Discover and publish cutting edge, open research.

Nick K. Jones1,2*, Lucy Rivett1,2*, Chris Workman3, Mark Ferris3, Ashley Shaw1, Cambridge COVID-19 Collaboration1,4, Paul J. Lehner1,4, Rob Howes5, Giles Wright3, Nicholas J. Matheson1,4,6¶, Michael P. Weekes1,7¶1 Cambridge University NHS Hospitals Foundation Trust, Cambridge, UK2 Clinical Microbiology & Public Health Laboratory, Public Health England, Cambridge, UK3 Occupational Health and Wellbeing, Cambridge Biomedical Campus, Cambridge, UK4 Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, UK5 Cambridge COVID-19 Testing Centre and AstraZeneca, Anne Mclaren Building, Cambridge, UK6 NHS Blood and Transplant, Cambridge, UK7 Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK*Joint first authorship¶Joint last authorshipCorrespondence: [email protected] UK has initiated mass COVID-19 immunisation, with healthcare workers (HCWs) given early priority because of the potential for workplace exposure and risk of onward transmission to patients. The UK’s Joint Committee on Vaccination and Immunisation has recommended maximising the number of people vaccinated with first doses at the expense of early booster vaccinations, based on single dose efficacy against symptomatic COVID-19 disease.1-3At the time of writing, three COVID-19 vaccines have been granted emergency use authorisation in the UK, including the BNT162b2 mRNA COVID-19 vaccine (Pfizer-BioNTech). A vital outstanding question is whether this vaccine prevents or promotes asymptomatic SARS-CoV-2 infection, rather than symptomatic COVID-19 disease, because sub-clinical infection following vaccination could continue to drive transmission. This is especially important because many UK HCWs have received this vaccine, and nosocomial COVID-19 infection has been a persistent problem.Through the implementation of a 24 h-turnaround PCR-based comprehensive HCW screening programme at Cambridge University Hospitals NHS Foundation Trust (CUHNFT), we previously demonstrated the frequent presence of pauci- and asymptomatic infection amongst HCWs during the UK’s first wave of the COVID-19 pandemic.4 Here, we evaluate the effect of first-dose BNT162b2 vaccination on test positivity rates and cycle threshold (Ct) values in the asymptomatic arm of our programme, which now offers weekly screening to all staff.Vaccination of HCWs at CUHNFT began on 8th December 2020, with mass vaccination from 8th January 2021. Here, we analyse data from the two weeks spanning 18thto 31st January 2021, during which: (a) the prevalence of COVID-19 amongst HCWs remained approximately constant; and (b) we screened comparable numbers of vaccinated and unvaccinated HCWs. Over this period, 4,408 (week 1) and 4,411 (week 2) PCR tests were performed from individuals reporting well to work. We stratified HCWs <12 days or > 12 days post-vaccination because this was the point at which protection against symptomatic infection began to appear in phase III clinical trial.226/3,252 (0·80%) tests from unvaccinated HCWs were positive (Ct<36), compared to 13/3,535 (0·37%) from HCWs <12 days post-vaccination and 4/1,989 (0·20%) tests from HCWs ≥12 days post-vaccination (p=0·023 and p=0·004, respectively; Fisher’s exact test, Figure). This suggests a four-fold decrease in the risk of asymptomatic SARS-CoV-2 infection amongst HCWs ≥12 days post-vaccination, compared to unvaccinated HCWs, with an intermediate effect amongst HCWs <12 days post-vaccination.A marked reduction in infections was also seen when analyses were repeated with: (a) inclusion of HCWs testing positive through both the symptomatic and asymptomatic arms of the programme (56/3,282 (1·71%) unvaccinated vs 8/1,997 (0·40%) ≥12 days post-vaccination, 4·3-fold reduction, p=0·00001); (b) inclusion of PCR tests which were positive at the limit of detection (Ct>36, 42/3,268 (1·29%) vs 15/2,000 (0·75%), 1·7-fold reduction, p=0·075); and (c) extension of the period of analysis to include six weeks from December 28th to February 7th 2021 (113/14,083 (0·80%) vs 5/4,872 (0·10%), 7·8-fold reduction, p=1x10-9). In addition, the median Ct value of positive tests showed a non-significant trend towards increase between unvaccinated HCWs and HCWs > 12 days post-vaccination (23·3 to 30·3, Figure), suggesting that samples from vaccinated individuals had lower viral loads.We therefore provide real-world evidence for a high level of protection against asymptomatic SARS-CoV-2 infection after a single dose of BNT162b2 vaccine, at a time of predominant transmission of the UK COVID-19 variant of concern 202012/01 (lineage B.1.1.7), and amongst a population with a relatively low frequency of prior infection (7.2% antibody positive).5This work was funded by a Wellcome Senior Clinical Research Fellowship to MPW (108070/Z/15/Z), a Wellcome Principal Research Fellowship to PJL (210688/Z/18/Z), and an MRC Clinician Scientist Fellowship (MR/P008801/1) and NHSBT workpackage (WPA15-02) to NJM. Funding was also received from Addenbrooke’s Charitable Trust and the Cambridge Biomedical Research Centre. We also acknowledge contributions from all staff at CUHNFT Occupational Health and Wellbeing and the Cambridge COVID-19 Testing Centre.

