Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Shape Reconstruction from Sparse Measurements with Neural Implicit Functions

Reconstructing anatomical shapes from sparse or partial measurements relies on prior knowledge of shape variations that occur within a given population. Such shape priors are learned from example shapes, obtained by segmenting volumetric medical images. For existing models, the resolution of a learned shape prior is limited to the resolution of the training data. However, in clinical practice, volumetric images are often acquired with highly anisotropic voxel sizes, e.g. to reduce image acquisition time in MRI or radiation exposure in CT imaging. The missing shape information between the slices prohibits existing methods to learn a high-resolution shape prior. We introduce a method for high-resolution shape reconstruction from sparse measurements without relying on high-resolution ground truth for training. Our method is based on neural implicit shape representations and learns a continuous shape prior only from highly anisotropic segmentations. Furthermore, it is able to learn from shapes with a varying field of view and can reconstruct from various
Drift rather than host or parasite control can explain within-host Wolbachia growth

Within-host parasite density is tightly linked to parasite fitness often determining both transmission success and virulence (parasite-induced harm to the host) (Alizon et al., 2009, Anderson & May, 1982). Parasite density may thus be controlled by selection balancing these conflicting pressures. Actual within-host density regulation may be under host or parasite control, or due to other environmental factors (Wale et al., 2019, Vale et al., 2011, Chrostek et al., 2013). Vertically transmitted parasites may also be more vulnerable to drift associated with bottlenecks between generations, which may also determine within-host population size (Mathe-Hubert et al., 2019, Mira & Moran, 2002).
New Method of Boundary Points Description with Given Accuracy for Objects Recognition

Nowadays digital video and image are made with very high speed. The computing power of modern computers allows the development of new methods to automate the data analysis process. The speed of useful information extracting from this digital data are very important. A review of the papers of recent years shows that the methods and means for obtaining and processing of digital image continue to develop. The article deals with the problem of image processing for analyzing video data. High-resolution videocamera increases the number of contour points and re-assigning points with curves or array of points with given permission variable (accuracy) significantly reduces time of processing. A method for the obtaining of boundary points with a given accuracy to select objects with different levels of detailing is proposed.
Application of RLS-SVM in Identification and Control for Inverse Dynamics of System

To overcome the large memory expense in the process of on-line identification by utilizing support vector machine(SVM), least squares support vector machine (LS-SVM) was combined with recursive least square(RLS), the weigh vector and bias were adjusted on-line by RLS algorithm, and on-line identification of inverse dynamic model of system was realized. Based on the inverse dynamic model acquired, a control algorithm based on recursive least squares support vector machine (RLS-SVM) of inverse dynamics was designed. The parameters of controller were adjusted on-line by RLS algorithm. The simulations on superheated steam temperature identification and control system show that the inverse dynamic model identified has high precision and the controller designed has good control performance and strong adaptability.
Sex makes them sleepy: change in host reproductive status induces diapause in 1 parasitoidsa parasitoid population experiencing harsh winters 2

21 When organisms coevolve, any change in one species can induce phenotypic changes in 22 traits and ecology of the other species. The role such interactions play in ecosystems is central, 23 but their mechanistic bases remain underexplored. Upper trophic level species have to 24 synchronize their life-cycle to both abiotic conditions and to lower trophic level species’ 25 phenology and phenotypic variations. We tested the effect of host seasonal strategy on 26 parasitoid diapause induction by using a holocyclic clone of the pea aphid Acyrthosiphon pisum 27 producing two morphs with either asexual (and sexual morphs that are viviparous females) or 28 sexual ( (i.e. laying embryos) and oviparous females) reproduction (laying eggs), respectively, 29 the latter being only present at the end of the growing season. Aphidius ervi parasitoids from 30 populations of contrasted climatic origin (harsh vs. mild winter areas) were allowed to parasitize 31 each morph in a split-brood design and developing parasitoids were next reared under either 32 fall-like or summer-like temperature-photoperiod conditions. We next examined aspects of the 33 host physiological state by comparing the relative proportion of forty-seven metabolites and 34 lipid reserves in both morphs. produced under the same conditions. We found that oviparous 35 morphs are cues per se for diapause induction; parasitoids entered diapause at higher levels 36 when developing in oviparous hosts (19.4 ± 3.0 %) than in viviparous ones (3.6 ± 1.3 %), under 37 summer-like conditions (i.e., when oviparous aphids appear in the fields). This pattern was only 38 observed in parasitoids from the harsh winter area since low diapause levels were observed in 39 the other population-dependent, suggesting local adaptations to overwintering 40 cues. Metabolomics analyses show parasitoids’ response to be mainly influenced by the host’s 41 physiology, with higher proportion of polyols and sugars, and more fat reserves being found in 42 oviparous morphs. Host quality thus varies across the seasons. and represents one of the 43 multiple environmental parameters affecting parasitoid diapause. Our results underline strong 44 coevolutionary processes between hosts and parasitoids in their area of origin, likely leading to 45 phenological synchronization, and we point out theirthe importance of such bottom-up effects 46 for trait-expression, and for the provision of ecosystem serviceservices such as biological 47 control in the context of climate change. 48 49 that
Observation and motor imagery balance tasks evaluation: An fNIRS feasibility study

