Browsing by Author "Chen, Fang"

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    Convolutional neural network model for soil moisture prediction and its transferability analysis based on laboratory Vis-NIR spectral data
    (Elsevier, 2021-12) Chen, Yu; Li, Lin; Whiting, Michael; Chen, Fang; Sun, Zhongchang; Song, Kaishan; Wang, Qinjun; Earth Sciences, School of Science
    Laboratory visible near infrared reflectance (Vis-NIR, 400–2500 nm) spectroscopy has the advantages of simplicity, fast and non-destructive which was used for SM prediction. However, many previously proposed models are difficult to transfer to unknown target areas without recalibration. In this study, we first developed a suitable Convolutional Neutral Network (CNN) model and transferred the model to other target areas for two situations using different soil sample backgrounds under 1) the same measurement conditions (DSSM), and 2) under different measurement conditions (DSDM). We also developed the CNN models for the target areas based on their own datasets and traditional PLS models was developed to compare their performances. The results show that one dimensional model (1D-CNN) performed strongly for SM prediction with average R2 up to 0.989 and RPIQ up to 19.59 in the laboratory environment (DSSM). Applying the knowledge-based transfer learning method to an unknown target area improved the R2 from 0.845 to 0.983 under the DSSM and from 0.298 to 0.620 under the DSDM, which performed better than data-based spiking calibration method for traditional PLS models. The results show that knowledge-based transfer learning was suitable for SM prediction under different soil background and measurement conditions and can be a promising approach for remotely estimating SM with the increasing amount of soil dataset in the future.
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    Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci
    (Springer Nature, 2019-01-07) Erzurumluoglu, A. Mesut; Liu, Mengzhen; Jackson, Victoria E.; Barnes, Daniel R.; Datta, Gargi; Melbourne, Carl A.; Young, Robin; Batini, Chiara; Surendran, Praveen; Jiang, Tao; Adnan, Sheikh Daud; Afaq, Saima; Agrawal, Arpana; Altmaier, Elisabeth; Antoniou, Antonis C.; Asselbergs, Folkert W.; Baumbach, Clemens; Bierut, Laura; Bertelsen, Sarah; Boehnke, Michael; Bots, Michiel L.; Brazel, David M.; Chambers, John C.; Chang-Claude, Jenny; Chen, Chu; Corley, Janie; Chou, Yi-Ling; David, Sean P.; Boer, Rudolf A. de; Leeuw, Christiaan A. de; Dennis, Joe G.; Dominiczak, Anna F.; Dunning, Alison M.; Easton, Douglas F.; Eaton, Charles; Elliott, Paul; Evangelou, Evangelos; Faul, Jessica D.; Foroud, Tatiana; Goate, Alison; Gong, Jian; Grabe, Hans J.; Haessler, Jeff; Haiman, Christopher; Hallmans, Göran; Hammerschlag, Anke R.; Harris, Sarah E.; Hattersley, Andrew; Heath, Andrew; Hsu, Chris; Iacono, William G.; Kanoni, Stavroula; Kapoor, Manav; Kaprio, Jaakko; Kardia, Sharon L.; Karpe, Fredrik; Kontto, Jukka; Kooner, Jaspal S.; Kooperberg, Charles; Kuulasmaa, Kari; Laakso, Markku; Lai, Dongbing; Langenberg, Claudia; Le, Nhung; Lettre, Guillaume; Loukola, Anu; Luan, Jian’an; Madden, Pamela A. F.; Mangino, Massimo; Marioni, Riccardo E.; Marouli, Eirini; Marten, Jonathan; Martin, Nicholas G.; McGue, Matt; Michailidou, Kyriaki; Mihailov, Evelin; Moayyeri, Alireza; Moitry, Marie; Müller-Nurasyid, Martina; Naheed, Aliya; Nauck, Matthias; Neville, Matthew J.; Nielsen, Sune Fallgaard; North, Kari; Perola, Markus; Pharoah, Paul D. P.; Pistis, Giorgio; Polderman, Tinca J.; Posthuma, Danielle; Poulter, Neil; Qaiser, Beenish; Rasheed, Asif; Reiner, Alex; Renström, Frida; Rice, John; Rohde, Rebecca; Rolandsson, Olov; Samani, Nilesh J.; Samuel, Maria; Schlessinger, David; Scholte, Steven