Miracle-dz/UniFAR-FTUs huggingface dataset:
One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection

PURPOSE Computed tomography for the reconstruction of region of interest (ROI) has advantages in reducing the x-ray dose and the use of a small detector. However, standard analytic reconstruction methods such as filtered back projection (FBP) suffer from severe cupping artifacts, and existing model-based iterative reconstruction methods require extensive computations. Recently, we proposed a deep neural network to learn the cupping artifacts, but the network was not generalized well for different ROIs due to the singularities in the corrupted images. Therefore, there is an increasing demand for a neural network that works well for any ROI size. METHOD Two types of neural networks are designed. The first type learns ROI size-specific cupping artifacts from FBP images, whereas the second type network is for the inversion of the truncated Hilbert transform from the truncated differentiated backprojection (DBP) data. Their generalizabilities for different ROI sizes, pixel sizes, detector pitch and starting angles for a short scan are then investigated. RESULTS Experimental results show that the new type of neural networks significantly outperform existing iterative methods for all ROI sizes despite significantly lower runtime complexity. In addition, performance improvement is consistent across different acquisition scenarios. CONCLUSIONS Since the proposed method consistently surpasses existing methods, it can be used as a general CT reconstruction engine for many practical applications without compromising possible detector truncation.
Sequence-structure relations of biopolymers.

Motivation DNA data is transcribed into single-stranded RNA, which folds into specific molecular structures. In this paper we pose the question to what extent sequence- and structure-information correlate. We view this correlation as structural semantics of sequence data that allows for a different interpretation than conventional sequence alignment. Structural semantics could enable us to identify more general embedded ‘patterns’ in DNA and RNA sequences. Results We compute the partition function of sequences with respect to a fixed structure and connect this computation to the mutual information of a sequence–structure pair for RNA secondary structures. We present a Boltzmann sampler and obtain the a priori probability of specific sequence patterns. We present a detailed analysis for the three PDB-structures, 2JXV (hairpin), 2N3R (3-branch multi-loop) and 1EHZ (tRNA). We localize specific sequence patterns, contrast the energy spectrum of the Boltzmann sampled sequences versus those sequences that refold into the same structure and derive a criterion to identify native structures. We illustrate that there are multiple sequences in the partition function of a fixed structure, each having nearly the same mutual information, that are nevertheless poorly aligned. This indicates the possibility of the existence of relevant patterns embedded in the sequences that are not discoverable using alignments. Availability and Implementation The source code is freely available at http://staff.vbi.vt.edu/fenixh/Sampler.zip Contact duckcr@vbi.vt.edu Supplimentary Information Supplementary data are available at Bioinformatics online.
Low Microfilaremia Levels in Three Districts in Coastal Ghana with at Least 16 Years of Mass Drug Administration and Persistent Transmission of Lymphatic Filariasis

Ghana has been implementing mass drug administration (MDA) of ivermectin and albendazole for the elimination of lymphatic filariasis (LF) since the year 2000, as part of the Global Programme to Eliminate Lymphatic Filariasis (GPELF). It was estimated that 5–6 years of treatment would be sufficient to eliminate the disease. Tremendous progress has been made over the years, and treatment has stopped in many disease endemic districts. However, despite the successful implementation of MDA, there are districts with persistent transmission. In this study we assessed the epidemiology of LF in three adjoining districts that have received at least 16 years of MDA. The assessments were undertaken one year after the last MDA. 1234 adults and 182 children below the age of 10 years were assessed. The overall prevalence of circulating filarial antigen in the study participants was 8.3% (95% CI: 6.9–9.9), with an estimated microfilaria prevalence of 1.2%. The microfilarial intensity in positive individuals ranged from 1 to 57 microfilariae/mL of blood. Higher antigen prevalence was detected in males (13.0%; 95% CI: 10.3–16.2) compared to females (5.5%; 95% CI: 4.1–7.2). The presence of infection was also highest in individuals involved in outdoor commercial activities, with the risks of infection being four- to five-fold higher among farmers, fishermen, drivers and artisans, compared to all other occupations. Using bednets or participating in MDA did not significantly influence the risk of infection. No children below the age of 10 years were found with infection. Detection of Wb123 antibodies for current infections indicated a prevalence of 14.4% (95% CI: 8.1–23.0) in antigen-positive individuals above 10 years of age. No antibodies were detected in children 10 years or below. Assessment of infection within the An. gambiae vectors of LF indicated an infection rate of 0.9% (95% CI: 0.3–2.1) and infectivity rate of 0.5% (95% CI: 0.1–1.6). These results indicate low-level transmission within the districts, and suggest that it will require targeted interventions in order to eliminate the infection.
Neural correlates of facial emotion processing in infancy.

