Modeling of Shear Flows over Superhydrophobic Surfaces: From Newtonian to Non-Newtonian Fluids.

The design and use of superhydrophobic surfaces have gained special attentions due to their superior performances and advantages in many flow systems, e.g., in achieving specific goals including drag reduction and flow/droplet handling and manipulation. In this work, we conduct a brief review of shear flows over superhydrophobic surfaces, covering the classic and recent studies/trends for both Newtonian and non-Newtonian fluids. The aim is to mainly review the relevant mathematical and numerical modeling approaches developed during the past 20 years. Considering the wide ranges of applications of superhydrophobic surfaces in Newtonian fluid flows, we attempt to show how the developed studies for the Newtonian shear flows over superhydrophobic surfaces have been evolved, through highlighting the major breakthroughs. Despite the fact that, in many practical applications, flows over superhydrophobic surfaces may show complex non-Newtonian rheology, interactions between the non-Newtonian rheology and superhydrophobicity have not yet been well understood. Therefore, in this Review, we also highlight emerging recent studies addressing the shear flows of shear-thinning and yield stress fluids in superhydrophobic channels. We focus on reviewing the models developed to handle the intricate interaction between the formed liquid/air interface on superhydrophobic surfaces and the overlying flow. Such an intricate interaction will be more complex when the overlying flow shows nonlinear non-Newtonian rheology. We conclude that, although our understanding on the Newtonian shear flows over superhydrophobic surfaces has been well expanded via analyzing various aspects of such flows, the non-Newtonian counterpart is in its early stages. This could be associated with either the early applications mainly concerning Newtonian fluids or new complexities added to an already complex problem by the nonlinear non-Newtonian rheology. Finally, we discuss the possible directions for development of models that can address complex non-Newtonian shear flows over superhydrophobic surfaces.

GradMDM: Adversarial Attack on Dynamic Networks.

Dynamic neural networks can greatly reduce computation redundancy without compromising accuracy by adapting their structures based on the input. In this paper, we explore the robustness of dynamic neural networks against energy-oriented attacks targeted at reducing their efficiency. Specifically, we attack dynamic models with our novel algorithm GradMDM. GradMDM is a technique that adjusts the direction and the magnitude of the gradients to effectively find a small perturbation for each input, that will activate more computational units of dynamic models during inference. We evaluate GradMDM on multiple datasets and dynamic models, where it outperforms previous energy-oriented attack techniques, significantly increasing computation complexity while reducing the perceptibility of the perturbations https://github.com/lingengfoo/GradMDM.

Human Action Recognition From Various Data Modalities: A Review.

Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action. It has a wide range of applications, and therefore has been attracting increasing attention in the field of computer vision. Human actions can be represented using various data modalities, such as RGB, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, radar, and WiFi signal, which encode different sources of useful yet distinct information and have various advantages depending on the application scenarios. Consequently, lots of existing works have attempted to investigate different types of approaches for HAR using various modalities. In this article, we present a comprehensive survey of recent progress in deep learning methods for HAR based on the type of input data modality. Specifically, we review the current mainstream deep learning methods for single data modalities and multiple data modalities, including the fusion-based and the co-learning-based frameworks. We also present comparative results on several benchmark datasets for HAR, together with insightful observations and inspiring future research directions.

QbD-guided pharmaceutical development of Pembrolizumab biosimilar candidate PSG-024 propelled to industry meeting primary requirements of comparability to Keytruda®.

