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FIGURE 3. A in A Review of Genus Cyclosomus Latreille (Coleoptera: Carabidae: Lebiinae: Cyclosomini) in Asia.
FIGURE 3. A. Illustration of measurements recorded. EL = elytral length; EW = elytral width; HL = head length; PL.= pronotal width; PWA = width between apices of apical angles of pronotum; PWM = width across widest part of pronotum. B. Illustration of terms used for describing the dark color pattern in Cyclosomus species (example image is of a widespread African species, Cyclosomus buqueti Kolbe). bdb = basal dark band; ldb = longitudinal dark band; lpb = lateral pale band of pronotum; mdb = middle transverse dark band; pas = preapical dark spot.
FIGURE 9 in A Review of the Pennatulacean Genus Stylatula, with the Description of a New Species from Japan (Cnidaria: Octocorallia)
FIGURE 9. Map of the Pacific and Indian Oceans showing the known geographic distribution of the genus Stylatula. Arrows point to type localities of 11 of the species. Localities of S. lacazi and S. kinsbergi are not known.
FIGURE 6 in A Review of the Pennatulacean Genus Stylatula, with the Description of a New Species from Japan (Cnidaria: Octocorallia)
FIGURE 6. Stylatula diminutiva sp. nov. SEM of sclerites from region of the polyp leaf armature (Paratype CAS 203514); scale bars = 0.10 mm.
FIGURE 1 in A Review of the Pennatulacean Genus Stylatula, with the Description of a New Species from Japan (Cnidaria: Octocorallia)
FIGURE 1. Stylatula diminutiva sp. nov. A-B. Living colony. C. Wet preserved holotype (CAS 198016). D-F. Wet preserved paratype (CAS 203514). Scale bars = 2.5 mm.
FIGURE 2 in A Review of the Pennatulacean Genus Stylatula, with the Description of a New Species from Japan (Cnidaria: Octocorallia)
FIGURE 2. Stylatula diminutiva sp. nov. SEM of sur- face of axis (CAS 203513), showing surface pores. A. Portion of axis; scale bar = 0.2 mm. B. Detail of axial FIGURE 3. Stylatula diminutiva sp. nov. SEM of surface; scale bar = 0.02 mm. C. Ultrastructural detail internal morphology of the axis (CAS 203513). A. of axial surface; scale bar = 0.01 mm. Transverse section of axis, 0.23 mm in diameter, showing elongated pores; scale bar = 0.02 mm; lightened rectangle is shown enlarged in B below. B. Internal structural detail of axis; scale bar = 0.01 mm. adjacent pairs in the proximal region of the rachis, or are separated by <2 mm of bare rachis in the middle portion of the rachis. The uppermost polyp leaf pairs on the distal part of the rachis are separated from adjacent pairs of polyp leaves by approximately 2 mm of bare rachis (Fig. 1C). In the largest polyp leaves of the upper rachis, there are 2–4 polyps comprising each leaf. The peduncle is approximately 7 mm in length (Fig 7E). The sclerites are predominantly threeflanged spindles and rods (0.04–0.85 mm in length). Each polyp leaf is subtended by a conspicuous V-shaped or fan-shaped polyp leaf armature of relatively large sclerites (Fig 1). The fan-shaped armature is narrowly V-shaped and gradually tapers proximally. The ends of each FIGURE 4. Stylatula diminutiva sp. nov. SEM of internal morphology of the axis 203514), show- sclerite may be acute and pointed or truncated (CAS ing radial arrangement of calcareous material. Trans- and blunt. Some sclerites are minutely toothed verse sections of axis; scale bars = 0.02 mm. on a portion of the margins or on one terminal
Figure 7 in A Review of the Pennatulacean Genus Stylatula, with the Description of a New Species from Japan (Cnidaria: Octocorallia)
Figure 7. Wet-preserved colonies of Pacific Ocean species of Stylatula. A-C. Stylatula elongata (CAS 173213); scale bars: A = 40 mm, B & C = 15 mm. D-F. Stylatula diminutiva sp. nov.; scale bar = 10 mm. D. (CAS 203513). E. Holotype (CAS 198016). F. Paratype (CAS 198017). G. Stylatula austropacifica Paratype (CAS 173209); scale bar = 30 mm.
AProtocol for Managing Orthodontic Complications in Patients with Thalassemia and Haemoglobin Disorder; Article Review
<p>Hemoglobinopathies are known as haemoglobin production disorders. Familial disorders caused by thalassemia or sickle cell anaemia are autosomal recessive disorders affecting haemoglobin production and structure. Objectives:This article review aimed to clarify protocols for managing orthodontic complications in patients with thalassemia and haemoglobin disorder. The article explains how to deal with common dental and orthodontic issues that patients with these conditions may experience.It provides guidance on diagnosing and treating these issues to ensure the best possible outcomes for these patients. Conclusion:Hemoglobinopathies can cause dental diseases, which can be particularly concerning for children with this condition. While specialists are responsible for treating dental diseases, prevention is the best approach. Physicians with adequate knowledge of the diseases can address this issue safely and effectively. Paying attention to this matter is essential to ensure the best possible patient outcomes.</p>
Figure 11 in Exploring the potential of electric weed control: a review
Figure 11. The RootWave™ handheld electric weeder, the RootWave™ Pro (Table 1). Image sourced from T. Archer (personal communication, April 2, 2022).
