Using synthetic genome readers/regulators to interrogate chromatin processes: A brief review.

Aberrant gene expression underlies numerous human ailments. Hence, developing small molecules to target and remedy dysfunctional gene regulation has been a long-standing goal at the interface of chemistry and medicine. A major challenge for designing small molecule therapeutics aimed at targeting desired genomic loci is the minimization of widescale disruption of genomic functions. To address this challenge, we rationally design polyamide-based multi-functional molecules, i.e., Synthetic Genome Readers/Regulators (SynGRs), which, by design, target distinct sequences in the genome. Herein, we briefly review how SynGRs access chromatin-bound and chromatin-free genomic sites, then highlight the methods for the study of chromatin processes using SynGRs on positioned nucleosomes in vitro or disease-causing repressive genomic loci in vivo.

Over 10% Efficient Sb2(S,Se)3 Solar Cells Enabled by CsI-Doping Strategy.

Antimony selenosulfide (Sb2(S,Se)3) is an emerging quasi-1D photovoltaic semiconductor with exceptional photoelectric properties. The low-symmetry chain structure contains complex defects and makes it difficult to improve electrical properties via doping method. This article reports a doping strategy to enhance the efficiency of Sb2(S,Se)3 solar cells by using alkali halide (CsI) as the hydrothermal reaction precursor. It is found that the Cs and I ions are effectively doped and atomically coordinate with Sb ions and S/Se ions. The CsI-doping Sb2(S,Se)3 absorbers exhibit enhanced grain morphologies and reduced trap densities. The consequential CsI-doping Sb2(S,Se)3 based solar cells demonstrate favorable band alignment, suppressed carrier recombination, and improved device performance. An efficiency as high as 10.05% under standard AM1.5 illumination irradiance is achieved. This precursor-based alkali halide doping strategy provides a useful guidance for high-efficiency antimony selenosulfide solar cells.

Comparison of baseline correction algorithms for in vivo 1H-MRS.

Proton MRS is used clinically to collect localized, quantitative metabolic data from living tissues. However, the presence of baselines in the spectra complicates accurate MRS data quantification. The occurrence of baselines is not specific to short-echo-time MRS data. In short-echo-time MRS, the baseline consists typically of a dominating macromolecular (MM) part, and can, depending on B0 shimming, poor voxel placement, and/or localization sequences, also contain broad water and lipid resonance components, indicated by broad components (BCs). In long-echo-time MRS, the MM part is usually much smaller, but BCs may still be present. The sum of MM and BCs is denoted by the baseline. Many algorithms have been proposed over the years to tackle these artefacts. A first approach is to identify the baseline itself in a preprocessing step, and a second approach is to model the baseline in the quantification of the MRS data themselves. This paper gives an overview of baseline handling algorithms and also proposes a new algorithm for baseline correction. A subset of suitable baseline removal algorithms were tested on in vivo MRSI data (semi-LASER at TE = 40 ms) and compared with the new algorithm. The baselines in all datasets were removed using the different methods and subsequently fitted using spectrIm-QMRS with a TDFDFit fitting model that contained only a metabolite basis set and lacked a baseline model. The same spectra were also fitted using a spectrIm-QMRS model that explicitly models the metabolites and the baseline of the spectrum. The quantification results of the latter quantification were regarded as ground truth. The fit quality number (FQN) was used to assess baseline removal effectiveness, and correlations between metabolite peak areas and ground truth models were also examined. The results show a competitive performance of our new proposed algorithm, underscoring its automatic approach and efficiency. Nevertheless, none of the tested baseline correction methods achieved FQNs as good as the ground truth model. All separately applied baseline correction methods introduce a bias in the observed metabolite peak areas. We conclude that all baseline correction methods tested, when applied as a separate preprocessing step, yield poorer FQNs and biased quantification results. While they may enhance visual display, they are not advisable for use before spectral fitting.

Simultaneous Incorporation of CsI in the Two-step Deposition Process Boosts the Power Conversion Efficiency and Stability of Perovskite Solar Cells.

Two-step deposition method has been widely exploited to fabricate FA1-x Csx PbI3 perovskite solar cells. However, in previous studies, CsI is mainly added into the PbI2 precursor with DMF/DMSO as solvent. Here in this study, a novel method to fabricate FA1-x Csx PbI3 perovskite has been proposed. The CsI is simultaneously added into the PbI2 precursor and the organic FAI/MACl salts solution in our modified two-step deposition process. The resulting FA1-x Csx PbI3 film exhibits larger perovskite crystals and suppressed defect density (4.05×1015  cm-3 ) compared with the reference perovskite film (9.23×1015  cm-3 ) without CsI. Therefore, the obtained FA1-x Csx PbI3 perovskite solar cells have demonstrated superior power conversion efficiencies (PCE=21.96 %) together with better long-term device stability.

