Modulating the granularity of category formation by global cortical states

The unsupervised categorization of sensory stimuli is typically attributed to feedforward processing in a hierarchy of cortical areas. This purely sensory-driven view of cortical processing, however, ignores any internal modulation, e.g., by top-down attentional signals or neuromodulator release. To isolate the role of internal signaling on category formation, we consider an unbroken continuum of stimuli without intrinsic category boundaries. We show that a competitive network, shaped by recurrent inhibition and endowed with Hebbian and homeostatic synaptic plasticity, can enforce stimulus categorization. The degree of competition is internally controlled by the neuronal gain and the strength of inhibition. Strong competition leads to the formation of many attracting network states, each being evoked by a distinct subset of stimuli and representing a category. Weak competition allows more neurons to be co-active, resulting in fewer but larger categories. We conclude that the granularity of cortical category formation, i.e., the number and size of emerging categories, is not simply determined by the richness of the stimulus environment, but rather by some global internal signal modulating the network dynamics. The model also explains the salient non-additivity of visual object representation observed in the monkey inferotemporal (IT) cortex. Furthermore, it offers an explanation of a previously observed, demand-dependent modulation of IT activity on a stimulus categorization task and of categorization-related cognitive deficits in schizophrenic patients.

Land use/cover change using Remote Sensing and Geographic Information Systems : Pic Macaya National Park, Haiti

A post classification change detection technique based on a hybrid classification approach (unsupervised and supervised) was applied to Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Plus (ETM+), and ASTER images acquired in 1987, 2000 and 2004 respectively to map land use/cover changes in the Pic Macaya National Park in the southern region of Haiti. Each image was classified individually into six land use/cover classes: built-up, agriculture, herbaceous, open pine forest, mixed forest, and barren land using unsupervised ISODATA and maximum likelihood supervised classifiers with the aid of field collected ground truth data collected in the field. Ground truth information, collected in the field in December 2007, and including equalized stratified random points which were visual interpreted were used to assess the accuracy of the classification results. The overall accuracy of the land classification for each image was respectively: 1987 (82%), 2000 (82%), 2004 (87%). A post classification change detection technique was used to produce change images for 1987 to 2000, 1987 to 2004, and 2000 to 2004. It was found that significant changes in the land use/cover occurred over the 17- year period. The results showed increases in built up (from 10% to 17%) and herbaceous (from 5% to 14%) areas between 1987 and 2004. The increase of herbaceous was mostly caused by the abandonment of exhausted agriculture lands. At the same time, open pine forest and mixed forest areas lost (75%) and (83%) of their area to other land use/cover types. Open pine forest (from 20% to 14%) and mixed forest (from18 to 12%) were transformed into agriculture area or barren land. This study illustrated the continuing deforestation, land degradation and soil erosion in the region, which in turn is leading to decrease in vegetative cover. The study also showed the importance of Remote Sensing (RS) and Geographic Information System (GIS) technologies to estimate timely changes in the land use/cover, and to evaluate their causes in order to design an ecological based management plan for the park.

Data-driven estimates of the number of clusters in multivariate time series

An important problem in unsupervised data clustering is how to determine the number of clusters. Here we investigate how this can be achieved in an automated way by using interrelation matrices of multivariate time series. Two nonparametric and purely data driven algorithms are expounded and compared. The first exploits the eigenvalue spectra of surrogate data, while the second employs the eigenvector components of the interrelation matrix. Compared to the first algorithm, the second approach is computationally faster and not limited to linear interrelation measures.