Figure 10 in Exploring the potential of electric weed control: a review
Figure 10. The XPower electric weed control machine with the XP300 applicator, developed by Zasso™ (Table 1). Images sourced from Zasso Group AG (2021a).
Figure 7 in Exploring the potential of electric weed control: a review
Figure 7. crop.zone and Nufarm's electrochemical weeding machine, NUCROP (Table 1). Image sourced from D. Vandenhirtz (personal communication, September 10, 2021).
Figure 8 in Exploring the potential of electric weed control: a review
Figure 8. The XPower electric weed control machine with an XPS applicator, developed by Zasso™ (Table 1). Image sourced from Zasso Group AG (2021i).
Figure 9 in Exploring the potential of electric weed control: a review
Figure 9. The XPower electric weed control machine with the XPU applicator, developed by Zasso™ (Table 1). Images sourced from Zasso Group AG (2021g).
Figure 6 in Exploring the potential of electric weed control: a review
Figure 6. The Weed Zapper™ electric weed control machine, produced by Old School Manufacturing (Table 1). The image includes the Annihilator Tractor Series (right, 12R30 model) and the Terminator Self-Propelled Series (left, T3 model), each fitted with flexible front applicator booms of 9.1 and 18.3 m (30 and 60 feet), respectively. The image is sourced from B. Kroeger and N. Kroeger (personal communication, March 25, 2022).
Figure 1 in Exploring the potential of electric weed control: a review
Figure 1. Schematic representation of electric weed control technology using the spark-discharge method; produced by Guanhao Cheng from the information presented in Diprose and Benson (1984), Savchuk and Bayev (1975), Slesarev (1972), and Wilson and Anderson (1981). The process starts when the plant comes into close proximity to or contact with the electrode (ti). Electricity is then transferred through the plant's foliage and into the roots before dissipating into the soil. The application is grounded by the groundcontact device (GCD). Each object through which the current passes is depicted as having individual resistance, such as the target vegetation (Rv), soil and machinery (Rs), or parallel objects (Rp). This continues over time until the final point of electrode–plant contact (tf). The efficacy of weed control depends on contact time (tc), which is the duration of the electrode's contact with the plant. Contact time is determined by the electrode's effective contact surface and the distance traveled while the electrode is transferring the current to or in contact with the plant (Se).
Figure 5 in Exploring the potential of electric weed control: a review
Figure 5. The electric weed control machine, the Lightning Weeder, developed by Lasco (Table 1). Image sourced from Lasco (2021).
Figure 4 in Exploring the potential of electric weed control: a review
Figure 4. Representative diagram of the theoretical distribution of maximum electrical power (Ep max) during electric weed control application in a constant application direction under different weed population density scenarios. In the scenario where only one plant (plant one; left) is initially in contact with the electrode(s) (ti), Ep max is delivered to the plant until the final point of plant–electrode contact (tf). However, when multiple plant contacts occur (plants one, two, and three; right), Ep max is divided among each plant in contact at that time. Note that this diagram is not to scale and was produced by Guanhao Cheng from the information presented in Vigneault and Benoit (2001).
Figure 2 in Exploring the potential of electric weed control: a review
Figure 2. Schematic representation of electric weed control technology using the continuous electrode–plant contact method; produced by Guanhao Cheng and adapted from Vigneault and Benoit (2001) and Bauer et al. (2020). The process starts when the electrode initially contacts the plant (ti). Electricity is then transferred through the plant's foliage and into the roots and soil before returning to the machine via a ground-contact device, forming a complete electrical circuit. Each object through which the current passes is depicted as having individual resistance, such as the target vegetation (Rv), soil and machinery (Rs), or parallel objects (Rp). The circuit continues over time until the final point of electrode–plant contact (tf). The efficacy of weed control depends on contact time (tc), which is the duration of the electrode's contact with the plant. Contact time is determined by the electrode's effective contact surface, the distance traveled while the electrode is in contact with the plant (Se), which will always be greater than the electrode's actual contact surface (Sa).
Figure 3 in Exploring the potential of electric weed control: a review
Figure 3. Representative diagram of the theoretical relationship between electrical flow and plant electrical resistance (Rv) when using electric weed control measures. This diagram is not to scale and was produced by Guanhao Cheng from the information presented in Diprose et al. (1980) and Diprose and Benson (1984).
Figure 4 in First occurrence of Panthera atrox (Felidae, Pantherinae) in the Mexican state of Hidalgo and a review of the record of felids from the Pleistocene of Mexico
Figure 4. Potential common prey-size range for Panthera atrox from the late Pleistocene of southeastern Hidalgo, including the herbivores that have been reported at the El Barrio locality (HGO-47). Diamond and line indicate the mean and observed range of body mass (from Van Valkenburgh et al., 2016).
Figure 1 in First occurrence of Panthera atrox (Felidae, Pantherinae) in the Mexican state of Hidalgo and a review of the record of felids from the Pleistocene of Mexico
Figure 1. (a) Index map showing the study area in southeastern Hidalgo, central Mexico; the capital of the state (Pachuca) and the late Pleistocene locality El Barrio (HGO-47) are depicted. (b) Stratigraphic section of the El Barrio locality (HGO-47); the arrow indicates the fossil-bearing level.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.