Miracle-dz/UniFAR-FTUs huggingface dataset:
Production of afucosylated antibodies in CHO cells by coexpression of an anti‐FUT8 intrabody

Some effector functions prompted by immunoglobulin G (IgG) antibodies, such as antibody‐dependent cell‐mediated cytotoxicity (ADCC), strongly depend on the N‐glycans linked to asparagine 297 of the Fc region of the protein. A single α‐(1,6)‐fucosyltransferase (FUT8) is responsible for catalyzing the addition of an α‐1,6‐linked fucose residue to the first GlcNAc residue of the N‐linked glycans. Antibodies missing this core fucose show a significantly enhanced ADCC and increased antitumor activity, which could help reduce therapeutic dose requirement, potentially translating into reduced safety concerns and manufacturing costs. Several approaches have been developed to modify glycans and improve the biological functions of antibodies. Here, we demonstrate that expression of a membrane‐associated anti‐FUT8 intrabody engineered to reside in the endoplasmic reticulum and Golgi apparatus can efficiently reduce FUT8 activity and therefore the core‐fucosylation of the Fc N‐glycan of an antibody. IgG1‐producing CHO cells expressing the intrabody secrete antibodies with reduced core fucosylation as demonstrated by lectin blot analysis and UPLC‐HILIC glycan analysis. Cells engineered to inhibit directly and specifically alpha‐(1,6)‐fucosyltransferase activity allows for the production of g/L levels of IgGs with strongly enhanced ADCC effector function, for which the level of fucosylation can be selected. The quick and efficient method described here should have broad practical applicability for the development of next‐generation therapeutic antibodies with enhanced effector functions.
A review on alginate-based bioinks, combination with other natural biomaterials and characteristics

The advent of three-dimensional (3D) Bioprinting increased the need for a suitable bioink in which Cells can live, proliferate and generate specific tissue and organ. Therefore, bioinks must have several physical and chemical characteristics that depend on the bioprinting modality and the target tissue. Alginate is considered a promising biomaterial for bioprinting due to its distinct physicochemical properties and diverse biological functions. However, some characteristics, such as cell adherence and biodegradability, are lacking, which can compensate when combined with other biomaterials, for example, gelatin, gelatin methacryloyl (GelMa), cellulose, silk fibroin, and hyaluronic acid. The alginate–gelatin blend receives considerable attention since gelatin has Arginine, Glycine, and Aspartate, the tripeptide Arg–Gly–Asp (RGD) sequence that could sustain cell attachment. Some parameters assist the optimization of bioink features like temperature, biomaterials' concentration, and crosslinking time. For instance, the viscosity of alginate increases by enhancing its concentration, and while it exhibits shear thinning property, it will be printed correctly. This review interprets the alginate-based bioink, focusing on its composite with other natural biomaterials, especially gelatin. Also, it discusses the parameters that affect bioink functionality and cell viability.
A multi-attack intrusion detection model based on Mosaic coded convolutional neural network and centralized encoding

With the development of the Internet of Vehicles (IoV), attacks to the vehicle-mounted control area network (CAN) have seriously jeopardized the security of automobiles. As an important security measure, intrusion detection technologies have aroused great interest in researchers and many detection methods have also been proposed based on the vehicle’s CAN bus. However, many studies only considered one type of attack at a time but in real environments there may contain a variety of attack types simultaneously. In view of the deficiency in the current methods, this paper proposed a method to detect multi-intrusions at one time based on a Mosaic coded convolutional neural network (CNN) and a centralized coding method. A Mosaic-like data block was created to convert the one-dimensional CAN ID into a two-dimensional data grid for the CNN to effectively extract the data characteristics and maintain the time characteristics between the CAN IDs. Four types of attacks and all combinations of them were used to train and test our model. Finally, a centralized coding method was used to increase the discrimination capability of the model. Experimental results showed that this single model could successfully detect any combinations of the intrusion types with very high and stable performance.
An image analysis and machine learning approach to measuring the quality of individual colonoscopy procedures

