Positive Number Research Articles

TEGLIE: Transformer encoders as strong gravitational lens finders in KiDS. From simulations to surveys

With the current and upcoming generation of surveys, such as the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory and the Euclid mission, tens of billions of galaxies will be observed, with a significant portion (sim 10$^5$) exhibiting lensing features. To effectively detect these rare objects amidst the vast number of galaxies, automated techniques such as machine learning are indispensable. We applied a state-of-the-art transformer algorithm to the 221 deg$^2$ of the Kilo Degree Survey (KiDS) to search for new strong gravitational lenses (SGLs). We tested four transformer encoders trained on simulated data from the Strong Lens Finding Challenge on KiDS data. The best performing model was fine-tuned on real images of SGL candidates identified in previous searches. To expand the dataset for fine-tuning, data augmentation techniques were employed, including rotation, flipping, transposition, and white noise injection. The network fine-tuned with rotated, flipped, and transposed images exhibited the best performance and was used to hunt for SGLs in the overlapping region of the Galaxy And Mass Assembly (GAMA) and KiDS surveys on galaxies up to $z$=0.8. Candidate SGLs were matched with those from other surveys and examined using GAMA data to identify blended spectra resulting from the signal from multiple objects in a GAMA fiber. Fine-tuning the transformer encoder to the KiDS data reduced the number of false positives by 70<!PCT!>. Additionally, applying the fine-tuned model to a sample of sim 5\,000\,000 galaxies resulted in a list of sim 51\,000 SGL candidates. Upon visual inspection, this list was narrowed down to 231 candidates. Combined with the SGL candidates identified in the model testing, our final sample comprises 264 candidates, including 71 high-confidence SGLs; of these 71, 44 are new discoveries. We propose fine-tuning via real augmented images as a viable approach to mitigating false positives when transitioning from simulated lenses to real surveys. While our model shows improvement, it still does not achieve the same accuracy as previously proposed models trained directly on galaxy images from KiDS with added simulated lensing arcs. This suggests that a larger fine-tuning set is necessary for a competitive performance. Additionally, we provide a list of 121 false positives that exhibit features similar to lensed objects, which can be used in the training of future machine learning models in this field.

Histopathology-based breast cancer prediction using deep learning methods for healthcare applications.

Breast cancer (BC) is the leading cause of female cancer mortality and is a type of cancer that is a major threat to women's health. Deep learning methods have been used extensively in many medical domains recently, especially in detection and classification applications. Studying histological images for the automatic diagnosis of BC is important for patients and their prognosis. Owing to the complication and variety of histology images, manual examination can be difficult and susceptible to errors and thus needs the services of experienced pathologists. Therefore, publicly accessible datasets called BreakHis and invasive ductal carcinoma (IDC) are used in this study to analyze histopathological images of BC. Next, using super-resolution generative adversarial networks (SRGANs), which create high-resolution images from low-quality images, the gathered images from BreakHis and IDC are pre-processed to provide useful results in the prediction stage. The components of conventional generative adversarial network (GAN) loss functions and effective sub-pixel nets were combined to create the concept of SRGAN. Next, the high-quality images are sent to the data augmentation stage, where new data points are created by making small adjustments to the dataset using rotation, random cropping, mirroring, and color-shifting. Next, patch-based feature extraction using Inception V3 and Resnet-50 (PFE-INC-RES) is employed to extract the features from the augmentation. After the features have been extracted, the next step involves processing them and applying transductive long short-term memory (TLSTM) to improve classification accuracy by decreasing the number of false positives. The results of suggested PFE-INC-RES is evaluated using existing methods on the BreakHis dataset, with respect to accuracy (99.84%), specificity (99.71%), sensitivity (99.78%), and F1-score (99.80%), while the suggested PFE-INC-RES performed better in the IDC dataset based on F1-score (99.08%), accuracy (99.79%), specificity (98.97%), and sensitivity (99.17%).

