Man has always been interested in animal electricity, which seems to be measured in every living cell. He has been fascinated by trying to elucidate the mechanisms by which this potential is created and maintained. Biology is the science that seeks to explain this mystery. Biology is based on basic sciences such as physics or chemistry. The latter, in turn, make systematic use of mathematics to measure, evaluate and predict certain phenomena and to develop "laws" and models that are as general as possible while respecting, as closely as possible, observations and facts. The Nernst equation was one of the pillars of electrochemistry. Biology also uses this same equation as one of the indispensable bases for the computation of membrane potential. Man has established a cellular model that highlights this equation in several forms. However, we are going to show by various means that this model is inadequate or even inapplicable.

Purpose: While resultant maximal voluntary contraction (MVC) is commonly used to assess muscular performance, the simultaneous activation of antagonist muscles could dramatically underestimate the strength of the agonist muscles. While quantification of antagonist torque has been performed in plantar- (PF) and dorsi-flexion (DF) joint in isometric conditions, it has yet to be determined in anisometric (concentric and eccentric) conditions. Methods: The experiment was performed in 9 participants through 2 sessions (reliability). The MVCs in DF and PF were measured in isometric, concentric and eccentric conditions (10°.s^-1). Electromyographic (EMG) activities from the soleus, gastrocnemius medialis and lateralis, and tibialis anterior muscles were simultaneously recorded. The EMG biofeedback method was used to quantify antagonist torque, where participants were asked to maintain a level of EMG activity, corresponding to antagonist EMG activity and related to the muscle contraction type, according to a visual EMG bio-feedback displayed on a screen. Results: Resultant torque significantly underestimated agonist torque in DF MVC (30-65%) and to a lesser extent in PF MVC (3%). Triceps surae antagonist torque was significantly modified with muscle contraction type, showing higher antagonist torque in isometric (29 Nm) than eccentric (23 Nm, p < 0.001) and concentric (14 Nm, p < 0.001) conditions and resulting in modification of the DF MVC torque-velocity shape. The difference between DF eccentric and concentric MVC was attenuated when considered agonist torque (12%) rather than resultant torque (45%). Conclusion: Estimation of the antagonist torque in isometric or anisometric condition brings new insights to assessment of muscular performance and could result in costly misinterpretation in strength training and/or rehabilitation programs.

The physiological mechanism of membrane potential generation relies on the passage of mobile ions across the plasma membrane, which is selectively permeable to mobile ions. The existence of a membrane and the transport of ions across it are essential for the generation of membrane potential. However, a long-forgotten theory, Association-Induction hypothesis, asserts that the heterogeneous distribution of charges in space, due to ion adsorption, is the true cause of membrane potential generation. Using an artificial living cell model consisting of a viscous aqueous solution, the mechanism of membrane potential generation is restudied in this work. The results indicate that membrane potential can be generated even in the absence of a plasma membrane and that ion adsorption leading to a heterogeneous spatial charge distribution is an essential factor for membrane potential generation. Consequently, the authors invites people to reconsider the Association-Induction hypothesis as a genuine mechanism for generating membrane potential.

Accurate prediction of the membrane potential by membrane theory is possible on the basis that the plasma membrane is selectively permeable to ions and that permeability determines the characteristics of the membrane potential. However, an experimental and artificial cell system with an impermeable membrane serving as a model plasma membrane has a non-zero membrane potential, and this potential generated across the membrane is somehow consistent with the potential characteristics predicted by the membrane theory, despite the impermeability of the membrane to ions. A long-forgotten theory, called the association-induction hypothesis (AIH), has emerged as a more plausible mechanism for generating the membrane potential than the membrane theory to explain this unexpected behavior. The AIH asserts that ion-selective membrane permeability is not necessary for the generation of the membrane potential, which is contrary to the membrane theory. Although such an idea is not easy to accept, the experimental results clearly suggest the correctness of the AIH.

Current physiology attributes the mechanism of membrane potential generation to transmembrane ion transport, but ion adsorption could just as well play this fundamental role. The evidence shows that the ion adsorption mechanism accurately reproduces the experimentally measured membrane potential. The Goldman-Hodgkin-Katz equation (GHK eq.) and the Nernst equation (Nernst eq.) are the typical mathematical formulas representing the membrane potential in current physiology. However, the authors were able to show that the potential formulas by ion adsorption mechanism give identical results to GHK eq. and Nernst. eq. Our experimental and theoretical analyses suggest that there is a special relationship between the membrane potential and the membrane surface charge density, and this unique equation inevitably leads to the establishment of a GHK eq and/or a Nernst eq. The authors found that the unique equation is the foundation of thermodynamics “Boltzmann distribution”. Thus, the GHK eq. and the Nernst eq. are simply the natural consequence of thermodynamics from the view of the ion adsorption mechanism.

In this age of mass media and, in particular, social media-driven perception of reality, coupling disease and prophylactic opinion dynamics models can provide better insights into disease evolution than using a disease model alone. We develop in this work two disease-opinion dynamics models based on the epidemiology of the new coronavirus disease (COVID-19) and the availability or not of imperfect vaccines. We assume that susceptibility to infection decreases with the level of prophylactic attitude (personal hygiene, social distancing), and changes in prophylactic attitudes of susceptible individuals occur in response to perceived disease prevalence and vaccination coverage and efficacy in the population. We derive and discuss the disease-free equilibriums and reproduction numbers in the introduced models. We further assess the impacts of the distribution of opinions at disease introduction, the ability to detect presymptomatic, asymptomatic and symptomatic positive COVID-19 cases, the behavioural responses to the outbreak and the introduction of vaccination, and the effects of distortions of disease prevalence by public policy and mass media on disease dynamics. The insights highlighted from the proposed models are expected to make informative contributions to public policy in a context of opinion fluxes in response to perceived disease prevalence.

In January 2019, the French Agency for Food, Environmental and Occupational Health and Safety (ANSES) published an opinion on risks related to the presence of hazardous chemicals in infant diapers. ANSES found that health reference values were largely exceeded for polycyclic aromatic hydrocarbons (PAHs), dioxins (PCCD/Fs) and dioxin-like polychlorobiphenyls (DL-PCBs). The levels of formaldehyde and of some fragrances were also considered potentially unsafe. Therefore, ANSES concluded that actions have to be taken to restrict levels of these contaminants in diapers. Under the exposure scenario deemed the most reliable by ANSES, estimates of cancer risks of the most potent PAHs detected in diapers exceeded 10-3 and hazard quotients for neurobehavioral effects attained values up to 66. Regarding dioxins and DL-PCBs, ANSES derived a hazard quotient of 12 for the risk of decreased sperm count at adult age. The aim of this critical review was to examine whether the exposure and risk assessment conducted by ANSES contained potential flaws that could explain such a high exceedance of health reference values. The review will also put into perspective the exposure from diapers with that from breast milk whose benefits for child’s health are undisputable despite contamination by PAHs, dioxins and DL-PCBS.

Inherited mutations in BRCA1 and BRCA2 genes increase risks for breast, ovarian, and other cancers. Both genes encode proteins for accurately repairing chromosome breaks. If mutations inactivate this function, broken chromosomes may not be restored correctly, allowing breaks to persist or rearrange chromosomes. These abnormalities are potentially catastrophic events that can originate from viral infections. I used bioinformatic analyses of publicly available breast cancer patient data to show that the distribution of chromosome breaks in hereditary breast cancers differs markedly from sporadic breast cancers. Then I tested hereditary breast cancer sequence data around chromosome breaks for DNA similarity to all known viruses. Human DNA flanking breakpoints usually had decisive matches to Epstein-Barr virus (EBV / HHV4) tumor variants HKHD40 and HKNPC60. Many breakpoints were near EBV genome anchor sites, human EBV tumor-like sequences, EBV-associated epigenetic marks, and some fragile sites. On chromosomes 2 and 12, sequences near EBV genome anchor sites accounted for 90% and 88% of breakpoints (p<0.0001), respectively. On chromosome 4, 51/52 inter-chromosomal breakpoints were close to EBV-like sequences in 19 hereditary breast cancers. In contrast, 19 sporadic breast cancers only had 12 interchromosomal breakpoint regions on chromosome 4 near EBV-like sequences. On various other chromosomes, five EBV genome anchor sites were near hereditary breast cancer breakpoints at precisely defined, disparate gene or LINE locations. Independent evidence further implicating EBV in hereditary breast cancer breakpoints is that 25 breast cancer break positions are within 1.25% of breakpoints in model EBV cancers. In addition to BRCA1 or BRCA2 mutations, all the hereditary breast cancers had mutated genes essential for immune responses. This compromise facilitates reactivation of herpes viruses which produce nucleases capable of breaking chromosomes. EBV also causes other deleterious effects: anchored EBV episomes can interfere with normal replication and obstruct DNA break repairs; even very early infection causes massive transcription changes. The results, therefore, imply proactive treatment and prevention of herpes viral infections may prevent some chromosome breaks and benefit BRCA mutation carriers.

Inherited mutations in BRCA1 and BRCA2 genes increase risks for breast, ovarian, and other cancers. Both genes encode proteins for accurately repairing chromosome breaks. If mutations inactivate this function, chromosome fragments may not be restored correctly. Resulting chromosome rearrangements can become critical breast cancer drivers. Because I had data from thousands of cancer structural alterations that matched viral infections, I wondered whether infections contribute to chromosome breaks and rearrangements in hereditary breast cancers. There are currently no interventions to prevent chromosome breaks because they are thought to be unavoidable. However, if chromosome breaks come from infections, they can be treated or prevented. I used bioinformatic analyses to test publicly available breast cancer sequence data around chromosome breaks for DNA similarity to all known viruses. Human DNA flanking breakpoints usually had the strongest matches to Epstein-Barr virus (EBV) tumor variants HKHD40 and HKNPC60. Many breakpoints were near sites that anchor EBV genomes, human EBV tumor-like sequences, EBV-associated epigenetic marks, and fragile sites. On chromosome 2, sequences near EBV genome anchor sites accounted for 90% of breakpoints (p<0.0001). On chromosome 4, 51/52 inter-chromosomal breakpoints were close to EBV-like sequences. Five EBV genome anchor sites were near breast cancer breakpoints at precisely defined, disparate gene or LINE locations. Breakpoint flanking regions resembled known EBV-cancers. Twenty-five breakpoints in breast cancers were within 1.25% of EBV cancer breakpoints. In addition to BRCA1 or BRCA2 mutations, all the breast cancers had mutated genes essential for immune responses. Because of this immune compromise, herpes viruses can activate and produce nucleases that break chromosomes. Alternatively, anchored viral episomes can obstruct break repairs, whatever the cause. The results, therefore, imply proactive treatment and prevention of herpes viral infections may prevent some chromosome breaks and benefit BRCA mutation carriers.

In this work we introduce an end-to-end multi-modal neural network to segment the Gross Tumor Volume (GTV) from 3D-CBCT’s during radiotherapy. We improve the tumor segmentation by using a U-net which takes additional information such as the tumor mask generated at the planning phase along with the CBCT volume. The mask contains relevant information about the tumor’s location and can guide the model to use this knowledge appropriately to give a better prediction. This technique could become an alternative to produce segmentation masks of GTV in CBCT automatically during radiotherapy as in the traditional RT-pipeline, they are not segmented. We have evaluated our model on a dataset of 82 patients who have undergone radiotherapy. We compare the results of registered target volumes from planning CT as mask seed with 2 different types of multi-modal architectures. Our model shows a DSC of 0.706±0.002 with Late Fusion and 0.702±0.015 with Early Fusion using the GTV Mask. The performance of the two models on this mask is similar, so we perform further experiments with different types of masks which suggest that Late Fusion model produces a better segmentation of the tumor than the Early Fusion model. We also provide an ablation study consisting of a single modality U-net and a metric based on the Planning CT mask registration. It indicates a clear advantage of using our model to produce segmentation for this type of imaging.

From the first isolation of the cystovirus bacteriophage Φ6 from Pseudomonas syringae 50 years ago, we have progressed to a better understanding of the structure and transformations of the many parts of the virion. The three-layered virion encapsulating the tripartite double stranded RNA (dsRNA) genome, breaches the cell envelope on infection, generates its own transcripts, and coopts the bacterial machinery to produce its proteins. The generation of new virions starts with a procapsid with a contracted shape, followed by packaging single stranded RNA segments with concurrent expansion of the capsid, and finally replication to reconstitute the dsRNA genome. The outer two layers are then added, and the fully formed virions released by cell lysis. Most of the procapsid structure composed of the proteins P1, P2, P4 and P7 is now known, as well as its transformations to the mature, packaged nucleocapsid. The outer two layers are less well studied. One additional study investigated the binding of the host protein YajQ to the infecting nucleocapsid, where it enhances the transcription of the large RNA segment that codes for the capsid proteins. Finally, we relate the structural aspects of bacteriophage Φ6 top those of other dsRNA viruses, noting the similarities and differences.