How Wastewater Reflects Human Metabolism─Suspect Screening of Pharmaceutical Metabolites in Wastewater Influent.

Pharmaceuticals and their human metabolites are contaminants of emerging concern in the aquatic environment. Most monitoring studies focus on a limited set of parent compounds and even fewer metabolites. However, more than 50% of the most consumed pharmaceuticals are excreted in higher amounts as metabolites than as parents, as confirmed by a literature analysis within this study. Hence, we applied a wide-scope suspect screening approach to identify human pharmaceutical metabolites in wastewater influent from three Swiss treatment plants. Based on consumption amounts and human metabolism data, a suspect list comprising 268 parent compounds and over 1500 metabolites was compiled. Online solid phase extraction combined with liquid chromatography coupled to high-resolution tandem mass spectrometry was used to analyze the samples. Data processing, annotation, and structure elucidation were achieved with various tools, including molecular networking as well as SIRIUS/CSI:FingerID and MetFrag for MS2 spectra rationalization. We confirmed 37 metabolites with reference standards and 16 by human liver S9 incubation experiments. More than 25 metabolites were detected for the first time in influent wastewater. Semiquantification with MS2Quant showed that metabolite to parent concentration ratios were generally lower compared to literature expectations, probably due to further metabolite transformation in the sewer system or limitations in the metabolite detection. Nonetheless, metabolites pose a large fraction to the total pharmaceutical contribution in wastewater, highlighting the need for metabolite inclusion in chemical risk assessment.

Sleep disorders in patients with endometriosis; a cross-sectional study.

BACKGROUND: Endometriosis affects 10-15% of women of reproductive age and is considered a critical gynecological problem. Endometriosis causes pain and infertility, both of which can impair the patient's quality of life. Sleep disorders account for the most bothersome presentation of impaired quality of life. This study investigated the frequency and severity of sleep disorders in women with endometriosis. METHODS: In this analytical cross-sectional study, 665 women referred to three hospitals in Tehran, Rasool-e-Akram, Pars, and Nikan, were included (463 patients with endometriosis and 202 women without endometriosis). All of them were informed about the study design and the aim of the research, and then they were asked to sign the consent form and complete the Pittsburgh Sleep Quality Index (PSQI). After data gathering and entering, they were analyzed by SPSS version 22 and were considered significant with P < 0.05. RESULTS: The study population's mean age was 35.4 ± 7.9 years. The mean global PSQI score in the case group (endometriosis patients) was higher than in the control group (non-endometriosis patients) (10.6 vs. 7.1; P < 0.001). Patients with dyspareunia, dysuria, pelvic pain, and dyschezia had a significantly higher PSQI score (P < 0.05). CONCLUSION: According to the findings of the present study, the sleep quality in endometriosis patients is low, and there is a need to pay greater attention to these patients. This may result in some changes in the therapeutic strategies for this disease.

Csi-let-7a-5p delivered by extracellular vesicles from a liver fluke activates M1-like macrophages and exacerbates biliary injuries.

Chronic infection with liver flukes (such as Clonorchis sinensis) can induce severe biliary injuries, which can cause cholangitis, biliary fibrosis, and even cholangiocarcinoma. The release of extracellular vesicles by C. sinensis (CsEVs) is of importance in the long-distance communication between the hosts and worms. However, the biological effects of EVs from liver fluke on biliary injuries and the underlying molecular mechanisms remain poorly characterized. In the present study, we found that CsEVs induced M1-like activation. In addition, the mice that were administrated with CsEVs showed severe biliary injuries associated with remarkable activation of M1-like macrophages. We further characterized the signatures of miRNAs packaged in CsEVs and identified a miRNA Csi-let-7a-5p, which was highly enriched. Further study showed that Csi-let-7a-5p facilitated the activation of M1-like macrophages by targeting Socs1 and Clec7a; however, CsEVs with silencing Csi-let-7a-5p showed a decrease in proinflammatory responses and biliary injuries, which involved in the Socs1- and Clec7a-regulated NF-κB signaling pathway. Our study demonstrates that Csi-let-7a-5p delivered by CsEVs plays a critical role in the activation of M1-like macrophages and contributes to the biliary injuries by targeting the Socs1- and Clec7a-mediated NF-κB signaling pathway, which indicates a mechanism contributing to biliary injuries caused by fluke infection. However, molecules other than Csi-let-7a-5p from CsEVs that may also promote M1-like polarization and exacerbate biliary injuries are not excluded.