In this study, we aimed at exploring the feasibility of functional near-infrared spectroscopy (fNIRS) for studying the observation and/or motor imagination of various postural tasks. Thirteen healthy adult subjects followed five trials of static and dynamic standing balance tasks, throughout three different experimental setups of action observation (AO), a combination of action observation and motor imagery (AO+MI), and motor imagery (MI). During static and dynamic standing tasks, both the AO+MI and MI experiments revealed that many channels in prefrontal or motor regions are significantly activated while the AO experiment showed almost no significant increase in activations in most of the channels. The contrast between static and dynamic standing tasks showed that with more demanding balance tasks, relative higher activation patterns were observed, particularly during AO and in AO+MI experiments in the frontopolar area. Moreover, the AO+MI experiment revealed a significant difference in premotor and supplementary motor cortices that are related to balance control. Furthermore, it has been observed that the AO+MI experiment induced relatively higher activation patterns in comparison to AO or MI alone. Remarkably, the results of this work match its counterpart from previous functional magnetic resonance imaging studies. Therefore, they may pave the way for using the fNIRS as a diagnostic tool for evaluating the performance of the non-physical balance training during the rehabilitation period of temporally immobilized patients.
Whole genome sequencing of Klebsiella pneumoniae clinical isolates sequence type 627 isolated from Egyptian patients

Klebsiella pneumoniae is considered a threat to public health especially due to multidrug resistance emergence. It is largely oligoclonal based on multi-locus sequence typing (MLST); in Egypt, ST 627 was recently detected. Despites the global dissemination of this ST, there is still paucity of information about it. Herein, we used 4 K. pneumoniae ST627 for whole genome sequencing utilizing an Illumina MiSeq platform. Genome sequences were examined for resistance and virulence determinants, capsular types, plasmids, insertion sequences, phage regions, and Clustered Regularly Interspaced Palindromic Repeats (CRISPR) regions using bioinformatic analysis. The molecular characterization revealed 15 and 65 antimicrobial resistance and virulence genes, respectively. Resistance genes such as tet(D), aph(3’’)-Ib, aph(6)-Id, blaTEM-234, fosA, and fosA6; were mainly responsible for tetracycline, aminoglycoside, and fosfomycin resistance; respectively. The capsular typing revealed that the four strains are KL-24 and O1v1. One plasmid was found in all samples known as pC17KP0052-1 and another plasmid with accession no. NZ_CP032191. 1 was found only in K90. IncFIB(K) and IncFII(K) are two replicons found in all samples, while ColRNAI replicon was found only in K90. Entero P88, Salmon SEN5, and Klebsi phiKO2 intact phage regions were identified. All samples harbored CRISPR arrays including CRISPR1 and CRISPR2. Our results shed light on critical tasks of mobile genetic elements in ST 627 in antibiotic resistance spreading.
Nutritional self-care practices and skills of patients with diabetes mellitus: A study at a tertiary hospital in Ghana

Introduction Nutritional management decreases and/or prevents the complications and deaths associated with diabetes mellitus. However, the majority of patients living with diabetes do not engage in optimal nutritional management of diabetes because they see it as the most difficult aspect of managing the condition. This study aimed to explore and describe the practices and skills on nutritional management of diabetes mellitus among patients living with diabetes attending a Ghanaian hospital. Materials and methods This study employed an exploratory, descriptive qualitative research design. Fifteen participants were recruited using purposive sampling, and interviewed with a semi-structured interview guide. Content analysis was performed on the data gathered, following which three main themes emerged. Results More than two-thirds of the participants of this study had adequate meal planning skills, ate the right quantity of foods, engaged in healthy eating habits, and consumed healthy sources of carbohydrates, fats and protein when eating. However, more than half of the participants had insufficient knowledge and skills in the reading and usage of food labels. Conclusions The participants of this study largely engaged in optimal nutritional management of diabetes due to their healthy dietary practices and preferences. It is recommended that health care professionals in Ghana find practical and robust ways to factor the reading and usage of food labels into the care and management of patients with diabetes.
COMMIT: Consideration of metabolite leakage and community composition improves microbial community reconstructions

