Finite element analysis of Neanderthal and early Homo sapiens maxillary central incisor.

Neanderthal anterior teeth are very large and have a distinctive morphology characterized by robust 'shovel-shaped' crowns. These features are frequently seen as adaptive responses in dissipating heavy mechanical loads resulting from masticatory and non-masticatory activities. Although the long-standing debate surrounding this hypothesis has played a central role in paleoanthropology, is still unclear if Neanderthal anterior teeth can resist high mechanical loads or not. A novel way to answer this question is to use a multidisciplinary approach that considers together tooth architecture, dental wear and jaw movements. The aim of this study is to functionally reposition the teeth of Le Moustier 1 (a Neanderthal adolescent) and Qafzeh 9 (an early Homo sapiens adolescent) derived from wear facet mapping, occlusal fingerprint analysis and physical dental restoration methods. The restored dental arches are then used to perform finite element analysis on the left central maxillary incisor during edge-to-edge occlusion. The results show stress distribution differences between Le Moustier 1 and Qafzeh 9, with the former displaying higher tensile stress in enamel around the lingual fossa but lower concentration of stress in the lingual aspect of the root surface. These results seem to suggest that the presence of labial convexity, lingual tubercle and of a large root surface in Le Moustier 1 incisor helps in dissipating mechanical stress. The absence of these dental features in Qafzeh 9 is compensated by the presence of a thicker enamel, which helps in reducing the stress in the tooth crown.

Voxel-based dosimetry predicting treatment response and related toxicity in HCC patients treated with resin-based Y90 radioembolization: a prospective, single-arm study.

BACKGROUND: There is an increasing body of evidence indicating Y90 dose thresholds for tumor response and treatment-related toxicity. These thresholds are poorly studied in resin Y90, particularly in hepatocellular carcinoma (HCC). PURPOSE: To evaluate the efficacy of prospective voxel-based dosimetry for predicting treatment response and adverse events (AEs) in patients with HCC undergoing resin-based Y90 radioembolization. MATERIALS AND METHODS: This correlative study was based on a prospective single-arm clinical trial (NCT04172714), which evaluated the efficacy of low/scout (555 MBq) activity of resin-based Y90 for treatment planning. Partition model was used with goal of tumor dose (TD) > 200 Gy and non-tumoral liver dose (NTLD) < 70 Gy for non-segmental therapies. Single compartment dose of 200 Gy was used for segmentectomies. Prescribed Y90 activity minus scout activity was administered for therapeutic Y90 followed by Y90-PET/CT. Sureplan® (MIM Software, Cleveland, OH) was used for dosimetry analysis. Treatment response was evaluated at 3 and 6 months. Receiver operating characteristic curve determined TD response threshold for objective response (OR) and complete response (CR) as well as non-tumor liver dose (NTLD) threshold that predicted AEs. RESULTS: N = 30 patients were treated with 33 tumors (19 segmental and 14 non-segmental). One patient died before the first imaging, and clinical follow-up was excluded from this analysis. Overall, 26 (81%) of the tumors had an OR and 23 (72%) had a CR. A mean TD of 253 Gy predicted an OR with 92% sensitivity and 83% specificity (area under the curve (AUC = 0.929, p < 0.001). A mean TD of 337 Gy predicted a CR with 83% sensitivity and 89% specificity (AUC = 0.845, p < 0.001). A mean NTLD of 81 and 87 Gy predicted grade 3 AEs with 100% sensitivity and 100% specificity in the non-segmental cohort at 3- and 6-month post Y90, respectively. CONCLUSION: In patients with HCC undergoing resin-based Y90, there are dose response and dose toxicity thresholds directly affecting outcomes. CLINICAL TRIAL NUMBER: NCT04172714.

Accuracy and Safety of Scout Dose Resin Yttrium-90 Microspheres for Radioembolization Therapy Treatment Planning: A Prospective Single-Arm Clinical Trial.

