In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson's disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals of the PD patients. Data acquired are pre-processed by advanced knowledge processing methods, integrated by fusion algorithms to allow health professionals to remotely monitor the overall status of the patients, adjust medication schedules and personalize treatment. The information collected by the sensors (accelerometers and gyroscopes) is processed by several classifiers. As a result, it is possible to evaluate and quantify the PD motor symptoms related to end of dose deterioration ) as well as those related to over-dose concentration (Levodopa-induced dyskinesia (LID)). Based on this information, together with information derived from tests performed with a virtual reality glove and information about the medication and food intake, a patient specific profile can be built. In addition, the patient specific profile with his evaluation during the last week and last month, is compared to understand whether his status is stable, improving or worsening. Based on that, the system analyses whether a medication change is needed—always under medical supervision—and in this case, information about the medication change proposal is sent to the patient. The performance of the system has been evaluated in real life conditions, the accuracy and acceptability of the system by the PD patients and healthcare professionals has been tested, and a comparison with the standard routine clinical evaluation done by the PD patients' physician has been carried out. The PERFORM system is used by the PD patients and in a simple and safe non-invasive way for long-term record of their motor status, thus offering to the clinician a precise, long-term and objective view of patient's motor status and drug/food intake. Thus, with the PERFORM system the clinician can remotely receive precise information for the PD patient's status on previous days and define the optimal therapeutical treatment. The most common disorders are Alzheimer's disease and Parkinson's disease (PD) . The risetc.). Also, objective methods have been suggested to quantify rigidity [In the current medical practice, assessment of PD motor disabilities is based on neurological examination during patient's visits to the clinic and home diaries that the patient or the caregiver keeps. However, the short-time examination may not reveal important information to the neurologist while data from the daily diaries are highly subjective since they rely on the patient's memory and perception of his own symptoms. In addition, most of the patients may not be aware of mild symptoms or not be able to identify early “wearing off”, while they may unconsciously exaggerate or attenuate symptoms' severity. To address these problems and to find more objective assessments, several rating scales have been used, with the Unified Parkinson's Disease Rating Scale (UPDRS) being the most widely used . UPDRS irigidity . While tA multi-parametric wearable monitoring system to recognize health status and detect the cardinal motor symptoms of PD patients.An intelligent closed-loop mechanism, which inform the patient about the appropriate actions to reduce diseases signs and progression.A remote monitoring and health care support with intelligent decision tools that assist general practitioners and specialized physicians to design better and personalized patient-specific treatment regime when and where needed.In this paper, an intelligent system for monitoring neurodegenerative disease evolution through the employment of a wide range of wearable accelerometers and gyroscopes has been developed. This system has been evaluated using short-term and long-term recordings from several PD patients. The system provides to health professionals a powerful decision support tool, and to patients a ubiquitous robust health evaluation and monitoring system, which feedback them with the appropriate medical information when necessary. The main contributions of the system are summarized below:2.A limited number of movement analysis systems have been described for the ambulatory measurement of the various aspects of movement disorders in PD. Electromyography (EMG) has been used for a long time to study tremor in PD, detect basic body postures and study gait in PD patients . HoweverPhysilog  is an aPortable Motus System  quantifiThe Kinesia ,31 systeAPDM  producesLift Labs  has deveThe market offers plenty of solutions concerning the remote health monitoring of people with pathologies involving the motor system such as PD ,26. SomeThe Rescue Project  investigPARREHA (PARkinsonian REHAbilitation)  assistivDAPHNE   is anothThe HELP (Home-based Empowered Living for Parkinson's Disease)  intends CuPiD   providesREMPARK   developsHowever, most of these commercial products have limited focus on a single motor symptom and not overall assessment and lack important characteristics for Parkinson's disease and monitoring services such as: long-term recording, qualitative and quantitative assessments, high reliability, sensitivity and specificity. Also, a few projects have been funded, most of them covering the assessment of motor performance and the design of training and rehabilitation programs for PD patients, by incorporating virtual reality auditory feedback, interactive video conference technologies and conventional kinematic analysis:In this paper, the PERFORM system is presented . The sys3.The PERFORM system consists of three subsystems: the Wearable Multi-Sensor Monitor Unit, the Local Base Unit and the Centralized Hospital Unit .3.1.3.1.1.The PERFORM wearable multi-sensor monitor unit (WMSMU) is physically attached to PD patient's body. The key role of this unit is to facilitate the monitoring of patient's daily motor activity and status through the continuous recording of specific signals. WMSMU is a light-weight wearable device composed of four tri-axial accelerometers ) used to record the accelerations of the movements at each patient extremity, one accelerometer/gyroscope on the waist ) used to record body movement accelerations and angular body velocity during body turning, and one data acquisition unit which is called Parkinson Daily Data Set Logger ), receiving all recorded signals. The AGYRO device must be permanently attached to the PDSL-1 device by means of a long wire, while the ALA-6g accelerometers communicate wirelessly making up a body sensor network .i.e., sensors' locations and combination) has been used in the PERFORM system. The signals recorded through the five sensors are transferred to the Local Base Unit (see details in Section 3.1.2) where they are stored and processed.The sensors position was chosen after a detailed comparative study, with different sensors' locations and combinations. The outcome of the study (The PDSL-1 logger supports two modes of operation: (i) Personal Computer (PC)-Connected Mode; and (ii) Normal Operating Mode .(i)PC-Connected Mode. The PDSL-1 logger enters automatically to this mode of operation, when a connection with a PC via a standard miniB USB cable is detected during power-on. In PC-Connected Mode the user can perform the following tasks by means of the Simple Detector software tool:Get/set the device date and time.Configure the device operating parameters.Design the Monitoring and Testing schedules.Acquire the Monitoring and Testing sessions sensor data stored into the SD card.Design the patient Medicine and Appointment schedules.Acquire events occurred.Acquire device malfunctions.Wireless sensor's contact loss/re-connection.(ii)Normal Operating Mode . The PDSL-1 logger enters into this mode of operation, when a USB connection is not detected at power-on  with the glove device. All recorded data are played back/viewed by the medical doctor in order to have a better assessment of the patient's motor status.The Local Base Unit (LBU) is composed of a touch screen computer which is located in the patient's setting along with the WMSMU and the test devices. It is mainly responsible for the downloading, storage and processing of the raw signals coming from the test devices and the WMSMU, the identification and quantification of motor symptoms, the UPDRS evaluation of the patient and the patient's diary keeping (entries of time of drug and food intake). The signals coming from the PERFORM WMSMU are processed by the Daily Monitoring Processor (DMP) which is composed of the following modules: Tremor (Posture and Resting) Recognizer, LID Recognizer, FoG Recognizers, Bradykinesia Recognizer and Activity Recognizer. Furthermore, additional data coming from the test devices  are obtained through the Device Controller and are processed by the Test Processor, which is the component of LBU, responsible for controlling the functionality of test devices. The Test Processor acts as an intermediate between the Test Devices and the LBU, implementing operations concerning the calibration of the test devices, the management of the connection established between the LBU and the test devices, as well as the data handling of the content recorded by the device. Finally, the Test Processor provides the functionality for performing patient facial expressions and voice recordings, using the camera and the microphone, and recording tests  and providing reminders to the patient through the LBU user interfaces. It is also responsible for the synchronisation of old and new schedules  and the transfer of these schedules to the wearable multi-sensor monitor unit. The Information Handler controls all the processes and workflows executed and provide a data access layer to the LBU Repository. All modules in the LBU are interfacing with the Information Handler and their outputs are transformed internally in order to be used as inputs for other modules. Finally, the Communicator is responsible to retrieve the latest data from the LBU, compose XML messages, encrypt them and transfer them to the Centralized Hospital Unit.The Patient Graphical User Interface (P-GUI) of LBU supports different modes of operation . Emphasi(i)Changing Personal Data. The patient data are shown in the P-GUI. The personal information can be changed according to the patient's inputs or doctor's suggestions.