Miracle-dz/UniFAR-FTUs huggingface dataset:
Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data

Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution. The resulting datasets are quite large (with scales of TB/hour in some cases), which has presented a barrier to routine open sharing of this data, slowing progress in reproducible research. State of the art methods for analyzing this data are based on non-negative matrix factorization (NMF); these approaches solve a non-convex optimization problem, and are highly effective when good initializations are available, but can break down e.g. in low-SNR settings where common initialization approaches fail. Here we introduce an improved approach to compressing and denoising functional imaging data. The method is based on a spatially-localized penalized matrix decomposition (PMD) of the data to separate (low-dimensional) signal from (temporally-uncorrelated) noise. This approach can be applied in parallel on local spatial patches and is therefore highly scalable, does not impose non-negativity constraints or require stringent identifiability assumptions (leading to significantly more robust results compared to NMF), and estimates all parameters directly from the data, so no hand-tuning is required. We have applied the method to a wide range of functional imaging data (including one-photon, two-photon, three-photon, widefield, somatic, axonal, dendritic, calcium, and voltage imaging datasets): in all cases, we observe ~2-4x increases in SNR and compression rates of 20-300x with minimal visible loss of signal, with no adjustment of hyperparameters; this in turn facilitates the process of demixing the observed activity into contributions from individual neurons. We focus on two challenging applications: dendritic calcium imaging data and voltage imaging data in the context of optogenetic stimulation. In both cases, we show that our new approach leads to faster and much more robust extraction of activity from the video data.
HAase-sensitive dual-targeting irinotecan liposomes enhance the therapeutic efficacy of lung cancer in animals

Among all cancers, lung cancer is one of the most common and serious types of cancer. It is challenging for site-specific delivery of anticancer therapeutics to tumor cells. Herein, we developed a novel“smart” dual-targeting liposomal platform to respond to the highly expressed hyaluronidase (HAase) in the tumor microenvironment and improve tumor targeting and antitumor efficacy. Methods: In our design, the HA was used as a sensitive linker between a liposomal lipid and long chain PEG block to synthesize three functional conjugates in order to prepare“smart” liposomal platform modified with epidermal growth factor receptor (EGFR) antibody (GE11) and cell-penetrating peptide (TATp). Using irinotecan as a model therapeutic, evaluations were performed on the human lung adenocarcinoma A549 cells as well as the xenografted A549 cancer cells in nude mice. Results: The GE11/HA/TATp-irinotecan liposomes evidently increased the uptake of irinotecan and showed significant antitumor efficacy in the xenografted A549 cancer cells in nude mice by intravenous administration. The mechanisms were defined to be two aspects: GE11 exhibits high affinity for EGFR binding and the degradation of the HA by HAase results in the long-chain PEG removal and exposure of the previously hidden surface-attached TATp to enhance the target cell internalization. Conclusion: Our findings suggest that this functional liposomal platform may provide a novel strategy for treating lung cancers because of effective intracellular delivery.
Reproductive seasonality and early life temperature sensitivity reflect vulnerability of a seaweed undergoing range reduction

Temperature is a major determinant of the performance and geographical ranges of marine species. Changes in temperature can therefore result in localised mortality and shifts in species distributions, but the phenology and temperature sensitivity of many important habitatforming seaweed species has not yet been investigated. Through field observations and culture growth experiments, the present study investigated the temperature sensitivity of reproductive timing, early post-settlement growth and survival, and recruitment success of the widespread foundation seaweed Scytothalia dorycarpa in Australia. In culture, the highest settlement densities and lowest mortality rates were achieved at 15°C, and optimal temperatures for germling fertilisation occurred at 18°C, whereas temperatures greater than 20°C delayed germling settlement and significantly increased mortality rates, with no germlings surviving at temperatures greater than 23°C. Experimental findings were consistent with field observations that found adult reproductive development and gamete release to occur in synchronous pulses throughout the winter months, when seawater temperatures were ~18°C. Surveys of the latitudinal distribution of S. dorycarpa recruits showed significantly lower recruit densities in warmer, low-latitude reefs where summer maximum temperatures often exceed 23°C compared to much higher recruit densities in cooler pole-ward reefs where average summer maximum temperatures are 21 to 22°C. The timing of reproduction, latitudinal distribution of recruits and culture temperature optima all indicate high temperature sensitivity among the early life stages of S. dorycarpa. These findings help to explain the rapid range contraction of this species following a 2011 marine heatwave off the southwest coast of Australia.
Demand Response Scheduling in Production Lines Constrained by Available Power

