New Method For Detection Research Articles

Improving breast-cancer diagnostics: A study using AI-ML with computational prognostics and feature extraction

: Though and even though advancements are high during last two decades, yet ‘breast—cancer’ continues to be a sizable causal transience amongst females (womankind) globally. Whilst mammography has distinctly lowered death rates, detection and categorization and accuracy of breast sizes (forms in quantities) in mammograms remains argue. Thus we present a new method of detection and discovery of key region-of-interest(ROI) plus its labeling as natural usual ‘0’, benign ‘1’, or cancerous ‘2’ sizes. : For sorting of identified ‘ROIs’ as usual, benign or malicious exactly. : We use the removal of temporally sequential digital mammograms, merged via machine-learning (ML). We estimated feature-selection algorithmic techniques (ATs) over the novel database (data—set) comprises images~circa352 as of 88 diseased exactly noted by sites-of-mass. 98 feature extractions were done and classified by 8 dissimilar feature-selection ATs to detect the best discriminative features. We then computed 10classifiers via first in first out (FIFO) diseased plus a k-fold cross validation. In our computation, we observed the artificial neural nets (ANNs) appeared as the highest classifier with 99.5%accuracy, 0.98AUC (for label ‘0’), 0.92AUC (for label ‘1’, plus 0.94AUC (for class ‘2’). : Our findings showed significant progress in divergence (disparity) through existing advanced high-tech algorithms and techniques, which feature worth of using temporally successive mammographs in amalgamation, with advanced computational intelligence ML, aimed at the exact sorting of identified ‘ROIs’ as usual, benign or malicious. : Clinical significancy/ when clinically applied, this study improves the clinical prognosis precision of breast-cancer possibly steering to reliable diseased outcomes plus added personalized therapeutic attempts.

Dosimetry for low-energy electron beam applications at Fraunhofer FEP

Abstract Accelerating electrons to achieve chemical and biological effects is a well-established competence of Fraunhofer FEP. Today, there is a large variety of low-energy electron beam applications with a broad range of absorbed doses, e.g. modification of plastics, plasma-chemical syntheses, pollutant removal in wastewaters and exhaust gases, sterilization of medical products, disinfection of seeds, biocompatible functionalization of implants and stimulation of biotechnological processes. This calls for reliable, sensitive, and flexible methods for dosimetry. Radiochromic films are suitable tools to measure electron dose distributions for the characterization and quality control of Fraunhofer FEP’s irradiation facilities. Risø B3 radiochromic film from DTU Health Tech, the dosimeter of choice at FEP, reliably detects doses in the range of 10–100 kGy. However, with new upcoming applications, doses in the single-digit kGy range and even lower come to the fore. Hence, the palette of dosimeters at FEP must be extended. Gafchromic’s HD-V2 film is a welcome complement and widens the accessible dose range down to 10 Gy. Using a UV-VIS spectrophotometer for read-out of the films and custom analysis algorithms further increase the sensitivity of the dosimetric setup. Additionally, dosimeters based on the optically stimulated luminescence (OSL) of beryllium oxide offer a wide dose range and high sensitivity. They were used to measure doses induced by secondary X-ray components to gain more information about a specific radiation field. The work gives an overview of the dosimetric toolbox at Fraunhofer FEP and the efforts to implement new methods of detection for low-dose applications and X-ray dosimetry.

A gap analysis of UK biobank publications reveals SNPs associated with intrinsic subtypes of breast cancer

