Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic cycle selection in active flow networks

Significance Nature often uses interlinked networks to transport matter or information. In many cases, the physical or virtual flows between network nodes are actively driven, consuming energy to achieve transport along different links. However, there are currently very few elementary principles known to predict the behavior of this broad class of nonequilibrium systems. Our work develops a generic foundational understanding of mass-conserving active flow networks. By merging previously disparate concepts from mathematical graph theory and physicochemical reaction rate theory, we relate the self-organizing behavior of actively driven flows to the fundamental topological symmetries of the underlying network, resulting in a class of predictive models with applicability across many biological scales. Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models.
Oxazaphosphorines: new therapeutic strategies for an old class of drugs

Importance of the field: The oxazaphosphorines (cyclophosphamide, ifosfamide and trofosfamide) are widely used in clinical practice for their antitumor and immunomodulatory activities. However, their use is associated with toxicities. The metabolism of oxazaphosphorines involves cytochrome P450 biotransformations, leading to highly reactive metabolites such as acrolein and chloroacetaldehyde responsible for urotoxicity, neurotoxicity and nephrotoxicity. While the mechanisms behind these toxicities remain under investigation, some advances have been made, as exemplified by the use of mesna to limit acrolein related urotoxicity. Areas covered in this review: This review highlights potential strategies for limiting side effects commonly associated with the oxazaphosphorine drugs, through pharmacological or medicinal chemistry-based approaches. What the reader will gain: The readers will gain a comprehensive review of these approaches to treatment in terms of: i) pharmacology: use of antidotes and modification of metabolism through inhibition/induction of CYP enzymes or use of gene therapy; and ii) medicinal chemistry: the design of new drugs to target cancer cells and avoid CYP biotransformation with pre-activated prodrugs or with side-chain substituted analogues. Take home message: An increased knowledge of oxazaphosphorines' metabolism and toxicity may allow the development of new anticancer drugs combined with drug delivery systems to circumvent drug toxicity, providing increased tumoral specificity and greater anticancer activity.
Towards Micro-video Understanding by Joint Sequential-Sparse Modeling

Like the traditional long videos, micro-videos are the unity of textual, acoustic, and visual modalities. These modalities sequentially tell a real-life event from distinct angles. Yet, unlike the traditional long videos with rich content, micro-videos are very short, lasting for 6-15 seconds, and they hence usually convey one or a few high-level concepts. In the light of this, we have to characterize and jointly model the sparseness and multiple sequential structures for better micro-video understanding. To accomplish this, in this paper, we present an end-to-end deep learning model, which packs three parallel LSTMs to capture the sequential structures and a convolutional neural network to learn the sparse concept-level representations of micro-videos. We applied our model to the application of micro-video categorization. Besides, we constructed a real-world dataset for sequence modeling and released it to facilitate other researchers. Experimental results demonstrate that our model yields better performance than several state-of-the-art baselines.
Local Variance Driven Self-Organization for Unsupervised Clustering

We propose a new, novel unsupervised clustering technique based on traditional Kohonen self organization, competitive Hebbian learning (CHL), and the Hebbian based maximum eigenfilter (HME). This method fits into the family of dynamic self-generating, self-organizing map (SOM) algorithms. The approach uses a vigilance based, global parsing strategy as a guide for the hierarchical partitioning of an underlying data distribution into a set of dominant prototypes: each consisting of a dual memory element for the online estimation of both position and maximal local variance. A co-operative scheme exploits the interplay between global vigilance and maximal local variance such that an informed choice may be made regarding insertion sites for new nodes into the map. The network is related to self-organizing tree maps (SOTM), growing neural gas (GNG) and their variants. A framework is presented and performance demonstrated against GNG
Burnout: a fashionable diagnosis.

BACKGROUND "Burnout syndrome" is now a common reason for medical excuses from work, and thus an important topic in health-related economics. Much research is still needed, however, to establish the scientific basis for this entity, the criteria by which it might be diagnosed and classified, and how it should be treated. METHODS A systematic review of this topic, previously published as an HTA report, is presented here together with a selective overview of pertinent literature. RESULTS There currently exists neither an officially accepted definition nor a valid instrument for the differential diagnosis of burnout syndrome. Its manifestations are generally considered to lie along three dimensions: emotional exhaustion, depersonalization, and reduced performance ability and/or motivation. Most of the available studies on its epidemiology and differential diagnosis provide no more than a low level of evidence for their conclusions. There have been no controlled trials of treatments for burnout. CONCLUSION High-quality controlled studies on burnout syndrome are lacking. A standardized and internationally accepted diagnostic instrument with a validated rating scale should be developed. There is also a need for epidemiological and health-economic studies on the prevalence, incidence, and cost of burnout. The etiology and pathogenesis of burnout should be studied with special regard to the possible role of neurobiological factors. Treatments for it should be studied systematically so that their effects can be judged at a high level of evidence. In view of the current lack of knowledge about what is called "burnout," the term should not be used as a medical diagnosis or as a basis for decisions regarding disability or other socioeconomic matters.
On Training Neural Networks with Transfer Function