Energy efficiency in factories is a new paradigm arising with automatic Demand Response (DR) protocols like OpenADR that enables energy providers to ask their clients to reduce their power consumption for a given time. DR is used to smooth peaks of energy demand at country level by energy providers and the clients get paid for not consuming. Upon reception of a DR, a client should decide in a bounded time if he agrees on DR and for how long. In this paper, we characterize a DR scheduling problem for a production line with available power constraint. Our objective is to respect the DR constraint in a way that its impact on production rate (number of finished items per time unit) is limited as much as possible. To do so, we first propose a scheduling algorithm based on an exhaustive search approach to find an optimal schedule. As complexity of this approach is high, we then develop a heuristic approach based on Integer Linear Programming (ILP) model to find sub-optimal solutions. We conduct simulations to compare performances of the two approaches in terms of production rate, run time and monetary gain.
Accordion: Better Memory Organization for LSM Key-Value Stores

Log-structured merge (LSM) stores have emerged as the technology of choice for building scalable write-intensive key-value storage systems. An LSM store replaces random I/O with sequential I/O by accumulating large batches of writes in a memory store prior to flushing them to log-structured disk storage; the latter is continuously re-organized in the background through a compaction process for efficiency of reads. Though inherent to the LSM design, frequent compactions are a major pain point because they slow down data store operations, primarily writes, and also increase disk wear. Another performance bottleneck in today's state-of-the-art LSM stores, in particular ones that use managed languages like Java, is the fragmented memory layout of their dynamic memory store. In this paper we show that these pain points may be mitigated via better organization of the memory store. We present Accordion - an algorithm that addresses these problems by re-applying the LSM design principles to memory management. Accordion is implemented in the production code of Apache HBase, where it was extensively evaluated. We demonstrate Accordion's double-digit performance gains versus the baseline HBase implementation and discuss some unexpected lessons learned in the process.
Composition of blue-winged teal eggs in relation to egg size, clutch size, and the timing of laying

Blue-winged Teal (Anas discors) eggs from 172 nests were analyzed to determine how organic composition was affected by clutch size, laying sequence, nest attempt, laying date, and egg size. On average, eggs weighed 28.1 g and were 46% yolk and 44% albumen. Eggs contained 3.6 g lipid and 3.2 g protein almost equally distributed between yolk and albumen. Yolk increased a disproportionate amount and the proportion of albumen tended to decrease with increased egg mass. Consequently, large eggs contained proportionately more lipid than small eggs. Shell decreased in proportion with increasing egg mass. For most egg traits 60 to 80% of the variation occurred between clutches rather than within clutches. Teal eggs did not vary in composition in relation to clutch size, laying sequence, nest attempt, or the timing of laying on either a seasonal or yearly basis. Eggs in renests weighed 0.8 g less than eggs from the first clutch of the same females; however, this disparity was not reflected in differences in lipid or lean dry mass for the collected eggs of known renesters.
Enabling High Order SCMA Systems in Downlink Scenarios With a Serial Coding Scheme

