Egyptian adaptation and validation of the Edinburgh Cognitive and Behavioral Amyotrophic Lateral Sclerosis Screen (ECAS-EG).

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is the most common, fatal adult neuromuscular disease. It is a multi-system disorder characterized primarily by motor manifestations, but there is established evidence for cognitive and behavioral impairment, which is associated with poor prognosis, hence, the importance of tools for its assessment. The Edinburgh Cognitive and Behavioral Assessment Screen (ECAS) is an invaluable assessment tool for cognition in ALS-front temporal spectrum dementia (FTSD), as it accommodates physical challenges that usually confound traditional neuropsychological testing in those patients. OBJECTIVE AND METHODS: To validate the Egyptian Arabic version of ECAS (ECAS-EG) based on the original English scale. This is a prospective study. The ECAS was adapted and administered to 62 Egyptian ALS patients and 60 healthy controls. Patients were recruited from the Neuromuscular Unit, Ain Shams University Hospital. The ECAS was adapted to Egyptian Arabic after being translated using the back translation method. Internal consistency of the test, inter-rater reliability, and construct validity were assessed. RESULTS: The Egyptian Arabic version of ECAS (ECAS-EG) showed good internal consistency using Cronbach's alpha of 0.84. Inter-rater reliability was tested, values for all variables were compared, and no statistically significant differences were found (ICC = .997). ECAS-EG discriminated significantly between the patients from the control subjects (p-value of 0.001). There was a strong positive correlation between the ECAS-EG total score and the MoCA total score with a p-value of 0.001, thus indicating convergent validity. The test showed that 63% of Egyptian ALS patients were cognitively affected; most affected domains were executive functions and verbal fluency. CONCLUSION: The current study proves that the Egyptian version of the ECAS (ECAS-EG) is valid and reliable among Egyptian ALS patients and it would be applicable to the general Arabic-speaking population.

Assessment of X-ray shielding properties of polystyrene incorporated with different nano-sizes of PbO.

PbO (lead oxide) particles with different sizes were incorporated into polystyrene (PS) with various weight fractions (0, 10, 15, 25, 35%). These novel PS/PbO nano-composites were produced by roll mill mixing and compressing molding techniques and then investigated for radiation attenuation of X-rays (N-series/ISO 4037) typically used in radiology. Properties of the PbO particles were studied by X-ray diffraction (XRD). Filler dispersion and elemental composition of the prepared nano-composites were characterized using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS), revealing better filler distribution and fewer agglomerations with smaller PbO particle size. Linear and mass attenuation coefficients (µ and µm), total molecular and atomic cross-sections (σmol and σatm), as well as effective atomic number and electron density (Zeff and Neff), were calculated for the energy range N40 to N200. The influence of PbO weight percentage on the enhancement of the shielding parameters of the nano-composites was expected; however, the effect of PbO particle size was surprising. Linear and mass attenuation coefficients for PS/PbO composites increased gradually with increasing PbO concentrations, and composites with a small size of nanoparticles showed best performance. In addition, increasing PbO concentration raised the effective atomic number Zeff of the composite. Hence, the electron density Neff increased, which provided a higher total interaction cross-section of X-rays with the composites. Maximum radiation shielding was observed for PS/PbO(B). It is concluded that this material might be used in developping low-cost and lightweight X-ray shielding to be used in radiology.

Interactive Deep Learning for Shelf Life Prediction of Muskmelons Based on an Active Learning Approach.

A pivotal topic in agriculture and food monitoring is the assessment of the quality and ripeness of agricultural products by using non-destructive testing techniques. Acoustic testing offers a rapid in situ analysis of the state of the agricultural good, obtaining global information of its interior. While deep learning (DL) methods have outperformed state-of-the-art benchmarks in various applications, the reason for lacking adaptation of DL algorithms such as convolutional neural networks (CNNs) can be traced back to its high data inefficiency and the absence of annotated data. Active learning is a framework that has been heavily used in machine learning when the labelled instances are scarce or cumbersome to obtain. This is specifically of interest when the DL algorithm is highly uncertain about the label of an instance. By allowing the human-in-the-loop for guidance, a continuous improvement of the DL algorithm based on a sample efficient manner can be obtained. This paper seeks to study the applicability of active learning when grading 'Galia' muskmelons based on its shelf life. We propose k-Determinantal Point Processes (k-DPP), which is a purely diversity-based method that allows to take influence on the exploration within the feature space based on the chosen subset k. While getting coequal results to uncertainty-based approaches when k is large, we simultaneously obtain a better exploration of the data distribution. While the implementation based on eigendecomposition takes up a runtime of O(n3), this can further be reduced to O(n·poly(k)) based on rejection sampling. We suggest the use of diversity-based acquisition when only a few labelled samples are available, allowing for better exploration while counteracting the disadvantage of missing the training objective in uncertainty-based methods following a greedy fashion.