(ii)Checking Appointment Schedules and Monitoring Session Set-Ups. The appointment and monitoring sessions are shown daily in the P-GUI. Once the doctor schedule a new appointment or monitoring session, the patient can check it in the Schedules Menu, respectively.(iii)Inserting Medication, Food Intake Information and Filling out a Self-Assessment Questionnaire. Through the Questionnaire Menu screen  and the extent by which the metabolism of the drug is influenced by the diet (proteins or fats).(iv)Performing Test Sessions. Through Tests Menu screen, the patient is able to initiate any of the available tests such as Speech, Face Expression, Finger Tapping, Fist Open/Close, and Alternative Hand Movement using the virtual reality glove  as well as the microphone and video camera of the touch screen. The patient performs the tests as instructed by the visual interface of the P-GUI  is positioned to the clinician's setting. The CHU is dedicated to processing all patient data and assisting the treating clinician in making appropriate treatment decisions. The CHU subsystem is responsible to further process the classified results of LBU in order to extract further knowledge and to generate alerts to inform the clinician for the patient condition. The three core components of the CHU subsystem are: (i) the Alert Manager; (ii) the Information Manager; and (iii) the Interoperability Manager.The Alert Manager is the administrator of the Clinical Decision Support Systems (CDSS). It is the only module at the CHU subsystem which is aware of the internal data flow and data dependencies of the various modules in the CDSS, so its role is crucial for the system operation. Depending on the data arriving from the numerous LBUs, the Alert Manager creates the workflow that needs to be executed and then triggers all relevant submodules or CHU models/manager . Depending on the alerts generated by these submodules, the Alert Manager handles the prioritization and representation of the newly produced alerts. During this process, the Alert Manager is responsible to keep a log, that contains information about the start and end time of each sub module's process for a specific patient, so as to check if every operation runs on time and the overall system's performance.The Information Manager is responsible of fulfilling the needs for data retrieval and storage of the rest of CHU modules. It triggers the execution of other modules, depending on the information received, and stores the produced data into the central unit Repository. All PERFORM submodules interact with the information manager and cannot interact with the PERFORM repository . Also, tThe Interoperability manager is the link between the PERFORM system and external hospital and clinical information systems. It coordinates the overall information exchange between these legacy systems and through the information handler it may provide a more holistic view of the patient status, providing past information stored in external systems. All functions of the information manager are utilized by the PERFORM clinicians, yet they are transparent to these end users.The Clinician Graphical User Interface (C-GUI) is a web-based application, which can be accessed either locally or remotely by the treating clinician and the general practitioner, using either a large or small screen access device . Clinicians are directed to the home system screen, which presents the produced patient alerts to the patient specific screen, which provides the information needed to evaluate visually the patient condition. On request, the actual recorded signal and tests are downloaded from the patient-side to the healthcare center for review. The focus is on the provision of an adequate visual description of the patient status within one screen, minimizing the time spend by a clinician. Clinicians access the system periodically to check patient status, but the option to be alerted when the patient's status changes is also available.The key functionalities of C-GUI include: (i) Searching for a Specific Patient; (ii) Extracting Patient Summary; (iii) Performing Patients Tests; (iv) Extracting Information on Symptoms Appearance; (v) Extracting Information about ON/OFF Periods; (vi) Changing medication intake; (vii) Changing an appointment.(i)Searching for A Specific Patient. This functionality allows the clinician to search for a specific patient in the system's database(ii)Extracting Patient Summary. An overview of all patient events is presented on a page Performing Patient Tests. Each patient performs tests by using a monitoring set. All the results of these tests are available to the clinician on a Test Page (est Page .(iv)Extracting Information on Symptoms Appearance. Tremor, LID, events and falls are the four symptoms which are represented employing graphs (g graphs . For eac(v)Extracting Information about ON/OFF Periods. The clinician can extract information about the ON/OFF period accessing a specific interface page Changing Medication Intake. The clinician can prescribe a new medication to the patient and ask to perform some tests or day monitoring sessions.(vii)Changing An Appointment. Clinicians can define and review their appointments with their patients, using the scheduler, on which all appointments are depicted as boxes on the time line which corresponds to the time of the appointment. If the clinician has made his/her customizations concerning working hours, this scheduler  starts from the working hours of each clinician.3.2.The core of the PERFORM system is a set of intelligent modules capable of automatically assessing PD common motor disabilities based on processing of the accelerometer and gyroscope signals and machine learning techniques. In order to provide a complete tool to the neurologist, the PERFORM system includes intelligent modules for tremor, bradykinesia, LID and FoG, which comprise the most common PD motor disabilities, along with several additional tools.3.2.1.i.e., from the data logger to the LBU. Then the selected signal is downloaded and processed using the set of intelligent modules. After processing is finished, the results menu is displayed. By selection the appropriate action, the results of the analysis of the signal is displayed  with respect to time, 3.2.2.All intelligent modules have been developed using a database of short-term (∼15 min) recordings, created using the recording device described above, in hospital environment. The recording started with the subject lying on the bed and the protocol consisted of three major tasks: (i) lying on the bed; (ii) rising from the bed and sitting on a chair located by the bed; and (iii) standing up from the chair and performing a series of tasks . During the recording, the subjects were instructed to act freely, speak and make voluntary movements if they need to. The procedure (performing the tasks) was videotaped. Clinical annotation was provided by expert neurologists during the recording and afterwards via visual inspection of the video footage. For the clinical annotation, the UPDRS was used. The short-term dataset included 39 recordings from 24 patients in different states (ON/OFF) and included all motor symptoms. The short-term dataset is presented in The PERFORM system was also used for long-term recordings. These included two recordings  for five consecutive days, at patient's home. The patient (or the caregiver) was asked to record his/her motor condition in the home diary every half an hour during the day, using a different level of classification for each PD symptom . Twelve patients were enrolled in the long-term recordings phase. The long-term dataset is presented in Tremor assessment module is based on the analysis of signals obtained from accelerometers attached to specific body segments . SeveralThe LID assessment module is based on the analysis of the signals recorded from the sensors . The sigThe classification methodology used for the bradykinesia assessment module includes three steps . InitialThe FoG detection module methodology consists of three stages . In the 4.4.1.The intelligent modules were initially evaluated using the short-term dataset, based on the leave-one-patient-out cross validation technique . All dat4.2.et al. [The wearability analysis  was perfet al. . The reset al. [According to Knight et al. , there aetc.)Section 1: Personal information  score chart .Section 3: Energy Cost Analysis: this section was to be completed by the PD patient according to his perception of extra energy used in order to perform daily activities.Section 4: Pain/Discomfort