Miracle-dz/UniFAR-FTUs huggingface dataset:
Bacteriophage tRNA-dependent lysogeny: requirement of phage-encoded tRNA genes for establishment of lysogeny

ABSTRACT Bacteriophages are large and diverse components of the biosphere, and many phages are temperate. Upon infection, temperate phages can establish lysogeny in which a prophage is typically integrated into the bacterial chromosome. Here, we describe the phenomenon of tRNA-dependent lysogeny, a previously unrecognized behavior of some temperate phages. tRNA-dependent lysogeny is characterized by two unusual features. First, a phage-encoded tyrosine family integrase mediates site-specific recombination between a phage attP site and a bacterial attB site overlapping a host tRNA gene. However, attP and attB share only a short (~10 bp) common core such that a functional tRNA is not reconstructed upon integration. Second, the phage encodes a tRNA of the same isotype as the disrupted but essential host tRNA, complementing its loss, and consequently is required for the survival of lysogenic progeny. As expected, an integrase-defective phage mutant forms turbid plaques, and bacterial progeny are immune to superinfection, but they lack stability, and the prophage is rapidly lost. In contrast, a tRNA-defective phage mutant forms clear plaques and more closely resembles a repressor mutant, and lysogens are recovered only at very low frequency through the use of secondary attachment sites elsewhere in the host genome. Integration-proficient plasmids derived from these phages must also carry a cognate phage tRNA gene for efficient integration, and these may be useful tools for mycobacterial genetics. We show that tRNA-dependent lysogeny is used by phages within multiple different groups of related viruses and may be prevalent elsewhere in the broader phage community. IMPORTANCE Bacteriophages are the most numerous biological entities in the biosphere, and a substantial proportion of phages are temperate, forming stable lysogens in which a prophage copy of the genome integrates into the bacterial chromosome. Many phages encode a variety of tRNA genes whose roles are poorly understood, although it has been proposed that they enhance translational efficiencies in lytic growth or that they counteract host defenses that degrade host tRNAs. Here, we show that phage-encoded tRNAs play key roles in the establishment of lysogeny of some temperate phages. They do so by compensating for the loss of tRNA function when phages integrate at an attB site overlapping a tRNA gene but fail to reconstruct the tRNA at the attachment junction. In this system of tRNA-dependent lysogeny, the phage-encoded tRNA is required for lysogeny, and deletion of the phage tRNA gives rise to a clear plaque phenotype and obligate lytic growth.
Marginal identities, histories and negotiating spaces: life experiences of street children in Bulawayo, Zimbabwe

So much has been said and written about Zimbabwe’s political and economic problems with very little mention of Zimbabwe’s street children who are a common feature of the urban landscape. Street children’s life experiences can be viewed in the context of stigmatised identities and living on the margins of society. The research explored some of the conceptual terrain necessary to a study of the life experiences of street children in Bulawayo in the context of their day to day activities and claims for space and places. The study employed an ethnographic approach based on the understanding of street children as autonomous social actors. Their street image, their journeys and how they project themselves into the future emerge from their dynamic interactions amongst themselves and their immediate environment. Their life experiences define who they are and how they navigate living on the margins of society; crossing physical, social and moral boundaries. Finally, the study explores whether a child-centred model of practice could be used in relation to street children and the implications for policy and practice. KEY TERMS : Street children, stigmatised identities, space, place
ANALISIS PENGARUH UKURAN PERUSAHAAN, LEVERAGE, DAN MEKANISME CORPORATE GOVERNANCE TERHADAP MENAJEMEN LABA

Penelitian ini bertujuan menganalisis pengaruh ukuran perusahaan, leverage, dan mekanisme corporate governance terhadap manajemen laba. Ukuran perusahaan dengan menggunakan natural logaritma dari total penjualan perusahaan, leverage diukur dengan rasio antara total kewajiban dengan total asset dan mekanisme corporate governance diukur dengan menggunakan empat variabel (konsentrasi kepemilikan, komposisi dewan komisaris, ukuran KAP, dan komposisi komite audit). Manajemen laba diukur dengan discretionary accruals menggunakan Modified Jones Model. Populasi pada penelitian ini adalah 150 perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia tahun 2009-2011. Data penelitian diperoleh dari laporan keuangan perusahaan manufaktur periode tahun 2009-2011. Berdasarkan metode purposive sampling, sampel yang diperoleh sebanyak 50 perusahaan. Hipotesis dalam penelitian ini diuji menggunakan analisis regresi berganda. Hasil analisis menunjukkan bahwa variabel yang mempunyai pengaruh signifikan terhadap menejemen laba adalah ukuran perusahaan dan ukuran KAP. Selanjutnya, variabel independen leverage, konsentrasi kepemilikan, komposisi dewan komisaris dan komposisi komite audit tidak berpengaruh secara signifikan terhadap manajemen. Kata kunci : Ukuran Perusahaan, Leverage, Corporate Governance, Manajemen Laba
Insulitis in human type 1 diabetes: lessons from an enigmatic lesion

