Xiamen University

The last decade has seen a sharp increase in the number of scientific publications describing physiological and pathological functions of extracellular vesicles (EVs), a collective term covering various subtypes of cell-released, membranous structures, called exosomes, microvesicles, microparticles, ectosomes, oncosomes, apoptotic bodies, and many other names. However, specific issues arise when working with these entities, whose size and amount often make them difficult to obtain as relatively pure preparations, and to characterize properly. The International Society for Extracellular Vesicles (ISEV) proposed Minimal Information for Studies of Extracellular Vesicles ("MISEV") guidelines for the field in 2014. We now update these "MISEV2014" guidelines based on evolution of the collective knowledge in the last four years. An important point to consider is that ascribing a specific function to EVs in general, or to subtypes of EVs, requires reporting of specific information beyond mere description of function in a crude, potentially contaminated, and heterogeneous preparation. For example, claims that exosomes are endowed with exquisite and specific activities remain difficult to support experimentally, given our still limited knowledge of their specific molecular machineries of biogenesis and release, as compared with other biophysically similar EVs. The MISEV2018 guidelines include tables and outlines of suggested protocols and steps to follow to document specific EV-associated functional activities. Finally, a checklist is provided with summaries of key points.

AUTORES: Daniel J Klionsky1745,1749*, Kotb Abdelmohsen840, Akihisa Abe1237, Md Joynal Abedin1762, Hagai Abeliovich425,
\nAbraham Acevedo Arozena789, Hiroaki Adachi1800, Christopher M Adams1669, Peter D Adams57, Khosrow Adeli1981,
\nPeter J Adhihetty1625, Sharon G Adler700, Galila Agam67, Rajesh Agarwal1587, Manish K Aghi1537, Maria Agnello1826,
\nPatrizia Agostinis664, Patricia V Aguilar1960, Julio Aguirre-Ghiso784,786, Edoardo M Airoldi89,422, Slimane Ait-Si-Ali1376,
\nTakahiko Akematsu2010, Emmanuel T Akporiaye1097, Mohamed Al-Rubeai1394, Guillermo M Albaiceta1294,
\nChris Albanese363, Diego Albani561, Matthew L Albert517, Jesus Aldudo128, Hana Alg€ul1164, Mehrdad Alirezaei1198,
\nIraide Alloza642,888, Alexandru Almasan206, Maylin Almonte-Beceril524, Emad S Alnemri1212, Covadonga Alonso544,
\nNihal Altan-Bonnet848, Dario C Altieri1205, Silvia Alvarez1497, Lydia Alvarez-Erviti1395, Sandro Alves107,
\nGiuseppina Amadoro860, Atsuo Amano930, Consuelo Amantini1554, Santiago Ambrosio1458, Ivano Amelio756,
\nAmal O Amer918, Mohamed Amessou2089, Angelika Amon726, Zhenyi An1538, Frank A Anania291, Stig U Andersen6,