Breast cancer is a multifaceted disease and a leading cause of cancer morbidity and mortality in females across the globe. In 2020 alone, 2.3 million women were diagnosed and 685,000 died of breast cancer worldwide. With the number of diagnoses projected to increase to 3 million per year by 2040 it is essential that new methods of detection and disease stratification are sought to decrease this global cancer burden.Although significant improvements have been made in breast cancer diagnosis and treatment, the prognosis of breast cancer remains poor in some patient groups (i.e. triple negative breast cancer), necessitating research into better patient stratification, diagnosis and drug discovery. The UK Biobank, a comprehensive biomedical and epidemiological database with a wide variety of multiomics data (genomics, proteomics, metabolomics) offers huge potential to uncover groundbreaking discoveries in breast cancer research leading to improved patient stratification. Combining genomic, proteomic, and metabolic profiles of breast cancer in combination with histological classification, can aid treatment decisions through accurate diagnosis and prognosis prediction of tumor behaviour.Here, we systematically reviewed PubMed publications reporting the analysis of UK Biobank data in breast cancer research. Our analysis of UK Biobank studies in the past five years identified 125 publications, of which 76 focussed on genomic data analysis. Interestingly, only two studies reported the analysis of metabolomics and proteomics data, with none performing multiomics analysis of breast cancer. A meta-analysis of the 76 publications identified 2870 genetic variants associated with breast cancer across 445 genes. Subtype analysis revealed differential genetic alteration in 13 of the 445 genes and the identification of 59 well-established breast cancer genes. in differential pathways. Pathway interaction analyses illuminated their involvement in general cancer biomolecular pathways (e.g. DNA damage repair, Gene expression). While our meta-analysis only measured genetic differences in breast cancer due to current usage of UK Biobank data, minimal multi-omics analyses have been performed and the potential for harnessing multi-omics strategies within the UK Biobank cohort holds promise for unravelling the biological signatures of distinct breast cancer subtypes further in the future.

Evaluation of new method for detection of creatine and guanidinoacetate by ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS)

Evaluation of new method for detection of creatine and guanidinoacetate by ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS)

Research and application of the wavelet high-frequency coefficient direct mapping algorithm in the micro-defect target detection system of an infrared objective lens

Various lens detection techniques for military sighting telescopes have been the focus of military production. However, most of the micro-defect detection of lenses is detected manually, which cannot guarantee the measurement precision and quality. In order to solve these problems, the wavelet high-frequency coefficients direct mapping algorithm is applied to the automatic detection system of a micro-defect target in an infrared objective lens, thus obtaining a new method of detection. The detection method utilizes the correlation feature between wavelet coefficient scales to process the acquired images to suppress the background signals. Then the noise is further filtered by the pipelined filtering method to finally realize the detection of micro-targets. The experimental results show that a relatively high signal-to-noise ratio can be obtained, and the method can detect the small and dim target whose signal-to-noise ratio is more than 2.5 times. The measurement range is ≥3pixels and the error precision is 4±1 pixels.

Aptamers-based Strategies for the Treatment of Microbial Infections

Background: Nowadays, infectious diseases caused by bacteria are one of the major risks for the human population worldwide. Antimicrobial resistance determined the necessity to develop both, new drugs and therapeutic approaches as alternatives to antibiotics and novel methods to detect bacteria. Aptamers have revealed their potential in combating antimicrobial infections. Aptamers are small singlestranded DNA or RNA oligonucleotides obtained through an in vitro process able to bind several targets with high affinity and specificity. Objective: The aim of this review is to provide an overview of the state of the art of aptamer-based antimicrobial therapeutic strategies, new methods of detection of bacteria, and their potential use as delivery systems. Conclusion: Recent applications on research about aptamers suggest their important potential in discovering novel pharmacological tools for the treatment of microbial infections.

A new SOI sensor design for detecting cancer using hybrid waveguide with higher sensitivity than both strip and slot waveguides

A new highly accurate optical biomedical sensor is proposed for cancer detection characterized by high sensitivity, small footprint, low cost, and low limit of detection. The sensor is based on double-ring resonators made of silicon on insulator. The type of the waveguide is critical in determining the sensor performance. To compromise the advantages and disadvantages of strip and slot waveguides, a mixed design of both has been introduced in literature at the expense of increased footprint compared to traditional sensors. Indeed, almost 27-fold footprint increase is required to improve the sensitivity by only one third of that of slot waveguide’s sensitivity. In this paper, we introduce a new design that keeps the same footprint of traditional sensors, while achieving high sensitivity. This sensitivity depends on the resonance wavelength shift due to different refractive indices of the biosample. It has the value 109.8 nm/RIU compared to 55.57 nm/RIU and 129.621 nm/RIU for strip and slot waveguides, respectively. The hybrid waveguide quality factor is 537.7 while the quality factors of the strip and the slot waveguides are 627.99 and 380.76, respectively. In addition, the new design achieves the minimum limit of detection (0.0255) when compared to that of traditional designs. Furthermore, a new method of detection is proposed with the same design, providing a higher sensitivity over both traditional waveguide types with a value of 15.3, compared to 13.2 and 11.5 for strip and slot waveguides, respectively. In this method, the sensitivity relies on various values of output transmitted light at the same wavelength due to altering the biocell refractive index. The biosensor output equation is developed. In addition, the relationship between the supermodes and the sensitivity is determined at variance conditions. It is found that there is an inverse relation between them.

