Miracle-dz/UniFAR-FTUs huggingface dataset:
Layer-to-Layer Endurance Variation of 3D NAND Flash Memory

The block size of flash memory chips has increased significantly with the introduction of 3D NAND technology, causing "big-block" management issues in storage systems. In this paper, we characterize endurance of individual pages in a block and show that pages in the bottom and top layers exhibit lower endurance than pages in the middle layers. This variation in endurance among pages can cause severe underutilization of big memory blocks. We find that erase threshold voltage (Vt) variation between the layers is the root cause for the observed endurance variation.
Small-signal modeling for p-n junctions in the breakdown region at different temperatures using artificial neural networks

Abstract In this paper, an artificial neural network (ANN) approach is applied to efficiently and accurately determine small-signal parameters of a breakdown model for silicon p-n junctions operating in the impact ionization region at different temperatures for the first time. A feed-forward back propagation neural network program with a Levenberg Marquardt optimization algorithm is employed to implement this ANN-based parameter determination approach. Measured S-parameters at different temperatures which are regarded as a training data-set are fitted by this program to determine small-signal parameters of the breakdown equivalent circuit model at different temperatures. Multiplication factors of p-n junctions at different temperatures are also extracted. This simple and accurate breakdown characterization method based on the ANNs can be applicable to automatic parameter determination for devices operating in the breakdown region.
Analysis of retreat mining pillar stability

Preventing pillar line squeezes, massive pillar collapses, and coal pillar bumps is critical to the safe and efficient recovery of coal during retreat mining operations. To help prevent these problems, the US Bureau of mines has developed the Analysis of Retreat Mining Pillar Stability (ARMPS) computer program. ARMPS calculates stability factors (SF) based on estimates of the loads applied to, and the load-bearing capacities of, pillars during retreat mining operations. The program can accommodate various retreat mining scenarios including angled crosscuts, varied entry spacings, barrier pillars between the active section and old (side) gobs, and slab cuts in the barriers on retreat. It also features a pillar strength formula that considers the greater strength of rectangular pillars. The program may be used to evaluate bleeder energy designs as well as active workings. A data base of 130 pillar retreat case histories has been collected across the United States to verify the program. It was found that satisfactory conditions were very rare when the ARMPS SF was less than 0.75. Conversely, very few unsatisfactory designs were found where the ARMPS SF was greater than 1.5.
Deep Learning and Machine Learning based Facial Emotion Detection using CNN

Human emotions are the psychological conditions of sentiments that are encountered impulsively which imply the expressions on the face. By using Machine Learning and Deep Learning in this paper, the emotions will be detected and human behavior will be extracted. The various body language approaches like idiomatic expressions, eye stirring and body movement are significant while applying for the association between machines and people. Out of these methods, facial emotion is most commonly employed as it refers to the mental sentiments and frame of person's mind. The identification of the various emotions is sometimes a very difficult job as no specified prototype or framework is there to differentiate the various kinds of sentiments and also there are various complications while recognizing the facial emotion expression. Facial emotions are so much crucial in non-verbal type communication which appears to the internal feelings of an individual that reflects on the faces. Machine Learning techniques, Deep learning and Neural Network algorithms are used for emotion recognition. This work is going to propose an efficient technique using Convolutional Neural Networks (CNNs) to detect anger, disgust, happiness, fear, sadness, calmness and surprisingness.
Off-Road Navigation With End-to-end Imitation Learning for Continuously Parameterized Control*

Control of autonomous ground vehicles by deep neural networks has been an area of interest for over 30 years. However, the majority of this research has been directed to-wards urban navigation and traffic control. We instead attempt to develop an end-to-end controller for off-road navigation using a convolutional deep neural network. In pursuit of this, we make use of state-of-the-art machine learning algorithms to train a control policy using data collected from a limited number of human demonstrations. We evaluate a variety of network structures, primarily focusing on methods of goal parameterization within a continuous state space. We demonstrate the effectiveness and limitations of these methods in the real world using a 1/6 scale truck outfitted with an RGB-D camera and GPS sensor.
Experimental Dual-band Coherent Photonics-based Radar Network with ISAR Imaging

In this paper, an operative radar network based on microwave photonics techniques is presented. The proposed system employs a centralized architecture, with a central unit driving multiple remote radar peripherals. The wide distribution of the peripherals, the possibility to operate in dual-band mode and the coherent management of the transmitted and received signals are discussed. The surveillance system is deployed in a real freight port, exploiting the available optical fiber network, for the monitoring of the maritime traffic. Preliminary results demonstrate the possibility of exploiting geometric and frequency diversity on the target detection. Finally, the first experimental multi-sensor, multi-band inverse synthetic aperture radar target imaging results are presented.
Mechanically primed cells transfer memory to fibrous matrices for persistent invasion