In the future fifth generation communication systems, sparse code multiple access (SCMA) shows strong competitiveness as a novel non-orthogonal multiple access (NOMA) technique. Attributing to multi-dimensional codebooks, the SCMA can obtain shaping gain and provide a better performance than some other NOMA schemes, such as pattern division multiple access, low-density signature (LDS), and so on. However, under higher user load, each orthogonal resource in high-order SCMA systems is occupied by more users and the constellations of these systems possess smaller Euclidean distances, both of which will degrade the link performance. The iterative algorithm employed in SCMA decoding, namely message passing algorithm, will also bring an exponential growth of computational complexity. In this paper, a scheme with serial codes for system order reduction in downlink scenarios, namely serial SCMA, is proposed to tackle these issues. Based on the idea of hierarchical coding, the processes of encoding and decoding in a high-order system are decomposed into several processes in low-order systems. Specific mapping modules are designed to recover the original user binary bits. Simulation results show that serial SCMA will substantially cut down the detection complexity and enjoy better link performance while maintaining sparser codebooks. Besides, it still inherits the advantages of original SCMA systems such as overloading and average aggregate energy efficiency over OMA and other NOMA schemes.
Performance Evaluation of MongoDB and CouchDB Databases

The rise of new type of databases have seen over the last few years, known as NoSQL databases. NoSQL databases are challenging the dominance of relational databases. Scaling a relational databases on powerful servers are expensive and difficult to handle and Nosql databases are designed to expand horizontally. Also Nosql database is schema less and data can be inserted without any predefined schema. They represent a broad category of databases which allow large quantities of unstructured and semi structured data to be stored and managed. Additionally they are designed to handle high levels of reads and writes while scaling horizontally. There are various open sourced and licenced document oriented NoSQL databases , but all have different mechanism to store data in document format. However it is extremely diligent to decide which is to be used and when. So there is need for performance evaluation of various document oriented databases. This work comprises about a detailed comparative study between MongoDB and CouchDB, two leading document oriented databases.
Formalization in Constructive Type Theory of the Standardization Theorem for the Lambda Calculus using Multiple Substitution

We present a full formalization in Martin-L\"of's Constructive Type Theory of the Standardization Theorem for the Lambda Calculus using first-order syntax with one sort of names for both free and bound variables and Stoughton's multiple substitution. Our formalization is based on a proof by Ryo Kashima, in which a notion of beta-reducibility with a standard sequence is captured by an inductive relation. The proof uses only structural induction over the syntax and the relations defined, which is possible due to the specific formulation of substitution that we employ. The whole development has been machine-checked using the system Agda.
DeepSS: Exploring Splice Site Motif Through Convolutional Neural Network Directly From DNA Sequence

Splice sites prediction and interpretation are crucial to the understanding of complicated mechanisms underlying gene transcriptional regulation. Although existing computational approaches can classify true/false splice sites, the performance mostly relies on a set of sequence- or structure-based features and model interpretability is relatively weak. In viewing of these challenges, we report a deep learning-based framework (DeepSS), which consists of DeepSS-C module to classify splice sites and DeepSS-M module to detect splice sites sequence pattern. Unlike previous feature construction and model training process, DeepSS-C module accomplishes feature learning during the whole model training. Compared with state-of-the-art algorithms, experimental results show that the DeepSS-C module yields more accurate performance on six publicly donor/acceptor splice sites data sets. In addition, the parameters of the trained DeepSS-M module are used for model interpretation and downstream analysis, including: 1) genome factors detection (the truly relevant motifs that induce the related biological process happen) via filters from deep learning perspective; 2) analyzing the ability of CNN filters on motifs detection; 3) co-analysis of filters and motifs on DNA sequence pattern. DeepSS is freely available at http://ailab.ahu. edu.cn:8087/DeepSS/index.html.
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator

In this paper, we propose a new technique named \textit{Stochastic Path-Integrated Differential EstimatoR} (SPIDER), which can be used to track many deterministic quantities of interest with significantly reduced computational cost. We apply SPIDER to two tasks, namely the stochastic first-order and zeroth-order methods. For stochastic first-order method, combining SPIDER with normalized gradient descent, we propose two new algorithms, namely SPIDER-SFO and SPIDER-SFO\textsuperscript{+}, that solve non-convex stochastic optimization problems using stochastic gradients only. We provide sharp error-bound results on their convergence rates. In special, we prove that the SPIDER-SFO and SPIDER-SFO\textsuperscript{+} algorithms achieve a record-breaking gradient computation cost of $\mathcal{O}\left( \min( n^{1/2} \epsilon^{-2}, \epsilon^{-3} ) \right)$ for finding an $\epsilon$-approximate first-order and $\tilde{\mathcal{O}}\left( \min( n^{1/2} \epsilon^{-2}+\epsilon^{-2.5}, \epsilon^{-3} ) \right)$ for finding an $(\epsilon, \mathcal{O}(\epsilon^{0.5}))$-approximate second-order stationary point, respectively. In addition, we prove that SPIDER-SFO nearly matches the algorithmic lower bound for finding approximate first-order stationary points under the gradient Lipschitz assumption in the finite-sum setting. For stochastic zeroth-order method, we prove a cost of $\mathcal{O}( d \min( n^{1/2} \epsilon^{-2}, \epsilon^{-3}) )$ which outperforms all existing results.
Towards Batch-Processing on Cold Storage Devices

Large amounts of data in storage systems is cold, i.e., Written Once and Read Occasionally (WORO). The rapid growth of massive-scale archival and historical data increases the demand for petabyte-scale cheap storage for such cold data. A Cold Storage Device (CSD) is a disk-based storage system which is designed to trade off performance for cost and power efficiency. Inevitably, the design restrictions used in CSD's results in performance limitations. These limitations are not a concern for WORO workloads, however, the very low price/performance characteristics of CSDs makes them interesting for other applications, e.g., batch processes, too. Applications, however, can be very slow on CSD's if they do not take their characteristics into account. In this paper we design two strategies for data partitioning in CSDs -- a crucial operation in many batch analytics tasks like hash-join, near-duplicate detection, and data localization. We show that our strategies can efficiently use CSDs for batch processing of terabyte-scale data by accelerating data partitioning by 3.5x in our experiments.
Confirmatory Factor Analysis of the Inventory of Personality Organization-Reality Testing Subscale

The reality testing dimension of the Inventory of Personality Organization, the IPO-RT, has emerged as an important index of proneness to reality testing deficits. However, to date few studies have examined the factorial structure of the IPO-RT in isolation. This is an important and necessary development because studies use the IPO-RT as a discrete measure. Additionally, psychometric evaluation of the IPO suggests alternative factorial solutions. Specifically, recent work supports multidimensionality, whereas initial IPO assessment evinced a unidimensional structure. Accordingly, this study, using a heterogeneous sample (N = 652), tested the fit of several factorial models (one-factor, four-factor oblique, second-order, and bifactor) via maximum likelihood with bootstrapping due to multivariate non-normality. Analysis revealed superior fit for the bifactor solution (correlated errors) (CFI = 0.965, SRMR = 0.036, RMSEA = 0.042). This model comprised a general reality testing dimension alongside four subfactors (auditory and visual hallucinations, delusional thinking, social deficits, and confusion). Inter-factor correlations were in the moderate range. Item loadings and omega reliability supported the notion that the IPO-RT emphasizes a single latent construct. The model demonstrated invariance across gender and partial age invariance. Overall, from a psychometric perspective, the IPO-RT functioned effectively at both global and, to an extent, factorial levels. Findings recommend that the IPO-RT should be scored as a total scale, and rather than treat subscales independently, future studies should consider examining factor variance alongside overall scale scores.
Non-Orthogonal Multiple Access for DF Cognitive Cooperative Radio Networks

