Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibition of HSP90 as a Strategy to Radiosensitize Glioblastoma: Targeting the DNA Damage Response and Beyond

Radiotherapy is an essential component of multi-modality treatment of glioblastoma (GBM). However, treatment failure and recurrence are frequent and give rise to the dismal prognosis of this aggressive type of primary brain tumor. A high level of inherent treatment resistance is considered to be the major underlying reason, stemming from constantly activated DNA damage response (DDR) mechanisms as a consequence of oncogene overexpression, persistent replicative stress, and other so far unknown reasons. The molecular chaperone heat shock protein 90 (HSP90) plays an important role in the establishment and maintenance of treatment resistance, since it crucially assists the folding and stabilization of various DDR regulators. Accordingly, inhibition of HSP90 represents a multi-target strategy to interfere with DDR function and to sensitize cancer cells to radiotherapy. Using NW457, a pochoxime-based HSP90 inhibitor with favorable brain pharmacokinetic profile, we show here that HSP90 inhibition at low concentrations with per se limited cytotoxicity leads to downregulation of various DNA damage response factors on the protein level, distinct transcriptomic alterations, impaired DNA damage repair, and reduced clonogenic survival in response to ionizing irradiation in glioblastoma cells in vitro. In vivo, HSP90 inhibition by NW457 improved the therapeutic outcome of fractionated CBCT-based irradiation in an orthotopic, syngeneic GBM mouse model, both in terms of tumor progression and survival. Nevertheless, in view of the promising in vitro results the in vivo efficacy was not as strong as expected, although apart from the radiosensitizing effects HSP90 inhibition also reduced irradiation-induced GBM cell migration and tumor invasiveness. Hence, our findings identify the combination of HSP90 inhibition and radiotherapy in principle as a promising strategy for GBM treatment whose performance needs to be further optimized by improved inhibitor substances, better formulations and/or administration routes, and fine-tuned treatment sequences.
Identification and Validation of Reference Genes in Clostridium beijerinckii NRRL B-598 for RT-qPCR Using RNA-Seq Data

Gene expression analysis through reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) depends on correct data normalization by reference genes with stable expression. Although Clostridium beijerinckii NRRL B-598 is a promising Gram-positive bacterium for the industrial production of biobutanol, validated reference genes have not yet been reported. In this study, we selected 160 genes with stable expression based on an RNA sequencing (RNA-Seq) data analysis, and among them, seven genes (zmp, rpoB1, rsmB, greA, rpoB2, topB2, and rimO) were selected for experimental validation by RT-qPCR and gene ontology (GO) enrichment analysis. According to statistical analyses, zmp and greA were the most stable and suitable reference genes for RT-qPCR normalization. Furthermore, our methodology can be useful for selection of the reference genes in other strains of C. beijerinckii and it also suggests that the RNA-Seq data can be used for the initial selection of novel reference genes, however, their validation is required.
1.2 kV Vertical GaN Fin JFETs with Robust Avalanche and Fast Switching Capabilities

This work, for the first time, demonstrates a 1.2-kV-class, 4-A normally-off vertical GaN fin-channel JFET on GaN substrate, and characterizes its static and dynamic performance as well as avalanche robustness. The device shows an on/off current ratio of ~109, a specific on-resistance (RON) of 0.82 mΩ·cm2, and a threshold voltage (VTH) over 0.5 V extracted at a drain current of 1 mA. The on/off ratio and VTH exhibit very little changes at high temperatures up to 125°C. A robust avalanche is observed under the unclamped inductive switching (UIS) conditions, showing an avalanche breakdown voltage (BVAVA) of 1470 V and an avalanche current (IAVA) over 1 A. The IAVA is found to mainly flow through the p-GaN gate instead of the n-GaN fins. The JFET also shows small junction capacitances. Double-pulse tests at 600-V/4-A reveal a rise/fall time of 12.9 ns/10.3 ns and a total loss of 37 μJ. Almost no reverse recovery is observed on the body diode, due to the short lifetime of minority carriers. To the best of our knowledge, we report one of the highest Baliga’s figure-of-merit (BV2/RON) and the first avalanche robustness in vertical GaN power transistors. Our results suggest the great potential of vertical GaN JFETs for medium-voltage high-frequency power applications.
A Routing Protocol toward Reliable Mobile Communication in Wi-SUN FAN

