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FEATURED DOCUMENTS

Single-dose BNT162b2 vaccine protects against asymptomatic SARS-CoV-2 infection

Michael Weekes

and 11 more

February 24, 2021
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.
SFGAN: Unsupervised Generative Adversarial Learning of 3D Scene Flow from the 3D
Scen...

Guangming Wang

and 4 more

October 04, 2021
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: wanghesheng@sjtu.edu.cn
Artificial  Intelligence Enabled Reagent-free Imaging Hematology
Analyzer            

Xin Shu

and 6 more

November 01, 2021
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: 
  rjzhou@cuhk.edu.hk,  rishikesh.pandey@uconn.edu
The "easy part" of the Hard Problem: a resonance theory of consciousness

Tam Hunt

and 1 more

January 04, 2019
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.
The Cream of the Crop: Biology, Breeding and Applications of Cannabis sativa

Susanne Schilling*^

and 9 more

October 01, 2020
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.
Open Chemistry, JupyterLab, REST, and Quantum Chemistry

Marcus D. Hanwell

and 7 more

August 26, 2020
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.
Masks for the public: laying straw men to rest

Trisha Greenhalgh

April 28, 2020
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.
Supporting Information for "Learning Assembly Tasks in a Few Minutes by
Combining Imp...

Padmaja Kulkarni

and 3 more

October 18, 2021
This Supporting information includes interactive plots, videos, and data
captured while performing evaluation and validation experiments for our paper. 
Rethinking wellbeing: Toward a more ethical science of wellbeing that considers
curre...

Jessica mead

and 6 more

August 22, 2019
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.  
Global synthesis of the effectiveness of flower strips and hedgerows on pest
control,...

Matthias Albrecht

and 42 more

April 06, 2020
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.
Biomolecular Histology as a Novel Proxy for Ancient DNA and Protein Sequence
Preserva...

Landon A. Anderson

December 13, 2022
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.
FFP3 respirators protect healthcare workers against infection  with SARS-CoV-2

Mark Ferris

and 14 more

June 30, 2021
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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(Anisoptera...

Ali Salur

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Positive-to-negative tunable delay circuit designed with NGD RC network

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and 6 more

November 06, 2023
Despite the performed progressive research work, the interpretation of negative
group delay (NGD) function remains not familiar to non-specialist design and
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by using low-pass (LP) type NGD one is described. The design formulas for
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Machine learning approach for classifying and predicting depressive behavior
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mateo alzate

and 3 more

November 06, 2023
The creation of a system for depression detection is proposed, based on the
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Functional connectivity induced by social cognition task predict individual
differenc...

Li Geng

and 4 more

November 06, 2023
Background: Loneliness poses threats to both physical and mental well-being and
has become a critical public health concern. Loneliness is intricately connected
to social cognition, yet the precise brain mechanisms that underscore their
relationship need further exploration. Hence, the current study aims to unveil
the contribution of brain functional connectivity induced by social cognition to
the prediction of loneliness. Methods: The present study employed a theory of
mind processing task that engaged participants in assessing the trajectories of
geometric shapes while undergoing fMRI scans. The comprehensive data pool
encompassed loneliness assessments and brain imaging data from a cohort of 157
participants. Utilizing a machine learning approach, task-induced functional
connectivity data was used to predict individuals’ loneliness scores. Results：
The findings unveil that specific patterns of task-induced alterations in brain
functional connectivity could predict loneliness scores. Further dissection of
the data disclosed pivotal nodes, including the prefrontal cortex,
temporoparietal junction, and amygdala, among other cerebral regions.
Furthermore, functional connectivity among the pre-defined social network, the
default mode network, and somatomotor networks emerged as crucial factors in
prediction. Conclusion: Brain regions contributed strongly in prediction are
involved in a variety of social cognitive processes, including intention
inference, empathy, and information integration. The results illuminate the
association between brain functional connectivity induced by social cognition
and loneliness, which enhance the comprehensive understanding of this complex
emotional state and may have implications for its diagnosis and intervention.
OsJAZ5 promotes salt tolerance in rice and physical interactions with OsMYL1 and
OsMY...

