There are few reports of the adoption of continuous processes in bioproduction, particularly the implementation of end-to-end continuous processes, due to difficulties such as feed adjustment, production batch demarcation, and incorporating virus filtration. Here, we propose an end-to-end continuous process for a monoclonal antibody (mAb) with three integrated process segments: upstream production processes with pool-less direct connection, pooled low pH virus inactivation with automated pH control and a total flow-through integrated polishing process in which two columns were directly connected with a virus filter. The pooled virus inactivation step demarcates the batch, and high impurities reduction and mAb recovery were achieved for batches conducted in succession. Viral clearance tests also confirmed robust virus reduction for the flow-through two column chromatography and the virus filtration steps. Additionally, viral clearance tests with two different hollow fiber virus filters operated at flux ranging from 1.5 to 40 LMH confirmed robust virus reduction over these ranges. Complete clearance with LRV ≥ 4 was achieved even with a process pause at the lowest flux. The end-to-end continuous process proposed in this study is highly applicable to production processes, and the investigated virus filters have excellent applicability to continuous processes conducted at constant flux.

Identifying the population genetic structure is important for the development of species-specific management plans. Investigating the population genetics of cryptic species is even more critical. Here we focus on two cryptic duckweed species, Lemna minor L. and L. turionifera Landolt, which have overlapping ranges in our study region of Alberta, Canada, and elsewhere, and are easily mistaken for one another. We used genotyping-by-sequencing to determine the population genetic structure of both duckweed species. A total of 192 samples were sequenced and after filtering, >16,000 SNP were used to examine patterns of genetic diversity between and within L. minor and L. turionifera. The two species showed clear differentiation. When examining L. turionifera singly, we found no evidence of genetically distinct populations among 67 samples from 43 sites. In contrast, when examining L. minor singly, we discovered at least three genetically distinct populations among the 30 samples from eight sites, even though these were from a small geographic area. We also examined the relationship between surface water quality variables and the distribution of the two Lemna species. The sites containing L. turionifera had a wider range of water chemistry variables suggesting they are more tolerant of different environmental conditions. In contrast, each of the three genetically distinct L. minor groups had different water chemistry profiles. Large differences between L. minor and L. turionifera in their regional distributions and degrees of genetic differentiation highlight the importance of documentation and careful monitoring of Lemna species within Alberta, and in other regions where they co-occur.

Background and Purpose: β-arrestin2 plays an important role in opioid receptor signaling, but its involvement in morphine- and fentanyl-induced respiratory depression is widely debated. The aim of this study was to determine whether β-arrestin2 signaling is associated with fentanyl- and morphine-induced respiratory depression. Experimental Approach: This study investigated whether β-arrestin2 is involved in respiratory depression induced by fentanyl or morphine by inhibiting the upstream signaling molecule GRK2 and knocking out β-arrestin2 in mice, using whole-body plethysmography chambers to assess changes in respiratory function. Key Results: In the experiment of inhibiting GRK molecules, GRK inhibitors significantly improved the respiratory depression induced by morphine, but had no effect on fentanyl. In experiment of knocking out β-arrestin2, respiratory depression was significantly improved in the morphine group, but less affected in the fentanyl group. Conclusion and Implications: Our results suggested that inhibition of β-arrestin2 signaling alleviated morphine-induced respiratory depression but had little effect on fentanyl-induced respiratory depression in both models, suggesting differences in the respiratory depression mechanisms between fentanyl and morphine. This suggests that we may need to give a differentiated dosing regimen in clinical treatment of respiratory depression caused by the two drugs.

Biotic interactions are key to understanding the ecology of species and communities. As such, integrating biotic interactions into ecological niche modelling methods has been a central topic of research for the last decade. Yet, the role of biotic interactions remains overlooked. Mutualistic systems constitute perfect study cases for analysing the effect of biotic interactions on species niches and communities’ composition. Using the clownfish-sea anemone interaction, we integrate mutualistic interactions into a niche quantification framework to analyse the effect of biotic interactions in the estimation of species niches; and competition patterns among clownfish communities. Our results show that ignoring biotic interactions can strongly affect species ecological niche estimations. More importantly, sea anemones seem to mediate competition among clownfishes, structure communities and allow coexistence in competitive environments. These findings strongly support the importance of biotic interactions in shaping communities. Future studies could use the proposed analytical framework, which could also serve multiple conservation purposes.

