Original Target Research Articles

Robo2 Drives Target-Selective Peripheral Nerve Regeneration in Response to Glia-Derived Signals.

Peripheral nerves are divided into multiple branches leading to divergent synaptic targets. This poses a remarkable challenge for regenerating axons as they select their original trajectory at nerve branch-points. Despite implications for functional regeneration, the molecular mechanisms underlying target selectivity are not well characterized. Danio Rerio (zebrafish) motor nerves are composed of a ventral and a dorsal branch that diverge at a choice-point, and we have previously shown that regenerating axons faithfully select their original branch and targets. Here we identify robo2 as a key regulator of target-selective regeneration (sex of experimental subjects unknown). We demonstrate that robo2 function in regenerating axons is required and sufficient to drive target-selective regeneration, and that robo2 acts in response to glia located precisely where regenerating axons select the branch-specific trajectory to prevent and correct axonal errors. Combined, our results reveal a glia-derived mechanism that acts locally via axonal robo2 to promote target-selective regeneration.SIGNIFICANCE STATEMENT Despite its relevance for functional recovery, the molecular mechanisms that direct regenerating peripheral nerve axons toward their original targets are not well defined. Zebrafish spinal motor nerves are composed of a dorsal and a ventral branch that diverge at a stereotyped nerve branch-point, providing a unique opportunity to decipher the molecular mechanisms critical for target-selective regeneration. Using a combination of live cell imaging and molecular-genetic manipulations, we demonstrate that the robo2 guidance receptor is necessary and sufficient to promote target-selective regeneration. Moreover, we demonstrate that robo2 is part of a genetic pathway that generates transient, spatially restricted, and tightly coordinated signaling events that direct axons of the dorsal nerve branch toward their original, pre-injury targets.

Research on Target Tracking Algorithm of Twin Networks Integrating Attention Mechanism

Aiming at the current stage of the twin network target tracking algorithm, the tracking target is occluded, the tracking is affected by illumination, and the target's scale change from far to near or from near to far causes tracking failure. This article will optimize and improve from two directions. The twin neural network first uses an adaptive detailed feature extraction, adds a residual network to the twin network, and embeds a detailed feature retention module in each layer, amplifies the changes in the target feature, and retains the important structure of the original target feature Details: Secondly, the introduction of a spatial attention mechanism allows the main branch to pay more attention to the area to be matched, improves the ability to distinguish features, and makes the tracking effect better. In order to verify the effectiveness of this experiment, this experiment was tested on the data set OTB2015. The experiment proved that the proposed algorithm performs better in accuracy and success rate.

Evaluating Image Normalization via GANs for Environmental Mapping: A Case Study of Lichen Mapping Using High-Resolution Satellite Imagery

Illumination variations in non-atmospherically corrected high-resolution satellite (HRS) images acquired at different dates/times/locations pose a major challenge for large-area environmental mapping and monitoring. This problem is exacerbated in cases where a classification model is trained only on one image (and often limited training data) but applied to other scenes without collecting additional samples from these new images. In this research, by focusing on caribou lichen mapping, we evaluated the potential of using conditional Generative Adversarial Networks (cGANs) for the normalization of WorldView-2 (WV2) images of one area to a source WV2 image of another area on which a lichen detector model was trained. In this regard, we considered an extreme case where the classifier was not fine-tuned on the normalized images. We tested two main scenarios to normalize four target WV2 images to a source 50 cm pansharpened WV2 image: (1) normalizing based only on the WV2 panchromatic band, and (2) normalizing based on the WV2 panchromatic band and Sentinel-2 surface reflectance (SR) imagery. Our experiments showed that normalizing even based only on the WV2 panchromatic band led to a significant lichen-detection accuracy improvement compared to the use of original pansharpened target images. However, we found that conditioning the cGAN on both the WV2 panchromatic band and auxiliary information (in this case, Sentinel-2 SR imagery) further improved normalization and the subsequent classification results due to adding a more invariant source of information. Our experiments showed that, using only the panchromatic band, F1-score values ranged from 54% to 88%, while using the fused panchromatic and SR, F1-score values ranged from 75% to 91%.

