Miracle-dz/UniFAR-FTUs huggingface dataset:
The Study of Abnormal Uterine Bleeding, Estrogen And Progesterone Receptor Expression In Cases of Endometrial Hyperplasia And Neoplasia

AUB is the very common condition of patients in gynaecological OPD.The aim of study is to evaluate the histopatholological patterns of endometrium and to correlate it with different age groups and study ER and PR receptor expression in them. Meterials and Methods: It is a reterograde study from 1 st jan 2021 to 30th june 2022 in the Department of Pathology ,T S Misra Medical College and Hospital in collaboration with King George’s Medical University. The study includes around 87 patients in the age group 30 to 80 yrs. All the relevant cinical details and investigations was obtained from patients record.The study includes AUB cases of histopathologicaly diagnosed endometrial pathology . Those cases with inadequate sample or with history of pregnancy was excluded from study.The specimens were routinely processed and Hand E stained slides are studied for endometrial patterns and their frequencies and percentages were classified age group wise.ER and PR positivity percentage was determined in different patterns with immunohistochemistry. Results: Number of patients falling in different age groups were noted and the result concluded was maximum number of AUB cases were in 40 -50 yrs in perimenopausal and post menopausal age group less number was in the reproductive age group Number of patients in different histopathological pattern was also noted. Endometrial patterns included non malignant( 65/87,) proliferative (%) followed by secretary (%) and endometrial hyperplasia without atypia( %),simple hyperplasia with atypia (%) ,complex hyperplasia with atypia (%) and malignant(22/87)endometrial carcinoma (25.3%). ER and PR positivity rate was 95.4% and 96.6% respectively in whole study. ER and PR expression in each pattern was also studied. Conclusion; Histopathological examination plays a critical role in early diagnosis of endometrial pathology and to plan appropriate gynaecological management. Knowledge of ER ,PR expression is helpful in determining the prognosis for survival and hormone replacement therapy.
Research Progress of Early Non-motor Symptoms of Parkinson's Disease

: Parkinson's disease (PD) is a chronic central neurodegenerative disease with high morbidity, prevalence and disability rate.PD is generally divided into motor symptoms and non-motor symptoms, of which motor symptoms include myotonia, static tremor, slow movement and postural instability. Constipation, olfactory disorders, sleep disorders, anxiety, depression and cognitive disorders are the main manifestations of non-motor symptoms. At present, the research on the early non-motor symptoms of PD is relatively scattered. Now, the literature and data on the early non-motor symptoms of Parkinson's disease in recent years are summarized and discussed systematically, and some effective preventive measures are proposed in order to provide new ideas and methods for the follow-up clinical treatment.
Characterization of the tilt (tt) phenotype in Drosophila melanogaster

In the early 20th century, Calvin Bridges and Thomas Morgan identified a number of spontaneous mutations that displayed visible phenotypes in adult flies and subsequent analysis of these mutations over the past century have provided fundamental insights into subdisciplines of biology such as genetics, developmental, and cell biology. One of the mutations they identified in 1915 was named tilt ( tt ) and was described by Bridges and Morgan as having two visible phenotype characteristics in the wing. The wings were “held out at a wider angle from the body” and had a break in wing vein L3. Subsequent analysis of the tilt phenotype identified another phenotype: the wings were missing a varying number of campaniform sensilla on L3. Though Bridges and Morgan provided an ink drawing of the wing posture phenotype, only the vein and campaniform sensilla loss images have been published. Here we confirm and document the tilt phenotypes that have been previously described. We also show the penetrance of these phenotypes: the vein break and the distinct outward wing posture have decreased since its discovery.
N-methyl-D-aspartate receptor regulates the circadian clock in megakaryocytic cells and impacts cell proliferation through BMAL1