Scene flow tracks the three-dimensional (3D) motion of each point in adjacent point clouds. It provides fundamental 3D motion perception for autonomous driving and server robot. Although the Red Green Blue Depth (RGBD) camera or Light Detection and Ranging (LiDAR) capture discrete 3D points in space, the objects and motions usually are continuous in the macro world. That is, the objects keep themselves consistent as they flow from the current frame to the next frame. Based on this insight, the Generative Adversarial Networks (GAN) is utilized to self-learn 3D scene flow with no need for ground truth. The fake point cloud of the second frame is synthesized from the predicted scene flow and the point cloud of the first frame. The adversarial training of the generator and discriminator is realized through synthesizing indistinguishable fake point cloud and discriminating the real point cloud and the synthesized fake point cloud. The experiments on Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) scene flow dataset show that our method realizes promising results without ground truth. Just as human, the proposed method can identify the similar local structures of two adjacent frames even without knowing the ground truth scene flow. Then, the local correspondence can be correctly estimated, and further the scene flow is correctly estimated.   Corresponding author(s) Email: [email protected]

Leukocyte differential test is a widely performed clinical procedure for screening infectious diseases. Existing hematology analyzers require labor-intensive work and a panel of expensive reagents. Here we report an artificial-intelligence enabled reagent-free imaging hematology analyzer (AIRFIHA) modality that can accurately classify subpopulations of leukocytes with minimal sample preparation. AIRFIHA is realized through training a two-step residual neural network using label-free images of isolated leukocytes acquired from a custom-built quantitative phase microscope. By leveraging the rich information contained in quantitative phase images, we not only achieved high accuracy in differentiating B and T lymphocytes, but also classified CD4 and CD8 cells, therefore outperforming the classification accuracy of most current hematology analyzers. We validated the performance of AIRFIHA in a randomly selected test set and cross-validated it across all blood donors. Owing to its easy operation, low cost, and accurate discerning capability of complex leukocyte subpopulations, we envision AIRFIHA is clinically translatable and can also be deployed in resource-limited settings, e.g., during pandemic situations for the rapid screening of infectious diseases.  Corresponding author(s) Email:    [email protected],  [email protected]

