Correlation Plots Research Articles

Risk assessment modeling for childhood stunting using automated machine learning and demographic analysis

Over the last few decades, childhood stunting has persisted as a major global challenge. More than 154 million children under five are stunted across the world, with 95% of them residing in Asia and Africa alone. Apart from the small stature, stunting poses additional impediments including delayed cognitive development and health complications. Furthermore, the involvement of several demographic factors and limited timeframe of intervention effectiveness poses challenges to existing treatments. Thus, understanding the impact of demographic conditions on stunting and developing predictive methods is vital for advancing future research developing predictive models. In this study, we hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. The TPOT pipelines, for data sets from Kenya and Bangladesh, outscored previously reported ML models with most performance metrics improving by 0.1 or more. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. Additionally, the machine learning models trained by TPOT resulted in strong overall performances with the best metrics scoring over 0.8. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.

Machine learning enabled high-throughput screening of inorganic solid electrolytes for regulating dendritic growth in lithium metal anodes

Schematic of dendrite formation and correlation plot of shear modulus.

Role of immune mediators in predicting hospitalization of SARS-CoV-2 positive patients.

BackgroundSeveral immune mediators (IM) including cytokines, chemokines, and their receptors have been suggested to play a role in COVID-19 pathophysiology and severity.AimTo determine if early IM profiles are predictive of clinical outcome and which of the IMs tested possess the most clinical utility.MethodsA custom bead-based multiplex assay was used to measure IM concentrations in a cohort of SARS-CoV-2 PCR positive patients (n = 326) with varying disease severities as determined by hospitalization status, length of hospital stay, and survival. Patient groups were compared, and clinical utility was assessed. Correlation plots were constructed to determine if significant relationships exist between the IMs in the setting of COVID-19.ResultsIn PCR positive SARS-CoV-2 patients, IL-6 was the best predictor of the need for hospitalization and length of stay. Additionally, MCP-1 and sIL-2Rα were moderate predictors of the need for hospitalization. Hospitalized PCR positive SARS-CoV-2 patients displayed a notable correlation between sIL-2Rα and IL-18 (Spearman’s ρ = 0.48, P=<0.0001).ConclusionsIM profiles between non-hospitalized and hospitalized patients were distinct. IL-6 was the best predictor of COVID-19 severity among all the IMs tested.

Software platform for high-fidelity-data-based artificial neural network modeling and process optimization in chemical engineering

Artificial neural networks are revolutionizing the field of engineering because of their ability to model complex non-linear problems without explicit programming. Their applications in different areas, such as manufacturing and healthcare, have provided a new path away from traditional modeling techniques. Nonetheless, users of this technology, especially those without prior knowledge of neural networks, spend a considerable amount of time gaining a basic understanding of the use of technology. Traditional trial-and-error approaches are often employed in training these neural networks, which further increases the time spent in developing them. Owing to the laborious nature of the trial-and-error method, the optimal or best hyperparameters of a particular neural network may not be determined, thereby affecting the accuracy of the developed model. Hence, in this study, a software platform is presented to aid in the training and development of neural networks by using genetic algorithms (GAs) for optimizing the model's hyperparameters, such as the number of neurons, learning rate, and activation function. As an essential aspect of chemical engineering processes, design or operating-parameter optimization is also included in this software package, wherein the best (optimized) weights and biases from the neural network are saved and employed in another GA to optimize key process variables, such as temperature, and velocity, as required by the user. This dual-purpose provides a complete application of neural networks that are primarily encountered in many engineering disciplines. The software platform can also plot 3D contours, heat maps (correlation plots), and other line graphs. For the validation and generalization of the software, it was benchmarked against five cases presented by different authors across various chemical engineering fields. The prediction results obtained using the software package were higher than those presented in the published literature, demonstrating the superior performance of the software package.

Smplot: An R Package for Easy and Elegant Data Visualization.