Transfer functions play an important role in artificial neural networks. They enable neural networks to form decision boundaries of different shapes and forms. However, transfer function optimisation has received relatively little research attention. In this paper, we present an approach for training neural networks that use transfer functions pooling as a method of optimising transfer function, such as Neural Diversity Machines. The method we present regulates the dimensionality of the search space for computational strategies by detecting and searching potentially promising regions of this search space, then gradually growing the search to include other regions. This is done by growing the subset of transfer functions made available during the optimisation of the neural networks. Variants of this approach are tested on popular datasets such as the XOR, Double spiral, Iris, Sonar, and Diabetes datasets. The results suggest that transfer function complexification improves convergence properties in most cases, and generalization properties in some cases.
The Development of Shared Mental Models during Air Battlespace Collective Training using Dispersed Networked Systems

Abstract : The UK Ministry of Defence (MOD) has a vision of providing Mission Training through Distributed Simulation, or UK MTDS. The paper will report the human performance results from a three year MTDS Capability Concept Demonstrator (CCD) programme presenting data to qualify the development of shared situational awareness and mental models in a networked training environment. Evidence was collected during five national and multi-national air battlespace exercises, from subjective rating scales, interviews, observational and anecdotal data. The MTDS CCD facility, based at RAF Waddington, included 8 fast-jet Tornado GR4 and Typhoon simulators, a 7-seat AWACS mission crew training system, and extensive exercise management capability (including virtual role players and Computer Generated Forces (CGF)). Data were gathered during representative daily Planning, Briefing, Execution, and Debriefing (PBED) cycles of air battlespace missions which took place at the collective, joint and multi-national levels. The MTDS CCD programme assessed the training benefit, and the optimal balance, between co-located teams and dispersed training teams (both in the UK and US). The major source of data across all sites was the application of the Mission Essential Competency (MEC) framework. Each warfighter's expectations were recorded for a series of platform based MECs and compared to warfighter's experiences. These data were used to assess differences between role, platform and site. To allow analysis of the robustness of the networked environment for shared cognitive processes, these were then sub-divided into collective experiences, i.e. those that required interactions with other players or entities leading to collective training; and individual MEC experiences.
Fundamentals and applications of field/current assisted sintering

Abstract: The recent Field Activated Sintering Technique (FAST) yields enhanced powder densification due to an external electrical current/field application. FAST has been applied to a wide spectrum of materials, from electrically conductive materials to insulators. The chapter first discusses the fundamentals of electrical phenomena and sintering mechanisms under an external electrical current/field. It then describes the main applications and manufacturing benefits of FAST sintering.
Equilibrium order parameters of nematic liquid crystals in the Landau-de Gennes theory

We study equilibrium liquid crystal configurations in three-dimensional geometries, within the continuum Landau-de Gennes theory. We obtain explicit bounds for the equilibrium scalar order parameters in terms of the temperature and material-dependent constants. We explicitly quantify the temperature regimes where the Landau-de Gennes predictions match and the temperature regimes where the Landau-de Gennes predictions do not match the probabilistic second-moment definition of the Q-tensor order parameter. The regime of agreement may be interpreted as the regime of validity of the Landau-de Gennes theory since the Landau-de Gennes theory predicts large values of the equilibrium scalar order parameters – larger than unity, in the low-temperature regime. We discuss a modified Landau-de Gennes energy functional which yields physically realistic values of the equilibrium scalar order parameters in all temperature regimes.
Secure Information Transmission using Steganography and Morphological Associative Memory

This paper presents a new steganography algorithm based on Morphology associative memory. Often, steganalysis methods are created to detect steganography algorithms using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). In this paper, cover images are mapped to morphological representation by using morphology transform containing morphological coefficients, and each bit of secret message is inserted in the least significant bit of morphological coefficients. To evaluate stego quality, we measure the quality of the cover image after embedding by comparing with other image transformed steganography algorithms such as discrete cosine and Wavelet transforms. The quality of stego has considerably improved in comparison with the state-of-art methods. In the other experimentation, we test the robustness of our proposed method by using Wavelet and Block-based steganalysis methods. The results show a high level of robustness of our algorithm respect to other steganography algorithms.
Methodologic issues in assessing the quality of life of cancer patients

