Detection Of Biological Signals Research Articles

A complementary approach for detecting biological signals through a semi-automated feature selection tool.

Untargeted metabolomics is often used in studies that aim to trace the metabolic profile in a broad context, with the data-dependent acquisition (DDA) mode being the most commonly used method. However, this approach has the limitation that not all detected ions are fragmented in the data acquisition process, in addition to the lack of specificity regarding the process of fragmentation of biological signals. The present work aims to extend the detection of biological signals and contribute to overcoming the fragmentation limits of the DDA mode with a dynamic procedure that combines experimental and in silico approaches. Metabolomic analysis was performed on three different species of actinomycetes using liquid chromatography coupled with mass spectrometry. The data obtained were preprocessed by the MZmine software and processed by the custom package RegFilter. RegFilter allowed the coverage of the entire chromatographic run and the selection of precursor ions for fragmentation that were previously missed in DDA mode. Most of the ions selected by the tool could be annotated through three levels of annotation, presenting biologically relevant candidates. In addition, the tool offers the possibility of creating local spectral libraries curated according to the user's interests. Thus, the adoption of a dynamic analysis flow using RegFilter allowed for detection optimization and curation of potential biological signals, previously absent in the DDA mode, being a good complementary approach to the current mode of data acquisition. In addition, this workflow enables the creation and search of in-house tailored custom libraries.

A Contemporary Survey on Deepfake Detection: Datasets, Algorithms, and Challenges

Deepfakes are notorious for their unethical and malicious applications to achieve economic, political, and social reputation goals. Recent years have seen widespread facial forgery, which does not require technical skills. Since the development of generative adversarial networks (GANs) and diffusion models (DMs), deepfake generation has been moving toward better quality. Therefore, it is necessary to find an effective method to detect fake media. This contemporary survey provides a comprehensive overview of several typical facial forgery detection methods proposed from 2019 to 2023. We also analyze and group them into four categories in terms of their feature extraction methods and network architectures: traditional convolutional neural network (CNN)-based detection, CNN backbone with semi-supervised detection, transformer-based detection, and biological signal detection. Furthermore, it summarizes several representative deepfake detection datasets with their advantages and disadvantages. Finally, we evaluate the performance of these detection models with respect to different datasets by comparing their evaluating metrics. Across all experimental results on these state-of-the-art detection models, we find that the accuracy is largely degraded if we utilize cross-dataset evaluation. These results will provide a reference for further research to develop more reliable detection algorithms.

Nanoparticles of Push-Pull Triphenylamine-Based Molecules for Light-Controlled Stimulation of Neuronal Activity.

Organic semiconductor materials with a unique set of properties are very attractive for interfacing biological objects and can be used for noninvasive therapy or detection of biological signals. Here, we describe the synthesis and investigation of a novel series of organic push-pull conjugated molecules with the star-shaped architecture, consisting of triphenylamine as a branching electron donor core linked through the thiophene π-spacer to electron-withdrawing alkyl-dicyanovinyl groups. The molecules could form stable aqueous dispersions of nanoparticles (NPs) without the addition of any surfactants or amphiphilic polymer matrixes with the average size distribution varying from 40 to 120 nm and absorption spectra very similar to those of human eye retina pigments such as rods and green cones. Variation of the terminal alkyl chain length of the molecules forming NPs from 1 to 12 carbon atoms was found to be an efficient tool to modulate their lipophilic and biological properties. Possibilities of using the NPs as light nanoactuators in biological systems or as artificial pigments for therapy of degenerative retinal diseases were studied both on the model planar bilayer lipid membranes and on the rat cortical neurons. In the planar bilayer system, the photodynamic activity of these NPs led to photoinactivation of ion channels formed by pentadecapeptide gramicidin A. Treatment of rat cortical neurons with the NPs caused depolarization of cell membranes upon light irradiation, which could also be due to the photodynamic activity of the NPs. The results of the work gave more insight into the mechanisms of light-controlled stimulation of neuronal activity and for the first time showed that fine-tuning of the lipophilic affinity of NPs based on organic conjugated molecules is of high importance for creating a bioelectronic interface for biomedical applications.

Electrospinning Nanofibers as Stretchable Sensors for Wearable Devices.