In the present study we examined the neural correlates of facial emotion processing in the first year of life using ERP measures and cortical source analysis. EEG data were collected cross-sectionally from 5- (N = 49), 7- (N = 50), and 12-month-old (N = 51) infants while they were viewing images of angry, fearful, and happy faces. The N290 component was found to be larger in amplitude in response to fearful and happy than angry faces in all posterior clusters and showed largest response to fear than the other two emotions only over the right occipital area. The P400 and Nc components were found to be larger in amplitude in response to angry than happy and fearful faces over central and frontal scalp. Cortical source analysis of the N290 component revealed greater cortical activation in the right fusiform face area in response to fearful faces. This effect started to emerge at 5 months and became well established at 7 months, but it disappeared at 12 months. The P400 and Nc components were primarily localized to the PCC/Precuneus where heightened responses to angry faces were observed. The current results suggest the detection of a fearful face in infants' brain can happen shortly (~200-290 ms) after the stimulus onset, and this process may rely on the face network and develop substantially between 5 to 7 months of age. The current findings also suggest the differential processing of angry faces occurred later in the P400/Nc time window, which recruits the PCC/Precuneus and is associated with the allocation of infants' attention.
Application of the Hollow Core Fiber Ended with Fiber Microlens in the Multiphoton Excitation Setup

In this paper an ultrathin lensed fiber-optics probe for multiphoton fluorescence endoscopy is presented. The probe is made by attaching a segment of multimode (MM) fiber to the distal end of the hollow core, photonic bandgap fiber (HCPBF) and generating a curved surface at the tip of the MM fiber using a fiber arc fusion splicer the electric arc of a fusion splicer. The curved surface served as a micro fiber-optic lens and focused the exiting light beam. It simultaneously protected the HCF tip against contamination.
Analytical Study of Light Scattering Characteristics of Radially Inhomogeneous Subwavelength Spheres

IB054. The general mathematical treatment for the electrostatic polarizability will be presented in terms of scattering potentials. The case of a power-law profile and a new class of permittivity profiles that exhibit an exponential-radial dependence are considered. The presented theoretical results can open multiple avenues towards the exploration/implementation of scatterers with more exotic permittivity profiles for RF/optics and remote sensing applications.
Expression of a Grape VqSTS36-Increased Resistance to Powdery Mildew and Osmotic Stress in Arabidopsis but Enhanced Susceptibility to Botrytis cinerea in Arabidopsis and Tomato

Stilbene synthase genes make a contribution to improving the tolerances of biotic and abiotic stress in plants. However, the mechanisms mediated by these STS genes remain unclear. To provide insight into the role of STS genes defense against biotic and abiotic stress, we overexpressed VqSTS36 in Arabidopsis thaliana and tomato (Micro-Tom) via Agrobacterium-mediated transformation. VqSTS36-transformed Arabidopsis lines displayed an increased resistance to powdery mildew, but both VqSTS36-transformed Arabidopsis and tomato lines showed the increased susceptibility to Botrytis cinerea. Besides, transgenic Arabidopsis lines were found to confer tolerance to salt and drought stress in seed and seedlings. When transgenic plants were treated with a different stress, qPCR assays of defense-related genes in transgenic Arabidopsis and tomato suggested that VqSTS36 played a specific role in different phytohormone-related pathways, including salicylic acid, jasmonic acid, and abscisic acid signaling pathways. All of these results provided a better understanding of the mechanism behind the role of VqSTS36 in biotic and abiotic stress.
Bridging the Gap From Research to Practical Advice

