Array Platform Research Articles

Contribution of genetic polymorphism in ABCB1 to individual variations of imatinib plasma levels in patients with gastrointestinal stromal tumor.

Imatinib mesylate (IM) is a first-line treatment option for the majority of patients diagnosed with gastrointestinal stromal tumors (GISTs). Although the clinical benefit is high, interindividual response is variable. This study thus aimed to assess how genetic polymorphisms can affect the blood levels of IM and treatment outcomes in patients with GIST. A total of 31 single-nucleotide polymorphisms (SNPs) in selected cytochrome P450 (P450), ATP-binding cassette transporter (ABC), solute carrier family (SLC), interleukin-4 receptor (IL4R), and vascular endothelial growth factor (VEGF) genes were genotyped using an SNP mass array platform. A total of 192 consecutive patients with GIST who received 400 mg of IM daily were enrolled into the study, with 1,485 blood samples being analyzed. According to genotypes, IM trough concentrations were tested and compared. Progression-free survival (PFS) and overall survival (OS) were also assessed. With a mean follow-up of 75.99 months, trough concentrations of imatinib were examined at average time points of 7.73 for each patient. Polymorphism in ABCB1 rs1045642 was found to be associated with steady-state IM trough plasma levels (P=0.008). Patients with the C genotype (CT + CC) of rs1045642 exhibited higher IM trough concentrations (1,271.09±306.69 ng/mL) compared to those with the TT genotype (1,106.60±206.05 ng/mL). No statistically significant differences in IM plasma concentration were observed for the other SNPs tested. None of the tested SNPs displayed a significant association with patients' survival in this study. This is the largest cohort study evaluating the associations of SNP and imatinib blood trough levels. The ABCB1 rs1045642 genetic polymorphism may exert an effect on the pharmacokinetics of imatinib. The presence of the C allele in ABCB1 rs1045642 is predictive of a higher plasma concentration of IM.

Survey of convolutional neural network accelerators on field-programmable gate array platforms: architectures and optimization techniques

Survey of convolutional neural network accelerators on field-programmable gate array platforms: architectures and optimization techniques

17 variants interaction of Wnt/β-catenin pathway associated with development of osteonecrosis of femoral head in Chinese Han population

The genes of Wnt/β-catenin pathway may have potential roles in fat accumulation of Non-traumatic osteonecrosis of the femoral head (ONFH), but the effects of their variants in the pathway on ONFH development have been remained unclear. To explore the potential roles of the variants in the development of ONFH, we completed the investigation of the paired interactions as well as their related biological functions of 17 variants of GSK3β, LRP5, and FRP4 genes etc. in the pathway. The genotyping of the 17 variants were finished by MASS ARRAY PLATFORM in a 560 ONFH case–control system. The association of variants interactions with ONFH risk and clinical traits was evaluated by logistic regression analysis etc. and bioinformatics technology. The results showed that the genotype, allele frequency, and genetic models of Gsk3β rs334558 (G/A), SFRP4 rs1052981 (A/G), and LRP5 rs312778 (T/C) were significantly associated with the increased and decreased ONFH risk and clinical traits, respectively (P < 0.001–0.0002). Particularly, the paired interactions of six variants as well as eight variants also showed statistically increased and decreased ONFH risk, bilateral hip lesions risk and stage IV risk of ONFH, respectively (P < 0.044–0.004). Our results not only at the first time simultaneously showed exact serum lipid disorder and abnormal platelet function of ONFH in the same study system with the 17 variants polymorphisms of Wnt/β-catenin pathway but also shed light on the variants closely intervening the lipid disorder and abnormal coagulation of ONFH.

The fatal contribution of serine protease-related genetic variants to COVID-19 outcomes.