This article presents an analysis of monthly precipitation totals based on the Global Precipitation Climatology Centre database and monthly mean temperatures based on National Oceanic and Atmospheric Administration data for 377 catchments located around the world. Data sequences covering 110 years from 1901 to 2010 were analysed. The long-term sequences of precipitation and temperature were used to assess the variability of climate extremes, referred to here as polarisation. Measures of polarisation used in the natural sciences are discussed. A simple measure of polarisation was presented and applied to long-term sequences of monthly precipitation totals and monthly mean temperatures. Due to the nature of the proposed polarisation measure, other characteristics of precipitation and temperature sequences are also presented as a background for the discussion of the polarisation index. The study showed the existence of trends related to the polarisation of temperature and precipitation phenomena. As a result of the analysis, trends of polarisation factors in the area of precipitation and temperature were identified in 11 catchments out of the 377 analysed catchments. The trend analysis used Mann-Kendall tests at a level of significance of 5%. The Pettitt test was used to determine the point of trend change for precipitation and temperature data. The whole investigation is supported by rich graphical analyses, and the results are presented in tabular form.

The article presents an analysis of monthly precipitation totals based on the GPCC database, and monthly mean temperatures (NOAA data) for 377 catchments distributed across the globe. The analysis included 110-year data sequences from 1901 to 2010 calculated from grid data with a spatial resolution of 0.5°x 0.5° longitude and latitude. Long-term sequences of precipitation and temperature were used to assess the polarization of climatic phenomena. The noticeable impact of polarization in the area of extreme changes in temperature and precipitation is related to anthropogenic factors such as greenhouse gas emissions, deforestation and pollution, which affect ecosystem functioning, biodiversity, water resources and economies. The paper demonstrates the existence of trends related to the polarization of temperature and precipitation phenomena. The measures of polarization used in science are discussed. A simple measure of polarization was proposed and applied to both long-term sequences of monthly precipitation totals and monthly mean temperature. Due to the nature of the proposed polarization measure, other characteristics of the precipitation and temperature sequences are also presented as background for the discussion of the polarization index. The paper presents, for a selection of several hundred catchments from around the world, analyses of the assessment of precipitation and temperature trends using non-parametric tests. The trend analyses use Mann -Kendall tests at the 5% significance level. A Pettitt test was used to determine the trend change point for precipitation and temperature data. The whole is supported by rich graphical analyses and the results are presented tabularly.

The occurrence of potential spikes in a cell is a sign of life, and it is called action potential. There is a common notion that neuron signal conduction is the conduction of action potential. Hence, action potential is a typical and essential life activity. However, such potential spikes occur even in simple nonliving systems. According to the experimental observations by Pollack, structured water molecules can generate a negative potential environment. From this observation, the potential spike generation process for both living and nonliving systems caused by ion and water molecule adsorption-desorption process could be explained in this paper. So, taking into consideration the electrically neutral water molecules,the action potential generation mechanism could be explained. It is a fully inanimate model. Hence, the action potential may not be a life activity. Here, the role of water molecules in life is investigated further. It was found that the phase transition of the membrane is involved in the neuron signal conduction, but the membrane phase transition could be due to the change of state of the water molecules, which forms a large-scale structure in the cavities created by a number of lipids.

The generation and maintenance of membrane potential is considered a fundamental element of the cellular machinery. They are based on elements of physics and chemistry. Mathematics is used to validate the equations and evaluate the results. However, this computation process can lead to a misunderstanding of the phenomena involved or to the masking of crucial events. It is even possible that the calculation no longer corresponds to the hypothesis considered. Membrane theory is no exception to these trivial considerations, which are essential for normal theory validation.

Membrane potential generation and maintenance are fundamental parts of membrane theory. However, since its inception, theories and assumptions have remained simple, whereas the amount of knowledge available has only grown. Unfortunately, this simplification and reductionism lead to its loss of validity on both microscopic and macroscopic scales. This article shows that assumptions made outside the context of factual reality lead to contradictions that cannot be compared with science. Membrane theory must be thoroughly revised, taking into account all forgotten and unanswered assumptions and questions to conform to facts and science to maintain credibility.

The membrane potential or resting potential of the neuron has been the subject of many studies. Although this theory explains the generation and maintenance of the membrane potential by direct or even facilitated diffusion, there are too many contradictions to doubt that these forces are sufficient or even at work in a process whose initial conditions are of rare complexity. The aim of this article is to show that already in the past, a competing theory has been developed whose hypothesis seems more scientifically sound. To confirm this last theory, Hirohisa Tamagawa carried out an experiment of great simplicity which makes it possible to invalidate the current theory and to question the teaching and the knowledge in Biology and Biophysics.

The generation and maintenance of membrane potential is a fundamental part of Membrane Pump Theory. One of the key points of this hypothesis is based on a natural or facilitated molecular diffusion through several types of ion channels and pumps like the Na/K ATPase. Following the principles of chemistry, electrostatics and geometry, it becomes clear that ion channels cannot function in this way. The ions channels cannot by their location have both a filter function and be ion concentrators, and the Na/K pump by its position in the membrane and by the proposed assumptions is not able to perform its regulatory function. The current model must absolutely be revised according to the current state of our knowledge and allow an advance in the understanding of the phenomena opening new research perspectives.

The generation and maintenance of membrane potential is a fundamental part of Membrane Pump Theory. One of the key points of this hypothesis is based on a natural or facilitated molecular diffusion through several types of ion channels and pumps like the Na/K ATPase. Chemistry, physics and especially electrochemistry, however, bring strong contradictions to this theoretical assumption. By respecting the principles of chemistry and electrostatics, it becomes obvious that this theoretical hypothesis cannot work. The ionic diffusion that would be at the origin of this potential cannot take place. Indeed, the topology and the forces involved definitively exclude the current model, which must absolutely be revised according to the current state of our knowledge and allow an advance in the understanding of the phenomena and open new research perspectives.

The most common and taught membrane theory assumes that the membrane behaves as a kind of electrical capacitance that is exposed to an electrical current generated by an ionic flow. If this statement is verifiable, it can be confirmed by the laws of physics, mathematics and in particular electricity. We will demonstrate that this hypothesis is not verified and that it is necessary to modify biophysics according to already established and experimentally verified principles of physics.

The cellular model we teach and have theorized assumes that the cell is the basic unit of multicellular living beings. This fundamental element has been the subject of many theories concerning its properties and the exchanges that exist with its environment. In this article, we demonstrate that certain functional aspects, in particular the electrical aspects related to diffusion, have not been correctly assumed or that certain initial conditions have been purely ignored and are in contradiction with physics, chemistry and thermodynamics.

For more than 70 years, biologists and biophysicists have been trying to unravel the mystery that exists regarding the saltatory conduction of so-called myelinated neurons. Albert Einstein used the train metaphor to explain the theory of relativity. It is possible to use a similar metaphor to better understand this transient functioning of the neuron: the action potential. We will, once again, use a train to demonstrate unequivocally that the action potential does not jump from node of Ranvier to node of Ranvier (noR) as we thought it would. It is possible to describe that the neuron uses an elegant method to increase the speed of transmission of the neural message. It is also important to conclude that this increase in speed, contrary to the common idea, has a certain energy cost that is proportional to speed and in accordance with thermodynamics.

This work focuses on the theoretical study of the effect of entanglement in the Yttrium Barium Copper Oxide (YBCO) superconductor on the critical current density, critical magnetic field, and penetration depth. Using the Ginzburg Landau (GL) theory, the expression for the GL coherence length, the critical current density, the lower critical magnetic field (Hc1), and the upper critical magnetic field (Hc2) for the entangled superconducting YBCO was obtained. At zero external magnetic fields, YBCO was found to undergo a transition from the normal state to the superconducting state at 93K and returned to normal. We found a direct relationship between the GL coherence length penetration depth and the temperature of the entangled superconducting YBCO. The graphs of the lower critical magnetic field (Hc1) and critical current density against temperature show a non-linear dependence.In contrast, the graph of the upper critical magnetic field (Hc2) against temperature (T) shows the linear dependence, which is in agreement with existing theoretical observations. On the other hand, the graph of critical current density against the distance of separation between the entangled electrons that form a cooper pair showed that the critical current density increases between 1.8 and 6 nanometers attaining a constant value after reaching 7 nanometers for infinite distances. Finally, for lower critical fields, the magnetic field decreases between 1.5 to 6 nanometers attaining a constant value of the magnetic field, for infinite values of distance of separation between the entangled pair of electrons.

In this paper, the Shannon entropy measure was used to assess changes in precipitation and temperature conditions. Due to the short, low-volume sequences of precipitation and temperature data analysed, a bootstrap method was used in the procedure for calculating Shannon entropy. The analysis used minimum and maximum values of monthly precipitation totals and monthly mean temperatures for 377 catchments distributed across the globe. A 110-year data series from 1901 to 2010 was analysed. Entropy values for the estimated parameters of the generalised extreme value distribution (GEV) were calculated for the adopted data. Entropy value calculations were performed for the left-hand constraint, based on minimum values, and for the right-hand constraint, based on maximum values. The applicability of Shannon's entropy measure in the analysis of climate change was demonstrated by allowing the degree of disorder and complexity of the distributions describing climate variables in the form of precipitation and temperature to be measured. This made it possible to obtain information on the directions of changes occurring with regard to minimum and maximum values in the field of monthly precipitation and mean temperatures in the analysed catchments. The study demonstrated the existence of Shannon entropy trends. The evaluation of entropy trends for precipitation and temperature sequences was performed using non-parametric tests. Mann -Kendall tests at the 5% significance level were used for trend analyses. The Pettitt test was performed to determine the point of change in trend for the rainfall and temperature data. The performed analysis was supported by graphical presentations.

The emergence of COVID-19 has prompted an unprecedented scientic publication with the aim of better understanding this new disease. This study assessed the scientic impact and disciplinary priorities of the published papers on the pandemic by comparing epidemiological (EP) and social sciences (SS) research interests. Papers were identified via keywords searching using Google Scholar and Scopus databases. From an initial 1720 papers, we identified 597 relevant articles, of which 347 were for EP researches and 250 for SS studies. We extracted information, such as authors' countries, and research thematic related to EP and SS. The results revealed that most papers were authored by Asian (37.5%), European (30.5%) and American (19.6%) scientists. Only 10.1% and 2.3% of authors were aliated with African and Oceanian institutions, respectively, indicating that the regions most affected by the pandemic mainly contributed to the scientic publications. In total, 26 research themes were recorded from both EP and SS studies. There was a high signicant dierence among themes in both research fields (Chi-square = 1204.3, df = 1, p-value < 0.001). EP papers mostly dealt with clinical trials (54.5%) and diagnosis (53.3%). These papers assessed the incidence and epidemiological characteristics of the disease (incubation period, symptomatic period, recovering or death), testing tests developed, drugs and vaccines used. SS papers were mainly concerned with the sociocultural analyses (78%) and economic impact (55.6%) of the pandemic. They mainly focused on behavioral changes induced by the pandemic and strategies developed to mitigate its impacts. This study highlights the difference between regions and gaps between scientific disciplines concerning the proposed responses to control the pandemic. It is important to promote collaborative and interdisciplinary studies for health emergencies.

We propose an experimental method for the unidirectional excitation of spin waves. By structuring Au nanowires arrays within a coplanar waveguide onto a thin yttrium iron garnet (YIG) film, we observe a chiral coupling between the excitation field geometry of the nanowires grating and several well-resolved propagating magnon modes. We report a propagating spin wave spectroscopy study with unprecedented spectral definition, wavelengths down to 130 nm and attenuation lengths well above the 100 μm over the 20 GHz frequency band. The proposed experiment paves the way for future non-reciprocal magnonic devices.

Water quality is the measure of chemical, physical and biological suitability of water in relation to natural effects and intended purpose which may affect human health and aquatic life. Assessment of water quality is very essential for the management of water resources and human health. Traditionally, in-situ measurements have been used to obtain the water quality parameters of the water bodies. However, with the availability of satellite images, researchers have shown that satellite images are a reliable tool that can be used to estimate water quality. Satellite image-derived water quality parameters provide extensive spatial extent and large temporal variations when compared to traditional in situ sample collection and laboratory measurements. The present work estimated several parameters for quality of water in the Kamuzu reservoir of Lilongwe River for the 2013-2020 period using Sentinel-2 and Landsat-8 satellite images. The band ratio algorithms were used to retrieve Chlorophyll a (Chl-a), Turbidity, Total Suspended Matter (TSM), Secchi depth, Coloured Dissolved Organic Matter (CDOM), and Cyanobacteria from the reservoir. Turbidity and TSM were compared with the in-situ data collected over the same period. The comparison indicated R² of 0.9 and 0.69 for TSM and Turbidity respectively from Sentinel-2 images whereas R² of 0.56 and 0.61 was obtained using Landsat 8 images which are quite encouraging. The other set of results included the spatial distribution maps of water quality parameters using Landsat-8 and Sentinel-2 satellite data. It was observed that the spatial distribution of water quality parameters, except for CDOM and Cyanobacteria, showed very good distribution and matches with the theoretical results. However, for CDOM and Cyanobacteria, the distribution was almost similar for the entire study area and the band ratio algorithm may not be able to estimate them quite reasonably. This research reiterates the need for the use of remote sensing in estimating the water quality parameters and may be a substitute to the in-situ data, in terms of spread and frequency, which is very common to most of the water bodies, across the globe.

Covariant integral quantization with rotational symmetry SO(3) is established for the quantum motion on this group manifold, or, alternatively, for Gabor signal analysis on this group. We revisit the action of the related (non-unitary) Weyl-Gabor operator on the Hilbert space of square integrable functions on SO(3) and disclose a set of various properties. By selecting weight functions on the corresponding discrete-continuous phase space, one derives related coherent states and Wigner transform.