Effect of 6-month supervised exercise on low-density lipoprotein apolipoprotein B kinetics in patients with type 2 diabetes mellitus

Although low-density lipoprotein (LDL) cholesterol is often normal in patients with type 2 diabetes mellitus, there is evidence for a reduced fractional catabolic rate and consequently an increased mean residence time (MRT), which can increase atherogenic risk. The dyslipidemia and insulin resistance of type 2 diabetes mellitus can be improved by aerobic exercise, but effects on LDL kinetics are unknown. The effect of 6-month supervised exercise on LDL apolipoprotein B kinetics was studied in a group of 17 patients with type 2 diabetes mellitus (mean age, 56.8 years; range, 38-68 years). Patients were randomized into a supervised group, who had a weekly training session, and an unsupervised group. LDL kinetics were measured with an infusion of 1-(13)C leucine at baseline in all groups and after 6 months of exercise in the patients. Eight body mass index-matched nondiabetic controls (mean age, 50.3 years; range, 40-67 years) were also studied at baseline only. At baseline, LDL MRT was significantly longer in the diabetic patients, whereas LDL production rate and fractional clearance rates were significantly lower than in controls. Percentage of glycated hemoglobin A(1c), body mass index, insulin sensitivity measured by the homeostasis model assessment, and very low-density lipoprotein triglyceride decreased (P < .02) in the supervised group, with no change in the unsupervised group. After 6 months, LDL cholesterol did not change in either the supervised or unsupervised group; but there was a significant change in LDL MRT between groups (P < .05) that correlated positively with very low-density lipoprotein triglyceride (r = 0.51, P < .04) and negatively with maximal oxygen uptake, a measure of fitness (r = -0.51, P = .035), in all patients. The LDL production and clearance rates did not change in either group. This study suggests that a supervised exercise program can reduce deleterious changes in LDL MRT.

Learning by the Dendritic Prediction of Somatic Spiking

Recent modeling of spike-timing-dependent plasticity indicates that plasticity involves as a third factor a local dendritic potential, besides pre- and postsynaptic firing times. We present a simple compartmental neuron model together with a non-Hebbian, biologically plausible learning rule for dendritic synapses where plasticity is modulated by these three factors. In functional terms, the rule seeks to minimize discrepancies between somatic firings and a local dendritic potential. Such prediction errors can arise in our model from stochastic fluctuations as well as from synaptic input, which directly targets the soma. Depending on the nature of this direct input, our plasticity rule subserves supervised or unsupervised learning. When a reward signal modulates the learning rate, reinforcement learning results. Hence a single plasticity rule supports diverse learning paradigms.

Comprehensive MicroRNA expression profiling identifies novel markers in follicular variant of papillary thyroid carcinoma

BACKGROUND Follicular variant of papillary thyroid carcinoma (FVPTC) shares features of papillary (PTC) and follicular (FTC) thyroid carcinomas on a clinical, morphological, and genetic level. MicroRNA (miRNA) deregulation was extensively studied in PTCs and FTCs. However, very limited information is available for FVPTC. The aim of this study was to assess miRNA expression in FVPTC with the most comprehensive miRNA array panel and to correlate it with the clinicopathological data. METHODS Forty-four papillary thyroid carcinomas (17 FVPTC, 27 classic PTC) and eight normal thyroid tissue samples were analyzed for expression of 748 miRNAs using Human Microarray Assays on the ABI 7900 platform (Life Technologies, Carlsbad, CA). In addition, an independent set of 61 tumor and normal samples was studied for expression of novel miRNA markers detected in this study. RESULTS Overall, the miRNA expression profile demonstrated similar trends between FVPTC and classic PTC. Fourteen miRNAs were deregulated in FVPTC with a fold change of more than five (up/down), including miRNAs known to be upregulated in PTC (miR-146b-3p, -146-5p, -221, -222 and miR-222-5p) and novel miRNAs (miR-375, -551b, 181-2-3p, 99b-3p). However, the levels of miRNA expression were different between these tumor types and some miRNAs were uniquely dysregulated in FVPTC allowing separation of these tumors on the unsupervised hierarchical clustering analysis. Upregulation of novel miR-375 was confirmed in a large independent set of follicular cell derived neoplasms and benign nodules and demonstrated specific upregulation for PTC. Two miRNAs (miR-181a-2-3p, miR-99b-3p) were associated with an adverse outcome in FVPTC patients by a Kaplan-Meier (p < 0.05) and multivariate Cox regression analysis (p < 0.05). CONCLUSIONS Despite high similarity in miRNA expression between FVPTC and classic PTC, several miRNAs were uniquely expressed in each tumor type, supporting their histopathologic differences. Highly upregulated miRNA identified in this study (miR-375) can serve as a novel marker of papillary thyroid carcinoma, and miR-181a-2-3p and miR-99b-3p can predict relapse-free survival in patients with FVPTC thus potentially providing important diagnostic and predictive value.

