Fast as possible gait velocity, timed-up-and-go time, 180°turn duration, and steps to turn 180°were impaired in PD compared to controls (P < 0.05) and did not improve following either intervention (P > 0.05). Preferred pace gait velocity and timing of yaw rotation onset of body segments  during 180°turns were not different in PD (P > 0.05) and did not change following either intervention. No improvements in gait or turning occurred after five days of rotating treadmill training, compared to one day. The rotating treadmill is not recommended for short-term rehabilitation of impaired in-place turning in the general PD population.Since turning is often impaired in Parkinson's disease (PD) and may lead to falls, it is important to develop targeted treatment strategies for turning. We determined the effects of rotating treadmill training on turning in individuals with PD. This randomized controlled study evaluated 180°in-place turns, functional turning (timed-up-and-go), and gait velocity before and after 15 minutes of rotating treadmill training or stepping in place in 26 people with PD and 27 age-matched controls. A subset of participants with PD  Parkinson's disease (PD) is a progressive neurodegenerative disease resulting in a variety of motor symptoms. Individuals with PD frequently experience difficulty with gait and turning, with more than half reporting difficulty turning –3 which Stepping in place on the rotating treadmill has been recommended as a possible rehabilitation option for those with PD . After sImmediately after stepping in place on a rotating disk for a total of 15 minutes on one day, turning performance was improved in two people with PD on medication who also experienced freezing of gait during turns [We recruited 29 participants from the Movement Disorders Center at Washington University School of Medicine who had been diagnosed with idiopathic Parkinson's disease according to standard criteria . We alson = 3) returned for an additional five consecutive days of training and testing. Training and testing sessions were identical for the one-day and five-day portions of the study. Surgical skin pens were used to ensure consistent placement of reflective markers across days. The Movement Disorders Society Unified Parkinson's Disease Rating Scale motor subscale (MDS-UPDRS-III) was given to all participants prior to testing to assess movement impairments [All participants with and without PD completed testing on one day, and a small subset of participants with PD  to an intervention condition (rotating treadmill (Train) or stepping in place (Step)). Those in the Train condition were asked to step in place on the perimeter of a rotating disk built into the floor  as it rotated approximately 45°/sec either clockwise or counterclockwise. The direction of treadmill rotation was selected for each participant to train turns in the worse direction , where clockwise rotation trained left turns and counterclockwise rotation trained right turns. For the five-day training sessions, right turns were trained for all participants. Participants walked on the rotating treadmill for a total of 15 minutes, divided into 5-minute blocks with interspersed 5-minute rest periods . Those iTurning and walking were assessed in two separate blocks, once before (PRE) and once after (POST) the assigned intervention. In each testing block, we examined gait, functional turning while walking, and in-place turns of 180°. Gait was assessed using a 4.8 m GAITRite instrumented walkway  to determine if the interventions had any effects on gait. The GAITRite calculated gait velocity in six walking trials in each block: three at a participant's preferred pace and three as fast as possible. Functional turning ability was assessed using the timed-up-and-go (TUG) test where participants rise from a chair, walk three meters, turn 180°, walk three meters back to the chair, and sit down. Participants completed the TUG six times in each block, with instruction to turn left or right on each trial . The time to complete this task was measured using a stopwatch. To assess in-place turning, we instructed participants to stand in the middle of the room and turn 180° to the right or left, 10 times in each direction (randomized), to face the wall behind them. Kinematic data were recorded at 100 Hz during these turns using an eight-camera 3-D motion capture system . Thirty-four reflective markers were placed on each participant: four on the head , seven on the trunk , five on the pelvis , and nine on each leg . The Motion Monitor software  was used to create body segment models for the head, trunk, pelvis, and feet based on marker positions and to export segment rotation data. Custom written Matlab programs were used  to determine rotation onsets and offsets for the head, trunk, pelvis, and feet body segments, using a 5° yaw plane rotation threshold criterion. The onset of rotation of the foot used for the first step was designated as the turn onset, and the offset of rotation of the foot used for the last step was designated as the turn offset. To quantify body segment rotation sequences during turn initiation, we examined the timing of the onsets of the head (HTO), trunk (TTO), and pelvis (PTO) relative to turn onset. All onset times were expressed as a percentage of the first gait cycle of the turn. To assess overall turn performance, we also determined the number of steps used to complete each turn and turn duration. Kinematic, TUG, and gait velocity data were averaged across trials within each participant for the PRE block and for the POST block.Our primary variables of interest were functional turning ability (TUG), 180° in-place turn duration, and normalized rotation onset of the head, trunk, and pelvis relative to turn onset, to quantify timing of body segment rotations during turn initiation. Secondarily, we looked at number of steps to turn 180°. We also examined velocity during preferred pace and fast as possible gait to determine if the interventions, specifically the rotating treadmill, impacted gait. In the Train groups, we trained the worse turn direction, and the other turn direction was untrained. In order to similarly compare turn performance for the Step groups, we designated the worse turn direction and the better turn direction based on turn durations. For statistical comparisons across groups, the trained direction of the Train group was compared with the worse direction of the Step group, and the untrained direction of the Train group was compared with the better direction of the Step group. We were primarily interested in the trained direction, as turns in this direction were expected to be affected by rotating treadmill training. Separate RM-ANOVAs were run  for our primary variables of interest for the trained/worse directions. We also ran RM-ANOVAs for step number and gait velocity. Only 3 participants with PD completed the 5-day training component, so formal statistical tests were not used. However, we examined POST data from Day 1 and Day 5 for primary variables of interest, step number, and gait velocity to determine trends. Secondarily, we also evaluated variables similarly for the untrained/better directions.P = 0.503). The PD Train and Step groups had similar ages, MDS-UPDRS-III scores, and disease durations (P > 0.05). Similar ages and MDS-UPDRS-III scores were also seen across CN Train and Step groups (P > 0.05). There were no significant differences in any variables at baseline for turns in the trained/worse direction or turns in the untrained/better direction (P > 0.05). There were also no significant differences between those with left as the trained/worse turn direction and those with right as the trained/worse direction (P > 0.05), so data were combined for analysis.The PD and CN groups did not differ significantly in age (P = 0.12) or Group  = 1.40, P = 0.24), nor any significant interaction effects (P > 0.05) for preferred pace gait velocity (P = 0.06), with individuals tending to demonstrate higher preferred pace gait velocity POST intervention. For fast as possible gait velocity (P = 0.62) or Condition  = 3.28, P = 0.08), nor any significant interaction effects (P > 0.05). There was a significant effect of Group  = 4.77, P = 0.034), with PD walking slower than CN. GAITRite data for two participants  were lost due to hard drive failure, but all remaining data for these participants was included in analyses. The mean velocity data are shown for PD and controls before and after the assigned intervention in velocity . There wvelocity , there wP = 0.58) or Condition  = 3.43, P = 0.070), nor any significant interactions (P > 0.05). There was a significant Group effect  = 5.25, P = 0.026), with PD requiring more time to complete the TUG, turning to the trained/worse direction, compared to controls. Results were similar for the untrained/better direction.For TUG where the turn component was in the trained/worse direction , there wP = 0.62) or Condition  = 0.99, P = 0.33), nor any significant interactions (P > 0.05). There was a significant Group effect  = 15.95, P < 0.001), with PD turning slower than CN. For the untrained/better direction, results were similar. For 180° turn duration in the trained/worse direction , there wP = 0.15) or Condition  = 1.33, P = 0.25), nor any significant interactions (P > 0.05). There was a significant Group effect  = 13.71, P = 0.001), with PD requiring more steps to turn. Similar results were seen for the untrained/better direction. For steps to turn 180° in the trained/worse direction , there wP = 0.30), Condition  = 0.60, P = 0.62), or Group  = 1.48, P = 0.23), nor any significant interactions (P > 0.05). For body segment  rotation onsets relative to turn onset for turns in the trained/worse direction, there were no significant effects of Time  = 1.26, A small subset of the original group of participants returned for 5 consecutive days of rotating treadmill training. All three individuals who returned for five consecutive days of training were able to tolerate the training program. On average, gait velocity, TUG, turn duration, steps to turn, and body segment rotation onsets relative to turn onset were very similar following a single session of training (Day 1 POST), compared to after five sessions of training (Day 5 POST) in either the trained or untrained direction. Difficulty with turning is common in individuals with PD, and the development of therapeutic approaches that target turn deficits might reduce the occurrence of falls and serious injuries in these individuals. As a result, it is important to evaluate potential treatment strategies in individuals with PD who demonstrate a range of turning ability and might benefit from these treatment options.Contrary to our initial hypotheses, we did not see improvements in turning in those with PD following one day or five consecutive days of rotating treadmill training. For most turning variables, group effects indicated turning was impaired in those with PD ON medication, compared to controls, as has been previously reported –10. Interestingly, the body segment rotation onset patterns we observed were similar between those with PD and controls. All groups initiated turns with the head, followed by the trunk, pelvis, and foot. Controls display this top-down rotation sequence during turns while walking , 18–24, Turning and gait velocity did not systematically worsen in any intervention group from PRE to POST. Also, participants were permitted to rest as often as necessary during testing and had mandatory five-minute rests between the five-minute blocks of treadmill training. As a result, we think our results were likely not confounded by fatigue. Further, we likely tested a higher-functioning group of people with PD than some prior studies, since preferred pace gait velocity did not differ between PD and controls. Despite smaller differences in gait performance, we detected distinct impairments in turn performance compared to controls, confirming turning impairments can be present in those with relatively normal gait . This hiThe most notable difference between the present study and the previous rotating treadmill study  is the fAnother limitation of the study is that the sessions were of relatively low intensity and were few in number. It may be that more intense rotating treadmill training sessions or increased number of sessions may result in detectable changes, as previous traditional treadmill studies report improvements after completion of 10–28 training sessions of 20–30 minutes each , 31. HowFifteen minutes of rotating treadmill training alone on one day or for five consecutive days did not affect turn performance in PD. As a result, this type of training is unlikely to serve as an effective short-term rehabilitation strategy for many individuals with PD. However, future studies should determine whether rotating treadmill training may improve turning impairments with longer training paradigms or when combined with other external cues, as well as assess its effects on performance of turns while walking in addition to the in-place turning studied here.