Tam Hunt [1], Jonathan SchoolerUniversity of California Santa Barbara Synchronization, harmonization, vibrations, or simply resonance in its most general sense seems to have an integral relationship with consciousness itself. One of the possible “neural correlates of consciousness” in mammalian brains is a combination of gamma, beta and theta synchrony. More broadly, we see similar kinds of resonance patterns in living and non-living structures of many types. What clues can resonance provide about the nature of consciousness more generally? This paper provides an overview of resonating structures in the fields of neuroscience, biology and physics and attempts to coalesce these data into a solution to what we see as the “easy part” of the Hard Problem, which is generally known as the “combination problem” or the “binding problem.” The combination problem asks: how do micro-conscious entities combine into a higher-level macro-consciousness? The proposed solution in the context of mammalian consciousness suggests that a shared resonance is what allows different parts of the brain to achieve a phase transition in the speed and bandwidth of information flows between the constituent parts. This phase transition allows for richer varieties of consciousness to arise, with the character and content of that consciousness in each moment determined by the particular set of constituent neurons. We also offer more general insights into the ontology of consciousness and suggest that consciousness manifests as a relatively smooth continuum of increasing richness in all physical processes, distinguishing our view from emergentist materialism. We refer to this approach as a (general) resonance theory of consciousness and offer some responses to Chalmers’ questions about the different kinds of “combination problem.”  At the heart of the universe is a steady, insistent beat: the sound of cycles in sync…. [T]hese feats of synchrony occur spontaneously, almost as if nature has an eerie yearning for order. Steven Strogatz, Sync: How Order Emerges From Chaos in the Universe, Nature and Daily Life (2003) If you want to find the secrets of the universe, think in terms of energy, frequency and vibration.Nikola Tesla (1942) I.               Introduction Is there an “easy part” and a “hard part” to the Hard Problem of consciousness? In this paper, we suggest that there is. The harder part is arriving at a philosophical position with respect to the relationship of matter and mind. This paper is about the “easy part” of the Hard Problem but we address the “hard part” briefly in this introduction.  We have both arrived, after much deliberation, at the position of panpsychism or panexperientialism (all matter has at least some associated mind/experience and vice versa). This is the view that all things and processes have both mental and physical aspects. Matter and mind are two sides of the same coin.  Panpsychism is one of many possible approaches that addresses the “hard part” of the Hard Problem. We adopt this position for all the reasons various authors have listed (Chalmers 1996, Griffin 1997, Hunt 2011, Goff 2017). This first step is particularly powerful if we adopt the Whiteheadian version of panpsychism (Whitehead 1929).  Reaching a position on this fundamental question of how mind relates to matter must be based on a “weight of plausibility” approach, rather than on definitive evidence, because establishing definitive evidence with respect to the presence of mind/experience is difficult. We must generally rely on examining various “behavioral correlates of consciousness” in judging whether entities other than ourselves are conscious – even with respect to other humans—since the only consciousness we can know with certainty is our own. Positing that matter and mind are two sides of the same coin explains the problem of consciousness insofar as it avoids the problems of emergence because under this approach consciousness doesn’t emerge. Consciousness is, rather, always present, at some level, even in the simplest of processes, but it “complexifies” as matter complexifies, and vice versa. Consciousness starts very simple and becomes more complex and rich under the right conditions, which in our proposed framework rely on resonance mechanisms. Matter and mind are two sides of the coin. Neither is primary; they are coequal.  We acknowledge the challenges of adopting this perspective, but encourage readers to consider the many compelling reasons to consider it that are reviewed elsewhere (Chalmers 1996, Griffin 1998, Hunt 2011, Goff 2017, Schooler, Schooler, & Hunt, 2011; Schooler, 2015).  Taking a position on the overarching ontology is the first step in addressing the Hard Problem. But this leads to the related questions: at what level of organization does consciousness reside in any particular process? Is a rock conscious? A chair? An ant? A bacterium? Or are only the smaller constituents, such as atoms or molecules, of these entities conscious? And if there is some degree of consciousness even in atoms and molecules, as panpsychism suggests (albeit of a very rudimentary nature, an important point to remember), how do these micro-conscious entities combine into the higher-level and obvious consciousness we witness in entities like humans and other mammals?  This set of questions is known as the “combination problem,” another now-classic problem in the philosophy of mind, and is what we describe here as the “easy part” of the Hard Problem. Our characterization of this part of the problem as “easy”[2] is, of course, more than a little tongue in cheek. The authors have discussed frequently with each other what part of the Hard Problem should be labeled the easier part and which the harder part. Regardless of the labels we choose, however, this paper focuses on our suggested solution to the combination problem.  Various solutions to the combination problem have been proposed but none have gained widespread acceptance. This paper further elaborates a proposed solution to the combination problem that we first described in Hunt 2011 and Schooler, Hunt, and Schooler 2011. The proposed solution rests on the idea of resonance, a shared vibratory frequency, which can also be called synchrony or field coherence. We will generally use resonance and “sync,” short for synchrony, interchangeably in this paper. We describe the approach as a general resonance theory of consciousness or just “general resonance theory” (GRT). GRT is a field theory of consciousness wherein the various specific fields associated with matter and energy are the seat of conscious awareness.  A summary of our approach appears in Appendix 1.  All things in our universe are constantly in motion, in process. Even objects that appear to be stationary are in fact vibrating, oscillating, resonating, at specific frequencies. So all things are actually processes. Resonance is a specific type of motion, characterized by synchronized oscillation between two states.  An interesting phenomenon occurs when different vibrating processes come into proximity: they will often start vibrating together at the same frequency. They “sync up,” sometimes in ways that can seem mysterious, and allow for richer and faster information and energy flows (Figure 1 offers a schematic). Examining this phenomenon leads to potentially deep insights about the nature of consciousness in both the human/mammalian context but also at a deeper ontological level.

