Showing papers by "Macquarie University published in 2020"

Pan-cancer analysis of whole genomes

Abstract: Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1,2,3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10,11,12,13,14,15,16,17,18.

TRY plant trait database : Enhanced coverage and open access

Abstract: Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

The 16th Data Release of the Sloan Digital Sky Surveys: First Release from the APOGEE-2 Southern Survey and Full Release of eBOSS Spectra

Abstract: This paper documents the sixteenth data release (DR16) from the Sloan Digital Sky Surveys; the fourth and penultimate from the fourth phase (SDSS-IV). This is the first release of data from the southern hemisphere survey of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2); new data from APOGEE-2 North are also included. DR16 is also notable as the final data release for the main cosmological program of the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), and all raw and reduced spectra from that project are released here. DR16 also includes all the data from the Time Domain Spectroscopic Survey (TDSS) and new data from the SPectroscopic IDentification of ERosita Survey (SPIDERS) programs, both of which were co-observed on eBOSS plates. DR16 has no new data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey (or the MaNGA Stellar Library "MaStar"). We also preview future SDSS-V operations (due to start in 2020), and summarize plans for the final SDSS-IV data release (DR17).

Conducting systematic literature reviews and bibliometric analyses

Abstract: Literature reviews play an essential role in academic research to gather existing knowledge and to examine the state of a field. However, researchers in business, management and related disciplines...

The mental health of healthcare workers in the COVID-19 pandemic: A systematic review.

Abstract: The novel coronavirus 2019 (COVID-19) is widely spreading all over the world, causing mental health problems for most people. The medical staff is also under considerable psychological pressure. This study aimed to review all research carried out on the mental health status of health care workers (HCWs) to bring policymakers and managers’ attention. A literature search conducted through e-databases, including PubMed, EMBASE, Scopus, and Web of Science (WoS) from December 2019 up to April 12th 2020. All cross- sectional studies published in English which assessed the health workers’ psychological well-being during the SARS-CoV-2 pandemic included. Study quality was analyzed using NHLBI Study Quality assessment tools. One hundred relevant articles were identified through systematic search; of which eleven studies were eligible for this review. Their quality score was acceptable. The lowest reported prevalence of anxiety, depression, and stress among HCWs was 24.1%, 12.1%, and 29.8%, respectively. In addition, the highest reported values for the aforementioned parameters were 67.55%, 55.89%, and 62.99%, respectively. Nurses, female workers, front-line health care workers, younger medical staff, and workers in areas with higher infection rates reported more severe degrees of all psychological symptoms than other health care workers. Moreover, vicarious traumatization in non-front-line nurses and the general public was higher than that of the front-line nurses. During SARS-CoV-2 outbreak, the health care workers face aggravated psychological pressure and even mental illness. It would be recommended to the policymakers and managers to adopt the supportive, encouragement & motivational, protective, and training & educational interventions, especially through information and communication platform.

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

Abstract: COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

Managing patients with chronic pain during the COVID-19 outbreak: considerations for the rapid introduction of remotely supported (eHealth) pain management services.

Abstract: Across the world, pain treatment centres have closed their doors. Because of the COVID-19 pandemic, healthcare providers are abruptly changing their care delivery to protect patients and staff from infection and to reallocate resource towards the greatest acute needs. Elective, routine, and nonemergency casework has stopped in secondary and tertiary centres, while in primary care, patients are requested to stay away or “socially distance,” and in residential care facilities and hospices, strict isolation and separation protocols have been introduced. Before the COVID-19 pandemic, telemedicine and eHealth approaches were being developed and tested in a gradual fashion with many studies focusing on lessons learned and barriers to using digital solutions.3,37,39,51 Overnight, however, treating or supporting people with non-urgent and long-term conditions at a distance from healthcare providers has become imperative. These immediate changes are happening across healthcare systems. Telemedicine is being used to demand-manage the flow of patients with respiratory distress accessing emergency departments25; video consultation is being introduced in multiple settings23; and using social media is being discussed positively for its potential to direct people to trusted resources, to counteract misinformation, and to provide psychological first aid.36 Pain management providers face the challenge of delivering face-to-face service through different modes. Fortunately, there is a rich stream of research and clinical experience in the use of different technological solutions. Table ​Table11 provides a summary of the definitions and terminology in use. Table 1 Definitions and terminology used in remotely supported pain management.

