Miracle-dz/UniFAR-FTUs huggingface dataset:
SARs Investigation of α-, β-, γ-, δ-, ε-RuCl2(Azpy)2 Complexes as Antitumor Drugs

Structure Activity-Relationships (SARs) of the five possible isomers of RuCl2(Azpy)2 were predicted thanks to DFT method. Azpy stands for 2-phenylazopyridine and the structure of the isomers α-RuCl2(Azpy)2, β-RuCl2(Azpy)2, γ-RuCl2(Azpy)2, δ-RuCl2(Azpy)2 and e-RuCl2(Azpy)2 call respectively α-Cl, β-Cl, γ-Cl, δ-Cl and e-Cl are defined according to chlorine atoms orientations. Hence, they are divided into two groups. In the first group comprising α-Cl, β-Cl and e-Cl, both chlorine atoms are in cis position and Azpy ligands are intervertical. Whereas the two others isomers (γ-Cl and δ-Cl), they form the second group. Here, both chlorine are in trans position and Azpy are planar. The five synthesized isomers were investigated as potential antitumor agents. Then, regarding the DNA, its bases are stacked by pair. Therefore, complexes are assumed to insert and to stack on them through intercalative mode. So the electronic and geometric structures become more important to describe their SARs. Consequently, group 2 regarding γ-Cl and δ-Cl presents the best structure to allow intercalation between DNA base-pairs. Besides, the energy order of the lower unoccupied molecular orbital (LUMO) of the isomers is ELUMO(β-Cl) > ELUMO(α-Cl) > ELUMO(e-Cl) > ELUMO(γ-Cl) > ELUMO(δ-Cl). The energy gap between LUMO and HOMO was also sorted as Δ(L-H)(β-Cl) > Δ(L-H)(α-Cl) > Δ(L-H)(e-Cl) > Δ(L-H)(γ-Cl) > Δ(L-H)(δ-Cl). In addition, the total dipole moment was classified as μ(e-Cl) > μ(β-Cl) > μ(α-Cl) > μ(γ-Cl) > μ(δ-Cl). Finally, net charge of the ligand Azpy was also classified as QL(δ-Cl) > QL(γ-Cl) > QL(e-Cl) > QL(α-Cl) > QL(β-Cl). All those parameters show that δ-Cl isomer displays the highest activity as antitumor drug when intercalating between the DNA basepairs Cytosine-Guanine/Cytosine-Guanine (CG/CG).
A Lightweight Fourier Convolutional Attention Encoder for Multi-Channel Speech Enhancement

Beamforming weights prediction via deep neural networks has been one of the main methods in multi-channel speech enhancement tasks. The spectral-spatial cues are crucial in beamforming weights estimation, however, many existing works fail to optimally predict the beamforming weights with an absence of adequate spectral-spatial information learning. To tackle this challenge, we propose a Fourier convolutional attention encoder (FCAE) to provide a global receptive field over the frequency axis and boost the learning of spectral contexts and cross-channel features. Besides, a new convolutional recurrent encoder-decoder (CRED) structure is proposed in this work, within which FCAEs, attention blocks with skip connections and a deep feedback sequential memory network (DFSMN) serving as recurrent module are involved. The proposed CRED structure is exploited to capture the spectral-spatial joint information to obtain accurate estimation of beamforming weights. Experimental results demonstrate the superiority of the proposed approach with only 0.74M parameters and a PESQ improvement from 2.225 to 2.359 on the ConferencingSpeech2021 challenge development test set.
Dynamic characteristics of rectangular single-layered black phosphorus-based mass sensor

Recently, anisotropic black phosphorus (BP) has been reported to have significant physical and mechanical properties, and BP-based mass sensors have the latent applications in detecting the mass of nanoparticles attached on BP. In this paper, an orthotropic plate model (OPM) attached concentrated mass and molecular dynamics (MD) simulations are adopted to investigate the dynamic characteristics of rectangular single-layered BP-based (RSLBP-based) mass sensors. The influences of mass, position of the attachment and RSLBP size on the fundamental frequency of RSLBP-based mass sensor are discussed. The results show that when attachment mass increases, or the position of attachment moves from the edge to the middle of RSLBP, the fundamental frequency of the RSLBP-based mass sensor decreases. The fundamental frequency of RSLBP-based mass sensors decreases more significantly with increasing the attachment mass when the size of RSLBP is very small. The results calculated by OPM attached concentrated mass are agreed well with the results calculated by MD simulations, which indicates that the OPM attached concentrated mass can forecast the dynamic characteristics of RSLBP-based mass sensors significantly.
Amperometric Biosensor for Quantitative Measurement Using Sandwich Immunoassays

