Miracle-dz/UniFAR-FTUs huggingface dataset:
SER Analysis of Adaptive Threshold-Based Relay Selection With Limited Feedback for Type II Relay

In this paper, via the exact closed-form expressions, we statistically investigate the symbol-error-rate performance of the limited feedback-based adaptive threshold-based relay selection (ATRS) scheme under multiple type II relay environments. Our analytical results are thoroughly validated by cross-checking them against the results obtained via the Monte Carlo simulations. Some selected results show that the performance degradation caused by minimizing the feedback can be compensated by refining the feedback information, specifically by applying a 1-bit increase in terms of the feedback data rate and an additional single-comparison process in terms of complexity. Note that our derived results are valid for both symmetric and asymmetric link conditions. Additionally, the analytical results can be easily evaluated numerically with the standard mathematical packages. Furthermore, the analytical framework and the derived results can be applied for the expansion of the $n$ -bit feedback-based ATRS scheme. Finally, for practical consideration, we provide useful discussion on the plausibility that the extended scheme can be implemented with only a 1-bit feedback.
Interacting with a “Transparent” Upper-Limb Exoskeleton: A Human Motor Control Approach

Establishing a symbiotic relationship between a human and a exoskeleton is the end goal in many applications in order to provide benefits to the user. However, the literature focusing on the human side of human-exoskeleton interaction has remained less exhaustive than the literature focusing on the design (hardware/software) of the exoskeleton device itself. It is, though, essential to understand how a human adapts his motor control when interacting with an exoskeleton. Motor adaptation is an implicit process carried out by the central nervous system when the body encounters a perturbation, a paradigm that has been extensively studied in the field of human motor control research. When wearing an exoskeleton, even “as-transparent-as-possible”, contact/interaction forces may impact well-known motor control laws in a way that may be detrimental to the user, and even compromise usability in real applications. The present paper investigates how interaction with a backdrivable upper-limb exoskeleton (ABLE) set in “transparent” mode of control affects the kinematics/dynamics of human movement in a simple task. We find that important motor control features are preserved when moving with ABLE but an overall movement slowness occurs, likely as a response to increased inertia according to optimal control simulations. Such a human motor control approach illustrates one possible way to assess the degree of symbiosis between human and exoskeleton, i.e. by grounding on well-known findings in motor control research.
Optimization of Neural Network Computation with use of Residual Number System for Tasks of Design of Neural Network Systems of Automatic Control

The article is devoted to the description of approaches to the solution of the actual problem of optimization of neural network calculations using the non-position number system (residual number system) for design problems of neural network automatic control systems. The problems arising in neural network control systems of industrial objects characterized by astatic properties are identified and analyzed. The approach proposed to solve these problems is confirmed by an example of solving a real problem.
Performance of a Classifier Based on Time-Domain Features for Incipient Fault Detection in Inverter Drives

In the field of industrial informatics and fault diagnostic applications, an important topic is about detecting and locating fault in an industrial machinery in its incipient stage. This enables avoiding severe faults that are often followed by unscheduled process interruptions and escalated maintenance cost. Inverter-driven induction motor drives, among other irregularities, suffer from degradation of switching device (such as insulated gate bipolar transistor (IGBT)) junctions giving rise to spurious resistance at those junction points. The present research attempts to identify those incipient yet progressive faults from motor current signature by a time-domain computation technique for feature extraction followed by the use of support vector machine classifier. Additionally, it is observed that spurious resistance faults in an inverter can be grouped in a particular pattern using mean current vector distribution. Different experimental results confirm that the proposed method is robust to change in motor parameters and load while giving satisfactory classification accuracy.
Entire Domain Basis Function With Accurate Edge Condition for Rectangular Microstrip Antennas

This letter presents the study of a segmented entire domain basis function suitable for fast analysis of rectangular microstrip antennas with the method of moments (MoM). In contrast to other basis functions, whereby the edge condition is valid only for narrow lines, the proposed basis function can be applied to microstrip lines and antennas with arbitrary width. For validation, a probe-fed rectangular microstrip antenna was modeled and the results were validated with measurements. The deviation of the calculated resonance frequency from the measured value could be reduced to less than 0.1%. This has been obtained with the use of only two expansion modes along each direction for the construction of the MoM matrices.
Robust finite-time synchronization of non-identical fractional-order hyperchaotic systems and its application in secure communication

