Bern University of Applied Sciences

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,
\nNeelam Azad404, Tiziana Bachetti568, Jonathan M Backer28, Dong-Hun Bae1933, Jae-sung Bae677, Ok-Nam Bae409,
\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,
\nBen Berkhout1441, Laura Berliocchi714, Am elie Bernard1749, Monique Bernard1354, Francesca Bernassola1880,
\nAnne Bertolotti791, Amanda S Bess272, S ebastien Besteiro1351, Saverio Bettuzzi1828, Savita Bhalla913,
\nShalmoli Bhattacharyya973, Sujit K Bhutia838, Caroline Biagosch1159, Michele Wolfe Bianchi520,1378,1381,
\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,

This article reports on a research synthesis of the relation between alliance and the outcomes of individual psychotherapy. Included were over 200 research reports based on 190 independent data sources, covering more than 14,000 treatments. Research involving 5 or more adult participants receiving genuine (as opposed to analogue) treatments, where the author(s) referred to one of the independent variables as "alliance," "therapeutic alliance," "helping alliance," or "working alliance" were the inclusion criteria. All analyses were done using the assumptions of a random model. The overall aggregate relation between the alliance and treatment outcome (adjusted for sample size and non independence of outcome measures) was r = .275 (k = 190); the 95% confidence interval for this value was .25-.30. The statistical probability associated with the aggregated relation between alliance and outcome is p < .0001. The data collected for this meta-analysis were quite variable (heterogeneous). Potential variables such as assessment perspectives (client, therapist, observer), publication source, types of assessment methods and time of assessment were explored.

Multiproxy reconstructions of monthly and seasonal surface temperature fields for Europe back to 1500 show that the late 20th- and early 21st-century European climate is very likely (>95% confidence level) warmer than that of any time during the past 500 years. This agrees with findings for the entire Northern Hemisphere. European winter average temperatures during the period 1500 to 1900 were reduced by approximately 0.5 degrees C (0.25 degrees C for annual mean temperatures) compared to the 20th century. Summer temperatures did not experience systematic century-scale cooling relative to present conditions. The coldest European winter was 1708/1709; 2003 was by far the hottest summer.

A classification for peri-implant diseases and conditions was presented. Focused questions on the characteristics of peri-implant health, peri-implant mucositis, peri-implantitis, and soft- and hard-tissue deficiencies were addressed. Peri-implant health is characterized by the absence of erythema, bleeding on probing, swelling, and suppuration. It is not possible to define a range of probing depths compatible with health; Peri-implant health can exist around implants with reduced bone support. The main clinical characteristic of peri-implant mucositis is bleeding on gentle probing. Erythema, swelling, and/or suppuration may also be present. An increase in probing depth is often observed in the presence of peri-implant mucositis due to swelling or decrease in probing resistance. There is strong evidence from animal and human experimental studies that plaque is the etiological factor for peri-implant mucositis. Peri-implantitis is a plaque-associated pathological condition occurring in tissues around dental implants, characterized by inflammation in the peri-implant mucosa and subsequent progressive loss of supporting bone. Peri-implantitis sites exhibit clinical signs of inflammation, bleeding on probing, and/or suppuration, increased probing depths and/or recession of the mucosal margin in addition to radiographic bone loss. The evidence is equivocal regarding the effect of keratinized mucosa on the long-term health of the peri-implant tissue. It appears, however, that keratinized mucosa may have advantages regarding patient comfort and ease of plaque removal. Case definitions in day-to-day clinical practice and in epidemiological or disease-surveillance studies for peri-implant health, peri-implant mucositis, and peri-implantitis were introduced. The proposed case definitions should be viewed within the context that there is no generic implant and that there are numerous implant designs with different surface characteristics, surgical and loading protocols. It is recommended that the clinician obtain baseline radiographic and probing measurements following the completion of the implant-supported prosthesis.

