaneity of visual contact and force exertion changed, reflecting a shift in perception of time offset. In addition, timing of catching motion initiation relative to visual contact changed, reflecting a shift in estimation of force timing. We also found that participants began to perceive the ball as lighter after conditioning to 60 ms advanced offset and heavier after the 60 ms delayed offset. These results suggest that perceived heaviness depends not on the actual time offset between force exertion and visual contact but on the subjectively perceived time offset between them and/or estimation error in force timing.

A computational model for optimal muscle activity considering muscle viscoelasticity in wrist movements

To understand the mechanism of neural motor control, it is important to clarify how the central nervous system organizes the coordination of redundant muscles. Previous studies suggested that muscle activity for step-tracking wrist movements are optimized so as to reduce total effort or end-point variance under neural noise. However, since the muscle dynamics were assumed as a simple linear system, some characteristic patterns of experimental EMG were not seen in the simulated muscle activity of the previous studies. The biological muscle is known to have dynamic properties in which its elasticity and viscosity depend on activation level. The motor control system is supposed to consider the viscoelasticity of the muscles when generating motor command signals. In this study, we present a computational motor control model that can control a musculoskeletal system with nonlinear dynamics. We applied the model to step-tracking wrist movements actuated by five muscles with dynamic viscoelastic properties. To solve the motor redundancy, we designed the control model to generate motor commands that maximize end-point accuracy under signal-dependent noise, while minimizing the squared sum of them. Here, we demonstrate that the muscle activity simulated by our model exhibits spatiotemporal features of experimentally observed muscle activity of human and nonhuman primates. In addition, we show that the movement trajectories resulting from the simulated muscle activity resemble experimentally observed trajectories. These results suggest that, by utilizing inherent viscoelastic properties of the muscles, the neural system may optimize muscle activity to improve motor performance.

Prediction of muscle activities from electrocorticograms in primary motor cortex of primates

Electrocorticography (ECoG) has drawn attention as an effective recording approach for brain-machine interfaces (BMI). Previous studies have succeeded in classifying movement intention and predicting hand trajectories from ECoG. Despite such successes, however, there still remains considerable work for the realization of ECoG-based BMIs as neuroprosthetics. We developed a method to predict multiple muscle activities from ECoG measurements. We also verified that ECoG signals are effective for predicting muscle activities in time varying series when performing sequential movements. ECoG signals were band-pass filtered into separate sensorimotor rhythm bands, z-score normalized, and smoothed with a Gaussian filter. We used sparse linear regression to find the best fit between frequency bands of ECoG and electromyographic activity. The best average correlation coefficient and the normalized root-mean-square error were 0.92±0.06 and 0.06±0.10, respectively, in the flexor digitorum profundus finger muscle. The δ (1.5∼4Hz) and γ2 (50∼90Hz) bands contributed significantly more strongly than other frequency bands (P<0.001). These results demonstrate the feasibility of predicting muscle activity from ECoG signals in an online fashion.

A Power Assist Device Based on Joint Equilibrium Point Estimation from EMG Signals

In some researches about power assist devices, surface ElectroMyoGraphy (EMG) signals are used to estimate user intentions to move their limbs. These conventional methods mainly focus on estimation of joint torque. However, the devices based on torque estimation are inclined to cause the vibration of users’ posture originating from the waviness of the EMG signals. Focusing on estimation of states related to the joint angle may improve the performance of the power assist devices. This paper proposes a new method that estimates user joint equilibrium point and stiffness separately from the EMG and that amplifies the stiffness while tuning the device joints according to user equilibrium points. To evaluate the method, we constructed a power assist system for the wrist and compared the method with a method based on simple torque estimation during posture maintenance tasks. Our results showed that the proposed method offers a more stable operation at the same assist ratio and proved the effectiveness of the method.