New methods for detection of early loss of rapid flicker, spatial vision, and chromatic sensitivity in glaucoma

Purpose: To examine whether ocular hypertension and early glaucoma cause measurable changes in spatial vision, rapid flicker sensitivity and red/green and yellow/blue colour vision.Methods: We examined subjects aged 40 to 78 years with ocular hypertension (OHT), primary open angle glaucoma (POAG), normal tension glaucoma (NTG), and age matched controls. Monocular best corrected visual acuity (BCVA) was tested using EDTRS charts.VA and Functional Contrast Sensitivity (FCS) with Landolt ring stimuli of both positive and negative contrast were also measured monocularly with the Acuity‐Plus test. Red/green (RG) and yellow/blue (YB) colour thresholds were measured with the Colour Assessment and Diagnostics (CAD) test. Photopic (cone‐enhanced) and mesopic (rod‐enhanced) rapid flicker thresholds were measured at the fovea (0°) and at an eccentricity of 5° in each of the four quadrants using the Flicker‐Plus test. All subjects had their visual fields measured using the Humphrey visual field analyser (24–2 SITA standard protocol). Peripapillary retinal nerve fibre layer thickness (pRNFLT) and macular retinal layer thicknesses were measured using the Heidelberg Spectralis OCT.Results: The mean, logMAR BCVA for OHT patients was 0.00 and for POAG and NTG patients was −0.10 and 0.10, respectively. For control participants, mean logMAR BCVA was 0.00. Mean pRNFL thickness was significantly reduced for glaucoma patients (59.5 ± 7.7 μm) as compared to control subjects (94.6 ± 5.13 μm) and OHT patients (115.5 ± 3.5 μm). POAG and NTG patients showed significantly higher colour thresholds (mean red/green threshold 4.97, blue/yellow 5.28 CAD units) compared to control subjects (mean red/green threshold 2.07, blue/yellow 1.62 CAD units). One OHT patient showed acquired loss of blue/yellow colour vision (3.23 CAD units), with normal VA. The Acuity‐Plus test which employs briefly presented stimuli, also revealed significant loss of VA (3.78 and 3.64 min of arc) and FCS (141.32% and 100%) in an NTG patient, with results outside normal limits for both positive and negative contrast, respectively. Central FCS was higher and outside the normal age limits for POAG patients (73.41% and 89.27%) as compared to age matched controls (61.27% and 33.63%). OHT patients, on the other hand, produced FCS thresholds within normal limits. Flicker modulation thresholds were higher for both rod and cone enhanced stimulus conditions in glaucoma and OHT patients, when compared to control subjects.Conclusion: The results reveal non‐uniform, losses that can affect selectively each of the visual attributes examined in this study. These preliminary findings suggest that in addition to VA and FCS tests, colour vision and rapid flicker sensitivity should also be examined in patients with optic nerve pathologies like glaucoma.