Analysis: this section was to be completed by the patient according to his perception of pain or discomfort to specific body areas (using the body maps) .Section 5: Comfort Rating Scales: this section was to be completed by the patient in order to provide his/her thoughts and feelings regarding to several issues related to comfort like emotions, anxieties and harm caused.Section 6: Interview and complementary questions: this section included both some complementary questions that are related to allergic reactions, excess heat and sweat caused by the device and also enable the user to provide his/her own suggestions on how the system can be improved.Based on the aforementioned method and factors, an evaluation questionnaire was developed and employed during short-term and long-term recordings. This questionnaire was composed of the following sections:The PERFORM WMSMU does not change the posture of the patient and that it is not causing significant problems to the patients .The PERFORM WMSMU is considered wearable by all subjects .The wearability analysis of the PERFORM WMSMU was completed with the involvement of 20 PD patients during short-term recordings and 24 patients during long-term recordings. From the analysis of results, two main conclusions were drawn:5.The PERFORM system aims to tackle problems associated with the efficient remote health status monitoring, the qualitative and quantitative assessment and the treatment personalization for people suffering from neurodegenerative diseases and movement disorders, such as PD. The system is based on wearable monitoring devices (accelerometers and gyroscopes), which are wirelessly connected and seamlessly integrated to produce a user-friendly and patient-customized monitoring tool. The recorded signals are pre-processed and stored at the patient site. At the point of care , the supervising health professionals are able to remotely monitor their patients, personalize their treatment and medication schedules and generate statistical data, so as to study and evaluate the efficacy of medication, based on the patients' specific personal and medical characteristics.The PERFORM system aims to improve the prevalent philosophy in the follow up of PD patients and to change the paradigm in PD treatment. The new paradigm supports the continuous follow up of patients with easy to wear sensors and computer intelligence to be used by doctors in order to design optimized personalized treatment, delay the appearance of symptoms (such as LID) and improve quality of patients' lives. These are achieved through an assistive tool for assessing the course of the disease (in terms of motor disabilities) and evaluating the medication efficiency, through continuous monitoring and assessment of the disease evolution, automated diagnosis and decision support technologies. Disease motor symptoms are long-term monitored and quantified, significantly improving the current short-term subjective medical practice, allowing the health professionals to assess the disease's progress and evaluation/adaptation of the medical treatment based on the changing symptoms and efficiency of drug intake .Better control of the disease symptoms will have a synergistic positive effect on the psychosocial and physical functioning of the patients. Therefore patients are empowered to have their role in a society by living independently, working, participating in social activities and enjoying life. Although the final system has to be tested on many Parkinson's disease patients, bearing in mind the needs of other neurodegenerative diseases and Parkinsonian syndromes with similar motor and mental disabilities. Thus it offers a great potential for easy future use for most neurodegenerative syndromes .PERFORM system targets cost and effort savings related to frequent visits to hospitals for assessment and treatment modification , since it provides a monitoring tool for out-of-hospital long-term monitoring. Also, it provides work efficiency to healthcare professionals, through the utilization of multi-parametric information fusion, allowing all related information to be included in a single system.vs. the PERFORM system is presented. Also, in In 6.In contrast with other diseases there is no treatment and therapeutic schema for PD. The dosage and the way of medication administration are totally personalized for every patient. When the PD disease appears the treatment seems very simple but in the course of time the treatment becomes complicated and requires more and more the patient's participation. During the short visit of PD patient the clinician must be informed for the patient's day motor status. This is required to configure the treatment strategy, drug time intake, drug doses, intervals between doses, combination of drugs depending on the food intake and other details. The clinician tries to retrieve information for patient's motor status for the previous days or weeks. This is almost impossible since it is difficult for PD patients to describe their symptoms and they cannot assess exactly their reaction to the drug. Consequently, the clinician cannot receive proper information to define realistically the drug administration treatment. The PERFORM system offers daily assistance to the clinician neurologist who tries through conflicting information from the patients and their relatives to determine the optimal therapeutically schema. The PERFORM system is used by the PD patients and in a simple, safe, painless and non-invasive way to record patient motor status for long-time intervals. In this way, the clinician can have a precise, long-term and objective view of patient's motor status in relation to drug and food intake; all the aforementioned factors are directly involved in the drug absorption and action. With the PERFORM system the clinician can remotely receive precise information for the PD patient's motor status on previous days and define the optimal therapeutical treatment.


To date the anatomic extent of tumor (TNM classifications) has been by far the most important factors to predict the prognosis of cancer patients. However, this classification provides limited prognostic information in estimating the outcome in cancer and does not predict response to therapy.large-scale approaches, and quantitative measurements, we evaluated the importance of the host-immune response within human tumors.immune contexture, a standardized, simple and powerful immune stratification system, termed the “Immunoscore”, was delineated that may bear a prognostic power superior to that of the currently used cancer staging system. Tumor invasion parameters were statistically dependent on the host-immune reaction. A worldwide Immunoscore consortium is testing the prognostic value of the Immunoscore, using a standardized assay to routinely measure the immune status of a cancer patient.We showed that tumors from human colorectal cancer with a high density of infiltrating memory and effector memory T-cells (TEM) are less likely to disseminate to lymphovascular and perineural structures and to regional lymph-nodes. We showed that the combination of immune parameters associating the nature, the density, the functional orientation and the location of immune cells within the tumor was essential to accurately define the impact of the local host immune reaction on patients prognosis. We defined these parameters as the “immune contexture”, and factors modulating it will be discussed. Based on the immune contexture is characterized by immune signatures qualitatively similar to those predicting response to immunotherapy, Thus, the continuum of immune response existing, spanning a balance between tumor cell growth and elimination, will be discussed.The functional orientation of the


The dropped head syndrome (DHS) is a disabling condition caused by severe weakness of the neck extensor muscles causing progressive reducible kyphosis of the cervical spine and the inability to hold the head up. Weakness can occur in isolation or in association with a generalized neuromuscular disorder. Isolated cases are owed to the late onset of noninflammatory myopathy designated as INEM, where persistent chin to chest deformity may gradually cause or aggravate preexisting degenerative changes of the cervical spine and ultimately result in myelopathy. In review of the literature, we could find only 5 cases, with no unique guidelines to address the management of these two concomitant pathologies. Herein, a 69-year-old man who had developed cervical myelopathy 2 years after being affected by isolated dropped head syndrome is presented. Chin to chest deformity and cervical myelopathy were managed through three-level anterior cervical discectomy and fusion (ACDF) combined with decompressive cervical laminectomy and stabilization with C2 to C7 pedicle screw-rod construct. At 4-month follow-up, despite recovery in patient's neurological status, flexion deformity reappeared with recurrence of dropped head due to C7 pedicle screws pull-out. However, this was successfully managed with extension of the construct to the upper thoracic levels. Dropped head syndrome or head ptosis is a reducible flexion deformity of the neck that is caused from a weakness of the extensor muscles or increased tone of the flexor muscles of the neck resulting in the chin-on-chest deformity and at the extreme the patient will be unable to look straight ahead –6. NotabThe isolated type of dropped syndrome is a disease of the elderly that is caused by noninflammatory myopathy restricted to the paraspinal muscles of the neck being described by Suarez and Kelly Jr. for the first time in 1992 .Isolated dropped head syndrome proceeding cervical spondylotic myelopathy and their ultimate association is quite rare. This combination was first described by Kawaguchi in 2004 and since then only four more cases have been described in the literature –14.Herein, a new case of cervical myelopathy developing two years after the appearance of dropped head syndrome, as a sequel of isolated neck extensor myopathy, is presented and a brief review of the literature on the condition is also provided –14.This previously healthy 67-year-old man was admitted with chin to chest deformity in February 2011. The deformity had progressed rapidly from mild difficulty in keeping the head up to head drop over a period of 5 months . Upon adRoutine laboratory studies, such as serum creatine kinase (CK) and lactate dehydrogenase (LDH), were normal. Thyroid function tests, parathyroid hormone, acetylcholine receptor antibodies, and tumor markers were negative.As he refused to undergo surgery, he was advised to wear a cervical collar to improve his neck posture and social interactions. However, according to his wife, he rarely wore the collar.Over a period of two years and in particular during the last season, he exhibited mild but progressive weakness of all his extremities with difficulty in buttoning or unbuttoning his shirt and mild difficulty in walking owing to an unsteady gait. He also experienced tingling in both hands. These new difficulties along with the chin to chest deformity impaired his activities of daily living more than before and forced him to seek medical advice. This time, he could keep his head up for only one minute.His neurological examination revealed spastic quadriparesis with positive Hoffman's sign, hyperactive reflexes, and equivocal extensor planter response in both sides.Cervical plain radiographs in dropped position disclosed osteoporotic cervical spine with severe kyphosis as well as instability with forward subluxation at C3-C4, C4-C5, and C5-C6 levels . FlexionOne stage circumferential surgery was decided with respect to the osteoporosis. Therefore, a three-level anterior cervical discectomy fusion with cage at C3-C4, C4-C5, and C5-C6 was accomplished initially. Anterior procedure was followed with C3 to C6 laminectomy from C2 to C7 screw-rod stabilization. With the application of this strategy, simultaneous decompression of the spinal cord and correction of the deformity could be achieved. Postoperative course was uneventful and he was discharged in three days. Postoperative radiographs disclosed normal position of the neck . Two monBut surprisingly, four months after surgery, his head has tendency to drop again, X-ray revealed recurrence of flexion deformity of the neck and out-pulling of both pedicle screws from the body of C7 . RedoingWith the patient in prone position the site of previous surgery was reopened and the rods and subsequently the screws of C7 were removed. Pedicle screws from T1 to T4 were inserted and the construct was extended from C2 to T4. Finally, the nuts were tightened with the head in normal position. Postoperatively, the patient was discharged after 3 days in Minerva collar, whereas the control radiographs were quite satisfactory . Now 18 Development of cervical spondylotic myopathy a few years after appearance of dropped head syndrome is a rare scenario. The information obtained from systematic review of the literature indicates that the since the report of the first example of this combination described by Kawaguchi et al. in 2004, four more cases have been published so far –14. The This association might be explained with two different theories. In the first theory, disturbances of spinal cord microcirculatory are regarded as the major factor. Accordingly, ischemia caused by cervical spondylosis results in preferential degeneration of the anterior horn cells of the cervical spinal cord. Eventually, this will result in weakness limited to the extensor muscles of neck causing dropped head syndrome. Later, with consideration of the natural course of cervical spondylosis, with further affection of the cord, the clinical picture of myelopathy will appear –13.According to the second theory, with consideration of the age of the patients suffering from DHS due to INEM, association of asymptomatic cervical spondylosis with this syndrome should be quite frequent. Actually, as the head falls forward greater stress will be imposed on the neck extensors where restless efforts for correction of the kyphotic deformity combined with frequent failure of these efforts for holding the head up increase the work-load on the discoligamentous structures of the cervical spine. Gradually, this scenario can aggravate preexisting cervical spondylosis and, with progression of degenerative changes, cervical myelopathy will appear –13.However, the rarity of this association remains a question, if we accept the fact that dropped head syndrome due to INEM is confined to the elderly and in this age group asymptomatic cervical spondylosis is not infrequent.Nonetheless, coexistence of dropped head syndrome (DHS) and cervical spondylotic myelopathy (CSM) sooner or later will severely compromise the patient's quality of life and may result in significant disability if left untreated –14.For achieving a good outcome and long life expectancy, appropriate surgical intervention for this association is required –14. ConsSurgery seems to be an obvious therapeutic option in association of DHS with cervical spondylotic myelopathy. However, owing to the paucity of information regarding surgical intervention, there is no clear consensus on the optimal approach or timing –14. NoneActually, the combination of DHS and CSM is a complex cervical spine pathology where the compressive effect of protruded multilevel degenerated intervertebral discs is aggravated by cervical kyphosis. This complex situation might benefit from circumferential surgery, if the protruded cervical disc causes canal compromise in particular at the site of myelopathy , 16. ComIn fact, in DHS with kyphotic cervical deformity, once lordosis is not achieved with neck extension, disc release and reconstruction of the anterior column will facilitate correction and prevent failure which happens with gradual degeneration and subsequent collapse of the disc spaces that might occur with time. This progressive scenario might result in failure of posterior construct. Suboptimal correction has been demonstrated in some reported cases with DHS , 16.Moreover, severe osteoporosis which coexists in the elderly with dropped head syndrome may complicate laminectomy plus posterior instrumentation. In osteoporotic subjects, strengthening of the anterior column with anterior discectomy and fusion might be helpful in prevention of posterior construct failure.The distal length of posterior instrumentation was not clearly defined in the literature till recently that a formulation was proposed by Riew . AccordiWhether the cranium should be included in the construct or not had been a matter of controversies in the past. In pre-screw-rod era, a contoured Steinman pin or a rod connecting the cranium to the cervical spine with the aid of the wires or hooks was the only choice for correction and stabilization of a dropped head . This meInformation about the long-term outcome in combination of head drop and cervical spondylotic myelopathy, owing to its rarity in the literature, is limited. However, in this association, if the deformity is left untreated and decompression is not done, catastrophic results owing to the progression of myelopathy will ensue. In contrast to the dropped head syndrome secondary to serious neuromuscular diseases, which usually have a grave prognosis, in the combination of INEM and CSM, the outcome depends on the time of the surgery. If surgery is accomplished before the establishment of myelopathy, prognosis will be good, but with delay in diagnosis and from diagnosis to management the myelopathic changes may become irreversible with poor prognosis.It should be noted that the dangers that are hidden in the correction of the fixed cervical kyphosis are not usually seen in this flexible kyphotic deformity. However, the patient should be informed about the restricted motions of the neck and the risk of increased falls because of an inability to see the walking surface.In summary, progression of spondylotic changes with appearance of myelopathy should be expected in a patient with dropped head syndrome as a sequel of INEM. Periodic neurological examination every six months and control MRI in one-year interval seems justified. However, once DHS is complicated with early symptoms CSM, in the absence of serious comorbidities, in order to prevent disability, early surgery is indicated. Mainstay of surgery in this association is combination of decompression and instrumentation. Overall trend of instrumentation should be toward C2 to upper thoracic spine which provides lower-profile constructs with multiple points of fixation yielding stronger stabilization with an enhanced likelihood of successful fusion. However, extension of the construct to the cranium is not mandatory and usually not necessary.