Determinants of Eastern Indonesia's Human Development Index

This study aims to determine the underlying Human Development Index in Eastern Indonesia Region by using Health expenditure and Education expenditure as variables that affect it. Using a new numerical perspective where the measurement of the Human Development Index according to the UNDP is reviewed by tracing and analysing Health and Education expenditure data using panel data, namely cross-section and time series to see the effect statistically. The results of this study which obtained positive and insignificant numbers and the low contribution value of the influence variables in this study are challenging data to face global progress, various forms of access to social services, especially in health and education, are basic things that need to be considered, some regions included in Eastern Indonesia may have experienced progress, but equity itself is what we must pay attention to together, continuous efforts to overcome the challenges of progress must ensure that all Indonesians can enjoy prosperity.

IDENTIFICATION OF POSITIONS OF SEPARATED RIGID INCLUSIONS BY BOUNDARY MEASUREMENTS FOR A SIGNORINI PROBLEM

A variational inequality describing an elastic body with a finite set of rigid inclusions is considered. The Signorini condition is imposed on a part of the boundary of the body. On the other part a homogeneous Dirichlet boundary condition is specified. The inclusions are arranged such that distance between any two inclusions is not less than a given positive number. All inclusions are located at a nonzero distance from the outer boundary. An inverse problem is investigated, which consists in identification of positions of the rigid inclusions from the measurement of displacements on an observation boundary. Continuous dependency of the solution of the forward problem on parameters of inclusions’ location and rotation is established. This provides existence of a solution for the inverse identification problem.

A Novel Method to Train Classification Models for Structure Detection in In Situ Spacecraft Data

AbstractWe present a method for creating spacecraft‐like data which can be used to train Machine Learning (ML) models to detect and classify structures in in situ spacecraft data. First, we use the Grad‐Shafranov equation to numerically solve for several magnetohydrostatic equilibria which are variations on a known analytic equilibrium. These equilibria are then used as the initial conditions for Particle‐In‐Cell simulations in which the structures of interest are observed and labeled. We then take one‐dimensional slices through the simulations to replicate what a spacecraft collecting data from the simulation would observe. This sliced data then can be used as training data for the initial training of ML models intended for use on spacecraft data. We demonstrate the method applied to the problem of detecting small‐scale plasmoids in the magnetotail, which is important for understanding complex magnetotail reconnection dynamics. The simple 1D classifier we train is able to detect more than 70% of the plasmoid points in the data set but also produces a large number of false positives. Our further work on this example problem is detailed, and further potential uses of the method are discussed.

A nomogram to predict the risk of axillary upstaging in patients with Z0011-eligible breast cancer.

e12606 Background: ACOSOG Z0011 trial showed that in early cT1-2N0 breast cancer patients with 1-2 positive lymph nodes in sentinel lymph node biopsy (SLNB) can safely omit axillary lymph node dissection (ALND) without compromising local control and long-term survival. Although it is widely accepted worldwide, controversy and concerns remain that the incidence of non-sentinel lymph node metastasis may lead to pathological lymph node (pN) upstaging and insufficient systemic treatment. This study aims to identify risk factors for having ≥4 metastatic lymph node (pN2-3) in patients who met Z0011 criteria and received SLNB. Methods: This is a retrospective study enrolled 733 cT1-2N0 breast cancer patients with 1-2 positive SLNs treated between 2011-2022 at Sun Yat-sen Memorial Hospital. All patients had received SLNB followed by ALND. Patients included were divided into the training and validation sets at a 7:3 ratio. Differences were compared between pN1(1-3 LN+) and pN2-3(≥4 LN+) in training set using either t test or chi-squared test. Multivariate logistic regression was conducted to identify risk factors for pN upstaging and to form the basis of a predictive nomogram. The validation was processed in the other validation set and nomogram performance was evaluated using the area under ROC curve (AUC). Results: Among the 733 patients enrolled, 127 (17.3%) patients had pN upstaging (pN2-3) after ALND. There was a big difference of pN upstaging between the patients with 1 (47/511, 9.2%) and 2 (80/222, 36%) positive SLNs. 488 patients were allocated to the training set, among which 18.2% had pN upstaging. In univariate analysis, pN were differed by cT stage, axillary ultrasound appearance, histological grade, molecular subtype, Ki67, lymph vascular invasion, pathological tumor size, SLN metastasis type (micrometastasis or macrometastasis), positive SLN numbers and negative SLN numbers. Multivariate logistic analysis resulted six independent risk factors associated to pN upstaging: ultrasound suspicious node (OR=1.93; 95% CI: 1.08-3.47), positive lympho-vascular invasion (OR=2.27; 95% CI: 1.25-4.13), larger tumor size (OR=2.07; 95% CI: 1.15-3.73), SLN macro-metastasis (OR= 9.15; 95% CI: 1.07-78.13), two positive SLNs (OR= 6.59; 95% CI:3.70-11.76) and less negative SLN number (OR=1.54; 95% CI: 1.22-1.89). These factors were used to construct a predictive nomogram, exhibiting excellent performance with AUC values of 0.819 and 0.827 in the training and validation cohorts respectively. Conclusions: The Z0011-eligible patients with two positive SLNs may have a high risk of pN upstaging and need to be cautious to spare ALND. A practical nomogram is helpful to identify patients at high risk of pN upstaging and avoid insufficient systemic treatment.