It is quite easy to stochastically distort an original count variable to obtain a new count variable with relatively more variability than in the original variable. Many popular overdispersion models (variance greater than mean) can indeed be obtained by mixtures, compounding or randomlystopped sums. There is no analogous stochastic mechanism for the construction of underdispersed count variables (variance less than mean), starting from an original count distribution of interest. This work proposes a generic method to stochastically distort an original count variable to obtain a new count variable with relatively less variability than in the original variable. The proposed mechanism, termed condensation, attracts probability masses from the quantiles in the tails of the original distribution and redirect them toward quantiles around the expected value. If the original distribution can be simulated, then the simulation of variates from a condensed distribution is straightforward. Moreover, condensed distributions have a simple mean-parametrization, a characteristic useful in a count regression context. An application to the negative binomial distribution resulted in a distribution allowing under, equi and overdispersion. In addition to graphical insights, fields of applications of special cases of condensed Poisson and condensed negative binomial distributions were pointed out as an indication of the potential of condensation for a flexible analysis of count data

Studies on Protein-Protein interactions (PPI) can be helpful for the annotation of unknown protein function and for the understanding of cellular processes, such as specific virulence mechanisms developed by bacterial pathogens. In that context, several methods have been extensively used in recent years for the characterization of Mycobacterium tuberculosis PPI to further decipher TB pathogenesis. This review aims at compiling the most striking results based on in vivo methods (yeast and bacterial two-hybrid systems, protein complementation assays) for the specific study of PPI in mycobacteria. Moreover, newly developed methods, such as in-cell native mass resonance and proximity-dependent biotinylation identification, will have a deep impact on future mycobacterial research, as they are able to perform dynamic (transient interactions) and integrative (multiprotein complexes) analyses.

An embryonic version of membrane theory can be date back to the Bernstein's work reported more than a hundred years ago. Such an originally old work has evolved conceptually and mathematically up until today, and it plays a central role in current membrane theory. Goldman-Hodgkin-Katz equation (GHK eq.) is one of the math-based monumental works, which constitutes the present membrane theory. Goldman theoretically derived GHK eq., but its physiological meaning was provided by the two renowned scientists, Hodgkin and Katz. These two employed an assumption that the electric field (EF) across the plasma membrane is constant to validate the GHK eq. physiologically. Proposal of Hodgkin-Huxley model (HH model) is another math-based monumental works developed from the membrane theory and now forms a fundamental part of the current membrane theory. GHK eq. and HH model are quite fundamental central concepts in the current physiology. Despite the broad acceptance of GHK eq. at present time, its prerequisite that the EF within the plasma membrane is constant is hardly believable. Especially when the action potential is generated, it sounds totally nonsense. Furthermore, the existence of constant EF within the plasma membrane is conceptually almost in conflict with the HH model. The authors will discuss those problematic issues the membrane theory inherits.

Abstract: The purpose of this study was to understand the role of infection in the origin of chromosomal anomalies linked to neurodevelopmental disorders. In patients with neurodevelopmental disorders, DNA’s from viruses and bacteria including known teratogens were tested against chromosome anomalies known to cause the disorders. Results support a theory that parental infections disrupt elaborate multi-system gene coordination needed for neurodevelopment. Genes essential for neurons, lymphatic drainage, immunity, circulation, angiogenesis, barriers, structure, and chromatin activity were all found close together in polyfunctional clusters that were deleted in neurodevelopmental disorders. These deletions account for immune, circulatory, and structural deficits that accompany neurologic deficits. In deleted clusters, specific and repetitive human DNA matched infections and passed rigorous artifact tests. In some patients, epigenetic driver mutations were found and may be functionally equivalent to deleting a cluster or changing topologic chromatin interactions because they change access to large chromosome segments. In three families, deleted DNA sequences were associated with intellectual deficits and were not included in any database of genomic variants. These sequences were thousands of bp and unequivocally matched foreign DNAs. Analogous homologies were also found in chromosome anomalies of a recurrent neurodevelopmental disorder. Viral and bacterial DNAs that match repetitive or specific human DNA segments are thus proposed to interfere with highly active break repair during meiosis, and sometimes delete polyfunctional clusters, and disable epigenetic drivers. Mis-repaired gametes produce zygotes containing rare chromosome anomalies which cause neurologic disorders and accompanying non-neurologic signs. Neurodevelopmental disorders may be examples of assault on the human genome by foreign DNA with some infections more likely tolerated because they resemble human DNA segments. Further tests of this model await new technology.

Although there is a common physiological notion that the origin of the membrane potential is attributed to transmembrane ion transport, it is theoretically possible to explain its generation by the mechanism of ion adsorption. It was previously suggested that the ion adsorption mechanism led even to the potential formulas which are even identical to either the famous Nernst equation or Goldman-Hodgkin-Katz equation. Our further analysis shown in this paper indicates that the potential formula based on the ion adsorption mechanism leads to one equation which is the function of material surface charge density and the material surface potential. Furthermore, we confirmed that the equation holds in all the different experimental systems we studied. Although we have not succeeded in elucidating why such an equation is established, the equation appears to be the key equation governing the characteristics of the membrane potential regardless of the systems in question.

The normal and Poisson distribution assumptions in the normal-Poisson mixed effects regression model are often too restrictive for many real count data. Several works have independently relaxed the Poisson conditional distribution assumption for counts or the normal distribution assumption for random effects. This work couples some recent advances in these two regards to develop a skew t–discrete gamma regression model in which the count outcomes have full dispersion flexibility and random effets can be skewed and heavy tailed. Inference in the model is achieved by maximum likelihood using pseudo-adaptive Gaussian quadature. The use of the proposal is demonstrated on a popular owl sibling negotiation data. It appears that, for this example, the proposed approach outperforms models based on normal random effects and the Poisson or negative binomial count distribution.

Most existing flexible count regression models allow only approximate inference. Balanced discretization is a simple method to produce a mean-parametrizable flexible count distribution starting from a continuous probability distribution. This makes easy the definition of flexible count regression models allowing exact inference under various types of dispersion (equi-, under- and overdispersion). This study describes maximum likelihood (ML) estimation and inference in count regression based on balanced discrete gamma (BDG) distribution and introduces a likelihood ratio based latent equidispersion (LE) test to identify the parsimonious dispersion model for a particular dataset. A series of Monte Carlo experiments were carried out to assess the performance of ML estimates and the LE test in the BDG regression model, as compared to the popular Conway-Maxwell-Poisson model (CMP). The results show that the two evaluated models recover population effects even under misspecification of dispersion related covariates, with coverage rates of asymptotic 95% confidence interval approaching the nominal level as the sample size increases. The BDG regression approach, nevertheless, outperforms CMP regression in very small samples (n = 15 − 30), mostly in overdispersed data. The LE test proves appropriate to detect latent equidispersion, with rejection rates converging to the nominal level as the sample size increases. Two applications on real data are given to illustrate the use of the proposed approach to count regression analysis.

Background: Skin is exposed to ultraviolet radiation (UV) and air pollution and recent works demonstrated these factors have additive effects in the disturbance of skin homeostasis. Nuclear factor erythroid 2-related factor 2 (Nrf2) and aryl hydrocarbon receptor (AHR) appears appropriate targets in the management of combined environmental stressors. The protective effects of Silymarin (SM), an antioxidant and anti-inflammatory complex of flavonoids, were evaluated. Methods: Reactive oxygen species (ROS) and interleukin 1-alpha (IL-1a) were quantified in UV+urban dust stressed reconstructed human epidermis (RHE) treated with SM. Gene expression study was conducted on targets related to AHR and Nrf2. SM agonistic activity on cannabinoid receptor type 2 (CB2R) was evaluated on mast cells. Clinical study quantified the performance of SM and cannabidiol (CBD), in skin exposed to solar radiation and air pollution. Results: SM decreased morphological alterations, ROS and IL-1a in UV+urban dust stressed RHE. AHR and Nrf2 related genes were upregulated that control antioxidant effector and barrier function. Interleukin 8 gene expression was decreased. Clinical study confirmed SM to improve homogeneity and perceived well-being of urban skins exposed to UV, outperforming CBD. SM activated CB2R and release of β-endorphin from mast cells. Conclusions: SM provides protection of skin from oxidative stress and inflammation caused by two major factors of exposome and appears mediated by AHR-Nrf2. SM activation of CB2R opens a new understanding of SM anti-inflammatory properties.

Paratemporal methods based on tree expansion have proven to be effective in efficiently generating trajectories of stochastic systems. However, combinatorial explosion of branching arising from multiple choice points presents a major hurdle that must be overcome to implement such techniques. In this paper we tackle this scalability problem by developing a systems theory-based framework covering both conventional and proposed tree expansion algorithms for speeding up discrete event system stochastic simulations while preserving desired accuracy. An example is discussed to illustrate the tree expansion framework in which a discrete event system specification (DEVS) Markov stochastic model takes the form of a tree isomorphic to a free monoid over the branching alphabet. We derive the computation times for baseline, non-merging, and merging tree expansion algorithms to compute the distribution of output values at any given depth. The results show the remarkable reduction from exponential to polynomial dependence on depth effectuated by node merging. We relate these results to the similarly reduced computation time of binomial coefficients underlying Pascal’s triangle. Finally, we discuss application of tree expansion to estimating temporal distributions in stochastic simulations involving serial and parallel compositions with potential real world use cases.

Within the framework of multiblock data analysis, a unified approach of supervised methods is discussed. It encompasses multiblock redundancy analysis (MB-RA) and multiblock partial least squares (MB-PLS) regression. Moreover, we develop new supervised strategies of multiblock data analysis, which can be seen as variants of one or the other of these two methods. They are respectively refered to as multiblock weighted redundancy analysis (MB-WRA) and multiblock weighted covariate analysis (MB-WCov). The four methods are based on the determination of latent variables associated with the various blocks of variables. They are derived from clear optimization criteria whose aim is to maximize either the sum of the covariances or the sum of squared covariances between the latent variable associated with the response block of variables and the block latent variables associated with the various explanatory blocks of variables. We also propose indices to help better interpreting the outcomes of the analyses. The methods are illustrated and compared based on simulated and real datasets.

Despite improved knowledge regarding diagnosis and treatment of osteomyelitis of the jaw (OMJ), it remains a clinical challenge for oral and maxillofacial surgeons. This study aimed to identify risk factors associated with severe forms of OMJ, i.e. related to the occurrence of major complications or the refractory course of the disease. A retrospective study was performed based on the medical records of all patients diagnosed with OMJ from the past 20 years. Collected data included demographic information, medical and dental history, clinical, radiological, and bacterial findings as well as treatment modalities. The main outcome variables were the onset of major complications and treatment results. Fifty-four patients were included. Our results showed that alcohol and smoking habits, as well as malnutrition, were significantly associated with the occurrence of major complications. We also established that dental implant–induced OMJ should be considered as an aggressive subtype of OMJ. Finally, clinical bone exposure was significantly associated with unfavorable outcomes, whereas dental causes or radiological evidence of periosteal reaction were predictive of successful outcomes. Identifying such factors could be useful in preventing serious complication and informing patients about the refractory course of the disease based on the presence of these factors.

The extent of correlation or co-movement among the returns of developed and/or emerging stock markets remains pivotal for efficiently diversifying global portfolios. This correlation is prone to variation over time as a consequence of escalating economic interdependence fostered by international trade and financial markets. In this study, we analyzed the time-varying correlation and co-movement between the JSE.JO stock market of South Africa and its developed and developing stock market partners. The Dynamic Conditional Correlation - Exponential Generalized Autoregressive Conditional Heteroscedasticity (DCC-EGARCH) methodology is employed with different multivariate distributions to explore the time-varying correlation and volatilities between the JSE.JO stock market and its partners. Based on the conditional correlation results, the JSE.JO stock market is integrated and co-moves with its partners, and the conditional correlation for all markets exhibit time-variant behavior. The conditional volatility results show that the JSE.JO stock market behaves differently from other markets, especially after 2015, indicating a positive sign for investors to diversify between JSE.JO and its partners. The highest value of conditional volatility for markets was in 2020 during the COVID-19 pandemic, representing the riskiest period that investors should avoid due to the lack of diversification opportunities during crises.

This paper deals with the viscoelastic behavior during crystallization and melting of semicrystalline polymers with the aim of later modeling the residual stresses after processing in case where crystallization occurs in quasi static conditions (in additive manufacturing for example). Despite of an abundant literature on polymer crystallization, the current state of scientific knowledge does not yet allow ab initio modeling. Therefore, an alternative and pragmatic way has been explored to propose a first approximation of the impact of crystallization and melting on the storage and the loss moduli during crystallization-melting-crystallization cycle. An experimental approach, combining DSC, optical microscopy and oscillatory shear rheology was used to define macroscopic parameters related to the microstructure. These parameters have been integrated into a phenomenological model. Isothermal measurements were used to describe the general framework, and crystallization at a constant cooling rate was used to evaluate the feasibility of a general approach. It can be concluded that relying solely on the crystalline fraction is inadequate to model the rheology. Instead accounting for the microstructure at the spherulitic level could be more useful. Additionally, the results obtained from the experiments help to enhance our understanding of the correlations between crystallization kinetics and its mechanical effects.