Since the sensors used in the new calibration process are already installed in the commercial incubator, no additional hardware is necessary; and the calibration necessity can be diagnosed in real time without the presence of technical professionals in the neonatal intensive care unit (NICU). Experimental tests involving the aforementioned calibration system are carried out in a commercial incubator in order to validate the proposal.Measurement and diagnostic systems based on electronic sensors have been increasingly essential in the standardization of hospital equipment. The technical standard IEC  60601-2-19 establishes requirements for neonatal incubators and specifies the calibration procedure and validation tests for such devices using sensors systems. This paper proposes a new procedure based on an inferential neural network to evaluate and calibrate a neonatal incubator. The proposal presents significant advantages over the standard calibration process,  Within this context, a neonatal incubator helps in taking care of the health of newborns . A neonaA neonatal incubator is one of the most important pieces of neonatal intensive care unit (NICU) equipment ,4. For eAs with any other electro-medical equipment, a neonatal incubator must be calibrated periodically, because its malfunction may cause serious damage to the newborn's health or even lead to the newborn's death. The technical standard IEC 60601-2-19 establishes operating specifications for neonatal incubators, so that a safe environment can be offered for newborns . Such spIn order to perform the aforementioned tests, the incubator is removed from service, and a calibration system is installed, so that temperature and humidity data are provided for a standardized calibration procedure. Considering that it is necessary to turn off the incubator to run the tests and, also, that the number of available incubators in neonatal centers is limited in most Brazilian hospitals, a maintenance strategy that minimizes the incubator down time will prove to be very useful.et al. [In this work, a literature survey is carried out by considering the published studies addressing the identification and control of the dynamics involving temperature and humidity in neonatal incubators. Zermani et al.  use geneet al. . Such coet al. [et al. [Zermani et al.  design a [et al. , the resAmer and Al-Aubidy  declaredANNs are massively-distributed parallel structures, consisting of simple processing units known as neurons, which have a natural propensity for storing experimental knowledge and making it available for use . Among tet al. [In the literature, several studies report applications of ANNs as a potential technique for identification of the dynamical behavior for temperature and humidity inside controlled environments. Although these next briefly described works are not directly related to neonatal incubators, there is some similarity with the scope of this paper. Martnez et al.  presenteet al. [et al. [i.e., temperature or relative humidity. The last model takes into account both variables as outputs at the same time. Various aspects described in the aforementioned papers may be considered in the proposed study.In another paper, Ferreira et al.  develope [et al.  in orderThe present work introduces a new method for calibrating neonatal incubators based on multilayer perceptron (MLP) neural network models, with a reduced number of sensors if compared with that used in the traditional calibration procedure. The basic idea is the use of an ANN to infer the temperature and humidity of the extra sensors positioned inside the equipment according to standard IEC 60601-2-19 by using only the sensors included in the commercial neonatal incubators. The proposed method has four significant advantages over the standard calibration process: (i) while the standard technique requires five temperature sensors and one humidity sensor, the proposed method dramatically reduces the number of sensors to one for temperature and one for humidity; (ii) the calibration process works with the incubator under operation; (iii) no hardware calibration is necessary, and the sensors used in the new process are already installed in the traditional incubator; and (iv) the necessity for calibration is diagnosed in real time, and the presence of technical experts in the NICU is not necessary.This paper is organized as follows. The next section presents a brief description of the incubator used in this research and the traditional calibration procedure for such equipment. In Section 3, a summary of the main points established by the IEC standard is presented. The theoretical referential of MLP and inferential systems is presented in Section 4. Finally, the simulations and relevant results are discussed in Section 5, while the main conclusions are given in Section 6.2.A neonatal incubator, which is represented in The arrangement is a multivariable system characterized by nonlinearities and interaction among the variables. Within this context, two variables are the most important ones: temperature and humidity, which are regulated by controlling the electric current that flows through two resistors. Temperature and humidity are measured by sensors positioned on the dome. The relative humidity sensor, SHT11, represented in To perform the standardized calibration procedure, an embedded digital system was designed. The system is composed of a data acquisition platform based on the microcontroller PIC18LF2620 and a data communication wireless link based on ZigBee protocols. The embedded system is connected to a PC  through a ZigBee radio link. This embedded system is responsible for monitoring the sensor states and sending commands to the power drivers that control temperature, humidity and oxygen levels and the air flowing inside the neonatal incubator. The main objective of the embedded system is to provide the data acquisition and the communication link with the PC.The communication link is a ZigBee network, which is a low-cost, low-powered wireless network standard . The reduced cost allows the technology to be widely used in wireless control and monitoring applications. The low-powered ZigBee devices often transmit data over long distances by transferring data through an intermediate device in a mesh configuration. ZigBee operates in industrial, science and medical radio bands. The prototype uses the ZigBee radio MRF24J40MA (Microchip), which is shown in The designed microcontrolled system is based on standard IEC 60601-2-19. This standard specifies the behavior of the temperature in specific points inside the incubator. The aforementioned points are outlined in The mechanical part of the calibration system is comprise of a rigid base made of acrylic, five supports for sensors arranged according to the standard recommendation (given in Section 3) and a control board. At points B, C, D and E, the temperature sensors, MCP9808 (Microchip), are placed. Point A integrates a temperature and humidity sensor, SHT11. The acrylic base with sensors for calibration purposes must be positioned inside the incubator during the tests and removed from the dome when the incubator is under normal operation. The sensor system that integrates the commercial incubator is maintained during the test. 3.The parameters for quality and safety in Brazilian neonatal incubators must comply with standard IEC 60601-2-19 “Particular requirements for the safety of neonatal incubator”. This standard determines the variation range of environmental variables in the neonatal incubator, such as temperature, humidity, velocity of air flow, and noise level.According to the referred IEC standard, five positions within the incubator are defined to carry out the measurements of temperature and relative humidity, as shown in Before starting the tests, verify if the environment temperature is between 21 °C and 26 °C.Adjust the incubator temperature control (set by the operator) to 11 °C over the environment temperature. The incubator must reach the temperature control at the time instant specified by the manufacturer with a tolerance of up to 20%. The incubator temperature control is measured by the incubator sensor.i.e., 32 °C and 36 °C. At each point, wait for the stabilization temperature condition (the measured incubator temperature at point A must not vary more than 1 °C within the period of one hour).Adjust the incubator temperature control to two operation points, In step number three, the average temperature measured by the sensors at points A, B, C, D and E cannot differ by more than 0.8 °C from the average temperature of the incubator within the period of one hour. Moreover, the average temperature cannot differ more than ±1.5 °C from the incubator control temperature. The incubator average temperature is obtained by means of temperatures measured in regular intervals.With the incubator temperature at 32 °C, set the temperature control reference in steady state to 36 °C and verify if the overshoot is below 2 °C and that the setting is reached in less than 15 min.The humidity value shown by the incubator sensor must not differ by more than 10% of the value measured by the sensor at position A during the whole period of the incubator functioning.ccording to standard IEC 60601-2-19, besides the correct positioning of the sensors, a set of six steps must be followed to calibrate the incubator, which are:The aforementioned procedures must be performed periodically after each maintenance procedure or when the incubator sensors are found to be uncalibrated. The accuracy of temperature and humidity measured by the designed embedded digital system (Section 2) are given by the IEC standard and listed in 4.Inferential systems (or soft sensors) represent an attractive approach to estimate the primary process variables , particuInferential systems are mathematical algorithms that have been used in a wide range of applications in industry, such as supervision, control and optimization of processes due to their advantages over some physical sensors that present measurement problems. Such systems provide faster measurements with more accuracy and reliability at a lower cost in terms of development, implementation and maintenance.et al. [According to Warne et al. , despiteThe MLP neural network architecture consists of a set of neurons arranged in layers, where a layer sends its outputs only to the neurons of the next layer. In addition, all neurons in a given layer are connected to all neurons of the next layer through adjustable parameters of the network, called synaptic weights. These connections are responsible for the MLP's ability to learn and store the knowledge extracted from the problem context. An MLP has at least one intermediate layer, called a hidden layer. If only one hidden layer is used, the MLP is able to solve problems of low complexity. Cybenko  demonstrThe synaptic weights between the MLP layers are adjusted through a process called learning or training. The supervised training process can be applied to the MLP architecture to solve mapping problems. In this learning process, the synaptic weights are adjusted based on a set of experimental input-output pairs of the problem and a cost function . A commoN pairs of vectors, {x(n), d(n)}, n = 1, 2, …, N, where x(n) ∈ ℝ(p+1) represents the input vector at discrete time n and d(n) ∈ ℝm denotes the desired vector of outputs (responses) for such an input vector.A supervised neural network requires an external source to provide information about the analyzed problem. This information is given by a set of p > 0 denotes the effective number of input variables used in the problem. The term “effective” is used here, because the input component, x0(n), is not really a variable in the usual sense, since it has a fixed value equal to −1 and is known as the threshold or (bias). Similarly, the n – th output vector at a discrete time is represented as follows:m > 0 indicates the number of output variables in the neural network. Any component of the input vector is symbolized by jx(n) ∈ ℝ, while the component of a given vector output is symbolized as kd(n) ∈ ℝ.Each input vector is represented as:n) and d(n) are related according to some unknown mathematical relationship, f(·):For a given problem, it is considered that vectors x(f (·), denoted by fˆ (·):n) generated by the neural network to be very close to the desired output d(n), even when the inputs presented to the network are different from those used in the training process. This is possible because of the generalization ability of neural networks.For this purpose, one can use a supervised neural network, such as MLP, by adjusting its synaptic weights during the training procedure to generate a mathematical model that acts as an approximation of n) represents the real behavior of such variables and y(n) correspond to the values inferred by the network.The main goal of this work is to use the supervised neural network MLP to infer the humidity and temperature values at the points specified by IEC 60601-2-19. Thus, in the learning process, vector d as inputs, which were instantaneously measured by the sensor, SHT11, available in a commercial incubator. By using measurements and a trained MLP, the system is able to estimate primary variables, which are: humidity at point A and temperature at points A, B, C, D and E, as shown in sT and sH are the measurements of temperature and humidity at the incubator's dome output, respectively, d is the transport delay of the system and, finally, m1 and m2 are the dimensions of the regressors applied