Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel ex vivo high-throughput assay reveals antiproliferative effects of idelalisib and ibrutinib in chronic lymphocytic leukemia

PI3Kδ (idelalisib) and BTK (ibrutinib) inhibitors have demonstrated significant clinical activity in chronic lymphocytic leukemia (CLL) interfering with the cross-talk between CLL cells and the lymph node microenviroment, yet their mechanism of action remains to be fully elucidated. Here, we developed an ex vivo model with the aim of reproducing the effects of the microenvironment that would help shed light on the in vivo mechanism of action of idelalisib and ibrutinib and predict their clinical efficacy in individual patients. First we explored the effects of various cell-extrinsic elements on CLL apoptosis and proliferation and found that the combination of CpG+IL2+HS5 stromal cell line + human serum +CLL plasma and erythrocyte fractions represented the best co-culture conditions to test the effects of the novel inhibitors. Then, using this assay, we investigated the impact of idelalisib and ibrutinib on both survival and proliferation in 30 CLL patients. While both drugs had a limited direct pro-apoptotic activity, a potent inhibition of proliferation was achieved at clinically achievable concentrations. Notably, up to 10% of CLL cells still proliferated even at the highest concentrations, likely mirroring the known difficulty to achieve complete responses in vivo. Altogether, this novel assay represents an appropriate ex vivo drug testing system to potentially predict the clinical response to novel inhibitors in particular by quantifying the antiproliferative effect.
Digital video stabilization-review with a perspective of real time implemention

Installed Onboard cameras considering cases of off road unmanned navigating ground vehicles experience severe jitter and vibration. This leads to the prerequisite that the video images acquired from these platforms need to be heavily preprocessed to eliminate the jitter induced variations before human analysis. Digital Video stabilization system is the process of using electronic processing to control the image stability. That is, only software algorithms are used rather than hardware components such as motion sensors, actuators or floating lenses to compensate the disturbances. This makes digital stabilization more portable and cost effective among other methods. Digital stabilization can be used for real time and offline applications if the algorithms are optimized. This literature discusses the state of the art in the field of DVS with an implementation aspect of its use in challenging environment of unmanned ground vehicles where due to the dynamic nature of the vehicle, vibrations and oscillations are affect the camera resulting in a shaky and unstable video feed.
Characterization of second-order fading statistics of 28GHz indoor radio propagation channels

Extension of usable frequency spectrum from microwave to millimeter-wave (mmWave) range is one of the key research direction in addressing the capacity demands of 5th generation (5G) land mobile radio communication networks. This paper presents a thorough analysis on the second-order fading statistics of indoor mmWave range radio propagation channels. The well established analytical relationship of plain angular statistics of a radio propagation channel with the channel's fading statistics is utilized to study the fading characteristics of indoor mmWave radio propagation channels. The plain angle-of-arrival (AoA) measurement results published in the open literature for three different indoor radio propagation scenarios at 28GHz band are extracted by using different graphical interpretation techniques. The extracted plain AoA statistics are extended to study the dispersion of energy in angular domain and second-order fading statistics of the channel. The considered quantifiers for energy dispersion in angular domain and second-order fading statistics are true standard-deviation, angular spread, angular constriction, and direction of maximum fading; and spatial coherence distance, spatial auto-covariance, average fade duration (AFD), and level-crossing-rate (LCR); respectively. The conducted analysis on angular spread and second-order fading statistics is of high significance in designing antenna-beams, error-correction techniques, channel estimation and equalization schemes, modulation schemes, and interleaving algorithms; for 28GHz indoor radio propagation environments.
Weight Initialization without Local Minima in Deep Nonlinear Neural Networks

In this paper, we propose a new weight initialization method called even initialization for wide and deep nonlinear neural networks with the ReLU activation function. We prove that no poor local minimum exists in the initial loss landscape in the wide and deep nonlinear neural network initialized by the even initialization method that we propose. Specifically, in the initial loss landscape of such a wide and deep ReLU neural network model, the following four statements hold true: 1) the loss function is non-convex and non-concave; 2) every local minimum is a global minimum; 3) every critical point that is not a global minimum is a saddle point; and 4) bad saddle points exist. We also show that the weight values initialized by the even initialization method are contained in those initialized by both of the (often used) standard initialization and He initialization methods.
On Periodic Dissipativity Notions in Economic Model Predictive Control

Dissipativity properties have become the tool to analyze economic model predictive control (MPC) schemes and optimal system operation. In this letter we establish equivalences between different characterizations of periodic dissipativity used in economic MPC. This allows us to unify various results and use a standard dissipativity condition instead of periodic dissipativity notions in order to characterize optimal periodic operation. As a corollary, we can formulate sufficient conditions that ensure practical asymptotic stability of the optimal periodic orbit when using an economic MPC scheme without terminal constraints.
Analysis of using an active artificial spine in a quadruped robot