Numerical optimization methods are widely used for designing turbomachinery components due to the cost and time savings they can provide. In the available literature, shape optimization of radial compressors is mainly focused on improving the impeller alone. However, it is a well-established knowledge that the volute plays a key role in the overall performance of the compressor. The aim of the present paper is to perform the adjoint-based optimization of a volute that is designed for the SRV2-O compressor. The CAD model is first created, using a parametrization of 33 design parameters. Then, a butterfly topology is applied to mesh the computational domain with a multi-block structured grid, and an elliptic smoothing procedure is then used to improve the quality of the fluid grid. A steady-state RANS CFD solver with Spalart-Allmaras turbulence model is used to solve the Navier-Stokes equations, and flow sensitivities are computed with an adjoint solver. The objective function consists in minimizing the loss coefficient of the volute. The optimization is performed to obtain an improved design with 14% losses reduction. Detailed flow and design analysis is carried out to highlight the loss reduction mechanisms followed by the optimizer. Finally, the compressor map of the full stage is compared between the baseline and the optimized volute through CFD simulations using a mixing plane interface. This research demonstrates the successful use of a gradient-based optimization technique to improve the volute of a radial compressor and opens the door towards optimizing simultaneously the wheel and the volute.

(1) Background: A global pandemic due to COVID-19 emerged in November 2019 and hit France in early March 2020. It not only resulted in a loss of lives but also in very strict confinement measures. The objective of this study was to understand what the determinants of the change in participants’ behavior and mental state were during the confinement. (2) Methods: An online survey was launched on April 23rd, 2020, and closed on May 7th, 2020. The final sample included 1454 participants from 24 to 65 years old. Descriptive and multivariate analyses were then performed. (3) Results: 28.7% reported having a more balanced diet, against 17.1% with a less balanced diet. 22.7% of respondents reported an increased alcohol consumption, as opposed to only 12.2% declaring a decrease and 11.2% of respondents increased their tobacco consumption, while 6.3% decreased it. 50.6% of the participants reported being more depressed, stressed, or irritable since the beginning of the lockdown. Confinement had a negative effect on every behavior studied in this survey, except for nutrition. We also found that negative mental state changes were strongly associated with nutrition, sleep, physical activity, and alcohol consumption changes.

This paper presents the development of a new system designed to measure the local temperature field in adiabatic shear band. Transient temperature field are simultaneously recorded by an array of 32 InSb infrared (IR) detectors and a streak camera working in visible-near infrared (VIS-NIR). Observations in IR offer a low temperature detection limit (350°C) but they are highly sensitive to uncertainty in the emissivity. Observations in VIS-NIR allow for measurement only at high temperatures (750°C) but they are less affected by uncertainty on emissivity and present a higher temperature sensitivity. By performing simultaneous measurements, it is possible to obtain data on a large temperature range with an improved accuracy at high temperature. The different sources of errors caused by uncertainty in the emissivity, spatial and temporal resolution of the detectors has been analyzed and an estimation of the total measurement uncertainty of the system is given.

A statistical analysis of semantic memory should reflect the complex, multifactorial structure of the relations among its items. Still, a dominant paradigm in the study of semantic memory has been the idea that the mental representation of concepts is structured along a simple branching tree spanned by superordinate and subordinate categories. We propose a generative model of item representation with correlations that overcomes the limitations of a tree structure. The items are generated through "factors" that represent semantic features or real-world attributes. The correlation between items has its source in the extent to which items share such factors and the strength of such factors: if many factors are balanced, correlations are overall low; whereas if a few factors dominate, they become strong. Our model allows for correlations that are neither trivial nor hierarchical, but may reproduce the general spectrum of correlations present in a data-set of nouns. We provide an estimate of the number of concepts that can be stored and retrieved by a large-scale cortical network, the Potts network, which is perhaps approximately 10⁷ with human cortical parameters. When this storage capacity is exceeded, however, retrieval fails completely only for balanced factors; above a critical degree of imbalance, a phase transition leads to a regime where the network still extracts considerable information about the cued item, even if not recovering its detailed representation: partial categorization seems to emerge spontaneously as a consequence of the dominance of particular factors, rather than being imposed ad hoc. We argue this to be a relevant model of semantic memory resilience in Tulving’s remember/know paradigms.

Vitamin A, or retinol, is one of the most effective anti-ageing molecules, but it presents issues with photo-sensitivity and irritation. Retinol-like alternatives are emerging, but have so far been less effective. Here, we present a Silibum marianum extract (SME) as a retinol-like ingredient providing both safety and efficacy. SME was compared to the reference compound, retinol, and to the main alternative, bakuchiol. In ex vivo tests, skin explants from a 58-year-old donor were treated with pure retinol (0.1%), bakuchiol (0.2%) or SME (0.8%). After 5 days, collagen and hyaluronic acid levels were analysed. A placebo-controlled study involving 57 volunteers (ages 45-73 years) was also conducted, with products applied twice-daily for 56 days. Results were measured by AEVA-HE and VISA. Levels of collagen III were significantly increased by SME, by 23% and 16% compared to bakuchiol and retinol, respectively. Compared to bakuchiol, SME treatment increased hyaluronic acid production by 36%. In clinical tests, SME had a significantly stronger anti-wrinkle effect than bakuchiol – reducing the number of wrinkles on the forehead by 21%, and their circumference by 17% – producing effects similar to retinol, and better than bakuchiol. In the self-assessment, 43% of volunteers reported discomfort while using retinol, as compared to 0% for the SME formulation. By enhancing levels of collagen III – the youth collagen – and hyaluronic acid in the skin, SME paves the way for the maturation of collagen I fibrils and skin plumping. With its stronger efficacy compared to bakuchiol and enhanced safety profile compared to retinol, SME may be the next-generation of natural alternatives to retinoids.

Alteration of land use and climate change are among the main variables affecting watershed hydrology. Characterizing the impacts of climate variation and land use alteration on water resources is essential in managing watersheds. Thus, in this research, streamflow and baseflow responses to climate and land use variation were modeled in two watersheds, the Upper West Branch DuPage River (UWBDR) watershed in Illinois and Walzem Creek watershed in Texas. The variations in streamflow and baseflow were evaluated using the Soil and Water Assessment Tool (SWAT) hydrological model. The alteration in land use between 1992 and 2011 was evaluated using transition matrix analysis. The non-parametric Mann-Kendall test was adopted to investigate changes in meteorological data from 1980-2017. Our results indicated that the baseflow accounted for almost 55.3% and 33.3% of the annual streamflow in the UWBDR and Walzem Creek watersheds, respectively. The contribution of both land use alteration and climate variability on the flow variation is higher in the UWBDR watershed. In Walzem Creek, the alteration in streamflow and baseflow appears to be driven by the effect of urbanization more than that of climate variability. The results reported herein are compared with results reported in recent work by the authors in order to provide necessary information for water resources management planning, as well as soil and water conservation, and to broaden the current understanding of hydrological components variation in different climate regions.

Purpose Prospective life cycle assessment (pLCA) is a key method for providing environmental decision support for future technologies. The specific technology under study is often modeled by the practitioner (the so-called foreground system), whereas data for supply chains are sourced from life cycle inventory databases (the so-called background system). Within pLCA, it is paramount to consider possible future developments for both foreground and background systems to avoid a temporal mismatch between the specific technology under study and the wider economy, which could lead to incorrect conclusions. Therefore, and given the importance of future-oriented environmental decision support, efforts to develop prospective life cycle inventory (pLCI) databases have recently increased. However, there is still much to be improved to realize the widespread use of these databases and to have a stronger real-world effect on how future technology is designed. Methods This paper summarizes recent developments and structures the information chain behind pLCI databases. We then discuss conditions for the broad application of pLCI databases, and we propose priorities and ways forward to overcome existing challenges to foster the use of pLCI databases. Results The conditions are scientific integrity, usefulness to LCA practitioners, accessibility, usability, interpretability, and a continuous improvement process in which core stakeholders coordinate efforts to streamline the generation, sharing, and use of pLCI databases. Conclusions Priority should be given to installing a continuous process to improve the coverage and consistency of pLCI databases, to improve related guidance and documentation, and to remove practical barriers relating to the access and usability of pLCI databases in LCA software.

Phagnalon saxatile subsp. saxatileest une espèce sauvage très répandue en Algérie qui est utilisée à des fins médicinales comme analgésique et anticholestérolémique. Cependant, les informations sur son contenu phytochimique sont encore rares. L'objectif de cette étude était d'identifier et de quantifier les composés phénoliques de différents extraits de ses tiges feuillues. A cet effet, les effets de quatre solvants d'extraction ont été étudiés sur la teneur en composés phénoliques et l'activité antioxydante de cette plante. Les extraits préparés avec des solvants polaires (méthanol et eau) contenaient des quantités plus élevées de composés phénoliques et présentaient une meilleure activité antioxydante que les extraits avec des solvants apolaires (hexane, dichlorométhane). L'extrait méthanolique, le plus riche en phénoliques totaux et en flavonoïdes totaux, avait une activité antioxydante significative au regard de la capacité de piégeage du DPPH ° (IC50 de 5,47 µg / mL), Capacité de piégeage ABTS (CI50 de 63,77 µg / mL) et inhibition de l'oxydation de l'acide linoléique (CI50 de 22,71 µg / mL), par rapport aux antioxydants synthétiques. Les acides chlorogéniques et quelques flavonoïdes ont été identifiés et quantifiés par UPLC-DAD-MSn. Les isomères des acides di-O-caféoylquiniques étaient les composés phénoliques les plus concentrés (25,4 mg / g DW) dans l'extrait méthanolique.

The widely used logistic model for epidemic case reporting data may be either restrictive or unrealistic in presence of containment measures when implemented after an epidemic outbreak. For flexibility in epidemic case reporting data modelling, we combined an exponential growth curve for the early epidemic phase with a flexible growth curve to account for the potential change in growth pattern after implementation of containment measures. We also fitted logistic regression models to recoveries and deaths from the confirmed positive cases. In addition, the growth curves were integrated into a SIQR (Susceptible, Infective, Quarantined, Recovered) model framework to provide an overview on the modelled epidemic wave. We focused on the estimation of: (1) the delay between the appearance of the first infectious case in the population and the outbreak (“epidemic latency period"); (2) the duration of the exponential growth phase; (3) the basic and the time-varying reproduction numbers; and (4) the peaks (time and size) in confirmed positive cases, active cases and new infections. The application of this approach to COVID-19 data from West Africa allowed to discuss the effectiveness of some containment measures implemented across the region.

Artificial Intelligence based on Deep Learning is opening new horizons in Biomedical research and promises to revolutionize the Microscopy field. Slowly, it now transitions from the hands of experts in Computer Sciences to researchers in Cell Biology. Here, we introduce recent developments in Deep Learning applied to Microscopy, in a manner accessible to non-experts. We overview its concepts, capabilities and limitations, presenting applications in image segmentation, classification and restoration. We discuss how Deep Learning shows an outstanding potential to push the limits of Microscopy, enhancing resolution, signal and information content in acquired data. Its pitfalls are carefully discussed, as well as the future directions expected in this field.

Foot drop is a common disability in post-stroke patients and represents a challenge for the clinician. To date, Ankle Foot Orthosis (AFO) combined with conventional rehabilitation is the gold standard of rehabilitation management. AFO has a palliative mechanical action without actively restoring the associated neural function. Functional Electrical Stimulation (FES), consisting in stimulation of the peroneal nerve pathway, represents an alternative approach. By providing a FES device (Bioness L-300, BIONESS, USA) for 6 months to a post-stroke 22-year-old woman with a foot drop, our goal was to quantify its potential benefit on walking capacity. Gait parameters and the temporal evolution of the speed were collected with a specific connected sole device (Feet Me®) during the 10-meter walking, the Time Up and Go, and the 6-minute walking tests with AFO, FES or without any device (NO). As a result, the walking speed changes on 10-meters were clinically significant with an increase from baseline to 6 months in AFO and FES conditions (+0.14m-1 and +0.36m-1), without any changes in NO condition. In addition, speed decreased at about 4-minutes of the 6-minute walking test in NO and AFO conditions, while speed increased in FES conditions at baseline and after 1, 3 and 6 months. Monitoring gait speed in an endurance test after an ecological rehabilitation training program helps to examine walking performance in post-stroke patients and to propose a specific rehabilitation program depending on a fatigue threshold.

In September 2021, the European Chemicals Agency evaluated a dossier for restricting polycyclic aromatic hydrocarbons (PAHs) in infant diapers and concluded that risks were not demonstrat-ed because of inconclusive exposure data. To fill this gap, we measured the 16 priority PAHs of the U.S. Environmental Protection Agency in the diaper core of four brands and in the sheets and fastening tapes of six brands of commercially available diapers. Health risks were conservatively assessed by assuming that dermally absorbed PAHs can cause both local (skin cancer) and sys-temic critical effects (neurobehavioral changes). Total concentrations of PAHs in diaper core and top sheet, the only significant contributors to skin exposure, averaged 34.5 μg/kg and 66.8 μg/kg, respectively. Excess skin cancer risks and hazard quotients for neurobehavioral effects calculated with the daily dose of total PAHs from combined diaper core and top sheet averaged 1.4 x 10-7 and 1.19 x 10-2, respectively. The median daily dose of total PAHs and of its ben-zo[a]pyrene-equivalent from breast milk estimated worldwide are 171 and 30 times greater than that from combined diaper core and top sheet, respectively. Altogether these findings indicate that trace levels of PAHs found in infant diapers are unlikely to pose health risks.