Serine proteases play a critical role during SARS-CoV-2 infection. Therefore, polymorphisms of transmembrane protease serine 2 (TMPRSS2) and serpine family E member 1 (SERPINE1) could help to elucidate the contribution of variability to COVID-19 outcomes. To evaluate the genetic variants of the genes previously associated with COVID-19 outcomes, we performed a cross-sectional study in which 1536 SARS-CoV-2-positive participants were enrolled. TMPRSS2 (rs2070788, rs75603675, rs12329760) and SERPINE1 (rs2227631, rs2227667, rs2070682, rs2227692) were genotyped using the Open Array Platform. The association of polymorphisms with disease outcomes was determined by logistic regression analysis adjusted for covariates (age, sex, hypertension, type 2 diabetes, and obesity). According to our codominant model, the GA genotype of rs2227667 (OR=0.55; 95% CI = 0.36-0.84; p=0.006) and the AG genotype of rs2227667 (OR=0.59; 95% CI = 0.38-0.91; p=0.02) of SERPINE1 played a protective role against disease. However, the rs2227692 T allele and TT genotype SERPINE1 (OR=1.45; 95% CI = 1.11-1.91; p=0.006; OR=2.08; 95% CI = 1.22-3.57; p=0.007; respectively) were associated with a decreased risk of death. Similarly, the rs75603675 AA genotype TMPRSS2 had an OR of 1.97 (95% CI = 1.07-3.6; p=0.03) for deceased patients. Finally, the rs2227692 T allele SERPINE1 was associated with increased D-dimer levels (OR=1.24; 95% CI = 1.03-1.48; p=0.02). Our data suggest that the rs75603675 TMPRSS2 and rs2227692 SERPINE1 polymorphisms are associated with a poor outcome. Additionally, rs2227692 SERPINE1 could participate in hypercoagulable conditions in critical COVID-19 patients, and this genetic variant could contribute to the identification of new pharmacological targets and treatment strategies to block the inhibition of TMPRSS2 entry into SARS-CoV-2.

Abstract 5798: Preclinical safety assessment for specificity of biotherapeutics using the membrane proteome array

Abstract Rigorous specificity analysis is critical for the development of antibody-based therapies, as even minimal off-target binding can lead to toxicity and clinical failures. Long believed to be exquisitely specific, recent preclinical data and our own work indicate that monoclonal antibodies (MAbs) frequently (~25%) display cross-reactivity. In many cases, off-target interactions occur with unrelated proteins that cannot be predicted by protein sequence homology. Cross-reactivity can lead to serious or even life-threatening consequences especially when MAbs are configured as bispecifics, antibody-drug conjugates (ADC) or CAR-T cell therapies, and IND applications for biotherapeutics require cross-reactivity assessment to prevent adverse events. Tissue cross-reactivity (TCR) studies have traditionally been used to screen for off-target binding, however, with poor predictive value for in vivo safety and toxicity. We developed the Membrane Proteome Array (MPA) platform to de-risk MAb-based therapeutics by testing for specificity across 6,000 human membrane proteins expressed in unfixed cells. The MPA assesses binding interactions by high-throughput flow cytometry allowing for high sensitivity detection and rapid analysis. All targets identified on the MPA screen are validated by secondary titration analysis. In contrast to TCR studies, proteins in the MPA exist in their native conformations and are not altered by fixation. Recently, we used the MPA to select a lead antibody candidate targeting the oncotherapeutic target Claudin 6 (CLDN6). Through MPA screening, we successfully identified a lead candidate devoid of cross-reactivity against other CLDN family members or any other membrane protein across the human genome. The MPA has also been used to rapidly evaluate the specificity of novel CAR-T cell therapies for cancer and autoimmunity. The MPA has been used for lead selection and data from the MPA has been accepted by the FDA as a part of IND applications for antibody-based therapies. The MPA is currently under consideration for being developed as an FDA-qualified Drug Development Tool. Citation Format: Kristopher S. Raghavan, Jonathan T. Sullivan, Carmen Navia, Rachel H. Fong, Benjamin J. Doranz. Preclinical safety assessment for specificity of biotherapeutics using the membrane proteome array [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5798.