This paper proposes a novel adaptive sliding mode control ( SMC ) method for synchronization of non-identical fractional-order ( FO ) chaotic and hyper-chaotic systems. Under the existence of system uncertainties and external disturbances, finite-time synchronization between two FO chaotic and hyperchaotic systems is achieved by introducing a novel adaptive sliding mode controller ( ASMC ). Here in this paper, a fractional sliding surface is proposed. A stability criterion for FO nonlinear dynamic systems is introduced. Sufficient conditions to guarantee stable synchronization are given in the sense of the Lyapunov stability theorem. To tackle the uncertainties and external disturbances, appropriate adaptation laws are introduced. Particle swarm optimization ( PSO ) is used for estimating the controller parameters. Finally, finite-time synchronization of the FO chaotic and hyper-chaotic systems is applied to secure communication.
Competition for Chiasma Formation Between Identical and Homologous (But Not Identical) Chromosomes in Synthetic Autotetraploids of Arabidopsis thaliana

Polyploid organisms provide additional opportunities to study meiosis in a more complex context since more than two potential homologous chromosomes are available. When the chromosome complement of a diploid individual is duplicated, each chromosome is accompanied by one identical and two homologous chromosomes within the same nucleus. In this situation, a competition in pairing/synapsis/chiasma formation between identical and homologous (but not necessarily identical) chromosomes can occur. Several studies have been conducted in different species to address whether there are preferences in crossover formation between identical rather than homologous chromosomes. In this study, multivalent and chiasma frequencies were cytologically analyzed in synthetic autotetraploids of Arabidopsis thaliana including the accessions Col, Ler, and the Col/Ler hybrid. Fluorescence in situ hybridization was conducted to identify each chromosome at metaphase I. The new Col and Ler tetraploids showed high multivalent frequencies, exceeding the theoretical 66.66% expected on a simple random end-pairing model, thus indicating that there are more than two autonomous synaptic sites per chromosome despite their small size. However, a significant excess of bivalent pairs was found in the Col/Ler hybrid, mainly due to the contribution of chromosomes 2 and 3. The mean chiasma frequencies of the three artificial autotetraploids were about twofold the corresponding mean cell chiasma frequencies of their diploid counterparts. The relative contribution of each chromosome to the total chiasma frequency was similar in the three genotypes, with the exception of a lower contribution of chromosome 3 in the hybrid. Preferences for chiasma formation between identical and homologous chromosomes were analyzed in Col/Ler 4x, taking advantage of the cytological differences between the accessions: variations in the size of the 45S rDNA region on the short arm of chromosome 2 and changes in the size and localization of the 5S rDNA region in chromosome 3. We observed a different behavior of chromosomes 2 and 3, i.e., random chiasma formation between identical and homologous chromosomes 2, and preferences for chiasma formation between homologous chromosomes 3. Hence, our results reveal the existence of chromosome-specific mechanisms responsible for these preferences.
Locus Coeruleus Optogenetic Light Activation Induces Long-Term Potentiation of Perforant Path Population Spike Amplitude in Rat Dentate Gyrus

Norepinephrine (NE) in dentate gyrus (DG) produces NE-dependent long-term potentiation (NE-LTP) of the perforant path-evoked potential population spike both in vitro and in vivo. Chemical activators infused near locus coeruleus (LC), the source of DG NE, produce a NE-LTP that is associative, i.e., requires concurrent pairing with perforant path (PP) input. Here, we ask if LC optogenetic stimulation that allows us to activate only LC neurons can induce NE-LTP in DG. We use an adeno-associated viral vector containing a depolarizing channel (AAV8-Ef1a-DIO-eChR2(h134r)-EYFP-WPRE) infused stereotaxically into the LC of TH:Cre rats to produce light-sensitive LC neurons. A co-localization of ~62% in LC neurons was observed for these channels. Under urethane anesthesia, we demonstrated that 5–10 s 10 Hz trains of 30 ms light pulses in LC reliably activated neurons near an LC optoprobe. Ten minutes of the same train paired with 0.1 Hz PP electrical stimulation produced a delayed NE-LTP of population spike amplitude, but not EPSP slope. A leftward shift in the population spike input/output curve at the end of the experiment was also consistent with long-term population spike potentiation. LC neuron activity during the 10 min light train was unexpectedly transient. Increased LC neuronal firing was seen only for the first 2 min of the light train. NE-LTP was more delayed and less robust than reported with LC chemo-activation. Previous estimates of LC axonal conduction times suggest acute release of NE occurs 40–70 ms after an LC neuron action potential. We used single LC light pulses to examine acute effects of NE release and found potentiated population spike amplitude when a light pulse in LC occurred 40–50 ms, but not 20–30 ms, prior to a PP pulse, consistent with conduction estimates. These effects of LC optogenetic activation reinforce evidence for a continuum of NE potentiation effects in DG. The single pulse effects mirror an earlier report using LC electrical stimulation. These acute effects support an attentional role of LC activation. The LTP of PP responses induced by optogenetic LC activation is consistent with the role of LC in long-term learning and memory.
Relationships Between Neurofibromatosis-2, Progesterone Receptor Expression, the Use of Exogenous Progesterone, and Risk of Orbitocranial Meningioma in Females