Non-Contact Heart Rate Monitoring Method Based on Wi-Fi CSI Signal.

This paper introduces an innovative non-contact heart rate monitoring method based on Wi-Fi Channel State Information (CSI). This approach integrates both amplitude and phase information of the CSI signal through rotational projection, aiming to optimize the accuracy of heart rate estimation in home environments. We develop a frequency domain subcarrier selection algorithm based on Heartbeat to subcomponent ratio (HSR) and design a complete set of signal filtering and subcarrier selection processes to further enhance the accuracy of heart rate estimation. Heart rate estimation is conducted by combining the peak frequencies of multiple subcarriers. Extensive experimental validations demonstrate that our method exhibits exceptional performance under various environmental conditions. The experimental results show that our subcarrier selection method for heart rate estimation achieves an average accuracy of 96.8%, with a median error of only 0.8 bpm, representing an approximately 20% performance improvement over existing technologies.

Critical Analysis of Data Leakage in WiFi CSI-Based Human Action Recognition Using CNNs.

WiFi Channel State Information (CSI)-based human action recognition using convolutional neural networks (CNNs) has emerged as a promising approach for non-intrusive activity monitoring. However, the integrity and reliability of the reported performance metrics are susceptible to data leakage, wherein information from the test set inadvertently influences the training process, leading to inflated accuracy rates. In this paper, we conduct a critical analysis of a notable IEEE Sensors Journal study on WiFi CSI-based human action recognition, uncovering instances of data leakage resulting from the absence of subject-based data partitioning. Empirical investigation corroborates the lack of exclusivity of individuals across dataset partitions, underscoring the importance of rigorous data management practices. Furthermore, we demonstrate that employing data partitioning with respect to humans results in significantly lower precision rates than the reported 99.9% precision, highlighting the exaggerated nature of the original findings. Such inflated results could potentially discourage other researchers and impede progress in the field by fostering a sense of complacency.

CSI-F: A Human Motion Recognition Method Based on Channel-State-Information Signal Feature Fusion.

The recognition of human activity is crucial as the Internet of Things (IoT) progresses toward future smart homes. Wi-Fi-based motion-recognition stands out due to its non-contact nature and widespread applicability. However, the channel state information (CSI) related to human movement in indoor environments changes with the direction of movement, which poses challenges for existing Wi-Fi movement-recognition methods. These challenges include limited directions of movement that can be detected, short detection distances, and inaccurate feature extraction, all of which significantly constrain the wide-scale application of Wi-Fi action-recognition. To address this issue, we propose a direction-independent CSI fusion and sharing model named CSI-F, one which combines Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). Specifically, we have introduced a series of signal-processing techniques that utilize antenna diversity to eliminate random phase shifts, thereby removing noise influences unrelated to motion information. Later, by amplifying the Doppler frequency shift effect through cyclic actions and generating a spectrogram, we further enhance the impact of actions on CSI. To demonstrate the effectiveness of this method, we conducted experiments on datasets collected in natural environments. We confirmed that the superposition of periodic actions on CSI can improve the accuracy of the process. CSI-F can achieve higher recognition accuracy compared with other methods and a monitoring coverage of up to 6 m.

Fluoroscopic-guided procedures of the lower extremity.

This article reviews the literature and the authors' experiences regarding the performance of lower extremity fluoroscopically guided procedures from the hip to the toes. An overview of injections and aspirations, their indications, risks, and complications are provided, focusing on anesthetics, corticosteroids, and contrast agents. A variety of approaches to each joint and the associated pearls and pitfalls of each approach will be discussed.

Enhancing System Performance through Objective Feature Scoring of Multiple Persons' Breathing Using Non-Contact RF Approach.

Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system's performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system's performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively.

Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator.

