Input Threshold Research Articles

Moving beyond the initial stages in L3/Ln research: toward theorising L3 development

ABSTRACT This article serves as an introduction to the present special issue, which focuses on third language (L3) development. It provides a brief overview of the theoretical foundations necessary to map L3 (grammatical) development, with particular attention to the recently proposed Cumulative Input Threshold Hypothesis (Cabrelli & Iverson, 2023). Additionally, the introduction offers a brief overview of the six contributions included in this issue, originally presented at the Workshop on Multilingual Language Acquisition, Processing, and Use, held at King’s College London in 2024. The introduction concludes by outlining what I consider to be the next logical steps in the study of L3 grammatical development.

Enhancement of Q-Switched Erbium-Doped Fibre Laser Performance using Graphene Oxide-Calcium Oxide Thin Film Saturable Absorbers

This paper presents an experiment utilizing calcium oxide (CaO) from the cuttlefish bone combined with graphene oxide (GO) to create a graphene oxide-calcium oxide (GO-CaO) based saturable absorber (SA) for Q-switched erbium-doped fibre lasers. The experiment aims to investigate the impact of calcium oxide when added to graphene oxide to form the SA thin film. The laser performance of both types of SA was compared, considering their input pump power, repetition rate, pulse width, and output power. The results indicated that the graphene oxide – calcium oxide SA thin film provided the best output power and the lowest threshold input pump power at 10.76 µW and 53.12 mW, respectively. In the characterization, UV-Vis spectroscopy was utilized to determine the number of discrete wavelengths in UV or visible light that the materials absorbed or transmitted. The UV-Vis results indicated that the graphene oxide thin film exhibited an absorbance value of 0.049 AU and a transmittance value of 89.25 % at a wavelength of 1565 nm. In comparison, the GO-CaO SA thin film displayed an absorbance value of 0.037 AU and a transmittance value of 91.89 % at the same wavelength. Raman spectroscopy was also conducted to provide details about the chemical structure and crystallinity of the thin film samples. The results showed high-intensity peaks for the GO thin film at wavenumbers of 1353.74 cm-1 and 1609.85 cm-1, and for GO – CaO thin film at wavenumbers of 1358.56 cm-1 and 1612.18 cm-1. Additionally, SEM was utilized to study the morphology of the thin film samples. Based on the characterization and laser performance results, it was concluded that the addition of calcium oxide to graphene oxide enhances the properties of the SA thin film, leading to improved laser performance due to its higher thermal conductivity.

Nonlinearities and timescales in neural models of temporal interference stimulation.

In temporal interference (TI) stimulation, neuronal cells react to two interfering sinusoidal electric fields with a slightly different frequency ( , in the range of about 1-4 kHz, in the range of about 1-100 Hz). It has been previously observed that for the same input intensity, the neurons do not react to a purely sinusoidal signal at or . This study seeks a better understanding of the largely unknown mechanisms underlying TI neuromodulation. To this end, single-compartment models are used to simulate computationally the response of neurons to the sinusoidal and TI waveform. This study compares five different neuron models: Hodgkin-Huxley (HH), Frankenhaeuser-Huxley (FH), along with leaky, exponential, and adaptive-exponential integrate-and-fire (IF). It was found that IF models do not entirely reflect the experimental behavior while the HH and FH model did qualitatively replicate the observed neural responses. Changing the time constants and steady state values of the ion gates in the FH model alters the response to both the sinusoidal and TI signal, possibly reducing the firing threshold of the sinusoidal input below that of the TI input. The results show that in the modified (simplified) model, TI stimulation is not qualitatively impacted by nonlinearities in the current-voltage relation. In contrast, ion channels have a significant impact on the neuronal response. This paper offers insights into neuronal biophysics and computational models of TI stimulation.

A Tunable Threshold Colorimetric DNA Logic Gate for Intuitive Assessment of Chemical Contaminant Exceedance.