The measurement of quality in colonoscopy is an active topic in medical research. Studies report significant miss rates in the detection of colorectal lesions. This has raised the concern among gastroenterologists that the present mechanisms for quality assurance are insufficient. The current clinical practice of quality assurance is based on long term statistics, while the quality of individual colonoscopy procedures is judged by self-assessment. For training and auditing, there exist validated subjective assessment methods, which involve the rating of procedures by trained experts using predefined assessment forms. We focus our research on one such assessment method, the Direct Observation of Procedure and Skill (DOPS), developed by the Joint Advisory Group on Gastrointestinal Endoscopy (JAG) in the UK. One of our main objectives is to investigate to what degree the JAG DOPS assessment can be automated. We have developed a system to automatically measure the quality of colonoscopy procedures according to JAG DOPS criteria and have performed a pilot validation of the system using two trained clinical assessors. The system is based on two different types of data: video data from the endoscopic camera and measurements of the longitudinal and circular motion of the shaft of the endoscope outside the anus. We have developed a number of algorithms that measure different quality related characteristics in endoscopic images and complete colonoscopy procedures. While the development of these measures is oriented towards the overall objective of assessing JAG DOPS criteria, each measure represents clinically relevant image or procedure characteristics on its own. For single images, we propose methods for the measurement of the clarity of the endoscopic field of view, the position and presence of the lumen, the quality of luminal views and the distance to the nearest bend in the colon. The image measures are based on models which are trained using a universal machine learning framework involving automatic feature selection and different variants of support vector machines. The quality of the features is enhanced by a number of pre-processing steps, most notably by a novel algorithm for detection and inpainting of specular highlights. We estimate the depth of insertion of the endoscope using the measurements of the motion sensor, which allows us to divide the colon into a number of spatial segments. This representation is the basis for the development of novel measures of colonoscopy procedure characteristics, reflecting handling patterns and summarising image based measures over the course of complete procedures. The individual procedure measures are then used as features for the training of predictive models for the automatic assessment of JAG DOPS criteria.
Gross motor skills trajectory variation between WEIRD and LMIC countries: A cross-cultural study

Aim This study aimed to examine the prevalence of delays and borderline impaired performance for Brazilian girls and boys and the differences in the motor trajectories (locomotor and ball skills) of girls and boys (3- to 10-years-old) across WEIRD (Western, Educated, Industrial, Rich, and Democratic) countries and Brazil–a low- and middle-income country (LMIC). Methods We assessed 1000 children (524 girls; 476 boys), 3- to 10.9-year-old (M = 6.9, SD = 2.1; Girls M = 6.9, SD = 2.0; Boys M = 6.9, SD = 2.1), using the Test of Gross Motor Development-3. Using systematic search, original studies investigating FMS in children using the TGMD-3 were eligible; 5 studies were eligible to have the results compared to the Brazilian sample. One sample t-test to run the secondary data from Irish, American, Finnish, and German children (i.e., mean, standard deviation). Results The prevalence of delays and borderline impaired performance was high among Brazilian girls (28.3% and 27.5%) and boys (10.6% and 22.7%). The cross-countries comparisons showed significant (p values from .048 and < .001) overall lower locomotor and ball skills scores for Brazilian children; the only exceptions were skipping, catching, and kicking. We observed stability in performance, across countries, after 8-years-old, and no ceiling effects were found in the samples. Conclusions The Brazilian sample emphasized the need for national strategies to foster children’s motor proficiency. Differences in motor opportunities may explain the differences in motor trajectories between children in WEIRD and LMIC countries.
Neddylation of Enterovirus 71 VP2 Protein Reduces Its Stability and Restricts Viral Replication