Online Social Networks (OSNs) have grown exponentially in the last few years due to their applications in real life like marketing, recommendation systems, and social awareness campaigns. One of the most important research areas in this field is Influence Maximization (IM). IM pertains to finding methods to maximize the spread of information (or influence) across a social network. Previous works in IM have focused on using a pre-defined edge propagation probability or using the Hurst exponent (H) to identify which nodes to be activated. This is calculated on the basis of self-similarity in the time series depicting a user’s (node) past temporal interaction behaviour. In this work, we propose a Time Series Characteristic based Hurst-based Diffusion Model (TSC-HDM). The model calculates Hurst Exponent (H) based on the stationary or non-stationary characteristic of the time series. Furthermore, our model selects a handful of seed nodes and activates every seed node’s inactive successor only if H>0.5 . The process is continued until the activation of successor nodes is not possible. The proposed model was tested on 4 datasets - UC Irvine messages, Email EU-Core, Math Overflow 3, and Linux Kernel mailing list. We have also compared the results against 4 other Influence Maximisation models - Independent Cascade (IC), Weighted Cascade (WC), Trivalency (TV), and Hurst-based Influence Maximisation (HBIM). Our model achieves as much as 590% higher expected influence spread as compared to the other models. Moreover, our model attained 344% better average influence spread than other state-of-the-art models.

The prediction of news popularity is having substantial importance for the digital advertisement community in terms of selecting and engaging users. Traditional approaches are based on empirical data collected through surveys and applied statistical measures to prove a hypothesis. However, predicting news popularity based on statistical measures applied to past data is highly questionable. Therefore, in this paper, we predict news popularity using machine learning classification models and deep residual neural network models. Articles are usually made up of textual content and in many cases, images are also used. Although it is evident that the appropriate amount of textual data is required to extract features and create models, image data is also helpful in gaining useful information. In this paper, we present a novel multimodal online news popularity prediction model based on ensemble learning. This research work acts as a guide for extensive feature engineering, feature extraction, feature selection, and effective modeling to create a robust news popularity Prediction Model. Three kinds of features – meta features, text features, and image features are used to design an influential and robust model. The performance measure Root Mean Squared logarithmic error (RMSLE) is used to validate the outcome of the proposed model. Further, the most important features are sought out for the proposed model to verify the dependence of the model on text and image features.

As the latest information exchange model, ontology is favored by information systems, but the heterogeneity of ontology has seriously influenced the interaction and cooperation between these systems. Ontology matching is considered an effective method to solve the ontology heterogeneity problem whose kernel technology is a similarity measure. However, a single measure cannot achieve satisfactory ontology alignments. To this end, integrating different similarity measures is feasible. First of all, due to the difference in user preferences for alignment quality, the ontology matching problem is modeled as a continuous multi-objective optimization model. Particle Swarm Optimization (PSO) is suitable for solving continuous optimization problems and previous studies have found that decomposition-based methods are more suitable for solving ontology matching. Then, considering the user’s preference, a knee solution-driven, decomposition-based multi-objective particle swarm algorithm (K-MOPSO/D) is designed to solve the ontology matching. Finally, the effectiveness of our proposed method is verified by standard test cases from the well-known OAEI (Ontology Alignment Evaluation Initiative), and its performance is compared with the state-of-the-art matching methods.

Traumatic rupture of an extraocular muscle is rare. We report an unusual case of Superior oblique and medial rectus rupture after a penetrating trauma by metallic hook. Surgical repair was combined with botulinum toxin injection into the ipsilateral antagonist to prevent consequent contracture thus also augmenting the orthoptic outcome.