PURPOSE: To compare the accuracy and safety of 0.56 GBq resin yttrium-90 (90Y) (scout90Y) microspheres with those of technetium-99m macroaggregated albumin (MAA) in predicting the therapeutic 90Y (Rx90Y) dose for patients with hepatocellular carcinoma (HCC). MATERIALS AND METHODS: This prospective single-arm clinical trial (Clinicaltrials.gov: NCT04172714) recruited patients with HCC. Patients underwent same-day mapping with MAA and scout90Y. Rx90Y activity was administered 3 days after mapping. Using paired t test and Pearson correlation, the tumor-to-normal ratio (TNR), lung shunt fraction (LSF), predicted mean tumor dose (TD), and nontumoral liver dose (NTLD) by MAA and scout90Y were compared with those by Rx90Y. Bland-Altman plots compared the level of agreement between the TNR and LSF of scout90Y and MAA with that of Rx90Y. The safety of scout90Y was evaluated by examining the discrepancy in extrahepatic activity between MAA and scout90Y. RESULTS: Thirty patients were treated using 19 segmental and 14 nonsegmental (ie, 2 contiguous segments or nonsegmental) therapies. MAA had weak LSF, moderate TNR, and moderate TD linear correlation with Rx90Y. Scout90Y had a moderate LSF, strong TNR, strong TD, and very strong NTLD in correlation with those of Rx90Y. Furthermore, the TNR and LSF of scout90Y had a stronger agreement with those of Rx90Y than with those of MAA. In the nonsegmental subgroup, MAA had no significant correlation with the TD and NTLD of Rx90Y, whereas scout90Y had a very strong correlation with both of these factors. In the segmental subgroup, both MAA and scout90Y had a strong linear correlation with the TD and NTLD of Rx90Y. CONCLUSIONS: Compared with MAA, scout90Y is a more accurate surrogate for Rx90Y biodistribution for nonsegmental therapies.

A Data-Driven Approach to Predict Fatigue in Exercise Based on Motion Data from Wearable Sensors or Force Plate.

Fatigue increases the risk of injury during sports training and rehabilitation. Early detection of fatigue during exercises would help adapt the training in order to prevent over-training and injury. This study lays the foundation for a data-driven model to automatically predict the onset of fatigue and quantify consequent fatigue changes using a force plate (FP) or inertial measurement units (IMUs). The force plate and body-worn IMUs were used to capture movements associated with exercises (squats, high knee jacks, and corkscrew toe-touch) to estimate participant-specific fatigue levels in a continuous fashion using random forest (RF) regression and convolutional neural network (CNN) based regression models. Analysis of unseen data showed high correlation (up to 89%, 93%, and 94% for the squat, jack, and corkscrew exercises, respectively) between the predicted fatigue levels and self-reported fatigue levels. Predictions using force plate data achieved similar performance as those with IMU data; the best results in both cases were achieved with a convolutional neural network. The displacement of the center of pressure (COP) was found to be correlated with fatigue compared to other commonly used features of the force plate. Bland-Altman analysis also confirmed that the predicted fatigue levels were close to the true values. These results contribute to the field of human motion recognition by proposing a deep neural network model that can detect fairly small changes of motion data in a continuous process and quantify the movement. Based on the successful findings with three different exercises, the general nature of the methodology is potentially applicable to a variety of other forms of exercises, thereby contributing to the future adaptation of exercise programs and prevention of over-training and injury as a result of excessive fatigue.

Enhanced laser speckle optical sensor for in situ strain sensing and structural health monitoring.

In this Letter, an enhanced laser speckle optical sensor (LSOS) for nondestructive, noncontact, and high-accuracy strain measurement has been developed. Subsystems of laser beam shaping and telecentric imaging were incorporated into the LSOS to achieve optimized speckle patterns, and a field-of-view (FOV) separation was introduced to extend sensor gauge length. Validation tests confirmed that the LSOS achieved consistent results with resistive strain gauges in laboratory conditions with maximum RMS error (RMSE) of $ 9.44\;\unicode{x00B5} \unicode{x03B5} $9.44µÎµ. Sensing practicality was demonstrated in field tests. The results showed that the LSOS is capable of achieving accurate strain measurements in an external environment with maximum RMSE of $ 13.34\;\unicode{x00B5}\unicode{x03B5} $13.34µÎµ.

Effects of Lumbar Spine Assemblies and Body-Borne Equipment Mass on Anthropomorphic Test Device Responses During Drop Tests.

