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Michael Weekes

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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: mpw1001@cam.ac.ukThe 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.

Guangming Wang

and 4 more

Tam Hunt

and 1 more

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.

Susanne Schilling*^

and 9 more

Jessica mead

and 6 more

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.  

Mark Ferris

and 14 more

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.

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Rui-Rui Wang

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Maternal lineages of mitochondrial DNA (mtDNA) are recognized as important components of intra- and inter-specific biodiversity and help us to disclose the phylogeny and divergence times of many taxa. Species of the genus Capra are canonical mountain dwellers. Among these is the Siberian ibex (Capra sibirica), which is regarded as a relic species whose intra-specific classification has been controversial so far. We collected 54 samples in Xinjiang, China, and analyzed the mtDNA genes to shed light on the intra-specific relationships of the C. sibirica populations and estimate the divergence time. Intriguingly, we found that the mtDNA sequences of C. sibirica split into two main lineages in both phylogenetic and network analyses: the southern lineage, sister to C. falconeri, consisting of samples from India, Ulugqat, and Kagilik in Xinjiang; and the northern lineage further divided into four monophyletic clades A–D corresponding to their geographic origins. Samples from Urumqi, Sawan, and Arturk formed a distinct monophyletic clade C within the northern lineage. The genetic distance between the C. sibirica clades ranges from 3 to 8.6 percent, with values of FST between 0.72 and 0.95, indicating notable genetic differentiation. The split of the genus Capra occurred approximately 6.75 Mya during the late Miocene. The northern lineage diverged around 5.88 Mya, following the divergence of Clades A–D from 3.3 Mya to 1.9 Mya during the late Pliocene and early Pleistocene. The radiation between the southern lineage and C. falconeri occurred at 2.29 Mya during the early Pleistocene. Our results highlight the importance of extensive sampling when relating to genetic studies of alpine mammals and call for further genomic studies to draw definitive conclusions.

Ping Yang

and 3 more

Abstract: Path planning is a crucial component for ensuring the safety and efficiency of flight missions, especially for fighter aircraft. To enhance the combat effectiveness of fighter aircraft, it is important to consider how to avoid danger sources and terrain obstacles, reduce fuel consumption, and utilize the aircraft’s own performance to accomplish the mission objectives. In the modern battlefield environment, the shortest path is not the only criterion for planning, but also other factors such as the threat level to the aircraft, fuel consumption, mission completion time, and minimum turning radius. In this paper, we propose a multi-constraint path planning method for fighter aircraft that incorporates these factors into an improved particle swarm algorithm. We transform the constraints of three-dimensional terrain, threat source, fuel consumption, and mission time into an aggregated fitness function. We construct a limit curvature matrix to evaluate the feasibility of the generated path. We also introduce an adaptive adjustment strategy based on the activation function for the parameters in the particle swarm algorithm. The weights of each constraint are determined according to the actual demand. The experiment results show that our method can efficiently plan the optimal path that satisfies the requirements. Compared with other improved particle swarm algorithms, our method has higher optimal search efficiency and better convergence effect. We also provide optimal values for important parameters such as mission energy consumption, mission time, flight speed and others to support the overall mission planning. Our method has certain practical application value.

Rachael Thomas

and 3 more

Bong Soo Park

and 8 more

Jinlin Lin

and 3 more

Introduction: Obesity is a major risk factor in the development of endometrial cancer (EC) in young patients of reproductive age. Fertility sparing treatment is a viable option for a select group of patients with early EC, and involves systemic and intra-uterine hormonal therapy. Weight loss has been associated with improved outcomes in this group. Bariatric surgery (BS) has been shown to be the most efficient and durable method of weight loss in obese patients. However, there is a paucity of data studying the benefit of BS as part of fertility sparing treatment. Methods: We present a retrospective case series of five patients who are undergoing fertility sparing treatment for early EC, who also underwent BS for treatment of obesity and related comorbidities. We aim to show early regression of EC for all the patients and also report on the other health benefits of BS. Results: All five patients in the series achieved regression of EC within six months of undergoing BS. They also achieved significant weight loss consistent with previous studies, and three patients who had comorbidities related to obesity had remission of these conditions. One of the patients with EC regression also managed to conceive with IVF. Conclusion: Patients on fertility sparing treatment for early EC who underwent BS was associated with early regression within 6 months, significant weight loss and resolution of comorbidities. BS could be a promising component of fertility sparing treatment. Long term, prospective studies are required to confirm the benefits reported in this case series.