Genome-wide identification of zero nucleotide recursive splicing in Drosophila.

Recursive splicing is a process in which large introns are removed in multiple steps by re-splicing at ratchet points--5' splice sites recreated after splicing. Recursive splicing was first identified in the Drosophila Ultrabithorax (Ubx) gene and only three additional Drosophila genes have since been experimentally shown to undergo recursive splicing. Here we identify 197 zero nucleotide exon ratchet points in 130 introns of 115 Drosophila genes from total RNA sequencing data generated from developmental time points, dissected tissues and cultured cells. The sequential nature of recursive splicing was confirmed by identification of lariat introns generated by splicing to and from the ratchet points. We also show that recursive splicing is a constitutive process, that depletion of U2AF inhibits recursive splicing, and that the sequence and function of ratchet points are evolutionarily conserved in Drosophila. Finally, we identify four recursively spliced human genes, one of which is also recursively spliced in Drosophila. Together, these results indicate that recursive splicing is commonly used in Drosophila, occurs in humans, and provides insight into the mechanisms by which some large introns are removed.

A Deep Learning Method for the Impact Damage Segmentation of Curve-Shaped CFRP Specimens Inspected by Infrared Thermography.

Advanced materials such as continuous carbon fiber-reinforced thermoplastic (CFRP) laminates are commonly used in many industries, mainly because of their strength, stiffness to weight ratio, toughness, weldability, and repairability. Structural components working in harsh environments such as satellites are permanently exposed to some sort of damage during their lifetimes. To detect and characterize these damages, non-destructive testing and evaluation techniques are essential tools, especially for composite materials. In this study, artificial intelligence was applied in combination with infrared thermography to detected and segment impact damage on curved laminates that were previously submitted to a severe thermal stress cycles and subsequent ballistic impacts. Segmentation was performed on both mid-wave and long-wave infrared sequences obtained simultaneously during pulsed thermography experiments by means of a deep neural network. A deep neural network was trained for each wavelength. Both networks generated satisfactory results. The model trained with mid-wave images achieved an F1-score of 92.74% and the model trained with long-wave images achieved an F1-score of 87.39%.

A Hierarchy of Time Scales for Discriminating and Classifying the Temporal Shape of Sound in Three Auditory Cortical Fields.

Auditory cortex is essential for mammals, including rodents, to detect temporal "shape" cues in the sound envelope but it remains unclear how different cortical fields may contribute to this ability (Lomber and Malhotra, 2008; Threlkeld et al., 2008). Previously, we found that precise spiking patterns provide a potential neural code for temporal shape cues in the sound envelope in the primary auditory (A1), and ventral auditory field (VAF) and caudal suprarhinal auditory field (cSRAF) of the rat (Lee et al., 2016). Here, we extend these findings and characterize the time course of the temporally precise output of auditory cortical neurons in male rats. A pairwise sound discrimination index and a Naive Bayesian classifier are used to determine how these spiking patterns could provide brain signals for behavioral discrimination and classification of sounds. We find response durations and optimal time constants for discriminating sound envelope shape increase in rank order with: A1 < VAF < cSRAF. Accordingly, sustained spiking is more prominent and results in more robust sound discrimination in non-primary cortex versus A1. Spike-timing patterns classify 10 different sound envelope shape sequences and there is a twofold increase in maximal performance when pooling output across the neuron population indicating a robust distributed neural code in all three cortical fields. Together, these results support the idea that temporally precise spiking patterns from primary and non-primary auditory cortical fields provide the necessary signals for animals to discriminate and classify a large range of temporal shapes in the sound envelope.SIGNIFICANCE STATEMENT Functional hierarchies in the visual cortices support the concept that classification of visual objects requires successive cortical stages of processing including a progressive increase in classical receptive field size. The present study is significant as it supports the idea that a similar progression exists in auditory cortices in the time domain. We demonstrate for the first time that three cortices provide temporal spiking patterns for robust temporal envelope shape discrimination but only the ventral non-primary cortices do so on long time scales. This study raises the possibility that primary and non-primary cortices provide unique temporal spiking patterns and time scales for perception of sound envelope shape.

