Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analysis of Microwave Ablation Parameters for Treatment of Liver Tumors from the 3D-IRCADb-01 Database

Simulation techniques are powerful tools for determining the optimal conditions necessary for microwave ablation to be efficient and safe for treating liver tumors. Owing to the complexity and computational resource consumption, most of the existing numerical models are two-dimensional axisymmetric models that emulate actual three-dimensional cancers and the surrounding tissue, which is often far from reality. Different tumor shapes and sizes require different input powers and ablation times to ensure the preservation of healthy tissues that can be determined only by the full three-dimensional simulations. This study aimed to tailor microwave ablation therapeutic conditions for complete tumor ablation with an adequate safety margin, while avoiding injury to the surrounding healthy tissue. Three-dimensional simulations were performed for a multi-slot microwave antenna immersed in two tumors obtained from the 3D-IRCADb-01 liver tumors database. The temperature dependence of the dielectric and thermal properties of healthy and tumoral liver tissues, blood perfusion, and water content are crucial for calculating the correct ablation time and, thereby, the correct ablation process. The developed three-dimensional simulation model may help practitioners in planning patient-individual procedures by determining the optimal input power and duration of the ablation process for the actual shape of the tumor. With proper input power, necrotic tissue is placed mainly in the tumor, and only a small amount of surrounding tissue is damaged.
Approximate inference for determinantal point processes

APPROXIMATE INFERENCE FOR DETERMINANTAL POINT PROCESSES Jennifer Gillenwater Ben Taskar Emily Fox In this thesis we explore a probabilistic model that is well-suited to a variety of subset selection tasks: the determinantal point process (DPP). DPPs were originally developed in the physics community to describe the repulsive interactions of fermions. More recently, they have been applied to machine learning problems such as search diversification and document summarization, which can be cast as subset selection tasks. A challenge, however, is scaling such DPP-based methods to the size of the datasets of interest to this community, and developing approximations for DPP inference tasks whose exact computation is prohibitively expensive. A DPP defines a probability distribution over all subsets of a ground set of items. Consider the inference tasks common to probabilistic models, which include normalizing, marginalizing, conditioning, sampling, estimating the mode, and maximizing likelihood. For DPPs, exactly computing the quantities necessary for the first four of these tasks requires time cubic in the number of items or features of the items. In this thesis, we propose a means of making these four tasks tractable even in the realm where the number of items and the number of features is large. Specifically, we analyze the impact of randomly projecting the features down to a lower-dimensional space and show that the variational distance between the resulting DPP and the original is bounded. In addition to expanding the circumstances in which these first four tasks are tractable, we also tackle the other two tasks, the first of which is known to be NP-hard (with no PTAS) and the second of which is conjectured to be NP-hard. For mode estimation, we build on submodular maximization techniques to develop an algorithm with a multiplicative approximation guarantee. For likelihood maximization, we exploit the generative process associated with DPP sampling to derive an expectation-maximization (EM) algorithm. We experimentally verify the practicality of all the techniques that we develop, testing them on applications such as news and research summarization, political candidate comparison, and product recommendation.
Physicochemical and Techno-Functional Properties of Dried and Defatted Porcine Liver

Porcine liver has a high nutritional value and is rich in proteins, minerals, and vitamins, making it an interesting co-product to alleviate the growing global demand for protein. The objective of this study was to analyze how the drying and defatting processes of porcine liver affect the physicochemical and techno-functional properties of its proteins. Two drying temperatures (40 and 70 °C) were studied, and dried samples were defatted using organic solvents. The drying process turned out to be an effective method for the stabilization of the protein fraction; however, when the drying temperature was high (70 °C), greater protein degradation was found compared to drying at a moderate temperature (40 °C). Regarding the defatting stage, it contributed to an improvement in certain techno-functional properties of the liver proteins, such as the foaming capacity (the average of the dried and defatted samples was 397% higher than the dried samples), with the degree of foaming stability in the liver dried at 40 °C and defatted being the highest (13.76 min). Moreover, the emulsifying capacity of the different treatments was not found to vary significantly (p > 0.05). Therefore, the conditions of the drying and defatting processes conducted prior to the extraction of liver proteins must be properly adjusted to maximize the stability, quality, and techno-functional properties of the proteins.
2D-QSAR and Molecular Docking Studies of N-Substituted Rhodanine Derivatives as Potent Cytotoxic Agents

