Throwing is a uniquely human skill that requires a high degree of coordination to successfully hit a target. Timing of ball release appears crucial as previous studies report required timing accuracies as short as 1-2ms, which however appear physiologically challenging. This study mathematically and experimentally demonstrates that humans can overcome these seemingly stringent timing requirements by shaping their hand trajectories to create extended timing windows, where ball releases achieve target hits despite temporal imprecision. Subjects practiced four task variations in a virtual environment, each with a distinct geometry of the solution space and different demands for timing. Model-based analyses of arm trajectories revealed that subjects first decreased timing error, followed by lengthening timing windows in their hand trajectories. This pattern was invariant across solution spaces, except for a control case. Hence, the exquisite skill that humans evolved for throwing is achieved by developing strategies that are less sensitive to temporal variability arising from neuromotor noise. This analysis also provides an explanation why coaches emphasize the “follow-through” in many ball sports.

Fusarium verticillioides, an important maize pathogen produces fumonisins and causes stalk and ear rot; thus, we are aimed to clarify its infection cycle by assessing enhanced green fluorescent protein (EGFP) expression in stalk and ear rot strains. Maize seeds were inoculated with stable and strongly pathogenic transformants. To investigate the degree of infection, inoculated plants were observed under a stereo fluorescence microscope, and affected tissue strains were detected using PCR. We found that both transformants infected maize. Hyphae infected the plants from radical to the stem and extended to the ear and infected ear kernels caused a second infection. This process formed the infection cycle.

An essential feature of goal-directed behavior is the ability to selectively respond to the diverse stimuli in one's environment. However, the neural mechanisms that enable us to respond to target stimuli while ignoring distractor stimuli are poorly understood. To study this sensory selection process, we trained male and female mice in a selective detection task in which mice learn to respond to rapid stimuli in the target whisker field and ignore identical stimuli in the opposite, distractor whisker field. In expert mice, we used widefield Ca 2+ imaging to analyze target-related and distractor-related neural responses throughout dorsal cortex. For target stimuli, we observed strong signal activation in primary somatosensory cortex (S1) and frontal cortices, including both the whisker representation of primary motor cortex (wMC) and anterior lateral motor cortex (ALM). For distractor stimuli, we observe strong signal activation in S1, with minimal propagation to frontal cortex. Our data support only modest subcortical filtering, with robust, step-like attenuation in distractor processing between mono-synaptically coupled regions of S1 and wMC. This study establishes a highly robust model system for studying the neural mechanisms of sensory selection and places important constraints on its implementation. SummaryResponding to task-relevant stimuli while ignoring task-irrelevant stimuli is critical for goaldirected behavior. Yet, the neural mechanisms involved in this selection process are poorly understood. We trained mice in a detection task with both target and distractor stimuli. During expert performance, we measured neural activity throughout cortex using widefield imaging. We observed responses to target stimuli in multiple sensory and motor cortical regions. In contrast, responses to distractor stimuli were abruptly suppressed beyond sensory cortex. Our findings localize the sites of attenuation when successfully ignoring a distractor stimulus, and provide essential foundations for further revealing the neural mechanism of sensory selection and distractor suppression.

Exercise can increase skeletal muscle sensitivity to insulin, improve insulin resistance and regulate glucose homeostasis in rat models of type 2 diabetes. However, the potential mechanism remains poorly understood. In this study, we established a male Sprague–Dawley rat model of type 2 diabetes, with insulin resistance and β cell dysfunction, which was induced by a high-fat diet and low-dose streptozotocin to replicate the pathogenesis and metabolic characteristics of type 2 diabetes in humans. We also investigated the possible mechanism by which chronic and acute exercise improves metabolism, and the phosphorylation and expression of components of AMP-activated protein kinase (AMPK) and downstream components of phosphatidylinositol 3-kinase (PI3K) signaling pathways in the soleus. As a result, blood glucose, triglyceride, total cholesterol, and free fatty acid were significantly increased, whereas insulin level progressively declined in diabetic rats. Interestingly, chronic and acute exercise reduced blood glucose, increased phosphorylation and expression of AMPKα1/2 and the isoforms AMPKα1 and AMPKα2, and decreased phosphorylation and expression of AMPK substrate, acetyl CoA carboxylase (ACC). Chronic exercise upregulated phosphorylation and expression of AMPK upstream kinase, LKB1. But acute exercise only increased LKB1 expression. In particular, exercise reversed the changes in protein kinase C (PKC)ζ/λ phosphorylation, and PKCζ phosphorylation and expression. Additionally, exercise also increased protein kinase B (PKB)/Akt1, Akt2 and GLUT4 expression, but AS160 protein expression was unchanged. Chronic exercise elevated Akt (Thr308) and (Ser473) and AS160 phosphorylation. Finally, we found that exercise increased peroxisome proliferator-activated receptor-γ coactivator 1 (PGC1) mRNA expression in the soleus of diabetic rats. These results indicate that both chronic and acute exercise influence the phosphorylation and expression of components of the AMPK and downstream to PIK3 (aPKC, Akt), and improve GLUT4 trafficking in skeletal muscle. These data help explain the mechanism how exercise regulates glucose homeostasis in diabetic rats.