Neddylation is a ubiquitin-like posttranslational modification by conjugation of neural precursor cell-expressed developmentally downregulated protein 8 (NEDD8) to specific proteins for regulation of their metabolism and biological activities. In this study, we demonstrated for the first time that EV71 VP2 protein is neddylated at K69 residue to promote viral protein degradation and consequentially suppress multiplication of the virus. ABSTRACT Posttranslational modifications (PTMs) of viral proteins play critical roles in virus infection. The role of neddylation in enterovirus 71 (EV71) replication remains poorly defined. Here, we showed that the structural protein VP2 of EV71 can be modified by neural precursor cell-expressed developmentally downregulated protein 8 (NEDD8) in an E3 ligase X-linked inhibitor of apoptosis protein (XIAP)-dependent manner. Mutagenesis and biochemical analyses mapped the neddylation site at lysine 69 (K69) of VP2 and demonstrated that neddylation reduced the stability of VP2. In agreement with the essential role of VP2 in viral replication, studies with EV71 reporter viruses with wild-type VP2 (enhanced green fluorescent protein [EGFP]-EV71) and a K69R mutant VP2 (EGFP-EV71-VP2 K69R) showed that abolishment of VP2 neddylation increased EV71 replication. In support of this finding, overexpression of NEDD8 significantly inhibited the replication of wild-type EV71 and EGFP-EV71, but not EGFP-EV71-VP2 K69R, whereas pharmacologic inhibition of neddylation with the NEDD8-activating enzyme inhibitor MLN4924 promoted the replication of EV71 in biologically relevant cell types. Our results thus support the notion that EV71 replication can be negatively regulated by host cellular and pathobiological cues through neddylation of VP2 protein. IMPORTANCE Neddylation is a ubiquitin-like posttranslational modification by conjugation of neural precursor cell-expressed developmentally downregulated protein 8 (NEDD8) to specific proteins for regulation of their metabolism and biological activities. In this study, we demonstrated for the first time that EV71 VP2 protein is neddylated at K69 residue to promote viral protein degradation and consequentially suppress multiplication of the virus. Our findings advance knowledge related to the roles of VP2 in EV71 virulence and the neddylation pathway in the host restriction of EV71 infection.
Oryza sativa PECTIN DEFECTIVE TAPETUM1 affects anther development through a pectin-mediated signaling pathway in rice

Abstract Galacturonosyltransferase (GalAT) is required for the synthesis of pectin, an important component of plant cell walls that is also involved in signal transduction. Here, we describe the rice (Oryza sativa) male-sterile mutant O. sativa pectin-defective tapetum1 (ospdt1), in which GalAT is mutated. The ospdt1 mutant exhibited premature programmed cell death (PCD) of the tapetum and disordered pollen walls, resulting in aborted pollen grains. Pectin distribution in the anther sac was comparable between the mutant and the wild-type, suggesting that the structural pectin was not dramatically affected in ospdt1. Wall-associated kinases are necessary for the signal transduction of pectin, and the intracellular distribution of O. sativa indica WALL-ASSOCIATED KINASE1 (OsiWAK1), which binds pectic polysaccharides to its extracellular domain, was affected in ospdt1. OsiWAK1 RNA interference lines exhibited earlier tapetal PCD, similar to ospdt1. Furthermore, overexpression of OsiWAK1 in ospdt1 lines partially rescued the defects observed in ospdt1, suggesting that OsiWAK1 plays pivotal roles in the function of OsPDT1. These results suggest that the mutation of OsPDT1 does not dramatically affect structural pectin but affects components of the pectin-mediated signaling pathway, such as OsiWAK1, and causes male sterility.
Development of a robust multi-objective history matching for reliable well-based production forecasts