Cannabis sativa is an extraordinarily versatile species. Hemp and its cousin marijuana, both C. sativa, have been used for millennia as a source of fibre, oil and for medicinal, spiritual and recreational purposes. Because the consumption of Cannabis can have psychoactive effects, the plant has been widely banned throughout the last century. In the past decade, evidence of its medicinal properties did lead to the relaxation of legislation in many countries around the world. Consequently, the genetics and development of Cannabis as well as Cannabis-derived products are the subject of renewed attention.Here, we review the biology of C. sativa, including recent insights from taxonomy, morphology and genomics, with an emphasis on the genetics of cannabinoid synthesis. Because the female Cannabis flower is of special interest as the site of cannabinoid synthesis, we explore flower development, flowering time well as the species’ unique sex determination system in detail. Furthermore, we outline the tremendous medicinal, engineering, and environmental opportunities that Cannabis bears. Together, the picture emerges that our understanding of Cannabis biology currently progresses at an unusual speed. A future challenge will be to preserve the multi-purpose nature of Cannabis, and to harness its medicinal properties and sustainability advantages simultaneously.

Quantum chemistry must evolve if it wants to fully leverage the benefits of the internet age, where the world wide web offers a vast tapestry of tools that enable users to communicate and interact with complex data at the speed and convenience of a button press. The Open Chemistry project has developed an open source framework that offers an end-to-end solution for producing, sharing, and visualizing quantum chemical data interactively on the web using an array of modern tools and approaches. These tools build on some of the best open source community projects such as Jupyter for interactive online notebooks, coupled with 3D accelerated visualization, state-of-the-art computational chemistry codes including NWChem and Psi4 and emerging machine learning and data mining tools such as ChemML and ANI. They offer flexible formats to import and export data, along with approaches to compare computational and experimental data.

This paper responds to one by Graham Martin and colleagues, who offered a critique of my previous publications on masks for the lay public in the Covid-19 pandemic. I address their charges that my co-authors and I had misapplied the precautionary principle; drawn conclusions that were not supported by empirical research; and failed to take account of potential harms. But before that, I remind Martin et al that the evidence on mask wearing goes beyond the contested trials and observational studies they place centre stage. I set out some key findings from basic science, epidemiology, mathematical modelling, case studies and natural experiments, and use this rich and diverse body of evidence as the backdrop for my rebuttal of their narrowly-framed objections. I challenge my critics’ apparent assumption that a particular kind of systematic review should be valorised over narrative and real-world evidence, since stories are crucial to both our scientific understanding and our moral imagination. I conclude by thanking my academic adversaries for the intellectual sparring match, but exhort them to remember our professional accountability to a society in crisis. It is time to lay straw men to rest and engage, scientifically and morally, with the dreadful tragedy that is unfolding across the world.

This Supporting information includes interactive plots, videos, and data captured while performing evaluation and validation experiments for our paper. 

The construct of wellbeing has been criticised as a neoliberal construction of western individualism that ignores wider systemic issues including increasing burden of chronic disease, widening inequality, concerns over environmental degradation and anthropogenic climate change. While these criticisms overlook recent developments, there remains a need for biopsychosocial models that extend theoretical grounding beyond individual wellbeing, incorporating overlapping contextual issues relating to community and environment. Our first GENIAL model \cite{Kemp_2017} provided a more expansive view of pathways to longevity in the context of individual health and wellbeing, emphasising bidirectional links to positive social ties and the impact of sociocultural factors. In this paper, we build on these ideas and propose GENIAL 2.0, focusing on intersecting individual-community-environmental contributions to health and wellbeing, and laying an evidence-based, theoretical framework on which future research and innovative therapeutic innovations could be based. We suggest that our transdisciplinary model of wellbeing - focusing on individual, community and environmental contributions to personal wellbeing - will help to move the research field forward. In reconceptualising wellbeing, GENIAL 2.0 bridges the gap between psychological science and population health health systems, and presents opportunities for enhancing the health and wellbeing of people living with chronic conditions. Implications for future generations including the very survival of our species are discussed.  

