Miracle-dz/UniFAR-FTUs huggingface dataset:
MIMO U-Net: efficient cell segmentation and counting in microscopy image sequences

Automatic cell quantification in microscopy images can accelerate biomedical research. There has been significant progress in the 3D segmentation of neurons in fluorescence microscopy. However, it remains a challenge in bright-field microscopy due to the low Signal-to-Noise Ratio and signals from out-of-focus neurons. Automatic neuron counting in bright-field z-stacks is often performed on Extended Depth of Field images or on only one thick focal plane image. However, resolving overlapping cells that are located at different z-depths is a challenge. The overlap can be resolved by counting every neuron in its best focus z-plane because of their separation on the z-axis. Unbiased stereology is the state-of-the-art for total cell number estimation. The segmentation boundary for cells is required in order to incorporate the unbiased counting rule for stereology application. Hence, we perform counting via segmentation. We propose to achieve neuron segmentation in the optimal focal plane by posing the binary segmentation task as a multi-class multi-label task. Also, we propose to efficiently use a 2D U-Net for inter-image feature learning in a Multiple Input Multiple Output system that poses a binary segmentation task as a multi-class multi-label segmentation task. We demonstrate the accuracy and efficiency of the MIMO approach using a bright-field microscopy z-stack dataset locally prepared by an expert. The proposed MIMO approach is also validated on a dataset from the Cell Tracking Challenge achieving comparable results to a compared method equipped with memory units. Our z-stack dataset is available at https://tinyurl.com/wncfxn9m.
Low-dose dual-tracer PET image reconstruction based on attention mechanism

Since the multi-tracer positron emission tomography (PET) technology will increase the total dose of tracers, it is necessary to study the low-dose multi-tracer PET technology. In this paper, we proposed a model based on attention mechanism to estimate the standard-dose single-tracer sinogram from the low-dose dual-tracer sinogram, that is, to achieve both standard-dose estimation and dual-tracer signal separation in sinogram domain. Both the spatial attention and channel attention were implemented. We cascaded the FBP-Net for reconstruction after the proposed model, and verified the proposed method in simulation experiment and rat experiment. At the same time, we compared the results of dual-tracer PET signal separation in sinogram domain and activity map domain. The results prove the effectiveness of the proposed method in the problem of low-dose dual-tracer PET image reconstruction, and also show that signal separation in sinogram domain is more effective than separation in activity map domain.
Simulation Study on a Planar Quasi-Optical Waveguide Circuit for a W-Band Gyro-TWT With Stability Improvement

In this article, a planar quasi-optical waveguide (PQOW) circuit is proposed for vacuum electron devices (VEDs) for the first time. The circuit is made of a series of metamaterial pillars attached to a pair of parallel plates for phase compensation. It can propagate quasi-optical modes as a pair of concave mirrors does, while the lower order HE $_{{0}{n}}$ modes experience much heavier circuit loss compared to that of convention confocal waveguide circuits, which can improve the backward oscillation (BWO) stability of gyro-amplifiers. As an example, a W-band gyro-amplifier with PQOW circuits to replace conventional confocal circuits is designed. The 3-D particle-in-cell simulation demonstrated that the circuit is capable of producing stable high-power radiation within 96–108 GHz, with an electron efficiency of about 15% over the entire bandwidth.
Three Simulation Cases of Automated Warehouse Picking Systems: A Comparative Analysis

Picking systems are great allies for companies that must manage a wide range of stock keeping units (SKU) and/or purchase order (PO) volume. The available systems allow optimization of the most valued resources within a company: time and labor; allowing organizations to gain greater competitiveness within their industry. Based on a descriptive and comparative scope design, this study focuses on the analysis of three automated simulation cases for order preparation systems to analyze its effectiveness within the warehouses: put-to-light, put-to-wall, and routing system. Likewise, it was sought to analyze the particularities of the three case studies in order to constitute a support framework for the management of the companies during their distribution center improvement process. The findings show that the first two simulation cases of the three chosen focus their study on solving makespan problems in picking, while the last case specializes in systemic preparations prior to the order preparation process; furthermore, these three selected cases have used the Gurobi Optimizer software to carry out the simulation.
A Note on a Simple Matrix Isomorphism

Since (f(a)+/(a))/2 and (f(a)-f(a))/2i are, respectively, the real and imginary parts of f(a), then f(a) is a ring isomorphism of C1 onto S2n. Again the question is asked: if f is a scalar function for which the matrices f(u) and f(U) are defined, under what condition does q(f(u)) f(q(u))? This question is answered by the following theorem. Theorem. Let U = ? )(u). Let a1, . ,. ar be the distinct eigenvalues of
Joint Resource Allocation and Trajectory Optimization in UAV-Enabled Wirelessly Powered MEC for Large Area