Abstract Type 1 diabetes is caused by a deficiency of insulin secretion which has been considered traditionally as the outcome of a precipitous decline in the viability of β-cells in the islets of Langerhans, brought about by autoimmune-mediated attack. Consistent with this, various classes of lymphocyte, as well as cells of the innate immune system have been found in association with islets during disease progression. However, analysis of human pancreas from subjects with type 1 diabetes has revealed that insulitis is often less intense than in equivalent animal models of the disease and can affect many fewer islets than expected, at disease onset. This is especially true in subjects developing type 1 diabetes in, or beyond, their teenage years. Such studies imply that both the phenotype and the number of immune cells present within insulitic lesions can vary among individuals in an age-dependent manner. Additionally, the influent lymphocytes are often mainly arrayed peripherally around islets rather than gaining direct access to the endocrine cell core. Thus, insulitis remains an enigmatic phenomenon in human pancreas and this review seeks to explore the current understanding of its likely role in the progression of type 1 diabetes.
Prior Clinico‐Radiological Features Informed Multi‐Modal MR Images Convolution Neural Network: A novel deep learning framework for prediction of lymphovascular invasion in breast cancer

Abstract Background Current methods utilizing preoperative magnetic resonance imaging (MRI)‐based radiomics for assessing lymphovascular invasion (LVI) in patients with early‐stage breast cancer lack precision, limiting the options for surgical planning. Purpose This study aimed to develop a sophisticated deep learning framework called “Prior Clinico‐Radiological Features Informed Multi‐Modal MR Images Convolutional Neural Network (PCMM‐Net)” to improve the accuracy of LVI prediction in breast cancer. By incorporating multiparameter MRI and prior clinical knowledge, PCMM‐Net should enhance the precision of LVI assessment. Methods A total of 341 patients with breast cancer were randomly divided into training and validation groups at a ratio of 7:3. Imaging features were extracted from T1‐weighted, T2‐weighted, and contrast‐enhanced T1‐weighted MRI sequences. Stepwise univariate and multivariate logistic regression were employed to establish a clinico‐radiological model for LVI prediction. The radiomics model was built using redundancy and the least absolute shrinkage and selection operator. Then, two deep learning frameworks were developed: the Multi‐Modal MR Images Convolutional Neural Network (MM‐Net), which does not consider prior radiological features, and PCMM‐Net, which incorporates multiparameter MRI and prior clinical knowledge. Receiver operating characteristic curves were used, and the corresponding areas under the curves (AUCs) were calculated for evaluation. Results PCMM‐Net achieved the highest AUC of 0.843. The clinico‐radiological features displayed the lowest AUC value of 0.743, followed by MM‐Net with an AUC of 0.774, and radiomics with an AUC of 0.795. Conclusions This study introduces PCMM‐Net, an innovative deep learning framework that integrates prior clinico‐radiological features for accurate LVI prediction in breast cancer. PCMM‐Net demonstrates excellent diagnostic performance and facilitates the application of precision medicine.
Sagebrush Ecosystem Characterization, Monitoring, and Forecasting with Remote Sensing: Quantifying Future Climate and Wildlife Habitat Change

xvi CHAPTER 1, INTRODUCTION. 1 Sagebrush ecosystem background. 2 Sagebrush ecosystem remote sensing monitoring. 3 Climate change detection and forecasting. 7 Wildlife habitat applications. 9 Research goals and hypothesis questions. 9 Summary of chapters. 10 References. 13 CHAPTER 2, MULTI-SCALE REMOTE SENSING SAGEBRUSH CHARACTERIZATION WITH REGRESSION TREES OVER WYOMING, USA; LAYING A FOUNDATION FOR MONITORING. 20 Abstract.21. 21
Postpartum depression: Impact on pregnant women and the postnatal physical, emotional, and cognitive development of their children. An ecological perspective.

Maternal mental health problems during pregnancy, childbirth, and the postpartum period are a challenge for public health. Not recognizing them hinders a timely diagnosis and treatment and has an impact on the mother and the establishment of the fundamental bond of the mother-child dyad. We must recognize the risk factors (age, socioeconomic status, mental health history, family dysfunction, unfavorable environment), clinical manifestations, and screening tools. There is evidence that the effect of stress, anxiety, and depression during pregnancy negatively affect fetal neurodevelopment and condition child developmental outcomes. Here we describe the negative impact of postpartum depression during the first months of life, which affects mother-child bonding, postnatal development (emotional, behavioral, cognitive, language), and the maintenance of breastfeeding. We also recognize protective factors that mitigate its effects. It is essential to establish preventive strategies and interdisciplinary diagnostic and therapeutic approaches to minimize the risks to the mother and her children.
Methods to Protect Global Warming