Xiangyang Li

and 8 more

November 06, 2023
Salt stress is one of the main abiotic stresses that affect plants. The jasmonic
acid ( JA) signaling pathway is an important pathway that converts the normal
growth state into the stress state when plant cells encounter abiotic stress.
OsJAZ5 is a JAZ protein family gene, but how the JA signaling pathway promotes
rice responses to salt stress is unclear. In this study, after screening for the
OsJAZ5 gene, a yeast function analysis using the yeast mutant Axt3k was
performed to determine its salt resistance sensitivity. The phenotype analysis
showed that it was overexpressed in rice and promoted growth under salt stress.
An analysis of the superoxide dismutase, peroxidase, phenylalanine ammonolyase,
and catalase activities showed that the OE-3 and OE-5 lines promoted OsJAZ5
activity to maintain the reactive oxygen species dynamic balance under salt
stress. In addition, the protein-protein interaction analysis and
protein-protein docking confirmed that OsJAZ5 interacts with OsMYL1 and OsMYL2,
demonstrated that OsJAZ5 promotes reactions by OsMYL1, OsMYL2, and OsMYC2 and
that their expressions were further enhanced by OsJAZ5 through JA signaling of O
sMYC2. Thus, expression of the JA signaling response gene was activated by salt
stress and its effect on OsJAZ5 promoted salt tolerance in rice.
Unraveling the neglected role of UV radiation on stomata: a meta-analysis with
implic...

Otmar Urban

and 3 more

November 06, 2023
Stomata play a pivotal role in regulating gas exchange between terrestrial
plants and the atmosphere controlling water and carbon cycles at organismal,
ecosystem and global levels. Accordingly, our objective was to investigate the
impact of ultraviolet-B radiation, a neglected environmental factor varying with
ongoing global change, on stomatal morphology and function by means of a
comprehensive meta-analysis. We found 45 peer-reviewed publications containing
altogether 143 case studies for analysis. The overall UV effect at the leaf
level is to decrease stomatal conductance, stomatal aperture and stomatal size,
although stomatal density was increased. The significant decline in conductance
is marked in short-term experiments, with more modest decreases noted in
long-term UV studies. We found that short-term experiments in growth chambers
are not representative of long-term field UV effects on stomatal conductance.
Further, we found a stronger UV effect in grasses than in herbs, while the
reduction of stomatal conductance was insignificant in trees. It is hypothesised
that these alterations in stomatal function have important potential
consequences for plant life. In the short term, UV-mediated stomatal closure may
reduce transpiration and alleviate drought stress. However, in the long term
more complex changes in stomatal aperture, size and density may reduce carbon
sink capacity, and enhance leaf and surface warming, potentially exacerbating
the negative effects of drought and/or heatwaves on plant ecosystems and
endangering long-term plant survival.
Overexpression of AhTDC under waterlogging stress reduces kernel filling rate
due to...

Yong Chen

and 7 more

November 06, 2023
Summary: The hindrance of kernel development caused by waterlogging stress (WS)
is the direct reason for the peanut yield reduction. Currently, the mechanism of
kernel filling responding to WS remains unknown. The waterlogging-sensitive
variety Huayu 39 was subjected to WS for 3 days after 7 days after gynophores
touched the ground (DAG), and the key stage of WS affecting kernel filling is
14, 21, and 28 DAG. WS decreased the average filling rate and kernel dry weight.
Therefore, transcriptome sequencing and widely-targeted metabolomic analysis
were conducted on kernel to elucidate the mechanism for the decrease in average
filling rate under WS, revealing that overexpression of the gene encoding
tryptophan decarboxylase ( AhTDC), which caused the accumulation of melatonin,
reduced kernel weight. The sucrose transformation rate was limited by the
crosstalk between melatonin and ethylene, thereby reducing the kernel filling
rate and hindering kernel development. Our results are crucial for formulating
measures to alleviate the negative impact of WS on peanut yield and quality,
providing a basis for exploring high-yield and high-quality cultivation,
molecular-assisted breeding, and waterlogging prevention.
Optimization of subcritical carbon dioxide isobaric extraction process and fatty
acid...

