Commercial Platforms Research Articles

Quantitative Structure-Activity Relationship Models to Predict Cardiac Adverse Effects.

Drug-induced cardiotoxicity represents one of the most common causes of attrition of drug candidates in preclinical and clinical development. For this reason, the evaluation of cardiac toxicity is essential during drug development and regulatory review. In the present study, drug-induced postmarket adverse event combinations from the FDA Adverse Event Reporting System were extracted for 2002 drugs using 243 cardiac toxicity-related preferred terms (PTs). These PTs were combined into 12 groups based on their clinical relevance to serve as training sets. The optimal classification scheme was determined using a combination of data sources that included drug labeling information, published literature, clinical study data, and postmarket surveillance data. Two commercial QSAR platforms were used to construct 12 models, including general cardiac toxicity, cardiac ischemia, heart failure, cardiac valve disease, myocardial disease, pericardial disease, structural heart disease, cardiac arrhythmia, Torsades de Pointes, long QT syndrome, atrial fibrillation and ventricular arrhythmia, and cardiac arrest. The cross-validated performance for the new models reached a sensitivity of up to 80% and negative predictivity of up to 80%. These new models covering a wide range of cardiac endpoints will provide fast, reliable, and comprehensive predictions of potential cardiotoxic compounds in drug discovery and regulatory safety assessment.

EXTH-11. PEDIATRIC GLIOMA IMMUNE PROFILING IDENTIFIES TIM3 AS A THERAPEUTIC TARGET IN BRAF-FUSION PILOCYTIC ASTROCYTOMA

Abstract Despite being the leading cause of childhood mortality, pediatric gliomas have been relatively understudied. Repurposing of adult brain tumor immunotherapies in pediatric patients has not been successful, highlighting the need for pediatric-specific immunotherapies. Pilocytic astrocytomas (PA) are the most common pediatric glioma. While surgical resection of PA is the first-line treatment, the ten-year progression-free survival rate for patients with residual tumors is <50%. Notably, some PAs undergo spontaneous regression after partial resection, suggesting potential baseline tumor immunoreactivity. Whole transcriptome sequencing performed on a commercial platform of pediatric gliomas (n=250) showed that MAPK-driven gliomas have higher interferon signatures compared to other molecular subgroups. Single-cell sequencing identified a unique activated and cytotoxic microglia population designated MG-Act in BRAF-fused MAPK-activated PA (n=13; p < 0.05), but not in pediatric high-grade gliomas (n=5) or normal brain (n=3). TIM3 was expressed on the luminal side of endothelial cells and MG-Act, but not in the surrounding normal brain based on sequential multiplex imaging. Flow cytometry showed that TIM3 intensity is upregulated on immune cells in the PA tumor microenvironment (TME) relative to matched peripheral blood immune cells (n=6; p < 0.01). Anti-TIM3 reprogrammed fresh ex vivo immune cells from the TME of human PAs (n=3) to a pro-inflammatory cytotoxic phenotype. In a genetically engineered murine model of MAPK-driven low-grade gliomas, median survival (MS) was 252 days in mice treated with anti-TIM3 (n=21; p<0.01) relative to IgG control (MS: 110 days; n=20) or anti-PD-1 (MS: 134 days; n=20). The therapeutic activity of anti-TIM3 was abrogated in the CX3CR1 microglia knockout background. ScRNA sequencing data during the therapeutic window of anti-TIM3 in wild-type mice demonstrated enrichment of the MG-Act population within low-grade gliomas at two different longitudinal time points but not in IgG-treated controls. Together, these data indicate that anti-TIM3 should be considered for clinical trials in pediatric low-grade MAPK-driven gliomas.

Behaviors and Perceptions of Human-Chatbot Interactions Based on Top Active Users of a Commercial Social Chatbot

Natural language processing is enabling machines to communicate with humans naturally, yet the dynamics of extended user-chatbot interactions remain much unexplored. This study characterizes the conversational styles, demographics, psychologies, and emotional tendencies of the most active users (i.e., top 1% by message count) of a commercial chatbot platform (SimSimi.com), whom we refer to as superusers. We analyzed the linguistic patterns and topics of 1,988,971 messages written by 1,994 superusers over a period of three years. We further surveyed 76 users to observe their emotional dispositions and perceptions towards the chatbot. We find that SimSimi superusers empathize and humanize the chatbot more than less active users, and they show a higher tendency to share personal and negative feelings. Our findings suggest that chatbots require new design considerations for users who are vulnerable due to their high anthropomorphism and openness toward machines. Our work also shows that chatbots should have functions to offer social support when necessary.

