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PubMed1977

Development of psychomotor objectives for classroom of clinical education in physical therapy.

A systematic method for developing and evaluating psychomotor objectives for students in clinical and laboratory education was presented. In preparing objectives for motor learning, the instructor must perform a task analysis of each skill to be taught. The analysis involves 1) identifying components of the movement, 2) salient features of facilitating stimuli, such as placement of grips, intensity and speed of stimulation, and 3) timing of commands or resistance. The senses, such as vision or kinesthesia, through which the student is to perceive the selected features must be chosen. After determining the level of skill to be attained, teaching methods are easily fit to the objectives. A simple checklist aids evaluation of student competency and provides immediate feedback on performance level.

Humans↗
PubMed2003

The use of artificial neural networks for the selection of the most appropriate formulation and processing variables in order to predict the in vitro dissolution of sustained release minitablets.

The objective of this work was to apply artificial neural networks (ANNs) to examine the relative importance of various factors, both formulation and process, governing the in-vitro dissolution from enteric-coated sustained release (SR) minitablets. Input feature selection (IFS) algorithms were used in order to give an estimate of the relative importance of the various formulation and processing variables in determining minitablet dissolution rate. Both forward and backward stepwise algorithms were used as well as genetic algorithms. Networks were subsequently trained using the back propagation algorithm in order to check whether or not the IFS process had correctly located any unimportant inputs. IFS gave consistent rankings for the importance of the various formulation and processing variables in determining the release of drug from minitablets. Consistent ranking was achieved for both indices of the release process; ie, the time taken for release to commence through the enteric coat (T(lag)) and that for the drug to diffuse through the SR matrix of the minitablet into the dissolution medium (T9(0-10)). In the case of the T(lag) phase, the main coating parameters, along with the original batch blend size and the blend time with lubricant, were found to have most influence. By contrast, with the T(90-10 phase), the amounts of matrix forming polymer and direct compression filler were most important. In the subsequent training of the ANNs, removal of inputs regarded as less important led to improved network performance. ANNs were capable of ranking the relative importance of the various formulations and processing variables that influenced the release rate of the drug from minitablets. This could be done for all main stages of the release process. Subsequent training of the ANN verified that removal of less relevant inputs from the training process led to an improved performance from the ANN.

Artificial Intelligence↗
PubMed1999

Temporal encoding of the voice onset time phonetic parameter by field potentials recorded directly from human auditory cortex.

Voice onset time (VOT) is an important parameter of speech that denotes the time interval between consonant onset and the onset of low-frequency periodicity generated by rhythmic vocal cord vibration. Voiced stop consonants (/b/, /g/, and /d/) in syllable initial position are characterized by short VOTs, whereas unvoiced stop consonants (/p/, /k/, and t/) contain prolonged VOTs. As the VOT is increased in incremental steps, perception rapidly changes from a voiced stop consonant to an unvoiced consonant at an interval of 20-40 ms. This abrupt change in consonant identification is an example of categorical speech perception and is a central feature of phonetic discrimination. This study tested the hypothesis that VOT is represented within auditory cortex by transient responses time-locked to consonant and voicing onset. Auditory evoked potentials (AEPs) elicited by stop consonant-vowel (CV) syllables were recorded directly from Heschl's gyrus, the planum temporale, and the superior temporal gyrus in three patients undergoing evaluation for surgical remediation of medically intractable epilepsy. Voiced CV syllables elicited a triphasic sequence of field potentials within Heschl's gyrus. AEPs evoked by unvoiced CV syllables contained additional response components time-locked to voicing onset. Syllables with a VOT of 40, 60, or 80 ms evoked components time-locked to consonant release and voicing onset. In contrast, the syllable with a VOT of 20 ms evoked a markedly diminished response to voicing onset and elicited an AEP very similar in morphology to that evoked by the syllable with a 0-ms VOT. Similar response features were observed in the AEPs evoked by click trains. In this case, there was a marked decrease in amplitude of the transient response to the second click in trains with interpulse intervals of 20-25 ms. Speech-evoked AEPs recorded from the posterior superior temporal gyrus lateral to Heschl's gyrus displayed comparable response features, whereas field potentials recorded from three locations in the planum temporale did not contain components time-locked to voicing onset. This study demonstrates that VOT at least partially is represented in primary and specific secondary auditory cortical fields by synchronized activity time-locked to consonant release and voicing onset. Furthermore, AEPs exhibit features that may facilitate categorical perception of stop consonants, and these response patterns appear to be based on temporal processing limitations within auditory cortex. Demonstrations of similar speech-evoked response patterns in animals support a role for these experimental models in clarifying selected features of speech encoding.