Composition and functions of microbial communities affect important traits in diverse hosts, from crops to humans. Yet, mechanistic understanding of how metabolism of individual microbes is affected by the community composition and metabolite leakage is lacking. Here, we first show that the consensus of automatically generated metabolic reconstructions improves the quality of the draft reconstructions, measured by comparison to reference models. We then devise an approach for gap filling, termed COMMIT, that considers metabolites for secretion based on their permeability and the composition of the community. By applying COMMIT with two soil communities from the Arabidopsis thaliana culture collection, we could significantly reduce the gap-filling solution in comparison to filling gaps in individual reconstructions without affecting the genomic support. Inspection of the metabolic interactions in the soil communities allows us to identify microbes with community roles of helpers and beneficiaries. Therefore, COMMIT offers a versatile fully automated solution for large-scale modelling of microbial communities for diverse biotechnological applications.
Processing time affects sequential memory performance beginning at the level of visual encoding

Electrophysiological studies have demonstrated that theta-band activity is useful for investigating neural mechanisms of memory. However, mechanisms specifically driving memory performance remain poorly understood. In sequential memory, performance can be artificially attenuated by shortening the inter-stimulus interval (ISI) between memory item presentations. Therefore, we sought to clarify the mechanisms of sequential memory performance by analyzing theta-band (4–8 Hz) activity recorded via magnetoencephalogram in 33 participants during performance of a sequential memory task where memory items were presented at either slow or fast rates in accordance with longer or shorter ISIs, respectively. Particularly in the slow task, theta activity clearly modulated in accordance with the presentation of memory items. Common cortical target regions in the occipital and frontal cortex were identified in both tasks and related to visual encoding and memory maintenance, respectively. Compared to the slow task, occipital-theta activity was significantly lower in the fast task from the midterm until the ending of encoding, in correspondence with significantly lower recall for memory items in this same period. Meanwhile, despite a loss of clarity in responsiveness to individual memory items in the fast task, frontal-theta activity was not different between tasks and exhibited particularly strong responses in both tasks during the holding period prior to recall. Our results indicate that shorter processing time erodes sequential memory performance beginning at the level of visual encoding.
Provide energy-aware routing protocol in wireless sensor networks using bacterial foraging optimization algorithm and mobile sink

Wireless sensor networks (WSNs) include small sensor nodes with battery and processing power and limited memory units then improving power consumption is a major design challenge for any sensor network. In this paper, a new algorithm for routing in the wireless sensor network is proposed using the ultra-innovative algorithm for bacterial Foraging and mobile sink, which leads to energy efficiency. In the proposed method, the number of sensor nodes is determined according to two criteria: the amount of energy on the battery surface and the distance to the sink ahead, which leads to the formation of regular clusters in the network. Nodes adopt a multi-step routing scheme within the network to communicate with the sink. also, the mobile sink is used to balance the load and help consume uniform energy throughout the network. The simulation results show better performance of the proposed method in terms of energy consumption by 17.99%, throughput rate by 30.04%, end-to-end delay by 46.04%, signal-to-noise ratio by 32.81%, delivery rate successfully Data to the sink is 0.80 times higher than the AFSRP (Artificial Fish Swarm Routing Protocol).
Galectin-1 and -3 in high amounts inhibit angiogenic properties of human retinal microvascular endothelial cells in vitro

Purpose Galectin-1 and -3 are β-galactoside binding lectins with varying effects on angiogenesis and apoptosis. Since in retinal pigment epithelial cells high amounts of human recombinant galectin (hr-GAL)1 and 3 inhibit cell adhesion, migration and proliferation, we investigated if hr-GAL1 and 3 have homologous effects on human retinal microvascular endothelial cells (HRMEC) in vitro. Methods To investigate the effect of galectin-1 and -3 on HRMEC, proliferation, apoptosis and viability were analyzed after incubation with 30, 60 and 120 μg/ml hr-GAL1 or 3 by BrdU-ELISA, histone-DNA complex ELISA, live/dead staining and the WST-1 assay, respectively. Further on, a cell adhesion as well as tube formation assay were performed on galectin-treated HRMEC. Migration was investigated by the scratch migration assay and time-lapse microscopy. In addition, immunohistochemical staining on HRMEC for β-catenin, galectin-1 and -3 were performed and β-catenin expression was investigated by western blot analysis. Results Incubation with hr-GAL1 or 3 lead to a decrease in proliferation, migration, adhesion and tube formation of HRMEC compared to the untreated controls. No toxic effects of hr-GAL1 and 3 on HRMEC were detected. Intriguingly, after treatment of HRMEC with hr-GAL1 or 3, an activation of the proangiogenic Wnt/β-catenin signaling pathway was observed. However, incubation of HRMEC with hr-GAL1 or 3 drew intracellular galectin-1 and -3 out of the cells, respectively. Conclusion Exogenously added hr-GAL1 or 3 inhibit angiogenic properties of HRMEC in vitro, an effect that might be mediated via a loss of intracellular endogenous galectins.
Mokey: enabling narrow fixed-point inference for out-of-the-box floating-point transformer models

