Miracle-dz/UniFAR-FTUs huggingface dataset:
Ecological Opportunity and Necessity: Biotic and Abiotic Drivers Interact During Diversification of Digital Host-Parasite Communities

Most of Earth’s diversity has been produced in rounds of adaptive radiation, but the ecological drivers of diversification, such as abiotic complexity (i.e., ecological opportunity) or predation and parasitism (i.e., ecological necessity), are hard to disentangle. However, most of these radiations occurred hundreds of thousands if not millions of years ago, and the mechanisms promoting contemporary coexistence are not necessarily the same mechanisms that drove diversification in the first place. Experimental evolution has been one fruitful approach used to understand how different ecological mechanisms promote diversification in simple microbial microcosms, but these microbial systems come with their own limitations. To test how ecological necessity and opportunity interact, we use an unusual system of self-replicating computer programs that diversify to fill niches in a virtual environment. These organisms are subject to ecological pressures just like their natural counterparts. They experience biotic interactions from digital parasites, which steal host resources to replicate their own code and spread in the population. With the control afforded by experimenting with computational ecologies, we begin to unweave the complex interplay between ecological drivers of diversification. In particular, we find that the complexity of the abiotic environment and the size of the phenotypic space in which organisms are able to interact play different roles depending on the ecological driver of diversification. We find that in some situations, both ecological opportunity and necessity drive similar levels of diversity. However, the phenotypes that hosts uncover while coevolving with parasites are dramatically more complex than hosts evolving alone.
Biomimetic Cucurbitacin B-Polydopamine Nanoparticles for Synergistic Chemo-Photothermal Therapy of Breast Cancer

Breast cancer is the most common malignant tumor in women. Researchers have found that the combined use of multiple methods to treat tumors is a promising strategy. Here, we have developed a biomimetic nano-platform PDA@MB for tumor targeted photothermal therapy (PTT) combined with chemotherapy. The 4T1 cell membrane loaded with cucurbitacin B (CuB) was used to coat polydopamine (PDA) nanoparticles, which gave PDA@MB nanoparticles the ability to target tumors and escape immune cells from phagocytosis. PDA@MB showed excellent photothermal performance including high photothermal conversion efficiency and photostability, and exhibited outstanding in vitro PTT effect under NIR laser irradiation. The high temperature ruptured the PDA@MB membrane to release CuB, which changed the tumor hypoxic environment, down-regulated the FAK/MMP signaling pathway, and significantly inhibited the metastasis and proliferation of tumor cells. The results of in vivo experiments indicated that the tumor growth of the 4T1 mouse tumor model was significantly inhibited. Additionally, toxicity studies showed that PDA@MB had good biocompatibility and safety. In conclusion, this study provides a promising chemo-photothermal therapy (CPT) nano-platform for precise and effective breast cancer therapy.
Genomic Comparisons and Phenotypic Diversity of Dickeya zeae Strains Causing Bacterial Soft Rot of Banana in China