A novel 5.1–7.1 GHz front-end power amplifier for wireless applications with [formula omitted] dB Error Vector Magnitude

A Power Amplifier (PA) is proposed for WiFi-6E applications, specifically designed to support WLAN 802.11ax standard to achieve significant linear power. The proposed PA is designed to operate in the entire 5 GHz and 6 GHz frequency bands. The design involves a two-stage configuration, initial stage adopts a linear driver, while the subsequent amplification stage is structured as a differential cascode. UMC 65 nm CMOS process technology is used to design a PA in commercial Cadence platform. The layout occupies core area of 0.24 mm2. Proposed PA delivers a power gain of 20 dB, Power Added Efficiency (PAE) of 19% and 1 dB compression point (P1dB) is of 24.5 dB is achieved for continuous wave (CW) signal. For the 802.11ax source, the recorded average power output (Pavg) is 17.1 dBm at an Error Vector Magnitude (EVM) ≤−35 dB, across the 5–7 GHz. The PA delivers Pavg of 10 dBm at worst corner case in PVT variation analysis. While the majority of recent works in WLAN PA confines the investigation to 5 GHz band, this paper broadens the scope by extending the study by up to 7.050 GHz in 6 GHz band. The incorporation of 6GHz band enables WiFi-6E to utilize up to 1,200 MHz of unencumbered spectrum, significantly enhancing the available bandwidth relative to earlier WiFi standards. The increased bandwidth facilitates high data rate applications, such as streaming and gaming.

Developing remote patient monitoring infrastructure using commercially available cloud platforms

Wearable sensor devices for continuous patient monitoring produce a large volume of data, necessitating scalable infrastructures for efficient data processing, management and security, especially concerning Patient Health Information (PHI). Adherence to the Health Insurance Portability and Accountability Act (HIPAA), a legislation that mandates developers and healthcare providers to uphold a set of standards for safeguarding patients’ health information and privacy, further complicates the development of remote patient monitoring within healthcare ecosystems. This paper presents an Internet of Things (IoT) architecture designed for the healthcare sector, utilizing commercial cloud platforms like Microsoft Azure and Amazon Web Services (AWS) to develop HIPAA-compliant health monitoring systems. By leveraging cloud functionalities such as scalability, security, and load balancing, the architecture simplifies the creation of infrastructures adhering to HIPAA standards. The study includes a cost analysis of Azure and AWS infrastructures and evaluates data processing speeds and database query latencies, offering insights into their performance for healthcare applications.

Ultra-Short-Term Wind Power Forecasting in Complex Terrain: A Physics-Based Approach

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting.

Comparison of Four Chimeric Antigens and Commercial Serological Assays for the Diagnosis of Trypanosoma cruzi Infection

Chagas disease (CD), a neglected tropical disease caused by Trypanosoma cruzi, is a significant public health issue particularly in Latin America, affecting millions worldwide. Diagnosis is a challenge owing to the genetic diversity of T. cruzi and the complexities involved in selecting antigens for the detection of anti–T. cruzi antibodies. This study evaluated four chimeric recombinant antigens (IBMP-8.1, IBMP-8.2, IBMP-8.3, and IBMP-8.4) designed to enhance diagnostic accuracy by addressing assay variability. We compared the diagnostic performance of these chimeric antigens using indirect ELISA as a diagnostic platform, with three commercial serological assays in Brazil, analyzing 100 serum samples from individuals with confirmed CD and 86 from non-infected controls. The results revealed that all assays and antigens demonstrated an area under the receiver operating characteristic curve of 100%, signifying their exceptional ability to distinguish between CD-positive and CD-negative samples. Notably, the chimeric antigens achieved 100% sensitivity, specificity, accuracy, and kappa index, equaling or surpassing the commercial assays. This research highlights the efficacy of IBMP chimeric antigens as reliable diagnostic tools for CD, suggesting their potential integration into commercial diagnostic platforms to enhance the accuracy and reliability of CD detection.