Floral plantings are promoted to foster ecological intensification of agriculture through provisioning of ecosystem services. However, a comprehensive assessment of the effectiveness of different floral plantings, their characteristics and consequences for crop yield across global regions is lacking. Here we quantified the impacts of flower strips and hedgerows on pest control and pollination services in adjacent crops using a global dataset of 529 sites. Flower strips, but not hedgerows, enhanced pest control services in adjacent fields by 16% on average. However, effects on crop pollination and yield were more variable. Our synthesis identifies several important drivers of variability in effectiveness of plantings: pollination services declined exponentially with distance from plantings, and perennial and older flower strips with higher flowering plant diversity enhanced pollination more effectively. These findings provide promising pathways to optimize floral plantings to more effectively contribute to ecosystem service delivery and ecological intensification of agriculture in the future.

Researchers' ability to accurately screen fossil and subfossil specimens for preservation of DNA and protein sequences remains limited. Thermal exposure and geologic age are usable proxies for sequence preservation on a broad scale but are of nominal use for specimens of similar depositional environments. Cell and tissue biomolecular histology is thus proposed as a novel proxy for determining sequence preservation potential of ancient specimens with improved accuracy. Biomolecular histology as a proxy is hypothesized to elucidate why fossils/subfossils of some depositional environments preserve sequences while others do not and to facilitate selection of ancient specimens for use in molecular studies.

IntroductionConsistent with World Health Organization (WHO) advice [1], UK Infection Protection Control guidance recommends that healthcare workers (HCWs) caring for patients with coronavirus disease 2019 (COVID-19) should use fluid resistant surgical masks type IIR (FRSMs) as respiratory protective equipment (RPE), unless aerosol generating procedures (AGPs) are being undertaken or are likely, when a filtering face piece 3 (FFP3) respirator should be used [2]. In a recent update, an FFP3 respirator is recommended if “an unacceptable risk of transmission remains following rigorous application of the hierarchy of control” [3]. Conversely, guidance from the Centers for Disease Control and Prevention (CDC) recommends that HCWs caring for patients with COVID-19 should use an N95 or higher level respirator [4]. WHO guidance suggests that a respirator, such as FFP3, may be used for HCWs in the absence of AGPs if availability or cost is not an issue [1].A recent systematic review undertaken for PHE concluded that: “patients with SARS-CoV-2 infection who are breathing, talking or coughing generate both respiratory droplets and aerosols, but FRSM (and where required, eye protection) are considered to provide adequate staff protection” [5]. Nevertheless, FFP3 respirators are more effective in preventing aerosol transmission than FRSMs, and observational data suggests that they may improve protection for HCWs [6]. It has therefore been suggested that respirators should be considered as a means of affording the best available protection [7], and some organisations have decided to provide FFP3 (or equivalent) respirators to HCWs caring for COVID-19 patients, despite a lack of mandate from local or national guidelines [8].Data from the HCW testing programme at Cambridge University Hospitals NHS Foundation Trust (CUHNFT) during the first wave of the UK severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic indicated a higher incidence of infection amongst HCWs caring for patients with COVID-19, compared with those who did not [9]. Subsequent studies have confirmed this observation [10, 11]. This disparity persisted at CUHNFT in December 2020, despite control measures consistent with PHE guidance and audits indicating good compliance. The CUHNFT infection control committee therefore implemented a change of RPE for staff on “red” (COVID-19) wards from FRSMs to FFP3 respirators. In this study, we analyse the incidence of SARS-CoV-2 infection in HCWs before and after this transition.

This article presents a novel approach to obfuscating database connection strings using Keyword Cipher, which is based on the Roman Caesar Cipher and Greek Scytale Cipher. This is an important and open problem because database connection strings typically have long substrings of identical and well-known character substrings. These known substrings in related database connection strings greatly increase the risk of their encryption key’s being broken, in addition to having identical initial substrings in their encrypted versions. Our experience applying these two obfuscation techniques to database connection strings show that the simple and easily implemented string obfuscation functions effectively solve the problem of common initial substrings. It also greatly reduces risk of breaking the connections strings encryption keys by hiding the known substrings and making the number of possible string needed to search for grow geometrically. Lastly, the use of obfuscation functions completely eliminates all the commonality between related database connection strings.