An Ultraconserved Element (UCE) controls homeostatic splicing of ARGLU1 mRNA.

Arginine and Glutamate-Rich protein 1 (ARGLU1) is a protein whose function is poorly understood, but may act in both transcription and pre-mRNA splicing. We demonstrate that the ARGLU1 gene expresses at least three distinct RNA splice isoforms - a fully spliced isoform coding for the protein, an isoform containing a retained intron that is detained in the nucleus, and an isoform containing an alternative exon that targets the transcript for nonsense mediated decay. Furthermore, ARGLU1 contains a long, highly evolutionarily conserved sequence known as an Ultraconserved Element (UCE) that is within the retained intron and overlaps the alternative exon. Manipulation of the UCE, in a reporter minigene or via random mutations in the genomic context using CRISPR/Cas9, changed the splicing pattern. Further, overexpression of the ARGLU1 protein shifted the splicing of endogenous ARGLU1 mRNA, resulting in an increase in the retained intron isoform and nonsense mediated decay susceptible isoform and a decrease in the fully spliced isoform. Taken together with data showing that functional protein knockout shifts splicing toward the fully spliced isoform, our data are consistent with a model in which unproductive splicing complexes assembled at the alternative exon lead to inefficient splicing and intron retention.

Eddy current pulsed thermography for ballistic impact evaluation in basalt-carbon hybrid composite panels.

In this paper, eddy current pulsed thermography was used to evaluate ballistic impact damages in basalt-carbon hybrid fiber-reinforced polymer composite laminates for the first time, to our knowledge. In particular, different hybrid structures including intercalated stacking and sandwich-like sequences were used. Pulsed phase thermography, wavelet transform, principle component thermography, and partial least-squares thermography were used to process the thermographic data. Ultrasound C-scan testing and X-ray computed tomography were also performed for comparative purposes. Finite element analysis was used for validation. Finally, an analytical and comparative study was conducted based on signal-to-noise ratio analysis.

Enhanced Infrared Image Processing for Impacted Carbon/Glass Fiber-Reinforced Composite Evaluation.

In this paper, an infrared pre-processing modality is presented. Different from a signal smoothing modality which only uses a polynomial fitting as the pre-processing method, the presented modality instead takes into account the low-order derivatives to pre-process the raw thermal data prior to applying the advanced post-processing techniques such as principal component thermography and pulsed phase thermography. Different cases were studied involving several defects in CFRPs and GFRPs for pulsed thermography and vibrothermography. Ultrasonic testing and signal-to-noise ratio analysis are used for the validation of the thermographic results. Finally, a verification that the presented modality can enhance the thermal image performance effectively is provided.

Neural spike-timing patterns vary with sound shape and periodicity in three auditory cortical fields.

Mammals perceive a wide range of temporal cues in natural sounds, and the auditory cortex is essential for their detection and discrimination. The rat primary (A1), ventral (VAF), and caudal suprarhinal (cSRAF) auditory cortical fields have separate thalamocortical pathways that may support unique temporal cue sensitivities. To explore this, we record responses of single neurons in the three fields to variations in envelope shape and modulation frequency of periodic noise sequences. Spike rate, relative synchrony, and first-spike latency metrics have previously been used to quantify neural sensitivities to temporal sound cues; however, such metrics do not measure absolute spike timing of sustained responses to sound shape. To address this, in this study we quantify two forms of spike-timing precision, jitter, and reliability. In all three fields, we find that jitter decreases logarithmically with increase in the basis spline (B-spline) cutoff frequency used to shape the sound envelope. In contrast, reliability decreases logarithmically with increase in sound envelope modulation frequency. In A1, jitter and reliability vary independently, whereas in ventral cortical fields, jitter and reliability covary. Jitter time scales increase (A1 < VAF < cSRAF) and modulation frequency upper cutoffs decrease (A1 > VAF > cSRAF) with ventral progression from A1. These results suggest a transition from independent encoding of shape and periodicity sound cues on short time scales in A1 to a joint encoding of these same cues on longer time scales in ventral nonprimary cortices.