Current molecular logic gates are predominantly focused on the qualitative assessment of target presence, which has certain limitations in scenarios requiring quantitative assessment, such as chemical contaminant monitoring. To bridge this gap, we have developed a novel DNA logic gate featuring a tunable threshold, specifically tailored to the limits of contaminants. At the core of this logic gate is a DNA-gold nanoparticle (AuNP) hybrid film that incorporates aptamer sequences to selectively bind to acetamiprid (ACE) and atrazine (ATR). Upon interaction with these contaminants, the film degrades, releasing AuNPs that, in the presence of Hg2+, catalyze the oxidation of TMB, resulting in a visible blue coloration on test paper. This aptamer-enabled process effectively establishes an OR logic gate, with ACE and ATR as inputs and the appearance of blue color as the output. A key innovation of our system is its tunable input threshold. By adjusting the concentration of Hg2+, we can fine-tune the color mutation points to match the input threshold to predefined limits, such as Maximum Residue Limits (MRLs). This alignment allows semiquantitative assessment of contaminant levels, providing intuitive visual feedback of contaminant exceedance. Validation experiments with spiked samples confirm its accuracy and reliability by closely matching HPLC results. Therefore, our colorimetric DNA logic gate is emerging as a promising tool for easy and semiquantitative monitoring of chemical contaminants across diverse applications.

How does a structurally similar background language influence L3 grammars? A study on the syntax of L2/L3 Mandarin [SI 21/3]

ABSTRACT This article reports on an empirical study of L3 Mandarin, aiming to shed light on transfer effects and their interaction with other factors throughout the L3 acquisition trajectory. A fill-in-the-blank task was employed to examine L2 and L3 acquisition of three types of Mandarin sentence-final particle clusters. Participants in the study were Mandarin native speakers, L1 English-L2 Mandarin speakers, L1 English-L2 Cantonese-L3 Mandarin speakers, and L1 Cantonese-L2 English-L3 Mandarin speakers at low and high Mandarin proficiency levels. Our data support arguments in the L3 literature that transfer at the L3 initial stages is from a structurally more similar language. Facilitation takes place when the instantiation of the target property in the L3 is exactly the same as that in the transfer source language. Overall typological similarity might be helpful when transfer is from the L1, but it does not always facilitate the L3 acquisition of individual properties. For later L3 development, construction frequency and mapping paradigms have a great impact on the acquisition outcome. Moreover, a certain kind of non-facilitative transfer from an L1 is found to persist in L3 Mandarin grammars, which qualitatively supports the Cumulative Input Threshold Hypothesis.

Phosphorus fertilizer input threshold shifts bacterial community structure and soil multifunctionality to maintain dryland wheat production

As an important factor affecting crop yield in agroecosystems, phosphorus (P) fertilizer addition can significantly alter the soil microbial community, functional gene composition, and multifunctionality. However, the major factors that determine wheat grain yield and the changes in wheat yield caused by the P fertilizer input threshold in dryland wheat production have not been adequately characterized. Based on a long-term field experiment (18 years) with five P addition gradients (0, 50, 100, 150, and 200 kg P2O5 ha−1 yr−1) in the Loess Plateau of China, amplicon (16 S rRNA) and metagenomic sequencing were used to investigate the effects of long-term P fertilization on soil bacterial communities, rhizosphere microbial functions, soil multifunctionality, and wheat grain yield. Long-term P input significantly affected the compositions and functions of the soil bacterial communities. Conversely, long-term high-P treatment (P200) inhibited soil bacterial diversity and soil P cycling genes but increased carbon (C) cycling genes and soil multifunctionality. Meanwhile, there is a threshold of P fertilizer (71.6−78.1 kg ha−1 yr−1) input for wheat grain yield, beyond which the grain yield of wheat did not significantly increase. In addition, the rhizosphere selection effect significantly increased the complexity of the rhizosphere network and microbial interactions and directed the recruitment of microorganisms and genes with special functions to maintain wheat grain yield. Therefore, this study revealed that the threshold of P fertilizer input can provide a basis for reducing P fertilizer and regulating microorganisms to maintain soil multifunctionality and network complexity in the dryland agriculture system of the Loess Plateau to maintain wheat grain yield.

