Detection of Myocardial Infarction using ECG and Multi-Scale Feature Concatenate

Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However; issues; particularly overfitting and underfitting; were not being taken into account. In other words; it is unclear whether the network structure is too simple or complex. Toward this end; the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally; multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result; the N-Net reached a 95.76% accuracy in the MI detection task; whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p \u3c 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion; testing throughout the simple and complex network structure is indispensable. However; the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed

Path-dependent selection—a bridge between natural selection and neutral selection

Path-dependent selection follows the premise of complete symmetry in the neutral theory of selection; mutations in the natural world are entirely based on statistical randomness, lack directionality, and thus do not exhibit differences in fitness. Under specific spatiotemporal conditions, however, evolutionary positive feedback effects resulting from the specific environment will result in the breakdown of symmetry pre-assumed in neutral selection. This evolutionary positive feedback, a recursive effect, is of Lamarckian active selection or inheritance of acquired characteristics. The mutual antagonistic interactions between the positive selection of recursive effect and the passive selection under natural selection pressure of the environment in multidimensional conditions will result in evolutionary paths. Path-dependent selection proposes that the evolutionary process of organisms is a selection process based on path frequencies rather than an increase in fitness, with a strong reliance on the paths that it has taken in the past. Because of the existence of transition probabilities between different paths or within the same path (such as plasmid transfer, transposons, and function transfer in ecological interactions), path formation will exhibit acceleration or deceleration effects, explaining Gould’s principles such as punctuated equilibrium. When environmental selection pressure is weak or zero, most or all paths (like neutral selection outcomes) may be possible. The frequencies of different paths will differentiate as environmental selection increases, and the paths with higher frequencies will be more easily selected. When the evolutionary process or history has no impact on the evolution of the paths themselves (a static, equilibrium state), the path with the highest frequency is the shortest or optimal path used by evolution—a result consistent with Darwin’s theory of natural selection. Path-dependent selection, which draws inspiration from modern physics, particularly path integral methods in quantum mechanics, may provide us with a new perspective and approach to explaining the evolution of life

Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning

Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, particularly deep machine learning (ML), severely suffer from non-interpretability, which might uncontrollably lead to incorrect predictions. Interpretability is particularly crucial to ML for clinical diagnosis as the consumers must gain necessary sense of security and trust from firm grounds or convincing interpretations. In this work, we propose a tensor-network (TN)-ML method to reliably predict lung cancer patients and their stages via screening Raman spectra data of Volatile organic compounds (VOCs) in exhaled breath, which are generally suitable as biomarkers and are considered to be an ideal way for non-invasive lung cancer screening. The prediction of TN-ML is based on the mutual distances of the breath samples mapped to the quantum Hilbert space. Thanks to the quantum probabilistic interpretation, the certainty of the predictions can be quantitatively characterized. The accuracy of the samples with high certainty is almost 100$\%$. The incorrectly-classified samples exhibit obviously lower certainty, and thus can be decipherably identified as anomalies, which will be handled by human experts to guarantee high reliability. Our work sheds light on shifting the ``AI for biomedical sciences'' from the conventional non-interpretable ML schemes to the interpretable human-ML interactive approaches, for the purpose of high accuracy and reliability.Comment: 10 pages, 7 figure

MicroRNA-221 and microRNA-222 regulate gastric carcinoma cell proliferation and radioresistance by targeting PTEN

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) can function as either oncogenes or tumor suppressor genes via regulation of cell proliferation and/or apoptosis. MiR-221 and miR-222 were discovered to induce cell growth and cell cycle progression via direct targeting of p27 and p57 in various human malignancies. However, the roles of miR-221 and miR-222 have not been reported in human gastric cancer. In this study, we examined the impact of miR-221 and miR-222 on human gastric cancer cells, and identified target genes for miR-221 and miR-222 that might mediate their biology.</p> <p>Methods</p> <p>The human gastric cancer cell line SGC7901 was transfected with AS-miR-221/222 or transduced with pMSCV-miR-221/222 to knockdown or restore expression of miR-221 and miR-222, respectively. The effects of miR-221 and miR-222 were then assessed by cell viability, cell cycle analysis, apoptosis, transwell, and clonogenic assay. Potential target genes were identified by Western blot and luciferase reporter assay.</p> <p>Results</p> <p>Upregulation of miR-221 and miR-222 induced the malignant phenotype of SGC7901 cells, whereas knockdown of miR-221 and miR-222 reversed this phenotype via induction of PTEN expression. In addition, knockdonwn of miR-221 and miR-222 inhibited cell growth and invasion and increased the radiosensitivity of SGC7901 cells. Notably, the seed sequence of miR-221 and miR-222 matched the 3'UTR of PTEN, and introducing a PTEN cDNA without the 3'UTR into SGC7901 cells abrogated the miR-221 and miR-222-induced malignant phenotype. PTEN-3'UTR luciferase reporter assay confirmed PTEN as a direct target of miR-221 and miR-222.</p> <p>Conclusion</p> <p>These results demonstrate that miR-221 and miR-222 regulate radiosensitivity, and cell growth and invasion of SGC7901 cells, possibly via direct modulation of PTEN expression. Our study suggests that inhibition of miR-221 and miR-222 might form a novel therapeutic strategy for human gastric cancer.</p