Wearable devices attract great attention in intelligent medicine, electronic skin, artificial intelligence robots, and so on. However, boundedness of traditional sensors based on rigid materials unconstrained self-multilayer structure assembly and dense substrate in stretchability and permeability limits their applications. The network structure of the elastomeric nanofibers gives them excellent air permeability and stretchability. By introducing metal nanofillers, intrinsic conductive polymers, carbon materials, and other methods to construct conductive paths, stretchable conductors can be effectively prepared by elastomeric nanofibers, showing great potential in the field of flexible sensors. This perspective briefly introduces the representative preparations of conductive thermoplastic polyurethane, nylon, and hydrogel nanofibers by electrospinning and the application of integrated electronic devices in biological signal detection. The main challenge is to unify the stretchability and conductivity of the fiber structure.

Interface Chirality: From Biological Effects to Biomedical Applications.

Chiral surface is a critical mediator that significantly impacts interaction with biological systems on regulating cell behavior. To better understand how the properties of interfacial Chirality affect cell behavior and address the limitations of chiral materials for biomedical applications, in this review, we mainly focus on the recent developments of chiral bio-interfaces for the controllable and accurate guidance of chiral biomedical phenomena. In particular, we will discuss how cells or organisms sense and respond to the chiral stimulus, as well as the chirality mediating cell fate, tissue repair, and organism immune response will be reviewed. In addition, the biological applications of chirality, such as drug delivery, antibacterial, antivirus and antitumor activities, and biological signal detection, will also be reviewed. Finally, the challenges of chiral bio-interfaces for controlling biological response and the further application of interface chirality materials for biomedical will be discussed.

Auto interpretable depth learning model to analyze the hemodynamic changes and pulmonary complications in laparoscopic gynecologic tumor surgery with nalmefene hydrochloride combined with general anesthesia.

In this work, based on intelligent computing, the biological signals of patients were analyzed to investigate the hemodynamic changes and pulmonary complications of Nalmefene Hcl combined with general anesthesia (GA) in laparoscopic gynecological tumor surgery (GTS). Eighty computer-aided GTS patients were randomly divided into a control group (n = 40) and an observation group (n = 40). Biomedical electrocardiogram (ECG) signals were detected by wavelet neural network in all patients undergoing laparoscopic gynecological tumor surgery and were computerized according to the android interface definition language model (AIDL). GA was used during surgery. The observation group was injected intravenously with 0.2 μg/kg naproxenacin hydrochloride after operation. The control group was given 1 mL 0.9% sodium chloride solution after operation. Mean arterial pressure (MAP), heart rate (HR), respiratory rate (RR), pulse oxygen saturation (SPO), coma score, and adverse reactions (AR) were compared between the two groups at 10, 20, and 30 minutes after wakefulness. The hemodynamic parameters between the two groups were compared. Serum urocholine (URO) and creatinine (Cre) levels were analyzed in patients without complications. ECG waveform based on wavelet neural network has a high recognition rate and strong generalization ability. 37 patients in the observation group recovered within 10 minutes after surgery, and the recovery rate at 30 minutes was 95%. 30 patients in the control group awoke 10 minutes after the operation, and the recovery rate at 30 minutes m-AR was 75%. The average abstract windows toolkit (AWT) of the observation group and control group was 11.87 ± 5.78 min and 16.46 ± 5.32 min, respectively, and the difference was significant (p < 0.05). There were significant differences in HR, systolic blood pressure (SBP), and diastolic blood pressure (DBP) between the observation group and the control group during the extubation (p < 0.05). Blood gas indexes PaO2, PvO2, PaCO2, and PvCO2 in the observation group were significantly different from those in the control group half an hour after the operation and half an hour after pneumoperitoneum (p > 0.05). Intelligent computational biological signal detection was beneficial to the development of surgery. Nalmefene Hcl combined with GA on the basis of the AIDL model has a significant effect on the awakening of GTS patients and can shorten sleep time. Patients with underlying cardiac disease were more likely to develop postoperative lung complications.

Algorithm for biological second messenger analysis with dynamic regions of interest.