In the present multi-center study, non-submerged ITI implants were prospectively followed to evaluate their long-term prognosis in fully and partially edentulous patients. In a total of 1003 patients, 2359 implants were consecutively inserted. Following a healing period of 3-6 months, the successfully integrated implants were restored with 393 removable and 758 fixed restorations. Subsequently, all consecutive implants were documented annually up to 8 years. At each examination, the clinical status of all implants was evaluated according to predefined criteria of success. Therefore, the data base allowed the evaluation of 8-year cumulative survival and success rates for 2359 implants. In addition, cumulative success rates were calculated for implant subgroups divided per implant type, implant length, and implant location. Furthermore, the actual 5-year survival and success rates could be determined for 488 implants. During the healing period, 13 implants did not successfully integrate, whereas 2346 implants fulfilled the predefined criteria of success. This corresponds with an early failure rate of 0.55%. During follow-up, 19 implants were classified as failures due to several reasons. In addition, 17 implants (approximately 0.8%) demonstrated at the last annual examination a suppurative periimplant infection. Including 127 drop out implants (= 5.4% drop out rate) into the calculation, the 8-year cumulative survival and success rates resulted in 96.7% and 93.3%, respectively. The analysis of implant subgroups showed slightly more favorable cumulative success rates for screw type implants (> 95%) compared to hollow-cylinder implants (91.3%), and clearly better success rates for mandibular implants (approximately 95%) when compared to maxillary implants (approximately 87%). The actual 5-year survival and success rates of 488 implants with 98.2% and 97.3%, respectively, were slightly better than the estimated 5-year cumulative survival and success rates of 2359 implants indicating that the applied life table analysis is a reliable statistical method to evaluate the long-term prognosis of dental implants. It can be concluded that non-submerged ITI implants maintain success rates well above 90% in different clinical centers for observation periods up to 8 years.

Periodontal health is defined by absence of clinically detectable inflammation. There is a biological level of immune surveillance that is consistent with clinical gingival health and homeostasis. Clinical gingival health may be found in a periodontium that is intact, i.e. without clinical attachment loss or bone loss, and on a reduced periodontium in either a non-periodontitis patient (e.g. in patients with some form of gingival recession or following crown lengthening surgery) or in a patient with a history of periodontitis who is currently periodontally stable. Clinical gingival health can be restored following treatment of gingivitis and periodontitis. However, the treated and stable periodontitis patient with current gingival health remains at increased risk of recurrent periodontitis, and accordingly, must be closely monitored. Two broad categories of gingival diseases include non-dental plaque biofilm-induced gingival diseases and dental plaque-induced gingivitis. Non-dental plaque biofilm-induced gingival diseases include a variety of conditions that are not caused by plaque and usually do not resolve following plaque removal. Such lesions may be manifestations of a systemic condition or may be localized to the oral cavity. Dental plaque-induced gingivitis has a variety of clinical signs and symptoms, and both local predisposing factors and systemic modifying factors can affect its extent, severity, and progression. Dental plaque-induced gingivitis may arise on an intact periodontium or on a reduced periodontium in either a non-periodontitis patient or in a currently stable "periodontitis patient" i.e. successfully treated, in whom clinical inflammation has been eliminated (or substantially reduced). A periodontitis patient with gingival inflammation remains a periodontitis patient (Figure 1), and comprehensive risk assessment and management are imperative to ensure early prevention and/or treatment of recurrent/progressive periodontitis. Precision dental medicine defines a patient-centered approach to care, and therefore, creates differences in the way in which a "case" of gingival health or gingivitis is defined for clinical practice as opposed to epidemiologically in population prevalence surveys. Thus, case definitions of gingival health and gingivitis are presented for both purposes. While gingival health and gingivitis have many clinical features, case definitions are primarily predicated on presence or absence of bleeding on probing. Here we classify gingival health and gingival diseases/conditions, along with a summary table of diagnostic features for defining health and gingivitis in various clinical situations.

Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is suboptimal. This paper presents a random forest for spatial predictions framework (RFsp) where buffer distances from observation points are used as explanatory variables, thus incorporating geographical proximity effects into the prediction process. The RFsp framework is illustrated with examples that use textbook datasets and apply spatial and spatio-temporal prediction to numeric, binary, categorical, multivariate and spatiotemporal variables. Performance of the RFsp framework is compared with the state-of-the-art kriging techniques using fivefold cross-validation with refitting. The results show that RFsp can obtain equally accurate and unbiased predictions as different versions of kriging. Advantages of using RFsp over kriging are that it needs no rigid statistical assumptions about the distribution and stationarity of the target variable, it is more flexible towards incorporating, combining and extending covariates of different types, and it possibly yields more informative maps characterizing the prediction error. RFsp appears to be especially attractive for building multivariate spatial prediction models that can be used as "knowledge engines" in various geoscience fields. Some disadvantages of RFsp are the exponentially growing computational intensity with increase of calibration data and covariates and the high sensitivity of predictions to input data quality. The key to the success of the RFsp framework might be the training data quality-especially quality of spatial sampling (to minimize extrapolation problems and any type of bias in data), and quality of model validation (to ensure that accuracy is not effected by overfitting). For many data sets, especially those with lower number of points and covariates and close-to-linear relationships, model-based geostatistics can still lead to more accurate predictions than RFsp.

The phosphatidylinositol-3-kinase (PI3K)/Akt and the mammalian target of rapamycin (mTOR) signaling pathways are two pathways crucial to many aspects of cell growth and survival, in physiological as well as in pathological conditions (e.g., cancer). Indeed, they are so interconnected that, in a certain sense, they could be regarded as a single, unique pathway. In this paper, after a general overview of the biological significance and the main components of these pathways, we address the present status of the development of specific PI3K, Akt, and mTOR inhibitors, from already registered medicines to novel compounds that are just leaving the laboratory bench.

Results obtained from completed and on-going clinical studies indicate huge therapeutic potential of stem cell-based therapy in the treatment of degenerative, autoimmune and genetic disorders. However, clinical application of stem cells raises numerous ethical and safety concerns. In this review, we provide an overview of the most important ethical issues in stem cell therapy, as a contribution to the controversial debate about their clinical usage in regenerative and transplantation medicine. We describe ethical challenges regarding human embryonic stem cell (hESC) research, emphasizing that ethical dilemma involving the destruction of a human embryo is a major factor that may have limited the development of hESC-based clinical therapies. With previous derivation of induced pluripotent stem cells (iPSCs) this problem has been overcome, however current perspectives regarding clinical translation of iPSCs still remain. Unlimited differentiation potential of iPSCs which can be used in human reproductive cloning, as a risk for generation of genetically engineered human embryos and human-animal chimeras, is major ethical issue, while undesired differentiation and malignant transformation are major safety issues. Although clinical application of mesenchymal stem cells (MSCs) has shown beneficial effects in the therapy of autoimmune and chronic inflammatory diseases, the ability to promote tumor growth and metastasis and overestimated therapeutic potential of MSCs still provide concerns for the field of regenerative medicine. This review offers stem cell scientists, clinicians and patient's useful information and could be used as a starting point for more in-depth analysis of ethical and safety issues related to clinical application of stem cells.

Abstract A classification for peri‐implant diseases and conditions was presented. Focused questions on the characteristics of peri‐implant health, peri‐implant mucositis, peri‐implantitis, and soft‐ and hard‐tissue deficiencies were addressed. Peri‐implant health is characterized by the absence of erythema, bleeding on probing, swelling, and suppuration. It is not possible to define a range of probing depths compatible with health; Peri‐implant health can exist around implants with reduced bone support. The main clinical characteristic of peri‐implant mucositis is bleeding on gentle probing. Erythema, swelling, and/or suppuration may also be present. An increase in probing depth is often observed in the presence of peri‐implant mucositis due to swelling or decrease in probing resistance. There is strong evidence from animal and human experimental studies that plaque is the etiological factor for peri‐implant mucositis. Peri‐implantitis is a plaque‐associated pathological condition occurring in tissues around dental implants, characterized by inflammation in the peri‐implant mucosa and subsequent progressive loss of supporting bone. Peri‐implantitis sites exhibit clinical signs of inflammation, bleeding on probing, and/or suppuration, increased probing depths and/or recession of the mucosal margin in addition to radiographic bone loss. The evidence is equivocal regarding the effect of keratinized mucosa on the long‐term health of the peri‐implant tissue. It appears, however, that keratinized mucosa may have advantages regarding patient comfort and ease of plaque removal. Case definitions in day‐to‐day clinical practice and in epidemiological or disease‐surveillance studies for peri‐implant health, peri‐implant mucositis, and peri‐implantitis were introduced. The proposed case definitions should be viewed within the context that there is no generic implant and that there are numerous implant designs with different surface characteristics, surgical and loading protocols. It is recommended that the clinician obtain baseline radiographic and probing measurements following the completion of the implant‐supported prosthesis.

