Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification

Introduction: Essential genes are essential for the survival of various species. These genes are a family linked to critical cellular activities for species survival. These genes are coded for proteins that regulate central metabolism, gene translation, deoxyribonucleic acid replication, and fundamental cellular structure and facilitate intracellular and extracellular transport. Essential genes preserve crucial genomics information that may hold the key to a detailed knowledge of life and evolution. Essential gene studies have long been regarded as a vital topic in computational biology due to their relevance. An essential gene is composed of adenine, guanine, cytosine, and thymine and its various combinations.Methods: This paper presents a novel method of extracting information on the stationary patterns of nucleotides such as adenine, guanine, cytosine, and thymine in each gene. For this purpose, some co-occurrence matrices are derived that provide the statistical distribution of stationary patterns of nucleotides in the genes, which is helpful in establishing the relationship between the nucleotides. For extracting discriminant features from each co-occurrence matrix, energy, entropy, homogeneity, contrast, and dissimilarity features are computed, which are extracted from all co-occurrence matrices and then concatenated to form a feature vector representing each essential gene. Finally, supervised machine learning algorithms are applied for essential gene classification based on the extracted fixed-dimensional feature vectors.Results: For comparison, some existing state-of-the-art feature representation techniques such as Shannon entropy (SE), Hurst exponent (HE), fractal dimension (FD), and their combinations have been utilized.Discussion: An extensive experiment has been performed for classifying the essential genes of five species that show the robustness and effectiveness of the proposed methodology

Discovery of a massive giant planet with extreme density around a sub-giant star TOI-4603

We present the discovery of a transiting massive giant planet around TOI-4603, a sub-giant F-type star from NASA's Transiting Exoplanet Survey Satellite (TESS). The newly discovered planet has a radius of $1.042^{+0.038}_{-0.035}$ $R_{J}$, and an orbital period of $7.24599^{+0.00022}_{-0.00021}$ days. Using radial velocity measurements with the PARAS {and TRES} spectrographs, we determined the planet's mass to be $12.89^{+0.58}_{-0.57}$ $M_{J}$, resulting in a bulk density of $14.1^{+1.7}_{-1.6}$ g ${cm^{-3}}$. This makes it one of the few massive giant planets with extreme density and lies in the transition mass region of massive giant planets and low-mass brown dwarfs, an important addition to the population of less than five objects in this mass range. The eccentricity of $0.325\pm0.020$ and an orbital separation of $0.0888\pm0.0010$ AU from its host star suggest that the planet is likely undergoing high eccentricity tidal (HET) migration. We find a fraction of heavy elements of $0.13^{+0.05}_{-0.06}$ and metal enrichment of the planet ($Z_{P}/Z_{star}$) of $4.2^{+1.6}_{-2.0}$. Detection of such systems will offer us to gain valuable insights into the governing mechanisms of massive planets and improve our understanding of their dominant formation and migration mechanisms.Comment: accepted for publication in A&A Letter

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Background: Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke. Methods: We did a double-blind, randomised, placebo-controlled trial in 610 sites across 28 countries. We randomly assigned patients aged 40 years and older with type 2 diabetes and cardiovascular disease (at a 1:1 ratio) to groups that either received a subcutaneous injection of albiglutide (30–50 mg, based on glycaemic response and tolerability) or of a matched volume of placebo once a week, in addition to their standard care. Investigators used an interactive voice or web response system to obtain treatment assignment, and patients and all study investigators were masked to their treatment allocation. We hypothesised that albiglutide would be non-inferior to placebo for the primary outcome of the first occurrence of cardiovascular death, myocardial infarction, or stroke, which was assessed in the intention-to-treat population. If non-inferiority was confirmed by an upper limit of the 95% CI for a hazard ratio of less than 1·30, closed testing for superiority was prespecified. This study is registered with ClinicalTrials.gov, number NCT02465515. Findings: Patients were screened between July 1, 2015, and Nov 24, 2016. 10 793 patients were screened and 9463 participants were enrolled and randomly assigned to groups: 4731 patients were assigned to receive albiglutide and 4732 patients to receive placebo. On Nov 8, 2017, it was determined that 611 primary endpoints and a median follow-up of at least 1·5 years had accrued, and participants returned for a final visit and discontinuation from study treatment; the last patient visit was on March 12, 2018. These 9463 patients, the intention-to-treat population, were evaluated for a median duration of 1·6 years and were assessed for the primary outcome. The primary composite outcome occurred in 338 (7%) of 4731 patients at an incidence rate of 4·6 events per 100 person-years in the albiglutide group and in 428 (9%) of 4732 patients at an incidence rate of 5·9 events per 100 person-years in the placebo group (hazard ratio 0·78, 95% CI 0·68–0·90), which indicated that albiglutide was superior to placebo (p<0·0001 for non-inferiority; p=0·0006 for superiority). The incidence of acute pancreatitis (ten patients in the albiglutide group and seven patients in the placebo group), pancreatic cancer (six patients in the albiglutide group and five patients in the placebo group), medullary thyroid carcinoma (zero patients in both groups), and other serious adverse events did not differ between the two groups. There were three (<1%) deaths in the placebo group that were assessed by investigators, who were masked to study drug assignment, to be treatment-related and two (<1%) deaths in the albiglutide group. Interpretation: In patients with type 2 diabetes and cardiovascular disease, albiglutide was superior to placebo with respect to major adverse cardiovascular events. Evidence-based glucagon-like peptide 1 receptor agonists should therefore be considered as part of a comprehensive strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. Funding: GlaxoSmithKline

