Miracle-dz/UniFAR-FTUs huggingface dataset:
Poorly differentiated clusters and tumor budding are important prognostic factors in colorectal carcinomas

The aim of our study was to assess the prognostic value of the two new grading systems based on the quantification of tumor budding - (GBd) and poorly differentiated clusters - (PDCs-G) in colorectal carcinomas (CRC). We performed a retrospective study on 71 CRC patients who underwent surgery at the Emergency County Hospital, Timişoara. CRC cases were classified based on hematoxylin-eosin slides, using the conventional grading system, GBd, and PDCs-G, respectively. We used two-tier and three-tier grading schemes for each system. Subsequently, we evaluated associations with other prognostic factors in CRC. Based on the three-tier GBd (GBd-3t) most cases (34/69, 49.27%) were classified as G3Bd-3t, while based on the conventional grading system, the majority of the cases (55/69, 79.71%) were considered G2. On the other hand, based on the three-tier PDCs-G system (PDCs-G-3t), most cases (31/69, 44.93%) were PDCs-G2-3t. We also noted a more significant association of GBd-3t with other prognostic parameters analyzed, as compared to the conventional grading system. Nodal status, tumor stage, and lymphovascular invasion were strongly correlated with GBd-3t (p=0.0001). Furthermore, we noted that PDCs-G-3t correlated more significantly than the conventional grading system with nodal status (p<0.0001), tumor stage (p=0.0003), lymphovascular invasion (p<0.0001), perineural invasion (p=0.005), and the tumor border configuration (p<0.0001). High GBd and PDCs-G grades correlate directly with other negative prognostic factors in CRC. Thus, these new parameters/classification methods could be used as additional tools for risk stratification in patients with CRC.
Adenovirus Diversity in Fur Seal and Penguin Colonies of South America

Abstract: Adenoviruses are medium size nonenveloped viruses with a trend of coevolution with their hosts. We surveyed South American fur seals (Arctocephalus australis) and Humboldt penguins (Spheniscus humboldti) for adenoviruses at two sites from 2009 to 2012. Despite the common pattern of host specificity, some of the adenoviruses in our study were present in samples from unexpected host species. We identified mastadenoviruses, aviadenoviruses, and siadenoviruses in A. australis from Peru and Chile and in S. humboldti from Peru. The El Niño Southern Oscillation (ENSO) significantly reduces the productivity of the Humboldt upwelling system, which can change trophic and other ecological interactions, facilitating exposure to new pathogens. One aviadenovirus was detected in both the penguins and the fur seals, an interclass distance. This finding occurred only during the 2009 ENSO and not in 2010 or 2012. Further studies of viral diversity in sites with high-density mixed species populations are necessary to better understand viral evolution and the effect of environmental change on viral evolution and host specificity.
Lab Resource: Genetically-Modified Single Cell Line Generation and characterization of an endogenously tagged SPG11-human iPSC line by CRISPR/Cas9 mediated knock-in

Pathogenic bi-allelic variants in the SPG11 gene result in rare motor neuron disorders such as Hereditary Spastic Paraplegia type 11, Charcot-Marie Tooth, and Juvenile Amyotrophic Lateral Sclerosis-5. The main challenge in SPG11-linked disease research is the lack of antibodies against SPG11 encoded spatacsin. Here, we describe the CRISPR/Cas9 mediated generation and validation of an endogenously tagged SPG11human iPSC line that contains an HA tag at the C-terminus of SPG11. The line exhibits multi-lineage differentiation potential and holds promise for studying the role of spatacsin and for the elucidation of SPG11-associated pathogenesis. Resource Table:
Extracorporeal photopheresis and multimodality therapy in patients with T‐cell cutaneous lymphomas: Real‐life experience in Argentina

Extracorporeal photopheresis (ECP) as a part of multimodality therapy, is one of the treatments for Sézary syndrome (SS) and advanced stage mycosis fungoides (MF). This study aims to describe cutaneous and peripheral blood responses of patients with MF and SS who received multimodality therapy.
Preoperative high-dose glucocorticoids for early recovery after liver resection: randomized double-blinded trial

