Earlier Estimates Research Articles

Systematic assessment of early brain injury severity at admission with aneurysmal subarachnoid hemorrhage

Early brain injury (EBI) after aneurysmal subarachnoid hemorrhage (aSAH) has been increasingly recognized as a risk factor for delayed cerebral ischemia (DCI). While several clinical and radiological EBI biomarkers have been identified, no tool for systematic assessment of EBI severity has been established so far. This study aimed to develop an EBI grading system based on clinical signs and neuroimaging for estimation of EBI severity at admission. This is a retrospective observational study assessing imaging parameters (intracranial blood amount, global cerebral edema (GCE)), and clinical signs (persistent loss of consciousness [LOC]) representative for EBI. The intracranial blood amount was semi-quantitatively assessed. One point was added for GCE and LOC, respectively. All points were summed up resulting in an EBI grading ranging from 1 to 5. The estimated EBI severity was correlated with progressive GCE requiring decompressive hemicraniectomy (DHC), DCI-associated infarction, and outcome according to the modified Rankin scale (mRS) at 3-month-follow up. A consecutive cohort including 324 aSAH-patients with a mean age of 55.9 years, was analyzed. The probability of developing progressive GCE was 9% for EBI grade 1, 28% for EBI grade 2, 43% for EBI grade 3, 61% for EBI grade 4, and 89% for EBI grade 5. The EBI grading correlated significantly with the need for DHC (r = 0.25, p < 0.0001), delayed infarction (r = 0.30, p < 0.0001), and outcome (r = 0.31, p < 0.0001). An EBI grading based on clinical and imaging parameters allowed an early systematic estimation of EBI severity with a higher EBI grade associated not only with a progressive GCE but also with DCI and poor outcome.

DeepCt: Predicting Pharmacokinetic Concentration-Time Curves and Compartmental Models from Chemical Structure Using Deep Learning.

After initial triaging using in vitro absorption, distribution, metabolism, and excretion (ADME) assays, pharmacokinetic (PK) studies are the first application of promising drug candidates in living mammals. Preclinical PK studies characterize the evolution of the compound's concentration over time, typically in rodents' blood or plasma. From this concentration-time (C-t) profiles, PK parameters such as total exposure or maximum concentration can be subsequently derived. An early estimation of compounds' PK offers the promise of reducing animal studies and cycle times by selecting and designing molecules with increased chances of success at the PK stage. Even though C-t curves are the major readout from a PK study, most machine learning-based prediction efforts have focused on the derived PK parameters instead of C-t profiles, likely due to the lack of approaches to model the underlying ADME mechanisms. Herein, a novel deep learning approach termed DeepCt is proposed for the prediction of C-t curves from the compound structure. Our methodology is based on the prediction of an underlying mechanistic compartmental PK model, which enables further simulations, and predictions of single- and multiple-dose C-t profiles.

Early Season Crop Acreage Estimation for Pigeon Pea using SAR Data: A Case Study of Kalaburagi District, Karnataka

Crop acreage estimates using space-based observations during mid-season and pre-harvest periods are operationally provided for major Kharif and Rabi crops in India. These are critical inputs for planning and decision-making concerning food security, storage, pricing, and procurement. This work explores the potential of Satellite C-band Synthetic Aperture Radar (SAR) data in VV and VH polarization, acquired during monsoon season, for early crop acreage assessment of Pigeon Pea. The present study evaluates two classification approaches based on multi-temporal SAR data to generate a spatial distribution map of the pigeon pea crop in the Kalaburagi region of Karnataka. The temporal profiles of sigma nought (backscatter coefficient) and Entropy vs. Alpha Angle were studied with reference to crop phenology. Random forest algorithm has been used to classify multi-temporal SAR Ground Range Detected (GRD) data from June to November 2019. Temporal SAR data from June to early November with VV+VH polarization was optimal for tur area assessment with 85% accuracy. This study also examines phase and intensity information from 3-date SAR Single Look Complex (SLC) data with an unsupervised Wishart Classification approach for improved crop acreage estimation. The results emphasize the effectiveness of dual-polarimetric SAR data for timely crop inventory of Pigeon Pea during the Kharif season.