Physiological function is regulated through cellular communication that is facilitated by multiple signaling molecules such as second messengers. Analysis of signal dynamics obtained from cell and tissue imaging is difficult because of intricate spatially and temporally distinct signals. Signal analysis tools based on static region of interest analysis may under- or overestimate signals in relation to region of interest size and location. Therefore, we developed an algorithm for biological signal detection and analysis based on dynamic regions of interest, where time-dependent polygonal regions of interest are automatically assigned to the changing perimeter of detected and segmented signals. This approach allows signal profiles to be rigorously and precisely tracked over time, eliminating the signal distortion observed with static methods. Integration of our approach with state-of-the-art image processing and particle tracking pipelines enabled the isolation of dynamic cellular signaling events and characterization of biological signaling patterns with distinct combinations of parameters including amplitude, duration, and spatial spread. Our algorithm was validated using synthetically generated datasets and compared with other available methods. Application of the algorithm to volumetric time-lapse hyperspectral images of cyclic adenosine monophosphate measurements in rat microvascular endothelial cells revealed distinct signal heterogeneity with respect to cell depth, confirming the utility of our approach for analysis of 5-dimensional data. In human tibial arteries, our approach allowed the identification of distinct calcium signal patterns associated with atherosclerosis. Our algorithm for automated detection and analysis of second messenger signals enables the decoding of signaling patterns in diverse tissues and identification of pathologic cellular responses.

Advanced Temporally-Spatially Precise Technologies for On-Demand Neurological Disorder Intervention.

Temporal-spatial precision has attracted increasing attention for the clinical intervention of neurological disorders (NDs) to mitigate adverse effects of traditional treatments and achieve point-of-care medicine. Inspiring steps forward in this field have been witnessed in recent years, giving the credit to multi-discipline efforts from neurobiology, bioengineering, chemical materials, artificial intelligence, and so on, exhibiting valuable clinical translation potential. In this review, the latest progress in advanced temporally-spatially precise clinical interventionis highlighted, including localized parenchyma drug delivery, precise neuromodulation, as well as biological signal detection to trigger closed-loop control. Their clinical potential in both central and peripheral nervous systems is illustrated meticulously related to typical diseases. The challenges relative to biosafety and scaled production as well as their future perspectives are also discussed in detail. Notably, these intelligent temporally-spatially precision intervention systems could lead the frontier in the near future, demonstrating significant clinical value to support billions of patients plagued with NDs.

Gas-permeable and stretchable on-skin electronics based on a gradient porous elastomer and self-assembled silver nanowires

On-skin electronics offers a noninvasive method for continuous and stable signal monitoring, which has played a significant role in healthcare and rehabilitation engineering. However, most present on-skin electronicses are highly restricted by their gas permeability and stretchability due to the materials, fabrication process, and cost. This study develops gas-permeable and stretchable on-skin electronics fabricated via a gradient porous elastomer and self-assembled silver nanowires. The introduction of a gradient porous structure offers the elastomer substrate a good water vapor permeability of 3073.3 g day−1 m−2, which is much higher than the average evaporation rate of water vapor on human skin (432 g day−1m−2). Additionally, the examined electronic device has no adverse effects on the skin and possesses good stretchability and flexibility (110%, 0.07778 MPa). In addition, it offers excellent fatigue resistance (0.70% electrical resistance change within 1000 stretch cycles). The electronic device achieves conformal and comfortable contact with human skin for a long period of wearing and accomplishes high-quality biopotential measurement with a high signal-to-noise ratio (SNR) during electromyography (EMG) signal detection. The proposed simple fabrication process may supply inspiration for new on-skin electronics used in biological signal detection and rehabilitation engineering.

Wireless Lateral Flow Device for Biosensing.