In this paper, we study a power domain non-orthogonal multiple access (NOMA) scheme for cognitive cooperative radio networks (CCRNs). In the proposed scheme, a secondary transmitter communicates with two secondary users (SUs) by allocating transmit powers inversely proportional to the channel power gains on the links to the respective SUs. A decode-and-forward (DF) secondary relay is deployed which decodes the superimposed signals associated with the two SUs. Then, power domain NOMA is used to forward the signals from the relay to the two SUs based on the channel power gains on the corresponding two links. Mathematical expressions for the outage probability and ergodic capacity of each SU and the overall power domain NOMA CCRN are derived. Numerical results are provided to reveal the impact of the power allocation coefficients at the secondary transmitter and secondary relay, the interference power threshold at the primary receiver, and the normalized distances of the SUs on the outage probability and ergodic capacity of each SU and the whole NOMA system.
Achievable Rate and Ergodic Capacity for Power Line Communication Systems under Impulsive Noise

Power line communications (PLC) enable modern and flexible network applications in smart grids. Due to severe channel fading and impulsive noise, the performance analysis for a PLC channel is a great challenge. In this paper, we analyze the performance of the PLC channel under impulsive noise. Specifically, we obtain the achievable rate of the PLC channel under the assumption that the impulsive occurrence number (ION) follows Poisson distribution. We derive the cumulative distribution function (CDF) for the received signal-to-noise ratio (SNR) based on the statistical characteristics of the PLC channel. Furthermore, we derive the ergodic capacity for the PLC channel according to the CDF of the received SNR. Simulation results are provided to verify the analytical results.
North American Football Fans Show Neurofunctional Differences in Response to Violence: Implications for Public Health and Policy

While social and behavioral effects of violence in the media have been studied extensively, much less is known about how sports affect perceptions of violence. The current study examined neurofunctional differences between fans and non-fans of North American football (a contact sport) while viewing violent imagery. Participants viewed images of violence in both football and non-football settings while high-resolution functional magnetic resonance imaging (fMRI) data were acquired from their brains. Neurological activation was compared between these violence types and between groups. Fans of football show diminished activation in brain regions involved in pain perception and empathy such as the anterior cingulate cortex, fusiform gyrus, insula, and temporal pole when viewing violence in the context of football compared to more broadly violent images. Non-fans of football showed no such effect for the types of violent imagery and had higher activation levels than fans of football for the specified brain regions. These differences show that fans of football may perceive violence differently when it is in the context of football. These fan attitudes have potential policy implications for addressing the issue of concussions in North American football.
Cooperative Spectrum Sensing Algorithm Based on Support Vector Machine against SSDF Attack

In cognitive radio networks (CRNs), a spectrum-sensing-data-falsification (SSDF) attack is a security issue. To deal with SSDF attacks, a support vector machine (SVM) based scheme is proposed here. Our scheme analyzes secondary user's behaviors from multi-round records of energy values, and obtains a novel evaluation index, classification accuracy. In particular, the concepts of recognition probability and misclassification probability are introduced, and the tradeoff relationship between misclassification probability and threshold of classification accuracy is theoretically obtained. Moreover, the asymptotic optimal property is derived. It enables excellent adaptability for malicious secondary user (MSU) detection in various scenarios. Exhaustive simulation results demonstrate that our proposed scheme outperforms other existing approaches.
Genetic structure of the populations of Spilostethus pandurus in the wadis of the St Katherine Protectorate, South Sinai

The genetic diversity of Spilostethus pandurus was studied using RAPD markers in individuals sampled from four wadis within the St Katherine area. A total of 109 different RAPD bands were generated for the whole sample: site-specific bands occurred at low frequency. Even though there were many genetic differences among individuals within sites, the sites were statistically distinct. Thus even in thus fairly long-lived and apparently fairly mobile insect, there is evidence of genetic isolation among the wadis of this highly dissected environment. Keywords : aposematism, gene flow, genetic diversity, RAPD, Sinai Egyptian Journal of Biology Vol. 10 2008: pp. 87-94