Although quality of life assessments have been employed successfully in descriptive and evaluative studies in oncology, their use in cancer clinical trials has, to date, been limited. A range of issues have impeded the conduct of clinical trial‐based quality of life investigations. These include: the absence of theoretical models to guide the development of quality of life measures; over‐reliance on ad hoc approaches to quality of life assessment; and insufficient attention to the practical constraints operating in clinical research settings. Of primary importance is the need to develop multidimensional quality of life instruments that are brief and psychometrically robust. It is suggested that future work on instrument development focus on refining currently available generic or cancer‐specific measures, and on developing new diagnostic‐specific questionnaire modules. This psychometric work should be guided by appropriate theoretical models of the relationship among health‐related quality of life domains. Although it is widely accepted that the patient represents the most appropriate source of quality of life data, it is suggested that efforts also be directed toward improving the validity and reliability of physician‐generated assessments of patients' performance status and of treatment toxicities, and toward determining the feasibility of employing family members as proxy raters of the psychologic and social health status of patients who are unwilling or unable to provide such information. Additional attention should be paid to the many logistical problems that arise in clinical trial‐based quality of life investigations. In particular, research designs and data collection procedures should be selected that minimize patient, medical staff, and institutional burden.
Analysis of Transcription Factor HY5 Genomic Binding Sites Revealed Its Hierarchical Role in Light Regulation of Development[W]

The transcription factor LONG HYPOCOTYL5 (HY5) acts downstream of multiple families of the photoreceptors and promotes photomorphogenesis. Although it is well accepted that HY5 acts to regulate target gene expression, in vivo binding of HY5 to any of its target gene promoters has yet to be demonstrated. Here, we used a chromatin immunoprecipitation procedure to verify suspected in vivo HY5 binding sites. We demonstrated that in vivo association of HY5 with promoter targets is not altered under distinct light qualities or during light-to-dark transition. Coupled with DNA chip hybridization using a high-density 60-nucleotide oligomer microarray that contains one probe for every 500 nucleotides over the entire Arabidopsis thaliana genome, we mapped genome-wide in vivo HY5 binding sites. This analysis showed that HY5 binds preferentially to promoter regions in vivo and revealed >3000 chromosomal sites as putative HY5 binding targets. HY5 binding targets tend to be enriched in the early light-responsive genes and transcription factor genes. Our data thus support a model in which HY5 is a high hierarchical regulator of the transcriptional cascades for photomorphogenesis.
Immunity to Salmonella typhimurium Infection: Characterization of Antigens in Active Protection by Polyacrylamide Gel Electrophoresis

Immunity to challenge with Salmonella typhimurium (strain SR-11) was studied in NIH/NMRI Swiss mice. Viable organisms induced the best protection, but ribosomes were the best immunogen of the subcellular fractions. Antigens with lower concentrations of protein did not induce protection as well as antigens which had higher concentrations of protein. Acrylamide gel electrophoresis of the SR-11 subcellular components, fractionation, and differential staining of the gel columns indicated that the immunogen is a high-molecular-weight glycoprotein or mucopolysaccharide.
Stabilization and activation of p53 induced by Cdk5 contributes to neuronal cell death

The p53 tumor suppressor protein is a key regulator of cellular functions including responses to numerous stress signals, and triggers apoptosis in many cell types, including neurons. The major mechanisms known to regulate p53 stabilization and activation include phosphorylation and ubiquitin ligase-mediated proteasomal degradation. Cyclin-dependent kinase 5 (Cdk5), a proline-directed serine/threonine kinase, is most active in the central nervous system and plays a variety of roles in neuronal degeneration. Here, we demonstrate for the first time that Cdk5 interacts with p53 and increases its stability through posttranslational regulation, leading to accumulation of p53, particularly in the nucleus. We show that Cdk5 phosphorylates p53 on Ser15, Ser33 and Ser46 in vitro, and that increased Cdk5 activity in the nucleus mediates these phosphorylation events in response to genotoxic and oxidative stresses. Cdk5 mediates disruption of the interaction between p53 and Hdm2 (also known as Mdm2), and prevents Hdm2-induced p53 ubiquitylation and downregulation. Cdk5 additionally enhances phosphorylation-dependent binding of the p300 coactivator, inducing acetylation of p53. Cdk5-stabilized p53 protein is transcriptionally active, resulting in the induction of pro-apoptotic genes and subsequent mitochondria-mediated apoptosis in response to genotoxic or oxidative stress. Collectively, these novel findings help define the mechanisms underlying neuronal apoptosis occurring as a result of Cdk5-mediated p53 stabilization and transcriptional activation.
Intrinsic projections of the retrohippocampal region in the rat brain. I. The subicular complex