Advances in machine learning have widened the range of its applications in many fields. In particular, deep learning has attracted much interest for its ability to provide solutions where the derivation of a rigorous mathematical model of the problem is troublesome. Our interest was drawn to the application of deep learning for channel state information feedback reporting, a crucial problem in frequency division duplexing (FDD) 5G networks, where knowledge of the channel characteristics is fundamental to exploiting the full potential of multiple-input multiple-output (MIMO) systems. We designed a framework adopting a 5G New Radio convolutional neural network, called NR-CsiNet, with the aim of compressing the channel matrix experienced by the user at the receiver side and then reconstructing it at the transmitter side. In contrast to similar solutions, our framework is based on a 5G New Radio fully compliant simulator, thus implementing a channel generator based on the latest 3GPP 3-D channel model. Moreover, realistic 5G scenarios are considered by including multi-receiving antenna schemes and noisy downlink channel estimation. Simulations were carried out to analyze and compare the performance with current feedback reporting schemes, showing promising results for this approach from the point of view of the block error rate and throughput of the 5G data channel.

Human Activity Recognition via Score Level Fusion of Wi-Fi CSI Signals.

Wi-Fi signals are ubiquitous and provide a convenient, covert, and non-invasive means of recognizing human activity, which is particularly useful for healthcare monitoring. In this study, we investigate a score-level fusion structure for human activity recognition using the Wi-Fi channel state information (CSI) signals. The raw CSI signals undergo an important preprocessing stage before being classified using conventional classifiers at the first level. The output scores of two conventional classifiers are then fused via an analytic network that does not require iterative search for learning. Our experimental results show that the fusion provides good generalization and a shorter learning processing time compared with state-of-the-art networks.

Device-Free Tracking through Self-Attention Mechanism and Unscented Kalman Filter with Commodity Wi-Fi.

Recent advancements in target tracking using Wi-Fi signals and channel state information (CSI) have significantly improved the accuracy and efficiency of tracking mobile targets. However, there remains a gap in developing a comprehensive approach that combines CSI, an unscented Kalman filter (UKF), and a sole self-attention mechanism to accurately estimate the position, velocity, and acceleration of targets in real-time. Furthermore, optimizing the computational efficiency of such approaches is necessary for their applicability in resource-constrained environments. To bridge this gap, this research study proposes a novel approach that addresses these challenges. The approach leverages CSI data collected from commodity Wi-Fi devices and incorporates a combination of the UKF and a sole self-attention mechanism. By fusing these elements, the proposed model provides instantaneous and precise estimates of the target's position while considering factors such as acceleration and network information. The effectiveness of the proposed approach is demonstrated through extensive experiments conducted in a controlled test bed environment. The results exhibit a remarkable tracking accuracy level of 97%, affirming the model's ability to successfully track mobile targets. The achieved accuracy showcases the potential of the proposed approach for applications in human-computer interactions, surveillance, and security.

CSI-Based Human Activity Recognition Using Multi-Input Multi-Output Autoencoder and Fine-Tuning.

Wi-Fi-based human activity recognition (HAR) has gained considerable attention recently due to its ease of use and the availability of its infrastructures and sensors. Channel state information (CSI) captures how Wi-Fi signals are transmitted through the environment. Using channel state information of the received signals transmitted from Wi-Fi access points, human activity can be recognized with more accuracy compared with the received signal strength indicator (RSSI). However, in many scenarios and applications, there is a serious limit in the volume of training data because of cost, time, or resource constraints. In this study, multiple deep learning models have been trained for HAR to achieve an acceptable accuracy level while using less training data compared to other machine learning techniques. To do so, a pretrained encoder which is trained using only a limited number of data samples, is utilized for feature extraction. Then, by using fine-tuning, this encoder is utilized in the classifier, which is trained by a fraction of the rest of the data, and the training is continued alongside the rest of the classifier's layers. Simulation results show that by using only 50% of the training data, there is a 20% improvement compared with the case where the encoder is not used. We also showed that by using an untrainable encoder, an accuracy improvement of 11% using 50% of the training data is achievable with a lower complexity level.

Use of Domain Labels during Pre-Training for Domain-Independent WiFi-CSI Gesture Recognition.