Electrochemical Aptamer-Based Biosensor Developed to Monitor PCA3 and PSA Released by Prostate Cancer Cells

1. Introduction Detection of prostate cancer (PCa) in the early stage of the disease is fundamental for the prevention of metastases and for the initiation of treatment, which may bring greater chance of patient cured; so several research groups have sought the manufacture of a simple and non-invasive methodology [1]. The early diagnosis relies on prostate specific antigen (PSA) serum test, even if it showed clear limits [2]. Recently, prostate cancer gene 3 (PCA3), a non-coding mRNA which is overexpressed in tumor cells when compared with noncancerous prostate tissue [3]. Here, we report a new method for detection of prostate cancer-specific antigen 3 (PCA3) and prostate specific antigen (PSA) released by cancer cells using an aptasensor. Electrochemical impedance spectroscopy (EIS), SEM and EDS were used to evaluate biosensor surface. 2. Experimental Part The aptamers were modified through Biosearch Technologies at 5′-terminus in C6-disulfide linker to have it bond to the gold electrode; and at 3′-end in methylene blue (MB) (as redox probe) to perform the electrochemical analysis, which was followed by the HPLC purification process to remove truncated-synthesis products and to enrich the purity of the aptamer sequences. Aptamer immobilization was done by incubation of 1 μM thiolated aptamer on the top of the gold-coated silicon electrode overnight, in the dark, at 4°C. Subsequently, the electrodes were washed with PBS and a backfilling consisted of 3 mM of MCH aqueous solution for 30 minutes was done to displace the nonspecifically adsorbed aptamer molecules and to passivate the electrode surface [26]. Finally, the electrodes were once more rinsed with PBS to remove MCH excesses. The electrochemical characterization was done in a potentiostat μAutolabIII (Metrohn, NL), using a three-electrode system, which consisted of Ag/AgCl (3M KCl), as reference electrode; Pt wire, as a counter electrode; and gold-coated silicon wafer, as a working electrode. The prostate cell lines used were normal human prostate cells (RWPE-1; ATCC® CRL-11™), androgen-sensitive human prostate cancer cells (LNCaP; ATCC® CRL-1740™), and androgen-independent human prostate cancer cells (PC-3; ATCC® CRL-1435™). The three lineages were chosen to better understand PCA3 and PSA production patterns by different cell types. 3. Results and Discussion The gold-coated silicon wafe electrode was analyzed by SEM and EDS, which acts as working electrode and substrate to anchor the thiolated aptamer on the surface. By using square wave voltammetry, the present aptasensor was able of monitoring the PCA3 and PSA released from prostate cells (normal and cancerous). The PSA and PCA3 detection limits were 1 ng/mL and 9.2 ng/mL, respectively, at linear range up to 150 ng/mL, and it was also able to detect both biomarker even in presence of several interfering compounds (UA, AA, BSA and MUC1). In addition, the herein developed aptasensor was able to verify, at accurate and robust response, the patterns of the PCA3 and PSA released by different cell lines (RWPE-1, LNCaP and PC3). The detection of PSA and PCA3 by aptasensor agree with the cell lineage type, the LNCaP is an androgen-dependent lineage. In this way, an overexpressed both biomarkres was release was verified [49,50] and it can be compared to early-stage patients. On the other hand, the PC3 cells PSA and PCA3 releasing was nearly background signal, demonstrating almost absent production of studied targets, the PC3 lineage is androgen-independent which can be compared to advanced-disease patients. Funding This work has been supported by a grant from FAPESP-Brazil, CNPq and Capes.

Inductive detection of influence operations via graph learning

Influence operations are large-scale efforts to manipulate public opinion. The rapid detection and disruption of these operations is critical for healthy public discourse. Emergent AI technologies may enable novel operations that evade detection and influence public discourse on social media with greater scale, reach, and specificity. New methods of detection with inductive learning capacity will be needed to identify novel operations before they indelibly alter public opinion and events. To this end, we develop an inductive learning framework that: (1) determines content- and graph-based indicators that are not specific to any operation; (2) uses graph learning to encode abstract signatures of coordinated manipulation; and (3) evaluates generalization capacity by training and testing models across operations originating from Russia, China, and Iran. We find that this framework enables strong cross-operation generalization while also revealing salient indicators-illustrating a generic approach which directly complements transductive methodologies, thereby enhancing detection coverage.

