Miracle-dz/UniFAR-FTUs huggingface dataset:
CD-DFT: A Current-Difference Design-for-Testability to Detect Short Defects of STT-MRAM Under Process Variations

This work presents an efficient test technique for detecting resistive short defects in STT-MRAM arrays. The proposed technique is based on monitoring the current mismatch flowing into and out of the cell caused by a weak or strong short defect. This technique is used to propose a low area overhead Design-for-Testability (DFT) circuit to employ in STT-MRAM arrays to distinguish defect-free cells from faulty ones. The operation of the proposed test approach is resilient to the parameter uncertainties of the array circuit induced by process variations. The variations, however, may lower defect detection ranges. The efficacies of the proposed DFT technique under the nominal and the process variation cases are studied. Simulation results indicate that the proposed DFT circuit reduces the number of test escapes and improves the fault coverage by a factor of at least <inline-formula> <tex-math notation="LaTeX">$10 \times $ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$5 \times $ </tex-math></inline-formula>) under short defect to ground (short defect to <inline-formula> <tex-math notation="LaTeX">${V}_{DD}$ </tex-math></inline-formula>) cases compared to the corresponding maximum ones detected by conventional test schemes. The technique works through a single read operation with a negligible area overhead, especially, in large size arrays.
A K-Band Broadband Circularly Polarized Slot Antenna Based on L-Shaped Waveguide Cavity

A new broadband circularly polarized antenna is presented for K-band application in this letter. The circularly polarized radiation is realized by orthogonal-polarized slots based on a special structure of the L-shaped waveguide cavity. To verify the proposed idea, a single element and a 2 × 4-element array are designed, fabricated, and measured. The simulations are agree well with experiments. The results show that the single element achieves the 10 dB impedance bandwidth of 27.5% (18.5–24.5 GHz) and the 3 dB axial ratio bandwidth of 7.1% (19.6–21.2 GHz), while the 2 × 4 element array achieves the 10 dB impedance bandwidth of 27.1% (18.5–24 GHz) and the 3 dB axial ratio bandwidth of 18.7% (19.3–22.6 GHz). Moreover, all-metal structure of the proposed antenna is very appropriate for tough environment applications.
QoS-Based Data Aggregation and Resource Allocation Algorithm for Machine Type Communication Devices in Next-Generation Networks

Machine Type Communication (MTC) becomes one of enablers of the internet of things, it faces many challenges in its integration with human-to-human (H2H) communication methods. To this aim, the Long Term Evolution (LTE) needs some adaptation in the scheduling algorithms that assign resources efficiently to both MTC devices (MTCDs) and H2H users. The minimum amount of LTE resources that can be assigned to one user is much larger than the requirements of a single MTCD. In this paper, a QoS-enabled algorithm is proposed to aggregate MTCD traffic coming from many sources at the Relay Node (RN) that classifies and aggregates the MTCD traffic based on the source type and delay requirements. In this study, three types of MTCD and one H2H sources will be considered. Each type of MTCD traffic will be grouped into a separate queue, and will be served with the appropriate priority. Resources are then assigned to the aggregated MTC traffic instead of an individual assignment for each MTCD, while the H2H users will be directly connected to the LTE. Two schemes of resource partitioning and sharing between the MTCDs and the H2H users will be considered: one proportional and the other moving-boundary. Simulation models will be built to evaluate the proposed algorithms. While the obtained results for the first scheme showed a clear improvement in LTE resource utilization for the MTCDs, a negative effect was noticed in the performance of the H2H users. The second scheme achieved a positive improvement for both MTCDs and H2H users.
Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters

The coronavirus disease 2019 (COVID-19) after outbreaking in Wuhan increasingly spread throughout the world. Fast, reliable, and easily accessible clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. The objective of the study was to develop and validate an early scoring tool to stratify the risk of death using readily available complete blood count (CBC) biomarkers. A retrospective study was conducted on twenty-three CBC blood biomarkers for predicting disease mortality for 375 COVID-19 patients admitted to Tongji Hospital, China from January 10 to February 18, 2020. Machine learning based key biomarkers among the CBC parameters as the mortality predictors were identified. A multivariate logistic regression-based nomogram and a scoring system was developed to categorize the patients in three risk groups (low, moderate, and high) for predicting the mortality risk among COVID-19 patients. Lymphocyte count, neutrophils count, age, white blood cell count, monocytes (%), platelet count, red blood cell distribution width parameters collected at hospital admission were selected as important biomarkers for death prediction using random forest feature selection technique. A CBC score was devised for calculating the death probability of the patients and was used to categorize the patients into three sub-risk groups: low (<=5%), moderate (>5% and <=50%), and high (>50%), respectively. The area under the curve (AUC) of the model for the development and internal validation cohort were 0.961 and 0.88, respectively. The proposed model was further validated with an external cohort of 103 patients of Dhaka Medical College, Bangladesh, which exhibits in an AUC of 0.963. The proposed CBC parameter-based prognostic model and the associated web-application, can help the medical doctors to improve the management by early prediction of mortality risk of the COVID-19 patients in the low-resource countries.
The correlation between cell and nucleus size is explained by an eukaryotic cell growth model