Bio-mimetic deconstruction of the digital business ecosystem

The advent of the Internet Plus era, digital technologies, and the digital economy has instigated profound transformations in the commercial landscape, particularly evident in the systematic reshaping of the Digital Business Ecosystem (DBE), encompassing innovations in business models, norms of commercial conduct, and the exploration of business value. This paper delves into the panoramic view of digital business operations of typical companies to uncover the fundamental structural framework of digital commerce. Through deductive reasoning and drawing upon the theoretical framework of natural niche, we construct a niche model for the digital business ecosystem, thereby achieving a bionic deconstruction of the digital business ecosystem. The significance of this research lies in offering a novel research perspective for enterprises, economic regulatory bodies, and scholars in the field of business management, proposing a systemic approach rooted in niche theory models to competition. This approach provides a fresh theoretical framework for enterprises to devise their own ecological and sustainable development strategies. The key findings are as follows: (1) Most business firms establish competitive advantages by constructing commercial cloud platforms that facilitate internal digital transformation and enable digital synergy with external economic entities; (2) Within the digital business ecosystem, enterprises extend their digital capabilities externally through four modalities: data development, data application, data services, and data manufacturing. Externally, six primary forces and roles shape the ecosystem: suppliers, governments, social institutions, consumers, as well as external and internal industry players; (3) The digital business niche is a multidimensional and hyper volumetric relationship positioning between enterprises and the digital business environment. The niche factors include six dimensions: market, personnel, resources, social relationships, technology, and institutions; (4) Given limited ecological factors, the non-exclusivity between static resource allocation and dynamic technological investments in digital enablement leads to the generalization of property rights boundaries and industrial values within the digital business ecosystem. Consequently, this fosters extensive business applications and diversified business models, thereby resulting in less competition and more cooperation, symbiosis, and complementarity within the digital business niche.

Identification of genetic variants associated with exercise-induced atrial fibrillation in high-performing racehorses

Abstract Background Atrial fibrillation (AF) is the most common cardiac arrhythmia in athletes. Despite a lower burden of classical cardiovascular risk factors, the prevalence of AF in athletes is comparable to that in the general population of similar age (~13%, mean age 65.5), suggesting that exercise-induced AF may have different underlying mechanisms. The prevalence of AF in thoroughbred (TB) and standardbred (STB) racehorses is also relatively high (~5%), suggesting exercise as an independent risk factor. Due to selective breeding and complete control of pedigrees, racehorses are well-suited for genetic studies. Common and rare genetic variation explain a substantial part of AF variance, but no study has identified genetic variants associated with exercise-induced AF in any species. Objective In this project, we aimed to use the racehorse as a model to identify genetic variants associated with exercise-induced AF, and in a future study, possibly replicate the results in human athletes. Here we present preliminary results from the genome-wide association study (GWAS) of AF in racehorses. Methods We collected blood or hair samples from 181 STB and TB racehorses in which 71 had paroxysmal or persistent AF verified by ECG. We extracted DNA and performed whole-genome genotyping using a commercial equine genotyping platform (670k markers). We phased and imputed un-genotyped markers and increased the sample size of the control group, using publicly available equine data from similar breeds from the European Nucleotide Archive. We conducted a GWAS of 7.85 million markers in 71 cases and 296 controls using PLINK2 with covariates adjusted for population stratification, batch and sex. Results We identified one significant locus downstream of the protocadherin gene PCDH18 associated with AF in racehorses. The same locus has been associated with the ECG-trait P-wave terminal force in humans. Changes in P-wave terminal force is associated with increased atrial size, delayed interatrial conduction, left ventricular fibrosis and left atrial function, and considered a good predictor of AF occurrence. Several non-significant but interesting signals were also identified including the genes SLC6A15 (amino acid transport), MYBPH (myosin binding), MYF6 and MYOG (myoblast differentiation). Conclusion In these preliminary results, we have identified a new genetic locus associated with AF in racehorses. In addition, we have identified several candidate genes for further studies to elucidate the genetic background of exercise-induced AF. We aim to replicate our findings in a population of human athletes. Our results may aid the development of risk prediction tools for exercise-induced AF and new drugs for tailored treatment of AF in general.