Pharmaceutical development of biosimilars is primarily focused on meeting the regulatory requirements for analytical comparability of the product's critical quality attributes (CQAs), concerning safety and efficacy, to those of the originator drug of interest. To this end, the early adoption of a systematic science-based approach, as guided by quality-by-design (QbD) principles, is crucial due to the blind starting point where the same insights of an originator developer into the challenges of a given biopharmaceutical and its manufacturing process are lacking. In this study, we devised a pharmaceutical QbD-guided approach to undertake the biosimilar development of Pembrolizumab (Keytruda®), the ace of therapeutic monoclonal antibodies (mAbs) in terms of approved indications and market sales, and its manufacturing process development. Quality target product profile (QTPP) for Pembrolizumab biosimilar product was assembled using publicly available information on Keytruda®. Upon preliminary analyses of four different lots of Keytruda®, the product CQAs and their acceptable ranges of specification were determined via risk assessment based on the relevant pharmaceutical development quality guidelines, particularly those of the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH). The development and clone selection of Chinese Hamster Ovary (CHO) DG44 cell line was performed using DHFR expression vectors and Methotrexate (MTX) selective pressure. The CHO clone stably expressing relatively higher mAb titer (∼1200 mg/l) in small-scale shake-flask cultures, with the highest similarity of the CQAs charge variants contents (CVCs), N-glycan profile, and biological potency to those of Keytruda® reference standard was selected as the lead clone and the produced Pembrolizumab candidate was named PSG-024. The upstream process (USP) and downstream process (DSP) developments for production were started with the process evaluation screening experiments for the identification of critical process parameters (CPPs) founded upon the prior knowledge on different process stages, input process parameters (iPPs), output process parameters (oPPs), and their impacts on product CQAs. Thereby, screening experiments of USP fed-batch cell culture in 5-liter bioreactor resulted in improvement of PSG-024 expression titer to 2060 ± 70 mg/l and selection of the iPPs feed amount (A), glucose setpoint (B), culture temperature (C), and agitation rate (D) for the optimization design of experiments (DoEs) mainly focused on the CQA acidic CVC and the oPPs mAb expression yield. The USP optimization DoEs using response surface methodology (RSM) yielded valid prediction models and optimal conditions of A = 35%, B = 4.5 g/l, C = 37 °C, and D = 160-220 rpm, which resulted in the final PSG-024 expression titer of 3170 ± 40 mg/l without an excessive rise in acidic CVC. The DSP screening experiments led to achieving the mAb recovery rates of 94% ± 3% and 71.5% ± 3.5% for affinity (capture) and cation-exchange (polishing) chromatography stages, respectively. The capture eluate buffer and viral inactivation conditions were optimized to prevent mAb eluate turbidity and protein aggregation. Moreover, the polishing stage optimization DoEs via one-factor-at-a-time method focused on wash and elution steps for control of the acidic CVC CQA and achieving >80% mAb recovery rate. By shifting to Step elution from the primary salt gradient method and considering an additional intermediate wash step, the maximum mAb recovery of 87% ± 1.5% was achievable while maintaining the CQA acidic CVC within the acceptable range. The consistency of final analytical comparability of PSG-024 demonstrated the effectiveness of the adopted pharmaceutical QbD approach for Pembrolizumab biosimilar development, paving the way for the technology transfer to the client to proceed further development.

Proposing a novel deep network for detecting COVID-19 based on chest images.

The rapid outbreak of coronavirus threatens humans' life all around the world. Due to the insufficient diagnostic infrastructures, developing an accurate, efficient, inexpensive, and quick diagnostic tool is of great importance. To date, researchers have proposed several detection models based on chest imaging analysis, primarily based on deep neural networks; however, none of which could achieve a reliable and highly sensitive performance yet. Therefore, the nature of this study is primary epidemiological research that aims to overcome the limitations mentioned above by proposing a large-scale publicly available dataset of chest computed tomography scan (CT-scan) images consisting of more than 13k samples. Secondly, we propose a more sensitive deep neural networks model for CT-scan images of the lungs, providing a pixel-wise attention layer on top of the high-level features extracted from the network. Moreover, the proposed model is extended through a transfer learning approach for being applicable in the case of chest X-Ray (CXR) images. The proposed model and its extension have been trained and evaluated through several experiments. The inclusion criteria were patients with suspected PE and positive real-time reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2. The exclusion criteria were negative or inconclusive RT-PCR and other chest CT indications. Our model achieves an AUC score of 0.886, significantly better than its closest competitor, whose AUC is 0.843. Moreover, the obtained results on another commonly-used benchmark show an AUC of 0.899, outperforming related models. Additionally, the sensitivity of our model is 0.858, while that of its closest competitor is 0.81, explaining the efficiency of pixel-wise attention strategy in detecting coronavirus. Our promising results and the efficiency of the models imply that the proposed models can be considered reliable tools for assisting doctors in detecting coronavirus.

Ensemble-Based Bounding Box Regression for Enhanced Knuckle Localization.