Effect of throughflow on the study of linear and weakly nonlinear stability analysis of Jeffrey fluids in an anisotropic porous layer

The study investigates the linear and weakly nonlinear stability of Jeffrey fluids in an anisotropic porous medium with throughflow. In the linear stability analysis, the study examines how convective motion changes with variations in the Jeffrey parameter, Peclet number, and anisotropic parameters. The study finds that thermal anisotropy parameter, positive Peclet number, and mechanical anisotropy parameter delay the onset of fluid convection, whereas the Jeffrey parameter enhances the onset of convection in the presence of throughflow. In the nonlinear analysis, heat transfer is calculated using the Ginzburg–Landau Equation (GLE). The study observes that heat transfer increases with the Vadasz number and positive Peclet number, making the system unstable, whereas heat transfer decreases with an increased value of the Jeffrey parameter, thermal anisotropy parameter, mechanical anisotropic parameter, and negative Peclet number, making the system stable.

Design and Implementation of Braun Multiplier using Verilog: Research

Abstract: Multiplication is widely used in places like digital signal processing, image processing, instrumentation that require multilayer components. Carry Save Adder and ripple carry adder both are used in parallel processing architecture. They are widely used for signed multiplication i.e. for both negative and positive numbers. We have now designed a Braun multiplier (BM) to improve the speed, power and area capability. We are using the Xilinx tool to verify Braun multipliers using Verilog. We are taking the following considerations: checking using FPGA based instruments and implementing code using Verilog-VHDL. The adders are integrated into multipliers. The result of this research is to modify BM to improve the performance along with reduction in power consumed and also using less area.

Application of improved machine learning in large-scale investigation of plastic waste distribution in tourism Intensive artificial coastlines

Oceans are ultimately a sink of plastic waste. Complex artificial coastlines pose remarkable challenges for coastal plastic waste monitoring. With the development of machine learning methods, high detection accuracy can be achieved; however, many false positives have been noted in various network models used for plastic waste investigation. In this study, extensive surveys of artificial coastlines were conducted using drones along the Dongjiang Port artificial coastline in the Binhai District, Tianjin, China. The deep learning model YOLOv8 was enhanced by integrating the InceptionNeXt and LSK modules into the network to improve its detection accuracy for plastic waste and reduce instances of tourists being misidentified as plastic. In total, 553 high-resolution coastline images with 3488 items of detected plastic waste were compared using the original and improved YOLOv8 models. The improved YOLOv8s-IL model achieved a detection rate of 64.9%, a notable increase of 11.5% compared with that of the original model. The number of false positives in the improved YOLOv8s-IL model was reduced to 32.3%, the multi-class F-score reached 76.5%, and the average detection time per image was only 2.7 s. The findings of this study provide technical support for future large-scale monitoring of plastic waste on artificial coastlines.