Infrared Laser-Based Selected Reaction Monitoring Mass Spectrometry Imaging of Banana (Musa spp.) Tissue—New Method for Detection and Spatial Localization of Metabolites in Food

In this study, for the first time, we present the application of an infrared (IR) laser ablation-remote-electrospray ionization (LARESI) platform coupled to a tandem mass spectrometer (MS/MS) operated in selected reaction monitoring (SRM) mode for targeted metabolite imaging in intact plant tissues. We examined the distribution of specific metabolites in two banana varieties: Red Dacca and Cavendish. To support MSI results, an extensive analysis of banana tissue extracts was conducted using ultra-high-performance liquid chromatography and ultra-high-resolution mass spectrometry (UHPLC-UHRMS). In the Cavendish banana, 12 metabolites were successfully identified, while Red Dacca bananas were found to contain 16 amino acids. The spatial distribution of some of these compounds found in bananas was presented for the first time. This approach eliminates the need for high vacuum conditions and the pretreatment of biological materials, making it an efficient and promising tool for studying metabolites in plant tissues.

Mitigation of the diagenesis risk in biological apatite δ18O interpretation

The assessment of the potential blurring effect of diagenetic processes represents the greatest challenge for geochemists whose purpose is to reconstruct environmental parameters based on isotopic proxies. Numerous studies performed on phosphatic remains from terrestrial and marine sedimentary deposits have shown post-depositional isotopic modifications due to exchange reactions with surrounding waters. If diagenesis may be diagnosed through several lines of evidence such as mineralogical or chemical modifications of the pristine in vivo apatite after burial in soils and sediments, diagenesis does not prove that bioapatite phosphate exchanged oxygen isotopes with aqueous fluids. Therefore, we propose a new method of detection of diagenetic alteration of bioapatite phosphate based on a statistical approach designed to analyze sample collections. The approach requires that phosphatic remains belonged to animals living during a short time interval and within an environment characterized by steady air or water temperature and the δ18O of ambient water. We show that the distribution of bioapatite phosphate δ18O follows a normal distribution resulting from the combination of independent factors such as those related to the genetics, environment and analytical measurements. In the case of oxygen isotope exchange between the bioapatite phosphate and various amounts of circulating aqueous fluids, our numerical simulations reveal that the distributions of bioapatite phosphate δ18O are asymmetric with shapes depending on the temperature of reaction and the composition of the reacting aqueous fluid. This new statistical tool based on mass balance equations and Monte Carlo simulations is considered valuable to mitigate the diagenesis risk in the interpretation of bioapatite phosphate δ18O as paleoecological and paleoclimatic proxies.

Effective Segmentation and Brain Tumor Classification Using Sparse Bayesian ELM in MRI Images

Classification of tumors from MRI plays very important role for diagnosing various diseases. But, it consumes an enormous amount of time for classification. Due to the similar structure of anomalous and typical tissues in the brain, it is difficult to complete the detection process successfully. Many researchers have developed new methods for detection and classification of tumors. But most of them failed at some point due to these limitations. Therefore in our work, we introduced a new machine learning algorithm for detection and classification of tumors. In addition to this, an intellectual segmentation technique known as Improved Binomial Thresholding technique is also introduced in this paper. This newly developed approach is used to differentiate the normal and abnormal slices from brain MRI. We can extract different features from the segmented image. The extracted features may be Wavelet Transform based (WT) or Scattering Wavelet Transform based (SWT). Feature selection process is achieved using a hybrid algorithm known as CS-FS (Hybrid Cuckoo Search with Fish Swarm) to minimize the dimension of extracted features. Finally, feature classification process is performed using Sparse Bayesian Extreme Learning Machine (SBELM) classifier. The proposed method is executed with the help of BRATS (Brain Tumor Image Segmentation) 2015 dataset. The result of the proposed method is evaluated on different parameters like accuracy, specificity, and sensitivity. The values of these parameters are obtained by computing four factors such as TP, TN, FN and FP. The final evaluation results showed that, our proposed SBELM classifier has attained 97.2% accuracy, which is better than the other existing method like state-of-the-art method.

