Missing observations constitute one of the most important issues in data analysis in applied research studies. The magnitude and their structure impact parameters estimation in the modeling with important consequences for decision-making. This study aims to evaluate the efficiency of imputation methods combined with the backpropagation algorithm in a nonlinear regression context. The evaluation is conducted through a simulation study including sample sizes (50, 100, 200, 300 and 400) with different missing data rates (10, 20, 30 40 and 50%) and three missingness mechanisms (MCAR, MAR and MNAR). Four imputation methods (Last Observation Carried Forward, Random Forest, Amelia and MICE) were used to impute datasets before making prediction with backpropagation. 3-MLP model was used by varying the activation functions (Logistic-Linear, Logistic-Exponential, TanH-Linear and TanH-Exponentiel), the number of nodes in the hidden layer (3 - 15) and the learning rate (20 - 70%). Analysis of the performance criteria (R2, r and RMSE) of the network revealed good performances when it is trained with TanH-Linear functions, 11 nodes in the hidden layer and a learning rate of 50%. MICE and Random Forest were the most appropriate for data imputation. These methods can support up to 50% of missing rate with an optimal sample size of 200.

As the coronavirus disease 2019 (COVID-19), which is caused by the novel SARS-CoV-2, continues to spread rapidly around the world, there is a need for well validated serological assays that allow the detection of viral specific antibody responses in COVID-19 patients or recovered individuals. In this study, we established and used multiple indirect Enzyme Linked Immunosorbent Assay (ELISA)-based serological assays to study the antibody response in COVID-19 patients. In order to validate the assays we determined the cut off values, sensitivity and specificity of the assays using sera collected from pre-pandemic healthy controls, COVID-19 patients at different time points after disease-onset, and seropositive sera to other human coronaviruses. The developed SARS-CoV-2 S1 subunit of the spike glycoprotein and nucleocapsid (N)-based ELISAs not only showed high specificity and sensitivity but also did not show any cross-reactivity with other CoVs. We also show that all RT-PCR confirmed COVID-19 patients tested in our study developed both virus specific IgM and IgG antibodies as early as week one after disease onset. Our data also suggest that the inclusion of both S1 and N in serological testing would capture as many potential SARS-CoV-2 positive cases as possible than using any of them alone. This is specifically important for tracing contacts and cases and conducting large-scale epidemiological studies to understand the true extent of virus spread in populations.

The rapid development of 2019-2020 Wuhan seafood market pneumonia currently posed a major public health concern in China. Genome sequencing identified a novel beta-coronavirus closely related to SARS-CoV, named 2019-nCoV by WHO, as the cause of this pandemic disease. Viruses with single stranded RNA genome are prone to evolve quickly by accumulation of mutations, such as SNV, INDEL and cross viral recombination, aiding fast transmission among hosts and cross species. Here we collected related genome sequences and investigated variations shared by different strains of 2019-nCoV, identified reoccurrence of SNV mutations in clusters of patients, an indication of rapid evolution of 2019-nCoV at the transmission from animal host to human. The information collected herein would help to understand the dynamics of current pandemic.

Kidney transplantation is the treatment of choice for patients with end-stage disease. To expand the donor reserve, it is necessary to use marginal/sub optimal donors that provide marginal organs. We retrospectively evaluated the short and long-term outcome of elderly kidney transplantation using allografts with vascular abnormalities. Between January 1999 and December 2018, 740 transplants from cadaveric donors were performed. Thirty-four elderly patients received a kidney transplantation with vascular anatomical variants (Group 1) were compared with 34 patients who received a kidney transplantation with single renal artery (SRA) (Grroup2) pair-matched by age, dialysis age, donor age, comorbidity. All participants completed the Long Form International Physical Activity Questionnaire (IPAQ) at baseline and after 4, 8, and 12 weeks after transplantation. The overall rate of surgical complications was 17.6% in Group 1 and 20.6% in Group 2, indicating that kidney with vascular anatomical variant might be successfully transplanted. Our data also emphasizes the importance of individualized physical activity in kidney transplantation with multiple arteries. Physical activity should be considered as an essential part of the medical care for renal-transplanted recipients.

Sound absorbing materials are used in building to dissipate sound energy into heat by viscous and thermal processes. Sound absorbers increase the transmission loss of walls, decrease the reverberation time of rooms and attenuate the noise generated by internal sound sources. Porous absorbers (fibrous, cellular or granular) are the most used materials in noise control applications, since their high performance-to-cost ratio in the frequency band of interest. However, when cleaning and health reasons are of concern, microperforated panel (MPP) absorbers can be preferred. MPPs, consisting of many minute (sub-millimetric) holes in a panel, are tunable absorbers in a prescribed frequency band, which main shortcomings are high manufacturing cost and limited absorption frequency band. Production cost of MPP can nowadays be drastically cut down by means of modern techniques. Absorption frequency band can be considerably enlarged by designing multiple-layer MPPs (ML-MPPs). The aim of this article is to review the high potential of ML-MPPs as a modern, clean and healthy alternative of porous materials for sound absorption.

This work presents a novel approach to construct surrogate models of parametric Differential Algebraic Equations based on a tensor representation of the solutions. The procedure consists in building simultaneously, for every output of the reference model, an approximation given in tensor-train format. A parsimonious exploration of the parameter space coupled with a compact data representation allows to alleviate the curse of dimensionality. The approach is thus appropriate when many parameters with large domains of variation are involved. The numerical results obtained for a nonlinear elasto-viscoplastic constitutive law show that the constructed surrogate model is sufficiently accurate to enable parametric studies such as the calibration of material coefficients.

Vibrotactile displays have been reported effective in enhancing awareness of flight attitude for pilots and releasing other heavily loaded sensory channels. Although some work have been done on vibrotactile coding of flight altitude, there is lack of a systematic investigation into coding methods with combination of multiple coding parameters. In this paper, seven coding methods with combinations of multiple coding parameters (location, rhythm, intensity, and mode) were systematically studied to cue flight attitude for pilots with a vibrotactile vest. We conducted two psychophysical experiments in a static tactile sensory environment in which the attitude commands in the form of vibrotactile feedback are presented randomly, and quantitatively evaluated the effectiveness of the vest according to the users’ recognition accuracy, reaction time and information transfer rate. The results show that vibrotactile vest is effective to cue attitude information. The preferred coding method with combinations of location, rhythm and mode allowed users to perform with lowest reaction time and highest recognition accuracy and yield about 255 bits/min of information transfer rate. Overall, the presented work provides valuable insights and guidance for the design of haptic displays for vibrotactile aids for the pilots.

Traditional grain vinegar is fermented using multiple acetic acid bacteria (AAB) at various temperatures. A single AAB showed high acid–producing ability at 30 °C with a 5% alcohol concentration and an initial pH adjusted to 4.0. Multiple AAB were similar to single AAB, but the optimal initial pH was 3.0. Acid production ability according to the type of AAB was higher in multiple AAB than that for single AAB. That is, using multiple AAB helped to increase the titratable acidity of traditional grain vinegar. In addition, increasing the titratable acidity and content of volatile flavor compounds was advantageous when two, rather than four, AAB types were mixed and used. Titratable acidity was high at medium temperatures (30 °C), but volatile flavor compounds increased at low temperatures (20 °C) under multiple AAB. A 16S rDNA–based microbiome taxonomic profiling analysis identified differences in beta diversity due to multiple AAB and fermentation temperatures. In particular, beta diversity analysis revealed a specific pattern when a mixture of Acetobacter ascedens GV–8 and Acetobacter pasteurianus GV–22 were fermented at low temperature (20 °C). Therefore, we propose the application of multiple AAB with acidic and flavor–producing properties in traditional grain vinegar.

The geometry of mesoscopic inhomogeneities plays an important role in determining the macroscopic propagation behaviors of elastic waves in a heterogeneous medium. Non-equiaxed inhomogeneities can lead to anisotropic wave velocity and attenuation. Developing an accurate scattering theory to describe the quantitative relation between the microstructure features and wave propagation parameters is of fundamental importance for seismology and ultrasonic nondestructive characterization. This work presents a multiple scattering theory for strongly scattering elastic media with general tri-axial heterogeneities. A closed analytical expression of the shape-dependent singularity of the anisotropic Green’s tensor for the homogeneous reference medium is derived by introducing a proper non-orthogonal ellipsoidal coordinate. Renormalized Dyson’s equation for the coherent wave field is then derived with the help of Feynman’s diagram technique and the first-order-smoothing approximation. The exact dispersion curves and the inverse Q-factors of coherent waves in several representative medium models for the heterogeneous lithosphere are calculated numerically. Numerical results for small-scale heterogeneities with the aspect ratio varying from 1 to 7 show satisfactory agreement with those obtained from real earthquakes. The results for velocity dispersion give rise to a novel explanation to the formation mechanism of different seismic phases. The new model has potential applications in seismology and ultrasonic microstructure characterization.

Gender has been a rarely addressed aspect in digital game-based learning (DGBL). Likewise, mixed results have been presented regarding the effects according to gender and the conditions that generate these effects. The present work studies the effects of a drill-and-practice mathe-matical game on primary students. The study focused on an analysis by gender, measuring learning performance, and motivation in the practice activity. Also, two instructional mechanics were considered regarding the question answering, a multiple-try (MTF) and a single-try (STF) condition, to search for possible differences. A total of 81 students from 4 courses and 2 schools participated in the experiment. The study's main findings were that: (a) the girls outperformed boys in terms of students' learning gains, (b) girls presented lower levels of competence and autonomy than boys, (c) in the MTF, girls presented lower levels of autonomy but no differences in competence contrasted to boys, (d) in the STF, girls presented lower levels of competence but no differences in autonomy against boys, and (e) no differences existed in interest, effort and value among gender overall o per instructional condition. This study enhances the knowledge of gender differences under diverse instructional settings, in particular providing insights into possible differences by gender when varying the number of attempts provided to students.

(1) Background: Prevention of acute cardiovascular events in patients with cardiovascular diseases (CVDs) requires promoting health-protective behaviors (e.g., physical activity) and preventing health-compromising behaviors (sitting). Digital health interventions provide much potential. Based on multiple behavioral change theory, an intervention (reCardial app) was evaluated, and the following hypotheses were tested: H1: Health behaviors (physical activity, sitting) and disease self-management (self-care maintenance, self-care confidence) are closely interrelated. H2: Change in health behaviors and disease self-management indicators over time is more pronounced in reCardial app-users. H3: In the intervention group, changes in systolic and diastolic blood pressure indicate a positive trajectory. (2) Methods: A study with the design of a randomized controlled trial over 12 weeks was conducted. 40 CVD-patients were randomized equally to the Intervention Group (35% women; Mage=60.6years) and Control Group (45% women; Mage=61.5years). (3) Results: Findings support H1 with significant correlations between health behaviors (r=0.45-0.63**), and disease self-management (r=-0.32-0.54**), H2 with Eta²=0.21 (not statistically significant) and H3 partially with d=0.101 for systolic blood pressure but not regarding diastolic blood pressure. (4) Conclusions: Replications are needed with larger samples and more objective measures. The app can help prevent and manage CVD by means of promoting health-protective behaviors and preventing health-compromising behaviors. Taking different behaviors into account can increase intervention effects and with that support of users.

Keywords: Wall-like RC columns; Strengthening; FRP; NSM rebars; Steel plates; Testing; FE modeling; Preloading.

In this paper, we propose a method to find all efficient solutions of an interval multiple objective linear programming (IMOLP) problem with interval coefficients in the objective functions, the constraint matrix and the right-hand side vector. The set of all efficient solutions of an IMOLP problem can completely supply information to the decision makers in finding their most preferred solutions but, up to now, not all efficient solutions of an IMOLP problem can be found by known methods. A numerical example is given to illustrate the working of the proposed method.

Object-Oriented Programming (OOP) has long been a dominant paradigm in software development, but it's not without its challenges. One major issue is the problem of tight coupling between objects, which can hinder flexibility and make it difficult to modify or extend code. Additionally, the complexity of managing inheritance hierarchies can lead to rigid and fragile designs, making it hard to maintain and evolve the software over time. This paper introduces a software development pattern that seeks to offer a renewed approach to writing code in Object-Oriented (OO) environments. Addressing some of the limitations of the traditional approach, the Puzzle Pattern focuses on extreme modularity, favoring writing code exclusively in interfaces. Concrete classes are subsequently assembled through the implementation of those interfaces, reducing coupling and introducing a new level of flexibility and adaptability in software construction. The highlighted pattern offers significant benefits in software development, promoting extreme modularity through interface-based coding, enhancing adaptability via multiple inheritance, and upholding the SOLID principles, though it may pose challenges such as complexity and a learning curve for teams.

Edge computing reduces the response time of real-time services with handling dynamic traffics reliably. Edge servers with limited resources utilize container technology that provides a lightweight execution environment. When deploying containers in edge servers, a container image is required and is downloaded from a remote registry. Therefore, these operations are dependent on network overhead between the container deployment system and the remote registry. Through motivation experiments, we show that container pooling time increases in proportion to physical distance and has characteristics that vary flexibly depending on runtime time. In this study, we define a system model for high-speed registry deployment in this edge system and propose a clustering technique into k groups based on the network overhead and affinity of the regionally distributed edge servers that make up the edge system. Also, considering idle resources, we propose a technique to deploy a registry after electing a leader for each cluster. A simulation experiment is conducted to verify the performance of the proposed technique and shows that performance improvement can be achieved regardless of the number of edge servers and the k value.