Abstract Peripheral circadian clocks control cell proliferation and survival, but little is known about their role and regulation in megakaryocytic cells. N-methyl-D-aspartate receptor (NMDAR) regulates the central clock in the brain. The purpose of this study was to determine whether NMDAR regulates the megakaryocytic cell clock and whether the megakaryocytic clock regulates cell proliferation and cell death. We found that both the Meg-01 megakaryocytic cell line and native murine megakaryocytes expressed circadian clock genes. Megakaryocyte-directed deletion of Grin1 in mice caused significant disruption of the circadian rhythm pathway at the transcriptional level and increased expression of BMAL1 at the protein level. Similarly, both pharmacological (MK-801) and genetic (GRIN–/–) inhibition of NMDAR in Meg-01 cells in vitro resulted in widespread changes in clock gene expression including increased expression of BMAL1, the core clock transcription factor. BMAL1 overexpression reduced Meg-01 cell proliferation and altered the time-dependent expression of the cell cycle regulators MYC and WEE1, whereas BMAL1 knockdown led to increased cell death in Meg-01-GRIN1–/– cells. Our results demonstrate that NMDAR regulates the circadian clock in megakaryocytic cells and that the circadian clock component BMAL1 contributes to the control of Meg-01 cell proliferation and survival. Plain Language Summary Why was the study done? Time of day impacts platelet function and production. Our bodies are informed about external time by the brain, but all other cells including platelet precursors megakaryocytes also have their own clock. Circadian disruption contributes to disorders such as thrombosis (e.g. stroke and heart attacks) and blood cancers (e.g. leukemia). However, the mechanism of circadian control in megakaryocytes remains poorly elucidated. N-methyl-D-aspartate receptor (NMDAR) regulates circadian clock in the brain and is expressed in megakaryocytes, thus we hypothesized that NMDAR also regulates circadian clock in megakaryocytic cells. What did the researchers do and find? We used Meg-01 cell line, its genetically modified version with deleted NMDAR, and data from murine megakaryocytes to determine the NMDAR impact on the clock in these cells. We found that megakaryocytic cells had all the machinery required to maintain their own circadian clock. NMDAR deletion disrupted circadian clock in megakaryocytic cells. Manipulation of circadian clock in Meg-01 cells (through BMAL1 overexpression) impacted proliferation and survival of cells. What do the results mean? Megakaryocytic cells have their own circadian clock regulated by NMDAR, and its disruption impacts cell proliferation. What is the objective influence on the wider field? It is possible that deregulated function of megakaryocytes that occurs in disease can be corrected through the modulation of NMDAR or other components of the cellular circadian clock, thus further studies to develop and test such strategies in disease models are warranted.
Five Precision Points Synthesis of Spatial RRR Manipulators Using Interval Analysis

In this paper, the geometric design problem of serial-link robot manipulators with three revolute (R) joints is solved for the first time using an interval analysis method. In this problem, five spatial positions and orientations are defined and the dimensions of the geometric parameters of the 3-R manipulator are computed so that the manipulator will be able to place its end-effector at these pre-specified locations. Denavit and Hartenberg parameters and 4×4 homogeneous matrices are used to formulate the problem and obtain the design equations and an interval method is used to search for design solutions within a predetermined domain. At the time of writing this paper, six design solutions within the search domain and an additional twenty solutions outside the domain have been found.Copyright © 2002 by ASME
Binary Classification of Heart Failures Using k-NN with Various Distance Metrics

Magnetocardiography is a sensitive technique of measuring low magnetic fields generated by heart functioning, which is used for diagnostics of large number of cardiovascular diseases. In this paper, k -nearest neighbor ( k-NN ) technique is used for binary classification of myocardium current density distribution maps ( CDDM ) from patients with negative T-peak, male and female patients with microvessels (diffuse) abnormalities and sportsmen, which are compared with normal control subjects. Number of neighbors for k - NN classifier was selected to obtain highest classification characteristics. Specificity, accuracy, precision and sensitivity of classification as functions of number of neighbors in k - NN are obtained for classification with several distance measures: Mahalanobis, Cityblock, Eucleadian and Chebyshev. Increase of the accuracy of classification for all groups up to 10% was obtained using Cityblock distance metric in binary k-NN classifier with 19 - 27 neighbors, comparing to other metrics. Obtained results are acceptable for further patient’s state evaluation.
Abnormal thyroid function: an unusual presentation of pituitary stalk interruption syndrome

