Genuinely Distributed Byzantine Machine Learning

Machine Learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component failures, which can be all encompassed within the spectrum of a Byzantine behavior. Several approaches have been proposed recently to tolerate Byzantine workers. Yet all require trusting a central parameter server. We initiate in this paper the study of the ``general'' Byzantine-resilient distributed machine learning problem where no individual component is trusted. We show that this problem can be solved in an asynchronous system, despite the presence of $\frac{1}{3}$ Byzantine parameter servers and $\frac{1}{3}$ Byzantine workers (which is optimal). We present a new algorithm, ByzSGD, which solves the general Byzantine-resilient distributed machine learning problem by relying on three major schemes. The first, Scatter/Gather, is a communication scheme whose goal is to bound the maximum drift among models on correct servers. The second, Distributed Median Contraction (DMC), leverages the geometric properties of the median in high dimensional spaces to bring parameters within the correct servers back close to each other, ensuring learning convergence. The third, Minimum-Diameter Averaging (MDA), is a statistically-robust gradient aggregation rule whose goal is to tolerate Byzantine workers. MDA requires loose bound on the variance of non-Byzantine gradient estimates, compared to existing alternatives (e.g., Krum). Interestingly, ByzSGD ensures Byzantine resilience without adding communication rounds (on a normal path), compared to vanilla non-Byzantine alternatives. ByzSGD requires, however, a larger number of messages which, we show, can be reduced if we assume synchrony.Comment: This is a merge of arXiv:1905.03853 and arXiv:1911.07537; arXiv:1911.07537 will be retracte

Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA

BackgroundDilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarkers and therapeutic options for DCM.MethodsThe hub genes for the DCM were screened using Weighted Gene Co-expression Network Analysis (WGCNA) and three different algorithms in Cytoscape. These genes were then validated in a mouse model of doxorubicin (DOX)-induced DCM. Based on the validated hub genes, a prediction model and a neural network model were constructed and validated in a separate dataset. Finally, we assessed the diagnostic efficiency of hub genes and their relationship with immune cells.ResultsA total of eight hub genes were identified. Using RT-qPCR, we validated that the expression levels of five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) were considerably higher in DCM mice compared to normal mice, and this was consistent with the microarray results. Additionally, the risk prediction and neural network models constructed from these genes showed good accuracy and sensitivity in both the combined and validation datasets. These genes also demonstrated better diagnostic power, with AUC greater than 0.7 in both the combined and validation datasets. Immune cell infiltration analysis revealed differences in the abundance of most immune cells between DCM and normal samples.ConclusionThe current findings indicate an underlying association between DCM and these key genes, which could serve as potential biomarkers for diagnosing and treating DCM

Balance Cell Apoptosis and Pyroptosis of Caspase-3-Activating Chemotherapy for Better Antitumor Therapy

Chemotherapy is a standard treatment modality in clinic that exerts an antitumor effect via the activation of the caspase-3 pathway, inducing cell death. While a number of chemotherapeutic drugs have been developed to combat various types of tumors, severe side effects have been their common limitation, due to the nonspecific drug biodistribution, bringing significant pain to cancer patients. Recently, scientists found that, besides apoptosis, chemotherapy could also cause cell pyroptosis, both of which have great influence on the therapeutic index. For example, cell apoptosis is, generally, regarded as the main mechanism of killing tumor cells, while cell pyroptosis in tumors promotes treatment efficacy, but in normal tissue results in toxicity. Therefore, significant research efforts have been paid to exploring the rational modulation mode of cell death induced by chemotherapy. This critical review aims to summarize recent progress in the field, focusing on how to balance cell apoptosis and pyroptosis for better tumor chemotherapy. We first reviewed the mechanisms of chemotherapy-induced cell apoptosis and pyroptosis, in which the activated caspase-3 is the key signaling molecule for regulating both types of cell deaths. Then, we systematically discussed the rationale and methods of switching apoptosis to pyroptosis for enhanced antitumor efficacy, as well as the blockage of pyroptosis to decrease side effects. To balance cell pyroptosis in tumor and normal tissues, the level of GSDME expression and tumor-targeting drug delivery are two important factors. Finally, we proposed potential future research directions, which may provide guidance for researchers in the field

Big Data Analysis Guides Landscape Architecture Method Research

In the construction of urbanization, landscape architecture is the only kind of ecological construction with life, and it has an irreplaceable role in urban development and human landscape. The construction of urban landscape gardens can not only improve the ecological environment in the city, but also achieve the functions of purifying the urban air and beautifying the appearance of the city. However, considering the current situation of garden construction, there are not only many types of problems, but also large and complex problems. At the same time, its design concept and construction plan still stay in the traditional consciousness. The development of various industries today requires the integration of big data. In the context of the era of big data, data life has gradually penetrated into people's lives. Big data is not only a product of the development of social science and technology information, but also an inevitable trend of industry development. It can not only promote the construction of urban development, but also has great significance for social progress[1]. Therefore, the construction and design research of landscape architecture needs to combine the analysis of big data, and use the advantages of big data to promote the construction of gardens to be more complete, reasonable and humane

A New Sythetic Hybrid (A1D5) between Gossypium herbaceum and G. raimondii and Its Morphological, Cytogenetic, Molecular Characterization.

