Metabolic classification of microbial genomes using functional probes

<p>Abstract</p> <p>Background</p> <p>Microorganisms able to grow under artificial culture conditions comprise only a small proportion of the biosphere's total microbial community. Until recently, scientists have been unable to perform thorough analyses of difficult-to-culture microorganisms due to limitations in sequencing technology. As modern techniques have dramatically increased sequencing rates and rapidly expanded the number of sequenced genomes, in addition to traditional taxonomic classifications which focus on the evolutionary relationships of organisms, classifications of the genomes based on alternative points of view may help advance our understanding of the delicate relationships of organisms.</p> <p>Results</p> <p>We have developed a proteome-based method for classifying microbial species. This classification method uses a set of probes comprising short, highly conserved amino acid sequences. For each genome, <it>in silico </it>translation is performed to obtained its proteome, based on which a probe-set frequency pattern is generated. Then, the probe-set frequency patterns are used to cluster the proteomes/genomes.</p> <p>Conclusions</p> <p>Features of the proposed method include a high running speed in challenge of a large number of genomes, and high applicability for classifying organisms with incomplete genome sequences. Moreover, the probe-set clustering method is sensitive to the metabolic phenotypic similarities/differences among species and is thus supposed potential for the classification or differentiation of closely-related organisms.</p

Use of dietary supplements by breast cancer patients undergoing conventional cancer treatment

&lt;b&gt;Background&lt;/b&gt; Many breast cancer patients use some form of dietary supplement (DS) to complement their conventional cancer treatment, in the hope that they might lessen the side effects of treatment, improve quality of life, give a greater sense of control, and reduce stress. This pilot study assessed the level of DS usage by breast cancer patients undergoing conventional cancer treatment, and their concerns about the use of DS. &lt;p&gt;&lt;/p&gt; &lt;b&gt;Method&lt;/b&gt; A cross-sectional descriptive survey in three breast cancer centers in Hong Kong using face-to-face interviewing was performed. &lt;p&gt;&lt;/p&gt; &lt;b&gt;Results&lt;/b&gt; Of 82 female Chinese breast cancer patients who completed the survey, 99% reported that they had been using DS since their cancer was diagnosed. The most frequently used DS were Chinese herbal medicines, and patients spent about US$258 on DS every month. The reason given for using DS was to enhance their recovery from cancer, but at the same time the patients had safety concerns. However, most patients did not feel able to discuss these concerns with health professionals. &lt;p&gt;&lt;/p&gt; &lt;b&gt;Conclusion&lt;/b&gt; The majority of the patients had some safety concerns, and said that they would welcome detailed and reliable information on DS. The lack of reliable information on the potential risks and benefits of using such supplements as an adjuvant to conventional treatment and the reluctance of patients to discuss their use of DS with health professionals is a major area of concern that warrants further attention

Unsupervised Learning Method for the Wave Equation Based on Finite Difference Residual Constraints Loss

The wave equation is an important physical partial differential equation, and in recent years, deep learning has shown promise in accelerating or replacing traditional numerical methods for solving it. However, existing deep learning methods suffer from high data acquisition costs, low training efficiency, and insufficient generalization capability for boundary conditions. To address these issues, this paper proposes an unsupervised learning method for the wave equation based on finite difference residual constraints. We construct a novel finite difference residual constraint based on structured grids and finite difference methods, as well as an unsupervised training strategy, enabling convolutional neural networks to train without data and predict the forward propagation process of waves. Experimental results show that finite difference residual constraints have advantages over physics-informed neural networks (PINNs) type physical information constraints, such as easier fitting, lower computational costs, and stronger source term generalization capability, making our method more efficient in training and potent in application.Comment: in Chinese languag

Deferoxamine retinopathy: spectral domain-optical coherence tomography findings

Al-Djamiʿ li Ibn al-BaïtharNumérisation effectuée à partir d'un document de substitution

Multi-Hop Routing Mechanism for Reliable Sensor Computing

Current research on routing in wireless sensor computing concentrates on increasing the service lifetime, enabling scalability for large number of sensors and supporting fault tolerance for battery exhaustion and broken nodes. A sensor node is naturally exposed to various sources of unreliable communication channels and node failures. Sensor nodes have many failure modes, and each failure degrades the network performance. This work develops a novel mechanism, called Reliable Routing Mechanism (RRM), based on a hybrid cluster-based routing protocol to specify the best reliable routing path for sensor computing. Table-driven intra-cluster routing and on-demand inter-cluster routing are combined by changing the relationship between clusters for sensor computing. Applying a reliable routing mechanism in sensor computing can improve routing reliability, maintain low packet loss, minimize management overhead and save energy consumption. Simulation results indicate that the reliability of the proposed RRM mechanism is around 25% higher than that of the Dynamic Source Routing (DSR) and ad hoc On-demand Distance Vector routing (AODV) mechanisms

Deferoxamine retinopathy: spectral domain-optical coherence tomography findings

BACKGROUND: To describe the spectral domain optical coherence tomography (SD-OCT) findings of a patient who developed pigmentary retinopathy following high-dose deferoxamine administration. CASE PRESENTATION: A 34-year-old man with thalassemia major complained of nyctalopia and decreased vision following high-dose intravenous deferoxamine to treat systemic iron overload. Fundus examination revealed multiple discrete hypo-pigmented lesions at the posterior pole and mid-peripheral retina. Recovery was partial following cessation of desferrioxamine six weeks later. A follow-up SD-OCT showed multiple accumulated hyper-reflective deposits primarily in the choroid, retina pigment epithelium (RPE), and inner segment and outer segment (IS/OS) junction. CONCLUSION: Deferoxamine retinopathy primarily targets the RPE–Bruch membrane–photoreceptor complex, extending from the peri-fovea to the peripheral retina with foveola sparing. An SD-OCT examination can serve as a simple, noninvasive tool for early detection and long-term follow-up