Compositional and functional differences of the mucosal microbiota along the intestine of healthy individuals

Abstract: Gut mucosal microbes evolved closest to the host, developing specialized local communities. There is, however, insufficient knowledge of these communities as most studies have employed sequencing technologies to investigate faecal microbiota only. This work used shotgun metagenomics of mucosal biopsies to explore the microbial communities' compositions of terminal ileum and large intestine in 5 healthy individuals. Functional annotations and genome-scale metabolic modelling of selected species were then employed to identify local functional enrichments. While faecal metagenomics provided a good approximation of the average gut mucosal microbiome composition, mucosal biopsies allowed detecting the subtle variations of local microbial communities. Given their significant enrichment in the mucosal microbiota, we highlight the roles of Bacteroides species and describe the antimicrobial resistance biogeography along the intestine. We also detail which species, at which locations, are involved with the tryptophan/indole pathway, whose malfunctioning has been linked to pathologies including inflammatory bowel disease. Our study thus provides invaluable resources for investigating mechanisms connecting gut microbiota and host pathophysiology.

Nivolumab plus ipilimumab versus sunitinib for first-line treatment of advanced renal cell carcinoma: extended 4-year follow-up of the phase III CheckMate 214 trial.

Abstract: Purpose To report updated analyses of the phase III CheckMate 214 trial with extended minimum follow-up assessing long-term outcomes with first-line nivolumab plus ipilimumab (NIVO+IPI) versus (vs) sunitinib (SUN) in patients with advanced renal cell carcinoma (aRCC).

A census of baryons in the Universe from localized fast radio bursts.

Abstract: More than three-quarters of the baryonic content of the Universe resides in a highly diffuse state that is difficult to detect, with only a small fraction directly observed in galaxies and galaxy clusters1,2. Censuses of the nearby Universe have used absorption line spectroscopy3,4 to observe the ‘invisible’ baryons, but these measurements rely on large and uncertain corrections and are insensitive to most of the Universe’s volume and probably most of its mass. In particular, quasar spectroscopy is sensitive either to the very small amounts of hydrogen that exist in the atomic state, or to highly ionized and enriched gas4–6 in denser regions near galaxies7. Other techniques to observe these invisible baryons also have limitations; Sunyaev–Zel’dovich analyses8,9 can provide evidence from gas within filamentary structures, and studies of X-ray emission are most sensitive to gas near galaxy clusters9,10. Here we report a measurement of the baryon content of the Universe using the dispersion of a sample of localized fast radio bursts; this technique determines the electron column density along each line of sight and accounts for every ionized baryon11–13. We augment the sample of reported arcsecond-localized14–18 fast radio bursts with four new localizations in host galaxies that have measured redshifts of 0.291, 0.118, 0.378 and 0.522. This completes a sample sufficiently large to account for dispersion variations along the lines of sight and in the host-galaxy environments11, and we derive a cosmic baryon density of $${\varOmega }_{{\rm{b}}}={0.051}_{-0.025}^{+0.021}{h}_{70}^{-1}$$ (95 per cent confidence; h70 = H0/(70 km s−1 Mpc−1) and H0 is Hubble’s constant). This independent measurement is consistent with values derived from the cosmic microwave background and from Big Bang nucleosynthesis19,20. The baryon density determined along the lines of sight to localized fast radio bursts is consistent with that determined from the cosmic microwave background and required by Big Bang nucleosynthesis.

Forced Disruption of Anatomy Education in Australia and New Zealand: An Acute Response to the Covid-19 Pandemic.