Abstract Background Glucocorticoids modulate the surgical stress response. Previous studies showed that high-dose preoperative glucocorticoids reduce levels of postoperative inflammatory markers and specific biomarkers of liver damage compared with placebo, and suggested a reduced complication rate and shorter hospital stay after liver surgery. However, there are no studies with a clinical primary outcome or of early recovery outcomes. The aim of this study was to investigate whether a single high dose of preoperative glucocorticoid reduces complications in the immediate postoperative phase after liver surgery. Methods This was a single-centre, double-blinded, parallel-group RCT investigating preoperative methylprednisolone 10 mg/kg (high dose) versus dexamethasone 8 mg (standard-dose postoperative nausea prophylaxis) in patients scheduled for open liver resection. The primary outcome was number of patients with a complication in the postanaesthesia care unit; secondary outcomes included duration of hospital stay, pain and nausea during admission, and 30-day morbidity. Results A total of 174 patients (88 in high-dose group, 86 in standard-dose group) were randomized and analysed (mean(s.d.) age 65(12) years, 67.2 per cent men); 31.6 per cent had no serious co-morbidities and 25.3 per cent underwent major liver resection. Complications occurred in the postanaesthesia care unit in 51 patients (58 per cent) in the high-dose group and 58 (67 per cent) in the standard-dose group (risk ratio 0.86, 95 per cent c.i. 0.68 to 1.08; P = 0.213). Median duration of hospital stay was 4 days in both groups (P = 0.160). Thirty-day morbidity and mortality rates were similar in the two groups. Conclusion A high dose of preoperative glucocorticoids did not reduce acute postoperative complications after open liver resection compared with a standard dose. Registration number: NCT03403517 (http://www.clinicaltrials.gov); EudraCT 2017–002652-81 (https://eudract.ema.europa.eu/).
Sourcing Monitors to Monitor Your Sourcing: Strategies to Manage Shilling and Shirking by Supply Chain Auditors

Multinational corporations (MNCs) hire auditors to assess business partners’ compliance with quality, working conditions, and environmental standards. Independent third-party auditors are widely assumed to outperform second-party auditors employed and thus controlled by MNCs. However, outsourcing audits to third parties creates agency problems that can likewise undermine auditor performance. Synthesizing literatures on auditor independence, agency theory, and sourcing, we compare the implications for auditor performance of four sourcing strategies: insourcing, outsourcing, concurrent sourcing, and rotational sourcing. Using proprietary data from a global apparel brand, we find that insourced second-party auditors outperform outsourced third-party auditors and that third-party auditors’ performance improves when MNCs concurrently source audits, using both second- and third-party auditors. We also show that both types of auditors perform better when MNCs rotate auditing firms.
Study of the Structure, Microstructure and Temperature Dependent Thermal Conductivity Properties of SrTiO3: Via Y3+ Substitution

Yttrium (Y) modified strontium titanate (SrTiO3) powders with initial concentration of Y in the range of 0 to 15 mol% were produced through sol-gel technique. X-ray diffraction (XRD) studies show that all the prepared compounds have a perovskite cubic structure with the space group (Pm3m). The lattice constant, lattice strain and crystallite size of the as-prepared samples were estimated from the XRD pattern which reveals the incorporation of Y into SrTiO3 system, moreover to investigate the quality of the prepared SrYT ceramics powder, the scanning electron microscopy (SEM) was used to determined investigate the morphology, grain size and its distribution. The analysis of the thermal conductivity measurements performed on the obtained powders revealed the effect of the combination of temperature and Y content on the thermal conductivity value, Indeed, the minimum thermal conductivity was 4.12 W/(mK) obtained with 15%Y at 464 K.
Soft-Gated Self-Supervision Network for Action Reasoning: Soft-Gated Self-Supervision Network with Attention Mechanism and Joint Multi-Task Training Strategy for Action Reasoning