Changes in global climate have an important relationship with CO2 emission into the atmosphere. The implementation of integrated systems in some regions in the Amazon can mitigate CO 2 emissions and improve soil attributes in the medium to long term. In this context, this study aimed to evaluate the CO 2 emission dynamics and its relationship with soil attributes under different uses in the Amazon. The experiment consisted of four treatments characterized by different land uses (Degraded Pasture – DP, Managed Pasture – MP, Native Forest – NF, and Livestock Forest Integration – LF), with 25 replications. Soil CO 2 emission (FCO 2), soil temperature (Ts), and soil moisture (Ms) were evaluated over a period of 114 days. Soil samples were collected at the end of this period at a depth of 0 to 0.20 m to determine soil chemical, physical, and biological attributes. The mean FCO 2 reached values of 3.80; 4.44; 3.88; and 3.14 µmol m −2 s −1 in NF, DP, MP, and LF, respectively. In addition to the direct relationship between soil CO 2 emissions and soil temperature for all land uses, soil density indirectly influenced emissions in NF. The amount of humic acid induced the highest emission in DP. The increase in H+Al contents and the reduction in microbial biomass carbon increased emissions in MP. The highest CO 2 emissions came from DP, which had the lowest values of soil moisture and the highest values of soil temperature. Soil organic carbon and carbon stock were higher in MP (8.20 g kg −1 and 21.49 g kg −1) and LF (6.01 g kg −1 and 16.12 g kg −1), respectively. These values demonstrate that FCO 2 was influenced by the Amazon land uses and highlight LF as a low CO 2 emission system with a higher potential for carbon stock in the soil

A 19-year-old Cob mare with atrial fibrillation (AF) presented for transvenous electric cardioversion (TVEC). Under general anaesthesia, three successive incremental electric shocks were given (total 750 Joules) and sinus rhythm was temporarily restored. After 13 seconds, AF recurred so a fourth shock (300 Joules) was given and intravenous (IV) amiodarone started. Shortly after, capnography revealed an acute drop in the end-tidal carbon dioxide, which was followed by agonal gasping. Echocardiography showed severe cardiac dysfunction. Successful cardiac resuscitation consisted, amongst others, of cessation of amiodarone, and administration of dobutamine and adrenaline. This case report highlights the importance of capnography as a non-invasive means of cardiac output monitoring in equine anaesthesia which definitely contributed to a positive outcome. It also emphasises the critical importance of early recognition and communication of complications within anaesthetic and cardiology teams.

A two-dimensional analytical model is proposed for slotless brushless machines with arc-shaped spoke permanent magnets assisted by hub magnets to calculate the magnetic field distribution due to magnets and armature reaction, the electromagnetic torque, and back electromotive force. To verify the model, the analytical results of the field distribution, electromagnetic torque and back electromotive force have been compared with those achieved from finite element method. Moreover, this model is used as an equivalent analytical model for a cubic spoke motor, and it is shown that the proposed equivalent sub domain model satisfactorily estimates the electromagnetic quantities.

Evaluating the IgG titers targeting the S protein of SARS-CoV-2 by ELISA assays have been used in the development of several anti-SARS-CoV-2 vaccines in China. It is unclear whether the IgG levels against S protein tested by ELISA can monitor immune evasion against different Omicron variants. In the study, 88 recipients immunized with three doses of a COVID-19-inactivated vaccine were enrolled, whose serum samples were collected within 2 months after the third immunization. The IgG antibody levels were detected by using three commercial ELISA kits, which coated the S protein of the prototype, Omicron BA.1 and BA.5 variants respectively. The neutralizing activities of sera against the pseudotyped Omicron variant, prototype was determined. We also analyzed the correlation between the IgG titers with the neutralizing antibodies. The results showed that, after the third dose of the homologous inactivated vaccine, the neutralizing activity against the Omicron variant BA.1(GMT, 60) and BA. 5(GMT, 42) were decreased significantly compared with the prototype (GMT, 331) respectively ( P<0.05). However, the IgG titers against the S of Omicron BA.1(GMT, 2334) and BA.5(GMT, 2447) variants showed no significant difference with the prototype (GMT, 2797). Our results showed different correlation levels between anti-Spike protein of SARS-CoV-2 IgG titers and neutralizing antibodies, against SARS-CoV-2 prototype, Omicron variant BA.1 and BA.5 In summary, our result highlight that the Omicron variant BA.1 and BA.5 escape vaccine-induced immunity by neutralization activity test, and IgG titers against the SARS-CoV-2 Spike protein can not predict the viral immune evasion against different Omicron variants.