Xing Chen

and 4 more

November 06, 2023
This article studied the process of subcritical carbon dioxide isobaric
extraction of chive seed oil. Box Behnken design was used for experimental
design and optimization, and the effects of main parameters such as extraction
pressure (11-23 MPa), temperature (50-70 ℃), and extraction time (60-150
minutes) on the experimental results were analyzed, the response surface method
(RSM) and artificial neural network (ANN) were applied for modeling and
predicting of the extraction yield. the performance of RSM and ANN models were
analyzed and compared by statistical parameters such as coefficient of
determination (R 2), root mean square error (RMSE), mean absolute error (MAE),
and chi-square (χ 2). the RSM model is more accurate than the ANN model.
Subsequently, optimization was carried by two different approaches viz. RSM and
ANN-GA, by comparing the RSM and ANN models and the results of RSM and ANN-GA
optimization, the RSM model is closer to perfection than the predictions of ANN.
the optimal extraction conditions obtained by the RSM model were: extraction
pressure of 15.63 MPa, separation temperature of 57.3°C, extraction time of
121.2 min,and predicted value 15.89%, mean value of three sets of parallel
experiments 15.79%, the fatty acid composition analysis of leek seed oil
optimized by RSM showed that linoleic acid (60.871%), oleic acid (19.185%),
palmitic acid (11.517%) and stearic acid (3.174%) were the main components, and
the content of four fatty acids was more than 94%.
Enzymatic Conversion of Camellia Seed Oil into Glycerol Esters: Synthesis,
Characteri...

Jingzhi Wu

and 7 more

November 06, 2023
The conversion of triglycerides in edible oils into diacylglycerols (DAGs) is of
great significance for obtaining products with health benefits. Camellia seed
oil (C-oil), which is rich in oleic acid and linoleic acid, is an excellent raw
material for the production of DAGs. In this study, the hydrolysis rate reached
87.14% under optimal hydrolysis conditions (reaction temperature of 60 °C,
reaction time 24 h, 30% water content and 4% enzyme addition) with RMIM as
catalyst, and camellia seed oil diacylglycerol (C-DAG) with a content of 62.49%
was also given under optimal esterification conditions (vacuum system, 3% enzyme
addition, 2% water addition, reaction temperature of 50 oC, and substrate molar
ratio of free fatty acid to glycerol of 1:1). The high content of DAG was
obtained by a coupled method, which eliminated the purification steps and
reduced production costs. C-oil, and C-DAG have been characterized by means of
GC, TG, DSC, and GC-IMS. Our results showed that the enzymatic coupling method
did not affect the structural composition of the substances themselves, but did
affect the crystallization and melting properties of the oils. Moreover, the
taste of C-DAG was more delicate flavor than that of C-oil. Finally, the
reaction mechanism has been analyzed by means of infrared spectroscopy, which
showed that C-oil was mostly hydrolyzed to free fatty acids. C-DAG exhibited
ester C-O stretching vibrations in the range 1280–1030 cm-1, indicating
successful esterification reaction between camellia seed oil free fatty acids
(C-FFAs) and glycerol under catalysis by the enzyme.
Effectiveness of symbiotic nitrogen fixing bacteria on Stylosanthes guianensis
from a...

Cristian Richard Sierra Redhead

and 4 more

November 06, 2023
The Madre de Dios region in southeastern Peru, known for its biological and
cultural diversity, is threatened by alluvial gold mining, which has led to
deforestation and ecosystem degradation. This article focuses on the nodulation
capacity of symbiotic nitrogen-fixing bacteria in the plant Stylosanthes
guianensis in areas degraded by alluvial gold mining in Madre de Dios. The
objectives were to characterize bacterial strains, evaluate their nodulation
capacity and their influence on plant growth in mining substrate. The results
indicate that the inoculation of these bacteria had a positive impact on the
development of S. guianensis, promoting growth and the formation of effective
nodules in all test conditions. In addition, it was observed that the
slow-growing strains in vitro had a more beneficial effect on plant growth than
the fast-growing strains. These findings suggest that inoculation of
nitrogen-fixing bacteria may be an effective strategy for the restoration of
degraded areas, highlighting the importance of symbiosis between these bacteria
and the plant in the recovery of degraded ecosystems, and raises the need for
further research to better understand the species involved in biological
nitrogen fixation and to determine the best inoculants to increase forage yields
of S. guianensis.

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DOCUMENTS RECENTLY ACCEPTED IN SCHOLARLY JOURNALS

Outer-Loop Admittance And Motion Control Dual Improvement via A Multi-function
Observ...