Incipient Fault Detection and Reconstruction Using an Adaptive Sliding-Mode Observer for the Actuators of Fixed-Wing Aircraft

This paper proposes a new method of detection and reconstruction of the incipient fault of fixed-wing actuators based on an adaptive sliding-mode observer. First, a mathematical model of a fixed-wing aircraft is derived under certain assumptions, considering nonlinear terms and system disturbances. Second, by introducing a nonsingular coordinate transformation, the incipient faults are separated from the disturbances. For a subsystem with no disturbances, the Luenberger observer can estimate the incipient fault. For a subsystem with disturbances, the sliding-mode observer is robust against these disturbances. The Lyapunov stability theory guarantees dynamic error convergence and system stability. The evaluation function was designed to realize residual evaluation and threshold judgment. Third, based on the concept of equivalent output injection, an adaptive sliding-mode observer method is proposed to reconstruct actuator faults precisely under the condition of the uncertain system structure. The design steps of the proposed reconstruction method are introduced in the form of a linear matrix inequality problem, which provides an effective method for calculating the design parameters. Finally, the simulation results of the De Havilland DHC-2 “Beaver” aircraft demonstrate the correctness and effectiveness of the proposed method.

A Simple Stuck-at-faults Detection Method in Digital Combinational Circuits

This paper considers the new method of detection (diagnostic) stuck-at-faults (0/1) in digital combinational circuits based on a numerical set-theoretical approach. Compared to known methods and algorithms, the proposed approach differs in simpler implementation of searching for vectors of test codes at arbitrary points of the studied logic circuit. A few simple set-theoretical operations and procedures are sufficient to determine the location and the type of a stuck-at-fault (0/1). This is evidenced by the presented examples of application of the proposed method, that are borrowed from the publications of well-known authors.

Size-Dependent Oscillation in Optical Spectra from Fly Ash Cenospheres: Particle Sizing Using Darkfield Hyperspectral Interferometry

Abstract Fly ash by-products are emerging biocompatible fillers for a number of construction materials. The value of fly ash as a filler is higher if the content of hollow cenospheres is increased. Here we describe a new method for detection and sizing of fly ash spheres based on darkfield microscopy with hyperspectral image capture to perform white light interferometry. Our method is cost-effective and can provide rapid means for evaluating cenosphere content during the enrichment process. We show that fly ash cenospheres produce a strong oscillation over wavelength in optical recordings. The phenomenon is easiest to observe using microscope imaging techniques that preserve both spatial and spectral information. Frequency is observed to increase in direct proportion to the sphere diameter. The oscillation appears in light recorded from any focal plane on the sphere which indicates that the entire sphere is involved in sustaining the signal, making the detection of cenospheres of different size and displacement within a recording volume productive. There is no oscillation from nonspherical particles of fly ash or other material, so this detection method is highly selective for the cenospheres.

Temporal enhanced Ultrasound: A new method for detection of porosity defects in composites

Non-Destructive Evaluation (NDE) methods are commonly employed for identifying porosity, which is one of the most common manufacturing defects observed in composite structures. Among current widely used approaches are conventional ultrasonic methods such as pulse-echo analysis based on loss of signal amplitude. Application of these conventional ultrasonic methods, however, can be challenging in cases where the loss of signal is negligible, such as with porosity. In this paper, we propose Temporal-enhanced Ultrasound (TeUS) as a novel ultrasound-based imaging technique for NDE of composites. TeUS represents the analysis of a sequence of ultrasound images obtained from composites by varying an image acquisition parameter, such as the focal point, over the sequence. We present details on the analytical formulation of TeUS, followed by extensive simulation and experimental results to demonstrate the efficacy of TeUS for porosity detection, compared to a conventional ultrasound-based porosity detection method. The results indicate that TeUS has excellent sensitivity for identifying subtle changes in the level of porosity within composite samples, with the possibility to reduce the need for reference samples.