Emerging applications of single-cell diagnostics

The performance of DNA sequencers (next generation sequencing) is rapidly enhanced these days, being used for genetic diagnostics. Although many phenomena could be elucidated with such massive genome data, it is still a big challenge to obtain comprehensive understanding of diseases and the relevant biology at the cellular level. In general terms, the data obtained to date are averages of ensembles of cells, but it is not certain whether the same features are the same inside an individual cell. Accordingly, important information may be masked by the averaging process. As the technologies for analyzing bio-molecular components in single cells are being developed, single cell analysis seems promising to address the current limitations due to averaging problems. Although the technologies for single cell analysis are still at the infant stage, the single cell approach has the potential to improve the accuracy of diagnosis based on knowledge of intra- and inter-cellular networks. In this review several technologies and applications (especially medical applications) of genome and transcriptome analysis or single cells are described.

Learning Strategy in Time-to-Contact Estimation of Falling Objects

The ability to estimate the time that remains before contact (Time-To-Contact or TTC) of a falling object is critical in daily life. In this paper, we investigated how the Central Nervous System (CNS) becomes able to estimate the TTC of a ball falling at various accelerations. According to experiments on the human ability to catch a ball falling at various accelerations, we assumed that the CNS can hold multiple TTC estimators each of which is trained for a different acceleration, and one of them is adopted for TTC estimation in a ball-catching trial. Here we made a hypothesis about how each TTC estimator is trained when there is an estimation error. (1) If the estimation error is small, the TTC estimator adopted in the trial is recalibrated. (2) If the estimation error is large, a new TTC estimator is created. To test this hypothesis, we conducted two types of ball-catching experiments in a virtual environment where the acceleration of a virtual ball is changed gradually or suddenly in each experiment. The difference in catching performances in the two experiments supported our hypothesis.

Evaluation of a game controller using human stiffness estimated from electromyogram

A motion controller which has an acceleration sensor increases reality and intuitiveness in sports games. But we adjust not only visible posture but also invisible force like stiffness and viscosity when we play sports. We propose a game controller using player's movement and force by using acceleration and electromyogram(EMG). In this research, we compared conventional motion controller and proposed method by using a golf game. The score was the distance between cup position and carried ball position. For beginner of video games, proposed method is superior than conventional. For well-trained video game players conventional button type controller wins on accurate input. Because it was difficult to keep arm stiffness constant than button. Using coarsely-quantized EMG might resolve this problem, then achieve intuitive and easy-to-use game controller.

A dictionary-driven P300 speller with a modified interface

P300 spellers are mainly composed of an interface, by which alphanumerical characters are presented to users, and a classification system, which identifies the target character by using acquired EEG data. In this study, we proposed modifications both to the interface and to the classification system, in order to reduce the number of required stimulus repetitions and consequently boost the information transfer rate. We initially incorporated a custom-built dictionary into the classification system, and conducted a study on 14 healthy subjects who copy-spelled 15 four letter words. Incorporating the dictionary, the mean accuracy at five trials increased from 72.86% to 95.71%. To further increase the system performance, we first validated the hypothesis that for a conventional P300 system, most target-error pairs lie on the same row or column. Then based on the validated hypothesis, we adjusted letter positions on the well-known from A to Z interface. The same subjects spelled the same 15 words using the modified interface as well, and the mean information transfer rate at two trials reached 55.32 bits/min.

Application of covariate shift adaptation techniques in brain-computer interfaces

A phenomenon often found in session-to-session transfers of brain-computer interfaces (BCIs) is nonstationarity. It can be caused by fatigue and changing attention level of the user, differing electrode placements, varying impedances, among other reasons. Covariate shift adaptation is an effective method that can adapt to the testing sessions without the need for labeling the testing session data. The method was applied on a BCI Competition III dataset. Results showed that covariate shift adaptation compares favorably with methods used in the BCI competition in coping with nonstationarities. Specifically, bagging combined with covariate shift helped to increase stability, when applied to the competition dataset. An online experiment also proved the effectiveness of bagged-covariate shift method. Thus, it can be summarized that covariate shift adaptation is helpful to realize adaptive BCI systems.