Carbapenemase-producing organisms (CPOs) present a major threat to public health, demanding precise diagnostic techniques their detection. Discrepancies among CPO tests have raised concerns, partly due to limitations in detecting bacterial diversity within host/specimen. We explored the impact of unbiased colony selection on carbapenemase testing and assessed its relevance on various tests. Based on "FirstAll" for unbiased colony selection to reduce bias, we compared modified carbapenem inactivation method/EDTA-modified carbapenem inactivation method (mCIM/eCIM), Carba5, the CPO panel, and multiplex PCR (M-PCR). Initially, we compared FirstAll to conventional colony selection for mCIM. Second, we used M-PCR as a reference, to evaluate test performance across seven CPO species. The results revealed that FirstAll selection improved carbapenemase detection, revising false-negative in 10.5% of K. pneumoniae isolates. In addition, 12.4% of CPOs tested positive for multiple carbapenemase genes. Both the Carba5 test and CPO panel showed suboptimal performance (sensitivity/specificity: Carba5 75.5%/89.0%, CPO panel 78.1%/74.0%). Carba5 test provided specific carbapenemase class assignments but CPO panel failed in 20.3% of cases. Carba5 test and the CPO panel results correlated well with ceftazidime-avibactam minimal inhibitory concentrations (MICs). Concordance for class A/D with MICs was 88.3% for Carba5 and 92.0% for the CPO panel; whereas for class B, it was 86.5% for Carba5 and 76.2% for the CPO panel. In conclusion, FirstAll as the unbiased colony selection impacted carbapenemase testing. With FirstAll, the diagnostic performance of either Carba5 or the CPO panel was compromised. The utilization of ceftazidime-avibactam guided by either the CPO panel or Carba5 was appropriate.

Ceramide is a lipid that regulates cell death including apoptosis and necroptosis and acts as a powerful tumor suppressor. These characteristics have supported the development of ceramide-based cancer therapeutics. In the present study, ceramide mimetics (RT-25, RT-71 and YM-07) were designed and chemically synthesized, and their potential as necroptosis-inducing chemotherapeutic reagents was examined. RT-71 and YM-07, but not RT-25, potently decreased cell viability in multiple cancer cell lines. The ranges of IC50 values for RT-71 and YM-07 were 8.4–40.8 µM and 4.4–23.7 µM, respectively. Cleavage of PARP, an apoptosis marker, was not observed in MCF-7 cells treated with the mimetics. To determine the effects on necroptotic cell death, we used siRNAs specific to mixed lineage kinase domain-like protein (MLKL). MLKL knockdown significantly suppressed the cell death induced by YM-07 but not RT-71. These results suggest that YM-07 exhibits pro-necroptotic activity. Taken together, these findings indicate ceramide mimetic YM-07 as a lead compound for developing MLKL-activating necroptotic reagents. Our results help establish a foundation for pro-necroptotic therapy for cancer.

Recently, astroviruses (AstVs) have been associated with neurological symptoms in pigs, cattle, sheep, minks, muskoxes, alpacas, and humans. To investigate genotypes of porcine astrovirus (PAstV) and neuroinvasive (Ni) PAstV in South Korea, five domestic pigs with neurological symptoms from three farms were tested using reverse transcriptase PCR, histopathology, and in situ hybridization (ISH). All five genotypes of PAstV were detected in brain tissues and coinfection of PAstV with multiple genotypes was observed. To the best of our knowledge, this is the first study to report pig infection by Ni-PAstV with multiple genotypes in South Korea.

The prediction of drug-target interactions plays a fundamental role in facilitating drug discovery, where the goal is to find prospective drug candidates. With the increase in the number of drug-protein interactions, machine learning techniques, especially deep learning methods, have become applicable for drug-target interaction discovery because they significantly reduce the required experimental workload. In this paper, we present a spontaneous formulation of the drug-target interaction prediction problem as an instance of multi-instance learning. We address the problem in three stages, first organizing given drug and target sequences into instances via a private-public mechanism, then identifying the predicted scores of all instances in the same bag, and finally combining all the predicted scores as the output prediction. A comprehensive evaluation demonstrates that the proposed method outperforms other state-of-the-art methods on three benchmark datasets.

With the advancement of photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D measurement, and its registration accuracy directly affects the accuracy of 3D measurements. In this study, we designed a novel MPCR-Net for multiple partial point cloud registration networks. First, an ideal point cloud was extracted from the CAD model of the workpiece and was used as the global template. Next, a deep neural network was used to search for the corresponding point groups between each partial point cloud and the global template point cloud. Then, the rigid body transformation matrix was learned according to these correspondence point groups to realize the registration of each partial point cloud. Finally, the iterative closest point algorithm was used to optimize the registration results to obtain a final point cloud model of the workpiece. We conducted point cloud registration experiments on untrained models and actual workpieces, and by comparing them with existing point cloud registration methods, we verified that the MPCR-Net could improve the accuracy and robustness of the 3D point cloud registration.

The adoption of agricultural technology is an important path for raising agricultural productivity, and thereby for reducing food insecurity and poverty. Despite the efforts to promote adoption in most of the rural areas of Ethiopian farmers, the adoption rate has always been very low. So, it is essential to understand the barriers to adoption. As a result, this study examined the determinants of adoption of multiple agricultural technologies in rural Amhara region of Ethiopia. The study is based on Ethiopian socio-economic survey of 2015/16. A sample of 656 farm households was considered. The paper used multinomial logit model to assess the factors affecting adoption. The result shows that farmers with more educational level, family size, off-farm participation, livestock, extension contact, credit access, advisory service, and farmers closer to plot, all-weather road, zonal town, and farmers with lower remittance income are more likely to adopt new or improved agricultural technology. Therefore, the study recommends the need of policies and interventions on adoption of agricultural technology should pay attention and move along with those variables significantly influencing adoption of agricultural technology.

The largest part of the image encryption algorithms operates on the pixel as a central element by implementing diffusion confusion and eventually a permutation. On the other side, a permutation applied at the bit level changes not only the pixel value, but also its location within the image. In this work, we will propose a new technology of medical and color image encryption, based on chaotic permutations acting at the bit level, and a diffusion confusion ensured by an application of the method of Vigenere largely improved and adapted to the subject. Simulations performed on a large number of images of different sizes and formats ensure that our method is not subject to any known attacks.

Non-orthogonal multiple access (NOMA) has been investigated to support massive connectivity for Internet-of-things (IoT) networks. However, since most IoT devices suffer from limited power and decoding capabilities, it is not desirable to pair a large number of devices simultaneously, which encourages two-user NOMA grouping. Additionally, most existing techniques have not considered the diversity in the target QoS of IoT devices, which may lead to spectrum inefficiency. Few investigations have partially considered that issue by using an order-based power allocation (OPA) approach, where the power is allocated according to the order to the user's target throughput within a priority-based NOMA (PNOMA) group. However, this does not fully capture the effects of diversity in the values of the users' target throughputs. In this work, we handle both problems by considering a throughput-based power allocation (TPA) approach, that captures the QoS diversity, within a three-users PNOMA group as a compromise between spectral efficiency and complexity. Specifically, we investigate the performance of a time-division PNOMA (TD-PNOMA) scheme, where the transmission time is divided into two-time slots with two-users per PNOMA group. The performance of such TD-PNOMA is compared with a fully PNOMA (F-PNOMA) scheme, where the three users share the whole transmission time, in terms of the ergodic capacity under imperfect successive interference cancellation (SIC). The results reveal the superiority of TPA compared with OPA approach in both schemes, besides that the throughput of both schemes can outperform each other under imperfect SIC based on the transmit signal-to-noise ratio and the deployment scenarios.

Mitochondrial disorders, although heterogeneous, are traditionally described as conditions characterized by encephalomyopathy, hypotonia and progressive postnatal organ failure. Here we provide a systematic review of Linear Skin Defects with Multiple Congenital Anomalies (LSDMCA), a rare unconventional mitochondrial disorder which presents as a developmental disease; its main clinical features include microphthalmia with different degrees of severity, linear skin lesions, and central nervous system malformations. The molecular basis of this disorder has been elusive for several years. Mutations were eventually identified in three X-linked genes, i.e., HCCS, COX7B, and NDUFB11, which are all endowed with defined roles in the mitochondrial respiratory chain. A peculiar feature of this condition is its inheritance pattern: X-linked dominant male-lethal. Only female or XX male individuals can be observed, implying that nullisomy for these transcripts is incompatible with normal embryonic development in mammals. All three genes undergo X-inactivation that, according to our hypothesis, may contribute to the extreme variable expressivity observed in this condition. We propose that mitochondrial dysfunction should be considered as an underlying cause in developmental disorders. Moreover, LSDMCA should be taken into consideration by clinicians when dealing with patients with microphthalmia with or without associated skin phenotypes.

Rainfall retrieval using geostationary satellites provides critical means to the monitoring of extreme rainfall events. Using the relatively new Himawari 8 meteorological satellite with three times more channels than its predecessors, the deep learning framework of “convolutional autoencoder” (CAE) was applied to the extraction of cloud and precipitation features. The CAE method was incorporated into the Convolution Neural Network version of the PERSIANN precipitation retrieval that uses GOES satellites. By applying the CAE technique with the addition of Residual Blocks and other modifications of deep learning architecture, the presented derivation of PERSIANN operated at the Central Weather Bureau of Taiwan (referred to as PERSIANN-CWB) expands four extra convolution layers to fully use Himawari 8’s infrared and water vapor channels, while preventing degradation of accuracy caused by the deeper network. The development of PERSIANN-CWB was trained over Taiwan for its diverse weather systems and localized rainfall features, and the evaluation reveals an overall improvement from its CNN counterpart and superior performance over all other rainfall retrievals analyzed. Limitation of this model was found in the derivation of typhoon rainfall, an area requiring further research.

Background – The profound impact of COVID-19 on the U.S. healthcare industry cannot be overstated. Telemedicine utilization exploded virtually overnight as healthcare systems, hospitals, and clinical practices rushed to implement this delivery model to ensure accessibility and continuity of patient care access across myriad service lines and dimensions. Objective – The purpose of this systematic literature review is to examine the measures that were implemented to accommodate community and individual patient needs not only to afford access to critical services, but to also maintain safety standards for affected parties. Methods – Boolean operators were crafted with the expressed intent of identifying articles within multiple database domains germane to our chosen topic. Results – 52,206 articles were captured from a general search query and subsequently distilled to 44 through group consensus based on pertinence to our basic research question. The four health service lines identified encountered similar, surmountable obstacles in their care delivery models, but adapted accordingly to the respective needs of their patient populations. Conclusion – This review showcased the healthcare industry’s ability to rapidly acclimate and change due to the pervasive spread of COVID-19 throughout the U.S. Although imperfect, unique responses were developed within telemedicine platforms to broadly and effectively mitigate disruptions in care and treatment modalities.

The remaining capacity can only be measured with offline method. This brings great challenge for the online prediction of Li-ion battery’s RUL. A novel online prediction method for Li-ion battery’s RUL was proposed, which is based on multiple health indicators (HIs) and can be derived from the batteries’ historical operation data. Firstly, four indirect HIs were built according to the battery’s operation current, voltage and temperature data respectively. On that basis, a generalized regression neural network (GRNN) was developed to estimate the battery’s remaining capacity, and the non-linear autoregressive approach (NAR) was utilized to predict the battery’s RUL based on the estimated capacity value. Furthermore, to reduce the interference, twice wavelet denoising were performed with different thresholds. A case study is conducted with a NASA battery dataset to demonstrate the effectiveness of the method. The result shows that the proposed method can obtain Li-ion batteries’ RUL effectively.

Mediterranean catchments are characterized by significant spatial and temporal hydrological variability caused by the interaction of natural as well human-induced abiotic and biotic factors. This study investigates the (non-)linearity rainfall-runoff relationship at multiple temporal scales in representative small Mediterranean catchments (i.e., < 10 km2) to achieve a better understanding of the hydrological response. Rainfall-runoff relationship was evaluated in 44 catchments at annual and event –203 events in 12 of these 44 catchments– scales. A linear rainfall-runoff relation was observed at annual scale with higher scatter in pervious than impervious catchments. Larger scattering was observed at event scale, although pervious lithology and agricultural land use promoted significant rainfall-runoff linear relations in winter and spring. These relationships were particularly analysed during five hydrological years in Es Fangar catchment (3.35 km2; Mallorca, Spain) as a temporal downscaling to assess intra-annual variability in which antecedent wetness conditions played a significant role in runoff generation.

This paper presents an extension of the results obtained in previous work concerning the application of global optimization techniques to the design of finite strategic games with mixed strategies. In that publication the Fuzzy ASA global optimization method was applied to many examples of synthesis of strategic games with one previously specified Nash equilibrium, evidencing its ability in finding payoff functions whose respective games present those equilibria, possibly among others. That is to say, it was shown it is possible to establish in advance a Nash equilibrium for a generic finite state strategic game and to compute payoff functions that will make it feasible to reach the chosen equilibrium, allowing players to converge to the desired profile, con- sidering that it is an equilibrium of the game as well. Going beyond this state of affairs, the present article shows that it is possible to ”impose” multiple Nash equilibria to finite strategic games by following the same reasoning as before, but with a fundamental change: using the same fundamental theorem of Richard McKelvey, modifying the originally prescribed objective function and globally minimizing it. The proposed method, in principle, is able to find payoff functions that result in games featuring an arbitrary number of Nash equilibria, paving the way to a substantial number of potential applications.

A picture is a powerful and convenient medium for inducing the illusion that one perceives a real three-dimensional scene. The relative invariance of picture perception across viewing positions has aroused the interest of painters, photographers and visual scientists. Many studies have been devoted to perceptual invariance when pictures are viewed from oblique directions. Invariance across viewing distances has received less attention. This study presents a computational analysis of pictures of perspective scenes taken from different distances between camera and physical objects. Distances and directions of pictorial objects were computed as function of viewing distance to the picture and compared with distances and directions of the physical objects as function of camera position. The computations show that pictorial distance and direction are determined by angular size of the depicted objects. Pictorial distance and direction are independent of camera position, focal length of the lens, and picture size. Ratios of pictorial distances, directions and sizes are constant as function of viewing distance. The constant ratios are proposed as the reason for invariance of picture perception over a range of viewing distances. Reanalysis of distance judgments obtained from the literature shows that perspective space, previously proposed as the model for visual space, is also a good model for pictorial space. The geometry of pictorial space contradicts some conceptions about picture perception.