No abstract

Triple-negative breast cancer (TNBC), characterized by the lack of expression of the estrogen receptor, the progesterone receptor, and the human epidermal growth factor receptor 2, is an aggressive form of cancer that conveys unpredictable and poor prognosis due to limited treatment options and lack of effective targeted therapies. Wnt/β-catenin signaling is hyperactivated in TNBC, which promotes the progression of TNBC. However, the molecular mechanism of Wnt/β-catenin activation in TNBC remains unknown. Here, we report the drastic overexpression of miR-221/222 in all of four TNBC cell lines and TNBC primary tumor samples from patients. Furthermore, we demonstrate by both ex vivo and xenograft experiments that inhibiting miR-221/222 expression in a TNBC cell line (MDA-MB-231) suppresses its proliferation, viability, epithelial-to-mesenchymal transition, and migration; whereas expressing miR-221/222 in a non-TNBC line (MCF7) promotes all of the above cancer properties. miR-221/222 achieve so by directly repressing multiple negative regulators of the Wnt/β-catenin signaling pathway, including WIF1, SFRP2, DKK2, and AXIN2, to activate the pathway. Notably, the level of miR-221/222 expression is inversely correlated whereas that of WIF1, DKK2, SFRP2, and AXIN2 expression is positively correlated with the patient survival. Last, we show that anti-miR-221/222 significantly increases apoptotic cells with tamoxifen/Wnt3a treatment but not with cyclophosphamide/Wnt3a treatment. These results demonstrate that miR-221/222 activate the Wnt/β-catenin signaling to promote the aggressiveness and TNBC properties of breast cancers, and thus reveal a new prospect for TNBC treatment.

Variability in motor performance results from the interplay of error correction and neuromotor noise. This study examined whether visual amplification of error, previously shown to improve performance, affects not only error correction, but also neuromotor noise, typically regarded as inaccessible to intervention. Seven groups of healthy individuals, with six participants in each group, practiced a virtual throwing task for three days until reaching a performance plateau. Over three more days of practice, six of the groups received different magnitudes of visual error amplification; three of these groups also had noise added. An additional control group was not subjected to any manipulations for all six practice days. The results showed that the control group did not improve further after the first three practice days, but the error amplification groups continued to decrease their error under the manipulations. Analysis of the temporal structure of participants’ corrective actions based on stochastic learning models revealed that these performance gains were attained by reducing neuromotor noise and, to a considerably lesser degree, by increasing the size of corrective actions. Based on these results, error amplification presents a promising intervention to improve motor function by decreasing neuromotor noise after performance has reached an asymptote. These results are relevant for patients with neurological disorders and the elderly. More fundamentally, these results suggest that neuromotor noise may be accessible to practice interventions.

The detection of an error in the motor output and the correction in the next movement are critical components of any form of motor learning. Accordingly, a variety of iterative learning models have assumed that a fraction of the error is adjusted in the next trial. This critical fraction, the correction gain, learning rate, or feedback gain, has been frequently estimated via least-square regression of the obtained data set. Such data contain not only the inevitable noise from motor execution, but also noise from measurement. It is generally assumed that this noise averages out with large data sets and does not affect the parameter estimation. This study demonstrates that this is not the case and that in the presence of noise the conventional estimate of the correction gain has a significant bias, even with the simplest model. Furthermore, this bias does not decrease with increasing length of the data set. This study reveals this limitation of current system identification methods and proposes a new method that overcomes this limitation. We derive an analytical form of the bias from a simple regression method (Yule-Walker) and develop an improved identification method. This bias is discussed as one of other examples for how the dynamics of noise can introduce significant distortions in data analysis.