Auditory Cortex↗
PubMed2003

Discriminative motifs.

This paper takes a new view of motif discovery, addressing a common problem in existing motif finders. A motif is treated as a feature of the input promoter regions that leads to a good classifier between these promoters and a set of background promoters. This perspective allows us to adapt existing methods of feature selection, a well-studied topic in machine learning, to motif discovery. We develop a general algorithmic framework that can be specialized to work with a wide variety of motif models, including consensus models with degenerate symbols or mismatches, and composite motifs. A key feature of our algorithm is that it measures overrepresentation while maintaining information about the distribution of motif instances in individual promoters. The assessment of a motif's discriminative power is normalized against chance behaviour by a probabilistic analysis. We apply our framework to two popular motif models and are able to detect several known binding sites in sets of co-regulated genes in yeast.

Algorithms↗
PubMed2001

A polyphasic taxonomic study of thermophilic bacilli from shallow, marine vents.

Eighty-seven thermophilic, aerobic, spore-forming bacteria were isolated from shallow, marine, thermal vents of the Eolian Islands (Italy) and tested for a broad spectrum of phenotypic characteristics. A numerical taxonomy study was performed on these isolates and 8 thermophilic Bacillus and Geobacillus reference strains by 89 selected features. Results from cluster analysis showed the formation of nine clusters. Most of the isolates (83%) fell into several phenetically well distinguished clusters, loosely related to Geobacillus thermodenitrificans. The remaining isolates grouped together with different reference strains. Eighteen isolates, representative of the different clusters, were selected for subsequent genotypic characterisation, including partial 16S rDNA sequence analysis of 18 strains and almost complete 16S rDNA sequences of 9 strains. Subsequent DNA/DNA reassociation studies and determination of the base composition of DNA identified seven isolates as Geobacillus thermodenitrificans, two isolates as G. thermoleovorans and one isolate as Bacillus pallidus. Four isolates represented two novel species of Bacillus. The remaining four represented novel Geobacillus species, one of which has recently been described as Bacillus vulcani DSMZ 13174 T.

Bacillus↗
PubMed1986

Classification of lung carcinoma by means of digital nuclear image analysis.

An investigation was performed of the maximum discriminating efficiency for each subgroup of digital nuclear image features and of the overall classification of nuclei from three types of human lung carcinomas in histologic sections: adenocarcinoma, small-cell carcinoma and squamous-cell carcinoma. The results indicate that, for each subgroup of features, the nuclei of the small-cell carcinomas are generally "correctly" classified in a higher percentage (80% to 100%) than are the nuclei of the adenocarcinomas (46% to 74%) and squamous-cell carcinomas (29% to 68%). The discriminant analysis for the overall classification selected features from most of the subgroups, suggesting that it is useful to perform nuclear image analysis with many subgroups having different properties. The overall classifications for the nuclei of the adenocarcinomas, small-cell carcinomas and squamous-cell carcinomas were, respectively, 81.4%, 93.2% and 74.7%. Before this technique can be applied to histopathologic diagnosis, a larger number of unselected lung carcinomas must be evaluated.

Adenocarcinoma↗
PubMedDate not supplied

Conformational and physicochemical DNA features specific for transcription factor binding sites.