to the incubator's secondary variables. At the output of the structure, iŷ is the estimation of the i-th primary variable provided by the i-th inferential system, according to The general structure of MLP inferential systems is shown in 5.The results and the methodology used are detailed in the next two subsections. In Section 5.1, the analysis of the results obtained by the neural network is presented. The results are related to the inferential values of the temperature and humidity inside the incubator. In Section 5.2, the procedural steps for the evaluation and calibration of the incubator with the inferential MLP system are presented.5.1.For the training and testing of the MLP network, 900 samples of temperature and humidity were acquired from points A, B, C, D and E and the incubator sensor, with sampling periods of 24 s. The collected data were divided into two sets: one for training, containing 750 samples and 150 samples for tests. The samples were collected by applying two PRBS (pseudo-random binary sequence) electric current signals to the incubator resistors. The amplitude limits of the PRBS electric currents are set by IEC 60601-2-19 for humidity and temperature inside the incubator. Main text paragraph.SH) and temperature (ST) values measured by the incubator sensors at the air outlet of the dome were used as input data in the network, aiming to infer the magnitudes measured by the humidity sensors at point A (AH) and temperature sensors at points A, B, C, D and E . Therefore, the IEC 60601-2-19 standardized temperature and humidity points can be determined by the inferential MLP process using only the temperature and humidity values (SH and ST) measured by the incubator sensor.The humidity , SH(n) and SH(n — 1) are used in AĤ(n).SH(n), ST(n) and ST(n — 1) to estimate humidity iTˆ(n)), where i is the reference temperature at points A, B, C, D and E. Such values are used in the training process of the network based on a backpropagation algorithm [The values of lgorithm . After tBoxplot (or box diagram) is a graphical way to represent numerical data aligned horizontally or vertically through five quantities: the smallest observation, lower quartile, median, upper quartile and largest observation . The boxaT. It can be seen that the boxplots for this variable provide results that are very close to those for variable aH.By analyzing the boxplots in −4 and 7.69 × 10−4. The results for each network are detailed in From the aforementioned results, it is clear that MLP has succeeded in inferring the humidity presented by the sensor at point A and, also, the temperatures given by the sensors positioned at all points specified by the standard. At points B, C, D and E, the network has the best performance, with the MSE ranging between 4.53 × 10−3 and the MSE for humidity is 4.55 × 10−3. Point A corresponds to the central position of the incubator, as it tends to present higher nonlinearity in the dynamics by being more susceptible to variations. This is less likely to happen at points B, C, D and E, which are located at the ends of the incubator and are less susceptible to temperature variation.The results show higher values of MSE for the temperature and humidity at point A. The temperature MSE at this point is 4.18 × 10i.e., the humidity and temperature at point A represents a variation of temperature slightly higher if compared to the other specified points. At point A, the moisture presented a relative percentage error ranging from −5% to +5% and a temperature error ranging from −7.5% to +8.0%. At other points, the variation is always within the range of −3.0% to +6.0%, showing that the difference between the desired and inferred temperatures is small. This demonstrates that the MLP achieved satisfactory performance, serving as an effective tool for evaluating the calibration of sensors in the incubator.Regarding the analysis of the aforementioned results, the calculation of the relative percentage error between the desired values and the inferred network was performed. In terms of this error, there is the same trend as the MSE, 5.2.The aforementioned inferential neural network system can be used to implement a software-based calibration system as a substitute or a complementary strategy to hardware calibration. The steps described in the previous section can be summarized as the following instructions in the form of the flowchart presented in When the software detects that the incubator control loops are not within the standard specified range through the MLP inferential sensors, a warning is automatically sent to the NICU computer and to the responsible nurse's mobile phone number, notifying that the incubator needs to be calibrated. This trigger may also occur manually, when the operator detects that the incubator is operating under a nonstandard state. After sending the alert message, the software checks if there is a newborn in the incubator. This check is performed by means of a load sensor. If the presence of a newborn is detected, an alarm signal is triggered, which persists, until the newborn is removed. Then, the software automatically sends a command that starts the calibration procedure of the temperature and humidity loops. Finally, a test is performed to check whether the temperatures at points A, B, C, D and E and, also, humidity at point A are adequate after the calibration process. Numerically, this test results in 6.The proposed neural network inferential calibration process of neonatal incubators can be used as an option other than the traditional calibration system based on hardware sensors. This inferential method based on standard IEC 60601-2-19 has shown the advantages of such a method over the hardware calibration process, such as the fact that only one temperature and one humidity sensor are used, that it is not necessary to turn off the incubator to evaluate the incubator condition, that if a more accurate calibration is necessary, it can be detected in runtime, and that during the occurrence of a process warning, alert signals can be sent directly to NICU personnel for repair. Therefore, the intelligent sensor system based on an inferential neural network can reduce the period of the evaluation and calibration of the incubator.Regarding the accuracy of the estimated values, the neural network architecture has shown small errors with respect to the actual values for humidity and temperature. Therefore, it can be stated that this technique allows for the accurate identification of the dynamic model of the incubator, which is capable of providing estimation in possible advanced risk cases, such as the temperature being out of a safe range and failures in the heater system and/or air flow in the equipment.With the use of the incubator over the years, it may be required that an MLP retraining be performed, due to natural changes in the operational conditions of this equipment. This is supposed to be the unique limitation of the proposed system. This limitation could be overcome with the implementation of a more sophisticated self-training MLP algorithm.


Bc, an approximately temperature-independent point in the measured longitudinal resistivity ρxx, which is ascribed to the direct insulator-quantum Hall (I-QH) transition, is observed. By analyzing the amplitudes of the magnetoresistivity oscillations, we are able to measure the quantum mobility μq of our device. It is found that at the direct I-QH transition, μqBc ≈ 0.37 which is considerably smaller than 1. In contrast, at Bc, ρxx is close to the Hall resistivity ρxy, i.e., the classical mobility μBc is ≈ 1. Therefore, our results suggest that different mobilities need to be introduced for the direct I-QH transition observed in multi-layered graphene. Combined with existing experimental results obtained in various material systems, our data obtained on graphene suggest that the direct I-QH transition is a universal effect in 2D.We have performed magnetotransport measurements on a multi-layer graphene flake. At the crossing magnetic field  Graphene, which is an ideal two-dimensional system , has attν > 2 QH state which is normally dubbed as the direct I-QH transition continues to attract interest [The insulator-quantum Hall (I-QH) transition -13 is a interest . The dirinterest , GaAs muinterest , GaAs twinterest -18, a deinterest ,20, GaN-interest  and on Sinterest , InAs-bainterest , and eveinterest , suggestinterest  and metainterest -29, haveinterest -33, grapinterest , reducedinterest , and fluinterest ,37, the interest , but notinterest , multi-linterest -42, makiρxx which can be ascribed to experimental evidence for the direct I-QH transition. At the crossing field Bc in which ρxx is approximately T-independent, ρxx is close to ρxy. In contrast, the product of the quantum mobility determined from the oscillations in ρxx and Bc is ≈ 0.37 which is considerably smaller than 1. Thus, our experimental results suggest that different mobilities need to be introduced when considering the direct I-QH transition in graphene-based devices.In this paper, we report magnetotransport measurements on a multi-layer graphene flake. We observe an approximately temperature-independent point in the measured longitudinal resistivity 2/Si substrate. Optical microscopy was used to locate the graphene flakes, and the thickness of multi-layer graphene is 3.5 nm, checked by atomic force microscopy. Therefore, the layer number of our graphene device is around ten according to the 3.4 Å graphene inter-layer distance [I = 20 nA in a He3 cryostat equipped with a superconducting magnet.A multi-layer graphene flake, mechanically exfoliated from natural graphite, was deposited onto a 300-nm-thick SiOdistance ,43. Ti/Adistance . Similardistance ,46. The ρxx(B) and ρxy(B) at T = 0.28 K. Features of magnetoresistivity oscillations accompanied by quantum Hall steps are observed at high fields. In order to further study these results, we analyze the positions of the extrema of the magnetoresistivity oscillations in B as well as the heights of the QH steps. Although the steps in the converted Hall conductivity ρxy are not well quantized in units of 4e2/h, they allow us to determine the Landau-level filling factor as indicated in the inset of Figure 16 m−2 following the procedure described in [Figure ribed in ,48.ρxx(B) and ρxy(B) as a function of magnetic field at various temperatures T. An approximately T-independent point in the measured ρxx at Bc = 3.1 T is observed. In the vicinity of Bc, for B <Bc, the sample behaves as a weak insulator in the sense that ρxx decreases with increasing T. For B >Bc, ρxx increases with increasing T, characteristic of a quantum Hall state. At Bc, the corresponding Landau-level filling factor is about 125 which is much bigger than 1. Therefore, we have observed evidence for a direct insulator-quantum Hall transition in our multi-layer graphene. The crossing points for B > 5.43 T can be ascribed to approximately T-independent points near half filling factors in the conventional Shubnikov-de Haas (SdH) model [We now turn to our main experimental finding. Figure H) model .m* of our device which is an important physical quantity. The amplitudes of the SdH oscillations ρxx is given by [By analyzing the amplitudes of the observed SdH oscillations at various magnetic fields and temperatures, we are able to determine the effective mass given by :ρ0, kB, h, and e are a constant, the Boltzmann constant, Plank's constant, and electron charge, respectively. When where C1 is a constant. Figure m0 to 0.07m0 where m0 is the rest mass of an electron. Interestingly, the measured effective mass is quite close to that in GaAs (0.067m0).where ρxx is close to ρxy. In this case, the classical Drude mobility is approximately the inverse of the crossing field 1/Bc. Therefore, the onset of Landau quantization is expected to take place near Bc[B at various temperatures. As shown in Figure 2/V/s. Therefore, near μqBc≈ 0.37 which is considerably smaller than 1. Our results obtained on multi-layered graphene are consistent with those obtained in GaAs-based weakly disordered systems [In our system, for the direct I-QH transition near the crossing field, e near Bc. Howevere near Bc. In orde systems ,21.T-independent suggests that Coulomb interactions do not seem to be dominant in our system.It has been shown that the elementary neutral excitations in graphene in a high magnetic field are different from those of a standard 2D system . In thisρxx is ascribed to the direct I-QH transition. Near the crossing field Bc, ρxx is close to ρxy, indicating that at Bc, the classical mobility is close to 1/Bc such that Bc is close to 1. On the other hand, μqBc≈ 0.37 which is much smaller than 1. Therefore, different mobilities must be considered for the direct I-QH transition. Together with existing experimental results obtained on various material systems, our new results obtained in a graphene-based system strongly suggest that the direct I-QH transition is a universal effect in 2D.In conclusion, we have presented magnetoresistivity measurements on a multi-layered graphene flake. An approximately temperature-independent point in 2D: Two-dimensional; 2DEGs: Two-dimensional electron gases; I-QH: Insulator-quantum Hall; SdH: Shubnikov-de Haas.The authors declare that they have no competing interests.CC and LHL performed the experiments. CC, TO, and AMM fabricated the device. NA, YO, and JPB coordinated the project. TPW and STL provided key interpretation of the data. CC and CTL drafted the paper. All the authors read and agree the final version of the paper.