Kangwagye Samuel

and 3 more

April 28, 2023
Safe environment contact and high performance motion control are typically
conflicting design goals. Admittance control can improve safety and stability in
contact with a stiff environment, but remains challenging on industrial robots.
Typically high-performance motion control is achieved by low-admittance systems,
which can give high transient forces or instability in contact with
high-stiffness environments. This paper proposes a linear admittance control
framework from which a Multi-function OBserver (MOB)-based control scheme that
succeeds in directly improving the motion control accuracy by suppressing
disturbances, while achieving better loop shaping in the outer-loop admittance
control is developed. By using the task space force and position measurement of
the robot, combined with linearized robot and payload models to design the MOB,
the outer-loop controller can render improved interactive dynamics. In addition,
a methodology to design the proposed MOB based on the reduced-order model is
developed. Theoretical evaluations and experiments verify the effectiveness of
the proposed MOB-based control method, in contact with an environment stiffness
and with a 7~kg payload.
The importance of Weber-Maxwell electrodynamics in electrical engineering

Steffen Kühn

December 30, 2022
Weber-Maxwell electrodynamics combines classical Weber electrodynamics and
Maxwell's equations, including all four field equations and the Lorentz force,
into a single de facto equivalent three-dimensional wave equation. From
classical Weber electrodynamics, Weber-Maxwell electrodynamics inherits
properties in which the concept of the magnetic field is unnecessary, and
Newton's third law is satisfied under all circumstances. From Maxwell's
electrodynamics, Weber-Maxwell electrodynamics inherits the ability to be
compatible with electromagnetic waves. This article shows that in Weber-Maxwell
electrodynamics, all conservation laws are satisfied, and that electromagnetic
waves in an isolated system do not possess energy and momentum, but only mediate
them between particles of matter. Furthermore, the article shows that the modern
formulation of Weber electrodynamics is clearly superior to standard
electrodynamics in electrical engineering, because it not only eliminates the
internal contradictions, but also represents considerable simplification and
compression.
Automated Detection and Depth Determination of Melt Ponds on Sea Ice in ICESat-2
ATLA...

Ute Herzfeld

and 5 more

October 10, 2022
As climate warms and the transition from a perennial to a seasonal Arctic
sea-ice cover is imminent, understanding melt ponding is central to
understanding changes in the new Arctic. NASA’s Ice, Cloud and land Elevation
Satellite (ICESat-2) has the capacity to provide measurements  and monitoring of
the onset of melt in the Arctic and on melt progression. Yet ponds are currently
not reported on the ICESat-2 standard sea-ice products because of the low
resolution of the products, in which only a single surface is determined. The
objective of this paper is to introduce a mathematical algorithm that
facilitates automated detection of melt ponds in ICESat-2 ATLAS data, retrieval
of two surface heights, pond surface and bottom, and measurements of depth and
width of melt ponds. With the Advanced Topographic Laser Altimeter System
(ATLAS), ICESat-2 carries the first space-borne multi-beam micro-pulse
photon-counting laser altimeter system, operating at 532~nm frequency. ATLAS
data are recorded as clouds of discrete photon points. The Density-Dimension
Algorithm for bifurcating sea-ice reflectors (DDA-bifurcate-seaice) is an
auto-adaptive algorithm that solves the problem of pond detection near  the 0.7m
nominal alongtrack resolution of ATLAS data, utilizing the radial basis function
for calculation of a density field and a threshold function that automatically
adapts to changes in background, apparent surface reflectance and some
instrument effects. The DDA-bifurcate-seaice is applied to large ICESat-2 data
sets from the 2019 and 2020 melt seasons in the multi-year Arctic sea-ice
region. Results are evaluated by comparison to those from a manually forced
algorithm.
Comprehensive Review on ML-based RIS-enhanced IoT Systems: Basics, Research
Progress...

Sree Krishna Das

and 6 more

January 09, 2023
Sixth generation (6G) internet of things (IoT) networks will modernize the
applications and satisfy user demands through implementing smart and automated
systems. Intelligence-based infrastructure, also called reconfigurable
intelligent surfaces (RISs), have been introduced as a potential technology
striving to improve system performance in terms of data rate, latency,
reliability, availability, and connectivity. A huge amount of cost-effective
passive components are included in RISs to interact with the impinging
electromagnetic waves in a smart way. However, there are still some challenges
in RIS system, such as finding the optimal configurations for a large number of
RIS components. In this paper, we first provide a complete outline of the
advancement of RISs along with machine learning (ML) algorithms and overview the
working regulations as well as spectrum allocation in intelligent IoT systems.
Also, we discuss the integration of different ML techniques in the context of
RIS, including deep reinforcement learning (DRL), federated learning (FL), and
FL-deep deterministic policy gradient (FL-DDPG) techniques which are utilized to
design the radio propagation atmosphere without using pilot signals or channel
state information (CSI). Additionally, in dynamic intelligent IoT networks, the
application of existing integrated ML solutions to technical issues like user
movement and random variations of wireless channels are surveyed. Finally, we
present the main challenges and future directions in integrating RISs and other
prominent methods to be applied in upcoming IoT networks.
A multi-modal model-fusion approach for improved prediction of Freezing of Gait
in Pa...