External evaluation of a commercial artificial intelligence-augmented digital auscultation platform in valvular heart disease detection using echocardiography as reference standard

ObjectiveThere are few studies evaluating the accuracy of commercially available AI-powered digital auscultation platforms in detecting valvular heart disease (VHD). Therefore, the utility of these systems for diagnosing clinically significant VHD remains unclear. We conducted a comprehensive external evaluation of the Eko murmur analysis software (EMAS) and report its accuracy in detecting murmurs associated with VHD using echocardiography (ECHO) as the reference standard. MethodsWe analyzed phonocardiogram (PCG) and ECHO data from 1,029 individuals (461 females, mean (SD) age: 61 (29) years, BMI: 29 (9)) at a single academic medical center. PCGs were recorded using the EkoDUO and EkoCORE stethoscopes from the four standard auscultation positions immediately before transthoracic ECHO (TTE) testing. TTE diagnostics were used as reference to calculate the EMAS sensitivity and specificity in detecting murmurs associated with VHD. The 95% confidence intervals are reported. ResultsOf the 4,081 PCGs, 79% were of sufficient quality for murmur analysis. The sensitivity and specificity of the EMAS in detecting VHD were 39.3% (95% CI: 37.2–41.3) and 82.3% (95% CI: 80.0–84.5), respectively. EMAS sensitivity in detecting murmurs associated with common VHD types was 62.5%, 75.0%, 88.9%, and 63.3% for moderate-severe and severe cases of mitral stenosis, aortic regurgitation, aortic stenosis, and mitral regurgitation, respectively. ConclusionThe EMAS algorithm exhibits limited overall sensitivity in detecting VHD. The sensitivity of the algorithm varies across VHD types. These findings suggest that EMAS can be used for diagnosis of specific lesions, but not all VHD types, which limits its clinical applicability as a screening tool.

Hyperspectral acquisition with ScanImage at the single pixel level: application to time domain coherent Raman imaging

We present a comprehensive strategy and its practical implementation using the commercial ScanImage software platform to perform hyperspectral point scanning microscopy when a fast time-dependent signal varies at each pixel level. In the proposed acquisition scheme, the scan along the X-axis is slowed down while the data acquisition is maintained at a high pace to enable the rapid acquisition of the time-dependent signal at each pixel level. The ScanImage generated raw 2D images have a very asymmetric aspect ratio between X and Y, the X axis encoding both for space and time acquisition. The results are X-axis macro-pixel where the associated time-dependent signal is sampled to provide hyperspectral information. We exemplified the proposed hyperspectral scheme in the context of time-domain coherent Raman imaging, where a pump pulse impulsively excites molecular vibrations that are subsequently probed by a time-delayed probe pulse. In this case, the time-dependent signal is a fast acousto-optics delay line that can scan a delay of 4.5ps in 25μs at each pixel level. With this acquisition scheme, we demonstrate ultra-fast hyperspectral vibrational imaging in the low frequency range [10cm−1, 150 cm−1] over a 500 μm field of view (64 x 64 pixels) in 130ms (∼ 7.5 frames/s). The proposed acquisition scheme can be readily extended to other applications requiring the acquisition of a fast-evolving signal at each pixel level.

Towards an understanding of the political economy of earlychildhood education platforms. Pro Business or Pro Children?

There is a rapidly expanding and proliferating number of commercial early childhood platforms, competing for market share in what has become a crowded marketplace. Early childhood platforms provide a wide range of functions including an all-in-one digital ecosystem offering a learning management system; social media communication between educators, families and children; invoicing and payment; attendance monitoring; data tracking; individual profiling; documentation; pedagogic advice and even policy interpretation. Platforms are a sociocultural and ideological phenomenon that ‘can replace or profoundly disrupt educational systems’ (Cobo and Rivas) and hence their advent demands urgent critical thinking. However, within early years there is comparatively limited research of platforms' impacts upon families, educators and children. The aims of this colloquium are firstly to open a critical space to think about the political economy of commercial education platforms and secondly, to ask questions about platforms' impacts upon the neoliberal subjectivities of educators, families and children.

