Miracle-dz/UniFAR-FTUs huggingface dataset:
Interferon-stimulated gene 15 promotes progression of endometrial carcinoma and weakens antitumor immune response

Endometrial carcinoma (EC) is one of the most common gynecological cancers with a poor prognosis. Therefore, clarifying the details of the molecular mechanisms is of great importance for EC diagnosis and clinical management. Interferon-stimulated gene 15 (ISG15) plays an important role in the development of various cancers. However, its role in EC remains unclear. High ISG15 expression was observed in EC, which was associated with poor clinical outcomes and pathological stage of patients with EC, thus representing a promising marker for EC progression. Further exploratory analysis revealed that the elevated ISG15 levels in EC were driven by aberrant DNA methylation, independent of copy number variation and specific transcription factor aberrations. Accordingly, knockdown of ISG15 by small interfering RNA attenuated the malignant cellular phenotype of EC cell lines, including proliferation and colony formation in vitro. Finally, investigation of the molecular mechanisms indicated that ISG15 promoted the cell cycle G1/S transition in EC. Furthermore, ISG15 promoted EC progression by activating the MYC proto-oncogene protein signaling pathway. Moreover, ECs with high levels of ISG15 harbored a more vital immune escape ability, evidenced not only by significantly less invasive CD8+ T cells, but also higher expression of T cell inhibitory factors, such as programmed death-ligand 1. These results suggest a tumor-promoting role of ISG15 in EC, which may be a promising marker for diagnosis, prognosis and therapeutic immunity.
Truncated jarid2 and kdm6b transcripts are associated with temperature-induced sex reversal during development in a dragon lizard

Sex determination and differentiation in reptiles are complex. In the model species, Pogona vitticeps, high incubation temperature can cause male to female sex reversal. To elucidate the epigenetic mechanisms of thermolabile sex, we used an unbiased genome-wide assessment of intron retention during sex reversal. The previously implicated chromatin modifiers (jarid2 and kdm6b) were two of three genes to display sex reversal–specific intron retention. In these species, embryonic intron retention resulting in C-terminally truncated jarid2 and kdm6b isoforms consistently occurs at low temperatures. High-temperature sex reversal is uniquely characterized by a high prevalence of N-terminally truncated isoforms of jarid2 and kdm6b, which are not present at low temperatures, or in two other reptiles with temperature-dependent sex determination. This work verifies that chromatin-modifying genes are involved in highly conserved temperature responses and can also be transcribed into isoforms with new sex-determining roles.
Collaborative co-design and evaluation of an immersive virtual reality application prototype for communication rehabilitation (DISCOVR prototype)

Abstract Purpose Virtual reality (VR) lends itself to communication rehabilitation by creating safe, replicable, and authentic simulated environments in which users learn and practice communication skills. The aim of this research was to obtain the views of health professionals and technology specialists on the design characteristics and usability of a prototype VR application for communication rehabilitation. Materials and Methods Nine professionals from different health and technology disciplines participated in an online focus group or individual online interview to evaluate the application and use of the VR prototype. Data sources were analysed using a content thematic analysis. Results Four main themes relating to VR design and implementation in rehabilitation were identified: (i) designing rehabilitation-focused virtual worlds; (ii) understanding and using VR hardware; (iii) making room for VR in rehabilitation and training; and (iv) implementing VR will not replace the health professional’s role. Discussion Health professionals and technology specialists engaged in co-design while evaluating the VR prototype. They identified software features requiring careful consideration to ensure improved usability, client safety, and success in communication rehabilitation outcomes. Continuing inclusive co-design, engaging health professionals, clients with communication disability, and their families will be essential to creating useable VR applications and integrating these successfully into rehabilitation. Implications for rehabilitation Health and technology professionals, along with clients, are integral to the co-design of new VR technology applications. Design of VR applications needs to consider the client’s communication, physical, cognitive, sensory, psychosocial, and emotional needs for greater usability of these programs. Realism and authenticity of interactions, characters, and environments are considered important factors to allow users to be fully immersed in virtual simulations to enhance rehabilitation.
Detection of glyphosate residues in feed, saliva, urine and faeces from a cattle farm: a pilot study

Abstract Forty-two samples of feed, saliva, urines, and faeces collected from a cattle farm were investigated with the aim to evaluate the occurrence of glyphosate in faeces, urine and saliva. Glyphosate in the feed was also quantified to understand how it was assimilated by mammals. All cows excreted glyphosate in their faeces at concentrations between 57 and 983 ng g−1. In contrast, only 55% of urine and one sample of saliva tested positive. Most of the feeds demonstrated a non-negligible presence of glyphosate. In particular, a silage containing soybeans from genetically modified cultivation showed a concentration one order of magnitude higher than the other feeds. This study aims to provide the first complete determination of glyphosate in a cattle farm, considering the possible re-entry into the environment through the spreading of liquid and solid sewage and its possible impact on groundwater. Graphical Abstract
Addressing the challenges of novel therapies in rare diseases with mechanistic perspectives: Missed opportunities or the way forward?