148. Single-amplicon, Multiplex Real-time RT-PCR with Tiled Probes to Detect SARS-CoV-2 spike Mutations Associated with Variants of Concern

BackgroundDetection and surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants is of great public health importance. Broadly accessible and inexpensive assays are needed to enhance variant surveillance and detection globally. We developed and validated a single-reaction multiplex real-time RT-PCR (the Spike SNP assay) to detect specific mutations associated with variants of concern (VOC).MethodsA single primer pair was designed to amplify a 348 bp region of spike. Probes were initially designed with locked nucleic acids (LNAs) to increase probe melting temperature, shorten probe length, and specifically detect 417K, E484K, and N501Y (Figure). The assay was optimized and evaluated using characterized variant sample pools. Clinical evaluation was performed on a convenience set of residual nasopharyngeal swabs, and variant calls were confirmed by SARS-CoV-2 genomic sequencing in a subset of samples. Following the initial evaluation, unmodified probes (without LNAs) were designed to detect L452R, L452Q, and E484Q.Figure. Spike SNP distinguishes mutations occurring in different lineages (A-C). Representative results of variant detection a single Spike SNP run are shown for mutations in the codons for 4177K (A) and mutations that encode 484K (B) and 501Y (C). Curves show dilutions of the following variants: blue, BEI 52286 (wild type); pink B.1.1.7; purple, B1.525; and green, P.1. Variant pools were used for B.1.17, B.1.525, and P.1 strains. Curves are displayed for a given dilution in each channel and result interpretation is shown (D).ResultsThe lower limit of 95% detection was 2.46 to 2.48 log10 GE/mL for the three targets (~1-2 GE/reaction). Among 253 nasopharyngeal swabs with detectable SARS-CoV-2 RNA, the Spike SNP assay was positive in 238 (94.1%), including all samples with Ct values < 30 (220/220) for the N2 target and 18/33 samples with N2 Ct values ≥ 30. Results were confirmed by SARS-CoV-2 genomic sequencing in 50/50 samples (100%). Subsequent addition of the 452R probe did not affect performance for the original targets, and probes for 452Q and 484Q performed similarly to LNA-modified probes.ConclusionThe Spike SNP assay provides fast, inexpensive and sensitive detection of specific mutations associated with SARS-CoV-2 VOCs, and the assay can be quickly modified to detect new mutations in the receptor binding domain. Similar analytical performance of LNA-modified and unmodified probes presents options for future assay customization that balance the shorter probe length (LNAs) and increased accessibility (unmodified). The Spike SNP assay, if implemented across laboratories offering SARS-CoV-2 testing, could greatly increase capacity for variant detection and surveillance globally. Disclosures Colleen S. Kraft, MD, MSc, Rebiotix (Individual(s) Involved: Self): Advisor or Review Panel member

Text-to-speech system for low-resource language using cross-lingual transfer learning and data augmentation

Deep learning techniques are currently being applied in automated text-to-speech (TTS) systems, resulting in significant improvements in performance. However, these methods require large amounts of text-speech paired data for model training, and collecting this data is costly. Therefore, in this paper, we propose a single-speaker TTS system containing both a spectrogram prediction network and a neural vocoder for the target language, using only 30 min of target language text-speech paired data for training. We evaluate three approaches for training the spectrogram prediction models of our TTS system, which produce mel-spectrograms from the input phoneme sequence: (1) cross-lingual transfer learning, (2) data augmentation, and (3) a combination of the previous two methods. In the cross-lingual transfer learning method, we used two high-resource language datasets, English (24 h) and Japanese (10 h). We also used 30 min of target language data for training in all three approaches, and for generating the augmented data used for training in methods 2 and 3. We found that using both cross-lingual transfer learning and augmented data during training resulted in the most natural synthesized target speech output. We also compare single-speaker and multi-speaker training methods, using sequential and simultaneous training, respectively. The multi-speaker models were found to be more effective for constructing a single-speaker, low-resource TTS model. In addition, we trained two Parallel WaveGAN (PWG) neural vocoders, one using 13 h of our augmented data with 30 min of target language data and one using the entire 12 h of the original target language dataset. Our subjective AB preference test indicated that the neural vocoder trained with augmented data achieved almost the same perceived speech quality as the vocoder trained with the entire target language dataset. Overall, we found that our proposed TTS system consisting of a spectrogram prediction network and a PWG neural vocoder was able to achieve reasonable performance using only 30 min of target language training data. We also found that by using 3 h of target language data, for training the model and for generating augmented data, our proposed TTS model was able to achieve performance very similar to that of the baseline model, which was trained with 12 h of target language data.