Many biosensing methods rely on signals produced by enzyme-catalyzed reactions and efficient methods to detect and record this activity. Herein, we report a wireless lateral flow device and demonstrate the conversion of oxidase reactions to changes in the resonance of radio frequency identification (RFID) circuits. The detection is triggered by polyoxometalate-catalyzed oxidative doping of polypyrrole (pPy) when exposed to oxidase-generated H2O2. We have integrated this transduction and RFID capability into a lateral flow device to create a low-cost, rapid, and portable method for quantitative biological signal detection. We further report a new method for creating functional coatings from pPy core-shell colloidal particles bioconjugated for streptavidin-biotin recognition with glucose oxidase or pyruvate oxidase. The biofunctionalized pPy particles coalesce on the nitrocellulose membrane to produce a chemiresistive band. Glucose or pyruvate solutions result in formation of H2O2 at the pPy bands, functionalized with the respective oxidase, to produce conductivity enhancements exceeding 7·105%. Placing the pPy band in the RFID circuit converts the resistivity response to a change of RF resonance. The enzymatic response of glucose oxidase is recorded within 30 min with as low as 0.6 mM of glucose using this lateral flow device. Pyruvate is also shown to produce large responses. The oxidase enzymes/pPy transduction establishes a resistivity-based platform for the construction of a new family of lateral flow devices capable of detecting and quantifying biological targets.

Microscale Optoelectronic Devices for Biomedical Sensing

Conventional bioelectrical sensors and systems integrate multiple power harvesting, signal amplification and data transmission components for wireless biological signal detection. Here we report that the real-time biophysical and biochemical activities can be optically captured using a microscale light-emitting diode (micro-LED), eliminating the need for complicated sensing circuit. Such a thin-film diode based device simultaneously absorbs and emits photons, enabling wireless power harvesting and signal transmission. Additionally, owing to its strong photon-recycling effects, the micro-LED’s photoluminescence (PL) emission exhibits a superlinear dependence on the external conductance. Taking advantage of these unique mechanisms, instantaneous biophysical signals including galvanic skin response, pressure and temperature, and biochemical signals like ascorbic acid concentration, can be optically monitored, and it demonstrates that such an optoelectronic sensing technique outperforms a traditional tethered, electrically based sensing circuit, in terms of its footprint, accuracy and sensitivity. This presented optoelectronic sensing approach could establish promising routes to advanced biological sensors.

Detection of biological signals from a live mammalian muscle using an early stage diamond quantum sensor

The ability to perform noninvasive and non-contact measurements of electric signals produced by action potentials is essential in biomedicine. A key method to do this is to remotely sense signals by the magnetic field they induce. Existing methods for magnetic field sensing of mammalian tissue, used in techniques such as magnetoencephalography of the brain, require cryogenically cooled superconducting detectors. These have many disadvantages in terms of high cost, flexibility and limited portability as well as poor spatial and temporal resolution. In this work we demonstrate an alternative technique for detecting magnetic fields generated by the current from action potentials in living tissue using nitrogen vacancy centres in diamond. With 50 pT/sqrt{text {Hz}} sensitivity, we show the first measurements of magnetic sensing from mammalian tissue with a diamond sensor using mouse muscle optogenetically activated with blue light. We show these proof of principle measurements can be performed in an ordinary, unshielded lab environment and that the signal can be easily recovered by digital signal processing techniques. Although as yet uncompetitive with probe electrophysiology in terms of sensitivity, we demonstrate the feasibility of sensing action potentials via magnetic field in mammals using a diamond quantum sensor, as a step towards microscopic imaging of electrical activity in a biological sample using nitrogen vacancy centres in diamond.

Optoelectronic sensing of biophysical and biochemical signals based on photon recycling of a micro-LED

Conventional bioelectrical sensors and systems integrate multiple power harvesting, signal amplification and data transmission components for wireless biological signal detection. This paper reports the real-time biophysical and biochemical activities can be optically captured using a microscale light-emitting diode (micro-LED), eliminating the need for complicated sensing circuit. Such a thin-film diode based device simultaneously absorbs and emits photons, enabling wireless power harvesting and signal transmission. Additionally, owing to its strong photon-recycling effects, the micro-LED’s photoluminescence (PL) emission exhibits a superlinear dependence on the external conductance. Taking advantage of these unique mechanisms, instantaneous biophysical signals including galvanic skin response, pressure and temperature, and biochemical signals like ascorbic acid concentration, can be optically monitored, and it demonstrates that such an optoelectronic sensing technique outperforms a traditional tethered, electrically based sensing circuit, in terms of its footprint, accuracy and sensitivity. This presented optoelectronic sensing approach could establish promising routes to advanced biological sensors.

Application of Transcriptional Gene Modules to Analysis of Caenorhabditis elegans' Gene Expression Data.