Background: The pathogenesis of meningioma in females and its association with exogenous progesterone is remained unclear. This study was aimed to examine expression of Progesterone receptor (PR) and Neurofibromatosis-2 (NF2) and assess their relationships to history of exogenous progesterone use and risk of meningioma. Methods: Our study was a case-control study that involves 115 females, 40 cases who diagnosed with orbito-cranial meningioma and 75 controls of healthy, that has been presented in previous study. The demographic characteristics, reproductive factors, and history of progesterone use were obtained in–depth face-to-face interviews. PR and NF2 mRNA were assessed by real-time quantitative polymerase chain reaction (RT-qPCR) on serum specimens. Results: The mean age of participants in cases vs. controls were 46.6 ± 6.2 vs. 46.5 ± 7.45 (P = 0.969). The expression of PR and NF2 in cases was significantly lower than in controls. The longer duration of progesterone exposure was significantly associated with lower expression of PR and NF2. Significant association between lower expression of PR (OR 11.7; 95% CI 4.17–32.9; P < 0.001 comparing the lowest quartile vs. 3 highest quartile of PR) and NF2 (OR 4.23; 95% CI 1.85–9.67; P = 0.001 comparing the 2 lowest quartiles vs. 2 highest quartiles) with increased risk of meningioma were also reported. Conclusion: In this study we showed that the longer the exposure to exogenous progesterone, the lower the expression of PR and NF2 mRNA in the serum. Low expression of PR and NF2 were associated with higher risk of meningioma, suggesting that low PR expression and inactivation of NF2 might play a key role in progesterone-associated meningioma tumorigenesis and may be potential clinical marker for females at higher risk of meningioma.
Distributed Learning in the Classroom: Effects of Rereading Schedules Depend on Time of Test

Research with adults in laboratory settings has shown that distributed rereading is a beneficial learning strategy but its effects depend on time of test. When learning outcomes are measured immediately after rereading, distributed rereading yields no benefits or even detrimental effects on learning, but the beneficial effects emerge two days later. In a preregistered experiment, the effects of distributed rereading were investigated in a classroom setting with school students. Seventh-graders (N = 191) reread a text either immediately or after 1 week. Learning outcomes were measured after 4 min or 1 week. Participants in the distributed rereading condition reread the text more slowly, predicted their learning success to be lower, and reported a lower on-task focus. At the shorter retention interval, massed rereading outperformed distributed rereading in terms of learning outcomes. Contrary to students in the massed condition, students in the distributed condition showed no forgetting from the short to the long retention interval. As a result, they performed equally well as the students in the massed condition at the longer retention interval. Our results indicate that distributed rereading makes learning more demanding and difficult and leads to higher effort during rereading. Its effects on learning depend on time of test, but no beneficial effects were found, not even at the delayed test.
Effects of Water Quality Adjusted by Submerged Macrophytes on the Richness of the Epiphytic Algal Community

Submerged macrophytes and epiphytic algae play significant roles in the functioning of aquatic ecosystems. Submerged macrophytes can influence the epiphytic algal community by directly or indirectly modifying environmental conditions (nutrients, light, etc.). From December to June of the following year, we investigated the dynamics of the dominant winter species Potamogeton crispus, its epiphytic algae, and water quality parameters in the shallow Liangzi Lake in China. The richness of epiphytic algae had a trend similar to that of P. crispus coverage, which increased in the first four months and then decreased in the following three months. The structural equation model (SEM) showed that P. crispus affected the richness of epiphytic algae by reducing nutrient concentrations (reduction in total organic carbon, total nitrogen and chemical oxygen demand) and enhancing water transparency (reduction in turbidity and total suspend solids) to enhance the richness of epiphytic algae. The results indicated that high amounts of submerged macrophyte cover can increase the richness of the epiphytic algal community by changing water quality.
Transactivated Epidermal Growth Factor Receptor Recruitment of α-actinin-4 From F-actin Contributes to Invasion of Brain Microvascular Endothelial Cells by Meningitic Escherichia coli