Abstract: Australian and New Zealand universities commenced a new academic year in February/March 2020 largely with "business as usual." The subsequent Covid-19 pandemic imposed unexpected disruptions to anatomical educational practice. Rapid change occurred due to government-imposed physical distancing regulations from March 2020 that increasingly restricted anatomy laboratory teaching practices. Anatomy educators in both these countries were mobilized to adjust their teaching approaches. This study on anatomy education disruption at pandemic onset within Australia and New Zealand adopts a social constructivist lens. The research question was "What are the perceived disruptions and changes made to anatomy education in Australia and New Zealand during the initial period of the Covid-19 pandemic, as reflected on by anatomy educators?." Thematic analysis to elucidate "the what and why" of anatomy education was applied to these reflections. About 18 anatomy academics from ten institutions participated in this exercise. The analysis revealed loss of integrated "hands-on" experiences, and impacts on workload, traditional roles, students, pedagogy, and anatomists' personal educational philosophies. The key opportunities recognized for anatomy education included: enabling synchronous teaching across remote sites, expanding offerings into the remote learning space, and embracing new pedagogies. In managing anatomy education's transition in response to the pandemic, six critical elements were identified: community care, clear communications, clarified expectations, constructive alignment, community of practice, ability to compromise, and adapt and continuity planning. There is no doubt that anatomy education has stepped into a yet unknown future in the island countries of Australia and New Zealand.

Gas chromatography-mass spectrometry analyses of encapsulated stable perovskite solar cells.

Abstract: INTRODUCTION Although advances in materials and processing have led to remarkable advancements in the energy conversion efficiency of perovskite solar cells (PSCs), increasing from 3.8% to 25.2% in only 10 years, these solar cells cannot become commercially viable unless their underperforming durability is improved. The instability of perovskites must be addressed if PSCs are to compete with silicon technology, which currently offers a 25-year performance warranty. Previous approaches to this problem include the use of metal oxide barrier layers and butyl rubber sealants. Here, we report a low-cost polymer/glass stack encapsulation scheme that enables PSCs to pass the demanding International Electrotechnical Commission (IEC) 61215:2016 Damp Heat and Humidity Freeze tests. These tests help to determine whether solar cell modules can withstand the effects of outdoor operating conditions by exposing them to repeated temperature cycling (–40° to 85°C) as well as 85% relative humidity. Our airtight encapsulation scheme prevented moisture ingress. It was also effective in suppressing outgassing of decomposition products, which limits decomposition reactions of organic hybrid PSCs by allowing these reactions to come to equilibrium. The gas compositions were verified by gas chromatography–mass spectrometry (GC-MS). RATIONALE In the GC-MS technique, gas chromatography separates the components in a mixture, and the chemical identity of each component is determined with mass spectrometry. We could directly identify with high specificity the decomposition products of multi-cation perovskite precursors, of unencapsulated perovskite test structures, and of encapsulated full cells at elevated temperatures. The results allowed us to identify thermal degradation pathways by determining the outgassing products of mixed-cation perovskites during heating. We then used GC-MS to evaluate the effectiveness of different packaging techniques developed for PSCs. The packaging schemes were a polyisobutylene (PIB)–based polymer blanket encapsulation, a polyolefin-based blanket encapsulation, and a PIB edge seal. These packaging layers were then capped by a glass cover. For the edge seal, the decomposition gases inside the cell were sampled with a syringe. The feasibilities of these packaging techniques were also demonstrated by IEC photovoltaic module standard Damp Heat and Humidity Freeze testing. RESULTS Signature decomposition products such as CH3I, CH3Br, and NH3 were identified and decomposition pathways were proposed for CH3NH3I (MAI), HC(NH2)2I (FAI), CH3NH3Br (MABr), and mixed-cation and mixed-halide (FAI)0.85 + (MABr)0.15 perovskite precursors, including their secondary decomposition reactions at 350°, 140°, and 85°C. The GC-MS results confirmed that the Br-containing precursor was less prone to thermal decomposition than an I-containing precursor. Also, CsFAMA cells were found to outgas one-fifth as much decomposition product as their FAMA counterparts, which indicated that the Cs-containing cells had better thermal stability. Although the decomposition of FAI is reversible, the mixing of MA with FA precursors caused decomposition products to participate in the secondary reaction that was irreversible. This finding confirmed the disadvantage of mixing of MA with FA perovskite through the reduction in chemical stability. The blanket-encapsulated PSCs sustained no efficiency degradation after 1800 hours of Damp Heat testing or 75 cycles of Humidity Freeze testing. CONCLUSION GC-MS identified signature volatile products of the decomposition of organic hybrid perovskites under thermal stress, thereby informing decomposition pathways. The findings are important for developing potential cell-stabilizing strategies, given that cells in the field typically experience high operating temperatures. In addition, results of GC-MS confirm that the low-cost pressure-tight encapsulation we developed is effective in suppressing such outgassing and therefore decomposition reactions of PSCs. This encapsulation scheme is the simplest of all for perovskite cells to pass IEC photovoltaic module standard tests. Our approach can be applied to evaluating the effectiveness of other packaging approaches, as well as testing the effectiveness of coatings and material compositions aimed at limiting light and thermal degradation.