Abstract 2985: RXR agonist, 9cUAB30 impacts epigenetic regulations to achieve chemoprevention of UVB-induced Skin cancer by altering global miRNA profiling

Abstract Basal cell carcinoma (BCCs) and Squamous cell carcinoma (SCCs) are the most common skin neoplasms in the Caucasian population. UVB is considered as the most important etiologic factor for these cancers. The molecular mechanism regulating pathogenesis of these neoplasms is not well defined. Similar to humans Ptch+/−/SKH-1 mice develop both BCCs and SCCs, following chronic UVB-irradiation. We used TaqMan-based Open Array platform containing 796 miRNAs (miRs) to profile miRs associated with BCCs and SCCs. Pathway enrichment analysis was performed using miRPathDB databases at https://mpd.bioinf.uni-sb.de/. 81 miRNAs were differentially expressed in BCCs, of which 54 miRs had cumulative frequency of greater than 1 (FC&amp;gt;1) and 78 miRs were differentially expressed in SCCs, out of which 20 miRs had FC&amp;gt;1. miR-721 was highly induced (123.75 fold, P&amp;lt;0.0430) in BCCs followed by miR-2182, miR-21, miR-451, miR-124a, and miR-685 while downmodulated miRs includes miR-1, miR-133a, miR-133b, and miR-196b. Likewise, miR-21 had maximum fold induction (45.94, P&amp;lt;0.007) in SCCs followed by miR-2182, miR-685, miR-1938, miR-2138, and miR-451 while miR-196b, miR196c, and miR-1 were downmodulated. Kegg pathways analysis revealed that altered miRs belong to Hepatitis B, HIF-1, FoxO, chemokine, adipocytokine, NFκB, cholinergic and TLR signaling pathways. WikiPathway database showed alterations in miRs associated with neovascularization process, Notch Signaling, Pregnane X receptor, α6β4, and BDNF-TrkB signaling. To further investigate the importance of pathways associated with tumor progression, we profiled miRs in residual BCCs and SCCs procured from UAB30 treatment groups. The chemopreventive agent UAB30 is an RXR agonist. In BCCs, UAB30 treatment significantly downmodulated UVB-upregulated miRs. These miRs includes miR-210, miR-546, miR-324-3p, miR-706 and miR-1898. However, UAB30 treatment significantly upregulates miR-380-5p which was downmodulated by UVB-alone. These miRs were involved in immune regulation, Fanconi anemia pathways, FoxO signaling, prostate cancer and p53 signaling. In SCCs, UAB30 treatment significantly reversed the expression of various miRs which were downmodulated by UVB. These miRs were miR-24, miR-26a, miR-30c, miR-328, miR-30b, miR-495 and miR-2100. The expression of miR-21a-3p was downmodulated by UAB30 in SCCs of UVB-exposed group relative to SCCs of untreated but UVB-exposed group. These miRs belong to pathways involved in cell cycle progression, p53 signaling, viral carcinogenesis, Hepatitis B, Melanoma, Prostate cancer, FoxO and PI3K-AKT signaling. In summary, this profiling of miRs in UVB-induced BCC and SCC represents the underlying epigenetic mechanisms that regulate various signaling pathways in these cancers. Our data also show the critical role of miRs responsive to UAB30 treatment in cancer chemoprevention. Citation Format: Mahendra P. Kashyap, Rajesh Sinha, Craig A. Elmets, Venkatram R. Atigadda, Mohammad Athar. RXR agonist, 9cUAB30 impacts epigenetic regulations to achieve chemoprevention of UVB-induced Skin cancer by altering global miRNA profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2985.