In recent years, the Wi-SUN field area network (FAN), a wireless multi-hop communication standard, has emerged in smart city infrastructures. Wi-SUN FAN adopts the Internet protocol version 6 (IPv6) routing protocol for low power and lossy networks (RPL) as its routing protocol in the network layer. RPL is originally developed for fixed nodes, such as smart metering devices. Nevertheless, as smart city infrastructures develop, mobile nodes' deployment becomes inevitable, and the Wi-SUN FAN needs to support the routing of mobile nodes. This paper evaluates the delivery rate of data packets from a mobile node to the network's gateway under RPL by computer simulations. The results specify that the slow parent handover deteriorates the delivery rate. Then, we propose a routing algorithm that considers the mobile node's movement to achieve reliable mobile communication in the Wi-SUN FAN. The computer simulation results show that the proposed routing algorithm increases the delivery rate by up to 80.7% compared to the original RPL.
Investigating cyber alerts with graph-based analytics and narrative visualization

In real-world situations, several threat alerts are being investigated by the specialised staff. In order to prompt response to serve incidents or ignore false alarms, alerts are prioritised and analysed. Security professionals rely on information provided in the alert message. Insufficient information in alert messages raises challenges for security analysts that require them to keep track of all internal and external sources to identify the relevant information. In this paper, a Narrative Analytics-Assisted System (NAAS) is proposed, and a knowledge graph is used in the proposed system to present the relationships. The knowledge graph is proposed to capture the complex relationships between the alert and relevant information from the Internal and External knowledge bases to reduce the cognitive effort in information digestion and to understand a wealth of security data. To enable cooperation in the cyber risk management process, it is an inevitable necessity to generate the knowledge graph and interpret it in a human-friendly format. The current machine-friendly formats for reporting incidents from alerts are complex and of an extensive nature. These characteristics hamper the readability and contribution, therefore preventing humans from understanding and being up to date about the incident. NAAS contains four life cycles to assist an analyst to have a better perception of the elements of the environment by involving more staff in the risk management: (1) Analyses the alert, (2) designs the knowledge graph with the natural language sentences, (3) automatically implements the incident report in natural language by applying novel storytelling techniques from the knowledge graph, and (4) maintains it with the contribution of different levels of expertise. The performance of various NAAS’s cycles is demonstrated in a case study with an example scenario from the Security Operations Centre (SOC) at an educational institution, highlighting its useability.
Latency-Aware Fair Scheduling for Spatial Reuse in WLANs: A Lyapunov Optimization Approach

The IEEE 802.11ax has introduced the concurrent transmissions among neighboring wireless local area networks (WLANs) to facilitate spatial reuse. As a side effect, the scheduling problem has become more challenging due to the appearance of unmanaged co-channel interference. This paper proposes a latency-aware fair resource scheduling scheme in dense WLANs with Lyapunov optimization. In the scheduling scheme, we formulate a scheduling problem for WLANs with the stabilization of the transmission queue, which is one of the quality of service (QoS) issues and the fairness of the instantaneous data rate as a stochastic optimization problem. This problem is solved by using Lyapunov optimization, which eliminates the time average constraints of the problem. We performed numerical simulations in dense WLANs, and the results confirm that the proposed scheme guarantees an allowable queue size. the results also show that the proposed scheme achieves a higher fairness index and a smaller queue size than compared methods.
In vitro cytocompatibility and blood compatibility of polysulfone blend, surface-modified polysulfone and polyacrylonitrile membranes for hemodialysis