МОДЕЛЮВАННЯ МУЛЬТИАГЕНТНИХ СИСТЕМ ЗАХИСТУ ІНФОРМАЦІЙНИХ РЕСУРСІВ

Traditional centralized information protection systems do not provide an adequate level of adaptability and efficiency in a dynamically changing threat environment. Multi-agent systems, having a distributed architecture and the possibility of autonomous decision-making, represent a promising direction for solving information security problems. The novelty of the research lies in the development of a multi-agent information protection system with enhanced capabilities for vulnerability analysis and intrusion detection. Unlike existing solutions, the proposed system provides active analysis of threats, reduction of the number of false positives, adaptation to new types of attacks, and minimization of the impact on the target traffic of information flows. In the proposed formal model and prototype of the agent-oriented attack modeling system (ASMA), distributed coordinated attacks on a computer network are considered as a sequence of joint actions of hacker agents, which are performed from different hosts. Hackers are supposed to coordinate their actions according to some common script. At each step of the attack scenario, they try to implement a specific private subgoal. The developed agent-oriented attack modeling system (ASMA) allows simulating distributed coordinated attacks on computer networks. It is based on a hierarchy of grammars, each of which is interpreted as a finite automaton. The system takes into account both macro- and micro-levels of attack description, providing detailed simulation of each stage of the attack. The use of KQML for communication between agents and XML for describing the content of messages allows effective coordination of agents' actions and ensures realistic simulation of attacks.

Diallyl trisulfide induces pyroptosis and impairs lung CSC-like properties by activating the ROS/Caspase 1 signaling pathway

Lung cancer stem cells (CSCs) drive continuous cancer growth and metastatic dissemination; thus, there is an urgent requirement to acquire effective therapeutic strategies for targeting lung CSCs. Diallyl trisulfide (DATS), a garlic organosulfide, possesses suppressive potential in lung cancer; however, its underlying mechanism is still unclear. In this study, we identified DATS as a pyroptosis inducer in lung cancer cells. DATS-treated A549 and H460 cells exhibited pyroptotic cell death, with characteristic large bubbles appearing on their plasma membrane and LDH release. DATS induced cell death, arrested the cell cycle at the G2/M phase, and inhibited colony formation in lung cancer cells. Meanwhile, we found that DATS significantly suppressed the malignant features by impairing lung CSC-like properties, including sphere formation ability, CD133 positive cell number, and lung CSCs marker expression. Mechanistically, DATS induced cell pyroptosis via increasing the expression of NLRP3, ASC, Pro Caspase 1, Cleaved Caspase 1, GSDMD, GSDMD-N, and IL-1β. The verification experiments showed that the effects of DATS on pyroptosis and lung CSC-like properties were weakened after Caspase 1 inhibitor VX-765 treatment, indicating that DATS activated NLRP3 inflammasome-mediated pyroptosis by targeting Caspase 1 in lung cancer cells. Moreover, DATS increased ROS overproduction and mitochondrial dysfunction, which contributed to DATS-induced pyroptosis of lung cancer cells. NAC treatment reversed the effects of DATS on pyroptosis and CSC-like properties. In vivo experiment further confirmed that DATS restrained tumor growth. Together, our results suggest that DATS promotes pyroptosis and impairs lung CSC-like properties by activating ROS/Caspase 1 signaling pathway, thereby retarding lung cancer progression.

Leveraging conformal prediction to annotate enzyme function space with limited false positives.

Machine learning (ML) is increasingly being used to guide biological discovery in biomedicine such as prioritizing promising small molecules in drug discovery. In those applications, ML models are used to predict the properties of biological systems, and researchers use these predictions to prioritize candidates as new biological hypotheses for downstream experimental validations. However, when applied to unseen situations, these models can be overconfident and produce a large number of false positives. One solution to address this issue is to quantify the model's prediction uncertainty and provide a set of hypotheses with a controlled false discovery rate (FDR) pre-specified by researchers. We propose CPEC, an ML framework for FDR-controlled biological discovery. We demonstrate its effectiveness using enzyme function annotation as a case study, simulating the discovery process of identifying the functions of less-characterized enzymes. CPEC integrates a deep learning model with a statistical tool known as conformal prediction, providing accurate and FDR-controlled function predictions for a given protein enzyme. Conformal prediction provides rigorous statistical guarantees to the predictive model and ensures that the expected FDR will not exceed a user-specified level with high probability. Evaluation experiments show that CPEC achieves reliable FDR control, better or comparable prediction performance at a lower FDR than existing methods, and accurate predictions for enzymes under-represented in the training data. We expect CPEC to be a useful tool for biological discovery applications where a high yield rate in validation experiments is desired but the experimental budget is limited.