Tumour genomic and microenvironmental heterogeneity for integrated prediction of 5-year biochemical recurrence of prostate cancer: a retrospective cohort study.

BACKGROUND Clinical prognostic groupings for localised prostate cancers are imprecise, with 30-50% of patients recurring after image-guided radiotherapy or radical prostatectomy. We aimed to test combined genomic and microenvironmental indices in prostate cancer to improve risk stratification and complement clinical prognostic factors. METHODS We used DNA-based indices alone or in combination with intra-prostatic hypoxia measurements to develop four prognostic indices in 126 low-risk to intermediate-risk patients (Toronto cohort) who will receive image-guided radiotherapy. We validated these indices in two independent cohorts of 154 (Memorial Sloan Kettering Cancer Center cohort [MSKCC] cohort) and 117 (Cambridge cohort) radical prostatectomy specimens from low-risk to high-risk patients. We applied unsupervised and supervised machine learning techniques to the copy-number profiles of 126 pre-image-guided radiotherapy diagnostic biopsies to develop prognostic signatures. Our primary endpoint was the development of a set of prognostic measures capable of stratifying patients for risk of biochemical relapse 5 years after primary treatment. FINDINGS Biochemical relapse was associated with indices of tumour hypoxia, genomic instability, and genomic subtypes based on multivariate analyses. We identified four genomic subtypes for prostate cancer, which had different 5-year biochemical relapse-free survival. Genomic instability is prognostic for relapse in both image-guided radiotherapy (multivariate analysis hazard ratio [HR] 4·5 [95% CI 2·1-9·8]; p=0·00013; area under the receiver operator curve [AUC] 0·70 [95% CI 0·65-0·76]) and radical prostatectomy (4·0 [1·6-9·7]; p=0·0024; AUC 0·57 [0·52-0·61]) patients with prostate cancer, and its effect is magnified by intratumoral hypoxia (3·8 [1·2-12]; p=0·019; AUC 0·67 [0·61-0·73]). A novel 100-loci DNA signature accurately classified treatment outcome in the MSKCC low-risk to intermediate-risk cohort (multivariate analysis HR 6·1 [95% CI 2·0-19]; p=0·0015; AUC 0·74 [95% CI 0·65-0·83]). In the independent MSKCC and Cambridge cohorts, this signature identified low-risk to high-risk patients who were most likely to fail treatment within 18 months (combined cohorts multivariate analysis HR 2·9 [95% CI 1·4-6·0]; p=0·0039; AUC 0·68 [95% CI 0·63-0·73]), and was better at predicting biochemical relapse than 23 previously published RNA signatures. INTERPRETATION This is the first study of cancer outcome to integrate DNA-based and microenvironment-based failure indices to predict patient outcome. Patients exhibiting these aggressive features after biopsy should be entered into treatment intensification trials. FUNDING Movember Foundation, Prostate Cancer Canada, Ontario Institute for Cancer Research, Canadian Institute for Health Research, NIHR Cambridge Biomedical Research Centre, The University of Cambridge, Cancer Research UK, Cambridge Cancer Charity, Prostate Cancer UK, Hutchison Whampoa Limited, Terry Fox Research Institute, Princess Margaret Cancer Centre Foundation, PMH-Radiation Medicine Program Academic Enrichment Fund, Motorcycle Ride for Dad (Durham), Canadian Cancer Society.

Developmental self-construction and -configuration of functional neocortical neuronal networks.