Sustained delivery of neurotrophic factors to treat spinal cord injury.

Acute spinal cord injury (SCI) is a devastating condition that results in tremendous physical and psychological harm and a series of socioeconomic problems. Although neurons in the spinal cord need neurotrophic factors for their survival and development to reestablish their connections with their original targets, endogenous neurotrophic factors are scarce and the sustainable delivery of exogeneous neurotrophic factors is challenging. The widely studied neurotrophic factors such as brain-derived neurotrophic factor, neurotrophin-3, nerve growth factor, ciliary neurotrophic factor, basic fibroblast growth factor, and glial cell-derived neurotrophic factor have a relatively short cycle that is not sufficient enough for functionally significant neural regeneration after SCI. In the past decades, scholars have tried a variety of cellular and viral vehicles as well as tissue engineering scaffolds to safely and sustainably deliver those necessary neurotrophic factors to the injury site, and achieved satisfactory neural repair and functional recovery on many occasions. Here, we review the neurotrophic factors that have been used in trials to treat SCI, and vehicles that were commonly used for their sustained delivery.

A Study on Current Characteristics Based on Design and Performance Test of Current Generator of KRISO&amp;apos;s Deep Ocean Engineering Basin

To build an environment facility of a large-scale ocean basin, various detailed reviews are required, but it is difficult to find data that introduces the related research or construction processes on the environment facility. The current generator facility for offshore structure safety evaluation tests should be implemented by rotating the water of the basin. However, when the water in the large basin rotates, relatively large flow irregularities may occur and the uniformity may not be adequate. In this paper, design and review were conducted to satisfy the performance goals of the DOEB through computational numerical analysis on the shape of the waterway and the flow straightening devices to form the current in the large tank. Based on this, the head loss, which decreases the flow rate when the large tank water rotates through the water channel, was estimated and used as the pump capacity (impeller) design data. The impeller of the DOEB current generator was designed through computational numerical analysis (CFD) based on the lift surface theory from the axial-type impeller shape for satisfying the head loss of the waterway and maximum current velocity. In order to confirm the performance of the designed impeller system, the flow rate and flow velocity performance were checked through factory test operation. And, after installing DOEB, the current flow rate and velocity performance were reviewed compare with the original design target values.&lt;/br&gt;Finally, by measuring the current velocity of the test area in DOEB formed through the current generator, the spatial current distribution characteristics in the test area were analyzed. Through the analysis of the current distribution characteristics of the DOEB test area, it was confirmed that the realization of the maximum current velocity and the average flow velocity distribution, the main performance goals in the waterway design process, were satisfied.