In eukaryotes, the cell volume is observed to be strongly correlated with the nuclear volume. The slope of this correlation depends on the cell type, growth condition, and the physical environment of the cell. We develop a computational model of cell growth and proteome increase, incorporating the kinetics of amino acid import, protein/ribosome synthesis and degradation, and active transport of proteins between the cytoplasm and the nucleoplasm. We also include a simple model of ribosome biogenesis and assembly. Results show that the cell volume is tightly correlated with the nuclear volume, and the cytoplasm-nucleoplasm transport rates strongly influences the cell growth rate as well as the cytoplasm/nucleoplasm ratio. Ribosome assembly and the ratio of ribosomal proteins to mature ribosomes also influence the cell volume and the cell growth rate. We find that in order to regulate the cell growth rate and the cytoplasm/nucleoplasm ratio, the cell must optimally control groups of kinetic parameters together, which could explain the quantitative roles of canonical growth pathways. Finally, using an extension of our model and single cell RNAseq data, it is possible to construct a detailed proteome distribution, provided that a quantitative cell division mechanism is known. Author summary We develop computational model of cell proteome increase and cell growth to compute the cell volume to nuclear volume ratio. The model incorporates essential kinetics of protein and ribosome synthesis/degradation, and their transport across the nuclear envelope. The model also incorporates ribosome biogenesis and assembly. The model identifies the most important parameters in determining the cell growth rate and the nucleoplasm/cytoplasm ratio, and provides a computational starting point to construct the cell proteome based on the RNAseq data.
Sequential transmission of task-relevant information in cortical neuronal networks

During auditory task performance, cortical processing of task-relevant information enables mammals to recognize sensory input and flexibly select behavioral responses. In mouse auditory cortex, small neuronal networks encode behavioral choice during a pure-tone detection task, but it is poorly understood how neuronal networks encode behavioral choice during a pure-tone discrimination task where tones have to be categorized into targets and non-targets. While the interactions between networked neurons are thought to encode behavioral choice, it remains unclear how patterns of neuronal network activity indicate the transmission of task-relevant information within the network. To this end, we trained mice to behaviorally discriminate target vs. non-target pure-tones while we used in vivo 2-photon imaging to record neuronal population activity in primary auditory cortex layer 2/3. We found that during task performance, a specialized subset of neurons transiently encoded intersection information, i.e., sensory information that was used to inform behavioral choice. Granger causality analysis showed that these neurons formed functional networks in which task-relevant information was transmitted sequentially between neurons. Differences in network structure between target and non-target sounds encoded behavioral choice. Correct behavioral choices were associated with shorter timescale communication between neurons. In summary, we find that specialized neuronal populations in auditory cortex form functional networks during auditory task performance whose structures depend on both sensory input and behavioral choice.
Multiple sclerosis patients have an altered gut mycobiome and increased fungal to bacterial richness

Trillions of microbes such as bacteria, fungi, and viruses exist in the healthy human gut microbiome. Although gut bacterial dysbiosis has been extensively studied in multiple sclerosis (MS), the significance of the fungal microbiome (mycobiome) is an understudied and neglected part of the intestinal microbiome in MS. The aim of this study was to characterize the gut mycobiome of patients with relapsing-remitting multiple sclerosis (RRMS), compare it to healthy controls, and examine its association with changes in the bacterial microbiome. We characterized and compared the mycobiome of 20 RRMS patients and 33 healthy controls (HC) using Internal Transcribed Spacer 2 (ITS2) and compared mycobiome interactions with the bacterial microbiome using 16S rRNA sequencing. Our results demonstrate an altered mycobiome in RRMS patients compared with HC. RRMS patients showed an increased abundance of Basidiomycota and decreased Ascomycota at the phylum level with an increased abundance of Candida and Epicoccum genera along with a decreased abundance of Saccharomyces compared to HC. We also observed an increased ITS2/16S ratio, altered fungal and bacterial associations, and altered fungal functional profiles in MS patients compared to HC. This study demonstrates that RRMS patients had a distinct mycobiome with associated changes in the bacterial microbiome compared to HC. There is an increased fungal to bacterial ratio as well as more diverse fungal-bacterial interactions in RRMS patients compared to HC. Our study is the first step towards future studies in delineating the mechanisms through which the fungal microbiome can influence MS disease.
Breast tumor Insulin-like growth factor receptor regulates cell adhesion and metastasis: Alignment of mouse single cell and human breast cancer transcriptomics