Emily, a software engineer at a mid-sized company, recently oversaw a software release with a critical user-facing bug. In the postmortem, someone asks Emily to evaluate static analysis for preventing this type of bug in the future. Web searches about static analysis reveal dozens of companies selling static-analysis tools. Their claims about identifying a wide variety of bugs are clearly marketing material. They boast of different technologies, at a range of prices. It’s difficult to compare them, let alone decide whether they should be used at all.
D B ] 1 O ct 2 01 8 Chasing Similarity : Distribution-aware Aggregation Scheduling ( Extended Version )

Parallel aggregation is a ubiquitous operation in data analytics that is expressed as GROUP BY in SQL, reduce in Hadoop, or segment in TensorFlow. Parallel aggregation starts with an optional local pre-aggregation step and then repartitions the intermediate result across the network. While local preaggregation works well for low-cardinality aggregations, the network communication cost remains significant for highcardinality aggregations even after local pre-aggregation. The problem is that the repartition-based algorithm for highcardinality aggregation does not fully utilize the network. In this work, we first formulate a mathematical model that captures the performance of parallel aggregation. We prove that finding optimal aggregation plans from a known data distribution is NP-hard, assuming the Small Set Expression conjecture. We propose GRASP, a GReedy Aggregation Scheduling Protocol that decomposes parallel aggregation into phases. GRASP is distribution-aware as it aggregates the most similar partitions in each phase to reduce the transmitted data size in subsequent phases. In addition, GRASP takes the available network bandwidth into account when scheduling aggregations in each phase to maximize network utilization. The experimental evaluation on real data shows that GRASP outperforms repartition-based aggregation by 3.5× and LOOM by 2.0×.
On Exact and $\infty$-R\'enyi Common Informations

Recently, two extensions of Wyner's common information\textemdash exact and R\'enyi common informations\textemdash were introduced respectively by Kumar, Li, and El Gamal (KLE), and the present authors. The class of common information problems involves determining the minimum rate of the common input to two independent processors needed to exactly or approximately generate a target joint distribution. For the exact common information problem, exact generation of the target distribution is required, while for Wyner's and $\alpha$-R\'enyi common informations, the relative entropy and R\'enyi divergence with order $\alpha$ were respectively used to quantify the discrepancy between the synthesized and target distributions. The exact common information is larger than or equal to Wyner's common information. However, it was hitherto unknown whether the former is strictly larger than the latter for some joint distributions. In this paper, we first establish the equivalence between the exact and $\infty$-R\'enyi common informations, and then provide single-letter upper and lower bounds for these two quantities. For doubly symmetric binary sources, we show that the upper and lower bounds coincide, which implies that for such sources, the exact and $\infty$-R\'enyi common informations are completely characterized. Interestingly, we observe that for such sources, these two common informations are strictly larger than Wyner's. This answers an open problem posed by KLE. Furthermore, we extend Wyner's, $\infty$-R\'enyi, and exact common informations to sources with countably infinite or continuous alphabets, including Gaussian sources.
Communication Through Real-Time Video Oculography Using Face Landmark Detection

An alternate method of communication for Amyotropic Lateral Sclerosis (ALS) patients using realtime video oculography is introduced in this paper. Proposed method is user-friendly and assist the patients to communicate their basic requirements. The algorithm can be used to address large number of outputs through various input eye wink combinations. The system is built using openCV and dlib. Eye wink is detected by computing Eye Aspect Ratio(EAR) using face landmarks obtained from dlib. Pattern in eye winks is considered to provide corresponding audio and visual outputs. This method can easily differentiate between voluntary and involuntary blinks with better accuracy.
Self-Supervised Low-Rank Representation (SSLRR) for Hyperspectral Image Classification