The paper proposes the concepts of integrators for sinusoidal signals. A proportional-integral (PI) current controller using stationary-frame generalized integrators is applied for current control of active power filters. Zero steady-state error for the concerned current harmonics is realized, with reduced computation, under unbalanced utility or load conditions. Designing of the PI constants, digital realization of the generalized integrators, as well as compensation of the computation delay are studied. Extensive test results from a 10-kW prototype are demonstrated.

Indices for M-mode measurements in dogs usually have been based on the assumption that a linear relationship exists between these measurements and body weight (BW) or body surface area (BSA). The relationships between the geometry of 3-dimensional objects do not support this assumption. The purposes of this study were to retrospectively examine M-mode data from a large number of dogs of varying sizes and breeds that were examined by a large number of ultrasonographers, to use the allometric equation to determine the appropriate BW exponent required to predict these cardiac dimensions, and to determine normal mean values and prediction intervals for common M-mode variables. Linear regression analyses of data from 494 dogs (2.2-95 kg) revealed a good correlation between M-mode measurements and BW after logarithmic transformation of the data (r2 = .55-.88). Most variables were most closely related to an index of body length, BW(1/3), although the exponent that best predicted diastolic and systolic left ventricular wall thicknesses was closer to 0.25. No variable indexed well to BW or BSA. With these data, appropriate mean values and prediction intervals were calculated for normal dogs, allowing veterinarians to correctly and appropriately index M-mode values. The equations developed from this study appear to be applicable to adult dogs of most breeds.

Current implant placement utilizes both nonsubmerged and submerged techniques. However, the implications of the location of a rough/smooth implant interface as well as the location of a microgap between implant and abutment on crestal bone changes are not well understood. The purpose of this study was to radiographically evaluate crestal bone changes around unloaded nonsubmerged and submerged titanium implants in a side-by-side comparison. Fifty-nine (59) implants were placed at different levels to the alveolar crest in 5 foxhounds. Standardized radiographs were taken at baseline and at monthly intervals until sacrifice at 6 months. Radiographic assessment was carried out by measuring the distance between the top of the implant/abutment and the most coronal bone-to-implant contact (DIB), and by evaluation of bone density changes using computer-assisted densitometric image analysis (CADIA). DIB measurements revealed that in 1-part, nonsubmerged implants, the most coronal bone-to-implant contact followed at all time points the rough/smooth implant interface. In all 2-part implants, nonsubmerged and submerged, the most coronal bone-to-implant contact was consistently located approximately 2 mm below the microgap. In addition, CADIA values for all 2-part implants were decreased in the most coronal area-of-interest (AOI). All bone changes were statistically significant and detectable 1 month after implant placement in nonsubmerged implants or 1 month after abutment connection in submerged implants. Neither implant position nor individual dog effects were statistically significant. These results demonstrate that the rough/smooth implant interface as well as the location of the microgap have a significant effect on marginal bone formation as evaluated by standardized longitudinal radiography. Bone remodeling occurs rapidly during the early healing phase after implant placement for non-submerged implants and after abutment connection for submerged implants.

The paper proposes several concepts of integrators for sinusoidal signals. Parallel and series associations of the basic PI units using the stationary frame generalized integrators are used for current control of active power filters. Zero steady state error for the concerned current harmonics are realized, with reduced computation, under unbalanced utility or load conditions. Designing of the PI constants, digital realization of the generalized integrators, as well as compensation of the computation delay etc. are studied. Extensive test results from a 10 kW active power filter prototype are demonstrated.