Behavior decision-making in the dialogue domain is the key to the success of the dialogue system. Existing machine learning algorithm based on statistics and an deep learning algorithm based on pre-training are difficult to adapt to all tasks. Therefore, this paper proposes a multi-task classification model based on soft gating. Firstly, based on the existing models, a multi-task model suitable for specific fields is designed, which makes the model fully consider the characteristics of the data set itself. Secondly, soft gating is used to divide the model to reduce the interaction between model parameters. Then, we manually annotate and extend the existing session behavior data set. Finally, the trained model is used to recognize dialogue behavior. The experimental results show that the accuracy of the score is 89.1%. The experimental results show that the performance of SGSAM better than other models.
A Survey on Multimodal Deep Learning for Image Synthesis: Applications, methods, datasets, evaluation metrics, and results comparison

In recent years, deep learning technology has made breakthroughs in computer vision. After using large-scale data training, the deep neural network represented by GAN is significantly better than previous technologies in image generation, including generating more reasonable, higher-definition, more complex, and more accurate images. With the continuous development of datasets, models, and applications, the fusion of different modal information, including fusion of natural language, semantic layouts, tags, edge maps, and other different modal information, to generate images has become a new demand and challenge. There are related reviews on image generation and multimodal deep learning. However, there has not been a review dedicated to multimodal deep-learning image generation to discuss the current status, existing problems, and challenges of this task. Therefore, this review proposes a survey on multimodal deep learning image generation. It aims to provide readers with an application scenario for multimodal deep learning image generation. Also, it provides readers with new multimodal deep learning image generation technologies, the relevant datasets, evaluation metrics used, and some results comparison. Finally, this article describes some of the challenges and future topics of multimodal deep learning image generation.
Review of Method to Automatic Detection of COVID-19

COVID-19 has begun to spread around the world, and this situation is so serious that we need to find the patient as soon as possible. To detect COVID-19 at an early stage, artificial intelligence (AI) is applied because of its powerful data processing capabilities. Therefore, many scholars have designed algorithms to automatically identify whether they are patients with COVID-19 from CT or X-ray of the lungs. Although nucleic acid detection is the main method for the diagnosis of COVID-19, lung image detection is still a method worthy of consideration, and X-ray would still be useful in the evaluation of therapy for the patients who are confirmed and hospitalized. This paper examines the state-of-the-art method for the automatic diagnosis of COVID-19 and provides a detailed description of its model and results, and expresses our opinions on it. Then, we list some common CT and X-ray databases. Most importantly, we conducted a survey on issues related to the automatic detection of COVID-19 for some people who are computer vision professionals and have published the results of the survey. This paper is an all-around review of the automatic detection methods and related survey of COVID-19, which will play an enlightening role in future research.
Chinese Named Entity Recognition Based on Bi-directional Quasi-Recurrent Neural Networks improved with BERT: new method to solve chinese ner

Named Entity Recognition is a basic and important task in the field of natural language processing. In the field of Chinese NER, the BiLSTM-CRF model has always been favored by many researchers due to its excellent information extraction capabilities. However, with the advent of the big data era, LSTM cannot be calculated in parallel, exposing the shortcomings of low training efficiency. In this paper, we focused on model training efficiency, using Bi-directional Quasi-Recurrent Neural Networks (BiQRNN) to replace BiLSTM, the experimental results show that the model we proposed achieves good results and increases the training speed of the model by 35%.
Target detection algorithm based on CNN and its FPGA implementation

When the deep learning algorithm is deployed on FPGA platform, it is difficult to deploy different network structures with a single hardware structure. The iteration of the algorithm becomes complex and increases the iteration time. Aiming at these problems of Deploying deep learning algorithm on FPGA platforms, This paper presents a neural network accelerator based on FPGA, the proposed accelerator has high adaptability to different networks, the accelerator beneficial to accelerate and optimize the hardware of convolutional neural network for image recognition, efficiently use the limited resources on FPGA chip to realize the calculation of large-scale convolutional neural network, our work explores a high-performance, low-power and low-cost embedded solution for image recognition applications. Finally, this paper takes the Xilinx FPGA platform ultra96v2 as the hardware platform, we realizes the deployment of the accelerator on the FPGA platform, implements and verifies the yolov3 algorithm on the platform and achieves good detection results.
Pathogenic Politics: Authoritarianism, Inequality, and Capitalism in the COVID-19 Crisis