A-356 A hand-held microfluidic platform for rapid and sensitive identification of respiratory viruses from nasal swab samples

Abstract Background Identifying the infected individual with a fast and accurate test is essential to constrain the spread of viruses and provide the proper treatment. Antigen rapid test (ART) provides quick screening of infection for patients; however, it shows less promising in the detection of early infection and patent’s full recovery as its inferior sensitivity to the nucleic acid detection method. On the other hand, bulky and costly instrument, tedious and prolonged procedures in traditional nucleic acid testing is also unfavorable for the screening. Delta is developing a fully automated hand-held POC using molecular technology to simultaneously detect 4 targets in 20 mins. The POC can be stand-alone or linked to mobile or PC app for different application scenarios. The proprietary recipe of the lysis buffer could neutralize the inhibition in the sample with effective pathogen lysis in seconds. The microfluidic cartridge containing lyophilized reagent can be stored at room temperature. The amplification signal is detected via the optical system from the colorimetric LAMP. This study is to evaluate the performance of the POC system in detecting SARS-CoV2 (SC2), FluA and B viruses in nasal swab. In addition, the assay performance was further compared with other commercial products available. Methods The attenuated SC2 virus, FluA and B viruses were diluted with 1xTE into different concentrations. The diluted viruses were spiked into the lysis buffer in 5% v/v ratio. Nasal samples collected from donors were soaked and swirled inside the cartridges containing lysis buffer according to the manufacturers’ instructions. For commercial products without lysis buffer, direct swabs were used. The cartridges were then put into the respective devices for further testing. Results The lyophilized SC2 assay consistently detected the viruses at 1000 copies/ml (20 out of 20) in real nasal samples in the presence of Delta Lysis Buffer (containing lyophilized neutralizer). However, without Lysis buffer, there was no signal detected even in the presence of 240x LoD viruses. The whole workflow sensitivity was tested with SC2 positive samples at 1x, 4x, 10x LoD and negative samples in the nasal sample matrix on Delta hand-held system. The results were processed via embedded algorithm and displayed on device as well as on PC and mobile app respectively. Out of 79 positive samples tested, the detection rate was 100%. There was no false positive detection for 31 negative samples tested. Positive internal control indicated the valid runs. 5 major SC2 subtypes (99.8% in silico analysis of 366,707 strains), 10 FluA and 9 FluB subtypes were successfully detected. Further, Delta hand-held platform outperformed in the LoD of SC2 with 500 copies/swab comparing to the ≥50,000 copies/swab of the benchmarked commercial platforms. Conclusions The performance of the hand-held molecular detection system we developed demonstrated a rapid, sensitive, and competitive analytical performance in the respiratory panel detection. The capability of device linking to apps and clouds for data management and the extendable docking system for multiple devices to run samples simultaneously and independently, make it applicable for the infectious diseases screening in both clinical settings and for home-use.

Quantitative Blood Serum IVDr NMR Spectroscopy in Clinical Metabolomics of Cancer, Neurodegeneration, and Internal Medicine.

Despite more than two decades of metabolomics having joined the "omics" scenery, to date only a few novel blood metabolite biomarkers have found their way into the clinic. This is changing now by massive large-scale population metabolic phenotyping for both healthy and disease cohorts. Here, nuclear magnetic resonance (NMR) spectroscopy is a method of choice, as typical blood serum markers can be easily quantified and by knowledge of precise reference concentrations, more and more NMR-amenable biomarkers are established, moving NMR from research to clinical application. Besides customized approaches, to date two major commercial platforms have evolved based on either 600MHz (14.1 Tesla) or 500MHz (11.7 Tesla) high-field NMR systems. This chapter provides an introduction into the field of quantitative in vitro diagnostics research (IVDr) NMR at 600 MHzand its application within clinical research of cancer, neurodegeneration, and internal medicine.

Utilizing multi-point temperature sensing to evaluate the low frequency noise of phasemeter for intersatellite laser interferometer.