Distinct trajectories of symptomatic response in ulcerative colitis during filgotinib therapy: A post hoc analysis from the SELECTION study.

Filgotinib is an oral, once-daily, Janus kinase 1 preferential inhibitor approved for treatment of ulcerative colitis (UC) following the phase 2b/3 SELECTION trial. Identification of patient populations and factors associated with long-term treatment response trajectories may improve UC management. We aimed to identify and describe distinct patient subgroups of response to filgotinib based on partial Mayo Clinic Score (pMCS) trajectories over time. In these post hoc analyses of SELECTION, group-based trajectory modeling (GBTM) was applied to pMCS to describe groups of distinct, symptom-based patient trajectories using data from patients who responded to filgotinib 200 or 100mg and continued receiving filgotinib up to week 58. Patient demographics, disease characteristics, and week 10 response were compared between the groups. Achievement of a patient-level multi-component endpoint of comprehensive disease control (CDC) was assessed in each group. GBTM identified five distinct patient populations with different response trajectories; 67.5% of patients had beneficial trajectories. The beneficial trajectory groups generally had higher proportions of patients who were recently diagnosed (<1year), were receiving filgotinib 200mg and were biologic-naive versus the relapsing trajectory groups (4%-9% vs. 4%-5%; 43%-65% vs. 36%-46%; 54%-70% vs. 35%-58%, respectively). Furthermore, 55.4% of patients had sustained beneficial trajectories, with low baseline endoscopic subscores (≥43% of patients had a subscore of 2) and strong week 10 FCP responses (≥61% of patients with >50% decrease in FCP from baseline). Sustained beneficial trajectory groups had a higher probability of achieving CDC at week 58 than other groups (31%-32% vs. 0%-7%). Beneficial long-term response trajectories and achievement of CDC with filgotinib were associated with being biologic-naive and having less severe disease at baseline. Early estimation of sustained and CDC may facilitate patient identification and development of personalized management strategies in UC. NCT02914522.

Early estimation of glutelin to gliadin ratio in wheat grain using high-dimensional and hyperspectral reflectance

Precise and timely estimation of the glutelin-to-gliadin ratio (Glu/Gli) in wheat grain is pivotal for crop monitoring, as it is a crucial quality indicator ensuring the production of high-quality wheat flour. Despite the recognized potential of hyperspectral technology in crop phenotype estimation, its application to estimate Glu/Gli in wheat grains faces challenges due to complex spectral-chemical relationships and the influence of growing seasons. This study addresses this gap by cultivating 11 wheat varieties and collecting high-dimensional hyperspectral data from field experiments during various growth stages of wheat (2018–2019 and 2019–2020). Utilizing vegetation indices (VIs) in conjunction with linear mixed-effects model (LMM) and random forest regression model (RFR), it constructs a robust Glu/Gli estimation model (with a Glu/Gli range of 1.063 to 2.218). Results reveal that a singular VI application suffers from data limitations, while the integration of multiple VIs significantly enhances estimation accuracy. The mid-grain filling period emerges as a critical stage for accurate Glu/Gli estimation, with TCARI (transformed chlorophyll absorption reflectance index) demonstrating notable significance and high correlation. In model performance, RFR (R2 = 0.691, rRMSE = 0.096, RPD = 1.872, RER = 6.028) outperforms LMM (R2 = 0.477, rRMSE = 0.131, RPD = 1.383, RER = 4.453), exhibiting superior accuracy in estimating grain Glu/Gli for diverse wheat varieties. This study introduces a rapid and accurate approach for early wheat grain Glu/Gli estimation, offering valuable insights for wheat value chain and precision farming.

Fixel-Based Analysis of White Matter Alterations in Facial Palsy.