We develop a simple and low-cost technique based on chemical vapor deposition from which large-size graphene films with 5-10 graphene layers can be produced reliably and the graphene films can be transferred easily onto HgCdTe (MCT) thin wafers at room temperature. The proposed technique does not cause any thermal and mechanical damages to the MCT wafers. It is found that the averaged light transmittance of the graphene film on MCT thin wafer is about 80% in the mid-infrared bandwidth at room temperature and 77 K. Moreover, we find that the electrical conductance of the graphene film on the MCT substrate is about 25 times larger than that of the MCT substrate at room temperature and 77 K. These experimental findings suggest that, from a physics point of view, graphene can be utilized as transparent electrodes as a replacement for metal electrodes while producing better and cheaper MCT infrared detectors. It is much larger than that in conventional III-V and SiGe-based 2DESs. More importantly, the carrier density in graphene can be turned easily and efficiently through applying the gate voltages [2/Vs at room temperature. This value of carrier mobility is about 100 times larger than that in conventional Si-based materials. Furthermore and optically, graphene has a very high light transmittance across the spectrum from the UV to the infrared. The light transmission coefficient for monolayer or bilayer graphene on SiO2 or Si substrates is about 98 or 96%, respectively, in the visible regime [As an ideal two-dimensional 2D) electronic system (2DES), graphene   has exceD electrovoltages . From anvoltages  which cae regime . Such anσ of graphene on a dielectric substrate depends on dielectric constant κ of the substrate material according to the following equation:From a physics point of view, two basic requirements have to be satisfied when a material is selected to be used as transparent electrodes. The selected material must have both high electrical conductance and high light transmittance on the substrate. We found recently  that theσ0 = e2/h, ne is the areal electron density in graphene, nI is the charged impurity concentration in graphene, and EF is the Fermi energy  in graphene. This equation implies that graphene has a better electrical conductance on a substrate with a larger dielectric constant. The physics reason behind this effect is that the main scattering mechanism to determine the carrier mobility in graphene layer is the charged impurity scattering within a relatively wide temperature range [where re range , especiare range ,9. In anre range (2)1= 1 for air, ϵ2 is the high-frequency dielectric constant of the substrate material, and σ(ω) is the optical conductance of graphene. This equation suggests that the light transmittance of the graphene layer on a substrate decreases with the increasing dielectric constant of the wafer material at a fixed σ(ω). Moreover, it was found, both experimentally [σ(ω) = Ne2/4ħ is a universal optical conductance with N being the number of layers in graphene film, whereas in the mid-infrared (MIR) bandwidth, there is an optical absorption window existing in graphene. The MIR absorption window in graphene is induced by inter- and intra-band optical absorption channels required for different transition energies [where ϵ mentally  and theomentally ,13, thatenergies ,13 and, energies  and tempenergies ,13. The On the other hand, HgCdTe (MCT)-based infrared detectors are popularly used as high-quality night-vision devices for MIR detection . The MCT4 is taken as carbon precursor flowing over a 500-nm-thick Ni film catalyst on a SiO2 substrate. The reaction temperature is 900°C, and the flow rates of CH4 and H2 are about 50 and 150 sccm, respectively. The reaction time is around 5 min. In this way, we can produce reliably the large-size and high-quality graphene films with a fewer layers (5-10) of graphene. This is verified by the measurements of optical transmittance and transmission electron microscopy (TEM). Figure 3 solution; and (iv) transferring the graphene film onto MCT wafer in water at room temperature. In addition to the large size of the graphene film that can be transferred onto the MCT wafer, another advantage of this technique is that there is not at all a thermal or mechanical damage to the MCT wafer samples. We know that Hg in MCT evaporates at about 180°C. Thus, the conventional method used for growing graphene film on substrate, such as MBE growth and thermal expansion, cannot be used for growing graphene directly on the MCT substrates. The MCT wafers used in this investigation are with the approximate thicknesses of 1 μm and the sizes of 1 cm2. The MCT wafers are placed on sapphire substrate.In this study, the graphene films are grown using the standard chemical vapor deposition (CVD) technique. CHLMCT) is approximately of the radiation wavelength λ. Here the transmission coefficient of the graphene film TG is deducted from the total transmittance Tt of the air-graphene-substrate system, and the transmittance of the MCT wafer alone TMCT from Tg = Tt/TMCT. We note that such a formula holds only for a case where the thicknesses of two material layers are much larger than λ. There is no simple and analytic formula to deduct the transmittance of a thin film (L1 ≪ λ) from the total transmittance and the transmittance of the wafer (L2 ~ λ). Because, in this case, the thickness of the graphene film is much smaller than λ and LMCT ~ λ, TG may be larger than that in the oscillation regime as shown in Figure TG is nearly 80% in the mid-IR spectrum at room temperature and 77 K. The stronger oscillations of TG can be observed at 77 K. These results indicate that a graphene film with 5-10 graphene layers can have nearly 80% light transmittance when it is placed on top of a thin MCT wafer.In Figure e wafer L ~ λ. BecI-V relations obtained from the four-point measurements for graphene film on MCT substrate and for MCT wafer alone are shown in Figure RG ~ 623 Ω/sq) is nearly 25 times less than that of the MCT wafer (RMCT ~ 15265 Ω/sq). The resistivity of the graphene film with 5-10 graphene layers on MCT wafer is about 0.17 Ω·m at room temperature. There is a small difference between RMCT and RG values at T = 77 K and at room-temperature. We have also measured the photo-resistance of the graphene film on the thin MCT substrate when MIR radiation fields, polarized linearly, are applied. It is found that there is a slight decrease in both RG and RMCT in the presence of MIR radiation. Those experimental findings indicate that the conductance and photo-conductance of a graphene film with 5-10 graphene layers are much lager than those of the MCT thin wafer in the MIR bandwidth at room temperature and 77 K (the operating temperature of the MCT infrared detectors).The κ ~ 14). This is one of the reasons why relatively high electric conductance can be achieved for graphene film on the MCT substrate. For 5-10 layers of graphene film on the MCT wafer, the light transmittance in the MIR bandwidth is about 80%. This is slightly lower than that on the SiO2/Si substrate in the visible regime. We find that the graphene films can be placed nicely on the MCT thin wafers with smooth surface. No crack or folding of the graphene film is found in our samples.HgCdTe or MCT is a material with relatively high dielectric constant  graphene films on MCT thin wafer can have high light transmittance in the MIR bandwidth and relatively high electrical conductance at room temperature and 77 K. The most important conclusion that we drew from this study is that the light transmittance (about 80% in the MIR bandwidth) and the electrical conductance (about 25 times larger than that in the wafer itself at room temperature and 77 K) of the graphene film on the MCT thin wafer can meet nicely the requirements for the infrared transparent electrodes. These interesting findings allow us to propose that graphene can be used as a replacement for metal electrodes to produce better and cheaper MCT infrared detectors. Graphene has been proposed as a replacement for the ITO as transparent electrodes for optical devices such as LCD and LED [2D: two-dimensional; 2DES: 2D electronic system; CVD: chemical vapor deposition; MIR: mid-infrared; ITO: tin oxide; TEM: transmission electron microscopy.The authors declare that they have no competing interests.WX proposed the research work, coordinated the collaborations, and carried out theoretical study and analyzes of the experimental results. YPG, LWL and HQ participated in the growth of graphene samples, in the transformation of graphene films onto the MCT wafers and in the electrical and optical measurements of the study. YLS prepared the HgCdTe wafers used in this study. All authors read and approved the final manuscript.


