Advanced Techniques in Automated High Resolution Scanning Transmission Electron Microscopy

Scanning transmission electron microscopy is a common tool used to study the atomic structure of materials. It is an inherently multimodal tool allowing for the simultaneous acquisition of multiple information channels. Despite its versatility, however, experimental workflows currently rely heavily on experienced human operators and can only acquire data from small regions of a sample at a time. Here, we demonstrate a flexible pipeline-based system for high-throughput acquisition of atomic-resolution structural data using a custom built sample stage and automation program. The program is capable of operating over many hours without human intervention improving the statistics of high-resolution experiments

Indefinite and Bidirectional Near Infrared Nanocrystal Photoswitching

Materials whose luminescence can be switched by optical stimulation drive technologies ranging from superresolution imaging1-4, nanophotonics5, and optical data storage6-8, to targeted pharmacology, optogenetics, and chemical reactivity9. These photoswitchable probes, including organic fluorophores and proteins, are prone to photodegradation, and often require phototoxic doses of ultraviolet (UV) or visible light. Colloidal inorganic nanoparticles have significant stability advantages over existing photoswitchable materials, but the ability to switch emission bidirectionally, particularly with NIR light, has not been reported with nanoparticles. Here, we present 2-way, near-infrared (NIR) photoswitching of avalanching nanoparticles (ANPs), showing full optical control of upconverted emission using phototriggers in the NIR-I and NIR-II spectral regions useful for subsurface imaging. Employing single-step photodarkening10-13 and photobrightening12,14-18, we demonstrate indefinite photoswitching of individual nanoparticles (>1000 cycles over 7 h) in ambient or aqueous conditions without measurable photodegradation. Critical steps of the photoswitching mechanism are elucidated by modeling and by measuring the photon avalanche properties of single ANPs in both bright and dark states. Unlimited, reversible photoswitching of ANPs enables indefinitely rewritable 2D and 3D multi-level optical patterning of ANPs, as well as optical nanoscopy with sub-{\AA} localization superresolution that allows us to distinguish individual ANPs within tightly packed clusters.Comment: 15 pages, 5 figure

Monensin Controlled-release Capsules do not Change Performance and Metabolic Profile in Unchallenged Beef Cattle

ABSTRACT Background: Some additives are able to improve animal performance in growing and finishing periods. Monensin was first used to control coccidiosis in poultry and was extended to other animals, like ruminants, to act also as a growth promoter and improve cattle performance. In this species, monensin improves the synthesis of propionic acid in the rumen and decreases methane synthesis and protein degradation, resulting in better performance in protein and energy metabolism. The objective of this study was to evaluate the use of monensin controlled-release capsules on animals grazing Lolium multiflorum intercropped with Trifolium repens on metabolic profile and performance. Materials, Methods & Results: Thirty Hereford cows were randomly distributed into two groups: control (CG) and monensin group (MG). Monensin was individually administered by controlled-release capsules placed in the rumen through oroesophageal pathway. All animals were identified through earring and kept under the same management condition, grazing on upland pasture mixture of Trifolium repens and Lolium multiflorum. Data from biochemical profile and performance were collected during 45 days. Blood samples started on the day of monensin controlled-release capsule placement (day 0) and continued in periods of 15, 30 and 45 days, after initial placement. Serum levels of albumin, glucose, urea, lactate dehydrogenase (LDH) and aspartate aminotransferase (AST) were evaluated using colorimetric diagnostic kits. In the rumen fluid, pH was measured and protozoa count was performed. All statistical analyses were made using software SAS. Albumin, AST, glucose, LDH and urea were analyzed through MIXED procedure and Tukey-Kramer test was applied for comparison of means. For average daily gain, the orthogonal polynomials test was applied. Treatments did not differ in BSC, body weight and average daily gain (ADG). None of these performance parameters were significantly affected by the addition of monensin. Blood biomarkers did not show statistical differences between treatments and markers of rumen activity did not suffer interference from monensin supplementation. There was only a tendency (P = 0.07) for the first time (0) to a higher pH value in CG. Discussion: Animals grazing in the finishing period, characterized by a continuous and linear weight gain, did not suffer any kind of stress situation. This condition did not provide a striking challenge that could reach the level of a metabolic change in animals. Facing feed shortages, or other stressful condition, supplementation with monensin and other additives, such as yeast, showed to be more effective, compared to animals in nutritional comfort. Weight gain increase is related to the expected changes in biochemical profile, as increased AST, glucose and LDH. The increase in AST levels on day 30 (P < 0.0001) is explained by the greater weight gain of animals in the previous period (day 15, P < 0.0001), where there was a higher hepatic activity to meet this anabolism and also by AST been an enzyme indicator of liver activity. This study did not show statistical treatment differences in relation to ruminal pH but, just a trend (P = 0.07) of higher pH in CG which is not caused by monensin supplementation that occurred since the first time (0), when animals were moved to pasture and receiving the monensin capsule. Since there was a low consumption of monensin capsules, the results were consistent with environment conditions and the phase in which the animals were. The results were also in agreement with finishing period, metabolic changes and animal performance at the same moment