With increasing mechanization of our daily lives, the expectations and demands in robotic systems increase in the general public and in scientists alike. Especially disaster scenarios shows that the robotic systems not only have to face a variety of different tasks during operation but also have to deal with different demands regarding the robot's mobility characteristics. To be able to cope with future requirements, it seems necessary to develop kinematically complex systems that feature several operating modes. Often disregarded in robotics, yet extensively used in nature, are the degrees of freedom introduced by the spine. This paper presents the latest work on the hominid robot Charlie, whose morphology is oriented on chimpanzees and which has the possibility due to its electromechanical structure and the degrees of freedom to walk with different gaits in different postures. Besides its degrees of freedom in its limbs, the robot features an active artificial spine, equipped with sensors in the structure to allow a dual use; both as a structural part as well as a 6-DoF force/torque sensor. This paper analyses the benefits of an active spine experimentally. The results show, that the exploration of the range of motion is improved and that less requirements on joint velocities are lowered.
Defect prediction by using cluster ensembles

Software defect prediction becomes an active research topic in recent years and receives lots of attention. Many researches focus on within project defect prediction, which needs historical data of the project. However, in practice, there exists insufficient training data available for a new project. So cross project defect prediction (CPDP) as well as unsupervised learning defect prediction were proposed to address this problem. Generally, CPDP models use training data from other projects and predict defect proneness for modules in a particular project of interest. However, due to the different data distribution between different projects, the performance of CPDP is highly volatile. To find a better way to solve the problem on unlabeled datasets, this paper focus on unsupervised learning, and proposed a new approach, Cluster Ensembles and Labeling (CEL), to predict defect proneness for unlabeled datasets. The experiment results on 15 open source projects show that CEL model show comparable predictive power compared to supervised learning models.
Outage Capacity Analysis of MIMO System With Survival Probability

In this letter, the outage capacity analysis for a proposed multiple-input multiple-output survival system model is investigated. The proposed model with survival probability in transmitter and receiver is applicable for realistic military scenarios. A closed form expression for the outage capacity is derived based on the matrix of survival probability under Rayleigh fading. The outage capacity is a staircase function of ergodic capacity when the states of antennas are taken into account in survival systems. As special cases, one-sided receive survival system and transmit survival system are addressed to obtain closed form expressions of outage capacity. Simulation results confirm the analysis and demonstrate that better survival probability leads to a more stable and favorable signal transmission rate.
A high data rate LiFi integrated system with inter-cell interference management

In this paper, we describe the first fully integrated Optical Wireless Communication (OWC) network solution with inter-cell interference management. This network uses novel LiFi integrated and real-time modems that implement advanced physical (PHY), Medium Access Control (MAC) and Logical Link Control (LLC) layers allowing adaptive rates up to 42 Mbit/s at PHY layer and a configurable number of supported users per lamp. On top of that, a network layer is designed and implemented to manage inter-cellular interference optimizing the spatial reuse of the resources. Hence, link quality of interfered unserved users is improved and the aggregated network throughput is drastically increased for typical indoor deployment compared to existing solutions without interference management. This whole system eases the deployment of LiFi lighting access points' networks in a dynamic auto-discovery manner (i.e., reconfiguration without operator's intervention).
Linear-scale filterbank for deep neural network-based voice activity detection

Voice activity detection (VAD) is an important preprocessing module in many speech applications. Choosing appropriate features and model structures is a significant challenge and an active area of current VAD research. Mel-scale features such as Mel-frequency cepstral coefficients (MFCCs) and log Mel-filterbank (LMFB) energies have been widely used in VAD as well as speech recognition. The reason for feature extraction in Mel- frequency scale to be one of the most popular methods is that it mimics how human ears process sound. However, for certain types of sound, in which important characteristics are reflected more in the high frequency range, a linear-scale in frequency may provide more information than the Mel- scale. Therefore, in this paper, we propose a deep neural network (DNN)-based VAD system using linear-scale feature. This study shows that the linear-scale feature, especially log linear-filterbank (LLFB) energy, can be used for the DNN-based VAD system and shows better performance than the LMFB for certain types of noise. Moreover, a combination of LMFB and LLFB can integrates both advantages of the two features.
Low complexity DoA estimation in millimeter wave MIMO with RF lens

In this paper, we propose a low complexity direction of arrival (DoA) estimation algorithm that operates in radio frequency (RF) lens-embedded millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. In mmWave system, to compute the beamforming weighting coefficients in real-time, precise DoA estimation with low complexity is essential. We first investigate the conventional DoA estimation algorithms in mmWave MIMO systems and evaluate its performance. Then we introduce the Power pROfile Based Estimation (PROBE) algorithm for DoA estimation that operates in the RF lens-embedded system. We elaborate on the computational complexity of each algorithm in terms of the number of multiplication operations. We show that the proposed algorithm has a significant performance advantage in terms of estimation accuracy over other conventional algorithms in a complexity-constrained environment.
Temperature-Dependent Development of Immature Phyllonorycter ringoniella (Lepidoptera: Gracillariidae) and Its Stage Transition Models