In disease and development, cells sense and migrate across mechanically dissimilar environments. We investigated whether mechanical memory of past environments empowers cells to navigate new, three-dimensional environments. Here, we show that cells primed by stiff matrices apply higher forces, compared to soft-primed cells, to accumulate and align collagen fibers towards sustained invasion. This priming advantage persists in dense or stiffened collagen. Through an energy-minimization model, we elucidate how memory-laden cells overcome mechanosensing of softer or challenging environments via a cell-matrix transfer of memory. Consistent with model predictions, depletion of α-catenin and YAP hamper coordinated forces and cellular memory required for collagen remodeling before invasion. We release tension in collagen fibers via laser ablation and disable fiber remodeling by lysyl-oxidase inhibition; both of which disrupt cell-to-matrix transfer of memory and reduce invasion. These results have implications for cancer, fibrosis, and aging, where potential matrix memory may generate prolonged cellular response. One-Sentence Summary Cell invasion across mechanically dissimilar environments is mediated by force-based storage and extraction of cell and matrix memory.
Comparison of selective genotyping strategies in genomic selection programs for broilers using stochastic simulation

Breeding programs using genomic information have become common in broilers. In broilers, the main traits for selection are body weight and feed efficiency. These traits are measured in both sexes and before sexual maturity. Thus, increases in genetic gain in broiler breeding programs due to the use of genomic information are attributable primarily to the increased accuracy of predicted breeding values of potential parents. As not all birds can be genotyped due to economic constraints, an optimal genotyping strategy needs to be chosen. The objective of this study was to investigate the rates of genetic gain and inbreeding obtained by varying three factors: the percentage of birds genotyped (PG), the percentage of birds genotyped randomly (PRG) or selectively, and the percentage of males among genotyped birds (PMG). Stochastic computer simulation with a full factorial experimental design was used. Significant interaction among the factors (PG, PRG, and PMG) was observed for the rate of genetic gain in body weight and aggregate breeding value, but not for the gain in residual feed intake or the rate of inbreeding per generation. Our findings indicate that the PG, PRG, and PMG need to be considered when selecting a genotyping strategy for a broiler breeding program. If available resources allow only to genotype a small percentage (e.g., 2.5% or 5%) of all birds, the genotyping of 50–100% of male birds and selection of birds to be genotyped using phenotypic information is best. If resources allow to genotype more (e.g., ≥20%) candidates, genotyping of equal numbers of each sex, and low PRG level (i.e., preselection of the majority of candidate based on performance) is best. Provided that a proper genotyping strategy is chosen, we conclude that the incorporation of genomic information in broiler breeding programs can substantially increase the rate of genetic gain.
The first multi-tissue diel genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways in Quercus suber

In the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behavior at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871), the first of a woody plant. The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyze the pathways associated with the synthesis of suberin monomers. Nevertheless, the models developed in this work can provide insights into other aspects of the metabolism of Q. suber, such as its secondary metabolism and cork formation.
Inferring the T cell repertoire dynamics of healthy individuals

Significance Immune repertoires carry a diverse set of antigen-specific receptors that protect us from disease and keep the memory of past infections. How stable are those repertoires over time? We combine longitudinal repertoire sequencing data, stochastic evolutionary modeling, and tailored inference techniques to determine the characteristic time scales of T cell repertoire evolution. These timescales are in quantitative agreement with long tails observed in the distribution of clone sizes. We show that repertoire turnover slows down with age and is maximal in the largest clones of young individuals.
APT: A Practical Transit Mapping Service

The size of the global routing table is a rapidly growing problem. Several solutions have been proposed. These solutions commonly divide the Internet into two parts, one for customers and one for providers, where only provider addresses are globally routable. Packets destined for customer addresses are tunneled through provider space. For this process to work, there must be a mapping service that can supply an appropriate provider-edge address for any given customer address. We present a design for such a mapping service. We adhere to a "do no harm" design philosophy: maintain all desirable features of the current architecture without negatively affecting its security or reliability. Our design aims to minimize delay and prevent loss in packet encapsulation, minimize the number of new or modified devices, and keep the level of control traffic manageable.
The Capsid Precursor Protein of Astrovirus VA1 Is Proteolytically Processed Intracellularly

