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Abstract Background Computerized posturography obtained in standing conditions has been applied to classify fall risk for older adults or disease groups. Combining machine learning (ML) approaches is superior to traditional regression analysis for its ability to handle complex data regarding its characteristics of being high-dimensional, non-linear, and highly correlated. The study goal was to use ML algorithms to classify fall risks in community-dwelling older adults with the aid of an explainable artificial intelligence (XAI) approach to increase interpretability. Methods A total of 215 participants were included for analysis. The input information included personal metrics and posturographic parameters obtained from a tracker-based posturography of four standing postures. Two classification criteria were used: with a previous history of falls and the timed-up-and-go (TUG) test. We used three meta-heuristic methods for feature selection to handle the large numbers of parameters and improve efficacy, and the SHapley Additive exPlanations (SHAP) method was used to display the weights of the selected features on the model. Results The results showed that posturographic parameters could classify the participants with TUG scores higher or lower than 10 s but were less effective in classifying fall risk according to previous fall history. Feature selections improved the accuracy with the TUG as the classification label, and the Slime Mould Algorithm had the best performance (accuracy: 0.72 to 0.77, area under the curve: 0.80 to 0.90). In contrast, feature selection did not improve the model performance significantly with the previous fall history as a classification label. The SHAP values also helped to display the importance of different features in the model. Conclusion Posturographic parameters in standing can be used to classify fall risks with high accuracy based on the TUG scores in community-dwelling older adults. Using feature selection improves the model’s performance. The results highlight the potential utility of ML algorithms and XAI to provide guidance for developing more robust and accurate fall classification models. Trial registration Not applicable

Abstract Neutral comet assay has been available for two decades to evaluate sperm double-strand breaks (DSBs). However, its clinical usability is limited due to its complex and time-consuming procedure, as well as the lack of a standardized scoring system. The aim of this study was to: develop a rapid diagnostic method for DSBs, Sperm DNA Fragmentation Releasing Assay (SDFR), and explore the association between DSBs and reproductive outcomes. We pioneered the use of polyacrylamide (PA) for embedding sperm chromatin and optimized the porosity of PA to be between 10 and 13%. The refined PA network allowed the trapping of DSBs, which dispersed halo on an immunological slide; in contrast, intact chromatin failed to develop a halo. A strong correlation was showed between reproducible values obtained from SDFR and neutral comet assay. SDFR were responsive to dose-/time-dependent simulated DSBs, indicating high sensitivity and specificity. Furthermore, we conducted a retrospective study of couples with embryonic aneuploidy screening, and recording DSB profiles of the male partners. Our findings revealed that DSB enabled to predict embryonic aneuploidy whereas basic semen parameters did not. In conclusion, SDFR offers a rapid and user-friendly approach for evaluating DSBs, with potential implications for predictive healthcare in reproductive medicine.

Background: To demonstrate an interpretable machine learning (ML) model for a clinical prediction of corifollitropin alfa protocol. Methods: The retrospective study involved 1,221 cycles from 1,180 patients undergoing corifollitropin alfa protocol with oocyte retrieval events from a single in vitro fertilization (IVF) center. The ML models were assigned to the following tasks, which are the dosage of corifollitropin alfa, trigger type, the dosage of recombinant FSH (rFSH), the dosage of recombinant LH (rLH), the duration between the follow-up visit (FUV), and oocyte retrieval. Interpretable SHapley Additive exPlanations (SHAP) were selected to analyze the input features. The ranking of the prediction powers from each input feature reveals the level of contribution to the model. Result(s): Two series of interpretable ML models were developed to predict classification tasks and regression tasks. The areas under ROC (AUC) for predicting the dosage of corifollitropin alfa and trigger type were 0.933 ([Formula: see text] CI 0.907–0.958) and 0.891 ([Formula: see text] CI 0.864–0.918), while accuracies were 0.944 and 0.904. The mean absolute errors (MAEs) that predict the dosage of rFSH, the dosage of rLH, and the duration between the FUV and oocyte retrieval were 97.08 IU (rFSH), 105.61 IU (rLH), and 0.45 days. Conclusions: The study demonstrates a set of interpretable ML models predicting tasks involved in corifollitropin alfa protocol. The potential for the clinical application is to provide consistency in corifollitropin protocol adjustments.