The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors. Although knowledge of various genetic and cellular aspects of development is accumulating rapidly, there is less systematic understanding of how these various processes play together in order to construct such functional networks. Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative ('winner-take-all', WTA) network architecture can arise by development from a single precursor cell. This precursor is granted a simplified gene regulatory network that directs cell mitosis, differentiation, migration, neurite outgrowth and synaptogenesis. Once initial axonal connection patterns are established, their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns. We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks, and compare our simulation results with biological data.

The trouble with quality filtering based on relative Cramér-Rao lower bounds.

Cramér Rao Lower Bounds (CRLB) have become the standard for expression of uncertainties in quantitative MR spectroscopy. If properly interpreted as a lower threshold of the error associated with model fitting, and if the limits of its estimation are respected, CRLB are certainly a very valuable tool to give an idea of minimal uncertainties in magnetic resonance spectroscopy (MRS), although other sources of error may be larger. Unfortunately, it has also become standard practice to use relative CRLB expressed as a percentage of the presently estimated area or concentration value as unsupervised exclusion criterion for bad quality spectra. It is shown that such quality filtering with widely used threshold levels of 20% to 50% CRLB readily causes bias in the estimated mean concentrations of cohort data, leading to wrong or missed statistical findings-and if applied rigorously-to the failure of using MRS as a clinical instrument to diagnose disease characterized by low levels of metabolites. Instead, absolute CRLB in comparison to those of the normal group or CRLB in relation to normal metabolite levels may be more useful as quality criteria. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.

The information bottleneck method for genome-wide association studies

In population studies, most current methods focus on identifying one outcome-related SNP at a time by testing for differences of genotype frequencies between disease and healthy groups or among different population groups. However, testing a great number of SNPs simultaneously has a problem of multiple testing and will give false-positive results. Although, this problem can be effectively dealt with through several approaches such as Bonferroni correction, permutation testing and false discovery rates, patterns of the joint effects by several genes, each with weak effect, might not be able to be determined. With the availability of high-throughput genotyping technology, searching for multiple scattered SNPs over the whole genome and modeling their joint effect on the target variable has become possible. Exhaustive search of all SNP subsets is computationally infeasible for millions of SNPs in a genome-wide study. Several effective feature selection methods combined with classification functions have been proposed to search for an optimal SNP subset among big data sets where the number of feature SNPs far exceeds the number of observations. ^ In this study, we take two steps to achieve the goal. First we selected 1000 SNPs through an effective filter method and then we performed a feature selection wrapped around a classifier to identify an optimal SNP subset for predicting disease. And also we developed a novel classification method-sequential information bottleneck method wrapped inside different search algorithms to identify an optimal subset of SNPs for classifying the outcome variable. This new method was compared with the classical linear discriminant analysis in terms of classification performance. Finally, we performed chi-square test to look at the relationship between each SNP and disease from another point of view. ^ In general, our results show that filtering features using harmononic mean of sensitivity and specificity(HMSS) through linear discriminant analysis (LDA) is better than using LDA training accuracy or mutual information in our study. Our results also demonstrate that exhaustive search of a small subset with one SNP, two SNPs or 3 SNP subset based on best 100 composite 2-SNPs can find an optimal subset and further inclusion of more SNPs through heuristic algorithm doesn't always increase the performance of SNP subsets. Although sequential forward floating selection can be applied to prevent from the nesting effect of forward selection, it does not always out-perform the latter due to overfitting from observing more complex subset states. ^ Our results also indicate that HMSS as a criterion to evaluate the classification ability of a function can be used in imbalanced data without modifying the original dataset as against classification accuracy. Our four studies suggest that Sequential Information Bottleneck(sIB), a new unsupervised technique, can be adopted to predict the outcome and its ability to detect the target status is superior to the traditional LDA in the study. ^ From our results we can see that the best test probability-HMSS for predicting CVD, stroke,CAD and psoriasis through sIB is 0.59406, 0.641815, 0.645315 and 0.678658, respectively. In terms of group prediction accuracy, the highest test accuracy of sIB for diagnosing a normal status among controls can reach 0.708999, 0.863216, 0.639918 and 0.850275 respectively in the four studies if the test accuracy among cases is required to be not less than 0.4. On the other hand, the highest test accuracy of sIB for diagnosing a disease among cases can reach 0.748644, 0.789916, 0.705701 and 0.749436 respectively in the four studies if the test accuracy among controls is required to be at least 0.4. ^ A further genome-wide association study through Chi square test shows that there are no significant SNPs detected at the cut-off level 9.09451E-08 in the Framingham heart study of CVD. Study results in WTCCC can only detect two significant SNPs that are associated with CAD. In the genome-wide study of psoriasis most of top 20 SNP markers with impressive classification accuracy are also significantly associated with the disease through chi-square test at the cut-off value 1.11E-07. ^ Although our classification methods can achieve high accuracy in the study, complete descriptions of those classification results(95% confidence interval or statistical test of differences) require more cost-effective methods or efficient computing system, both of which can't be accomplished currently in our genome-wide study. We should also note that the purpose of this study is to identify subsets of SNPs with high prediction ability and those SNPs with good discriminant power are not necessary to be causal markers for the disease.^