The response of traits and trait-related fitness to natural selection is crucial for the study of natural selection in populations. In this paper, we used a complex number to describe a quantitative trait, in which the frequency in a population and the fitness of the trait denote the modular square and the argument of the complex number. Based on this description, we introduced Fourier transform to relate the population distribution of a quantitative trait with the population distribution of its fitness and applied the uncertainty principle of the Fourier transform to describe the response of trait and fitness to natural selection in an inequality. This formula showed that under certain selection conditions, there was a minimum in the combined response of the fitness and the trait to the selection, which we called the principle of least response (PLR) to natural selection. It was suitable for the long-term evolutionary dynamics of polygenic adaptation, allowing for gene interactions (epistatic effects), not requiring the assumption of a normal distribution of traits in a population, and using only the variance of phenotype and fitness without the need to use complex statistics such as higher-order moments. The simulation results verified the high accuracy of the inequality relation in the case of a single population size. We hoped that this point could throw novel light on the theoretical basis of the evolution of complex traits.

Protein glycosylation is the most complex and diverse form of post-translational modification in human body. Meanwhile, glycosylation of peptides and proteins emerges as a promising strategy to improve the pharmacokinetic profile of peptide- and protein-based therapeutics. Owing to the importance of protein glycosylation, rigorous evaluation of the relationship between the precise structure and biological function of glycoproteins has to be per-formed. Recently, chemical synthesis, chemoenzymatic synthesis and semisynthesis strategy have attracted extensive attentions towards the prepara-tion of structurally defined glycopeptides and glycoproteins; the obtained synthetic glycoforms thus enable the thorough investigation of specific effects of protein glycosylation. This review highlights the recent progress in the development of novel strategies, preparation of homogeneous glycoproteins and exploration of structure-activity relationships. On this basis, the challenges and prospects are discussed.

The ancient Greeks took the entertainment of their crowds very seriously and used drama as a way of investigating the world they lived in. The theatrical re-enactment of suffering and grief in front of a large audience was to foster compassion, as well as a cathartic release of emotion that was restorative through a deeply felt interconnection between actors and audience. When our patients encounter tragedy, we are also drawn into their ordeal and experience it first hand. Such it was with Rose.

In this paper, we aim to improve the tracking performance of the manipulator joint system under the presence of the uncertainties, such as modelling error, friction, and external disturbance. Firstly, the nonsingular fast terminal sliding mode control is developed to guarantees a finite-time convergence and to solve the singular issue of the terminal sliding mode control. Secondly, in view of the established system model, an adaptive sliding mode controller (SMC) based on radial basis function neural network (RBFNN) and sliding mode variable structure control theory is designed for the tracking of the bi-joint manipulator and six-degree of freedom parallel robot. Finally, the results show that our method improves the robustness of the adaptive RBFNN controller further, weakens the chattering phenomenon, reduces error, and has an excellent control performance.

We report a 20-year-old female, adopted Indian patient with over 662 Mb regions of homozygosity who presented with variants associated with symptoms in BBS6, STRC, and DUOX2 genes. And a VUS in the TNNT2 gene, so far without clinical correlation. Symptoms are not explained by only one gene.

Although serum albumin and neuroticism have been found to be strongly associated with suicidal ideation in depressed patients, the causal relationship is uncertain. In the present study, the separate causal association of serum albumin with neuroticism and suicidal ideation was analyzed using univariable Mendelian Randomization (UVMR) methods via large-scale GWAS data. The multivariable MR (MVMR) analysis was used to explore causal pathways. UVMR results showed that genetically determined serum albumin was causally associated with neuroticism (β=0.009 S.D.; 95% CI: 0.001, 0.016; p=0.037) and suicidal ideation (β=0.120 S.D.; 95% CI: 0.059, 0.0180; p<0.001). MVMR results showed that neuroticism mediated 100% of the causal association between serum albumin and suicidal ideation in depressed patients. These findings suggest strong genetic evidence for the causal effect of serum albumin on suicidal ideation in depressed patients, and the complete mediation effect of neuroticism on this causal association. This study provided an evidence that serum albumin and personality traits are potential risks of suicidal ideation in patients with depression and a better approach for the intervention of suicidal ideation in depressed patients.