State-of-the-art clinical detection methods typically involve standard immunoassay methods, requiring specialized equipment and trained personnel. This impedes their use in the Point-of-Care (PoC) environment, where ease of operation, portability, and cost efficiency are prioritized. Small, robust electrochemical biosensors provide a means with which to analyze biomarkers in biological fluids in PoC environments. Optimized sensing surfaces, immobilization strategies, and efficient reporter systems are key to improving biosensor detection systems. The signal transduction and general performance of electrochemical sensors are determined by surface properties that link the sensing element to the biological sample. We analyzed the surface characteristics of screen-printed and thin-film electrodes using scanning electron microscopy and atomic force microscopy. An enzyme-linked immunosorbent assay (ELISA) was adapted for use in an electrochemical sensor. The robustness and reproducibility of the developed electrochemical immunosensor were investigated by detecting Neutrophil Gelatinase-Associated Lipocalin (NGAL) in urine. The sensor showed a detection limit of 1 ng/mL, a linear range of 3.5–80 ng/mL, and a CV% of 8%. The results demonstrate that the developed platform technology is suitable for immunoassay-based sensors on either screen-printed or thin-film gold electrodes.
PMMSD: Development of the Matrix Sentence Intelligibility Dataset for Mandarin with Lombard Effect

This paper presents a Paired Mandarin Matrix Sentence Dataset (PMMSD), which will be available after publication. PMMSD is the first Mandarin matrix sentence intelligibility dataset containing both plain and Lombard speech for scientific research. The results verify that different Lombard styles would affect word intelligibility to different degrees and the Lombard effect helps maintain homogeneous intelligibility against contextual interference. All of the discoveries indicate that the Lombard effect should be considered when building intelligibility datasets with noise in the future.
Bayesian Network Modeling and Prediction of Transitions Within the Homelessness System

Administrative data collected by homeless service providers offer a unique opportunity to understand how homeless individuals navigate the homeless system towards securing stable housing. However, the literature on predictive models in the context of homeless service provision has neglected the sequential nature of services that an individual receives over time. Our work addresses this gap by learning, from administrative data, a Bayesian network, which in turn can be used to accurately predict whether an individual will exit the system, or alternatively, the service she would be assigned to the next time she experiences homelessness. Experimental evaluation shows that the proposed approach outperforms prior art not only at predicting exit, but also the less frequent services (and thus more challenging to predict).
Exploiting Speaker Embeddings for Improved Microphone Clustering and Speech Separation in ad-hoc Microphone Arrays

For separating sources captured by ad hoc distributed microphones a key first step is assigning the microphones to the appropriate source-dominated clusters. The features used for such (blind) clustering are based on a fixed length embedding of the audio signals in a high-dimensional latent space. In previous work, the embedding was hand-engineered from the Mel frequency cepstral coefficients and their modulation-spectra. This paper argues that embedding frameworks designed explicitly for the purpose of reliably discriminating between speakers would produce more appropriate features. We propose features generated by the state-of-the-art ECAPA-TDNN speaker verification model for the clustering. We benchmark these features in terms of the subsequent signal enhancement as well as on the quality of the clustering where, further, we introduce 3 intuitive metrics for the latter. Results indicate that in contrast to the hand-engineered features, the ECAPA-TDNN-based features lead to more logical clusters and better performance in the subsequent enhancement stages - thus validating our hypothesis.
LE-DTA: Local Extrema Convolution for Drug Target Affinity Prediction