Predicting individual prognosis is difficult despite various risk factor studies, due to the complex recovery involving peripheral nerve restoration and cerebral plasticity. This study uses fixel-based analysis (FBA) to better understand neural pathway alterations, hypothesizing that FBA can effectively link functional outcomes with diffusion metrics in the process of rehabilitation. The authors recruited 50 participants including 30 patients and 20 healthy controls into the study. All of them underwent assessment 3 months and 6 months after injury. Both whole-brain fixel-based analysis and tract of interest analysis were performed. The authors observed multiple regions including the superior corona radiata, posterior limb of the internal capsule, and cerebral peduncle displaying significant changes of FD (fiber density), FC (fiber-bundle cross-section), and FDC (fiber density and cross-section). Patients with unfavorable outcomes showed increased FD in the ipsilesional side and decreased FD in the contralesional side compared with patients with favorable outcomes. In addition, FD values of the contralesional side exhibited positive correlations with differences in FGS at 3 months and 6 months. Conversely, FD values in the ipsilesional side showed a negative correlation. Our findings suggest that FD metrics within the corticobulbar tract could serve as predictive biomarkers for symmetry outcomes. Thus, FBA holds promise for early estimation of behavioral outcomes in facial palsy, guiding more targeted interventions.

Prognostic Role of Specific KRAS Mutations Detected in Aspiration and Liquid Biopsies from Patients with Pancreatic Cancer

Background/Objectives: Although the overall survival prognosis of patients in advanced stages of pancreatic ductal adenocarcinoma (PDAC) is poor, typically ranging from days to months from diagnosis, there are rare cases of patients remaining in therapy for longer periods of time. Early estimations of survival prognosis would allow rational decisions on complex therapy interventions, including radical surgery and robust systemic therapy regimens. Understandably, there is great interest in finding prognostic markers that can be used for patient stratification. We determined the role of various KRAS mutations in the prognosis of PDAC patients using biopsy samples and circulating tumor DNA. Methods: A total of 118 patients with PDAC, clinically confirmed by endoscopic ultrasound-guided fine-needle aspiration biopsy (EUS-FNB), were included in the study. DNA was extracted from cytological slides following a standard cytology evaluation to ensure adequacy (viability and quantity) and to mark the tumor cell fraction. Circulating tumor DNA (ctDNA) was extracted from plasma samples of 45 patients in stage IV of the disease. KRAS mutations in exons 12 and 13 were detected by denaturing capillary electrophoresis (DCE), revealing a minute presence of mutation-specific heteroduplexes. Kaplan–Meier survival curves were calculated for individual KRAS mutation types. Results:KRAS mutations were detected in 90% of tissue (106/118) and 44% of plasma (20/45) samples. All mutations were localized at exon 2, codon 12, with G12D (GGT &gt; GAT) being the most frequent at 44% (47/106) and 65% (13/20), followed by other types including G12V (GGT &gt; GTT) at 31% (33/106) and 10% (2/20), G12R (GGT &gt; CGT) at 17% (18/106) and 10% (2/20), G12C (GGT/TGT) at 5% (5/106) and 0% (0/20) and G12S (GGT/AGT) at 1% (1/106) and 5% (1/20) in tissue and plasma samples, respectively. Two patients had two mutations simultaneously (G12V + G12S and G12D + G12S) in both types of samples (2%, 2/106 and 10%, 2/20 in tissue and plasma samples, respectively). The median survival of patients with the G12D mutation in tissues was less than half that of other patients (median survival 101 days, 95% CI: 80–600 vs. 228 days, 95% CI: 184–602), with a statistically significant overall difference in survival (p = 0.0080, log-rank test), and furthermore it was less than that of all combined patients with other mutation types (101 days, 95% CI: 80–600 vs. 210 days, 95% CI: 161–602, p = 0.0166). For plasma samples, the survival of patients with this mutation was six times shorter than that of patients without the G12D mutation (27 days, 95% CI: 8–334 vs. 161 days, 95% CI: 107–536, p = 0.0200). In contrast, patients with detected KRAS G12R in the tissue survived nearly twice as long as other patients in the aggregate (286 days, 95% CI: 70–602 vs. 162 days, 95% CI: 122–600, p = 0.0374) or patients with other KRAS mutations (286 days, 95% CI: 70–602 vs. 137 days, 95% CI: 107–600, p = 0.0257). Conclusions: Differentiation of specific KRAS mutations in EUS-FNB and ctDNA (above all, the crucial G12D and G12R) is feasible in routine management of PDAC patients and imperative for assessment of prognosis.

