Neural Analytics (United States)

Parkinson's disease (PD) is a chronic, neurodegenerative condition characterized by motor symptoms such as bradykinesia, rigidity, and tremor, alongside multiple nonmotor symptoms. The appearance of motor symptoms is linked to progressive dopaminergic neuron loss within the substantia nigra. PD incidence increases sharply with age, suggesting a strong association between mechanisms driving biological aging and the development and progression of PD. However, the role of aging in the pathogenesis of PD remains understudied. Numerous models of PD, including cell models, toxin-induced models, and genetic models in rodents and nonhuman primates (NHPs), reproduce different aspects of PD, but preclinical studies of PD rarely incorporate age as a factor. Studies using patient neurons derived from stem cells via reprogramming methods retain some aging features, but their characterization, particularly of aging markers and reproducibility of neuron type, is suboptimal. Investigation of age-related changes in PD using animal models indicates an association, but this is likely in conjunction with other disease drivers. The biggest barrier to drawing firm conclusions is that each model lacks full characterization and appropriate time-course assessments. There is a need to systematically investigate whether aging increases the susceptibility of mouse, rat, and NHP models to develop PD and understand the role of cell models. We propose that a significant investment in time and resources, together with the coordination and sharing of resources, knowledge, and data, is required to accelerate progress in understanding the role of biological aging in PD development and improve the reliability of models to test interventions.

Yenilenebilir enerji kaynaklarından güneş enerji sistemlerinin tasarımında güneş radyasyonunu tahmin etmek oldukça önemlidir. Güneş enerjisi, global güneş radyasyonuna bağlıdır. Güneş radyasyonunun Yapay Sinir ağı modeli (YSA) ile tahmin edilmesinde girdi değişkeni olarak, hava sıcaklığı, güneşlenme süresi, buhar basıncı, bulutluluk gibi çeşitli meteorolojik değişkenler kullanılmaktadır. Bu çalışmada, Samsun bölgesi için Mart 2017-Şubat 2019 tarihleri arasındaki günlük güneş radyasyonu YSA yöntemi ile tahmin edilmeye çalışılmıştır. YSA yönteminde farklı giriş değişkenleri için Levenberg-Marquardt eğitim algoritması, logaritmik sigmoid ve doğrusal transfer fonksiyonu kullanılmıştır. Model performansı en yüksek 9 girişli meterolojik veriler (ortalama sıcaklık, ortalama nispi nem, ortalama rüzgar hızı, ortalama buhar basıncı, ortalama bulutluluk oranı, güneşlenme süresi, maksimum sıcaklık, minimum sıcaklık, 5 cm’de toprak sıcaklığı) ile elde edilmiştir. Test verileri için R değeri 0.9603, MSE değeri 0.3516 olarak bulunmuştur. İleri beslemeli YSA modeli yaklaşımının diğer meteorolojik değişkenler ile birlikte güneş radyasyonunu tahmin etmek için yüksek bir performans sağladığı görülmüştür. Ayrıca, YSA’ya güneşlenme süresi giriş olarak verildiğinde, R değeri 0.9032 olarak elde edilmiştir.

Background: The current lack of effective tools for prehospital identification of Large Vessel Occlusion (LVO) represents a significant barrier to efficient triage of stroke patients and detriment to treatment efficacy. The validation of objective Transcranial Doppler (TCD) metrics for LVO detection could provide first responders with requisite tools for informing stroke transfer decisions, dramatically improving patient care.Objective: To compare the diagnostic efficacy of two such candidate metrics: Velocity Asymmetry Index (VAI), which quantifies disparity of blood flow velocity across the cerebral hemispheres, and Velocity Curvature Index (VCI), a recently proposed TCD morphological biomarker. Additionally, we investigate a simple decision tree combining both metrics.Methods: We retrospectively compare accuracy/sensitivity/specificity (ACC/SEN/SPE) of each method (relative to standard CT-Angiography) in detecting LVO in a population of 66 subjects presenting with stroke symptoms (33 with CTA-confirmed LVO), enrolled consecutively at Erlanger Southeast Regional Stroke Center in Chattanooga, TN.Results: Individual VCI and VAI metrics demonstrated robust performance, with area under receiver operating characteristic curve (ROC-AUC) of 94% and 88%, respectively. Additionally, leave-one-out cross-validation at optimal identified thresholds resulted in 88% ACC (88% SEN) for VCI, vs. 79% ACC (76% SEN) for VAI. When combined, the resultant decision tree achieved 91% ACC (94% SEN).Discussion: We conclude VCI to be superior to VAI for LVO detection, and provide evidence that simple decision criteria incorporating both metrics may further optimize.Performance: Our results suggest that machine-learning approaches to TCD morphological analysis may soon enable robust prehospital LVO identification.Registration: Was not required for this feasibility study.