Abstract: This study aims to evaluate the effectiveness of plant-based diets and supplement interventions in mitigating the severity and mortality of COVID-19 patients. Observational research suggests that a diet high in plant-based foods may decrease the likelihood and severity of SARS-CoV-2 infection. However, several studies have faced criticism regarding the accuracy of obtaining dietary questionnaires and the insufficient intake of essential vitamins and minerals in such diets. To address these issues, this study employed a prospective interventional design that strictly controlled the quality, quantity, and processing of plant-based foods provided. Furthermore, the study incorporated supplementation, which enhanced the anti-viral, anti-inflammatory, antioxidant, antithrombotic, and immunomodulatory properties of a plant-based diet against the SARS-CoV-2 virus. Of the 3,470 COVID-19 elderly patients, half already followed a plant-based diet, and the other half new patients, both with various comorbidities, were intervened with a plant-based diet and supplementation. The results showed that the participants in the non-plant-based diet group were twice as likely to have experienced moderate disease (with 95% confidence limit: 1.42-3.12) and 2.4 times more likely to have experienced severe disease (with 95% confidence limit: 1.2-5.0) with a significant p-value of less than 0.0005 and 0.016 respectively, compared to those in the PBD group. During the interventions, none of the participants experienced disease progression or hospitalization, and there were no reported deaths by the end of the study. The outcome was highly favorable and aligned with the researchers' expectations, as COVID-19 morbidity and mortality rates in Indonesia were among the highest in Asia. This research may provide significant insights into the prevention and management of COVID-19 and aid in better preparation for future pandemics.

Carotenoids are essential pigments that play a vital role in the photosynthetic process of algae and are important for human health and nutrition. Despite their potential value, algal carotenoids are being neglected by biofuel industries. Recent research is aimed at producing microalgal lipids and carotenoids simultaneously under abiotic stresses. In this study, the effect of different abiotic stress factors such as Nitrogen (N), Potassium (K), Phosphorous (P), Sulphur (S), light intensity, and photoperiod on biomass, lipid, and carotenoids synthesis in Monoraphidium sp. (MP) were explored utilizing a statistical method. In addition, 2, 7-dichlorofluorescein diacetate (DCF-DA) mediated spectrofluorimetry was employed to detect the ele-vation in ROS level. The results revealed that variations in nutrients and light con-ditions are the key factors in the up-regulation of biomolecules lipids and carote-noids in the algae. The carotenoids profiling was done by Ultra Performance Liquid Chromatography Quadrupole Time-of-Flight Mass Spectroscopy (UPLC-Q-ToF-MS). Among consisting carotenoids violaxanthin, astaxanthin, and beta-carotene were enhanced by 1.3 folds, 1.19 folds, 1.08 folds, while lutein was declined by 1.32 folds under stressed condition compared to control. The identified carotenoids are rec-ognized for their importance in the pharmaceutical and nutraceutical industries, highlighting the significant role of MP in these sectors.

The outbreak of yellow fever transmitted by Aedes aegypti has been of major concern in Nigeria, this mosquito also transmits several other arboviruses globally. The control of many of the Aedes aegypti borne diseases relies heavily on the use of insecticides. Therefore, constant monitoring of insecticide resistance status and associated mechanisms in crucial within the local population. Here, we determined the resistance profile of adult Aedes aegypti from Ikorodu Local Government Area of Lagos State, Nigeria to different classes of insecticides using WHO procedures. The presence of kdr mutations F1534C, S989P and V1016G were also determined among resistant populations using molecular methods. High level of resistance to DDT and pyrethroid was rec-orded in Aedes aegypti in this study, though possible resistance to deltamethrin was reported in one of the locations. Resistance to bendiocarb was recorded in Majidun community while Aedes aegypti in both locations were susceptible to malathion. The presence of F1534C mutation associated with resistance in Aedes aegypti was detected for the first time in Nigeria, and the presence of S989P mutation was detected singly and in co-occurrence with F1534C for the first time in Africa. The role of these mutations in resistance phenotype expressed in Aedes aegypti in this study area need to established.

The insufficient flexibility of the hybrid renewable integrated energy system (HRIES) makes it difficult to accommodate renewable energy with a high proportion. It is thus necessary to excavate its potential flexibility to improve its operational performance and consume renewable energy. This paper unlocks the adjustment potential of flexible buildings and builds a flexible thermal load regulation model according to the dynamic thermal characteristics and thermal comfort elastic interval of the buildings and a regulation model of the flexible electrical load based on its transferability, resectability, and rigidity. An operation optimization model, which incorporates multiple flexible load regulations and the variable load of devices, is then developed. A case study is presented to analyze the regulation and synergy mechanism in different types of flexible loads. Its results show a saturation effect between the flexible thermal and electrical loads in increasing renewable energy consumption and a synergy effect in decreasing the total cost. This synergy can reduce the total cost by 0.73%. Besides, the total cost can be reduced by 15.13%, and the renewable energy curtailment rate can decrease by 12.08% if the flexible electric and thermal loads are integrated into the operation optimization of HRIES.

1. Background Septic shock is a rare and serious infectious disease. Patients surviving the early phase are at high risk of multiple amputations of limbs. They are potential sources of serious disability. 2. Methods The objective was to describe the functional prognosis and quality of life of the four-limb amputee patients following septic shock. This is a descriptive, retrospective and mono-centric study. The patients included between 2010 and 2019 had septic shock after 18 years, responsible for amputation of the four limbs. We studied the mean age of septic shock, the causative organisms, the infectious door enter, the different levels of amputation, the fitting process, the resumption of walking, the Functional Independence Measure (FIM) and the SF-12 Quality of Life Questionnaire. 3. Results We suceed to include 15 patients.. 53.3% women and 46.7% men with an average age of 46.1 years. 80% presented bilateral trans-tibial amputations, all were able to return walking even over short distances, within an average of 8.4 months. 40% did not require a technical walking aid, 33.3% had no walking perimeter limitation. 47% had a higher mental and social quality of life score than the general population. The younger the patients, the better their physical quality of life. After two years of follow-up, the younger the patients, the better their functional autonomy. Patients with better functional autonomy had a better physical quality of life. 4. Conclusion The number of amputations due to spetic shock is likely to increase in the years to come throughout the world. Our study prove that quadri-amputee patient can regain a fully acceptable functional and quality of life being monitored in a specialized health center with multidisciplinary health professional to help him.

Typhoid fever is an infectious disease that affects humanity worldwide particularly in the lower socio-economic communities where many individuals are exposed to a dirty environment and unclean food. A mathematical model is formulated to analyze the impact of control measures such as vaccination of susceptible humans, treatment of infected humans and sanitation in the different socio-economic communities. The model assumed that the population comprises of two socio-economic classes. The essential dynamical system analysis of our model was appropriately carried out. The impact of the control measures was analyzed, and the optimal control theory was applied on the control model to explore the impact of the different control measures. Numerical simulation of the models and the optimal controls were carried out and results obtained indicates that the overall combination of the control measures eradicates Typhoid fever in the population but the controls are more optimal in the higher socio-economic communities.

In this study, we investigate the existence of positive solutions within a system of Riemann-Liouville fractional differential equations that incorporate the (r1,r2,r3)-Laplacian operator while being subject to three-point boundary conditions. These equations incorporate various fractional derivatives and are influenced by parameters represented as (ψ1,ψ2,ψ3). Our approach involves employing techniques such as cone expansion and compression of the functional type, in conjunction with the Leggett-Williams fixed point theorem, to establish the existence of positive solutions. To emphasize the practical significance of our findings in the realm of fractional differential equations, we provide two illustrative examples.

Fisetin has been shown to be beneficial for brain injury and age-related brain disease via different mechanisms. The purpose of this study was to determine the presence of senescent cells and the effects of fisetin on cellular senescence in the brain and other organs in old sheep, a more translational model. Approximately 6-7 years old female sheep (N=6) were treated with 100mg/kg fisetin or vehicle two consecutive days a week for 8 weeks. All organs were harvested at the time of sacrifice. Histology, immunofluorescent staining, as well as Q-PCR was performed on different regions of brain tissues and organs. Our results indicated that Fisetin treatment at the current regimen did not affect general morphology of the brain. The presence of senescent cells in both cerebral brain cortex and cerebellum was detected by SA-β-Gal staining. More senescent cells were observed in the gray matter when compared to the white matter of the cerebral brain cortex. These senescent cells are mainly neurons in both gray and white matter of either cerebral brain cortex or cerebellum. Fisetin treatment showed a trend of decrease SA-β-Gal cells in gray matter of both cerebral brain cortex and cerebellum and significantly decreased in brain cortex white matter. Furthermore, fisetin treatment significantly decreased P16+ cells in brain cortex NEUN+ neurons, GFAP+ astrocytes, IBA+ microglia cells in both gray and white matter of cerebral brain cortex. Fisetin treatment also significantly decreased P16+ cells in microglia cells and a trend of decrease of P16+ cells in astrocytes in the non- (Cornu Ammonis) CA area of hippocampus. However, fisetin treatment did not change P16+ cells in the NEUN+ neurons in the CA1-4 area of the hippocampus. At the mRNA level, fisetin showed a decreased in GLB1 in heart ventricle muscle tissue and spleen tissues but not in other organs tested. Fisetin treatment also showed a decreased antioxidant gene SOD1 and increased CAT in spleen and bone marrow. Fisetin treatment showed variable effects in SASP and inflammasome genes in different organs. In conclusion, we found senescent cells are widely present in the cerebral brain cortex and cerebellum of old sheep. In addition, fisetin treatment decreased senescent cells as well as P16+ cells in the neurons in both gray and white matter of cerebral brain cortex and in astrocytes and microglia cells of cerebral brain cortex and non-CA area of hippocampus. Fisetin treatment represents a promising therapeutic strategy for age-related brain disease.

Data-driven models based on artificial intelligence are efficiently used to solve complex problems. The quality of groundwater is of utmost importance, as it directly impacts human health and the environment. In major parts of the world groundwater is the main source of drinking water, it is essential to periodically monitor water quality. Conventional water quality monitoring techniques involve periodical collection of water samples and analysis in the laboratory. This process is expensive, time consuming and involves lot of manual labor. The aim of our study is to build an ant colony optimized neural network for predicting groundwater quality parameters. We have proposed artificial neural network comprising of six hidden layers. The approach was validated using our groundwater quality dataset of a hard rock region in the northern part of Karnataka, India. Groundwater samples were collected by us periodically from March 2014 to October 2020 from 50 wells in this region. These samples where analysed for measuring the pH, Electrical Conductivity, Na+, Ca+, Na+, K+, Mg2+, F-, Cl- and U+. The temporal dataset was split for training, testing and validation of our model. Metrics such as R2 (Coefficient of Determination), RMSE (Root Mean Squared Error), NSE (Nash–Sutcliffe efficiencies) and MAE (Mean Absolute Error) were used to evaluate the prediction error and model performance. These performance evaluation metrics indicated the efficiency of our model in predicting the temporal variation in groundwater quality parameters. The method proposed by us can be used for prediction and the temporal frequency of sample collection can be reduced to save time and cost. The results also confirm that the combination of artificial neural network with ACO is a promising tool to optimize weights while training the network, and hence will help in reasonable prediction of groundwater quality parameters.

Biomarker directed selection of targeted anti-neoplastic agents such as immune check-point inhibitors, small molecule inhibitors and monoclonal antibodies form an important aspect of cancer treatment. Immunohistochemistry (IHC) analysis of the tumor tissue is the method of choice to evaluate the presence of these biomarkers. However, a significant barrier to biomarker testing on tissue is the availability of an adequate amount of tissue and need for repetitive sampling due to tumor evolution. Also, tumor tissue testing is not immune to inter- and intra-tumor heterogeneity. We describe the analytical and clinical validation of a Circulating Tumor Cell (CTC) assay to accurately assess the presence of PD-L1 22C3 and PD-L1 28.8, ER, PR and HER2, from patients with solid tumors to guide the choice of suitable targeted therapies. Analytically, the test has high sensitivity, specificity, linearity and precision. Based on a blinded case control study, the clinical sensitivity and specificity for PD-L1 (22C3 and 28.8) was determined to be 90% and 100% respectively. The clinical sensitivity and specificity was 83% and 89% for ER; 80% and 94% for PR; 63% and 89% for HER2 (by ICC); and 100% and 92% for HER2 (by FISH), respectively. The performance characteristics of the test support its suitability and adaptability for routine clinical use.

In this paper we compare using computational fluid dynamics the aerothermal performance of two candidate casing manifolds for supplying an impingement-actuated active tip clearance control system for an aero-engine high-pressure turbine. The two geometries are (a) single-entry: an annular manifold fed at one circumferential location; (b) multiple-entry: a casing manifold split into four annular sectors, each sector supplied separately from an annular ring main. Both the single-entry and multiple-entry systems analysed in this paper are idealised version of active clearance control systems in current production engines. Aerothermal performance is quantitatively assessed on the basis of the heat transfer coefficient distribution, driving temperature difference for heat transfer between the jet and casing wall, and total pressure loss within the high-pressure turbine active clearance control system. We predict that the mean heat transfer coefficient (defined with respect to the inlet temperature and local wall temperature) of the single-entry active clearance control system is 77% greater than the multiple-entry system; primarily because the coolant in the multiple-entry case picks up approximately 40 K of temperature from the ring main walls, and secondarily because the average jet Reynolds number of impingement holes in the single entry system is 1.2 times greater than in the multiple entry system. The multiple-entry system exhibits many local hot and cold spots, depending on the position of the transfer boxes, while the single-entry case has a more predictable aerothermal field across the system. The multiple-entry feed system uses an average of 20% of the total available pressure drop, while the feed system for the single-entry geometry uses only 2% of the total available pressure drop. From the aerothermal results of this computational study, and in consideration of holistic aero-engine design factors, we conclude that a single-entry system is closer to an optimal solution than a multiple-entry system.