Localized distributions of quasi-two-dimensional electronic states near defects artificially created at graphite surfaces in magnetic fields

We measured the local density of states of a quasi two-dimensional electron system (2DES) near defects, artificially created by Ar-ion sputtering, on surfaces of highly oriented pyrolytic graphite (HOPG) with scanning tunneling spectroscopy (STS) in high magnetic fields. At valley energies of the Landau level spectrum, we found two typical localized distributions of the 2DES depending on the defects. These are new types of distributions which are not observed in the previous STS work at the HOPG surface near a point defect [Y. Niimi, Phys. Rev. Lett. 97, 236804 (2006).10.1103/PhysRevLett.97.236804]. With increasing energy, we observed gradual transformation from the localized distributions to the extended ones as expected for the integer quantum Hall state. We show that the defect potential depth is responsible for the two localized distributions from comparison with theoretical calculations.

Construction of a versatile ultralow temperature scanning tunneling microscope

We constructed a dilution-refrigerator (DR)-based ultralow temperature scanning tunneling microscope (ULT-STM) which works at temperatures down to 30 mK, in magnetic fields up to 6 T and in ultrahigh vacuum (UHV). Besides these extreme operation conditions, this STM has several unique features not available in other DR-based ULT-STMs. One can load STM tips as well as samples with clean surfaces prepared in an UHV environment to a STM head keeping low temperature and UHV conditions. After then, the system can be cooled back to near the base temperature within 3 h. Due to these capabilities, it has a variety of applications not only for cleavable materials but also for almost all conducting materials. The present ULT-STM has also an exceptionally high stability in the presence of magnetic field and even during field sweep. We describe details of its design, performance, and applications for low temperature physics.

Learning arm's posture control using reinforcement learning and feedback-error-learning

In this paper, we propose a learning model using the Actor-Critic method and the feedback-error-learning scheme. The Actor-Critic method, which is one of the major frameworks in reinforcement learning, has attracted attention as a computational learning model in the basal ganglia. Meanwhile, the feedback-error-learning is learning architecture proposed as a computationally coherent model of cerebellar motor learning. This learning architecture's purpose is to acquire a feed-forward controller by using a feedback controller's output as an error signal. In past researches, a predetermined constant gain feedback controller was used for the feedback-error-learning. We use the Actor-Critic method for obtaining a feedback controller in the feedback-error-earning. By applying the proposed learning model to an arm's posture control, we show that high-performance feedback and feed-forward controller can be acquired from only by using a scalar value of reward.

Real-space imaging of alternate localization and extension of quasi-two-dimensional electronic States at graphite surfaces in magnetic fields

We measured the local density of states (LDOS) of a quasi-two-dimensional (2D) electron system near point defects on a surface of highly oriented pyrolytic graphite with scanning tunneling microscopy and spectroscopy. Differential tunnel conductance images taken at very low temperatures and in high magnetic fields show a clear contrast between localized and extended spatial distributions of the LDOS at the valley and peak energies of the Landau level spectrum, respectively. The localized electronic state has a single circular distribution around the defects with a radius comparable to the magnetic length. The localized LDOS is in good agreement with a spatial distribution of a calculated wave function for a single electron in 2D in a Coulomb potential in magnetic fields.

STS observations of Landau levels at graphite surfaces

Scanning tunneling spectroscopy (STS) measurements were made on surfaces of two different kinds of graphite samples, Kish graphite and highly oriented pyrolytic graphite (HOPG), at very low temperatures and in high magnetic fields. We observed a series of peaks in the tunnel spectra associated with Landau quantization of the quasi-two-dimensional electrons and holes. A comparison with the calculated local density of states at the surface layers allows us to identify Kish graphite as bulk graphite and HOPG as graphite with a finite thickness of 40 layers. This explains the qualitative difference between the two graphites reported in the recent transport measurements which suggested the quantum-Hall effect in HOPG. This work demonstrates how powerful the combined approach between the high quality STS measurement and the first-principles calculation is in material science.