Bacterial penetration of the blood-brain barrier requires its successful invasion of brain microvascular endothelial cells (BMECs), and host actin cytoskeleton rearrangement in these cells is a key prerequisite for this process. We have reported previously that meningitic Escherichia coli can induce the activation of host's epidermal growth factor receptor (EGFR) to facilitate its invasion of BMECs. However, it is unknown how EGFR specifically functions during this invasion process. Here, we identified an important EGFR-interacting protein, α-actinin-4 (ACTN4), which is involved in maintaining and regulating the actin cytoskeleton. We observed that transactivated-EGFR competitively recruited ACTN4 from intracellular F-actin fibers to disrupt the cytoskeleton, thus facilitating bacterial invasion of BMECs. Strikingly, this mechanism operated not only for meningitic E. coli, but also for infections with Streptococcus suis, a Gram-positive meningitis-causing bacterial pathogen, thus revealing a common mechanism hijacked by these meningitic pathogens where EGFR competitively recruits ACTN4. Ever rising levels of antibiotic-resistant bacteria and the emergence of their extended-spectrum antimicrobial-resistant counterparts remind us that EGFR could act as an alternative non-antibiotic target to better prevent and control bacterial meningitis.
Bio-inspired Analysis of Deep Learning on Not-So-Big Data Using Data-Prototypes

Deep artificial neural networks are feed-forward architectures capable of very impressive performances in diverse domains. Indeed stacking multiple layers allows a hierarchical composition of local functions, providing efficient compact mappings. Compared to the brain, however, such architectures are closer to a single pipeline and require huge amounts of data, while concrete cases for either human or machine learning systems are often restricted to not-so-big data sets. Furthermore, interpretability of the obtained results is a key issue: since deep learning applications are increasingly present in society, it is important that the underlying processes be accessible and understandable to every one. In order to target these challenges, in this contribution we analyze how considering prototypes in a rather generalized sense (with respect to the state of the art) allows to reasonably work with small data sets while providing an interpretable view of the obtained results. Some mathematical interpretation of this proposal is discussed. Sensitivity to hyperparameters is a key issue for reproducible deep learning results, and is carefully considered in our methodology. Performances and limitations of the proposed setup are explored in details, under different hyperparameter sets, in an analogous way as biological experiments are conducted. We obtain a rather simple architecture, easy to explain, and which allows, combined with a standard method, to target both performances and interpretability.
The Critical Role of Genome Maintenance Proteins in Immune Evasion During Gammaherpesvirus Latency

Gammaherpesviruses are important pathogens that establish latent infection in their natural host for lifelong persistence. During latency, the viral genome persists in the nucleus of infected cells as a circular episomal element while the viral gene expression program is restricted to non-coding RNAs and a few latency proteins. Among these, the genome maintenance protein (GMP) is part of the small subset of genes expressed in latently infected cells. Despite sharing little peptidic sequence similarity, gammaherpesvirus GMPs have conserved functions playing essential roles in latent infection. Among these functions, GMPs have acquired an intriguing capacity to evade the cytotoxic T cell response through self-limitation of MHC class I-restricted antigen presentation, further ensuring virus persistence in the infected host. In this review, we provide an updated overview of the main functions of gammaherpesvirus GMPs during latency with an emphasis on their immune evasion properties.
Behind the Scenes of Developmental Language Disorder: Time to Call Neuropsychology Back on Stage

Although the Developmental Language Disorder (DLD), also known as Specific Language Impairment in children has been the focus of unceasing scientific attention for decades, the nature and mechanisms of this disorder remain unclear. Most importantly, we still cannot reliably identify children requiring urgent intervention among other ‘late talkers’ at an early age and understand the high prevalence of comorbidity with psychiatric phenomena such as Autism Spectrum Disorder. One of the main reasons for this is the traditional ‘diagnosis-by-exclusion,’ resulting in heterogeneity of the DLD population. This paper proposes an alternative approach to the diagnosis, treatment and research of DLD, claiming that it is these children’s multiple deficits in neuropsychological development, which impede the spontaneous acquisition of their first language. Specifically, this review of the state-of-the-art in DLD research demonstrates deep and systematic interconnections between the speech and other higher cognitive functions developing in early childhood, including perception, attention and executive functions. In the proposed framework, speech is, therefore, considered as one of neuropsychological abilities, and the delay in its development is explained by other neuropsychological deficits, resulting in highly individual clinical profiles. By considering DLD as a complex neuropsychological syndrome, whose successful treatment depends on a holistic approach to diagnosis and intervention, we may significantly increase the efficacy of speech therapy, and also better understand the flexibility of the developing brain, its compensatory mechanisms and hence the comorbidity of DLD with psychiatric symptoms. Implications for using this paradigm in future scientific research are discussed.