Traditional Chinese medicine physicians’ insights into inter-professional tensions between traditional Chinese medicine and biomedicine: a critical perspective

In Singapore, the institutional preference for biomedicine and the cultural importance of traditional Chinese medicine (TCM) have created tensions between the two medical systems and erected barriers to a more collaborative healthcare system. This study foregrounds TCM physicians’ voice to reveal ideological struggles and power imbalances that underlie the inter-professional tensions and accompanying marginalization of TCM. Through in-depth interviews with 22 TCM physicians in Singapore, this study reveals the incongruences in ideological underpinnings between biomedicine and TCM, reflected in their different worldviews and epistemological approaches to knowledge formation and evaluation. Power differentials between the two medical systems are manifest in TCM physicians’ inferior position in relation to their biomedical peers, the patients’ internalization of biomedical standards to question the TCM profession and their own interest in seeking TCM treatments, and the state’s limited support for TCM research, subsidies, and service provision in hospital settings. The results suggest that more open dialogue about the dichotomous framings of biomedicine and TCM is key to disrupting the mutual reinforcement of ideology and power, as well as to creating increased mutual understanding between the two medical systems

Cost-Sensitive Learning for Recurrence Prediction of Breast Cancer

Breast cancer is one of the top cancer-death causes and specifically accounts for 10.4% of all cancer incidences among women. The prediction of breast cancer recurrence has been a challenging research problem for many researchers. Data mining techniques have recently received considerable attention, especially when used for the construction of prognosis models from survival data. However, existing data mining techniques may not be effective to handle censored data. Censored instances are often discarded when applying classification techniques to prognosis. In this paper, we propose a cost-sensitive learning approach to involve the censored data in prognostic assessment with better recurrence prediction capability. The proposed approach employs an outcome inference mechanism to infer the possible probabilistic outcome of each censored instance and adopt the cost-proportionate rejection sampling and a committee machine strategy to take into account these instances with probabilistic outcomes during the classification model learning process. We empirically evaluate the effectiveness of our proposed approach for breast cancer recurrence prediction and include a censored-data-discarding method (i.e., building the recurrence prediction model by only using uncensored data) and the Kaplan-Meier method (a common prognosis method) as performance benchmarks. Overall, our evaluation results suggest that the proposed approach outperforms its benchmark techniques, measured by precision, recall and F1 score

Entropy Projection Curved Gabor with Random Forest and SVM for Face Recognition

In this work, we propose a workflow for face recognition under occlusion using the entropy projection from the curved Gabor filter, and create a representative and compact features vector that describes a face. Despite the reduced vector obtained by the entropy projection, it still presents opportunity for further dimensionality reduction. Therefore, we use a Random Forest classifier as an attribute selector, providing a 97% reduction of the original vector while keeping suitable accuracy. A set of experiments using three public image databases: AR Face, Extended Yale B with occlusion and FERET illustrates the proposed methodology, evaluated using the SVM classifier. The results obtained in the experiments show promising results when compared to the available approaches in the literature, obtaining 98.05% accuracy for the complete AR Face, 97.26% for FERET and 81.66% with Yale with 50% occlusion

A single sub-km Kuiper Belt object from a stellar Occultation in archival data

The Kuiper belt is a remnant of the primordial Solar System. Measurements of its size distribution constrain its accretion and collisional history, and the importance of material strength of Kuiper belt objects (KBOs). Small, sub-km sized, KBOs elude direct detection, but the signature of their occultations of background stars should be detectable. Observations at both optical and X-ray wavelengths claim to have detected such occultations, but their implied KBO abundances are inconsistent with each other and far exceed theoretical expectations. Here, we report an analysis of archival data that reveals an occultation by a body with a 500 m radius at a distance of 45 AU. The probability of this event to occur due to random statistical fluctuations within our data set is about 2%. Our survey yields a surface density of KBOs with radii larger than 250 m of 2.1^{+4.8}_{-1.7} x 10^7 deg^{-2}, ruling out inferred surface densities from previous claimed detections by more than 5 sigma. The fact that we detected only one event, firmly shows a deficit of sub-km sized KBOs compared to a population extrapolated from objects with r>50 km. This implies that sub-km sized KBOs are undergoing collisional erosion, just like debris disks observed around other stars.Comment: To appear in Nature on December 17, 2009. Under press embargo until 1800 hours London time on 16 December. 19 pages; 7 figure