Axial Seamount, an extensively instrumented submarine volcano, lies at the intersection of the Cobb-Eickelberg hot spot and the Juan de Fuca Ridge. Since late 2014, the Ocean Observatories Initiative (OOI) has operated a seven-station cabled ocean bottom seismometers (OBS) array that captured Axial's last eruption in April 2015. This network streams data in real-time, facilitating seismic monitoring and analysis for volcanic unrest detection and eruption forecasting. In this study, we introduce a machine learning (ML) based real-time seismic monitoring framework for Axial Seamount. Combining both supervised and unsupervised ML and double-difference techniques, we constructed a comprehensive, high-resolution earthquake catalog and effectively discriminated between various seismic and acoustic events. These signals include earthquakes generated by different physical processes, acoustic signals of lava-water interaction, and oceanic sources such as whale calls. We first built a labeled ML-based earthquake catalog that extends from November 2014 to the end of 2021, and then implemented real-time monitoring and seismic analysis starting in 2022. With rapid determination of high-resolution earthquake locations and the capability to track potential precursory signals and co-eruption indicators of magma outflow, this system may improve eruption forecasting by providing short-term constraints on Axial's next eruption. Furthermore, our work demonstrated an effective application that integrates unsupervised learning for signal discrimination in real-time operation, which could be generalized to other regions for volcanic unrest detection and enhanced eruption forecasting.

Ewing sarcoma is a rare primary mesenchymal tumor of the bone that requires an intensive multimodal therapeutic approach. Multidrug chemotherapy regimens are also the backbone for relapsing/recurring Ewing sarcoma treatment, yet when relapse occurs as bone marrow infiltration, combination chemotherapy might be difficult to be administered and prognosis is poor. This report describes the case of a 22-year-old patient with Ewing sarcoma who developed severe pancytopenia due to bone marrow infiltration, who was treated with high-dose continuous infusion ifosfamide, obtaining both clinical, radiological and hematological response lasting for about 7 months.

Explaining variation in individual fitness is a key goal in evolutionary biology. Recently, telomeres, repeating DNA sequences capping the ends of chromosomes, have gained attention as a biomarker for body state, individual quality, and ageing. However, existing research has provided mixed evidence for whether telomere length correlates with fitness components, including survival and reproductive output. Moreover, few studies have examined how telomere shortening correlates with fitness in wild populations. Here, we intensively monitored an insular population of house sparrows on Lundy Island, UK, and collected longitudinal telomere and life history data spanning 16 years from 1,225 individuals. We tested whether telomere length and/or shortening predict fitness measures, namely survival, lifespan, as well as annual and lifetime reproductive success. Telomere length positively predicted immediate survival up to one year after measurement, independent of age, but did not predict lifespan, suggesting either a diminishing telomere length – survival correlation with age, or other extrinsic factors of mortality. The positive effect of telomere length on survival translated to reproductive benefits, as birds with longer telomeres produced more genetic recruits over their lifetime, but not annually, suggesting variation in individual quality. The rate of telomere shortening, however, correlated with neither lifespan nor lifetime reproductive success. Our results provided further evidence that telomere length correlates with fitness, and they contributed to our understanding of how telomere dynamics link with individual quality.

Mountain biota survived the Quaternary cold stages in peripheral refugia and/or ice-free peaks within ice-sheets (nunataks). While survival in peripheral refugia has been broadly demonstrated, evidence for nunatak refugia is still scarce. We generated RADseq data from three mountain plant species occurring at different elevations in the southeastern European Alps to investigate the role of different glacial refugia during the Last Glacial Maximum (LGM). We tested the following hypotheses. (i) The deep Piave Valley forms the deepest genetic split in the species distributed across it, delimiting two peripheral refugia. (ii) The montane to alpine species Campanula morettiana and Primula tyrolensis survived the LGM in peripheral refugia, while high-alpine to subnival Saxifraga facchinii likely survived in several nunatak refugia. (iii) The lower-elevation species suffered a strong population decline during the LGM. By contrast, the higher-elevation species shows long-term stability of population sizes due to survival on permanently ice-free peaks and small population sizes at present. We found peripheral refugia on both sides of the Piave Valley, which acted as a major genetic barrier. Demographic modeling confirmed nunatak survival not only for S. facchinii, but also for montane to alpine C. morettiana. Altitudinal segregation influenced the species’ demographic fluctuations, with the lower-elevation species showing a significant population increase at the end of the LGM, and the higher-elevation species either showing decrease towards the present or stable population sizes with a short bottleneck. Our results highlight the role of nunatak survival and species ecology in the demographic history of mountain species.