CASA: A Compact and Scalable Accelerator for Approximate Homomorphic Encryption

Approximate arithmetic-based homomorphic encryption (HE) scheme CKKS [CKKS17] is arguably the most suitable one for real-world data-privacy applications due to its wider computation range than other HE schemes such as BGV [BGV14], FV and BFV [Bra12, FV12]. However, the most crucial homomorphic operation of CKKS called key-switching induces a great amount of computational burden in actual deployment situations, and creates scalability challenges for hardware acceleration. In this paper, we present a novel Compact And Scalable Accelerator (CASA) for CKKS on the field-programmable gate array (FPGA) platform. The proposed CASA addresses the aforementioned computational and scalability challenges in homomorphic operations, including key-exchange, homomorphic multiplication, homomorphic addition, and rescaling.On the architecture layer, we propose a new design methodology for efficient acceleration of CKKS. We design this novel hardware architecture by carefully studying the homomorphic operation patterns and data dependency amongst the primitive oracles. The homomorphic operations are efficiently mapped into an accelerator with simple control and smooth operation, which brings benefits for scalable implementation and enhanced pipeline and parallel processing (even with the potential for further improvement).On the component layer, we carry out a detailed and extensive study and present novel micro-architectures for primitive function modules, including memory bank, number theoretic transform (NTT) module, modulus switching bank, and dyadic multiplication and accumulation.On the arithmetic layer, we develop a new partially reduction-free modular arithmetic technique to eliminate part of the reduction cost over different prime moduli within the moduli chain of the Residue Number System (RNS). The proposed structure can support arbitrary numbers of security primes of CKKS during key exchange, which offers better security options for adopting the scalable design methodology.As a proof-of-concept, we implement CASA on the FPGA platform and compare it with state-of-the-art designs. The implementation results showcase the superior performance of the proposed CASA in many aspects such as compact area, scalable architecture, and overall better area-time complexities.In particular, we successfully implement CASA on a mainstream resource-constrained Artix-7 FPGA. To the authors’ best knowledge, this is the first compact CKKS accelerator implemented on an Artix-7 device, e.g., CASA achieves a 10.8x speedup compared with the state-of-the-art CPU implementations (with power consumption of only 5.8%). Considering the power-delay product metric, CASA also achieves 138x and 105x improvement compared with the recent GPU implementation.

Asymmetrical multi‐level inverter by using space vector pulse width modulation technique for low and medium power applications

SummaryA DC to AC converter is developed in this paper. This proposes a single‐phase five‐level inverter is developed in such a way that one DC source is used. The structure of the inverter is modular and the capacitor has self‐voltage balancing capability, which eliminates the need for sensors or an additional control loop. This paper presents the schematic diagram of the proposed topology, principle of operation, and the concepts of sinusoidal pulse width modulation (SPWM) for single‐phase and space vector pulse width modulation (SVPWM) for three‐phase. To further prove the advantage, power loss, and parameter computations are calculated mathematically. Low‐voltage stress is achieved in the proposed topology because the blocking voltages of all the switches are constrained to the input voltage. A comparison table is presented between SPWM and SVPWM schemes in three‐phase inverter. The proposed asymmetrical five‐level configuration setup is constructed, and experiments are performed inside the laboratory. The controller of the converter has been implemented in the field‐programmable gate array (FPGA) platform. In order to confirm the viability of the suggested topology, experimental results are provided for single‐phase arrangement. By using the FPGA platform, real‐time implementation is done for a three‐phase inverter topology.

The Data Compression Method and FPGA Implementation in the Mars Rover Subsurface-Penetrating Radar on the Tianwen-1 Mission

Since Mars is far away from Earth, the propagation delay between Mars and Earth is very large. To ensure the effective use of the link transmission bandwidth, China’s first Mars exploration mission has put forward a demand for data compression for all scientific payloads. The on-board mature algorithms for data compression are mainly focused on optical images and microwave imaging radar applications. No articles have been published on data compression methods that are applied to subsurface-penetrating radar. Based on the background of this application, this paper proposes a logarithmic lossy compression algorithm which can meet the mission requirements for high compression ratios of 4:1 and 2.5:1. Its compression error is less than that of the block adaptive quantization (BAQ) algorithm. The algorithm is not only easy to implement on field-programmable gate array (FPGA) platforms, but also offers simple ground decompression and fast imaging. The experimental results show that high compression ratios of 4:1 and 2.5:1 can be realized, even if the data in and between traces do not have a strong correlation. And its relative error is less than 2%, which is a new type of high-efficiency data compression method that can be implemented on-board to meet with the demand of subsurface penetrating radar.

Construction of a Novel Semiautomated Electrochemical Sensor Array Platform and Its Application in Multiplexed Monitoring of Antibiotic Therapy.