Inclusive double-pomeron exchange at the fermilab tevatron p p collider

We report results from a study of events with a double-Pomeron exchange topology produced in p p collisions at sqrt[s]=1800 GeV. The events are characterized by a leading antiproton and a large rapidity gap on the outgoing proton side. We find that the differential production cross section agrees in shape with predictions based on Regge theory and factorization, and that the ratio of double-Pomeron exchange to single diffractive production rates is relatively unsuppressed as compared to the O(10) suppression factor previously measured in single diffractive production.

Search for Kaluza-Klein graviton emission in pp collisions at square root[s] = 1.8 TeV using the missing energy signature

We report on a search for direct Kaluza-Klein graviton production in a data sample of 84 pb(-1) of ppmacr; collisions at sqrt[s]=1.8 TeV, recorded by the Collider Detector at Fermilab. We investigate the final state of large missing transverse energy and one or two high energy jets. We compare the data with the predictions from a (3+1+n)-dimensional Kaluza-Klein scenario in which gravity becomes strong at the TeV scale. At 95% confidence level (C.L.) for n=2, 4, and 6 we exclude an effective Planck scale below 1.0, 0.77, and 0.71 TeV, respectively.

Search for pair production of scalar top quarks in R-parity violating decay modes in pp collisions at square root of s=1.8 TeV

We present the results of a search for pair production of scalar top quarks (t(1)) in an R-parity violating supersymmetry scenario in 106 pb(-1) of pp collisions at square root of s=1.8 TeV collected by the Collider Detector at Fermilab. In this mode each t(1) decays into a tau lepton and a b quark. We search for events with two tau's, one decaying leptonically (e or mu) and one decaying hadronically, and two jets. No candidate events pass our final selection criteria. We set a 95% confidence level lower limit on the t(1) mass at 122 GeV/c(2) for Br(t(1)-->tau b)=1.

Search for lepton flavor violating decays of a heavy neutral particle in p(-)p collisions at sqrt[s]=1.8 TeV

We report on a search for a high mass, narrow width particle that decays directly to emu, etau, or microtau. We use approximately 110 pb(-1) of data collected with the Collider Detector at Fermilab from 1992 to 1995. No evidence of lepton flavor violating decays is found. Limits are set on the production and decay of sneutrinos with R-parity violating interactions.

Comparative analysis for expressed genes by polymerase chain reaction using module-shuffling primers

We have developed a method for comparative analysis of gene expression. It is based on competitive PCR amplification with module-shuffling primers, followed by gel electrophoresis in a fluorescent DNA sequencer. In this method, tagged-cDNA restriction fragments derived from different sources were amplified in one tube at the same amplification efficiency. The method can detect different amounts of each expressed gene, up to difference in amounts of 30%. The method was successfully used for comparative analysis of expressed genes in yeast.

Classified fingerprinting: A method of comprehensive analysis for comparing megabase genomes

The objective of the work we describe is to establish elementary methods for investigating the functions of genes; specifically, a fingerprinting method for analyzing entire DNA fragments in a mixture. Our goal is to develop a method for comparing genes with a size of several megabases. We improved a method of amplified fragment length polymorphism (AFLP) so that it could be used to analyze all the restriction fragments in a mixture. This method could be used to detect 90% of the DNA fragments produced from 100-kb model genomes by using a four-base cutter enzyme.

DNA hybridization using "bead-array": probe-attached beads arrayed in a capillary in a predetermined order

We present a DNA analysis device called "Bead-Array", in which DNA-probe-attached beads (100 microns) are arrayed in a capillary in a predetermined order. We developed this device to overcome the problems of using DNA microarray technology, including high cost and a lengthy analysis time. Bead-arrays can easily be mass-produced even for many different probe combinations, and the small reaction volume and the use of sample flows enables faster hybridization. To demonstrate this, we examined DNA hybridization experiments using 18-mer DNA probes and targets and compared them to ones with beads in tubes. The results show that hybridization in bead-arrays progresses more than 100 times faster and reach the plateau in less than three min. These features suggest that the bead-array particularly meets the need for high-speed and disposable devices, for example diagnosis devices.