A hybrid feature weighted attention based deep learning approach for an intrusion detection system using the random forest algorithm.

Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. Multiple network attacks by an intruder are unavoidable. Our research targets the critical issue of class imbalance in intrusion detection, a reflection of the real-world scenario where legitimate network activities significantly out number malicious ones. This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. The primary goal is to create models that are highly sensitive to intrusions while minimizing false alarms, an essential aspect of effective IDS. This approach is not only practical for real-world applications but also enhances the theoretical understanding of managing class imbalance in machine learning. Our research, by addressing these significant challenges, is positioned to make substantial contributions to cybersecurity, providing valuable insights and applicable solutions in the fight against digital threats and ensuring robustness and relevance in IDS development. An intrusion detection system (IDS) checks network traffic for security, availability, and being non-shared. Despite the efforts of many researchers, contemporary IDSs still need to further improve detection accuracy, reduce false alarms, and detect new intrusions. The mean convolutional layer (MCL), feature-weighted attention (FWA) learning, a bidirectional long short-term memory (BILSTM) network, and the random forest algorithm are all parts of our unique hybrid model called MCL-FWA-BILSTM. The CNN-MCL layer for feature extraction receives data after preprocessing. After convolution, pooling, and flattening phases, feature vectors are obtained. The BI-LSTM and self-attention feature weights are used in the suggested method to mitigate the effects of class imbalance. The attention layer and the BI-LSTM features are concatenated to create mapped features before feeding them to the random forest algorithm for classification. Our methodology and model performance were validated using NSL-KDD and UNSW-NB-15, two widely available IDS datasets. The suggested model's accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. The model's binary and multi-class classification accuracies on the UNSW-NB15 dataset are 99.56% and 99.45%, respectively. Further, we compared the suggested approach with other previous machine learning and deep learning models and found it to outperform them in detection rate, FPR, and F-score. For both binary and multiclass classifications, the proposed method reduces false positives while increasing the number of true positives. The model proficiently identifies diverse network intrusions on computer networks and accomplishes its intended purpose. The suggested model will be helpful in a variety of network security research fields and applications.

Microfluidics-free single-cell genomics with templated emulsification.

Current single-cell RNA-sequencing approaches have limitations that stem from the microfluidic devices or fluid handling steps required for sample processing. We develop a method that does not require specialized microfluidic devices, expertise or hardware. Our approach is based on particle-templated emulsification, which allows single-cell encapsulation and barcoding of cDNA in uniform droplet emulsions with only a vortexer. Particle-templated instant partition sequencing (PIP-seq) accommodates a wide range of emulsification formats, including microwell plates and large-volume conical tubes, enabling thousands of samples or millions of cells to be processed in minutes. We demonstrate that PIP-seq produces high-purity transcriptomes in mouse-human mixing studies, is compatible with multiomics measurements and can accurately characterize cell types in human breast tissue compared to a commercial microfluidic platform. Single-cell transcriptional profiling of mixed phenotype acute leukemia using PIP-seq reveals the emergence of heterogeneity within chemotherapy-resistant cell subsets that were hidden by standard immunophenotyping. PIP-seq is a simple, flexible and scalable next-generation workflow that extends single-cell sequencing to new applications.

Recent Advances in Halide Perovskite Resistive Switching Memory Devices: A Transformation from Lead-Based to Lead-Free Perovskites.

Due to their remarkable electrical and light absorption characteristics, hybrid organic-inorganic perovskites have recently gained popularity in several applications such as optoelectronics, lasers, and light-emitting diodes. Through this, there has recently been an increase in the use of halide perovskites (HPs) in resistive switching (RS) devices. However, lead-based (Pb-based) perovskites are notorious for being unstable and harmful to the environment. As a result, lead-free (Pb-free) perovskite alternatives are being investigated in achieving the long-term and sustainable use of RS devices. This work describes the characteristics of Pb-based and Pb-free perovskite RS devices. It also presents the recent advancements of HP RS devices, including the selection strategies of perovskite structures. In terms of resistive qualities, the directions of both HPs appear to be identical. Following that, the possible impact of switching from Pb-based to Pb-free HPs is examined to determine the requirement in RS devices. Finally, this work discusses the opportunities and challenges of HP RS devices in creating a stable, efficient, and sustainable memory storage technology.