\nUsha P Andley2079, Catherine K Andreadi1690, Nathalie Andrieu-Abadie502, Alberto Anel2027, David K Ann58,
\nShailendra Anoopkumar-Dukie388, Manuela Antonioli832,858, Hiroshi Aoki1791, Nadezda Apostolova2007,
\nSaveria Aquila1500, Katia Aquilano1876, Koichi Araki292, Eli Arama2098, Agustin Aranda456, Jun Araya591,
\nAlexandre Arcaro1472, Esperanza Arias26, Hirokazu Arimoto1225, Aileen R Ariosa1749, Jane L Armstrong1930,
\nThierry Arnould1773, Ivica Arsov2120, Katsuhiko Asanuma675, Valerie Askanas1924, Eric Asselin1867, Ryuichiro Atarashi794,
\nSally S Atherton369, Julie D Atkin713, Laura D Attardi1131, Patrick Auberger1787, Georg Auburger379, Laure Aurelian1727,
\nRiccardo Autelli1992, Laura Avagliano1029,1755, Maria Laura Avantaggiati364, Limor Avrahami1166, Suresh Awale1986,
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\nSoo Han Bae2117, Eric H Baehrecke1729, Seung-Hoon Baek17, Stephen Baghdiguian1368,
\nAgnieszka Bagniewska-Zadworna2, Hua Bai90, Jie Bai667, Xue-Yuan Bai1133, Yannick Bailly884,
\nKithiganahalli Narayanaswamy Balaji473, Walter Balduini2002, Andrea Ballabio316, Rena Balzan1711, Rajkumar Banerjee239,
\nG abor B anhegyi1052, Haijun Bao2109, Benoit Barbeau1363, Maria D Barrachina2007, Esther Barreiro467, Bonnie Bartel997,
\nAlberto Bartolom e222, Diane C Bassham550, Maria Teresa Bassi1046, Robert C Bast Jr1273, Alakananda Basu1798,
\nMaria Teresa Batista1578, Henri Batoko1336, Maurizio Battino970, Kyle Bauckman2085, Bradley L Baumgarner1909,
\nK Ulrich Bayer1594, Rupert Beale1553, Jean-Fran¸cois Beaulieu1360, George R. Beck Jr48,294, Christoph Becker336,
\nJ David Beckham1595, Pierre-Andr e B edard749, Patrick J Bednarski301, Thomas J Begley1135, Christian Behl1419,
\nChristian Behrends757, Georg MN Behrens406, Kevin E Behrns1627, Eloy Bejarano26, Amine Belaid490,
\nFrancesca Belleudi1041, Giovanni B enard497, Guy Berchem706, Daniele Bergamaschi983, Matteo Bergami1401,
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\nMartine Biard-Piechaczyk210, Viktor Billes298, Claudia Bincoletto1314, Baris Bingol350, Sara W Bird1128, Marc Bitoun1112,
\nIvana Bjedov1258, Craig Blackstone843, Lionel Blanc1183, Guillermo A Blanco1496, Heidi Kiil Blomhoff1812,
\nEmilio Boada-Romero1297, Stefan B€ockler1464, Marianne Boes1423, Kathleen Boesze-Battaglia1835, Lawrence H Boise286,287,
\nAlessandra Bolino2063, Andrea Boman693, Paolo Bonaldo1823, Matteo Bordi897, J€urgen Bosch608, Luis M Botana1308,
\nJoelle Botti1375, German Bou1405, Marina Bouch e1038, Marion Bouchecareilh1331, Marie-Jos ee Boucher1901,
\nMichael E Boulton481, Sebastien G Bouret1926, Patricia Boya133, Micha€el Boyer-Guittaut1345, Peter V Bozhkov1141,
\nNathan Brady374, Vania MM Braga469, Claudio Brancolini1997, Gerhard H Braus353, Jos e M Bravo-San Pedro299,393,508,1374,