The knuckle creases present on the dorsal side of the human hand can play significant role in identifying the offenders of serious crime, especially when evidence images of more recognizable biometric traits, such as the face, are not available. These knuckle creases, if localized appropriately, can result in improved identification ability. This is attributed to ambient inclusion of the creases and minimal effect of background, which lead to quality and discerning feature extraction. This paper presents an ensemble approach, utilizing multiple object detector frameworks, to localize the knuckle regions in a functionally appropriate way. The approach leverages from the individual capabilities of the popular object detectors and provide a more comprehensive knuckle region localization. The investigations are completed with two large-scale public hand databases which consist of hand-dorsal images with varying backgrounds and finger positioning. In addition to that, effectiveness of the proposed approach is also tested with a novel proprietary unconstrained multi-ethnic hand dorsal dataset to evaluate its generalizability. Several novel performance metrics are tailored to evaluate the efficacy of the proposed knuckle localization approach. These metrics aim to measure the veracity of the detected knuckle regions in terms of their relation with the ground truth. The comparison of the proposed approach with individual object detectors and a state-of-the-art hand keypoint detector clearly establishes the outperforming nature of the proposed approach. The generalization of the proposed approach is also corroborated through the cross-dataset framework.

Biological activities of two polypore macrofungi (Basidiomycota) and characterization of their compounds using HPLC-DAD and LC-ESI-MS/MS.

Members of Hymenochaetaceae fungi are among well-known macromycetes with various medicinal properties. The aim of this study was to investigate the biological activities of Phellinus tuberculosus and Fuscoporia ferruginosa collected in Iran. The antimicrobial, antioxidant, and cytotoxic activities of the two species were examined, and their phenolic and polysaccharide contents were quantified. Compounds were characterized by HPLC-DAD chromatography and LC-ESI-MS/MS spectroscopy. According to our results, the antibacterial and antioxidant effects of P. tuberculosus extracts were stronger than F. ferruginosa. Also, the effect of hydroalcoholic extracts was higher than the aqueous extract. Gram-positive bacteria were more sensitive to all extracts, especially Streptococcus mutans with a MIC of 0.7 mg/mL and MBC of 6.25 mg/mL. HPLC-DAD analyses detected gallic acid, caffeic acid, and syringic acid in both fungi. The LC-ESI-MS/MS confirmed the detected compounds in HPLC-DAD and showed the presence of several phenolic compounds such as phellifuropyranone, phelligridin, and hispidin, besides others. This study showed that F. ferruginosa and P. tuberculosus are potent medicinal fungi with antibacterial and antioxidant properties, with no toxic effect on normal HDF cells, and possess various bioactive compounds including styrylpyrone-type phenols with well-known bioactivities.

Modification of chitosan using amino acids for wound healing purposes: A review.

Chitosan, a well-known biopolymer due to its unique properties, has received considerable attention as a result of the amine group activity that locates on the backbone of chitosan. To improve the mechanical and antibacterial characteristics of chitosan, various modifications have been used. Amino acids, the monomeric units of proteins, among all other compounds have been chosen to discuss as promising materials for wound healing in combination with chitosan. This review aims to provide an up-to-date overview of the methods used for modification of chitosan and the potential biomedical application, in particular wound healing, reported in the literature during the last five years.

Tarvajeh: Word Association Norms for Persian Words.

In recent years, word associations have played a key role in cognitive research. The goal of this study is to propose a new system called Tarvajeh to construct and analyze the association norms found in the Persian language. In this paper, we present Tarvajeh, a data set for Persian words in a continuous word association task. For data collection, cue words have been categorized into groups and then, 30 of them were presented to each participant in two phases. Afterward, the participants were asked to write the first three words that are related to the initial cue words. After data collection, Tarvajeh included 240 frequent Persian cues and more than 20,000 different responses. Furthermore, we also propose a method that allows participants to compare their responses as well as their subconscious mind with those of their peers using a unique graph that is tailored to their responses and is displayed to them. Finally, we compared our data with those of associated words in other languages. The comparison reveals that, for some cues, most associated words are the same, while for others, the most frequent responses are unique within each data set. In addition, some gender-related differences were observed with male participants spending more time on their responses than female participants.

Interplay of isoform 1N4R tau protein and amyloid-ß peptide fragment 25-35 in reducing and non-reducing conditions.

