Miracle-dz/UniFAR-FTUs huggingface dataset:
Polymer-Coated Fiber Optic Sensor as a Process Analytical Tool for Biopharmaceutical Impurity Detection

Large-scale purification of monoclonal antibodies using Protein A affinity chromatography continues to be one of the dominant processes in the production of biopharmaceuticals. In order to reduce the processing cost, ensure the purity of the product, and enable safe resin reuse, it is important to (a) estimate the quality of these resins, and (b) quantify the process-related impurities. This article presents the possibility of using a sensitive, rugged optical system as a process analysis tool for the assessment of resin fouling by quantification of the impurities like Protein A, host cell proteins (HCPs), and histones present in the elute. We have developed a conducting polymer-coated fiber optic sensor to detect these impurities, a flow cell to bypass the column elute directly toward the sensor, and associated electronics to make it a portable device for onsite applications. Protein A impurities (3 to 100 ng/mL) have been measured in pure buffer samples, while HCPs (100 to 350 ng/mL) and histone impurities (5 ng/mL to $1~\mu \text{g}$ /mL approximately) were detected in composite elute samples. In comparison to enzyme linked immunosorbent assay (ELISA)-based techniques, this low-cost, user-friendly sensing device demonstrates fast detection and high sensitivity for real-time monitoring of all process-related impurities.
Online Robot Kinematic Calibration Using Hybrid Filter With Multiple Sensors

With the development of Industry 4.0, future robots will no longer be fixed on the production line for doing repetitive tasks but will be adaptable and versatile. This means that they can correct their own parameters almost instantaneously. However, traditional offline calibration methods cannot meet such requirements. This article proposes an online robot calibration method that can provide quickly identify robotic kinematic parameters without stopping the robot, thereby greatly improving the operating efficiency of the robot. The method can also play a role in improving position accuracy even if the robot works in a complicated and fickle environment. The proposed method applies an inertial measurement unit (IMU) and a position marker fixed on the robot tool to measure the robot’s orientation and position. A hybrid filter, which integrates the unscented Kalman filter (UKF) and iterative particle filter (IPF), is developed to further improve the measurement accuracy of the robotic orientation and position, respectively. UKF is also adopted to reckon the errors of the robotic kinematic parameters in order to obtain better results. Particularly, the proposed method does not cover the complicated procedures, such as image capturing or laser tracking for improving the accuracy and efficiency of kinematic identification. To testify this method, a series of experiments have been performed. The experimental results indicate that the proposed method shows autonomy and precision in the robotic calibration process.
Clothing Image Detection and Recognition Based on Faster R-CNN

Clothing image detection and recognition algorithm is proposed in this paper, which involves the design of two neural network models based on improved Faster R-CNN [1]. In our work, in order to train neural networks, ModaNet [2] dataset is selected and improvements on it are made, that is, the improper bounding boxes of footwear and boots in ModaNet dataset are modified based on their polygon annotations. Moreover, by making the following improvements on Faster R-CNN, our two neural network models are established, which use VGG16[3] and the first 40 layers of ResNet50 [4] as feature extration network, respectively. When using ResNet50 for feature extraction, the last 10 layers of ResNet50 are selected as part of the final classification and regression network. In addition, the softmax activation function in RPN is replaced with sigmoid. Through experimental comparison, it is found that both of these two models can well accomplish the task.
AB-SMOTE: An Affinitive Borderline SMOTE Approach for Imbalanced Data Binary Classification

—SMOTE is an oversampling approach previously proposed to solve the imbalanced data binary classification problem. SMOTE managed to improve the classification accuracy, however it needs to generate large number of synthetic instances, which is not efficient in terms of memory and time. To overcome such drawbacks, the Borderline-SMOTE (BSMOTE) is previously proposed to minimize the number of generated synthetic instances by generating such instances based on the borderline between the majority and minority classes. Unfortunately, BSMOTE could not provide big savings regarding the number of generated instances, trading to the classification accuracy. To improve BSMOTE accuracy, this paper proposes an Affinitive Borderline SMOTE (AB-SMOTE) that leverages the BSMOTE, and improves the quality of the generated synthetic data by taking into consideration the affinity of the borderline instances. Experiments’ results show the AB-SOMTE, when compared with BSMOTE, managed to produce the most accurate results in the majority of the test cases adopted in our study.
A Tool for Suggesting Ayurvedic Remedies from Curated and Classified Clinical Trial Reports