There is an ever-expanding range of technologies that generate very large numbers of biomarkers for research and clinical applications. Choosing the most informative biomarkers from a high-dimensional data set, combined with identifying the most reliable and accurate classification algorithms to use with that biomarker set, can be a daunting task. Existing surveys of feature selection and classification algorithms typically focus on a single data type, such as gene expression microarrays, and rarely explore the model's performance across multiple biological data types.This paper presents the results of a large scale empirical study whereby a large number of popular feature selection and classification algorithms are used to identify the tissue of origin for the NCI-60 cancer cell lines. A computational pipeline was implemented to maximize predictive accuracy of all models at all parameters on five different data types available for the NCI-60 cell lines. A validation experiment was conducted using external data in order to demonstrate robustness.As expected, the data type and number of biomarkers have a significant effect on the performance of the predictive models. Although no model or data type uniformly outperforms the others across the entire range of tested numbers of markers, several clear trends are visible. At low numbers of biomarkers gene and protein expression data types are able to differentiate between cancer cell lines significantly better than the other three data types, namely SNP, array comparative genome hybridization (aCGH), and microRNA data.Interestingly, as the number of selected biomarkers increases best performing classifiers based on SNP data match or slightly outperform those based on gene and protein expression, while those based on aCGH and microRNA data continue to perform the worst. It is observed that one class of feature selection and classifier are consistently top performers across data types and number of markers, suggesting that well performing feature-selection/classifier pairings are likely to be robust in biological classification problems regardless of the data type used in the analysis. Due to the recent rise of big-data in biology, predictive models based on small panels of biomarkers are becoming increasingly important in clinical, translational and basic biomedical research. In clinical applications such predictive models are increasingly being used for diagnosis , patientMany types of biological data can be used to identify informative biomarker panels. Common ones include microarray based gene expression, microRNA, genomic copy number, and SNP data, but the rise of new technologies including high-throughput transcriptome sequencing (RNA-Seq) and mass spectrometry will continue to increase the diversity of biomarker types readily available for biomarker mining.feature selection, to identify a short list of informative biomarkers, and classification, used to make predictions for new samples based on patterns extracted from the training data. Both of these steps have been explored extensively in the statistics and machine learning literature, and many alternative algorithms are available for each. Due to the sheer number of available choices and the lack of predictable interactions between feature selection method, classification algorithm, and data type, assembling the most robust biomarker assay for a given biomedical application is rarely undertaken systematically. Rather, it is more often driven by the intuition and a priori preferences of the statistician.Useful predictive models are typically restricted to use a small number of biomarkers that can be cost-effectively assayed in the lab . The useAvailable feature selection methods can be grouped into three broad categories: filter, wrapper and embedded. Filtering approaches use an easy to calculate metric which allows quick ranking of the features, with top ranking features being selected. Wrapper methods use a classification algorithm to interrogate the effect of various biomarker subsets. Embedded approaches are classification algorithms which eliminate features as part of the training process. Recent studies -8 investThere are hundreds of publications describing classification algorithms and their applications to genetic research and medicine. Many publications advocating a new method employ a limited comparison between similar approaches. However non-uniform validation strategies make it difficult to assess performance of a wide variety of approaches. A previous study compared both classification and feature selection approaches in a unified framework , howeverThis work presents a large scale empirical comparison of the effects of the interaction between the main components of the predictive model , the number of features utilized, and the underlying data type on the performance of the overall model. This study also implements exhaustive parametrization of all models to ensure a fair comparison between models.In order to test the performance of the large number of models tested in this study, and in order to be able to run direct comparisons of the models on different biological data types, we took advantage of the publicly available NCI-60 cancer cell line data set . The NCICancers are widely believed to derive from a single event in which one cell escapes the many surveillance mechanisms in place to prevent uncontrolled proliferation. Once this has occurred, the cancer often evolves quickly, rapidly acquiring large numbers of mutations, ranging from small point mutations to very large chromosomal aberrations and regional amplifications (DNA duplications). The original identity of the cancer cell (its cell type or tissue type) appears to exert a very strong influence on the course of evolution of the cancer. For this reason, characteristic mutations will often be found in cancers derived from the same tissue, even in different patients. In addition, because identical cell types from different patients will share very similar gene expression signatures, cancers derived from these tissues will often do the same. In the present study we take advantage of these two features of cancer to test the ability of various statistical models to correctly infer the cell type (or "tissue-of-origin") of each cancer cell line. The ability to make this inference correctly not only represents an excellent test of these models on real biological data, it is a good example of the type of classification ability required for targeted cancer therapeutics.In order to test the predictive models in this study we use publicly available data from the NCI-60 cancer cell lines as provided by CellMiner . For theThe area of feature selection in machine learning has recently been quite robust. There are numerous specialized feature selection algorithms which identify the most informative biomarkers from high-dimensional data. This study utilized at least one approach from each of the three broad categories identified above . Every approach utilized allowed for a specific number of features to be chosen. No requirement was established that induced a relationship between feature sets from the same algorithm. So the 16 features chosen by one approach are not required to be a subset of the 32 features chosen by the same. For all algorithms we used the implementations in the Scikit-learn  Python pThe fastest and most simplistic selection method is univariate filtering. These approaches rank features according to some score, and the user selects the best k features accordingly. Here the F-statistic (Anova), a generalization of the t-test, is used as a filter, as suggested in  and 6]..6]. TherWrapper approaches typically use some type of greedy strategy to select influential features using a black box classifier. They are more computationally intensive, however SVM recursive feature elimination (SVM-RFE) is extensively used in medical applications . The parThe final class of feature selection algorithms is embedded approaches where the features are chosen while building the classifier. To represent this class two tree-based methods were adapted; random forest (RF)  and extrA summary of parameters of all considered feature selection methods along with the range of values searched for each parameter are given in Supplemental Table S3.An exhaustive comparison of all classification algorithms would be quite challenging. Therefore only a small number of approaches was explored, chosen to represent most common machine learning approaches used in bioinformatics. Identifying the cancer type from the NCI-60 dataset is inherently a multi-category classification problem. Therefore each considered approach must accommodate this setting or be adaptable by one-vs-one  or equivLinear classifiers use a linear function to score classes by taking the dot product of feature values and feature weights computed during training. One of the most powerful, flexible and ubiquitous linear classifier is the support vector machine (SVM) with linear kernel . SVM hasClassification trees are a machine learning tool which has found extensive use in the biological and medical communities. This is partially due to both their resilience to over-fitting and ease of interpretation. This work looks at three related approaches; vanilla decision trees (DT) , random k  closest matches.Distance based methods surveyed are k-nearest neighbors (KNN), cosine (Cos) and correlation (Corr). Cosine and correlation are simple classifiers which calculate the distance to all training samples from the test sample and assigns the label based on the closest match. KNN is a slightly more advanced version of the same concept in which class membership is assigned by majority voting among the A summary of parameters