\nLisa A Brennan322, Emery H Bresnick2022, Patrick Brest490, Dave Bridges1939, Marie-Agn es Bringer124, Marisa Brini1822,
\nGlauber C Brito1311, Bertha Brodin631, Paul S Brookes1872, Eric J Brown352, Karen Brown1690, Hal E Broxmeyer480,
\nAlain Bruhat486,1339, Patricia Chakur Brum1893, John H Brumell446, Nicola Brunetti-Pierri315,1171,
\nRobert J Bryson-Richardson781, Shilpa Buch1777, Alastair M Buchan1819, Hikmet Budak1022, Dmitry V Bulavin118,505,1789,
\nScott J Bultman1792, Geert Bultynck665, Vladimir Bumbasirevic1470, Yan Burelle1356, Robert E Burke216,217,
\nMargit Burmeister1750, Peter B€utikofer1473, Laura Caberlotto1987, Ken Cadwell896, Monika Cahova112, Dongsheng Cai24,
\nJingjing Cai2099, Qian Cai1018, Sara Calatayud2007, Nadine Camougrand1343, Michelangelo Campanella1700,
\nGrant R Campbell1525, Matthew Campbell1249, Silvia Campello556,1876, Robin Candau1769, Isabella Caniggia1983,
\nLavinia Cantoni560, Lizhi Cao116, Allan B Caplan1656, Michele Caraglia1051, Claudio Cardinali1043, Sandra Morais Cardoso1579, Jennifer S Carew208, Laura A Carleton874, Cathleen R Carlin101, Silvia Carloni2002,
\nSven R Carlsson1267, Didac Carmona-Gutierrez1643, Leticia AM Carneiro312, Oliana Carnevali971, Serena Carra1318,
\nAlice Carrier120, Bernadette Carroll900, Caty Casas1324, Josefina Casas1116, Giuliana Cassinelli324, Perrine Castets1462,
\nSusana Castro-Obregon214, Gabriella Cavallini1841, Isabella Ceccherini568, Francesco Cecconi253,555,1884,
\nArthur I Cederbaum459, Valent ın Ce~na199,1281, Simone Cenci1323,2064, Claudia Cerella444, Davide Cervia1996,
\nSilvia Cetrullo1478, Hassan Chaachouay2028, Han-Jung Chae187, Andrei S Chagin634, Chee-Yin Chai626,628,
\nGopal Chakrabarti1502, Georgios Chamilos1601, Edmond YW Chan1142, Matthew TV Chan181, Dhyan Chandra1003,
\nPallavi Chandra548, Chih-Peng Chang818, Raymond Chuen-Chung Chang1653, Ta Yuan Chang345, John C Chatham1434,
\nSaurabh Chatterjee1910, Santosh Chauhan527, Yongsheng Che62, Michael E Cheetham1263, Rajkumar Cheluvappa1783,
\nChun-Jung Chen1153, Gang Chen598,1676, Guang-Chao Chen9, Guoqiang Chen1078, Hongzhuan Chen1077, Jeff W Chen1514,
\nJian-Kang Chen370,371, Min Chen249, Mingzhou Chen2104, Peiwen Chen1823, Qi Chen1674, Quan Chen172,
\nShang-Der Chen138, Si Chen325, Steve S-L Chen10, Wei Chen2125, Wei-Jung Chen829, Wen Qiang Chen979, Wenli Chen1113,
\nXiangmei Chen1133, Yau-Hung Chen1157, Ye-Guang Chen1250, Yin Chen1447, Yingyu Chen953,955, Yongshun Chen2135,
\nYu-Jen Chen712, Yue-Qin Chen1145, Yujie Chen1208, Zhen Chen339, Zhong Chen2123, Alan Cheng1702,
\nChristopher HK Cheng184, Hua Cheng1728, Heesun Cheong814, Sara Cherry1836, Jason Chesney1703,