This article investigates a wirelessly powered mobile edge computing (MEC) framework with the cooperation between an unmanned aerial vehicle (UAV) and a center Cloud. In this system, we minimize the waiting delay of user equipments (UEs) under controllable energy consumption for a UAV serving a large area, where the UAV is equipped with an MEC server and an energy transmitter (ET). Thus, it can provide energy and computing services for UEs and transmit intractable tasks to the Cloud for further execution. To make the UAV serve all UEs, we propose the method by using a long and short time slots mixed mechanism, and handle the offloading decision and flying area selection by a mixed strategy with the Stackelberg game architecture during long-time slots, while the resource allocation and trajectory optimization issues are settled within short time slots. Specifically, considering limited battery capacity and severe energy propagation loss of the wireless power transfer (WPT), energy consumption can be controlled through queue optimization in short time slots. We decompose the original nonconvex problem into a series of deterministic optimization subproblems based on the Lyapunov optimization theory. Furthermore, using the Lagrangian duality theory, alternative optimization, and successive convex approximation (SCA) technique, we can obtain the closed-form solutions to the original problem. Performance analysis and simulation results demonstrate the convergence and the effectiveness of our proposed algorithm.
CONTINUOUS CHARACTERS: A SIMULATION STUDY

We use computer simulation to compare the statistical properties of several methods that have been proposed for estimating the evolutionary correlation between two continuous traits, and define alternative evolutionary correlations that may be of interest. We focus on Felsenstein's (1985) method and some variations of it and on several "minimum evolution" methods (of which the procedure of Huey and Bennett (1987) is a special case), as compared with a nonphylogenetic correlation. The last, a simple correlation of trait values across the tips of a phylogeny, virtually always yields inflated Type I error rates, relatively low power, and relatively poor estimates of evolutionary correlations. We therefore cannot recommend its use. In contrast, Felsenstein's (1985) method yields acceptable significance tests, high power, and good estimates of what we term the input correlation and the standardized realized evolutionary correlation, given complete phylo- genetic information and knowledge of the rate and mode of character change (e.g., gradual and proportional to time ("Brownian motion") or punctuational, with change only at speciation events). Inaccurate branch length information may affect any method adversely, but only rarely does it cause Felsenstein's (1985) method to perform worse than do the others tested. Other proposed methods generally yield inflated Type I error rates and have lower power. However, certain min- imum evolution methods (although not the specific procedure used by Huey and Bennett (1987)) often provide more accurate estimates of what we term the unstandardized realized evolutionary correlation, and their use is recommended when estimation of this correlation is desired. We also demonstrate how correct Type I error rates can be obtained for any method by reference to an empirical null distribution derived from computer simulations, and provide practical suggestions on choosing an analytical method, based both on the evolutionary correlation of interest and on the availability of branch lengths and knowledge of the model of evolutionary change appropriate for the characters being analyzed. Computer programs that implement the various methods and that will simulate (correlated) character evolution along a known phylogeny are available from the authors on request. These programs can be used to test the effectiveness of any new methods that might be proposed, and to check the generality of our conclusions with regard to other phylogenies.
Recent Advancements on the Use of Exosomes as Drug Carriers for the Treatment of Glioblastoma

Glioblastoma (GBM) is the most common and aggressive cancer of the brain. Presently, GBM patients have a poor prognosis, and therapy primarily aims to extend the life expectancy of affected patients. The current treatment of GBM in adult cases and high-grade gliomas in the pediatric population involves a multimodal approach that includes surgical resection followed by simultaneous chemo/radiotherapy. Exosomes are nanoparticles that transport proteins and nucleic acids and play a crucial role in mediating intercellular communication. Recent evidence suggests that these microvesicles may be used as biological carriers and offer significant advantages in targeted therapy. Due to their inherent cell-targeting properties, circulation stability, and biocompatibility, exosomes are emerging as promising new carriers for drugs and biotherapeutics. Furthermore, these nanovesicles are a repository of potential diagnostic and prognostic markers. In this review, we focus on the therapeutic potentials of exosomes in nano-delivery and describe the latest evidence of their use as a therapeutic tool in GBM.
Mining of both interesting positive and negative association rules based on MLMS model