Periodontal health is defined by absence of clinically detectable inflammation. There is a biological level of immune surveillance that is consistent with clinical gingival health and homeostasis. Clinical gingival health may be found in a periodontium that is intact, i.e. without clinical attachment loss or bone loss, and on a reduced periodontium in either a non-periodontitis patient (e.g. in patients with some form of gingival recession or following crown lengthening surgery) or in a patient with a history of periodontitis who is currently periodontally stable. Clinical gingival health can be restored following treatment of gingivitis and periodontitis. However, the treated and stable periodontitis patient with current gingival health remains at increased risk of recurrent periodontitis, and accordingly, must be closely monitored. Two broad categories of gingival diseases include non-dental plaque biofilm-induced gingival diseases and dental plaque-induced gingivitis. Non-dental plaque biofilm-induced gingival diseases include a variety of conditions that are not caused by plaque and usually do not resolve following plaque removal. Such lesions may be manifestations of a systemic condition or may be localized to the oral cavity. Dental plaque-induced gingivitis has a variety of clinical signs and symptoms, and both local predisposing factors and systemic modifying factors can affect its extent, severity, and progression. Dental plaque-induced gingivitis may arise on an intact periodontium or on a reduced periodontium in either a non-periodontitis patient or in a currently stable "periodontitis patient" i.e. successfully treated, in whom clinical inflammation has been eliminated (or substantially reduced). A periodontitis patient with gingival inflammation remains a periodontitis patient (Figure 1), and comprehensive risk assessment and management are imperative to ensure early prevention and/or treatment of recurrent/progressive periodontitis. Precision dental medicine defines a patient-centered approach to care, and therefore, creates differences in the way in which a "case" of gingival health or gingivitis is defined for clinical practice as opposed to epidemiologically in population prevalence surveys. Thus, case definitions of gingival health and gingivitis are presented for both purposes. While gingival health and gingivitis have many clinical features, case definitions are primarily predicated on presence or absence of bleeding on probing. Here we classify gingival health and gingival diseases/conditions, along with a summary table of diagnostic features for defining health and gingivitis in various clinical situations.

Abstract Quantifying rates of climate change in mountain regions is of considerable interest, not least because mountains are viewed as climate “hotspots” where change can anticipate or amplify what is occurring elsewhere. Accelerating mountain climate change has extensive environmental impacts, including depletion of snow/ice reserves, critical for the world's water supply. Whilst the concept of elevation‐dependent warming (EDW), whereby warming rates are stratified by elevation, is widely accepted, no consistent EDW profile at the global scale has been identified. Past assessments have also neglected elevation‐dependent changes in precipitation. In this comprehensive analysis, both in situ station temperature and precipitation data from mountain regions, and global gridded data sets (observations, reanalyses, and model hindcasts) are employed to examine the elevation dependency of temperature and precipitation changes since 1900. In situ observations in paired studies (using adjacent stations) show a tendency toward enhanced warming at higher elevations. However, when all mountain/lowland studies are pooled into two groups, no systematic difference in high versus low elevation group warming rates is found. Precipitation changes based on station data are inconsistent with no systematic contrast between mountain and lowland precipitation trends. Gridded data sets (CRU, GISTEMP, GPCC, ERA5, and CMIP5) show increased warming rates at higher elevations in some regions, but on a global scale there is no universal amplification of warming in mountains. Increases in mountain precipitation are weaker than for low elevations worldwide, meaning reduced elevation‐dependency of precipitation, especially in midlatitudes. Agreement on elevation‐dependent changes between gridded data sets is weak for temperature but stronger for precipitation.

Abstract Global deep‐time plate motion models have traditionally followed a classical rigid plate approach, even though plate deformation is known to be significant. Here we present a global Mesozoic–Cenozoic deforming plate motion model that captures the progressive extension of all continental margins since the initiation of rifting within Pangea at ~240 Ma. The model also includes major failed continental rifts and compressional deformation along collision zones. The outlines and timing of regional deformation episodes are reconstructed from a wealth of published regional tectonic models and associated geological and geophysical data. We reconstruct absolute plate motions in a mantle reference frame with a joint global inversion using hot spot tracks for the last 80 million years and minimizing global trench migration velocities and net lithospheric rotation. In our optimized model, net rotation is consistently below 0.2°/Myr, and trench migration scatter is substantially reduced. Distributed plate deformation reaches a Mesozoic peak of 30 × 10 6 km 2 in the Late Jurassic (~160–155 Ma), driven by a vast network of rift systems. After a mid‐Cretaceous drop in deformation, it reaches a high of 48 x 10 6 km 2 in the Late Eocene (~35 Ma), driven by the progressive growth of plate collisions and the formation of new rift systems. About a third of the continental crustal area has been deformed since 240 Ma, partitioned roughly into 65% extension and 35% compression. This community plate model provides a framework for building detailed regional deforming plate networks and form a constraint for models of basin evolution and the plate‐mantle system.