clinical outcome. They discuss how this search included drugs from a huge variety of classes from fibrates to stem cells, antioxidants, anticonvulsants and vitamins in attempts to identify new possible therapies. However, none of the drug candidates have been approved for use, partially due to the lack of supporting real world evidence, such as biomarkers. In addition, the small numbers of patients may be inadequate to provide the mandatory pharmacokinetic and pharmaco-dynamics information needed for the approval of these drugs in such a rare condition.
Places for learning engineering: a preliminary report on informal learning spaces

This is a report on an investigation of undergraduate engineering learning spaces that were newly introduced in 2008. The new spaces include formal learning spaces designed to enable student-centred, small group learning, and informal cafe-style spaces. The project investigates the research question: Does an availability of spaces for informal learning lead to an increase in behaviours that one might expect to be conducive to increased informal or collaborative learning? The initial findings from the survey data suggest that there may be some increased use of informal learning spaces if they are made available to students.
The Views and Experience of Audiologists Working in Flemish Hearing Aid Centers Concerning Cognition Within Audiological Practice.

PURPOSE This study aimed to get insight into the views and experience of audiologists, employed in Flemish hearing aid centers, concerning cognition within audiological practice. METHOD An online 49-item questionnaire was developed and subdivided into five categories: (a) work setting, (b) practical experience regarding hearing aid fitting linked to cognition, (c) knowledge regarding the auditory-cognitive perspective of speech understanding, (d) willingness and guidelines to implement cognitive measures within audiological practice, and (e) demographics. Respondents were surveyed during January and February 2021. RESULTS One hundred twenty-nine audiologists working in Flemish hearing aid centers responded to the entire questionnaire and showed a mean work experience of 8.0 years. Results revealed that cognition was taken into account, especially within the anamnesis interview and general communication strategy, whereas only a minority took cognition into account when actually fitting hearing aids. Knowledge and experience did not determine whether or not respondents took cognition into account. A willingness to implement cognitive measures in a time-efficient manner in audiological practice was observed among respondents. CONCLUSION Evidence-based guidelines regarding hearing aid fitting based on an individual's auditory-cognitive profile are needed to improve the quality of hearing rehabilitation. SUPPLEMENTAL MATERIAL https://doi.org/10.23641/asha.19593388.
TRUE SURFACE PROCESSING OF SPRING COULEE

The true surface processing approach takes into account the difference in the source and receiver raypaths introduced by variable elevation and weathering. In this project seismic data from the Spring Coulee line, in this case P-P, are processed using the true surface methodology. The post-stack time migrated P-P section obtained shows important differences with respect to other published results.
Closing the Control Loop with Time-Variant Embedded Soft Sensors and Recurrent Neural Networks

Embedded soft sensors can significantly impact the design and control of soft-bodied robots. Although there have been considerable advances in technology behind these novel sensing materials, their application in real-world tasks, especially in closed-loop control tasks, has been severely limited. This is mainly because of the challenge involved with modeling a nonlinear time-variant sensor embedded in a complex soft-bodied system. This article presents a learning-based approach for closed-loop force control with embedded soft sensors and recurrent neural networks (RNNs). We present learning protocols for training a class of RNNs called long short-term memory (LSTM) that allows us to develop accurate and robust state estimation models of these complex dynamical systems within a short period of time. Using this model, we develop a simple feedback force controller for a soft anthropomorphic finger even with significant drift and hysteresis in our feedback signal. Simulation and experimental studies are conducted to analyze the capabilities and generalizability of the control architecture. Experimentally, we are able to develop a closed-loop controller with a control frequency of 25 Hz and an average accuracy of 0.17 N. Our results indicate that current soft sensing technologies can already be used in real-world applications with the aid of machine learning techniques and an appropriate training methodology.
Tenosynovial giant cell tumors (TGCT): molecular biology, drug targets and non-surgical pharmacological approaches