of all considered classification algorithms along with the range of values searched for each parameter are given in Supplemental Table S4.k-fold cross-validation [k equal size subsets with each set used in turn for testing while the other k − 1 subsets are used as training data. Care must be taken taken to avoid substantial biases [k-fold cross-validation is used to tune parameter values. This requires an additional cross-validation experiment on each training dataset, where a grid-search over the considered parameter range is performed. The inner phase identifies the best parameter values which are then used exclusively in the outer cross-validation. In order to build stronger evidence for the models' performance, the outer cross-validation phase was repeated 100 times, however the parameterization was only performed in the first iteration. Biases towards selecting more complex models with more parameters or overly fine grid-steps are still a possibility, however nested cross-validation should largely mitigate them. More advanced techniques presented in [A common validation strategy used in evaluating machine-learning methods is lidation ,8. Here l biases  by ensurented in  could bek-fold cross-validation strategy is computationally very intensive. With 4 × 9 = 36 models (combinations of feature selection and classifier) to evaluate, dozens of parameter values and different number of selected markers there can be upwards of 1,000,000 individual classifier runs per data type. The majority of the jobs occur in the inner cross-validation loop, and fortunately can all be run in parallel on a cluster or multi-core server. Also, a pre-filtering heuristic was applied to speed up the feature selection process. For all datasets with more than 1,000 features we retained only the top 1,000 features as ranked by the F-statistic prior to any additional feature selection.The nested To further validate the results on external datasets, eight primary tumor cohorts from The Cancer Genome Atlas (TCGA) were identified to match five NCI-60 tissue-of-origin cell lines; central nervous system, colon, non-small cell lung, ovarian, and renal. The mapping of the TCGA cohorts to the NCI-60 cell lines can be found in Supplemental Table 7. The TCGA derived gene expression microarray data was obtained from the Broad Institute's GDAC Firehose utility -34. The area under the receiver operating characteristic (ROC) curve. The ROC curve is a plot of the true positive rate against the false positive rate. The AUC is then the area under this curve and is used as a single measurement of classifier performance. This definition is typically for binary classification tasks, however there are several extensions to multiclass classification problems [AUC(ic) is the typical binary classification AUC for class i cand p(ic) is the prevalence in the data of class ic.There are numerous metrics used in evaluating the accuracy of a predictive model. One common metric is AUC, or problems . Since tThis study is evaluating the effect of three parameters simultaneously: the model, the data type and the number of markers. Therefore conclusions about the best predictive model are presented from the perspective of each parameter individually. In Figure The accuracy of the predictive models varies greatly, with the various combinations of feature selection and classification algorithms. If the feature selection and classification algorithms are grouped by class, a high-level ranking becomes much clearer. In Figure If the data type and number of features are fixed the effects of the models can be explored further. As seen in Figure This conclusion is contrary to that of , where iThe relatively small effect of classifier choice is interesting and unexpected. This indicates that much more care should be given to choosing the right features, as this has the biggest effect on model performance.The rich selection of data types available for these cell lines provides the opportunity to compare the ability of many types of biological data to classify the tissue of origin of a tumor cell line. Some of these data types fundamentally reflect gene expression levels: mRNA, protein and microRNA. The other two: CNV and SNP, are generally assumed to reflect genomic changes at large (CNV) and small (SNP) scales. Comparisons of data type effects at all marker sizes are best seen in Figure ground truth and measure the ability of each data type to correctly infer the tissue of origin of a sample based upon each data type.The transition from normal tissue to cancerous tissue is generally associated with changes at the level of both gene expression and the genome. Frequent mutations, genomic rearrangements and large scale changes in gene expression are all characteristic of oncogenic transformation. However, cancer cells also retain many, if not most, of the essential hallmarks of the tissue of origin of the cancer. In this study, we use the tissue of origin as the A priori, we expect some of these data types to be better at this task than others. For instance, mRNA profiles are highly distinct between different tissue types. For this reason, even after oncogenic transformation, an mRNA transcriptional profile characteristic of the tissue of origin is expected to resemble that of the normal tissue, more than it would the transcriptional profile of tumors derived from other tissues. For this reason, we expect (and find) that mRNA transcriptional profiles reliably and accurately infer the tissue of origin of tumor cell lines. Similarly, protein expression profiles are also very reliable indicators of the tissue of origin of a tumor. microRNA profiles are less powerful than either mRNA or protein expression profiles, but still fairly powerful indicators of tissue of origin. The relative weakness of microRNA profiles compared to mRNA and protein expression profiles may in part result from lower tissue specificity of microRNA expression relative to mRNA and protein.The ability of genomic data to infer the tissue of origin of the tumor is subject to a very different set of biological constraints than expression data. While expression data is expected to be approximately identical across tissues regardless of patient identity, and thus similar between tumors derived from the same tissue but from different individuals; genomic data is identical across normal tissues in an individual, and differs between individuals. Thus, at first glance, genomic data would be expected to track with the individual, and be a very poor predictor of the tissue of origin of a cancer. However, dramatic genomic alterations are a hallmark of cancer progression, and distinct genomic alterations are often found in distinct cancer types. Accordingly, we find that copy number variation is about as powerful as microRNA profiles at inferring the tissue of origin of a cancer cell. This is likely due to the preferential occurrence of specific DNA rearrangements in cancers derived from specific cell types . The SNPAs one uses more biomarkers to classify samples, one expects increased performance, the possibility of overfitting, and the appearance of a plateau beyond which additional markers do not increase the power of classification. However, the rate at which these changes occur as more markers are used to classify a sample can be very different for various types of data.∼0.97).Our analysis shows that mRNA, protein, and SNP data all plateau at about the same AUC  or coverage (protein), and thus power of discrimination. The mRNA arrays used to characterize the NCI-60 cell lines provide direct assessment of the activity of thousands of protein-coding genes, while the protein arrays measure only somewhat more than 300 proteins. With thousands of potential markers to choose from, the mRNA-based models can select informative markers from a larger marker pool, and thus maximize the performance of a gene expression-based model more quickly than the protein arrays, which are restricted to a small subset of the protein coding genes represented on the mRNA arrays. The more direct nature of the protein measurement  does not appear to outweigh the disadvantage of the lower coverage in the starting set of protein markers. As discussed in the preceding section, the SNP array appears to be measuring, in part, gene expression levels resulting from the amplification of specific regions of the genome in specific cancer types. However, there is likely to be a complex and possibly heterogeneous and non-linear relationship between signal intensity on the SNP array, and gene expression levels. Thus, despite the very large number of markers to choose from on the SNP array, highly informative markers are not as abundant in this data as they appear to be in the mRNA data. As a result, many more SNP markers are required to achieve the same level of performance as mRNA-based markers. It is hard to predict how the power of SNPs to infer cancer type might change when newer arrays, that control for copy number changes, are used to characterize these cell lines.Similarly, CNV and microRNA markers approach the same level of performance as one another, but do so at different rates. While microRNA markers plateau quickly (at about 16 markers) CNV markers require 64-96 markers to reach the same level of performance. The quick plateau of microRNA-based markers is likely due to the highly tissue-specific expression of a minority of microRNA's, and the more global expression of the remaining majority. Once the few highly informative microRNA's have been selected and used, adding more provides little additional classification power. In the case of CNV's, like SNP's these markers reflect changes in the cancer cells genome that can lead to changes in gene expression that are