1,104 research outputs found
sj-png-1-tam-10.1177_17588359221108693 – Supplemental material for Survival benefit of surgery in patients with clinical T4 esophageal cancer who achieved complete or partial response after neoadjuvant chemoradiotherapy or radiotherapy
No full textSupplemental material, sj-png-1-tam-10.1177_17588359221108693 for Survival benefit of surgery in patients with clinical T4 esophageal cancer who achieved complete or partial response after neoadjuvant chemoradiotherapy or radiotherapy by Lin-Rui Gao, Chen Li, Weiming Han, Wenjie Ni, Wei Deng, Lijun Tan, Zongmei Zhou, Dongfu Chen, Qinfu Feng, Jun Liang, Jima Lv, Wenqing Wang, Wenyang Liu, Lei Deng, Xin Wang, Tao Zhang, Jianyang Wang, Yirui Zhai, Nan Bi, Lvhua Wang, Zhouguang Hui, Ye-Xiong Li and Zefen Xiao in Therapeutic Advances in Medical Oncology</p
sj-docx-6-tam-10.1177_17588359221108693 – Supplemental material for Survival benefit of surgery in patients with clinical T4 esophageal cancer who achieved complete or partial response after neoadjuvant chemoradiotherapy or radiotherapy
No full textSupplemental material, sj-docx-6-tam-10.1177_17588359221108693 for Survival benefit of surgery in patients with clinical T4 esophageal cancer who achieved complete or partial response after neoadjuvant chemoradiotherapy or radiotherapy by Lin-Rui Gao, Chen Li, Weiming Han, Wenjie Ni, Wei Deng, Lijun Tan, Zongmei Zhou, Dongfu Chen, Qinfu Feng, Jun Liang, Jima Lv, Wenqing Wang, Wenyang Liu, Lei Deng, Xin Wang, Tao Zhang, Jianyang Wang, Yirui Zhai, Nan Bi, Lvhua Wang, Zhouguang Hui, Ye-Xiong Li and Zefen Xiao in Therapeutic Advances in Medical Oncology</p
sj-png-4-tam-10.1177_17588359221108693 – Supplemental material for Survival benefit of surgery in patients with clinical T4 esophageal cancer who achieved complete or partial response after neoadjuvant chemoradiotherapy or radiotherapy
No full textSupplemental material, sj-png-4-tam-10.1177_17588359221108693 for Survival benefit of surgery in patients with clinical T4 esophageal cancer who achieved complete or partial response after neoadjuvant chemoradiotherapy or radiotherapy by Lin-Rui Gao, Chen Li, Weiming Han, Wenjie Ni, Wei Deng, Lijun Tan, Zongmei Zhou, Dongfu Chen, Qinfu Feng, Jun Liang, Jima Lv, Wenqing Wang, Wenyang Liu, Lei Deng, Xin Wang, Tao Zhang, Jianyang Wang, Yirui Zhai, Nan Bi, Lvhua Wang, Zhouguang Hui, Ye-Xiong Li and Zefen Xiao in Therapeutic Advances in Medical Oncology</p
sj-png-2-tam-10.1177_17588359221108693 – Supplemental material for Survival benefit of surgery in patients with clinical T4 esophageal cancer who achieved complete or partial response after neoadjuvant chemoradiotherapy or radiotherapy
No full textSupplemental material, sj-png-2-tam-10.1177_17588359221108693 for Survival benefit of surgery in patients with clinical T4 esophageal cancer who achieved complete or partial response after neoadjuvant chemoradiotherapy or radiotherapy by Lin-Rui Gao, Chen Li, Weiming Han, Wenjie Ni, Wei Deng, Lijun Tan, Zongmei Zhou, Dongfu Chen, Qinfu Feng, Jun Liang, Jima Lv, Wenqing Wang, Wenyang Liu, Lei Deng, Xin Wang, Tao Zhang, Jianyang Wang, Yirui Zhai, Nan Bi, Lvhua Wang, Zhouguang Hui, Ye-Xiong Li and Zefen Xiao in Therapeutic Advances in Medical Oncology</p
sj-png-5-tam-10.1177_17588359221108693 – Supplemental material for Survival benefit of surgery in patients with clinical T4 esophageal cancer who achieved complete or partial response after neoadjuvant chemoradiotherapy or radiotherapy