We aimed to evaluate the effect of defibrillator component positioning on high-voltage impedance. In two human specimens with BMI 19.1 kg/m 2 and 31.4 kg/m 2, shocks were administered with a subcutaneous implantable cardioverter-defibrillator (S-ICD) and an automated external defibrillator placed in various positions. High-voltage impedance increased with more fat tissue between the defibrillator components, or with air in the subcutaneous pocket of the S-ICD. High-voltage impedance was lowest when defibrillator components were placed anteriorly, but due to adjacent positioning, the electrical current will not be transmitted through the myocardium and effective defibrillation is unlikely. In conclusion, positioning of defibrillator components greatly affects high-voltage impedance, but the course of the current should always be considered.

The distribution of gas-liquid two-phase flow is one of significant effects on heterogeneous catalytic reactions. Ceramic membrane gas distributors (CMGD) were applied in improving gas-liquid distribution, and flow behavior of gas as dispersed phase in liquid phase was visualized via high-speed photograph. The average diameters of multi-scale bubbles were measured and modeled ranging from 10-5 to 10-2 m. The coalescence and trajectory of bubbles during rising process were observed, and two typical trajectories straight and spiral types were tracked. In order to inhibit coalescence of bubbles during rising process, internals manufactured by 3D printing were installed in the channel of ceramic membrane. The average bubble size of CMGD decreases 12 % from 392 to 345 μm compared to that of the original CMGD. The CMGD with internals enhances the heterogeneous catalytic reaction performance via providing large quantity of stabile multi-scale bubbles which could match the porous structure of catalyst.

The investigation of the Boudouard reaction and methane cracking was performed over Ni/CaO, Ni/Al2O3, and Ni/MgO. The catalysts were synthesized using co-precipitation. Both catalysts and carbon deposits were studied using AAS, BET, TPD, TPR, TPO, TOC, and XRD. The optimal temperatures for the graphite-like carbon species formation during the reactions were found to be 450 and 700 °C, respectively. The results showed that the activity of the catalytic systems is related to the number of weakly interacted Ni particles with the support. This research provides insight into the carbon deposit formation mechanism and the support role in the reactions.

Tumor cells meet the drastic energy requirements for cell survival and unlimited proliferation through metabolic reprogramming, and one of the important aspects of metabolic reprogramming is changes in lipid metabolism. Increasing evidence suggests that, like tumor cells in general, host cells infected with onco-viruses significantly alter metabolic requirements and generate substances for viral replication and virion production by altering the metabolism of host cells. HPV, EBV and KSHV are the three important contributors to virus-related tumors that appear in people before the age of 50. On the basis of reviewing the changes of lipid metabolism in tumor cells, this review summarizes the interesting association between HPV, EBV or KSHV and lipid metabolism from these three major tumor viruses in order to provide new insight into the treatment of virus-related tumors.

Basic thermodynamic data plays an important role in chemical applications. However, the traditional acquisition of thermodynamic data through experiments is laborious. Thermodynamic data prediction is considered as an alternative to the experiments, especially when qualitative analysis is needed prior to experimental studies. In this work, we report a successful machine-learning based approach to predict the fundamental thermodynamics characteristics of vapor-liquid equilibrium (VLE) process. A new dataset of the VLE experimental data of 210 kinds of binary mixture with screened descriptors were constructed. The obtained results show that the VLE characteristics of the target system can be fully revealed for a pre-analysis by ML methods and the RF model has more excellent predictive ability on the VLE behavior than the ANN model. This work pioneers the development of the generalized model on the prediction of the VLE data and provide useful information for mechanistic study on the VLE phenomenon.

Geophysical flows impacting a flexible barrier can create complex flows and solid-fluid-structure interactions, which are challenging to quantify and characterize towards a unified description. Here, we examine the common physical laws of multiphase, multiway interactions during debris flows, debris avalanches and rock avalanches against a flexible barrier system using a coupled computational fluid dynamics and discrete element (CFD-DEM) method. This model captures essential physics observed in experiments and fields. The bi-linear, positive correlations are found between peak impact load and Fr or maximum barrier deflection, with inflection points due to the transitions from trapezoid-to triangle-shaped dead zones. Our findings quantitatively elucidate how flow materials (wet versus dry) and impact dynamics (slow versus fast) control the patterns of the identified bi-linear correlations. This work offers a physics-based reference and insights for improving widely-used impact solutions for geophysical flows against flexible barriers.