Automated identification of minimal myocardial motion for improved image quality on MR angiography at 3 T

OBJECTIVE: Imaging during a period of minimal myocardial motion is of paramount importance for coronary MR angiography (MRA). The objective of our study was to evaluate the utility of FREEZE, a custom-built automated tool for the identification of the period of minimal myocardial motion, in both a moving phantom at 1.5 T and 10 healthy adults (nine men, one woman; mean age, 24.9 years; age range, 21-32 years) at 3 T. CONCLUSION: Quantitative analysis of the moving phantom showed that dimension measurements approached those obtained in the static phantom when using FREEZE. In vitro, vessel sharpness, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were significantly improved when coronary MRA was performed during the software-prescribed period of minimal myocardial motion (p < 0.05). Consistent with these objective findings, image quality assessments by consensus review also improved significantly when using the automated prescription of the period of minimal myocardial motion. The use of FREEZE improves image quality of coronary MRA. Simultaneously, operator dependence can be minimized while the ease of use is improved.

Automated identification and tracking of polar-cap plasma patches at solar minimum

A method of automatically identifying and tracking polar-cap plasma patches, utilising data inversion and feature-tracking methods, is presented. A well-established and widely used 4-D ionospheric imaging algorithm, the Multi-Instrument Data Assimilation System (MIDAS), inverts slant total electron content (TEC) data from ground-based Global Navigation Satellite System (GNSS) receivers to produce images of the free electron distribution in the polar-cap ionosphere. These are integrated to form vertical TEC maps. A flexible feature-tracking algorithm, TRACK, previously used extensively in meteorological storm-tracking studies is used to identify and track maxima in the resulting 2-D data fields. Various criteria are used to discriminate between genuine patches and "false-positive" maxima such as the continuously moving day-side maximum, which results from the Earth's rotation rather than plasma motion. Results for a 12-month period at solar minimum, when extensive validation data are available, are presented. The method identifies 71 separate structures consistent with patch motion during this time. The limitations of solar minimum and the consequent small number of patches make climatological inferences difficult, but the feasibility of the method for patches larger than approximately 500 km in scale is demonstrated and a larger study incorporating other parts of the solar cycle is warranted. Possible further optimisation of discrimination criteria, particularly regarding the definition of a patch in terms of its plasma concentration enhancement over the surrounding background, may improve results.

Automated identification of neural correlates of continuous variables

Background: The electroencephalogram (EEG) may be described by a large number of different feature types and automated feature selection methods are needed in order to reliably identify features which correlate with continuous independent variables. New method: A method is presented for the automated identification of features that differentiate two or more groups inneurologicaldatasets basedupona spectraldecompositionofthe feature set. Furthermore, the method is able to identify features that relate to continuous independent variables. Results: The proposed method is first evaluated on synthetic EEG datasets and observed to reliably identify the correct features. The method is then applied to EEG recorded during a music listening task and is observed to automatically identify neural correlates of music tempo changes similar to neural correlates identified in a previous study. Finally,the method is applied to identify neural correlates of music-induced affective states. The identified neural correlates reside primarily over the frontal cortex and are consistent with widely reported neural correlates of emotions. Comparison with existing methods: The proposed method is compared to the state-of-the-art methods of canonical correlation analysis and common spatial patterns, in order to identify features differentiating synthetic event-related potentials of different amplitudes and is observed to exhibit greater performance as the number of unique groups in the dataset increases. Conclusions: The proposed method is able to identify neural correlates of continuous variables in EEG datasets and is shown to outperform canonical correlation analysis and common spatial patterns.

Candida spp.: manual identification (reference method) and automated identification (Vitek system platform)

Yeasts are becoming a common cause of nosocomial fungal infections that affect immunocompromised patients. Such infections can evolve into sepsis, whose mortality rate is high. This study aimed to evaluate the viability of Candida species identification by the automated system Vitek-Biomerieux (Durham, USA). Ninety-eight medical charts referencing the Candida spp. samples available for the study were retrospectively analyzed. The system Vitek-Biomerieux with Candida identification card is recommended for laboratory routine use and presents 80.6% agreement with the reference method. By separate analysis of species, 13.5% of C. parapsilosis samples differed from the reference method, while the Vitek system wrongly identified them as C. tropicalis, C. lusitaneae or as Candida albicans. C. glabrata presented a discrepancy of only one sample (25%), and was identified by Vitek as C. parapsilosis. C. guilliermondii also differed in only one sample (33.3%), being identified as Candida spp. All C. albicans, C. tropicalis and C. lusitaneae samples were identified correctly.

Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.

This study was carried out to detect differences in locomotion and feeding behavior in lame (group L; n = 41; gait score ≥ 2.5) and non-lame (group C; n = 12; gait score ≤ 2) multiparous Holstein cows in a cross-sectional study design. A model for automatic lameness detection was created, using data from accelerometers attached to the hind limbs and noseband sensors attached to the head. Each cow's gait was videotaped and scored on a 5-point scale before and after a period of 3 consecutive days of behavioral data recording. The mean value of 3 independent experienced observers was taken as a definite gait score and considered to be the gold standard. For statistical analysis, data from the noseband sensor and one of two accelerometers per cow (randomly selected) of 2 out of 3 randomly selected days was used. For comparison between group L and group C, the T-test, the Aspin-Welch Test and the Wilcoxon Test were used. The sensitivity and specificity for lameness detection was determined with logistic regression and ROC-analysis. Group L compared to group C had significantly lower eating and ruminating time, fewer eating chews, ruminating chews and ruminating boluses, longer lying time and lying bout duration, lower standing time, fewer standing and walking bouts, fewer, slower and shorter strides and a lower walking speed. The model considering the number of standing bouts and walking speed was the best predictor of cows being lame with a sensitivity of 90.2% and specificity of 91.7%. Sensitivity and specificity of the lameness detection model were considered to be very high, even without the use of halter data. It was concluded that under the conditions of the study farm, accelerometer data were suitable for accurately distinguishing between lame and non-lame dairy cows, even in cases of slight lameness with a gait score of 2.5.

Automated traffic route identification through the shared nearest neighbour algorithm

Many organisations need to extract useful information from huge amounts of movement data. One example is found in maritime transportation, where the automated identification of a diverse range of traffic routes is a key management issue for improving the maintenance of ports and ocean routes, and accelerating ship traffic. This paper addresses, in a first stage, the research challenge of developing an approach for the automated identification of traffic routes based on clustering motion vectors rather than reconstructed trajectories. The immediate benefit of the proposed approach is to avoid the reconstruction of trajectories in terms of their geometric shape of the path, their position in space, their life span, and changes of speed, direction and other attributes over time. For clustering the moving objects, an adapted version of the Shared Nearest Neighbour algorithm is used. The motion vectors, with a position and a direction, are analysed in order to identify clusters of vectors that are moving towards the same direction. These clusters represent traffic routes and the preliminary results have shown to be promising for the automated identification of traffic routes with different shapes and densities, as well as for handling noise data.

Identification of environmental Serratia plymuthica strains with the new Combo panels Type 1S

Automated systems are required when numerous samples need to be processed, offering both high through put and test of a multiple simultaneously. This study was performed to compare the MicroScan WalkAway automated identification system in conjunction with the new MicroScan Combo Neg Panels Type 1S with conventional biochemical methods for identifying ten environmental Serratia plymuthica strains. High correlation between both methods were observed for all the 21 tests evaluated, and the MicroScan system was found capable of correctly identifying all S. plymuthica strains tested. In all tests, the percentage of correlation was 100%, except in raffinose test (91%).

TCRep 3D: an automated in silico approach to study the structural properties of TCR repertoires.

TCRep 3D is an automated systematic approach for TCR-peptide-MHC class I structure prediction, based on homology and ab initio modeling. It has been considerably generalized from former studies to be applicable to large repertoires of TCR. First, the location of the complementary determining regions of the target sequences are automatically identified by a sequence alignment strategy against a database of TCR Vα and Vβ chains. A structure-based alignment ensures automated identification of CDR3 loops. The CDR are then modeled in the environment of the complex, in an ab initio approach based on a simulated annealing protocol. During this step, dihedral restraints are applied to drive the CDR1 and CDR2 loops towards their canonical conformations, described by Al-Lazikani et. al. We developed a new automated algorithm that determines additional restraints to iteratively converge towards TCR conformations making frequent hydrogen bonds with the pMHC. We demonstrated that our approach outperforms popular scoring methods (Anolea, Dope and Modeller) in predicting relevant CDR conformations. Finally, this modeling approach has been successfully applied to experimentally determined sequences of TCR that recognize the NY-ESO-1 cancer testis antigen. This analysis revealed a mechanism of selection of TCR through the presence of a single conserved amino acid in all CDR3β sequences. The important structural modifications predicted in silico and the associated dramatic loss of experimental binding affinity upon mutation of this amino acid show the good correspondence between the predicted structures and their biological activities. To our knowledge, this is the first systematic approach that was developed for large TCR repertoire structural modeling.