Abnormal co-doping effect on the red persistent luminescence SrS:Eu2+,RE3+ materials

Persistent luminescence materials are a reality in several applications. However, there is still a lack of efficient red-emitting materials. SrS:Eu2+ phosphor is a potential candidate since its strong nephelauxetic effect shifts Eu(2+)4f(6)5d(1) -> 4f(7) to red, and its weak bond between strontium and sulphide, due to the soft base-hard acid character, generates a high number of intrinsic defects. SrS:Eu2+,RE3+ materials were efficiently prepared by two rounds of 22 min microwave-assisted solid-state synthesis. The highly crystalline purity and the material organization at the micro-scale were observed with X-ray powder diffraction and scanning electron microscopy, respectively. X-ray absorption spectroscopy revealed a low amount of Eu2+ compared to Eu3+ due to the efficient Eu2+ photo-oxidation by X-ray irradiation in the high storage capability SrS host matrix. Electron paramagnetic resonance spectra confirmed that at least 50% of Eu2+ ions in the material are photo-oxidized during excitation, reinforcing the previously established mechanisms. The RE2+ energy level positioned very close to or into the conduction band led to an abnormal co-doping effect, with similar effects independent of the co-dopant. The high concentration of intrinsic defects in SrS indicates that the soft-hard pair host is an excellent approach to develop efficient persistent luminescence materials

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Optics-Free Chip-Scale Intraoperative Imaging Using NIR-Excited Upconverting Nanoparticles

We present an optics-free CMOS image sensor that incorporates a novel time-gated dual-photodiode pixel design to allow filter- and lens-less image acquisition of near-infrared-excited (NIR-excited) upconverting nanoparticles. Recent biomedical advances have highlighted the benefits of NIR excitation, but NIR interaction with silicon has remained a challenge, even with high-performance optical blocking filters. Using a secondary diode and a dual-photodiode design, this sensor is able to remove the 100s of mV of NIR background on pixels and bring it down to single-digit mV level, nearing its noise floor of 2.2 mV rms, not achievable with any optical filter. Non-linear effects of background cancellation using the diode pair has been mitigated using an initial one-time pixel-level curve fitting and calibration in a post-processing setting. This imager comprises a highly linear 11 fF metal-oxide-metal (MOM) capacitor and includes integrated angle-selective gratings to reject oblique light and enhance sharpness. Each pixel also includes two distinct correlated double sampling schemes, to remove low frequency flicker noise and systematic offset in the datapath. We demonstrate the performance of this imager using pulsed NIR-excited upconverting nanoparticles on standard United-States-Air-Force (USAF) resolution targets and achieve an SNR of 15 dB, while keeping NIR background below 6 mV. This 36-by-80-pixel array measures only 2.3 mm by 4.8 mm and can be thinned down to 25 µm, allowing it to become surgically compatible with intraoperative instruments and equipment, while remaining optics-free