Durvalumab alone and durvalumab plus tremelimumab versus chemotherapy in previously untreated patients with unresectable, locally advanced or metastatic urothelial carcinoma (DANUBE): a randomised, open-label, multicentre, phase 3 trial

Abstract: Background: Survival outcomes are poor for patients with metastatic urothelial carcinoma who receive standard, first-line, platinum-based chemotherapy. We assessed the overall survival of patients who received durvalumab (a PD-L1 inhibitor), with or without tremelimumab (a CTLA-4 inhibitor), as a first-line treatment for metastatic urothelial carcinoma. Methods: DANUBE is an open-label, randomised, controlled, phase 3 trial in patients with untreated, unresectable, locally advanced or metastatic urothelial carcinoma, conducted at 224 academic research centres, hospitals, and oncology clinics in 23 countries. Eligible patients were aged 18 years or older with an Eastern Cooperative Oncology Group performance status of 0 or 1. We randomly assigned patients (1:1:1) to receive durvalumab monotherapy (1500 mg) administered intravenously every 4 weeks; durvalumab (1500 mg) plus tremelimumab (75 mg) administered intravenously every 4 weeks for up to four doses, followed by durvalumab maintenance (1500 mg) every 4 weeks; or standard-of-care chemotherapy (gemcitabine plus cisplatin or gemcitabine plus carboplatin, depending on cisplatin eligibility) administered intravenously for up to six cycles. Randomisation was done through an interactive voice–web response system, with stratification by cisplatin eligibility, PD-L1 status, and presence or absence of liver metastases, lung metastases, or both. The coprimary endpoints were overall survival compared between the durvalumab monotherapy versus chemotherapy groups in the population of patients with high PD-L1 expression (the high PD-L1 population) and between the durvalumab plus tremelimumab versus chemotherapy groups in the intention-to-treat population (all randomly assigned patients). The study has completed enrolment and the final analysis of overall survival is reported. The trial is registered with ClinicalTrials.gov, NCT02516241, and the EU Clinical Trials Register, EudraCT number 2015-001633-24. Findings: Between Nov 24, 2015, and March 21, 2017, we randomly assigned 1032 patients to receive durvalumab (n=346), durvalumab plus tremelimumab (n=342), or chemotherapy (n=344). At data cutoff (Jan 27, 2020), median follow-up for survival was 41·2 months (IQR 37·9–43·2) for all patients. In the high PD-L1 population, median overall survival was 14·4 months (95% CI 10·4–17·3) in the durvalumab monotherapy group (n=209) versus 12·1 months (10·4–15·0) in the chemotherapy group (n=207; hazard ratio 0·89, 95% CI 0·71–1·11; p=0·30). In the intention-to-treat population, median overall survival was 15·1 months (13·1–18·0) in the durvalumab plus tremelimumab group versus 12·1 months (10·9–14·0) in the chemotherapy group (0·85, 95% CI 0·72–1·02; p=0·075). In the safety population, grade 3 or 4 treatment-related adverse events occurred in 47 (14%) of 345 patients in the durvalumab group, 93 (27%) of 340 patients in the durvalumab plus tremelimumab group, and in 188 (60%) of 313 patients in the chemotherapy group. The most common grade 3 or 4 treatment-related adverse event was increased lipase in the durvalumab group (seven [2%] of 345 patients) and in the durvalumab plus tremelimumab group (16 [5%] of 340 patients), and neutropenia in the chemotherapy group (66 [21%] of 313 patients). Serious treatment-related adverse events occurred in 30 (9%) of 345 patients in the durvalumab group, 78 (23%) of 340 patients in the durvalumab plus tremelimumab group, and 50 (16%) of 313 patients in the chemotherapy group. Deaths due to study drug toxicity were reported in two (1%) patients in the durvalumab group (acute hepatic failure and hepatitis), two (1%) patients in the durvalumab plus tremelimumab group (septic shock and pneumonitis), and one (<1%) patient in the chemotherapy group (acute kidney injury). Interpretation: This study did not meet either of its coprimary endpoints. Further research to identify the patients with previously untreated metastatic urothelial carcinoma who benefit from treatment with immune checkpoint inhibitors, either alone or in combination regimens, is warranted. Funding: AstraZeneca.