Application of stacking ensemble learning model in quantitative analysis of biomaterial activity

Quantitative analysis techniques based on attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) are widely used for component detection of cells rather than activity levels. In this study, a rapid nondestructive detection method for the activity of biomaterials is proposed. The method is based on the infrared spectroscopy technique, which analyzes the infrared absorption peaks of three different biomaterials before and after inactivation, and then obtains the changes of their surface functional groups after inactivation. According to the regular difference of their absorption spectra, the stacked ensemble learning model is used to accurately detect the activity ratio of the biomaterials. In the two-level fusion framework of the ensemble model, partial least squares regression (PLSR), gradient boosted decision tree (GBDT), random forest (RF) and extra tree (ET) are used as primary learners, linear regression is used as secondary learner. Duplicates and interfering data in the raw spectral can be eliminated by multiplicative scatter correction (MSC) and principal component analysis (PCA). The coefficient of determination of prediction set (R2p) for three biomaterials were 0.9641, 0.9946 and 0.9939, respectively. The root mean square error of prediction (RMSEP) were 5.7%, 2.1% and 2.3%, respectively. Compared with these single algorithm models, the stacking ensemble learning model has the highest and lowest values for R2p and RMSEP, respectively. The results reveal that the fusion model could enhance the generalization ability and prediction accuracy for detecting activity of biomaterial with the influence of various factors. This study not only provides a new method for detection of biomaterial activity, but also guidance for fusion algorithms as well.

Atypical Viral Infections in Gastroenterology.

Enteric viruses are commonly found obligate parasites in the gastrointestinal (GI) tract. These viruses usually follow a fecal-oral route of transmission and are characterized by their extraordinary stability as well as resistance in high-stress environments. Most of them cause similar symptoms including vomiting, diarrhea, and abdominal pain. In order to come in contract with mucosal surfaces, these viruses need to pass the three main lines of defense: mucus layer, innate immune defenses, and adaptive immune defenses. The following atypical gastrointestinal infections are discussed: SARS-CoV2, hantavirus, herpes simplex virus I, cytomegalovirus, and calicivirus. Dysbiosis represents any modification to the makeup of resident commensal communities from those found in healthy individuals and can cause a patient to become more susceptible to bacterial and viral infections. The interaction between bacteria, viruses, and host physiology is still not completely understood. However, with growing research on viral infections, dysbiosis, and new methods of detection, we are getting closer to understanding the nature of these viruses, their typical and atypical characteristics, long-term effects, and mechanisms of action in different organ systems.

Signatures of midsummer droughts over Central America and Mexico

The annual cycle of precipitation over most parts of Central America and southern Mexico is climatologically characterized by a robust bimodal distribution, normally termed as the midsummer drought (MSD), influencing a large range of agricultural economic and public insurances. Compared to studies focusing on mechanisms underpinning the MSD, less research has been undertaken related to its climatological signatures. This is due to a lack of generally accepted methods through which to detect and quantify the bimodal precipitation accurately. The present study focuses on characterizing the MSD climatological signatures over Central America and Mexico using daily precipitation observations between 1979 and 2017, aiming to provide a comprehensive analysis of MSD in fine scale over this region. This was completed using a new method of detection. The signatures were analyzed from three aspects, namely (1) climatological mean states and variability; (2) connections with large scale modes of climate variability (El Niño–Southern Oscillation (ENSO) and the Madden–Julian Oscillation (MJO)); and (3) the potential afforded by statistical modelling. The development of MSDs across the region is attributed to changes of surface wind–pressure composites, characterized by anomalously negative (positive) surface pressure and onshore (offshore) winds during the peak (trough) of precipitation. ENSO’s modulation of MSDs is also shown by modifying the surface wind–pressure patterns through MSD periods, inducing the intensified North Atlantic Subtropical High and associated easterlies from the Caribbean region, which induce relatively weak precipitation at corresponding time points and subsequently intensify the MSD magnitude and extend the MSD period. Building on previous research which showed MSDs tend to start/end in MJO phases 1 and 8, a fourth–order polynomial was used here to statistically model the precipitation time series during the rainy season. We show that the strength of the bimodal precipitation can be well modelled by the coefficient of the polynomial terms, and the intra-seasonal variability is largely covered by the MJO indices. Using two complete MJO cycles and the polynomial, the bimodal precipitation during the rainy season over Central America and Mexico is synoptically explained, largely contributing to our understanding of the MJO’s modulation on the MSD.