A variety of data in data mining can be an open gate to extensive studies of various aspects in business. This research was aimed to analyze the data mining association rules to find ways for improving process efficiency. This would be of great benefits to people involved in commercial transactions in terms of competitive advantages of information data and the inventory development. In this research, we studied using pruning strategy of both positive and negative association rules with only single minimum support. In addition, frequent itemsets and infrequent itemsets in the database were focused. Further study was on the work which multiple levels of minimum supports in relation to the length of itemsets were employed. From the study, the application of a prune strategy, together with minimum supports to the multiple levels had resulted in a new technique which helped reduce the process of the association rules of interest, both positive and negative on the MLMS model.
Symptoms Experienced at Home and Self-Care Requisites of Patients After Heart Transplantation: A Retrospective Study

Heart transplantation is the most important treatment option in end-stage heart failure, and heart transplant recipients should be monitored at home by healthcare professionals in terms of symptoms and self-care requisites. The aim of this study is to examine symptoms and self-care requisites of patients with heart transplantation after discharge. Symptoms and self-care requisites of 81 patients who had heart transplantation between 2015 and 2020 were evaluated retrospectively in this study. Survey data were collected using the telephone interview method. It was determined that 77.8% of the patients were male and their mean age was 41.87 ± 2.51 years. Fatigue (91.4%), pain in the back, neck, and joints (86.4%), restlessness/agitation (81.5%), weight gain (71.6%), and edema in the lower extremities (71.6%) were the first 5 common symptoms experienced at home after heart transplantation. It was also found that the vast majority of patients (80.2%) answered negatively to the question of eliminating the lack of knowledge about the disease, and some of the patients were found not to pay attention to some important issues (e.g., regular blood pressure check, quitting smoking, etc.). Findings revealed that patients with heart transplantation experience a series of symptoms, and their self-care requisites were mostly focused on education and information specific to the disease after discharge. As a part of public health nursing practices, home care concentrating on symptoms and learning needs of patients should be planned and organized with a multidisciplinary approach to decrease rehospitalization and contribute to cost-effectiveness after heart transplantation.
A Brazilian case of IFAP syndrome with severe congenital ichthyosis and limb malformations caused by a rare variant in MBTPS2.

OBJECTIVE The classic triad, which defines IFAP syndrome, is ichthyosis follicularis, alopecia, and photophobia. It is a rare X-linked genetic disorder characterized by multiple congenital anomalies with variable severity, caused by pathogenic variants in the MBTPS2 gene, which encodes a zinc metalloprotease that is essential for normal development. This study aimed to report a case of a Brazilian patient with IFAP syndrome presenting skeletal anomalies, which is a rare finding among patients from different families. CASE DESCRIPTION We describe a male proband with IFAP syndrome showing severe ichthyosis congenita, cryptorchidism, limb malformation, and comprising the BRESHECK syndrome features. Using whole-exome sequencing, we identified a rare missense variant in hemizygosity in the MBTPS2 gene, which had not been identified in other family members. COMMENTS This is the first diagnosis of IFAP syndrome in Brazil with a molecular investigation. The present case study thus expands our knowledge on the mutational spectrum of MBPTS2 associated with IFAP syndrome.
Foreshock Ion Motion Across Discontinuities: Formation of Foreshock Transients

In the ion foreshock, hot flow anomalies (HFAs) and foreshock bubbles (FBs) are two types of foreshock transients that have the strongest fluctuations, which can disturb the magnetosphere‐ionosphere system and increase shock acceleration efficiency. They form due to interaction between the foreshock ions and solar wind discontinuities: the direction of the foreshock ion‐driven current and whether it decreases or increases the magnetic field strength behind the discontinuity determine whether the transient's formation can be promoted or suppressed. Thus, to predict the HFA and FB formation and forecast their space weather effects, it is necessary to predict the foreshock ion‐driven current direction. In this study, we derive analytical equations of foreshock ion velocities within discontinuities to estimate foreshock ion‐driven current direction, which provides a quantitative criterion of HFA and FB formation. To validate the criterion, we use Acceleration Reconnection Turbulence & Electrodynamics of Moon's Interaction with the Sun to observe pristine solar wind discontinuities and calculate discontinuity parameters. We use Magnetospheric Multiscale to observe the foreshock ion motion around the discontinuities and show that the data support our model. This study is another step toward a predictive model of HFA and FB formation so that we can forecast their space weather effects at Earth using solar wind observations at lunar orbit or L1.
Specializing Neural Networks for Cryptographic Code Completion Applications