ABSTRACT Introduction Tenosynovial giant cell tumor (TGCT) is a mono-articular, benign or locally aggressive and often debilitating neoplasm. Systemic therapies are becoming part of the multimodal armamentarium when surgery alone will not confer improvements. Since TGCT is characterized by colony-stimulating factor-1 (CSF1) rearrangements, the most studied molecular pathway is the CSF1 and CSF1 receptor (CSF1R) axis. Inhibiting CSF1-CSF1R interaction often yields considerable radiological and clinical responses; however, adverse events may cause treatment discontinuation because of an unfavorable risk-benefit ratio in benign disease. Only Pexidartinib is approved by the US FDA; however, the European Medicines Agency has not approved it due to a uncertain risk-benefit ratio. Thus, there is a need for safer and effective therapies. Areas covered Light is shed on disease mechanisms and potential drug targets. The safety and efficacy of different systemic therapies are evaluated. Expert opinion The CSF1-CSF1R axis is the principal drug target; however, the effect of CSF1R inhibition on angiogenesis and the role of macrophages, which are essential in the postoperative course, needs further elucidation. Systemic therapies have a promising role in treating mainly diffuse-type, TGCT patients who are not expected to clinically improve from surgery. Future drug development should focus on targeting neoplastic TGCT cells.
Rat and Mouse Brain Tumor Models for Experimental Neuro-Oncology Research

Abstract Rodent brain tumor models have been useful for developing effective therapies for glioblastomas (GBMs). In this review, we first discuss the 3 most commonly used rat brain tumor models, the C6, 9L, and F98 gliomas, which are all induced by repeated injections of nitrosourea to adult rats. The C6 glioma arose in an outbred Wistar rat and its potential to evoke an alloimmune response is a serious limitation. The 9L gliosarcoma arose in a Fischer rat and is strongly immunogenic, which must be taken into consideration when using it for therapy studies. The F98 glioma may be the best of the 3 but it does not fully recapitulate human GBMs because it is weakly immunogenic. Next, we discuss a number of mouse models. The first are human patient-derived xenograft gliomas in immunodeficient mice. These have failed to reproduce the tumor-host interactions and microenvironment of human GBMs. Genetically engineered mouse models recapitulate the molecular alterations of GBMs in an immunocompetent environment and “humanized” mouse models repopulate with human immune cells. While the latter are rarely isogenic, expensive to produce, and challenging to use, they represent an important advance. The advantages and limitations of each of these brain tumor models are discussed. This information will assist investigators in selecting the most appropriate model for the specific focus of their research.
Application-Layer DDoS Detection by K-means Algorithm

Lots of methods have been proposed to detect Distributed Denial-of–Service (DDoS) attacks focus on the transport layer and the network layer. However, these methods may not work well when application-layer DDoS attack is launched. In this paper, we introduce a clustering method based on some features to detect application-layer DDoS attack. Firstly, we extract features from normal users’ sessions. Then, we cluster users’ sessions by K-means algorithm and build normal users’ behavior model. Finally, we detect the application-layer DDoS attack based on the normal users’ behavior model. Introduction Distributed denial of service attack (DDoS) is a major threat in the Internet. There are many classical DDoS attack methods and tools. Traditionally, DDoS attacks such as ICMP flooding, SYN flooding and UDP flooding [1] are carried out at the network layer and transport layer. Lots of methods have been proposed to defend this serious security threaten. Statistical approaches [2] to DDoS attacks detection and mitigation involve packet attributes like source IP and destination IP address, time to live (TTL), and so on. These attributes are used to build kinds of defense mechanisms. Clustering Method can be also found to detect DDoS attacks focus on IP or TCP layers. Lee et al. [3] cluster IP addresses and TCP and UDP ports on backbone routers to find DDoS attacks. These methods are effective for network-layer DDoS attack detection. But they cannot protect against application-layer DDoS attack effectively. Application-layer DDoS attacks use true IP address. Attack agent simulates a normal web user and sends requests to application server. As we known, application server can handle limit requests in a short time. All attack agents cooperate to overload the application server. Application-layer DDoS can bypass defense methods which use IP or TCP header attributes’ distribution by making full and successful TCP connections. In this paper, we use k-means clustering method to detect application-layer DDoS attack. We select proper features of users’ sessions, and cluster these normal user sessions to build normal users behavior models. There are many differences between normal users’ sessions and attackers’ sessions, like request rate, requested resources, and so on. As we have built the normal users behavior models, we can use these models to detect anomaly of behavior. 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016) Copyright © 2016, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Computer Science Research, volume 50
BALL MILL MODELS: THEIR EVOLUTION AND PRESENT STATUS.

The evolution of ball mill models from empirical energy-size reduction relationships to the modern mathematical models that describe the various comminution and material transport mechanisms is described. The methods used by the various research workers for the determination of ball mill models parameters are compared. This comparison shows that, while all the modern ball mill models use a similar method for describing the breakage rate and the breakage distribution functions, each model has its own way of representing the material transport mechanisms. The ball mill scale-up techniques using laboratory batch milling data and single particle breakage data are discussed. The predictions from the ball mill scale-up scheme using single particle breakage data and the multi-segment ball mill model are illustrated from a typical example.
ranking-based choice network management.