It requires great effort to search through huge number of published articles that provide information we need. Therefore it is necessary to find a solution that helps researchers in gaining accurate and deep understanding about diseases. Thus drug discovery and drug repurposing are gaining significance with the current onics tools. Traditional Medical practices like Ayurveda needs to be more visible to practitioners with evidence based approach. The clinical trials conducted have to be shared with the world for attaining the very philosophy of Ayurveda. This paper presents a survey on various text mining technologies developed to classify theories and literature pertaining to the clinical observations of practitioners and suggests a possible solution to match a patient’s symptoms.
Toward a Novel Pharmacology and Therapeutic Understanding of Brazilian Propolis: A Meta-Analytical Approach

Cite this article: Sousa ARS, Moraes SZC, Viana-Junior AB, Araujo ED. Toward a Novel Pharmacology and Therapeutic Understanding of Brazilian Propolis: A Meta-Analytical Approach. Pharmacog Rev. 2020;14(27):1-7. ABSTRACT This review article aims to use a meta-analytical approach to systematize and compare the main effects of green and red propolis, as determined through biological assays, in order to inform future preclinical tests and drug development for this natural product. A search for the full spectrum of their biological properties and possible pharmacological and medical applications found that propolis has anti-inflammatory, antioxidant, antiviral, antibacterial, antifungal, antiparasitic, anti-proliferative, anticarcinogenic, antinociceptive and neuroprotective effects, arising mainly from polyphenolic compounds. In order to identify and retrieve the most important published literature in this field in the last ten years a database search (LILACS, PubMed, Scopus and Web of Science) was performed. The MeSH terms and free text words used were: green propolis, red propolis, anti-inflammatory, antioxidant, antimicrobial, antiviral, antibacterial, anti-fungal, anti-parasitic, anti-carcinogenic, anti-proliferative, cytotoxicity, analgesic, anti-nociceptive, neuroprotective and neuroregenerative as well as their respective combinations. The meta-analysis outcomes showed that red propolis is superior to green propolis for healing, cytotoxic, antiparasitic and antibacterial applications; however, there are no significant differences between them as regards antifungal, antioxidant and antiinflammatory activities. Furthermore, it was found that red propolis contains greater amounts of flavonoids than green propolis, and that the supercritical extraction method was better in relation to phenolic acid yields, whereas the ethanolic method was better in respect of flavonoids. The key findings of this study can help to direct future work on this natural product in the pharmacological and medical fields.
Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail

This paper proposes a novel forecasting method that combines the deep learning method – long short-term memory (LSTM) networks and random forest (RF). The proposed method can model complex relationships of both temporal and regression type which gives it an edge in accuracy over other forecasting methods. We evaluated the new method on a real-world multivariate dataset from a multi-channel retailer. We benchmark the forecasting performance of the new proposition against neural networks, multiple regression, ARIMAX, LSTM networks, and RF. We employed forecasting performance metrics to measure bias, accuracy, and variance, and the empirical evidence suggests that the new proposition is (statistically) significantly better. Furthermore, our method ranks the explanatory variables in terms of their relative importance. The empirical evaluations are replicated for longer forecasting horizons, and online and offline channels and the same conclusions hold; thus, advocating for the robustness of our forecasting proposition as well as the suitability in multi-channel retail demand forecasting.
DEVELOPMENT AND UTILIZATION OF BAYESIAN PROGNOSTIC MODELS IN A LEFT VENTRICULAR ASSIST DEVICE DECISION SUPPORT TOOL