MOTIVATION: A reliable recognition of transcription factor binding sites is essential for analysis of regulatory genomic sequences. The experimental data make evident an important role of DNA conformational features for site functioning. However, Internet-available tools for revealing conformational and physicochemical DNA features significant for the site functioning and subsequent use of these features for site recognition have not been developed up to now. RESULTS: We suggest an approach for revealing significant conformational and physicochemical properties of functional sites implemented in the database B-DNA-VIDEO. This database is designed to study the sets of various transcription factor binding sites, providing evidence that transcription factor binding sites are characterized by specific sets of significant conformational and physicochemical DNA properties. For a fixed site, by using the B-DNA features selected for this site recognition, the C-program recognizing this site may be generated, control tested and stored in the database B-DNA-VIDEO. Each B-DNA-VIDEO entry links to the Web-applet recognizing the site, whose significant B-DNA features are stored in this entry as the 'site recognition programs'. The pairwise linked entry-applet pairs are compiled within the B-DNA-VIDEO system, which is simultaneously the database and the program tools package applicable immediately for recognizing the sites stored in the database. Indeed, this is the novelty. Hence, B-DNA-VIDEO is the Web resource of both 'searching for static data' and 'active computation' type, that is why it was called an 'activated database'. AVAILABILITY: B-DNA-VIDEO is available at http://wwwmgs.bionet.nsc.ru/systems/BDNAVideo/ and the mirror site at http://www.cbil.upenn.edu/mgs/systems/c onsfreq/.

Base Sequence↗
PubMed1995

Nitric oxide-based possibilities for pharmacotherapy.

The goal of nitric oxide (NO) based pharmacotherapy is to reach proper homeostasis of NO metabolism in the target tissue where endogenous production of NO is either too weak or excessively increased. In addition to the classic NO-based therapy of cardiovascular conditions with nitrates, a variety of new therapeutic possibilities have emerged including sexual disorders, gastrointestinal system, immunology, tumour growth regulation and respiratory disorders. NO levels of target tissues can be affected directly by NO donors, or indirectly by increasing the level of L-arginine, a substrate of nitric oxide synthase (NOS). While increased production of NO by induceable NO (iNOS) by, for example, cytokines does not at present seem therapeutically meaningful, increased NO production by constitutive NOS (cNOS) may be involved in the beneficial effects of ACE inhibitors or oestrogens. NO production may be pharmacologically decreased by inhibition of expression of iNOS by glucocorticoids while both cNOS and iNOS derived NO production is inhibited by administration of false substrates, for example L-NAME. Additionally, the respiratory system and related vessels can be reached directly and more selectively by inhalation of pure NO gas. Possible problems in administering NO and perhaps some NO-donors include the toxic nature of the compound itself whereby vital enzyme systems may be inhibited and tissue damaging radicals formed. Future prospects of NO-based pharmacotherapy may feature selective ligands to different NOS isoforms and tissue selective donors that release NO in a controlled fashion.

Adult↗
PubMed1997

Classification of organelle trajectories using region-based curve analysis.

A method based on analysis of the region of movement and the functioning of the acto-myosin cytoskeleton has been elaborated to quantify and classify patterns of organelle movement in tobacco pollen tubes. The trajectory was dilated to the region of movement, which was then reduced to give a one-pixel-wide skeleton, represented by a graph structure. The longest line in this skeleton was hypothesized to represent the basic track of the organelle along a single actin filament. Quantitative features were derived from the graph structure, direction of movement on the longest skeletal line, and distance between skeletal line and particle. These features corresponded to biological events like the amount of linear movement or the probability of attachment of an organelle to the actin filament. From 81 analyzed organelle trajectories, 17 had completely linear, 17 had completely non-linear, and 47 had alternating linear and non-linear movement. Selected features were employed for classification and ranking of the movement patterns of a representative sample of the population of organelles moving in the cell tip. The presented methods can be applied to any field where analysis and classification of particle motion are intended.

Actin Cytoskeleton↗
PubMed1977

Microbial carbohydrate specific antibodies distinguish between different stages of differentiating mouse cerebellum.

