An Ultrathin Conformable Vibration-Responsive Electronic Skin for Quantitative Vocal Recognition

Flexible and skin-attachable vibration sensors have been studied for use as wearable voice-recognition electronics. However, the development of vibration sensors to recognize the human voice accurately with a flat frequency response, a high sensitivity, and a flexible/conformable form factor has proved a major challenge. Here, we present an ultrathin, conformable, and vibration-responsive electronic skin that detects skin acceleration, which is highly and linearly correlated with voice pressure. This device consists of a crosslinked ultrathin polymer film and a hole-patterned diaphragm structure, and senses voices quantitatively with an outstanding sensitivity of 5.5 V Pa-1 over the voice frequency range. Moreover, this ultrathin device (<5 mu m) exhibits superior skin conformity, which enables exact voice recognition because it eliminates vibrational distortion on rough and curved skin surfaces. Our device is suitable for several promising voice-recognition applications, such as security authentication, remote control systems and vocal healthcare.11Ysciescopu

Prognostic significance of sealed-off perforation in colon cancer: a prospective cohort study

Background Perforated colon cancer is a rare complication, but has a high risk of recurrence. However, most studies have not distinguished sealed-off perforation from free perforation, and the prognosis is unclear. The aim of this study was to evaluate the oncologic outcome of colon cancer with sealed-off perforation. Methods Eighty-six consecutive patients who underwent resection for colon cancer with sealed-off or free perforation were included. We defined sealed-off perforation as a colon perforation with localized abscess identified on operative, computed tomography, or pathologic findings, with no evidence of free perforation, including fecal contamination and dirty fluid collection in the peritoneal cavity. Oncologic outcomes were compared between patients with colon cancer with sealed-off perforation and free perforation using a log-rank test and Cox regression analysis. Results The sealed-off perforation group included 62 patients, and 24 patients were in the free perforation group. TNM stage and lymphatic, venous, and perineural invasion were similar between the groups. The median follow-up period was 28.9 months (range 0–159). The sealed-off perforation group had better prognosis compared with the free perforation group in terms of progression-free survival (PFS) and overall survival (OS), although there were no statistically significant differences in PFS (5-year PFS 53.7% vs. 40.5%, p = 0.148; 5-year OS 53.6% vs. 22.9%, p = 0.001). However, in multivariable analysis using the Cox progression test, sealed-off perforation did not show a significant effect on cancer progression (p = 0.138) and OS (p = 0.727). Conclusions Colon cancer with sealed-off perforation showed no difference in prognosis compared with free perforation.Not applicable

The acidic tumor microenvironment enhances PD-L1 expression via activation of STAT3 in MDA-MB-231 breast cancer cells

Abstract Tumor acidosis, a common phenomenon in solid cancers such as breast cancer, is caused by the abnormal metabolism of cancer cells. The low pH affects cells surrounding the cancer, and tumor acidosis has been shown to inhibit the activity of immune cells. Despite many previous studies, the immune surveillance mechanisms are not fully understood. We found that the expression of PD-L1 was significantly increased under conditions of extracellular acidosis in MDA-MB-231 cells. We also confirmed that the increased expression of PD-L1 mediated by extracellular acidosis was decreased when the pH was raised to the normal range. Gene set enrichment analysis (GSEA) of public breast cancer patient databases showed that PD-L1 expression was also highly correlated with IL-6/JAK/STAT3 signaling. Surprisingly, the expression of both phospho-tyrosine STAT3 and PD-L1 was significantly increased under conditions of extracellular acidosis, and inhibition of STAT3 did not increase the expression of PD-L1 even under acidic conditions in MDA-MB-231 cells. Based on these results, we suggest that the expression of PD-L1 is increased by tumor acidosis via activation of STAT3 in MDA-MB-231 cells.This work was supported by grants from the National Research Foundation of Korea (NRF) funded by the Korean government (NRF-2018R1A5A2025964) and the Seoul National University Hospital (SNUH) Research Fund (04–20200230). This study was also carried out with support from the R&D Program for Forest Science Technology (Project No. 2020195A00–2122-BA01) of the Korea Forest Service (Korea Forestry Promotion Institute) and Cooperative Research Program for the Agriculture Science and Technology Development (Project No. PJ01589402 and No. PJ016202022) Rural Development Administration, Republic of Korea