Low-rank representation (LRR) can construct the relationships among pixels for hyperspectral image (HSI) classification with a given dictionary and a noise term. However, the accuracy of HSI classification based on LRR methods is degraded with the redundant and noise information existed in pixels. The neglect of semantic information around pixels in the LRR methods may cause “salt-and-pepper” problem in HSI classification. To avoid the aforementioned problems, a novel self-supervised low-rank representation method called SSLRR is developed. In SSLRR, the LRR and spectral–spatial graph regularization are developed as the pixel-level constraints to remove the redundant and noise information in HSIs. Superpixel constraints including data structure and relationship construction are further utilized to provide supervised feedback information to the subspace learning to avoid the “salt-and-pepper” problem generated in the pixel-based classification methods, and simultaneously enhance the performance of LRR. The pixel-level and superpixel-level regularizations are explicitly integrated into a unified objective function for LRR. By means of the linearized alternating direction method with adaptive penalty, the solution to the objective function is achieved by employing a customized iterative algorithm. We perform comprehensive evaluation of the proposed method on three challenging public HSI data sets. We obtain new state-of-the-art performance on these data sets, and achieve improvements of 44.3%, 13.4%, and 30.1% in overall accuracy compared to the best LRR method.
SWIPT-Aware Fog Information Processing: Local Computing vs. Fog Offloading

This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing by using a simple model, where a sensor harvests energy and receives information from a hybrid access point (HAP) through power splitting (PS) receiver architecture. Two information processing modes, local computing and fog offloading modes are investigated. For such a system, two optimization problems are formulated to minimize the sensor’s required power for the two modes under the information rate and energy harvesting constraints by jointly optimizing the time assignment and the transmit power, as well as the PS ratio. The closed-form and semi-closed-form solutions to the proposed optimization problems are derived based on convex optimization theory. Simulation results show that neither mode is always superior to the other one. It also shows that when the number of logic operations per bit associated with local computing is less than a certain value, the local computing mode is a better choice; otherwise, the fog offloading mode should be selected. In addition, the mode selection associated with the positions of the user for fixed HAP and fog server (FS) is also discussed.
Different culture media modulate growth, heterogeneity, and senescence in human mammary epithelial cell cultures

The ability to culture normal human mammary epithelial cells (HMEC) greatly facilitates experiments that seek to understand both normal mammary cell biology and the many differences between normal and abnormal human mammary epithelia. To maximize in vivo relevance, the primary cell culture conditions should maintain cells in states that resemble in vivo as much as possible. Towards this goal, we compared the properties of HMEC strains from two different reduction mammoplasty tissues that were grown in parallel using different media and culture conditions. Epithelial organoids were initiated into three different media: two commonly used serum-free-media, MCDB 170-type (e.g. MEGM) and WIT-P, and a low stress media, M87A. Growth, lineage heterogeneity, p16 protein expression, and population doublings to senescence were measured for each culture condition. MCDB 170 caused rapid senescence and loss of heterogeneity within 2 to 3 passages, but some cultures went through the 1 to 2 month process of selection to generate clonal finite post-selection post-stasis cells. WIT-P caused impressive expansion of luminal cells in 2nd passage followed by their near complete disappearance by passage 4 and senescence shortly thereafter. M87A supported as much as twice the number of population doublings compared to either serum-free medium, and luminal and myoepithelial cells were present for as many as 8 passages. Thus, of the three media compared, WIT-P and MCDB 170 imposed rapid senescence and loss of lineage heterogeneity, phenotypes consistent with cells maintained in high-stress conditions, while M87A supported cultures that maintained multiple lineages and robust growth for up to 60 population doublings. In conjunction with previous studies examining the molecular properties of cultures grown in these media, we conclude that M87A medium is most able to support long-term culture of multiple lineages similar to in vivo conditions, thereby facilitating investigations of normal HMEC biology relevant to the mammary gland in situ.
A Survey: Comparative Analysis of Different Variants of Local Binary Pattern