The acquisition of a metastatic phenotype is the critical event that determines patient survival from breast cancer. Several receptor tyrosine kinases have functions both in promoting and inhibiting metastasis in breast tumors. Although the insulin-like growth factor 1 receptor (IGF-1R) has been considered a target for inhibition in breast cancer, low levels of IGF-1R expression are associated with worse overall patient survival. To determine how reduced IGF-1R impacts tumor phenotype, we used weighted gene correlation network analysis (WGCNA) of METABRIC patient data and identified gene modules specific to cell cycle, adhesion, and immune cell signaling inversely correlated with IGF-1R expression in human breast cancers. Integration of human patient data with data from mouse tumors revealed similar pathways necessary for promoting metastasis in basal-like tumors with reduced signaling or expression of the IGF-1R. Functional analyses revealed the basis for the enhanced metastatic phenotype including alterations in E- and P-cadherins.
The information signature of diverging lineages

Divergence and speciation are the generators of biodiversity on Earth. Yet, we still seek to understand why species are unevenly distributed across taxa and geographic settings. This is partly because we lack a way to directly compare aspects of population divergence across unrelated species and an integrative quantitative framework for how geographical setting relates to genetic divergence. Here we postulate that, based on communications theory, there is a communications channel between Earth’s surface and the structuring of genomic information among populations. Each species is a separate receiver of this message. Organism traits determine the clarity of that channel. Calculating the partial information decomposition (PID) of genetic metrics under different evolutionary scenarios can measure the signature of this communication. We show in silico how the informational decomposition of 97,200 genetic lattices varies as lineages diverge. The decomposed nodes of Tajima’s D, θW, and π show strong context-dependent information signatures, while FST was least informative. Specific decompositions detected whether lineages divergence with or without gene flow better than current SFS-based tools. The study of information in an evolutionary genomic and landscape context suggests it may be a helpful framework and for understanding the diversity of life across Earth and their coevolution.
Dynamic analysis of lower leg muscles response to whole body vibration stimulation at different frequencies and postures: implications for training

Purpose To characterise the mechanical and neuromuscular response of lower limb muscles in subjects undergoing Whole Body Vibration (WBV) at different frequencies while holding two static postures. Methods Twenty-five participants underwent WBV at 15, 20, 25 and 30 Hz while holding a static ‘hack squat’ and on ‘fore feet’ posture. Surface electromyography (sEMG) and soft tissue accelerations were collected from Gastrocnemius Lateralis (GL), Soleus (SOL) and Tibialis Anterior (TA) muscles. Results Only specific WBV settings led to a significant increase in muscle contraction. Specifically, the WBV-induced activation of SOL and GL was maximal in fore-feet and in response to higher frequencies. Estimated displacement at muscle bellies revealed a resonant pattern never highlighted before. After stimulation starts, muscle oscillation reaches a peak followed by a drop and a further stabilisation (few seconds after the peak) that suggests the occurrence of a neuromuscular activation to reduce the vibration-induced oscillation. Conclusion Lower leg muscles need a response time to tune to a vibratory stimulation, which discourages the use of dynamic exercises on vibrating platforms. To maximize calf muscle response to WBVs, a stimulation frequency in the range of 25-30 Hz and an ‘on fore feet’ posture are recommended.
Thalamic bursts modulate cortical synchrony locally to switch between states of global functional connectivity in a cognitive task

Performing a cognitive task requires going through a sequence of functionally diverse stages. Although it is typically assumed that these stages are characterized by distinct states of cortical synchrony that are triggered by sub-cortical events, little reported evidence supports this hypothesis. To test this hypothesis, we first identified cognitive stages in single-trial MEG data of an associative recognition task, showing with a novel method that each stage begins with local modulations of synchrony followed by a state of directed functional connectivity. Second, we developed the first whole-brain model that can simulate cortical synchrony throughout a task. The model suggests that the observed synchrony is caused by thalamocortical bursts at the onset of each stage, targeted at cortical synapses and interacting with the structural anatomical connectivity. These findings confirm that cognitive stages are defined by distinct states of cortical synchrony and explains the network-level mechanisms necessary for reaching stage-dependent synchrony states. Author summary A novel machine-learning method was applied to unveil the dynamics of local and cortex-wide neural coordination underlying the fundamental cognitive processes involved in a memory task. To explain how neural activity – and ultimately behavior – was coordinated throughout the task, we developed a whole-brain model that incorporates cognitive mechanisms, anatomy, and neural biophysics. Similar models are used with resting state data, however, simulating a cognitive task remained elusive. The model showed that sub-cortical pulses at the onset of cognitive processes – as hypothesized by cognitive and neurophysiological theories – were sufficient to switch between the states of neural coordination observed. These findings have implications to understand goal-directed cognitive processing and the mechanisms to reach states of neural coordination.
Quantifying biochemical reaction rates from static population variability within complex networks