Particle swarm optimization-extreme learning machine model combined with the ADA boost algorithm for short-term wind power prediction

In our proposed approach, we integrate ADA boosting with particle swarm optimization-extreme learning machine (PSO-ELM) to enhance the accuracy of wind power estimation, addressing the inherent unpredictability and variability in wind energy. Initially, we refine the thresholds and input weights of the extreme learning machine (ELM) and then construct the PSO-ELM prediction model. ADA Boost is utilized to generate multiple weak predictors, each comprising a distinct hidden layer node. The PSO technique is then employed to optimize the input weights and thresholds for each weak predictor. The final forecast is attained by amalgamating and weighting the outcomes from each weak predictor using a robust wind power forecast model. Experimental validation utilizing data from Turkish wind turbines underscores the efficacy of our approach. Comparative analysis against contemporary techniques such as ensemble learning models and optimal neural networks reveals that our ADA-PSO-ELM model demonstrates superior accuracy and generalizability in predicting wind power output under real-world conditions. The proposed approach offers a promising framework for addressing the challenges associated with wind power estimation, thereby facilitating more reliable and efficient utilization of wind energy resources.

Effect of exceptional point on the performance of a bistable all-optical switch.

Microring cavities based on whispering-gallery modes (WGMs) have a very high-quality factor (Q) and a small mode volume, greatly improving the interaction between light and matter, which has attracted great attention in microlaser, nonlinear, and sensing fields. Plasmonics in the microcavity further enhance compression of the optical field. Recently, research on enhanced optical sensing sensitivity and low threshold laser based on exceptional points (EPs) is quite impressive. In this work, we propose a new, to our knowledge, all-optical switch by using the bistable effect under the EP of an ultra-compact plasmonic racetrack resonator and perform numerical simulations using the finite-difference time-domain (FDTD) method. The introduction of EPs further enhances the localization of the light field and thus improves the Kerr nonlinear effect of the microcavity; low threshold optical bistability is achieved. The results show that the device under an EP has a relatively lower threshold (input optical power threshold of 2.2 MW/cm2), shorter switching time (1.725 ps), and significantly improved switching contrast (17.16 dB) compared with those without EP. Our research lays the groundwork for optical switches that are chip-integrated, have low power consumption, and exhibit short switching times.

Moderately reducing N input to mitigate heat stress in maize

In a warming climate, high temperature stress greatly threatens crop yields. Maize is critical to food security, but frequent extreme heat events coincide temporally and spatially with the period of kernel number determination (e.g., flowering stage), greatly limiting maize yields. In this context, how to increase or at least maintain maize yield has become more important. Nitrogen fertilizer (N) is widely used to improve maize yields, but its effect in heat stress is unclear. For this, we collected 1536 pairs of comparisons from 113 studies concerning N conducted in the past 20 years over China. We classified the data into two groups – without high temperature stress (NHT) and with high temperature stress during the critical period for maize kernel number determination (HT) – based on the national meteorological data. We comprehensively evaluated N effects on grain yield under HT and NHT using meta-analysis. The effect of N on maize yield became significantly smaller in HT than that in NHT. In NHT, soil characteristics, crop management practices, and climatic conditions all significantly affected N effects on maize yield, but in HT, only a few factors such as soil organic matter and mean annual precipitation significantly affected N effects. Hence, it is difficult to improve N effect by improving soil characteristics and crop management when meeting with high temperature stress during flowering. On average, N effect increased with increased N input, but there were respective N input thresholds in NHT and HT, beyond which N effects on maize yield remained stable. According to the thresholds, it is speculated that moderately reducing N input (~20 %) likely increased high temperature tolerance of maize during flowering. These findings have important implications for the optimization of N management under a warming climate.

