Comparison of Doxorubicin Plus Docetaxel Neoadjuvant Chemotherapy with Doxorubicin Plus Vinorelbine in Primary Breast Cancer

Purpose: This study was performed to compare the therapeutic efficacy and toxicity of doxorubicin plus docetaxel neoadjuvant chemotherapy (NC) with doxorubicin plus vinorelbine NC. Methods: Fifty-three patients underwent 4 cycles of NC consisted of intravenous injection of doxorubicin (50 mg/m 2) plus docetaxel (75 mg/m 2) administered every 3 weeks (AD), while 49 patients underwent 4 cycles of NC consisted of intravenous injection of doxorubicin (50 mg/m 2) and vinorelbine (25 mg/m 2) administered every 3 weeks (AN). Response rate and treatment-related toxicities were analyzed by administered chemotherapeutics. Response to NC was also analyzed according to clinicobiological characteristics of the primary tumors. Results: Clinical response was observed in 66 % with AN and 81.6 % with AD chemotherapy. A complete pathologic response (pCR) was confirmed in 6 patients (11.3%) with AN and in 7 patients (14.3%) with AD afte

Baseline characteristics of the Korean genetic cohort of inherited cystic kidney disease

Background Identifying genetic mutations in individuals with inherited cystic kidney disease is necessary for precise treatment. We aimed to elucidate the genetic characteristics of cystic kidney disease in the Korean population. Methods We conducted a 3-year prospective, multicenter cohort study at eight hospitals from May 2019 to May 2022. Patients with more than three renal cysts were enrolled and classified into two categories, typical autosomal dominant polycystic kidney disease (ADPKD) and atypical PKD. We identified the clinical characteristics and performed a genetic analysis using a targeted gene panel. Results A total of 725 adult patients were included in the study, of which 560 (77.2%) were diagnosed with typical ADPKD and 165 (22.8%) had atypical PKD. Among the typical ADPKD cases, the Mayo imaging classification was as follows: 1A (55, 9.9%), 1B (149, 26.6%), 1C (198, 35.8%), 1D (90, 16.3%), and 1E (61, 11.0%). The atypical PKD cases were classified as bilateral cystic with bilateral atrophic (31, 37.3%), lopsided (27, 32.5%), unilateral (nine, 10.8%), segmental (eight, 9.6%), bilateral cystic with unilateral atrophic (seven, 8.4%), and asymmetric (one, 1.2%). Pathogenic variants were found in 64.3% of the patients using the ciliopathy-related targeted gene panel. The typical ADPKD group demonstrated a higher discovery rate (62.3%) than the atypical PKD group (41.8%). Conclusion We present a nationwide genetic cohort’s baseline clinical and genetic characteristics for Korean cystic kidney disease

Temperature dependence of velocity saturation in a multilayer molybdenum disulfide transistor

© 2020 IOP Publishing Ltd Velocity saturation in semiconducting devices is an important parameter that involves in the determination of the upper limit of the operational speed. The large contact resistance in two-dimensional semiconducting nano-devices is troublesome for extracting the saturation velocity from the electrical transport measurements, particularly at low temperatures. We obtain the saturation velocity for multilayer MoS2 from 10 to 300 K using the four-probe method to exclude contact effects. The saturation velocity exhibits strong temperature dependence, with a substantial enhancement at low temperature similar to the mobility, that is, similar to 5.2 x 10(5) cm s(-1) at 300 K and similar to 4.8 x 10(6) cm s(-1) at 10 K. The overall temperature dependence of the saturation velocity can be well represented using an empirical model previously used for Si11sciescopu

Estimation of the closest in-path vehicle by low-channel lidar and camera sensor fusion for autonomous vehicles

In autonomous driving, using a variety of sensors to recognize preceding vehicles at middle and long distances is helpful for improving driving performance and developing various functions. However, if only LiDAR or cameras are used in the recognition stage, it is difficult to obtain the necessary data due to the limitations of each sensor. In this paper, we proposed a method of converting the vision-tracked data into bird’s eye-view (BEV) coordinates using an equation that projects LiDAR points onto an image and a method of fusion between LiDAR and vision-tracked data. Thus, the proposed method was effective through the results of detecting the closest in-path vehicle (CIPV) in various situations. In addition, even when experimenting with the EuroNCAP autonomous emergency braking (AEB) test protocol using the result of fusion, AEB performance was improved through improved cognitive performance than when using only LiDAR. In the experimental results, the performance of the proposed method was proven through actual vehicle tests in various scenarios. Consequently, it was convincing that the proposed sensor fusion method significantly improved the adaptive cruise control (ACC) function in autonomous maneuvering. We expect that this improvement in perception performance will contribute to improving the overall stability of ACC. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.TRU