Similarities between natural languages and programming languages have prompted researchers to apply neural network models to software problems, such as code generation and repair. However, program-specific characteristics pose unique prediction challenges that require the design of new and specialized neural network solutions. In this work, we identify new prediction challenges in application programming interface (API) completion tasks and find that existing solutions are unable to capture complex program dependencies in program semantics and structures. We design a new neural network model Multi-HyLSTM to overcome the newly identified challenges and comprehend complex dependencies between API calls. Our neural network is empowered with a specialized dataflow analysis to extract multiple global API dependence paths for neural network predictions. We evaluate Multi-HyLSTM on 64,478 Android Apps and predict 774,460 Java cryptographic API calls that are usually challenging for developers to use correctly. Our Multi-HyLSTM achieves an excellent top-1 API completion accuracy at 98.99%. Moreover, we show the effectiveness of our design choices through an ablation study and have released our dataset.
Adaptive semi-supervised learning material estimation network in dual-energy CT

Dual Energy CT (DECT) has ability to characterize different materials and quantify the densities or proportions of different contrast agents. However, the basis images decomposition is an ill-posed problem and the traditional model-based and image-domain direct inversion methods always suffer from serious degradation of the signal-to-noise ratios (SNRs). To this issue, we propose a new strategy by combining model-based and learning-based methods, which suppresses noise in the material images after direct inverse, and design a semi-supervised framework, Adaptive Semi-supervised Learning Material Estimation Network (ASLME-Net), to balance the detail structure preservation and noise suppression when fed little paired data in training stage of the deep learning. Specifically, the ASLME-Net contains two sub-networks, i.e., supervise sub-network and unsupervised sub-network. The supervised sub-network aims at capturing key features learned by with the labeled data, and the unsupervised sub-network adaptively learns the transferred feature distribution from supervised sub-network with Kullback-Leibler (KL) divergence. Experiment shows that the presented method can suppress the noise propagation in decomposition and yield qualitatively and quantitatively accurate results during the process of material decomposition. To this issue, we propose a new strategy by combining model-based and learning-based methods, which suppresses noise in the material images after direct inverse, and design a semi-supervised framework, Adaptive Semi-supervised Learning Material Estimation Network (ASLME-Net), to balance the detail structure preservation and noise suppression when fed little paired data in training stage of the deep learning.
Weakly supervised deep learning for predicting the response to hormonal treatment of women with atypical endometrial hyperplasia: a feasibility study

Endometrial cancer (EC) is the most common gynecologic malignancy in the US and complex atypical hyperplasia (CAH) is considered a high-risk precursor to EC. Treatment options for CAH and early-stage EC include hormone therapies and hysterectomy with the former preferred by certain patients, e.g., for fertility preservation or poor surgical candidates. Accurate prediction of response to hormonal treatment would allow for personalized and potentially improved recommendations for the treatment of these conditions. In this study, we investigate the feasibility of utilizing weakly supervised deep learning models on whole slide images of endometrial tissue samples for the prediction of patient response to hormonal treatment. We curated a clinical whole-slide-image (WSI) dataset of 112 patients from two clinical sites. We developed an end-to-end machine learning model using WSIs of endometrial specimens for the prediction of hormonal treatment response among women with CAH/EC. The model takes patches extracted from pathologist-annotated CAH/EC regions as input and utilizes an unsupervised deep learning architecture (Autoencoder or ResNet50) to embed the images into a low-dimensional space, followed by fully connected layers for binary prediction. Our autoencoder model yielded an AUC of 0.79 with 95% CI [0.61, 0.98] on a hold-out test set in the task of predicting a patient with CAH/EC as a responder vs non-responder to hormonal treatment. Our results, demonstrate the potential for using weakly supervised machine learning models on WSIs for predicting response to hormonal treatment of CAH/EC patients.
Experimental and theoretical investigation of oxygen glow discharge structure at low pressures

An experimental and theoretical investigation of the oxygen glow discharge structure at low pressures has been performed. Radial dependencies of the electron energy distribution function, the ambipolar plasma potential, and the negative ion concentration, as well as the axial electric field and the concentrations of atomic and singlet oxygen were measured. A new approach to the application of laser photodetachment method has been used to measure the negative ion concentration. It allows one to obtain information about fast processes after the photodetachment at low frequencies (/spl sim/100-200 Hz) by using the simplest modulation technique. A self-consistent model involving the electrodynamics and kinetics of the discharge was developed. The observed variations of the negative ion densities with current density and oxygen pressure were explained in the model frame by a dependence of the detachment rate constant of the O/sup -/+O/spl rarr/e+O/sub 2/ process on the effective ion temperature (k=1.9/spl middot/10/sup -10/-/spl radic/1100/T/sub i//sup eff/). It was shown that the feature of oxygen dc discharge at low pressures is a possibility to change the basic type of negative ions from the O/sup -/ to the O/sub 2//sup -/. This effect become more pronounced with decreasing current density.