Assessment of diagnostic potential of some circulating microRNAs in Amyotrophic Lateral Sclerosis Patients, an Egyptian study.

OBJECTIVE: Numerous studies have been carried out to identify the role of microRNA (miRNA) as potential biomarkers for many diseases including amyotrophic lateral sclerosis (ALS). The aim of this study was to explore the circulating levels of some miRNAs in cohort of Egyptian ALS patients in an attempt to correlate the selected miRNA profiles with disease progression. METHODS: Thirty ALS patients and 20 age and sex matched healthy controls were enrolled. Circulating miRNA levels were determined in venous blood samples, collected on EDTA, from all the study subjects. The selection of miRNA species (miR-206, miR-142-3p, miR-143-3p, miR-181a-5p, miR-106b-3p, miR-4516 and Let7f-5p) was based on their involvement in the pathophysiology of ALS and was further confirmed by data mining of specific miRNA databases (miRBase and miRDB). RESULTS: As compared to the control group, significant consistent upregulation was found in the levels of miR-206, miR-143-3p and to a lesser extent in miR-142-3p. An elevation trend, although not significant, was also found in the levels of miR-181a-5p, miR-106b-3p, and miR-4516. Interestingly, we found that the levels of miR-142-3p were elevated in familial cases, while that of miR-4516 were significantly increased in sporadic cases. Furthermore, the levels of Let7f-5p, although were generally lowered in ALS patients but were also decreased in familial cases as well as in spinal onset ALS as compared to bulbar onset. CONCLUSION: This is the first study investigating miRNA profiles in Egyptian ALS patients. We found that some miRNAs are significantly altered in ALS patients, and some may be used to distinguish familial and sporadic cases and bulbar and spinal onset. Larger study is needed, in which we will conduct a correlation of miRNA levels against variations in disease onset, progression as well as systemic inflammatory responses and the extent of neuromuscular involvement in Egyptian ALS patients in an attempt to identify environmental/occupational risk factors.

Poverty, Income, and Unemployment as Determinants of Life Expectancy: Empirical Evidence from Panel Data of Thirteen Malaysian States.

BACKGROUND: The primary indicator of public health, which all nations aim to prolong, is life expectancy at birth. Uncovering its socioeconomic determinants is key to extending life expectancy. This study examined the determinants of life expectancy in Malaysia. METHODS: This observational study employs secondary data from various official sources of 12 states and one federal territory in Malaysia (2002-2014). Panel data of 78 observations (13 cross-sections at six points in time) were used in multivariate, fixed-effect, regressions to estimate the effects of socioeconomic variables on life expectancy at birth for male, female and both-gender. RESULTS: Poverty and income significantly determine female, male, and total life expectancies. Unemployment significantly determines female and total life expectancies, but not male. Income inequality and public spending on health (as a percentage of total health spending) do not significantly determine life expectancy. The coefficients of the multivariate regressions suggest that a 1% reduction in poverty, 1% reduction in unemployment, and around USD 23.20 increase in household monthly income prolong total life expectancy at birth by 17.9, 72.0, and 16.3 d, respectively. The magnitudes of the effects of the socioeconomic variables on life expectancy vary somewhat by gender. CONCLUSION: Life expectancy in Malaysia is higher than the world average and higher than that in some developing countries in the region. However, it is far lower than the advanced world. Reducing poverty and unemployment and increasing income are three effective channels to enhance longevity.

The Power of Multidimensional Poverty in Explaining Life Expectancy: Empirical Evidence from Cross-Sectional Data of 62 Developing Countries.