Abstract We provide an introduction to this special issue on the politics of the COVID-19 pandemic, presenting some of the key findings and central arguments of the articles collected herein, and discussing their significance in relation to the broader political context of the pandemic. We address the roles of necropolitics and “necrosecurity”, pointing to their relationships to colonial and eugenicist histories, as well as some of the ways in which a globally-ascendant authoritarian populism contributed to the often-disastrous mismanagement of the pandemic. We consider how unequal structures of social and public obligation were reproduced to the detriment of lives and livelihoods, and the challenges facing institutions of protection, care, and social reproduction. Finally, we consider some of the ways in which the pandemic may have opened up avenues for more systemic transformations—for good or for ill.
Concerted Ion-Exchange Mechanism for Sodium Diffusion and Its Promotion in Na3V2(PO4)3 Framework

Design and development of novel cathode materials for room-temperature sodium ion batteries is of great importance to solve the shortage of lithium resources. As a promising candidate, the Na3V2(PO4)3 cathode (NVP) exhibits stable structure and rapid Na diffusion kinetics. A detailed insight to the transportation behavior of Na+ ions in the interstitials of NVP lattice should be of great importance for understanding the ionic conductivity as well as the electrochemical performances. In this paper, we proposed three different sodium diffusion pathways, among which the concerted ion-exchange route is found to be energetically most favorable. During the migration process, Na ions at both Na(1) and Na(2) sites are engaged in the transportation. Several dopants, including Li, K, Ca, Mg, and Al, were introduced at the Na(1) site to promote the electrochemical performance of the NVP cathode. It was found that the K-doped NVP exhibits the highest voltage and lowest lattice variation during the charge/discharge pr.
Reducing Catheter-Associated Urinary Tract Infection: The Impact of Routine Screening in the Geriatric Hip Fracture Population

Background: Catheter-associated urinary tract infection (CAUTI) is a noted complication among geriatric hip fracture patients. This complication results in negative outcomes for both the patients and the institution providing care. Screening measures to identify predisposing factors, with early diagnosis and treatment of urinary tract infection (UTI) present on admission, may lead to reduced rates of CAUTI. Objective: The goals of this study were to determine the prevalence of UTI on admission among geriatric hip fracture patients and whether routine screening for UTI or predisposing factors at presentation resulted in reduced rates of CAUTI. Methods: A retrospective observational study of geriatric hip fracture patients from January 2017 to December 2018 at a Level I trauma center was performed. Rates of UTI on admission and CAUTI were calculated using routine admission urinalysis. Results: Of the 183 patients in the sample, 36.1% had UTI on admission and 4.4% of patients developed CAUTI. There were no significant differences in patient demographics, comorbidities, and complications between those with UTI on admission and those without. Conclusions: Urinary tract infection on admission may be present among a large portion of geriatric hip fracture patients, leading to increased rates of CAUTI. Routine screening for UTI and its predisposing factors at admission can identify these patients earlier and lead to earlier treatments and prevention of CAUTI.
Hardware Veriﬁcation In An Open Source Context

—The last few decades have seen the complexity of commercial hardware designs increase by multiple orders of magnitude. This has driven corresponding increases in commercial tool capability and the development of industry standard methods to drive them. Over the same time period, open source hardware development has lagged severely behind in terms of the scale of attempted designs, as well as the tooling and methodology capability to realise them. This capability gap is particularly acute for veriﬁcation ﬂows. In this paper, we try to explain how this capability gap appeared, and what it means for a nascent open source EDA community. We also survey the state of the art in commercial veriﬁcation techniques, and list alternative open source tools where available. Where open source alternatives are lacking, we make suggestions for closing the gap. We also discuss several human factors and challenges relevant to veriﬁcation in an open source context, and suggest a change in mindset is needed to make open source hardware designs more trustworthy
Evaluating The Quality of Blended Learning Strategy Based Module for Islamic Civilization and Asian Civilization Course