Noninvasive preimplantation genetic testing for aneuploidy (niPGT-A) using cell-free DNA (cfDNA) present in the spent blastocyst medium (SBM) of human blastocysts has emerged as an alternative for classical preimplantation genetic testing for aneuploidy (PGT-A). The major benefit is to avoid possible damage to the trophectoderm and subsequently increase the implantation potential of the embryo. niPGT-A provides more information including the ploidy state of both trophectoderm and inner cell mass compared to invasive PGT-A in which only a few trophectoderm cells are assessed. This study was to report our first successful experience solely using niPGT-A to select embryos to transfer resulting in a successful delivery. This practical application is believed to be the first case report in Taiwan and can serve as an innovative model for other clinics to apply niPGT-A.

Motion: For The Outdated Significance of Morphological Assessment in Embryo Selection and the Rise of Advanced Technologies in Reproductive Medicine This symposium lecture presents a compelling debate, shedding light on the diminishing relevance of traditional static morphological assessment as a primary criterion for embryo. The speaker aims to challenge the long-standing reliance on morphological evaluation and advocate for the integration of cutting-edge technologies as a more reliable and objective means of embryo quality assessment. During the debate, the speaker will examine the limitations of morphological assessment, acknowledging its subjective nature, potential bias, and lack of comprehensive insight into embryonic development. The traditional morphological assessment based solely on visual appearance may not adequately predict an embryo’s true potential for successful implantation and healthy pregnancy. A variety of available new technologies will be highlighted during the lecture, showcasing alternatives to morphological embryo assessment. Attendees will gain insight into how these innovative methods can offer more objective and precise measures of embryo quality, enhancing the success rates of assisted reproductive procedures. The symposium will foster an engaging environment, encouraging active participation and thoughtful discussions among attendees from the reproductive medicine community. Participants will have the opportunity to challenge prevailing notions and explore the implications of adopting new technologies in clinical practice. In summary, this debate aims to catalyze a paradigm shift in embryo evaluation, emphasizing the need to move beyond traditional morphological assessment towards embracing advanced technologies, to optimize embryo selection and lead to improved outcomes in IVF. Motion: Against Morphological Assessment of Embryos is Not Outdated In the era of new technologies and artificial intelligence, looking good has never been more important! On-time progression through specific events is requisite for human preimplantation development and provides important visual insights into the health and physiology of the embryo. The formation of 2 distinct pronuclei, the times and patterns of cleavage, and how the cells compact are all key markers of development. Following compaction, the formation of the blastocyst is not only a temporally regulated event, but also an energetically demanding process, one that reflects the metabolic activity (a biomarker of viability) of the embryo. Further, at the blastocyst stage it is possible to visualise and quantitate the formation of both the inner cell mass (ICM) and trophectoderm, with all parameters of blastocyst development linked to transfer outcomes. The development of time-lapse incubation, which can be described as embryo morphology over time, has led to the incorporation of several key morphological features to support embryo selection, further reflecting the significance of embryo morphology. Interestingly, AI algorithms used for embryo selection reflect predominantly those morphological parameters that are known to affect transfer outcomes, such as blastocyst quality. While only specific molecular tests can accurately determine the precise chromosomal composition of an embryo, key morphological features and events can also be used to tag those embryos at greatest risk of being genetically abnormal. In conclusion, key morphological developmental events indirectly inform us of the physiology of the embryo (degree of blastocyst expansion reflects metabolic activity for example), and hence this is why morphological grading systems (such as the Gardner Grade for blastocysts) have been shown to be effective in embryo selection for over the past 25 years, and further have proven to be as useful as AI or biomarker analysis for the majority of patients.

This talk is an engaging format to address crucial aspects of selecting the most suitable blastocyst for biopsy to optimize the success of PGT procedures and successful pregnancies. The talk commences with an overview of various blastocyst stages and relevant morphological characteristics, elucidating the critical developmental milestones and key aspects of an embryo must attain to be considered for biopsy. Special attention is given to the decision and timing of biopsy to ensure embryo survival and sampling outcome accuracy. This salon talk cultivates an interactive environment and sets the stage for the afternoon hands-on sessions which will further enrich the attendees’ understanding of the topic.