Identification and visualization of multidimensional antigen-specific T-cell populations in polychromatic cytometry data.

An important aspect of immune monitoring for vaccine development, clinical trials, and research is the detection, measurement, and comparison of antigen-specific T-cells from subject samples under different conditions. Antigen-specific T-cells compose a very small fraction of total T-cells. Developments in cytometry technology over the past five years have enabled the measurement of single-cells in a multivariate and high-throughput manner. This growth in both dimensionality and quantity of data continues to pose a challenge for effective identification and visualization of rare cell subsets, such as antigen-specific T-cells. Dimension reduction and feature extraction play pivotal role in both identifying and visualizing cell populations of interest in large, multi-dimensional cytometry datasets. However, the automated identification and visualization of rare, high-dimensional cell subsets remains challenging. Here we demonstrate how a systematic and integrated approach combining targeted feature extraction with dimension reduction can be used to identify and visualize biological differences in rare, antigen-specific cell populations. By using OpenCyto to perform semi-automated gating and features extraction of flow cytometry data, followed by dimensionality reduction with t-SNE we are able to identify polyfunctional subpopulations of antigen-specific T-cells and visualize treatment-specific differences between them.

Graphical Model-Based Vertebra Identification from X-Ray Image(s)

Automated identification of vertebrae from X-ray image(s) is an important step for various medical image computing tasks such as 2D/3D rigid and non-rigid registration. In this chapter we present a graphical model-based solution for automated vertebra identification from X-ray image(s). Our solution does not ask for a training process using training data and has the capability to automatically determine the number of vertebrae visible in the image(s). This is achieved by combining a graphical model-based maximum a posterior probability (MAP) estimate with a mean-shift based clustering. Experiments conducted on simulated X-ray images as well as on a low-dose low quality X-ray spinal image of a scoliotic patient verified its performance.

A fully automated system for multispectral ore microscopy

A joint research to develop an efficient method for automated identification and quantification of ores [1], based on Reflected Light Microscopy (RLM) in the VNIR realm (Fig. 1), provides an alternative to modern SEM based equipments used by geometallurgists, but for ~ 1/10th of the price.

Differentiation between Candida albicans and Candida dubliniensis using hypertonic Sabouraud broth and tobacco agar

INTRODUCTION: Opportunistic fungal infections in immunocompromised hosts are caused by Candida species, and the majority of such infections are due to Candida albicans. However, the emerging pathogen Candida dubliniensis demonstrates several phenotypic characteristics in common with C. albicans, such as production of germ tubes and chlamydospores, calling attention to the development of stable resistance to fluconazole in vitro. The aim of this study was to evaluate the performance of biochemistry identification in the differentiating between C. albicans and C. dubliniensis, by phenotyping of yeast identified as C. albicans. METHODS: Seventy-nine isolates identified as C. albicans by the API system ID 32C were grown on Sabouraud dextrose agar at 30°C for 24-48h and then inoculated on hypertonic Sabouraud broth and tobacco agar. RESULTS: Our results showed that 17 (21.5%) isolates were growth-inhibited on hypertonic Sabouraud broth, a phenotypic trait inconsistent with C. albicans in this medium. However, the results observed on tobacco agar showed that only 9 (11.4%) of the growth-inhibited isolates produced characteristic colonies of C. dubliniensis (rough colonies, yellowish-brown with abundant fragments of hyphae and chlamydospores). CONCLUSIONS: The results suggest that this method is a simple tool for screening C. albicans and non-albicans yeast and for verification of automated identification.

