Search

Background: Universal spinal muscular atrophy (SMA) newborn screening was implemented in California on June 24, 2020. Objective: We describe California’s experience with the first 18 months of SMA newborn screening, including our assay methodology, timeliness of screening and follow-up milestones, and clinical and epidemiological outcomes observed. Methods: Dried blood spots are screened for SMA using multiplex real time polymerase chain reaction (RT-PCR) to detect deletions of exon 7 in the survival of motor neuron 1 (SMN1) gene. Short-term follow-up data is collected from clinical staff via an online data collection tool. Results: In the first 18 months, 628,791 newborns from California’s diverse population were tested for SMA. Thirty-four screened positive and were confirmed to have the disorder. Infants were referred, diagnosed, and treated at a median of 8, 12, and 33 days of life, respectively. Nearly all infants received the desired treatment modality, and 62% received treatment while still asymptomatic. Conclusions: SMA newborn screening is a highly sensitive and specific test which identifies infants with SMA early when treatment is most effective. Even with newborn screening’s success in facilitating early intervention, there is still work to be done to expedite treatment, especially for infants with the most severe form of the disease.

With the advent of healthcare-based genotyped biobanks, genome-wide association studies (GWAS) leverage larger sample sizes, incorporate patients with diverse ancestries and introduce noisier phenotypic definitions. Yet the extent and impact of phenotypic misclassification on large-scale datasets is not currently well understood due to a lack of statistical methods to estimate relevant parameters from empirical data. Here, we develop a statistical method and scalable software, PheMED,PhenotypicMeasurement ofEffectiveDilution, to quantify phenotypic misclassification across GWAS using only summary statistics. We illustrate how the parameters estimated by PheMED relate to the negative and positive predictive value of the labeled phenotype, compared to ground truth, and how misclassification of the phenotype yields diluted effect-sizes of variant-phenotype associations. Furthermore, we apply our methodology to detect multiple instances of statistically significant dilution in real-world data. We demonstrate how effective dilution biases downstream GWAS replication and heritability analyses despite utilizing current best practices, and provide a dilution-aware meta-analysis approach that outperforms existing methods. Consequently, we anticipate that PheMED will be a valuable tool for researchers to address phenotypic data quality issues both within and across cohorts.

We describe the Predicting Protein Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between nearly 7 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence and structural similarity-based metrics are established between template proteins in the Protein Data Bank, T, that bind small molecules, C, and proteins in the models database, Q. When these metrics pass a sequence threshold value, it is assumed that C also binds to Q with a probability derived from machine learning. If the relationship is based on structure, this probability is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto Coefficient identifies other small molecules that may bind to Q. A likelihood ratio for the binding of C to Q is obtained from naïve Bayesian statistics. The PrePCI algorithm performs well under different validations. It can be queried by entering a UniProt ID for a protein and obtaining a list of compounds predicted to bind to it along with associated probabilities. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database are described and a strategy is introduced to use PrePCI as a first step in a docking screen.

The generation volatility of photovoltaics (PVs) has created several control and operation challenges for grid operators. For a secure and reliable day or hour-ahead electricity dispatch, the grid operators need the visibility of their synchronous and asynchronous generators’ capacity. It helps them to manage the spinning reserve, inertia and frequency response during any contingency events. This study attempts to provide a machine learning-based PV power generation forecasting for both the short and long-term. The study has chosen Alice Springs, one of the geographically solar energy-rich areas in Australia, and considered various environmental parameters. Different machine learning algorithms, including Linear Regression, Polynomial Regression, Decision Tree Regression, Support Vector Regression, Random Forest Regression, Long Short-Term Memory, and Multilayer Perceptron Regression, are considered in the study. Various comparative performance analysis is conducted for both normal and uncertain cases and found that Random Forest Regression performed better for our dataset. The impact of data normalization on forecasting performance is also analyzed using multiple performance metrics. The study may help the grid operators to choose an appropriate PV power forecasting algorithm and plan the time-ahead generation volatility.