This lecture is a concise introduction designed to equip participants with essential knowledge and practical skills in performing embryo biopsy for PGT. The workshop emphasizes the significance of procedure and preparation which may impact the blastocyst biopsy and its role in selecting embryos. During the lecture, attendees are introduced to the fundamental steps of the blastocyst biopsy, starting with the optimal time frame for performing the biopsy, followed by techniques for safe and accurate zona drilling without compromising the embryo’s viability, as well as the importance of using a suitable biopsy medium that provides the necessary nutrients and support for the embryo during the biopsy. This lecture also delves into the selection and preparation of the biopsy dish, outlining the key factors to consider and practical tips on maintaining optimal conditions for the embryo within the biopsy dish and minimizing potential stress, to ensure successful outcomes. The instructor will share experiences and provide valuable insights into troubleshooting potential challenges that may arise during the biopsy process. Participants are encouraged to engage in interactive discussions and following hands-on sessions, allowing for a comprehensive understanding of the procedure, and fostering a collaborative learning environment. In conclusion, this lecture offers a valuable platform for embryologists to enhance their knowledge and skills in performing blastocyst biopsies with precision and confidence.

Early diagnosis, prioritization, screening, clustering, and tracking of patients with COVID-19, and production of drugs and vaccines are some of the applications that have made it necessary to use a new style of technology to involve, manage, and deal with this epidemic. Strategies backed by artificial intelligence (A.I.) and the Internet of Things (IoT) have been undeniably effective to understand how the virus works and prevent it from spreading. Accordingly, the main aim of this survey is to critically review the ML, IoT, and the integration of IoT and ML-based techniques in the applications related to COVID-19, from the diagnosis of the disease to the prediction of its outbreak. According to the main findings, IoT provided a prompt and efficient approach to tracking the disease spread. On the other hand, most of the studies developed by ML-based techniques aimed at the detection and handling of challenges associated with the COVID-19 pandemic. Among different approaches, Convolutional Neural Network (CNN), Support Vector Machine, Genetic CNN, and pre-trained CNN, followed by ResNet have demonstrated the best performances compared to other methods.

Detection of malignant lung nodules from Computed Tomography (CT) images is a significant task for radiologists. But, it is time-consuming in nature. Despite numerous breakthroughs in studies on the application of deep learning models for the identification of lung cancer, researchers and doctors still face challenges when trying to deploy the model in clinical settings to achieve improved accuracy and sensitivity on huge datasets. In most situations, deep convolutional neural networks are used for detecting the region of the main nodule of the lung exclusive of considering the neighboring tissues of the nodule. Although the accuracy achieved through CNN is good enough but this models performance degrades when there are variations in image characteristics like: rotation, tiling, and other abnormal image orientations. CNN does not store relative spatial relationships among features in scanned images. As CT scans have high spatial resolution and are sensitive to misalignments during the scanning process, there is a requirement of a technique which helps in considering spatial information of image features also. In this paper, a hybrid model named VCNet is proposed by combining the features of VGG-16 and capsule network (CapsNet). VGG-16 model is used for object recognition and classification. CapsNet is used to address the shortcomings of convolutional neural networks for image rotation, tiling, and other abnormal image orientations. The performance of VCNeT is verified on the Lung Image Database Consortium (LIDC) image collection dataset. It achieves higher testing accuracy of 99.49% which is significantly better than MobileNet, Xception, and VGG-16 that has achieved an accuracy of 98, 97.97, and 96.95%, respectively. Therefore, the proposed hybrid VCNet framework can be used for the clinical purpose for nodule detection in lung carcinoma detection.

With the global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, several reports highlight its effects on pregnant women. Based on scant available data, vertical transmission is considered unlikely. We present here a preterm neonate born to a critically ill mother with SARV-CoV-2 with early evidence of infection with a positive reverse transcription polymerase chain reaction on day 1. Lack of parental contact prior to testing and strict adherence to recommended airborne precautions perinatally suggest vertical transmission of infection. Critical maternal illness and medications may have contributed to the need for extensive resuscitation at birth and highlight the importance of close fetal monitoring. Infant lacked immunoglobulin G antibody response by 3 weeks, presumably secondary to mild clinical course and prematurity. Effects of SARS-CoV-2 in preterm infants, their antibody response and potential for asymptomatic carriage remain uncertain.