Heart failure is a chronic, progressive condition that affects over 6 million Americans. The gold standard treatment for advanced heart failure is heart transplant. However, when a donor heart is not available, or the patient is not eligible, patients may receive a mechanical circulatory support device such as a left ventricular assist device (LVAD). LVADs can improve patient survival and increase patient quality of life but they also require significant changes in lifestyle and carry with them risks of adverse events, such as re-hospitalization, gastrointestinal bleeding (GI), stroke, or right heart failure. LVAD decision making for physicians and patients requires extensive discussion of the trade-off between benefits, risks, and associated lifestyle changes. Decision support tools for patients and their caregivers are in development but are not personalized and are limited to general educational information. Using Bayesian modeling, a machine learning method of data analysis, I developed novel predictive models for three sets of LVAD outcomes: all-cause mortality, recurrent gastrointestinal (GI) bleeding, and pump-dependent ischemic stroke. The mortality models performed better than current risk scores with receiver operating characteristic area under the curve (ROC AUC) of 70-71% in a multi-center validation cohort and 76-79% in a contemporary single-center study. The recurrent GI bleeding models performed with ROC AUCs of 68% and 60%, revealed the importance of hemoglobin/hematocrit levels and inflammation in driving risk, and are the first models for this outcome. The ischemic stroke models out-performed the current ischemic risk score with ROC AUCs of 64-66%. In addition to model development, I explored how to present prognostic information to decision making stakeholders: physicians, patients, and caregivers. I accomplished this with three studies: pilot testing the usability of an online application for physicians, surveying potential LVAD patients’ interest in healthcare engagement, and comparing the interpretation of prognostic information in different visual formats between patients and the general population. The results of these studies indicated that survival predictions are the most important outcome in decision making; patient numeracy is a key determinant of decision making engagement; and use of line graphs to present prognostic information is well-suited to all stakeholders.
Assessment of Low Level Diode Laser Therapy versus Arthrocentesis in Management of Temporomandibular Joint Internal Derangement.

Introduction/Aims: Many nonsurgical therapies have been suggested for the management of Disc derangement disorders such as physical therapy, pharmacologic therapy, through psychological counselling, cognitive behavioural therapy, occlusal splints and acupuncture. Among physical therapy procedures, low level laser therapy (LLLT) has recently been proposed to reduce symptoms and improve function in TMD patients. The aim of this study is to evaluate effectiveness of Low level diode laser therapy as a treatment modality in temporomandibular joint internal derangement. Patients/Methods: Thirty six patients presented with jaw pain, clicking and limited mouth openings were involved in this study. Clinical, radiographic and magnetic resonance imaging (MRI) were performed to exclude muscular, hyperplastic or degenerative causes. They were divided randomly into two groups. group I, subjected to arthrocentesis and group II, subjected to ten sessions of LLLT on fixed predetermined points. Results: Statistical analysis has shown a significant improvement in both groups after six month regarding Pain, range of mandibular movements and clicking. In comparison between both groups the change in maximal mouth opening (MMO) and clicking was higher in arthrocentesis group than LLLT group.
The Need Analysis of Participation in an English Immersion Village at Kampung Inggris Pare

The privilege of English as an international lingua franca force many countries to open a wider opportunity for society to learn the language. However, the unbalance between societal needs and the role of English as a foreign language in Indonesia sometimes find the obstacles in the process of learning. This paper reveals the learners’ initial perception of English and analyzes the need for participation in an English immersion program. 24 participants from six English institutions contributed to this qualitative study. The data collected through participant observation and semi-structured interviews for three months in Kampung Inggris Pare, Kediri, East Java, Indonesia. Most of the learners had a negative perception of English before participating in this immersion program that they perceived this language was difficult. This perception is associated with negative experiences during the process of learning English at school. However, their awareness of low self-confidence and poor speaking performance in facing globalization invited them to participate in an English immersion village in Kampung Inggris Pare. This paper concludes that the necessities, the lacks, and students' desire underlie the need analysis of participating in an English immersion program in Kampung Inggris Pare.
A Complex Association of Invertebrates, Vertebrates and Trace Fossils in the Marine Camacho Formation (Late Miocene of Uruguay): Biostratigraphy and Paleoenvironments