Lignocellulose biomass pyrolysis for bio-oil production: a review of biomass pre-treatment methods for production of drop-in fuels

Abstract: Bio-oil upgrading can be achieved mainly via three types of methods that are biomass pre-treatment, catalytic upgrading and downstream bio-oil upgrading. The article aim is to review the different physicochemical biomass pre-treatment methods used to improve the physiochemical properties of the bio-oils produced from pyrolysis of treated biomass. Biomass pre-treatment could be classified as physical, thermal, chemical and biological methods. The physical methods, such as grinding and densification improve the biomass particle size and density, affecting the heat flow and mass transfer during pyrolysis, while thermal methods, such as torrefaction, decrease the activation energy of the pyrolysis process and increase the amount of hydrocarbons in the produced bio-oil. The chemical methods generally remove the minerals and alkali metals from the biomass, improve its calorific value and enhance other biomass properties. The biomass pre-treatment methods can be integrated with catalytic pyrolysis to enhance the total carbon yield and aromatic hydrocarbons in the bio-oil. This article provides review of the basic principles of the methods, important parameters that affect biomass properties, highlights the key challenges involved in each treatment method and suggests possible future recommendations to further understand the influence of the pre-treatment methods on bio-oil upgrading. In the last section, the effect of integrated catalytic pyrolysis and pre-treatment methods on bio-oil upgrading is provided.

An encrusting kleptoparasite-host interaction from the early Cambrian

Abstract: Parasite–host systems are pervasive in nature but are extremely difficult to convincingly identify in the fossil record. Here we report quantitative evidence of parasitism in the form of a unique, enduring life association between tube-dwelling organisms encrusted to densely clustered shells of a monospecific organophosphatic brachiopod assemblage from the lower Cambrian (Stage 4) of South China. Brachiopods with encrusting tubes have decreased biomass (indicating reduced fitness) compared to individuals without tubes. The encrusting tubes orient tightly in vectors matching the laminar feeding currents of the host, suggesting kleptoparasitism. With no convincing parasite–host interactions known from the Ediacaran, this widespread sessile association reveals intimate parasite–host animal systems arose in early Cambrian benthic communities and their emergence may have played a key role in driving the evolutionary and ecological innovations associated with the Cambrian radiation. Parasitic interactions are difficult to document in the fossil record. Here, Zhang et al. analyze a large population of a Cambrian brachiopod and show it was frequently encrusted by tubes aligned to its feeding currents and that encrustation was associated with reduced biomass, suggesting a fitness cost.

Cybersecurity data science: an overview from machine learning perspective

Abstract: In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

A new nickel-based co-crystal complex electrocatalyst amplified by NiO dope Pt nanostructure hybrid; a highly sensitive approach for determination of cysteamine in the presence of serotonin.

