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Space weather prediction is a central focus of solar-terrestrial studies, with forecasts of geomagnetic storms deemed critical due to their significant practical implications. We have gathered facts that highlight the effects of geomagnetic storms on electric power systems and satellites. Recent studies indicate that geomagnetic storms of moderate intensity are statistically associated with larger spike amplitudes of telluric currents potentially leading to power outages compared to those caused by major storms. This underscores the importance of building reliable forecasts for all geomagnetic storms, especially given that solar cycles 20–24 saw less than 1% of storms classified as severe or extreme. A major challenge in current prediction models, even those utilizing advanced machine learning techniques, is the decline in accuracy for forecast lead times beyond 3 h, limiting the ability to mitigate infrastructure damage effectively. In this work, we provide a concise overview of geomagnetic storm statistics, describe key forecasting methods, recent advancements, and discuss the challenges in achieving accurate and timely storm predictions.

Pressure-volume (PV) loop analysis is a sophisticated invasive approach to quantifying load-dependent and independent measures of cardiac function. Biventricular (BV) PV loops allow left and right ventricular function to be quantified simultaneously and independently, which is important for conditions and certain physiologic states, such as ventricular decoupling or acute physiologic changes. BV PV loops can be performed in an entirely endovascular, percutaneous, and closed-chest setting. This technique is helpful in a survival animal model, as a percutaneous monitoring system during endovascular device experiments, or in cases where chest wall compliance is being tested or may be a confounder. In this article, we describe the end-to-end implementation of a completely endovascular, totally percutaneous, and closed-chest large animal model to obtain contemporaneous BV PV loops in 40 to 70 kg swine. We describe the associated surgical and technical challenges and our solutions to obtaining endovascular BV PV loops, closed-chest cardiac output, and stroke volume (including validation of the correction factor necessary for thermodilution), as well as how to perform endovascular inferior vena cava occlusion in this swine model. We also include techniques for data acquisition and analysis that are required for this method. : Clinical Relevance: This article describes the end-to-end implementation of a completely endovascular, percutaneous, and closed-chest large animal model for obtaining biventricular pressure-volume loops in swine. This will allow researchers to obtain contemporaneous, continuous left and right ventricular physiology through a minimally invasive procedure that animals can survive and does not alter chest wall compliance. This model may be used to better quantify the physiology during clinical scenarios of ventricular decoupling, during procedures such as transcatheter aortic valve repair or those that alter left ventricular (LV) function, or during the initiation of resuscitative endovascular balloon occlusion of the aorta or extracorporeal cardiopulmonary resuscitation when LV and right ventricular filling become misaligned.

Abstract The divergence of total alkalinity (TA) from conservation with salinity (S) and relatively acidic conditions (pH) in surface seawater was suggested to explain the high prevalence of lightning superbolts in the Mediterranean sea, North sea and upwelling regions of the oceans. In this study we tested the combined effects of changes in S, TA and pH of Mediterranean sea surface water on the intensity of laboratory generated electrical sparks, which are considered to be analogous to cloud to sea-surface intensity of lightning discharges. The experimental results were used to develop a multivariate linear equation (MLE) of Lightning Flash Intensity (LFI) as a function of S, TA/S and pH. This relation was validated with wintertime (DJF) LFI measurements along a Mediterranean sea zonal profile during the period 2009–2020 compared to corresponding climate model outputs of S, TA and pH. Based on the resulting MLE, the combined effects of climate change, ocean acidification and the damming of the Nile, may have increased LFI in the Levantine Sea by 16 ± 14% until now relative to the pre-Aswan Dam period. Furthermore, assuming that salinization and acidification of the Levantine Sea will continue at current trends, the LFI is predicted to increase by 25 ± 13% by the year 2050.

The composition of metagenomic communities within the human body often reflects localized medical conditions such as upper respiratory diseases and gastrointestinal diseases. Fast and accurate computational tools to flag anomalous metagenomic samples from typical samples are desirable to understand different phenotypes, especially in contexts where repeated, long-duration temporal sampling is done. Here, we present Automated Metagenome Anomaly Detection (AMAnD), which utilizes two types of Deep Support Vector Data Description (DeepSVDD) models; one trained on taxonomic feature space output by the Pan-Genomics for Infectious Agents (PanGIA) taxonomy classifier and one trained on kmer frequency counts. AMAnD's semi-supervised one-class approach makes no assumptions about what an anomaly may look like, allowing the flagging of potentially novel anomaly types. Three diverse datasets are profiled. The first dataset is hosted on the National Center for Biotechnology Information's (NCBI) Sequence Read Archive (SRA) and contains nasopharyngeal swabs from healthy and COVID-19-positive patients. The second dataset is also hosted on SRA and contains gut microbiome samples from normal controls and from patients with slow transit constipation (STC). AMAnD can learn a typical healthy nasopharyngeal or gut microbiome profile and reliably flag the anomalous COVID+ or STC samples in both feature spaces. The final dataset is a synthetic metagenome created by the Critical Assessment of Metagenome Annotation Simulator (CAMISIM). A control dataset of 50 well-characterized organisms was submitted to CAMISIM to generate 100 synthetic control class samples. The experimental conditions included 12 different spiked-in contaminants that are taxonomically similar to organisms present in the laboratory blank sample ranging from one strain tree branch taxonomic distance away to one family tree branch taxonomic distance away. This experiment was repeated in triplicate at three different coverage levels to probe the dependence on sample coverage. AMAnD was again able to flag the contaminant inserts as anomalous. AMAnD's assumption-free flagging of metagenomic anomalies, the real-time model training update potential of the deep learning approach, and the strong performance even with lightweight models of low sample cardinality would make AMAnD well-suited to a wide array of applied metagenomics biosurveillance use-cases, from environmental to clinical utility.