Human astrovirus VA1 has been associated with neurological disease in immunocompromised patients. Its recent propagation in cell culture has facilitated the study of its biology. ABSTRACT Human astrovirus VA1 has been associated with neurological disease in immunocompromised patients, and its recent propagation in cell culture has opened the possibility to study its biology. Unlike classical human astroviruses, VA1 growth was found to be independent of trypsin during virus replication in vitro. In this work, we show that despite its independence on trypsin activation for cell infection, the VA1 capsid precursor protein, of 86 kDa (VP86), is processed intracellularly, and this proteolytic processing is important for astrovirus VA1 infectivity. Antibodies raised against different regions of the capsid precursor showed that the polyprotein can be processed starting at either its amino- or carboxy-terminal end, and they allowed us to identify those proteins of about 33 (VP33) and 38 (VP38) kDa constitute the core and the spike proteins of the mature infectious virus particles, respectively. The amino-terminal end of the spike protein was found to be Thr-348. Whether the protease involved in intracellular cleavage of the capsid precursor is of viral or cellular origin remains to be determined, but the cleavage is independent of caspases. Also, trypsin is able to degrade the capsid precursor but has no effect on VP33 and VP38 proteins when assembled into virus particles. These studies provide the basis for advancement of the knowledge of astrovirus VA1 cell entry and replication. IMPORTANCE Human astrovirus VA1 has been associated with neurological disease in immunocompromised patients. Its recent propagation in cell culture has facilitated the study of its biology. In this work, we show that despite the ability of this virus to grow in the absence of trypsin, a marked feature of human classical astroviruses, the capsid precursor protein of astrovirus VA1 is cleaved intracellularly to yield the mature infectious particles, formed by two polypeptides, VP33 that constitutes the core domain of the virus particle, and VP38 that forms the spike of the virus. These studies provide a platform to advance our knowledge on astrovirus VA1 cell entry and replication.
GD2-targeting CAR-T cells enhanced by transgenic IL-15 expression are an effective and clinically feasible therapy for glioblastoma

Background Aggressive primary brain tumors such as glioblastoma are uniquely challenging to treat. The intracranial location poses barriers to therapy, and the potential for severe toxicity. Effective treatments for primary brain tumors are limited, and 5-year survival rates remain poor. Immune checkpoint inhibitor therapy has transformed treatment of some other cancers but has yet to significantly benefit patients with glioblastoma. Early phase trials of chimeric antigen receptor (CAR) T-cell therapy in patients with glioblastoma have demonstrated that this approach is safe and feasible, but with limited evidence of its effectiveness. The choices of appropriate target antigens for CAR-T-cell therapy also remain limited. Methods We profiled an extensive biobank of patients’ biopsy tissues and patient-derived early passage glioma neural stem cell lines for GD2 expression using immunomicroscopy and flow cytometry. We then employed an approved clinical manufacturing process to make CAR- T cells from patients with peripheral blood of glioblastoma and diffuse midline glioma and characterized their phenotype and function in vitro. Finally, we tested intravenously administered CAR-T cells in an aggressive intracranial xenograft model of glioblastoma and used multicolor flow cytometry, multicolor whole-tissue immunofluorescence and next-generation RNA sequencing to uncover markers associated with effective tumor control. Results Here we show that the tumor-associated antigen GD2 is highly and consistently expressed in primary glioblastoma tissue removed at surgery. Moreover, despite patients with glioblastoma having perturbations in their immune system, highly functional GD2-specific CAR-T cells can be produced from their peripheral T cells using an approved clinical manufacturing process. Finally, after intravenous administration, GD2-CAR-T cells effectively infiltrated the brain and controlled tumor growth in an aggressive orthotopic xenograft model of glioblastoma. Tumor control was further improved using CAR-T cells manufactured with a clinical retroviral vector encoding an interleukin-15 transgene alongside the GD2-specific CAR. These CAR-T cells achieved a striking 50% complete response rate by bioluminescence imaging in established intracranial tumors. Conclusions Targeting GD2 using a clinically deployed CAR-T-cell therapy has a sound scientific and clinical rationale as a treatment for glioblastoma and other aggressive primary brain tumors.
An in situ quantitative map of initial human colorectal HIV transmission