\nChun Hei Antonio Cheung817, Eric Chevet1359, Hsiang Cheng Chi140, Sung-Gil Chi656, Fulvio Chiacchiera308,
\nHui-Ling Chiang958, Roberto Chiarelli1826, Mario Chiariello235,567,577, Marcello Chieppa835, Lih-Shen Chin290,
\nMario Chiong1285, Gigi NC Chiu878, Dong-Hyung Cho676, Ssang-Goo Cho650, William C Cho982, Yong-Yeon Cho105,
\nYoung-Seok Cho1064, Augustine MK Choi2095, Eui-Ju Choi656, Eun-Kyoung Choi387,400,685, Jayoung Choi1563,
\nMary E Choi2093, Seung-Il Choi2116, Tsui-Fen Chou412, Salem Chouaib395, Divaker Choubey1574, Vinay Choubey1936,
\nKuan-Chih Chow822, Kamal Chowdhury730, Charleen T Chu1856, Tsung-Hsien Chuang827, Taehoon Chun657,
\nHyewon Chung652, Taijoon Chung978, Yuen-Li Chung1194, Yong-Joon Chwae18, Valentina Cianfanelli254,
\nRoberto Ciarcia1775, Iwona A Ciechomska886, Maria Rosa Ciriolo1876, Mara Cirone1042, Sofie Claerhout1694,
\nMichael J Clague1698, Joan Cl aria1457, Peter GH Clarke1687, Robert Clarke361, Emilio Clementi1045,1398, C edric Cleyrat1781,
\nMiriam Cnop1366, Eliana M Coccia574, Tiziana Cocco1459, Patrice Codogno1375, J€orn Coers271, Ezra EW Cohen1533,
\nDavid Colecchia235,567,577, Luisa Coletto25, N uria S Coll123, Emma Colucci-Guyon516, Sergio Comincini1829,
\nMaria Condello578, Katherine L Cook2073, Graham H Coombs1929, Cynthia D Cooper2076, J Mark Cooper1395,
\nIsabelle Coppens601, Maria Tiziana Corasaniti1387, Marco Corazzari485,1884, Ramon Corbalan1566,
\nElisabeth Corcelle-Termeau251, Mario D Cordero1899, Cristina Corral-Ramos1289, Olga Corti507,1109, Andrea Cossarizza1767,
\nPaola Costelli1993, Safia Costes1518, Susan L Cotman721, Ana Coto-Montes946, Sandra Cottet566,1688, Eduardo Couve1301,
\nLori R Covey1015, L Ashley Cowart762, Jeffery S Cox1536, Fraser P Coxon1427, Carolyn B Coyne1846, Mark S Cragg1919,
\nRolf J Craven1679, Tiziana Crepaldi1995, Jose L Crespo1300, Alfredo Criollo1285, Valeria Crippa558, Maria Teresa Cruz1576,
\nAna Maria Cuervo26, Jose M Cuezva1277, Taixing Cui1907, Pedro R Cutillas987, Mark J Czaja27, Maria F Czyzyk-Krzeska1572,
\nRuben K Dagda2068, Uta Dahmen1404, Chunsun Dai800, Wenjie Dai1187, Yun Dai2059, Kevin N Dalby1940,
\nLuisa Dalla Valle1822, Guillaume Dalmasso1340, Marcello D’Amelio557, Markus Damme188, Arlette Darfeuille-Michaud1340,
\nCatherine Dargemont950, Victor M Darley-Usmar1433, Srinivasan Dasarathy205, Biplab Dasgupta202, Srikanta Dash1254,
\nCrispin R Dass242, Hazel Marie Davey8, Lester M Davids1560, David D avila227, Roger J Davis1731, Ted M Dawson604,
\nValina L Dawson606, Paula Daza1898, Jackie de Belleroche470, Paul de Figueiredo1180,1182,
\nRegina Celia Bressan Queiroz de Figueiredo135, Jos e de la Fuente1023, Luisa De Martino1775,
\nAntonella De Matteis1171, Guido RY De Meyer1443, Angelo De Milito631, Mauro De Santi2002,

