Mcconnellmahmoud5490

This paper uses Monte Carlo simulations to investigate the interaction of short-wave infrared (SWIR) light with vascular tissue as a step toward the development of a non-invasive optical sensor for measuring blood lactate in humans. The primary focus of this work was to determine the optimal source-detector separation, penetration depth of light at SWIR wavelengths in tissue, and the optimal light power required for reliable detection of lactate. The investigation also focused on determining the non-linear variations in absorbance of lactate at a few select SWIR wavelengths. SWIR photons only penetrated 1.3 mm and did not travel beyond the hypodermal fat layer. The maximum output power was only 2.51% of the input power, demonstrating the need for a highly sensitive detection system. Simulations optimized a source-detector separation of 1 mm at 1684 nm for accurate measurement of lactate in blood.The usefulness of magnetic resonance imaging (MRI) in predicting gait ability in stroke patients remains unclear. Therefore, MRI evaluations have not yet been standardized in stroke rehabilitation. We performed a systematic review to consolidate evidence regarding the use of MRIs in predicting gait ability of stroke patients. The Medline, Cumulative Index to Nursing and Allied Health Literature, and SCOPUS databases were comprehensively searched. We included all literature published from each source's earliest date to August 2020. We included 19 studies 8 were classified as structure- or function-based MRI studies and 11 as neural tract integrity-based MRI studies. Most structure- or function-based MRI studies indicated that damage to motor-related areas (primary motor cortex, corona radiata, internal capsule, and basal ganglia) or insula was related to poor gait recovery. learn more In neural tract integrity-based MRI studies, integrity of the corticospinal tract was related to gait ability. Some studies reported predictive value of the corticoreticular pathway. All included studies had some concerns, at least one, based on the Cochrane risk of bias instrument. This review suggests that MRIs are useful in predicting gait ability of stroke patients. However, we cannot make definitive conclusion regarding the predictive value, due to the lack of quantitative evaluations.The acute non-image forming (NIF) effects of daytime light on momentary mood had been-although not always-established in the current literature. It still remains largely unknown whether short-time light exposure would modulate emotion perception in healthy adults. The current study (N = 48) was conducted to explore the effects of illuminance (100 lx vs. 1000 lx at eye level) and correlated color temperature (CCT, 2700 K vs. 6500 K) on explicit and implicit emotion perception that was assessed with emotional face judgment task and emotional oddball task respectively. Results showed that lower CCT significantly decreased negative response bias in the face judgment task, with labeling ambiguous faces less fearful under 2700 K vs. 6500 K condition. Moreover, participants responded slightly faster for emotional pictures under 6500 K vs. 2700 K condition, but no significant effect of illuminance or CCT on negativity bias was revealed in the emotional oddball task. These findings highlighted the differential role of illuminance and CCT in regulating instant emotion perception and suggested a task-dependent moderation of light spectrum on negativity bias.Inhibitory G proteins (Gi proteins) are highly homologous but play distinct biological roles. However, their isoform-specific detection remains challenging. To facilitate the analysis of Gαi3 expression, we generated a Gnai3- iresGFP reporter mouse line. An internal ribosomal entry site (IRES) was inserted behind the stop-codon of the Gnai3 gene to initiate simultaneous translation of the GFP cDNA together with Gαi3. The expression of GFP was confirmed in spleen and thymus tissue by immunoblot analysis. Importantly, the GFP knock-in (ki) did not alter Gαi3 expression levels in all organs tested including spleen and thymus compared to wild-type littermates. Flow cytometry of thymocytes, splenic and blood cell suspensions revealed significantly higher GFP fluorescence intensities in homozygous ki/ki animals compared to heterozygous mice (+/ki). Using cell-type specific surface markers GFP fluorescence was assigned to B cells, T cells, macrophages and granulocytes from both splenic and blood cells and additionally blood-derived platelets. Moreover, immunofluorescent staining of the inner ear from knock-in mice unraveled GFP expression in sensory and non-sensory cell types, with highest levels in Deiter's cells and in the first row of Hensen's cells in the organ of Corti, indicating a novel site for Gαi3 expression. In summary, the Gnai3- iresGFP reporter mouse represents an ideal tool for precise analyses of Gαi3 expression patterns and sites.We present the use of a power limiting apparatus to evaluate ultrafast optical nonlinearities of transparent liquids (water and ethanol) in the femtosecond filamentation regime. The setup has been previously employed for the same purpose, however, in a longer pulsewidth (> 20 ps) regime, which leads to an ambiguous evaluation of the critical power for self-focusing. The uncertainty originates from the existence of a threshold power for optical breakdown well below the critical power for self-focusing within this timeframe. Contrarily, using the proposed apparatus in the femtosecond regime, we observe for the first time a unique optical response, which features the