One of the essential parts of drug discovery and design is the prediction of drug-target affinity (DTA). Researchers have proposed computational approaches for predicting DTA to circumvent the more expensive in vivo and in vitro tests. More recent approaches employed deep network architectures to obtain the features from the drug molecules and protein sequences. The drug compounds are represented as graphs and the target protein as a sequence to extract this information. In this work, we develop a new graph-based prediction model, termed LE-DTA, that utilizes local extrema convolutions for effective feature extraction. It focuses on the local and global extrema of graphs for node embedding. We investigated the performances of both the proposed models on three different benchmark datasets. Our proposed model showed improvement in CI by 1.12% and 0.35% and a reduction in MSE by 7.7% and 3.33% on the KIBA and BindingDB datasets, respectively. we also showed that despite using various pooling operations on our proposed model, we achieved an average reduction in MSE by 7% on the KIBA dataset and 3% improvement on the BindingDB dataset.
The 2nd Clarity Enhancement Challenge for Hearing Aid Speech Intelligibility Enhancement: Overview and Outcomes

This paper reports on the design and outcomes of the 2nd Clarity Enhancement Challenge (CEC2), a challenge for stimulating novel approaches to hearing-aid speech intelligibility enhancement. The challenge was for a listener attending to a target speaker in a noisy, domestic environment. The challenge extends the previous edition, CEC1, in a number of key respects: scenes have multiple interferers including speech, noise and music; ambisonics are used to model listener head movement; target speaker identity is provided to encourage speaker extraction approaches. Systems are evaluated both via the HASPI intelligibility metric and with listening tests using a panel of hearing-impaired listeners. The paper reviews the 18 systems that were submitted describing them in terms of their enhancement and amplification stages. HASPI is seen to be a good predictor of listener performance. The top system, using carefully engineered neural approaches, produces highly intelligible signals for complex scenes with SNRs down to -12 dB while obeying the challenges 5 ms latency constraint.
Exploration of Bitflip’s Effect on Deep Neural Network Accuracy in Plaintext and Ciphertext

Neural networks (NNs) are increasingly deployed to solve complex classification problems and produce accurate results on reliable systems. However, their accuracy quickly degrades in the presence of bit flips from memory errors or targeted attacks on dynamic random-access main memory. Prior work has shown that a few bit errors significantly reduce NN accuracies, but it is unclear which bits have an outsized impact on network accuracy and why. This article first investigates the relationship of the number representation for NN parameters with the impacts of bit flips on NN accuracy. We then explore the bit flip detection framework— four software-based error detectors that detect bit flips independent of NN topology. We discuss exciting findings and evaluate the various detectors’ efficacy, characteristics, and tradeoffs.
Restoration of liver function in malnourished mice orally administered with Pleurotus ostreatus fruiting bodies extract

Context: Malnutrition is considered worldwide as an important burden because it provokes serious damage in several physiological and metabolic mechanisms, among them the hepatic function. Currently, natural products are being used to reduce the negative impact of some pathological disorders in liver. Pleurotus genus of edible mushrooms has shown a wide spectrum of medicinal effects, but its role in the management of liver complications associated to malnutrition is not clear. Aims: To evaluate the restoration of hepatic function in BALB/c malnourished mice, orally administered with a crude water extract (CW-P) from Pleurotus ostreatus fruiting bodies. Methods: Animals (8-week old female BALB/c mice) were starved for 3 days and then refed with commercial diet supplemented with or without CW-P (100 mg/kg) for 8 days. Serum ALP, GGT and amylase activities, hepatic enzymes (ALT, AST, ALP, LDH, GGT), and liver histological examination were assayed. Results: CW-P (34.3 % proteins, 42.6 % carbohydrates and 3.8 g/ 100 g of phenolics) administered to malnourished mice: (i) increased total serum proteins concentration that was correlated with the stimulation in liver protein anabolism, (ii) alleviated hepatic damage markers such as decrease serum ALP levels as well as, in ALP, GGT y AST activities in liver samples, and (iii) improved liver histological architecture similar to control group with decreased lipid accumulation. Conclusions: CW-P supplementation favored liver restoration in malnourished mice. Nutritional interventions with this mushroom extract may be suggested to prevent liver complications associated to malnutrition.
The Role of Posterior Neural Plate-Derived Presomitic Mesoderm (PSM) in Trunk and Tail Muscle Formation and Axis Elongation

