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We carried out light curve solutions of four detached binaries observed by Kepler. As a result, their orbital inclinations, temperatures and relative stellar radii were determined. KIC 10031409 and KIC 11228612 reveal partial eclipses while the components of KIC 11403216 and KIC 11913071 undergo total eclipses. The secondary component of KIC 11403216 is probably a very late M dwarf or brown dwarf. The out-of-eclipse brightness of KIC 10031409, KIC 11228612 and KIC 11913071 vary with the orbital period and might be explained by spots on synchronously-rotating star(s). The out-of-eclipse variability of KIC 11403216 is with a period that is a third of its orbital period and may be due to spot on asynchronous rotating component. The resonance 1:3 needs future study of KIC 11403216.

The health and welfare of livestock are significant for ensuring the sustainability and profitability of the agricultural industry. Addressing efficient ways to monitor and report the health status of individual cows is critical to prevent outbreaks and maintain herd productivity. The purpose of the study is to develop a machine learning (ML) model to classify the health status of milk cows into three categories. In this research, data are collected from existing non-invasive IoT devices and tools in a dairy farm, monitoring the micro- and macroenvironment of the cow in combination with particular information on age, days in milk, lactation, and more. A workflow of various data-processing methods is systematized and presented to create a complete, efficient, and reusable roadmap for data processing, modeling, and real-world integration. Following the proposed workflow, the data were treated, and five different ML algorithms were trained and tested to select the most descriptive one to monitor the health status of individual cows. The highest result for health status assessment is obtained by random forest classifier (RFC) with an accuracy of 0.959, recall of 0.954, and precision of 0.97. To increase the security, speed, and reliability of the work process, a cloud architecture of services is presented to integrate the trained model as an additional functionality in the Amazon Web Services (AWS) environment. The classification results of the ML model are visualized in a newly created interface in the client application.

Matrix metalloproteinases (MMPs) are a large family of Ca 2+ and Zn 2+ dependent proteolytic enzymes, able to cleave the various components of the extracellular matrix (ECM), as well as a range of other regulatory molecules. Several reports have proven the important role of both MMPs and their endogenous inhibitors, TIPMs, in oral health, the initial development of the tooth, and during enamel maturation. In this mini-review, we aim to summarize the literature information about the functions of MMPs, paying more attention to MMP-8 (collagenase-2 or neutrophil collagenase) in the development and progression of periodontitis, peri-implantitis, and carious lesions. We also emphasize the role of particular gene variants in MMP8 as predisposing factors for some oral diseases.

Early life stress (ELS) results in enduring dysfunction of the corticolimbic circuitry, underlying emotional and social behavior. However, the neurobiological mechanisms involved remain elusive. Here, we have combined viral tracing and electrophysiological techniques to study the effects of maternal separation (MS) on frontolimbic connectivity and function in young (P14-21) rats. We report that aberrant prefrontal inputs to basolateral amygdala (BLA) GABAergic interneurons transiently increase the strength of feed-forward inhibition in the BLA, which raises LTP induction threshold in MS treated male rats. The enhanced GABAergic activity after MS exposure associates with lower functional synchronization within prefrontal-amygdala networks in vivo . Intriguingly, no differences in these parameters were detected in females, which were also resistant to MS dependent changes in anxiety-like behaviors. Impaired plasticity and synchronization during the sensitive period of circuit refinement may contribute to long-lasting functional changes in the prefrontal-amygdaloid circuitry that predispose to neuropsychiatric conditions later on in life., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

Smart farms, as a part of high-tech agriculture, collect a huge amount of data from IoT devices about the conditions of animals, plants, and the environment. These data are most often stored locally and are not used in intelligent monitoring systems to provide opportunities for extracting meaningful knowledge for the farmers. This often leads to a sense of missed transparency, fairness, and accountability, and a lack of motivation for the majority of farmers to invest in sensor-based intelligent systems to support and improve the technological development of their farm and the decision-making process. In this paper, a data-driven intelligent monitoring system in a cloud environment is proposed. The designed architecture enables a comprehensive solution for interaction between data extraction from IoT devices, preprocessing, storage, feature engineering, modelling, and visualization. Streaming data from IoT devices to interactive live reports along with built machine learning (ML) models are included. As a result of the proposed intelligent monitoring system, the collected data and ML modelling outcomes are visualized using a powerful dynamic dashboard. The dashboard allows users to monitor various parameters across the farm and provides an accessible way to view trends, deviations, and patterns in the data. ML models are trained on the collected data and are updated periodically. The data-driven visualization enables farmers to examine, organize, and represent collected farm's data with the goal of better serving their needs. Performance and durability tests of the system are provided. The proposed solution is a technological bridge with which farmers can easily, affordably, and understandably monitor and track the progress of their farms with easy integration into an existing IoT system.