Emily C. Chow

and 6 more

Background: As the ongoing public health crisis from Coronavirus Disease 2019 (COVID-19) pandemic puts strains on current models of cancer care, many health care centers had to adapt to minimize the risk of exposure and infection. The effects of the COVID-19 pandemic in a comprehensive cancer center were determined. Purpose: To measure the impact of the COVID-19 pandemic on care delivery at a comprehensive cancer center. Methods: The number of on-site and telehealth visits (TH) were obtained from scheduling software. Multiple factors including total visits, telehealth visits, screenings for cancer diagnosis, and cancer treatments were tracked from two years before the pandemic onset through 2022. The length of stay (LOS) and Case Mix Index (CMI) were calculated using hospital database. Results: In the third quarter of FY 2020, telehealth visits (TH) represented a fifth of total patient encounters. Cancer treatments, such as chemotherapy, radiation therapy, and surgery, decreased during the pandemic with number of surgeries being most affected (23% decrease in 2020 compared to the previous fiscal year). The average length of stay (LOS) was also longer with less discharges per given time during the pandemic. The increased LOS was related to increased severity of patient illnesses since CMI was higher. Screening mammograms decreased to a nadir of 58% in 2021 as compared to those screened in pre-pandemic fiscal years. Conclusions : The COVID-19 pandemic impacted many aspects of care, such as treatment and screenings. Many of these factors had to be postponed due to the fear of acquiring COVID-19 and access to care. The findings presented implicate that the delays and changes in cancer care during the pandemic resulted in less screening and treatment of more advanced disease.

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Mulugeta Tuji Dugda

and 4 more

Large earthquakes, especially those occurring in a city or population centers, create devastation and havoc, and often times kindle several deaths and injuries, and significant infrastructure damage that lead to several billions of dollars in losses. Marine earthquakes are the leading cause of large tsunamis which cause deaths, destruction, displacement of population, and a possible nuclear meltdown. Thus, prediction of earthquake or its aftershocks or earthquake early warning system has a great potential to mitigate the loss of life as well as different kinds of damage. Earthquake prediction would mean forecasting the occurrence of an earthquake by providing both its magnitude estimate and accurate location. Earthquake prediction has been an important area of seismology research for quite a while, and it looks like it will continue to be an important area of research. Recently, with the implementation of deep learning in seismology, scientists have been able to detect, predict, and model seismic waves and earthquake aftershocks. Earthquake aftershocks are generally triggered by changes in stress formed by large earthquakes that happen within, or surrounding a given fault network system. The main goal of this study is to investigate the improvement of aftershock pattern predictions with the implementation of tuning and optimizing of deep learning parameters. To achieve these goals, we have developed an algorithm that can help first gather mainshock-aftershock sequence data. Some of the criteria used in identifying earthquakes that initiate an aftershock is to look at earthquakes that happen within a certain radius, the values we attempted are within about 0.5 degrees range, and within a certain period, from few seconds to several weeks of the occurrence of the main shock. For the sequence identification, we have been using seismic data from the United States Geological Survey (USGS)-National Earthquake Information Center (NEIC). We are also looking at different open-source data gathered by researchers for a similar study. The deep neural networks we are implementing make use of Keras python Toolkit, and Theano and Tensorflow libraries, with a plan to use PyTorch python library instead of Theano library in the future because of some maintenance issues. To this point our attempts have shown a good progress.