Abstract Background The initiation of primate embryo invasion into the endometrium and the formation of the placenta from trophoblasts, fetal mesenchyme, and vascular components are essential for the establishment of a successful pregnancy. The mechanisms which direct morphogenesis of the chorionic villi, and the interactions between trophectoderm-derived trophoblasts and the fetal mesenchyme to direct these processes during placentation are not well understood due to a dearth of systems to examine and manipulate real-time primate implantation. Here we describe an in vitro three-dimensional (3-D) model to study implantation which utilized IVF-generated rhesus monkey embryos cultured in a Matrigel explant system. Methods Blastocyst stage embryos were embedded in a 3-D microenvironment of a Matrigel carrier and co-cultured with a feeder layer of cells generating conditioned medium. Throughout the course of embryo co-culture embryo growth and secretions were monitored. Embedded embryos were then sectioned and stained for markers of trophoblast function and differentiation. Results Signs of implantation were observed including enlargement of the embryo mass, and invasion and proliferation of trophoblast outgrowths. Expression of chorionic gonadotropin defined by immunohistochemical staining, and secretion of chorionic gonadotropin and progesterone coincident with the appearance of trophoblast outgrowths, supported the conclusion that a trophoblast cell lineage formed from implanted embryos. Positive staining for selected markers including Ki67, MHC class I, NeuN, CD31, vonWillebrand Factor and Vimentin, suggest growth and differentiation of the embryo following embedding. Conclusions This 3-D in vitro system will facilitate further study of primate embryo biology, with potential to provide a platform for study of genes related to implantation defects and trophoblast differentiation.

The Markov jump systems (MJSs) are a special case of parametric switching system. However, we know that time delay inevitably exists in many practical systems, and is known as the main source of efficiency reduction, and even instability. In this paper, the stochastic stable control design is discussed for time delay MJSs. In this regard, first, the problem of modeling of MJSs and their stability analysis using Lyapunov-Krasovsky functions is studied. Then, a state-feedback controller (SFC) is designed and its stability is proved on the basis of the Lyapunov theorem and linear matrix inequalities (LMIs), in the presence of polytopic uncertainties and time delays. Finally, by various simulations, the accuracy and efficiency of the proposed methods for robust stabilization of MJSs are demonstrated.

In this study, the challenges of the controller design of a class of Uncertain Switched Neutral Systems (USNSs) in the presence of discrete, neutral, and time-varying delays are considered by using a robust observer-based control technique. The cases where the uncertainties are normbounded and time-varying are emphasized in this research. The adopted control approach reduces the prescribed level of disturbance input on the controlled output in the closed-loop form and the robust exponential stability of the control system. The challenge of parametric uncertainty in USNSs is solved by designing a robust output observer-based control and applying the Yakubovich lemma. Since the separation principle does not generally hold in this research, the controller and observer cannot be designed separately, sufficient conditions are suggested. These conditions are composed of applying the average dwell time approach and piecewise Lyapunov function technique in terms of linear matrix inequalities, which guarantees robust exponential stability of the observer-based output controller. Finally, two examples are given to determine the effectiveness of the proposed method.

In this study, a novel fast terminal sliding mode control technique based on the disturbance observer is recommended for the stabilization of underactuated robotic systems. The finite time disturbance observer is employed to estimate the exterior disturbances of the system and develop the finite time control law. The proposed controller can regulate the state trajectories of the underactuated systems to the origin within a finite time in the existence of external disturbances. The stability analysis of the proposed control scheme is verified via the Lyapunov stabilization theory. The designed control law is enough to drive a switching surface achieving the fast terminal sliding mode against severe model nonlinearities with large parametric uncertainties and external disturbances. Illustrative simulation results and experimental validations on a cart-inverted pendulum system are provided to display the success and efficacy of the offered method.

In this study, the observer-based state feedback stabilizer design for a class of chaotic systems in the existence of external perturbations and Lipchitz nonlinearities is presented. This manuscript aims to design a state feedback controller based on a state observer by the linear matrix inequality method. The conditions of linear matrix inequality guarantee the asymptotical stability of the system based on the Lyapunov theorem. The stabilizer and observer parameters are obtained using linear matrix inequalities, which make the state errors converge to the origin. The effects of the nonlinear Lipschitz perturbation and external disturbances on the system stability are then reduced. Moreover, the stabilizer and observer design techniques are investigated for the nonlinear systems with an output nonlinear function. The main advantages of the suggested approach are the convergence of estimation errors to zero, the Lyapunov stability of the closed-loop system and the elimination of the effects of perturbation and nonlinearities. Furthermore, numerical examples are used to illustrate the accuracy and reliability of the proposed approaches.

In recent years, due to the several advantages of permanent magnet synchronous generator (PMSG), the number of wind farms utilizing this technology has been significantly grown. The determination of the failure mechanism in these devices is the major challenge which has been addressed in many studies. Particularly, response to grid code compliance by wind power in the voltage drop situation needs to be comprehensively analyzed. In this paper, a small signal model of a PMSG-based wind turbine for low voltage ride-through (LVRT) and suitable for stability and artificial intelligent studies is presented. Accordingly, the generator side converter controls the dc-link voltage, and the maximum power point tracking is performed by the grid side converter. Given the proposed model, the speed of the simulation for stability analysis studies can be significantly increased by intelligent methods. Furthermore, the simplified approach can be achieved for calculating the optimal coefficients of the proportionality-integral controller by intelligent methods in a short time. By simulating the proposed small-signal model and comparing it with the block-based simulation in MATLAB/SIMULINK software, the appropriate accuracy and efficiency of the proposed model are confirmed.