Pursuing integrated research and decision-making to advance action on the sustainable development goals (SDGs) fundamentally depends on understanding interactions between the SDGs, both negative ones ("trade-offs") and positive ones ("co-benefits"). This quest, triggered by the 2030 Agenda, has however pointed to a gap in current research and policy analysis regarding how to think systematically about interactions across the SDGs. This paper synthesizes experiences and insights from the application of a new conceptual framework for mapping and assessing SDG interactions using a defined typology and characterization approach. Drawing on results from a major international research study applied to the SDGs on health, energy and the ocean, it analyses how interactions depend on key factors such as geographical context, resource endowments, time horizon and governance. The paper discusses the future potential, barriers and opportunities for applying the approach in scientific research, in policy making and in bridging the two through a global SDG Interactions Knowledge Platform as a key mechanism for assembling, systematizing and aggregating knowledge on interactions.

Abstract Twenty-five years since foundational publications on valuing ecosystem services for human well-being 1,2 , addressing the global biodiversity crisis 3 still implies confronting barriers to incorporating nature’s diverse values into decision-making. These barriers include powerful interests supported by current norms and legal rules such as property rights, which determine whose values and which values of nature are acted on. A better understanding of how and why nature is (under)valued is more urgent than ever 4 . Notwithstanding agreements to incorporate nature’s values into actions, including the Kunming-Montreal Global Biodiversity Framework (GBF) 5 and the UN Sustainable Development Goals 6 , predominant environmental and development policies still prioritize a subset of values, particularly those linked to markets, and ignore other ways people relate to and benefit from nature 7 . Arguably, a ‘values crisis’ underpins the intertwined crises of biodiversity loss and climate change 8 , pandemic emergence 9 and socio-environmental injustices 10 . On the basis of more than 50,000 scientific publications, policy documents and Indigenous and local knowledge sources, the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) assessed knowledge on nature’s diverse values and valuation methods to gain insights into their role in policymaking and fuller integration into decisions 7,11 . Applying this evidence, combinations of values-centred approaches are proposed to improve valuation and address barriers to uptake, ultimately leveraging transformative changes towards more just (that is, fair treatment of people and nature, including inter- and intragenerational equity) and sustainable futures.