When simulating or conducting land mine blast tests on armored vehicles to assess potential occupant injury, the preference is to use the Hybrid III anthropomorphic test device (ATD). In land blast events, neither the effect of body-borne equipment (BBE) on the ATD response nor the dynamic response index (DRI) is well understood. An experimental study was carried out using a drop tower test rig, with a rigid seat mounted on a carriage table undergoing average accelerations of 161 g and 232 g over 3 ms. A key aspect of the work looked at the various lumbar spine assemblies available for a Hybrid III ATD. These can result in different load cell orientations for the ATD which in turn can affect the load measurement in the vertical and horizontal planes. Thirty-two tests were carried out using two BBE mass conditions and three variations of ATDs. The latter were the Hybrid III with the curved (conventional) spine, the Hybrid III with the pedestrian (straight) spine, and the Federal Aviation Administration (FAA) Hybrid III which also has a straight spine. The results showed that the straight lumbar spine assemblies produced similar ATD responses in drop tower tests using a rigid seat. In contrast, the curved lumbar spine assembly generated a lower pelvis acceleration and a higher lumbar load than the straight lumbar spine assemblies. The maximum relative displacement of the lumbar spine occurred after the peak loading event, suggesting that the DRI is not suitable for assessing injury when the impact duration is short and an ATD is seated on a rigid seat on a drop tower. The peak vertical lumbar loads did not change with increasing BBE mass because the equipment mass effects did not become a factor during the peak loading event.

Polymeric fiber sensors for insertion forces and trajectory determination of cochlear implants in hearing preservation.

The level of hearing restoration in patients with severe to profound sensorineural hearing loss by means of cochlear implants (CIs) has drastically risen since the introduction of these neuroprosthetics. The proposed CI integrated with polymer optical fiber Bragg gratings (POFBGs) enables real-time evaluation of insertion forces and trajectory determination during implantation irrespective of the speed of insertion, as well as provides high signal quality, low stiffness levels, minimum induced stress even under forces of high magnitudes and exhibits significant reduction of the risk of fiber breakage inside the constricted cochlear geometry. As such, the proposed device opens new avenues towards atraumatic cochlear implantations and provides a direct route for the next generation of CIs with intraoperative insertion force assessment and path planning capacity crucial for surgical navigation. Hence, adaptation of this technology to clinical reality holds promising prospects for the hearing impaired.

Real-time forecasting of exercise-induced fatigue from wearable sensors.

Although a number of studies attempt to classify human fatigue, most models can only identify fatigue after fatigue has already occurred. In this paper, we propose a novel time series approach to forecasting wearable sensor data and associated fatigue progression during exercise. The proposed framework consists of spatio-temporal attention-based Transformer with an auxiliary critic and a fatigue classifier. The Transformer network is used to analyze the person-independent pattern underlying the past kinematic sequence obtained from wearable sensors and generate short term predictions of the human motion. Adversarial training is employed to regularize the Transformer and improve the time series forecasting performance. A fatigue classifier is used to estimate person-independent fatigue levels based on the forecasted wearable sensor data from the Transformer model. The proposed approach is validated with simulated and real squat datasets which were collected from young healthy participants. The proposed network can accurately forecast a time horizon of up to 80 timesteps for motion signal forecasting and fatigue classification. In terms of fatigue prediction, an accuracy of 83% and a Pearson correlation coefficient of 0.92 were achieved on forecasted motion data with unseen participant data. The experimental results show that our model can predict fatigue progression and outperforms other state-of-the-art techniques, achieving 95% correlation compared to 83% for the best performing baseline method. Successfully predicting fatigue progression can help a patient or athlete monitor and adjust their exercise session to prevent overexertion and fatigue-induced injury.

Model-based data augmentation for user-independent fatigue estimation.

OBJECTIVE: User-independent recognition of exercise-induced fatigue from wearable motion data is challenging, due to inter-participant variability. This study aims to develop algorithms that can accurately estimate fatigue during exercise. METHODS: A novel approach for wearable sensor data augmentation was used to generate (via OpenSim) a large corpus of simulated wearable human motion data, based on a small corpus of human motion data measured using optical sensors. Simulated data is generated using detailed kinematic modelling with variations based on human anthropometry datasets. Using both the recorded and generated data, we trained three different neural networks (Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), DeepConvLSTM) to perform person-independent fatigue estimation from wearable motion data. RESULTS: The estimation performance increased with the amount of simulated training data. Accuracy and correlation values were higher with the proposed data augmentation method as compared to other general time series augmentation methods (e.g, rotation, jettering, magnitude wrapping) with the same amount of training data. An accuracy of 87% and a Pearson correlation coefficient of 90% were achieved on unseen data when the DeepConvLSTM model was trained with the proposed augmented dataset. CONCLUSION: The enlarged dataset significantly improves the prediction of inter-individual fatigue. SIGNIFICANCE: Appropriate augmentation techniques for biomechanical data can improve model accuracy and reduce the need for expensive data collection.