Amyloid-ß (Aß) peptide and tau protein are two hallmark proteins in Alzheimer's disease (AD); however, the parameters, which mediate the abnormal aggregation of Aß and tau, have not been fully discovered. Here, we have provided an optimum method to purify tau protein isoform 1N4R by using nickel-nitrilotriacetic acid agarose chromatography under denaturing condition. The biochemical and biophysical properties of the purified protein were further characterized using in vitro tau filament assembly, tubulin polymerization assay, circular dichroism (CD) spectroscopy and atomic force microscopy. Afterwards, we investigated the effect of tau protein on aggregation of Aß (25-35) peptide using microscopic imaging and cell viability assay. Incubation of tau at physiologic and supra-physiologic concentrations with Aß25-35 for 40 days under reducing and non-reducing conditions revealed formation of two types of aggregates with distinct morphologies and dimensions. In non-reducing condition, the co-incubated sample showed granular aggregates, while in reducing condition, they formed annular protofibrils. Results from cell viability assay revealed the increased cell viability for the co-incubated sample. Therefore, the disassembling action shown by tau protein on Aß25-35 suggests the possibility that tau may have a protective role in preventing Aß peptide from acquiring the cytotoxic, aggregated form against oxidative stress damages.

ADDI: Recommending alternatives for drug-drug interactions with negative health effects.

Investigating the interactions among various drugs is an indispensable issue in the field of computational biology. Scientific literature represents a rich source for the retrieval of knowledge about the interactions between drugs. Predicting drug-drug interaction (DDI) types will help biologists to evade hazardous drug interactions and support them in discovering potential alternatives that increase therapeutic efficacy and reduce toxicity. In this paper, we propose a general-purpose method called ADDI (standing for Alternative Drug-Drug Interaction) that applies deep learning on PubMed abstracts to predict interaction types among drugs. As an application, ADDI recommends alternatives for drug-drug interactions (DDIs) which have Negative Health Effects Types (NHETs). ADDI clearly outperforms state-of-the-art methods, on average by 13%, with respect to accuracy by using only the textual content of the online PubMed papers. Additionally, manual evaluation of ADDI indicates high precision in recommending alternatives for DDIs with NHETs.

Enhanced sensitivity of VEGF detection using catalase-mediated chemiluminescence immunoassay based on CdTe QD/H2O2 system.

BACKGROUND: Since vascular endothelial growth factor (VEGF) is a significant regulator of cancer angiogenesis, it is essential to develop a technology for its sensitive detection. Herein, we sensitized a chemiluminescence (CL) immunoassay through the combination of H2O2-sensitive TGA-CdTe quantum dot (QD) as signal transduction, dextran as a cross-linker to prepare enzyme-labeled antigen and the ultrahigh bioactivity of catalase (CAT) as reporter enzyme. RESULTS: Under the optimized experimental conditions, the chemiluminescence enzyme-linked immunosorbent assay (CL-ELISA) method can detect VEGF in the excellent linear range of 2-35,000 pg mL-1, with a detection limit (S/N = 3) of 0.5 pg mL-1 which was approximately ten times lower than the commercial colorimetric immunoassay. This proposed method has been successfully applied to the clinical determination of VEGF in the human serum samples, and the results illustrated an excellent correlation with the conventional ELISA method (R2 = 0.997). The suitable recovery rate of the method in the serum ranged from 97 to 107%, with a relative standard deviation of 1.2% to 13.4%. CONCLUSIONS: The novel immunoassay proposes a highly sensitive, specific, and stable method for very low levels detection of VEGF that can be used in the primary diagnosis of tumors. With the well-designed sensing platform, this approach has a broad potential to be applied for quantitative analysis of numerous disease-related protein biomarkers for which antibodies are available.

EMDIP: An Entropy Measure to Discover Important Proteins in PPI networks.

Discovering important proteins in Protein-Protein Interaction (PPI) networks has attracted a lot of attention in recent years. Most of the previous work applies different network centrality measures such as Closeness, Betweenness, PageRank and many others to discover the most influential proteins in PPI networks. Although entropy is a well-known graph-based method in computer science, according to our knowledge, it is not used in the biology domain for this purpose. In this paper, first, we annotate the human PPI network with available annotation data. Second, we introduce a new concept called annotation-context that describes each protein according to annotation data of its neighbors. Third, we apply an entropy measure to discover proteins with varied annotation-context. Empirical results indicate that our proposed method succeeded in (1) differentiating essential and non-essential proteins in PPI networks with annotation data; (2) outperforming centrality measures in the task of discovering essential nodes; (3) predicting new annotated proteins based on existing annotation data.

Preparation of chitosan-based composites with urethane cross linkage and evaluation of their properties for using as wound healing dressing.