This article presents a dynamic reservoir characterization using a new multi-objective optimization algorithm to quantify the reservoir uncertainties in history matching. The proposed method formulated Pareto-optimality with preference-ordering to derive multiple trade-off history-matched reservoir models for probabilistic production estimation. The integration of linear programming with multi-objective genetic algorithm enhances the efficiency of a multi-directional search by prioritizing the reservoir models that satisfy the aspiration levels on the discrepancy between the observed and the calculated production data. The preference levels are automatically adjusted in correspondence to the quality of the reservoir models for facilitating the model update process during optimization. An oil-field application result indicates the method outperforms the conventional multi-objective optimization method in terms of the relative average error for the production data despite a small loss of diversity-preservation among the reservoir models.
Cultural adaptation, translation and validation of the Spanish version Debriefing Experience Scale

Clinical simulation as a teaching methodology allows the student to train and learn technical abilities and/or non-technical abilities. One of the key elements of this teaching methodology is the debriefing, which consists of a conversation between several people, in which the participants go over a real or simulated event in order to analyze their actions and reflect on the role that thought processes, psychomotor skills and emotional states can play in maintaining, or improving their performance in the future. The Debriefing Experience Scale allows the experience of students in debriefing to be measured. The objective of this study is to translate the Debriefing Experience Scale (DES) into Spanish and analyze its reliability and validity to measure the experience of nursing students during the debriefing. The study was developed in two phases: One: the adaption of the instrument to Spanish, two: a transversal study carried out in a sample of 290 nursing students. The psychometric properties were analyzed in terms of reliability and construct validity using confirmatory factorial analysis (CFA). Cronbach’s alpha was adequate for all the scales and for each one of the dimensions. The confirmatory factorial analysis showed that the 4-dimensional model is acceptable for both scales (experience and opinion). The Spanish version Debriefing Experience Scale questionnaire is useful, valid and reliable for use to measure the debriefing experience of university students in a simulation activity.
Process Evaluation of Community Energy Development Programme Projects

This report sets out the findings, conclusions and recommendations of a Process Evaluation, conducted between January and March 2015, of the Community Energy Development Programme (CEDP) projects as part of the Malawi Renewable Energy Acceleration Programme (MREAP). It was commissioned by the Scottish Government (SG) as a product of the Institutional Support Programme Component (ISP) of MREAP. Its main purpose is to assess what has been delivered, how this has been achieved and to compile learning from the process for policy and future projects. The agreed scope for the process evaluation was the portfolio of 46 CEDP projects implemented across the 3 regions of Malawi and the relevant processes and systems in place to design, implement and manage these projects. Due consideration was also given to framing the scope of the evaluation through the choice of evaluation questions and the feasibility of what was possible.
A risk prediction model for contrast-induced nephropathy associated with gadolinium-based contrast agents

Abstract Objective This is the first study to explore the risk factors for nephropathy caused by gadolinium-based contrast agents and establish a prediction model to identify high-risk patients. Methods A total of 1404 patients who received gadolinium-based contrast agents in our hospital were included. The participants were randomly assigned in a 7:3 ratio to the modeling and validation groups. The modeling group was divided into a contrast-induced nephropathy group and a non-contrast-induced nephropathy group. The clinical characteristics before the use of contrast agents were compared between the two groups. The risk factors for contrast-induced nephropathy were analyzed by logistic regression. A nomogram that could predict the incidence of contrast-induced nephropathy was plotted. The validation group was used to verify the predictive model. Results The incidence of contrast-induced nephropathy caused by gadolinium-based contrast agents was 3.92% (55/1404). The logistic stepwise regression analysis showed that sex, systolic pressure (SBP), absolute neutrophil count, albumin, fasting blood glucose level, and furosemide use were significant predictors of contrast-induced nephropathy caused by gadolinium-based contrast agents. The above predictors were then included in the nomogram construction. The area under the receiver operating characteristic (ROC) curve was 0.82 (p < 0.001). The specificity and sensitivity corresponding to the optimal cutoff point (0.039) based on the area under the ROC curve were 71.9% and 80.5%, respectively. Conclusion Sex, SBP, absolute neutrophil count, albumin, fasting blood glucose levels, and furosemide use are significant predictors of contrast-induced nephropathy caused by gadolinium-based contrast agents. Therefore, the incidence of contrast-induced nephropathy may be estimated by the prediction model established in this study before the use of contrast agents.
Warfarin sensitivity is associated with increased hospital mortality in critically Ill patients