Characterization of local and systemic immune-modulation in melanoma patients

Le mélanome cutané est un des cancers les plus agressifs et dont l'incidence augmente le plus en Suisse. Une fois métastatique, le pronostic de survie moyenne avec les thérapies actuelles est d'environ huit mois, avec moins de 5% de survie à cinq ans. Les récents progrès effectués dans la compréhension de la biologie de la cellule tumorale mais surtout dans l'importance du système immunitaire dans le contrôle de ce cancer ont permis le développement de nouveaux traitements novateurs et prometteurs. Ces thérapies, appelées immunothérapies, reposent sur la stimulation et l'augmentation de la réponse immunitaire à la tumeur. Alors que les derniers essais cliniques ont démontré l'efficacité de ces traitements chez les patients avec des stades avancés de la maladie, le contrôle de la maladie à long- terme est seulement atteint chez une minorité des patients. La suppression locale et systémique de la réponse immunitaire spécifique anti-tumorale apparaitrait comme une des raisons expliquant la persistance d'un mauvais pronostic clinique chez ces patients. Des études sur les souris ont montré que les vaisseaux lymphatiques joueraient un rôle primordial dans ce processus en induisant une tolérance immune, ce qui permettrait à la tumeur d'échapper au contrôle du système immunitaire et métastatiser plus facilement. Ces excitantes découvertes n'ont pas encore été établi et prouvé chez l'homme. Dans cette thèse, nous montrons pour la première fois que les vaisseaux lymphatiques sont directement impliqués dans la modulation de la réponse immunitaire au niveau local et systémique dans le mélanome chez l'homme. Ces récentes découvertes montrent le potentiel de combiner des thérapies visant le système lymphatique avec les immunothérapies actuellement utilisées afin d'améliorer le pronostic des patients atteint du mélanome. -- Cutaneous melanoma is one of the most invasive and metastatic human cancers and causes 75% of skin cancer mortality. Current therapies such as surgery and chemotherapy fail to control metastatic disease, and relapse occurs frequently due to microscopic residual lesions. It is, thus, essential to develop and optimize novel therapeutic strategies to improve curative responses in these patients. In recent decades, tumor immunologists have revealed the development of spontaneous adaptive immune responses in melanoma patients, leading to the accumulation of highly differentiated tumor-specific T cells at the tumor site. This remains one of the most powerful prognostic markers to date. Immunotherapies that augment the natural function of these tumor-specific T cells have since emerged as highly attractive therapeutic approaches to eliminate melanoma cells. While recent clinical trials have demonstrated great progress in the treatment of advanced stage melanoma, long-term disease control is still only achieved in a minority of patients. Local and systemic immune suppression by the tumor appears to be responsible, in part, for this poor clinical evolution. These facts underscore the need for a better analysis and characterization of immune- related pathways within the tumor microenvironment (TME), as well as at the systemic level. The overall goal of this thesis is, thus, to obtain greater insight into the complexity and heterogeneity of the TME in human melanoma, as well as to investigate immune modulation beyond the TME, which ultimately influences the immune system throughout the whole body. To achieve this, we established two main objectives: to precisely characterize local and systemic immune modulation (i) in untreated melanoma patients and (ii) in patients undergoing peptide vaccination or checkpoint blockade therapy with anti-cytotoxic T- lymphocyte-asisctaed protein-4 (CTLA-4) antibody. In the first and main part of this thesis, we analyzed lymphatic vessels in relation to anti-tumor immune responses in tissues from vaccinated patients using a combination of immunohistochemistry (IHC) techniques, whole slide scanning/analysis, and an automatic quantification system. Strikingly, we found that increased lymphatic vessel density was associated with high expression of immune suppressive molecules, low functionality of tumor-infiltrating CD8+ T cells and decreased cytokine production by tumor-antigen specific CD8+ T cells in the blood. These data revealed a previously unappreciated local and systemic role of lymphangiogenesis in modulating T cell responses in human cancer and support the use of therapies that target lymphatic vessels combined with existing and future T cell based therapies. In the second objective, we describe a metastatic melanoma patient who developed pulmonary sarcoid-like granulomatosis following repetitive vaccination with peptides and CpG. We demonstrated that the onset of this pulmonary autoimmune adverse event was related to the development of a strong and long-lasting tumor-specific CD8+ T cell response. This constitutes the first demonstration that a new generation tumor vaccine can induce the development of autoimmune adverse events. In the third objective, we assessed the use of Fourier Transform Infrared (FTIR) imaging to identify melanoma cells and lymphocyte subpopulations in lymph node (LN) metastasis tissues, thanks to a fruitful collaboration with researchers in Brussels. We demonstrated that the different cell types in metastatic LNs have different infrared spectral features allowing automated identification of these cells. This technic is therefore capable of distinguishing known and novel biological features in human tissues and has, therefore, significant potential as a tool for histopathological diagnosis and biomarker assessment. Finally, in the fourth objective, we investigated the role of colony- stimulating factor-1 (CSF-1) in modulating the anti-tumor response in ipilimumab-treated