Early life stress (ELS) is a well-characterized risk factor for mood and anxiety disorders. GABAergic microcircuits in the amygdala are critically implicated in anxiety; however, whether their function is altered after ELS is not known. Here we identify a novel mechanism by which kainate receptors (KARs) modulate feedforward inhibition in the lateral amygdala (LA) and show that this mechanism is downregulated after ELS induced by maternal separation (MS). Specifically, we show that in control rats but not after MS, endogenous activity of GluK1 subunit containing KARs disinhibit LA principal neurons during activation of cortical afferents. GluK1 antagonism attenuated excitability of parvalbumin (PV)-expressing interneurons, resulting in loss of PV-dependent inhibitory control and an increase in firing of somatostatin-expressing interneurons. Inactivation of Grik1 expression locally in the adult amygdala reduced ongoing GABAergic transmission and was sufficient to produce a mild anxiety-like behavioral phenotype. Interestingly, MS and GluK1-dependent phenotypes showed similar gender specificity, being detectable in male but not female rodents. Our data identify a novel KAR-dependent mechanism for cell-type and projection-specific functional modulation of the LA GABAergic microcircuit and suggest that the loss of GluK1 KAR function contributes to anxiogenesis after ELS., (© 2021. The Author(s).)

In the ecological future of the planet, intelligent agriculture relies on CPS and IoT to free up human resources and increase production efficiency. Due to the growing number of connected IoT devices, the maximum scalability capacity, and available computing power of the existing architectural frameworks will be reached. This necessitates finding a solution that meets the continuously growing demands in smart farming. Cloud-based IoT solutions are achieving increasingly high popularity. The aim of this study was to design a scalable cloud-based architecture for a smart livestock monitoring system following Agile methodology and featuring environmental monitoring, health, growth, behaviour, reproduction, emotional state, and stress levels of animals. The AWS services used, and their specific tasks related to the proposed architecture are explained in detail. A stress test was performed to prove the data ingesting and processing capability of the proposed architecture. Experimental results proved that the proposed architecture using AWS automated scaling mechanisms and IoT devices are fully capable of processing the growing amount of data, which in turn allow for meeting the required needs of the constantly expanding number of CPS systems.

Despite extensive and intensive research efforts in recent decades, there is still no effective treatment for neurodegenerative diseases. On this background, the use of drugs inhibiting the enzyme acetylcholinesterase (AChE) remains an eternal evergreen in the symptomatic treatment of mild to moderate cognitive impairments. Even more, the cholinergic hypothesis, somewhat forgotten in recent years due to the shift in focus on amyloid cascade, is back to life, and the search for new, more effective AChE inhibitors continues. We generated a fragment-based library containing aromatic moieties and linkers originating from a set of novel AChE inhibitors. We used this library to design 1220 galantamine (GAL) derivatives following the model GAL (binding core) - linker (L) - aromatic fragment (Ar). The newly designed compounds were screened virtually for blood-brain barrier (BBB) permeability and binding to AChE. Among the top 10 best-scored compounds, a representative lead molecule was selected and tested for anti-AChE activity and neurotoxicity. It was found that the selected compound was a powerful non-toxic AChE inhibitor, 68 times more active than GAL, and could serve as a lead molecule for further optimization and development.