Quantifying biochemical reaction rates within complex cellular processes remains a key challenge of systems biology even as high-throughput single-cell data have become available to characterize snapshots of population variability. That is because complex systems with stochastic and non-linear interactions are difficult to analyze when not all components can be observed simultaneously and systems cannot be followed over time. Instead of using descriptive statistical models, we show that incompletely specified mechanistic models can be used to translate qualitative knowledge of interactions into reaction rate functions from covariability data between pairs of components. This promises to turn a globally intractable problem into a sequence of solvable inference problems to quantify complex interaction networks from incomplete snapshots of their stochastic fluctuations.
Functional connectivity gradients as a common neural architecture for predictive processing in the human brain

Predictive processing is emerging as a common neurocomputational hypothesis to account for diverse psychological functions subserved by a brain, enabling characterization of computational capacities of its distinct substructures. However, the literature is currently lacking a systems-level framework for understanding brain structure-function relationships based on the unified computational principles. Here, we contribute to this framework by examining gradients of functional connectivity as a low dimensional spatial representation of functional variation within a given structure. Specifically, we investigated functional connectivity gradients in the cerebral cortex, the cerebellum, and the hippocampus using resting-state functional MRI data collected from large samples of healthy young adults. We then evaluated the degree to which these structures share common principles of functional organization by assessing the correspondence of their gradients. We show that the organizing principles of these structures primarily follow two functional gradients consistent with the existing accounts of predictive processing: A model-error gradient that describes the flow of prediction and prediction error signals, and a model-precision gradient that differentiates regions involved in the representation and attentional modulation of such signals in the cerebral cortex. Using these gradients, we also demonstrate triangulation of functional connectivity involving distinct subregions of the three structures, suggesting the existence of parallel functional circuits that may subserve different aspects of predictive processing in the brain. These findings allow formulation of novel computational hypotheses about the functional relationships between the cerebral cortex, the cerebellum, and the hippocampus that may be instrumental for understanding the brain’s dynamics within its large-scale predictive architecture.
Domestication reshaped the genetic basis of inbreeding depression in a maize landrace compared to its wild relative, teosinte

Inbreeding depression is the reduction in fitness and vigor resulting from mating of close relatives observed in many plant and animal species. The extent to which the genetic load of mutations contributing to inbreeding depression is due to rare large-effect variation versus potentially more common variants with very small individual effects is unknown and may be affected by population history. We compared the effects of outcrossing and self-fertilization on 18 traits in a landrace population of maize, which underwent a population bottleneck during domestication, and a neighboring population of its wild relative teosinte. Inbreeding depression was greater in maize than teosinte for 15 of 18 traits, congruent with the greater segregating genetic load predicted from sequence data in the maize population. For many traits - and more commonly in maize - genetic variation among selffertilized families was less than expected based on additive and dominance variance estimated in outcrossed families, suggesting that a negative covariance between additive and homozygous dominance effects limits the variation available to selection under partial inbreeding. We identified quantitative trait loci (QTL) representing large-effect rare variants carried by only a single parent, which were more important in teosinte than maize. Teosinte also carried more putative juvenile-acting lethal variants identified by segregation distortion. These results suggest a mixture of mostly polygenic, smalleffect recessive variation underlying inbreeding depression, with an additional contribution from rare larger-effect variants that was more important in teosinte but depleted in maize following to the domestication bottleneck. Purging associated with the maize domestication bottleneck may have selected against large effect variants, but polygenic load is harder to purge and segregating mutational burden increased in maize compared to teosinte.
Right fronto-parietal networks mediate the neurocognitive benefits of enriched environments

Exposure to enriched environments (EE) throughout a lifetime, providing so called reserve, protects against cognitive decline in later years. It has been hypothesised that high levels of alertness necessitated by EE might strengthen the right fronto-parietal networks (FPN) to facilitate this neurocognitive resilience. We have previously shown that EE offset age-related deficits in selective attention by preserving grey matter within right fronto-parietal regions. Here, using neurite orientation dispersion and density imaging (NODDI), we examined the relationship between EE, microstructural properties of fronto-parietal white matter association pathways (three branches of the superior longitudinal fasciculus, SLF), structural brain health (atrophy), and attention (alertness, orienting and executive control) in a group of older adults. We show that EE is associated with a lower orientation dispersion index (ODI) within the right SLF1 which in turn mediates the relationship between EE and alertness, as well as grey- and white-matter atrophy. This suggests that EE may induce white matter plasticity (and prevent age-related dispersion of axons) within the right FPN to facilitate the preservation of neurocognitive health in later years.