Enhancing Robots Navigation in Internet of Things Indoor Systems

In this study, an effective local minima detection and definition algorithm is introduced for a mobile robot navigating through unknown static environments. Furthermore, five approaches are presented and compared with the popular approach wall-following to pull the robot out of the local minima enclosure namely; Random Virtual Target, Reflected Virtual Target, Global Path Backtracking, Half Path Backtracking, and Local Path Backtracking. The proposed approaches mainly depend on changing the target location temporarily to avoid the original target’s attraction force effect on the robot. Moreover, to avoid getting trapped in the same location, a virtual obstacle is placed to cover the local minima enclosure. To include the most common shapes of deadlock situations, the proposed approaches were evaluated in four different environments; V-shaped, double U-shaped, C-shaped, and cluttered environments. The results reveal that the robot, using any of the proposed approaches, requires fewer steps to reach the destination, ranging from 59 to 73 m on average, as opposed to the wall-following strategy, which requires an average of 732 m. On average, the robot with a constant speed and reflected virtual target approach takes 103 s, whereas the identical robot with a wall-following approach takes 907 s to complete the tasks. Using a fuzzy-speed robot, the duration for the wall-following approach is greatly reduced to 507 s, while the reflected virtual target may only need up to 20% of that time. More results and detailed comparisons are embedded in the subsequent sections.

Research on the Generation and Design Method of Residential Community Scheme Based on Improving Design

Along with the urban renewal and development, the urban living environment has given rise to various problems that need to be solved. With an eye on the future development model of residential communities, an experimental preliminary design for the construction of architectural space, public space, and landscape space based on people’s actual needs is carried out in an attempt to alleviate the more urgent symbiotic relationship between people and urban environment. To this end, this paper proposes a planning and design generation framework for the constructed external spatial environment of building groups based on a recursive double-adversarial network model. Firstly, we extract the features of the constructed external spatial environment of the building group in depth and generate the expression feature map, which is used as a supervisory signal to generate an expression seed image of the constructed external spatial environment of the building group; then we use the generated seed image together with the constructed external spatial environment of the original target building group as the input to generate a feature-holding image as the output of the current frame, and the feature-holding image is also used as the input for the next. Finally, the seed image generation network and the feature-holding image generation network are recursively used to generate the next frame, and the video sequence of the expressions of the constructed external spatial environment of the building group with the same feature-holding expressions as the original input is recursively obtained several times. The experimental results on the building group database show that the proposed method can generate clear and natural video frames of the constructed external spatial environment of the building group, which can be gradually derived from the design of building units to the construction of the building group and penetrate into the planning and design of the external spatial environment in order to comprehensively improve the living environment of urban population and provide a design method and theoretical support for the design of future urban residential communities.

Robust economic model predictive control with zone tracking

This paper presents a robust economic model predictive control (EMPC) formulation with zone tracking for discrete-time uncertain nonlinear systems. The proposed design ensures that the zone tracking objective is achieved in finite steps and at the same time optimizes the economic performance. In the proposed design, instead of tracking the original target zone, a robust control invariant set within the target zone is determined and is used as the actual zone tracked in the proposed EMPC. This approach ensures that the zone tracking objective is achieved within finite steps and once the zone tracking objective is achieved (the system state enters the robust control invariant set), the system state does not come out of the target zone anymore. To optimize the economic performance within the zone in the presence of disturbances, we introduce the notion of risk factor in the controller design. An algorithm to determine the economic zone to be tracked is provided. The risk factor determines the conservativeness of the controller and provides a way to tune the EMPC for better economic performance. A nonlinear chemical example is presented to demonstrate the performance of the proposed formulation.

Novel Multitarget Hydroxamic Acids with a Natural Origin CAP Group against Alzheimer's Disease: Synthesis, Docking and Biological Evaluation.

Hydroxamic acids are one of the most promising and actively studied classes of chemical compounds in medicinal chemistry. In this study, we describe the directed synthesis and effects of HDAC6 inhibitors. Fragments of adamantane and natural terpenes camphane and fenchane, combined with linkers of various nature with an amide group, were used as the CAP groups. Accordingly, 11 original target compounds were developed, synthesized, and exposed to in vitro and in vivo biological evaluations, including in silico methods. In silico studies showed that all synthesized compounds were drug-like and could penetrate through the blood–brain barrier. According to the in vitro testing, hydroxamic acids 15 and 25, which effectively inhibited HDAC6 and exhibited anti-aggregation properties against β-amyloid peptides, were chosen as the most promising substances to study their neuroprotective activities in vivo. All in vivo studies were performed using 5xFAD transgenic mice simulating Alzheimer’s disease. In these animals, the Novel Object Recognition and Morris Water Maze Test showed that the formation of hippocampus-dependent long-term episodic and spatial memory was deteriorated. Hydroxamic acid 15 restored normal memory functions to the level observed in control wild-type animals. Notably, this effect was precisely associated with the ability to restore lost cognitive functions, but not with the effect on motor and exploratory activities or on the level of anxiety in animals. Conclusively, hydroxamic acid 15 containing an adamantane fragment linked by an amide bond to a hydrocarbon linker is a possible potential multitarget agent against Alzheimer’s disease.