Binge-eating disorder (BED) is the most common eating disorder yet its genetic architecture remains largely unknown. Studying BED is challenging because it is often comorbid with obesity, a common and highly polygenic trait, and it is underdiagnosed in biobank datasets. To address this limitation, we apply a supervised machine learning approach to estimate the probability of each individual having BED based on electronic medical records from the Million Veteran Program. We perform a genome-wide association study on individuals of African (n = 77,574) and European (n = 285,138) ancestry while controlling for body mass index to identify three independent loci near the HFE, MCHR2 and LRP11 genes, which are reproducible across three independent cohorts. We identify genetic association between BED and several neuropsychiatric traits and implicate iron metabolism in the pathophysiology of BED. Overall, our findings provide insights into the genetics underlying BED and suggest directions for future translational research.

Leading From the North: Rethinking Northern Australia Development points out that understanding land, environment and people of northern Australia should be the first step in any government policy formation. It is an anthology of articles by researchers from Charles Darwin University, James Cook University and Australian National University, and is an important reference work for anyone working in Northern development.

In this special edition of Rural Society, we present a collection of papers developed from a research symposium held in Alice Springs, in November 2019. The symposium, which was established in 2017...

BackgroundNanostructures generated by self-assembly of peptides yield nanomaterial that has many therapeutic applications, including drug delivery and biomedical engineering, due to their low cytotoxicity and higher uptake by targeted cells owing to their high affinity and specificity towards cell surface receptors. Despite the promising implications of this rapidly expanding field, there is no dedicated resource to study peptide nanostructures.ResultThis study endeavours to create a dedicated repository of short peptides, which may prove to be the best models to study ordered nanostructures formed by peptide self-assembly. SAPdb has a repertoire of 1,049 entries of experimentally validated nanostructures formed by the self-assembling of small peptides. It includes 701 entries are of dipeptides, 328 entries belong to tripeptides, and 20 entries of single amino acid with some conjugated partners. Each entry encompasses comprehensive information about the peptide such as chemical modifications in the peptide sequences, the type of nanostructure formed, and experimental conditions like pH, temperature, and solvent required for the self-assembly of the peptide, etc. Further, our analysis has shown that the occurrence of aromatic amino acids favours the formation of self-assembling nanostructures, as indicated by a large number of entries in SAPdb contain aromatics amino acids. Besides, we have observed that these peptides form different nanostructures under different experimental conditions. SAPdb provides this comprehensive information in a hassle-free tabulated manner at a glance. User-friendly browsing, searching, and analysis modules are integrated for easy retrieval and comparison of data and examination of properties. We anticipate SAPdb to be a valuable repository for researchers engaged in the burgeoning arena of nanobiotechnology.AvailabilityThe database can be accessed on the web at https://webs.iiitd.edu.in/raghava/sapdb.

As the use of solar systems as an alternate source of energy increases, so too does the volume of waste from decommissioned systems. While several options have been suggested as solutions for the end of life of solar systems in terms of their component elements in urban regions, there is little discussion of managing waste arising in regional areas where volumes are often below necessary thresholds. This paper discusses why solar energy systems in Australia’s Northern Territory are being removed and how they are disposed of. Results suggest that there are several social and economic reasons for their removal, as it is not just a matter of failure to generate electric power. Since solar energy generation waste is a new problem, there are few or no social mechanisms in place to manage this waste. The results of this study highlight the need for addressing this gap. This paper inquires into, and attempts to formulate an early definition of this newly emergent social problem, conceptualising it as a sociomaterial issue.

The construction sector represents one of the most significant contributors to global waste production and is responsible for over 30% of the waste that ends up in landfill. Sending construction waste to landfill results in a broad range of environmental consequences including: degradation of land, habitat destruction, contamination of soil and groundwater, and release of methane. There is a growing awareness of the need to divert construction and demolition (C&D) waste from landfill for reuse or recycling. This helps maximise the value of the resources embodied in these materials and reduce the demand for virgin raw materials and the associated environment effects resulting from their extraction, processing and manufacture. However, diversion of C&D waste to reuse or recycling in remote communities can be difficult and costly. This poses a significant challenge for improving the environmental performance of construction waste management in these communities. A housing refurbishment project in Alice Springs, a remote town in central Australia, was used to identify the barriers associated with improving the environmental performance of construction waste management in remote communities. This study considers the materials removed as part of the demolition phase of the project. Material types and quantities were documented and on-site and off-site waste management practices observed. Reasons for waste management decisions were recorded. The study identified a range of barriers to improving the environmental performance of construction waste management in remote communities. These include cost and time associated with on-site waste management, industry culture, lack of education, competing project priorities, and lack of financial incentive. Greater incentives to encourage the diversion of C&D waste from landfill are needed, in particular. This and other strategies for improving construction waste management practices in remote communities must be targeted at the context of individual communities though, due to their unique characteristics.