At present, traditional analytical methods suffer from issues such as complex operation, expensive equipment, and a lengthy testing time. Electrochemical sensors have shown great advantages and application potential as an alternative solution. In this study, we proposed a novel semiautomated electrochemical sensor array (SAESA) platform. The sensor array was fabricated using screen-printed technology with a tubular design where all electrodes were printed on the inner wall. The integration of the tubular sensor array with a pipet allows for a semiautomated process including sampling and rinsing, which simplifies operation and reduces overall time. Each working electrode in the tubular sensor array underwent distinct decoration to get specific sensing responses toward the target analytes in a mixture environment (e.g., blood samples). To demonstrate the applicability of the developed sensing platform for simultaneous multianalyte detection, we chose antibiotic treatment for inflammatory infection as a model scenario and continuously measured three biomarkers, namely, tigecycline (TGC), procalcitonin (PCT), and alanine aminotransferase (ALT). The detection limits were 0.3 μM, 0.3 ng/L, and 2.76 U/L, respectively. The developed semiautomated electrochemical sensor array exhibits characteristics such as rapid and simple operation, portability, good selectivity, and excellent stability.

Marine magnetotelluric and controlled source electromagnetics signal simulative generator

Ocean bottom electromagnetic receivers (OBEMs) are invaluable tools for observing seafloor electromagnetic signals, primarily used in magnetotelluric (MT) sounding and marine controlled source electromagnetics (MCSEM). However, the high operational costs of deploying OBEMs require thorough pre-deployment testing to enhance the data reliability and quality. Commercial signal generators used for MT and controlled source electromagnetic (CSEM) testing often fall short of the restricted terms of the frequency range, signal-to-noise ratio, storage depth, time synchronization, and amplitude dynamics. To address these limitations, we developed a specialized marine MT and CSEM signal simulative generator. The generator employs pseudorandom number code-based white noise to simulate MT signals and uses multi-frequency synthesis and amplitude modulation (AM) to generate CSEM signals. The system is developed using a low-power microcontroller unit and a field programmable gate array platform, integrating various circuits such as power, clock, encoding, control, data storage, chopper, and amplification circuits. The generator simultaneously outputs four-channel white noise and AM signals. The white noise feature provides a wide and adjustable frequency band and amplitude, while the AM feature provides a large dynamic amplitude range and multi-frequency synthesis at various carrier frequency points. Comprehensive testing verified the performance of the proposed simulative generator. The test results demonstrate that the MT simulated signals cover a frequency range from 1 E4s to 100 Hz, providing adjustable amplitudes between 10 μVpp and 10 mVpp. The CSEM simulated signals include standard square waves, third harmonic waves, and seventh harmonic waves, with amplitudes adjustable from 10 mVpp to 10 Vpp. The proposed simulative generator enhances the efficiency of OBEM functional testing and provides essential equipment support for maintaining the quality of marine data.

Multiplex signal amplification for ultrasensitive CRP assay via integrated electrochemical biosensor array using MOF-derived carbon material and aptamers

Accurate and precise detection of disease-associated proteins, such as C-reactive protein (CRP), remains a challenge in biosensor development. Herein, we present a novel approach－an integrated disposable aptasensor array－designed for precise, ultra-sensitive, and parallel detection of CRP in plasma samples. This integrated biosensing array platform enables multiplex parallel testing, ensuring the accuracy and reliability in sample analysis. The ultra-sensitivity of this biosensor is achieved through multiplex signal amplification. Leveraging the superior conductivity and extensive surface area of MOF-derived nanoporous carbon material (CMOF), the biosensor enhances recognition elements (aptamers) by catalyzing the horseradish peroxidase (HRP) label enzyme reaction to multiply the number of probe molecules. Optimized conditions yielded exceptional performance, exhibiting high accuracy (relative standard deviation, RSD≤10.0 %), a low detection limit (0.3 pg/mL, S/N = 3), ultra-sensitivity (0.16 μA/ng mL−1 mm−2), and a rapid response (seven parallel tests within 60 min). Importantly, this multi-unit integrated disposable aptasensor array accurately quantified CRP in human serum, demonstrating comparable results to commercial enzyme–linked immunosorbent assay (ELISA). This technology showcases promise for detecting various biomarkers using a unified approach, presenting an appealing strategy for early disease diagnosis and biological analysis.

