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The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations.

Music plagiarism is defined as using tune, or melody that would closely imitate with another author's music without proper attributions. It may occur either by stealing a musical idea (a melody or motif) or sampling (a portion of one sound, or tune is copied into a different song). Unlike the traditional music, the Indian cinematic music is extremely popular amongst the public. Since the expectations of the public for songs that are enjoyable are high, many music directors are seeking elsewhere to "borrow" tunes. Whilst a vast majority of Indian cinemagoers may not have noticed these plagiarised tunes, some journalists and vigilant music lovers have noticed these activities. This study has taken the initiative to investigate the extent of plagiaristic activities within one Indian cinematic music industry. A list of plagiarised songs was produced by using YouTube® searches for "comparative videos" made by the vigilant music lovers about accused/detected music plagiarism. Some of these individuals were also interviewed to understand their views on this. During the investigation, it was possible to identify a vast number of plagiarised tunes, snippets, or even the full songs. In fact, some of these examples' dates to 1954, during the era when no one would have noticed plagiarism. The paper would highlight the similarities of these music files. It will also show some examples of the excuses/denial given by the composers and would try to highlight the attitudes of general public towards these types of activities.

One of the major threats in computer vison and image processing areas is image tarnishing due to noisy pixels. Image tarnishing occurs when clean images are subjected to multiple feature changes, length transmission, or due to improper image capturing. The growth of Autoencoders in Artificial Neural Networks has paved way to efficient noise removal. In this work, an architecture oriented analysis of stacked denoising autoencoders is implemented by considering two perspectives of noise, namely noise as an inverse problem and noise as a residual problem. Autoencoders have been modelled by considering the approaches: Shallow Model, Deep-Series and Parallel Series models. Autoencoders with Skip Connections and Denoising CNNs have also been implemented and analyzed in order to propose an efficient, optimized and best working model for denoising. The dataset used is Common Object in Context (COCO) and the performance of the model is evaluated based on Structural Similarity Index (SSIM) and Peak Signal to Noise Ratio (PSNR). The analysis has presented the impact of the model structures of the autoencoders, which implicitly tend to treat noise as an inverse problem. [ABSTRACT FROM AUTHOR]

Background: Significant proportion of haemodialysis (HD) patients fails to comply with their prescribed medication regimens, leading to poor health outcomes and survival rates. This highlights the need for interventions that improve medication adherence thereby leading to improved patient outcomes. Objectives: To evaluate the impact of motivational interviewing, a skilful clinical method to improve medication adherence among HD patients. Methods: A pre-post study was conducted prospectively among 63 HD patients at multicentre dialysis units. The recruited patients received three sessions of MI which helps in addressing patient beliefs around medication and overcome barriers for nonadherence. General medication adherence scale (GMAS) was used to evaluate the medication adherence among enrolled patients. An independent t-test was used to analyze the impact of MI in improving adherence among HD patients. In this study, the level of significance was 0.05. Results: A total of 63 patients (27 males and 36 females; mean age 48.5±13.9 years) were included in the study. While the mean of dialysis duration and number of medications was 7.7±6.0 years and 8.1±2.2 respectively. Paired t-test showed a significant increase in medication adherence score from a baseline for nonadherence domain of patient's attitude and nonadherence domain of additional illness and pill overload at the end of the study (p<0.05). However, the non-adherence domain of financial constraints, was not statistically significant, p=0.507. Motivational interviewing was effective in increasing intrinsic positive attitude by resolving their ambivalence thus improving the adherence towards medication. Conclusion: This study emphasized that MI conducted by pharmacist effectively improves HD patients' medication adherence. Healthcare professionals ought to employ this strategy in order to assist HD patients in better taking their medications. [ABSTRACT FROM AUTHOR]

In this paper, the stabilization and set point tracking of the utility boiler is achieved via the Proportional-Integral-Derivative (PID) controller, tuned using the versions of metaheuristic Particle Swarm Optimization (PSO) algorithms. The utility boiler model dynamics are based on the Bell and Astrom boiler turbine system. Initially, the non-linear mathematical model is obtained using the first principles data from the plant model. Further, Taylor series approximation is applied to linearize the non-linear equations around the equilibrium point to implement a stable controller for the plant. Finally, the PID controller gains for the plant model are determined using the PSO algorithms to achieve the desired response. For tuning the PID controller, three PSO algorithms namely standard PSO, Adaptive PSO (APSO) and Improved Adaptive PSO (IAPSO) are proposed. The performance of the proposed PSO based PID control algorithms are evaluated using error metrics such as Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE) and Integral Square Error (ISE). The simulation results show that the proposed APSO and IAPSO algorithms have improved performance when compared to regular PSO algorithms and are capable of optimizing the control parameters. [ABSTRACT FROM AUTHOR]