Transition From Reverse to Left‐Slip on the Eastern Haiyuan Fault, NE Tibetan Plateau, From the Structure and Age of the Ganyanchi Pull‐Apart Basin

AbstractBecause the active, left‐slip Haiyuan fault is a first‐order structure along the NE margin of the Tibetan Plateau, its tectonic evolution provides insight into deformation processes along this margin over time. Late Cenozoic deformation in the area of the eastern Haiyuan fault initiated as thrust faulting, followed by left‐lateral strike slip displacement. However, the time of this kinematic change has been long debated. Here we use the structural evolution and the age of the Ganyanchi basin to date the onset of this kinematic transition. Seismic reflection data, integrated with published structural mapping, indicate that the basin is a pull‐apart controlled by strands of the eastern Haiyuan fault. Previously reported magnetostratigraphy of a core from the basin interior indicates deposition started at ∼2.8 Ma. However, this age likely postdates initiation of the bounding strike‐slip faults. We estimate that an additional 0.2–1.0 Myr of lateral slip occurred before the dated section formed. Thus, we find that the age of the eastern Haiyuan fault is at least ∼3.5 Ma, which is significantly older than early estimates of less than 2 Ma. Integrating our new data with prior work reveals that the kinematic change from NE‐SW shortening to left‐slip along the Haiyuan fault youngs to the east, which we interpret to result from growth of the Haiyuan strike‐slip fault via progressive lateral propagation from west to east. Miocene‐Pliocene onset of the Haiyuan strike‐slip fault coincides with initiation ages of other major strike‐slip faults in NE Tibet, likely implying that the NE margin of the Tibetan Plateau had switched to a strike‐slip dominated mode of deformation by this time.

Reevaluation of the Cosmic-Ray Ionization Rate in Diffuse Clouds

All current estimates of the cosmic-ray (CR) ionization rate rely on assessments of the gas density along the probed sight lines. Until now, these have been based on observations of different tracers, with C2 being the most widely used in diffuse molecular clouds for this purpose. However, dust extinction maps have recently reached sufficient accuracy to give an independent measurement of the gas density on parsec scales. In addition, they allow us to identify the gas clumps along each sight line, thus localizing the regions where CR ionization is probed. We reevaluate H3+ observations, which are often considered as the most reliable method to measure the H2 ionization rate ζH2 in diffuse clouds. The peak density values derived from the extinction maps for 12 analyzed sight lines turn out to be, on average, an order of magnitude lower than the previous estimates and agree with the values obtained from revised analysis of C2 data. We use the extinction maps in combination with the 3d-pdr code to self-consistently compute the H3+ and H2 abundances in the identified clumps for different values of ζH2 . For each sight line, we obtain the optimum value by comparing the simulation results with observations. We show that ζH2 is systematically reduced with respect to the earlier estimates by a factor of ≈9 on average, to ≈6 × 10−17 s−1, primarily as a result of the density reduction. We emphasize that these results have profound consequences for all available measurements of the ionization rate.