Product-closing approximation for Summary: Most recent research in network revenue management incorporates choice behavior that models the customers’ buying logic. These models are consequently more complex to solve, but they return a more robust policy that usually generates better expected revenue than an independent-demand model. Choice network revenue management has an exact dynamic programming formulation that rapidly be-comes intractable. Approximations have been developed, and many of them are based on the multinomial logit demand model. However, this parametric model has the property known as the independence of ir-relevant alternatives and is often replaced in practice by a nonparametric model. We propose a new approximation called the product closing program that is speciﬁcally designed for a ranking-based choice model representing a nonparametric demand. Numerical experiments show that our approach quickly returns expected revenues that are slightly better than those of other approximations, especially for large instances. Our approximation can also supply a good initial solution for other approaches.
A Novel Space-Time-Speed Method for Increasing the Passing Capacity with Safety Guaranteed of Railway Station

A method for improving the passing capacity of a station without adding any track and equipment is proposed in this paper. In the process of handling train routes, by transforming the existing fixed train-approaching locking section into a variable mode, the route locking time is shortened and in-station resource consumption is reduced. This approach improves the capacity of the station. At the same time, delay of the train can be quickly returned to normal. A method of variable train-approaching locking section is discussed; a mathematical model for increasing station passing capacity is shown. Comparison between the impact of a variable train-approaching locking section and a fixed mode on the station passing capacity is shown.
CLUSTER FORMATION ALGORITHM FOR LIFE TIME MAXIMIZATION IN HETEROGENEOUS WIRELESS SENSOR NETWORKS

Typically, a wireless sensor network (WSN) is a new technology about acquiring and processing information from environment. Wireless sensor network contains an important number of inexpensive power constrained sensors, which collect data from the environment and transmit them towards the base station. Sensor networks have limited and non-rechargeable energy resources. Saving energy and therefore, extending the wireless sensor networks lifetime, imposes a great challenge. The clustering technique is a kind of key technique used to reduce energy consumption. Many clustering protocols had designed for the characteristic of heterogeneous wireless sensor networks. In this paper we propose energy efficient cluster formation algorithm for heterogeneous wireless sensor network. Our technique differs from DEEC protocol in cluster formation phase. In our technique the member node join that cluster head which has minimum distance between member node to cluster head and minimum distance between cluster head to base station. Simulation result shows that our technique will increase the network lifetime around 25% and number of packet sent up to 10%.
Dynamic Tomography Reconstruction by Projection-Domain Separable Modeling

In dynamic tomography the object undergoes changes while projections are being acquired sequentially in time. The resulting inconsistent set of projections cannot be used directly to reconstruct an object corresponding to a time instant. Instead, the objective is to reconstruct a spatio-temporal representation of the object, which can be displayed as a movie. We analyze conditions for unique and stable solution of this ill-posed inverse problem, and present a recovery algorithm, validating it experimentally. We compare our approach to one based on the recently proposed GMLR variation on deep prior for video, demonstrating the advantages of the proposed approach.
Singularity-Avoiding Multi-Dimensional Root-Finder

We proposed in this paper a new method, which we named the W4 method, to solve nonlinear equation systems. It may be regarded as an extension of the Newton-Raphson~(NR) method to be used when the method fails. Indeed our method can be applied not only to ordinary problems with non-singular Jacobian matrices but also to problems with singular Jacobians, which essentially all previous methods that employ the inversion of the Jacobian matrix have failed to solve. In this article, we demonstrate that (i) our new scheme can define a non-singular iteration map even for those problems by utilizing the singular value decomposition, (ii) a series of vectors in the new iteration map converges to the right solution under a certain condition, (iii) the standard two-dimensional problems in the literature that no single method proposed so far has been able to solve completely are all solved by our new method.
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines

A misspeciﬁed reward can degrade sample efﬁciency and induce undesired behaviors in reinforcement learning (RL) problems. We propose symbolic reward machines for incorporating high-level task knowledge when specifying the reward signals. Symbolic reward machines augment ex-isting reward machine formalism by allowing transitions to carry predicates and symbolic reward outputs. This formalism lends itself well to inverse reinforcement learning, whereby the key challenge is determining appropriate assignments to the symbolic values from a few expert demonstrations. We propose a hierarchical Bayesian approach for inferring the most likely assignments such that the concretized reward machine can dis-criminate expert demonstrated trajectories from other trajectories with high accuracy. Experimental results show that learned reward machines can signiﬁcantly improve training efﬁciency for complex RL tasks and generalize well across different task environment conﬁgurations.