The role of T cell immunity has been acknowledged in recent vaccine development and evaluation. We tested the humoral and cellular immune responses to Flucelvax®, a quadrivalent inactivated seasonal influenza vaccine containing two influenza A (H1N1 Singapore/GP1908/2015 IVR-180 and H3N2 North Carolina/04/2016) and two influenza B (Iowa/06/2017 and Singapore/INFTT-16-0610/2016) virus strains, using peripheral blood mononuclear cells stimulated by pools of peptides overlapping all the individual influenza viral protein components. Baseline reactivity was detected against all four strains both at the level of CD4 and CD8 responses and targeting different proteins. CD4 T cell reactivity was mostly directed to HA/NA proteins in influenza B strains, and NP/M1/M2/NS1/NEP proteins in the case of the Influenza A strains. CD8 responses to both influenza A and B viruses preferentially targeted the more conserved core viral proteins. Following vaccination, both CD4 and CD8 responses against the various influenza antigens were increased in day 15 to day 91 post vaccination period and maintained a Th1 polarized profile. Importantly, no vaccine interference was detected, with the increased responses balanced across all 4 included viral strains for both CD4 and CD8 T cells, and targeting HA and multiple additional viral antigens.

Chronic diseases are still known as incurable diseases, and we suspect that the medical research model is unfit for characterizing chronic diseases. In this study, we examined accuracy and reliability required for characterizing chronic diseases, reviewed implied presumptions in clinical trials and assumptions used in statistical analysis, examined sources of variances normally encountered in clinical trials, and conducted numeric simulations by using hypothetical data for several theoretical and hypothetical models. We found that the sources of variances attributable to personal differences in clinical trials can distort hypothesis test outcomes, that clinical trials introduce too many errors and too much inaccuracies that tend to hide weak and slow effects of treatments, and that the means of treatments used in statistical analysis have little or no relevance to specific patients. We further found that a large number of uncontrolled co-causal or interfering factors normally seen in human subjects can greatly enlarge the means and the variances of the experimental errors, and the use of high rejection criteria (e.g., low p values) further raises the chances of failing to find treatment effects. As a whole, we concluded that the research model using clinical trials is wrong on multiple grounds, under any of our realistic theoretical and hypothetical models, and that misuse of statistical analysis is most probably responsible for failure to identify treatment effects for chronic diseases and to detect harmful effects of toxic substances in the environment. We proposed alternative experimental models involving the use of single-person or mini optimization trials for studying low-risk weak treatments.

Both ricin and R. communis agglutinin (RCA120), belonging to the type II ribosome-inactivating proteins (RIPs-Ⅱ), are derived from the seeds of castor bean plant. They share very similar amino acid sequences, but ricin is much more toxic than RCA120. It is urgently necessary to distinguish ricin and RCA120 in response to public safety. Currently, mass spectrometric assays are well established for unambiguous identification of ricin by accurate analysis of differentiated amino acid residues after trypsin digestion. However, diagnostic peptides are relatively limited for unambiguous identification of trace ricin, especially in complex matrices. Here, we demonstrate a digestion strategy of multiple proteinases to produce novel peptide markers for unambiguous identification of ricin. LC-HRMS was used to verified the resulting peptides, among which only the peptides with uniqueness and good MS response were selected as peptide markers. Seven novel peptide markers were obtained from tandem digestion of trypsin and endoproteinase Glu-C in PBS buffer. From the chymotrypsin digestion under reduction and non-reduction conditions, eight and seven novel peptides were selected respectively. Using pepsin under pH 1~2 and proteinase K digestion, 6 and 5 peptides were selected as novel peptide markers. In conclusion, the obtained novel peptides from the established digestion methods can be recommended for the unambiguous identification of ricin during the investigation of illegal use of the toxin.

Great Himalayan range with regions namely, Jammu & Kashmir, Ladakh and high altitude parts of north eastern states inherently experience high solar exposure, cold, arid climate and significantly challenging geography. With a scarcity of affordable public housing projects in these regions a grid enabled low energy residential prototype is proposed having the capability to operate on nearly zero consumption of heating and cooling energy with the application of indigenous and contemporary energy efficient passive design techniques. Preliminary study, development of design and reading of simulation results were applied for the performance and benefit analysis of the low energy building prototype. Analysis of the climatic zone further classified and narrowed the design criterion by confirming upon beneficial architectural components proven to be energy efficient. These architectural components include, optimization of direct solar gain and orientation of building envelope, modified trombe wall system, traditional sunrooms, solar chimney with openable vents, efficient spatial planning and compliance of relevant parameters such as window-wall-ratio (WWR). The proposed low energy housing for the economically weaker section is estimated to save nearly 50% of active fuel based household heating energy. Site has been planned in such a way that about 100% of required electricity can be generated from on site renewable energy for the proposed infrastructure, it is proven more beneficial if additional electricity is generated to offset the power supply and result in lower or subsidized electricity bills. Design is compliant to different codes relevant to energy conservation and low-income housing with an embodied carbon of about 1.2 kgCO2/m2 per dwelling unit giving ‘A’ grade in cradle to gate impacts. The findings and performance of this prototype can serve as a guideline for energy efficient design in low income group housing situated in severe cold and arid regions of India such as Ladakh (U.T.).

The clustering methods of Software Defined Networking (SDN) gained popularity due to their ability to offer improved scalability, consistency, dependability, and load balancing within overlay networks and SDN partitions. This paper delved into the effects of increasing the number of OpenFlow-enabled southbound devices on the establishment and coordination of SDN-Controller clusters. Specifically, we examined the volume of east-west cluster packet communication concerning the number of southbound devices within the topology. Many research studies focused on bandwidth and the number of bytes in East-West communication. While bandwidth referred to the maximum rate at which data could be transferred, and the number of bytes reflected the volume of data being transmitted, the number of packet communications directly influenced the efficiency and responsiveness of network operations. Our investigation encompassed the impact of SDN controllerto- controller communication within the cluster concerning the rising number of OpenFlow switches connected to various topologies, including tree (star-bus network), linear, and torus configurations. This study presented data on communication patterns within Open Network Operating Systems (ONOS) and OpenDaylight (ODL) clusters, revealing differing levels of controller communication with southbound network expansions. We evaluated the scalability of ODL and ONOS controllers by scrutinising the effect of increasing the number of southbound devices on control communication volume. Our analysis revealed varied communication patterns within ONOS and ODL clusters, resulting in different volumes of control communication with southbound expansions. The findings indicated that in small to medium-sized SDNs, ODL outperformed ONOS, notably with faster cluster discovery. Conversely, ONOS demonstrated greater efficiency in larger networks owing to its centralised communication architecture. Finally, we provided recommendations for selecting the most suitable controllers based on the size of southbound networks, aiming to provide practical guidelines for optimal network performance.

Immune-cell reprogramming driven by mitochondria-derived reactive electrophilic immunometabolites (mt-REMs—e.g., fumarate, itaconate) is an emerging phenomenon of major biomedical importance. Despite their localized production, mt-REMs elicit significantly-large local and global footprints within and across cells, through mechanisms involving electrophile signaling. Burgeoning efforts are being put into profiling mt-REMs’ potential protein-targets and phenotypic mapping of their multifaceted inflammatory behaviors. Yet, precision indexing of mt-REMs’ first-responders with spatiotemporal intelligence and locale-specific function assignments remains elusive. Highlighting the latest advances and overarching challenges, this perspective aims to stimulate thoughts and spur interdisciplinary innovations to address these unmet chemical-biotechnological needs at therapeutic immuno-signaling frontiers.

In the present paper, we efficiently solve the two-body problem for extreme cases such as those with high eccentricities. The use of numerical methods, with the usual variables, cannot maintain the perihelion passage accurately. In previous articles, we have verified that this problem is treated more adequately through temporal reparametrizations related to the mean anomaly through the partition function. The biparametric family of anomalies, with an appropriate partition function, allows a systematic study of these transformations. In the present work, we consider the elliptical orbit as a meridian section of the ellipsoid of revolution, and the partition function depends on two variables raised to specific parameters. One of the variables is the mean radius of the ellipsoid at the secondary, and the other is the distance to the primary. One parameter regulates the concentration of points in the apoapsis region, and the other produces a symmetrical displacement between the polar and equatorial regions. The three most used geodesy latitude variables are also studied, resulting in one not belonging to the biparametric family. However, it is in the one introduced now, which implies an extension of the biparametric method. The results obtained by the method presented here now allow a causal interpretation of the operation of numerous reparametrizations used in the study of orbital motion.

(1) Background: donor-derived circulating free DNA (dd-cfDNA) is an innovative biomarker with great potential for the early identification and prevention of graft damage; (2) Methods: Prospective study to analyze dd-cfDNA plasma levels in 30 kidney transplant patients during their post-transplant follow-up (15 days, 3, 6 and 9 months), to determine if the result could be of interest in the identification of possible adverse events, specially allograft rejection. In addition, the aim is also to verify the obtained data on sensitivity, specificity, NPV and PPV; (3) Results: Plasma levels of dd-cfDNA could be considered a novel biomarker of graft rejection after first term post-transplant up to several months before its clinical presentation, mainly in TCMR or ABMR. In this study, it appears to have no positive predictive value for borderline rejection or TCMR IA. Our results show a low NPV of the technique because other pathologies or biological post-transplant complications can also affect dd-cfDNA levels and they must be considered in the interpretation of the result.; (4) Conclusions: dd cfDNA could allow, together with other additional biomarkers in the context of a future score, avoid invasive biopsies in most cases, mainly in the case of suspected TCMR or ABMR.

This article, based on network science, aims to contribute to overcoming its geometric and technological phases. The novelty consists in considering links of networks as linked by superimposed networks, termed here multiple superimposed soft networks (MSSN), which is introduced as a research issue. Such links of links (termed here as passive links) concern, for instance, correspondences, incompatibilities, and temporal synchronizations between the occurrences of pairs of active links of effective networks, such as those based on electrical and telecommunication. A possible constitutive mechanism of such passive linkage consists of linkage representations for practices and histories of use expressed by their validating statistical reoccurrences. We consider the possible emergent nature of the passive linkage. The reason for introducing the design and usage of MSSN properties as a research issue involves making new approaches to profile and manage networks available. Correspondence between active linkage and MSSN properties should be a matter for an experiential, machine-learning approach. Research issues relate their possible usage on the active linkage such as for classification, comparations, detection of criticalities, diagnosis, performance evaluation, and regulatory as weak forces. Furthermore, the possible identification of standard corresponding configurations of passive and active linkage are finalized to avoid their establishment or, conversely, in facilitating their establishment and keeping their replication in different contexts (or partially and in combinations) and identifying related standardized approaches (also for classes of configurations having significant levels of equivalence). This research project has methodological generalizing aspects of trans-disciplinarity. We conclude by mentioning related research issues.

Multiple myeloma (MM) is a common type of cancer that unfortunately leads to a significant number of deaths each year. The majority of the reported MM cases are detected in the advanced stages, posing significant challenges for treatment. Additionally, all MM patients eventually develop resistance or experience relapse, therefore advances in treatment are needed. However, developing new anti-cancer drugs, especially for MM, requires significant financial investment and a lengthy development process. The study of drug repurposing involves exploring the potential of existing drugs for new therapeutic uses. This can significantly reduce both time and costs, which is typically a major concern for MM patients. The utilization of pre-existing non-cancer drugs for various myeloma treatments presents a highly efficient and cost-effective strategy, considering their prior preclinical and clinical development. The drugs have shown promising potential in targeting key pathways associated with MM progression and resistance. Thalidomide exemplifies the success that can be achieved through this strategy. This review delves into the current trends, the challenges faced by conventional therapies for MM, and the importance of repurposing drugs for MM. This review highlights a noncomprehensive list of conventional therapies that have potentially significant anti-myeloma properties and anti-neoplastic effects. Additionally, we offer valuable insights into the resources that can help streamline and accelerate drug repurposing efforts in the field of MM.

The most commonly used real-time augmentation services in China are International GNSS Service (IGS) real-time service, PPP-B2b service, and BeiDou Satellite-Based Augmentation System (BDSBAS) dual-frequency augmentation service. However, the research on the performance evaluation, comparison and application scope of these three products is still unknown and unrevealed. This article introduces methods for obtaining real-time augmentation information and real-time orbit and clock offset recovery. Based on real-time orbit and clock offset accuracy, positioning accuracy, and positioning availability, it systematically evaluates the performance of CNES, PPP-B2b, and BDSBAS augmentation information. The results indicate that the radial accuracy of CNES and PPP-B2b real-time orbit products is consistent, and the Root Mean Square (RMS) is better than 5 cm. CNES can achieve centimeter-level accuracy in both along-track and cross-track components, surpassing the decimeter-level accuracy of PPP-B2b. Both services demonstrate consistent accuracy in real-time clock offset, with PPP-B2b showing similar standard deviations (STD) of 0.16 ns for different satellites. However, for CNES, the STD of real-time clock offset varies for GPS, BDS-3 MEO, and BDS-3 IGSO satellites, with values of 0.10 ns, 0.19 ns, and 0.60 ns, respectively. The accuracy and availability of the two PPP services exhibit the same levels, with centimeter-level accuracy achieved after convergence and positioning availability exceeding 99%. Therefore, the difference between the two services in application areas depends on the acquisition of augmentation information. However, BDSBAS, which concentrates on code observations, demonstrates lower accuracy in real-time orbit and clock offset, positioning accuracy, and positioning availability compared to the other two services. Its primary application is in the aviation and maritime domains, where there is a greater need for service integrity, continuity, and reliability.