In recent years, there has been a notable surge in lightning events within the Arctic region. This is possibly due to the ongoing trend of global warming that is particularly pronounced in the Arctic. This study focuses on instances of rare lightning activity in the proximity to the North Pole in the last decade. We hypothesize that these occurrences are linked to augmented land heating in Eurasia rather than solely due to the increasing temperatures in the Arctic region. This assertion is substantiated by model simulations using the Weather Research and Forecasting model.

Magnetic field oscillations resulting from atmospheric events could have an effect on growth and development of plants and on the responsive reactions of plants to other environmental factors. In the current work, extremely low-frequency magnetic field (14.3 Hz) was shown to modulate light-induced electric reactions of wheat (Triticum aestivum L.). Blue light-induced electric reaction in wheat leaf comprises depolarization and two waves of hyperpolarization resulting in an increase of the potential to a higher level compared to the dark one. Fluorescent and inhibitory analysis demonstrate a key role of calcium ions and calcium-dependent H+-ATPase of the plasma membrane in the development of the reaction. Activation of H+-ATPase by the increased calcium influx is suggested as a mechanism of the influence of magnetic field on light-induced electric reaction.

We report ground-based measurements of the atmospheric electric field (Ez = −potential gradient (PG)) and current density (Jz) that were conducted at two locations in Israel. One is at the Emilio Segre cosmic ray station located on Mt. Hermon (34.45° N, 2020 m AMSL) in northern Israel near the Syrian-Lebanon border, and the other is at the Wise astronomical observatory in the Negev desert highland plateau of southern Israel (31.18° N, 870 m AMSL). We searched for possible effects of strong, short-term solar events on the potential gradient and the vertical current density, as disruptions to the global electric circuit are often observed following strong solar events. The first case study (St. Patrick’s Day, 17 March 2015) was classified as the strongest event of 2015. The second case study (8 September 2017) was categorized as the strongest event of 2017 and one of the twenty strongest events on record to date. The results show that the electrical parameters measured at ground level at both stations were not affected during the two massive proton events and the ensuing geomagnetic storms. The magnetospheric shielding in lower latitudes is strong enough to shield against the flux of energetic particles from solar events, obscuring any impact that may be noticeable above the local daily variations induced by local meteorological conditions (aerosol concentrations, clouds, high humidity, and wind speed), which were investigated as well.

Flash floods in the Eastern Mediterranean (EM) region are considered among the most destructive natural hazards, which pose a significant challenge to model due to their high complexity. Machine learning (ML) methods have made a significant contribution to the advancement of flash flood prediction systems by providing cost-effective solutions with improved performance, enabling the modeling of the complex mathematical expressions underlying physical processes of flash floods. Thus, the development of ML methods for flash flood prediction holds the potential to mitigate risks, inform policy recommendations, minimize loss of human life, and reduce property damage caused by flash floods. Here, we present a novel approach for improving flash flood predictions in the EM region using Support Vector Machines (SVMs) with a combination of precipitable water vapor (PWV) data, derived from ground-based global navigation satellite system (GNSS) receivers, along with surface pressure measurements, and nearby lightning occurrence data to predict flash floods in an arid region of the EM. The SVM model was trained on historical data from 2004 to 2019 and was used to forecast the likelihood of flash floods in the region. The study found that integrating nearby lightning data with the other variables significantly improved the accuracy of flash flood prediction compared to using only PWV and surface pressure measurements. The results of the SVM model were validated using observed flash flood events, and the model was found to have a high predictive accuracy with an area under the receiver operating characteristic curve of 0.93 for the test set. The study provides valuable insights into the potential of utilizing a combination of meteorological and lightning data for improving flash flood forecasting in the Eastern Mediterranean region.