The intrahippocampal projections of the subicular complex were studied in the rat with the aid of the anterogradely transported lectin Phaseolus vulgaris leucoagglutinin (PHA‐L). After iontophoretic injections of the lectin into the subiculum proper, presubiculum, or the parasubiculum, axons and terminal processes immunoreactive for PHA‐L were traced to their respective terminal fields within the hippocampal region. After subicular injections PHA‐L‐stained axons could be followed both in a caudal and a rostral direction. The caudally directed fibers course around or within the angular bundle to enter layers VI and V of the medial entorhinal area (MEA). Many fibers penetrate through these layers to terminate in layer IV of the medial and the lateral entorhinal area, which contains a major terminal field of this projection. At more ventral levels, all layers of the entorhinal area are innervated by cells located in the subiculum. Other retrohippocampal projections of the subiculum proper include the deep and the outer two layers of the presubiculum and the medial sector of the parasubiculum, in addition to a massive projection which terminates in the retrosplenial cortex. The rostrally directed projections from the subiculum form a dense innervation of strata lacunosum, radiatum, oriens, and of individual pyramidal cells in the regio superior of the Ammon's horn. All these projections of the subiculum are exclusively ipsilateral.
AnyviewC: A Visual Practice Platform for Data Structures Course

AnyviewC is a C-language Integrated Development Environment (IDE) with visual debugging feature. Besides editing, compiling, and debugging C-language source code, with the help of the special algorithm to recognize typical data structure, and the layout strategy to visualize corresponding data structure, AnyviewC can identify the relationship among the data and apply visualization techniques to demonstrate data structure as suitable graphics. AnyviewC has been used as a practice platform for data structures course in Guangdong University of Technology, and received widely approval in enhancing students' performance in the course.
True Random Number Generator using Fish Tank Image

Pseudo Random Number Generator (PRNG) uses a deterministic system and an initial seed to generate random numbers. In order for the output sequence to be truly random, a truly random input seed is used. Most True Random Number Generators (TRNG), use noise in the form nuclear decay, atmospheric noise, electrical noise or Brownian motion as their initial seed. In order to reduce the computational complexity, we use a simple setup of a fish tank as the variable environment, capturing its images over time. The image data is then applied to a reduction algorithm and hash function to generate the initial seed. We propose a cost efficient method of extracting the true seed from the image data and applying it to a pseudo random generator, a Linear Congruential Generator (LCG) in our case to give true random numbers.
Endoscopic surveillance of gastrointestinal premalignant lesions: current knowledge and future directions

Purpose of review Provide an evidence-based resource for the surveillance of gastrointestinal premalignant lesions, focusing on the scientific articles reported recently. Recent findings No randomized controlled clinical trials exist to definitively support the efficacy of surveillance programs for Barrett's esophagus and gastric intestinal metaplasia. However, surveillance of these premalignant lesions is recommended by some of the leading organizations. To optimize the usefulness of surveillance programs, targeting high-risk patients might maximize its benefits. A Barrett's esophagus segment of at least 3 cm and evidence of intestinal metaplasia can help stratify those patients at highest risk for progression to esophageal adenocarcinoma. The location, extent, and severity of intestinal metaplasia are indicators of risk of developing gastric cancer. Patients with extensive intestinal metaplasia should be offered endoscopic surveillance. Quality in the baseline colonoscopy is crucial to decrease the risk of interval colorectal cancers. The importance of serrated polyps, as well as their surveillance intervals, is emphasized. Summary To optimize the usefulness of surveillance programs, targeting high-risk patients might maximize its benefits. Future research is needed to design more effective surveillance strategies. Recently, emerging imaging techniques hold promise for improving sensitivity of endoscopic surveillance of premalignant conditions in the gastrointestinal tract.
Estimating the supply chain efficiency loss when the seller has to estimate the buyer's willingness to pay

We study the pricing problem between two firms when the manufacturer’s willingness to pay (wtp) for the supplier’s good is not known by the latter. We demonstrate that it is in the interest of the manufacturer to hide this information from the supplier. The precision of the information available to the supplier modifies the rent distribution. The risk of opportunistic behaviour entails a loss of efficiency in the supply chain. The model is extended to the case of a supplier submitting offers to several manufacturers. Some managerial insight through a numerical illustration is provided.