Extensive use of waste glass in one-part alkali-activated materials: Towards sustainable construction practices.

The feasibility of the extensive recycling of waste glass in alkali-activated materials (AAMs) was evaluated. The waste glass was utilised in AAMs for two purposes: a partial activator and a mineral precursor. The waste glass was blended with commercial sodium hydroxide and then heated to produce the solid activator powder. The technical performance of waste glass-based activator was investigated to replace commercial sodium silicate, a common alkali-activator used in AAMs. The effect of waste glass using only as the activator (WGA) and using as both activator and precursor (WGAP) in fly ash/slag-based one-part AAMs was studied using strength and microstructure characterisations. A mass-cost and emission analysis of waste glass-based AAMs (WGA and WGAP) was conducted, comparing the results with ordinary Portland cement (OPC). Characterisation tests of waste glass-based activator showed the effective formation of sodium silicate minerals with the adequate dissolution of activator in water by releasing reactive alkali and silica. Both WGA and WGAP showed comparable strengths at 56 days with a denser microstructure under ambient curing. According to mass analysis, waste glass could be utilised up to 17% by mass of total binder. Based on the analysis of cost and CO2 emissions, WGA and WGAP are around 23% and 15% cheaper and 84% and 82% greener than OPC. The dual role of waste glass in AAMs as an activator and as a precursor broadens the recycling of glass waste in the cement industry by favouring technical and environmental outcomes.

On the Efficiency of Haptic Based Object Identification: Determining Where to Grasp to Get the Most Distinguishing Information.

Haptic perception is one of the key modalities in obtaining physical information of objects and in object identification. Most existing literature focused on improving the accuracy of identification algorithms with less attention paid to the efficiency. This work aims to investigate the efficiency of haptic object identification to reduce the number of grasps required to correctly identify an object out of a given object set. Thus, in a case where multiple grasps are required to characterise an object, the proposed algorithm seeks to determine where the next grasp should be on the object to obtain the most amount of distinguishing information. As such, the paper proposes the construction of the object description that preserves the association of the spatial information and the haptic information on the object. A clustering technique is employed both to construct the description of the object in a data set and for the identification process. An information gain (IG) based method is then employed to determine which pose would yield the most distinguishing information among the remaining possible candidates in the object set to improve the efficiency of the identification process. This proposed algorithm is validated experimentally. A Reflex TakkTile robotic hand with integrated joint displacement and tactile sensors is used to perform both the data collection for the dataset and the object identification procedure. The proposed IG approach was found to require a significantly lower number of grasps to identify the objects compared to a baseline approach where the decision was made by random choice of grasps.

Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images.

BACKGROUND: Computer-aided segmentation and border detection in dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. In this study, we compare two approaches for automatic border detection in dermoscopy images: density based clustering (DBSCAN) and Fuzzy C-Means (FCM) clustering algorithms. In the first approach, if there exists enough density--greater than certain number of points--around a point, then either a new cluster is formed around the point or an existing cluster grows by including the point and its neighbors. In the second approach FCM clustering is used. This approach has the ability to assign one data point into more than one cluster. RESULTS: Each approach is examined on a set of 100 dermoscopy images whose manually drawn borders by a dermatologist are used as the ground truth. Error rates; false positives and false negatives along with true positives and true negatives are quantified by comparing results with manually determined borders from a dermatologist. The assessments obtained from both methods are quantitatively analyzed over three accuracy measures: border error, precision, and recall. CONCLUSION: As well as low border error, high precision and recall, visual outcome showed that the DBSCAN effectively delineated targeted lesion, and has bright future; however, the FCM had poor performance especially in border error metric.

A lobster-inspired bending module for compliant robotic applications.

