High-throughput non-destructive nuclear magnetic resonance method to measure intramuscular fat content in beef

High intake of saturated fat from meats has been associated with cardiovascular disease, cancer, diabetes, and others diseases. In this paper, we are introducing a simple, high-throughput, and non-destructive low-resolution nuclear magnetic resonance method that has the potential to analyze the intramuscular fat content (IMF) in more than 1,000 beef portions per hour. The results can be used in nutritional fact labels, replacing the currently used average value. The method is based on longitudinal (T(1)) and transverse (T(2)) relaxation time information obtained by a continuous wave-free precession (CWFP) sequence. CWFP yields a higher correlation coefficient (r=0.9) than the conventional Carr-Purcell-Meiboom- Gill (CPMG) method (r=-0.25) for IMF in beef and is just as fast and a simpler pulse sequence than CPMG. The method can also be applied to other meat products.

Measure functional differential equations and functional dynamic equations on time scales

We study measure functional differential equations and clarify their relation to generalized ordinary differential equations. We show that functional dynamic equations on time scales represent a special case of measure functional differential equations. For both types of equations, we obtain results on the existence and uniqueness of solutions, continuous dependence, and periodic averaging.

Osteoporotic hip fractures in non-elderly patients: relevance of associated co-morbidities

Osteoporotic hip fractures (OHF) are not limited to elderly; however, studies in non-elderly are scarce. Thus, the aim of this study was to evaluate co-morbidities in non-elderly patients with OHF in a Community Teaching Hospital. All hospitalizations due to OHF during a 3-year period in a Community Teaching Hospital were retrospectively evaluated for co-morbidities, and patients 18-64 years old were compared with those a parts per thousand yen65 years old. Of all hospitalizations, 232 (0.73%) were due to hip fractures, and 120/232 (51.7%) patients had OHF. The comparison of the 13 (10.8%) OHF patients < 65 years old (47.3 +/- A 9.7 years) with 107 (89.2%) a parts per thousand yen65 years old (80.4 +/- A 7.7 years) revealed a male predominance (61.5 vs. 27.1%, P = 0.022) and a distinct ethnic distribution with a lower proportion of Caucasians in the former (61.5 vs. 86.9%, P = 0.033). Moreover, non-elderly OHF patients had higher frequencies of insulin-dependent DM (38.5 vs. 3.7%, P = 0.001) and alcoholism (38.5 vs. 4.7%, P = 0.001) than aged patients. In contrast, rates of age-related co-morbidities such as stroke (7.7 vs. 18.7%, P = 0.461), heart failure (23.1 vs. 14.0%, P = 0.411), and dementia (7.7 vs. 15.9%, P = 0.689) were comparable in both groups. Logistic regression analysis demonstrated that insulin-dependent DM (OR = 25.4, 95% CI = 4.7-136.8, P < 0.001) and alcoholism (OR = 20.3, 95% CI = 3.9-103.3, P < 0.001) remained as independent risk factors for OHF in non-elderly patients. Osteoporosis is an important cause of HF in Community Hospital. Non-elderly patients with OHF have a peculiar demographic profile and associated co-morbidities. These findings reinforce the need of early osteoporosis diagnosis and rigorous fracture prevention in patients with DM and alcoholism.

Morphological heterogeneity of Paracoccidioides brasiliensis: relevance of the Rho-like GTPase PbCDC42

Paracoccidioides brasiliensis budding pattern and polymorphic growth were previously shown to be closely linked to the expression of PbCDC42 and to influence the pathogenesis of the fungus. In this work we conducted a detailed morphogenetic evaluation of the yeast-forms of 11 different clinical and environmental P. brasiliensis isolates comprising four phylogenetic lineages (S1, PS2, PS3 and Pb01-like), as well as a PbCDC42 knock-down strain. High variations in the shape and size of mother and bud cells of each isolate were observed but we did not find a characteristic morphologic profile for any of the phylogenetic groups. In all isolates studied, the bud size and shape were demonstrated to be highly dependent on the mother cell. Importantly, we found strong correlations between PbCDC42 expression and both the shape of mother and bud cells and the size of the buds in all isolates and the knock-down strain. Our results suggested that PbCDC42 expression can explain approximately 80% of mother and bud cell shape and 19% of bud cell size. This data support PbCDC42 expression level as being a relevant predictor of P. brasiliensis morphology. Altogether, these findings quantitatively describe the polymorphic nature of the P. brasiliensis yeast form and provide additional support for the key role of PbCDC42 expression on yeast cell morphology.