Dual threshold input receiver FPGA-only signal digitization method for time-of-flight positron emission tomography

As silicon photomultiplier (SiPM)-based time-of-flight (TOF) positron emission tomography (PET) becomes popular, the need for sophisticated PET data acquisition (DAQ) systems is increasing. One promising solution to this challenge is the adoption of a field-programmable gate array (FPGA)-only signal digitization method. In this paper, we propose a new approach to efficiently implement an FPGA-only digitizer. We configured the input/output (IO) port of the FPGA to function as a dual-threshold voltage comparator through the use of simple passive circuitry and heterogeneous IO standards. This configuration overcomes the limitations of existing methods by allowing different threshold voltages for adjacent IO pins, effectively reducing routing complexity and lowering manufacturing costs. An FPGA-only digitizer was implemented by integrating the dual-threshold voltage comparator and FPGA-based time-to-digital converter. By combining the dual-threshold time-over-threshold (TOT) method and curve fitting, precise energy information could be obtained. The performance of the FPGA-only digitizer was assessed using a detector setup comprising a 3 × 3 × 20 mm3 LYSO scintillation crystal and a single pixel SiPM. Using the configured evaluation setup, an energy resolution of 12.5% and a time resolution of 146 ± 9 ps were achieved for a 20 mm scintillation crystal. The dual-threshold TOT implemented using the proposed method showed consistent linearity across an energy range of 100 keV to 600 keV. The proposed method is well-suited for the development of cost-effective DAQ systems in highly integrated TOF PET systems.

Thresholds of Nitrogen and Phosphorus Input Substantially Alter Arbuscular Mycorrhizal Fungal Communities and Wheat Yield in Dryland Farmland.

Arbuscular mycorrhizal (AM) fungi are essential for preserving the multifunctionality of ecosystems. The nitrogen (N)/phosphorus (P) threshold that causes notable variations in the AM fungus community of the soil and plant productivity is still unclear. Herein, a long-term (18 years) field experiment with five N and five P fertilizer levels was conducted to investigate the change patterns of soil AM fungus, multifunctionality, and wheat yield. High-N and -P fertilizer inputs did not considerably increase the wheat yield. In the AM fungal network, a statistically significant positive correlation was observed between ecosystem multifunctionality and the biodiversity of two primary ecological clusters (N: Module #0 and P: Module #3). Furthermore, fertilizer input thresholds for N (92-160 kg ha-1) and P (78-100 kg ha-1) significantly altered the AM fungal community, soil characteristics, and plant productivity. Our study provided a basis for reduced N and P fertilizer application and sustainable agricultural development from the aspect of soil AM fungi.

Abstract 6251: Validation and implementation of whole-transcriptome sequencing assay into cancer care: An experience from a cancer center