Identification of co-expressed sets of genes (gene modules) is used widely for grouping functionally related genes during transcriptomic data analysis. An organism-wide atlas of high-quality gene modules would provide a powerful tool for unbiased detection of biological signals from gene expression data. Here, using a method based on independent component analysis we call DEXICA, we have defined and optimized 209 modules that broadly represent transcriptional wiring of the key experimental organism C. elegans. These modules represent responses to changes in the environment (e.g., starvation, exposure to xenobiotics), genes regulated by transcriptions factors (e.g., ATFS-1, DAF-16), genes specific to tissues (e.g., neurons, muscle), genes that change during development, and other complex transcriptional responses to genetic, environmental and temporal perturbations. Interrogation of these modules reveals processes that are activated in long-lived mutants in cases where traditional analyses of differentially expressed genes fail to do so. Additionally, we show that modules can inform the strength of the association between a gene and an annotation (e.g., GO term). Analysis of “module-weighted annotations” improves on several aspects of traditional annotation-enrichment tests and can aid in functional interpretation of poorly annotated genes. We provide an online interactive resource with tutorials at http://genemodules.org/, in which users can find detailed information on each module, check genes for module-weighted annotations, and use both of these to analyze their own gene expression data (generated using any platform) or gene sets of interest.

Synthetic metabolic computation in a bioluminescence-sensing system.

Bioluminescence is visible light produced and emitted by living cells using various biological systems (e.g. luxCDABE cassette). Today, this phenomenon is widely exploited in biological research, biotechnology and medical applications as a quantitative technique for the detection of biological signals. However, this technique has mostly been used to detect a single input only. In this work, we re-engineered the complex genetic structure of luxCDABE cassette to build a biological unit that can detect multi-inputs, process the cellular information and report the computation results. We first split the luxCDABE operon into several parts to create a genetic circuit that can compute a soft minimum in living cells. Then, we used the new design to implement an AND logic function with better performance as compared to AND logic functions based on protein-protein interactions. Furthermore, by controlling the reverse reaction of the luxCDABE cassette independently from the forward reaction, we built a comparator with a programmable detection threshold. Finally, we applied the redesigned cassette to build an incoherent feedforward loop that reduced the unwanted crosstalk between stress-responsive promoters (recA, katG). This work demonstrates the construction of genetic circuits that combine regulations of gene expression with metabolic pathways, for sensing and computing in living cells.

Magnetocardiography on an isolated animal heart with a room-temperature optically pumped magnetometer

Optically pumped magnetometers are becoming a promising alternative to cryogenically-cooled superconducting magnetometers for detecting and imaging biomagnetic fields. Magnetic field detection is a completely non-invasive method, which allows one to study the function of excitable human organs with a sensor placed outside the human body. For instance, magnetometers can be used to detect brain activity or to study the activity of the heart. We have developed a highly sensitive miniature optically pumped magnetometer based on cesium atomic vapor kept in a paraffin-coated glass container. The magnetometer is optimized for detection of biological signals and has high temporal and spatial resolution. It is operated at room- or human body temperature and can be placed in contact with or at a mm-distance from a biological object. With this magnetometer, we detected the heartbeat of an isolated guinea-pig heart, which is an animal widely used in biomedical studies. In our recordings of the magnetocardiogram, we can detect the P-wave, QRS-complex and T-wave associated with the cardiac cycle in real time. We also demonstrate that our device is capable of measuring the cardiac electrographic intervals, such as the RR- and QT-interval, and detecting drug-induced prolongation of the QT-interval, which is important for medical diagnostics.

Design and Experiment of Electrooculogram (EOG) System and Its Application to Control Mobile Robot

In this paper, we design and investigate a biological signal detection of eye movements (Electrooculogram). To detect a signal of Electrooculogram (EOG) used 4 instrument amplifier process; differential instrumentation amplifier, High Pass Filter (HPF) with 3 stage filters, Low Pass Filter (LPF) with 3 stage filters and Level Shifter circuit. The total of amplifying is 1000 times of gain, with frequency range 0.5-30 Hz. IC OP-Amp OP07 was used for all amplifying process. EOG signal will be read as analog input for Arduino microprocessor, and will interfaced with serial communication to PC Monitor using Processing® software. The result of this research show a differences value of eye movements. Differences signal of EOG have been applied to navigation control of the mobile robot. In this research, all communication process using Bluetooth HC-05.

