Accurate and immediate diagnosis of malaria is important for medication of the infectious disease. Conventional methods for diagnosing malaria are time consuming and rely on the skill of experts. Therefore, an automatic and simple diagnostic modality is essential for healthcare in developing countries that lack the expertise of trained microscopists. In the present study, a new automatic sensing method using digital in-line holographic microscopy (DIHM) combined with machine learning algorithms was proposed to sensitively detect unstained malaria-infected red blood cells (iRBCs). To identify the RBC characteristics, 13 descriptors were extracted from segmented holograms of individual RBCs. Among the 13 descriptors, 10 features were highly statistically different between healthy RBCs (hRBCs) and iRBCs. Six machine learning algorithms were applied to effectively combine the dominant features and to greatly improve the diagnostic capacity of the present method. Among the classification models trained by the 6 tested algorithms, the model trained by the support vector machine (SVM) showed the best accuracy in separating hRBCs and iRBCs for training (n = 280, 96.78%) and testing sets (n = 120, 97.50%). This DIHM-based artificial intelligence methodology is simple and does not require blood staining. Thus, it will be beneficial and valuable in the diagnosis of malaria.

A liquid gallium electrode confined in a porous carbon matrix was prepared by vaporization and pyrolysis of Ga͑III͒-phthalocyanine chloride on a nanosized Ga 2 O 3 powder surface, which was followed by carbothermal reduction of Ga 2 O 3 by a carbon matrix. When the electrode was charge/discharge cycled, the liquid Ga component was restored to its original liquid state at the final stage of delithiation, such that any electrode failure modes, for instance, crack formation and electric disconnection that are caused by severe volume change associated with multistage, solid-state Li x Ga ͑0 Ͻ x Յ 2͒ phase transitions, are self-healed by cohesion between liquid Ga droplets.

Lithium ion batteries (LIBs) have been emerging as a major power source for portable electronic devices and hybrid electric vehicles (HEV) with their superior performance to other competitors. The performance aspects of energy density and rate capability of LIBs should, however, be further improved for their new applications. Towards this end, many Li‐alloy materials, metal oxides, and phosphides have been tested, some of which have, however, been discarded because of poor activity at ambient temperature. Here, it is shown that the InCu binary intermetallic compound (Cu7In3), which shows no activity at room temperature as a result of activation energy required for InCu bond cleavage, can be made active by discharge–charge cycling at elevated temperatures. Upon lithiation at elevated temperatures (55–120 °C), the Cu7In3 phase is converted into nanograins of metallic Cu and a lithiated In phase (Li13In3). The underlying activation mechanism is the formation of new In‐rich phase (CuIn). The de‐lithiation temperature turns out to be the most important variable that controlling the nature of the In‐rich compounds.

Upon lithiation, the active (Ga) and inactive component (Cu) in a binary intermetallic CuGa 2 electrode are converted to nanograins (<50 nm) of Li x Ga and metallic Cu, respectively. It was found that the Cu nanograins are not idling as an inactive ingredient but have a strong influence on the thermodynamic and kinetic properties of Li x Ga phases through a partial bonding to Ga atoms of Li x Ga (CufGa-Li). The Li x Ga phase diagram is altered by the presence of Cu nanograins, eloquently demonstrating that the surface energy becomes more important than internal energy in controlling thermodynamics of nanosized materials. The lithiation rate is slower than that for pure Ga electrode because of activation energy needed for bond cleavage of the partial bonding. The delithiation rate capability is, however, exceptionally good; the capacity at 26 C amounts to 91% of that at 0.13 C, which is indebted to a weakening in the Ga-Li bond by the CufGa partial bonding.

Background and objectives:Functional outcomes after stroke are strongly related to focal injury measures. However, the role of global brain health is less clear. Here, we examined the impact of brain age, a measure of neurobiological aging derived from whole brain structural neuroimaging, on post-stroke outcomes, with a focus on sensorimotor performance. We hypothesized that more lesion damage would result in older brain age, which would in turn be associated with poorer outcomes. Related, we expected that brain age would mediate the relationship between lesion damage and outcomes. Finally, we hypothesized that structural brain resilience, which we define in the context of stroke as younger brain age given matched lesion damage, would differentiate people with good versus poor outcomes.Methods:We conducted a cross-sectional observational study using a multi-site dataset of 3D brain structural MRIs and clinical measures from ENIGMA Stroke Recovery. Brain age was calculated from 77 neuroanatomical features using a ridge regression model trained and validated on 4,314 healthy controls. We performed a three-step mediation analysis with robust mixed-effects linear regression models to examine relationships between brain age, lesion damage, and stroke outcomes. We used propensity score matching and logistic regression to examine whether brain resilience predicts good versus poor outcomes in patients with matched lesion damage.Results:We examined 963 patients across 38 cohorts. Greater lesion damage was associated with older brain age (β=0.21; 95% CI 0.04,0.38,P=0.015), which in turn was associated with poorer outcomes, both in the sensorimotor domain (β=-0.28; 95% CI: -0.41,-0.15,P<0.001) and across multiple domains of function (β=-0.14; 95% CI: -0.22,-0.06,P<0.001). Brain age mediated 15% of the impact of lesion damage on sensorimotor performance (95% CI: 3%,58%,P=0.01). Greater brain resilience explained why people have better outcomes, given matched lesion damage (OR=1.04, 95% CI: 1.01,1.08,P=0.004).Conclusions:We provide evidence that younger brain age is associated with superior post-stroke outcomes and modifies the impact of focal damage. The inclusion of imaging-based assessments of brain age and brain resilience may improve the prediction of post-stroke outcomes compared to focal injury measures alone, opening new possibilities for potential therapeutic targets.