Mulugeta Tuji Dugda

and 4 more

Seismology is a data-driven science with a huge amount of data gathered for over a century. Though seismic data recording started in 1900, the growth of seismic data has obviously been exponentially in the last three decades. This data growth can be easily noticed if one takes a close look at just one of the largest seismological data centers in the US, the Data Management Center (DMC) of the Integrated Research Institutions in Seismology (IRIS). Data at the DMC grew from less than 10 Tebibytes in 1992 to about 800 Tebibytes in 2022. With the availability of such a large amount of seismic data, it is paramount to develop new seismic data processing and management tools to help analyze and find new and better seismic models. Developing new big seismic data processing and management tools will be helpful to make the best use of such growing big seismological data sets. The main goal of this investigation is the development of efficient data manipulation and processing tools for retrieval, processing, merging, aggregation, and management of big seismic data from disparate data sources. In this study, such big seismic data processing tools are being developed using python programming language and open source python libraries, and the tools we are developing will be helpful to extract, split, and convert, merge and process big seismic data. In addition, python is very suitable for data science and has powerful libraries to process and manage data and applications. Significant contributions have been made in recent python based libraries for seismic data processing, though there are still some rooms for improvement when it comes to seismic applications to merge, convert, manage  and process big seismic data from disparate data sources and converting different file formats. Seismic data from different networks surrounding the Rio Grande Valley have been collected from different data sources. Our attempt is to test the developed tools and evaluate their performance. This study has made important progress in this regard and the results are promising. 
The true bottleneck of artificial intelligence (AI) is not access to the data, but rather labeling this data. We have tons of raw agriculture image data coming from various sources and manual labelling remains to be a crucial step to keep the data well organized which requires considerable amount of time, money, and labor. This process can be made more efficient if we can automatically label the raw data. We propose contrastive learning representations for agriculture images (AgCLR) model that uses self-supervised representation learning approach on unlabeled Encirca Notes data (customer generated field notes data), to learn the useful image feature representations from the real-world agriculture images. Contrastive learning is a self-supervised approach that enables model to learn attributes by contrasting samples against each other without the use of labels. AgCLR leverages the state-of-the-art SimCLRv2 framework to learn representations by maximizing the agreement between differently augmented views of same sample. We have incorporated critical enablers like mixed precision, multi-GPU distributed parallel computing, and use of Google Cloud's Tensor Processing Units (TPU) for optimizing the training process. We achieved 80.2% accuracy while classifying the test data. We further applied AgCLR to unrelated task to determine the alleys and rows in corn field videos for corn phenotyping and we observed two cluster formations for alleys and rows when plotted embeddings in a 3-dimensional space. We also developed a content-based image retrieval tool (pixel affinity) to identify similar images in our database and results were visually very promising.

Selim Polat

and 3 more

Objective: The aim of this research was to elucidate the effect of deep brain stimulation on apathy, and cognitive functions in the pre and post-operative period. Materials & Methods: This study was conducted in Adana City Training & Research Hospital, Parkinson and Movement Disorders Center between January to December 2022. Individuals were evaluated by a multidisciplinary commission consisting of neurology, neurosurgery and psychiatrists. Thirty six, aged between 18–70 years who underwent Deep Brain Stimulation at the neurosurgery clinic were included in the study. Hamiltonanxiety and depression, apathy assessment, standard mini-mental test and Montreal Cognitive Assessment scales are applied to the patients. Results: The mean Apathy Score at the pre-op was 47.77±15.83 in patients who had undergone DBS operation while it was 30.83±13.59 in the post–op. This decrease was statistically significant (p<0.003) and indicated clinical improvement. The average Hamilton Anxiety scale scores at the pre–op was 11.50±5.14, and s 10.22±5.57 at the post-op with no clinical significance (p=0.28). The UPDRS-ON value was determined as 22.55±7.53 in the pre–op and 14.50±6.99 in the post–op significantly (p<0.001). UPDRS-OFF was found to be significant with pre–op 37.44±9.85, compared to post–op 23.44±7.86 (p<0.001). Conclusion: Regarding the results of this study, it was found that sub – thalamic stimulation led to stabilization of both motor and non-motor complications. Additionally DBS ameliorated apathy and Parkinson’s Disease symptoms of patients significantly. Future studies with larger sample size that focus on both pharmacological and non-pharmacological treatments might provide better clinical aspects.

Yuyun Yang

and 1 more

It is widely recognized that fluid injection can trigger fault slip. However, the processes by which the fluid-rock interactions facilitate or inhibit slip are poorly understood and some are neglected or oversimplified in most models of injection-induced slip. In this study, we perform a 2D antiplane shear investigation of aseismic slip that occurs in response to fluid injection into a permeable fault governed by rate-and-state friction. We account for pore dilatancy and permeability changes that accompany slip, and quantify how these processes affect pore pressure diffusion, which couples to aseismic slip. The fault response to injection has two phases. In the first phase, slip is negligible and pore pressure closely follows the standard linear diffusion model. Pressurization of the fault eventually triggers aseismic slip in the immediate vicinity of the injection site. In the second phase, the aseismic slip front expands outward and dilatancy causes pore pressure to depart from the linear diffusion model. Aseismic slip front overtakes pore pressure contours, with both subsequently advancing at constant rate along fault. We quantify how prestress, initial state variable, injection rate, and frictional properties affect the migration rate of the aseismic slip front, finding values ranging from less than 50 to 1000 m/day for typical parameters. Additionally, we compare to the case when porosity and permeability evolution are neglected. In this case, the aseismic slip front migration rate and total slip are much higher. Our modeling demonstrates that porosity and permeability evolution, especially dilatancy, fundamentally alters how faults respond to fluid injection.