Genome-wide association study of blood lipid levels in Southern Han Chinese adults with prediabetes.

Dyslipidemia is highly prevalent among individuals with prediabetes, further exacerbating their cardiovascular risk. However, the genetic determinants underlying diabetic dyslipidemia in Southern Han Chinese remain largely unexplored. We performed a genome-wide association study (GWAS) of blood lipid traits in 451 Southern Han Chinese adults with prediabetes. Fasting plasma lipids, including triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were assayed. Genotyping was conducted using the Precision Medicine Diversity Array and Gene Titan platform, followed by genotype imputation using IMPUTE2 with the 1000 Genomes Project (Phase 3, Southern Han Chinese) as reference. Single nucleotide polymorphisms (SNPs) associated with lipid levels were identified using mixed linear regression, with adjustment for covariates. We identified 58, 215, 74 and 81 novel SNPs associated with TG, TC, HDL-C and LDL-C levels, respectively (P < 5×10-5). Several implicated loci were located in or near genes involved in lipid metabolism, including SRD5A2, PCSK7, PITPNC1, IRX3, BPI, and LBP. Pathway enrichment analysis highlighted lipid metabolism and insulin secretion. This first GWAS of dyslipidemia in Southern Han Chinese with prediabetes identified novel genetic variants associated with lipid traits. Our findings provide new insights into genetic mechanisms underlying heightened cardiovascular risk in the prediabetic stage. Functional characterization of implicated loci is warranted.

Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

Reconfigurable intelligent surfaces (RIS) offer the potential to customize the radio propagation environment for wireless networks, and will be a key element for 6G communications. However, due to the unique constraints in these systems, the optimization problems associated to RIS configuration are challenging to solve. This paper illustrates a new approach to the RIS configuration problem, based on the use of artificial intelligence (AI) and deep learning (DL) algorithms. Concretely, a custom convolutional neural network (CNN) intended for edge computing is presented, and implementations on different representative edge devices are compared, including the use of commercial AI-oriented devices and a field-programmable gate array (FPGA) platform. This FPGA option provides the best performance, with ×20 performance increase over the closest FP32, GPU-accelerated option, and almost ×3 performance advantage when compared with the INT8-quantized, TPU-accelerated implementation. More noticeably, this is achieved even when high-level synthesis (HLS) tools are used and no custom accelerators are developed. At the same time, the inherent reconfigurability of FPGAs opens a new field for their use as enabler hardware in RIS applications.

Genetic testing for fetal loss of heterozygosity using single nucleotide polymorphism array and whole-exome sequencing

The study explored the clinical significance of fetal loss of heterozygosity (LOH) identified by single-nucleotide polymorphism array (SNP array). We retrospectively reviewed data from pregnant women who underwent invasive diagnostic procedures at prenatal diagnosis centers in southeastern China from December 2016 to December 2021. SNP array was performed by the Affymetrix CytoScan 750 K array platform. Fetuses with LOH were further identified by parental verification, MS-MLPA, and/or trio whole-exome sequencing (trio-WES). The genetic results, fetal clinical manifestations, and perinatal outcome were analyzed. Of 11,062 fetuses, 106 (0.96%) had LOH exhibiting a neutral copy number, 88 (83.0%) had LOH in a single chromosome, whereas 18 (17.0%) had multiple LOHs on different chromosomes. Sixty-six fetuses had ultrasound anomalies (UAs), most frequently fetal growth restriction (18/66 (27.3%)). Parental SNP array verification was performed in 21 cases and trio-WES in 21 cases. Twelve cases had clinically relevant uniparental disomy, five had pathogenic variants, four had likely pathogenic variants, six had variants of unknown significance, and eight had identity by descent. The rate of adverse pregnancy outcomes in fetuses with LOH and UAs (24/66 (36.4%)) was higher than in those without UAs (6/40 (15.0%)) (p < 0.05). LOH is not uncommon. Molecular genetic testing techniques, including parental SNP array verification, trio-WES, methylation-specific multiplex ligation-dependent probe amplification, regular and systematic ultrasonic monitoring, and placental study, can accurately assess the prognosis and guide the management of the affected pregnancy.