Abstract Asiatic apple leafminer, Phyllonorycter ringoniella (Matsumura), is an important insect pest of apple in Northeast Asia. Its larvae mine the leaves and are best managed by targeted sprays. Little is known about its thermal development characteristics, thus, immature P. ringoniella development was investigated under eight constant temperatures (13.3, 15.3, 20.7, 23.6, 26.1, 30.0, 32.3, and 35.0°C) in the laboratory. The total developmental period of the immature stage (egg–pupa) was inversely related to temperature and decreased from 76.4 d at 13.3°C to 25.9 d at 26.1°C.The developmental periods of eggs, larvae, and pupae ranged from 4.8 to 15.2, 15.5 to 40.3, and 5.7 to 21.6 d, respectively. Eggs, larvae, and pupae did not survive at 35.0°C. According to the ordinary linear model, the lower developmental threshold temperatures for egg, larva, pupa, and total immature stage were estimated as 6.7, 6.7, 9.6, and 7.1°C, and the thermal constants as 93.5, 286.5, 94.6, and 480.1 degree-days (DD), respectively. There were large differences among 11 nonlinear models for estimating lower and upper temperature thresholds, and small differences in estimating optimal temperature. Nonlinear temperature-dependent developmental rates were best represented by Lactin-1 model for egg and Briere-1 model for other stages. Stage transition models from eggs to adults of P. ringoniella were constructed by two-parameter Weibull function with the respective selected nonlinear developmental rate models. This study provides a fundamental understanding of thermal traits of P. ringoniella development which will be a useful tool to predict stage transition and for developing decision-based spray timings for integrated pest management.
Learning Visual Knowledge Memory Networks for Visual Question Answering

Visual question answering (VQA) requires joint comprehension of images and natural language questions, where many questions can't be directly or clearly answered from visual content but require reasoning from structured human knowledge with confirmation from visual content. This paper proposes visual knowledge memory network (VKMN) to address this issue, which seamlessly incorporates structured human knowledge and deep visual features into memory networks in an end-to-end learning framework. Comparing to existing methods for leveraging external knowledge for supporting VQA, this paper stresses more on two missing mechanisms. First is the mechanism for integrating visual contents with knowledge facts. VKMN handles this issue by embedding knowledge triples (subject, relation, target) and deep visual features jointly into the visual knowledge features. Second is the mechanism for handling multiple knowledge facts expanding from question and answer pairs. VKMN stores joint embedding using key-value pair structure in the memory networks so that it is easy to handle multiple facts. Experiments show that the proposed method achieves promising results on both VQA v1.0 and v2.0 benchmarks, while outperforms state-of-the-art methods on the knowledge-reasoning related questions.
Modelling time-limited capacity of a wireless channel as a Markov reward process

One of the key issues in industrial-radio-based control applications is supporting their stringent requirements over a wireless link. In this paper, we propose a new model called Time-Limited Wireless Channel Capacity (TL-WChC) to describe the achievable data rate within a given duration of time and within a given violation probability. We establish a connection between fading characteristics of the wireless link with the channel capacity of our model and derive a closed-form solution; we apply it to Rayleigh fading channels as example. Our simulation results show the applicability and effectiveness of the proposed approach and model.
Prefrontal Neuronal Excitability Maintains Cocaine-Associated Memory During Retrieval

Presentation of drug-associated cues provokes craving and drug seeking, and elimination of these associative memories would facilitate recovery from addiction. Emotionally salient memories are maintained during retrieval, as particular pharmacologic or optogenetic perturbations of memory circuits during retrieval, but not after, can induce long-lasting memory impairments. For example, in rats, inhibition of noradrenergic beta-receptors, which control intrinsic neuronal excitability, in the prelimbic medial prefrontal cortex (PL-mPFC) can cause long-term memory impairments that prevent subsequent cocaine-induced reinstatement. The physiologic mechanisms that allow noradrenergic signaling to maintain drug-associated memories during retrieval, however, are unclear. Here we combine patch-clamp electrophysiology ex vivo and behavioral neuropharmacology in vivo to evaluate the mechanisms that maintain drug-associated memory during retrieval in rats. Consistent with previous studies, we find that cocaine experience increases the intrinsic excitability of pyramidal neurons in PL-mPFC. In addition, we now find that this intrinsic plasticity positively predicts the retrieval of a cocaine-induced conditioned place preference (CPP) memory, suggesting that such plasticity may contribute to drug-associated memory retrieval. In further support of this, we find that pharmacological blockade of a cAMP-dependent signaling cascade, which allows noradrenergic signaling to elevate neuronal excitability, is required for memory maintenance during retrieval. Thus, inhibition of PL-mPFC neuronal excitability during memory retrieval not only leads to long-term deficits in the memory, but this memory deficit provides protection against subsequent cocaine-induced reinstatement. These data reveal that PL-mPFC intrinsic neuronal excitability maintains a cocaine-associated memory during retrieval and suggest a unique mechanism whereby drug-associated memories could be targeted for elimination.
A multi-pulse AC-DC converter fed 5-level NPC inverter based VCIMD