High-capacity nickel-rich layered oxides are promising cathode materials for high-energy-density lithium batteries. However, the poor structural stability and severe side reactions at the electrode/electrolyte interface result in unsatisfactory cycle performance. Herein, the thin layer of two-dimensional (2D) graphitic carbon-nitride (g-C3N4) is uniformly coated on the LiNi0.8Co0.1Mn0.1O2 (denoted as NCM811@CN) using a facile chemical vaporization-assisted synthesis method. As an ideal protective layer, the g-C3N4 layer effectively avoids direct contact between the NCM811 cathode and the electrolyte, preventing harmful side reactions and inhibiting secondary crystal cracking. Moreover, the unique nano-pore structure and abundant nitrogen vacancy edges in g-C3N4 facilitate the adsorption and diffusion of lithium ions, which enhances the lithium deintercalation/intercalation kinetics of the NCM811 cathode. As a result, the NCM811@CN-3wt% cathode exhibits 161.3 mAh g-1 and capacity retention of 84.6% at 0.5 C and 55 °C after 400 cycles and 95.7 mAh g-1 at 10 C, which is greatly superior to the uncoated NCM811 (i.e. 129.3 mAh g-1 and capacity retention of 67.4% at 0.5 C and 55 °C after 220 cycles and 28.8 mAh g-1 at 10 C). The improved cycle performance of the NCM811@CN-3wt% cathode is also applicable to solid-liquid-hybrid cells composed of PVDF:LLZTO electrolyte membranes, which show 163.8 mAh g-1 and the capacity retention of 88.1% at 0.1 C and 30 °C after 200 cycles and 95.3 mAh g-1 at 1 C.

In this research, the radially increasing axisymmetric discharge of an electrically conducting fluid across a surface is accurately evaluated mathematically using the influence of Soret number. The surface is extended at an exponential speed in a radial direction, which causes flow trends. New similarity transformations are discussed in order to transform the governing, transform nonlinear partial differential equations into standard derivatives. The Successive Linearization Method is used to perform mathematical analysis for flow performing. The Chebyshev spectral technique is utilized to resolve the linear system in order to provide accurate solutions that converge effectively to the whole numerical solution. Comparisons with earlier research are conducted to evaluate the validity of the results on the distribution of velocity, temperature and concentration. Verify convergence and accuracy of the solution, the impacts of a few fluid factors are identified and explained.

A document by Bhupendra Bahadur Singh. Click on the document to view its contents.

Aim: The genus Stasimopus is endemic to South Africa but has never undergone a phylogeographic review. This study aims to unravel the phylogeographic patterns and history of the many Stasimopus species which occur in the greater Karoo region. Location: The Karoo region, South Africa Time period: 2017-2018 Major taxa studied: Cork-lid trapdoor spider, Stasimopus (15 species). Methods: A fossil-calibrated phylogeny was produced based on three gene regions (CO1, 16S and EF-1ɣ) for Stasimopus specimens collected in the Karoo region, to infer dates of origin and diversification. Demographic analyses were performed on species with sufficient sample sizes (>4). Haplotype networks were constructed for each gene region and plotted on a map to infer phylogeographic patterns. Lastly, Mantel tests were performed to test for isolation by distance. Results: It was found that 15 species occur in the Karoo and that the genus radiation in the area is in the early Paleocene. Most diversification occurred between the late Eocene and the Miocene. Several species show signals of demographic expansions. Isolation by distance was detected, but only with a slight correlation. Main conclusions: It is apparent that aridification has played a vital role in the diversification of the genus in the Karoo region. This is a shared biogeographic influence between the mygalomorph fauna of the Karoo and arid region of western Australia. Stasimopus has radiated from the late Eocene and through the Miocene resulting in 15 extant species in the region. The Tankwa Karoo has been identified as a possible Pleistocene glacial cycle refugia for the species S. leipoldti. Many of the species in the Karoo are short range endemics, making them of high conservation concern. This study provided vital information as the Karoo is undergoing further desertification due to factors such as climate change, which may affect the future of short-range endemic spiders.