High titered anticarbohydrate antibodies were used to identify cell surface carbohydrates during different stages in histogenesis of mouse cerebellum in a micro tissue-culture system which mimics selected features of in vivo cerebellum development. Blockage of fiber formation within the first few days in vitro and inhibition of cell migrations by carbohydrate-specific antibodies served as an assay system for possible contributions of surface carbohydrates to the behavior of developing cerebellar cells. Microbial strains were selected on the basis of carbohydrate structures of their cell wall antigens, and anticarbohydrate antibodies were raised against treated whole bacteria and yeast in rabbits. We found that antibodies to mannan were active at all stages of development tested (embryonic day 13, E13; the day of birth, PO; and postnatal day 7, P7). Antibodies to sialic acids prepared against strains B and C of Neisseria meningitidis distinguish different subterminal structures: anti-B reacted with E13 and PO cerebellar cells, and anti-C mostly with cells older than P7. Antifetuin antibody recognized E13 and PO but not P7 cell populations. Pneumococcus C strain R36A-specific antibodies were effective only after coating cells to C type carbohydrate before application of the antibody. The results demonstrate that antimicrobiol carbohydrate antibodies cross-react with mammalian cell surface carbohydrate structures and therefore can be used as a powerful tool in tissue culture to analyse those structures which might control cell behaviors pertinent to cerebellar development.

Animals↗
PubMed1982

Some practical conclusions following a longitudinal study of common macular lesions.

In a project which lasted 18 years, the natural history of five common macular lesions was followed in 268 patients, and the most significant clinical features selected in each. Methodology consisted of trichromatic analysis with direct ophthalmoscopy, contact lens fundoscopy including the triple mirror, refraction, and fundus photography. In several subjects in each group repeated fluorescence angiographies were carried out. Single eye records were kept and scattergrams of corrected visual acuities plotted. The latter--besides key clinical features--were then used to predict the probable course. Examples are related both to the biographies and to the clinical features to make the method more comprehensible. The five lesions studied were: cystoid macular degeneration, retinal pigment epithelial detachments, choroidal sclerosis, hyaline degenerations, and macular (foveal) degenerations. A new paramacular 'window' defect called a 'target' lesion is described and attention drawn to its close association with migraine. A feature of the study throughout was the frequency with which more than one of the lesions appeared in the same eye, although there was no set sequence. Thus a patient suffering from one macular lesion not infrequently ended by being blinded by another.

Adolescent↗
PubMed1994

Prenatal detection of neuroblastoma by fetal ultrasonography.

PURPOSE: We report three cases of neuroblastoma diagnosed by prenatal ultrasound examination and examine the biologic features of tumors diagnosed prenatally. PATIENTS AND METHODS: Neuroblastoma is the most common tumor detected in the newborn period. Thus, some of these tumors develop prenatally and should be detectable by maternal ultrasound. Here we report three cases in which a neuroblastoma was suspected on prenatal ultrasonography. In addition, we review selected features of 17 additional cases reported in the literature. RESULTS AND CONCLUSIONS: These data indicate that, although the majority of patients have favorable clinical and biological features and do well, some patients do not, and the DNA index may be the most important predictor of outcome.

Female↗
PubMed2025

Opportunities for machine learning to predict cross-neutralization in FMDV serotype O.

Accurately estimating cross-neutralization between serotype O foot-and-mouth disease viruses (FMDVs) is critical for guiding vaccine selection and disease management. In this study, we developed a machine learning approach to estimate r1 values-an established measure of antigenic similarity-using VP1 sequence data and published virus neutralization titer (VNT) results. Our dataset comprised 108 serum-virus pairs representing 73 distinct FMDV strains. We applied Boruta feature selection and random forest classifiers, optimizing model performance through tenfold cross-validation and sub-sampling to address class imbalance. Predictors included pairwise amino acid distances, site-specific polymorphisms, and differences in potential N-glycosylation sites. Using a 0.3 r1 threshold to define cross-neutralization, the final model achieved high accuracy (0.96), sensitivity (0.93), and specificity (0.96) in training, and performed robustly on independent test sets - accuracy was 0.75 (95% CI 0.60 and 0.90), F1 score 0.86% and PPV 0.77. Importantly, key VP1 residues-positions 48, 100, 135, 150, and 151-emerged as strong predictors of antigenic relationships. Our results demonstrate the utility of integrating routinely generated genomic data with machine learning to inform vaccine candidate selection and anticipate immune interactions among circulating FMDV strains. This approach offers a practical tool for accelerating vaccine decision-making and can be adapted to other FMDV serotypes. The latest version of the r1 predictive model is available for access via a Shiny dashboard (https://dmakau.shinyapps.io/PredImmune-FMD/).