Elongation of the posterior body axis is distinct from that of the anterior trunk and head. Early drivers of posterior elongation are the neural plate/tube and notochord, later followed by the presomitic mesoderm (PSM), together with the neural tube and notochord. In axolotl, posterior neural plate-derived PSM is pushed posteriorly by convergence and extension of the neural plate. The PSM does not go through the blastopore but turns anteriorly to join the gastrulated paraxial mesoderm. To gain a deeper understanding of the process of axial elongation, a detailed characterization of PSM morphogenesis, which precedes somite formation, and of other tissues (such as the epidermis, lateral plate mesoderm and endoderm) is needed. We investigated these issues with specific tissue labelling techniques (DiI injections and GFP+ tissue grafting) in combination with optical tissue clearing and 3D reconstructions. We defined a spatiotemporal order of PSM morphogenesis that is characterized by changes in collective cell behaviour. The PSM forms a cohesive tissue strand and largely retains this cohesiveness even after epidermis removal. We show that during embryogenesis, the PSM, as well as the lateral plate and endoderm move anteriorly, while the net movement of the axis is posterior.
3D Point Cloud Completion Based on Multi-Scale Degradation

Recent advances in 3D point cloud completion adopt unsupervised deep learning-based methods, which does not rely on labeled data and improves generalization ability. However, existing methods tend to focus more on the generation overall shape rather than detailed structure. To explore unsupervised 3D point cloud completion methods that give attention to both, we propose a multi resolution completion net (MRC-Net) which introduces a multi-scale degradation (KM- mask) and multi-discriminator into GAN inversion paradigm. First, degrade the point clouds completed by the generator under three different resolutions. Then, the losses of multi-stage reconstruction and feature matching by multi-scale discriminator are used to jointly optimize the generator. The experimental results demonstrate that MRC-Net outperforms existing unsupervised point cloud completion methods and has better completion performances on virtual scanning dataset.
First-line treatment options for advanced gastric/gastroesophageal junction cancer patients with PD-L1-positive: a systematic review and meta-analysis

Background: Lately, many trials have paid much attention on the oncological outcomes of immunotherapy combined with chemotherapy as a first-line treatment. The authors perform a systematic meta-analysis to assess the efficacy and safety of programmed death 1 inhibitor plus chemotherapy for first-line treatment in advanced gastric/gastroesophageal junction cancer. Materials and methods: Literature search through major databases in English and Chinese: PubMed, Embase, Cochrane library, web of Science and CNKI updated on 10 March 2023. Randomized controlled trials were selected to investigate chemotherapy plus programmed death 1 inhibitor versus chemotherapy. Results: A total of 7 randomised controlled trials including 5788 participants were included. The overall survival (hazard ratio=0.79;95% CI: 0.74–0.85, P<0.01), progression-free survival (hazard ratio=0.72; 95% CI: 0.67–0.77, P<0.01) and objective response rate (risk ratio=1.24,95% CI: 1.18–1.31, P<0.05) were longer than chemotherapy alone in the pooled analysis. For subgroup analyses of overall survival, programmed death 1 inhibitors plus chemotherapy had a significant advantage in patients with combined positive score greater than or equal to 5, in Asia, in men and in those younger than 65 years (P<0.01), as were immune-mediated adverse events (odds ratio=8.86;95% CI: 1.26–62.47,P<0.05) and treatment-related grade 3–5 adverse events (odds ratio=1.40,95% CI:1.20–1.62, P<0.01). Conclusion: Programmed death 1 inhibitors plus chemotherapy have significant antitumour activity compared to chemotherapy alone. However, it is riskier in terms of toxicity than chemotherapy. The authors recommend programmed death 1 inhibitors plus chemotherapy as the optimal treatment regimen for patients with positive programmed death ligand 1 expression, in Asia, male and less than 65 years of age. More well-designed studies are needed to investigate the efficacy and safety of different immune plus chemotherapy drug doses and regimens.
Interpolation Filter Model For Ramanujan Subspace Signals