Electropolymerized-molecularly imprinted polymers (E-MIPS) as sensing elements for the detection of dengue infection.

A straightforward in situ detection method for dengue infection was demonstrated through the molecular imprinting of a dengue nonstructural protein 1 (NS1) epitope into an electropolymerized molecularly imprinted polyterthiophene (E-MIP) film sensor. The key enabling step in the sensor fabrication is based on an epitope imprinting strategy, in which short peptide sequences derived from the original target molecules were employed as the main template for detection and analysis. The formation of the E-MIP sensor films was facilitated using cyclic voltammetry (CV) and monitored in situ by electrochemical quartz crystal microbalance (EC-QCM). Surface properties were analyzed using different techniques including atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), and polarization modulation-infrared reflection-adsorption (PM-IRRAS). The standard calibration curve (R = 0.9830) was generated for the detection of the epitope, Ac-VHTWTEQYKFQ-NH2, with a linear range of 0.2 to 30μg/mL and detection limit of 0.073μg/mL. A separate calibration curve (R = 0.9786) was obtained using spiked buffered solutions of dengue NS1 protein, which resulted in a linear range of 0.2 to 10μg/mL and a detection limit of 0.056μg/mL. The fabricated E-MIP sensor exhibited long-term stability, high sensitivity, and good selectivity towards the targeted molecules. These results indicated that the formation of the exact and stable cavity imprints in terms of size, shape, and functionalities was successful. In our future work, we aim to use our E-MIP sensors for NS1 detection in real-life samples such as serum and blood.

Dispersion and shattering strength of rocky and frozen planetesimals studied by laboratory experiments and numerical simulations

We have developed a method to investigate the whole mass–velocity distribution of impact fragments generated by catastrophic disruption of simulated planetesimals. Flash X-ray radiography including 12 iron particles for tracers was used to visualize the interior of the target, and the velocity distribution of the whole target was estimated by using the velocities of the tracers measured by X-ray images. High-velocity impact experiments in the laboratory and numerical simulations were conducted for four types of targets simulating rocky and frozen planetesimals at various specific energies, Q. These targets consisted of frozen clays with three different water contents ranging from 25 to 45 wt% and porous gypsum with a porosity of 51%. The shattering strength, QS*, and the mass–velocity distribution (MVD) were studied for these targets. The QS* of the frozen clays varied by a factor of 3–4 times, depending on the water content, and the QS* for porous gypsum was almost the same as that for the frozen clays with lower water contents. The numerical impact simulations led to slightly different QS* and MVD values for the frozen clay targets, possibly because of the partly ductile behavior of these samples. The MVDs resulting from the porous gypsum targets were well reproduced in the simulations. The cumulative mass of fragments with an ejection velocity slower than a specific velocity was examined to introduce a median velocity, v⁎, charactering the mass–velocity distribution. The v⁎ is defined as the velocity at which the cumulative mass corresponds to a half of the original target mass in the distribution. The v⁎ values of the frozen clays were described by the empirical equation v∗ = εQγ with almost the same ε and γ, irrespective of the water content, but the v⁎ of porous gypsum was about 1/3 that of the frozen clays. These experimental results were well reproduced by the numerical simulations for both frozen clays and porous gypsum targets. The dispersion strength, QD*, could be derived by comparing v⁎ with the escape velocity, vesc, of a target body with an effective mass, M, and radius, R. From this, a semi-theoretical equation showing the dispersion strength was derived: QD∗=1ε2GMR1/21/γ. Numerical simulations of catastrophic disruptions including self-gravity were conducted to directly determine QD* at large scale. These calculations showed that the effective mass of the target body, which is used in the computation of vesc = v⁎, should be a half of the original target mass, M = Mtarget /2. Our results suggest that this approach for computing the semi-theoretical dispersion strength is suitable for bodies larger than ~10 km.