Foot-and-Mouth Disease Virus↗
PubMed2000

Direct identification of pure Penicillium species using image analysis.

This paper presents a method for direct identification of fungal species solely by means of digital image analysis of colonies as seen after growth on a standard medium. The method described is completely automated and hence objective once digital images of the reference fungi have been established. Using a digital image it is possible to extract precise information from the surface of the fungal colony. This includes color distribution, colony dimensions and texture measurements. For fungal identification, this is normally done by visual observation that often results in a very subjective data recording. Isolates of nine different species of the genus Penicillium have been selected for the purpose. After incubation for 7 days, the fungal colonies are digitized using a very accurate digital camera. Prior to the image analysis each image is corrected for self-illumination, thereby gaining a set of directly corresponding images with respect to illumination. A Windows application has been developed to locate the position and size of up to three colonies in the digitized image. Using the estimated positions and sizes of the colonies, a number of relevant features can be extracted for further analysis. The method used to determine the position of the colonies will be covered as well as the feature selection. The texture measurements of colonies of the nine species were analyzed and a clustering of the data into the correct species was confirmed. This indicates that it is indeed possible to identify a given colony merely by macromorphological features. A classifier (in the normal distribution) based on measurements of 151 colonies incubated on yeast extract sucrose agar (YES) was used to discriminate between the species. This resulted in a correct classification rate of 100% when used on the training set and 96% using cross-validation. The same methods applied to 194 colonies incubated on Czapek yeast extract agar (CYA) resulted in a correct classification rate of 98% on the training set and 71% using cross-validation.

Color↗
PubMed2003

The compressed feature matrix--a fast method for feature based substructure search.

The compressed feature matrix (CFM) is a feature based molecular descriptor for the fast processing of pharmacochemical applications such as adaptive similarity search, pharmacophore development and substructure search. Depending on the particular purpose, the descriptor may be generated upon either topological or Euclidean molecular data. To assure a variable utilizability, the assignment of the structural patterns to feature types is arbitrarily determined by the user. This step is based on a graph algorithm for substructure search, which resembles the common substructure descriptors. While these merely allow a screening for the predefined patterns, the CFM permits a real substructure/subgraph search, presuming that all desired elements of the query substructure are described by the selected feature set. In this work, the CFM based substructure search is evaluated with regard to both the different outputs resulting from varying feature sets and the search speed. As a benchmark we use the programmable atom typer (PATTY) graph algorithm. When comparing the two methods, the CFM based matrix algorithm is up to several hundred times faster than PATTY and when using the CFM as a basis for substructure screening, the search speed is accelerated by three orders of magnitude. Thus, the CFM based substructure search complies with the requirements for interactive usage, even for the evaluation of several hundred thousand compounds. The concept of the CFM is implemented in the software COFEA. FIGURE CFM based substructure search using the compounds dopamine and benzene-1,2-diol

Algorithms↗
PubMed2000

Automated classification of clustered microcalcifications into malignant and benign types.

The objectives in this study were to design and test a fully automated method for classification of microcalcification clusters into malignant and benign types, and to compare the method's performance with that of radiologists. A novel aspect of the approach is that the relative location and orientation of clusters inside the breast was taken into account for feature calculation. Furthermore, correspondence of location of clusters in mediolateral oblique (MLO) and cranio-caudal (CC) views, was used in feature calculation and in final classification. Initially, microcalcifications were automatically detected by using a statistical method based on Bayesian techniques and a Markov random field model. To determine malignancy or benignancy of a cluster, a method based on two classification steps was developed. In the first step, classification of clusters was performed and in the second step a patient based classification was done. A total of 16 features was used in the study. To identify meaningful features, a feature selection was applied, using the area under the receiver operating characteristic (ROC) curve (Az value) as a criterion. For classification the k-nearest-neighbor method was used in a leave-one-patient-out procedure. A database of 192 mammograms with 280 true positive detected microcalcification clusters was used for evaluation of the method. The set consisted of cases that were selected for diagnostic work up during a 4 year period of screening in the Nijmegen region (The Netherlands). Because of the high positive predictive value in the screening program (50%), this set did not contain obvious benign cases. The method's best patient-based performance on this set corresponded with Az = 0.83, using nine features. A subset of the data set, containing mammograms from 90 patients, was used for comparing the computer results to radiologists' performance. Ten radiologists read these cases on a light-box and assessed the probability of malignancy for each patient. All participants had experience in clinical mammography and participated in our observer study during the last 2 days of a 2-week training session leading to screening mammography certification. Results on the subset showed that the method's performance (Az = 0.83) was considerably higher than that of the radiologists (Az = 0.63).

