Hippocampal and prefrontal contributions to memory retrieval: Examination of immediate early gene,NMDA receptor and environmental interactions

Animals can use a range of strategies to recall important locations. These include simple stimulus–response strategies and more complex spatial (place) strategies, which are thought to have distinct neural substrates. The hippocampus—and NMDA receptor activation therein—is considered to be crucial for spatial, but not response strategies. The medial prefrontal cortex has also been implicated in memory retrieval; however, evidence concerning its specific role is equivocal. Both hippocampal and prefrontal regions have been associated with flexible behavioural responding (e.g. when task demands change). Here, we investigated the use of spatial and non‐spatial strategies in the Morris water maze and their associated brain areas in rats using immediate early gene (IEG) imaging of Zif268 and c‐Fos. Specifically, we charted the involvement of hippocampal and prefrontal subregions during retrieval of spatial and non‐spatial memories. Behavioural flexibility was also examined using intact and partial cue configurations during recall. Results indicated that regions of both the hippocampus (area CA3) and prefrontal cortex (anterior cingulate cortex) were preferentially engaged in spatial memory recall compared to response learning. In addition, both spatial and non‐spatial memories were dependent on NMDA receptor activation. MK801 impaired recall performance across all groups and reduced IEG activation across hippocampal and prefrontal regions. Finally, IEG results revealed divergent patterns of Zif268 and c‐Fos activity and support the suggestion that Zif268 plays a functional role in the recall of long‐term memories.     

Ependymoma and pilocytic astrocytoma: Differentiation using radiomics approach based on machine learning

Mandatory accurate and specific diagnosis demands have brought about increased challenges for radiologists in pediatric posterior fossa tumor prediction and prognosis. With the development of high-performance computing and machine learning technologies, radiomics provides increasing opportunities for clinical decision-making. Several studies have applied radiomics as a decision support tool in intracranial tumors differentiation. Here we seek to achieve preoperative differentiation between ependymoma (EP) and pilocytic astrocytoma (PA) using radiomics analysis method based on machine learning. A total of 135 Magnetic Resonance Imaging (MRI) slices are divided into training sets and validation sets. Three kinds of radiomics features, including Gabor transform, texture and wavelet transform based ones are used to obtain 300 multimodal features. Kruskal–Wallis test score (KWT) and support vector machines (SVM) are applied for feature selection and tumor differentiation. The performance is investigated via accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. Results show that the accuracy, sensitivity, specificity, and AUC of the selected feature set are 0.8775, 0.9292, 0.8000, and 0.8646 respectively, having no significant differences compared with the overall feature set. For different types of features, texture features yield the best differentiation performance and the significance analysis results are consistent with this. Our study demonstrates texture features perform better than the other features. The radiomics approach based on machine learning is efficient for pediatric posterior fossa tumors differentiation and could enhance the application of radiomics methods for assisted clinical diagnosis.     

Biohorology and biomarkers of aging: Current state-of-the-art,challenges and opportunities

The aging process results in multiple traceable footprints, which can be quantified and used to estimate an organism's age. Examples of such aging biomarkers include epigenetic changes, telomere attrition, and alterations in gene expression and metabolite concentrations. More than a dozen aging clocks use molecular features to predict an organism's age, each of them utilizing different data types and training procedures. Here, we offer a detailed comparison of existing mouse and human aging clocks, discuss their technological limitations and the underlying machine learning algorithms. We also discuss promising future directions of research in biohorology — the science of measuring the passage of time in living systems. Overall, we expect deep learning, deep neural networks and generative approaches to be the next power tools in this timely and actively developing field.     

基于预测剂量引导的宫颈癌自动计划研究

目的:使用机器学习方法建立宫颈癌计划剂量预测回归模型，并将预测剂量引导生成Monaco 计划系统（TPS）可调用 的优化模板文件，实现宫颈癌的自动计划设计。方法:对50例宫颈癌术后调强治疗计划中的危及器官采集基于重叠体积直 方图的几何特征值和基于剂量直方图的剂量目标值，建模后将模型预测剂量结果自动生成Monaco TPS模板文件，进而由TPS 调用优化。使用该方法对另外10例未参与模型训练的测试病例进行自动计划设计，并和人工设计的计划进行对比分析。 结果:自动计划比手动计划的平均设计时间减少了40 min（P<0.05），且平均调优次数降低了3次（P<0.05），剂量学指标和计 划执行效率上两者无明显差异（P>0.05）。结论:基于预测剂量引导的宫颈癌自动计划可以达到临床要求，并且提高了计划 设计效率。     

Short‐term plasticity following motor sequence learning revealed by diffusion magnetic resonance imaging

Current noninvasive methods to detect structural plasticity in humans are mainly used to study long‐term changes. Diffusion magnetic resonance imaging (MRI) was recently proposed as a novel approach to reveal gray matter changes following spatial navigation learning and object‐location memory tasks. In the present work, we used diffusion MRI to investigate the short‐term neuroplasticity that accompanies motor sequence learning. Following a 45‐min training session in which participants learned to accurately play a short sequence on a piano keyboard, changes in diffusion properties were revealed mainly in motor system regions such as the premotor cortex and cerebellum. In a second learning session taking place immediately afterward, feedback was given on the timing of key pressing instead of accuracy, while participants continued to learn. This second session induced a different plasticity pattern, demonstrating the dynamic nature of learning‐induced plasticity, formerly thought to require months of training in order to be detectable. These results provide us with an important reminder that the brain is an extremely dynamic structure. Furthermore, diffusion MRI offers a novel measure to follow tissue plasticity particularly over short timescales, allowing new insights into the dynamics of structural brain plasticity.     