Texture is one of the important properties of an image. Local Binary Pattern (LBP) is popular descriptor to extract Local texture information from an image. The main aim of LBP is to efficiently outline local structures of an image. In recent year, there are many variants of LBP have been enlarged to extract texture (locally and globally) of the image efficiently. These descriptors are used in many problems and applications like face recognition, motion analysis and image and video retrieval etc. A large number of variations of LBP have been advanced to increase the image discriminative capability and, to boost its robustness and applicability of different type of problems. This paper presents a survey of the current and important variants of LBP and finally comparison between different variants of LBP are done.
Reinstating plasticity and memory in a tauopathy mouse model with an acetyltransferase activator

Chromatin acetylation, a critical regulator of synaptic plasticity and memory processes, is thought to be altered in neurodegenerative diseases. Here, we demonstrate that spatial memory and plasticity (LTD, dendritic spine formation) deficits can be restored in a mouse model of tauopathy following treatment with CSP‐TTK21, a small‐molecule activator of CBP/p300 histone acetyltransferases (HAT). At the transcriptional level, CSP‐TTK21 re‐established half of the hippocampal transcriptome in learning mice, likely through increased expression of neuronal activity genes and memory enhancers. At the epigenomic level, the hippocampus of tauopathic mice showed a significant decrease in H2B but not H3K27 acetylation levels, both marks co‐localizing at TSS and CBP enhancers. Importantly, CSP‐TTK21 treatment increased H2B acetylation levels at decreased peaks, CBP enhancers, and TSS, including genes associated with plasticity and neuronal functions, overall providing a 95% rescue of the H2B acetylome in tauopathic mice. This study is the first to provide in vivo proof‐of‐concept evidence that CBP/p300 HAT activation efficiently reverses epigenetic, transcriptional, synaptic plasticity, and behavioral deficits associated with Alzheimer's disease lesions in mice.
Joint Activity Detection and Channel Estimation for IoT Networks: Phase Transition and Computation-Estimation Tradeoff

Massive device connectivity is a crucial communication challenge for Internet of Things (IoT) networks, which consist of a large number of devices with sporadic traffic. In each coherence block, the serving base station needs to identify the active devices and estimate their channel state information for effective communication. By exploiting the sparsity pattern of data transmission, we develop a structured group sparsity estimation method to simultaneously detect the active devices and estimate the corresponding channels. This method significantly reduces the signature sequence length while supporting massive IoT access. To determine the optimal signature sequence length, we study the phase transition behavior of the group sparsity estimation problem. Specifically, user activity can be successfully estimated with a high probability when the signature sequence length exceeds a threshold; otherwise, it fails with a high probability. The location and width of the phase transition region are characterized via the theory of conic integral geometry. We further develop a smoothing method to solve the high-dimensional structured estimation problem with a given limited time budget. This is achieved by sharply characterizing the convergence rate in terms of the smoothing parameter, signature sequence length and estimation accuracy, yielding a tradeoff between the estimation accuracy and computational cost. Numerical results are provided to illustrate the accuracy of our theoretical results and the benefits of smoothing techniques.
An Application of ASP Theories of Intentions to Understanding Restaurant Scenarios: Insights and Narrative Corpus

Abstract This paper presents a practical application of Answer Set Programming to the understanding of narratives about restaurants. While this task was investigated in depth by Erik Mueller, exceptional scenarios remained a serious challenge for his script-based story comprehension system. We present a methodology that remedies this issue by modeling characters in a restaurant episode as intentional agents. We focus especially on the refinement of certain components of this methodology in order to increase coverage and performance. We present a restaurant story corpus that we created to design and evaluate our methodology.