Data-driven modeling approaches for crop yield prediction have exponentially increased in the last decade due to the greater availability of spatial data from various sensors. Yet, most yield modeling has focused on major commodities, leaving lesser-cultivated horticultural crops like sweetpotato relatively undertooled, though these crops considerably contribute to the global economy and food supply. The U.S. is the primary exporter of sweetpotato (271 K tonnes), with 21% of U.S.-grown sweetpotatoes being exported. Early yield forecasting at the county scale offers crucial insights for growers, packers, wholesalers, and associated industries, enabling them to anticipate variations in yield to make informed decisions. While roots and tubers have demonstrated a relationship between yields and above-ground plant characteristics, it remains uncertain whether forecasting models that utilize remotely sensed data, including vegetation indices, are suitable for sweetpotato. We developed county-scale in-season sweetpotato yield forecast models using machine learning (ML) algorithms and multitemporal remote sensing environmental data. Four of the most commonly used ML algorithms for predicting crop yield-Random Forest Regression (RFR), Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB)-were applied using stationary (topography and soil characteristics), and temporal (weather, NDVI, and Growing Degree Days) variables as potential predictors. Six predictor sets were tested to identify key predictor variables, optimal aggregation time (16 or 32 days composite) of the temporal variables, and how early in the growing season the models can reliably predict end-of-season yields. U.S. Annual CropScape land cover layers were used to identify sweetpotato fields, over which temporal variables were aggregated, and sweetpotato yields were tabulated from the USDA Agricultural Survey from 2008 to 2022. The Boruta method was used for feature selection across each predictor set before training the ML models. RFR outperformed other ML algorithms and the RFR models' evaluation metrics were the most consistent across the six predictor sets. The RFR model that incorporated early and mid season temporal variables as 16-day composites was selected and proposed for future sweetpotato yield forecasting due to its performance (R 2 = 0.44, RMSE = 3.53 tonnes.ha-1), as well as ability to predict early enough 1 in the season to provide actionable information. In the final model, several stationary variables (elevation, nitrogen, cec, soc, and clay content) were the most predictive of sweetpotato yield. After these stationary variables, NDVI and precipitation from the time around storage root initiation and bulking (July), and minimum temperature around planting (June) followed in importance.

An open question in epidemiology is why transmission is often overdispersed, meaning that most new infections are driven by few infected individuals. For example, around 10\% of COVID-19 cases cause 80\% of new COVID-19 cases. This overdispersion in pathogen transmission is likely driven by intrinsic biological heterogeneity among hosts, i.e. variability in SARS-CoV-2 viral loads. However, host heterogeneity could also indirectly increase transmission dispersion by driving pathogen adaptation. Specifically, transmission variation among hosts could drive pathogen specialization to highly-infectious hosts. Adaptation to rare, highly-infectious hosts could amplify transmission dispersion by simultaneously decreasing transmission from common, less-infectious hosts. This study considers whether increased transmission dispersion can be, in part, an emergent property of parasite adaptation to heterogeneous host populations. We develop a mathematical model using a Price equation framework to address this question that follows the epidemiological and evolutionary dynamics of a general host-parasite system. The results predict that parasite adaptation to heterogeneous host populations drives high transmission dispersion early in epidemics. Further, parasite adaptation can maintain increased transmission dispersion at endemic equilibria as long as virulence differs between hosts in a heterogeneous population. More broadly, this study provides a framework for predicting how parasite adaptation determines transmission dispersion for emerging and re-emerging infectious disease.

The primary external energy input to planetary atmospheres comes from solar radiation. Accurate estimates of solar irradiance are needed in order to understand atmospheric conditions at other planets. Many studies have interpolated Earth-based irradiance measurements at other planets, disregarding the evolution of solar features, which are responsible for the irradiance variability. We present an approach for assessing solar irradiance at other planets by using synthetic full surface magnetograms of the Sun, generated by the Surface Flux Transport Model, and then converted into sunspots and faculae areas. Following the Spectral And Total Irradiance REconstruction approach, we compute the irradiance as the sum of contributions from sunspots, faculae, and the quiet Sun. We calculate the S-index, which is a proxy for near-ultraviolet irradiance, and the total solar irradiance, which is the wavelength-integrated value. We demonstrate that a simple phase shift of the Earth-observed total solar irradiance does not produce reliable estimates in the ecliptic. By comparing and correlating our results with the interpolated measurements, we find that the two models agree in the S-index variability, because faculae have a long lifetime, so the effect of the solar rotation becomes less important. Conversely, the methods disagree strongly in the total solar irradiance, since sunspots have a short lifetime, and the interpolation does not take into account their emergence and evolution on the far-side of the Sun, as it only uses the information of the disc visible for an Earth-bound observer.