distinctive features of cancer subtypes. However, not only do the CNV markers suffer from the indirect relationship between the marker and gene expression expected for SNP's, they are also a much lower resolution marker than SNP's (megabases vs single bases), and far fewer CNV's were measured on the arrays, thus limiting the likelihood that the most informative CNV's were available for selection. Thus, the power of the CNV biomarker panel climbs slowly.Taken together, these observations suggest that the absolute performance of a given biomarker data type to classify a cancer can be understood in the context of: the number of available markers for the model to choose from, the power of the most informative markers in the set, and the directness with which the data type reflects an informative aspect of the sample biology. Data types with a large number of markers to choose from, that are closely related to the biology of the sample, are most likely to yield highly effective small biomarker panels. On another hand, data types with lower saturation (fewer markers measured), and/or a less direct relationship to the biological differences between samples, will require more markers to reach maximal performance.Ultimately all parameters should be considered simultaneously when attempting to build the best targeted predictive model. In order to do this it is necessary to build a validation framework to explore all parameters fairly and efficiently. Although it is a difficult task it is not impractical and interesting nuances can be extracted.In this study it was observed that at the lowest number of markers (8) mRNA and protein were the best data types for cancer identification. For mRNA, SNP and protein the SVM-RFE was the best feature selection choice and ET was the best classifier. For CNV and microRNA the best classifier was LR and ET respectively. Interestingly for all data types at 8 markers except CNV a tree based classifier performed the best as seen in Table At the highest number of markers tests (96) both RFE and ET perform strongly on all data types, however LR is the best classifier for all types except SNP where KNN is the best. Both of these classification tools are technically simple, yet they perform best which lends credence to the Occam's razor principle which when applied to machine learning places preference on simpler explanations.The amount of over-fitting when building a predictive model is always a concern. This effect was measured in an external validation experiment utilizing analogous gene expression microarray data obtained from several studies which are part of the TCGA project -34. The The NCI-60 data is derived from decades old cell lines, while the TCGA data was derived from recently sampled primary solid tumors. Additionally the matched cancer types did not have comparable histological classification. Finally, there were three aditional cancer types  which were present in NCI-60 but not included in the external validation set. These classes were included in the training. Despite these differences the presented method was able to perform biomarker selection and build accurate predictive models for this challenging external validation experiment. A complete breakdown of the per-class prediction rate by cancer marker set size is provided in Supplemental Table 9.The initial hypothesis motivating this research was that certain predictive models will perform better on different data types at different dimensionalities. While this hypothesis holds, the difference in accuracy between models is often small and allows for several generalizations. Namely that RFE is clearly the best feature selection algorithm and both SVM and LR are the best classifiers as seen in Figures NCI-60. National Cancer Institutes tumor cell line project; TCGA. The Cancer Genome Atlas; SVM. Support vector machine; DT. Decision tree; RF. Random forest; ET. Extra tree; GB. Gradient boosting; KNN. K-nearest neighbor; RFE. Recursive feature elimination; LR. Logistic regression; COS. Cosine; COR. Correlation; BR. Breast; CNS. Central nervous system; CO. Colon; LC. Lung cancer; RE. Renal; OV. Ovarian; ME. Melanoma; SNP. Single nucleotide polymorphismIIM, CEN, EH and JL are co-founders and own shares of Smpl Bio LLC a for-profit business created to commercialize the application of the work presented here. Smpl Bio LLC has not contributed financially to this publication. The University of Connecticut has filed a patent application on work related to this publication.IIM and CEN conceived the study. EH, JL, and CL conducted the experiments. All the authors contributed valuable ideas to the study, drafted the manuscript, and were involved in manuscript revision. All authors have read and approved the final manuscript.Supplementary figures, methods, and tables are supplied in PDF format.Click here for file


The familiarity of an object depends on the spatial arrangement of its parts; when the parts are spatially rearranged, they form a novel, unrecognizable configuration. Yet the same collection of parts comprises both the familiar and novel configuration. Is it possible that the collection of familiar parts activates a representation of the intact familiar configuration even when they are spatially rearranged? We presented novel configurations as primes before test displays that assayed effects on figure-ground perception from memories of intact familiar objects. In our test displays, two equal-area regions shared a central border; one region depicted a portion of a familiar object. Previous research with such displays has shown that participants are more likely to perceive the region depicting a familiar object as the figure and the abutting region as its ground when the familiar object is depicted in its upright orientation rather than upside down. The novel primes comprised either the same or a different collection of parts as the familiar object in the test display . We found that participants were more likely to perceive the familiar region as figure in upright vs. inverted displays following part-rearranged primes but not control primes. Thus, priming with a novel configuration comprising the same familiar parts as the upcoming figure-ground display facilitated orientation-dependent effects of object memories on figure assignment. Similar results were obtained when the spatially rearranged collection of parts was suggested on the groundside of the prime's border, suggesting that familiar parts in novel configurations access the representation of their corresponding intact whole object before figure assignment. These data demonstrate that familiar parts access memories of familiar objects even when they are arranged in a novel configuration. Objects consist of collections of parts, but objects are also usually encountered in a particular orientation and with their parts arranged in a particular way. For instance, when we perceive or identify a table lamp, it is usually the case that the base of the lamp is on the table and is topped with the lampshade. The visual system may capitalize on the regularity of the spatial arrangement of object parts by representing objects not only  by their individual parts, but also (or solely) by the spatial arrangement of those parts  cortex respond selectively either to individual object parts  or the configuration they form , but not both  structures—specifically, the perirhinal cortex (PRC) and anterior temporal lobe (ATL)—assess the familiarity of both parts and configurations  shaped by the shared border, while the other region is perceived as a shapeless ground continuing behind that figure at their shared border. On each trial, participants are asked to report which region (left or right) they perceive as the figure. Using this paradigm, studies have shown that figure assignment is influenced by the presence of a portion of an intact familiar object on one side of the border. Specifically, the region that depicts a familiar object at the central border is more likely to be perceived as the figure at that border than a region that depicts a novel, meaningless shape , reports of that region as figure are not significantly above chance and are equivalent for upright and inverted displays  occur implicitly; explicit, declarative knowledge pertaining to the familiar configurations in these bipartite displays is neither necessary nor sufficient for these effects. This has been demonstrated in prior studies where telling participants up-front about the relationship between upright familiar configurations and their inverted or their part-rearranged counterparts did not bias participants' figure reports regarding those regions, suggesting that explicit knowledge is not sufficient to produce OMEFA effects  parts even when those parts are arranged in a novel configuration. To test this, we employed a figure assignment task using the upright and inverted familiar configuration bipartite displays . We predicted that if the collection of parts comprising the part-rearranged silhouette, though rearranged, nevertheless activated memory representations pertaining to the whole intact familiar configuration, then we should see a larger difference in figure reports on upright vs. inverted trials following the part-rearranged silhouette prime than the control silhouette prime.A total of 55 undergraduate students from the University of Arizona participated in this experiment in order to partially fulfill course requirements . All participants provided informed consent prior to the experiment and reported normal or corrected-to-normal visual acuity. An additional three participants were removed from the analysis for failing to respond on more than 15% of trials during the experiment.N = 32) were those used previously  