Changes in word production occur across the lifespan. Previous studies have shown electrophysiological, temporal, and functional differences between children and adults accompanying behavioral changes in picture-naming tasks (Laganaro, Tzieropoulos, Fraunfelder, & Zesiger, 2015). Thus, a shift toward adult-like processes in referential word production occurs somewhere between the ages of 13 and 20. Our aim was to investigate when and how children develop adult-like behavior and brain activation in word production. Toward this aim, performance and event-related potentials (ERP) in a referential word production task were recorded and compared for two groups of adolescents (aged 14 to 16 and 17 to 18), children (aged 10 to 13), and young adults (aged 20 to 30). Both groups of adolescents displayed adult-like production latencies, which were longer only for children, while accuracy was lower in the younger adolescents and in children, compared to adults. ERP waveform analysis and topographic pattern analysis revealed significant intergroup differences in key time-windows on stimulus-locked ERPs, both early (150-220 ms)-associated with pre-linguistic processes-and late (280-330 ms)-associated with lexical processes. The results indicate that brain activation underlying referential word production is completely adult-like in 17-year-old adolescents, whereas an intermediate pattern is still observed in adolescents aged 14 to 16 years old, although their production speed, but not their accuracy, is already adult-like., Competing Interests: Competing Interests: The authors have declared that no competing interests exist., (© 2020 Massachusetts Institute of Technology.)

Galantamine (GAL) and curcumin (CU) are alkaloids used to improve symptomatically neurodegenerative conditions like Alzheimer's disease (AD). GAL acts mainly as an inhibitor of the enzyme acetylcholinesterase (AChE). CU binds to amyloid-beta (Aβ) oligomers and inhibits the formation of Aβ plaques. Here, we combine GAL core with CU fragments and design a combinatorial library of GAL-CU hybrids as dual-site binding AChE inhibitors. The designed hybrids are screened for optimal ADME properties and BBB permeability and docked on AChE. The 14 best performing compounds are synthesized and tested in vitro for neurotoxicity and anti-AChE activity. Five of them are less toxic than GAL and CU and show activities between 41 and 186 times higher than GAL.

Auditory cortex volume and shape differences have been observed in the context of phonetic learning, musicianship and dyslexia. Heschl's gyrus, which includes primary auditory cortex, displays large anatomical variability across individuals and hemispheres. Given this variability, manual labelling is the gold standard for segmenting HG, but is time consuming and error prone. Our novel toolbox, called 'Toolbox for the Automated Segmentation of HG' or TASH, automatically segments HG in brain structural MRI data, and extracts measures including its volume, surface area and cortical thickness. TASH builds upon FreeSurfer, which provides an initial segmentation of auditory regions, and implements further steps to perform finer auditory cortex delineation. We validate TASH by showing significant relationships between HG volumes obtained using manual labelling and using TASH, in three independent datasets acquired on different scanners and field strengths, and by showing good qualitative segmentation. We also present two applications of TASH, demonstrating replication and extension of previously published findings of relationships between HG volumes and (a) phonetic learning, and (b) musicianship. In sum, TASH effectively segments HG in a fully automated and reproducible manner, opening up a wide range of applications in the domains of expertise, disease, genetics and brain plasticity.

Kainate-type glutamate receptors (KARs) are highly expressed in the developing brain, where they are tonically activated to modulate synaptic transmission, network excitability and synaptogenesis. NETO proteins are auxiliary subunits that regulate biophysical properties of KARs; however, their functions in the immature brain are not known. Here, we show that NETO1 guides the development of the rodent hippocampal CA3-CA1 circuitry via regulating axonal KARs. NETO deficiency reduced axonal targeting of most KAR subunits in hippocampal neurons in a subtype independent manner. As an interesting exception, axonal delivery of GluK1c was strongly and selectively impaired in the Neto1 -/- , but not Neto2 -/- , neurons. Correspondingly, the presynaptic GluK1 KAR activity that tonically inhibits glutamate release at immature CA3-CA1 synapses was completely lost in the absence of NETO1 but not NETO2. The deficit in axonal KARs at Neto1 -/- neurons resulted in impaired synaptogenesis and perturbed synchronization of CA3 and CA1 neuronal populations during development in vitro . Both these Neto1 -/- phenotypes were fully rescued by overexpression of GluK1c, emphasizing the role of NETO1/KAR complex in development of efferent connectivity. Together, our data uncover a novel role for NETO1 in regulation of axonal KARs and identify its physiological significance in development of the CA3-CA1 circuit.