Organizational climate is the ascribed psychological meanings and significance associated with the procedures, policies and practices that are recognized and rewarded in the workplace, and hence mediates the effects of environmental stimuli on individuals’ response. Safety climate is a specific organizational climate, i.e., organizational climate for safety. Previous research claimed that organizational climate provides foundation for safety climate, but without elaboration on the foundational mechanisms. This paper attempts to fill up this knowledge gap. As organizational climate is a multi-dimensional phenomenon, this paper chooses two dimensions, i.e., perceived organizational support (POS) and participative decision-making (PaDM), for illustrative purposes. Drawing on an interactive approach to forming climate perceptions, this paper introduces two interactive constructs, i.e., leader-member exchange (LMX) and team member exchange (TMX), and establishes a multiple mediation model depicting the foundational effect of organizational climate on safety climate. A random sample of Hong Kong based construction personnel is used to validate the model. The results show that both POS and PaDM are positively associated with perceived safety climate, both LMX and TMX fully mediate the effect of PaDM on safety climate, and only LMX partially mediates the effect of POS on safety climate. This study sheds light on the foundational effects of organizational climate on safety climate. POS can improve the quality of reciprocal exchange about safety matters between construction personnel and their supervisors, and hence raise construction personnel’s awareness of the priority of safety. PaDM can improve the quality of reciprocal exchange about safety matters vertically and horizontally, and hence have construction personnel aware the importance of safety. In practice, this paper suggests that project managers timely recognize and reward construction personnel’s contribution, genuinely cares about their well-being, and take their suggestions seriously in making decisions. In this way, the quality of both vertical and horizontal exchange about safety matters improves, and a sound and positive safety climate ensues.

Porcine epidemic diarrhea virus (PEDV) has caused great damage to the global pig industry. The innate immunity plays a significant role in resisting viral infection, however, the exact role of innate immunity in the anti-PEDV response has not been fully elucidated. In this study, we observed that various porcine innate immune signaling adaptors are involved in the anti-PEDV activity. Among these, TRIF and MAVS showed the strongest anti-PEDV activity. The endogenous TRIF, MAVS and STING were selected for further examination of anti-PEDV activity. Agonist stimulation experiments showed that TRIF, MAVS and STING signaling all have obvious anti-PEDV activity. The siRNA knockdown assay showed that TRIF, MAVS and STING are also all involved in anti-PEDV response, and their remarkable effects on PEDV replication were confirmed in TRIF-/-, MAVS-/- and STING-/- Vero cells. For further verification, the anti-PEDV activity of TRIF, MAVS and STING could be reproduced in porcine IPEC-DQ cells. In summary, this study reveals that multiple pattern recognition receptor (PRR) signaling pathways of porcine innate immunity play an important role in the anti-PEDV infection, providing new and useful antiviral knowledge for prevention and control of PEDV spreading.

In this paper, an iterative procedure to find the solution of a nonlinear structural model is introduced. The model presents different multiplicities where parameters are randomly selected within a solvability region. To achieve this aim, a class of multipoint fixed-point iterative schemes for single roots is modified to find multiple roots, reaching the fourth order of convergence. Complex discrete dynamics techniques are employed to select the members with the most stable performance. The structural problem, as well as some academic problems involving multiple roots, are solved numerically to verify the theoretical analysis, robustness and applicability of the proposed scheme.

In this study we present a hybrid approach of ACO with fuzzy logic and clustering methods to solve multi-objective path planning problems in case of swarm USVs. This study aims to enhance the performance of ACO algorithm by integrating fuzzy logic in order to cope with the multiple contradicting objectives and generate quality solutions by in parallel identifying the mission areas of each USV to reach the desired targets. The objectives that are taken into account are the minimization of traveled distance and energy consumption, and the maximization of path smoothness. A comparative evaluation is conducted among ACO and fuzzy inference systems, Mamdani (ACO-M) and Takagi–Sugeno–Kang (ACO-TSK). The results showed that depending on the needs of the application, each methodology can contribute respectively. ACO-M generates better paths but ACO-TSK presents higher computation efficiency.

Fringe projection profilometry(FPP) has been extensively applied in various fields for its superior fast speed, high accuracy and high data density. However, measuring some objects with high reflective surfaces or high dynamic range surfaces remains challenging for FPP. Some multiple exposure image fusion methods have been proposed and successfully improved the measurement performance for these kinds of objects. Normally, these methods have a relatively fixed sequence of exposure settings determined by artificial experience or trial and error experiments, which may decrease the efficiency of the entire measurement process and may have less robustness to various environmental lighting conditions and object reflective properties. In this paper, a novel self-adaptive multiple exposure image fusion method is proposed with two main aspects of improvement on adaptively optimizing the initial exposure and the exposure sequence. By introducing the theory of information entropy, combined with the analysis of the characterization of fringe image entropy, an adaptive initial exposure searching method is first proposed. Then, an exposure sequence generation method based on dichotomy is further described. On the base of these two improvements, a novel self-adaptive multiple exposure image fusion method for FPP as well as its detailed procedures are given. Experimental results validate the performance of the proposed self-adaptivity multiple exposure image fusion method by measuring the objects with different surface reflectivity and in different ambient lighting conditions.

We evaluate the nested sum $\sum_{a_{n - 1} = c}^{a_n } {\sum_{a_{n - 2} = c}^{a_{n - 1} } { \cdots \sum_{a_0 = c}^{a_1 } {x^{a_0 } } } }$ where $a_n$ and $c$ are any integers and $x$ is a real or complex variable. Consequently, we evaluate multiple sums involving the terms of a general second order sequence, the Horadam sequence (W_j (a, b; p, q)), defined for all non-negative integers j by the recurrence relation W₀ = a, W₁ = b; W_j = pW_j−1 − qW_j−2 (j≥2); where a, b, p and q are arbitrary complex numbers, with p ≠ 0, q ≠ 0.

Phenotypes are necessary for genomic evaluations and management. Sometimes genomics can be used to measure phenotypes when other methods are difficult or expensive. Prolificacy of bulls used in multiple-bull pastures for commercial beef production is an example. A retrospective study of 79 bulls aged 2-year-old and older used 141 times in 4-5 pastures across 4 years was used to estimate repeatability from variance components. Traits available before each season’s use were tested for predictive ability. Sires were matched to calves using individual genotypes and evaluating exclusions. A lower cost method of measuring prolificacy was simulated for 5 pastures using the bulls’ genotypes and pooled genotypes to estimate average allele frequencies of calves and of cows. Repeatability of prolificacy was 0.62 ± 0.09. A combination of age-class and scrotal circumference accounted for less than 5 % of variation. Simulated estimation of prolificacy by pooling DNA of calves was accurate. Adding pooling of cow DNA or actual genotypes both increased accuracy about the same. Knowing a bull’s prior prolificacy would help predict future prolificacy for management purposes and could be used in genomic evaluations and research with coordination of breeders and commercial beef producers.

Searching, recognizing and retrieving a video of interest from a large collection of a video data is an instantaneous requirement. This requirement has been recognized as an active area of research in computer vision, machine learning and pattern recognition. Flower video recognition and retrieval is vital in the field of floriculture and horticulture. In this paper we propose a model for the retrieval of videos of flowers. Initially, videos are represented with keyframes and flowers in keyframes are segmented from their background. Then, the model is analysed by features extracted from flower regions of the keyframe. A Linear Discriminant Analysis (LDA) is adapted for the extraction of discriminating features. Multiclass Support Vector Machine (MSVM) classifier is applied to identify the class of the query video. Experiments have been conducted on relatively large dataset of our own, consisting of 7788 videos of 30 different species of flowers captured from three different devices. Generally, retrieval of flower videos is addressed by the use of a query video consisting of a flower of a single species. In this work we made an attempt to develop a system consisting of retrieval of similar videos for a query video consisting of flowers of different species.

In recent years, autonomous robots have proven capable of solving tasks in complex environments. In particular, robot manipulations in activities of daily living (ADL) for service robots have been in wide use. However, manipulations such as loading a dishwasher or folding laundry are difficult to automate robustly. In addition, manipulations of grasping multiple objects in domestic environments present difficulty. To perform those applications better, we developed robotic systems based on shared autonomy by combining the cognitive skills of a human operator with autonomous robot behaviors. In this work, we present techniques for integration of a shared autonomy system for assistive mobile manipulation and new strategies to support users in the domestic environment. We demonstrate that the robot can grasp multiple objects with random size at known and unknown table heights. Specifically, we developed three strategies for manipulation. From experimental results, we observed that the first strategy has the highest success rate (70% for random objects) up to 70 cm table height. Two other strategies perform better for 80 cm to 100 cm table height. The success rate of the second and third strategies shows an average 63.3% and 73.3%, respectively, for grasping random objects. We also demonstrated these strategies using two intuitive interfaces, a visual interface in rviz and a voice user interface with speech recognition, which are suitable for elderly people. In addition, the robot can select strategies automatically in random scenarios, which make the robot intelligent and able to make decisions independently in the environment. We obtained interesting results showing that the robot adapts to the environmental variation automatically. After these experimental demonstrations, our robot shows the capabilities for employment in domestic environments to perform actual tasks.

Automotive manufacturers and suppliers develop new vehicle systems, such as Advanced Driver Assistance Systems (ADAS), to increase traffic safety and driving comfort. ADAS are technologies that provide drivers with essential information or take over demanding driving tasks. More complex and intelligent vehicle systems are being developed toward fully autonomous and cooperative driving. Apart from the technical development challenges, training of drivers with these complex vehicle systems represents an important concern for automotive manufacturers. This paper highlights the new evolving requirements concerning the training of drivers with future complex vehicle systems. In accordance with these requirements, a new training concept is introduced, and a prototype of a training platform is implemented for utilization in future driving schools. The developed training platform has a scalable and modular architecture so that more than one driving simulator can be networked to a common driving instructor unit. The participating driving simulators provide fully immersive visualization to the drivers by utilizing head-mounted displays instead of conventional display screens and projectors. The driving instructor unit consists of a computer with a developed software tool for training session control, monitoring, and evaluation. Moreover, the driving instructor can use a head-mounted display to participate interactively within the same virtual environment of any selected driver. A simulation model of an autonomous driving system was implemented and integrated in the participating driving simulators. Using this simulation model, training sessions were conducted with the help of a group of test drivers and professional driving instructors to prove the validity of the developed concept and show the usability of the implemented training platform.

Integration of multi-view datasets which are comprised of heterogeneous sources or different representations is challenging to understand the subtle and complex relationship in data. Such data integration methods attempt to combine efficiently the complementary information of multiple data types to construct a comprehensive view of underlying data. Nonnegative matrix factorization (NMF), an approach that can be used for signal compression and noise reduction, has aroused widespread attention in the last two decades. The Kullback–Leibler divergence (or relative entropy) information distance can be used to measure the loss function of NMF. In this article, we propose a fast and robust framework (RSNMF) based on symmetric nonnegative matrix factorization (SNMF) and similarity network fusion (SNF) for clustering human microbiome data including functional, metabolic and phylogenetic profiles. Many existing methods typically utilize all the information provided by each view to create a consensus representation, which often suffers a lot from noise in data and cannot provide a precise representation of the latent data structures. In contrast, RSNMF combines the strength of SNMF and the advantage of SNF to form a robust clustering indicator matrix thus can reduce the noise influence. We conduct experiments on one synthetic and two real dataset (microbiome data, text data) and the results show that the proposed RSNMF has better performance over the baseline and the state-of-art methods, which demonstrates the potential application of RSNMF for microbiome data analysis.

The green peach aphid, Myzus persicae (Sulzer) (Hemiptera: Aphididae), is an economically important pest of crops worldwide. A M. persicae (SEF-R) population from a cabbage field in southeastern China was tested for susceptibilities to 13 insecticides. Compared with the susceptible clone (FFJ-S), extremely high and high-resistance to beta-cypermethrin (324-fold) and imidacloprid (106.9-fold) were detected in SEF-R. More importantly, this is the first report of resistance in field M. persicae population to sulfoxaflor, flupyradifurone, pymetrozine, spirotetramat, flonicamid, and broflanilide in China. The resistance factor decayed to a low level to sulfoxaflor and pymetrozine after 15 generations without any selection pressure. The resistance-related mutations (R81T and kdr) were detected in SEF, conferring target-site resistance to neonicotinoids and pyrethroids, respectively. Biochemistry bioassays confirmed the involvement of monooxygenase enzyme, carboxylesterase, superoxide dismutase and peroxidase in multi-insecticide resistance mechanism. The overexpression of P450s, esterases, and a UDP-glycosyltransferase, might be responsible for the multi-insecticide resistance in SEF-R. The knockdown of CYP6CY3 in SEF-R increased its susceptibility to imidacloprid, thiacloprid, and thiamethoxam, which verified that P450s play vital roles in neonicotinoid metabolism. This study will be helpful for chemical control, as well as for resistance monitoring and management of GPA, in China.