The COVID-19 pandemic has triggered seismic shifts in how we work, and organizations behave. It has caused many organizations to transition from an office-centric culture to more flexible ways of working. This shift is necessitated by health conditions and the need for controls. It is also driven by the assumption that remote work or working from home offer a certain assurance of safety for employees against the threat of infection in highly vulnerable office conditions. The shift to remote work has so far been mainly experimental as most businesses continue to learn about what works and what doesn’t. There is also the assumption that all that matters for the efficient operability of a remote work regime is the availability and effectiveness of computing technologies and internet connectivity. Thus, once those supplies are in place remote work is expected to be fully functional and without problems. As learning continues about what works and what doesn’t, it has also become increasingly clear that beyond the threat of covid-19 and the relative safety that individual homes may offer, there are other vulnerabilities which are ignored or underestimated in the process. In most instances, the home environment of the employee, the conditions under which employees operate from home and the health and safety such environments provide (or not) are either ignored or underestimated in discussions around remote work practices. This paper highlights remote work as an emergent practice while exploring associated, but ignored, health and safety issues in employee homes.

Neural networks models are mostly represented by oriented graphs where only the components, constitutive elements of the graph, are transcribed into mathematical xpression. Indeed, accurate knowledge of the full expression of the model is required in certain situations such as selecting among several reference models, the one that best fits the available data or comparing the explanatory and predictive performance of an established model with respect to some reference models. In this paper, we establish a formalism of the mathematical expression for multilayer perceptron neural network in a general framework, MLP-p-n-q, with p, n and q natural integers and show its restriction to cases where one has a hidden layer and multivariate outputs (MLP-p-1-q), and then a single output (MLP-p-1-1). Then, we give some specific cases of the most commonly used models. An application case is presented in the context of solving a nonlinear regression problem.

We study here what can be learned from our experience with COVID-19 vaccination for an initially naïve population, that can inform planning for vaccination against the next novel, highly transmissible pathogen. We focus on the first two pandemic years (wild strain through Delta), because after the Omicron wave in early 2022, few people were still SARS-CoV-2-naïve. Almost all were vaccinated, infected, or often both. We review the evidence on COVID-19 vaccine effectiveness (VE), waning effectiveness over time, and what we should expect about VE and waning from a future pathogen. As a basis for our analysis, we conducted a PRISMA-compliant review of all studies on PubMed through August 15, 2022 reporting VE against four endpoints: any infection, symptomatic infection, hospitalization, and death, for the four principal vaccines used in developed Western countries (BNT162b2, mRNA1273, Ad26.CoV2.S, and ChAdOx1-S). The mRNA vaccines (BNT162b2, mRNA1273) had high initial VE against all endpoints but protection waned after approximately six months, with BNT162b2 declining faster than mRNA1273. Both mRNA vaccines initially outperformed the viral vector vaccines. A third “booster” dose, roughly six months after the primary doses, substantially reduced symptomatic infection, severe disease, and mortality, and in hindsight should be seen as part of the normal vaccination schedule.

There is increasing interdisciplinary interest in phytoplankton community dynamics as the growing environmental problems of water quality (particularly eutrophication) and climate change demand attention. This has led to a pressing need for improved biophysical and causal understanding of Phytoplankton Functional Type (PFT) optical signals, in order that satellite radiometry may be used to detect ecologically relevant phytoplankton assemblage changes. This understanding can best be achieved with biophysically and biogeochemically consistent phytoplankton Inherent Optical Property (IOP) models, as it is only via modelling that phytoplankton assemblage characteristics can be examined systematically in relation to the bulk optical waterleaving signal. The Equivalent Algal Populations (EAP) model is used here to investigate the source and magnitude of size- and pigment- driven PFT signals in the water-leaving reflectance, as well as the potential to detect these using satellite radiometry. This model places emphasis on explicit biophysical modelling of the phytoplankton population as a holistic determinant of IOPs, and a distinctive attribute is its comprehensive handling of the spectral and angular character of phytoplankton scattering. Selected case studies and sensitivity analyses reveal that phytoplankton spectral scattering is the primary driver of the PFT-related signal. Key findings are that the backscattering-driven signal in the 520 to 600 nm region is the critical PFT identifier at marginal biomass, and that while PFT information does appear at blue and red wavelengths, it is compromised by biomass/gelbstoff ambiguity in the blue and low signal in the red, due primarily to absorption by water. These findings are hoped to provide considerable insight into the next generation of PFT algorithms.

The change in both streamflow and baseflow in urban catchments has received significant attention in the latest decades as a result of their drastic variability. In this research, effects of climate variation and dynamics of land use are measured separately and in combination on streamflow and baseflow in the Little Eagle Creek (LEC) watershed (Indianapolis, Indiana). These effects are examined using land use maps, statistical tests, and hydrological modeling. Transition matrix analysis was used to investigate the change in land use between 1992 and 2011. Temporal trends and changes in meteorological data were evaluated from 1980-2017 using the Mann-Kendall test. Changes in streamflow and baseflow were assessed using the Soil and Water Assessment Tool (SWAT) hydrological model using multiple scenarios that varied in land use and climate change. Evaluation of the model outputs showed streamflow and baseflow in LEC are well represented using SWAT; however, comparing the calibration and validation period showed SWAT performs better for the calibration. During 1992-2011, roughly 30% of the watershed experienced change, typically cultivated agricultural areas became urbanized. Baseflow is significantly affected by the observed urbanization; however, the combination of land and climate variability has a larger effect on the baseflow in LEC. Generally, the variability in the baseflow and streamflow appears to be heavily driven by the response to climate change in comparison to variability due to altered land use. The results reported herein expand the current understanding of variation in hydrological components, and provides useful information for management planning regarding water resources, as well as water and soil conservation in urban watersheds in Indiana and beyond.

Improper illumination of cultural heritage buildings and monuments can be a source of enormous light pollution that can have a major impact on the overall environment of the illuminated area. Such lighting can be the result of carelessness or a wrong/badly designed lighting system. This paper presents one of the methodologies that can significantly reduce light pollution, especially the spilt light out of the façade. The methodology is based on using luminaires with specially made shutters with the appropriate silhouette of the object. The shutter is planned with the help of an object photo and sizes measured at the location. The methodology was tested during the renovation of the lighting system of different churches in Slovenia and is described in the example of the Church of St. Thomas near Ptuj, Slovenia. The results showed that the methodology is effective and can significantly reduce light pollution that occurs when such buildings are incorrectly lit.

While shoulder injuries represent the musculoskeletal disorders (MSDs) most encountered in physical therapy, there is no consensus on their management. As attempts to provide standardized and personalized treatment, a robot-ic-assisted device combined with EMG biofeedback specifically dedicated to shoulder MSDs has been developed. The aim of this study was to determine the efficacy of an 8-week rehabilitation program (≈3 sessions a week) using a ro-botic-assisted device combined with EMG biofeedback (RA-EMG group) in comparison with a conventional program (CONV group) in patients presenting with shoulder MSDs. This study is a retrospective cohort study including data from 2010 to 2013 on patients initially involved in a physical rehabilitation program in a private clinic of Chicoutimi (Canada) for shoulder MSDs. Shoul-der flexion strength and range of motion were collected before and after the rehabilitation program. Forty-four patients participated in a conventional pro-gram using dumbbell (CONV group) while 72 of them completed a program on robot-assisted device with EMG and visual biofeedback (RA-EMG group), whereby both programs consisted in 2 sets of 20 repetitions at 60% of maximal capacity. Results showed that the RA-EMG had significantly greater benefits than the Conv group for shoulder flexion strength (+103.1% vs 67%, p = 0.016) and range of motion (+14.4% vs 6.1%, p = 0.046). The current retrospective co-hort study showed that a specific and tailored rehabilitation program with constant effort by automatic adjustment of the level of resistance was able to potentiate strength and range of motion shoulder flexion after an 8-week reha-bilitation period in comparison with a conventional approach in patients with shoulder MSDs. This study provides new insight on shoulder MSD rehabilita-tion and future research should be pursued to determine the added potential of this approach for abduction and external rotation with a randomized controlled design.

Egg activation is a cellular transition of an arrested mature oocyte into a developing embryo through a coordinated series of events. Previous studies in Hymenoptera have indicated that mechanical pressure can induce egg activation. In this study, we developed the first egg activation protocol for the haplodiploid insect pest, Sirex noctilio (Hymenoptera: Siricidae), from two climatically different regions in South Africa. In addition, activated eggs were exposed to three treatments involving water, pine sawdust, and the fungal symbiont of S. noctilio, Amylostereum areolatum (Russulales: Amylostereaceae), to confirm the hypothesised prerequisite of the symbiotic fungus for egg development. A rearing protocol was developed for emerged first instar larvae using a modified Anoplophora glabripennis (Coleoptera: Cerambycidae) artificial diet. A significant difference between mean survival rates of activated eggs from the two different regions was observed. Amylostereum areolatum was shown to be unnecessary for egg survival and adversely affected egg eclosion. Maximum larval survival duration on the artificial diet was 92 days. The egg activation and rearing protocol developed in this study enables opportunities for research on the physiology, ecology, symbioses, and genetics of S. noctilio, which can be exploited for new genetic pest management strategies.

Glaphyrophlebia victoriensis sp. nov. (Paoliida: Blattinopsidae) is the third Gzhelian representative of the genus and is described based on a beautiful forewing from the Var department in Southern France. Together with the description of another forewing fragment of a Glaphyrophlebia sp. from the Province of León in NW Spain, they improve our knowledge of fossil insects from French and Spanish late Carboniferous deposits. The specimen of Glaphyrophlebia sp. is the first mention of the family in the Carboniferous of Spain and extends the geographical distribution of the genus. These descriptions suggest that the genus Glaphyrophlebia was speciose during the Upper Pennsylvanian, while otherwise, very diverse in the early and middle Permian strata of the Russian Federation. We proposed the first hypothesis to explain the diversification of family and of its most speciose genera, and argue their diversity dynamics were likely linked with the major environmental changes that followed the collapse of the Carboniferous rainforest notably the extension of arid biomes during the Permian period. The exquisite preservation and the fineness of the sediment from Tante Victoire, in which the new species was found, suggests that the locality is suitable for preserving other fossil insects and will require additional investigations.

The emergence of bispecific antibodies has transformed cancer immunotherapy, highlighting increased clinical efficacy, especially in hematological malignancies. These innovative molecules uniquely target two distinct tumor antigens or separate epitopes simultaneously, demonstrating potent antitumor activity across various cancers. Despite their promise, challenges like rapid drug clearance, off-target effects, and cytokine release syndrome hinder their widespread therapeutic application. Recent engineering advancements in bispecific antibody systems aim to overcome these challenges, broadening therapeutic coverage. This review offers insights into the latest clinical and preclinical progress in bispecific immunotherapy, outlining key challenges faced by the technique, and exploring emerging strategies to address these obstacles.

The identification of an emerging pathogen in humans can remain difficult by conventional methods such as enrichment culture assays that remain highly selective, require appropriate medium and cannot avoid misidentifications, or serological tests that use surrogate antigens and are often hampered by the level of detectable antibodies. Although not originally designed for this purpose, the implementation of polymerase-chain-reaction (PCR) has resulted in an increasing number of diagnostic tests for many diseases. However, the design of specific molecular assays relies on the availability and reliability of published genetic sequences for the target pathogen as well as enough knowledge on the genetic diversity of species and/or variants giving rise to the same disease symptoms. Usually designed for clinical isolates, molecular tests are often not suitable for environmental samples in which the target DNA is mixed with a mixture of environmental DNA. A key challenge of such molecular assays is thus to ensure high specificity of the target genetic markers when focusing on clinical and environmental samples in order to follow the dynamics of disease transmission and emergence in humans. Here we focus on Buruli ulcer (BU), a human necrotizing skin disease mainly affecting tropical and subtropical areas, commonly admitted to be caused by Mycobacterium ulcerans worldwide although other mycolactone-producing mycobacteria and even mycobacterium species were found associated with BU or BU-like cases. By revisiting the literature, we show that many studies have used non-specific molecular markers (IS2404, IS2606, KR) to identify M. ulcerans from clinical and environmental samples and propose that all mycolactone-producing mycobacteria should be definitively considered as variants from the same group rather than different species. Importantly, we provide evidence that the diversity of mycolactone-producing mycobacteria variants as well as mycobacterium species potentially involved in BU or BU-like skin ulcerations might have been underestimated. We also suggest that the specific variants/species involved in each BU or BU-like case should be carefully identified during the diagnosis phase, either via the key of genetic identification proposed here or by broader metabarcoding approaches, in order to guide the medical community in the choice for the most appropriate antibiotic therapy.