The impact of generative artificial intelligence (AI) on medical publishing practices is currently unknown. However, as our experiences underline, generative AI could have substantial ethical implications. ChatGPT (OpenAI, San Francisco, CA, USA) is an AI chatbot released in November, 2022.1Open AI ChatGPT.https://openai.com/blog/chatgpt/Date: 2022Date accessed: December 21, 2022Google Scholar Developed using human feedback and freely accessible, the platform has already attracted millions of interactions.2Grant N Metz C A New chat bot is a ‘code red’ for Google's search business. The New York Times, Dec 21, 2022https://www.nytimes.com/2022/12/21/technology/ai-chatgpt-google-search.htmlDate accessed: December 23, 2022Google Scholar When presented with a query, ChatGPT will automatically generate a response, which is based on thousands of internet sources, often without further input from the user. Resultantly, individuals have reportedly used ChatGPT to formulate university essays and scholarly articles3Bowman E AI bot ChatGPT stuns academics with essay-writing skills and usability. NPR, Dec 19, 2022https://www.npr.org/2022/12/19/1143912956/chatgpt-ai-chatbot-homework-academiaDate accessed: December 21, 2022Google Scholar and, if prompted, the system can deliver accompanying references. Given these accounts and its popular usage, we requested that ChatGPT write a Comment for The Lancet Digital Health about AI and medical publishing ethics. We then asked ChatGPT how the editorial team should address academic content produced by AI. The results make for interesting reading (appendix). The functionality of ChatGPT highlights the growing necessity of implementing robust AI author guidelines in scholarly publishing. Ethical considerations abound concerning copyright, attribution, plagiarism, and authorship when AI produces academic text. These concerns are especially pertinent because whether copy is AI generated is currently imperceptible to human readers and anti-plagiarism software. Studies across various fields have already listed ChatGPT as an author,4Frye B Should using an AI text generator to produce academic writing be plagiarism?.SSRN. 2022; (published online Dec 20.) (preprint).https://ssrn.com/abstract=4292283Google Scholar but whether generative AI fulfils the International Committee of Medical Journal Editors' criteria for authorship is a point of debate: can a chatbot really provide approval for work and be accountable for its contents? The Committee on Publication Ethics has developed AI recommendations for editorial decision making5Committee on Publication EthicsArtificial intelligence (AI) in decision making.https://doi.org/10.24318/9kvAgrnJDate: 2021Date accessed: December 20, 2022Google Scholar and the trade body for scholarly publishers, the International Association of Scientific, Technical, and Medical Publishers, produced a white paper on AI ethics.6International Association of Scientific, Technical, and Medical PublishersAI ethics in scholarly communication—STM best practice principles for ethical, trustworthy and human-centric AI.https://www.stm-assoc.org/2021_05_11_STM_AI_White_Paper_April2021.pdfDate: 2021Date accessed: December 21, 2022Google Scholar As technologies become better tailored to user needs and more commonly adopted, we believe comprehensive discussions about authorship policies are urgent and essential. Elsevier, who publish the Lancet family of journals, alongside other major publishers, have stated that AI cannot be listed as an author and its use must be properly acknowledged.7ElsevierPublishing ethics. Elsevier.https://www.elsevier.com/about/policies/publishing-ethicsDate accessed: February 1, 2023Google Scholar ChatGPT is available to use without cost.1Open AI ChatGPT.https://openai.com/blog/chatgpt/Date: 2022Date accessed: December 21, 2022Google Scholar However, OpenAI's leadership have affirmed that free use is temporary and the product will eventually be monetised.8Karpf D Money will kill ChatGPT's magic. The Atlantic, Dec 21, 2022https://www.theatlantic.com/technology/archive/2022/12/chatgpt-ai-chatbots-openai-cost-regulations/672539/Date accessed: December 23, 2022Google Scholar One commercial option for the platform could conceivably involve some form of paywall, which might entrench existing international inequalities in scholarly publishing. Although institutions in socioeconomically advantaged areas could probably afford access, those in low-income and middle-income countries might not be able to, thus widening existing disparities in knowledge dissemination and scholarly publishing. In our opinion, as the program remains freely available in the short term, ChatGPT's ease of use and accessibility could substantially increase scholarly output. ChatGPT might democratise the dissemination of knowledge since the chatbot can receive and produce copy in multiple languages, circumventing English-language requirements that can be a publishing barrier for speakers of other languages. Nonetheless, the functionality of ChatGPT has the capacity to cause harm by producing misleading or inaccurate content,3Bowman E AI bot ChatGPT stuns academics with essay-writing skills and usability. NPR, Dec 19, 2022https://www.npr.org/2022/12/19/1143912956/chatgpt-ai-chatbot-homework-academiaDate accessed: December 21, 2022Google Scholar thereby eliciting concerns around scholarly misinformation. As the so-called COVID-19 infodemic shows, the potential spread of misinformation in medical publishing can entail significant societal hazards.9The Lancet Infectious DiseasesThe COVID-19 infodemic.Lancet Infect Dis. 2020; 20: 875Summary Full Text Full Text PDF PubMed Scopus (198) Google Scholar Listed by OpenAI as a limitation, “ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers”;1Open AI ChatGPT.https://openai.com/blog/chatgpt/Date: 2022Date accessed: December 21, 2022Google Scholar interestingly, the chatbot itself highlighted this possibility when responding to us (appendix). The early rollout of ChatGPT will inevitably spawn competitors, potentially rendering this a far-reaching problem. Accordingly, per ChatGPT's response to our query, The Lancet Digital Health should “carefully consider the ethical implications of publishing articles produced by AI.” We would go further: as pioneers of publishing ethics and academic standards, we call on The Lancet Digital Health and the Lancet family to initiate rigorous exchanges around the implications of AI-generated content within scholarly publishing, with a view to creating comprehensive guidance. ChatGPT's burgeoning popularity and our experiences illustrate that the time for these conversations is now; after all, can you really be sure that what you are currently reading was written by human authors? We declare no competing interests. Download .pdf (.19 MB) Help with pdf files Supplementary appendix