Intracranial Hypertension, a disorder characterized by elevated pressure in the brain, is typically monitored in neurointensive care and diagnosed only after elevation has occurred. This reaction-based method of treatment leaves patients at higher risk of additional complications in case of misdetection. The detection of intracranial hypertension has been the subject of many recent studies in an attempt to accurately characterize the causes of hypertension, specifically examining waveform morphology. We investigate the use of Deep Learning, a hierarchical form of machine learning, to model the relationship between hypertension and waveform morphology, giving us the ability to accurately detect presence hypertension. Data from 60 patients, showing intracranial pressure levels over a half hour time span, was used to evaluate the model. We divided each patient's recording into average normalized beats over 30 sec segments, assigning each beat a label of high (i.e. greater than 15 mmHg) or low intracranial pressure. The model was tested to predict the presence of elevated intracranial pressure. The algorithm was found to be 92.05± 2.25% accurate in detecting intracranial hypertension on our dataset.

Finding a path between locations is a routine task in daily life. Mental navigation is often used to plan a routeto a destination that is not visible from the current location. We first used functional magnetic resonance imaging (fMRI)and surface-based averaging methods to find high-level brain regions involved in imagined navigation between locationsin a building very familiar to each participant. This revealed a mental navigation network that includes the precuneus,retrosplenial cortex (RSC), parahippocampal place area (PPA), occipital place area (OPA), supplementary motor area(SMA), premotor cortex, and areas along the medial and anterior intraparietal sulcus. We then visualized retinotopic mapsin the entire cortex using wide-field, natural scene stimuli in a separate set of fMRI experiments. This revealed fivedistinct visual streams or ‘fingers’ that extend anteriorly into middle temporal, superior parietal, medial parietal,retrosplenial and ventral occipitotemporal cortex. By using spherical morphing to overlap these two data sets, we showedthat the mental navigation network primarily occupies areas that also contain retinotopic maps. Specifically, scene-selectiveregions RSC, PPA and OPA have a common emphasis on the far periphery of the upper visual field. Theseresults suggest that bottom-up retinotopic organization may help to efficiently encode scene and location information inan eye-centered reference frame for top-down, internally generated mental navigation. This study pushes the border ofvisual cortex further anterior than was initially expected.

Little is known about the neural networks supporting value computation during complex social decisions. We investigated this question using functional magnetic resonance imaging while subjects made donations to different charities. We found that the blood oxygenation level-dependent signal in ventral medial prefrontal cortex (VMPFC) correlated with the subjective value of voluntary donations. Furthermore, the region of the VMPFC identified showed considerable overlap with regions that have been shown to encode for the value of basic rewards at the time of choice, suggesting that it might serve as a common valuation system during decision making. In addition, functional connectivity analyses indicated that the value signal in VMPFC might integrate inputs from networks, including the anterior insula and posterior superior temporal cortex, that are thought to be involved in social cognition.

Several studies have found that Parkinson's disease (PD) disrupts the organization of complex motor sequences regardless of the influence of parkinsonian medications. A clear candidate for the neural bases of such deficits, which we term “coordinative,” is the failure to integrate propioceptive and visual information by cortico-striatal circuits in a timed fashion. Recent reports, however, have indicated that deep-brain stimulation of the subthalamic nucleus (STN DBS) may result in an improvement in coordinative deficits beyond the amelioration of “intensive deficits” such as bradykinesia and scaling errors. The present study examined the spatio-temporal organization underlying the shaping of the hand during reaching to grasp objects differing in shape. Six PD patients ON and OFF their STN DBS when OFF their concomitant medications and six age-matched controls participated in this study. STN DBS improved the coordination involved in preshaping the hand while grasping. We discuss these results in light of our earlier work with PD patients on and off dopamine replacement therapy.