Objectives: To investigate the etiology of amyotrophic lateral sclerosis (ALS) in an adult female patient. Methods: We conducted trio genome sequencing on a 35-year-old woman with progressive weakness in her left upper limb, as well as on her parents. Prior to sequencing, we performed a comprehensive neurological work-up on the patient, including neurological exam, electrophysiology, biomarker assessment, and brain and spinal cord MRI. Results : The propositus experienced weakness and atrophy in her left hand 6 months before the evaluation. This was associated with brisk reflexes and Hoffman sign in the same arm. Electroneuromyography revealed lower motor neuron involvement in three body regions. Neurofilament light chains were elevated in her cerebrospinal fluid. Brain imaging showed asymmetrical T2 hyperintensity of the corticospinal tracts and T2 linear hypointensity of the precentral gyri. Genome trio sequencing identified a likely pathogenic de novo variant in the propositus’s KIF1A gene (NM_001244008.2): c.574A&gt;G, p.(Ile192Val). Discussion: Pathogenic variants in the KIF1A gene have been associated with a wide range of neurological manifestations called KIF1A-associated neurological diseases (KAND). The present report describe a likely pathogenic de novo variant in KIF1A associated with ALS, expanding the phenotypic spectrum of KAND and our understanding of the pathophysiology of ALS.

Conditions under which compositions of component systems form a well-defined system-of-systems here are formulated at a fundamental level. Statement of what defines a well-defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: 1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state, and 2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and if so, whether it is non-deterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

With an estimated 3 to 5 million human cases annually and the potential to infect domestic and wild animal populations, influenza viruses are one of the greatest health and economic burdens to our society [1] and pose an ongoing threat of large-scale pandemics. Despite our knowledge of many important aspects of influenza virus biology, there is still much to learn about how influenza viruses replicate in infected cells, for instance how they use entry receptors or exploit host cell trafficking pathways. These gaps in our knowledge are due, in part, to the difficulty of directly observing viruses in living cells. In recent years, advances in light microscopy, including super-resolution microscopy and single-molecule imaging, have enabled many viral replication steps to be visualised dynamically in living cells. In particular, the ability to track single virions and their components, in real time, now allows specific pathways to be interrogated providing new insights to various aspects of the virus-host cell interaction. In this review, we discuss how state-of-the-art imaging technologies, notably quantitative live-cell and super-resolution microscopy, are shedding new nanoscale and molecular insights into influenza virus replication and revealing new opportunities for developing antiviral strategies.

Species distribution models have become tools of great importance in ecology since the advanced knowledge of suitable habitat of species is needed in the process of the world's biodiversity conservation. Models that use presence-only data are of great interests and are widely used in ecology due to their easy access. However, these models do not estimate accurately the true spatial species distribution based solely on presence-only data since they do not account for biases induced by the sampling techniques used and imperfect detection. To address this gap, Hierarchical integrated models have been recently introduced. Through this study, we assessed the relative performance of these new SDMs models using simulated data. The performance of the models was tested by comparing the estimates of parameters of the distribution models they provide with parameters used to simulate the distribution of the virtual species. The best model was the one whose estimates were close to the true distribution parameters of the virtual species. Results showed that analyzing Presence-only data in conjunction with Point-counts data through the Dorazio's Hierarchical model produced estimates of the coecients of the species intensity models with high precision and less bias while the Koshkina integrated model showed poor performance. Site-occupancy data, being not informative of species abundance, did not allow reducing biases in Presence-only data. The Dorazio's Hierarchical model produced estimates with high precision even with low detection probability. We have also found that the species rarity tends to in ate the variability of the models' estimates making modelling abundant species to be more accurate than modelling less abundant species. Hence, to model the species distribution with high precision based on Presence-only data, additional Point-counts data are required to account for sampling bias and imperfect detection.

The Harmonious Protected Landscape of Ambohitr'Antsingy Montagne des Français is one of the protected area located in the extreme northern of Madagascar. An assessment of the Herpe-tofauna community were made on the eastern and western slopes regarding the forest degrada-tion level, the availability of the streams and the habitat types in the massif during the years of 2014 and 2020. This survey was done in order to updating the herpetofaunal database and re-cording the distribution ranges of the Herpetofauna community in the PA. Three methods were used, including direct observation, systematic microhabitat searching and pitfall-traps with drift fences at three sampling sites during the day and night with headlamp. Twenty species of am-phibians and 50 species of reptiles were recorded. Four species of Amphibians, and 03 species of Reptiles (snake) are potentially new to science and/or newly recorded at Montagne des Français. One snake (Langaha pseudoalluaudi) last inventoried in 2007 has reappeared during this investiga-tion. Here, we present the mapping of all species recorded. This new database can help the manager site to update the management plan to guarantee the herpetofaunal conservation facing habitat change. That is what we think also for all of the current PA in Madagascar.

Estimating the vehicle crashworthiness parameters experimentally is expensive and time consuming. For these reasons different modelling approaches are utilized to predict the vehicle behaviour and reduce the need for full-scale crash testing. The earlier numerical methods used for vehicle crashworthiness analysis were based on the use of lumped parameters models (LPM), a combination of masses and nonlinear springs interconnected in various configurations. Nowadays, the explicit nonlinear finite element analysis (FEA) is probably the most widely recognized modelling technique. Although informative, finite element models (FEM) of vehicle crash are expensive both in terms of man-hours put into assembling the model and related computational costs. A simpler analytical tool for early analysis of vehicle crashworthiness could greatly assist the modelling and save time. In this paper a simple piecewise LPM composed of a mass-spring-damper system, is used to estimate the vehicle crashworthiness parameters, focusing on the dynamic crush and the acceleration severity index (ASI). The model is first calibrated against a full-scale crash test and a FEM, post-processed with the LS-DYNA software, at an impact velocity of 56 km/h. The genetic algorithm is used to calibrate the model by estimating the piecewise lumped parameters (stiffness and damping of the front structure of the vehicle). After calibration, the LPM is applied to a range of velocities (40, 48, 64 and 72 km/h). The predictions for crashworthiness parameters from the LPM were compared with the predictions from the FEA and the results are much similar. It is shown that the LPM can assist in crash analysis, since LPM has some predictive capabilities and requires less computation time in comparison with the explicit nonlinear FEA.

Background: Media coverage of Lyme disease (LD) has led to an increase in consultations for presumed LD in Europe. However, LD is confirmed in only 10-20% of patients, with a significant number remaining in a diagnostic dead-end. Objectives: To reach a deeper understanding of how patients themselves contribute to the diagnostic process. To describe the genesis of the LD hypothesis in care pathways. Methods: In 2019, 30 patients from a prospective cohort consulting in the infectious diseases department at University Hospital in Marseille for presumed LD were recruited for semi-structured interviews. The inclusion criteria were : suffering from subjective symptoms for 6 months, no clinical or paraclinical argument suggesting current LD. The patients’ medical trajectories were collected using a biographical approach. Results: The diagnosis of Lyme disease was primarily triggered by identification with personal testimonies found on the internet. Most of patients were leading their own diagnostic investigation. The majority of participants were convinced they had LD despite the lack of medical evidence and the scepticism of their referring GP. Conclusion: GPs should first systematically explore patients’ etiologic representations in order to improve adherence to the diagnosis especially in the management of medically unexplained symptoms. Long COVID-19 syndrome challenge offers an opportunity to promote active patient involvement in diagnosis.

Non-vertebrate species represent about ~95% of known metazoan (animal) diversity. They remain to this day relatively unexplored genetically, but understanding their genome structure and function is pivotal for expanding our current knowledge of evolution, ecology and biodiversity. Following the continuous improvements and decreasing costs of sequencing technologies, many genome assembly tools have been released, leading to a significant amount of genome projects being completed in recent years. In this review, we examine the current state of genome projects of non-vertebrate animal species. We present an overview of available sequencing technologies, assembly approaches, as well as pre and post-processing steps, genome assembly evaluation methods, and their application to non-vertebrate animal genomes.

The redefined vacuum approach, which is frequently employed in the many-body perturbation theory, proved to be a powerful tool for formula derivation. Here, we elaborate this approach within the bound-state QED perturbation theory. In addition to general formulation, we consider the particular example of a single particle (electron or vacancy) excitation with respect to the redefined vacuum. Starting with simple one-electron QED diagrams, we deduce first- and second-order many-electron contributions: screened self-energy, screened vacuum polarization, one-photon exchange, and two-photon exchange. The redefined vacuum approach provides a straightforward and streamlined derivation and facilitates its application to any electronic configuration. Moreover, based on the gauge invariance of the one-electron diagrams, we can identify various gauge-invariant subsets within derived many-electron QED contributions.

Most existing flexible count regression models allow only approximate inference. This work proposes a new framework to provide an exact and flexible alternative for modeling and simulating count data with various types of dispersion (equi-, under- and overdispersion). The new method, referred as “balanced discretization”, consists in discretizing continuous probability distributions while preserving expectations. It is easy to generate pseudo random variates from the resulting balanced discrete distribution since it has a simple stochastic representation in terms of the continuous distribution. For illustrative purposes, we have developed the family of balanced discrete gamma distributions which can model equi-, under- and overdispersed count data. This family of count distributions is appropriate for building flexible count regressionmodels because the expectation of the distribution has a simple expression in terms of the parameters of the distribution. Using the Jensen–Shannon divergence measure, we have shown that under equidispersion restriction, the family of balanced discrete gamma distributions is similar to the Poisson distribution. Based on this, we conjecture that while covering all types of dispersion, a count regression model based on the balanced discrete gamma distribution will allow recovering a near Poisson distribution model fit when the data is Poisson distributed.

The vast array of omics data in microbiology presents significant opportunities for studying bacterial pathogenesis and creating computational tools for predicting pathogenic potential. However, the field lacks a comprehensive, curated resource that catalogs bacterial strains and their ability to cause human infections. Current methods for identifying pathogenicity determinants often introduce biases and miss critical aspects of bacterial pathogenesis.In response to this gap, we introduce BacSPaD (Bacterial Strains’ Pathogenicity Database), a thoroughly curated database focusing on pathogenicity annotations for a wide range of high-quality, complete bacterial genomes. Our rule-based annotation workflow combines metadata from trusted sources with automated keyword matching, extensive manual curation, and detailed literature review. Our analysis classified 5,502 genomes as pathogenic to humans (HP) and 490 as non-pathogenic to humans (NHP), encompassing 532 species, 193 genera, and 96 families. Statistical analysis demonstrated a significant but moderate correlation between virulence factors and HP classification, highlighting the complexity of bacterial pathogenicity and the need for ongoing research. This resource is poised to enhance our understanding of bacterial pathogenicity mechanisms and aid in the development of predictive models. To improve accessibility and provide key visualization statistics, we developed a user-friendly web interface, accessible at https://bacspad.altrabio.com.

Cloud computations are based on the computer networks such as Internet which presents a new pattern to provide, consume and deliver services such as infrastructure, software, ground and other resources using network. The inappropriate timing of assigning loads to the virtual machines in the computational space could lead to unbalance in the system. One of the challenging planning problems. In the cloud data centers is considering both assigning and migration (transfer) of the virtual machines with the ability of reconfiguration and the integrated features of the hosting physical machines. In this article, we introduce an integrated and dynamic timing algorithm based on the Genetic evolution algorithm. The suggested method was evaluated based on these factors and different inputs. Our suggested method is done using Java programming language and cloud-SME simulation. The results show that the execution time and the response time were improved by 12 and 1 percent respectively.

Human rhabdomyosarcomas are rarely cured by surgical resection alone. This is also true for high grade soft tissue sarcomas in dogs. Dogs with spontaneous sarcoma are good models for clinical responses to new cancer therapies. Strategic combinations of immunotherapy and oncolytic virotherapy (OV) could improve treatment responses in canine and human cancer patients. To develop an appropriate combination of immunotherapy and OV for dogs with soft tissue sarcoma (STS), canine cancer cells were inoculated with myxoma viruses (MYXVs) and gene transcripts were quantified. Next, cytokine concentrations in canine cancer cells were altered to evaluate their effect on MYXV replication. These studies indicated that, as in murine and human cells, type I interferons (IFN) play an important role in limiting MYXV replication in canine cancer cells. To reduce type I IFN production during OV, oclacitinib (a JAK1 inhibitor) was administered twice daily to dogs for 14 days starting ~7 days prior to surgery. STS tumors were excised and MYXV deleted for serp2 (MYXV∆SERP2) was administered at the surgical site at two time points post-operatively to treat any remaining microscopic tumor cells. Tumor regrowth in dogs treated with OV was decreased relative to historical controls. However, regrowth was not further inhibited in patients given combination therapy.

The fall armyworm, Spodoptera frugiperda, arrived during the 2016/2017 cropping season in Limpopo, South Africa and it continues to cause devastating damage on maize in this region. The present study investigated knowledge, perception and management of S. frugiperda by smallholder farmers at Dzindi and Tshiombo irrigation schemes in this region. The study was conducted in 16 focus group discussion interviews, 12 at Dzindi and four at Tshiombo irrigation scheme. Although farmers failed to identify S. frugiperda eggs and pupal stages, they positively identified the pest by its larval stage and refer to it by several local names. All farmers relied extensively on insecticides for S. frugiperda control. With the exception of the Dzindi irrigation scheme farmers, who have adopted Bt maize, very few farmers have adopted alternative methods to chemical control such as mechanical control, physical control (crushing egg and handpicking of larvae), biological control, cultural practices (such as Push-Pull Technology), possible indigenous knowledge, or the use of monitoring tools, for the management of S. frugiperda. The study provided valuable information for designing sustainable management practices to control S. frugiperda on maize in smallholder farming communities.