underlying physics of laser filamentation. Importantly, we demonstrate a dependence of the optical transmission of the power limiter on its geometrical, imaging characteristics and the conditions under which a distinct demarcation for the critical power for self-focusing can be determined. The result is supported by numerical simulations, which indicate that the features of the observed power-dependent optical response of the power limiting setup are physically related to the spontaneous transformation of the laser pulses into nonlinear conical waves.Numerous applications in diffusion MRI involve computing the orientationally-averaged diffusion-weighted signal. Most approaches implicitly assume, for a given b-value, that the gradient sampling vectors are uniformly distributed on a sphere (or 'shell'), computing the orientationally-averaged signal through simple arithmetic averaging. One challenge with this approach is that not all acquisition schemes have gradient sampling vectors distributed over perfect spheres. To ameliorate this challenge, alternative averaging methods include weighted signal averaging; spherical harmonic representation of the signal in each shell; and using Mean Apparent Propagator MRI (MAP-MRI) to derive a three-dimensional signal representation and estimate its 'isotropic part'. Here, these different methods are simulated and compared under different signal-to-noise (SNR) realizations. With sufficiently dense sampling points (61 orientations per shell), and isotropically-distributed sampling vectors, all averaging methods give comparable results, (MAP-MRI-based estimates give slightly higher accuracy, albeit with slightly elevated bias as b-value increases). As the SNR and number of data points per shell are reduced, MAP-MRI-based approaches give significantly higher accuracy compared with the other methods. We also apply these approaches to in vivo data where the results are broadly consistent with our simulations. A statistical analysis of the simulated data shows that the orientationally-averaged signals at each b-value are largely Gaussian distributed.The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) featurevelopment of better therapeutic interventions.The global spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has casted a significant threat to mankind. As the COVID-19 situation continues to evolve, predicting localized disease severity is crucial for advanced resource allocation. This paper proposes a method named COURAGE (COUnty aggRegation mixup AuGmEntation) to generate a short-term prediction of 2-week-ahead COVID-19 related deaths for each county in the United States, leveraging modern deep learning techniques. Specifically, our method adopts a self-attention model from Natural Language Processing, known as the transformer model, to capture both short-term and long-term dependencies within the time series while enjoying computational efficiency. Our model solely utilizes publicly available information for COVID-19 related confirmed cases, deaths, community mobility trends and demographic information, and can produce state-level predictions as an aggregation of the corresponding county-level predictions. Our numerical experiments demonstrate that our model achieves the state-of-the-art performance among the publicly available benchmark models.Surgeons must visually distinguish soft-tissues, such as nerves, from surrounding anatomy to prevent complications and optimize patient outcomes. An accurate nerve segmentation and analysis tool could provide useful insight for surgical decision-making. Here, we present an end-to-end, automatic deep learning computer vision algorithm to segment and measure nerves. Unlike traditional medical imaging, our unconstrained setup with accessible handheld digital cameras, along with the unstructured open surgery scene, makes this task uniquely challenging. We investigate one common procedure, thyroidectomy, during which surgeons must avoid damaging the recurrent laryngeal nerve (RLN), which is responsible for human speech. We evaluate our segmentation algorithm on a diverse dataset across varied and challenging settings of operating room image capture, and show strong segmentation performance in the optimal image capture condition. This work lays the foundation for future research in real-time tissue discrimination and integration of accessible, intelligent tools into open surgery to provide actionable insights.We examined the effect of total and afferent renal denervation (RDN) on hypertension and the renin-angiotensin system (RAS) in a rodent model of juvenile-onset polycystic kidney disease (PKD). Lewis Polycystic Kidney (LPK) and control rats received total, afferent or sham RDN by periaxonal application of phenol, capsaicin or normal saline, respectively, and were monitored for 4-weeks. Afferent RDN did not affect systolic blood pressure (SBP) determined by radiotelemetry in either strain (n = 19) while total RDN significantly reduced SBP in Lewis rats 4-weeks post-denervation (total vs. sham, 122 ± 1 vs. 130 ± 2 mmHg, P = 0.002, n = 25). Plasma and kidney renin content determined by radioimmunoassay were significantly lower in LPK vs. Lewis (plasma 278.2 ± 6.7 vs. 376.5 ± 11.9 ng Ang I/ml/h; kidney 260.1 ± 6.3 vs. 753.2 ± 37.9 ng Ang I/mg/h, P  less then  0.001, n = 26). These parameters were not affected by RDN. Intrarenal mRNA expression levels of renin, angiotensinogen, angiotensin-converting enzyme (ACE)2, and angiotensin II receptor type 1a were significantly lower, whereas ACE1 expression was significantly higher in the LPK vs.