patients using IHC and in vitro co-cultures, revealing that melanoma cells produce CSF-1 via CTL-derived cytokines when attacked by cytotoxic T lymphocytes (CTLs), resulting in the recruitment of immunosuppressive monocytes. These findings support the combined use of CSF-1R blockade with T cell based immunotherapy for melanoma patients. Taken together, our results reveal the existence of novel mechanisms of immune modulation and thus promote the optimization of combination immunotherapies against melanoma. -- Le mélanome cutané est un des cancers humains les plus invasifs et métastatiques et est responsable de 75% de la mortalité liée aux cancers de la peau. Les thérapies comme la chirurgie et la chimiothérapie ont échoué à contrôler le mélanome métastatique, par ailleurs les rechutes sous ces traitements ont été montrées fréquentes. Il est donc essentiel de développer et d'optimiser de nouvelles stratégies thérapeutiques pour améliorer les réponses thérapeutiques de ces patients. Durant les dernières décennies, les immunologistes spécialisés dans les tumeurs ont démontré qu'un patient atteint du mélanome pouvait développer spontanément une réponse immune adaptative à sa tumeur et que l'accumulation de cellules T spécifiques tumorales au sein même de la tumeur était un des plus puissants facteurs pronostiques. Les immunothérapies qui ont pour but d'augmenter les fonctions naturelles de ces cellules T spécifiques tumorales ont donc émergé comme des approches thérapeutiques très attractives pour éliminer les cellules du mélanome. Alors que les derniers essais cliniques ont démontré un progrès important dans le traitement des formes avancées du mélanome, le contrôle de la maladie à long-terme est seulement atteint chez une minorité des patients. La suppression immune locale et systémique apparaitrait comme une des raisons expliquant la persistance d'un mauvais pronostic clinique chez ces patients. Ces considérations soulignent la nécessité de mieux analyser et caractériser les voies immunitaires non seulement au niveau local dans le microenvironement tumoral mais aussi au niveau systémique dans le sang des patients. Le but de cette thèse est d'obtenir une plus grande connaissance de la complexité et de l'hétérogénéité du microenvironement tumoral dans les mélanomes mais aussi d'investiguer la modulation immunitaire au delà du microenvironement tumoral au niveau systémique. Afin d'atteindre ce but, nous avons établi deux objectifs principaux : caractériser précisément la modulation locale et systémique du système immunitaire (i) chez les patients atteints du mélanome qui n'ont pas reçu de traitement et (ii) chez les patients qui ont été traités soit par des vaccins soit par des thérapies qui bloquent les points de contrôles. Dans la première et majeure partie de cette thèse, nous avons analysé les vaisseaux lymphatiques en relation avec la réponse immunitaire anti-tumorale dans les tissus des patients vaccinés grâce à des techniques d'immunohistochimie et de quantification informatisé et automatique des marquages. Nous avons trouvé qu'une densité élevée de vaisseaux lymphatiques dans la tumeur était associée à une plus grande expression de molécules immunosuppressives ainsi qu'à une diminution de la fonctionnalité des cellules T spécifiques tumoral dans la tumeur et dans le sang des patients. Ces résultats révèlent un rôle jusqu'à là inconnu des vaisseaux lymphatiques dans la modulation directe du système immunitaire au niveau local et systémique dans les cancers de l'homme. Cette recherche apporte finalement des preuves du potentiel de combiner des thérapies visant le système lymphatique avec des autres immunothérapies déjà utilisées en clinique. Dans le second objectif, nous rapportons le cas d'un patient atteint d'un mélanome avec de multiples métastases qui a développé à la suite de plusieurs vaccinations répétées et consécutives avec des peptides et du CpG, un évènement indésirable sous la forme d'une granulomatose pulmonaire sarcoid-like. Nous avons démontré que l'apparition de cet évènement était intimement liée au développement d'une réponse immunitaire durable et spécifique contre les antigènes de la tumeur. Par là- même, nous prouvons pour la première fois que la nouvelle génération de vaccins est aussi capable d'induire des effets indésirables auto-immuns. Pour le troisième objectif, nous avons voulu savoir si l'utilisation de la spectroscopie infrarouge à transformée de Fourier (IRTF) était capable d'identifier les cellules du mélanome ainsi que les différents sous-types cellulaires dans les ganglions métastatiques. Grâce à nos collaborateurs de Bruxelles, nous avons pu établir que les diverses composantes cellulaires des ganglions atteints par des métastases du mélanome présentaient des spectres infrarouges différents et qu'elles pouvaient être identifiées d'une façon automatique. Cette nouvelle technique permettrait donc de distinguer des caractéristiques biologiques connues ou nouvelles dans les tissus humains qui auraient des retombées pratiques importantes dans le diagnostic histopathologique et dans l'évaluation des biomarqueurs. Finalement dans le dernier objectif, nous avons investigué le rôle du facteur de stimulation des colonies (CSF-1) dans la modulation de la réponse immunitaire anti-tumorale chez les patients qui ont été traités par l'Ipilimumab. Nos expériences in vivo au niveau des tissus tumoraux et nos co-cultures in vitro nous ont permis de démontrer que les cytokines secrétées par les cellules T spécifiques anti-tumorales induisaient la sécrétion de CSF-1 dans les cellules du mélanome ce qui résultait en un recrutement de monocytes immunosuppresseurs. Dans son ensemble, cette thèse révèle donc l'existence de nouveaux mécanismes de modulation de la réponse immunitaire anti-tumorale et propose de nouvelles optimisations de combinaison d'immunothérapies contre le mélanome.