R, a programming language, is an attractive tool for data visualization because it is free and open source. However, learning R can be intimidating and cumbersome for many. In this report, we introduce an R package called “smplot” for easy and elegant data visualization. The R package “smplot” generates graphs with defaults that are visually pleasing and informative. Although it requires basic knowledge of R and ggplot2, it significantly simplifies the process of plotting a bar graph, a violin plot, a correlation plot, a slope chart, a Bland-Altman plot and a raincloud plot. The aesthetics of the plots generated from the package are elegant, highly customisable and adhere to important practices of data visualization. The functions from smplot can be used in a modular fashion, thereby allowing the user to further customise the aesthetics. The smplot package is open source under the MIT license and available on Github (https://github.com/smin95/smplot), where updates will be posted. All the example figures in this report are reproducible and the codes and data are provided for the reader in a separate online guide (https://smin95.github.io/dataviz/).

Chitin Determination in Residual Streams Derived From Insect Production by LC-ECD and LC-MS/MS Methods

Chitin, a biopolymer present in fungi and arthropods, is a compound of interest for various applications, such as in the agricultural and medical fields. With the recently growing interest in the development of insect farming, the availability of chitin-containing residual streams, particularly the molting skins (exuviae), is expected to increase in the near future. For application purposes, accurate quantification of chitin in these insect sources is essential. Previous studies on chitin extraction and quantification often overlooked the purity of the extracted chitin, making the outcomes inconsistent and prone to overestimation. The present study aims to determine chitin content in the exuviae of three insect species mass-reared worldwide: black soldier fly (BSF), mealworm, and house cricket. Chitin was chemically extracted using acid and alkali treatments to remove minerals and proteins. The purity of extracted chitin was evaluated by hydrolyzing the chitin into glucosamine, followed by quantitative determination of the latter using two liquid chromatography methods: electrochemical detection (ECD) and tandem mass spectrometry (MS/MS). Both methods proved accurate and precise, without the need for labor-intensive derivatization steps. Pearson's correlation and Bland-Altman plots showed that the glucosamine determination results obtained by the two methods were comparable, and there is no consistent bias of one approach vs. the other. The chitin content in extracted residues ranged between 7.9 and 18.5%, with the highest amount found in BSF puparium. In summary, the study demonstrated that (1) the residual streams of the insect farming industry have a great potential for utilization as an alternative chitin source, and (2) both LC-ECD and LC-MS/MS are reliable for the quantitative determination of glucosamine in insect chitin.

Optimization of MoCl5 intercalation for low-resistance and low-damage exfoliated highly-oriented pyrolytic graphite

This study developed a damageless molybdenum pentachloride (MoCl5) intercalation doping process of exfoliated highly-oriented pyrolytic graphite (e-HOPG). We optimized the chemical concentration and reaction temperature and time for doping crystalline multilayer graphene. We found that thick e-HOPG films are more susceptible to intercalation damage than previously reported few-layer graphene. Lowering the chemical concentration reduced the damage; however, there was a trade-off between doping efficiency and damage. Efficient doping with a 77% reduction of sheet resistance without significant damage was achieved by further optimizing the reaction temperature and time with reduced chemical concentration. The correlation plots of G and 2D peak positions in the Raman spectra were used to analyze the strain and carrier density induced during the intercalation process and investigate the cause of damage. The stability of the intercalated e-HOPG was confirmed for storage in a nitrogen box for 40 weeks.

Memory Effect and Crystallization of (R, S)-2-Chloromandelic Acid Glass.

(R, S)-2-Chloromandelic acid, which can crystallize in racemic crystals (forms α and β) or a conglomerate (form γ), has been studied for its glass-forming behavior. Below the glass transition temperature, samples of the title compound crack into pieces. Correlation plots of DSC results have been used to investigate what determines the cracking and its occurrence temperature. We found that the latter is influenced by the polymorph from which the melt state has been obtained, showing that a certain memory of the previous crystalline phase persists in the undercooled melt. Moreover, this residual structure could be eliminated by elongating the annealing period or increasing the annealing temperature. Investigation using broadband dielectric spectroscopy confirmed such a memory effect. Finally, we studied the role of cracking in the control of the crystallization. In contrast with previous literature on other glass-forming molecular systems, we verified that the crystallization upon reheating is not impacted by the occurrence of cracks in the glassy state. This observation challenges the current views on polymorphic crystallization from organic glasses.