\n Numa área experimental de 25,2 ha formada com o capim-braquiarão (Brachiaria brizantha (Hochst) Stapf.) cv. Marandu e localizada no Campus da USP em Pirassununga/SP, durante o período de janeiro a julho de 2004, conduziu-se a presente pesquisa pela Faculdade de Zootecnia e Engenharia de Alimentos (FZEA/USP) com os seguintes objetivos: 1) Determinar as frações de carboidratos (A - açúcares solúveis com rápida degradação ruminal; B1- amido e pectina; B2 - parede celular com taxa de degradação mais lenta; C - fração não digerida) e as frações protéicas (A - NNP; B1 - peptídeos e oligopeptídeos; B2 - proteína verdadeira; B3 - NFDN; C - NIDA) na forragem da gramínea, baseados nas equações utilizadas pelo método de Cornell; 2) Relacionar outras variáveis com as medições em campo de experimentos paralelos e dados de elementos de clima com as frações protéicas e de carboidratos com o auxílio de um modelo computacional baseado em redes neurais artificiais (RNA). O delineamento foi em blocos completos e casualizados, com quatro tratamentos (ofertas de forragem de 5, 10, 15 e 20% - kg de massa seca por 100 kg de peso animal.dia) e quatro repetições. Cada bloco era dividido em quatro unidades experimentais de 1,575 ha, com cinco piquetes de 0,315 ha cada. Os animais eram manejados em cada unidade experimental em lotação rotacionada, com períodos de descanso de 28 dias no verão e 56 dias no inverno e período de ocupação de 7 dias, respectivamente. As amostras eram colhidas 2 dias antes da entrada dos animais à altura do resíduo do pastejo anterior. Foram determinados produção de massa seca (MS), alturas de pré e pós pastejo, fibras em detergente ácido (FDA) e neutro (FDN), sacarose, amido, lignina, extrato etéro (EE), carboidrato totais (CHO), carboidratos não estruturais (CNE), frações A, B1, B2 e C de carboidratos, proteína bruta (PB), frações A, B1, B2, B3 e C de proteínas e análise de uma rede neural artificial para uma predição dos teores de FDA, fibra em detergente neutro, PB e as frações protéicas. A produção de massa seca (MS) foi significativa, quando se estudou os efeitos da oferta de forragem (p<0,05), ciclo de pastejo (p<0,05) e da interação oferta de forragem x ciclo de pastejo (p<0,05). A maior produção foi no mês de março, quando se alcançou a média de 16140 kg MS/há para o oferta de 20%. Os teores de FDA foram significativos, quando se estudou a oferta de forragem (p<0,05) e seus maiores. Os teores médios da fibra em detergente neutro foram de 66,3 e 64,7% no verão e inverno respectivamente. Houve diferenças significativas para PB, quando se estudou a oferta de forragem (p<0,05), sendo seus teores médios de maior valor na OF a 5%. Observa aumento dos CNE em função de lâminas e colmos ao longo das estações do ano com interação no CP x OF (p<0,05) e seus maiores valores foram encontrados no ciclo de pastejo 3 na oferta de forragem 5%. Os teores de CHO totais apresentaram diferenças (p<0,10) em função da oferta de forragem, sendo os maiores teores médios encontrados na oferta de forragem de 20%. As frações A e B2 de CHO foram significativas em função da oferta de forragem (p<0,05), enquanto que os maiores teores médios da fração A foram encontrados nos ciclos de pastejo 3 e 4 e das frações B2 (%CHO) no ciclo de pastejo 1. As frações B2 e C de CHO apresentaram-se diferentes (p<0,05) nos ciclos de pastejo, sendo decrescentes para a fração B1 e crescentes para a fração C. As frações A (47%), B1 (11%) e B3 (10%) de proteínas foram significativas nos ciclos de pastejos. Os teores médios da fração B2 de proteínas apresentaram-se semelhantes (p>0,05) e os da fração C de proteínas foram diferentes (p<0,05) nas ofertas de forragem e ciclos de pastejo. Conclui-se que os ciclos de pastejos interferiram em todas as variáveis estudadas e que os teores das frações de proteínas e carboidratos estão dentro da variação (%) encontrada na literatura. A rede neural artificial conseguiu vincular as interações existentes de dados de campo e estimar os valores laboratoriais dentro de erros esperados, permitindo com isso desvincular análises laboratoriais, de qualidade de planta forrageira, à pesquisa agropecuária e com isso obter além de resultados mais rápidos, menor custo de pesquisa.\n

The therapeutic potential of glial cell line-derived neurotrophic factor (GDNF) for Parkinson's disease is likely to depend on sustained delivery of the appropriate amount to the target areas. Recombinant adeno-associated viral vectors (rAAVs) expressing GDNF may be a suitable delivery system for this purpose. The aim of this study was to define a sustained level of GDNF that does not affect the function of the normal dopamine (DA) neurons but does provide anatomical and behavioral protection against an intrastriatal 6-hydroxydopamine (6-OHDA) lesion in the common marmoset. We found that unilateral intrastriatal injection of rAAV resulting in the expression of high levels of GDNF (14 ng/mg of tissue) in the striatum induced a substantial bilateral increase in tyrosine hydroxylase protein levels and activity as well as in DA turnover. Expression of low levels of GDNF (0.04 ng/mg of tissue), on the other hand, produced only minimal effects on DA synthesis and only on the injected side. In addition, the low level of GDNF provided approximately 85% protection of the nigral DA neurons and their projections to the striatum in the 6-OHDA-lesioned hemisphere. Furthermore, the anatomical protection was accompanied by a complete attenuation of sensorimotor neglect, head position bias, and amphetamine-induced rotation. We conclude that when delivered continuously, a low level of GDNF in the striatum (approximately threefold above baseline) is sufficient to provide optimal functional outcome.