Bacterial soft rot of banana, caused by Dickeya zeae, is spreading rapidly in important banana growing areas in China and seriously threatens banana production. In this study, we sequenced the high-quality complete genomes of three typical banana strains, MS1 (size: 4,831,702-bp; genome coverages: 538x), MS_2014 (size: 4,740,000-bp; genome coverages: 586x) and MS_2018 (size: 4,787,201-bp; genome coverages: 583x), isolated in 2009, 2014, and 2018, respectively. To determine their genomic and phenotypic diversity with respect to their hosts of origin, they were compared with other D. zeae strains, including another representative banana strain MS2 from China. The sequenced strains were similar in utilization of carbon source and chemical substrates, and general genomic features of GC content, and tRNA and rRNA regions. They were also conserved in most virulence determinants, including gene-encoding secretion systems, plant cell wall degrading enzymes, and exopolysaccharides. We further explored their genomic diversity in the predicted genomic islands (GIs). These GIs were rich in integrases and transposases, where some genomic dissimilarity was observed in the flagellar gene cluster and several secondary metabolite gene clusters. Different constituents of core biosynthetic modules were found within the bacteriocin and aryl polyene (APE) pigment gene clusters, and the strains from banana showed different phenotypes with respect to antibiosis effects and colony pigmentation. Additionally, clustered regularly interspaced short palindromic repeat (CRISPR) and prophage elements, such as type I-F and III-A CRISPR arrays and an intact prophage of MS1-P5, contributed to bacterial diversity. Phylogenetic tree analysis and genome-genome nucleotide comparison confirmed the genomic divergence among the strains isolated from banana. Considering these characteristics, MS2 and MS_2014 probably diverged later than MS1, while MS_2018 was different and more similar to foreign strains isolated from other hosts in several characteristics. Strain MS_2018 caused severe symptoms on banana varieties previously considered moderately resistant or moderately susceptible, including varieties of Cavendish (Musa AAA) and Plantain (Musa ABB). Our study of genomic and phenotypic diversity raises public attention to the risk of spreading new pathogenic variants within banana growing regions and supports development of predictive strategies for disease control.
Impact of Obesity on Major Surgical Outcomes in Ovarian Cancer: A Meta-Analysis

Background The impact of obesity on the surgical outcomes in patients after primary ovarian cancer surgery is unclear. We aimed at conducting a meta-analysis to evaluate the associations between obesity and major surgical outcomes in ovarian cancer patients. Method Embase, PubMed and Web of Science databases were searched for eligible studies. Study-specific relative risks (RR) were pooled using fixed effect model when little evidence of heterogeneity was detected, otherwise random effect model was employed. Results Twelve eligible studies were identified. The pooled incidence rates of all complications were 38% (95% CI: 29%, 47%) for obese patients and 27% (95% CI: 18%, 36%) for non-obese patients. Compared with the non-obese patients, there was a significantly increased risk of all complications in obese patients after ovarian cancer surgery, with a pooled RR of 1.75 (95% CI: 1.26, 2.43). For advanced (stages III–IV) ovarian cancer, the pooled RR of all complications was 1.55 (95% CI: 1.07, 2.24). Obese patients after ovarian cancer surgery were at higher risks of wound complication (pooled RR: 7.06, 95% CI: 3.23, 15.40) and infection (pooled RR: 1.94, 95% CI: 1.47, 2.55) compared with non-obese patients. Such increased risk was not observed for other major complications, namely, venous thromboembolism, ileus and organ failure. Hospital stay days between obese patients and non-obese patients were similar (Standardized Mean Difference: −0.28, 95% CI: −0.75, 0.19). The rates of optimal debulking (pooled RR: 0.96, 95% CI: 0.90, 1.03), readmission/return to operation room (pooled RR: 1.20, 95% CI: 0.56, 2.57) and 30-day mortality (pooled RR: 0.95, 95% CI: 0.54, 1.66) were also comparable between obese patients and non-obese patients. Conclusion Obesity is associated with an increased risk of postoperative complications, especially wound complications and infection after primary ovarian cancer surgery. Obesity may not affect their optimal debulking rates and 30-day mortality in patients undergoing ovarian cancer surgery. Besides, to improve surgical outcomes, an advanced minimally invasive robotic approach seems to be feasible for the treatment of obese patients with ovarian cancer.
Polymeric Fibers as Scaffolds for Spinal Cord Injury: A Systematic Review