Abstract Introduction Transcriptome sequencing is a well-established diagnostic tool to characterize and quantify gene expression profiles and to detect fusion transcripts. The utilization of whole-transcriptome sequencing (WTS), in clinical workflows can tremendously help improve diagnosis, risk stratification, and therapy selection. However, there are multiple challenges to widespread clinical implementation of WTS including nucleic acid input, defining appropriate quality control (QC) criteria, tumor specific baseline controls and depth of sequencing. Here, we present a validation study to deploy routine whole transcriptome sequencing with automated analysis in a mid-size academic center. Methods Nucleic acid material was obtained through clinical residual samples in the Center for Genomics and Advanced Technology. RNA samples were extracted using the Purigen Ionic® FFPE to Pure RNA Kit. RNA input sample quantities ranged from 34-100ng. WTS libraries were prepared using the Illumina® RNA Prep with Enrichment (L) Tagmentation kit with the Illumina Exome Panel for FFPE tumor samples. The validation was performed on a set of patient samples with known clinically reported variants from the Illumina TruSight® Tumor 170 assay. A total of 84 true positives with reportable fusions, 5 splicing events, 16 negative samples, and 15 limit of detection (LOD) samples were used. Libraries were prepared in a three-plex format. Sequencing was performed on the Novaseq-6000 system using S2 and S4 flow cells with a target read count of 100 million. All data was processed through the AUGMET informatics platform for demultiplexing, quality control, fusion detection, transcript quantification and determination of alternatively spliced transcript. Results The WTS validation assay demonstrated 98% (n=49/50) sensitivity, with only one false negative, and 100% (n=68/68) specificity. A final review of our data was determined to develop stable QC standards for RNA sequenced libraries to be > 60M reads and > 130bp read length. When analyzing (LOD) for WTS, interestingly, read length was inversely proportional to input and as a result detection of the clinically relevant fusion. Based on our LOD experiments, a minimum input threshold of 20ng of RNA was determined to maintain the sensitivity and specificity. An LOD study for tumor purity was not performed due to the known lack of correlation between tumor purity and fusion transcript quantity. Discussion WTS can detect all clinically reported fusions and splicing events with comparable sensitivity to panel assays. Moreover, simultaneously available transcript quantification and variant calling provide the ability to implement several disease classifiers. This work lays the foundation for integration with our clinically available tumor exome sequencing assay to allow joint exome transcriptome analysis in a single informatics platform. Citation Format: Jillian A. Moran, Edward G. Hughes, Donald C. Green, Shrey Sukhadia, Gregory J. Tsongalis, Samantha F. Allen, Laura Tafe, Brianna Houde, Nevena B. Ognjenovic, Jennifer N. Barbuto, Ivy Riano, Parth Shah. Validation and implementation of whole-transcriptome sequencing assay into cancer care: An experience from a cancer center [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 6251.

Forecasting model for short-term wind speed using robust local mean decomposition, deep neural networks, intelligent algorithm, and error correction

Wind power generation has aroused widespread concern worldwide. Accurate prediction of wind speed is very important for the safe and economic operation of the power grid. This paper presents a short-term wind speed prediction model which includes data decomposition, deep learning, intelligent algorithm optimization, and error correction modules. First, the robust local mean decomposition (RLMD) is applied to the original wind speed data to reduce the non-stationarity of the data. Then, the salp swarm algorithm (SSA) is used to determine the optimal parameter combination of the bidirectional gated recurrent unit (BiGRU) to ensure prediction quality. In order to eliminate the predictable components of the error further, a correction module based on the improved salp swarm algorithm (ISSA) and deep extreme learning machine (DELM) is constructed. The exploration and exploitation capability of the original SSA is enhanced by introducing a crazy operator and dynamic learning strategy, and the input weights and thresholds in the DELM are optimized by the ISSA to improve the generalization ability of the model. The actual data of wind farms are used to verify the advancement of the proposed model. Compared with other models, the results show that the proposed model has the best prediction performance. As a powerful tool, the developed forecasting system is expected to be further used in the energy system.

Hydrodynamics and Mass Transfer in an Airlift Loop Reactor: Comparison between Using Two Kinds of Spargers

The effects of different spargers on the hydrodynamics and mass transfer of an airlift loop reactor were investigated. The gas holdup, liquid loop velocity, and volumetric mass transfer coefficient of the reactor were tested using a ring orifice distributor and a jet nozzle. The study was conducted in a 6 m high airlift loop reactor at a superficial gas velocity of 0.01~0.04 m/s, and the superficial liquid velocity was maintained at 0.0154 m/s. The results showed that using the jet nozzle provided a higher gas holdup, liquid loop velocity, and mass transfer. When the superficial gas velocity was less than 0.0325 m/s, the liquid loop velocity generated by the jet nozzle was approximately 1.1-fold higher than that generated by the ring orifice distributor, and the disparity in gas holdup between the riser and downcomer enhanced the power of liquid circulation. When the superficial gas velocity was more than 0.0325 m/s, the jet kinetic power dominated the improvement in the liquid loop velocity, and the energy input from the nozzle to the airlift loop reactor was greater than 10.8 J/(s·m2). This indicated a threshold of energy input for overcoming the friction loss. In this situation, the liquid loop velocity in the jet form increased considerably, thus favoring the mixing performance and temperature uniformity of the reactor. It was also of significance for avoiding the formation of a flow dead zone in scale-up airlift loop reactors.