The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article.

Teaser: Our review found the average R0 for 2019-nCoV to be 3.28, which exceeds WHO estimates of 1.4 to 2.5.

A summary of the technical advances that are incorporated in the fourth major release of the Q-Chem quantum chemistry program is provided, covering approximately the last seven years. These include developments in density functional theory methods and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and open-shell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces. In addition, a selection of example case studies that illustrate these capabilities is given. These include extensive benchmarks of the comparative accuracy of modern density functionals for bonded and non-bonded interactions, tests of attenuated second order Møller–Plesset (MP2) methods for intermolecular interactions, a variety of parallel performance benchmarks, and tests of the accuracy of implicit solvation models. Some specific chemical examples include calculations on the strongly correlated Cr2 dimer, exploring zeolite-catalysed ethane dehydrogenation, energy decomposition analysis of a charged ter-molecular complex arising from glycerol photoionisation, and natural transition orbitals for a Frenkel exciton state in a nine-unit model of a self-assembling nanotube.

The shapes of noble metal nanocrystals (NCs) are usually defined by polyhedra that are enclosed by {111} and {100} facets, such as cubes, tetrahedra, and octahedra. Platinum NCs of unusual tetrahexahedral (THH) shape were prepared at high yield by an electrochemical treatment of Pt nanospheres supported on glassy carbon by a square-wave potential. The single-crystal THH NC is enclosed by 24 high-index facets such as {730}, {210}, and/or {520} surfaces that have a large density of atomic steps and dangling bonds. These high-energy surfaces are stable thermally (to 800 degrees C) and chemically and exhibit much enhanced (up to 400%) catalytic activity for equivalent Pt surface areas for electro-oxidation of small organic fuels such as formic acid and ethanol.

In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN). In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values. In this process, training images with various levels of occlusion are generated, which reduces the risk of over-fitting and makes the model robust to occlusion. Random Erasing is parameter learning free, easy to implement, and can be integrated with most of the CNN-based recognition models. Albeit simple, Random Erasing is complementary to commonly used data augmentation techniques such as random cropping and flipping, and yields consistent improvement over strong baselines in image classification, object detection and person re-identification. Code is available at: https://github.com/zhunzhong07/Random-Erasing.

BACKGROUND: The novel coronavirus SARS-CoV-2 is a newly emerging virus. The antibody response in infected patients remains largely unknown, and the clinical value of antibody testing has not been fully demonstrated. METHODS: 173 patients with SARS-CoV-2 infection were enrolled. Their serial plasma samples (n = 535) collected during hospitalization were tested for total antibodies (Ab), IgM, and IgG against SARS-CoV-2. The dynamics of antibodies with disease progress were analyzed. RESULTS: Among 173 patients, the seroconversion rates for Ab, IgM, and IgG were 93.1%, 82.7%, and 64.7%, respectively. The reason for the negative antibody findings in 12 patients might be due to the lack of blood samples at the later stage of illness. The median seroconversion times for Ab, IgM, and then IgG were days 11, 12, and 4, respectively. The presence of antibodies was <40% among patients within 1 week of onset, and rapidly increased to 100.0% (Ab), 94.3% (IgM), and 79.8% (IgG) by day 15 after onset. In contrast, RNA detectability decreased from 66.7% (58/87) in samples collected before day 7 to 45.5% (25/55) during days 15-39. Combining RNA and antibody detection significantly improved the sensitivity of pathogenic diagnosis for COVID-19 (P < .001), even in the early phase of 1 week from onset (P = .007). Moreover, a higher titer of Ab was independently associated with a worse clinical classification (P = .006). CONCLUSIONS: Antibody detection offers vital clinical information during the course of SARS-CoV-2 infection. The findings provide strong empirical support for the routine application of serological testing in the diagnosis and management of COVID-19 patients.

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Inflammasome is an intracellular signaling complex of the innate immune system. Activation of inflammasomes promotes the secretion of interleukin 1β (IL-1β) and IL-18 and triggers pyroptosis. Caspase-1 and -11 (or -4/5 in human) in the canonical and non-canonical inflammasome pathways, respectively, are crucial for inflammasome-mediated inflammatory responses. Here we report that gasdermin D (GSDMD) is another crucial component of inflammasomes. We discovered the presence of GSDMD protein in nigericin-induced NLRP3 inflammasomes by a quantitative mass spectrometry-based analysis. Gene deletion of GSDMD demonstrated that GSDMD is required for pyroptosis and for the secretion but not proteolytic maturation of IL-1β in both canonical and non-canonical inflammasome responses. It was known that GSDMD is a substrate of caspase-1 and we showed its cleavage at the predicted site during inflammasome activation and that this cleavage was required for pyroptosis and IL-1β secretion. Expression of the N-terminal proteolytic fragment of GSDMD can trigger cell death and N-terminal modification such as tagging with Flag sequence disrupted the function of GSDMD. We also found that pro-caspase-1 is capable of processing GSDMD and ASC is not essential for GSDMD to function. Further analyses of LPS plus nigericin- or Salmonella typhimurium-treated macrophage cell lines and primary cells showed that apoptosis became apparent in Gsdmd(-/-) cells, indicating a suppression of apoptosis by pyroptosis. The induction of apoptosis required NLRP3 or other inflammasome receptors and ASC, and caspase-1 may partially contribute to the activation of apoptotic caspases in Gsdmd(-/-) cells. These data provide new insights into the molecular mechanisms of pyroptosis and reveal an unexpected interplay between apoptosis and pyroptosis.