Abstract. A complex fossil association from the San Pedro Member of the Camacho Formation (late Miocene) in Puerto Arazatí, San José department, southern Uruguay, is documented in this paper. Situated at the base of the coastal cliffs, this association is characterized by many land mammals, trace fossils indicative of the Psilonichnus Ichnofacies, and irregularly distributed oyster bioherms. The trace fossils are interpreted as crab galleries, which represent a clear marginal, or even intertidal to supratidal episode within the overall marine environment recognized for the Camacho Formation. Episodic subaerial exposure of the deposits under consideration is inferred by the presence of the Psilonichnus Ichnofacies and a set of trace fossils assigned to sarcosaprophagous insects, found in the skeleton of some specimens of glyptodonts. Finally, a review of the mastofauna and the mollusks from the main localities of the San Pedro Member was conducted. This study permits redefining the biostratigraphy of this unit and confirms its late Miocene age.
The Silence of the Muses in Sidonius Apollinaris (Carm. 12–13, Ep. 8.11): Aphasia and the Timelessness of Poetic Inspiration

Abstract:When the Muses and Apollo fall silent for Sidonius, this signifies neither a general silence of poetry in Late Antiquity nor a lasting silence on the poet's own part. It is paradoxically the "silence of the Muse," the trope for the poet's loss of speech, that most clearly displays the gamut of the emotions of the poetic persona. The "inward turn" and psychological approach will be used to show that Sidonius, in discourse with the changing voices of the Muses, created a series of self-portraits—petitioner of Majorian, host to the Burgundians, a human suffering deeply after the murder of Lampridius—that prove instrumental in explaining himself and the world. Using his allegory of the "silent Muse" and the literary technique of allusion, Sidonius evokes a multi-layered world of imagery that enables him to overcome his traumas and make his readers into spectators alert and responsive to the troubles of their epoch.
How Corporate Social Responsibility Influences Business Model Innovation: The Mediating Role of Organizational Legitimacy

Business model (BM) innovation driven by corporate social responsibility (CSR) has attracted considerable attention from scholars. However, the understanding of whether and how CSR influences BM innovation is limited. Therefore, this paper aimed to fill these gaps by exploring the influence of CSR on BM innovation through the mediating role of organizational legitimacy (OL). This paper proposed research hypotheses on the relationships among CSR, OL, and BM innovation and empirically tested these hypotheses by using the hierarchical regression analysis method with data collected from 186 firms. The results of this study show that both CSR and OL positively influence BM innovation. The results also show that CSR positively influences OL and OL mediates the relationship between CSR and BM innovation. This paper provides new insights into the relationship between CSR and BM innovation by answering questions of whether and how CSR influences BM innovation. This paper may help managers better understand how to link CSR and BM innovation.
Effects of Nanoparticle Additives on Spray Characteristics of Liquid Jets in Gaseous Crossflow

Nanofluids are attracting attention as future energy carriers owing to their high performance for improving combustion and heat transfer. In this study, the macroscopic characteristics of nanofluid jets in a subsonic gaseous crossflow were investigated by focusing on the influence of nanoparticle additives on the breakup process. Based on a distribution map of the image grayscale standard deviation, we propose an improved method to process transverse injection shadowgraphs. A simplified model of the transition mechanism from column breakup to surface breakup at a small Weber number was established. The effects of nanoparticles on the jet trajectory and column fracture position were analyzed according to the deviations from the pure liquid. To interpret the effects of the nanoparticles, a new nondimensional parameter was introduced into the empirical correlation of the column fracture position. The results indicated that at low concentrations of nanoparticles, the surface tension of the nanofluids increased slightly, while the viscosity increased significantly (by up to 23%). These changes in the physical properties had little effect on the breakup regimes or jet trajectory. Moreover, the nanoparticles promoted cavitation inside the liquid column, resulting in an additional primary breakup mode for the nanofluids. Consequently, the length of the column fracture was reduced by up to 20% compared with that of the basic fluid.
Strategyproof Reinforcement Learning for Online Resource Allocation