GAS SOURCE LOCALIZATION THROUGH DEEP LEARNING METHOD BASED ON GAS DISTRIBUTION MAP DATABASE

The incident of harmful gas leakage can cause severe damage to the environment and several casualties to human beings while the gas localization system plays a major role in mitigating those causalities. With the advances in artificial intelligence technology, deep learning is able to enhance the accuracy of the gas localization system to locate the gas source. This paper proposes a gas localization system that utilizes three different deep learning models namely DNN, 1DCNN, and 2DCNN to locate the gas source within the gas map. The proposed method involves generating the gas distribution map through the large gas sensor array platform in real-world indoor scenarios. Those models are then trained using the collected database which allows for accurate prediction of the gas source location. The performance of each proposed deep learning model was compared to find the best model demonstrating the highest effectiveness in identifying gas leaks. The study has shown that the 1DCNN has the highest effectiveness in predicting the gas source in the range between 0.0 m to 0.3 m with 90.3% compared to the DNN and 2DCNN models.

Fully synthetic phosphorylated Tau181, Tau217, and Tau231 calibrators for Alzheimer's disease diagnosis.

The calibrator in immunoassay plays an essential role in diagnosing Alzheimer's disease (AD). Presently, the most well-studied biomarkers for AD diagnosis are three phosphorylated Tau (p-Tau): p-Tau231, p-Tau217, and p-Tau181. Glycogen synthase-3beta (GSK3β)-phosphorated Tau-441 is the most commonly used calibrator for p-Tau immunoassays. However, the batch-to-batch inconsistency issue of the commonly used GSK3β-phosphorylated Tau-441 limits its clinical application. We have successfully generated and characterized 61 Tau monoclonal antibodies (mAbs) with distinct epitopes by using the hybridoma technique and employed them as capture or detection antibodies for p-Tau immunoassays. Through chemical synthesis, we synthesized calibrators, which are three peptides including capture and detection antibody epitopes, for application in immunoassays that detect p-Tau231, p-Tau217, and p-Tau181. The novel calibrators were applied to Enzyme-linked immunosorbent assay (ELISA) and Single-molecule array (Simoa) platforms to validate their applicability and establish a range of p-Tau immunoassays. By employing the hybridoma technique, 49 mAbs recognizing Tau (1-22), nine mAbs targeting p-Tau231, one mAb targeting p-Tau217, and two mAbs targeting p-Tau181 were developed. Peptides, including recognition epitopes of capture and detection antibodies, were synthesized. These peptides were used as calibrators to develop 60 immunoassays on the ELISA platform, of which six highly sensitive immunoassays were selected and applied to the ultra-sensitive Simoa platform. Remarkably, the LODs were 2.5, 2.4, 31.1, 32.9, 46.9, and 52.1 pg/ml, respectively. Three novel p-Tau calibrators were successfully generated and validated, which solved the batch-to-batch inconsistency issue of GSK3β-phosphorylated Tau-441. The novel calibrators exhibit the potential to promote the standardization of clinical AD diagnostic calibrators. Furthermore, we established a series of highly sensitive and specific immunoassays on the Simoa platform based on novel calibrators, which moved a steady step forward in p-Tau immunoassay application for AD diagnosis.

Multiplexed miRNA and Protein Analysis Using Digital Quantitative PCR in Microwell Arrays.