No full textSupplemental material, sj-png-5-tam-10.1177_17588359221108693 for Survival benefit of surgery in patients with clinical T4 esophageal cancer who achieved complete or partial response after neoadjuvant chemoradiotherapy or radiotherapy by Lin-Rui Gao, Chen Li, Weiming Han, Wenjie Ni, Wei Deng, Lijun Tan, Zongmei Zhou, Dongfu Chen, Qinfu Feng, Jun Liang, Jima Lv, Wenqing Wang, Wenyang Liu, Lei Deng, Xin Wang, Tao Zhang, Jianyang Wang, Yirui Zhai, Nan Bi, Lvhua Wang, Zhouguang Hui, Ye-Xiong Li and Zefen Xiao in Therapeutic Advances in Medical Oncology</p
sj-png-3-tam-10.1177_17588359221108693 – Supplemental material for Survival benefit of surgery in patients with clinical T4 esophageal cancer who achieved complete or partial response after neoadjuvant chemoradiotherapy or radiotherapy
No full textSupplemental material, sj-png-3-tam-10.1177_17588359221108693 for Survival benefit of surgery in patients with clinical T4 esophageal cancer who achieved complete or partial response after neoadjuvant chemoradiotherapy or radiotherapy by Lin-Rui Gao, Chen Li, Weiming Han, Wenjie Ni, Wei Deng, Lijun Tan, Zongmei Zhou, Dongfu Chen, Qinfu Feng, Jun Liang, Jima Lv, Wenqing Wang, Wenyang Liu, Lei Deng, Xin Wang, Tao Zhang, Jianyang Wang, Yirui Zhai, Nan Bi, Lvhua Wang, Zhouguang Hui, Ye-Xiong Li and Zefen Xiao in Therapeutic Advances in Medical Oncology</p
Liphistius liz Lin & Li 2023, sp. nov.
No full text<p>Liphistius liz Lin & Li, 2023 sp. nov.</p> <p>Materials</p> <p> <b>Type status:</b> Holotype. <b>Occurrence:</b> catalogNumber: IZCAS-Ar44748; recordedBy: Yicheng Lin; individualCount: 1; sex: male; lifeStage: adult; occurrenceID: 5BCC41FF-4DC2-53C5-836F-F9BBC80D4BDE; <b>Taxon:</b> scientificName: Liphistius liz; <b>Location:</b> country: China; stateProvince: Yunnan; county: Lianghe; locality: Jiubao Achang Township, Shizunao; verbatimElevation: 1200 m; decimalLatitude: 24.7478; decimalLongitude: 98.2106; <b>Identification:</b> identifiedBy: Yejie Lin; dateIdentified: 2023; <b>Event:</b> year: 2023; month: 5; day: 13 <b>Type status:</b> Paratype. <b>Occurrence:</b> catalogNumber: IZCAS-Ar44749; recordedBy: Yicheng Lin; individualCount: 1; sex: female; lifeStage: adult; occurrenceID: 2177DB32-CFCD-5FED-9AAF-D1629797C869; <b>Taxon:</b> scientificName: Liphistius liz; <b>Location:</b> country: China; stateProvince: Yunnan; county: Lianghe; locality: Jiubao Achang Township, Shizunao; verbatimElevation: 1200 m; decimalLatitude: 24.7478; decimalLongitude: 98.2106; <b>Identification:</b> identifiedBy: Yejie Lin; dateIdentified: 2023; <b>Event:</b> year: 2023; month: 8; day: 12 <b>Type status:</b> Paratype. <b>Occurrence:</b> catalogNumber: IZCAS-Ar44750; recordedBy: Yicheng Lin; individualCount: 1; sex: female; lifeStage: adult; occurrenceID: 4FEE7ED6-6BCF-50BB-A7A5-D3C318237341; <b>Taxon:</b> scientificName: Liphistius liz; <b>Location:</b> country: China; stateProvince: Yunnan; county: Lianghe; locality: Jiubao Achang Township, Shizunao; verbatimElevation: 1200 m; decimalLatitude: 24.7478; decimalLongitude: 98.2106; <b>Identification:</b> identifiedBy: Yejie Lin; dateIdentified: 2023; <b>Event:</b> year: 2023; month: 8; day: 12 <b>Type status:</b> Paratype. <b>Occurrence:</b> catalogNumber: IZCAS-Ar44751; recordedBy: Yicheng Lin; individualCount: 1; sex: female; lifeStage: adult; occurrenceID: 34FCBAD1-1985-59EA-8784-A3605859BC42; <b>Taxon:</b> scientificName: Liphistius liz; <b>Location:</b> country: China; stateProvince: Yunnan; county: Lianghe; locality: Jiubao Achang Township, Shizunao; verbatimElevation: 1200 m; decimalLatitude: 24.7478; decimalLongitude: 98.2106; <b>Identification:</b> identifiedBy: Yejie Lin; dateIdentified: 2023; <b>Event:</b> year: 2023; month: 8; day: 12 <b>Type status:</b> Paratype. <b>Occurrence:</b> catalogNumber: IZCAS-Ar44752; recordedBy: Yicheng Lin; individualCount: 1; sex: female; lifeStage: adult; occurrenceID: BB3338CB-0A61-516F-BEA2-1CA2A06BA8E9; <b>Taxon:</b> scientificName: Liphistius liz; <b>Location:</b> country: China; stateProvince: Yunnan; county: Lianghe; locality: Jiubao Achang Township, Shizunao; verbatimElevation: 1200 m; decimalLatitude: 24.7478; decimalLongitude: 98.2106; <b>Identification:</b> identifiedBy: Yejie Lin; dateIdentified: 2023; <b>Event:</b> year: 2023; month: 8; day: 12</p> <p>Description</p> <p>Male (holotype, Figs 2, 3 b, 4, 7 A). Total length 7.55. Carapace 4.19 long and 3.83 wide, earthy yellow in ethanol (slightly lighter than in life), margin and fovea colour darker, without obvious dark stripes between coxal elevations (Fig. 7 A). Eye sizes and interdistances: AME 0.06, ALE 0.49, PME 0.25, PLE 0.35, AME-AME 0.08, AME-ALE 0.08, PME-PME 0.04, PME-PLE 0.06, AME-PME 0.02, ALE-PLE 0.05. Chelicerae reduced, brown, with several short macrosetae. Labium 0.73 long and 0.44 wide, fused with sternum. Sternum 1.98 long and 0.75 wide, posterior tip elongated. Opisthosoma 3.54 long and 2.29 wide, with ten tergites. Leg measurements: leg I 11.86 (3.26, 3.85, 3.17, 1.58), leg II 13.46 (3.83, 4.07, 3.51, 2.05), leg III 14.88 (3.53, 4.30, 4.47, 2.58), leg IV 19.41 (4.69, 5.51, 5.91, 3.30).</p> <p>Palp (Figs 2, 3 b, 4). Tibial apophysis of palp almost as high as wide, situated near retrolateral margin of tibia, with four megaspines. Cymbium with two clavate trichobothria retrolaterally (Fig. 4 D). Paracymbium large and thick, almost as wide as cymbium, cumulus distinctly elevated with many long setae (Fig. 4). Subtegulum curved in prolaterodorsal and ventral views, without obvious apophysis. Tegulum with a well-developed and denticulate distal edge. Half of the contrategulum strongly sclerotised, with a ventral process (Figs 2, 3 b). Paraembolic plate slightly elevated. Embolus partly sclerotised, with some longitudinal ridges extending to the tip, margins of these ridges slightly dentated (Figs 2, 3 b).</p> <p>Female (paratype, Figs 1, 5, 7 B). Total length 10.32. Carapace 4.87 long, 4.16 wide, colour as in males, except shades being darker (Figs 1, 7 B). Eye sizes and interdistances: AME 0.06, ALE 0.45, PME 0.27, PLE 0.31, AME-AME 0.06, AME-ALE 0.07, PME-PME 0.04, PME-PLE 0.05, AME-PME 0.04, ALE-PLE 0.05. Chelicerae robust, reddish-brown, with a few short stripes on dorsal side and several long macrosetae on retrolateral edge of fang groove. Labium 1.03 long, 0.52 wide. Sternum 242 long, 1.23 wide. Opisthosoma 5.92 long, 4.52 wide, with ten tergites. Leg measurements: leg I 8.60 (3.04, 2.77, 1.75, 1.04), leg II 8.63 (2.68, 3.16, 1.65, 1.14), leg III 9.80 (2.98, 3.14, 2.28, 1.48), leg IV 14.34 (3.93, 4.47, 3.83, 2.11).</p> <p>Vulva (Fig. 5): Poreplate with four notobvious protuberances (two anterolateral and two posterolateral), two posterolateral protuberances not attached to ventral rim of poreplate. Central dorsal opening globular, receptacular cluster grape-shaped. Bulging margins on ventral poreplate only extending to the posterolateral corner of poreplate (Fig. 5 B) and distance between bulging margins almost as wide as poreplate. Genital atrium straight. Posterior area of posterior stalk located in the same plane of poreplate and almost as wide as poreplate (Fig. 5 A).