Towards a muon collider

A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10 TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work

Towards a muon collider

A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10 TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work

Erratum: Towards a muon collider

The original online version of this article was revised: The additional reference [139] has been added. Tao Han’s ORICD ID has been incorrectly assigned to Chengcheng Han and Chengcheng Han’s ORCID ID to Tao Han. Yang Ma’s ORCID ID has been incorrectly assigned to Lianliang Ma, and Lianliang Ma’s ORCID ID to Yang Ma. The original article has been corrected

A Self-Predictable Crop Yield Platform (SCYP) Based On Crop Diseases Using Deep Learning

This paper proposes a self-predictable crop yield platform (SCYP) based on crop diseases using deep learning that collects weather information (temperature, humidity, sunshine, precipitation, etc.) and farm status information (harvest date, disease information, crop status, ground temperature, etc.), diagnoses crop diseases by using convolutional neural network (CNN), and predicts crop yield based on factors such as climate change, crop diseases, and others by using artificial neural network (ANN). The SCYP consists of an image preprocessing module (IPM) to determine crop diseases through the Google Vision API and image resizing, a crop disease diagnosis module (CDDM) based on CNN to diagnose the types and extent of crop diseases through photographs, and a crop yield prediction module (CYPM) based on ANN by using information of crop diseases, remaining time until harvest (based on the date), current temperature, humidity and precipitation (amount of snowfall) in the area, sunshine amount, ground temperature, atmospheric pressure, moisture evaporation in the ground, etc. Four experiments were conducted to verify the efficiency of the SCYP. In the CDMM, the accuracy and operation time of each model were measured using three neural network models: CNN, region-CNN(R-CNN), and you only look once (YOLO). In the CYPM, rectified linear unit (ReLU), Sigmoid, and Step activation functions were compared to measure ANN accuracy. The accuracy of CNN was about 3.5% higher than that of R-CNN and about 5.4% higher than that of YOLO. The operation time of CNN was about 37 s less than that of R-CNN and about 72 s less than that of YOLO. The CDDM had slightly less operation time, but in this paper, we prefer accuracy over operation time to diagnose crop diseases efficiently and accurately. When the activation function of the ANN used in the CYPM was ReLU, the accuracy of the ANN was 2% higher than that of Sigmoid and 7% higher than that of Step. The CYPM prediction was about 34% more accurate when using multiple diseases than when not using them. Therefore, the SCYP can predict farm yields more accurately than traditional methods

A Total Crop-Diagnosis Platform Based on Deep Learning Models in a Natural Nutrient Environment

This paper proposes a total crop-diagnosis platform (TCP) based on deep learning models in a natural nutrient environment, which collects the weather information based on a farm’s location information, diagnoses the collected weather information and the crop soil sensor data with a deep learning technique, and notifies a farm manager of the diagnosed result. The proposed TCP is composed of 1 gateway and 2 modules as follows. First, the optimized farm sensor gateway (OFSG) collects data by internetworking sensor nodes which use Zigbee, Wi-Fi and Bluetooth protocol and reduces the number of sensor data fragmentation times through the compression of a fragment header. Second, the data storage module (DSM) stores the collected farm data and weather data in a farm central server. Third, the crop self-diagnosis module (CSM) works in the cloud server and diagnoses by deep learning whether or not the status of a farm is in good condition for growing crops according to current weather and soil information. The TCP performance shows that the data processing rate of the OFSG is increased by about 7% compared with existing sensor gateways. The learning time of the CSM is shorter than that of the long short-term memory models (LSTM) by 0.43 s, and the success rate of the CSM is higher than that of the LSTM by about 7%. Therefore, the TCP based on deep learning interconnects the communication protocols of various sensors, solves the maximum data size that sensor can transfer, predicts in advance crop disease occurrence in an external environment, and helps to make an optimized environment in which to grow crops