COMPLEXITY MEASURE: A QUANTUM INFORMATION APPROACH

In the past decades, all of the efforts at quantifying systems complexity with a general tool has usually relied on using Shannon's classical information framework to address the disorder of the system through the Boltzmann-Gibbs-Shannon entropy, or one of its extensions. However, in recent years, there were some attempts to tackle the quantification of algorithmic complexities in quantum systems based on the Kolmogorov algorithmic complexity, obtaining some discrepant results against the classical approach. Therefore, an approach to the complexity measure is proposed here, using the quantum information formalism, taking advantage of the generality of the classical-based complexities, and being capable of expressing these systems' complexity on other framework than its algorithmic counterparts. To do so, the Shiner-Davison-Landsberg (SDL) complexity framework is considered jointly with linear entropy for the density operators representing the analyzed systems formalism along with the tangle for the entanglement measure. The proposed measure is then applied in a family of maximally entangled mixed state.

Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

Motor function measure scale, steroid therapy and patients with Duchenne muscular dystrophy

Objective: To assess the evolution of motor function in patients with Duchenne muscular dystrophy (DMD) treated with steroids (prednisolone or deflazacort) through the Motor Function Measure (MFM), which evaluates three dimensions of motor performance (D1, D2, D3). Methods: Thirty-three patients with DMD (22 ambulant, 6 non-ambulant and 5 who lost the capacity to walk during the period of the study) were assessed using the MFM scale six times over a period of 18 months. Results: All the motor functions remained stable for 14 months in all patients, except D1 for those who lost their walking ability. In ambulant patients, D2 (axial and proximal motor capacities) motor functions improved during six months; an improvement in D3 (distal motor capacity) was noted during the total follow-up. D1 (standing posture and transfers) and total score were useful to predict the loss of the ability to walk. Conclusions: The use of the MFM in DMD patients confirms the benefits of the steroid treatment for slowing the progression of the disease.

Cosmological measure with volume averaging and the vacuum energy problem

In this paper, we give a possible solution to the cosmological constant problem. It is shown that the traditional approach, based on volume weighting of probabilities, leads to an incoherent conclusion: the probability that a randomly chosen observer measures Lambda = 0 is exactly equal to 1. Using an alternative, volume averaging measure, instead of volume weighting can explain why the cosmological constant is non-zero.

Seizures in Primary Antiphospholipid Syndrome: The Relevance of Smoking to Stroke

Objectives. To evaluate the frequency of seizures in primary antiphospholipid syndrome (PAPS) and their possible clinical and laboratory associations. Methods. Eighty-eight PAPS patients (Sydney's criteria) were analyzed by a standard interview, physical examination and review of medical charts. Risk factors for seizures, clinical manifestations, associated comorbidities, and antiphospholipid antibodies were evaluated. Results. Nine (10.2%) patients with seizures were identified, 77.8% had convulsions onset after PAPS diagnosis. Mean age, gender, and race were comparable in groups with or without seizures. Interestingly, a higher frequency of current smoking (44.4 versus 10.1%, P = 0.019) was observed in the first group. Stroke, Sneddon's syndrome, and livedo reticularis were more frequent in PAPS patients with seizures than those without seizures, although not statistically significant (P > 0.05). Comparison between patients with seizures onset after PAPS diagnosis (n = 7) and those without convulsions (n = 79) demonstrated a higher frequency of current smoking (42.9 versus 10%, P = 0.042) and stroke in the first group (71.4 versus 30.4%, P = 0.041). Regression analysis confirmed that smoking (P = 0.030) and stroke (P = 0.042) were independently associated to seizures. Conclusion. About 10.2% of PAPS patients had convulsions, predominantly after PAPS diagnosis, and seizures were associated to current smoking and stroke.

Speech Graphs Provide a Quantitative Measure of Thought Disorder in Psychosis

Background: Psychosis has various causes, including mania and schizophrenia. Since the differential diagnosis of psychosis is exclusively based on subjective assessments of oral interviews with patients, an objective quantification of the speech disturbances that characterize mania and schizophrenia is in order. In principle, such quantification could be achieved by the analysis of speech graphs. A graph represents a network with nodes connected by edges; in speech graphs, nodes correspond to words and edges correspond to semantic and grammatical relationships. Methodology/Principal Findings: To quantify speech differences related to psychosis, interviews with schizophrenics, manics and normal subjects were recorded and represented as graphs. Manics scored significantly higher than schizophrenics in ten graph measures. Psychopathological symptoms such as logorrhea, poor speech, and flight of thoughts were grasped by the analysis even when verbosity differences were discounted. Binary classifiers based on speech graph measures sorted schizophrenics from manics with up to 93.8% of sensitivity and 93.7% of specificity. In contrast, sorting based on the scores of two standard psychiatric scales (BPRS and PANSS) reached only 62.5% of sensitivity and specificity. Conclusions/Significance: The results demonstrate that alterations of the thought process manifested in the speech of psychotic patients can be objectively measured using graph-theoretical tools, developed to capture specific features of the normal and dysfunctional flow of thought, such as divergence and recurrence. The quantitative analysis of speech graphs is not redundant with standard psychometric scales but rather complementary, as it yields a very accurate sorting of schizophrenics and manics. Overall, the results point to automated psychiatric diagnosis based not on what is said, but on how it is said.