High-precision phasemeters are a key technology in intersatellite laser interferometers used for detecting gravitational waves (GWs) in space. As the core of the readout system, the phasemeter must operate in the bandwidth of 5-25MHz, and its resolution needs to reach the order of μrad/Hz at mHz. It presents significant challenges to electronic signal processing technology. To investigate the primary noise source in the low-frequency band, a mathematical model of thermal drift to phase noise was established, and a multi-point temperature sensing scheme for critical electronic components was proposed. In particular, we evaluated a phasemeter based on a commercial platform and assessed the thermal drift noise according to the proposed model. This study identifies and explains the effects of temperature linear drift and overcorrection in components, demonstrating that thermal drift noise is the main noise source for the phasemeter at frequencies from 0.1 to 1 mHz. In addition, the proposed scheme is universal in its applicability and may be implemented in any circuit for the evaluation of temperature effects on the components of interest.

A machine learning approach to forecast 5G metrics in a commercial and operational 5G platform: 5G and mobility

The demand for more secure, available, reliable, and fast networks emerges in a more interconnected society. In this context, 5G networks aim to transform how we communicate and interact. However, studies using 5G data are sparse since there are only a few number of publicly available 5G datasets (especially about commercial 5G network metrics with real users). ►In this work, we analyze the data of a commercial 5G deployment with real users, and propose forecasting techniques to help understand the trends and to manage 5G networks. We propose the creation of a metric to measure the traffic load. We forecast the metric using several machine learning models, and we choose LightGBM as the best approach. We observe that this approach obtains results with a good accuracy, and better than other machine learning approaches, but its performance decreases if the patterns contain unexpected events. Taking advantage of the lower accuracy in the performance, this is used to detect changes in the patterns and manage the network in real-time, supporting network resource elasticity by generating alarms and automating the scaling during these unpredictable fluctuations. ►Moreover, we introduce mobility data and integrate it with the previously traffic load metric, understanding its correlation and the prediction of 5G metrics through the use of the mobility data. We show again that LightGBM is the best model in predicting both types of 5G handovers, intra- and inter-gNB handovers, using the mobility information through Radars in the several roads, and lanes, near the 5G cells.

Trichomonas vaginalis: comparison of primers for implementation as an in-house PCR in rural Vellore, South India

BackgroundTrichomonas vaginalis (TV) accounts for the highest burden of curable, non-viral sexually transmitted infections worldwide. Prevalence in India ranges from 0.4 to 27.4% in women and 0.0-5.6% in men. In 2015, the prevalence of TV among pregnant women of rural Vellore was 3.11% using Sekisui OSOM® Trichomonas test and culture methods. Molecular methods are the most sensitive, rapid diagnostic tool for Sexually Transmitted Infection’s (STI) albeit cost hinders implementation of commercial platforms. To determine a sensitive, sustainable molecular method, we compared three targets (Adhesin AP65, cytoskeleton Beta-tubulin BTUB 9/2 and TVK 3/7) with the highest published diagnostic accuracy against microscopy, culture and Real Time PCR (RT- PCR).Materials & methodsSix-hundred adult, sexually active women attending the Obstetrics-Gynaecology rural out-patient clinic the Rural Unit for Health and Social Affairs (RUHSA) from July 2020 - February 2021 were enrolled. A vaginal lateral and posterior fornix specimen was inoculated, onsite, into Biomed InPouch® TV culture and smeared onto a slide for fluorescence microscopy using Acridine orange. A flocked nylon swab specimen for PCR was used to determine the sensitivities of the Adhesin AP65, cytoskeleton Beta-tubulin BTUB 9/2 and TVK 3/7 gene targets. Seegene Allplex™ STI Essential Assay, S.Korea was used to confirm TV positives.ResultsNine specimens (9/600, 1.5%) were positive for TV. There was a 100% correlation between Biomed InPouch TV® culture, PCR with TVK 3/7 and RT-PCR while a correlation of 66.6% with BTUB 9/2 and AP65 gene targets. Clinically, 77.7% (n = 7) presented with white-greenish discharge per vagina, 11% (n = 1) with infertility, 22.2% (n = 2) were asymptomatic. Eight of nine patients (88.9%) had co-infections with other bacterial STIs. Prevalence of TV coinfection with Neisseria gonorrhoea was 1.1%.ConclusionCurrent hospital-based prevalence of TV in rural Vellore was 1.5%. Repetitive DNA target TVK 3/7 was more sensitive than AP65 and BTUB 9/2 primers.