Estimation of the Closest In-Path Vehicle by Low-Channel LiDAR and Camera Sensor Fusion for Autonomous Vehicles

In autonomous driving, using a variety of sensors to recognize preceding vehicles at middle and long distances is helpful for improving driving performance and developing various functions. However, if only LiDAR or cameras are used in the recognition stage, it is difficult to obtain the necessary data due to the limitations of each sensor. In this paper, we proposed a method of converting the vision-tracked data into bird’s eye-view (BEV) coordinates using an equation that projects LiDAR points onto an image and a method of fusion between LiDAR and vision-tracked data. Thus, the proposed method was effective through the results of detecting the closest in-path vehicle (CIPV) in various situations. In addition, even when experimenting with the EuroNCAP autonomous emergency braking (AEB) test protocol using the result of fusion, AEB performance was improved through improved cognitive performance than when using only LiDAR. In the experimental results, the performance of the proposed method was proven through actual vehicle tests in various scenarios. Consequently, it was convincing that the proposed sensor fusion method significantly improved the adaptive cruise control (ACC) function in autonomous maneuvering. We expect that this improvement in perception performance will contribute to improving the overall stability of ACC

Semantic Knowledge-Based Hierarchical Planning Approach for Multi-Robot Systems

Multi-robot systems have been used in many fields by utilizing parallel working robots to perform missions by allocating tasks and cooperating. For task planning, multi-robot systems need to solve complex problems that simultaneously consider the movement of the robots and the influence of each robot. For this purpose, researchers have proposed various methods for modeling and planning multi-robot missions. In particular, some approaches have been presented for high-level task planning by introducing semantic knowledge, such as relationships and domain rules, for environmental factors. This paper proposes a semantic knowledge-based hierarchical planning approach for multi-robot systems. We extend the semantic knowledge by considering the influence and interaction between environmental elements in multi-robot systems. Relationship knowledge represents the space occupancy of each environmental element and the possession of objects. Additionally, the knowledge property is defined to express the hierarchical information of each space. Based on the suggested semantic knowledge, the task planner utilizes spatial hierarchy knowledge to group the robots and generate optimal task plans for each group. With this approach, our method efficiently plans complex missions while handling overlap and deadlock problems among the robots. The experiments verified the feasibility of the suggested semantic knowledge and demonstrated that the task planner could reduce the planning time in simulation environments

How medical education survives and evolves during COVID-19: Our experience and future direction.

BackgroundDue to the outbreak of coronavirus disease 2019 (COVID-19), school openings were postponed worldwide as a way to stop its spread. Most classes are moving online, and this includes medical school classes. The authors present their experience of running such online classes with offline clinical clerkship under pandemic conditions, and also present data on student satisfaction, academic performance, and preference.MethodsThe medical school changed every first-year to fourth-year course to an online format except the clinical clerkship, clinical skills training, and basic laboratory classes such as anatomy lab sessions. Online courses were pre-recorded video lectures or live-streamed using video communication software. At the end of each course, students and professors were asked to report their satisfaction with the online course and comment on it. The authors also compared students' academic performance before and after the introduction of online courses.ResultsA total of 69.7% (318/456) of students and 35.2% (44/125) of professors answered the questionnaire. Students were generally satisfied with the online course and 62.2% of them preferred the online course to the offline course. The majority (84.3%) of the students wanted to maintain the online course after the end of COVID-19. In contrast, just 13.6% of professors preferred online lectures and half (52.3%) wanted to go back to the offline course. With the introduction of online classes, students' academic achievement did not change significantly in four subjects, but decreased in two subjects.ConclusionsThe inevitable transformation of medical education caused by COVID-19 is still ongoing. As the safety of students and the training of competent physicians are the responsibilities of medical schools, further research into how future physicians will be educated is needed