The fabrication of dialysis membranes with significant biocompatibility is an active area of research. In this context, three types of asymmetric flat sheet membranes were fabricated and compared for potential use as hemodialysis membranes. A polysulfone–polyvinylpyrrolidone and polyethylene glycol-based polymer blend membrane, a polysulfone membrane surface-modified with trimesoyl chloride and m-phenylene diamine, and a polyacrylonitrile membrane were synthesized. All three types of membrane were characterized in terms of their surface morphology, permeability, hydrophilicity, surface charge, porosity and mechanical strength. They were then subjected to comprehensive cytocompatibility and hemocompatibility tests as well as analysing the transport of uremic toxins. On the basis of protein adsorption, oxidative stress, cell proliferation and adhesion, all three membranes were comparable. However, the blend and surface-modified membranes showed excellent results for hemolysis, platelet adhesion, blood cell aggregation and degree of thrombus formation. All these results indicated the suitability of the blend and surface-modified membranes for possible dialysis applications.
Surface Wave and Lamb Wave Acoustic Devices on Heterogenous Substrate for 5G Front-Ends

We demonstrate groups of surface wave (SH0 mode) and Lamb wave (S0 mode) acoustic devices on lithium niobate thin films on silicon carbide (LNOSiC) heterogeneous substrate. The 4-inch LNOSiC with an excellent thermal transport property is prepared by ion-cutting process. The fabricated acoustic resonators on the LNOSiC substrate show scalable resonances from 2.0 to 4.72 GHz, in which the SH0 (S0) mode resonator shows a $k_t^2$ of 24.1% (15.5%) and a maximum Bode-Q of 976 (577) at 2.54 (3.56) GHz. Moreover, the phase velocity (Vp) of the SH0 (S0) mode is greater than 5000 (6400) m/s, which is about 1.25 (1.6) times higher than that of the conventional SAWs, so as the operating frequency. The filter with a center frequency of 2.62 GHz, an insertion loss (IL) of 1.06 dB, and a 3-dB fractional bandwidth (FBW) of 12.6% (three times larger than that of the conventional SAWs) is also achieved. The acoustic devices on heterogeneous substrate are very promising for high frequency, wideband and high power 5G front-ends.
Optimal New Node Insertion for Strong Minimum Energy Topology in IoT Networks

To provide seamless services by low powered small devices having stringent energy constraint in Internet of Things (IoT) networks, it is highly sought over the past few decades to design a competent energy-aware network. In that sense, thereof, the strong minimum energy topology (SMET) is investigated in the existing works to minimize the total power consumption while maintaining the strong connectivity between any pair of nodes (small IoT devices) in the network consisting only bidirectional links. Nevertheless, to significantly improve it, in this paper, we further explore the SMET with respect to insertion of a new node among the existing nodes which is defined as node insertion problem (NIP) for SMET (NIP-SMET). It has been proved that the NIP-SMET is NP-complete, therefore, we propose a heuristic based on Prim-incremental power greedy heuristic to solve it in polynomial time. Also, analytically, it has been shown that the NIP-SMET can provide significant energy-aware improvement over SMET. Via obtained numerical results, we find that the proposed heuristic can reduce the total power dissipation by 50% against the existing heuristic for SMET.
A High Speed Voltage Comparator with Adjustable MOM Capacitor Based on a Strong-Arm Latch

This paper presents a high-speed voltage based on Strong-Arm comparator with a MOM capacitor array. The comparator increases the discharge path of output node to improve the speed performance. An adjustable MOM capacitor array is designed for the process variation and mismatch. The proposed comparator is implemented using a 40-nm CMOS technology. The simulation results indicated that the power dissipation is 215 µW with 1GHz sampling clock, and the delay is lower compared to the conventional. The proposed circuit shows a 4.33mV offset deviation during the Monte-Carlo analysis.
Analytical Determination of Fast-Scale Instability Boundaries for Current Mode Controlled DC–DC Converters With CPL and Closed Voltage Loop