The ceria-tin/alumina mixed metal oxides (Ce/Sn =1) with different proportions of Fe & Mn dopants were synthesized and investigated in detailed approach for diesel emission reduction. The dopants created structural defects enhancing the oxygen ion mobility for exhaust treatment. The existence of surface-active oxygen sites and oxygen ion vacancy sites generated for charge compensation due to reduction of Ce4+, Sn4+ and dopants incorporation evidenced from XPS analysis. The Mn doped sample holds better physicochemical properties than Fe doped sample. The Mn doped sample with higher surface area of about 101.32 m2 g−1 exhibits greater active sites for better catalytic activity. The redox couples in the Mn-doped sample Ce4+/Ce3+, Sn4+/Sn2+, and Mn3+/Mn2+ helps in oxygen regeneration to contribute to exhaust treatment by oxygen ion conduction from bulk to the surface. This sample exhibited the 92 % of NO x reduction and proved to be a dynamic candidate for diesel emission reduction. [Display omitted] • Fe and Mn dopants insertion in CeO2-SnO2/Al2O3 sample enhances the phase stability. • Mn doped sample possesses the better physicochemical properties than Fe doped sample. • Mn doped sample shows larger surface area of 101.32 m2 g-1 with more number of active sites. • The NOx emission is drastically reduced by the Mn doped sample. [ABSTRACT FROM AUTHOR]

• Eye-blinking interfering with aqueous flare assessments can lead to inaccurate diagnosis of uveitis inflammation. • The influence of blink artifacts was examined in healthy and synthetically generated uveitis participants' data. • Implemented an LSTM deep learning model to synthesize uveitis data, based on our previous study on experimental data. • Detection and offline removal of blink artifacts using unsupervised clustering algorithms in aqueous flare measurements. • The proposed technique effectively removed blink artifacts, allowing clinicians to grade uveitis with minimal medication. Anatomically inflammation in the anterior chamber of the eye, specifically in the iris and choroid is named as anterior uveitis. For the effective management of the disease it is essential for regular monitoring. Quantifying aqueous flare as a continuous measure of the intensity of light scatter (ILS) assists in accurately evaluating inflammation. Nevertheless, there is a potential for the subject's blinking to disrupt the ILS data. This leads to increased and misleading levels of aqueous flare when assessing the extent of inflammation. Thus, our objective was to use an EOG-based spot fluorometer to examine the influence of eyeblink artifacts on ILS outcomes. This approach was founded on empirical data collected by quantifying the blink-induced and blink-artifact-free ILS in individuals with good health. A dataset of synthetic uveitis was generated using the LSTM deep learning technique. In addition, unsupervised machine learning techniques including k-means clustering, agglomerative hierarchical clustering, and Gaussian mixture clustering were used to identify blink artifacts in both the healthy and synthetic uveitis data. The optimal choice for minimizing artifacts was found to be the model that demonstrated superior performance. Our study findings indicate that the Gaussian mixture model outperformed other models in predicting blink-induced ILS, resulting in the most substantial decrease in blink artifacts. Furthermore, we successfully resolved the ILS by using our artifact removal technique, resulting in an impressive accuracy rate of 89%. The experiment verifies that our methodology successfully mitigates the occurrence of blinking errors in ILS measurements, thereby allowing a spot fluorometer to precisely grade uveitis. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19) disease has rapidly spread around the world after initial identification in Wuhan, China, in December 2019. Most common presentation is mild or asymptomatic disease, followed by pneumonia, and rarely- multiorgan failure and Acute Respiratory Distress Syndrome (ARDS). Knowledge about the pathophysiology, imaging and treatment of this novel virus is rapidly evolving due to ongoing worldwide research. Most common imaging modalities utilized during this pandemic are chest radiography and HRCT with findings of bilateral peripheral, mid and lower zone GGO and/or consolidation, vascular enlargement and crazy paving. HRCT is also useful for prognostication and follow-up of severely ill COVID-19 patients. Portable radiography allows follow-up of ICU patients & obviates the need of shifting critically ill patients and disinfection of CT room. As the pandemic has progressed, numerous neurologic manifestations have been described in COVID-19 including stroke, white matter hyperintensities and demyelination on MRI. Varying abdominal presentations have been described, which on imaging either show evidence of COVID-19 pneumonia in lung bases or show abdominal findings including bowel thickening and vascular thrombosis. Numerous thrombo-embolic and cardiovascular complications have also been described in COVID-19 including arterial and venous thrombosis, pulmonary embolism and myocarditis. It is imperative for radiologists to be aware of all the varied faces of this disease on imaging, as they may well be the first physician to suspect the disease. This article aims to review the multimodality imaging manifestations of COVID-19 disease in various organ systems from head to toe. [ABSTRACT FROM AUTHOR]