This paper describes a web server developed for designing therapeutic peptides with desired half-life in blood. In this study, we used 163 natural and 98 modified peptides whose half-life has been determined experimentally in mammalian blood, for developing in silico models. Firstly, models have been developed on 261 peptides containing natural and modified residues, using different chemical descriptors. The best model using 43 PaDEL descriptors got a maximum correlation of 0.692 between the predicted and the actual half-life peptides. Secondly, models were developed on 163 natural peptides using amino acid composition feature of peptides and achieved a maximum correlation of 0.643. Thirdly, models were developed on 163 natural peptides using chemical descriptors and attained a maximum correlation of 0.743 using 45 selected PaDEL descriptors. In order to assist researchers in the prediction and designing of half-life of peptides, the models developed have been integrated into PlifePred web server (http://webs.iiitd.edu.in//raghava/plifepred/).

Preterm birth (born before 37 completed weeks of gestation) is one of the leading causes of death among children under 5 years of age. Several recent studies have examined the association between extreme temperature and preterm births, but there have been almost no such studies in arid Australia. In this paper, we explore the potential association between exposures to extreme temperatures during the last 3 weeks of pregnancy in a Central Australian town. An immediate effect of temperature exposure is observed with an increased relative risk of 1%–2% when the maximum temperature exceeded the 90th percentile of the summer season maximum temperature data. Delayed effects are also observed closer to 3 weeks before delivery when the relative risks tend to increase exponentially. Immediate risks to preterm birth are also observed for cold temperature exposures (0 to –6 °C), with an increased relative risk of up to 10%. In the future, Central Australia will face more hot days and less cold days due to climate change and hence the risks posed by extreme heat is of particular relevance to the community and health practitioners.

Short half-life is one of the key challenges in the field of therapeutic peptides. Various studies have reported enhancement in the stability of peptides using methods like chemical modifications, D-amino acid substitution, cyclization, replacement of labile aminos acids, etc. In order to study this scattered data, there is a pressing need for a repository dedicated to the half-life of peptides. To fill this lacuna, we have developed PEPlife (http://crdd.osdd.net/raghava/peplife), a manually curated resource of experimentally determined half-life of peptides. PEPlife contains 2229 entries covering 1193 unique peptides. Each entry provides detailed information of the peptide, like its name, sequence, half-life, modifications, the experimental assay for determining half-life, biological nature and activity of the peptide. We also maintain SMILES and structures of peptides. We have incorporated web-based modules to offer user-friendly data searching and browsing in the database. PEPlife integrates numerous tools to perform various types of analysis such as BLAST, Smith-Waterman algorithm, GGSEARCH, Jalview and MUSTANG. PEPlife would augment the understanding of different factors that affect the half-life of peptides like modifications, sequence, length, route of delivery of the peptide, etc. We anticipate that PEPlife will be useful for the researchers working in the area of peptide-based therapeutics.