We consider an online resource allocation problem where tasks with specific values, sizes and resource requirements arrive dynamically over time, and have to be either serviced or rejected immediately. Reinforcement learning is a promising approach for this, but existing work on reinforcement learning has neglected that task owners may misreport their task requirements or values strategically when this is to their benefit. To address this, we apply mechanism design and propose a novel mechanism based on reinforcement learning that aims to maximise social welfare, is strategyproof and individually rational (i.e., truthful reporting and participation are incentivised). In experiments, we show that our algorithm achieves results that are typically within 90% of the optimal social welfare, while outperforming approaches that use fixed pricing (by up to 86% in specific cases).
Dynamically Operated Fischer-Tropsch Synthesis in PtL-Part 1: System Response on Intermittent Feed

Society is facing serious challenges to reduce CO2 emissions. Effective change requires the use of advanced chemical catalyst and reactor systems to utilize renewable feedstocks. One pathway to long-term energy storage is its transformation into high quality, low-emission and CO2-neutral fuels. Performance of technologies such as the Fischer-Tropsch reaction can be maximized using the inherent advantages of microstructured packed bed reactors. Advantages arise not only from high conversion and productivity, but from its capability to resolve the natural fluctuation of renewable sources. This work highlights and evaluates a system for dynamic feed gas and temperature changes in a pilot scale Fischer-Tropsch synthesis unit for up to 7 L of product per day. Dead times were determined for non-reactive and reactive mode at individual positions in the setup. Oscillating conditions were applied to investigate responses with regard to gaseous and liquid products. The system was stable at short cycle times of 8 min. Neither of the periodic changes showed negative effects on the process performance. Findings even suggest this technology’s capability for effective, small-to-medium-scale applications with periodically changing process parameters. The second part of this work focuses on the application of a real-time photovoltaics profile to the given system.
Stem cell therapy and regenerative medicine in RPE degenerative disease: advances and challenges

ABSTRACT Introduction: Retinal degenerative disorders affect patients of all age groups and cause devastating, irreversible vision loss. To date there is no effective treatment capable of restoring retinal function. Pluripotent stem cells, like embryonic stem cells and induced pluripotent stem cells, have the capacity to differentiate into any cell derived from the three germinal layers, including retinal tissue. Considering that, research in transplanted retinal cells derived from pluripotent stem cells has shown progressive results. Areas covered: The article discusses recent relevant clinical trials in retinal pigment epithelium-derived embryonic stem cells and -induced pluripotent stem cells, as well as challenges stem cell research faces from bench to bed side. Expert commentary: Therapies involving stem cells and regenerative medicine have the potential to restore sight and revolutionize the treatment of retinal disease. Clinical trials show promising results. Research surrounding obstacles and challenges is underway.
Tachycardia-induced cardiomyopathy: a review of literature on clinical status and meta-analysis of diagnosis and clinical management method

Abbreviations: AF: Atrial Fibrillation; ANP: Atrial Natriuretic Peptide; ART: Atrioventricular Reentrant Tachycardia; AV: Atrioventricular; AVNRT: Atrioventricular Nodal Re-entrant Tachycardia; BNP: B-Type Natriuretic Peptide; BPM: Beats per Minute; CHF: Congestive Heart Failure; CRT: Cardiac Resynchronization Therapy; IHD: Ischemic Heart Disease; LV: Left Ventricular; LVEDV: Left Ventricular End-Diastolic Volume;LVEF: Left Ventricular Ejection Fraction; PJRT: Permanent Junctional Reciprocating Tachycardia; PV: Pulmonary Vein; PVC: Persistent Ventricular Ectopy; RAAS: Renin-Angiotensin-Aldosterone System; RF: Radiofrequency; RV: Right Ventricular; RVEDV: Right Ventricular End-Diastolic Volume; RVEF: Right Ventricular Ejection Fraction; RVOT: Right Ventricular Outflow Tract; TIC: Tachycardia-Induced Cardiomyopathy; VES: Ventricular Extrasystoles; VT: Ventricular Tachycardia