Unsupervised Learning for Feature Representation Using Spatial Distribution of Amino Acids in Aldehyde Dehydrogenase (ALDH2) Protein Sequences

Aldehyde dehydrogenase 2 (ALDH2) enzyme is required for alcohol detoxification. ALDH2 belongs to the aldehyde dehydrogenase family, the most important oxidative pathway of alcohol digestion. Two main liver isoforms of aldehyde dehydrogenase are cytosolic and mitochondrial. Approximately 50% of East Asians have ALDH2 deficiency (inactive mitochondrial isozyme), with lysine (K) for glutamate (E) substitution at position 487 (E487K). ALDH2 deficiency is also known as Alcohol Flushing Syndrome or Asian Glow. For people with an ALDH2 deficiency, their face turns red after drinking alcohol, and they are more susceptible to various diseases than ALDH2-normal people. This study performed a machine learning analysis of ALDH2 sequences of thirteen other species by comparing them with the human ALDH2 sequence. Based on the various quantitative metrics (physicochemical properties, secondary structure, Hurst exponent, Shannon entropy, and fractal dimension), these fourteen species were clustered into four clusters using the unsupervised machine learning (K-means clustering) algorithm. We also analyze these species using hierarchical clustering (agglomerative clustering) and draw the phylogenetic trees. The results show that Homo sapiens is more closely related to the Bos taurus and Sus scrofa species. Our experimental results suggest that the testing for discovering medicines may be done on these species before being tested in humans to alleviate the impacts of ALDH2 deficiency

PRMxAI: protein arginine methylation sites prediction based on amino acid spatial distribution using explainable artificial intelligence

Abstract Background Protein methylation, a post-translational modification, is crucial in regulating various cellular functions. Arginine methylation is required to understand crucial biochemical activities and biological functions, like gene regulation, signal transduction, etc. However, some experimental methods, including Chip–Chip, mass spectrometry, and methylation-specific antibodies, exist for the prediction of methylated proteins. These experimental methods are expensive and tedious. As a result, computational methods based on machine learning play an efficient role in predicting arginine methylation sites. Results In this research, a novel method called PRMxAI has been proposed to predict arginine methylation sites. The proposed PRMxAI extract sequence-based features, such as dipeptide composition, physicochemical properties, amino acid composition, and information theory-based features (Arimoto, Havrda-Charvat, Renyi, and Shannon entropy), to represent the protein sequences into numerical format. Various machine learning algorithms are implemented to select the better classifier, such as Decision trees, Naive Bayes, Random Forest, Support vector machines, and K-nearest neighbors. The random forest algorithm is selected as the underlying classifier for the PRMxAI model. The performance of PRMxAI is evaluated by employing 10-fold cross-validation, and it yields 87.17% and 90.40% accuracy on mono-methylarginine and di-methylarginine data sets, respectively. This research also examines the impact of various features on both data sets using explainable artificial intelligence. Conclusions The proposed PRMxAI shows the effectiveness of the features for predicting arginine methylation sites. Additionally, the SHapley Additive exPlanation method is used to interpret the predictive mechanism of the proposed model. The results indicate that the proposed PRMxAI model outperforms other state-of-the-art predictors

Geriatric Goalposts: Of Independence And Interdependence

Our elder population has a unique set of needs and necessities, challenges and concerns. This reflects in the approach of geriatric medicine, which aims to ensure functional freedom and independence, as well as healthy ageing, of older citizens. We propose another, higher, aim of geriatric medicine: that is interdependence. This creates a spirit of optimism, in persons of geriatric age group as well as in their health care providers, who are able to interpret goals of medical care in a broader perspective. Keywords: ADL, geriatrics, gerontology, independence, interdependence, Person centred care

Management of an anomalous maxillary lateral incisor fused with a supernumerary tooth and a coronal dens invaginatus