The α1‐adrenergic receptor is involved in hepcidin upregulation induced by adrenaline and norepinephrine via the STAT3 pathway

Elevated body iron stores are associated with hypertension progression, while hypertension is associated with elevated plasma catecholamine levels in patients. However, there is a gap in our understanding of the connection between catecholamines and iron regulation. Hepcidin is a key iron‐regulatory hormone, which maintains body iron balance. In the present study, we investigated the effects of adrenaline (AD) and norepinephrine (NE) on hepatic hepcidin regulation. Mice were treated with AD, NE, phenylephrine (PE, α1‐adrenergic receptor agonist), prazosin (PZ, α1‐adrenergic receptor antagonist), and/or propranolol (Pro, β‐adrenergic receptor antagonist). The levels of hepcidin, as well as signal transducer and activator of transcription 3 (STAT3), ferroportin 1 (FPN1), and ferritin‐light (Ft‐L) protein in the liver or spleen, were assessed. Six hours after AD, NE, or PE treatment, hepatic hepcidin mRNA levels increased. Pretreatment with PZ, but not Pro, abolished the effects of AD or NE on STAT3 phosphorylation and hepatic hepcidin expression. When mice were treated with AD or NE continuously for 7 days, an increase in hepatic hepcidin mRNA levels and serum hepcidin concentration was also observed. Meanwhile, the expected downstream effects of elevated hepcidin, namely decreased FPN1 expression and increased Ft‐L protein and non‐heme iron concentrations in the spleen, were observed after the continuous AD or NE treatments. Taken together, we found that AD or NE increase hepatic hepcidin expression via the α1‐adrenergic receptor and STAT3 pathways in mice. The elevated hepatic hepcidin decreased FPN1 levels in the spleen, likely causing the increased iron accumulation in the spleen

Concurrent TMS-fMRI: Technical Challenges, Developments, and Overview of Previous Studies

Transcranial magnetic stimulation (TMS) is a promising treatment modality for psychiatric and neurological disorders. Repetitive TMS (rTMS) is widely used for the treatment of psychiatric and neurological diseases, such as depression, motor stroke, and neuropathic pain. However, the underlying mechanisms of rTMS-mediated neuronal modulation are not fully understood. In this respect, concurrent or simultaneous TMS-fMRI, in which TMS is applied during functional magnetic resonance imaging (fMRI), is a viable tool to gain insights, as it enables an investigation of the immediate effects of TMS. Concurrent application of TMS during neuroimaging usually causes severe artifacts due to magnetic field inhomogeneities induced by TMS. However, by carefully interleaving the TMS pulses with MR signal acquisition in the way that these are far enough apart, we can avoid any image distortions. While the very first feasibility studies date back to the 1990s, recent developments in coil hardware and acquisition techniques have boosted the number of TMS-fMRI applications. As such, a concurrent application requires expertise in both TMS and MRI mechanisms and sequencing, and the hurdle of initial technical set up and maintenance remains high. This review gives a comprehensive overview of concurrent TMS-fMRI techniques by collecting (1) basic information, (2) technical challenges and developments, (3) an overview of findings reported so far using concurrent TMS-fMRI, and (4) current limitations and our suggestions for improvement. By sharing this review, we hope to attract the interest of researchers from various backgrounds and create an educational knowledge base

Multi-Modal Spectral Image Super-Resolution

Recent advances have shown the great power of deep convolutional neural networks (CNN) to learn the relationship between low and high-resolution image patches. However, these methods only take a single-scale image as input and require large amount of data to train without the risk of overfitting. In this paper, we tackle the problem of multi-modal spectral image super-resolution while constraining our-selves to a small dataset. We propose the use of different modalities to improve the performance of neural networks on the spectral super-resolution problem. First, we use multiple downscaled versions of the same image to infer a better high-resolution image for training, we refer to these inputs as a multi-scale modality. Furthermore, color images are usually taken at a higher resolution than spectral images, so we make use of color images as another modality to improve the super-resolution network. By combining both modalities, we build a pipeline that learns to super-resolve using multi-scale spectral inputs guided by a color image. Finally, we validate our method and show that it is economic in terms of parameters and computation time, while still producing state-of-the-art results