A Bi2Te3-Filled Nickel Foam Film with Exceptional Flexibility and Thermoelectric Performance

The past decades have witnessed surging demand for wearable electronics, for which thermoelectrics (TEs) are considered a promising self-charging technology, as they are capable of converting skin heat into electricity directly. Bi2Te3 is the most-used TE material at room temperature, due to a high zT of ~1. However, it is different to integrate Bi2Te3 for wearable TEs owing to its intrinsic rigidity. Bi2Te3 could be flexible when made thin enough, but this implies a small electrical and thermal load, thus severely restricting the power output. Herein, we developed a Bi2Te3/nickel foam (NiFoam) composite film through solvothermal deposition of Bi2Te3 nanoplates into porous NiFoam. Due to the mesh structure and ductility of Ni Foam, the film, with a thickness of 160 μm, exhibited a high figure of merit for flexibility, 0.016, connoting higher output. Moreover, the film also revealed a high tensile strength of 12.7 ± 0.04 MPa and a maximum elongation rate of 28.8%. In addition, due to the film’s high electrical conductivity and enhanced Seebeck coefficient, an outstanding power factor of 850 μW m−1 K−2 was achieved, which is among the highest ever reported. A module fabricated with five such n-type legs integrated electrically in series and thermally in parallel showed an output power of 22.8 nW at a temperature gap of 30 K. This work offered a cost-effective avenue for making highly flexible TE films for power supply of wearable electronics by intercalating TE nanoplates into porous and meshed-structure materials

Genetic Diversity and Differentiation of Dendrocalamus membranaceus (Poaceae: Bambusoideae), a Declining Bamboo Species in Yunnan, China, as Based on Inter-Simple Sequence Repeat (ISSR) Analysis

Dendrocalamus membranaceus Munro is a woody bamboo with a high economic and ecological value that often occurs as natural stands, such as in the large-scale forested areas of China’s Yunnan Province. Due to its overexploitation, the habitat of D. membranaceus in Yunnan has been dramatically reduced, and the quality of the stands has declined. As a preliminary analysis in considering the effective protection for these germplasm resources, we assessed the genetic diversity of 12 natural populations in Yunnan, using inter-simple sequence repeat (ISSR) markers. From 10 ISSR primers, we generated 155 bands, of which 153 were polymorphic (98.71%). Compared with other species in the genus, this species demonstrated a greater genetic diversity (S = 0.349) and lower genetic differentiation (GST = 0.252). Our analysis of molecular variance revealed that the genetic differentiation among the populations is significant. A large proportion of the genetic variation (78.95%) resides among the individuals within populations, whereas only 21.05% are found among populations. Mantel tests indicated no significant correlation between genetic and geographic distances among the populations. Given the low sexual reproducibility and characteristics of monocarpic plants, we recommend implementing in situ conservation measures for all of the D. membranaceus populations in Yunnan and collecting sufficient samples for ex situ conservation. Furthermore, the conservation area should be extended to its main natural habitats, the Lancang-Mekong River Valley

MiR-221 and miR-222 target PUMA to induce cell survival in glioblastoma

<p>Abstract</p> <p>Background</p> <p>MiR-221 and miR-222 (miR-221/222) are frequently up-regulated in various types of human malignancy including glioblastoma. Recent studies have reported that miR-221/222 regulate cell growth and cell cycle progression by targeting p27 and p57. However the underlying mechanism involved in cell survival modulation of miR-221/222 remains elusive.</p> <p>Results</p> <p>Here we showed that miR-221/222 inhibited cell apoptosis by targeting pro-apoptotic gene PUMA in human glioma cells. Enforced expression of miR-22/222 induced cell survival whereas knockdown of miR-221/222 rendered cells to apoptosis. Further, miR-221/222 reduced PUMA protein levels by targeting PUMA-3'UTR. Introducing PUMA cDNA without 3'UTR abrogated miR-221/222-induced cell survival. Notably, knockdown of miR-221/222 induces PUMA expression and cell apoptosis and considerably decreases tumor growth in xenograft model. Finally, there was an inverse relationship between PUMA and miR-221/222 expression in glioma tissues.</p> <p>Conclusion</p> <p>To our knowledge, these data indicate for the first time that miR-221/222 directly regulate apoptosis by targeting PUMA in glioblastoma and that miR-221/222 could be potential therapeutic targets for glioblastoma intervention.</p