Identificação de Enterobacter spp. através de técnicas convencionais e do sistema semi-automatizado Microscan (Autoscan-4)

A hundred and ninety-five (195) strains of Enterobacter spp., isolated from diverse clinical specimens - urine, feces, cateter, blood, wound, tracheal aspirate, vaginal fluid - were submitted to the conventional identification by biochemical tests, and were also submitted to the identification by panels NegCombo 20 of the system automated MicroScan - AutoScan- 4 (Dade Behring Inc., West Sacramento, CA, USA). The samples were from patients of the Clinical Laboratory from the School of Pharmacy and Biochemistry of UNOESTE, Presidente Prudente, SP, and from patients hospitalized at the University Hospital Domingos Leonardo Cerávolo, UNOESTE. Of the total of strains tested, 191 (97.9%) presented agreement between the two approaches utilized and 4 strains (2.1%) presented identification disagreement, that is, the genus identified was different in each approach. By this study, the conclusion is that both the approaches utilized for the identification presented advantages and disadvantages related to the cost, facility of execution, quickness, reliability and some other characteristics. Even so, our results showed that conventional methods represent a reliable tool for Enterobacter identification.

Analysis of behavioral changes in dairy cows associated with claw horn lesions

Detecting lame cows is important in improving animal welfare. Automated tools are potentially useful to enable identification and monitoring of lame cows. The goals of this study were to evaluate the suitability of various physiological and behavioral parameters to automatically detect lameness in dairy cows housed in a cubicle barn. Lame cows suffering from a claw horn lesion (sole ulcer or white line disease) of one claw of the same hind limb (n=32; group L) and 10 nonlame healthy cows (group C) were included in this study. Lying and standing behavior at night by tridimensional accelerometers, weight distribution between hind limbs by the 4-scale weighing platform, feeding behavior at night by the nose band sensor, and heart activity by the Polar device (Polar Electro Oy, Kempele, Finland) were assessed. Either the entire data set or parts of the data collected over a 48-h period were used for statistical analysis, depending upon the parameter in question. The standing time at night over 12 h and the limb weight ratio (LWR) were significantly higher in group C as compared with group L, whereas the lying time at night over 12 h, the mean limb difference (△weight), and the standard deviation (SD) of the weight applied on the limb taking less weight were significantly lower in group C as compared with group L. No significant difference was noted between the groups for the parameters of heart activity and feeding behavior at night. The locomotion score of cows in group L was positively correlated with the lying time and △weight, whereas it was negatively correlated with LWR and SD. The highest sensitivity (0.97) for lameness detection was found for the parameter SD [specificity of 0.80 and an area under the curve (AUC) of 0.84]. The highest specificity (0.90) for lameness detection was present for Δweight (sensitivity=0.78; AUC=0.88) and LWR (sensitivity=0.81; AUC=0.87). The model considering the data of SD together with lying time at night was the best predictor of cows being lame, accounting for 40% of the variation in the likelihood of a cow being lame (sensitivity=0.94; specificity=0.80; AUC=0.86). In conclusion, the data derived from the 4-scale-weighing platform, either alone or combined with the lying time at night over 12 h, represent the most valuable parameters for automated identification of lame cows suffering from a claw horn lesion of one individual hind limb.