Spinal cord injury (SCI) is a complex neurological condition caused by trauma, inflammation, and other diseases, which often leads to permanent changes in strength and sensory function below the injured site. Changes in the microenvironment and secondary injuries continue to pose challenges for nerve repair and recovery after SCI. Recently, there has been progress in the treatment of SCI with the use of scaffolds for neural tissue engineering. Polymeric fibers fabricated by electrospinning have been increasingly used in SCI therapy owing to their biocompatibility, complex porous structure, high porosity, and large specific surface area. Polymer fibers simulate natural extracellular matrix of the nerve fiber and guide axon growth. Moreover, multiple channels of polymer fiber simulate the bundle of nerves. Polymer fibers with porous structure can be used as carriers loaded with drugs, nerve growth factors and cells. As conductive fibers, polymer fibers have electrical stimulation of nerve function. This paper reviews the fabrication, characterization, and application in SCI therapy of polymeric fibers, as well as potential challenges and future perspectives regarding their application.
METRO: A Generic Graph Neural Network Framework for Multivariate Time Series Forecasting

Multivariate time series forecasting has been drawing increasing attention due to its prevalent applications. It has been commonly assumed that leveraging latent dependencies between pairs of variables can enhance prediction accuracy. However, most existing methods suffer from static variable relevance modeling and ignorance of correlation between temporal scales, thereby failing to fully retain the dynamic and periodic interdependencies among variables, which are vital for long- and short-term forecasting. In this paper, we propose METRO, a generic framework with multi-scale temporal graphs neural networks, which models the dynamic and cross-scale variable correlations simultaneously. By representing the multivariate time series as a series of temporal graphs, both intra- and inter-step correlations can be well preserved via message-passing and node embedding update. To enable information propagation across temporal scales, we design a novel sampling strategy to align specific steps between higher and lower scales and fuse the cross-scale information efficiently. Moreover, we provide a modular interpretation of existing GNN-based time series forecasting works as specific instances under our framework. Extensive experiments conducted on four benchmark datasets demonstrate the effectiveness and efficiency of our approach. METRO has been successfully deployed onto the time series analytics platform of Huawei Cloud, where a one-month online test demonstrated that up to 20% relative improvement over state-of-the-art models w.r. t. RSE can be achieved.
HVS: Hierarchical Graph Structure Based on Voronoi Diagrams for Solving Approximate Nearest Neighbor Search

Approximate nearest neighbor search (ANNS) is a fundamental problem that has a wide range of applications in information retrieval and data mining. Among state-of-the-art in-memory ANNS methods, graph-based methods have attracted particular interest owing to their superior efficiency and query accuracy. Most of these methods focus on the selection of edges to shorten the search path, but do not pay much attention to the computational cost at each hop. To reduce the cost, we propose a novel graph structure called HVS. HVS has a hierarchical structure of multiple layers that corresponds to a series of subspace divisions in a coarse-to-fine manner. In addition, we utilize a virtual Voronoi diagram in each layer to accelerate the search. By traversing Voronoi cells, HVS can reach the nearest neighbors of a given query efficiently, resulting in a reduction in the total search cost. Experiments confirm that HVS is superior to other state-of-the-art graph-based methods.
Natural Frequencies of Stator Core with Frame in Induction Motor.

The construction and the frame with a fin in a totally enclosed induction motor have almost the same design worldwide. A method for estimating natural frequencies of a stator core pressed into the frame of this type of motor was found by a finite-element method (FEM) analysis. First, the number of circumferential contact points between the inner surface of the frame and the outer surface of the core was determined by experiments and FEM analysis. It was confirmed that the number of circumferential contact points was three. In these analysis, spring elements with radial and circumferential spring constants were set at these contact points. Good agreement in natural frequencies obtained by measurement and by FEM analysis was attained when the number of spring constants in the radial direction was infinite and that in the circumferential direction was finite. FEM analysis with spring elements at contact points is useful in practical applications to obtain the natural frequencics of a stator core with frame.
Machine learning approaches for improving condition-based maintenance of naval propulsion plants