</p> <p>Diagnosis</p> <p> Males of the new species resemble <i>Liphistius nabang</i> Yu, Zhang & Zhang, 2021 by the general shape of the embolus and tegulum with a clearly outlined distal edge (Fig. 3) and similar body colouration (Fig. 7) and the female with a similar-shaped poreplate plate. However, <i>L. liz</i> sp. nov. can be distinguished by the male with curved subtegulum (Fig. 2) [vs. subtegulum straight in <i>L. nabang</i> (see Yu et al. (2021), figs. 3A and B)] and tibial apophysis almost as high as wide (Fig. 4) [vs. wider than high in <i>L. nabang</i> (see Yu et al. (2021), figs. 3 D-F)]. Females of the new species can be distinguished from those of <i>L. nabang</i> by the straight genital atrium (Figs 5, 6) [vs. genital atrium curved in <i>L. nabang</i> (see Yu et al. (2021), fig. 4)], posterior stalk and poreplate are located in the same plane (Figs 5, 6) [vs. posterior stalk perpendicular to poreplate in <i>L. nabang</i> (see Yu et al. (2021), fig. 4)] and posterior stalk two times longer than wide [vs. posterior stalk four times longer than wide in <i>L. nabang</i> (see Yu et al. (2021), fig. 4)].</p> <p>Etymology</p> <p>The specific name refers to the short name for the Laboratory of Invertebrate Zoology (LIZ), Institute of Zoology, Chinese Academy of Sciences in Beijing; noun in apposition. LIZ was founded by Shen Jia-Rui (see Dai (1997)) in 1928, later led by Daxiang Song (see Marusik (2008)) from 1975 to 1995 and has been led by the senior author Shuqiang Li from 1995 to the present.</p> <p>Distribution</p> <p>China (Yunnan; Fig. 8).</p> <p>DNA barcode</p> <p>CTGCGATGGTTATATTCAACAAATCACAAAGATATTGGAACTATATATTTAATTTTTGGTGTATGATCTGCCATAATCGGAACTGCACTAAGATTATTAATTCGAGCAGAATTAGGTCAACCAGGAAGATTAATCGGAGACGATCAAACATATAATGTAATTGTAACAGCTCATGCTTTTATTATAATTTTTTTTATAGTTATACCTATAATAATTGGAGGTTTTGGAAATTGATTAATCCCTCTTATACTAAGAGCCCCTGATATAGCTTTTCCTCGATTAAATAATTTAAGATTTTGATTATTACCCCCCTCTATCACCCTCTTATTGATTTCATCCATAGTAGAAAGAGGCTCCGGCACAGGTTGGACTATTTATCCCCCTATTGCTAGCATAGAATTTCACCCTGGTATATCTATTGATTATACTATTTTTTCATTACACCTTGCCGGGGCCTCTTCAATCTTAGGCGCAATTAATTTTATTACCACTATTATTAACATACGACCAAGAGGTATATTAATAGAGCGAGTACCATTATTTGTTTGATCTATTCTTATTACCGCAAGCCTACTGTTACTATCTTTACCTGTATTAGCTGGTGCGATTACTATGCTATTAACAGATCGAAATTTTAACACGTCATTTTTTGATCCAGCAGGAGGTGGTGACCCTATCCTATTCCAACATTTATTTTGATTTTTTGGTCATCCAGAAGTTTACATTCTTATTATTCCAGGTTTTGGGATAATTTCACATATTGTAAGACACAACGCTGGAAAAAAAGAACCTTTTGGGTCTTTAGGCATAATTTATGCAATATCCGCTATTGGATTACTAGGGTTTGTAGTCTGAGCACACCATATATTTACAGTAGGTATAGATGTTGATACACGAGCTTATTTCACAGCAGCAACCATAATTATTGCAATCCCCACAGGAATTAAAATTTTTAGATGATTAGCTACTCTTCATGGTACTAATTTAATCATAAGTACTTCCCTAATATGGTCTATTGGATTTATCTTCCTATTCACTATTGGTGGATTAACAGGCGTAATCCTAGCTAATTCATCTATTGATATTGTTCTTCATGATACATACTATGTAGTAGCTCATTTTCATTATGTTTTATCAATAGGAGCAGTTTTTGCAATTATAGCAAGAATTATTCACTGATTCCCTTTATTTTTTGGATTTTCATTTAATCAAACTTTATTAAAAATTAACTTTTTTTCCATATTTATTGGTGTAAATATAACCTTTTTCCCACAACACTTCTTAGGATTAAATGGAATACCACGACGATATTCAGATTACCCTGATATATTTATATCATGAAATGTAATTTCATCTTTAGGAAGAATTTTATCTTTTCTAGCAGTAATTATATTTATTTTAATTGTATGAGAAAGAATTATATCGAACCGTAATATTTATATTCCTACTCAATCACCTTCTTCAGTTGAATGAACTCAAAATATTCCTCCTTCTAATCATACCTTTAATCAACTCAATATACTCATTTTCTAA (GenBank accession number OR721885).</p> <p>Compared material examined</p> <p> <i>Liphistius nabang</i>: Holotype: ♂ (MHBU-ARA-00020000), CHINA, Yunnan Province, Dehong Dai and Jingpo Autonomous Prefecture, Yingjiang County, Nabang Town, 24.7521°N, 97.563°E, 265 m elev., 2 August 2019, leg. Quanyu Ji.</p> <p>Variation</p> <p>Vulvae of two paratype females, see Fig. 6.</p>Published as part of <i>Lin, Yejie & Li, Shuqiang, 2023, A new species of Liphistius Schiodte, 1849 (Araneae, Liphistiidae) from Yunnan, China, pp. 113290 in Biodiversity Data Journal 11</i> on page 113290, DOI: 10.3897/BDJ.11.e11329
Deep learning in virtual reality
No full textPhysical rehabilitation may be a vital component for patients who are recuperating from a surgery,
injury, or a disabling medical condition. During the treatment session, the patient relies greatly on the
physiotherapist’s verbal feedback. However, in the event that the patient has to undergo long periods
of rehabilitation, exercises done outside of the physiotherapist’s guidance could be ineffective as the
patient is unable to visualize and attain immediate feedback.