Increasingly larger and better Transformer models keep advancing state-of-the-art accuracy and capability for Natural Language Processing applications. These models demand more computational power, storage, and energy. Mokey reduces the footprint of state-of-the-art 32-bit or 16-bit floating-point transformer models by quantizing all values to 4-bit indexes into dictionaries of representative 16-bit fixed-point centroids. Mokey does not need fine-tuning, an essential feature as often the training resources or datasets are not available to many. Exploiting the range of values that naturally occur in transformer models, Mokey selects centroid values to also fit an exponential curve. This unique feature enables Mokey to replace the bulk of the original multiply-accumulate operations with narrow 3b fixed-point additions resulting in an area- and energy-efficient hardware accelerator design. Over a set of state-of-the-art transformer models, the Mokey accelerator delivers an order of magnitude improvements in energy efficiency over a Tensor Cores-based accelerator while improving performance by at least 4× and as much as 15× depending on the model and on-chip buffering capacity. Optionally, Mokey can be used as memory compression assist for any other accelerator transparently stashing wide floating-point or fixed-point activations or weights into narrow 4-bit indexes. Mokey proves superior to prior state-of-the-art quantization methods for Transformers.
Iterative Implicit Gradients for Nonconvex Optimization with Variational Inequality Constraints

We propose an implicit gradient based scheme for a constrained optimization problem with nonconvex loss function, which can be potentially used to analyze a variety of applications in machine learning, including meta-learning, hyperparameter optimization, and reinforcement learning. The proposed algorithm is based on the iterative differentiation (ITD) strategy. We extend the convergence and rate analysis of the current literature of bilevel optimization to a constrained bilevel structure with the motivation of learning under constraints. For addressing bilevel optimization using any firstorder scheme, it involves the gradient of the inner-level optimal solution with respect to the outer variable (implicit gradient). In this paper, taking into account of a possible large-scale structure, we propose an efficient way of obtaining the implicit gradient. We further provide error bounds with respect to the true gradients. Further, we provide nonasymptotic rate results.
Subjective and Objective Analysis of Streamed Gaming Videos

The rising popularity of online User-Generated-Content (UGC) in the form of streamed and shared videos, has hastened the development of perceptual Video Quality Assessment (VQA) models, which can be used to help optimize their delivery. Gaming videos, which are a relatively new type of UGC videos, are created when skilled gamers post videos of their gameplay. These kinds of screenshots of UGC gameplay videos have become extremely popular on major streaming platforms like YouTube and Twitch. Synthetically-generated gaming content presents challenges to existing VQA algorithms, including those based on natural scene/video statistics models. Synthetically generated gaming content presents different statistical behavior than naturalistic videos. A number of studies have been directed towards understanding the perceptual characteristics of professionally generated gaming videos arising in gaming video streaming, online gaming, and cloud gaming. However, little work has been done on understanding the quality of UGC gaming videos, and how it can be characterized and predicted. Towards boosting the progress of gaming video VQA model development, we conducted a comprehensive study of subjective and objective VQA models on UGC gaming videos. To do this, we created a novel UGC gaming video resource, called the LIVE-YouTube Gaming video quality (LIVE-YT-Gaming) database, comprised of 600 real UGC gaming videos. We conducted a subjective human study on this data, yielding 18,600 human quality ratings recorded by 61 human subjects. We also evaluated a number of state-of-the-art (SOTA) VQA models on the new database, including a new one, called GAME-VQP, based on both natural video statistics and CNN-learned features. To help support work in this field, we are making the new LIVE-YT-Gaming Database, publicly available through the link: https://live.ece. utexas.edu/research/LIVE-YT-Gaming/index.html .
Estimating Viral Genetic Linkage Rates in the Presence of Missing Data

Although the interest in the the use of social and information networks has grown, most inferences on networks assume the data collected represents the complete. How-ever, when ignoring missing data, even when missing completely at random, this results in bias for estimators regarding inference network related parameters. In this paper, we focus on constructing estimators for the probability that a randomly selected node has node has at least one edge under the assumption that nodes are ∗ R37 missing completely at random along with their corresponding edges. In addition, is-sues also arise in obtaining asymptotic properties for such estimators, because linkage indicators across nodes are correlated preventing the direct application of the Central Limit Theorem and Law of Large Numbers. Using a subsampling approach, we present an improved estimator for our parameter of interest that accommodates for missing data. Utilizing the theory U-statistics, we derive consistency and asymptotic normality of the proposed estimator. This approach decreases the bias in estimating our parameter of interest. We illustrate our approach using the HIV viral strains from a large cluster-randomized trial of a combination HIV prevention intervention the Botswana Combination Prevention Project (BCPP).