Summary: Background: Outbreaks of unexplained illness frequently remain under-investigated. In India, outbreaks of an acute neurological illness with high mortality among children occur annually in Muzaffarpur, the country's largest litchi cultivation region. In 2014, we aimed to investigate the cause and risk factors for this illness. Methods: In this hospital-based surveillance and nested age-matched case-control study, we did laboratory investigations to assess potential infectious and non-infectious causes of this acute neurological illness. Cases were children aged 15 years or younger who were admitted to two hospitals in Muzaffarpur with new-onset seizures or altered sensorium. Age-matched controls were residents of Muzaffarpur who were admitted to the same two hospitals for a non-neurologic illness within seven days of the date of admission of the case. Clinical specimens (blood, cerebrospinal fluid, and urine) and environmental specimens (litchis) were tested for evidence of infectious pathogens, pesticides, toxic metals, and other non-infectious causes, including presence of hypoglycin A or methylenecyclopropylglycine (MCPG), naturally-occurring fruit-based toxins that cause hypoglycaemia and metabolic derangement. Matched and unmatched (controlling for age) bivariate analyses were done and risk factors for illness were expressed as matched odds ratios and odds ratios (unmatched analyses). Findings: Between May 26, and July 17, 2014, 390 patients meeting the case definition were admitted to the two referral hospitals in Muzaffarpur, of whom 122 (31%) died. On admission, 204 (62%) of 327 had blood glucose concentration of 70 mg/dL or less. 104 cases were compared with 104 age-matched hospital controls. Litchi consumption (matched odds ratio [mOR] 9·6 [95% CI 3·6 – 24]) and absence of an evening meal (2·2 [1·2–4·3]) in the 24 h preceding illness onset were associated with illness. The absence of an evening meal significantly modified the effect of eating litchis on illness (odds ratio [OR] 7·8 [95% CI 3·3–18·8], without evening meal; OR 3·6 [1·1–11·1] with an evening meal). Tests for infectious agents and pesticides were negative. Metabolites of hypoglycin A, MCPG, or both were detected in 48 [66%] of 73 urine specimens from case-patients and none from 15 controls; 72 (90%) of 80 case-patient specimens had abnormal plasma acylcarnitine profiles, consistent with severe disruption of fatty acid metabolism. In 36 litchi arils tested from Muzaffarpur, hypoglycin A concentrations ranged from 12·4 μg/g to 152·0 μg/g and MCPG ranged from 44·9 μg/g to 220·0 μg/g. Interpretation: Our investigation suggests an outbreak of acute encephalopathy in Muzaffarpur associated with both hypoglycin A and MCPG toxicity. To prevent illness and reduce mortality in the region, we recommended minimising litchi consumption, ensuring receipt of an evening meal and implementing rapid glucose correction for suspected illness. A comprehensive investigative approach in Muzaffarpur led to timely public health recommendations, underscoring the importance of using systematic methods in other unexplained illness outbreaks. Funding: US Centers for Disease Control and Prevention.

In Device-to-Device (D2D) communications, the first step is to find all of the neighboring peers in the network by performing a peer discovery process. Most previous studies use the social behaviors of the users to adjust the sending rates of the peer discovery messages (i.e., beacons) under the constraint of consumed power for increasing the Peer Discovery Ratio (PDR). However, these studies do not consider the potential for energy harvesting, which allows for the User Equipments (UEs) to procure additional power within charging areas. Accordingly, this paper proposes an Energy-Ratio Rate Decision (ERRD) algorithm that comprises three steps, namely Social Ratio Allocation (SRA), Energy Ratio Allocation (ERA), and Beacon Rate Decision (BRD). The SRA step determines the allocated power quantum for each UE from the total budget power based on the social behavior of the UE. The ERA step then adjusts this allocated power quantum in accordance with the power that is harvested by the UE. Finally, the BRD step computes the beacon rate for the UE based on the adjusted power quantum. The simulation results show that ERRD outperforms the previously-reported Social-Based Grouping (SBG) algorithm by 190% on the PDR for a budget power of one watt and 8% for a budget power of 20 watts.