Estrogen-induced gene expression and patient survival in high-grade serous carcinoma of the ovary

Background. Assessment of estrogen receptor (ER) expression has inconsistent utility as a prognostic marker in epithelial ovarian carcinoma. In breast and endometrial cancers, the use of estrogen-induced gene panels, rather than ER expression alone, has shown improved prognostic capability. Specifically, over-expression of estrogen-induced genes in these tumors is associated with a better prognosis and signifies estrogen sensitivity that can be exploited with hormone antagonizing agents. It was therefore hypothesized that estrogen-induced gene expression in ovarian carcinoma would successfully predict outcomes and differentiate between tumors of varying estrogen sensitivities. Methods. Two hundred nineteen (219) patients with ovarian cancer who underwent surgery at M. D. Anderson between 2004 and 2007 were identified. Of these, eighty-three (83) patients were selected for inclusion because they had advanced stage, high-grade serous carcinoma of the ovary or peritoneum, had not received neoadjuvant chemotherapy, and had readily available frozen tissue for study. All patients had also received adjuvant treatment with platinum and taxane agents. The expression of seven genes known to be induced by estrogen in the female reproductive tract (EIG121, sFRP1, sFRP4, RALDH2, PR, IGF-1, and ER) was measured using qRT-PCR. Unsupervised cluster analyses of multiple gene permutations were used to categorize patients as high or low estrogen-induced gene expressors. QPCR gene expression results were then compared to ER and PR immunohistochemical (IHC) expression. Cox proportional hazards models were used to evaluate the effects of both individual genes and selected gene clusters on patient survival. Results. Median follow-up time was 38.7 months (range 1-68 months). In a multivariate model, overall survival was predicted by sFRP1 expression (HR 1.10 [1.02-1.19], p=0.01) and EIG121 expression (HR 1.28 [1.10-1.49], p<0.01). A cluster defined by EIG121 and ER was further examined because that combination appeared to reasonably segregate tumors into distinct groups of high and low estrogen-induced gene expressors. Shorter overall survival was associated with high estrogen-induced gene expressors (HR 2.84 [1.11-7.30], p=0.03), even after adjustment for race, age, body mass index, and residual disease at debulking. No difference in IHC ER or PR expression was noted between gene clusters. Conclusion. In sharp contrast to breast and endometrial cancers, high estrogen-induced gene expression predicts shorter overall survival in patients with high-grade serous ovarian carcinoma. An estrogen-induced gene biomarker panel may have utility as prognostic indicator and may be useful to guide management with estrogen antagonists in this population.^

What factors affect the visibility of Argentinean publications in humanities and social sciences in Scopus? Some evidence beyond the geographic realm of research