Summary An 11-year-old girl with past medical history of septic shock and multi-organ failure at age 5 presented to her primary care doctor with concern for pallor of the lips. Laboratory studies demonstrated low free thyroxine (T4) and normal thyroid-stimulating hormone (TSH). A referral to endocrinology was made where the patient was evaluated, and laboratory evaluation was repeated. The patient was asymptomatic and clinically euthyroid with a height consistent with her mid-parental height and was in mid- to late-puberty. The repeated laboratory evaluation demonstrated a pattern suggestive of primary hypothyroidism with low free T4 and an elevated TSH. However, the magnitude of elevation of TSH was less than expected, given the degree of lowering of free T4; therefore, central hypothyroidism was considered. Workup was initiated, and laboratory studies and MRI imaging confirmed an underlying diagnosis of panhypopituitarism in the setting of pituitary stalk interruption syndrome. Learning points Pituitary stalk interruption syndrome is a rare but important cause of panhypopituitarism. Central hypothyroidism should be suspected in patients with low free thyroxine with an inappropriate degree of elevation of thyroid-stimulating hormone. Workup of central hypothyroidism should include multi-pituitary hormone assessment, and, if evident, MRI imaging should be done. Adrenal insufficiency should be suspected in a hypotensive, critically ill patient who is failing to improve on standard-of-care therapy.
Maternal and infant microbiome: next-generation indicators and targets for intergenerational health and nutrition care

Abstract Microbes are commonly sensitive to shifts in the physiological and pathological state of their hosts, including mothers and babies. From this perspective, the microbiome may be a good indicator for diseases during pregnancy and has the potential to be used for perinatal health monitoring. This is embodied in the application of microbiome from multi body sites for auxiliary diagnosis, early prediction, prolonged monitoring, and retrospective diagnosis of pregnancy and infant complications, as well as nutrition management and health products developments of mothers and babies. Here we summarized the progress in these areas and explained that the microbiome of different body sites is sensitive to different diseases and their microbial biomarkers may overlap between each other, thus we need to make a diagnosis prudently for those diseases. Based on the microbiome variances and additional anthropometric and physical data, individualized responses of mothers and neonates to meals and probiotics/prebiotics were predictable, which is of importance for precise nutrition and probiotics/prebiotics managements and developments. Although a great deal of encouraging performance was manifested in previous studies, the efficacy could be further improved by combining multi-aspect data such as multi-omics and time series analysis in the future. This review reconceptualizes maternal and infant health from a microbiome perspective, and the knowledge in it may inspire the development of new options for the prevention and treatment of adverse pregnancy outcomes and bring a leap forward in perinatal health care.
Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery

Publicly available diabetic retinopathy (DR) datasets are imbalanced, containing limited numbers of images with DR. This imbalance contributes to overfitting when training machine learning classifiers. The impact of this imbalance is exacerbated as the severity of the DR stage increases, affecting the classifiers' diagnostic capacity. The imbalance can be addressed using Generative Adversarial Networks (GANs) to augment the datasets with synthetic images. Generating synthetic images is advantageous if high-quality and diversified images are produced. To evaluate the quality and diversity of synthetic images, several evaluation metrics, such as Multi-Scale Structural Similarity Index (MS-SSIM), Cosine Distance (CD), and Fr\'echet Inception Distance (FID) are used. Understanding the effectiveness of each metric in evaluating the quality and diversity of GAN-based synthetic images is critical to select images for augmentation. To date, there has been limited analysis of the appropriateness of these metrics in the context of biomedical imagery. This work contributes an empirical assessment of these evaluation metrics as applied to synthetic Proliferative DR imagery generated by a Deep Convolutional GAN (DCGAN). Furthermore, the metrics' capacity to indicate the quality and diversity of synthetic images and a correlation with classifier performance is undertaken. This enables a quantitative selection of synthetic imagery and an informed augmentation strategy. Results indicate that FID is suitable for evaluating the quality, while MS-SSIM and CD are suitable for evaluating the diversity of synthetic imagery. Furthermore, the superior performance of Convolutional Neural Network (CNN) and EfficientNet classifiers, as indicated by the F1 and AUC scores, for the augmented datasets demonstrates the efficacy of synthetic imagery to augment the imbalanced dataset.
Defending Against Disinformation Attacks in Open-Domain Question Answering

Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems. However, little to no work has proposed methods to defend against these attacks. To do so, we rely on the intuition that redundant information often exists in large corpora. To find it, we introduce a method that uses query augmentation to search for a diverse set of passages that could answer the original question but are less likely to have been poisoned. We integrate these new passages into the model through the design of a novel confidence method, comparing the predicted answer to its appearance in the retrieved contexts (what we call Confidence from Answer Redundancy, i.e. CAR). Together these methods allow for a simple but effective way to defend against poisoning attacks that provides gains of nearly 20% exact match across varying levels of data poisoning/knowledge conflicts.
On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm

Robust reinforcement learning (RRL) aims at seeking a robust policy to optimize the worst case performance over an uncertainty set of Markov decision processes (MDPs). This set contains some perturbed MDPs from a nominal MDP (N-MDP) that generate samples for training, which reflects some potential mismatches between training (i.e., N-MDP) and true environments. In this paper we present an elaborated uncertainty set by excluding some implausible MDPs from the existing sets. Under this uncertainty set, we develop a sample-based RRL algorithm (named ARQ-Learning) for tabular setting and characterize its finite-time error bound. Also, it is proved that ARQ-Learning converges as fast as the standard Q-Learning and robust Q-Learning while ensuring better robustness. We introduce an additional pessimistic agent which can tackle the major bottleneck for the extension of ARQ-Learning into the cases with larger or continuous state spaces. Incorporating this idea into RL algorithms, we propose double-agent algorithms for model-free RRL. Via experiments, we demonstrate the effectiveness of the proposed algorithms.
Comparison of Discrete Variable and Continuous Variable Quantum Key Distribution Protocols with Phase Noise in the Thermal-Loss Channel

Discrete-variable (DV) quantum key distribution (QKD) based on single-photon detectors and sources have been successfully deployed for long-range secure key distribution. On the other hand, continuous-variable (CV) quantum key distribution (QKD) based on coherent detectors and sources is currently lagging behind in terms of loss and noise tolerance. An important discerning factor between DV-QKD and CV-QKD is the effect of phase noise, which is known to be more relevant in CV-QKD. In this article, we investigate the effect of phase noise on DV-QKD and CV-QKD protocols, including the six-state protocol and squeezed-state protocol, in a thermal-loss channel but with the assumed availability of perfect sources and detectors. We find that in the low phase noise regime but high thermal noise regime, CV-QKD can tolerate more loss compared to DV-QKD. We also compare the secret key rate as an additional metric for the performance of QKD. Requirements for this quantity to be high vastly extend the regions at which CV-QKD performs better than DV-QKD. Our analysis addresses the questions of how phase noise affects DV-QKD and CV-QKD and why the former has historically performed better in a thermal-loss channel.
Detection and Mitigation of Byzantine Attacks in Distributed Training