The promotion of growth of plant and promotion of carbon dioxide assimilation are best methods to protect global warming. Plant growth by eating carbon dioxide, nutrient N and nutrient P. Supply of N and P is key point. Elimination process of Nitrogen oxide (NOx) at automobile exhaust gas and power station flue gas should be stopped. Elimination of phosphate, urea and ammonium salt in drainage should be stopped. By the supply of sufficient N and P, enough growth of plant and enough carbon dioxide assimilation are performed. Then increased absorption of carbon dioxide and heat will protect global warming.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of nearly 100 co-authors across over 50 institutions in industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we present initial performance baselines across various model architectures on the algorithm track and outline the system track benchmark tasks and guidelines. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community.
Uncertainty Modeling in Ultrasound Image Segmentation for Precise Fetal Biometric Measurements

Medical image segmentation, particularly in the context of ultrasound data, is a crucial aspect of computer vision and medical imaging. This paper delves into the complexities of uncertainty in the segmentation process, focusing on fetal head and femur ultrasound images. The proposed methodology involves extracting target contours and exploring techniques for precise parameter measurement. Uncertainty modeling methods are employed to enhance the training and testing processes of the segmentation network. The study reveals that the average absolute error in fetal head circumference measurement is 8.0833mm, with a relative error of 4.7347%. Similarly, the average absolute error in fetal femur measurement is 2.6163mm, with a relative error of 6.3336%. Uncertainty modeling experiments employing Test-Time Augmentation (TTA) demonstrate effective interpretability of data uncertainty on both datasets. This suggests that incorporating data uncertainty based on the TTA method can support clinical practitioners in making informed decisions and obtaining more reliable measurement results in practical clinical applications. The paper contributes to the advancement of ultrasound image segmentation, addressing critical challenges and improving the reliability of biometric measurements.
Higher-Point Gauge-Theory Couplings of Massive Spin-2 States in 4-Dimensional String Theories

We explicitly compute the (NS) sector conventional type-I superstring tree-level amplitudes at five points after compactifying to 4-D, express the QFT building block in the helicity basis, and give several attempts towards arbitrary $n$ points. More specifically, we consider the interaction of one first excited level and otherwise massless states of conventional type-I superstrings, where the four-dimensional states can, for instance, be realized via D$3$ branes. We construct the amplitude by using the Berends-Giele currents. From the recursion of Berends-Giele currents, we can generate the higher point amplitude. We also apply the BCFW recursion with massive external legs shifted and get the amplitude for arbitrary $n$ points.
Integrating Graceful Degradation and Recovery through Requirement-driven Adaptation

Cyber-physical systems (CPS) are subject to environmental uncertainties such as adverse operating conditions, malicious attacks, and hardware degradation. These uncertainties may lead to failures that put the system in a sub-optimal or unsafe state. Systems that are resilient to such uncertainties rely on two types of operations: (1) graceful degradation, to ensure that the system maintains an acceptable level of safety during unexpected environmental conditions and (2) recovery, to facilitate the resumption of normal system functions. Typically, mechanisms for degradation and recovery are developed independently from each other, and later integrated into a system, requiring the designer to develop an additional, ad-hoc logic for activating and coordinating between the two operations. In this paper, we propose a self-adaptation approach for improving system resiliency through automated triggering and coordination of graceful degradation and recovery.The key idea behind our approach is to treat degradation and recovery as requirement-driven adaptation tasks: Degradation can be thought of as temporarily weakening an original (i.e., ideal) system requirement to be achieved by the system, and recovery as strengthening the weakened requirement when the environment returns within an expected operating boundary. Furthermore, by treating weakening and strengthening as dual operations, we argue that a single requirement-based adaptation method is sufficient to enable coordination between degradation and recovery. Given system requirements specified in signal temporal logic (STL), we propose a run-time adaptation framework that automatically performs degradation and recovery in response to environmental changes. We describe a prototype implementation of our framework and demonstrate the feasibility of the proposed approach using a case study in unmanned underwater vehicles (UUVs).
Model-Assisted Learning for Adaptive Cooperative Perception of Connected Autonomous Vehicles