The diploid species G. herbaceum (A1) and G. raimondii (D5) are the progenitors of allotetraploid cotton, respectively. However, hybrids between G. herbaceum and G. raimondii haven't been reported. In the present study, hybridization between G. herbaceum and G. raimondii was explored. Morphological, cytogenetic and molecular analyses were used to assess the hybridity. The interspecific hybrid plants were successfully obtained. Most of the morphological characteristics of the hybrids were intermediate between G. herbaceum and G. raimondii. However, the color of glands, anther cases, pollen and corolla, and the state of bracteoles in hybrids were associated with the G. herbaceum. The color of staminal columns and filaments in hybrids were associated with G. raimondii. Cytogenetic analysis confirmed abnormal meiotic behavior existed in hybrids. The hybrids couldn't produce boll-set. Simple sequence repeat results found that besides the fragments inherited from the two parents, some novel bands were amplified in hybrids, indicating that potential mutations and chromosomal recombination occurred between parental genomes during hybridization. These results may provide some novel insights in speciation, genome interaction, and evolution of the tetraploid cotton species

Factors affecting medical students’ intention to use Rain Classroom: a cross-sectional survey

Abstract Background Rain Classroom was one of the most popular online learning platforms in Chinese higher education during the pandemic. However, there is little research on user intention under the guidance of technology acceptance and unified theory (UTAUT). Objective This research aims to determine factors influencing students' behavioural intention to use Rain Classroom. Methods In this cross-sectional and correlational investigation, 1138 medical students from five medical universities in Guangxi Province, China, made up the sample. This study added self-efficacy (SE), motivation (MO), stress (ST), and anxiety (AN) to the UTAUT framework. This study modified the framework by excluding actual usage variables and focusing only on intention determinants. SPSS-26 and AMOS-26 were used to analyze the data. The structural equation modelling technique was chosen to confirm the hypotheses. Results Except for facilitating conditions (FC), all proposed factors, including performance expectancy (PE), effort expectancy (EE), social influence (SI), self-efficacy (SE), motivation (MO), anxiety (AN), and stress (ST), had a significant effect on students' behavioural intentions to use Rain Classroom. Conclusions The research revealed that the proposed model, which was based on the UTAUT, is excellent at identifying the variables that influence students' behavioural intentions in the Rain Classroom. Higher education institutions can plan and implement productive classrooms

Additional file 1 of Factors affecting medical students’ intention to use Rain Classroom: a cross-sectional survey

Additional file 1: Appendix 1. Please choose the appropriate response that reflects your opinion for each of the following statements

Amplification results for three <i>Gossypium herbaceum</i> × <i>G</i>. <i>raimondii</i> F1 hybrids and two parents using 4 representative SSR primer pairs.

<p>A) BNL4053. B) NAU2026. C) NAU1157. D) NAU1164 respectively.1-5: <i>G</i>. <i>herbaceum</i>, hybrid plant 1, hybrid plant 2, hybrid plant 3, and <i>G</i>. <i>raimondii</i>, respectively. The novel bands produced in hybrid plants were indicated by arrows.</p

Data_Sheet_1_Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA.docx

BackgroundDilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarkers and therapeutic options for DCM.MethodsThe hub genes for the DCM were screened using Weighted Gene Co-expression Network Analysis (WGCNA) and three different algorithms in Cytoscape. These genes were then validated in a mouse model of doxorubicin (DOX)-induced DCM. Based on the validated hub genes, a prediction model and a neural network model were constructed and validated in a separate dataset. Finally, we assessed the diagnostic efficiency of hub genes and their relationship with immune cells.ResultsA total of eight hub genes were identified. Using RT-qPCR, we validated that the expression levels of five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) were considerably higher in DCM mice compared to normal mice, and this was consistent with the microarray results. Additionally, the risk prediction and neural network models constructed from these genes showed good accuracy and sensitivity in both the combined and validation datasets. These genes also demonstrated better diagnostic power, with AUC greater than 0.7 in both the combined and validation datasets. Immune cell infiltration analysis revealed differences in the abundance of most immune cells between DCM and normal samples.ConclusionThe current findings indicate an underlying association between DCM and these key genes, which could serve as potential biomarkers for diagnosing and treating DCM.</p