Availability, reliability and economic sustainability of naval propulsion plants are key elements to cope with because maintenance costs represent a large slice of total operational expenses. Depending on the adopted strategy, impact of maintenance on overall expenses can remarkably vary; for example, letting an asset running up until breakdown can lead to unaffordable costs. As a matter of fact, a desideratum is to progress maintenance technology of ship propulsion systems from breakdown or preventive maintenance up to more effective condition-based maintenance approaches. The central idea in condition-based maintenance is to monitor the propulsion equipment by exploiting heterogeneous sensors, enabling diagnosis and, most of all, prognosis of the propulsion system’s components and of their potential future failures. The success of condition-based maintenance clearly hinges on the capability of developing effective predictive models; for this purpose, effective use of machine learning methods is proposed in this article. In particular, authors take into consideration an application of condition-based maintenance to gas turbines used for vessel propulsion, where the performance and advantages of exploiting machine learning methods in modeling the degradation of the propulsion plant over time are tested. Experiments, conducted on data generated from a sophisticated simulator of a gas turbine, mounted on a Frigate characterized by a COmbined Diesel eLectric And Gas propulsion plant type, will allow to show the effectiveness of the proposed machine learning approaches and to benchmark them in a realistic maritime application.
Research on vehicle scheduling problem with transportation scope constraints

In view of transportation range of different vehicles, a vehicle scheduling model was established with with multiple vehicle types, multiple materials, multiple warehouse points, and multiple demand points. Targeting at the shortest overall transportation time, the influence of vehicle transportation range constraints on the overall transportation time is studied. Established material supply and demand constraints and vehicle transportation range constraints to ensure that the vehicle allocation plan in each genetic entity meets the transportation scope and the material transportation does not exceed the total supply and demand. A case containing 3 types of vehicle types and 4 types of transportation ranges is designed, and a genetic algorithm is used to solve the vehicle scheduling plan. The calculation results show that the optimal scheduling scheme satisfies all the constraints in the model and is an excellent solution. The corresponding scheduling scheme makes full use of scheduling resources. The results also show that the reasonable allocation of vehicles according to different vehicle transportation ranges can obtain a vehicle scheduling plan that is more in line with the scheduling goal and closer to the actual situation.
DDX50 Is a Viral Restriction Factor That Enhances IRF3 Activation

The transcription factors IRF3 and NF-κB are crucial in innate immune signalling in response to many viral and bacterial pathogens. However, mechanisms leading to their activation remain incompletely understood. Viral RNA can be detected by RLR receptors, such as RIG-I and MDA5, and the dsRNA receptor TLR3. Alternatively, the DExD-Box RNA helicases DDX1-DDX21-DHX36 activate IRF3/NF-κB in a TRIF-dependent manner independent of RIG-I, MDA5, or TLR3. Here, we describe DDX50, which shares 55.6% amino acid identity with DDX21, as a non-redundant factor that promotes activation of the IRF3 signalling pathway following its stimulation with viral RNA or infection with RNA and DNA viruses. Deletion of DDX50 in mouse and human cells impaired IRF3 phosphorylation and IRF3-dependent endogenous gene expression and cytokine/chemokine production in response to cytoplasmic dsRNA (polyIC transfection), and infection by RNA and DNA viruses. Mechanistically, whilst DDX50 co-immunoprecipitated TRIF, it acted independently to the previously described TRIF-dependent RNA sensor DDX1. Indeed, shRNA-mediated depletion of DDX1 showed DDX1 was dispensable for signalling in response to RNA virus infection. Importantly, loss of DDX50 resulted in a significant increase in replication and dissemination of virus following infection with vaccinia virus, herpes simplex virus, or Zika virus, highlighting its important role as a broad-ranging viral restriction factor.
Energy Efficient RF Transmitter Design using Enhanced Breakdown Voltage SOI-CMOS Compatible MESFETs