HER2 positive breast cancer is most common malignancy in women globally. Aberrant behavior of HER2 kinase protein is a hallmark of tumorigenesis, and as a result it has been considered as an emerging potential drug target for breast cancer therapy. In the present investigation a series of N-substituted rhodanine derivatives (33 compounds) were subjected to 2D-QSAR studies with the aid of genetic algorithm (GA) method to identify the essential structural features that responsible for cytotoxic activity. Based on the results, the cytotoxic activity of N-substituted rhodanine derivatives can be successfully explained in terms of twodimensional (ATSC7v), and three-dimensional (geomRadius and RDF45i) descriptors. The obtained model was vigorously validated and passed all validation metrics (R2train = 0.913, R2adj = 0.899, Q2LOO = 0.870, R2test = 0.848). Importantly, the model quality was good based on mean absolute error (MAE) criteria and the results were consistent with proposed limits by Golbraikh and Tropsha. Molecular docking study of the active compounds (ligands 4, 16, and 27) revealed the formation of hydrogen and hydrophobic interactions within the active site of HER2 protein. Keywords: HER2 proteins, breast cancer, cytotoxic agents, rhodanine, molecular docking, 2DQSAR.
Fermi surface instability in CeRh2Si2 under pressure

That the $4f$ electrons in the rare-earth antiferromagnetic compound ${\mathrm{CeRh}}_{2}{\mathrm{Si}}_{2}$ should be considered as localized electrons at cerium sites has been confirmed by comparing a de Haas\char21{}van Alphen experiment to the result of energy band calculations for ${\mathrm{LaRh}}_{2}{\mathrm{Si}}_{2}. $ When pressure p is applied to the compound, the N\'eel temperature ${T}_{\mathrm{N}1}=36 \mathrm{K}$ decreases and finally becomes zero at ${p}_{\mathrm{c}}\ensuremath{\simeq}1.0$\char21{}1.1 GPa. The topology of the Fermi surface is found to be almost unchanged up to 1.0 GPa, but the cyclotron effective mass increases with increasing pressure. Above 1.1 GPa, the topology of the Fermi surface changes abruptly. A new Fermi surface is explained by the $4f$-itinerant-band model. The corresponding cyclotron mass is large, being about ${22m}_{0}. $
Determinants of household waste disposal practices and implications for practical community interventions: lessons from Lilongwe

Open waste disposal has a negative effect on local ecology, economy, and public health. Understanding factors influencing waste disposal decisions is necessary for developing solutions to curb open waste disposal. This paper discusses the associations between household’s social demographic and spatial characteristics with preference for domestic waste disposal. The paper also utilizes this knowledge practical community action. This was achieved by gathering and examining a novel dataset of the waste disposal patterns of 200 randomly chosen households in Malawi. We observed that households were likely to dispose of their waste openly when the residential unit was closer to an existing open waste disposal site. A multinomial logistic regression model showed that the likelihood of choosing waste disposal methods, such as private garbage collection services, is higher when the household head is a woman, the housing unit is owned by the occupants, or in situations where a fence is present around the housing unit. We presented these findings to the neighborhood community development committee. A short-term community waste management plan was created using a participatory community planning approach. The plan included co-designing waste disposal solutions with landlords, setting up community waste bylaws, and intensifying civic education activities. In conclusion, our study provides insights into the factors that influence households’ disposal behavior. This unique case study highlights a potential approach for developing waste management policies using a bottom-up approach.
Stability results on random impulsive control for uncertain neutral delay differential systems

This study addresses the delay-dependent criteria like mean square robust exponential stability and mean square exponential stability for the uncertain neutral discrete time-varying and distributed delay differential systems under random impulsive control. By employing the random impulsive control, descriptor model transforms, and Lyapunov functional approach, we achieved the desired performance for the given system in the way of Linear matrix inequality. Finally, we justify the proposed results through numerical examples and their simulations.
Phase unwrapping of fringe images for dynamic 3D measurements without additional pattern projection

Fringe projection is an established method for contactless measurement of 3D object structure. Adversely, the coding of fringe projection is ambiguous. To determine object points with absolute position in 3D space, this coding has to be unique. We propose a novel approach of phase unwrapping without using additional pattern projection. Based on a stereo camera setup, an image segmentation of each view in areas without height jumps larger than a fringe period is necessary. Within these segments, phase unwrapping is potentially without error. Alignment of phase maps between the two views is realized by an identification process of one correspondence point.
Introduction to the Special Issue-Imagining Possible Futures: Disability Critical Race Theory as a Lever for Praxis in Education

This article provides the introduction to a special issue of the Teachers College Record (TCR) “Imagining Possible Futures: Disability Critical Race Theory as a Lever for Praxis in Education”. We begin by revisiting the seven tenets of DisCrit and citing a truncated intellectual geneology. Next, we trace the importance of a TCR special issue recognizing the mutually constiutive oppressions of racism and ableism. We then briefly describe the powerful premise and substantive contributions of the eight featured articles in this special issue. Each article is featured due to its commitments to link DisCrit theory to practice – engaging praxis through empirical research and/or theoretical engagements examining higher education, teacher education, state standards, k-12 education, carceral education, and international education using a variety of rigorous methods. By taking on racism and ableism across disciplinary boundaries and geographic borders, the contributors to this special issue break open new possibilities.
Fault diagnosis method for machinery based on multi-source conflict information fusion