In this paper fast-scale instability boundary in power converters loaded by a constant power load is addressed using an analytical approach. From a piecewise linearized model of the converter, the discrete-time steady-state values of the electrical variables are first determined. Based on justified and realistic approximations of the voltage and current ripples, simple and accurate expressions for these steady-state values are derived and validated. Starting from a matrix-based analytical expression representing the fast-scale instability boundary, a simple design-oriented approximate expression is derived showing insight into how the different system parameters influence this boundary. We highlight its dependency on ripple and slope quantities of the electrical variables of the converter and we explore the impact of variation of parameters in different projections of the multi-dimensional parameter space. An important parameter is the outer voltage controller gain which we demonstrate mathematically that it could have an either stabilizing or destabilizing effect depending whether the steady-state duty cycle is smaller or larger than 1/2. Experimental measurements from a laboratory prototype are in correlation with the theoretical predictions.
Continual Learning-Based Channel Estimation for 5G Millimeter-Wave Systems

Accurate channel estimation in the millimeter-wave (mmWave) based wireless communication systems is challenging and involves a lot of computational costs. The mmWave frequency band has its advantages and disadvantages. At higher frequency mmWave bands, due to smaller wavelengths, we can pack a large number of antennas compared to lower frequency bands. However, the main disadvantages of the mmWave system are computing accurate channel estimation, smaller coverage, and high signal absorption. Besides, when multiple-input multiple-output (MIMO) systems operated over mmWave frequencies, it makes the channel estimation even more intricate in terms of computational complexity and estimation accuracy. In this paper, we plan to address these limitations and improve channel accuracy; we proposed a Continual Learning (CL)-based method for channel estimation in mmWave MIMO systems. Besides, we also proposed an activation function that is numerically stable and robust against early saturation. We discussed several channel estimation algorithms from the literature, also evaluated and compared their performances via numerical simulations. Our simulation results show that the proposed CL-based method outperforms the existing minimum mean squared error (MMSE)-based channel estimators in terms of precision. Furthermore, based on our experiments, we give insight into spectral efficiency with respect to the number of available channel observations.
Thermodynamic properties of moisture desorption of raw pinhao (Araucaria angustifolia seeds)

Summary The seeds of Araucaria angustifolia, named pinhao, are consumed in the south and southeast of Brazil. They are big in size and have high nutritious value. The literature about technological aspects of pinhao is very scarce and there are no reports about moisture sorption models. In this work, moisture desorption isotherms of raw pinhao were determined at 15, 25, 30 and 40 °C. Results show that temperature has little effect on the sorption behaviour and the Chirife model was found to best represent the experimental data. The isosteric heat of sorption (differential enthalpy) was calculated through direct use of moisture desorption isotherm by applying the Clausius-Clapeyron equation. The differential enthalpy of desorption decreased with increasing moisture content. The enthalpy–entropy compensation theory was applied to desorption isotherms and plots of differential enthalpy vs. differential entropy for pinhao provided the isokinetic temperature, indicating an enthalpy-controlled desorption process.
Robust Auditory Functions Based on Probabilistic Integration of MUSIC and CGMM

Sound source localization and separation are essential functions for robot audition to comprehend acoustic environments. The widely-used multiple signal classification (MUSIC) can precisely estimate the directions of arrival (DoAs) of multiple sound sources if its hyperparameters are selected appropriately depending on the surrounding environment. A popular separation method based on a complex Gaussian mixture model (CGMM), on the other hand, can extract multiple sources even in noisy environments if its latent variables are properly initialized to avoid bad local optima. To overcome the drawbacks of both the MUSIC and CGMM, we propose a robot audition framework that complementarily combines the MUSIC and CGMM in a probabilistic manner. Our method is based on a variant of the CGMM conditioned by the localization results of MUSIC. The hyperparameters of MUSIC are estimated by the type II maximum likelihood estimation of the CGMM, and the CGMM itself is efficiently initialized and regularized by using the localization results of MUSIC. Experimental results show that our method outperformed conventional localization and separation methods even when the number of sound sources is unknown. we also demonstrate that our method can work even with moving sound sources in real time.
LSTM based brain-machine interface tool for text generation through eyes blinking detection