Analyzing Richtmyer–Meshkov Phenomena Triggered by Forward-Triangular Light Gas Bubbles: A Numerical Perspective

In this paper, we present a numerical investigation into elucidating the complex dynamics of Richtmyer–Meshkov (RM) phenomena initiated by the interaction of shock waves with forward-triangular light gas bubbles. The triangular bubble is filled with neon, helium, or hydrogen gas, and is surrounded by nitrogen gas. Three different shock Mach numbers are considered: Ms=1.12,1.21, and 1.41. For the numerical simulations, a two-dimensional system of compressible Euler equations for two-component gas flows is solved by utilizing the high-fidelity explicit modal discontinuous Galerkin technique. For validation, the numerical results are compared with the existing experimental results and are found to be in good agreement. The numerical model explores the impact of the Atwood number on the underlying mechanisms of the shock-induced forward-triangle bubble, encompassing aspects such as flow evolution, wave characteristics, jet formation, generation of vorticity, interface features, and integral diagnostics. Furthermore, the impacts of shock strengths and positive Atwood numbers on the flow evolution are also analyzed. Insights gained from this numerical perspective enhance our understanding of RM phenomena triggered by forward-triangular light gas bubbles, with implications for diverse applications in engineering, astrophysics, and fusion research.

Application of Partitions of Odd Numbers and their Odd Sums to Prove the Nonexistence of Odd Perfect Numbers

Perfect numbers, which are integers equal to the sum of their proper divisors, excluding themselves, have intrigued mathematicians for centuries. While it is established that even perfect numbers can be expressed as 2p-1(2p-1), where p and 2p-1 are prime numbers (Mersenne primes), the existence of odd perfect numbers remains an unsolved problem. This study aims to prove the nonexistence of odd perfect numbers by utilizing an algorithm which demonstrates that a positive even integer can be partitioned into all pairs of odd numbers. Using this approach, it is proven that any positive odd number 2n+1 can be partitioned into all pairs of both odd and even numbers and from the set of these partitions, we show that there exist a proper subset containing all proper divisors of 2n+1. Using these results, and the facts that there exist infinitely many odd numbers and the odd sums of odd numbers is always odd, we prove the nonexistence of odd perfect numbers contributing to the conjecture that they do not exist.

Nuance: Efficient Detection of Planets Transiting Active Stars

The detection of planetary transits in the light curves of active stars, featuring correlated noise in the form of stellar variability, remains a challenge. Depending on the noise characteristics, we show that the traditional technique that consists of detrending a light curve before searching for transits alters their signal-to-noise ratio and hinders our capability to discover exoplanets transiting rapidly rotating active stars. We present nuance, an algorithm to search for transits in light curves while simultaneously accounting for the presence of correlated noise, such as stellar variability and instrumental signals. We assess the performance of nuance on simulated light curves as well as on the Transiting Exoplanet Survey Satellite light curves of 438 rapidly rotating M dwarfs. For each data set, we compare our method to five commonly used detrending techniques followed by a search with the Box-Least-Squares algorithm. Overall, we demonstrate that nuance is the most performant method in 93% of cases, leading to both the highest number of true positives and the lowest number of false-positive detections. Although simultaneously searching for transits while modeling correlated noise is expected to be computationally expensive, we make our algorithm tractable and available as the JAX-powered Python package nuance, allowing its use on distributed environments and GPU devices. Finally, we explore the prospects offered by the nuance formalism and its use to advance our knowledge of planetary systems around active stars, both using space-based surveys and sparse ground-based observations.

#2468 2-Step-Scores with optional histopathology for the prediction of delayed graft function and 1-year transplant loss in Eurotransplant