Se analizan los patrones de publicación y citación en ciencias humanas y sociales en Scopus en el período 2003-2012, según el alcance geográfico de la investigación. Los resultados muestran que los temas de alcance nacional tienen un predominio del español como lengua de publicación y una marcada preferencia por la autoría única frente a los patrones observados en el grupo de otros temas, no situados geográficamente, donde el inglés y la colaboración institucional es más fuerte y está más consolidada. La citación no parece estar determinada solo por el alcance geográfico de las investigaciones, sino también por el idioma de publicación, la coautoría y los perfiles de las revistas donde se publica. Se espera que los resultados den lugar a una reflexión constructiva sobre la cultura investigadora y editorial y que sean útiles como referencia para establecer criterios de evaluación en las comisiones evaluadoras y las políticas editoriales a nivel nacional

What factors affect the visibility of Argentinean publications in humanities and social sciences in Scopus? Some evidence beyond the geographic realm of research

Se analizan los patrones de publicación y citación en ciencias humanas y sociales en Scopus en el período 2003-2012, según el alcance geográfico de la investigación. Los resultados muestran que los temas de alcance nacional tienen un predominio del español como lengua de publicación y una marcada preferencia por la autoría única frente a los patrones observados en el grupo de otros temas, no situados geográficamente, donde el inglés y la colaboración institucional es más fuerte y está más consolidada. La citación no parece estar determinada solo por el alcance geográfico de las investigaciones, sino también por el idioma de publicación, la coautoría y los perfiles de las revistas donde se publica. Se espera que los resultados den lugar a una reflexión constructiva sobre la cultura investigadora y editorial y que sean útiles como referencia para establecer criterios de evaluación en las comisiones evaluadoras y las políticas editoriales a nivel nacional

What factors affect the visibility of Argentinean publications in humanities and social sciences in Scopus? Some evidence beyond the geographic realm of research

Se analizan los patrones de publicación y citación en ciencias humanas y sociales en Scopus en el período 2003-2012, según el alcance geográfico de la investigación. Los resultados muestran que los temas de alcance nacional tienen un predominio del español como lengua de publicación y una marcada preferencia por la autoría única frente a los patrones observados en el grupo de otros temas, no situados geográficamente, donde el inglés y la colaboración institucional es más fuerte y está más consolidada. La citación no parece estar determinada solo por el alcance geográfico de las investigaciones, sino también por el idioma de publicación, la coautoría y los perfiles de las revistas donde se publica. Se espera que los resultados den lugar a una reflexión constructiva sobre la cultura investigadora y editorial y que sean útiles como referencia para establecer criterios de evaluación en las comisiones evaluadoras y las políticas editoriales a nivel nacional

Contiunous surface water uncorrected and quench-corrected active fluorescence and derived chlorophyll a concentration using calibration by HPLC pigments during POLARSTERN cruise ANT-XXV/1

The relationship between phytoplankton assemblages and the associated optical properties of the water body is important for the further development of algorithms for large-scale remote sensing of phytoplankton biomass and the identification of phytoplankton functional types (PFTs), which are often representative for different biogeochemical export scenarios. Optical in-situ measurements aid in the identification of phytoplankton groups with differing pigment compositions and are widely used to validate remote sensing data. In this study we present results from an interdisciplinary cruise aboard the RV Polarstern along a north-to-south transect in the eastern Atlantic Ocean in November 2008. Phytoplankton community composition was identified using a broad set of in-situ measurements. Water samples from the surface and the depth of maximum chlorophyll concentration were analyzed by high performance liquid chromatography (HPLC), flow cytometry, spectrophotometry and microscopy. Simultaneously, the above- and underwater light field was measured by a set of high spectral resolution (hyperspectral) radiometers. An unsupervised cluster algorithm applied to the measured parameters allowed us to define bio-optical provinces, which we compared to ecological provinces proposed elsewhere in the literature. As could be expected, picophytoplankton was responsible for most of the variability of PFTs in the eastern Atlantic Ocean. Our bio-optical clusters agreed well with established provinces and thus can be used to classify areas of similar biogeography. This method has the potential to become an automated approach where satellite data could be used to identify shifting boundaries of established ecological provinces or to track exceptions from the rule to improve our understanding of the biogeochemical cycles in the ocean.