We have developed deep learning models for automatic identification of the maternal heart rate (MHR) and, more generally, false signals (FSs) on fetal heart rate (FHR) recordings. The models can be used to preprocess FHR data prior to automated analysis or as a clinical alert system to assist the practitioner. Three models were developed and used to detect (i) FSs on the MHR channel (the FSMHR model), (ii) the MHR and FSs on the Doppler FHR sensor (the FSDop model), and (iii) FSs on the scalp ECG channel (the FSScalp model). The FSDop model was the most useful because FSs are far more frequent on the Doppler FHR channel. All three models were based on a multilayer, symmetric, GRU and were trained on data recorded during the first and second stages of delivery. The FSMHR and FSDop models were also trained on antepartum recordings. The training dataset contained 1030 expert-annotated periods (mean duration: 36 min) from 635 recordings. In an initial evaluation of routine clinical practice, 30 fully annotated recordings for each sensor type (mean duration: 5 h for MHR and Doppler sensors, and 3 h for the scalp ECG sensor) were analyzed. The sensitivity, positive predictive value (PPV) and accuracy were respectively 62.20%, 87.1% and 99.90% for the FSMHR model, 93.1%, 95.6% and 99.68% for the FSDop model, and 44.6%, 87.2% and 99.93% for the FSScalp model. We built a second test dataset with a more solid ground truth by selecting 45 periods (lasting 20 min, on average) on which the Doppler FHR and scalp ECG signals were recorded simultaneously. Using scalp ECG data, the experts estimated the true FHR value more reliably and thus annotated the Doppler FHR channel more precisely. The models achieved a sensitivity of 53.3%, a PPV of 62.4%, and an accuracy of 97.29%. In comparison, two experts (blinded to the scalp ECG data) respectively achieved a sensitivity of 15.7%, a PPV of 74.3%, and an accuracy of 96.91% and a sensitivity of 60.7%, a PPV of 83.5% and an accuracy of 98.24%. Hence, the models performed at expert level (better than one expert and worse than the other), although a well-trained expert with good knowledge of FSs could probably do better in some cases. The models and datasets have been included in the Fetal Heart Rate Morphological Analysis open-source MATLAB toolbox and can be used freely for research purposes.

Open-pit uranium mining in the south Texas Gulf Coast region was active during the second half of the 20th century. Although some mines have extensive records documenting the locations and extents with descriptions of reclamation procedures and timelines, abandoned and reclaimed mines with minimal documentation concurrently exist where little is known about locations, mine waste volumes, and reclamation status. Advanced mapping of these mining operations is helpful for producing basic mine land inventories and assessments of mine waste; identifying potential undeveloped resources and environmental health issues; and for developing proper management, restoration, and reclamation strategies. We studied known reclaimed and unreclaimed mine sites to develop a spatiotemporal mapping approach utilizing multiple techniques in remote sensing as a proof of concept. A mapping classification system was developed to identify uranium mine waste-rock piles, mine pits (with and without waste water), the total area of land disturbance due to mining, and to differentiate abandoned and reclaimed mine features using lidar (Light Detection and Ranging) analysis and multispectral temporal analysis. Visual interpretation of Landsat imagery and time-series analysis revealed the temporal life-cycle of mining, historical areas of disturbance, and reclamation; however, mine features below vegetation canopies are not always visible in satellite imagery. Lidar laser scanning penetrates the vegetation canopy to measure the topographic elevation and exposes anthropogenic modifications to the landscape. Lidar analysis (bare earth elevation surface, slope, topographic contours, topographic textures, and overland-flow hydrography) revealed mine features, and expansive areas of mining disturbance. The spatiotemporal mapping approach proved to be useful for identifying and characterizing known mine features, areas of disturbance, and reclamation status. Historical pre-mining topographic data were available at only 4 of the mines for comparison where the cut-and-fill tool estimated 21% less waste-rock volume, indicating that information about pre-mining terrain topography and/or subsurface mine waste geometry is needed to improve accuracies for quantifying buried mine waste volume at reclaimed mine sites.

The positional cloning of single nucleotide polymorphisms (SNPs) of the neutrophil cytosolic factor 1 (Ncf1) gene, advocating that a low oxidative burst drives autoimmune disease, demands an understanding of the underlying molecular causes. A cellular target could be T cells, which have been shown to be regulated by reactive oxygen species (ROS). However, the pathways by which ROS mediate T cell signaling remain unclear. The adaptor molecule linker for activation of T cells (LAT) is essential for coupling T cell receptor-mediated antigen recognition to downstream responses, and it contains several cysteine residues that have previously been suggested to be involved in redox regulation. To address the possibility that ROS regulate T cell-dependent inflammation through LAT, we established a mouse strain with cysteine-to-serine mutations at positions 120 and 172 (LATSS). We found that redox regulation of LAT through C120 and C172 mediate its localization and phosphorylation. LATSS mice had reduced numbers of double-positive thymocytes and naïve peripheral T cells. Importantly, redox insensitivity of LAT enhanced T cell-dependent autoimmune inflammation in collagen-induced arthritis (CIA), a mouse model of rheumatoid arthritis (RA). This effect was reversed on an NCF1-mutated (NCF1m1j), ROS-deficient, background. Overall, our data shows that LAT is redox-regulated, acts to repress T cell activation, and is targeted by ROS induced by NCF1 in antigen-presenting cells (APCs).

Stretch marks are far from being exclusively appearing on pregnant women and appear whenever the body experience a rapid growth. Into the dermis, collagen fibres are altered associated with a loss of orientation and the elastic network is disrupted leading to a fibrotic organisation. This results to epidermal tearing which produce skin lesions. Centella asiatica is a well-known medicinal plants rich in triterpenic actives molecules and traditionally used to treat wounds and help skin repair. The aim of this study was to evaluate Centella asiatica extract as a natural way to solve stretch marks concern and understand its mechanism of action. We have first evaluated the fibroblast’s proliferation based on scratch assay model and their genes expression by RT-qPCR. At the ex vivo level, elastin fibres were quantified by immunofluorescence. Collagen fibre’s orientation and their occupation of the dermis were analysed after Sirius red staining and specific software analysis. We have proven that Centella asiatica has stimulated fibroblasts proliferation, reduced extracellular matrix degradation and fibrosis. On stretch marked skin explant, Centella asiatica increased the occupation of collagen fibres and elastin production. Based on the mechanisms behind the formation of stretch marks, Centella asiatica restores dermis network by optimising fibres organisation for a visible skin remodelling effect.

Background: Globally, hot cooked refined rice is consumed in large quantities and is a major contributor to dietary glycaemic load. This study aimed to compare the glycaemic potency of hot and cold stored parboiled rice to widely available medium grain white rice. Method: Twenty-eight healthy volunteers participated in a three treatment experiment where postprandial blood glucose was measured over 120 minutes after consumption of 140g of rice. Three rice samples were freshly cooked medium-grain white rice, freshly cooked parboiled rice, and parboiled rice stored overnight at 4 ºC. All rice was served warm at 65 ºC. Chewing time was recorded. Results: The 24-hours cold-stored and reheated parboiled rice resulted in a 42% and 12% lower blood glucose concentration trajectory than freshly cooked medium-grain white rice and freshly cooked parboiled rice. Chewing time for 10g cold stored parboiled rice was 6 seconds (25%) longer and was considered more palatable, visually appealing and better tasting than freshly cooked medium grain (all P<0.05). Conclusions:. For regular consumers of rice reheating cooked rice after cold storage would lower the dietary glycaemic load and long term may reduce risk for type 2 and gestational diabetes. More trials are needed to identify the significance.

Mountain summit in Madagascar have generally a specific habitat and that figure home a local endemicity of herpetofauna. Mantidactylus pauliani, a typically aquatic and critically endangered amphibian from Ankaratra Mountain, is among of them. This species inhabits high elevation with restricted distribution range. Our study highlights here a new data of this species and its viability with environmental change. So that, we carried out annual monitoring from 2018 to 2021. Nine fixed transects of 100 m were done along the streams between 1762 and 2378 m a.s.l. We followed the herpetofaunal standard technique for collecting data (systematic ecological niche searching and direct observation), and data analysis was performed using χ² tests and Fac-tor Correspondence Analysis. Here we present the new ecological data of M. pauliani. This spe-cies home between 1900 - 2378 m a.s.l. within the humid forest and savannah habitat. We noticed also a fluctuation in population size and migration to the upper throughout the years across the season, and the stream quality and volume of water variation. This habitat change makes it vul-nerable and the annual bushfire surrounding plays a negative impact. Knowledge and under-standing of its ecological guilds is needed for running and updating conservation activities

Cold gas-dynamic spray (cold spray) is an evolving coating deposition and restoration technology in which particles are deposited above the sonic speed. This paper presents the non-destructive three-dimensional characterization of cold sprayed stainless steel coating. The visualization of coating morphology and volumetric porosity, and the analyses of porosity size and spatial distributions confirmed that dense stainless steel coating with non-connected, micron-sized gradient porosity is successfully produced by cold spray. The suitability of X-ray tomography for characterizing cold sprayed coatings is assessed.

Long-range radio transmissions open new sensor application fields, in particular for environment monitoring. As an example, the {\sl LoRa} radio protocol enables to connect remote sensors at distance as long as ten kilometers in line-of-sight. However, the large area covered also bring several difficulties, such as the placement of sensing devices in regard to geography topology, or the variability of communication latency. Sensing the environment also carries constraints related to the interest of sensing points in relation with a physical phenomenon. Criteria for designs are thus evolving a lot from the existing methods, especially in complex terrains. This article describes simulation techniques based on geography analysis to compute long-range radio coverages and radio characteristics in these situations. As radio propagation is just a particular case of physical phenomena, it is shown how a unified approach also allows to characterize the behavior of potential physical risks. The case of heavy rainfall and flooding is investigated. Geography analysis is achieved using segmentation tools to produce cellular systems which are in turn translated into code for high-performance computations. The paper provides results from practical complex terrain experiments using LoRa, that confirm the accuracy of the simulation, scheduling characteristics for sample networks, and performance tables for simulations on middle range Graphics Processing Units (GPUs).

Coronavirus disease 2019 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To contain the virus, numerous preventive measures have been taken including isolation of patients, careful infection control, social distancing, and taking vaccine. So far, new confirmed and death cases are still increasing. SARS-CoV-2 invades cells by using the angiotensin converting enzyme 2 (ACE2). ACE2 is an essential enzyme of the renin-angiotensin system (RAS) which converts angiotensin II (Ang II) to angiotensin (1-7). ACE2 is expressed in different organs, including lung, heart, and kidney. A high number of COVID-19 patients developed kidney injury has been reported. Renal impairment and acute injury are associated with mortality of COVID-19, which is 14-16 times higher than other general patients. Acute Kidney Injury has been occured in 2.9 up to 43% of intensive care unit patients. The increasing evidence show that the components of RAS can activate the complement cascade, and cytokines production. Kidney injury caused by SARS-CoV-2 is related mainly to systemic and local inflammation. Moreover, the uncontrolled immune responses mediated by SARS-CoV-2 including hypercytokinaemia, secondary hemophagocytic lymphohistiocytosis, antibody dependent enhancement, complement system, and phagocytic cells activation can contribute in the virus pathogenesis leading to associated renal dysfunction. However, the role and crosstalk between of RAS components and immune response in mediating kidney injury remain undefined. In this review, we focus on the recent studies to provide the pathogenesis of SARS-CoV-2 interacting with RAS and immune responses to mediate kidney injury.

In biology molecules and macromolecules like sugars, proteins, DNA, and RNA are of utter importance. Detecting their presence as well as their conformation is still a challenge in many cases. It is well known that the vibrational states of such molecules lie from the infrared to the TeraHertz range. Spectroscopy can be used to detect such compounds and probe their conformation. Still, terahertz spectroscopy on biosample is a challenge for two main reasons: water absorption; and the small size of the samples. The sample volume is smaller than the cube of the TeraHertz wavelength; the light matter interactions are thus extremely reduced. In this paper, we present the design, fabrication, characterization and the first typical uses of a biophotonic device aiming at increasing light matter interaction to enable terahertz spectroscopy of minute samples on a broad band (0.2–2 THz). We demonstrate time domain spectroscopy experiments on few µl samples showing the validity of our approach.

Abstract: The SARS-CoV-2 pandemic has reemphasized the urgent need for broad-spectrum antiviral therapies. We developed a computational workflow using scRNA-Seq data to assess cellular metabolism during viral infection. With this workflow we predicted the capacity of cells to sustain SARS-CoV-2 virion production in patients and found a tissue-wide induction of metabolic pathways that support viral replication. Expanding our analysis to influenza A and dengue viruses, we identified metabolic targets and inhibitors for potential broad-45 spectrum antiviral treatment. These targets were highly enriched for known interaction partners of all analyzed viruses. Indeed, phenformin, an NADH:ubiquinone oxidoreductase inhibitor, suppressed SARS-CoV-2 and dengue virus replication. Atpenin A5, blocking succinate dehydrogenase, inhibited SARS-CoV-2, dengue virus, respiratory syncytial virus, and influenza A with high selectivity indices. In vivo, phenformin showed antiviral activity against SARS-CoV-2 in a Syrian hamster model. Our work establishes host metabolism as druggable for broad-spectrum antiviral strategies, providing invaluable tools for pandemic preparedness.