Machine Learning-Based Identification of Potentially Novel Non-Alcoholic Fatty Liver Disease Biomarkers.

Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disease that presents a great challenge for treatment and prevention.. This study aims to implement a machine learning approach that employs such datasets to identify potential biomarker targets. We developed a pipeline to identify potential biomarkers for NAFLD that includes five major processes, namely, a pre-processing step, a feature selection and a generation of a random forest model and, finally, a downstream feature analysis and a provision of a potential biological interpretation. The pre-processing step includes data normalising and variable extraction accompanied by appropriate annotations. A feature selection based on a differential gene expression analysis is then conducted to identify significant features and then employ them to generate a random forest model whose performance is assessed based on a receiver operating characteristic curve. Next, the features are subjected to a downstream analysis, such as univariate analysis, a pathway enrichment analysis, a network analysis and a generation of correlation plots, boxplots and heatmaps. Once the results are obtained, the biological interpretation and the literature validation is conducted over the identified features and results. We applied this pipeline to transcriptomics and lipidomic datasets and concluded that the C4BPA gene could play a role in the development of NAFLD. The activation of the complement pathway, due to the downregulation of the C4BPA gene, leads to an increase in triglyceride content, which might further render the lipid metabolism. This approach identified the C4BPA gene, an inhibitor of the complement pathway, as a potential biomarker for the development of NAFLD.

Mir-671-5p, Mir-193b-5p, Mir-1307-5p Are Useful for Predicting Outcome in Diffuse Large B-Cell Lymphoma

Background: Diffuse large B-cell Lymphoma (DLBCL) is stratified histologically as GCB and ABC phenotypes based on Hans algorithm. Besides this categorization, there is a paucity of molecular markers useful for predicting response to CHOP/R-CHOP chemotherapy and outcome of DLBCL patients. We performed miRNA profiling to identify potential miRNAs for their ability to predict response to chemotherapy and outcome in DLBCL patients.Methods: A total of 41 cases of histologically and immunohistochemically confirmed DLBCL cases who received R-CHOP/CHOP were included in this study after IRB approval. The follow up ranged from 7 to 2273 days with a mean of, 874 days. Cases in sustained complete remission for ≥ 1095 days were identified as “responders” whereas cases with partial response/stable disease or primary progressive disease were identified as“non-responders”. In the first part of the study, miRNA transcriptome profiling was performed from lymph node tissue of 10 cases of DLBCL which included 3 responders and 7 non-responders (3 with refractory disease and 4 with relapse) for identification and selection of differentially expressed miRNAs. Total RNA was isolated from the biopsies, libraries constructed with ION TOTAL RNA-SEQ v2 and sequenced on S5 ION Torrent. The filtered reads were aligned to the reference genome hg19 using Bowtie. Differentially expressed miRNAs were identified by DESeq2. In the second part of the study, validation of the selected miRNAs was performed in a retrospective validation cohort of 41 cases including 17 responders and 24 non-responders by semi-quantitative qRT-PCR. ROC curves were obtained and best cut-off for the delta Ct values was selected for each of the miRNAs to distinguish the two cohorts of patients. In the third part of the study, an additional 38 cases of DLBCL were included prospectively as testing cohort, in whom mi-RNA were isolated from paired pre-treatment biopsy and plasma samples were evaluated for the expression of the selected miRNAs for prediction of response to treatment and outcome.Results: miRNA transcriptome profiling revealed a total of 30 differentially expressed miRNAs among DLBCL responders vs non-responders and the data was submitted to GEO database (GSE179760). The top 4 significantly downregulated miRNAs in non-responders selected for validation included miR-193b-5p, miR-671-5p, miR-1307-5p and miR-326 as shown in the volcano plot and heatmap in Fig.1. In-silico prediction for the interacting genes of the selected miRNAs revealed genes involved in lymphoma progression and chemoresistance . Validation by qRT-PCR revealed significant down-regulation of miR-1307-5p, miR-671-5p and miR-193b-5p in the non-responders compared to responders (Fig.2). There was no significant association of miRNA expression with disease stage, nodal or extranodal origin and the presence of B symptoms. However, the downregulation of miR-671-5p was found to be associated with the presence of B-symptoms. Downregulation of miR-193b-5p, miR-671-5p, miR-1307-5p expression showed a significant association with progression free survival as shown in Fig.3 (Spearman's correlation plot and Kaplan-Meier survival curves).In the prospective cohort of 38 DLBCL patients, all 3 miRNAs were expressed in cell free plasma and tissue but only miRNA-193b-5p showed correlation between them. Using the cut-off Ct values obtained from ROC curves of tissue miRNA levels in the validation cohort , 16/38 patients were predicted to be non-responders. The preliminary data showed that 8 of these 16 cases were correctly predicted (6 died of disease, 2 showed partial response). The remaining cases are still under follow-up. The treatment response could not be predicted on the corresponding plasma levels.Conclusion: Downregulation of miRNA-671-5p, miR-193b-5p and miR-1307-5p in pre-treatment biopsy samples is associated with poor outcome and shorter progression free survival and poor treatment response leading to refractory/relapse DLBCL, which needs confirmation in larger studies. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Prediksi Indeks Harga Saham Gabungan (IHSG) Menggunakan Algoritma Autoregressive Integrated Moving Average (ARIMA)