Recently, graphene family materials (GFMs) have been introduced among all fields of science and still get numerous attention. Also, the applicability of these materials in many areas makes them very attractive. GFMs have attracted both academic and industrial interest as they can produce a dramatic improvement in materials properties at very low filler content. This article presents recent findings on GFMs toxicity properties based on the most current literature. This article studies the effects of GFMs on bacteria, mammalian cells, animals, and plants. This article also reviews in vitro and in vivo test results as well as potential anticancer activity and toxicity mechanisms of GFMs. The effect of functionalization of graphene on pacifying its strong interactions with cells and associated toxic effects was also analyzed. The authors of the article believe that further work should focus on in vitro and in vivo studies on possible interactions between GFMs and different living systems. Further research should also focus on decreasing GFMs toxicity, which still poses a great challenge for in vivo biomedical applications. Consequently, the potential impact of graphene and its derivatives on humans and environmental health is a matter of academic interest. However, potential hazards sufficient for risk assessment first need to be investigated. Graphene is a newly emerging member of carbon materials with sp2-hybridized single-atom-layer structure. It is a typical two-dimensional material made of carbon atoms packed densely in a honeycomb crystal lattice. Graphene is believed to be composed of benzene rings stripped of their hydrogen atoms , graphene oxide dispersion showed the highest antibacterial activity, sequentially followed by reduced graphene oxide, graphite, and graphite oxide. SEM images suggested that cell direct contact with graphene nanosheets disrupted the cell membrane. However, no reactive oxygen species (ROS) production was detected together with the glutathione oxidization ability. The authors also concluded that antimicrobial actions resulted from both membrane and oxidation stress. Therefore, the researchers proposed a three-step antimicrobial mechanism, including: initial cell deposition on graphene-based materials, membrane stress caused by direct contact with sharp nanosheets, and the ROS-independent oxidation stress.Liu et al.  compared2 g−1 as an anodic catalyst of microbial fuel cells based on E. coli. Authors have found that lots of E. coli cells accumulated on the electrode surface and successfully adhered to one another with no inhibition of bacterial growth.In comparison, three studies reported lack of graphene 41  showed the evidence of neuronal cells apoptosis. Gollavelli and Ling  embryos microinjected with multi-function graphene (coated with PAA and FMA). The studies proved that this material was biocompatible with zebrafish and failed to induce any significant abnormalities or affect the survival rate of fish embryos. Confocal laser scanning microscopy images revealed that multi-function graphene was located only in the embryo’ cytoplasm region and exhibited good biodistribution from the head to tail in the zebrafish. However, it should be noted that the multi-function graphene used in the study was coated with PAA and PLL which could lead to the lack of toxicity of the material.As for in vivo studies on the GFM toxicity to other living organisms, Gollavelli and Ling experimented on fish (Gollavelli and Ling Caenorhabditis elegans (nematode). The absence of any acute or chronic toxicity of GNPs was observed. The authors examined longevity (life expectancy) as well as reproductive capability end points. Moreover, no effect on C. elegans reproductive potential was found. Good spatial distribution of the GFM inside the nematodes was demonstrated with the use of Fourier-transform infrared spectroscopy (FT-IR) mapping.Zanni et al.  evaluateUncertainty still prevails as to toxicity pathways for GFM. Two-dimensional graphene nanomaterials are unique in comparison with spherical nanoparticles or one-dimensional nanotubes or nanorods, and the chemical and physical determinants for their cellular interactions and biocompatibility are still under studies  membrane damage upon direct contact. The authors confirmed these results using transmission electron microscopy, which revealed that the bacterial cells lost their membrane integrity.In comparison to the studies mentioned above, Hu et al.  observedBy the close look at cellular functions at proteome level, Yuan et al.  clearly Several studies attempted to address the interactions of graphene and its derivatives with different molecules. There some evidence which proves that polymer chains, drugs, and targeting molecules can be covalently attached to the graphene surface and edge site, or polymers may be adsorbed onto the graphene surface to enhance solubility and biocompatibility  conjugation to graphene oxide was examined by several authors. Yang et al.  used nann et al.  in ordern et al.  showed t−1. Robinson et al. (−1). Also Gollavelli and Ling , polyethyleneimine graphene complexes significantly reduced in vitro toxicity to the treated human epithelial carcinoma (HeLa) cells. Markovic et al. . Authors demonstrated highly efficient tumor passive targeting of graphene sheets in several different tumor models, such as murine breast cancer tumors, human epidermoid carcinoma tumors, and human glioblastoma tumors, without utilizing any targeting ligands, such as antibodies. Thus, PEGylated nanographene sheets appeared to be an excellent in vivo tumor NIR photothermal therapy agent without exhibiting noticeable toxicity to the treated mice.Yang et al.  have alsp–p stacking. Functionalization with folic acid allowed specific targeting of the human breast cancer cells, exhibiting folic acid receptors. Authors demonstrated that FA–graphene oxide loaded with anticancer drugs showed in vitro specific targeting of cancer cells, and remarkably reduced their viability.Zhang et al.  functionE. coli and S. aureus bacteria caused by direct contact of the bacteria with the extremely sharp edges of the nanosheets was the effective mechanism in the bacterial inactivation. However, in contrast to these studies, two studies reported lack of GFM toxicity to E. coli and Shewanella species.There have been reported numerous studies focused on GFM biomedical applications. Some of studies regarding the bacterial toxicity of GFM suggest that they may find future application in antimicrobial products. Results suggest that the cell membrane damage of Only a limited number of publications attempted to address the interactions of graphene and its derivatives with living systems. In vitro toxicity investigation suggests that GFM exhibit dose-dependent toxicity to mammalian cells , which strongly suggests that their biocompatibility must be considered when GFMs are applied for biomedical engineering. However, only few studies reported biodistribution and toxicity of graphene oxide following intravenous injection in mice. Graphene oxide under low dose did not exhibit obvious toxicity to mice but under high dose exhibited chronic toxicity, causing significant pathological changes, such as granuloma formation, mainly located in the lungs, kidneys, liver, and spleen.The number of published study results is also greatly limited. The results of studies indicate that GFMs in the free form (highly dispersed and no-coated) exhibit high in vitro cellular toxicity. Nevertheless, GFM in the form of film exhibited good biocompatibility with investigated cells and promoted their growth and proliferation.Unfortunately, first alarming reports on validity of different toxicity assessment methodologies were found while compiling this review. Liao et al.  discoverLittle is also known about toxicity pathways for GFMs. Generation of ROS in target cells is considered to be a potential mechanism for toxicity. The extremely high hydrophobic surface area of graphene may also result in significant biomolecular and cellular interactions with membrane lipids leading to indirect toxicity. GFMs can also produce cytotoxicity in dose- and time-dependent means, decreasing cell adhesion, and inducing cell floating and apoptosis. Results indicate that graphene can induce cytotoxicity through the depletion of the mitochondrial membrane potential and the increase of intracellular ROS, and then trigger apoptosis by activation of the mitochondrial pathway. Sadly, the results in the literature are inconsistent, particularly concerning the ability of GFM to enter the cells. While some studies suggest that graphene oxide can enter the cytoplasm and nucleus of human lung epithelial cells and fibroblasts, other studies indicate that graphene oxide cannot enter human alveolar basal epithelial cells. Moreover, cytotoxicity of graphene oxide was found to occur mostly during the initial contact stage of graphene oxide and cells and was independent of exposure duration. Thus, it is possible to draw the conclusion that physical damage of the cell membrane observation excludes the contribution of an oxidative stress mechanism as that is a time-dependent process.Several studies attempted to address the interactions of graphene or its derivatives with different molecules. There is evidence which suggests that drugs and targeting molecules can be covalently attached to the graphene surface and edge site, or polymers may be adsorbed onto the GFM surface to enhance solubility and biocompatibility. The observations of different molecules related GFM cytotoxicity effects may lead to the creation of an alternative and convenient route to engineer nanomaterials for safe biomedical and environmental applications. The biocompatibility of the functionalized graphene oxide and reduced graphene was analyzed along with the potential biological effects of the used dispersants in cells. The toxicity depended on the type of dispersant and concentration of GFM in the suspensions. Although, different polymeric substrates were used to functionalize GFM for several in vitro and in vivo studies e.g., FBS, PEG derivatives, sodium DOC and DEX, the development of biocompatible surface coating seems to be critical to engineer various functional nanomaterials for biomedical applications. Surface modification of graphene was reported to alter its toxicity, whereas graphene oxide organic conjugates were reported to reduce cellular toxicity to a remarkably higher degree than their native counterparts.Only one study, so far, attempted to address the interactions of GFMs with plants. The effects of graphene on root and shoot growth, biomass, shape, cell death, and ROS on cabbage, tomato, red spinach, and lettuce were scrutinized. Results suggest that graphene can significantly inhibit plant growth and biomass in a dose- and time-dependent manner. The potential toxic effect of graphene also largely depends on plant species and, thus, should be given much further attention.There are relatively few studies concerning GFM anticancer activity. GFMs appeared to be excellent in vitro and in vivo tumor NIR photothermal therapy agents. Significantly reduced viability of in vitro human glioma and human epithelial carcinoma cells was observed without exhibiting noticeable toxicity. Highly efficient tumor passive targeting of GFM has been observed in several different in vivo tumor models without utilizing any targeting ligands, such as antibodies.What should also be taken into consideration is the fact that GFM synthesis technique determines GFM parameters and their resulting biological activity. In Figs. To summarize, the literature on potential health risks of GFM is being published. As for toxicity, a number of studies have been conducted, yet the field still requires further research as it is a newly emerged one and the literature is still greatly limited. The sources are not sufficient to reach conclusions as to potential hazards connected with risk assessment and regulation. The most likely source of the apparent lack of uniformity are different physicochemical properties of GFMs, such as chemical structure, thickness, lateral size, surface charge, surface area, and surface modifications. Undoubtedly, these properties have significant influence on biological/toxicological activity toward investigated cells and animals. However, mentioned GFM parameters are not always well-controlled and in some cases even analyzed. Moreover, some of these parameters may also be measured by different techniques, which makes the complied results of studies almost impossible to compare. Consequently, the need for further systematic studies which would address the role of GFM parameters as well as their methods of preparation in determining adverse environmental and health impacts is not emphasized. Furthermore, some guidelines should be drawn by the community. Such guidelines would enable choosing the right GFM parameters/properties while conducting the studies. Moreover, the most applicable measurement methodologies should be recommended, and verified during scientific report submission processes.The authors of this article believe that further work should also focus on in vitro and in vivo studies on possible mechanisms of interactions between GFMs and different living biosystems as well as decreasing GFM toxicity, which is still a great challenge for in vivo biomedical applications. Consequently, the potential impact of graphene and its derivatives  on humans and environmental health need to be given the right attention. However, in order to evaluate biocompatibility of GFMs potential hazards and a systematic characterization of cellular response at protein expression level should be analyzed beforehand.