Acoustic habitat of an oceanic archipelago in the Southwestern Atlantic

Underwater soundscapes can be highly variable, and in natural conditions are often dominated by biological signals and physical features of the environment. Few studies, however, focused on oceanic islands soundscapes. Islands in the middle of ocean basins can provide a good example of how untouched marine soundscapes are. Autonomous acoustic recordings were carried out in two different seasons in Trindade-Martin Vaz Archipelago, Southwestern Atlantic, providing nearly continuous data for both periods. Sound levels varied daily and between seasons. During summer, higher frequencies were noisier than lower frequencies, with snapping shrimp being the dominating sound source. During winter, lower frequencies were noisier than higher frequencies due to humpback whale constant singing. Biological signal detection had a marked temporal pattern, playing an important role in the soundscape. Over 1000 humpback whale sounds were detected hourly during winter. Fish vocalizations were detected mostly during night time during both summer and winter. The results show an acoustic habitat dominated by biological sound sources and highlight the importance of the island to humpback whales in winter.

3D Carbon-MEMS a Potent Molecular Recognition Platform for the Detection of DNA Molecule

Deoxyribonucleic acid (DNA) molecule is the building block which architects the genetic materials for development and functioning of most living organisms. The characteristics of the DNA molecule such as stability to the mechanical and thermal stress, universal presence in all tissue or cell and more informative with unique genetic code, makes it very useful for organisms species detection. Furthermore, recent discovery of the structural and functional properties of the DNA molecule extended its application for bio-imaging, bio-diagnostics and sensing for drug delivery. Thus a number of assays have been introduced for the detection of specific DNA target such as real-time polymerase chain reaction assay and molecular fluorophores labelled platforms, but those are suffering for high costing. Furthermore, traditional biological signal detection platform with two-dimensional (2D) patterns and 2D nature electrodes have the drawbacks of lower sensitivity. Microfabrication of 3D carbonaceous structures, carbon-microelectromechanical system (C-MEMS) based on the thermochemical decomposition of patterned organic photoresist at high temperatures in oxygen-free environment, draw recent attention for the detection of DNA. In comparison with fluorescence detection platform on 2D carbon micropatterns, 3D micro arrays not suffer from optical interferences obtained on oxidized silicon substrates. The architectural tools for 3D C-MEMS, such as lithography and pyrolysis facilitate to generate varied carbon structure with different crystallinity, size and shape. Furthermore, integration of certain nanomaterials such as grapheme, carbon nanotubes or nanofibers with C-MEMS can create high surface area for carbon microarray. Thus fabrication of the 3D carbon microarrays with higher surface area increases the loading capability of the biomolecule to improve the sensitivity and performance of the DNA detection platform. Acknowledgement This research is supported by University Malaya Flagship Grant project FL001A-14AET, MOSTI SF020-2014, and Transdisciplinary Research Grant Scheme (TRGS TR002A-2014) from Malaysian Ministry of Higher Education.

Wide Range Load Sensor Using Quartz Crystal Resonator for Detection of Biological Signals

High-sensitive, wide-measurement range, and small-sized load sensor was developed using AT-cut quartz crystal resonator (QCR). The quartz crystal generates a charge, which is proportional to the external force. Since it has high sensitivity and excellent temperature stability, it has been used for various sensors. In particular, QCR has superior characteristic for static load sensing in nature. However, QCR is fragile and easily broken by the stress concentration. Moreover, a retention mechanism is required to transmit the load efficiently, and we have to fix the QCR firmly while avoiding off axis force. Miniaturization of the retention mechanism is quite difficult to develop, since fabrication and assembly process is complicated. We have proposed a miniaturized sensor element using microfabrication. The QCR load sensor had enormously wide range of force sensing over 10 4 . However, output of previous sensor changes easily by parasitic capacitance change around QCR. The objective of this paper is to improve the resolution of load measurement and stability of sensor output for detection of biological signals. We fabricated QCR sensor whose sensitivity is 973 Hz/N. We succeeded in detection of multiple biological signals (breath, heartbeat, and posture) using proposed QCR load sensor with high stability.