There are various types of investment in Indonesia, one of which is the Indeks Harga Saham Gabungan (IHSG) or in English it is called the Indonesia Composite Index, ICI, or IDX Composite. IHSG is an important parameter to consider when making an investment considering that IHSG is a joint stock. This study aims to predict the price of the IHSG with data mining techniques using an algorithm that can be used as a reference for investors when making an investment. ARIMA is a model for generating estimates from historical data. Data in this study were collected from the monthly IHSG from January 4, 2010 - November 26, 2019. Based on the correlation plot, two autocorrelations (lag 1, lag 32) were found to be significant. This model can predict with an average percentage error of 0.004 so that this prediction is considered good enough to predict the stock price of the IHSG.

An alternative index to NINO3.4 for predicting monthly rainfall anomaly in East Java

ENSO and NINO3.4 index are known to have some relation with Indonesian monthly rainfall anomaly. There is a gap between scientific studies on one hand and forecasting operational problems on the other hand since previous studies are not giving enough attention to the N+1,2,3 concept. The concept is about giving three next month rainfall anomaly prediction rather than connecting ENSO index with three-monthly rainfall anomaly. Here we propose an alternative index for ENSO. The median of categorical gridded rainfall anomaly of East Java is used as a general representation. Plots of correlation between the median and anomaly sea surface temperature from ReynSmithOIv2 are used to determine locus candidate to be compared with NINO3.4. The Near Maritime Continent (NMC) index is selected and proven to have a significant average difference in correlation between based on bootstrap technique. Verification of prediction used in this study is simulation-based and only uses binary hit-miss final result. Prediction is generated by simple linear regression with three lag times (2,3, and 4). Verification based on three categories shows that NMC’s hits are higher than NINO.34 in lag-2 and lag-3. In lag-2, NMC’s verification is 57.5% compared to only 38.7% for NINO3.4. However, NINO3.4 is a still better predictor in lag-4. Radar charts of monthly verifications are also developed.

Corrosion inhibition and statistical data of low carbon steel in HCl media by admixed organic compounds

Corrosion inhibition of carbon steels with organic compounds exhibiting effective corrosion inhibition is an on-going research. The corrosion inhibition of low carbon steel in 1M HCl solution by the combined admixture of vanillin and benzonitrile (VBN), and salvia officinalis with lavendulan officinalis (SLV) was studied by weight loss method. Results obtained show that VBN and SLV performed effectively with maximum inhibition efficiency 91.03% at 1.5% VBN concentration and 97.89% at 5% SLV concentration. The inhibition efficiency of VBN increased with concentration but decreased with exposure time compared to the values obtained for SLV which showed non-dependence on concentration and exposure time after 1% SLV concentration. Correlation plots of inhibition efficiency versus inhibitor concentration show VBN to be more concentration dependent in performance. Calculated data for standard deviation shows the degree of variation from mean values for both compounds is significant at low inhibitor concentration due to time dependence action. Statistical analysis through ANOVA shows inhibitor concentration overwhelmingly influences the inhibition performance of the despite even though exposure time is statistically relevant to minimal degree.