Dynamics of unipolar charged particles flowing through a cylindrical tube at high number concentrations

This work provides a theoretical and non-dimensional numerical analysis of the dynamics of gas-borne unipolar charged particles flowing through a cylindrical tube at high number concentrations. It is discovered that above a threshold input particle number concentration, the concentration at the exit of the tube is nearly constant and independent of further increase in the input. Based on a maximum of 2% sensitivity of the system, the threshold input number concentration is quantified for a tube of any length more than 40 × the radius. The theoretical analysis developed in this work is used to derive correlations for the threshold input concentration and the exit concentration, as well as show that the exit particle concentration is lower than the threshold input value by a factor of 20/PD, where PD is the well-known diffusive particle penetration. The analysis demonstrates that a constant concentration can occur if and only if the ratio of non-dimensional electric particle mobility to the Gormley-Kennedy diffusion parameter is above 74, which is a hitherto unexplored regime of charged aerosol flow dynamics. It is shown that the constant concentration can occur in compact tube designs (L/R<100) as long as the mass Peclet number of the particles is high and reducing the mass Peclet number to around 10 or below makes it impossible to obtain an input-independent number concentration. It is also shown that the Reynolds number of the flow has very little effect on the charged transport dynamics, where the order of magnitude of the input-independent number concentration is not highly sensitive to the Reynolds number, provided the system remains laminar. Finally, practical examples for designing instrumentation to generate a constant concentration of charged particles are discussed.

Physics-based analytical model for trap assisted biosensing in dual cavity negative capacitance junctionless accumulation mode FET

The design of a ferroelectric-based biosensor for detecting various biomolecules like proteins and DNA has captivated the interest of researchers in early disease diagnostics and treatment monitoring. Doomed by this interest, an analytical model for trap-assisted biosensing in Dual Cavity Negative Capacitance Junctionless Accumulation Mode FET is proposed for detecting various biomolecules. The biomolecules are treated as traps that tunnel through the biosensor's ferroelectric layer. The analytical model is validated through extensive numerical device simulations. The device incorporates the detection of charged and uncharged biomolecules in terms of dielectric constants and biomolecule concentration. The nano-cavities are created inside the gate dielectric stack to collect biomolecules like proteins and DNA. As the biomolecules are infused as traps in cavity areas, electrical properties of the biosensor, like input characteristics, transconductance, threshold voltage, on-state current, and subthreshold swing change. The sensitivity obtained for the proposed biosensor is compared with the existing biosensor and found that it is best suited for susceptible biosensing applications.

Low threshold DC–AC power converter with optimized standby power consumption

The quest for universal energy access continues to be a major concern globally. Renewable energy technologies such as solar PV are viable options to meet this energy poverty with DC–AC power converters playing a major role in solar PV systems. Current designs of these converters suffer from high standby energy consumption and high input threshold voltage. This paper presents the design and implementation of a single-phase DC–AC power converter with low threshold input voltage and optimized standby power consumption. A multivibrator, pulse width modulation signal generator, MOSFETs, and a multiple secondary winding center tap transformer were used in the system design. The results show an optimized power converter having a low input threshold voltage of 10.5V and power consumption of 9.49W.

Optimizing a metabarcoding marker portfolio for species detection from complex mixtures of globally diverse fishes