复方党参对低氧暴露小鼠认知功能的改善作用

目的观察复方党参干预对低氧暴露致认知功能下降的改善作用,为进一步评价复方党参的抗缺氧效果提供依据。方法将成年雄性昆明小鼠按体质量随机分成3组(n=12):常氧对照组、低氧模型组、复方党参干预组,将后2组置于模拟海拔高度5500 m低压氧舱内,每天8 h,持续28 d。采用旷场、避暗和穿梭箱实验检测其自发活动能力、被动逃避及主动逃避学习记忆能力。结果总穿行格数及竖立次数低氧模型组小鼠明显低于常氧对照组,复方党参干预组明显高于低氧模型组(P均0.05)。避暗潜伏期低氧模型组〔(123.2±106.7)s〕显著低于常氧对照组〔(230.5±97.9)s〕,复方党参干预组小鼠的潜伏期〔(214.4±113.7)s〕显著长于低氧模型组(P均0.05)。穿梭箱测试中,3组小鼠的主动逃避时间随天数增加均呈下降趋势,但低氧模型组小鼠显著高于常氧对照组,复方党参干预组明显低于低氧模型组(P均0.05)。结论低氧暴露对小鼠的学习记忆造成损伤,复方党参可改善缺氧导致认知功能的下降。     

Input representations and classification strategies for automated human gait analysis

BackgroundQuantitative gait analysis produces a vast amount of data, which can be difficult to analyze. Automated gait classification based on machine learning techniques bear the potential to support clinicians in comprehending these complex data. Even though these techniques are already frequently used in the scientific community, there is no clear consensus on how the data need to be preprocessed and arranged to assure optimal classification accuracy outcomes.Research questionIs there an optimal data aggregation and preprocessing workflow to optimize classification accuracy outcomes?MethodsBased on our previous work on automated classification of ground reaction force (GRF) data, a sequential setup was followed: firstly, several aggregation methods – early fusion and late fusion – were compared, and secondly, based on the best aggregation method identified, the expressiveness of different combinations of signal representations was investigated. The employed dataset included data from 910 subjects, with four gait disorder classes and one healthy control group. The machine learning pipeline comprised principle component analysis (PCA), z-standardization and a support vector machine (SVM).ResultsThe late fusion aggregation, i.e., utilizing majority voting on the classifier's predictions, performed best. In addition, the use of derived signal representations (relative changes and signal differences) seems to be advantageous as well.SignificanceOur results indicate that great caution is needed when data preprocessing and aggregation methods are selected, as these can have an impact on classification accuracies. These results shall serve future studies as a guideline for the choice of data aggregation and preprocessing techniques to be employed.     

Evaluation of machine learning methods for organic apple authentication based on diffraction grating and image processing

Optical measuring technologies coupled with machine learning algorithms can be used to build a home-made sensor system. We built such a sensor system using a smartphone and a diffraction grating sheet. Diffraction images were captured under white light illumination and converted into a data matrix for data analysis. In this paper we present a systematic evaluation of this sensor system on the task of differentiating organic apples from conventional ones. We used the sensor system to measure 150 organic and conventional apples as rainbow images. We processed the rainbow images using computer vision techniques, built machine learning and chemometrics models, and used the resultant models to classify testing samples. Moreover, a comparative study was conducted where the same set of apples were scanned by a commercial spectrometer resulting in spectral data of the apple samples and classification was undertaken using partial least squares discriminant analysis (PLS-DA). Experimental results show that state of the art machine learning algorithms such as support vector machine (SVM) and locally weighted partial least squares classifier (LW-PLSC) are effective in handling low-quality image data with classification accuracies of 93 − 100%. These results suggest that the sensor system is convenient and low-cost, and provides a fast, effective, non-destructive and viable solution for in-line food authentication.     

Artificial intelligence-assisted psychosis risk screening in adolescents: Practices and challenges

Artificial intelligence-based technologies are gradually being applied to psych-iatric research and practice. This paper reviews the primary literature concerning artificial intelligence-assisted psychosis risk screening in adolescents. In terms of the practice of psychosis risk screening, the application of two artificial intelligence-assisted screening methods, chatbot and large-scale social media data analysis, is summarized in detail. Regarding the challenges of psychiatric risk screening, ethical issues constitute the first challenge of psychiatric risk screening through artificial intelligence, which must comply with the four biomedical ethical principles of respect for autonomy, nonmaleficence, beneficence and impartiality such that the development of artificial intelligence can meet the moral and ethical requirements of human beings. By reviewing the pertinent literature concerning current artificial intelligence-assisted adolescent psychosis risk screens, we propose that assuming they meet ethical requirements, there are three directions worth considering in the future development of artificial intelligence-assisted psychosis risk screening in adolescents as follows: nonperceptual real-time artificial intelligence-assisted screening, further reducing the cost of artificial intelligence-assisted screening, and improving the ease of use of artificial intelligence-assisted screening techniques and tools.