Numerous therapeutic peptides do not enter the clinical trials just because of their high hemolytic activity. Recently, we developed a database, Hemolytik, for maintaining experimentally validated hemolytic and non-hemolytic peptides. The present study describes a web server and mobile app developed for predicting and screening of peptides having hemolytic potency. Firstly, we generated a dataset HemoPI-1 that contains 552 hemolytic peptides extracted from Hemolytik database and 552 random non-hemolytic peptides (from Swiss-Prot). The sequence analysis of these peptides revealed that certain residues (e.g., L, K, F, W) and motifs (e.g., “FKK”, “LKL”, “KKLL”, “KWK”, “VLK”, “CYCR”, “CRR”, “RFC”, “RRR”, “LKKL”) are more abundant in hemolytic peptides. Therefore, we developed models for discriminating hemolytic and non-hemolytic peptides using various machine learning techniques and achieved more than 95% accuracy. We also developed models for discriminating peptides having high and low hemolytic potential on different datasets called HemoPI-2 and HemoPI-3. In order to serve the scientific community, we developed a web server, mobile app and JAVA-based standalone software (http://crdd.osdd.net/raghava/hemopi/).

ObjectiveTo investigate whether leptin and adiponectin are associated with body fat composition in a South Asian population independent of metabolic variables.DesignCross-sectional study.Subjects150 South Asian men and women, between the ages of 45-79 years, in the San Francisco Bay Area without pre-existing clinical cardiovascular disease.MeasurementsBlood samples were obtained to measure glucose metabolism variables, lipid profiles and adipokines. Total body fat was determined using dual-energy X-ray absorptiometry. Abdominal computed tomography was used to measure subcutaneous, visceral and hepatic fat.ResultsAverage body mass index (BMI) was overweight at 26.1±4.6 kg m(-2) and did not differ by sex. However, women had significantly more total body fat (P

Phosphoglucose isomerase is a ubiquitous enzyme that catalyzes the isomerization of D-glucopyranose-6-phosphate to D-fructofuranose-6-phosphate. The present investigation reports the expression, purification, crystallization and preliminary crystallographic studies of the phosphoglucose isomerase from Mycobacterium tuberculosis H37Rv, which shares 46% sequence identity with that of its human host. The recombinant protein, which was prepared using an Escherichia coli expression system, was crystallized by the hanging-drop vapour-diffusion method. The crystals diffracted to a resolution of 2.8 A and belonged to the orthorhombic space group I2(1)2(1)2(1), with unit-cell parameters a = 109.0, b = 119.8, c = 138.9 A.

ParaPep is a repository of antiparasitic peptides, which provides comprehensive information related to experimentally validated antiparasitic peptide sequences and their structures. The data were collected and compiled from published research papers, patents and from various databases. The current release of ParaPep holds 863 entries among which 519 are unique peptides. In addition to peptides having natural amino acids, ParaPep also consists of peptides having d-amino acids and chemically modified residues. In ParaPep, most of the peptides have been evaluated for growth inhibition of various species of Plasmodium, Leishmania and Trypanosoma. We have provided comprehensive information about these peptides that include peptide sequence, chemical modifications, stereochemistry, antiparasitic activity, origin, nature of peptide, assay types, type of parasite, mode of action and hemolytic activity. Structures of peptides consisting of natural, as well as modified amino acids have been determined using state-of-the-art software, PEPstr. To facilitate users, various user-friendly web tools, for data fetching, analysis and browsing, have been integrated. We hope that ParaPep will be advantageous in designing therapeutic peptides against parasitic diseases. Database URL: http://crdd.osdd.net/raghava/parapep/

SATPdb (http://crdd.osdd.net/raghava/satpdb/) is a database of structurally annotated therapeutic peptides, curated from 22 public domain peptide databases/datasets including 9 of our own. The current version holds 19192 unique experimentally validated therapeutic peptide sequences having length between 2 and 50 amino acids. It covers peptides having natural, non-natural and modified residues. These peptides were systematically grouped into 10 categories based on their major function or therapeutic property like 1099 anticancer, 10585 antimicrobial, 1642 drug delivery and 1698 antihypertensive peptides. We assigned or annotated structure of these therapeutic peptides using structural databases (Protein Data Bank) and state-of-the-art structure prediction methods like I-TASSER, HHsearch and PEPstrMOD. In addition, SATPdb facilitates users in performing various tasks that include: (i) structure and sequence similarity search, (ii) peptide browsing based on their function and properties, (iii) identification of moonlighting peptides and (iv) searching of peptides having desired structure and therapeutic activities. We hope this database will be useful for researchers working in the field of peptide-based therapeutics.