A plethora of modern machine learning tasks require the utilization of large-scale distributed clusters as a critical component of the training pipeline. However, abnormal Byzantine behavior of the worker nodes can derail the training and compromise the quality of the inference. Such behavior can be attributed to unintentional system malfunctions or orchestrated attacks; as a result, some nodes may return arbitrary results to the parameter server (PS) that coordinates the training. Recent work considers a wide range of attack models and has explored robust aggregation and/or computational redundancy to correct the distorted gradients. In this work, we consider attack models ranging from strong ones: $q$ omniscient adversaries with full knowledge of the defense protocol that can change from iteration to iteration to weak ones: $q$ randomly chosen adversaries with limited collusion abilities which only change every few iterations at a time. Our algorithms rely on redundant task assignments coupled with detection of adversarial behavior. We also show the convergence of our method to the optimal point under common assumptions and settings considered in literature. For strong attacks, we demonstrate a reduction in the fraction of distorted gradients ranging from 16%-99% as compared to the prior state-of-the-art. Our top-1 classification accuracy results on the CIFAR-10 data set demonstrate 25% advantage in accuracy (averaged over strong and weak scenarios) under the most sophisticated attacks compared to state-of-the-art methods.
Random walks in distributed networks and their applications

This thesis studies random walks and its algorithmic applications in distributed networks. Random walk is a fundamental technique which has found numerous applications in computer science, mathematics, statistics, physics, and among others. The simulation of random walks in a network is an important tool, with a lot of applications in algorithms and complexity theory. In particular, in communication networks, random walks have been used in various applications including token management, load balancing, network topology discovery and construction, search, and peer-to-peer membership management, local and lightweight algorithms for dynamic networks etc. While several such algorithms are ubiquitous, and use random walk sampling, the walks themselves have always been performed in a naive way — simply passing the random walk token from one node to its neighbor. Thus, to perform a random walk of length `, the naive approach requires ` steps. Therefore, a natural question is whether a better algorithm is possible in the distributed model. In this thesis, we focus on two main questions: (1) How efficiently random walks can be performed in distributed networks? (2) How random walks can be used in designing efficient distributed algorithms for important distributed computing problems? Towards the first question, the thesis studies efficient distributed random walk sampling in networks, where we focus on both static and dynamic networks. For static networks, we present a round and message optimal algorithm which can be used to output several random walk samples in a continuous online fashion. This significantly improves upon both the naive technique that requires linear time and messages, and the sophisticated algorithm presented by Das Sarma et al. [42] which has the same sub-linear (quadratic) running time as our algorithm, but requires a large number of messages (depending on the number of edges in the network). Moreover, we perform a comprehensive experimental evaluation on numerous network topologies which proves the effectiveness and efficiency of our algorithm. For dynamic networks, we present a fast distributed algorithm
PPO-ABR: Proximal Policy Optimization based Deep Reinforcement Learning for Adaptive BitRate streaming

Providing a high Quality of Experience (QoE) for video streaming in 5G and beyond 5G(B5G) networks is challenging due to the dynamic nature of the underlying network conditions. Several Adaptive Bit Rate (ABR) algorithms have been developed to improve QoE, but most of them are designed based on fixed rules and unsuitable for a wide range of network conditions. Recently, Deep Reinforcement Learning (DRL) based Asynchronous Advantage Actor-Critic (A3C) methods have recently demonstrated promise in their ability to generalise to diverse network conditions, but they still have limitations. One specific issue with A3C methods is the lag between each actor’s behavior policy and central learner’s target policy. Consequently, suboptimal updates emerge when the behavior and target policies become out of synchronization. In this paper, we address the problems faced by vanilla-A3C by integrating the on-policybased multi-agent DRL method into the existing video streaming framework. Specifically, we propose a novel system for ABR generation- Proximal Policy optimization-based DRL for Adaptive Bit Rate streaming (PPO-ABR). Our proposed method improves the overall video QoE by maximizing sample efficiency using a clipped probability ratio between the new and the old policies on multiple epochs of minibatch updates. The experiments on real network traces demonstrate that PPO-ABR outperforms stateof-the-art methods for different QoE variants.