The high cut-off frequency of deep sub-micron CMOS technologies has enabled the integration of radio frequency (RF) transceivers with digital circuits. However, the challenging point is the integration of RF power amplifiers, mainly due to the low breakdown voltage of CMOS transistors. Silicon-on-insulator (SOI) metal semiconductor field effect transistors (MESFETs) have been introduced to remedy the limited headroom concern in CMOS technologies. The MESFETs presented in this thesis have been fabricated on different SOI-CMOS processes without making any change to the standard fabrication steps and offer 2-30 times higher breakdown voltage than the MOSFETs on the same process. This thesis explains the design steps of high efficiency and wideband RF transmitters using the proposed SOI-CMOS compatible MESFETs. This task involves DC and RF characterization of MESFET devices, along with providing a compact Spice model for simulation purposes. This thesis presents the design of several SOI-MESFET RF power amplifiers operating at 433, 900 and 1800 MHz with ~40% bandwidth. Measurement results show a peak power added efficiency (PAE) of 55% and a peak output power of 22.5 dBm. The RF-PAs were designed to operate in Class-AB mode to minimize the linearity degradation. Class-AB power amplifiers lead to poor power added efficiency, especially when fed with signals with high peak to average power ratio (PAPR) such as wideband code division multiple access (W-CDMA). Polar transmitters have been introduced to improve the efficiency of RF-PAs at backed-off powers. A MESFET based envelope tracking (ET) polar transmitter was designed and measured. A low drop-out voltage regulator (LDO) was used as the supply modulator of this polar transmitter. MESFETs are depletion mode devices; therefore, they can be configured in a source follower configuration to have better stability and higher bandwidth that MOSFET based LDOs. Measurement results show 350 MHz bandwidth while driving a 10 pF capacitive load. A novel polar transmitter is introduced in this thesis to alleviate some of the limitations associated with polar transmitters. The proposed architecture uses the backgate terminal of a partially depleted transistor on SOI process, which relaxes the bandwidth and efficiency requirements of the envelope amplifier in a polar transmitter. The measurement results of the proposed transmitter demonstrate more than three times PAE improvement at 6-dB backed-off output power, compared to the traditional RF transmitters.
The Association between Mental Health Symptoms and Quality and Safety of Patient Care before and during COVID-19 among Canadian Nurses

(1) Background: While the association between nurse mental health and quality and safety of patient care delivery was well documented pre-pandemic, fewer research studies have examined this relationship in the context of COVID-19. This study examines the impact of various mental health symptoms experienced by nurses on quality and safety before and during the COVID-19 pandemic; (2) Methods: A secondary analysis of cross-sectional survey data from 4729 and 3585 nurses in one Canadian province between December 2019 and June-July 2020 was conducted. Data were analyzed using between group difference tests and logistic regression; (3) Results: Compared to pre-COVID-19, during COVID-19 nurses reported a higher safety grade, a greater likelihood of recommending their units for care and lower quality of nursing care. Most mental health symptoms were higher during COVID-19 and higher levels of mental health symptoms were correlated with lower ratings of quality and safety both pre- and during COVID-19; (4) Conclusion: Mental health symptoms have implications for nurses’ quality and safety of patient care delivery, with the association between mental health symptoms and quality and safety following a dose–response relationship before and during COVID-19. These findings suggest that it is worthwhile for nurse mental health symptoms to be included as hospital level performance metrics.
Accountability of Regional Officials of Sidoarjo Regency In The Distribution of Groceries During The Covid-19 Pandemic

This article aims to analyze the distribution of free groceries in the Sidoarjo regency during the Covid-19 pandemic. Free groceries program for citizens in almost every year by the local government. But it is slightly different in pandemic times like today, where the free Groceries program is more often done and on a large scale. One of them is carried out by the Sidoarjo Regency Government, East Java. Keep in mind that the thing that is affected by the outbreak of the Covid-19 Virus is an economic factor, which makes most people lose their eyes that make the weakening of the economic system in Indonesia no exception in Sidoarjo Regency itself. This paper takes socio-legal research to find out the problem of the distribution of free groceries in the Sidoarjo regency. The results of this study look at the extent of the accountability of the Sidoarjo Regency government with the Covid-19 outbreak in the community, namely from the start of the distribution of free food so that it is expected to provide more concrete results. Sidoarjo Regency government program is expected to be applied in the early stages of formulation, implementation, and evaluation of public policy.