Background Warfarin is a widely used anticoagulant with a narrow therapeutic index and large interpatient variability in the therapeutic dose. Warfarin sensitivity has been reported to be associated with increased incidence of international normalized ratio (INR) > 5. However, whether warfarin sensitivity is a risk factor for adverse outcomes in critically ill patients remains unknown. In the present study, we aimed to evaluate the utility of different machine learning algorithms for the prediction of warfarin sensitivity and to determine the impact of warfarin sensitivity on outcomes in critically ill patients. Methods Nine different machine learning algorithms for the prediction of warfarin sensitivity were tested in the International Warfarin Pharmacogenetic Consortium cohort and Easton cohort. Furthermore, a total of 7,647 critically ill patients was analyzed for warfarin sensitivity on in-hospital mortality by multivariable regression. Covariates that potentially confound the association were further adjusted using propensity score matching or inverse probability of treatment weighting. Results We found that logistic regression (AUC = 0.879, 95% CI: 0.834–0.924) was indistinguishable from support vector machine with a linear kernel, neural network, AdaBoost and light gradient boosting trees, and significantly outperformed all the other machine learning algorithms. Furthermore, we found that warfarin sensitivity predicted by the logistic regression model was significantly associated with worse in-hospital mortality in critically ill patients with an odds ratio (OR) of 1.33 (95% CI, 1.01–1.77). Conclusions Our data suggest that the logistic regression model is the best model for the prediction of warfarin sensitivity clinically and that warfarin sensitivity is likely to be a risk factor for adverse outcomes in critically ill patients.
Deep learning for dose assessment in radiotherapy by the super-localization of vaporized nanodroplets in high frame rate ultrasound imaging

Objective. External beam radiotherapy is aimed to precisely deliver a high radiation dose to malignancies, while optimally sparing surrounding healthy tissues. With the advent of increasingly complex treatment plans, the delivery should preferably be verified by quality assurance methods. Recently, online ultrasound imaging of vaporized radiosensitive nanodroplets was proposed as a promising tool for in vivo dosimetry in radiotherapy. Previously, the detection of sparse vaporization events was achieved by applying differential ultrasound (US) imaging followed by intensity thresholding using subjective parameter tuning, which is sensitive to image artifacts. Approach. A generalized deep learning solution (i.e. BubbleNet) is proposed to localize vaporized nanodroplets on differential US frames, while overcoming the aforementioned limitation. A 5-fold cross-validation was performed on a diversely composed 5747-frame training/validation dataset by manual segmentation. BubbleNet was then applied on a test dataset of 1536 differential US frames to evaluate dosimetric features. The intra-observer variability was determined by scoring the Dice similarity coefficient (DSC) on 150 frames segmented twice. Additionally, the BubbleNet generalization capability was tested on an external test dataset of 432 frames acquired by a phased array transducer at a much lower ultrasound frequency and reconstructed with unconventional pixel dimensions with respect to the training dataset. Main results. The median DSC in the 5-fold cross validation was equal to ∼0.88, which was in line with the intra-observer variability (=0.86). Next, BubbleNet was employed to detect vaporizations in differential US frames obtained during the irradiation of phantoms with a 154 MeV proton beam or a 6 MV photon beam. BubbleNet improved the bubble-count statistics by ∼30% compared to the earlier established intensity-weighted thresholding. The proton range was verified with a −0.8 mm accuracy. Significance. BubbleNet is a flexible tool to localize individual vaporized nanodroplets on experimentally acquired US images, which improves the sensitivity compared to former thresholding-weighted methods.