To minimize dependency on the availability of data labels, some WiFi-CSI based-gesture recognition solutions utilize an unsupervised representation learning phase prior to fine-tuning downstream task classifiers. In this case, however, the overall performance of the solution is negatively affected by domain factors present in the WiFi-CSI data used by the pre-training models. To reduce this negative effect, we propose an integration of the adversarial domain classifier in the pre-training phase. We consider this as an effective step towards automatic domain discovery during pre-training. We also experiment with multi-class and label versions of domain classification to improve situations, in which integrating a multi-class and single label-based domain classifier during pre-training fails to reduce the negative impact domain factors have on overall solution performance. For our extensive random and leave-out domain factor cross-validation experiments, we utilise (i) an end-to-end and unsupervised representation learning baseline, (ii) integration of both single- and multi-label domain classification, and (iii) so-called domain-aware versions of the aformentioned unsupervised representation learning baseline in (i) with two different datasets, i.e., Widar3 and SignFi. We also consider an input sample type that generalizes, in terms of overall solution performance, to both aforementioned datasets. Experiment results with the Widar3 dataset indicate that multi-label domain classification reduces domain shift in position (1.2% mean metric improvement and 0.5% variance increase) and orientation (0.4% mean metric improvement and 1.0% variance decrease) in domain factor leave-out cross-validation experiments. The results also indicate that domain shift reduction, when considering single- or multi-label domain classification during pre-training, is negatively impacted when a large proportion of negative view combinations contain views that originate from different domains within a substantial amount of mini-batches considered during pre-training. This is caused by the view contrastive loss repelling the aforementioned negative view combinations, eventually causing more domain shift in the intermediate feature space of the overall solution.

CSI Feedback Model Based on Multi-Source Characterization in FDD Systems.

In wireless communication, to fully utilize the spectrum and energy efficiency of the system, it is necessary to obtain the channel state information (CSI) of the link. However, in Frequency Division Duplexing (FDD) systems, CSI feedback wastes part of the spectrum resources. In order to save spectrum resources, the CSI needs to be compressed. However, many current deep-learning algorithms have complex structures and a large number of model parameters. When the computational and storage resources are limited, the large number of model parameters will decrease the accuracy of CSI feedback, which cannot meet the application requirements. In this paper, we propose a neural network-based CSI feedback model, Mix_Multi_TransNet, which considers both the spatial characteristics and temporal sequence of the channel, aiming to provide higher feedback accuracy while reducing the number of model parameters. Through experiments, it is found that Mix_Multi_TransNet achieves higher accuracy than the traditional CSI feedback network in both indoor and outdoor scenes. In the indoor scene, the NMSE gains of Mix_Multi_TransNet are 4.06 dB, 4.92 dB, 4.82 dB, and 6.47 dB for compression ratio η = 1/8, 1/16, 1/32, 1/64, respectively. In the outdoor scene, the NMSE gains of Mix_Multi_TransNet are 3.63 dB, 6.24 dB, 4.71 dB, 4.60 dB, and 2.93 dB for compression ratio η = 1/4, 1/8, 1/16, 1/32, 1/64, respectively.

Enhanced MIMO CSI Estimation Using ACCPM with Limited Feedback.

Multiple Input and Multiple Output (MIMO) is a promising technology to enable spatial multiplexing and improve throughput in wireless communication networks. To obtain the full benefits of MIMO systems, the Channel State Information (CSI) should be acquired correctly at the transmitter side for optimal beamforming design. The analytical centre-cutting plane method (ACCPM) has shown to be an appealing way to obtain the CSI at the transmitter side. This paper adopts ACCPM to learn down-link CSI in both single-user and multi-user scenarios. In particular, during the learning phase, it uses the null space beamforming vector of the estimated CSI to reduce the power usage, which approaches zero when the learned CSI approaches the optimal solution. Simulation results show our proposed method converges and outperforms previous studies. The effectiveness of the proposed method was corroborated by applying it to the scattering channel and winner II channel models.

Human Walking Direction Detection Using Wireless Signals, Machine and Deep Learning Algorithms.

The use of wireless signals for device-free activity recognition and precise indoor positioning has gained significant popularity recently. By taking advantage of the characteristics of the received signals, it is possible to establish a mapping between these signals and human activities. Existing approaches for detecting human walking direction have encountered challenges in adapting to changes in the surrounding environment or different people. In this paper, we propose a new approach that uses the channel state information of received wireless signals, a Hampel filter to remove the outliers, a Discrete wavelet transform to remove the noise and extract the important features, and finally, machine and deep learning algorithms to identify the walking direction for different people and in different environments. Through experimentation, we demonstrate that our approach achieved accuracy rates of 92.9%, 95.1%, and 89% in detecting human walking directions for untrained data collected from the classroom, the meeting room, and both rooms, respectively. Our results highlight the effectiveness of our approach even for people of different genders, heights, and environments, which utilizes machine and deep learning algorithms for low-cost deployment and device-free detection of human activities in indoor environments.