In an urban context, the street network can be considered the most substantial element, as its role is to create the whole urban setting. The street network is a spatial pattern that can be recognized as a linkage bonding other components of the urban context, such as buildings and open spaces. Basically, the street is not only a collection of lines that transfer people, vehicles and goods but also denote a broad range of activities and influences people’s responses. In this respect, some studies address the street as a network that entails two key elements, link and node; the link can be defined by the type of mechanism that it connects, while the node can be identified according to the analysis technique. Defining the street’s elements is a crucial step in ascertaining the expression of whatever is a link or/and a node. Thus, some scholars use the link to refer to a street, and node for the intersection, while others employ the opposite meaning. Two different viewpoints deal with spatial network analysis; the first is a technique based on topological depth, such as Space Syntax SS, and the second is Multiple Centrality Assessment (MCA) that is employed for metric analysis. This paper compares Space Syntax (SS) and Multiple Centrality Assessment (MCA) by measuring the integration and centrality of the street, and highlights the points of similarity and difference between the two techniques.

Ligand-based virtual screening (LBVS) is a promising approach for rapid and low-cost screening of potentially bioactive molecules in the early stage of drug discovery. Compared with traditional similarity-based machine learning methods, deep learning frameworks for LBVS can more effectively extract high-order molecule structure representations from molecular fingerprints or structures. However, the 3D conformation of a molecule largely influences its bioactivity and physical properties, and has rarely been considered in previous deep learning-based LBVS methods. Moreover, the relative bioactivity benchmark dataset is still lacking. To address these issues, we introduce a novel end-to-end deep learning architecture trained from molecular conformers for LBVS. We first extracted molecule conformers from multiple public molecular bioactivity data and consolidated them into a large-scale bioactivity benchmark dataset, which totally includes millions of endpoints and molecules corresponding to 954 targets. Then, we devised a deep learning-based LBVS called EquiVS to learn molecule representations from conformers for bioactivity prediction. Specifically, graph convolutional network (GCN) and equivariant graph neural network (EGNN) are sequentially stacked to learn high-order molecule-level and conformer-level representations, followed by attention-based deep multiple-instance learning (MIL) to aggregate these representations and then predict the potential bioactivity for the query molecule on a given target. We conducted various experiments to validate the data quality of our benchmark dataset, and confirmed EquiVS achieved better performance compared with 10 traditional machine learning or deep learning-based LBVS methods. Further ablation studies demonstrate the significant contribution of molecular conformation for bioactivity prediction, as well as the reasonability and non-redundancy of deep learning architecture in EquiVS. Finally, a model interpretation case study on CDK2 shows the potential of EquiVS in optimal conformer discovery. The overall study shows that our proposed benchmark dataset and EquiVS method have promising prospects in virtual screening applications.

Silicon is an essential trace nutrient for plant growth and is frequently employed to remediate soils contaminated with heavy metals in agriculture. However, silicon’s role and mechanism in reducing heavy metal toxicity have not been well understood, especially for multi-heavy metals. In this study, the effects of silicon-rich materials (silicate, rice husk biochar (RHB), and bentonite) on growth trait, antioxidant response, and heavy metal accumulation and distribution of wheat grown in two soils polluted by multiple heavy metals (Cd, Zn, Pb, and As) were investigated. The results revealed that the addition of silicon-rich materials enhanced plant growth, improved the photosynthetic attributes in leaf tissues, and decreased the contents of Cd, Zn, Pb, and As in wheat shoots and grains. The examination of the subcellular distribution of heavy metals in plants implied that silicon-rich materials transferred heavy metals as intracellular soluble fractions to the cell walls, indicating the reduction of mobility and toxicity of heavy metals in the plants. In addition, the application of the silicon-rich materials reduced oxidative damage in plants by downregulating plant antioxidant response systems and decreasing the production of malondialdehyde (MDA), ascorbic acid (AsA), and glutathione (GSH). Moreover, fractionation analysis of soil heavy metals showed that silicon-rich amendments could convert bioavailable heavy metals into immobilized forms. The results indicated that silicon-rich materials could remediate multi-heavy metal-polluted soils and promote wheat production.

Advanced machine learning algorithms, have the potential to be successfully applied to many areas of system modelling. In the present study the capability of ten machine learning algorithms in predicting the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well known ground motion intensity measures are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial mainshock–aftershock sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology deployed in a user-friendly web-application.

In this paper, the author utilizes Saddle Theorem and variational methods to deduce existence of at least six stationary solutions for reaction-diffusion Gilpin-Ayala competition model (RDGACM). To obtain the global stabilization of the positive stationary solution of the RDGACM, the author designs a suitable impulsive event triggered mechanism (IETM) to derive the global exponential stability of the the positive stationary solution. It is worth mentioning that the new mechanism can exclude Zeno behavior and effectively reduce the cost of impulse control through event triggering mechanism. Besides, compared with existing literature, the restrictions on the parameters of the RDGACM are relaxed so that the methods used in existing literature can not be applied to the relaxed case of this paper, and so the author makes comprehensive use of Saddle Theorem, orthogonal decomposition of Sobolev space $H_0^1(\Omega)$ and variational methods to overcome the mathematical difficulty. Numerical examples show the effectiveness of the methods proposed in this paper.

Several herbivores feed on maize in field and storage setups making the development of multiple-insect resistance a critical breeding target. In this study, an association mapping panel of 341 tropical maize lines was evaluated in three field environments for resistance to FAW (fall armyworm) whilst bulked grains were subjected to MW (maize weevil) bioassay, genotyped with Diversity Array Technologies single nucleotide polymorphisms (SNPs) markers. A multi-locus genome-wide association study (GWAS) revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits on all 10 maize chromosomes, of which, 47 and 31 were discovered at stringent Bonferroni genome-wide significance level of 0.05 and 0.01, respectively, and located within or close to multiple-insect resistance genomic regions (MIRGRs) concerning FAW, SB, and MW. Sixteen QTNs influenced multiple-traits of which six were associated with resistance to both FAW and MW suggesting a pleiotropic genetic control. Functional prioritization of candidate genes (CGs) located within 10-30kb of the QTNs revealed 64 putative GWAS-based CGs (GbCGs) showing evidence of involvement in plant defense mechanisms. Only one GbCG was associated with each of five of the six combined-resistance QTNs, thus, reinforcing the pleiotropy hypothesis. In addition, through In-silico co-functional network inferences, an additional 107 Network-based CGs (NbCGs), biologically connected to the 64 GbCGs, differentially expressed under biotic or abiotic stress were revealed within MIRGRs. The provided multiple-insect resistance physical map should contribute to the development of combined-insect resistance in maize.

Background: At present, the treatments for patients with advanced lung cancer focus on chemotherapy, targeted therapy, immunotherapy, or a combination of multiple treatments. Purpose: The main purpose of this study is to compare the various chemotherapy-based combination therapies and find the best one for patients with advanced lung cancer. Methods: Based on database (PubMed, EMBASE and Medline) for randomized controlled trials of advanced lung cancer with combination therapy from 2008 to 2020, we searched literatures with overall survival (OS), progression-free survival (PFS), objective response rate (ORR) and adverse as outcome indicators and established a Bayesian mesh meta-analysis for multiple treatment strategies. Then, we combined the results of four outcome indicators to find out the best chemotherapy-based combination therapy strategy for patients with advanced lung cancer, further, we tried to screen out the best drugs of which were commonly used now. Results: It contained a total of 51 studies, including five combination therapies: Chemotherapy/Chemotherapy plus placebo (CT), chemotherapy plus one targeted therapy drug (CT+T), chemotherapy plus two targeted therapy drugs (CT+T+T), chemotherapy combined with immunotherapy (CT+I) or chemotherapy combined with biotherapy (CT+B). In terms of four outcome indicators, CT+I showed the best therapeutic benefits. In the comparison of immunotherapy drugs, pembrolizumab showed the best effect. Conclusion: Our results showed that, among the multiple chemotherapy-based combination therapy strategies, chemotherapy combined with immunotherapy is the best choice for patients with advanced lung cancer, and pembrolizumab combined with chemotherapy has the best effect.

What do you want to do ?New mailCopyBackground: to analyze the changes that a therapeutic physical exercise program is capable of causing in the functionality of patients suffering from ALS and in addition, to analyze the respiratory capacity. Methods: a systematic review of the PubMed, SCOPUS, Cochrane, SciELO, PEDro, CINAHL and MEDline databases is carried out. The information was filtered using the following MeSH terms: "Amyotrophic lateral sclerosis", "Physical Therapy", "Physical and Rehabilitation Medicine". Clinical trials published in the last 5 years were included in which one of the interventions was therapeutic physical exercise in patients with ALS, which included the ALSFRS-R as a result variable. Results: 10 clinical trials with a total of 421 patients were analyzed, of which 183 underwent rehabilitation with physical exercise and were part of the case group; the rest belong to the control group and their treatment was mostly passive. The observed trend is of a decrease of approximately 6 points in the ALSFRS-R scale at 6 months in the case groups; however, no behavior pattern was met in the controls. Conclusions: Therapeutic physical exercise could contribute to slow down the deterioration of the musculature of people with ALS, thus facilitating the performance of their daily activities.

Background: to analyze the changes that a therapeutic physical exercise program is capable of causing in the functionality of patients suffering from ALS and in addition, to analyze the respiratory capacity. Methods: a systematic review of the PubMed, SCOPUS, Cochrane, SciELO, PEDro, CINAHL and MEDline databases is carried out. The information was filtered using the following MeSH terms: "Amyotrophic lateral sclerosis", "Physical Therapy", "Physical and Rehabilitation Medicine". Clinical trials published in the last 5 years were included in which one of the interventions was therapeutic physical exercise in patients with ALS, which included the ALSFRS-R as a result variable. Results: 10 clinical trials with a total of 421 patients were analyzed, of which 183 underwent rehabilitation with physical exercise and were part of the case group; the rest belong to the control group and their treatment was mostly passive. The observed trend is of a decrease of approximately 6 points in the ALSFRS-R scale at 6 months in the case groups; however, no behavior pattern was met in the controls. Conclusions: Therapeutic physical exercise could contribute to slow down the deterioration of the musculature of people with ALS, thus facilitating the performance of their daily activities.What do you want to do ?New mailCopy

The current research investigates the effects of material and riblets on cavitation-induced erosion morphology, depth, and cross-sectional area through experimental approaches. To achieve these aims, the erosion of pure aluminum (1xxxAl or Al) and alpha brass (CuZn37 or CZ108) in the presence and absence of bio-inspired sawtooth riblets was examined after exposure to multiple collapses of single cavitation bubbles with a wall distance of 1.8. The results indicate that the erosion morphology resembles a rounded cone with a circular cross-section. Brass provides 21.6% more erosion resistance compared to Al in terms of material properties. Furthermore, the erosion for both Al (depth by 3.8% and width by 18.3%) and brass (depth by 7.9% and width by 27.4%) decreases in the presence of riblets compared to flat surfaces. The greater erosion resistance of brass compared to Al is attributed to the superior mechanical stability of brass, making it a potentially suitable alloy for propellers and hulls. In sum, the results reveal that riblet-provided materials with high mechanical durability are promising erosion-resistant materials for the shipping industry. However, the potential for chemical reactions in a cathodic environment should be addressed to provide a comprehensive perspective on reducing corrosion intensity.

Sexual vs asexual reproduction—unisexual vs bisexual populations—diploid vs polyploid biotypes—genetic vs environmental sex determination: all these natural phenomena are associated with the genus of teleost fish, Carassius. Two Carassius entities with completely different biological characteristics exist: one globally widespread and invasive Carassius gibelio, and the other C. carassius with decreasing trend of natural occurrence. Comprehensive biological and cytogenetic knowledge of both entities, including the physical interactions between them, can help to balance advantages of highly invasive and disadvantages of threatened species. For example, benefits of a wide-ranged colonization can lead toward extinction of native species or be compensated by parasitic enemies and lead to equilibrium. This review emphasizes the comprehensive biology and cytogenetic knowledge and importance of the Carassius genus as one of the most useful experimental vertebrate models for evolutionary biology and genetics. Secondly, the review points out that effective molecular cytogenetics should be used for identification of various species, ploidy levels, and hybrids. The proposed investigation of these hallmark characteristics in Carassius may be applied in conservation efforts to sustain threatened populations in their native ranges. Also, the review focuses on consequences of co-occurrence of native and non-native species and outlines future perspectives of Carassius research.

Multiple births or twinning in equids are dangerous, undesirable situations that compromise the life of the dam and resulting offspring. However, embryo vitrification and freezing techniques take advantage of individuals whose multiple ovulations allow flushing more fertilised embryos from the oviduct to be collected, increasing the productivity and profitability of such techniques. Embryo preservation is especially important in highly endangered populations such as certain donkey (Equus asinus) breeds; for which conventional reproductive techniques have previously failed. For instance, becoming an effective alternative to artificial insemination with frozen semen to preserve the individuals’ genetic material. The objective of this study was to examine the historical foaling records of Andalusian donkeys to estimate genetic parameters for multiple births, assessing the historical foal number born per animal, maximum foal number per birth and multiple birth number per animal. We designed an Animal Model with single records considering the fixed effects of birthyear, birth season, sex, farm, and husbandry system, and age as a linear and quadratic covariate. Restricted maximum likelihood reported heritability estimates ranging from 0.18±0.01 to 0.24±0.01. Genetic and phenotypic correlations ranged from 0.01±0.01 to 0.83±0.01 and 0.12±0.01 and 0.53±0.01, respectively. These estimates enable the potential for selection against/for these traits, offering a new perspective for donkey breeding and conservation.