Abstract The anthropogenic increase in atmospheric CO2 is not only considered to drive global warming, but also ocean acidification. Previous studies have shown that acidification will affect many aspects of biogenic carbon uptake and release in the surface water of the oceans. In this report we present a potential novel impact of acidification on the flash intensity of lightning discharged into the oceans. Our experimental results show that a decrease in ocean pH corresponding to the predicted increase in atmospheric CO2 according to the IPCC RCP 8.5 worst case emission scenario, may increase the intensity of lightning discharged into seawater by approximately 30 ± 7% by the end of the twenty-first century relative to 2000.

This paper investigates the influence of tropical cyclones on water vapor concentrations in the upper atmosphere above these storms. We use independent data sets of tropical storm intensity, water vapor and lightning activity to investigate this relationship. Water vapor in the upper troposphere is a key greenhouse gas, with direct impacts on surface temperatures. Both the amount and altitude of water vapor impact the radiative balance and the greenhouse effect of the atmosphere. The water vapor enters the upper troposphere through deep convective storms, often associated with lightning activity. The intensity of the lightning activity represents the intensity of the convection in these storms, and hence the amount of water vapor transported aloft. In this paper, we investigate the role of tropical cyclones on the contribution of water vapor to the upper atmosphere moistening. Tropical cyclones are the largest most intense storms on Earth and can last for up to two weeks at a time. There is also evidence that the intensity of tropical cyclones is increasing, and will continue to increase, due to global warming. In this study we find that the maximum moistening of the upper atmosphere occurs at the 200 hPa level (~12 km altitude), with a lag of 1–2 days after the maximum sustained winds in the tropical cyclone. While the water vapor peaks after the maximum of the storm intensity, the lightning activity peaks before the maximum intensity of the storms, as shown previously. We show here that the absolute amount of water vapor in the upper troposphere above tropical storms increases linearly with the intensity of the storms. For every 10 hPa decrease in the minimum pressure of tropical storms, the specific humidity increases around 0.2 g/kg at the 200 hPa level.

Introduction: The relationship between Parkinson Disease (PD) pathology, dopamine replacement therapy (DRT), and impulse control disorder (ICD) development is still incompletely understood. Given the sensorimotor-lateral substantia nigra (SN) selective degeneration associated with PD, we posit that a relative sparing of the limbic-medial SN in the context of DRT drives impulsive, reward-seeking behavior in PD patients with recent history of severe impulsivity. Methods: Impulsive and control participants were selected from a consecutive list of PD patients receiving pre-operative deep brain stimulation (DBS) planning scans including 3T structural MRI and 64 direction diffusion tensor imaging (DTI). Using previously identified substantia nigra (SN) subsegment network connectivity profiles to develop classification targets, split-hemisphere target-based SN segmentation with probabilistic tractography was performed. The relative subsegment volumes and strength of connectivity between the SN and the limbic, associative, and motor network targets were compared. Results: Our results show that there is greater probability of connectivity between the SN and limbic network targets relative to motor and associative network targets in PD patients with recent history of severe impulsivity as compared to PD patients without impulsivity (P = 0.0075). We did not observe relative volumetric subsegment differences across groups. Conclusion: Firstly, our results suggest that fine-grained, atlas-derived classification targets may be used in PD to parcellate and classify functionally distinct subsegments of the SN, with the apparent preservation of previously reported topographical limbic-medial SN, associative-ventral SN, and sensorimotor-lateral SN orientation. We suggest that relative, as opposed to absolute, degeneration amongst SN-associated dopaminergic networks relates to the impulsivity phenotype in PD.

Photosynthesis is an important target of action of numerous environmental factors; in particular, stressors can strongly affect photosynthetic light reactions. Considering relations of photosynthetic light reactions to electron and proton transport, it can be supposed that extremely low frequency magnetic field (ELFMF) may influence these reactions; however, this problem has been weakly investigated. In this paper, we experimentally tested a hypothesis about the potential influence of ELFMF of 18 µT intensity with Schumann resonance frequencies (7.8, 14.3, and 20.8 Hz) on photosynthetic light reactions in wheat and pea seedlings. It was shown that ELFMF decreased non-photochemical quenching in wheat and weakly influenced quantum yield of photosystem II at short-term treatment; in contrast, the changes in potential and effective quantum yields of photosystem II were observed mainly under chronic action of ELFMF. It is interesting that both short-term and chronic treatment decreased the time periods for 50% activation of quantum yield and non-photochemical quenching under illumination. Influence of ELFMF on pea was not observed at both short-term and chronic treatment. Thus, we showed that ELFMF with Schumann resonance frequencies could influence photosynthetic light processes; however, this effect depends on plant species (wheat or pea) and type of treatment (short-term or chronic).