You Want It Extra CRISPERY? Legal Disruption through New Plant Breeding Technologies in the EU

Abstract I will illustrate how the ‘old’ European Union (EU) legal regime on genetically modified organisms (GMOs) is applied to regulate new plant breeding technologies (NPBT). I will discuss why and how this results in what elsewhere had been called ‘legal disruption’. Legal disruption will occur once legal rules which were initially confined to a specific identified issue start to be applied beyond their original target. Such legal disruption may be found in the example of NPBT interacting with the precautionary principle, the use of scientific evidence in preliminary ruling proceedings, and the enforcement of labelling and traceability requirements. In the case of NPBT, legal disruption took place because policy makers were unwilling to adopt legislation designed to meet the demands of the technology. Consequently, they shuffled the responsibility for regulatory decision to the Court of Justice of the EU (CJEU). The CJEU, in its current design, represents an ill-suited institution to decide on such complex technology questions. I will explain why. I will end with a plea for a regulation of NPBT, which likewise enables and protects, following the eight principles developed by Michéle Finck on the example of the governance of blockchain technology.

Planning Target Volume Implications of Residual Setup Uncertainty and Intrafraction Motion During MRI Guided Brain Radiotherapy

<h3>Purpose/Objective(s)</h3> Magnetic resonance image guided brain radiotherapy (MRIgRT) is a promising advance to maximize the therapeutic ratio. An unexplored uncertainty in this approach is the residual setup uncertainty after translation-only MR based patient setup. This work quantified this uncertainty, including intrafraction motion, and determined the impact on planning target volume (PTV) margins. <h3>Materials/Methods</h3> Sixty-six patients treated on a 1.5T-based MR-Linac were included. Of these, 49 and 17 patients received conventional (15-30 fractions for glioblastoma) and hypofractionated (5 fractions for post-operative brain metastases) treatments, respectively; 1,329 total patient fractions were analyzed. At each fraction, patients were setup using online MR guidance by translation-only (T) fusion of the T1 volumetric MR to the planning image set to determine the treatment isocenter shift. These fusions were independently validated offline with translational and rotational (T+R) fusion of bony anatomy. The difference between the T and T+R co-registrations was applied to the original clinical target volume (CTV) to yield the CTV at each fraction (CTV_treat). The set of all CTV and CTV_treat volumes was incorporated in a population-based model parameterized by the relative thresholds (<b>CTV_min, Fx_min, Pt_min</b>). This model determined the minimum planning target volume (PTV) margin such that the PTV encompassed <b>CTV_min</b> of the CTV_treat volume in ≥ <b>Fx_min</b> of fractions in ≥ <b>Pt_min</b> of patients. In this work, we determined the PTV margin that for two sets of thresholds: A) (<b>CTV_min, Fx_min, Pt_min</b>) = (98%, 95%, 90%) and B) (<b>CTV_min, Fx_min, Pt_min</b>) = (95%, 95%, 90%). In a subset of 412 fractions, intrafraction motion was determined as the spatial registration difference between a post-treatment acquired MRI and the pre-treatment MRI. <h3>Results</h3> Residual setup uncertainty (difference between T+R and T registrations) and intrafraction motion results are summarized in Table 1. Across all patient fractions, the vector magnitude of the setup uncertainty and intrafraction motion was 1.3 ± 0.8 mm (mean ± SD) and 0.6 ± 0.5 mm, respectively. To accommodate the setup uncertainty, PTV margins of 2.5 and 1.8 mm were required to meet criteria sets A and B, respectively. <h3>Conclusion</h3> We have quantified residual setup and motion uncertainties for a large series of treated MR-Linac brain tumor patients, generated a model for a population-based PTV, and recommend a minimum PTV of 2-3 mm for this indication. This work is a crucial step towards developing an adaptive brain treatment based on dynamic tumor changes and tumor response.