BACKGROUND: We examined whether multidimensional poverty index (MPI) explained variations in life expectancy (LE) better than income poverty; and assessed the relative importance of MPI indicators in influencing LE. METHODS: Cross-sectional data from 62 developing countries were used to run several multivariate linear regressions. R2 was used to compare the powers of MPI with income-poverties (income poverty gaps [IPG] at 1.9 and 3.1 USD) in explaining LE. RESULTS: Adjusting for controls, both MPI (ß =-0.245, P<0.001) and IPG at 3.1 USD (ß=-0.135, P=0.044) significantly correlates with LE, but not IPG at 1.9 USD (ß=-0.147, P=0.135). MPI explains 12.1% of the variation in LE compared to only 3.2% explained by IPG at 3.1 USD. The effect of MPI on LE is higher on female (ß=-0.210, P<0.001) than male (ß=-0.177, P<0.001). The relative influence of the deprivation indictors on LE ranks as follows (most to least): Asset ownership, drinking water, cooking fuel, flooring, child school attendance, years of schooling, nutrition, mortality, improved sanitation, and electricity. CONCLUSION: Interventions to reduce poverty and improve LE should be guided by MPI, not income poverty indices. Such policies should be female-oriented and prioritized based on the relative influence of the various poverty deprivation indicators on LE.

Demographic and Socioeconomic Variables Associated With Health Care-Seeking Behavior Among Foreign Workers in Malaysia.

Foreign workers in Malaysia face various barriers in accessing health care, which results in many of them being unable to obtain appropriate medical treatment in case of sickness. This study investigates the foreign workers' health care-seeking behavior and the demographic and socioeconomic variables that influence it. Data were collected from 502 foreign workers using a self-administered questionnaire. Multiple logistic regression was used to estimate the influence of demographic and socioeconomic variables on health care-seeking behavior among foreign workers. In cases of severe sickness, 20.5% of foreign workers stated that they will not go or are unlikely to go to a clinic or hospital. The multiple logistic regression revealed that foreign workers' tendency to avoid medical treatment is associated with gender, marital status, monthly income, preferred language of communication, and work classification. Nonetheless, in cases of mild sickness, demographic and socioeconomic variables do not influence foreign workers' health care-seeking behavior.

Continuous Time Level Crossing Sampling ADC for Bio-Potential Recording Systems.

In this paper we present a fixed window level crossing sampling analog to digital convertor for bio-potential recording sensors. This is the first proposed and fully implemented fixed window level crossing ADC without local DACs and clocks. The circuit is designed to reduce data size, power, and silicon area in future wireless neurophysiological sensor systems. We built a testing system to measure bio-potential signals and used it to evaluate the performance of the circuit. The bio-potential amplifier offers a gain of 53 dB within a bandwidth of 200 Hz-20 kHz. The input-referred rms noise is 2.8 µV. In the asynchronous level crossing ADC, the minimum delta resolution is 4 mV. The input signal frequency of the ADC is up to 5 kHz. The system was fabricated using the AMI 0.5 µm CMOS process. The chip size is 1.5 mm by 1.5 mm. The power consumption of the 4-channel system from a 3.3 V supply is 118.8 µW in the static state and 501.6 µW with a 240 kS/s sampling rate. The conversion efficiency is 1.6 nJ/conversion.

Design constraints for mobile, high-speed fluorescence brain imaging in awake animals.

In this paper we present a fully self-contained imaging instrument (30 mm overall length) that is capable of recording high speed and detect relatively small fluorescent signals (0.1% ΔF/F) from brain tissues potentially containing genetically-encoded sensors or dyes. This device potentially enables the study of neuronal activity in awake and mobile animals during natural behaviors without the stress and suppression of anesthesia and restraint. The device is a fully self-contained illumination system, wide field fluorescence microscope (~ 4.8 mm² FOV-25 um lateral resolution-1.8 × magnification-0.39 NA) and CMOS image sensor (32 × 32). The total weight of the system is 10 g and is capable of imaging up to 900 fps. We present voltage dye RH1692 experiments using the system to study the somatosensory cortex of mice during whisker movements using an air puff.

Head-mountable high speed camera for optical neural recording.

We report a head-mountable CMOS camera for recording rapid neuronal activity in freely moving rodents using fluorescent activity reporters. This small, lightweight camera is capable of detecting small changes in light intensity (0.2% ΔI/I) at 500fps. The camera has a resolution of 32×32, sensitivity of 0.62V/lxs, conversion gain of 0.52µV/e(-) and well capacity of 2.1Me(-). The camera, containing intensity offset subtraction circuitry within the imaging chip, is part of a miniaturized epi-fluorescent microscope and represents a first generation, mobile scientific-grade, physiology imaging camera.