Severe and extreme weather is a major natural hazard all over the world, oftenresulting in major natural disasters such as hail storms, tornados, wind storms, flash floods,forest fires and lightning damages. While precipitation, wind, hail, tornados, turbulence,etc. can only be observed at close distances, lightning activity in these damaging stormscan be monitored at all spatial scales, from local (using very high frequency [VHF]sensors), to regional (using very low frequency [VLF] sensors), and even global scales(using extremely low frequency [ELF] sensors). Using sensors that detect the radio wavesemitted by each lightning discharge, it is now possible to observe and track continuouslydistant thunderstorms using ground networks of sensors. In addition to the number oflightning discharges, these sensors can also provide information on lightningcharacteristics such as the ratio between intra-cloud and cloud-to-ground lightning, thepolarity of the lightning discharge, peak currents, charge removal, etc. It has been shownthat changes in some of these lightning characteristics during thunderstorms are oftenrelated to changes in the severity of the storms. In this paper different lightning observingsystems are described, and a few examples are provided showing how lightning may beused to monitor storm hazards around the globe, while also providing the possibility ofsupplying short term forecasts, called nowcasting.

Extremely low-frequency magnetic field disturbances from intense positive lightning discharges are compared to the convective cloud cover in central Africa, derived from infrared brightness temperatures recorded on board the geostationary satellite Meteosat during April 1998. The mean diurnal variation of the positive lightning charge moment is well correlated with the mean diurnal variation of the cloud cover at ca. 11.5 km height and constrains the mean lightning channel length. The daily integrated positive cloud to ground charge transfer exhibits a pronounced day to day variability which is well correlated with the cloud cover at ca. 15.5 km height, related to the charging of the thundercloud ca. 4 hours prior to the maximum cloud to ground charge transfer. The cloud cover area is used to calculate an effective cloud volume which is related to the cloud to ground charge transfer via the charge density. This charge density is used to determine promising locations for optical sprite observations in the central Congo basin and Cameroon with ca. 69 estimated sprite occurrences during an average night.

The Read Codes have been recognised as the standard for General Practice computing since 1988 and the original 4-byte set continues to be extensively used to record primary health care data. Read Version 3 (the Read Thesaurus) is an expanded clinical vocabulary with an enhanced file structure designed to meet the detailed requirements of specialist practitioners and to address some of the limitations of previous versions. A recent phase of integration of the still widely-used 4-byte set has highlighted the need to ensure that the new Thesaurus continues to support generalist requirements.

Lightning produces electromagnetic fields and waves in all frequency ranges. In the extremely low frequency (ELF) range below 100 Hz, the global Schumann Resonances (SR) are excited at frequencies of 8 Hz, 14 Hz, 20 Hz, etc. This review is aimed at the reader generally unfamiliar with the Schumann Resonances. First some historical context to SR research is given, followed by some theoretical background and examples of the extensive use of Schumann resonances in a variety of lightning-related studies in recent years, ranging from estimates of the spatial and temporal variations in global lighting activity, connections to global climate change, transient luminous events and extraterrestrial lightning. Both theoretical and experimental results of the global resonance phenomenon are presented. It is our hope that this review will increase the interest in SR among researchers previously unfamiliar with this phenomenon.

Estimates of global thunderstorm activity have been made predominately by direct measurements of lightning discharges around the globe, either by optical measurements from satellites, or using ground-based radio antennas. In this paper we propose a new methodology in which thunderstorm clusters are constructed based on the lightning strokes detected by the World Wide Lightning Location Network (WWLLN) in the very low frequency range. We find that even with low lightning detection efficiency on a global scale, the spatial and temporal distribution of global thunderstorm cells is well reproduced. This is validated by comparing the global diurnal variations of the thunderstorm cells, and the currents produced by these storms, with the well-known Carnegie Curve, which represents the mean diurnal variability of the global atmospheric electric circuit, driven by thunderstorm activity. While the Carnegie Curve agrees well with our diurnal thunderstorm cluster variations, there is little agreement between the Carnegie Curve and the diurnal variation in the number of lightning strokes detected by the WWLLN. When multiplying the number of clusters we detect by the mean thunderstorm conduction current for land and ocean thunderstorms (Mach et al 2011 J. Geophys. Res. http://dx.doi.org/10.1016/j.atmosres.2010.08.010 116 http://dx.doi.org/10.1016/j.atmosres.2010.08.010 ) we get a total average current of about 760 A. Our results show that thunderstorms alone explain more than 90% in the variability of the global electric circuit. However, while it has been previously shown that 90% of the global lightning occurs over continental landmasses, we show that around 50% of the thunderstorms are over the oceans, and from 00-09UTC there are more thunderstorm cells globally over the oceans than over the continents. Since the detection efficiency of the WWLLN system has increased over time, we estimate that the lower bound of the mean number of global thunderstorm cells in 2012 was around 1050 per hour, varying from around 840 at 03UTC to 1150 storms at 19UTC.