Brown adipose tissue transplantation ameliorates diabetic nephropathy through the miR-30b pathway by targeting Runx1

ObjectiveAdipose tissue is a major source of circulating microRNAs (miRNAs) that can regulate target genes in distant organs. However, the role of brown adipose tissue (BAT) in diabetic kidney disease (DKD) is still unknown. We studied the original BAT miR-30b targeting two key fibrotic regulators, Runt-related transcription factor 1 (Runx1) and snail family zinc finger 1 (Snail1), to combat DKD. MethodsFirst, we transplanted healthy BAT from normal mouse donors into diabetic mice (induced by a high-fat diet and streptozotocin injection). In vitro, we observed extracellular vesicles (EVs) secreted from brown adipocytes. AgomiR-30b was directly administered to the BAT of diabetic mice twice weekly for 4 consecutive weeks. Next, the role of Runx1 in DKD was determined by using siRUNX1 or pCMV-RUNX1 in HK-2 cells and in diabetic mice treated with AAV9-U6-shRunx1 or AAV9-EF1a-Runx1. ResultsBAT transplantation reactivated endogenous BAT activity in diabetic mice, increased circulating miR-30b levels and significantly ameliorated DKD. In TGFβ1-treated HK-2 cells, miR-30b expression was significantly suppressed. miR-30b overexpression markedly decreased fibronectin and downregulated Runx1 and Snail1 expression, while silencing of miR-30b had the opposite effects. Next, Runx1 knockdown and overexpression mimicked the above phenotype of miR-30b mimics and inhibitors, respectively, both in vitro and in vivo. Moreover, Runx1 promoted TGFβ1-induced fibrosis by upregulating the PI3K pathway. ConclusionBAT-derived miRNAs might be a promising target for kidney protection in diabetes mellitus.

A Novel Multi-Objective Process Parameter Interval Optimization Method for Steel Production

Customized small batch orders and sustainable development requirements pose challenges for product quality control and manufacturing process optimization for steel production. Building a multi-quality objective process parameter optimization method that converts the original single target optimization into multi-objective interval capability optimization has become a new method to ensure product quality qualification rate and reduce production costs. Aiming at the multi-quality objective control problem of plate products, we proposed a novel multi-objective process parameter interval optimization model (MPPIO) with equipment process control capability and parameter sensitive analysis. The multi-output support vector regression method was used to establish a multi-quality objective prediction model, which was settled as a verification model for the process parameter optimization results based on the particle swarm optimization algorithm (PSO). The process control capability functions of key parameters were fitted based on the real data in production. With these functions, each optimized particle of the classical PSO was converted into the particle beam of the MIPPO. The iteration process was weight controlled by calculating the Morris sensitivity between each input parameter and output index in the multi-quality objective prediction model, and finally the processing control window of each key parameter was determined according to the process parameter optimization results. The experimental results show that the MPPIO model can obtain the optimal parameter optimization results with the maximum processing capacity and meet the customized processing range requirements. The MPPIO model can reduce the difficulty of control and save production costs while ensuring the product properties is qualified.

Coaching in the shadows: critically examining the unintended (non)influence of pedagogical practice