High-Q integrated lithium tantalate microring resonators for on-chip comb generation.

Lithium tantalate on insulator (LTOI), taking advantage of high cost-effectiveness, ultra-low optical loss, and prominent electro-optic (EO) coefficient, shows great potential as an integrated waveguide-based optical platform for commercialization. Further research on monolithic nonlinear source generators with tunable features is crucial in its early stages. Here, we fabricate low-loss microring resonators (intrinsic Q value above 4 × 106) via universal subtractive manufacturing. Both Kerr and EO combs are realized based on X-cut LTOI high-Q resonators. Specifically, we elucidate the complicated synergy caused by a photorefractive (PR) effect and thermo-optic modulation, observing the soliton step using the facile laser scanning technique. Furthermore, the preliminary experimental result of the static EO comb is also exploited in a 20 GHz free spectral range (FSR) LTOI microring resonator, verifying the versatility of this unique photonic platform for on-chip microcomb generation.

Gestión de riesgos y obtención de financiamiento externo en las mypes comerciales

The objective of the study was to establish the relationship between risk management and obtaining external financing in the San José Commercial Platform of Juliaca, Puno 2023. The methodology used was applied, quantitative, correlational, non-experimental and transversal; in addition, the sample was made up of 342 businessmen, two questionnaires were designed, which were validated through expert judgment and were applied through the survey to the sample. The results showed that although the owners of these companies regularly carry out risk management, it is not solid, which means that they do not have a real overview of their financial situation, contributing to the increase in difficulties in obtaining financing. It was concluded that there is a high relationship between risk management and obtaining external financing in companies in this market.

DONGLE 2.0: Direct FPGA-Orchestrated NVMe Storage for HLS

Rapid growth in data size poses significant computational and memory challenges to data processing. FPGA accelerators and near-storage processing have emerged as compelling solutions for tackling the growing computational and memory requirements. Many FPGA-based accelerators have shown to be effective in processing large data sets by leveraging the storage capability of either host-attached or FPGA-attached storage devices. However, the current HLS development environment does not allow direct access to host-or FPGA-attached NVMe storage from the HLS code. As such, users must frequently hand off between HLS and host code to access data in storage, and such a process requires tedious programming to ensure functional correctness. Moreover, since the HLS code uses radically different methods to access storage compared to DRAM, the HLS codebase targeting DRAM-based platforms cannot be easily ported to NVMe-based platforms, resulting in limited code portability and reusability. Furthermore, frequent suspension of HLS kernel and synchronization between CPU and FPGA introduce significant latency overhead and require sophisticated scheduling mechanisms to hide latency. To address these challenges, we propose a new HLS storage interface named DONGLE 2.0 that enables direct FPGA-orchestrated NVMe storage access. By providing a unified interface for storage and memory access, DONGLE 2.0 allows a single-source HLS program to target multiple memory/storage devices, thus making the codebase cleaner, portable, and more efficient. DONGLE 2.0 is an extension to DONGLE 1.0 [ 1 ] but adds support for host-attached storage. While its primary focus is still on FPGA NVMe access in near-storage configurations, the added host storage support ensures its compatibility with platforms that lack native support for FPGA-attached NVMe storage. We implemented a prototype of DONGLE 2.0 using an AMD/Xilinx Alveo U200 FPGA and Solidigm DC-P4610 SSD. Our evaluation on various workloads showed a geometric mean speed-up of 2.3× and a reduction in lines of code (LoC) by 2.4× compared to the state-of-the-art commercial platform when using FPGA-attached NVMe storage. Moreover, DONGLE 2.0 demonstrated a geometric mean speed-up of 1.5× and a reduction in LoC by 2.4× compared to the state-of-the-art commercial platform when using host-attached NVMe storage.