The reactive oxygen species (ROS)-mediated mechanism is the major cause underlying the efficacy of photodynamic therapy (PDT). The PDT procedure is based on the cascade of synergistic effects between light, a photosensitizer (PS) and oxygen, which greatly favors the spatiotemporal control of the treatment. This procedure has also evoked several unresolved challenges at different levels including (i) the limited penetration depth of light, which restricts traditional PDT to superficial tumours; (ii) oxygen reliance does not allow PDT treatment of hypoxic tumours; (iii) light can complicate the phototherapeutic outcomes because of the concurrent heat generation; (iv) specific delivery of PSs to sub-cellular organelles for exerting effective toxicity remains an issue; and (v) side effects from undesirable white-light activation and self-catalysation of traditional PSs. Recent advances in nanotechnology and nanomedicine have provided new opportunities to develop ROS-generating systems through photodynamic or non-photodynamic procedures while tackling the challenges of the current PDT approaches. In this review, we summarize the current status and discuss the possible opportunities for ROS generation for cancer therapy. We hope this review will spur pre-clinical research and clinical practice for ROS-mediated tumour treatments.

Transparent, flexible and high efficient power sources are important components of organic electronic and optoelectronic devices. In this work, based on the principle of the previously demonstrated triboelectric generator, we demonstrate a new high-output, flexible and transparent nanogenerator by using transparent polymer materials. We have fabricated three types of regular and uniform polymer patterned arrays (line, cube, and pyramid) to improve the efficiency of the nanogenerator. The power generation of the pyramid-featured device far surpassed that exhibited by the unstructured films and gave an output voltage of up to 18 V at a current density of ∼0.13 μA/cm(2). Furthermore, the as-prepared nanogenerator can be applied as a self-powered pressure sensor for sensing a water droplet (8 mg, ∼3.6 Pa in contact pressure) and a falling feather (20 mg, ∼0.4 Pa in contact pressure) with a low-end detection limit of ∼13 mPa.

Necrosis can be induced by stimulating death receptors with tumor necrosis factor (TNF) or other agonists; however, the underlying mechanism differentiating necrosis from apoptosis is largely unknown. We identified the protein kinase receptor-interacting protein 3 (RIP3) as a molecular switch between TNF-induced apoptosis and necrosis in NIH 3T3 cells and found that RIP3 was required for necrosis in other cells. RIP3 did not affect RIP1-mediated apoptosis but was required for RIP1-mediated necrosis and the enhancement of necrosis by the caspase inhibitor zVAD. By activating key enzymes of metabolic pathways, RIP3 regulates TNF-induced reactive oxygen species production, which partially accounts for RIP3's ability to promote necrosis. Our data suggest that modulation of energy metabolism in response to death stimuli has an important role in the choice between apoptosis and necrosis.

Akt/PKB is a serine/threonine protein kinase that functions as a critical regulator of cell survival and proliferation. Akt/PKB family comprises three highly homologous members known as PKBalpha/Akt1, PKBbeta/Akt2 and PKBgamma/Akt3 in mammalian cells. Similar to many other protein kinases, Akt/PKB contains a conserved domain structure including a specific PH domain, a central kinase domain and a carboxyl-terminal regulatory domain that mediates the interaction between signaling molecules. Akt/PKB plays important roles in the signaling pathways in response to growth factors and other extracellular stimuli to regulate several cellular functions including nutrient metabolism, cell growth, apoptosis and survival. This review surveys recent developments in understanding the molecular mechanisms of Akt/PKB activation and its roles in cell survival in normal and cancer cells.