The initial immune response to HIV is critical in determining transmission. However, due to technical limitations we still do not have a comparative map of early mucosal transmission events. We combined RNAscope, cyclic-immunofluorescence and novel image analysis tools to quantify HIV transmission dynamics in intact human colorectal tissue. We mapped HIV enrichment to mucosal dendritic cells (DC) and submucosal macrophages, but not CD4+ T-cells, the primary targets of downstream infection. DCs appeared to funnel virus to lymphoid aggregates which acted as early sanctuaries of high viral titres whilst facilitating HIV passage to the submucosa. Finally, HIV entry induced rapid recruitment and clustering of target cells, facilitating DC and macrophage mediated HIV transfer and enhanced infection of CD4+ T-cells. These data demonstrate a rapid response to HIV structured to maximise the likelihood of mucosal infection, and provide a framework for in situ studies of host pathogen interactions and immune mediated pathologies. Highlights - in situ quantification of host cellular microenvironment response to pathogen invasion in human colorectal tissue. - HIV first localises to mucosal DCs and submucosal macrophages, but not CD4+ T cells. - Viral enrichment first occurs in lymphoid aggregates which is associated with passage into the submucosa. - Early localisation of HIV to CD4+ T cells is associated with interactions with DCs and macrophages. Graphical Abstract
Changes in Protonation States of In-Pathway Residues can Alter Ligand Binding Pathways Obtained From Spontaneous Binding Molecular Dynamics Simulations

Protein-ligand binding processes often involve changes in protonation states that can be key to recognize and orient the ligand in the binding site. The pathways through which (bio)molecules interplay to attain productively bound complexes are intricate and involve a series of interconnected intermediate and transition states. Molecular dynamics (MD) simulations and enhanced sampling techniques are commonly used to characterize the spontaneous binding of a ligand to its receptor. However, the effect of protonation state changes of in-pathway residues in spontaneous binding MD simulations remained mostly unexplored. Here, we used molecular dynamics simulations to reconstruct the trypsin-benzamidine binding pathway considering different protonation states of His57. This residue is part of the trypsin catalytic triad and is located more than 10 Å away from Asp189, which is responsible for benzamidine binding in the trypsin S1 pocket. Our MD simulations showed that the binding pathways that benzamidine follow to target the S1 binding site are critically dependent on the His57 protonation state. Binding of benzamidine frequently occurs when His57 is protonated in the delta nitrogen while the binding process is significantly less frequent when His57 is positively charged. Constant-pH MD simulations retrieved the equilibrium populations of His57 protonation states at trypsin active pH offering a clearer picture of benzamidine recognition and binding. These results indicate that properly accounting for protonation states of distal residues can be important in spontaneous binding MD simulations.
Consumer Decision Aid and Purchase Perception for In-Store Shopping

Purchase abandonment is a common problem for businesses since it translates to loss of sales and loss of potential customers. A review of the literature suggests that there is yet a study on in-depth exploration of purchase abandonment in in-store shopping. Hence, this study aims to examine the nature of product abandonment in in-store shopping, explore the factors that influence purchase abandonment in in-store shopping, and understand how the difference in the features of product categories affect purchase abandonment in in-store shopping. This qualitative study involved in-depth interviews with consumers and the data was thematically analysed. The findings of this study provided new definitions for purchase abandonment in in-store shopping where the definitions differ in terms of the formation of purchase intention, and uncovered additional factors that lead to purchase abandonment in in-store shopping that were not identified in previous studies. This study also revealed how product abandonment differs across product categories where specialty products are more likely to be abandoned than convenience products and shopping products due to the perception that specialty products are usually not a 'need' product and that specialty products are expensive.
Quasi-Distributed Vibration Sensing System for Transformers Using a Phase-Sensitive OFDR

A quasi-distributed vibration sensing system based on a phase-sensitive optical frequency-domain reflectometer is proposed for monitoring the vibration of power transformer oil tanks. Compared with traditional accelerometers, the proposed system has high electromagnetic interference immunity. In this article, the principles of localization and vibration sensing are introduced. Then, the system layout and the vibration signal demodulation process are explained in detail. Next, the vibration sensing performance of the system is calibrated experimentally. Finally, vibration monitoring tests are carried out in a laboratory environment and on-site to verify the vibration detection capability of the system. The proposed system can be used to measure the time domain, frequency domain, and spatial distribution of oil tank vibration, which can be used for the online monitoring of the mechanical state of power transformers.
The Impact of Oil Price Changes on the Stock Markets in Main Oil Exporting Countries: The Role of COVID-19

We contribute to the literature on the linkage among shocks in oil price and stock markets fluctuations in oil-exporting economies. We evaluate the effects of COVID-19 pandemic on the magnitude and persistence of responses of stock markets to oil price sudden changes both before and during the pandemic. We also identify the most important structural breaks in stock markets during the period 2013-2021. We find evidence that the majority of developing oil-exporting countries faced a major structural break in their stock markets in 2014 following the spectacular fall of oil price in that year. By contrast, their developed counterparts have been subject to structural break during the CIVID-19 pandemic. The outcomes of the generalize d impulse response analysis suggests that, shocks in oil price tend to hit oil exporters more severely throughout the COVID-19 pandemic than before the outbreak of the pandemic.