Cooperative perception (CP) is a key technology to facilitate consistent and accurate situational awareness for connected and autonomous vehicles (CAVs). To tackle the network resource inefficiency issue in traditional broadcast-based CP, unicast-based CP has been proposed to associate CAV pairs for cooperative perception via vehicle-to-vehicle transmission. In this paper, we investigate unicast-based CP among CAV pairs. With the consideration of dynamic perception workloads and channel conditions due to vehicle mobility and dynamic radio resource availability, we propose an adaptive cooperative perception scheme for CAV pairs in a mixed-traffic autonomous driving scenario with both CAVs and human-driven vehicles. We aim to determine when to switch between cooperative perception and stand-alone perception for each CAV pair, and allocate communication and computing resources to cooperative CAV pairs for maximizing the computing efficiency gain under perception task delay requirements. A model-assisted multi-agent reinforcement learning (MARL) solution is developed, which integrates MARL for an adaptive CAV cooperation decision and an optimization model for communication and computing resource allocation. Simulation results demonstrate the effectiveness of the proposed scheme in achieving high computing efficiency gain, as compared with benchmark schemes.
Tailoring Semantic Communication at Network Edge: A Novel Approach Using Dynamic Knowledge Distillation

Semantic Communication (SemCom) systems, empowered by deep learning (DL), represent a paradigm shift in data transmission. These systems prioritize the significance of content over sheer data volume. However, existing SemCom designs face challenges when applied to diverse computational capabilities and network conditions, particularly in time-sensitive applications. A key challenge is the assumption that diverse devices can uniformly benefit from a standard, large DL model in SemCom systems. This assumption becomes increasingly impractical, especially in high-speed, high-reliability applications such as industrial automation or critical healthcare. Therefore, this paper introduces a novel SemCom framework tailored for heterogeneous, resource-constrained edge devices and computation-intensive servers. Our approach employs dynamic knowledge distillation (KD) to customize semantic models for each device, balancing computational and communication constraints while ensuring Quality of Service (QoS). We formulate an optimization problem and develop an adaptive algorithm that iteratively refines semantic knowledge on edge devices, resulting in better models tailored to their resource profiles. This algorithm strategically adjusts the granularity of distilled knowledge, enabling devices to maintain high semantic accuracy for precise inference tasks, even under unstable network conditions. Extensive simulations demonstrate that our approach significantly reduces model complexity for edge devices, leading to better semantic extraction and achieving the desired QoS.
Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance

In the age of the infodemic, it is crucial to have tools for effectively monitoring the spread of rampant rumors that can quickly go viral, as well as identifying vulnerable users who may be more susceptible to spreading such misinformation. This proactive approach allows for timely preventive measures to be taken, mitigating the negative impact of false information on society. We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model. We pre-train network-based user embeddings and leverage a cross-attention mechanism between users and posts, together with a community-enhanced vulnerability propagation (CVP) method to improve user and propagation graph representations. Furthermore, we employ two multi-task training strategies to mitigate negative transfer effects among tasks in different settings, enhancing the overall performance of our approach. We also construct two datasets with ground-truth annotations on information virality and user vulnerability in rumor and non-rumor events, which are automatically derived from existing rumor detection datasets. Extensive evaluation results of our joint learning model confirm its superiority over strong baselines in all three tasks: rumor detection, virality prediction, and user vulnerability scoring. For instance, compared to the best baselines based on the Weibo dataset, our model makes 3.8\% and 3.0\% improvements on Accuracy and MacF1 for rumor detection, and reduces mean squared error (MSE) by 23.9\% and 16.5\% for virality prediction and user vulnerability scoring, respectively. Our findings suggest that our approach effectively captures the correlation between rumor virality and user vulnerability, leveraging this information to improve prediction performance and provide a valuable tool for infodemic surveillance.
AUTOMATED DETECTION OF SKIN DISEASES USING TEXTURE FEATURES

This paper proposes an automated system for recognizing disease conditions of human skin in context to health informatics. The disease conditions are recognized by analyzing skin texture images using a set of normalized symmetrical Grey Level Co-occurrence Matrices (GLCM). GLCM defines the probability of grey level i occurring in the neighborhood of another grey level j at a distance d in direction θ. Directional GLCMs are computed along four directions: horizontal (θ = 0o), vertical (θ = 90o), right diagonal (θ = 45o) and left diagonal (θ= 135o), and a set of features computed from each, are averaged to provide an estimation of the texture class. The system is tested using 180 images pertaining to three dermatological skin conditions viz. Dermatitis, Eczema, Urticaria. An accuracy of 96.6% is obtained using a multilayer perceptron (MLP) as a classifier.
Enhancing Image-Text Matching with Adaptive Feature Aggregation

Image-text matching aims to find matched cross-modal pairs accurately. While current methods often rely on projecting cross-modal features into a common embedding space, they frequently suffer from imbalanced feature representations across different modalities, leading to unreliable retrieval results. To address these limitations, we introduce a novel Feature Enhancement Module that adaptively aggregates single-modal features for more balanced and robust image-text retrieval. Additionally, we propose a new loss function that overcomes the shortcomings of original triplet ranking loss, thereby significantly improving retrieval performance. The proposed model has been evaluated on two public datasets and achieves competitive retrieval performance when compared with several state-of-the-art models. Implementation codes can be found here.