Breast Neoplasms↗
PubMed2025

Predicting telomerase reverse transcriptase promoter mutation status in glioblastoma by whole-tumor multi-sequence magnetic resonance texture analysis.

OBJECTIVE: This study aimed to determine the feasibility of preoperative multi-sequence magnetic resonance texture analysis (MRTA) for predicting TERT promoter mutation status in IDH-wildtype glioblastoma (IDHwt GB). METHODS: The clinical and imaging data of 111 patients with IDHwt GB at our hospital between November 2018 and June 2023 were retrospectively analyzed as the training set, and those of 23 patients with IDHwt GB between July 2023 and November 2023 were interpreted as the validation set. We used molecular sequencing results to classify the training set into TERT promoter mutation and wildtype groups. Textural features of the whole-tumor volume were extracted, including T2-weighted imaging (T2WI), T2-fluid-attenuated inversion recovery, apparent diffusion coefficient (ADC) map, and contrast-enhanced T1-weighted imaging (CE-T1). All textural features were obtained using open-source pyradiomics. After feature selection, logistic regression was used to build prediction models, and a nomogram was generated. Finally, the model was validated using validation cohort. RESULTS: The CE-T1_Model (AUC 0.704) had a better predictive ability than the T2_Model (AUC 0.684) and ADC_Model (AUC 0.624). The MRI_Combined_Model (CE-T1, T2, and ADC texture features) (AUC 0.780) had a better predictive ability than the Clinical_Model (AUC 0.758). The Combined_Model (CE-T1, T2, ADC texture features, and clinical features) had the best predictive performance (AUC 0.871), with a sensitivity, specificity, and accuracy of 82.60 %, 83.30 %, and 80.18 %, respectively. The AUC, sensitivity, specificity, and accuracy in the validation cohort were 0.775, 86.70 %, 75.00 %, and 69.57 %, respectively. CONCLUSIONS: Whole-tumor multi-sequence MRTA can be used as non-invasive quantitative parameters to assist in the preoperative clinical prediction of TERT promoter mutation status in IDHwt GB.

Humans↗
PubMed1999

Inherited and inducible chromosomal instability: a fragile bridge between genome integrity mechanisms and tumourigenesis.

Cancer is a multi-step process evolving as the result of the accumulation of a number of mutational events. The growing body of evidence implicating genetic instability as a key feature of this evolutionary process and the risk of malignancy associated with chromosomal instability syndromes highlight the importance of understanding the mechanisms that cells use to maintain the integrity of their genomes. Classic examples of inherited chromosomal instability with cancer predisposition are Bloom's syndrome, ataxia telangiectasia, and Fanconi anaemia, although the mechanisms involved are far from understood. Selected features of these inherited disorders are reviewed to provide a background to the more recently discovered inducible chromosomal instability, a phenotype in which apparently normal cells that have survived ionizing radiation and certain chemical insults may produce descendants exhibiting a high frequency of de novo chromosome aberrations and gene mutations. The phenotype is induced at frequencies considerably greater than conventional mutation frequencies but little is understood of the underlying mechanism(s). To date, chromosomal instability induced by ionizing radiation has been the most extensively studied phenotype and it is evident that the expression of inducible instability has a strong dependence on the type of radiation exposure, the cell type irradiated, and the genetic 'predisposition' of the irradiated cell.

Animals↗