Abstract: A highly sensitive electrocatalytic sensor was designed and fabricated by the incorporation of NiO dope Pt nanostructure hybrid (NiO–Pt–H) as conductive mediator, bis (1,10 phenanthroline) (1,10-phenanthroline-5,6-dione) nickel(II) hexafluorophosphate (B,1,10,P,1,10, PDNiPF6), and electrocatalyst into carbon paste electrode (CPE) matrix for the determination of cysteamine. The NiO–Pt–H was synthesized by one-pot synthesis strategy and characterized by XRD, elemental mapping analysis (MAP), and FESEM methods. The characterization data, which confirmed good purity and spherical shape with a diameter of ⁓ 30.64 nm for the synthesized NiO–Pt–H. NiO–Pt–H/B,1,10, P,1,10, PDNiPF6/CPE, showed an excellent catalytic activity and was used as a powerful tool for the determination of cysteamine in the presence of serotonin. The NiO–Pt–H/B,1,10, P,1,10, PDNiPF6/CPE was able to solve the overlap problem of the two drug signals and was used for the determination of cysteamine and serotonin in concentration ranges of 0.003–200 µM and 0.5–260 µM with detection limits of 0.5 nM and 0.1 µM, using square wave voltammetric method, respectively. The NiO–Pt–H/B,1,10,P,1,10,PDNiPF6/CPE showed a high-performance ability for the determination of cysteamine and serotonin in the drug and pharmaceutical serum samples with the recovery data of 98.1–103.06%.

A Robust Intrinsically Green Fluorescent Poly(Amidoamine) Dendrimer for Imaging and Traceable Central Nervous System Delivery in Zebrafish.

Abstract: Intrinsically fluorescent poly(amidoamine) dendrimers (IF-PAMAM) are an emerging class of versatile nanoplatforms for in vitro tracking and bio-imaging. However, limited tissue penetration of their fluorescence and interference due to auto-fluorescence arising from biological tissues limit its application in vivo. Herein, a green IF-PAMAM (FGP) dendrimer is reported and its biocompatibility, circulation, biodistribution and potential role for traceable central nervous system (CNS)-targeted delivery in zebrafish is evaluated, exploring various routes of administration. Key features of FGP include visible light excitation (488 nm), high fluorescence signal intensity, superior photostability and low interference from tissue auto-fluorescence. After intravenous injection, FGP shows excellent imaging and tracking performance in zebrafish. Further conjugating FGP with transferrin (FGP-Tf) significantly increases its penetration through the blood-brain barrier (BBB) and prolongs its circulation in the blood stream. When administering through local intratissue microinjection, including intracranial and intrathecal injection in zebrafish, both FGP and FGP-Tf exhibit excellent tissue diffusion and effective cellular uptake in the brain and spinal cord, respectively. This makes FGP/FGP-Tf attractive for in vivo tracing when transporting to the CNS is desired. The work addresses some of the major shortcomings in IF-PAMAM and provides a promising application of these probes in the development of drug delivery in the CNS.

Low intrinsic efficacy for G protein activation can explain the improved side effect profiles of new opioid agonists.

Abstract: Biased agonism at G protein–coupled receptors describes the phenomenon whereby some drugs can activate some downstream signaling activities to the relative exclusion of others. Descriptions of biased agonism focusing on the differential engagement of G proteins versus β-arrestins are commonly limited by the small response windows obtained in pathways that are not amplified or are less effectively coupled to receptor engagement, such as β-arrestin recruitment. At the μ-opioid receptor (MOR), G protein–biased ligands have been proposed to induce less constipation and respiratory depressant side effects than opioids commonly used to treat pain. However, it is unclear whether these improved safety profiles are due to a reduction in β-arrestin–mediated signaling or, alternatively, to their low intrinsic efficacy in all signaling pathways. Here, we systematically evaluated the most recent and promising MOR-biased ligands and assessed their pharmacological profile against existing opioid analgesics in assays not confounded by limited signal windows. We found that oliceridine, PZM21, and SR-17018 had low intrinsic efficacy. We also demonstrated a strong correlation between measures of efficacy for receptor activation, G protein coupling, and β-arrestin recruitment for all tested ligands. By measuring the antinociceptive and respiratory depressant effects of these ligands, we showed that the low intrinsic efficacy of opioid ligands can explain an improved side effect profile. Our results suggest a possible alternative mechanism underlying the improved therapeutic windows described for new opioid ligands, which should be taken into account for future descriptions of ligand action at this important therapeutic target.