Multi-source information fusion diagnosis is usually more reliable than fault diagnosis with a single source employed. However, fusion results may be absurd when fusing highly conflicting information. To address this problem, the Dempster–Shafer (DS) evidence theory is updated by weighting each piece of evidence according to the corresponding contribution to the decision, and a novel fault diagnosis method based on multi-source conflict information fusion is proposed. First, the basic probability assignment of evidence corresponding to the sensor information is given by introducing the feature parameters of electromyographic signals and using the back-propagation neural network. Then, the importance of each piece of evidence is determined by solving the difference degree and exclusion degree among the evidence, and the evidence is assigned weights according to the degree of importance of each piece of evidence in the fusion decision-making process. Next, the weighted evidence is combined for making decisions and further diagnosis after weighted averaging of the evidence with different weights. Finally, the performance of the proposed method is assessed using receiver operating characteristic (ROC) curves. The experimental results show that the areas under the ROC curves for the proposed method are 0.3229, 0.0729 and 0.9271 higher than those of the traditional DS method, Murphy’s method and Yager’s method, respectively, which proves that the proposed method has better diagnostic performance and reliability.
FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification

With the popularity of Android and its open source, the Android platform has become an attractive target for hackers, and the detection and classification of malware has become a research hotspot. Existing malware classification methods rely on complex manual operation or large-volume high-quality training data. However, malware data collected by security providers contains user privacy information, such as user identity and behavior habit information. The increasing concern for user privacy poses a challenge to the current malware classification scheme. Based on this problem, we propose a new android malware classification scheme based on Federated learning, named FedHGCDroid, which classifies malware on Android clients in a privacy-protected manner. Firstly, we use a convolutional neural network and graph neural network to design a novel multi-dimensional malware classification model HGCDroid, which can effectively extract malicious behavior features to classify the malware accurately. Secondly, we introduce an FL framework to enable distributed Android clients to collaboratively train a comprehensive Android malware classification model in a privacy-preserving way. Finally, to adapt to the non-IID distribution of malware on Android clients, we propose a contribution degree-based adaptive classifier training mechanism FedAdapt to improve the adaptability of the malware classifier based on Federated learning. Comprehensive experimental studies on the Androzoo dataset (under different non-IID data settings) show that the FedHGCDroid achieves more adaptability and higher accuracy than the other state-of-the-art methods.
Routine Screening for Central and Primary Adrenal Insufficiency during Immune-Checkpoint Inhibitor Therapy: An Endocrinology Perspective for Oncologists

Background: Immune checkpoint inhibitor (ICI)-associated hypothalamic–pituitary–adrenal axis disruption can lead to hypocortisolism. This is a life-threatening but difficult to diagnose condition, due to its non-specific symptoms that overlap with symptoms of malignancy. Currently, there is no consensus on how to best screen asymptomatic patients on ICI therapy for hypophysitis with serum cortisol. Methods: A retrospective chart review of patients treated with ICI in a tertiary care centre was conducted to assess the rate of screening with cortisol and whether this had an impact on diagnosis of ICI-hypophysitis in the preclinical stage. Patients were identified as having hypophysitis with an adrenocorticotropin hormone (ACTH) deficiency based on chart review of patients with cortisol values ≤ 140 nmol/L (≤5 mcg/dL). We also assessed what proportion of cortisol values were drawn at the correct time for interpretation (between 6 AM and 10 AM). Results: Two hundred and sixty-five patients had 1301 cortisol levels drawn, only 40% of which were drawn correctly (between 6 and 10 AM). Twenty-two cases of hypophysitis manifesting with ACTH deficiency were identified. Eight of these patients were being screened with cortisol following treatment and were detected in the outpatient setting. The remaining 14 patients were not screened and were diagnosed when symptomatic, after an emergency room visit or hospital admission. Sixty percent of the cortisol tests were uninterpretable as they were not drawn within the appropriate time window. Conclusion: Measuring morning serum cortisol in asymptomatic patients on ICI therapy is a fast and inexpensive way to screen for hypophysitis and should become the standard of care. Random serum cortisol measurement has no clinical value. Education needs to be provided on when to correctly perform the test and how to interpret it and we provide an algorithm for this purpose. The adoption and validation of such an algorithm as part of routine practice could significantly reduce morbidity and mortality in patients, especially as ICI therapy is becoming increasingly commonplace.
Novel silicon integrated magnetic devices for power system miniaturization