Surface-enhanced Raman spectroscopy (SERS) inherits the rich chemical fingerprint information on Raman spectroscopy and gains sensitivity by plasmon-enhanced excitation and scattering. In particular, most Raman peaks have a narrow width suitable for multiplex analysis, and the measurements can be conveniently made under ambient and aqueous conditions. These merits make SERS a very promising technique for studying complex biological systems, and SERS has attracted increasing interest in biorelated analysis. However, there are still great challenges that need to be addressed until it can be widely accepted by the biorelated communities, answer interesting biological questions, and solve fatal clinical problems. SERS applications in bioanalysis involve the complex interactions of plasmonic nanomaterials with biological systems and their environments. The reliability becomes the key issue of bioanalytical SERS in order to extract meaningful information from SERS data. This review provides a comprehensive overview of bioanalytical SERS with the main focus on the reliability issue. We first introduce the mechanism of SERS to guide the design of reliable SERS experiments with high detection sensitivity. We then introduce the current understanding of the interaction of nanomaterials with biological systems, mainly living cells, to guide the design of functionalized SERS nanoparticles for target detection. We further introduce the current status of label-free (direct) and labeled (indirect) SERS detections, for systems from biomolecules, to pathogens, to living cells, and we discuss the potential interferences from experimental design, measurement conditions, and data analysis. In the end, we give an outlook of the key challenges in bioanalytical SERS, including reproducibility, sensitivity, and spatial and time resolution.

In this paper, we designed an end-to-end spectral-spatial residual network (SSRN) that takes raw 3-D cubes as input data without feature engineering for hyperspectral image classification. In this network, the spectral and spatial residual blocks consecutively learn discriminative features from abundant spectral signatures and spatial contexts in hyperspectral imagery (HSI). The proposed SSRN is a supervised deep learning framework that alleviates the declining-accuracy phenomenon of other deep learning models. Specifically, the residual blocks connect every other 3-D convolutional layer through identity mapping, which facilitates the backpropagation of gradients. Furthermore, we impose batch normalization on every convolutional layer to regularize the learning process and improve the classification performance of trained models. Quantitative and qualitative results demonstrate that the SSRN achieved the state-of-the-art HSI classification accuracy in agricultural, rural-urban, and urban data sets: Indian Pines, Kennedy Space Center, and University of Pavia.

The synthesis of nanocrystals is in the limelight in modern nanotechnology. Biosynthesis of nanoparticles by plant extracts is currently under exploitation. Not only could silver nanoparticles ranging from 55 to 80 nm in size be fabricated, but also triangular or spherical shaped gold nanoparticles could be easily modulated by reacting the novel sundried biomass of Cinnamomum camphora leaf with aqueous silver or gold precursors at ambient temperature. The marked difference of shape control between gold and silver nanoparticles was attributed to the comparative advantage of protective biomolecules and reductive biomolecules. The polyol components and the water-soluble heterocyclic components were mainly responsible for the reduction of silver ions or chloroaurate ions and the stabilization of the nanoparticles, respectively. The sundried leaf in this work was very suitable for simple synthesis of nanoparticles.

A universal but simple procedure for identifying the α, β and γ phases in PVDF using FTIR is proposed and validated. An integrated quantification methodology for individual β and γ phase in mixed systems is also proposed.

Anatase TiO(2) nanosheets with highly reactive (001) facets exposed have been successfully synthesized by a facile hydrothermal route, taking advantage of a specific stabilization effect of fluorine ion on (001) facets. The percentage of highly reactive (001) facets in such TiO(2) nanosheets is very high (up to 89%). In addition, the as-prepared TiO(2) nanosheets exhibit excellent activity in the photocatalytic degradation of organic contaminants.

In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-theart methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods.