Miracle-dz/UniFAR-FTUs huggingface dataset:
Claudin 18.2 is a target for IMAB362 antibody in pancreatic neoplasms

The majority of pancreatic neoplasms are characterized by a generally lethal progress within a short period of time after primary diagnosis and the mortality of patients is expected to increase further. Due to lack of efficient screening programs and moderate response to treatments, novel compounds for treatment are needed. We investigated the CLDN18.2 expression in affected patients as in vitro feasibility study for a potential treatment with the novel antibody IMAB362. Therefore, we analyzed the expression of CLDN18.2 in normal pancreatic tissues (N = 24), primary lesions (N = 202), metastases (N = 84) and intra‐individually matched samples (N = 48) of patients with pancreatic ductal adenocarcinoma (PDAC), neuroendocrine neoplasia (NEN) and acinar cell carcinoma. A standardized method for evaluation by immunohistochemistry was developed. The specific staining was evaluated by two independent raters and analysis of staining intensities (range 0–3+) and relative proportions of tumor cells were performed. One hundred three (59.2%) samples of primary PDAC were found positive. The vast majority of positive samples were characterized to highly express CLDN18.2: 54.6% (N = 95) with staining intensities of ≥2+. NEN were positive in 20% of cases (all ≥2+). Metastases of pancreatic neoplasms were also frequently found positive with comparable high rates (69.4% of lymph node and 65.7% of liver metastases). The rate of CLDN18.2 positivity is high in pancreatic neoplasms whereby the expression is not limited to the primaries but is also maintained upon metastasis. Thus, a considerable number of patients with pancreatic neoplasms would be in principle eligible for a CLDN18.2‐targeting approach.
Task Allocation in the Edge Cloud Scenario Incorporating Customized Requirements

As Edge-Cloud architectures continue to evolve, users increasingly demand customized task scheduling solutions. Traditional task scheduling aims at optimizing resource utilization and task prioritization. While conventional methods use random task arrival and first-come-first-served (FCFS) allocation or machine learning techniques like Reinforcement Learning (RL), they often lack consideration for individual task preferences, such as cost-time trade-offs, wait time, and user experience. In our paper, we introduce a framework for task scheduling that takes into account user-specific non-essential preferences, such as cost versus execution time and waiting time. This framework allows for parameter customization to meet these specific requirements. We also compare results from RL-based approaches with our framework, examining the impact of using actual task arrival data versus random data, and the performance of non-essential parameters. Our prototype in an edge-cloud environment, using a doctor-patient scenario, demonstrates that our simulation-based technique offers flexibility in parameter selection, resulting in improvements in fairness, reduced task waiting times, better load balancing, and minimized server idle time. Furthermore, our framework shows sensitivity to changes in input datasets, which is a limitation of the RL approach.
The control of reasoning in resource-bounded agents

Autonomous agents are systems capable of autonomous decision-making in real-time environments. Computation is a valuable resource for such decision-making, and yet the amount of computation that an autonomous agent may carry out will be limited. It follows that an agent must be equipped with a mechanism that enables it to make the best possible use of the computational resources at its disposal. In this paper we review three approaches to the control of computation in resource-bounded agents. In addition to a detailed description of each framework, this paper compares and contrasts the approaches, and lists the advantages and disadvantages of each.
The Unobserved Returns from Entrepreneurship

This paper seeks to understand the returns to self-employment. The analysis is motivated by the empirical puzzle that most entrepreneurs enter and persist in self-employment, despite lower initial earnings and earnings growth (Hamilton, 2000). I propose a new hypothesis to make sense of this observation. In particular, reported income is unlikely to be a good measure of the return to self-employment given the underreporting incentive, and opportunities available to business owners. Moreover, business owners also have access to multiple avenues for compensating themselves and their employees. As such, any survey of business owners will face great difficulties in capturing the many ways in which business income can be received. As evidence for the potential importance of such understating, Slemrod (2007) reports that the evasion rate amongst non-farm proprietorships varies between 18 and 57 percent depending on industry. In this paper, I make use of the PSID to test my hypothesis. The estimation strategy relies on the presumption that reported consumption by the self-employed will not be systematically misreported, even though income can easily be. The results indicate that individuals report earning 27 percent less but appear to consume 5 percent more in self-employment. This implies a 32 percent differential between reported wage and consumption for the selfemployed, indicating that the former measure is not a good barometer of the financial returns to self-employment. Furthermore, this increased consumption does not seem to be offset by higher uncertainty as evidenced by my finding, that the variance in consumption while selfemployed is not significantly different than that in wage employment. Other results include that the self-employed work longer hours and that consumption is the same as that prior to self-employment for those who return to wage employment.
The Unobserved Returns from Entrepreneurship

This paper resolves a longstanding empirical puzzle: that most entrepreneurs persist despite lower initial earnings and earnings growth. Using 38 years of longitudinal data, I find that while individuals report earning 26.2% less in self-employment, their expenditures are 4.5% higher. This increased expenditure comes from those who persist and is not offset by lower savings or higher uncertainty. Consistent with standard occupational choice models, these results rationalize the decision to persist in self-employment using just financial motivations. Other findings are that hours worked grow with time in self-employment, and that post-self-employment, labor market outcomes vary by skill level.
MRF model parameter estimation for contextual supervised classification of remote-sensing images

In the context of remote sensing image analysis, Markov random field (MRF) models are relevant image-analysis tools, thanks to their ability to integrate the use of contextual information associated to the image data in the analysis process. However, especially when dealing with supervised classification, the estimation of the internal parameters of the adopted MRF model is still an open issue, typically addressed by using time-expensive "trial-and-error" procedures. In the present paper, an automatic supervised MRF parameter optimization algorithm is proposed, that can be applied to a broad class of MRF models. The method formulates the parameter estimation problem as the solution of a set of linear inequalities, solved by extending to the present context the Ho-Kashyap algorithm, proposed to compute a linear discriminant function for binary classification. The method is validated experimentally on three different (single-date and multitemporal) data sets, endowed with distinct MRF models.
Module generators for a regular analog layout

In general, automatic layout composition techniques based on pre-designed devices facilitate the production of small IC numbers by prefabricating their basic structures. They also enable a high degree of automatic layout synthesis. However for their correct electrical behavior it is essential, that potential problems caused by electro-magnetic compatibility (EMC) are fully considered during the design phase. This paper presents a new concept for generating analog layout components by combining module generators, known from digital technology, with a flexible guidance scheme driven by the structural synthesis of the analog circuit. By utilizing both pre-defined functional blocks and flexible analog interconnection methods our approach takes EMC problems fully into consideration. The layout concept introduced here has successfully been applied to different sample circuits.
Mechanism of gas sensing in carbon nanotube field effect transistors

Gas sensors based on carbon nanotubes in the eld e ect transistor con guration have exhibited impressive sensitivities compared to the existing technologies. However, the lack of an understanding of the gas sensing mechanism in these carbon nanotube eld e ect transistors (CNTFETs) has impeded setting-up a calibration standard and customization of these nano-sensors for speci ed gas sensing application. Calibration requires identifying fundamental transistor parameters and establishing how they vary in the presence of a gas and in uence the overall sensing behavior. This work focuses on modeling the sensing behavior of a CNTFET in the presence of oxidizing (NO2) and reducing (NH3) gases and determining how each of the transistor parameters, namely: the Schottky barrier height, Schottky barrier width and doping level of the nanotube are a ected by the presence of these gases. Earlier experiments have shown that the carbon nanotube-metal interface is responsible for the observed change in the CNTFET response. The interface consists of the metal contact and the depletion region in the carbon nanotube. A change in the metal work function will change the Schottky barrier height, whereas doping of the depletion region will a ect the Schottky barrier width and the doping level of the carbon nanotube. A theoretical model containing these parameters was systematically tted to the experimental transfer characteristics for di erent concentrations of NO2 and NH3. A direct correlation between the measured changes in the CNTFET
PePC: Popularity Based Early Predictive Caching in Named Data Networks

Caching technique used in Information Centric/Named Data Networks (ICN/NDN) governs the response time. Cache capacity constraints at routers have led to investigations on different caching mechanisms to improve effective caching and performance in terms of improved cache hits and response time for requested contents. However, most caching methods remain oblivious to the dynamics of cache occupancy. In this paper, we describe a new caching technique which predicts whether a new content has to be cached or not considering the current occupancy level of the cache. Our prediction based approach is inspired by the Random Early Detection (RED) method used for queue management. Similar to RED, our predictive caching algorithm bases its decision to cache a content using the average cache occupancy and also takes into account the content popularity. When the cache occupancy is low, we cache every possible content, and with the increasing cache occupancy, the decision to cache the content is decided based on the content popularity and the occupancy threshold parameters. We perform simulation based studies using discrete event simulator to assess its performance. We also compare the performance of our predictive caching method with five different popular caching methods used in Named Data Networks to show its superiority over others.
Geospatial data for developing nutrient SPARROW models for the Midcontinental region of Canada and the United States

Vous avez des questions? Nous pouvons vous aider. Pour communiquer directement avec un auteur, consultez la première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées. Si vous n’arrivez pas à les repérer, communiquez avec nous à PublicationsArchive-ArchivesPublications@nrc-cnrc.gc. ca. Questions? Contact the NRC Publications Archive team at PublicationsArchive-ArchivesPublications@nrc-cnrc.gc. ca. If you wish to email the authors directly, please see the first page of the publication for their contact information. https://publications-cnrc.canada. ca/fra/droits L’accès à ce site Web et l’utilisation de son contenu sont assujettis aux conditions présentées dans le site
What Are the Experiences of Mental Health Practitioners Involved in a Coroner’s Inquest and Other Inquiry Processes after an Unexpected Death of a Patient? A Systematic Review and Thematic Synthesis of the Literature

Grief after suicide or patient-perpetrated homicide can be complex for those involved in the patient’s care. Mental health practitioners with patients who die unexpectedly may be called to assist in the formal investigation processes that follow. The aim of this study was to examine the experience of mental health practitioners called to attend a coroner’s inquest or other forms of formal inquiry. A protocol for a systematic review was prospectively registered on PROSPERO (CRD42023400310). A thematic synthesis of existing literature was conducted. We identified six articles for inclusion and constructed three themes from our analysis: Blame and enduring hostility, In the dark, and Limited learning. We found mental health practitioners may construct narratives of self-blame. These can be reinforced by the investigatory processes that follow. Feedback from inquiries is often delivered haphazardly and may not reflect the realities of clinical work. The support given to assist practitioners through inquiry processes varied—both in amount and how helpful it was. The research conducted on this topic is limited. More qualitative research should be conducted to understand the factors that make this experience more or less difficult as well as well as what support is needed for whom.
Finding Optimal Models for Small Gene Networks

Finding gene networks from microarray data has been one focus of research in recent years. Given search spaces of super-exponential size, researchers have been applying heuristic approaches like greedy algorithms or simulated annealing to infer such networks. However, the accuracy of heuristics is uncertain, which--in combination with the high measurement noise of microarrays--makes it very difficult to draw conclusions from networks estimated by heuristics. We present a method that finds optimal Bayesian networks of considerable size and show first results of the application to yeast data. Having removed the uncertainty due to the heuristic methods, it becomes possible to evaluate the power of different statistical models to find biologically accurate networks.
IFN-type-I-mediated signaling is regulated by modulation of STAT2 nuclear export

Signaling through the IFN type I receptor is mediated by assembly of the ISGF3 complex consisting of STAT1, STAT2 and IRF9. Whereas STAT1 is instrumentalized by many cytokines, STAT2 is specifically used by type I IFNs. Here, we report that the main regulatory mechanism of nuclear accumulation of STAT2 is nuclear export. We determined the kinetics of nucleocytoplasmic shuttling of STAT2 in living cells. In the absence of IFN, a virtually exclusive cytoplasmic localisation of STAT2 can be detected. Nevertheless, STAT2 is permanently and rapidly shuttling between the cytoplasm and the nucleus. The steady-state localization is explained by a very efficient nuclear export. Our studies indicate that at least two pathways (one of which is CRM1-dependent, the other not yet identified) are responsible for clearing the nucleus from STAT2. The constitutive nucleocytoplasmic shuttling of STAT2 does neither depend on the presence of IRF9 or STAT1, nor does it require tyrosine phosphorylation. Upon treatment with IFN type I, nuclear export of STAT2 is completely abolished in cells used within this study, whereas nuclear import is functioning. This explains the observed nuclear accumulation of STAT2. We have identified a region in the C-terminus of STAT2 that is essential for its almost exclusively cytoplasmic localization in the absence of IFN and responsible for CRM1-specific export. In comparative studies we show that nucleocytoplasmic shuttling of STAT2 is significantly different from that of STAT1. STAT1 is also shuttling in the absence of IFN, but the exchange rate in unstimulated cells is more than ten times lower. We further show that the latent STAT2 protein has stronger intrinsic nuclear-export activity than STAT1. Together, these observations lead to a model for IFN-type-I-induction in which the receptor-mediated heterodimerization overcomes the slow nuclear import of STAT1 and blocks the strong STAT2 export activity that leads to the accumulation of both signal transducers in the nucleus.
Plasmid profiles and transfer of plasmid-encoded antibiotic resistance in Lactobacillus plantarum

Plasmids were visualized in strains of Lactobacillus plantarum by use of a rapid method. Plasmids pIP501 and pAM beta 1 were transferred by conjugation from Streptococcus strains to Lactobacillus plantarum, and recipient strains were shown to act as donors in crosses to S. lactis. Attempts to transfer these plasmids between strains of L. plantarum were not successful.
Despeckling of SAR clutter amplitude data using G0-MAP filter

Synthetic aperture radar (SAR) images are inherently affected by a multiplicative noise known as speckle. In this paper, we propose a despeckling filter based on G0-distribution and formulate maximum a posteriori (MAP) estimator for texture amplitude. Here, the texture is modeled using inverse Gamma distribution which portrays areas with varying degree of heterogeneity. We estimate the roughness parameter of G0-distribution using method of log-cumulants (MoLC). The G0-MAP estimation of texture amplitude is performed on multilook synthetic data and single look real clutter data. Simulation results are presented to illustrate that the proposed G0-MAP filter achieves better mean preservation and speckle reduction compared to state-of-the-art filters, and promises wider application.
Challenges in developing Capture-HPC exclusion lists

In this paper we discuss the challenges faced whilst developing exclusion lists for the high-interaction client honeypot, Capture-HPC. Exclusion lists are Capture client system behaviours which are used in the decision making process when determining if a particular behaviour is malicious or benign. As exclusion lists are the main decision making method used by Capture-HPC to classify a given webpage as benign or malicious, we identify a number of issues with current research which are often overlooked. Exclusion lists by nature require constant updating as they are developed to meet the specific requirements of a particular operating system, web browser and application system environment. Any changes to these would mean the possibility of a given client to display different benign behaviour which consequently means new exclusions required. As a result of their specific version requirements, exclusion lists are not transferable from clients. We propose a set of recommendations to aid in the creation of exclusion lists. We also present and discuss some common drive-by-download attacks which we have captured using our Windows 7 compatible exclusion lists.
On Using Artificial Intelligence to Predict Music Playlist Success

The emergence of digital music platforms has fundamentally transformed the way we engage with and organize music. As playlist creation has gained widespread popularity, there is an increasing desire among music aficionados and industry experts to comprehend the factors that drive playlist success. This paper presents a machine learning-based approach designed to predict the success of music playlists. By analyzing various musical characteristics of songs, our model achieves an impressive accuracy of 89.6% in predicting playlist success. Notably, it exhibits a remarkable 92.0% accuracy in forecasting the success of popular playlists, while also effectively identifying unpopular playlists with an accuracy of 89.4%. These findings provide invaluable insights into playlist creation, ultimately enhancing the overall music-listening experience. By harnessing the power of machine learning, our proposed approach unlocks new prospects for optimizing playlist design strategies and delivering personalized music recommendations. This has significant ramifications for music enthusiasts and industry professionals seeking to elevate playlist creation and enrich the music consumption experience.
The Effects of Optimal Dietary Vitamin D3 on Growth and Carcass Performance, Tibia Traits, Meat Quality, and Intestinal Morphology of Chinese Yellow-Feathered Broiler Chickens

Simple Summary Vitamin D3 is an essential trace element in the poultry diet and plays an important role in healthy poultry production. Establishing the amount of VD3 required in the yellow feather broiler diet is important for actual production. This experiment aims to determine the appropriate amount of VD3 for enhancing growth performance, tibia traits, slaughter performance, and meat quality and to further explore the metabolic pattern of VD3 in broilers by 16S rRNA and liver transcriptome sequencing. Abstract This study aimed to assess the effects of different dietary vitamin D3 (VD3) levels on growth and carcass performance, tibia traits, meat quality, and intestinal morphology of yellow-feathered broilers. One-day-old broilers (n = 1440) were assigned into four treatment groups with six replicates per group, and each replicate contained 60 chicks. Dietary VD3 significantly improved the growth performance and carcass traits of broilers, and only low-dose VD3 supplementation decreased the abdominal fat percentage. High-dose VD3 supplementation improved intestinal morphology in the finisher stage, while the b* value of breast muscle meat color decreased markedly under VD3 supplementation (p < 0.05). Serum Ca and P levels and the tibia composition correlated positively with dietary VD3 supplementation at the early growth stage. The weight, length, and ash contents of the tibia increased linearly with increasing dietary VD3, with maximum values achieved in the high-dose group at all three stages. Intestinal 16S rRNA sequencing and liver transcriptome analysis showed that dietary VD3 might represent an effective treatment in poultry production by regulating lipid and immune-related metabolism in the gut–liver axis, which promotes the metabolism through the absorption of calcium and phosphorus in the intestine and improves their protective humoral immunity and reduces infection mortality. Dietary VD3 positively affected the growth—immunity and bone development of broilers during the early stage, suggesting strategies to optimize poultry feeding.