Rishabh Bajpai

and 2 more

July 27, 2022
Freezing of gait (FoG) is a widely observed movement disorder in Parkinson’s
Disease patients (PD). Its prediction is crucial for effectively giving the cue
to avoid FoG occurrence. However, present methods of prediction of FoG are
inaccurate for large but practical prediction horizons (PH)s. Therefore, this
work presents a comprehensive analysis of the electroencephalography (EEG) and
inertial measurement units (IMU)s to predict FoG advance in time. An ensemble
model consisting of two neural networks, EEGFoGNet and IMUFoGNet, was developed
and tested at different PHs and ensemble weights. Moreover, the model is tested
for two practical scenarios: clinical or research applications and personal
uses. For clinical or research applications, stratified 5-Folds cross-validation
was used. For personal uses, a transfer learning technique was used for learning
user-specific FoG-related features. The model obtained the best accuracy of
92.1% at 1 second’s PH and the least accuracy of 86.2% at 5 seconds’ PH. The
presented results are encouraging and show the proposed model’s clinical
applicability. This study will also help practitioners in comparing the efficacy
of different cueing methods.
An All-Metal, Simple-Structured Reflectarray Antenna with 2-D Beam-Steerable
Capabili...

Shaofeng Bi

and 5 more

September 08, 2022
An all-metal reflectarray (RA) element based on the variable-rotation technique
(VRT) is presented in this letter. The RA element, based on an elliptical inner
conductor and a circular outer conductor, exhibits a continuous 360° phase
coverage with negligible element loss over a wide range of incident angles and
frequencies. A 10GHz prototype with 121 manually rotated elements was simulated,
fabricated and tested. The proposed RA antenna has 2-D beam-steerable capability
up to 45° elevation angle. The measured results agree well with the simulated
counterparts. For demonstration, four different steering cases are presented as
examples by manually adjusting the rotation angle of the RA elements. The
maximum measured antenna gain is 26.2dBi, corresponding to an aperture
efficiency of 66%. A 1.3 dB gain drop was measured during beam scanning from 0°
to 40°.
A New Approach to Improve the Voltage Conversion Ratio in Topological
Switched-capaci...

Yaqub Mahnashi

July 05, 2022
This paper provides a simple yet novel structure to achieve voltage conversion
ratio (VCR) that exceeds the theoretical attainable VCR of topological
switched-capacitor DC-DC converters (SCC). Typically, the SCC terminals are
connected to the input voltage, the output voltage, or the ground reference. The
proposed structure in this paper utilizes the ground-connected terminals and
instead connecting them to negative input voltage provided by a negator stage.
Here, two types of SCC are used as case studies: Series-parallel (SPSC) and
Fibonacci (FSC). The model for the proposed structure is constructed and
verified experimentally using 3-stage SPSC and FSC. The experimental results are
in good agreement with the proposed model with an error of less than 5% in all
cases. The VCR obtained from the proposed structure exceeds the theoretical
limits of conventional topological structures.
A novel instrumented outsole for real-time foot kinematic measurements:
validation ac...

Rishabh Bajpai

and 1 more

June 21, 2022
The unavailability of a reliable and affordable gait assessment system for
quantifying gait abnormality is responsible for the slow and inaccurate
diagnosis of gait disorders. This study explores the feasibility of using a
low-cost lower limb kinematics estimation device for clinical applications. The
low-cost lower limb kinematics estimation device used in the study consists of
an instrumented outsole (IO), an instrumented insole (II), and an artificial
intelligence model (foot2hip). The low-cost system was compared with the
standard system for reconstruction errors. Four analyses were performed to
understand the nature of performance variability concerning clinically relevant
parameters, including automated-gait assessment score (A-GAS). The study’s
results suggest that the proposed system is unsuitable for detailed gait
analysis (in the swing phase). However, the device can be used for personal use
for tracking global gait abnormalities, and for clinical use in the screening of
patients. The results of the analyses will help the users of the proposed system
in framing the optimum data collection protocol.
Emerging Technologies for Next Generation Remote Health Care and Assisted Living

Ijaz Ahmad

and 9 more

March 22, 2022
This article surveys the latest developments in the technological landscape that
enables remote health care. First, the most important technologies that are
crucial for remote health care such as 5G, medical IoT, big data analytics,
machine learning, and distributed ledger technologies, etc., are outlined, and
then a thorough survey on those technologies with latest state-of-the-art
applications in remote health care is conducted. Furthermore, very important
research challenges and future research directions are laid out to initiate
further research on those very important technologies that enable remote health
care.
Deep Insights of Learning based Micro Expression Recognition: A Perspective on
Promis...