The Trk family of receptors play a wide variety of roles in physiological and disease processes in both neuronal and non-neuronal tissues. Amongst these the TrkB receptor in particular has attracted major attention due to its critical role in signalling for brain derived neurotrophic factor (BDNF), neurotrophin-3 (NT3) and neurotrophin-4 (NT4). TrkB signalling is indispensable for the survival, development and synaptic plasticity of several subtypes of neurons in the nervous system. Substantial evidence has emerged over the last decade about the involvement of aberrant TrkB signalling and its compromise in various neuropsychiatric and degenerative conditions. Unusual changes in TrkB signalling pathway have also been observed and implicated in a range of cancers. Variations in TrkB pathway have been observed in obesity and hyperphagia related disorders as well. Both BDNF and TrkB have been shown to play critical roles in the survival of retinal ganglion cells in the retina. The ability to specifically modulate TrkB signalling can be critical in various pathological scenarios associated with this pathway. In this review, we discuss the mechanisms underlying TrkB signalling, disease implications and explore plausible ameliorative or preventive approaches. NTRK2) molecule, in particular, has attracted significant attention in recent years because of its involvement in several important biological processes. TrkB acts as a receptor for brain-derived neurotrophic factor (BDNF) and neurotrophin-4 (NT4) ligands. Neurotrophin-3 (NT3) in addition to binding and activating TrkC, can also bind TrkB with reduced affinity and plays a role in regulating neuronal survival [The Trk family of receptors includes TrkA, TrkB and TrkC which are instrumental in carrying out the cellular effects of neurotrophins. P75NTR, a member of the tumour necrosis receptor superfamily exhibits some structural similarities to Trk family of receptors and interacts with members of the Trk family including TrkB receptor and influences its signalling. There is a cross talk between various members of the Trk family and p75NTR by formation of the chimeric heteromeric complexes . TrkB  possesses an intracellular tyrosine kinase domain and rapidly transmits the effects of neurotrophin binding to downstream network. Alternative splicing of the primary transcript produces truncated TrkB isoforms  that lack the intracellular tyrosine kinase domain and cannot elicit rapid intracellular signalling [C-terminal part (87 residues) is missing while a large part of the amino (156 residues) and carboxy-terminal (345 residues) are missing in TrkB-N-T1 (Uniprot: Q16620-7), with both isoforms exhibiting some sequence variations from the canonical TrkB sequence (Uniprot: Q16620). TrkB-T1 is predominantly expressed in the brain but also detected in heart, kidney and pancreas. Isoform TrkB-T-Shc is mainly expressed in the brain [The gnalling ,56. TrkBhe brain . T1 can he brain . Increashe brain . The domhe brain . TrkB rehe brain . BDNF aphe brain  while ashe brain .An increasing number of studies have suggested the involvement of BDNF/TrkB in various pathological conditions. BDNF/TrkB dysregulation leads to cellular proliferative changes as well as degenerative and behavioural changes in the brain .TrkB is indicated to play a role in various types of malignancies including those of neuroendocrinal origin . A high TrkB may in particular play an important role in the susceptibility to geriatric depression [TrkB mutations have been linked to the progression of anxiety disorders [TrkB mRNA in the prefrontal cortex is significantly diminished in the suicide ideation patients has generated a considerable interest to study its role in the suicide behaviour [TrkB is also indicated to determine the susceptibility towards nicotine dependence [Molecular events associated with TrkB may play a role in predisposition to depression and other psychiatric disorders as well as drug abuse. pression . TrkB lepression . Severalisorders . Reductiisorders . Schizopisorders ,82. The ehaviour . TrkB ispendence . It has pendence .Loss of TrkB signalling is reported to play roles in pathogenesis of Alzheimer’s, Huntington’s and other neurodegenerative disorders. It is proposed to be a susceptibility gene contributing to Alzheimer’s disease pathology . TrkB siTrkB has been implicated in promoting genetic susceptibility to anorexia nervosa, minimum body mass index and harm avoidance [TrkB result in impaired ligand induced phosphorylation and downstream Erk1 activation leading to obesity related disorders [Several studies support the involvement of BDNF and TrkB in vulnerability to eating disorders. This may be due to the involvement of BDNF/TrkB signalling axis in brain in the regulation of eating behaviour and attitudes or perception towards body weight or shape. voidance . Defectsvoidance . Mice heisorders .TrkB supports oocyte-to-granulosa cell communication by promoting signalling between JAGGED1 and NOTCH2 leading to early follicle growth . DistribMolecular strategies to regulate TrkB signalling involve its activation to address neuroprotection or psychiatric issues and its suppression in cell proliferative conditions and epileptogenesis . The undBDNF mimetics have been shown to activate TrkB signalling and prevent neuronal degeneration in rodents . 7,8-dihIn the visual system, TrkB mAb therapy triggers TrkB activation, protects retina and delays RGC death in experimental glaucoma . FibroblN-acetyl serotonin activates TrkB in a circadian manner and exhibits antidepressant effects. It also enhances neurogenesis in the hippocampus [etc. act by upregulating BDNF expression in the brain leading to enhanced TrkB signalling while other studies suggest that antidepressant drugs may also transactivate TrkB receptors in the brain independent of their effects on BDNF expression [The findings that TrkB is important for long term survival, differentiation, and function of neurons in the hippocampus , and thapocampus . Accumulpression . Lithiumpression . Electripression . Electripression . TrkB sipression .Conversely, Trk signalling pathways can be targeted with selective inhibitors to potentially treat neuroblastomas and other tumours . TrkB siin vivo to our advantage using specific agonists and inhibitors could advance our knowledge in a range of aforementioned disease conditions. Remarkably, studying the interactions of neurotrophic factor signalling with angiogenic factors like VEGF would also open up new avenues in the understanding and control of several vascular proliferative and neurodegenerative diseases alike.To summarise, the TrkB pathway represents a critical and complex signalling network that is of great relevance to the understanding of pathophysiology of several proliferative, neurodegenerative and psychiatric disorders and to the potential design of next generation therapeutic interventions and combination therapies. Despite significant progress in the field of BDNF/TrkB signalling, our understanding of the central role of this network in various biochemical and physiological processes in various neuronal and non-neuronal tissues, involvement in a range of disease conditions and potential to alter specific components of this network for therapeutic applications is very limited. In this review, we have highlighted the current understanding of mechanisms regulating TrkB receptor activation and the signalling processes mediated by it. Aberrations in TrkB signalling lead to various neuropsychiatric and neurodegenerative disorders on one hand and proliferative conditions and epileptogenesis on the other. Ability to fine-tune the balance of TrkB signalling and various effectors of this complex network


The basic process of RNA splicing is conserved among eukaryotic species. Three signals (5' and 3' splice sites and branch site) are commonly used to directly conduct splicing, while other features are also related to the recognition of an intron. Although there is experimental evidence pointing to the significant species specificities in the features of intron recognition, a quantitative evaluation of the divergence of these features among a wide variety of eukaryotes has yet to be conducted.To better understand the splicing process from the viewpoints of evolution and information theory, we collected introns from 61 diverse species of eukaryotes and analyzed the properties of the nucleotide sequences relevant to splicing. We found that trees individually constructed from the five features  roughly reflect the phylogenetic relationships among the species but sometimes extensively deviate from the species classification. The degree of topological deviation of each feature tree from the reference trees indicates the lowest discordance for the 5' splicing signal, followed by that for the 3' splicing signal, and a considerably greater discordance for the other three features. We also estimated the relative contributions of the five features to short intron recognition in each species. Again, moderate correlation was observed between the similarities in pattern of short intron recognition and the genealogical relationships among the species. When mammalian introns were categorized into three subtypes according to their terminal dinucleotide sequences, each subtype segregated into a nearly monophyletic group, regardless of the host species, with respect to the 5' and 3' splicing signals. It was also found that GC-AG introns are extraordinarily abundant in some species with high genomic G + C contents, and that the U12-type spliceosome might make a greater contribution than currently estimated in most species.Overall, the present study indicates that both splicing signals themselves and their relative contributions to short intron recognition are rather susceptible to evolutionary changes, while some poorly characterized properties seem to be preserved within the mammalian intron subtypes. Our findings may afford additional clues to understanding of evolution of splicing mechanisms. The intron is a nucleotide sequence that is transcribed from the genome but finally removed in the process of maturation of the transcribed RNA. The process of intron removal is called RNA splicing. Nuclear  introns are abundant in most eukaryotes and are processed by the machinery known as spliceosomes. In this article, we confine our attention to this type of intron. Other types of introns, known as Group I and Group II introns, are present in some archaea, bacteria, viruses, and plant and fungal organelles and are processed by auto-catalytic machineries . Under nSaccharomyces cerevisiae [65 protein factor, shows highly variable lengths and signal strengths among species; although there is a certain bias for pyrimidines toward the 3' end of the intron among all organisms, this signal is weak among most fungi, stronger in plants and protozoans, and by far the strongest in vertebrates [Although the basic ability of eukaryotes to splice introns has remained conserved throughout evolution -9, the srevisiae ,16. It itebrates . It is atebrates .It is important to investigate the conservation and variation of features relevant to splicing throughout the evolution of eukaryotes from two points of view. First, it provides us fundamental knowledge of the evolution of this very complicated cellular process that involves many factors and their recognition sites. Second, the quantitative characterization of these features is indispensable for the development of accurate and efficient computational tools for gene recognition from the genomic sequence -21. A feIn this study, we characterized and analyzed splicing signals in 61 eukaryotic species. First, we analyzed the evolutionary relationships of individual splicing features. We found that the five evolutionary trees thus constructed (feature trees) roughly reflect the phylogenetic relationships among the species but sometimes extensively deviate from the species tree. Second, we