COMPARATIVE ANALYSIS OF METHODS AND MODELS FOR ESTIMATING SIZE AND EFFORT IN DESIGNING MOBILE APPLICATIONS

Mobile applications have long become an integral part of everyday life and have gained popularity among ordinary users, small companies, and large enterprises. However, this is a completely separate area of software. We can say that this is a whole separate ecosystem. Creating functional software for this ecosystem comes with new requirements, constraints, and characteristics that were not used in previous software metrics evaluation methods such as desktop programs or web applications. Mobile applications have new features, limitations and capabilities that are different from other software. Estimating the size and effort helps the software development team approximate the costs needed to more successfully develop a software product such as a mobile application. An early estimate of size and effort is necessary to identify resources that can help in the early stages of implementation a software project within budget and on time. The reliability of the assessment is also of great importance. Low reliability can lead to overestimation or underestimation, which can have bad consequences for any software development company, especially mobile applications. However, the suitability of methods and models for measuring size and efforts that are used for traditional software (PC applications and web applications) do not always provide sufficient reliability for mobile applications.The main goal of this article is to analyze the usage of various methods and models for estimating size and effort the development of mobile applications, including for the Android OS at the initial stages of their design. There is a brief description of the main and widespread methods and models that are used to estimate the size and effort, such as: Lines of Code (LOC), Function Points (FP), COCOMO and COCOMO 2, COSMIC, FiSMA, application of regression analysis for construction of mathematical models. The methods and models proposed by various authors are directly analyzed.

Rice yield prediction through integration of biophysical parameters with SAR and optical remote sensing data using machine learning models

In an era marked by growing global population and climate variability, ensuring food security has become a paramount concern. Rice, being a staple crop for billions of people, requires accurate and timely yield prediction to ensure global food security. This study was undertaken across two rice crop seasons in the Udham Singh Nagar district of Uttarakhand state to predict rice yield at 45, 60 and 90 days after transplanting (DAT) through machine learning (ML) models, utilizing a combination of optical and Synthetic Aperture Radar (SAR) data in conjunction with crop biophysical parameters. Results revealed that the ML models were able to provide relatively accurate early yield estimates. For summer rice, eXtreme gradient boosting (XGB) was the best-performing model at all three stages (45, 60, and 90 DAT), while for kharif rice, the best-performing models at 45, 60, and 90 DAT were XGB, Neural network (NNET), and Cubist, respectively. The combined ranking of ML models showed that prediction accuracy improved as the prediction date approaches harvest, and the best prediction of yield was observed at 90 DAT for both summer and kharif rice. Overall rankings indicate that for summer rice, the top three models were XGB, NNET, and Support vector regression, while for kharif rice, these were Cubist, NNET, and Random Forest, respectively. The findings of this study offer valuable insights into the potential of the combined use of remote sensing and biophysical parameters using ML models, which enhances food security planning and resource management by enabling more informed decision-making by stakeholders such as farmers, policy planners as well as researchers.

Tahiti Population (Re)Estimates and Ideologies

The question of Tahiti population at contact was first addressed by assessing the reliability of early and later estimates, and then by a retrodiction based on the first reliable censuses and data on trends. Cook’s famous estimate of 204,000 Tahitians, affected by a wrong number of districts, has been reworked based on his own and the Forsters’ observations, using coast length and the age and sex structure of Tahiti population at contact in relation to infanticide, resulting in a range of 156,000-188,000 persons in 1774, before Boenechea’s flu in 1772 and the 1774 flu epidemics. The discrepancies between Wilson’s 16,050 and other missionaries’ estimate of 50,000 as of 1797 are puzzling, but the 1829-1830 missionary estimates were strongly affected by Tahitians’ resistance to Christianization. The twentieth century estimates are affected by a political context related to colonization - decolonisation, leading to the fact that researchers favour low estimates. McArthur uses missionary data, assuming that baptisms are births, although before the conversion was completed, baptisms included adults and children of all ages. She bases her estimate of 30,000 Tahitians at contact in 1767 on Wilson’s number of 16,050, who applied the size of nuclear households, 6 persons, to clans. Oliver followed her, however not excluding numbers twice as high or more. Our retrodiction on the 1848 and 1881 censuses, based on qualitative missionary reports on sanitary situation and data of Rarotonga that experienced similar events as Tahiti, yield, with conservative assumptions, 180,000 persons at contact, showing consistent results with early navigators’ estimates. My own PhD experience in the late 1980s shows that scholars’ re-estimates were constrained, directly or indirectly, by political considerations related to the guilt syndrome and colonialist ideology. This appears in the frequent contradictions found in their works to support low estimates in order to reduce the decline, despite evidence of dramatic epidemics and a steady decline extending almost throughout the 19th century. The same attitude of denial is found in some missionary, administrative and medical reports, particularly on the Marquesas in the late 19th century. Colonisation was associated with various ideologies, related to anthropology’s early attempts based on theories of evolutionism, functionalism and structuralism, occasionally resulting in fictions such as “primitive thinking”, “ahistorical societies” and “collective unconscious”, that were part of the justification of colonisation and its mistakes.