Proteins and microRNAs (miRNAs) act in tandem within biological pathways to regulate cellular functions, and their misregulation has been correlated to numerous diseases. Because of their interconnectedness, both miRNAs and proteins must be evaluated together to obtain accurate insights into the molecular pathways of pathogenesis. However, few analytical techniques can measure both classes of biomolecules in parallel from a single biological sample. Here, microfluidic digital quantitative PCR (dqPCR) was developed to simultaneously quantify miRNA and protein targets in a multiplexed assay using a single detection chemistry. This streamlined analysis was achieved by integrating base-stacking PCR and immuno-PCR in a microfluidic array platform. Analyses of let-7a (miRNA) and IL-6 (protein) were first optimized separately to identify thermocycling and capture conditions amenable to both biomolecules. Singleplex dqPCR studies exhibited the expected digital signals and quantification cycles for both analytes over a range of concentrations. Multiplexed analyses were then conducted to quantify both let-7a and IL-6 with high sensitivity (LODs ∼ 3 fM) over a broad dynamic range (5-5000 fM) using only standard PCR reagents. This multiplexed dqPCR was then translated to the analysis of HEK293 cell lysate, where endogenous let-7a and IL-6 were measured simultaneously without sample purification or pretreatment. Collectively, these studies demonstrate that the integration of BS-PCR and immuno-PCR achieves a sensitive and streamlined approach for multiplexed analyses of miRNAs and proteins, which will enable researchers to gain better insights into disease pathogenesis in future applications.

The hazards of genotype imputation when mapping disease susceptibility variants

BackgroundThe cost-free increase in statistical power of using imputation to infer missing genotypes is undoubtedly appealing, but is it hazard-free? This case study of three type-2 diabetes (T2D) loci demonstrates that it is not; it sheds light on why this is so and raises concerns as to the shortcomings of imputation at disease loci, where haplotypes differ between cases and reference panel.ResultsT2D-associated variants were previously identified using targeted sequencing. We removed these significantly associated SNPs and used neighbouring SNPs to infer them by imputation. We compared imputed with observed genotypes, examined the altered pattern of T2D-SNP association, and investigated the cause of imputation errors by studying haplotype structure. Most T2D variants were incorrectly imputed with a low density of scaffold SNPs, but the majority failed to impute even at high density, despite obtaining high certainty scores. Missing and discordant imputation errors, which were observed disproportionately for the risk alleles, produced monomorphic genotype calls or false-negative associations. We show that haplotypes carrying risk alleles are considerably more common in the T2D cases than the reference panel, for all loci.ConclusionsImputation is not a panacea for fine mapping, nor for meta-analysing multiple GWAS based on different arrays and different populations. A total of 80% of the SNPs we have tested are not included in array platforms, explaining why these and other such associated variants may previously have been missed. Regardless of the choice of software and reference haplotypes, imputation drives genotype inference towards the reference panel, introducing errors at disease loci.

An ultra-fast and energy-efficient CNTFET-based image corner detection hardware for real-time image processing applications

In image processing and machine vision, corner detection is pivotal in diverse applications, including computer vision, 3D reconstruction, face detection, object tracking, and video technologies. Despite the wide usage, the real-time and energy-efficient hardware implementation of corner detection algorithms remains a critical challenge because of the computational resource limitations. On the other hand, owing to the complicated nature of corner detection algorithms, their hardware implementation has been limited to the graphics processing unit (GPU) and field programmable gate arrays (FPGA) platforms. In this regard, this work aims to propose a novel and ultra-efficient carbon nanotube field-effect transistor (CNTFET)-based hardware for image corner detection. Thanks to the proposed corner detection algorithm, the designed hardware has been realized using 2742 transistors with competitive accuracy. The proposed corner detection hardware indicated a remarkable salt-and-pepper noise immunity without using any noise reduction circuit. Our comprehensive simulations demonstrate 78%, 87%, and 94.5% total average improvements in delay, power, and energy compared to the other related corner detection hardware. Moreover, the proposed CNTFET-based corner detection hardware shows a 43 ps propagation delay, demonstrating its real-time operation. The proposed corner detection algorithm at the system level shows suitable accuracy metrics such as Recall, Precision, and error of detection (EoD) compared to the other well-known corner detectors. Our method has established a new pathway for real-time circuit-level hardware design for image processing and machine vision applications.