were presented in their upright orientation; the rest were inverted. Each participant saw a specific test display (depicting a particular familiar configuration) only once, in either the upright or the inverted orientation. The specific familiar configurations in each orientation (upright/inverted), color (black/white), and location (left/right) were counterbalanced across participants. The two regions of each display were equated for area and matched for convexity  were small, enclosed, and symmetric—these attributes all favor perceiving the figure on the inside of the border. There were two types of primes: control primes and part-rearranged primes . Instructions were presented on the computer screen and were simultaneously read to the participant by the experimenter. Participants were told that on each trial, they would see an outline image appear briefly on the screen followed by a black and white display, and their task was to report whether they saw the figure lying on the right side or left side of the central border. The instructions defined the “figure” as the region of the display that looks like it has a definite shape and seems closer than the other region (the background) at the central border. Participants were told to respond using a button box with the left button labeled “left” and the right button labeled “right.”A sample trial is shown in Figure before the bipartite test display as well.It should be noted that this trial structure differed from that of previous studies that have used these bipartite test displays  ISI, it could have served as a pre-mask for the bipartite test display and may have reduced the effective exposure duration of the display, rendering the figure assignment task more difficult than in previous studies. Indeed, our participants were asked in post-experiment questioning how easy or difficult the task was for them. The majority of participants reported having difficulty perceiving the bipartite test display due to the fast trial structure. This important point is one to which we will return in the Results section.N = 16) and inverted for the remaining half (N = 16). Upright and inverted trials were randomly intermixed. Silhouette condition (control vs. part-rearranged) was manipulated between-subjects; thus, the type of silhouette remained constant across all 32 trials for a given participant.Before the experimental trials, participants were given 16 practice trials. No stimuli presented during the practice trials were used during the experimental trials. Thirty-two experimental trials followed the practice trials. The bipartite displays were upright for half of the trials  and were asked if they noticed it during the experiment. For participants in the part-rearranged prime condition, the experimenter pointed out and traced the familiar, rearranged parts in the sample outline prime and asked whether the participant ever noticed familiar parts in the outlines during the experiment. Participants were also asked whether they felt that the presence of the prime influenced their response to the black and white test display. Most participants reported that they barely noticed the prime due to its short exposure duration, the presence of the mask after the prime, and because their attention was focused on the bipartite test display and their demanding figure assignment task; moreover, they reported that they did not notice that the parts of the prime and the upcoming display were the same. Three participants in the part-rearranged condition indicated that they did perceive (or thought they might have perceived) the familiar parts contained in the primes, and that that the presence of those familiar parts might have influenced their decision on the figure-ground task. The data from these three participants were eliminated from the analysis, as the influence from the prime to the bipartite test display would have been explicit rather than implicit.Figures F = 4.76, p < 0.05. Specifically, participants reported perceiving the familiar region as figure more often for upright vs. inverted displays in the part-rearranged outline condition, t(28) = 3.03, p < 0.05, but not in the control condition, p > 0.90.The results showed orientation-dependent OMEFA effects when bipartite displays were preceded by a part-rearranged outline silhouette Figure  but not Surprisingly, in the control condition, the familiar configuration was perceived as figure equally often in both upright and inverted displays, on 71% of trials for both orientations. These percentages are significantly above chance (50%), which suggests that object memories were influencing figure assignment, although no orientation dependency was observed. In what follows, we first attempt to understand why orientation-dependent OMEFA effects were not observed in the control prime condition. We then turn to a discussion of the orientation dependent effects obtained in the part-rearranged prime condition.In order to understand the results obtained in the control condition, we return to considering our trial structure. In Experiment 1, the bipartite display was both post-masked and effectively pre-masked because a mask appeared after the outline silhouette prime that preceded the bipartite display  can occur before figure assignment. To test this, we presented novel outline silhouettes before the bipartite test displays as in Experiment 1, but in Experiment 2, the rearranged parts of the familiar object were suggested on the groundside (outside) of the outline silhouette rather than on the figure side  while the experimenter pointed out and traced the familiar, rearranged parts suggested in the ground. They were then directly asked if, during the experiment, they noticed any familiar parts on the groundside of the prime and whether the prime influenced their figure decision. None of the participants reported having perceived the familiar, rearranged parts suggested along the groundside of the outlines or their relationship to the bipartite test display in Experiment 2. Given these reports and the Gestalt configural cues biasing the inside of the outline shape to be perceived as the figure, we are reasonably confident that participants perceived the inside as figure and were not aware of the rearranged parts suggested in the seemingly shapeless ground, nor of the relationship between the silhouettes and the familiar region in the bipartite test display.t(25) = 2.85, p < 0.05. An ANOVA comparing responses made by participants in Experiment 2 to those of participants in the control group from Experiment 1 revealed a significant interaction between group and orientation, F = 4.34, p < 0.05.The results of Experiment 2 can be seen in Figure ps > 0.50). This suggests that even when spatially rearranged, parts can implicitly facilitate perception of the intact whole with the same collection of familiar parts in a different spatial relationship, and that this occurs before figure assignment.Experiment 1 and 2 together showed that familiar but spatially rearranged parts are sufficient to access representations of the whole familiar configuration. This effect was found regardless of whether the parts were suggested on the figure side or ground side of the outline; a 2 × 2 ANOVA with factors of experimental condition (rearranged parts on figure vs. ground) and orientation of the test display (upright vs. inverted) revealed no significant differences in figure responses between the part-rearranged conditions in Experiments 1 and 2 , they engage in inhibitory competition for figural status  of the outline's borders. However, we are reasonably confident that participants did indeed perceive the inside as figure and the outside as the ground based on the presence of Gestalt configural cues—including small area, closure, surroundedness, and symmetry—which favored this percept. Additionally, the outlines were always presented at fixation and were expected, which also increases the interpretation of the central, inside region as the figure (Peterson and Gibson, Finally, we interpreted our difference between figure reports for upright vs. inverted displays in the part-rearranged condition as facilitation of orientation-dependent OMEFA effects. However, an alternative explanation could be that the upright vs. inverted difference arose due to interference from the rearranged parts in the prime on the processing of the inverted displays. Upon the appearance of the part-rearranged prime, the representations of those upright parts would be activated such that the visual system was primed for upright stimuli. In this way, it would be more difficult for the system to then process and activate representations for an inverted stimulus. This interference in the inverted condition might have led to decreased reports of the familiar configuration as figure as compared to the upright condition. This interpretation of our data would still support our claim that prior presentation of object parts can implicitly activate the representation of the whole, although the influence on perception would be in the form of interference rather than facilitation. Future studies should investigate this alternative explanation.In two experiments, we show that orientation-dependent object memory effects on figure assignment can be facilitated via prior presentation of the rearranged parts of the upcoming familiar configuration compared to a control condition. These results are consistent with the interpretation that familiar parts can access representations of familiar wholes even when they are spatially rearranged into a novel configuration, and therefore have enormous implications for understanding the mechanisms underlying object perception.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.