2D fluorescence correlation to visualize influence of size curvature and phase structure of silica nanoparticle-supported small unilamellar vesicle membrane

Single-molecule fluorescence correlation spectroscopy (SM-FCS) was utilized to probe the impact of size curvature and phase structure on membrane dynamics in silica nanoparticles-supported small unilamellar vesicles (Si-SUVs). DOPC and DPPC Si-SUVs were prepared with three different sizes (∼50, 25, and 15 nm in radius), in which DiD dye as a fluorescence probe was implanted in each Si-SUV. DOPC and DPPC membranes have liquid disordered (Lα) and gel phase (Lβ) structures at room temperature, respectively. The detailed spatial dynamics of the lipid membrane in such smaller SUVs (below 50 nm) have never been clarified due to the low spatial resolution limit of available optical microscopes. This work visualizes the membrane's curvature- and phase-dependent spatial dynamics by using a 2D correlation plot that characterizes the interplay between the diffusion motion and the triplet-state lifetime of embedded DiD in a membrane. Irrespective of the membrane phase, the DiD diffusion motion in the membrane slows down as increasing membrane curvature. The homogeneous nature of DOPC membranes reflects into a single block of correlation distribution for all three studied curvature sizes. In contrast, a rigid structure of DPPC membrane appears to have three blocks in correlation plots corresponding to the outer and inner leaflets, and bouquet clustering. These blocks became more distinct with an increase in membrane curvature size. The hydrophilic attraction between the inner leaflet lipids and silica nanoparticles restricts the motion of inner leaflet and also plays a role in relaxing the tense conjugation between inner and outer leaflets.

SigTools: exploratory visualization for genomic signals.

With the advancement of sequencing technologies, genomic data sets are constantly being expanded by high volumes of different data types. One recently introduced data type in genomic science is genomic signals, which are usually short-read coverage measurements over the genome. To understand and evaluate the results of such studies, one needs to understand and analyze the characteristics of the input data. SigTools is an R-based genomic signals visualization package developed with two objectives: (i) to facilitate genomic signals exploration in order to uncover insights for later model training, refinement and development by including distribution and autocorrelation plots; (ii) to enable genomic signals interpretation by including correlation and aggregation plots. In addition, our corresponding web application, SigTools-Shiny, extends the accessibility scope of these modules to people who are more comfortable working with graphical user interfaces instead of command-line tools. SigTools source code, installation guide and manual is freely available on http://github.com/shohre73.

(Invited) Towards Point of Care Detection of Iron Metabolism Parameters

Disorders in iron metabolism are endemic globally, affecting more than several hundred million individuals and often resulting in increased mortality rates or general deterioration of quality of life. To prevent and monitor iron-related disorders, we present a point of care multiplex system to measure four clinically relevant iron biomarkers: blood iron levels (iron bound to transferrin), total iron-binding capacity (TIBC), percent transferrin saturation, and blood ferritin. This system leverages three distinct channels: two colorimetric and one electrochemical emerging from the same sample injection port designed to accommodate 50 ul of whole blood, filter out cellular components, and transport the filtered sample to the three specified channels’ capillary action. The first channel measures iron levels. It uses a membrane impregnated with the working reagents that reduce iron (III) to iron (II) and chelate the reduced iron with ferene forming a blue- complex. A custom smartphone app quantifies the Fe-ferene complex and provides outputs iron levels in whole blood. The second channel measures TIBC. This channel uses the same membrane and detection method that the first channel but requires an extra preconditioning step of saturating the blood sample with iron standard and precipitating excess unbound iron with a specific binding agent (magnesium carbonate). Using the ratio of total iron (output of channel 1) and TIBC (output of channel 2) enables calculating the percentage of transferrin saturation. The two colorimetric channels were created at Forzani’s Team at Arizona State University, while the electrochemical channel is created by Diez-Perez’s Team at King’s College London. The detection of Ferritin consists of a novel method that combines the selectivity of antibodies with the electrochemical properties of Ferritin for high sensitivity detection. The sensor components are all 3D-printed and require a finger-prick sample for complete measurement of these clinically relevant iron biomarkers. Iron biomarkers. Comparative studies of results obtained by the new sensing device and the reference method in actual samples were performed to determine the device’s capacity to detect iron parameters’ concentrations. Correlation plots with a slope of ~ 1 and regression coefficient of higher than 0.82 were obtained for detection of blood iron levels, total iron-binding capacity, and percentage of transferrin saturation. This indicated that the new device is substantially equivalent to the reference method. With detection times of five minutes, fingerpick sample, and sensor cost less than 10 cents; the device shows excellent promise for point care testing of iron disorders.