Evaluation of sonographic and clinical measures in early versus late third trimester for birth weight prediction.

To evaluate the optimal timing for fetal weight estimation during the third trimester. This retrospective cohort study involved fetal weight estimations from both early (28+0-36+6 weeks) and late (37+0 weeks and beyond) third trimester. These estimations were converted to predicted birth weights using the gestation-adjusted projection formula. Birth weight predictions were compared with actual birth weights, to identify the most effective timing for weight prediction. The study included 3549 cases, revealing mean percentage errors (MPE) of -3.69% for early sonographic assessments, -2.5% for late sonographic assessments, and -1.9% for late clinical assessments. A significant difference was found between early and late sonographic estimations (P < 0.001), whereas late sonographic and clinical assessments did not differ significantly (P = 0.771). Weight predictions for fetuses below the 10th and above the 90th centiles were less accurate than for those within the 10th-90th centiles (P < 0.001). In women with obesity, late clinical estimations were less precise (MPE of -5.85) compared with non-obese women (MPE of -1.66, P < 0.001). For women with diabetes, early sonographic estimations were more accurate (MPE of -1.31) compared with non-diabetic patients (MPE of -3.94, P < 0.001) though this difference did not persist later in pregnancy. Sonographic and clinical weight predictions in the late third trimester were more accurate than earlier third-trimester sonographic assessments, hence continuous follow up and assessments closer to term are important. In women with diabetes, no adjustments in weight prediction methods are necessary. Accurately predicting birth weights for abnormally small or large fetuses remains challenging, indicating the need for improved screening and diagnostic strategies.

Remote Prediction of Soybean Yield Using UAV-Based Hyperspectral Imaging and Machine Learning Models

Early soybean yield estimation has become a fundamental tool for market policy and food security. Considering a heterogeneous crop, this study investigates the spatial and spectral variability in soybean canopy reflectance to achieve grain yield estimation. Besides allowing crop mapping, remote sensing data also provide spectral evidence that can be used as a priori knowledge to guide sample collection for prediction models. In this context, this study proposes a sampling design method that distributes sample plots based on the spatial and spectral variability in vegetation spectral indices observed in the field. Random forest (RF) and multiple linear regression (MLR) approaches were applied to a set of spectral bands and six vegetation indices to assess their contributions to the soybean yield estimates. Experiments were conducted with a hyperspectral sensor of 25 contiguous spectral bands, ranging from 500 to 900 nm, carried by an unmanned aerial vehicle (UAV) to collect images during the R5 soybean growth stage. The tests showed that spectral indices specially designed from some bands could be adopted instead of using multiple bands with MLR. However, the best result was obtained with RF using spectral bands and the height attribute extracted from the photogrammetric height model. In this case, Pearson’s correlation coefficient was 0.91. The difference between the grain yield productivity estimated with the RF model and the weight collected at harvest was 1.5%, indicating high accuracy for yield prediction.

Role of FIRE-MADE FI for diagnosing frailty in central rural India and its comparison with LASA FI.