Abstract Background and Aims The decision for acceptance or discard of the increasingly rare and marginal deceased donor kidneys in Eurotransplant (ET) countries has to be made without solid evidence. Thus, we developed and validated flexible clinicopathological scores we call 2-Step Scores for the prognosis of delayed graft function (DGF) as 2-Step-DGF and one-year death-censored transplant loss (1y-tl) as 2-Step-1y, reflecting current practice of six ET countries including Croatia and Belgium. Method The training set was n = 620 for DGF and n = 711 for 1y-tl, with validation sets n = 158 and n = 162. In step 1, stepwise logistic regression models including only clinical predictors were used to estimate the risks. In step 2, risk estimates were updated for statistically relevant intermediate risk percentiles with nephropathology. Results Step 1 revealed an increased risk of DGF with increased cold ischaemia time, donor and recipient BMI, dialysis vintage, number of HLA-DR mismatches or recipient CMV IgG positivity. On the training and validation set, c-statistics were 0.672 and 0.704, respectively. At a range between 18% and 36%, accuracy of DGF-prognostication improved with nephropathology including number of glomeruli and Banff cv (updated overall c statistics of 0.696 and 0.701, respectively). Risk of 1y-tl increased in recipients with cold ischaemia time, sum of HLA-A. -B, -DR mismatches and donor age. On training and validation sets, c-statistics were 0.700 and 0.769, respectively. Accuracy of 1y-tl prediction improved (c-statistics = 0.706 and 0.765) with Banff ct. Overall, calibration was good on the training, but moderate on the validation set; discrimination was at least as good as established scores when applied to the validation set. Receiver operating characteristics of both 2-Step-DGF and 2-Step-1y compared to established scores are shown in Figs 1 and 2. Conclusion Our flexible 2-Step Scores with optional inclusion of time-consuming and often unavailable nephropathology should yield good results for clinical practice in ET, and may be superior to established scores. Our scores are adaptable to donation after cardiac death and perfusion pumps use.

A hybrid feature weighted attention based deep learning approach for an intrusion detection system using the random forest algorithm.

Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. Multiple network attacks by an intruder are unavoidable. Our research targets the critical issue of class imbalance in intrusion detection, a reflection of the real-world scenario where legitimate network activities significantly out number malicious ones. This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. The primary goal is to create models that are highly sensitive to intrusions while minimizing false alarms, an essential aspect of effective IDS. This approach is not only practical for real-world applications but also enhances the theoretical understanding of managing class imbalance in machine learning. Our research, by addressing these significant challenges, is positioned to make substantial contributions to cybersecurity, providing valuable insights and applicable solutions in the fight against digital threats and ensuring robustness and relevance in IDS development. An intrusion detection system (IDS) checks network traffic for security, availability, and being non-shared. Despite the efforts of many researchers, contemporary IDSs still need to further improve detection accuracy, reduce false alarms, and detect new intrusions. The mean convolutional layer (MCL), feature-weighted attention (FWA) learning, a bidirectional long short-term memory (BILSTM) network, and the random forest algorithm are all parts of our unique hybrid model called MCL-FWA-BILSTM. The CNN-MCL layer for feature extraction receives data after preprocessing. After convolution, pooling, and flattening phases, feature vectors are obtained. The BI-LSTM and self-attention feature weights are used in the suggested method to mitigate the effects of class imbalance. The attention layer and the BI-LSTM features are concatenated to create mapped features before feeding them to the random forest algorithm for classification. Our methodology and model performance were validated using NSL-KDD and UNSW-NB-15, two widely available IDS datasets. The suggested model's accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. The model's binary and multi-class classification accuracies on the UNSW-NB15 dataset are 99.56% and 99.45%, respectively. Further, we compared the suggested approach with other previous machine learning and deep learning models and found it to outperform them in detection rate, FPR, and F-score. For both binary and multiclass classifications, the proposed method reduces false positives while increasing the number of true positives. The model proficiently identifies diverse network intrusions on computer networks and accomplishes its intended purpose. The suggested model will be helpful in a variety of network security research fields and applications.

Efficient SARS-CoV-2 variant detection and monitoring with Spike Screen next-generation sequencing.

The emergence and rapid spread of SARS-CoV-2 prompted the global community to identify innovative approaches to diagnose infection and sequence the viral genome because at several points in the pandemic positive case numbers exceeded the laboratory capacity to characterize sufficient samples to adequately respond to the spread of emerging variants. From week 10, 2020, to week 13, 2023, Slovenian routine complete genome sequencing (CGS) surveillance network yielded 41537 complete genomes and revealed a typical molecular epidemiology with early lineages gradually being replaced by Alpha, Delta, and finally Omicron. We developed a targeted next-generation sequencing based variant surveillance strategy dubbed Spike Screen through sample pooling and selective SARS-CoV-2 spike gene amplification in conjunction with CGS of individual cases to increase throughput and cost-effectiveness. Spike Screen identifies variant of concern (VOC) and variant of interest (VOI) signature mutations, analyses their frequencies in sample pools, and calculates the number of VOCs/VOIs at the population level. The strategy was successfully applied for detection of specific VOC/VOI mutations prior to their confirmation by CGS. Spike Screen complemented CGS efforts with an additional 22897 samples sequenced in two time periods: between week 42, 2020, and week 24, 2021, and between week 37, 2021, and week 2, 2022. The results showed that Spike Screen can be applied to monitor VOC/VOI mutations among large volumes of samples in settings with limited sequencing capacity through reliable and rapid detection of novel variants at the population level and can serve as a basis for public health policy planning.