In this research, biocomposite films containing chitosan (CS), polyvinyl alcohol (PVA) and polyvinyl pyrrolidone (PVP), with different ratios, have been provided. The effects of adding hexamethylene 1, 6-di(aminocarboxysulfonate) (HMDACS) as cross-linking agent and the formation of urethane linkage on mechanical properties such as tensile strength, elongation and dynamic-mechanical properties such as storage modulus and tan δ were studied. Also, the antibacterial properties of the composites were investigated by viable bacterial cell counting and compared in reducing the bacterial growth. The final results showed the composite containing CS (50 wt%), PVA (30 wt%), PVP (20 wt%) and HMDACS (2 wt%) had the highest mechanical properties. Scanning electron microscopy (SEM) micrographs confirmed uniform distribution of components in the polymer matrix. In general, low contact angle values revealed the hydrophilicity of the prepared films. It was found that the composites made by combining CS, PVA and PVP at concentration of 50, 25, 25 wt% (A3) and 60, 20, 20 wt% (B4), cross-linked with 2 wt% HMDACS, had the best antibacterial activity against Escherichia coli and Staphylococcus aureus, hence they can be used as promising materials for the preparation of wound dressings.

Learning Latent Global Network for Skeleton-based Action Prediction.

Human actions represented with 3D skeleton sequences are robust to clustered backgrounds and illumination changes. In this paper, we investigate skeleton-based action prediction, which aims to recognize an action from a partial skeleton sequence that contains incomplete action information. We propose a new Latent Global Network based on adversarial learning for action prediction. We demonstrate that the proposed network provides latent long-term global information that is complementary to the local action information of the partial sequences and helps improve action prediction. We show that action prediction can be improved by combining the latent global information with the local action information. We test the proposed method on three challenging skeleton datasets and report state-of-the-art performance.

Bioluminescence Detection of Superoxide Anion Using Aequorin.

Although the superoxide anion (O2-·) is generated during normal cellular respiration and has fundamental roles in a wide range of cellular processes, such as cell proliferation, migration, apoptosis, and homeostasis, its dysregulation is associated with a variety of diseases. Regarding these prominent roles in biological systems, the development of accurate methods for quantification of superoxide anion has attracted tremendous research attention. Here, we evaluated aequorin, a calcium-dependent photoprotein, as a potential bioluminescent reporter protein of superoxide anion. The mechanism is based on the measurement of aequorin bioluminescence, where the lower the concentration of coelenterazine under the oxidation of superoxide anion, the lower the amount aequorin regeneration, leading to a decrease in bioluminescence. The bioluminescence intensity of aequorin was proportional to the concentration of superoxide anion in the range from 4 to 40 000 pM with a detection limit (S/N = 3) of 1.2 pM, which was 5000-fold lower than those of the chemiluminescence methods. The proposed method exhibited high sensitivity and has been successfully applied to the determination of superoxide anion in the plant cell samples. The results could suggest a photoprotein-based bioluminescence system as a highly sensitive, specific, and simple bioluminescent probe for in vitro detection of superoxide anion.

Impact of Implementing the Critical Care Pain Observation Tool on Nurses' Performance in Assessing and Managing Pain in the Critically Ill Patients.

BACKGROUND AND AIMS: Pain management is one of the most important responsibilities of nurses in an intensive care unit (ICU). It is difficult to perform pain assessment appropriately in patients who are unable to report their pain. This study is aimed to determine the impact of implementing the critical care pain observation tool (CPOT) on the amount and frequency of analgesics' administration in ICUs. MATERIALS AND METHODS: This interventional study was conducted in 2014. Sixty nurses and 240 patients were studied. This study was carried out in three phases: first the data about amount and frequency of analgesic administration were extracted from patients' medical files. Then the CPOT was implemented into the nursing assessment process and finally, nurses' performance regarding the amount and frequency of analgesic administration was recorded. This data obtained before and after intervention were analyzed using chi-square and independent t-test p values less than 0.05 were considered significant. RESULTS: In this interventional study, we found that there was no difference in the demography and cause of ICU admission before and after implementation of CPOT (age p = 0.937, gender p = 0.996, and the cause of admission p = 0.996). We found that after implementing the CPOT into the nursing assessment process, the amount of analgesics administered (7.95 ± 8.77 mg vs. 11.01 ± 11.04 mg, p = 0.018) and the frequency of administration (2.91 ± 1.38 vs. 4.16 ± 0.99, p <0.001) increased significantly. Moreover, there was a significant increase in the frequency of pain assessment per patient per day in nursing practice after implementation of CPOT as compared to the practice before (7.2 ± 2.48 vs. 1.03 ± 1.63, p <0.001). The mean pain scores before and after the intervention (5.5 ± 1.08 vs.2.2 ± 0.48) were also significantly different. CONCLUSION: Applying CPOT, as an objective mean of pain assessment, was effective in improving the performance of ICU nurses in assessment and management of patients' pain. It increased the amount and frequency of analgesic administration. We can recommend that COPT is a useful tool for assessment and management of pain in ICU patients and should be implemented in all ICUs. HOW TO CITE THIS ARTICLE: Modanloo M, Mohsenpour A, et al. Impact of Implementing the Critical Care Pain Observation Tool on Nurses' Performance in Assessing and Managing Pain in the Critically Ill Patients. Indian J Crit Care Med 2019;23(4):165-169.