An aging population ultimately has deprived physiological stores making them vulnerable for development of a syndrome called frailty, which presents with a cumulative decrease in tolerance, immunity, vision, balance, organ functions, health, and independent living. All these result in rising prevalence of frailty and its components, along with burden of disease, dependence, and health care cost. Thus, early estimation and assessment and interventions to correct it mark the mile stone in geriatric medicine. The present study was conducted with an aim to compare and correlate FIRE-MADE (Frailty Index in Rural Elderly - Mental status, Activities of daily living, Depression, and Events) FI (frailty index) with LASA (Longitudinal Aging Study Amsterdam) FI in central rural India's geriatric population. A cross-sectional study for assessment of frailty in the geriatric population of central rural India, reporting to the medicine department of a tertiary hospital, situated at Wardha district, by using FIRE-MADE and LASA FI was undertaken. Their scores were compared. The efficiency of FIRE-MADE FI in comparison with LASA FI was calculated. A frailty threshold of ≥ 0.25 is considered for diagnosing frailty. Standard descriptive and inferential statistics were used to evaluate all parametric and non-parametric data. Out of 250 geriatric people, 224 (89.6%) were frail according to LASA FI and 204 (81.6%) were frail according to FIRE-MADE FI. As compared to LASA FI, FIRE-MADE FI was 91.07% sensitive and 73.08% specific, with a positive predictive value of 96.68% and a diagnostic accuracy of 89.20%. The Indian rural population of central India has high prevalence of frailty. FIRE-MADE FI can be used as a potential, effective, and validated tool for early diagnosing and management of frailty. Among the parameters of FIRE-MADE FI, IHD was the most important contributing factor for development of frailty, followed by cognitive impairment, polypharmacy, and remaining factors mentioned in FIRE-MADE FI.

Association of Serum Uric Acid Level and Thyroid Function in Chronic Kidney Disease: A Hospital-Based Cross-Sectional Study From Northeast India.

Although the prevalence of thyroid dysfunction and hyperuricemia are independently high in patients with chronic kidney disease (CKD), there are limited data showing the association of serum uric acid and thyroid function in those with CKD. The aim of this study was to observe the alteration of both the serum uric acid level and thyroid function in CKD patients and to find the association between both. This observational cross-sectional study was conducted in a tertiary care hospital over a period of one year in Northeast India. A total of 50 CKD patients were enrolled. Their demographic profiles were studied. Serum urea, creatinine, thyroid-stimulating hormone (TSH), total triiodothyronine (TT3), free triiodothyronine (FT3), total tetraiodothyronine (TT4), and free tetraiodothyronine (FT4) levels were measured to establish the correlation of serum uric acid along with each of the parameters separately. A p-value of <0.05 was considered statistically significant. In the CKD patients studied, serum uric acid exhibited positive correlations with serum creatinine (p = 0.001, r = 0.67), serum urea (p = 0.001, r = 0.69), and serum TSH levels (p = 0.001, r = 0.5). Conversely, serum uric acid showed negative correlations with serum TT4 (p = 0.001, r = -0.74), TT3 (p = 0.001, r = -0.6), FT4 (p = 0.001, r = -0.53), and FT3 (p = 0.001, r = -0.58) levels. There was a significant positive correlation between uric acid and TSH levels in CKD patients. Thus, early estimation of both parameters should be considered in CKD patients.

Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life

Preterm birth (PTB) remains a global health concern, impacting neonatal mortality and lifelong health consequences. Traditional methods for estimating PTB rely on electronic health records or biomedical signals, limited to short-term assessments in clinical settings. Recent studies have leveraged wearable technologies for in-home maternal health monitoring, offering continuous assessment of maternal autonomic nervous system (ANS) activity and facilitating the exploration of PTB risk. In this paper, we conduct a longitudinal study to assess the risk of PTB by examining maternal ANS activity through heart rate (HR) and heart rate variability (HRV). To achieve this, we collect long-term raw photoplethysmogram (PPG) signals from 58 pregnant women (including seven preterm cases) from gestational weeks 12–15 to three months post-delivery using smartwatches in daily life settings. We employ a PPG processing pipeline to accurately extract HR and HRV, and an autoencoder machine learning model with SHAP analysis to generate explainable abnormality scores indicative of PTB risk. Our results reveal distinctive patterns in PTB abnormality scores during the second pregnancy trimester, indicating the potential for early PTB risk estimation. Moreover, we find that HR, average of interbeat intervals (AVNN), SD1SD2 ratio, and standard deviation of interbeat intervals (SDNN) emerge as significant PTB indicators.