ABSTRACT Background Influence is at the very core of physical education and sport pedagogy. Indeed, a large and growing body of work has focused on the (inter)actions of sport pedagogues which are deemed to be influential in terms of shaping the thoughts, feelings and actions of others. In comparison, little attention has been paid to the practices of sport pedagogues that are noninfluential or unintentionally influential. That is, when pedagogues or learners choose not to do something, how they are not influenced/influential, or when practice (unintentionally) influences those who were (or were not) the original target. Paying greater attention to these issues holds strong potential to develop more critical and ethical understandings of influence that can inform the education and development of sport pedagogues. Aims The aims of this study are two-fold. Firstly, we seek to break new ground by providing novel insights into how, when, why, for whom, and under which circumstances pedagogical (inter)action is not influential, and where (inter)actions have had an unintended influence. Secondly, and relatedly, we seek to advance and illustrate methodological perspectives capable of critically understanding this topic. Data collection Data were generated using a bricolage of methods (i.e. participant observation, semi-structured interviews, and stimulated recall interviews) as part of a critical realist ethnography with one representative-level junior cricket squad in the UK. Data were subject to emic and etic readings in response to the aims of the study. In total, 182 h of observational data and 46 h of interview data were generated and analysed using the Critical Incident Technique (CIT). Here, the primary sense making devices were provided by Jones and Wallace’s (2005) theorising of orchestration, Elder-Vass’ (2010) causal power of social structures, and Mason’s (2002) concept of noticing. Analysis and discussion A small number of richly detailed and critical coaching (inter)actions are presented to illustrate the emergent meaning-making of different tacticians and targets of (non)influence. Specifically, the analysis introduces incidents that are illustrative of three novel types of (non)influence. The examples all highlight pathos between the coaches’ original intentions for (and reading of) influence, and the actual influence of practice for the athlete(s). Here, the discussion provides accounts of (a) noninfluence on targeted individuals, (b) unintended influence on non-targeted individuals, and (c) unintended influence on targeted individuals. Conclusion Overall, this paper contributes to a growing body of critical pedagogical research, positioning the work of influence less obtrusively. It provides a novel methodological, theoretical, and empirical contribution which practitioners, educators, researchers, and other stakeholders can engage with to critically consider how, when, why, for whom, and under which circumstances (inter)actions are (likely to be) influential or not. Further work could examine what pedagogues and learners decide to do and not do, as well as what they notice and do not notice as a basis to develop more critical and ethical practices.

Genetic dissection of the glutamatergic neuron system in cerebral cortex

Diverse types of glutamatergic pyramidal neurons mediate the myriad processing streams and output channels of the cerebral cortex1,2, yet all derive from neural progenitors of the embryonic dorsal telencephalon3,4. Here we establish genetic strategies and tools for dissecting and fate-mapping subpopulations of pyramidal neurons on the basis of their developmental and molecular programs. We leverage key transcription factors and effector genes to systematically target temporal patterning programs in progenitors and differentiation programs in postmitotic neurons. We generated over a dozen temporally inducible mouse Cre and Flp knock-in driver lines to enable the combinatorial targeting of major progenitor types and projection classes. Combinatorial strategies confer viral access to subsets of pyramidal neurons defined by developmental origin, marker expression, anatomical location and projection targets. These strategies establish an experimental framework for understanding the hierarchical organization and developmental trajectory of subpopulations of pyramidal neurons that assemble cortical processing networks and output channels.

Combination of ELM and BEMD for Target Recognition of SAR Images

For the problem of target recognition of synthetic aperture radar (SAR) images, a method based on the combination of bidimensional empirical mode decomposition (BEMD) and extreme learning machine (ELM) is proposed. BEMD performs feature extraction for SAR images, producing multi-layer bidimensional intrinsic mode functions (BIMF). These BIMFs covey the discrimination of the original target while effectively eliminating the noises. ELM conducts the classification of each BIMF with high efficiency and robustness. Finally, the decisions from different BIMFs are fused using a linear weighting strategy to reach a reliable decision on the target label. The proposed method compensates the relatively low adaptivity of ELM to noise corruption by BEMD feature extraction. Moreover, the multi-layer BIMFs provide more discriminative information for correct decision. Hence, the overall recognition performance can be improved. As an efficient recognition algorithm, the proposed method can be used in an embedded system for wide applications. Experiments are designed and implemented on the moving and stationary target acquisition and recognition (MSTAR) dataset. The proposed method is tested under both the standard operating condition (SOC) and extended operating conditions (EOCs). The results reflect its effectiveness and robustness via quantitative comparisons.