This paper presents the development of a tool that allows people to communicate, by using only their voluntary eyes blinks. This tool provides a communication link mainly for people with motor disabilities, who cannot communicate through voice. the electroencephalographic (EEG) signal provided by the Mindwave Mobile 2 headset is the source of information to detect the voluntary blinks. In order to perform the digital processing of the EEG signal, a Long-Short Term Memory (LSTM) neural network was implemented, for the classification of the EEG signal in one of 5 possible classes: No blink, One blink, Two blinks, Three blinks and Other. the results of the trained model achieved an average accuracy of 92%. the neural network was embedded in a native Android mobile application, that shows a virtual keyboard consisting of the 27 letters of the Spanish alphabet, plus three characters, that are the commands “delete”, “space”, and “enter”. Each character can be selected by the user only through a determined number of voluntary blinks executed at certain times. When the user types a word and selects the “enter” command, the word is converted to audio using a text-to-speech engine. Finally, to verify the performance of the application, an experiment with eight people was executed, and as result the system achieved an average spelling precision of 91,26%.
A fast and homogeneous illumination applied to full-field terahertz imaging

In order to overcome the coherence-induced artifacts, the illumination heterogeneity as well as a limited dynamic range when using full-field technique, a solution employing a galvanometric beam-steering is proposed, demonstrating the possibility for real-time imaging in terahertz domain. Working toward industrial applications, different imaging illumination process, including Lissajous pattern for fast beam steering, are evaluated thanks to the versatility of the lighting method.
Broadband Dielectric Waveguides for 0.5–1.1 THz Operation

Miniaturization of terahertz systems is necessary for a broad-scale application of Terahertz technology. Waveguides are an important component in this regard. We present design and characterization of broadband planar dielectric waveguides made of highly resistive silicon having $200\times 50\ \mu\mathrm{m}^{2}$ cross-sectional area on a quartz substrate. The waveguides are characterized between 450 GHz and 1.1 THz. The waveguides show a lower cut-off frequency of ≈ 500 GHz and power transmission losses between 0.32–1 cm−1 over a frequency range of 0.55–1.05 THz.
A Wideband Butterfly Antenna Based on Deep Learning Parameter Optimization Algorithm

A wideband butterfly antenna based on deep learning parameter optimization algorithm is proposed in this paper. Two neural networks are designed to model the mapping relationship of structure parameters to frequency response and frequency response to structure parameters respectively. The parameter optimization algorithm proposed consists of two stages: training NN1 and optimizing parameters using NN2. A wideband butterfly antenna is designed to verify the algorithm. The experiment shows that the structure parameter optimization algorithm proposed can quickly optimize the structure parameters of antenna with the best preformance and save a lot of manpower and time cost.
BuildIt: A Type-Based Multi-stage Programming Framework for Code Generation in C++

The simplest implementation of a domain-specific language is to embed it in an existing language using operator overloading. This way, the DSL can inherit parsing, syntax and type checking, error handling, and the toolchain of debuggers and IDEs from the host language. A natural host language choice for most high-performance DSLs is the de-facto highperformance language, C++. However, DSL designers quickly run into the problem of not being able to extract control flows due to a lack of introspection in C++ and have to resort to special functions with lambdas to represent loops and conditionals. This approach introduces unnecessary syntax and does not capture the side effects of updates inside the lambdas in a safe way. We present BuildIt, a type-based multi-stage execution framework that solves this problem by extracting all control flow operators like if-then-else conditionals and for and while loops using a pure library approach. BuildIt achieves this by repeated execution of the program to explore all control flow paths and construct the AST piece by piece. We show that BuildIt can do this without exponential blow-up in terms of output size and execution time. We apply BuildIt's staging capabilities to the state-of-the-art tensor compiler TACO to generate low-level IR for custom-level formats. Thus, BuildIt offers a way to get both generalization and programmability for the user while generating specialized and efficient code. We also demonstrate that BuildIt can generate rich control-flow from relatively simple code by using it to stage an interpreter for an esoteric language. BuildIt changes the way we think about multi-staging as a problem by reducing the PL complexity of a supposedly harder problem requiring features like introspection or specialized compiler support to a set of common features found in most languages.