In this paper multi-pulse AC-DC converter fed 5-level neutral point clamped (NPC), connected in cascaded manner is used to improve the power quality in vector controlled induction motor drive (VCIMD). To meet an IEEE-519 standard at the utility end of the proposed drive, a thirty six pulse AC-DC converter is used. Two isolated eighteen-pulse AC-DC converters are used at the utility end, with the help of the primary phase shift, these two eighteen-pulse AC-DC converters are configured as a thirty six pulse AC-DC converter. A 5-level NPC inverter is used at the drive end to improve the steady state, dynamic and power quality performances of the proposed IMD. To ensure the low switching losses, 5-level NPC inverter is switched at very low switching frequency. The proposed variable speed induction motor drive is modelled and its performance is simulated in Simulink/MATLAB environment to demonstrate its effectiveness for the medium rating applications.
Polarimetrie phased array calibration for large-scale multi-mission radar applications

Concepts for multi-mission weather and air traffic surveillance radar systems call for the use of large-scale phased array radar systems to meet practical beam flexibility and timeline requirements. While the individual air surveillance (defense) and air traffic missions have clear paths towards technical success using phased arrays, it has not yet been demonstrated that a large-scale phased array can meet the scientific needs of the dual polarization weather surveillance mission because there are inherent scan-dependent polarization biases in planar phased array radar systems. This paper highlights the challenges and potential technical solutions to the calibration and correction of these biases, summarizing current work towards the ultimate goal of demonstrating these techniques on a real system.
Design, characterization, and evaluation of aceclofenac-loaded Eudragit RS 100 nanoparticulate system for ocular delivery

Abstract The aim of the present study was to develop and evaluate positively charged nanoparticles of aceclofenac for ocular delivery. The nanoparticles were prepared by the nanoprecipitation method using Eudragit RS 100. The optimized nanoparticles were found to have narrow particle size range (238.9 ± 8 nm) with nearly spherical shape, positive zeta potential (40.3 ± 3.8). Higher entrapment efficiency of aceclofenac (94.53 ± 1.0%) with prolonged in vitro drug release profiles was also observed. Powder X-ray diffraction and differential scanning calorimetry studies indicated decrease in crystallinity of drug within the nanoparticulate polymeric matrix. The formulation was found to have higher permeation as compared to aceclofenac aqueous solution. Nanoparticle formulation was found to be quite stable and well tolerated with no signs of corneal damage. The in vivo studies involving the arachidonic acid-induced ocular inflammation in rabbits showed optimal efficacy of the nanoparticles with significantly higher inhibition of polymorphonuclear leukocytes migration (p < 0.05) and lid closure scores.
Smenamide A Analogues. Synthesis and Biological Activity on Multiple Myeloma Cells

Smenamides are an intriguing class of peptide/polyketide molecules of marine origin showing antiproliferative activity against lung cancer Calu-1 cells at nanomolar concentrations through a clear pro-apoptotic mechanism. To probe the role of the activity-determining structural features, the 16-epi-analogue of smenamide A and eight simplified analogues in the 16-epi series were prepared using a flexible synthetic route. The synthetic analogues were tested on multiple myeloma (MM) cell lines showing that the configuration at C-16 slightly affects the activity, since the 16-epi-derivative is still active at nanomolar concentrations. Interestingly, it was found that the truncated compound 8, mainly composed of the pyrrolinone terminus, was not active, while compound 13, essentially lacking the pyrrolinone moiety, was 1000-fold less active than the intact substance and was the most active among all the synthesized compounds.
BLT2, a leukotriene B4 receptor 2, as a novel prognostic biomarker of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is considered to be a notorious type of cancer due to its aggressive metastatic potential and poor prognosis. Recent evidence suggests that BLT2, a low-affinity LTB4 receptor is critically associated with the phenotypes of TNBC cells, including invasion, metastasis, and survival. Furthermore, in a group of 545 breast cancer patients with metastasis, we observed that the high-BLT2 subgroup had a lower disease-free-survival rate than the low-BLT2 subgroup. Thus, we theorized that anti-BLT2 strategies could facilitate the development of new therapies used for TNBC. This review focuses on recent discoveries regarding BLT2 and its roles in as a novel prognostic biomarker in TNBC.