Ideally, robots may be designed to adapt to different tasks such as heavy lifting and handling delicate objects, in which the requirements in force compliance and position accuracy vary dramatically. While conventional rigid actuators are usually characterized by high precision and large force output, soft actuators are designed to be more compliant and flexible. In this paper, a lobster-inspired bending module with compliant actuation, enhanced torque output, and reconfigurability in assembling is presented. It is also capable of accurate control of its angular position with variable stiffness. Inspired by the anatomic structure of the lobster leg joint, the bending module has antagonistic soft chambers for actuation and rigid shells for structural protection and support. Theoretical models have been developed and their capability of independently adjusting both the bending angle and stiffness has been evaluated through experiments. A control strategy is constructed to realize angle control and stiffness adaptation. In order to demonstrate various applications of the proposed bending module, reconfigurable robotic fingers are assembled and shown to be capable of generating different motion profiles. In addition, robotic grippers are built for lifting both delicate and heavy objects, demonstrating applications that require both high force and compliant handling.

Application of Fibre Bragg Grating Sensors in Strain Monitoring and Fracture Recovery of Human Femur Bone.

Fibre Bragg Grating (FBG) sensors are gaining popularity in biomedical engineering. However, specific standards for in vivo testing for their use are absolutely limited. In this study, in vitro experimental tests were performed to investigate the behaviors and applications of gratings attached to intact and fractured thighbone for a range of compression loading (<300 N) based around some usual daily activities. The wavelength shifts and the corresponding strain sensitivities of the FBG sensors were measured to determine their effectiveness in monitoring the femoral fracture healing process. Four different arrangements of FBG sensors were selected to measure strains at different critical locations on the femoral sawbones surface. Data obtained for intact and plated sawbones were compared using both embedded longitudinal and coiled FBG arrays. Strains were measured close to the fracture, posterior linea aspera and popliteal surface areas, as well as at the proximal and distal ends of the synthetic femur; their responses are discussed herein. The gratings on the longitudinally secured FBG arrays were found to provide high levels of sensitivity and precise measurements, even for relatively small loads (<100 N). Nevertheless, embedding angled FBG sensors is essential to measure the strain generated by applied torque on the femur bone. The maximum recorded strain of the plated femur was 503.97 µÎµ for longitudinal and -274.97 µÎµ for coiled FBG arrays, respectively. These project results are important to configure effective arrangements and orientations of FBG sensors with respect to fracture position and fixation implant for future in vivo experiments.

Protein local 3D structure prediction by Super Granule Support Vector Machines (Super GSVM).

BACKGROUND: Understanding the relationship between the protein sequence and the 3D structure is a major research area in bioinformatics. The prediction of complete protein tertiary structure based only on sequence information is still an impractical work. This paper aims at revealing the hidden knowledge of the sequence motifs and the local tertiary structure. RESULTS: In this paper, we propose a Super Granule Support Vector Machine (Super GSVM) model to obtain the high quality protein sequence motifs and to predict local tertiary structure information based on purely sequence information. CONCLUSION: The proposed model overcomes the innate shortcoming of using the SVM on such a large data set, which is the inherent computational complexity involved in training support vectors for huge datasets including half million of samples. The satisfactory prediction results show the Super GSVM model generates decent protein sequence clusters and has the ability to capture the hidden sequence-to-structure information. This model also has a strong potential in the application of SVMs on other research areas with huge datasets.

Integrated Force Sensor in a Cochlear Implant for Hearing Preservation Surgery.

Cochlear Implant is used for patients with severe hearing loss. It is a neural-prosthesis that stimulates the nerve endings within the cochlea, which is the organ of hearing. The surgical technique involves inserting the electrode array of the implant into a very small "snail-like" spiral structure. During this insertion process, the surgeon's finger tip is not able to perceive the resistance from the contact of the implant and the cochlea's internal structure, below the internal rupture threshold. This can potentially damage vital structures and result in the worsening of residual hearing and poor speech perception. Currently, there is no clinically and commercially available intra-operative force feedback system. A custom made sensor is therefore proposed, integrated within the implant to enable real-time force readings. The device will provide surgeons with the vital force feedback information related to the implants' position within the cochlea. This paper concentrates on demonstrating that the proposed sensor is capable of measuring the contact force below the rupture threshold of the cochlea's internal structure.

Novel implant for peri-prosthetic proximal tibia fractures.