Ramanujan sums have been shown to have interesting applications in signal processing. Ramanujan subspaces, Ramanujan dictionaries, and Ramanujan filter banks are useful in representing and denoising discrete-time periodic signals. In this paper, we theoretically investigate an ideal interpolation filter model for Ramanujan subspace signals wherein an expander ↑ M is followed by the ideal q-th Ramanujan filter Cq (ejω). The output space of this interpolation filter is, in general, only a proper subspace of the q-th Ramanujan subspace ${{\mathcal{S}}_q}$. For the special case when M and q are coprime, we prove that the output space is the entire Ramanujan subspace. We also discuss a more general form of this model for the representation of periodic signals, which may have a potential application in denoising periodic signals. When M and q are not coprime, we provide a bound on the dimension of the output space of the interpolation filter. For this general case, we also conjecture that the provided bound in fact equals the dimension of the output space.
Lasso-Based Fast Residual Recovery For Modulo Sampling

In practice, Analog-to-Digital Converter (ADC) is used to perform sampling. A practical bottleneck of ADC is its lower dynamic range, leading to loss of information. To address this issue, researchers suggested folding operation on the signal using a modulo operator before passing it as an input to ADC. Though this process preserves the signal information, an unfolding algorithm is required to get the true samples from the folded samples. Noise robustness and computational time are two key parameters of an unfolding algorithm. In this paper, we propose a fast and robust algorithm for unfolding. Specifically, we first show that the first-order difference of the residual samples (the difference between the folded and true samples) is sparse by deriving an upper bound on its sparsity, and can be recovered from its partial Fourier measurements by formulating a sparse recovery problem. We demonstrate that the proposed algorithm is robust to noise and computationally efficient compared to the existing methods.
Formation of complex two-dimensional dissipative solitons via spontaneous symmetry breaking

We propose a complex Ginzburg-Landau equation (CGLE) with localized linear gain as a two-dimensional model for pattern formation proceeding via spontaneous breaking of the axial symmetry. Starting from steady-state solutions produced by an extended variational approximation, simulations of the CGLE generate a vast class of robust solitary structures. These are varieties of asymmetric rotating vortices carrying the topological charge (TC), and four- to ten-pointed revolving stars, whose angular momentum is decoupled from the TC. The fourand five-pointed stars feature a cyclic change of their structure in the course of the rotation.
Relapse Prediction from Long-Term Wearable Data Using Self-Supervised Learning and Survival Analysis

The introduction of biometric signal analysis in psychiatry could potentially reshape the field by making it more accurate, proactive and personalized. Such biosignals usually acquired from wearables encompass the quantification of human behavior and traits. In this study, we use long-term data acquired from commercial smartwatches, including kinetic and physiological signals, to extract information-thick descriptors that are used for the prediction of subsequent relapses in patients in the psychotic spectrum. Specifically, we propose a novel combination of methods based on Self-Supervised Learning and Survival Analysis that operates on unlabeled and censored data. When combined with other static features that describe the past course of the patient’s health, the proposed methodology yields promising predictive results in terms of two standard survival analysis metrics.
MRML: Multimodal Rumor Detection by Deep Metric Learning

Multimodal rumor detection aims at detecting rumors using information from textual and visual modalities. The most critical difficulty in multimodal rumor detection lies in capturing both the intra-modal and inter-modal relationships from multimodal data. However, existing methods mainly focus on the multimodal fusion process while paying little attention to the intra-modal relationships. To address these limitations, we propose a multimodal rumor detection method with deep metric learning (MRML) to effectively extract multimodal relationships of news for detecting rumors. Specifically, we design the metric-based triplet learning to extract the intra-modal relationships between rumors and non-rumors in every modality and the contrastive pairwise learning to capture the inter-modal relationships across multimodal. Extensive experiments on two real-world multimodal datasets show the superior performance of our rumor detection method.
Miniaturization of folded circular SIW cavity filters

Abstract This contribution presents miniaturization techniques for folded circular substrate integrated waveguide (FCSIW) cavity filters. The cavities support dual-mode operation. Two of these cavities are combined to form a fourth-order filter. Two filter designs are implemented. The first one serves as a reference and exhibits good manufacturability. For the second one, the cavity is modified, which leads to $50\%$ more compactness. The designs are experimentally verified with 10 GHz bandpass filters. Even though the achieved level of compactness requires higher technological effort, the measurements validate the miniaturization concept. The out-of-band rejection exceeds 40 dB between 11.2 and 30 GHz for the reference filter and between 11.3 and 33 GHz for the miniaturized one. The implemented components are compared with state-of-the-art filters.