An abnormal union of two or more tooth germs in the development process results in fusion of teeth. Such clinical situations present a diagnostic dilemma and a challenge in treatment planning. This article reports the endodontic and esthetic management of an atypical permanent maxillary lateral incisor fused with a supernumerary tooth and a coronal dens invaginatus. A 22-year-old female reported an abnormally large and discolored permanent maxillary left lateral incisor (#22). Cone-beam computed tomographic evaluation revealed a complex, labiolingually thin ribbon-shaped canal system in the central portion interconnected with two other canals along with a coronal dens invaginatus. A 2-year follow-up demonstrated satisfactory clinical and radiographic outcomes after the endodontic therapy and a veneer placement on the concerned tooth

A Study of Neurodevelopemental Outcome in Hyperbilirubinaemic Neonates Admitted in NICU

Introduction: Hyperbilirubinaemia may be toxic to the developing central nervous system and may cause neurological impairment. The developing brain of premature babies is extremely vulnerable to injury. With increased level of bilirubin, the risk for neurodevelopmental deficit increases with decreasing gestational age and birth weight resulting in relatively high risk of cerebral palsy, developmental delay, hearing and vision impairment and subnormal academic achievement. Aim: This study was conducted to identify factors and pattern of abnormal neurodevelopment at three and 12 months in babies having birth weight >1.5 Kg and gestational age >34 weeks with neonatal hyperbilirubinaemia. Materials and Methods: This prospective study was conducted at Sardar Patel Medical College, Bikaner (Rajasthan), India, from 2014 to 2015. Hyperbilirubinaemia in newborns were examined at three month and 12 month age and their neurodevelopmental assessment done by DASI method. All the collected data was tabulated and stastically analysed by using SPSS software. Results: Out of 96, 67 (69.79%) of hyperbilirubinaemic neonates were males and 29 (30.21%) were females. The prevalence of neurodevelopmental abnormalities (DQ=70) was 10.42% at three months where as it was 6.25% at 12 months follow-up. Early onset of jaundice (=1 day), serum bilirubin level >25 mg/dL, duration of hospital stay >3 days and requirement of exchange transfusion was significantly associated with adverse neurodevelopmental outcomes (DQ=70) at three and 12 months of age. Conclusion: This study found a high prevalence of adverse neurodevelopmental outcome in neonates with hyperbilirubinaemia. Early detection of neurodevelopmental abnormalities and initiation of early intervention measures to reduce the prevalence of neurodevelopmental abnormalities in hyperbilirubinaemic neonates

Ultrasound elastography is a useful adjunct to conventional ultrasonography and needle aspiration in preoperative prediction of malignancy in thyroid nodules: A Northern India perspective

Introduction: Data on ultrasound elastography (USE) are scant from India. This study aimed to compare the sensitivity and specificity of USE with thyroid ultrasonography (USG) and fine-needle aspiration (FNA) as preoperative predictor of malignancy, using postoperative histopathology as gold standard. Materials and Methods: Consecutive patients with thyroid swelling/goiter underwent thyroid USG followed by USE. Patients with pure cystic nodules or eggshell calcification were excluded. Patients with nodules >10 mm with one or more high-risk USG features underwent FNA. Patients with no USG high-risk features, benign score on USE, and benign FNA were conservatively followed. All other patients underwent thyroidectomy. Results: 246 consecutive patients underwent USG. Data from 97 patients (117 nodules) were analyzed. Median age of patients was 43 years with 85.4% females. All patients with USE score-1 had benign USG and FNA characteristics. Of 86 nodules having USE score-2, 18.6% nodules were hypoechoic and 16.28% had microcalcification. Hypoechogenicity and microcalcifications were observed in 66.67% nodules with USE score-3. All nodules with USE score-4 and 5 were hypoechoic and had microcalcifications. Histopathology was benign in 84 and malignant in 33 patients. Occurrence of malignancy in USE scores 1–5 was 0, 4.65, 100, 90.5, and 100%, respectively. All eight nodules with diagnosis of follicular adenoma had preoperative USE score-2. The sensitivity of preoperative USG, USE, and FNA in picking up malignancy was 66.67, 87.88, and 69.70%, respectively. Specificity of USG, USE, and FNA in detecting thyroid malignancy was 88.10, 100, and 97.6%, respectively. False positivity rates for USG, USE, and FNA in diagnosing thyroid malignancy was 11.9, 0, and 2.4%, respectively. The overall diagnostic accuracy of USG, USE, and FNA cytology in this study was 82.05, 96.58, and 89.74%, respectively. Conclusion: USE may be better than USG for preoperative detection of malignancy in thyroid nodules