The development of multiple drug resistant (MDR) cancer all too often signals the need for alternative toxic therapy or palliative care. Our recent in vivo and in vitro studies using canine MDR lymphoma cancer cells demonstrated that the Anaphase Promoting Complex (APC) is impaired in MDR cells compared to normal canine control and drug sensitive cancer cells. Here, we sought to establish whether this phenomena is a generalizable mechanism independent of species, malignancy type or chemotherapy regime. To test the association of blunted APC activity with MDR cancer behavior, we used matched parental and MDR MCF7 human breast cancer cells, and a patient derived xenograft (PDX) model of human breast cancer. We show that APC activating mechanisms, such as APC subunit 1 (APC1) phosphorylation and CDC27/CDC20 protein associations, are reduced in MCF7 MDR cells when compared to chemosensitive matched cell lines. Consistent with impaired APC function in MDR cells, APC substrate proteins failed to be effectively degraded. Similar to our previous observations in canine MDR lymphoma cells, chemical activation of the APC using Mad2 Inhibitor-1 (M2I-1) in MCF7 MDR cells enhanced APC substrate degradation and resensitized MDR cells in vitro to the cytotoxic effects of the alkylating chemotherapeutic agent, Doxorubicin (DOX). Using cell cycle arrest/release experiments we show that chemo-sensitive and -resistant MCF7 cells progress through the cell cycle at similar rates, but mitosis is delayed in MDR cells with elevated substrate levels. When treated with M2I-1, MDR cells progress through mitosis at a faster rate that coincides with reduced levels of APC substrates. In our PDX model, mice growing a clinically-MDR human breast cancer tumor show significantly reduced tumor growth when treated with M2I-1, with evidence of increased DNA damage and apoptosis. Thus, our results strongly support the hypothesis that APC impairment is a driver of aggressive tumor development and that targeting the APC for activation has the potential for meaningful clinical benefits in treating recurrent cases of MDR malignancy.

Under the "dual carbon" target in China, virtual power plants (VPPs) play an important role in improving grid security and promoting clean and low-carbon energy transformation. VPPs can integrate and control distributed resources to participate in the energy market and make full use of distributed resources. However, the intermittency and volatility of renewable energy and the "heat-driven" working mode of CHP units create contradictions that seriously affect the peak-shaving ability of VPPs and lead to high carbon emissions. To solve these problems, this paper aggregates CHP units, wind power, photovoltaics, carbon capture, hydrogen energy storage, and electric boilers into a new type of virtual power plant. The "hydrogen energy storage-electric boiler" joint decoupling CHP working mode is used to strengthen the coupling relationship between electric-thermal-hydrogen load. At the same time, a tiered carbon trading mechanism is considered, with the net profit of the VPP as the optimization objective, balancing economic and environmental considerations. A low-carbon economic dispatch model for VPPs is established, and a genetic algorithm is used for optimization. Three different scheduling strategies are set, and simulations are conducted in three different seasonal scenarios. The results show that the net profit in the cooling season increased by 50.4%, and carbon emissions decreased by 42.3%. In the transitional season, the net profit increased by 39.2%, and carbon emissions decreased by 44.9%. In the heating season, the net profit increased by 19.4%, and carbon emissions decreased by 43.4%. Overall, the proposed dispatch strategy can improve the new energy consumption capacity and total revenue of VPPs while achieving the goal of reducing carbon emissions.

A separate group consists of compounds that are able to simultaneously interact with both orthosteric and allosteric sites, which are classified as bitopic GPCR ligands [74, 76-81]. They have two pharmacophores, one of which binds with high affinity to the orthosteric site, while the other, with lower affinity, binds to the allosteric site. If these sites are spatially separated in the receptor, then the pharmacophores in the bitopic ligand must be connected with a flexible linker, the length of which exactly corresponds to the distance between the orthosteric and allosteric sites. At the same time, it is important that the linker does not significantly affect the conformational rearrangements in the receptor caused by its activation by orthosteric and (or) allosteric agonists [74, 79].

Aggregated alpha-synuclein (-synuclein) is the main component of Lewy bodies (LBs), Lewy neurites (LNs), and glial cytoplasmic inclusions (GCIs), which are pathological hallmarks of idiopathic Parkinson’s disease (IPD) and multiple system atrophy (MSA), respectively. Initiating factors that culminate in forming LBs/LNs/GCIs remain elusive. Several species of -synuclein exist, including phosphorylated and nitrated forms. It is unclear which -synuclein post-translational modifications (PTMs) appear within aggregates throughout disease pathology. Herein we aimed to establish the predominant synuclein PTMs in post-mortem IPD and MSA pathology using immunohistochemistry. We examined the patterns of three -synuclein PTMs (pS87, pS129, nY39) simultaneously in pathology-affected regions of 15 PD, 5 MSA, 6 neurologically normal controls. All antibodies recognized LBs, LNs, and GCIs, albeit to a variable extent. pS129 -synuclein antibody was particularly immunopositive for LNs and synaptic dot-like structures followed by nY39 -synuclein antibody. GCIs, neuronal inclusions, and small threads were positive for nY39 -synuclein in MSA. Quantification of the LB scores revealed that pS129 -synuclein was the dominant and earliest -synuclein PTM followed by nY39 -synuclein, while lower amounts of pSer87 -synuclein appeared later in disease progression in PD. These results may have implications for novel biomarker and therapeutic developments.

Abstract: Long noncoding RNAs (lncRNAs) are deregulated in human cancers and are associated with disease progression. Plasmacytoma Variant Translocation 1 (PVT1), an lncRNA, is located adjacent to MYC, linked to multiple myeloma (MM). PVT1 is expressed in MM and is associated with carcinogenesis, however, its role and regulation machinery remain uncertain. We examined PVT1/MYC expression through real time PCR in plasma cells purified from 59 MGUS and 140 MM patients. MM cell lines KMS11, KMS12PE, OPM2, and RPMI8226 were treated with JQ1, a MYC superenhancer inhibitor, or MYC inhibitor 10058-F4. The expression levels of PVT1 and MYC were significantly higher in MM than in MGUS (p &lt; 0.0001), and showed positive correlation with disease progression (r = 0.394, p &lt; 0.0001). JQ1 inhibited cell proliferation and decreased the expression levels of MYC and PVT1. However, 10054-F4 did not alter the expression level of PVT1. The positive correlation between MYC and PVT1 in patients, synchronous downregulation of MYC and PVT1 by JQ1, and no effect of MYC inhibitor on PVT1 expression suggest that the expression of these two genes is coregulated by a superenhancer. Cooperative effects between these two genes may contribute to MM pathogenesis and progression.

COVID-19 is a pandemic with no cure. There is an urgent need for low-cost interventions. Macroclimate work through affecting microclimate. In many situations, man-made microclimate, such as air conditioning, may override the effect of natural macroclimate in determining SARS-CoV-2 pathogenicity. Ambient temperature (AT) has been roughly associated to SARS-CoV-2 transmission. To translate into a feasible practice in controlling COVID-19 pandemic, in-depth and implementable knowledge of AT role in SARS-CoV-2 transmission should be unveiled. This study aimed to determine if there is a ‘safe’ temperature that is comfortable to human beings while significantly inhibitory for SARS-CoV-2 pathogenicity. Data on monthly new deaths or new cases per million population (MDPM or MCPM) and monthly cumulated days with more cases than the previous day (DI) from March 2 to June 30, 2020 were collected from all 118 countries with population over five million. Monthly average AT negatively correlated with the transmission parameters. A significant decrease in transmission was observed when AT reached above 20 ºC. Monthly average (not average high) AT of countries with MDPM <2, MCPM<10, or DI<=7 was found to be between 24.54 and 26.89 ºC (25.18 ºC on average) with average standard error of 4.81. Thus, average AT <20, 20-25, >25 ºC were considered as high, medium, and low risk AT. Furthermore, MDPM in countries with AT <20 ºC were 80.93, 50.23, 13.52, and 5.05 times of those in countries with AT >25 ºC in March, April, May, and June, respectively. MDPM low-risk rates (<2) in countries with AT >25 ºC were 100, 83.33, 52.73, and 52.46%, respectively. In countries with AT <20 ºC, the trends were opposite. Setting indoor temperature to 25 ºC could decrease the need of social distancing for containing SARS-CoV-2 transmission. Ventilation and sanitizing the air with ultraviolet light in nonbusiness hours may be additionally effective. Cooling indoor temperature too low may be a reason of COVID-19 outbreak in some high AT countries. Authorities and the general population can evaluate COVID-19 risk level and manipulate microclimate to reduce the risk anywhere anytime based on local day average AT.

During the past two decades there has been a major shift in the choice of agents to treat multiple myeloma, whether newly diagnosed or in the relapsed/refractory stage. The introduction of new drug classes, such as proteasome inhibitors, immunomodulators, and anti-CD38 and anti-SLAMF7 monoclonal antibodies, coupled with autologous stem cell transplantation, have approximately doubled the disease’s five-year survival rate. However, this positive news is tempered by the realization that these measures are not curative and patients eventually relapse and/or become resistant to the drug’s effects. Thus, there is a need to discover newer myeloma-driving molecular markers and develop innovative drugs designed to precisely regulate the actions of such putative targets. B cell maturation antigen (BCMA), which is found almost exclusively on the surfaces of malignant plasma cells to the exclusion of other cell types, including their normal counterparts, has emerged as a specific target of interest in this regard. Immunotherapeutic agents have been at the forefront of research designed to block BCMA activity. These agents encompass monoclonal antibodies, such as the drug conjugate belantamab mafodotin; bispecific T-cell engager strategies exemplified by AMG 420; and chimeric antigen receptor (CAR) T-cell therapeutics that include idecabtagene vicleucel (bb2121) and JNJ-68284528.

The statistical technique, the multiple regression model, is employed to analyze the relationship between the dependent variable and several independent variables. The multicollinearity problem is one of the assumptions in the multiple regression model that occurred in the relationship among independent variables. The ordinal least square is the standard method to evaluate parameters in the regression model, but the multicollinearity problem affects the unstable estimator. The Liu regression is proposed to approximate the Liu estimators based on the Liu parameter to overcome multicollinearity. For this paper, we have proposed the modified Liu parameter to estimate the biasing parameter in scaling options to compare the ordinal least square estimator with two modified Liu parameters and six standard Liu parameters. The performance of the modified Liu parameter is considered with the generating independent variables from the multivariate normal distribution in the Toeplitz correlation pattern as the multicollinearity data, where the dependent variable is obtained from the independent variable multiplied with a coefficient of regression and with the error from the normal distribution. The mean absolute percentage error is computed as an evaluation criterion of estimation. For application, the Hepatitis C patients dataset is a real dataset to investigate the benefit of the modified Liu parameter. Through the simulation and real dataset, it can be seen from the results that the modified Liu parameter outperforms the other Liu parameters and the ordinal least square estimator. It can recommend the user for estimating parameters by using the modified Liu parameter when the independent variable exits the multicollinearity problem.

Background: The COVID-19 pandemic has affected Madagascar, Cameroon, and the Central African Republic (CAR), with each experiencing multiple waves by mid-2022. This study aimed to evaluate immunity against SARS-CoV-2 strains Wuhan (W) and BA.2 (BA.2) among healthcare workers (HCWs) in these countries, focusing on vaccination and natural infection effects. Methods: HCWs' serum samples were analyzed for neutralizing antibodies (nAbs) against W and BA.2 variants, with statistical analyses comparing responses between countries and vaccination statuses. Results: Madagascar showed significantly higher nAb titers against both strains compared to CAR and Cameroon. Vaccination notably increased nAb levels against W by 2.6-fold in CAR and 1.8-fold in Madagascar, and against BA.2 by 1.6-fold in Madagascar and 1.5-fold in CAR. However, in Cameroon, there was no significant difference in nAb levels between vaccinated and unvaccinated groups. Conclusion: The study highlights the complex relationship between natural and vaccine-induced immunity, emphasizing the importance of assessing immunity in regions with varied epidemic experiences and low vaccination rates.

The novel ternary composites, BiOBr-TiO2-attapulgite (BTA), were synthesized using a simple hydrothermal and water-bath method, exhibiting excellent photocatalytic performance to mul-tiple xanthates. For BTA photocatalyst, TiO2 and BiOBr were uniformly loaded on the surface of acid-activated attapulgite. As a widely used collector in mining processes, sodium ethyl-xanthate (SEX) was selected as the target pollutant due to its high toxicity. The BTA ternary photocatalyst demonstrated significantly higher adsorption and photocatalytic degradation performance compared to TiO2 nanoparticles, BiOBr nanosheets, and BiOBr-TiO2 heterojunction. Structural characterization and experimental results indicated that the exceptional photocatalytic degrada-tion efficiency of BTA was mainly attributed to the formation of heterojunction between BiOBr and TiO2, as well as the presence of additional active adsorption sites provided by attapulgite. Free radical scavenging experiments and EPR results confirmed that the photogenerated holes were the predominant active species to photodegrade SEX throughout the entire experiment. The LC-MS results provided insight into potential degradation pathways of SEX. This research demonstrates that BTA, as a novel triple composite material, achieves rapid and complete degradation to 20 mg/L SEX within 20 min. This work presents a novel approach to synthesize mineral-based photo-catalysts, which have broad prospects for application in flotation wastewater treatment.

This paper studies the finite-time synchronization problem of fractional-order stochastic memristive bidirectional associative memory neural networks (MBAMNNs) with discontinuous jumps. A novel criterion for finite-time synchronization is obtained by utilizing the properties of quadratic fractional-order Gronwall inequality with time delay and the comparison principle. This criterion provides a new approach to analyze the finite-time synchronization problem of neural networks with stochasticity. Finally, numerical simulations are provided to demonstrate the effectiveness and superiority of the obtained results.