Assessment of heart rate agreement on continuous photoplethysmography monitoring using a smartwatch versus beat-to-beat synchronized ECG monitoring

Abstract Background In the awakening era of mobile health, wearables equipped with photoplethysmography (PPG) technology to monitor the heart rate (HR) and rhythm are on the rise. Smartwatches and wristbands enable HR monitoring for consumers at massive scale. Unfortunately, once consumers become patients, physicians are limited by insufficient evidence to support the clinical use of PPG based wearables. Accurate identification of heartbeats is the first step in the interpretation of PPG traces and should be validated. Purpose To assess the agreement between continuous PPG monitoring using a smartwatch and continuous ECG Holter monitoring in the identification of heartbeats and calculation of the HR. Methods One hundred patients (≥18 years) without a pacemaker-dependent heart rhythm who were referred to a university hospital and a large tertiary hospital for elective 24-hour ECG Holter monitoring were asked to wear a continuous PPG monitoring smartwatch (i.e. Samsung GWA2 or Empatica E4) simultaneously with the 24-hour Holter monitor. All activities of daily life were allowed. The ECG trace and PPG waveform were synchronised and fragmented in one-minute fragments. The one-minute ECG fragments were labelled as AF, non-AF, or insufficient quality based on the routine clinical interpretation (i.e. software + physician overreading), and the average HR during each fragment was calculated by Holter algorithm. The PPG fragments were analysed by an artificial intelligence (AI) algorithm (i.e. FibriCheck) that labelled fragments as sufficient or insufficient quality, identified the number of heartbeats and calculated the HR. The agreement between the HR on ECG and PPG in sufficient quality tracings was analysed with linear regression, Pearson's product-moment correlation and Bland-Altman analysis. A subanalysis was performed for AF rhythm and non-AF rhythms. Results A total of 72,725 simultaneous ECG and PPG one-minute fragments were recorded in 96 patients, after excluding 4 patients (due to 3 Holter and 1 smartwatch technical error) and 42,520 minutes (36.9%) of insufficient quality (ECG 1,454 (1.3%); PPG 25,704 (22.3%), ECG and PPG 15,362 (13.3%)). The correlation (r=0.935) between ECG and PPG HR was statistically significant (CI 0.934–0.936; P&amp;lt;0.001), with a mean difference between ECG and PPG of 0.8bpm. The lower and upper limit boundary (LLB and ULB; defined as ±1.96 SD) were −8.0bpm and 9.7bpm, respectively, i.e. 95% of PPG measurements identified the HR within 8bpm below or 10bpm above the ECG reference. The mean difference between ECG and PPG HR in the AF subgroup (n=10,255 (14.1%)) was 0.9bpm (LLB −8.4bpm; ULB 10.2bpm) and 0.8bpm in the non-AF subgroup (LLB −0.8bpm; ULB 9.6bpm). Conclusion The AI algorithm analysing continuous out-of-hospital PPG tracings can annotate heartbeats and assess HR without a clinically significant bias compared to continuous ECG monitoring, both during AF and non-AF rhythms in a heterogenous patient population. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Research Foundation-Flanders, Strategic Basic Research Fund Correlation plot &amp; Bland-Altman plot

PSI-16 Estimation of pigs live body weight from digital images using reference objects