Menaka Revel

and 3 more

Understanding spatial and temporal variations in terrestrial waters is key to assessing the global hydrological cycle. The future Surface Water and Ocean Topography (SWOT) satellite mission will observe the elevation and slope of surface waters at <100 m resolution. Methods for incorporating SWOT measurements into river hydrodynamic models have been developed to generate spatially and temporally continuous discharge estimates. However, most of SWOT data assimilation studies have been performed on a local scale. We developed a novel framework for estimating river discharge on a global scale by incorporating SWOT observations into the CaMa-Flood hydrodynamic model. The local ensemble transform Kalman filter with adaptive local patches was used to assimilate SWOT observations. We tested the framework using multi-model runoff forcing and/or inaccurate model parameters represented by corrupted Manning’s coefficient. Assimilation of virtual SWOT observations considerably improved river discharge estimates for continental-scale rivers at high latitudes (>50°) and also downstream river reaches at low latitudes. High assimilation efficiency in downstream river reaches was due to both local state correction and the propagation of corrected hydrodynamic states from upstream river reaches. Accurate global river discharge estimates were obtained (Kling–Gupta efficiency [KGE] > 0.90) in river reaches with > 270 accumulated overpasses per SWOT cycle when no model error was assumed. Introducing model errors decreased this accuracy (KGE ≈ 0.85). Therefore, improved hydrodynamic models are essential for maximizing SWOT information. These synthetic experiments showed where discharge estimates can be improved using SWOT observations. Further advances are needed for data assimilation on global-scale.

Ge Li

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The Leech River fault (LRF) zone located on southern Vancouver Island is a major regional seismic source. We investigate potential interactions between earthquake ruptures on the LRF and the neighboring Southern Whidbey Island fault (SWIF), which can be interpreted as a step-over fault system. Using a linear slip-weakening frictional law, we perform 3D finite element simulations to study rupture jumping scenarios from the LRF (source fault) to the SWIF (receiver fault), focusing on the influences of the offset distance, fault initial stress level, and fault burial depth. We find a smaller offset distance, a higher initial stress level on either fault or a shallower fault burial depth will promote rupture jumping. Jumping scenarios can be interpreted as the response of the receiver fault to stress perturbations radiated from the source fault rupture. We demonstrate that the final rupture jumping scenario depends on various parameters, which can be collectively quantified by two keystone variables, the time-averaged Over Stressed Zone (where shear stress exceeds static frictional strength on the receiver fault) size $\overline{R_e}$ and the receiver fault initial stress level. Specifically, a smaller offset distance, a higher initial shear stress level, or a shallower burial depth will lead to a larger $\overline{R_e}$. The seismic moment on the receiver fault increases with increasing $\overline{R_e}$. When $\overline{R_e}$ reaches the threshold dependent on the receiver fault initial stress level, the rupture becomes break-away.
This study presents the micro- and macrophysical cloud properties as a function of their surface coupling state with the sea ice during the wintertime of the MOSAiC field experiment. Cloud properties such as cloud base height, liquid- and ice water content have been previously found to have statistically distinguished features under the presence of sea ice leads (characterized by sea ice concentration, SIC) along downwind direction from the central observatory RV  Polarstern. Those findings are mainly in an increase of liquid water content, and favored occurrence of low level clouds as contrasted to situations when the clouds are thermodynamically decoupled.The present contribution is an update considering two recent developments in the liquid detection in clouds and in the detection of sea ice leads. First, radar and lidar-based cloud droplet detection approaches like Cloudnet (Illingworth et al. 2007, Tukiainen et al. 2020) using Arctic wintertime observations and applied to measurements by the Atmospheric Radiation Measurement mobile facility (ARM) instrumental suite on-board the RV Polarstern during  MOSAiC.Secondly, we explore a new sea ice lead fraction product based on sea ice divergence. Sea ice divergence is estimated from sequential images of space-borne synthetic aperture radar with a spatial resolution of 700 m. The lead divergence product, being independent of cloud coverage, offers the unique advantage to detect opening leads at high spatial resolution.Statistics for the wintertime cloud properties based on the coupling state with the sea ice concentration and sea ice lead fraction will be presented as an approach to study Arctic clouds and their interaction with sea ice.

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