There has been growing interest in using permanent magnet synchronous motors (PMSMs) for pumping applications to improve energy efficiency. One promising approach for powering these motors in variable speed applications is using modular multilevel cascaded converters based on a Triple-Star Bridge Cell (M3C) due to their inherent fault tolerance capability. However, M3C converters require a more complex control system than simpler converters. For instance, A basic M3C control system for power transmission requires seventeen (17) PI controllers, whose adjustment depends on the M3C’s dynamical model parameters’ value knowledge needing extensive and time-consuming testing to obtain them. To solve this control system issue, we propose an adaptive M3C control system for variable speed drives powering multiple PMSM-driven centrifugal pumps that reduces the number of controllers to six (6). Furthermore, the proposal does not require knowledge of the converter, motor, or load parameters, making it more practical and versatile. The proposal introduces an ad-hoc hybrid passivity-based model reference adaptive controller in cascade with a passivity-based control. It has been validated through theoretical stability proof and comparative simulation results with a basic control system under normal and fault operations. As a result, the proposal effectively follows the required rotor speed while enhancing performance by decreasing the current consumption and recovering from a 10% input phase imbalance, a cell short circuit, and an open cell.

The CAN protocol is widely adopted for in-vehicle networks due to its cost efficiency and reliable transmission. However, despite its popularity, the protocol lacks built-in security mechanisms, making it vulnerable to various attacks such as flooding, fuzzing, and DoS. These attacks can exploit vulnerabilities and disrupt the normal behavior of the in-vehicle network. One of the main reasons for these security concerns is that the protocol relies on broadcast frames for communication between ECUs within the network. To tackle this issue, this paper presents an intrusion detection system that leverages multiple LSTM-Autoencoders. The proposed system utilizes diverse features, including transmission interval and payload value changes, to capture various characteristics of normal network behavior. By analyzing different types of features separately using the LSTM-Autoencoder model, the system effectively detects anomalies. In our evaluation, we conducted experiments using real vehicle network traffic, and the results demonstrated the system's high precision with a 99% detection rate in identifying anomalies.

Today, the digital economy has pursued the required social and economic development, and enterprises can achieve long-term sustainability by relying on the digital platform ecosystem. However, the development of the digital economy and the rapid growth of platform ecosystems have also generated some problems. To study effective digital platform ecosystem governance, this study used the tripartite evolutionary game method and analyzed the changes and effects of platform regulation under different development stages from the perspectives of government, platforms, and enterprises. The results indicate that in the early stages, digital platforms primarily relied on the government’s obligation to regulate. With the digital platform ecosystem’s evolution, the platform organization and settled enterprises’ governance effects gradually appeared, establishing the three common constraints of governance mode. Finally, when the digital platform ecosystem is in its self-renewal period, the government effect is reduced, with the platform itself and settled enterprises taking primary supervisory roles. This study provides reasonable methods for effective platform governance at different stages and enriches the theoretical basis and management practices of platform ecosystems.

Multimodal medical image fusion is a fundamental but challenging problem in the fields of brain science research and brain disease diagnosis, and it is challenging for sparse representation (SR)-based fusion to characterize activity level with single measurement and no loss of effective information. In this paper, the Kronecker-criterion-based SR framework is applied for medical image fusion with a patch-based activity level integrating salient features of multiple domains. Inspired by the formation process of vision system, the spatial saliency is characterized by textural contrast (TC), which is composed of luminance and orientation contrasts to promote more highlighted texture information to participate in the fusion process. As substitution of the conventional l1-norm-based sparse saliency, a metric of sum of sparse salient features (SSSF) is used for promoting more significant coefficients to participate in the composition of activity level measure. The designed activity level measure is verified to be more conducive to maintain the integrity and sharpness of detailed information. Various experiments on multiple groups of clinical medical images verify the effectiveness of the proposed fusion method on both visual quality and objective assessment. Furthermore, the research work of this paper is helpful for further detection and segmentation of medical images.

As one of the common techniques for measuring coseismic deformations, optical image correlation techniques are capable of overcoming the drawbacks of inadequate coherence and phase blurring which can occur in radar interferometry, as well as the problem of low spatial resolution in radar pixel offset tracking. However, the scales of the correlation window in optical image correlation techniques typically influence the results, the conventional SAR POT method faces a fundamental trade-off between the accuracy of matching and the preservation of details in the matching window size. This study regards co-seismic deformation as a two-dimensional vector, and develops a new post-processing workflow called VACI-OIC to reduce the dependence of shift estimation on the size of the matching window. This paper takes the coseismic deformations in both the east-west and north-south directions into account at the same time, treating them as vectors, while also considering the similarity of displacement between adjacent points on the surface. Herein, the angular continuity index of the coseismic deformation vector was proposed as a more reasonable constraint condition to fuse the deformation field results obtained by optical image correlation across different matching window. Taking the earthquake of 2021 in Maduo, China, as the study area, the deformation with the highest spatial resolution in the violent surface rupture area was determined (which could not be provided by SAR data). Compared to the results of single-scale optical correlation, the presented results were more uniform (i.e., more consistent with published results). At the same time, the proposed index also detected the strip fracture zone of the earthquake with impressive clarity.

The main problem in pursuing multiple extended targets tracking is distinguishing the origins of the measurements. The association of measurements to the possible origins within the target’s extent is difficult, especially for the occlusions or the detection blind zone which cause the intermittent measurements. To solve the problem, a hierarchical network-based tracklets data association algorithm is proposed. At the low level, the min cost network flow model is used to extract possible tracklets from the divided measurement set. At the high-level, the trajectories are estimated from the tracks produced by the previous low level network. The experimental results show that the hierarchical network-based tracklets data association algorithm outperforms the JPDA and RFS-based method when the measurement is intermittently unavailable.

In recent years, new treatments have been studied for relapsed-refractory multiple myeloma (RRMM), including two CAR-T products and a variety of non CAR-T agents. Since direct comparisons between these innovative treatments are not available, indirect comparisons can be of interest. Reconstruction of individual patient data from Kaplan-Meier graphs (Shiny method) has been employed in numerous reports that have validated its performance. In the present review, we evaluated six treatments proposed for RRMM including two CAR-T products (ciltacabtagene autoleucel and idecabtagene vicleucel) and four treatments not based on a CAR-T (melflufen plus dexamethasone, isatuximab plus dexamethasone, selinexor, and belantamab). The end-point was overall survival (OS); the Shiny method was used to generate reconstructed survival curves. Our results showed statistically significant differences in OS across these treatments. Compared with pharmacological treatments, CAR-T products significantly prolonged OS with an improvement of remarkable clinical relevance. In particular, ciltacabtagene autoleucel showed better OS than idecabtagene vicleucel. As regards non CAR-T treatments, the ranking in OS was headed by isatuximab plus dexamethasone, followed by belantamab, selinexor, and melflufen plus dexamethasone. In conclusion, while the Shiny method has confirmed its validity in reconstructing individual patient data, our indirect comparisons have offered some original clues to better interpret the information on OS published in these studies.

The giant challenge breeding flood-irrigated rice is to identify superior genotypes that present high-yielding with specific grain qualities, resistance to abiotic and biotic stresses, excellent adaptation to the target environment. Thus, the objectives of this study were to propose a bayesian multi-trait model, estimate genetic parameters, and select flood-irrigated rice genotypes with better genetic potentials in different evaluation environments. For this, twenty-five rice genotypes belonging to the flood-irrigated rice improvement program were evaluated. The grain yields, grain length, width and thickness, grain length, and grain width and weight of 100 grains in the agricultural year 2016/2017. The experimental design used in all experiments was a randomized block design with three replications. The Monte Carlo Markov Chain algorithm estimated genetic parameters and genetic values. The grain thickness trait was considered highly heritable, with a credibility interval ranging from: h^2: 0.9480; 0.9440; 0.8610, in environments 1, 2, and 3, respectively. The grain yields showed a low correlation estimate between grain thickness and 100-grain weight, in all environments, with a credibility interval ranging from (ρ= 0.5477; 0.5762; 0.5618 and 0.5973; 0.5247; 0.5632, grain thickness and 100-grain weight, in environments 1, 2, and 3, respectively). The Bayesian multi-trait model proved to be an adequate strategy for the genetic improvement of flood-irrigated. Genotypes 2 and 15 had similar potential in the three environments, they should be selected as high-performance multi-trait genotypes for the genetic breeding of flood-irrigated rice in the program.

Bats have been identified as the natural hosts of several emerging zoonotic viruses, including paramyxoviruses, such as Hendra and Nipah viruses, that can cause fatal disease in humans. Recently, African fruit bats with populations that roost in or near urban areas have been shown to harbour a great diversity of paramyxoviruses, posing potential spillover risks to public health. Understanding the circulation of these viruses in their reservoir populations is essential to predict and prevent future emerging diseases. Here, we identify a high incidence of multiple paramyxoviruses in urine samples collected from a closed, captive colony of circa. 115 straw-coloured fruit bats (Eidolon helvum). The sequences detected have high nucleotide identities with those derived from free ranging African fruit bats and form phylogenetic clusters with the Henipavirus genus, Pararubulavirus genus and other unclassified paramyxoviruses. As this colony had been closed for 5 years prior to this study, these results indicate that within-host paramyxoviral persistence underlies the role of bats as reservoirs of these viruses.

The coronavirus disease 2019 (COVID-19) was officially declared a pandemic on the 11^th March 2020. It is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), impacting the lower respiratory tract. International travel to Australia during the early stages of the pandemic prior to border closure provided avenues for this virus to spread into Australia. There is little understanding of the clonality of SARS-CoV-2 isolates in Australia, and where they originated. This study aimed to investigate the clonality and ancestral sources of SARS-CoV-2 isolates in Australia using in silico methods. We retrieved 1,346 complete genomes from Australia along with 153 genomes from other countries from the NCBI nucleotide database and Global Initiative On Sharing All Influenza Data (GISAID). We then constructed a representative population of 270 sequences for downstream phylogenetic analysis and ancestral area reconstruction. Overall, two major clusters, one stemming from Europe and another from Asia, especially East Asia, were observed, implying at least two major transmission events with subsequent clades confirming the multiclonality of Australian isolates. We also identified three potential dissemination routes of SARS-CoV-2 into Australia. This study supports the hypothesis of multiple clonality and dispersals of SARS-CoV-2 isolates into Australia.

Removal of the proliferation component of gene expression by PCNA adjustment has been addressed in numerous survival prediction studies for breast cancer and all cancers in the TCGA. These studies indicate that widespread co-regulation of proliferation upwardly biases survival prediction when gene selection is performed on a genome-wide basis. In addition, removal of the correlative effects of proliferation does not reduce the random bias associated with survival prediction using random gene selection. Since most cancers become addicted to DNA repair as a result of forced cellular replication, increased oxidation, and repair deficiencies from oncogenic loss or genetic polymorphisms, we pursued an investigation to remove the proliferation component of expression in DNA repair genes to determine survival prediction. This translational hypothesis-driven focus on DNA repair genes is directly amenable to finding new sets of DNA repair genes that could potentially be studied for inhibition therapy. Overall survival (OS) prediction was evaluated in 18 cancers by using normalized RNA-Seq data for 126 DNA repair genes with expression available in TCGA. Transformations for normality and adjustments for age at diagnosis, stage, and PCNA metagene expression were performed for all DNA repair genes. We also analyzed genomic event rates (GER) for somatic mutations, deletions, and amplification in driver genes and DNA repair genes. After performing empirical p-value testing with use of randomly selected gene sets, it was observed that OS could be predicted significantly by sets of DNA repair genes for 61% (11/18) of the cancers. Interestingly, PARP1 was not a significant predictor of survival for any of the 11 cancers. Results from cluster analysis of GERs indicates that the most opportunistic cancers for inhibition therapy may be AML, colorectal, and renal papillary, because of potentially less confounding due to lower GERs for mutations, deletions, and amplifications in DNA repair genes. However, the most opportunistic cancer for inhibition therapy is likely to be AML, since it showed the lowest GERs for mutations, deletions, and amplifications in DNA repair genes. In conclusion, our hypothesis-driven focus to target DNA repair gene expression adjusted for the PCNA metagene as a means of predicting OS in various cancers resulted in statistically significant sets of genes.

In this study, in order to explore the failure mode of ZnO varistors under multiple lightning stroke, a 5-pulse 8/20 μs nominal lightning current with pulse intervals of 50 ms was applied to the ZnO varistors. Scanning electron microscopy (SEM) and X-ray diffractometry (XRD) were used to analyze the microstructure of the material. The failure processes of ZnO varistors caused by multiple lightning impulse current were described. The performance changes of ZnO varistors after multiple lightning impulses were analyzed from macro and micro perspectives. According to the results of this study’s experiments, the macroscopic failure mode of the ZnO varistors after multiple lightning impulse was that the electrical parameters deteriorate rapidly with the increase of the number of impulse groups, and finally destroyed by side-corner cracking. The microstructural examination indicated that after the multiple lightning strokes, the proportion of Bi in the several crystal phases had been converted, the grain size of ZnO varistors became smaller, and the white intergranular phase (Bi-rich grain boundary layer) increased significantly. The failure mechanism was thermal damage and grain boundary structure damage caused by temperature gradient thermal stress generated by multiple lightning current.

Using the sustainable livelihoods analytical framework, adaptability of cattle raising to multiple stressors (e.g. climate change and market conditions) in the dry tropics of Chiapas, Mexico was evaluated. Three case studies located in the Frailesca region of Chiapas were analyzed: (I) peasant cattle raising in a rural village in the Frailesca Valley; (II) peasant cattle raising in a rural village in a natural protected area in the Frailesca Highlands; and (III) holistic cattle raising by farmers with private land ownership in the Frailesca Valley. Adaptability was evaluated using an index on a scale of one to a hundred; average values were: case I = 20.9 ± 1.4; case II = 32.1 ± 1.8; and case III = 63.6 ± 3.5. In order to increase farms adaptability and reduce the vulnerability of cattle raising families, there is a need to modify public policy to take into account the conditions of the most vulnerable farmers (cases I and II). Given the economic, environmental, and social context of Mexico´s dry tropics, establishing ecological or organic cattle raising and silvopastoral systems may reduce the vulnerability of farm families and increase their level of adaptability of their farms to multiple stressors.