AbstractDNA metabarcoding is used to enumerate and identify taxa in both environmental samples and tissue mixtures, but the effectiveness of particular markers depends on their sensitivity to the taxa involved. Using multiple primer sets that amplify different genes can mitigate biases in amplification efficiency, sequence resolution, and reference data availability, but few empirical studies have evaluated markers for complementary performance. Here, we assess the individual and joint performance of 22 markers for detecting species in a DNA pool of 98 species of marine and freshwater bony fishes from geographically and phylogenetically diverse origins. We find that a portfolio of four markers targeting 12S, 16S, and two regions of COI identifies 100% of reference taxa to family and nearly 60% to species. We then use these four markers to evaluate metabarcoding of heterogeneous tissue mixtures, using experimental fishmeal to test: (1) the tissue input threshold to ensure detection; (2) how read depth scales with tissue abundance; and (3) the effect of non‐target material in the mixture on recovery of target taxa. We consistently detect taxa that makeup &gt;1% of fishmeal mixtures and can detect taxa at the lowest input level of 0.01%, but rare taxa (&lt;1%) were detected inconsistently across markers and replicates. Read counts showed only a weak correlation with tissue input, suggesting they are not a reliable quantitative proxy for relative abundance. Despite the limitations arising from primer specificity and reference data availability, our results demonstrate that a modest portfolio of markers can perform well in detecting and identifying aquatic species in complex mixtures despite heterogeneity in tissue representation, phylogenetic affinities, and from a broad geographic range.

Artificial Neural Network for Classification of Dengue Fever Using Backpropagation Algorithm

Fever is an increase in body temperature to higher than usual. Normal human body temperature is at 37oC, if the body temperature is more than this figure, it indicates a fever caused by infectious or non-infectious factors. The main symptom of Dengue hemorrhagic fever is high fever with a temperature between 30oC - 40oC which appears suddenly, the fever lasts for 7 days and occurs continuously, body temperature can be normal or low, then will rise slowly every day and can reach 40oC . These two diseases are still a public health problem in urban areas, including in the cities of Binjai and Medan. The problem that has occurred so far is that people in general cannot differentiate the symptoms of Dengue Fever from Malaria, so the treatment given only provides ordinary fever medicine, so that within three days there is no change and the high body temperature makes the patient know that someone has dengue fever. Therefore, the solution provided in this research is to find out the physical characteristics experienced by the sufferer before further diagnosis is carried out. If someone has a fever above 38oC, the body has red spots, irregular breathing, immediately go to the doctor because these symptoms indicate symptoms of dengue hemorrhagic fever or malaria fever. Artificial neural networks are an information processing system designed to imitate the workings of the human brain by carrying out a learning process through changing the weights of synapses. The human brain consists of millions of interconnected neurons known as biological neurons. Each neuron consists of a cell that has a number of dendrites (input) and an axon (output). Axons connect to other neurons through connecting pathways that produce chemical reactions when responding to incoming input. The input required includes the number of input variables, input variable values, weights, learning rate, threshold, maximum epoh and target (output) with the error value classification used is Mean Absolute Error (MAE), there are 2 types of disease with fever symptoms used. The types of disease are dengue hemorrhagic fever and malaria and the system will be designed using the Visual Basic 2010 programming language. From the results of the research that has been carried out, classification results are obtained with a value of 0.893619481 or rounded to equal 1 and classified as dengue hemorrhagic fever.

A PINK1 input threshold arises from positive feedback in the PINK1/Parkin mitophagy decision circuit

Mechanisms that prevent accidental activation of the PINK1/Parkin mitophagy circuit on healthy mitochondria are poorly understood. On the surface of damaged mitochondria, PINK1 accumulates and acts as the input signal to a positive feedback loop of Parkin recruitment, which in turn promotes mitochondrial degradation via mitophagy. However, PINK1 is also present on healthy mitochondria, where it could errantly recruit Parkin and thereby activate this positive feedback loop. Here, we explore emergent properties of the PINK1/Parkin circuit by quantifying the relationship between mitochondrial PINK1 concentrations and Parkin recruitment dynamics. We find that Parkin is recruited to mitochondria only if PINK1 levels exceed a threshold and then only after a delay that is inversely proportional to PINK1 levels. Furthermore, these two regulatory properties arise from the input-coupled positive feedback topology of the PINK1/Parkin circuit. These results outline an intrinsic mechanism by which the PINK1/Parkin circuit can avoid errant activation on healthy mitochondria.