Magnetic devices are indispensable parts of modern power systems. For decades, size of the active components in power systems kept shrinking rapidly but the size of the magnetic devices had not been reduced accordingly. In portable and wearable applications, granular power-supplyon-chip applications, and other size or cost sensitive applications, the large size of the conventional magnetic devices resulted in bulky systems, larger parasitics, higher costs, and less design flexibility. Thus, magnetic devices have become the major obstacle for power system miniaturization nowadays. In this thesis, novel silicon integrated magnetic devices are proposed, modeled, optimized, and demonstrated for power system miniaturization in various applications. First, novel silicon integrated coreless power inductors are proposed and demonstrated for high efficiency, compact power conversions. A tapered spiral inductor is designed and fabricated
A Genome-First Approach to Estimate Prevalence of Germline Pathogenic Variants and Risk of Pancreatic Cancer in Select Cancer Susceptibility Genes

Simple Summary Prevalence and cancer risk estimates derived from the evaluation of affected individuals in the clinic are subject to ascertainment bias. This limitation can be mitigated using a genome-first approach in which genotypic data is analyzed before knowing a patient’s phenotype. Our study aimed to analyze two large cohorts with available exome and phenotype data unselected for a specific diagnosis from a genome-first perspective focusing on six pancreatic cancer predisposition genes. We provide estimates of (1) prevalence of heterozygotes for the general population and for individuals with pancreatic cancer and (2) cancer risk for pancreatic cancer for each gene evaluated. For mutation carriers, we found an elevated risk of pancreatic cancer for most genes evaluated, with variation among genes. This work expands our knowledge of the complex genetics of this cancer and will help identify patients at the highest risk who could benefit from future screening or therapeutic strategies. Abstract Patients with germline pathogenic variants (GPV) in cancer predisposition genes are at increased risk of pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer. The genes most frequently found to harbor GPV in unselected PDAC cases are ATM, BRCA1, BRCA2, CDKN2A, CHEK2, and PALB2. However, GPV prevalence and gene-specific associations have not been extensively studied in the general population. To further explore these associations, we analyzed genomic and phenotypic data obtained from the UK Biobank (UKB) and Geisinger MyCode Community Health Initiative (GHS) cohorts comprising 200,600 and 175,449 participants, respectively. We estimated the frequency and calculated relative risks (RRs) of heterozygotes in both cohorts and a subset of individuals with PDAC. The combined frequency of heterozygous carriers of GPV in the general population ranged from 1.22% for CHEK2 to 0.05% for CDKN2A. The frequency of GPV in PDAC cases varied from 2.38% (ATM) to 0.19% (BRCA1 and CDKN2A). The RRs of PDAC were elevated for all genes except for BRCA1 and varied widely by gene from high (ATM) to low (CHEK2, BRCA2). This work expands our understanding of the frequencies of GPV heterozygous carriers and associations between PDAC and GPV in several important PDAC susceptibility genes.
What Is behind the Correlation Analysis of Diarrheagenic E. coli Pathotypes?

Simple Summary To date, despite the efforts made to monitor the wide spread of resistant pathogens, especially multidrug-resistant (MDR) diarrheagenic E. coli, there are limitations in the correlation analysis for these pathogens worldwide. Therefore, it seems important and so timely to assess the E. coli pathotypes and their correlations with hosts, antimicrobial resistance, virulence gene profiles, and serotypes. Our promising results gave a clear indication for the epidemic situation of diarrheagenic E. coli (DEC) in Egypt and suggested that restricted recommendations and a search for novel alternative therapies are urgently needed due to the wide spread of MDR and multi-virulent E. coli strains in addition to their heterogeneous nature. This study can be implemented in the infection control guidance with enhanced protocols to hinder the spread of MDR E. coli pathotypes in Egyptian hospitals. Abstract The treatment failure recorded among patients and animals infected with diarrheagenic Escherichia coli (DEC) was increased due to the presence of specific virulence markers among these strains. These markers were used to classify DEC into several pathotypes. We analyzed the correlations between DEC pathotypes and antimicrobial resistances, the existence of virulence genes, serotypes, and hosts. The ETEC pathotype was detected with a high prevalence rate (25%). Moreover, the ETEC and EPEC pathotypes were highly associated with human infections in contrast to the EIEC and EAEC phenotypes, which were commonly recognized among animal isolates. Interestingly, the antimicrobial resistance was affected by E. coli pathotypes. With the exception of EIEC and STEC, imipenem represented the most effective antibiotic against the other pathotypes. There were fixed correlations between the DEC pathotypes and the presence of virulence markers and hosts; meanwhile, their correlation with serotypes was variable. Additionally, the vast majority of our isolates were highly diverse, based on both phenotypic and ERIC molecular typing techniques. Our promising results gave a clear indication for the heterogeneity and weak clonality of DEC pathotypes in Egypt, which can be utilized in the evaluation of the current therapeutic protocols and infection control guidelines.