A simple method to measure critical angles for high-sensitivity differential refractometry

A total internal reflection-based differencial refractometer, capable of measuring the real and imaginary parts of the complex refractive index in real time, is presented. The device takes advantage of the phase difference acquired by s- and p-polarized light to generate an easily detectable minimum at the reflected profile. The method allows to sensitively measuring transparent and turbid liquid samples. (C)2012 Optical Society of America

Use of Carr-Purcell pulse sequence with low refocusing flip angle to measure T-1 and T-2 in a single experiment

The Carr-Purcell pulse sequence, with low refocusing flip angle, produces echoes midway between refocusing pulses that decay to a minimum value dependent on T*(2). When the refocusing flip angle was pi/2 (CP90) and tau > T*(2), the signal after the minimum value, increased to reach a steady-state free precession regime (SSFP), composed of a free induction decay signal after each pulse and an echo, before the next pulse. When tau < T*(2), the signal increased from the minimum value to the steady-state regime with a time constant (T*) = 2T(1)T(2)/(T-1 + T-2). identical to the time constant observed in the SSFP sequence, known as the continuous wave free precession (CWFP). The steady-state amplitude obtained with M-cp90 = M0T2/(T-1+T-2) was identical to CWFP. Therefore, this sequence was named CP-CWFP because it is a Carr-Purcell sequence that produces results similar to the CWFP. However, CP-CWFP is a better sequence for measuring the longitudinal and transverse relaxation times in single scan, when the sample exhibits T-1 similar to T-2. Therefore, this sequence can be a useful method in time domain NMR and can be widely used in the agriculture, food and petrochemical industries because those samples tend to have similar relaxation times in low magnetic fields. (C) 2011 Elsevier Inc. All rights reserved.

Incorporating label dependency into the binary relevance framework for multi-label classification

In multi-label classification, examples can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multi-label learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. Aiming to accurately predict label combinations, in this paper we propose a simple approach that enables the binary classifiers to discover existing label dependency by themselves. An experimental study using decision trees, a kernel method as well as Naive Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.

Clinical, tomographic aspects and relevance of torus palatinus: case report of two sisters

Despite the nomenclature suggested to be a tumor, torus palatinus (TP) is an overgrowth of the bone in the palatal region and represents an anatomic variation. Its prevalence varies among the population studied and its etiology is still unclear; however, it seems to be a multifactorial disorder with genetics and environmental involvement. Surgical removal of the TP is indicated in the following circumstances: (1) deglutition and speech impairment, (2) cancer phobia, (3) traumatized mucosa over the torus, and (4) prosthetic reasons. The aim of this case report is describe cases that occurred in two sisters, emphasizing the genetic etiology of this anatomic variation. In addition, intra-oral exam and computed tomography scan (axial, coronal and sagittal view) provided a detailed assessment of the TP and elimination of other possible diagnoses, furthermore allowed a better analyzes of the anatomic relation with adjacentes structures. No surgical removal was indicated for both cases.

Landscape and soil regionalization in southern Brazilian Amazon and contiguous areas: methodology and relevance for ecological studies

Soils of a large tropical area with differentiated landscapes cannot be treated uniformly for ecological applications. We intend to develop a framework based on physiography that can be used in regional applications. The study region occupies more than 1.1 million km² and is located at the junction of the savanna region of Central Brazil and the Amazon forest. It includes a portion of the high sedimentary Central Brazil plateau and large areas of mostly peneplained crystalline shield on the border of the wide inner-Amazon low sedimentary plain. A first broad subdivision was made into landscape regions followed by a more detailed subdivision into soil regions. Mapping information was extracted from soil survey maps at scales of 1:250000-1:500000. Soil units were integrated within a homogenized legend using a set of selected attributes such as taxonomic term, the texture of the B horizon and the associated vegetation. For each region, a detailed inventory of the soil units with their area distribution was elaborated. Ten landscape regions and twenty-four soil regions were recognized and delineated. Soil cover of a region is normally characterized by a cluster composed of many soil units. Soil diversity is comparable in the landscape and the soil regions. Composition of the soil cover is quantitatively expressed in terms of area extension of the soil units. Such geographic divisions characterized by grouping soil units and their spatial estimates must be used for regional ecological applications.