Adversarial Attacks on Deep-learning Models in Natural Language Processing: A Survey

Abstract: With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs are vulnerable to strategically modified samples, named adversarial examples. These samples are generated with some imperceptible perturbations, but can fool the DNNs to give false predictions. Inspired by the popularity of generating adversarial examples against DNNs in Computer Vision (CV), research efforts on attacking DNNs for Natural Language Processing (NLP) applications have emerged in recent years. However, the intrinsic difference between image (CV) and text (NLP) renders challenges to directly apply attacking methods in CV to NLP. Various methods are proposed addressing this difference and attack a wide range of NLP applications. In this article, we present a systematic survey on these works. We collect all related academic works since the first appearance in 2017. We then select, summarize, discuss, and analyze 40 representative works in a comprehensive way. To make the article self-contained, we cover preliminary knowledge of NLP and discuss related seminal works in computer vision. We conclude our survey with a discussion on open issues to bridge the gap between the existing progress and more robust adversarial attacks on NLP DNNs.

From digital literacy to digital competence: the teacher digital competency (TDC) framework

Abstract: Over the years, a variety of frameworks, models and literacies have been developed to guide teacher educators in their efforts to build digital capabilities in their students, that will support them to use new and emerging technologies in their future classrooms. Generally, these focus on advancing students’ skills in using ‘educational’ applications and digitally-sourced information, or understanding effective blends of pedagogical, content and technological knowledge seen as supporting the integration of digital resources into teaching, to enhance subject learning outcomes. Within teacher education institutions courses developing these capabilities are commonly delivered as standalone entities, or there is an assumption that they will be generated by technology’s integration in other disciplines or through mandated assessment. However, significant research exists suggesting the current narrow focus on subject-related technical and information skills does not prepare students adequately with the breadth of knowledge and capabilities needed in today’s classrooms, and beyond. This article presents a conceptual framework introducing an expanded view of teacher digital competence (TDC). It moves beyond prevailing technical and literacies conceptualisations, arguing for more holistic and broader-based understandings that recognise the increasingly complex knowledge and skills young people need to function ethically, safely and productively in diverse, digitally-mediated environments. The implications of the framework are discussed, with specific reference to its interdisciplinary nature and the requirement of all faculty to engage purposefully and deliberately in delivering its objectives. Practical suggestions on how the framework might be used by faculty, are presented.

Inhaled corticosteroids and COVID-19: a systematic review and clinical perspective.

Abstract: There is no evidence on benefits or harms of inhaled steroids in COVID-19. It is essential that epidemiological studies of COVID-19 include detailed information on comorbidities and prior medication to help answer this question.https://bit.ly/2XVwIsa

Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT

Abstract: Edge computing provides a promising paradigm to support the implementation of Industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resource-limited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this article, we consider the optimization of channel selection that is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to long-term constraints of energy budget and service reliability. We propose a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory. We provide rigorous theoretical analysis, and prove that the proposed framework can achieve guaranteed performance with a bounded deviation from the optimal performance with global state information (GSI) based on only local and causal information. Finally, simulations are conducted under both single-MTD and multi-MTD scenarios to verify the effectiveness and reliability of the proposed framework.

Thresholds of mangrove survival under rapid sea level rise

Abstract: The response of mangroves to high rates of relative sea level rise (RSLR) is poorly understood. We explore the limits of mangrove vertical accretion to sustained periods of RSLR in the final stages of deglaciation. The timing of initiation and rate of mangrove vertical accretion were compared with independently modeled rates of RSLR for 78 locations. Mangrove forests expanded between 9800 and 7500 years ago, vertically accreting thick sequences of organic sediments at a rate principally driven by the rate of RSLR, representing an important carbon sink. We found it very likely (>90% probability) that mangroves were unable to initiate sustained accretion when RSLR rates exceeded 6.1 millimeters per year. This threshold is likely to be surpassed on tropical coastlines within 30 years under high-emissions scenarios.