BACKGROUND: Repair of peri-prosthetic proximal tibia fractures is very challenging in patients with a total knee replacement or arthroplasty. The tibial component of the knee implant severely restricts the fixation points of the tibial implant to repair peri-prosthetic fractures. A novel implant has been designed with an extended flange over the anterior of tibial condyle to provide additional points of fixation, overcoming limitations of existing generic locking plates used for proximal tibia fractures. Furthermore, the screws fixed through the extended flange provide additional support to prevent the problem of subsidence of tibial component of knee implant. METHODS: The design methodology involved extraction of bone data from CT scans into a flexible CAD format, implant design and structural evaluation and optimisation using FEM as well as prototype development and manufacture by selective laser melting 3D printing technology with Ti6Al4 V powder. RESULTS: A prototype tibia implant was developed based on a patient-specific bone structure, which was regenerated from the CT images of patient's tibia. The design is described in detail and being applied to fit up to 80% of patients, for both left and right sides based on the average dimensions and shape of the bone structure from a wide range of CT images. CONCLUSION: A novel tibial implant has been developed to repair peri-prosthetic proximal tibia fractures which overcomes significant constraints from the tibial component of existing knee implant.

Identifying pathogenicity islands in bacterial pathogenomics using computational approaches.

High-throughput sequencing technologies have made it possible to study bacteria through analyzing their genome sequences. For instance, comparative genome sequence analyses can reveal the phenomenon such as gene loss, gene gain, or gene exchange in a genome. By analyzing pathogenic bacterial genomes, we can discover that pathogenic genomic regions in many pathogenic bacteria are horizontally transferred from other bacteria, and these regions are also known as pathogenicity islands (PAIs). PAIs have some detectable properties, such as having different genomic signatures than the rest of the host genomes, and containing mobility genes so that they can be integrated into the host genome. In this review, we will discuss various pathogenicity island-associated features and current computational approaches for the identification of PAIs. Existing pathogenicity island databases and related computational resources will also be discussed, so that researchers may find it to be useful for the studies of bacterial evolution and pathogenicity mechanisms.

Finite element analysis of damage by cochlear implant electrode array's proximal section to the basilar membrane.

HYPOTHESIS: This study aims to examine the mechanism of damage to the basilar membrane caused by the proximal section of the cochlear implant electrode array. BACKGROUND: The electrode array has been found to severely damage the basilar membrane. Most previous studies on cochlear implant insertion damage largely focused on the injury by the front section (tip) of the electrode array to the membrane. Little attempt has been made to investigate the damage caused by the array's proximal section. METHODS: A computational model using the finite element method has been developed for assessing the likelihood of the damage based on two criteria: 1) frequency of contact between the proximal section of the electrode array and the upper wall of the scala tympani where the basilar membrane is located, and 2) magnitude of the associated shear stresses at the contact areas. The model has been validated and used for studying the effect of electrode array's stiffness properties on the damage. RESULTS: The proximal section of the contour array is most likely to hit the basilar membrane, compared with its previous versions (the straight array and the single wire electrode). In terms of shear stress magnitude, the proximal section of the contour array exerts higher stresses on the scala tympani's upper wall and, thus, is more likely to damage the basilar membrane, compared with that of the straight array. CONCLUSION: Results from this study are useful for cochlear implant surgeons in better understanding the mechanism of damage by the electrode array's proximal section to the basilar membrane and in establishing advanced insertion techniques for reducing the damage (in particular, the results strongly support the "advance off-stylet" technique). The outcomes of the study also are beneficial for cochlear implant designers in selecting appropriate stiffness profiles for future electrode arrays, which are expected to cause minimal damage to the basilar membrane (a new design of the contour array with stiffness increasing from the front to the proximal section is highly recommended).

GIST: Genomic island suite of tools for predicting genomic islands in genomic sequences.

UNLABELLED: Genomic Islands (GIs) are genomic regions that are originally from other organisms, through a process known as Horizontal Gene Transfer (HGT). Detection of GIs plays a significant role in biomedical research since such align genomic regions usually contain important features, such as pathogenic genes. We have developed a use friendly graphic user interface, Genomic Island Suite of Tools (GIST), which is a platform for scientific users to predict GIs. This software package includes five commonly used tools, AlienHunter, IslandPath, Colombo SIGI-HMM, INDeGenIUS and Pai-Ida. It also includes an optimization program EGID that ensembles the result of existing tools for more accurate prediction. The tools in GIST can be used either separately or sequentially. GIST also includes a downloadable feature that facilitates collecting the input genomes automatically from the FTP server of the National Center for Biotechnology Information (NCBI). GIST was implemented in Java, and was compiled and executed on Linux/Unix operating systems. AVAILABILITY: The database is available for free at http://www5.esu.edu/cpsc/bioinfo/software/GIST.