Which orthodontic articles are accessed online the most? Exploring article usage metrics along with citations and altmetrics.

To investigate the current state of article usage metrics in orthodontics. Out of all orthodontic journals listed in Journal Citation Reports 2022, the European Journal of Orthodontics and The Angle Orthodontist fulfilled the inclusion criteria. All journal issues published in 2021 were scrutinized for original research articles and systematic reviews/meta-analyses, and the following features were collected: article type, subject, title, number of words and authors, Altmetric Attention Score (AAS), X (formerly Twitter) posts, and Mendeley reads. Article citations, number of publications, and h-index of the last authors were retrieved from Web of Science (WoS). 181 articles were considered eligible. The median number of views and downloads of included articles was 1296 (range: 355-10,233) and 793 (range: 167-3629). Page views, downloads, and total views were significantly correlated with WoS citations (rho > 0.345; P < .001). There was no correlation between usage metrics, AAS, X posts, and Mendeley reads. Number of downloads were significantly higher in studies dealing with new technologies, and where the last author had 1-40 publications or an h-index of 0-30. Page views, downloads, and total views were positively correlated with WoS citations and, therefore, may serve as an early estimate of future citations. Significant variations in article downloads may be expected in relation to article subject, scientific productivity, and impact of the last authors.

Electric vehicle supply equipment monitoring and early fault detection through autoencoders

This paper presents a novel approach to detecting anomalies in Electric Vehicle charging unit power profiles using a combination of Autoencoders with LSTM techniques. This study presents a robust methodology, combining the two Machine Learning techniques, for early fault estimation in a real-world case study. The proposed methodology offers significant advantages over existing methods by providing a more comprehensive analysis of anomalous trends. To validate the effectiveness of the proposed methodology, the authors tested it on real Electric Vehicles charging power curves provided by an Italian Distribution System Operator recorded on a historical database and compared the performances with the ones of a traditional anomaly detection technique. The results of the study, tested on Electric Vehicles Supply Equipment or charging stations, demonstrate that the proposed approach is highly effective in detecting anomalous trends in Electric Vehicles charging profiles.

Has genomic selection fulfilled its promise?

Abstract Genomic selection has been increasingly implemented since its initial description in 2001. The initial focus of activity was in the dairy industry in the developed world but it has now been adopted in all major livestock species, and adoption is underway in most agronomically important plants. The promise of genomic selection includes increased accuracy of estimation of genetic merit, earlier estimation of genetic merit, the capacity to make improvements in traits previously less tractable to genetic change, and the capacity to estimate genetic merit in animals without phenotypic records. Each of these benefits has been realised in practice, with the significance of the opportunity varying with species. At the same time, ease of adoption and realisation of benefits has varied, based on three broad parameters: the availability of suitable (and sufficiently cheap) genotyping tools, the prior extent of systematic recording of phenotypes, and the use of estimates of genetic merit in selection. The effectiveness of genomic selection depends on the existence and composition of a genomic reference population, and this is the most significant challenge for any industry, breed or breeding program seeking to achieve the available benefits. The challenge is two-fold: first, to ensure that sufficient of the right phenotypes are recorded on the right animals, and second, flowing directly from the first, is to fund the phenotyping effort – and challenges aspects are ongoing. “Right” in the context of both phenotypes and animals means most informative, or relevant to the breeding objective or goal. No standard approach has been outlined for addressing these challenges, but the review presents some simple conceptual thinking that can assist. Genomic selection also heightens the importance of ensuring maintenance of genetic diversity, and of monitoring changes in both selected and non-selected traits. Genomic selection has fulfilled its promise where applied effectively and is increasing scope for genetic improvement both in rate of progress and trait coverage. At the same time, its dependencies must be addressed in order to the potential benefits to be realised, and this will likely require new organisational and funding arrangements in many situations.