Aequorin as a sensitive and selective reporter for detection of dopamine: A photoprotein inhibition assay approach.

Dopamine is a metabolite that plays a key role in the human body and in biomedical and diagnostic applications. Thus, the concentration of this analyte has been considered in various diseases in therapeutic drug monitoring (TDM). In the present study, for the first time, a photoprotein inhibition assay strategy was developed by utilizing aequorin for the direct detection of dopamine as a receptor and reporter simultaneously. The results showed that bioluminescence emission of aequorin was effectively quenched by increasing concentration of dopamine at the range of 1 nM to 100 µM with a detection limit of 53 nM. The viability of this method for the monitoring of dopamine in spiked biological fluids was also established and it was successfully applied for the direct determination of dopamine in a blood serum and urine without preliminary treatment with satisfactory quantitative recovery 90-95% and 82-93%, respectively. The structural investigation using circular dichroism, fluorescence spectroscopy, and docking simulation indicated that, changes in the microenvironment of aromatic residues were significant, while minor conformational alterations of the protein were observed. It seems dopamine inhibits bioluminescence activity with specific binding to the residues involved in the light production.

Nursing managers attitude toward spirituality and spiritual care in Khorasan Razavi Province hospitals in 2016.

BACKGROUND AND AIM: Spiritual care is an important part of nursing practice that seems to provide comprehensive care to patients. Nursing manager's attention to the spiritual dimension is one of the most important aspects affecting human health. Therefore, the aim of this study was to determine nursing managers' attitude to spirituality and spiritual care in hospitals in Khorasan Razavi Province in 2016. METHODS: In this cross-sectional study, 110 management nurses (8 matrons, 37 supervisors and 65 head nurses) of hospitals in Khorasan Razavi Province (2016) were recruited for the study. Data were collected using a Spirituality & Spiritual Care Rating Scale (SSCRS) questionnaire in two parts, demographic information and questions related to the research objectives. Data were collected and analyzed using SPSS 16 software and doing descriptive (frequency, mean, standard deviation) and inferential statistics (independent-samples t-test, ANOVA) and considering levels of significance (p<0.05). RESULTS: The results showed mean and standard deviation in management nursing attitude to spirituality (4.01±0.35) and spiritual care (4.03±0.78). The result showed significant differences between the attitude to spirituality and spiritual care and gender, age and work experience in nursing management (p<0.05). CONCLUSIONS: Management nursing has high level attitude to spirituality and spiritual care. Therefore, providing the perfect platform to provide this care, and for nurses to implement it in hospitals is recommended.

Learning a Deep Model for Human Action Recognition from Novel Viewpoints.

Recognizing human actions from unknown and unseen (novel) views is a challenging problem. We propose a Robust Non-Linear Knowledge Transfer Model (R-NKTM) for human action recognition from novel views. The proposed R-NKTM is a deep fully-connected neural network that transfers knowledge of human actions from any unknown view to a shared high-level virtual view by finding a set of non-linear transformations that connects the views. The R-NKTM is learned from 2D projections of dense trajectories of synthetic 3D human models fitted to real motion capture data and generalizes to real videos of human actions. The strength of our technique is that we learn a single R-NKTM for all actions and all viewpoints for knowledge transfer of any real human action video without the need for re-training or fine-tuning the model. Thus, R-NKTM can efficiently scale to incorporate new action classes. R-NKTM is learned with dummy labels and does not require knowledge of the camera viewpoint at any stage. Experiments on three benchmark cross-view human action datasets show that our method outperforms existing state-of-the-art.