Monu Verma

and 1 more

March 03, 2022
Micro expression recognition (MER) is a very challenging area of research due to
its intrinsic nature and finegrained changes. In the literature, the problem of
MER has been solved through handcrafted/descriptor-based techniques. However, in
recent times, deep learning (DL) based techniques have been adopted to gain
higher performance for MER. Also, rich survey articles on MER are available by
summarizing the datasets, experimental settings, conventional and deep learning
methods. In contrast, these studies lack the ability to convey the impact of
network design paradigms and experimental setting strategies for DL based MER.
Therefore, this paper aims to provide a deep insight into the DL-based MER
frameworks with a perspective on promises in network model designing,
experimental strategies, challenges, and research needs. Also, the detailed
categorization of available MER frameworks is presented in various aspects of
model design and technical characteristics. Moreover, an empirical analysis of
the experimental and validation protocols adopted by MER methods is presented.
The challenges mentioned earlier and network design strategies may assist the
affective computing research community in forge ahead in MER research. Finally,
we point out the future directions, research needs and draw our conclusions.
Foot2hip: A deep neural network model for predicting lower limb kinematics from
foot...

Rishabh Bajpai

and 1 more

March 21, 2022
Objective: This study aims to develop a neural network (foot2hip) for long-term
recording of gait kinematics with improved user comfort. Methods: Foot2hip
predicts ankle, knee, and hip joint angle profiles in the sagittal plane using
foot kinematics and kinetics during walking. Foot2hip consists of three
convolution, two max-pooling, two LSTM and three dense layers. An indigenously
developed insole and an outsole were used to measure the kinetics and kinematics
of the foot, respectively. Seven healthy participants were recruited to follow
an experimental protocol consisting of six walking conditions: slow, medium,
fast walking speed, rearfoot, flatfoot, and forefoot landing pattern. Results:
When tested for leave-one-out and nested cross-validation, foot2hip obtained
3.04° ±0.20 RMSE and 0.97±0.01 correlation coefficient for knee joint, 1.7°±
0.09 RMSE and 0.95±0.01 correlation coefficient for hip joint, and 1.32°±0.08
RMSE and 0.91±0.02 correlation coefficient for ankle joint (averaged across all
folds). Conclusion: The prediction performance of foot2hip is encouraging and
shows its applicability in accurately predicting lower limb kinematics with
minimal wearables. Significance: The hardware used along with foot2hip is low
cost ($268 + N× $35, N is the number of different foot sizes), comfortable, and
easy to use. Therefore, it is suitable for most clinical and personal
applications
Exploring Reconfigurable Intelligent Surfaces for 6G: State-of-the-Art and the
Road A...

Sarah Basharat

and 5 more

March 03, 2022
Reconfigurable intelligent surfaces (RISs) are envisioned to transform the
propagation space into a smart radio environment (SRE) to realize the diverse
applications of sixth-generation (6G) wireless communication. By smartly tuning
the massive number of elements via controller, an RIS can passively phase-shift
the electromagnetic (EM) waves to enhance the system performance. The absence of
radio-frequency (RF) chains makes RIS an energy-efficient and cost-effective
solution for future wireless networks. In this paper, we explore the
state-of-the-art research on different aspects of RIS-assisted communication.
Specifically, we first introduce the fundamentals of RIS, including the RIS’s
structure, operating principle, and deployment strategies. We then
comprehensively discuss the emerging applications of RISs for 6G wireless
networks. In addition, we elaborate on the crucial challenges for RIS-assisted
networks, namely, RIS channel state information (CSI) acquisition and passive
beamforming optimization. Furthermore, we present the recent research
contributions leveraging the artificial intelligence (AI) based techniques for
channel estimation, phase-shift optimization, and resource allocation in
RIS-assisted networks. Finally, to provide effective guidance for future
research, we highlight important research directions for realizing RIS-assisted
network.

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