Abstract The live body weight (LBW) is an important parameter providing guidance for estimation of growth and feed conversion efficiency, body condition, presence of disease, and management of housing, nutrition and animal health in different life stages of livestock. This research study explores the possibility of developing a semi-automatic analytic system that estimates the LBW of pigs by applying machine learning methods that use approximated biometric measurements extracted from digital images acquired with consumer-level cameras in the presence of a reference object. Images corresponding to 12 pigs were sampled on two different dates 1 month apart and acquired using a consumer-level Motorola X4 mobile phone. The best 3 images for each pig were selected for each time point. Six measurements were extracted from each image using ImageJ. A total of 72 data points were analyzed using RStudio, and the generated correlation plot confirmed the positive correlations between the 6 predictors and LBW. Five machine learning (ML) methods were used to model the dependency between the measured parameters and LBW using WEKA. The Random Forest model outperformed all the other models and predicted LBW with the highest prediction accuracy (97%) and the lowest prediction error (MAE = 6.54), which could represent a good candidate for further studies. In conclusion, the semi-automatic image-based system is a promising approach combining machine learning, digital image analysis and manual scale extraction with the aid of reference objects of known size for accurate pig LBW estimation. The next stage of this study aims to integrate automatic image acquisition and image processing solutions in the current approach. This novel system will be cost- and time-efficient, and it can contribute to the development of intelligent solutions for scientific research and enhancing animal productivity in commercial pig farms.

Excluded area of superellipse sector particles.

Superellipse sector particles (SeSPs) are segments of superelliptical curves that form a tunable set of hard-particle shapes for granular and colloidal systems. SeSPs allow for continuous parametrization of corner sharpness, aspect ratio, and particle curvature; rods, circles, rectangles, and staples are examples of shapes SeSPs can model. We investigate the space of allowable (nonoverlapping) configurations of two SeSPs, which depends on both the center-of-mass separation and relative orientation. Radial correlation plots of the allowed configurations reveal circular regions centered at each of the particle's two end points that indicate configurations of mutually entangled particle interactions. Simultaneous entanglement with both end points is geometrically impossible; the overlap of these two regions therefore represents an excluded area in which no particles can be placed regardless of orientation. The regions' distinct boundaries indicate a translational frustration with implications for the dynamics of particle rearrangements (e.g., under shear). Representing translational and rotational degrees of freedom as a hypervolume, we find a topological change that suggests geometric frustration arises from a phase transition in this space. The excluded area is a straightforward integration over excluded states; for arbitrary relative orientation this decreases sigmoidally with increasing opening aperture, with sharper SeSP corners resulting in a sharper decrease. Together, this work offers a path towards a unified theory for particle shape control of bulk material properties.

Observation of a fission mode with very short elongation for the neutron-rich Md257 nucleus at high excitation energy

Mass and total kinetic-energy (TKE) distributions of fission fragments from $^{257}\mathrm{Md}$ nucleus populated by complete fusion of $^{19}\mathrm{F}$ with $^{238}\mathrm{U}$ have been measured in the range of excitation energy ${E}^{*}\ensuremath{\approx}37\text{\ensuremath{-}}48\phantom{\rule{0.16em}{0ex}}\mathrm{MeV}$. Due to the skewness of the measured TKE distributions, two Gaussian distributions were required to explain the data. The events with high TKE must originate from the supershort mode of fission, the presence of which has been confirmed by measuring two additional reactions: $^{19}\mathrm{F}+^{232}\mathrm{Th}$ and $^{18}\mathrm{O}+^{238}\mathrm{U}$ involving first a different target and then a different projectile leading to compound nuclei $^{251}\mathrm{Es}$ and $^{256}\mathrm{Fm}$, respectively, at similar excitation energies and comparing their mass-TKE correlation plots. The existence of considerable supershort mode of fission for the $^{257}\mathrm{Md}$ nucleus up to ${E}^{*}\ensuremath{\approx}48$ MeV, observed for the first time, reveals that such rare fission modes for neutron-rich exotic nuclei are far off from the liquid drop model predictions, even at high excitation energies.