measured the relative contributions of the five splicing features to recognize correct splicing pairs of short introns by means of a method similar to that used by Lim and Burge . By comp6 introns examined. However, these fractions show considerable variation among organisms  and Micromonas pusilla (picophytoplankton) are exceptionally high, respectively accounting for 39.6% and 22.2% of all introns of the species. The high G + C contents of the genomic sequences of these species may be related to these observations. In fact, the genomic G + C content of A. anophagefferens is 69.5% and that of M. pusilla is 65.9%, which are the highest and the third highest among the 61 species examined, respectively. The fraction of GC-AG introns in Chlorella sp. with the second highest genomic G + C content is 7.7% and the third highest among the 61 species. Thus, it seems apparent that the high genomic G + C content is responsible for the high fraction of GC-AG introns. However, there must be other factors that affect the abundance of GC-AG introns, as the correlation between the genomic G + C content and the fraction of GC-AG introns is rather weak except for these three species as shown in Figure Micromonas RCC299, another picophytoplankton, is not as remarkable as that of M. pusilla despite their close taxonomic relationship and its relatively high genomic G + C content (64.1%).We classified introns into three subtypes according to their terminal dinucleotide sequences. GT-AG introns represented 98.6%, GC-AG introns, 1.3%, and AT-AC introns, 0.1% of ca. 2.46 × 10Caenorhabditis elegans seems to lack snRNAs U11, U12, and U4atac that comprise U12-type spliceosomes [C. elegans  average intron length and fungal Phanerochaete chrysosporium has the shortest (56 bp) one among the 61 species. When sorted by average intron length in descending order, all highly ranked species are occupied by animals (Mann-Whitney p = 0.18 × 10-8), in particular, by mammals within animals (Mann-Whitney p = 0.20 × 10-6). By contrast, lower ranked species are mostly fungi (Mann-Whitney p = 0.26 × 10-5).We aggregated intron length data separately for each species and for each of the three subtypes of introns. However, no significant difference was observed in intron lengths categorized into the two subtypes of GT-AG and GC-AG in each species. As the contributions of AT-AC type introns are negligible, we used the combined data in the following analyses. More primitive organisms tend to have shorter average lengths of introns, as has been often documented -26. HomoAdditional file H. sapiens, Danio rerio, Drosophila melanogaster, P. chrysosporium, S. cerevisiae, Phaeodactylum tricornutum, and Arabidopsis thaliana shown in Figure S. cerevisiae and P. chrysosporium but less remarkably conserved in the other species. In this way, the motif profiles are characteristic to species or groups.We analyzed the classical splicing signal motifs for each organism. The results of seven representative species from six groups, S. cerevisiae is the highest of all the 61 species. When examined for all the 61 species, the information contents of 3'ss and BP show a strong negative correlation, whereas those of 3'ss and the percentage of PPT-containing introns show a strong positive correlation  of the 61 species using those distances Figure . As statFor the nine mammalian species, we classified the introns into three subtypes according to their terminal dinucleotide sequences. This is feasible as considerably large amounts of data are available for most of the nine mammalian species. We then made profiles for individual subtypes and features, calculated the distance matrices, and constructed a dendrogram for each feature. As the number of AT-AC introns is small, we could not fit the observed length distribution of AT-AC introns to a superposition of two Frechet distributions . Hence, As shown in Figure It is obvious that AT-AC 5'ss and 3'ss boundary signals are clearly distinct from those of the other subtypes. This difference is certainly related to the fact that AT-AC introns are mainly processed by U12-type spliceosomes, whereas GY-AG introns are mainly processed by U2-type spliceosomes. It is reported that U12-type spliceosomes can also process GY-AG introns  and convThe feature tree for BP motif shows that AT-AC introns form a monophyletic group, while GT-AG and GC-AG introns are not clearly separated from each other. This observation is consistent with the report that U12-type introns possess a well-conserved BP motif .For the other feature, oligomer composition, the terminal dinucleotide types are even less correlated with the branching patterns of the feature trees. For oligomer composition, the same species rather than the same terminal dinucleotide type tend to make a cluster.The above observations suggest that GT-AG, GC-AG, and AT-AC introns comprise distinct functional groups that are conserved among various mammals. The 5'ss and 3'ss signals are most influential and BP may also be involved in the functional differentiation. In contrast, the contribution of the other features is minimal.To study the individual contributions of various intron features, we examined only short introns, because long introns are likely to be recognized by much more complicated mechanisms than the five features considered here. For example, exonic and intronic splicing enhancers/silencers, secondary structure of pre-mRNA, and epigenetic modulation of coding genes are likely to be involved in exon and intron definition mechanisms ,31 but ap = 0.58 × 10-4). A. anophagefferens has the highest fraction (over 45%) of information deficit among the species. By contrast, the fraction of information deficit is the smallest (approximately 7.3%) in Dictyostelium discoideum. When the 61 species are sorted by the information bit score of the information deficit, the nine mammals and the 15 vertebrates are ranked high , indicating that the intron splicing of higher eukaryotes requires more information other than the five features, compared with splicing in the other species.To analyze the contributions of the five features to the recognition of short introns, we employed a variant of the method of Lim and Burge   to obtain the first and second principal components , the ratio of information deficit is large, amounting to nearly 40%. Concomitantly, the fractions of BP, intron length, and oligomer composition are relatively small. Cluster (ii) resembles cluster (i) except for the significant contribution of oligomer composition (ca. 10%). In cluster (iii), the contribution of intron length is considerably large while that of information deficit is relatively small. Cluster (iv) is rather uncharacteristic; here, the five features moderately contribute to intron recognition, leaving an information deficit of approximately 20%. Cluster (v) is the smallest cluster and is composed of only one species, A. anophagefferens. The ratio of information deficit is the largest of the six clusters. This is probably related to the unusually high fraction of GC-AG introns found in this species as mentioned earlier in this section. In the last cluster (vi), the contribution of oligomer composition is the largest and the percentage of information deficit is the smallest among the six clusters.As the relative contributions of the features including information deficit vary considerably among species, we conducted cluster analyses of the patterns of these contributions among the 61 species. We tried the k-means clusters, some are well characterized by specific groups of species. For example, cluster (iii) consists of 14 species, 10 of which are fungi, while two fungal species Phycomyces blakesleeanus and S. cerevisiae are outside this cluster. We also found that all vertebrates except Xenopus tropicalis belong to cluster (i), and all Chordata except Branchiostoma florida belong to cluster (i) or cluster (ii). Likewise, five land plants belong to cluster (i) or cluster (iv); Populus trichocarpa and Vitis vinifera are in cluster (i), and Sorghum bicolor, Oryza sativa, and A. thaliana are in cluster (iv). Thus, evolutionarily close species tend to use similar strategies for intron recognition.Of the six However, it should also be noted that every cluster except cluster (v) consists of more than one group of species of distant evolutionary origins. For example, clusters (i), (ii), (iii), and (v) individually contain both animal and plant species. From the distribution of plant species over the clusters, we speculate that plants have adopted divergent strategies for intron recognition in the course of evolution.Spaln [Spaln is one of the most accurate alignment tools currently available [This work involves large-scale computational analysis of pre-mRNA splicing that has been enabled by taking advantage of recent progress in massive genomic and transcriptomic sequencing efforts. Our analysis is unique in that it uses only experimentally verified, high quality, and least redundant data collected by the use of our own mapping and alignment tool, Spaln . Note thvailable , and repvailable  or Ensemvailable .Our observations are largely consistent with and reinforces those reported previously ,12 with First, the five features that are involved in intron recognition are differentially conserved in the evolution of eukaryotes; 5'ss and 3'ss motifs are better conserved than BP, intron length, and oligomer composition. On one hand, this is revealed by the larger values of the information contents of 5'ss and 3'ss motifs than those of BP  of 100 U1 RNAs represented in the Rfam RF00003_seed alignment. The three putative U1 RNAs have the same 5'-terminal 11-nucleotide sequence that is perfectly identical to the consensus "AUACUUACCUG" that is directly involved in base pairing with the pre-mRNA donor site [M. pusilla and M. RCC299 putative U1 RNAs exhibit 82.2% nucleotide identities in their entire ranges of 162 and 161 nucleotides, and these two sequences are equally divergent (approximately 55% identities) from the A. anophagefferens sequence. Altogether, it is unlikely that the very different fractions of GC-AG introns, 22.1% and 2.71%, in the two picophytoplanktons could be ascribed to the difference in the primary sequences of U1 RNA. Either RNA base modifications or the protein components of the U1 snRNP complex may be responsible for the peculiar 5' intron end selection in A. anophagefferens and M. pusilla.The origin of the exceptionally abundant GC-AG introns in nor site . M. pusiab initio, transcript-dependent, and comparative genomes, from the genomic sequence. As generally conceived and confirmed in this study, the 5'ss and 3'ss signals are the most important for the recognition of introns. Therefore, in many tools, exon-intron boundaries are recognized by position-specific weight matrix (PWM) or similar methods [We have characterized representative features involved in RNA splicing in divergent eukaryotic species from the viewpoints of evolution and information theory. The quantitative characterization of these features is indispensable for the development of accurate and efficient computational tools for gene recognition of various types, such as  methods -42. Our However, it must be said that the present study has posed additional puzzles about the splicing mechanisms. Namely, the nearly monophyletic distribution of GT-AG, GC-AG, and AT-AC subtypes among mammalian introns with respect to 5'ss and 3'ss signals and the extraordinarily abundant GC-AG introns in some species are not easily explained by our present knowledge. Our results also suggest that U12-type introns may account for a larger fraction of AT-AC introns than existing estimates . More dehttp://www.ncbi.nlm.nih.gov/, Ensembl http://www.ensembl.org/ or Joint Genome Institute (JGI) http://genome.jgi-psf.org/. The "unique" set of Unigene data of each species was downloaded from NCBI [Chordata except mammal, animal except Chordata, fungus, protist, and