Inadequacy of a 12.5 cm MUAC as a cutoff for malnutrition for children aged three to five years

Introduction: MUAC (mid-upper arm circumference) is a simple and easily taught screening tool for identifying malnutrition in children. South Sudan use a MUAC of 12.5 cm for children aged between six months and five years as a cut-off for moderate acute malnutrition. Currently, in South Sudan, children from six months to five years have the same MUAC cutoff for malnutrition. Method: This study evaluated the sensitivity of using a MUAC of 12.5 cm as a screening tool for children between 6 and 60 months applied to data obtained from children enrolled in urban and rural primary schools in Maridi County, South Sudan. We used the 12.5 cm cutoff which was 2 standard deviations from the median for boys at nine months and for girls at 20 months. We also used 14.0 cm as the cutoff and compared the number of children identified, and also the false positive rate, assuming -2 SD from the median to be the accepted norm for malnutrition. Finally, we considered two different standards, using 14.0 cm for children aged 3-5 years and 12.5 cm for children under three years. Results: Comparing the results obtained using the two different MUAC standards (12.5 cm versus 14.0 cm) against a single MUAC standard there is a dramatic difference in outcome. Using the current standard (12.5 cm), only 7.3% of the children were found to be malnourished. Using 14.0 cm, 33.8 % were found to be malnourished (Table 4). If we accept the norm of 2 SD below the median for age as an adequate definition of malnutrition, the false positive rate using 14.0 cm is 9.6 %, an acceptable figure for a screening device, but the false negative rate for 12.5 cm is 17.4 %, a clearly unacceptable rate for a screening device. If, however, instead of using one cut-off for children from six months to five years, a period characterized normally by rapid growth and changes in body composition, we used two different standards—12.5 cm for those under three years and 14 cm for those aged 3-5 years, the number of false positives drops down to 2.7% and false negatives to 0.9 % Conclusion: We suggest that the use of 12.5 cm for malnourished children is inappropriate above two years, that 14 cm would be a more appropriate screening measurement from 3-5 years of age.

Intermediate ions as indicator for local new particle formation

Abstract. Atmospheric aerosol particles have a considerable influence on climate via both aerosol–radiation and aerosol–cloud interactions. A major fraction of global aerosol particles, in terms of their number concentration, is due to atmospheric new particle formation (NPF) that involves both neutral and charged clusters and particles. NPF is the major source of atmospheric intermediate ions, i.e., charged particles with mobility diameters between approx. 2 and 7 nm. We investigate ion concentrations between 1.7 and 3.1 nm at the SMEAR II (Station for Measuring Forest Ecosystem–Atmosphere Relations II) measurement station in Hyytiälä, Finland. Both negative and positive ion number size distributions measured by a Neutral cluster and Air Ion Spectrometer (NAIS) are used. Our aim is to find the best diameter size range of ions for identifying and evaluating the intensity of local intermediate ion formation (LIIF). Intermediate ion formation (IIF) refers to the formation of intermediate ions through NPF, while local means that the growth of such ions from smaller clusters has occurred in close proximity (e.g., within 500 m to 1 km) to the measurement site, i.e., locally. We find that the ions in the mobility diameter size range of 2.0–2.3 nm are the best suited for detection of LIIF. The ion concentrations in this size range indicate the elevated rates of IIF, and the potential distances the growing ions have traveled are smaller than those for larger ions. In addition, in Hyytiälä, the negative ion concentrations are more sensitive to IIF than the positive ion concentrations due to the higher difference in concentrations between periods of IIF and the background. Therefore, we recommend the concentrations of ions with diameters 2.0–2.3 nm as the best choice for identifying and evaluating the intensity of LIIF.