Transient Receptor Potential Vanilloid 4 (TRPV4) channel is a non-selective cation channel, mostly permeable to calcium (Ca2+), which participates to intracellular Ca2+ handling in cardiac cells. It is widely expressed through the body and is activated by a large spectrum of physico-chemical stimuli, conferring it a role in a variety of sensorial and physiological functions. With-in the cardiovascular system, TRPV4 expression was reported in cardiomyocytes, endothelial cells (ECs) and smooth muscle cells (SMCs) where it modulates mitochondrial activity, Ca2+ ho-meostasis, cardiomyocytes electrical activity and contractility, cardiac embryonic development, fibroblast proliferation but also vascular permeability, dilatation and constriction. On the other hand, TRPV4 channels participate in several cardiac pathological processes such as development of cardiac fibrosis, hypertrophy, ischemia-reperfusion injuries, heart failure, myocardial infarc-tion, and arrhythmia. In this manuscript, we provide an overview of TRPV4 channel implica-tions in cardiac physiology and discuss the potential of TRPV4 channel as therapeutic target against cardiovascular diseases.

Several factors enhance the possibility of vertical HIV transmission in the pediatric population. Unfortunately, the data of the prevalence of HIV and associated risk factors in these populations remain limited in Rwanda. The study aimed to assess HIV prevalence and risk factors for infants born to mothers on ARV treatment at CHUB/Rwanda. MethodsA cross-sectional study was carried out on infants who were born to mothers under ARV treatment at CHUB. The associated risk factors were retrospectively assessed using prevention vertical HIV transmission records, and Dried Blood spots (DBS) were prospectively tested using Polymerase Chain Reaction (PCR). Data were analyzed by logistic regression. Ethical clearance (Ref: CMHS/IRB/198/2017) was issued by University of Rwanda to fulfill research ethical consideration.ResultsAmong 185(100%) infants born to HIV-positive mothers under ARV treatment, 5(2.7%) were HIV positive. The most associated risk factors were increased to over 1log copies/ml mother’s viral load (OR 9.3, 95% CI 1.01-85.45, P= 0.04) and mother’s CD4 count lower than 350 cells/µl (OR 6.4, 95% CI 1.03-40.06, P=0.04). The factors found to reduce the rate of vertical transmission of HIV were health facility as a delivery place (P=0.03), exclusive breastfeeding for 6 months (P= 0.006), and attending the antenatal care (P=0.01) while feeding children and vaginal delivery were associated risks but not statistically significant.ConclusionThe current study supports that the more mothers’ viral load and CD4 count decrease, so does the risk of HIV to their infants. A fact which indicates that both prevalence and risk factors remain an alarming issue. Much effort and multi-disciplinary approach are highly recommended.

Temporal lobe epilepsy (TLE) is associated with reorganization of hippocampal neuronal net-works, gliosis, neuroinflammation, loss of integrity of the blood-brain barrier (BBB) in humans and animal models. More than 30% of epilepsies remain intractable and characterization of the molecular mechanisms involved in BBB dysfunction is essential to the identification of new thera-peutic strategies. In this work, we induced status epilepticus in rats by injection of the proconvul-sant drug pilocarpine that leads to TLE. Using RT-qPCR, double immunohistochemistry and con-focal imaging, we studied at different time points of epileptogenesis (latent phase, 3, 7, 14 days; chronic phase, 1 and 3 months) the regulation of reactive glia and vascular markers. In the hippo-campus, increased expression of mRNA encoding the glial proteins GFAP and Iba1 confirmed neu-roinflammatory status. We report for the first time the concomitant induction in endothelial cells, pericytes and basement membrane of the BBB of specific proteins CD31, PDGFR and ColIV, that peaks at the same time points as inflammation. The altered expression of these proteins occurs ear-ly in TLE, during the latent phase, suggesting that they could be associated with early rupture and pathogenicity of the BBB that will contribute to the chronic phase of epilepsy.

Cyanobacteria are photosynthetic microorganisms that colonize diverse environments worldwide, ranging from ocean to freshwaters, soils, and extreme environments. Their adaptation capacities and the diversity of natural products (molecules, metabolites, or compounds) that they synthesize support the cyanobacterial success for the colonization of their respective ecological niches. Although cyanobacteria are well-known for their toxin production and their relative deleterious consequences, they also produce a large variety of molecules that exhibit beneficial properties with high potential for various fields of application (e.g., synthetic analog of the dolastatin 10 used against Hodgkin lymphoma). The present review specially focuses on the beneficial activities of cyanobacterial molecules described so far. Based on an analysis of 670 papers, it appears that more than 90 genera of cyanobacteria have been found to produce compounds with potential beneficial activities, most of them belonging to the orders Oscillatoriales, Nostocales Chroococcales, and Synechococcales. The rest of the cyanobacterial orders (i.e., Pleurocapsales, Chroococcidiopsales, and Gloeobacterales) remain poorly explored in terms of their molecular diversity and relative bioactivity. The diverse cyanobacterial molecules presenting beneficial bioactivities belong to 10 different chemical classes (alkaloids, depsipeptides, lipopeptides, macrolides/lactones, peptides, terpenes, polysaccharides, lipids, polyketides, and others) that exhibit 14 major kinds of bioactivity. However, no direct relation between the chemical class and the bioactivity of these molecules has been demonstrated. We further selected and specifically described 50 molecule families according to their specific bioactivities and their potential uses in pharmacology, cosmetology, agriculture, or other specific fields of interest. This up-to-date review takes advantage of the recent progresses in genome sequencing and biosynthetic pathway elucidation, and presents new perspectives for the rational discovery of new cyanobacterial metabolites with beneficial bioactivity.

Missing observations constitute one of the most important issues in data analysis in applied research studies. The magnitude and their structure impact parameters estimation in the modeling with important consequences for decision-making. This study aims to evaluate the efficiency of imputation methods combined with the backpropagation algorithm in a nonlinear regression context. The evaluation is conducted through a simulation study including sample sizes (50, 100, 200, 300 and 400) with different missing data rates (10, 20, 30 40 and 50%) and three missingness mechanisms (MCAR, MAR and MNAR). Four imputation methods (Last Observation Carried Forward, Random Forest, Amelia and MICE) were used to impute datasets before making prediction with backpropagation. 3-MLP model was used by varying the activation functions (Logistic-Linear, Logistic-Exponential, TanH-Linear and TanH-Exponentiel), the number of nodes in the hidden layer (3 - 15) and the learning rate (20 - 70%). Analysis of the performance criteria (R2, r and RMSE) of the network revealed good performances when it is trained with TanH-Linear functions, 11 nodes in the hidden layer and a learning rate of 50%. MICE and Random Forest were the most appropriate for data imputation. These methods can support up to 50% of missing rate with an optimal sample size of 200.

We explored how dietary behaviors (sucrose, fructose, sodium, and potassium consumption) and endured psychological stress in young adult males and females impact the vascular aging index (VAI) and CVD risk by mid-life. Data were obtained from the Coronary Artery Risk Development in Young Adults Study, an ongoing longitudinal study. Included participants (n=2,656) had un-dergone carotid artery ultrasound at year 20 allowing VAIs to be calculated. Demographics, di-etary data and depression scores were obtained at baseline and year 20. Regression analyses were used to assess the predictors of VAI. Cox regression analyses were conducted to assess the risk of CVD, stroke and all-cause mortality. Predictors of vascular aging were found to be sex-specific. In females, CES-D scores at baseline were positively associated with VAI (B-weight=0.063, p=0.015). In males, sodium intake at year 20 positively predicted VAI (B-weight=0.145, p=0.003) and potassium intake inversely predicted VAI (B-weight=-0.160, p<0.001). BMI significantly predicted CVD, stroke, and death. Fructose consumption at year 20 was a significant predictor of CVD risk while having high blood pressure at baseline was signif-icantly associated with stroke risk. Our findings support the promotion of nutrient- specific be-havior changes to prevent vascular aging in early adulthood and CVD risk in mid-life.

Spinal Cord (SC) Anatomy is often assimilated to a morphologically encapsulated neural entity, but its functional anatomy remains only partially understood. We hypothesized that it could be possible to reexplore SC neural networks by performing live electrostimulation mapping, based on “super-selective” Spinal Cord Stimulation (SCS), originally designed as a therapeutical tool to address chronic refractory pain. As a starting point, we initiated a systematic SCS lead programming approach using live electrostimulation mapping on a chronic refractory perineal pain patient, previously implanted with multicolumn SCS at the level of the conus medullaris (T12-L1). It appeared possible to (re)explore the classical anatomy of the conus medullaris using statistical correlations of paresthesia coverage mappings, resulting from 165 different electrical configurations tested. We highlighted that sacral dermatomes were not only located more medially but also deeper than lumbar dermatomes at the level of the conus medullaris, in contrast with classical anatomical descriptions of SC somatotopical organization. As we were finally able to find a morpho-functional description of ‘Philippe-Gombault’s triangle’, in 19th century historical textbooks of neuroanatomy, matching remarkably with these conclusions, the concept of “Neuro-Fiber-Mapping” was introduced.

The genus Chaetogaster belongs to the subfamily Naidinae (Naididae), includes mostly species of small size and is diverse and abundant in surface coarse sediments in streams. The aim of the present study is to initiate an inventory of lineages of Chaetogaster in Switzerland. We used 135 specimens collected at six sites in four streams of four cantons. We sequenced the cytochrome c oxidase (COI) gene from all specimens and ITS2 and rDNA 28S from all or several specimens of each lineage delimited using COI data and preserved a voucher of almost all sequenced specimens. We could delimit one lineage (=species) for Chaetogaster diaphanus (Gruithuisen, 1828), three within Chaetogaster diastrophus (Gruithuisen, 1828), one for Chaetogaster langi Brestcher, 1896, one for Chaetogaster setosus Svetlov, 1925 and three unidentified Chaetogaster spp. Two lineages of Chaetogaster spp. could correspond to a new morphological group, but this should be confirmed on more specimens. We described the three C. diastrophus lineages and two Chaetogaster spp. as new species for science. The prospects of the present work are to complete the data of the molecular diversity of this genus in Switzerland and to describe on a molecular/morphological basis the new Chaetogaster species that we will probably find.

The severity of the global COVID-19 pandemic, with a high transmission rate, 2.6-4.7% lethality and a huge economic impact, poses an urgent need for efficient medical treatments and vaccines. Currently, there are only non-specific treatments to assist the patients in acute respiratory distress during the inflammatory step following the preliminary infection by SARS-CoV-2. Clinical trials of drug repurposing were quickly launched at the international level. Specific treatments such as the transfusion of plasma from patients who have recovered into infected patients or the use of specific inhibitors of the viral RNA-polymerase complex are promising strategies to block infection. To complete the therapeutic arsenal, we believe that the opportunity of targeting the SARS-CoV-2 genome by RNA therapy should be deeply investigated. In the present paper, we propose to design specific antisense oligonucleotides targeting transcripts encoding viral proteins associated to replication and transcription of SARS-CoV-2, aiming to block infection. We designed antisense oligonucleotides targeting the genomic 5’ untranslated region (5’-UTR), open reading frames 1a and 1b (ORF1a and ORF1b) governing expression of the replicase/transcriptase complex, and the gene N encoding the nucleoprotein that is genome-associated. To maximize the probability of efficiency, we predicted the antisense oligonucleotides by using two design methods: i) conventional antisense oligonucleotides with 100% phosphorothioate modifications (ASO); ii) antisense locked nucleic acids GapmeR. After binding the viral RNA target, the hetero-duplexes antisense oligonucleotide-RNA are cleaved by RNAse H1. Nine potent ASO candidates were found and we selected five of them targeting ORF1a (3), ORF1b (1) and N (1). Nine GapmeR candidates were predicted with excellent properties and we retained four of them targeting 5’-UTR (1), ORF1a (3), ORF1b (1) and N (1). The most potent GapmeR candidate targets the 5’-UTR, a key genomic domain with multiple functions in the viral cycle. By this open publication, we are pleased to share these in silico results with the scientific community in hopes of stimulating innovation in translational research in order to fight the unprecedented COVID-19 pandemic. These antisense oligonucleotide candidates should be now experimentally evaluated.

Recently, the phase-sensitive OTDR (Φ-OTDR) based vibration sensor system has gained the focus of many researchers and some efforts have been undertaken to push further the limitations imposed on the performance of the Φ-OTDR sensor system. Then, progress in the different areas of its performance evaluation factors such as: improvement of the signal-to-noise ratio (SNR), spatial resolution (SR) in the sub-meter range, enlargement of the sensing range, frequency response bandwidth over the conventional limit and phase signal demodulation for quantitative measurement have been realized. This paper presents an overview of the recent progress in the Φ-OTDR based vibration sensing system in the different areas mentioned above.

The fetal heart rate (FHR) is a screening signal for preventing fetal hypoxia during labor. When experts analyze this signal, they have to position a baseline and identify decelerations and accelerations. These steps can potentially be automated and made more objective by data processing analysis, but training and evaluation datasets are required. Here, we describe a dataset of 155 FHR recordings in which a reference baseline, accelerations and decelerations have been annotated by expert consensus. 66 FHR recordings with a shared expert analysis have been included in a training dataset, and 90 other FHR recordings with a non-shared expert analysis have been included in an evaluation dataset. Researchers wishing to evaluate their automatic analysis method should submit their results for comparison with the expert consensus. The dataset also contains the results produced by 11 re-coded automatic analysis methods from the literature. All the data are available at http://utsb.univ-catholille.fr/fhr-review.