BANDWIDTH EFFICIENT FORMATION OF BROADCAST NETWORK WITH MULTIPLE DESCRIPTION CODING

In this paper, we consider the delay and fault-tolerance of data  broadcasting in  Internet of Things (IoT) networks,  in  which  nodes  form  a network  topology to deliver  live data  from  a source  to the  end receivers. We first consider to build a Small Height Tree which gives an overlay  with small expected  end-to-end  delay. The end-to-end delay and the fault-tolerance can be improved by  adopting  appropriate topology  for  the  overlay  according  to the  characteristics of providers.   Efficient and fault-tolerant in service level agreement (SLA) guaranteed services can hardly be achieved solely by tree or mesh. By multiple-path data  delivery with  multiple  description coding,  service  operators can  use the scheme  to predict  the  amount  of resources to be acquired,  and hence the cost, from  the network

STUDENT-CONTROLLED SOCIAL NETWORKS FOR PROMOTING HOLISTIC DEVELOPMENT FROM THE PRESPECTIVES OF STUDENT COACHES

Many previous research works have studied the impact of online social networks for educational purposes. We examine in particular how Facebook is being used as a platform to communicate among students of an on-going student development project run by a local tertiary institute in Hong Kong so as to facilitate promotion and foster participation and interaction. The study focuses on the perspectives from student coaches and evaluate on the facilitation and difficulties in promoting self-initiated holistic development via Facebook. The study shows that instant interaction between participants and student coaches via Facebook can lead to information circulation in a much faster and effective manner compared with traditional communication channels such as email or bulletins. However, limitations are found on the lack of proactive discussions initiated by participants, and the difficulties in establishing active interactions between coaches and participants. This has undermined the effectiveness of promoting to participants’ in self-initiated holistic development

Selecting the Best K Features for Predicting Student Participation in Generic Competency Development Activities in Higher Education

Generic competency (GC) is an essential but often overlooked aspect of developing students in higher education. While there is much research about using technologies to develop discipline- specific skills for students, the use of technologies in GC development is insufficient. In particular, more research is needed on using technologies to predict student participation in GC development activities (GCDAs). Machine learning (ML) can use student characteristics, known as features, to predict their involvement in GCDAs. However, too many features will slow down the prediction process and reduce the ability to pinpoint the best features for prediction. This study explored an effective way to identify the minimal number of features essential for predicting student participation in GCDAs. The findings help educators develop recommendation systems to help students select the most beneficial GCDA for their holistic development. We collected 98 features from 9570 students from a community college. Then, we applied the Principal Component Analysis and SelectKBest algorithms to reduce the number of features from 98 to 8. Finally, we compared the accuracy of predictions using KNN and ANN based on the all-feature dataset with those based on the reduced-feature dataset. The results showed that the reduced-feature dataset maintained good prediction accuracy and enabled the educator to recommend the GCDAs to students. The findings could drive further research and development in applying machine learning technologies to enhance the recommendations for GCDAs for higher-education students

Some pattern recognitions for a recommendation framework for higher education students’ generic competence development using machine learning

The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students’ particulars such as their academic background, current study and student activity records, their attended higher education institution’s expectations of graduate attributes and self-assessment of their own generic competencies. The gap between the higher education students’ generic competence development and their current statuses such as their academic performance and their student activity involvement was incorporated into the framework to come up with a recommendation for the student activities that lead to their generic competence development. For the formulation of the recommendation framework, the data mining tool Orange with some programming in Python and machine learning models was applied on 14,556 students’ activity and academic records in the case higher education institution to find out three major types of patterns between the students’ participation of the student activities and (1) their academic performance change, (2) their programmes of studies, and (3) their English results in the public examination. These findings are also discussed in this paperPeer Reviewe

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Paclitaxel Induces Immunogenic Cell Death in Ovarian Cancer via TLR4/IKK2/SNARE-Dependent Exocytosis

Emerging evidence shows that the efficacy of chemotherapeutic drugs is reliant on their capability to induce immunogenic cell death (ICD), thus transforming dying tumor cells into antitumor vaccines. We wanted to uncover potential therapeutic strategies that target ovarian cancer by having a better understanding of the standard-of-care chemotherapy treatment. Here, we showed in ovarian cancer that paclitaxel induced ICD-associated damage-associated molecular patterns (DAMP, such as CALR exposure, ATP secretion, and HMGB1 release) in vitro and elicited significant antitumor responses in tumor vaccination assays in vivo. Paclitaxel-induced TLR4 signaling was essential to the release of DAMPs, which led to the activation of NF-κB–mediated CCL2 transcription and IkappaB kinase 2–mediated SNARE-dependent vesicle exocytosis, thus exposing CALR on the cell surface. Paclitaxel induced endoplasmic reticulum stress, which triggered protein kinase R–like ER kinase activation and eukaryotic translation initiation factor 2α phosphorylation independent of TLR4. Paclitaxel chemotherapy induced T-cell infiltration in ovarian tumors of the responsive patients; CALR expression in primary ovarian tumors also correlated with patients' survival and patient response to chemotherapy. These findings suggest that the effectiveness of paclitaxel relied upon the activation of antitumor immunity through ICD via TLR4 and highlighted the importance of CALR expression in cancer cells as an indicator of response to paclitaxel chemotherapy in ovarian cancer