Citation: Burhan Norhapizah M, Mahfuzah MZ, Mohamed A, Jusoh MKA and Ayub MN (2020) Evaluating The Quality of Blended Learning Strategy Based Module for Islamic Civilization and Asian Civilization Course. Halaqa. 4:2. doi: Evaluating The Quality of Blended Learning Strategy Based Module for Islamic Civilization and Asian Civilization Course Mengevaluasi Kualitas Modul Berbasis Strategi Pembelajaran Campuran untuk Peradaban Islam dan Kursus Peradaban Asia Mohd Burhan Norhapizah *, Mohamed Zabidi Mahfuzah , Asjad Mohamed , Mohd Kamal Azman Jusoh , Mohd Nasir Ayub 2 Academy of Contemporary, UiTM Cawangan Pahang (Kampus Pasir Gudang), Malaysia, UiTM Cawangan Kedah (KampusMerbok), Academy of Contemporary Islamic Studies, Malysia
\infty-former: Infinite Memory Transformer

Transformers are unable to model long-term memories effectively, since the amount of computation they need to perform grows with the context length. While variations of efficient transformers have been proposed, they all have a finite memory capacity and are forced to drop old information. In this paper, we propose the \infty-former, which extends the vanilla transformer with an unbounded long-term memory. By making use of a continuous-space attention mechanism to attend over the long-term memory, the \infty-former’s attention complexity becomes independent of the context length, trading off memory length with precision.In order to control where precision is more important, \infty-former maintains “sticky memories,” being able to model arbitrarily long contexts while keeping the computation budget fixed.Experiments on a synthetic sorting task, language modeling, and document grounded dialogue generation demonstrate the \infty-former’s ability to retain information from long sequences.
Blueprint for a Production-Ready Information Retrieval System based on Multi-Modal Embeddings

: Deep Learning models for mapping documents from different domains, e.g., text, images, and audio, into a common vector space, enable a seamless information retrieval between the different domains and, thus, significantly improve the user experience of many expert tools. Despite various models for multi-modal mappings presented in scientific literature, the implementation and integration remain a challenge within the industry, especially for small or medium-sized companies. Reasons are, that developing such retrieval systems for production use-cases is a non-trivial task, requiring scalable, reliable, and cost-efficient infrastructure, services as well as adequate Deep Learning models. We present a generic and flexible blueprint architecture, targeting the development of a production-ready image-text retrieval search system using Kubernetes , MLflow , Elasticsearch , and integrating state-of-the-art Deep Learning models.
Classification of Malware Attacks Using Machine Learning In Decision Tree

Predicting cyberattacks using machine learning has become imperative since cyberattacks have increased exponentially due to the stealthy and sophisticated nature of adversaries. To have situational awareness and achieve defence in depth, using machine learning for threat prediction has become a prerequisite for cyber threat intelligence gathering. Some approaches to mitigating malware attacks include the use of spam filters, firewalls, and IDS/IPS configurations to detect attacks. However, threat actors are deploying adversarial machine learning techniques to exploit vulnerabilities. This paper explores the viability of using machine learning methods to predict malware attacks and build a classifier to automatically detect and label an event as “ Has Detection or No Detection ” . The purpose is to predict the probability of malware penetration and the extent of manipulation on the network nodes for cyber threat intelligence. To demonstrate the applicability of our work, we use a decision tree (DT) algorithms to learn dataset for evaluation. The dataset was from Microsoft Malware threat prediction website Kaggle. We identify probably cyberattacks on smart grid, use attack scenarios to determine penetrations and manipulations. The results show that ML methods can be applied in smart grid cyber supply chain environment to detect cyberattacks and predict future trends.