plant.The complete genomic sequences of the 61 species were downloaded from NCBI rom NCBI . EST seqSpaln [Spaln can map exon-intron structures quite accurately [i.e., they must be GT-AG, GC-AG or AT-AC. (ii) The intron must be longer than 30 bp. (iii) The alignment flanking the intron must contain at most one mismatch and no gap within the ten nucleotide positions at each side. Moreover, to remove redundancy, we randomly selected only a representative intron when several exon-intron boundaries shared highly similar sequences . Thus, the introns in our study are all evidence-based, of high quality, and the least redundant.We used Spaln  to aligncurately . Using tIn analysis of information content and construction of feature trees, we used all intron which we detect. On the other hand, in analysis of evaluation of relative contribution of five features, we used only short intron (under 250 bp).PPT_score, which is defined as the sum of the following indices:We devised an algorithm to find the most probable location of PPT within 50 nucleotides upstream of 3'ss. First, we identified the end (3' most) point of PPT as the last pyrimidine dinucleotides upstream of 3'ss. Then, we scanned the nucleotide sequence from there toward the 5' direction to compute the PPT_score decreased by 2 or more from the maximum value so far attained, and identified the start (5' most) point of PPT as the position with the maximal PPT_score. When the percentage of C + T nucleotides inside the sequence is less than 50%, we regarded that the intron lacks PPT.We stopped the scan when the BP_score(i), at the genomic position i. The detailed procedures for constructing PWM and calculating an associated score will be described later in this section. If the maximal BP_score(i) exceeds 1.85 bits [To construct the BP signal profile, we developed a simple algorithm that extracts putative BP motifs from the introns of individual organisms. Assuming that a functional BP must be followed by a functional PPT of a certain length, we first scanned upstream of the PPT start point and located all possible BP sequences within 100 nucleotides upstream of 3'ss. In this scoring process, we used the "core" BP motif profile represented by a 4 × 5 PWM  to obtai.85 bits , we regaWe constructed the motif profiles for each species, using all the extracted introns of the species. We first obtained the gap-less alignment around each splicing junction juxtaposing the canonical dinucleotide of 5'ss (GY or AT) or 3'ss (AG or AC). Looking at the relative entropy at each aligned position calculated with the overall genomic nucleotide compositions as the background, we selected informative signal sequences for each of the 5'ss and 3'ss motifs. Specifically, we identified positions -3 to + 6 from the exon-intron junction (exonic 3 positions and intronic 6 positions) as the 5'ss motif, and positions -13 to + 1 from the intron-exon junction (intronic 13 positions and an exonic position) as the 3'ss motif. Each ss motif profile is represented by a 4 × 9 or a 4 × 14 PWM.From information theory, the amount of information held by a given motif is related to the relative entropy of the motif. Meanwhile, the likelihood of a given sequence belonging to the motif is estimated from the log-odds scores. Most importantly, log-odds can measure the amount of "information for discrimination" between the motif and the background . In geneFor 5'ss, 3'ss, and BP, the log-odds are tabulated in the form of PWM:fk,j is the relative frequency of observing nucleotide k at the motif position j, Fk is the background frequency of k, and ε is a small constant representing a pseudo count (10-4 by default). Note that we numerically encoded nucleotides A, C, G, and T into 1, 2, 3, and 4, respectively, and 1 ≤ j ≤ w, where w denotes the number of columns of PWM. As mentioned above, w = 9 for 5'ss, w = 14 for 3'ss, and w = 7 for BP in this study.where s(i:i + w-1) = si,si+ 1,...,si+ w-1, the information (log-odds) score ISfeature(s(i:i + w-1)) of the segment having a feature  is defined as:Given a segment of genomic nucleotide sequence, f(l), is characteristic to each species  by a simple log-odd:The distribution of intron lengths, c = 1/(Lmax - Lmin). Lmin was fixed to 30, whereas Lmax was determined by the 99% quantile of the longer components of the Frechet distributions.As the background, we used a uniform density of s of a potential intron, we applied the Markov models to s to calculate the probability of s having the intronic composition, PI(s), and the probability of s having the background composition, PG(s). Then, we defined the information score on the oligomer composition of an intron of length l asAlthough the coding potential is widely used in gene recognition programs, the usual coding potential is beyond the scope of this study as our present interest is in intron recognition. Although less remarkable than coding sequences, intronic sequences still have some compositional bias relative to the whole genomic sequences. We modeled both intronic and whole genomic sequences by the homogeneous fourth-order Markov models, which are nearly equivalent to accounting 5-mer frequencies. Given a nucleotide sequence IC) of a motif with a distribution f is defined as usual by the relative entropy against the background distribution g:The information content  between A and B concerning a certain feature as follows:For a motif profile of 5'ss, 3'ss or BP, vergence  to measuFor simplicity, we omitted the terms corresponding to pseudo counts here and below. The first term in the braces of equation (7) can be rewritten as:A calculated with the "cognate" log-odds PWM and that calculated with the heterogeneous log-odds PWM. The second term has the same meaning for species B. Note that Dfeature defined above and Dcomposition defined below are not guaranteed to be non-negative and so do not satisfy the axiom of distance. However, they have a straightforward meaning as a measure of divergence, and work fine in practice.which indicates that the value is the difference in the expected motif score for species f or g is the probability of occurrence of a 5-mer within introns, and F or G is the corresponding probability observed in the whole genomic sequence. Similar to equation (7), we defined the distance as:For oligomer compositions, where the summation was taken for all 5-mers.f(l) or g(l) as the frequency fitted to two Frechet distributions of length l. We evaluated the distance by the symmetric Kullback-Leibler divergence:To calculate the distance between the distributions of intron lengths of two species, we defined Lmin ≤ l < Min(Lmax(A), Lmax(B)).where the summation was taken over the range: Phylip package [Using the distance matrices separately derived for the five features, we generated UPGMA (Unweighted Pair Group Method with Arithmetic mean) trees  by the " package .Phylip package [As controls, we also constructed phylogenetic trees from 18S rRNA and U2 snRNA sequences. We collected a set of 18S rRNA sequences of the 61 species from Silva  and NCBI package . In thisTo evaluate how much a feature tree deviates from a reference tree, we used the nodal distance method implemented in TOPD/FMTS v3.3 . In thisWe employed a method similar to that proposed by Lim and Burge  to evaluAc = (the number of correctly predicted introns)/. We regarded a prediction to be successful if Ac exceeded 0.98 [For a given intron, we enumerated all possible 5' and 3' splicing signal pairs within the region from 100 bp upstream of the 5'ss to 100 bp downstream of the 3'ss, besides the true one, which follow the canonical GY-AG or AT-AC rule and are separated by typical lengths of short introns, and assigned them the "splicing score," which is defined as the sum of information scores of the five features. On average, 393 pairs were examined for each intron. We chose the pair with the highest splicing score as the predicted intron boundaries. The intron detection accuracy is defined as TAc = -log(1.0-Ac). We defined the "necessary amount of contribution" as TAc of accuracy 98% (-log(1.0-0.98=0.02)). The difference between the necessary amount of contribution and the maximal TAc is defined as the information deficit for intron recognition. Each contribution of the five features is defined as the difference in the amount of contribution corresponding to the gain in accuracy obtained with 5'ss + 3'ss + the feature from that with the minimal TAc. For example, the information value of BP is obtained by subtracting TAc of 5'ss + 3'ss from TAc of 5'ss + 3'ss + BP. When the gain is negative, the corresponding amount of contribution is zero. Finally, we calculated the ratio of each feature's TAc to the necessary amount of contribution and defined the ratio as the "information share" of the feature.We measured the contribution of each feature to intron recognition as follows. First, we examined the accuracies of intron recognition for all combinations of features involving both splicing signals, 5'ss and 3'ss. For example, 5'ss + 3'ss, 5'ss + 3'ss + BP, 5'ss + 3'ss + length + composition, etc. Next, we transformed the accuracy to k-means clustering method [k-means clustering method.We tried to classify all the 61 species according to the contributions of the five features plus the fraction of "information deficit." In the method, we first applied the PCA to the contribution data to obtain the first and second components. We used the raw contribution values of the six features as the input to PCA. Using these PCA components, we classified the 61 species by the g method ,55. As aBP: branch point; PCA: principal component analysis; PPT: polypyrimidine tract; PWM: position-specific weight matrix; SOM: self-organization map; ss: splice site.HI carried out the majority of the analyses and drafted the manuscript. OG conceived of the study, carried out a part of the analyses, and helped to draft the manuscript. Both authors read and approved the final manuscript.Synopsis of splice sites identified. This table shows the numbers and percentages of individual subtypes as well as the total numbers and the average lengths of introns identified by our procedure for each species. The numbers of short introns and their fractional percentage are also presented.Click here for fileThree PPT signals of the 61 species presented in the descending order of strength. (a) Percentages of introns in which PPTs are detected. (b) Average lengths of identified PPTs. (c) Percentages of C + T content within PPT. Color codes are identical to those in Figures 4 and 7.Click here for fileCharacteristics of PPTs. a) Percentages of introns in which PPTs are detected. b) Average lengths of identified PPTs. c) Average percentages of pyrimidine nucleotides in PPTs.Click here for fileInformation contents in bits for the three splicing signal motifs.Click here for fileRelative contributions of the five features and the information deficit to short introns recognition. The difference between the necessary amount of contribution and the maximal TAc is defined as the information deficit for intron recognition . The color codes are identical to those in Figures 4 and 7.Click here for fileContribution of the five features to short introns recognition and information deficit.Click here for filePCA plots of four main components. The plots show correlations between (a) the first and the second principal components, (b) the first and the third principal components, and (c) the first and the fourth principal components. The proportion of variance of the first, second, third and fourth principal components are 47.2%, 25.4%, 16.0% and 6.7%, respectively.Click here for fileStatistics of AT-AC introns. The total number of AT-AC introns, the number of AT-AC introns with 5'ss motif score greater than 9 bits, that with BP motif score greater than 6 bits, that with intron length < 20 kb, and that of U12-type introns (that satisfy these three criteria) in five representative species.Click here for file