To better facilitate the patient’s recovery process, this project applies deep reinforcement learning on
a humanoid model using Unity game engine and Unity’s ML-agents such that it is able to imitate a
given training animation. The project is tested within the premise of a golf swing – an exercise that
aims to benefit patients that suffer from shoulder arthritis. The implemented deep reinforcement
learning algorithm proves to be a promising step in the right direction towards developing a real-time
feedback system that could playback the patient’s movement and provide instant feedback to the
user. Partial results were published in:
Raymond Tan Rui Ming, Chengxuan Feng, Hock Soon Seah, Feng Lin, Movability Assessment on
Physiotherapy for Shoulder Periarthritis via Fine-Grained 3D ResNet Deep Learning, SPIE Proceedings
of International Forum on Medical Imaging Asia (IFMIA’21), Taiwan (Online), 24-27 January 2021Bachelor of Engineering (Computer Science
Constructing adversarial samples against deep learning-based sensing system (part II)
No full textAs deep learning become more popular and have grown to become crucial components in the daily device we use. Despite their effectiveness, they are not invincible. Adversarial examples initially discovered and applied to computer vision systems are now becoming a noticeable issue in DeepSpeech processing classifier as well.
Adversarial examples are input samples that have very bad classification accuracy. They are generated by adding imperceptible perturbations by human. These adversarial examples result in misclassification of results.
In late 2017, an attack was shown to be quite effective against the Speech Commands classification model. Speech commands are used very frequently in many applications, such as Google Assistant, Amazon Alexa and Apple's Siri. Thus, adversarial examples produced by this attack could have real-world consequences.
While previous work in defending again these malicious attacks has investigated using gradient masking to hide information of the model and audio pre-processing to reduce or distort adversarial noise, this project explores the idea of simple pink noise injection at different loudness to detect adversarial examples. This technique of noise injection does not require retraining or modifying the model. It is also possible to transfer this technique from the model used in this project to other DeepSpeech models.Bachelor of Engineering (Computer Science
Environment data processing for a data centre (1)
No full textData centres have aided in the development of this ever-digitalising world due to the increasing need for processing and data storage capacities. The huge increase in the number of data centres worldwide has led to concerns for their impact on the environment due to their significant power consumption. Part of this environmental impact by data centres is contributed by the immense amount of heat energy generated by the data centre servers and cooling systems. Due to these concerns, systems have been developed to capture the heat generated by data centres, and from there, the need
for methods to predict the heat generated by the data centres arise.
This study uses the Tropical Data Centre (TDC) 1.0 dataset to examine and explore two relationships. The first relationship is that between the power consumed by the data centre racks and the amount of heat they generate. The second relationship is that between the environmental factors and the total heat leaving the data centre racks. This
study used various machine learning models to study these relationships, with the first part using Linear Regression, Polynomial Regression, and Neural Networks. The second part used the aforementioned models, along with Classification and Regression Trees (CART), Random Forest, Gradient Boosting, and Support Vector Machines (SVM).
The results of the study suggest that there is no clear relationship in the first part of the study that could be explained with the machine learning models used, while the results of the second part of the study provided favourable results that could potentially explain its relationship. This study provides an insight into the possibility of using certain models to accurately predict the total heat leaving the data centre, meaning that environmental variables can possibly be used to enhance the heat capturing systems that data centres use.Bachelor's degre
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