Central pseudorapidity gaps in events with a leading antiproton at the fermilab tevatron pp collider

We report a measurement of the fraction of events with a large pseudorapidity gap deltaeta within the pseudorapidity region available to the proton dissociation products X in p+p-->p+X. For a final state p of fractional momentum loss xi(p) and 4-momentum transfer squared t(p) within 0.06<xi(p)<0.09 and |t(p)|<1.0 [0.2] GeV2 at sqrt[s]=1800 [630] GeV, the fraction of events with deltaeta>3 is found to be 0.246+/-0.001 (stat)+/-0.042 (syst) [0.184+/-0.001 (stat)+/-0.043 (syst)]. Our results are compared with gap fractions measured in minimum bias pp collisions and with theoretical expectations.

Search for the supersymmetric partner of the top quark in dilepton events from pp collisions at square root of s=1.8 TeV

We have searched for pair production of the supersymmetric partner of the top quark (stop) in 107 pb(-1) of pp collisions at square root of s=1.8 TeV collected by the Collider Detector at Fermilab (CDF). Each stop is assumed to decay into a lepton, bottom quark, and supersymmetric neutrino. Such a scenario would give rise to events with two leptons, two hadronic jets, and a substantial imbalance of transverse energy. No evidence of such a stop signal has been found. We exclude stop masses in the region (80</=m(t)</=135 GeV/c(2)) in the mass plane of stop versus sneutrino.

Search for associated production of Upsilon and vector boson in pp collisions at sqrt[s]=1.8 TeV

We present a search for associated production of the Upsilon(1S) and a vector boson in 83 pb(-1) of ppmacr; collisions at sqrt[s]=1.8 TeV collected by the CDF experiment in 1994-1995. We find no evidence of the searched signal in the data, and set upper limits to the production cross sections.

Search for long-lived charged massive particles in pp collisions at square root s = 1.8 TeV

We report a search for the production of long-lived charged massive particles in a data sample of 90 pb(-1) of square root[s]=1.8 TeV pp collisions recorded by the Collider Detector at Fermilab. The search uses the muonlike penetration and anomalously high ionization energy loss signature expected for such a particle to discriminate it from backgrounds. The data are found to agree with background expectations, and cross section limits of O(1) pb are derived using two reference models, a stable quark and a stable scalar lepton.

Search for a W' boson decaying to a top and bottom quark pair in 1.8 TeV pp collisions

We report the results of a search for a W' boson produced in pp; collisions at a center-of-mass energy of 1.8 TeV using a 106 pb(-1) data sample recorded by the Collider Detector at Fermilab. We observe no significant excess of events above background for a W' boson decaying to a top and bottom quark pair. In a model where this boson would mediate interactions involving a massive right-handed neutrino (nu(R)) and have standard model strength couplings, we use these data to exclude a W' boson with mass between 225 and 536 GeV/c(2) at 95% confidence level for M(W')>>M(nu(R)) and between 225 and 566 GeV/c(2) at 95% confidence level for M(W')<M(nu(R)).

Limits on extra dimensions and new particle production in the exclusive photon and missing energy signature in pp collisions at square root [s]=1.8 TeV

The exclusive gammaE(T) signal has a small standard model cross section and is thus a channel sensitive to new physics. This signature is predicted by models with a superlight gravitino or with large extra spatial dimensions. We search for such signals at the Collider Detector at Fermilab, using 87 pb(-1) of data at square root [s]=1.8 TeV, and extract 95% C.L. limits on these processes. A limit of 221 GeV is set on the scale |F|(1/2) in supersymmetric models. For 4, 6, and 8 extra dimensions, model-dependent limits on the fundamental mass scale M(D) of 0.55, 0.58, and 0.60 TeV, respectively, are found. We also specify a "pseudo-model-independent" method of comparing the results to theoretical predictions.