H.; Scott, Robert A.; Sever, Peter; Shao, Yaming; Shrine, Nick; Smith, Jennifer A.; Starr, John M.; Stirrups, Kathleen; Stram, Danielle; Stringham, Heather M.; Tachmazidou, Ioanna; Tardif, Jean-Claude; Thompson, Deborah J.; Tindle, Hilary A.; Tragante, Vinicius; Trompet, Stella; Turcot, Valerie; Tyrrell, Jessica; Vaartjes, Ilonca; Leij, Andries R. van der; Meer, Peter van der; Varga, Tibor V.; Verweij, Niek; Völzke, Henry; Wareham, Nicholas J.; Warren, Helen R.; Weir, David R.; Weiss, Stefan; Wetherill, Leah; Yaghootkar, Hanieh; Yavas, Ersin; Jiang, Yu; Chen, Fang; Zhan, Xiaowei; Zhang, Weihua; Zhao, Wei; Zhao, Wei; Zhou, Kaixin; Amouyel, Philippe; Blankenberg, Stefan; Caulfield, Mark J.; Chowdhury, Rajiv; Cucca, Francesco; Deary, Ian J.; Deloukas, Panos; Angelantonio, Emanuele Di; Ferrario, Marco; Ferrières, Jean; Franks, Paul W.; Frayling, Tim M.; Frossard, Philippe; Hall, Ian P.; Hayward, Caroline; Jansson, Jan-Håkan; Jukema, J. Wouter; Kee, Frank; Männistö, Satu; Metspalu, Andres; Munroe, Patricia B.; Nordestgaard, Børge Grønne; Palmer, Colin N. A.; Salomaa, Veikko; Sattar, Naveed; Spector, Timothy; Strachan, David Peter; Harst, Pim van der; Zeggini, Eleftheria; Saleheen, Danish; Butterworth, Adam S.; Wain, Louise V.; Abecasis, Goncalo R.; Danesh, John; Tobin, Martin D.; Vrieze, Scott; Liu, Dajiang J.; Howson, Joanna M. M.; Medical and Molecular Genetics, School of Medicine
    Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10−8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10−8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10−3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.
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    The effect of cyberbullying victimization, traditional bullying victimization and suicidal ideation among college students: do specific affective problems play a mediate role?
    (Springer Nature, 2024-12-18) Chen, Fang; Li, Irene Yinglin; Ling, Chenyi; Zou, Hongyu; Radiology and Imaging Sciences, School of Medicine
    Background: Suicidal ideation among college students and adolescents represents a significant public health issue worldwide. Research has indicated that emotional difficulties may mediate the relationship between bullying victimization and suicidal ideation. However, insights from Thomas Joiner's interpersonal theory and Klonsky's The Three-Step Theory suggest that suicidal ideation arises not merely from emotional distress but from the interplay of hopelessness along with these emotional challenges. Therefore, this study aims to investigate whether variouse affective problems mediated the relationship between bullying victimization and suicidal ideation among college students. Methods: In this study, we utilized a convenience sampling method to collect data from 2717 college students (mean age = 19.81 years, 22.49% male) in South China. We examined the relationships between variables using Spearman's correlation analysis. To analyze potential mediation effects, we employed PROCESS model 4 within SPSS. Results: Our findings revealed that different forms of bullying victimization can predict experiences of depression, anxiety, and social anxiety. However, these emotional responses did not significantly influenced suicidal ideation among college students. Additionally, while different forms of bullying victimization did not directly correlate with feelings of hopelessness, hopelessness was closely associated with the prevalence of suicidal ideation. Furthermore, our results highlighted a significant mediating role of psychiatric prodromal experiences in the relationship between different forms of bullying victimization and suicidal ideation. Conclusions: The results suggested that affective problems may not serve as a significant mediating factor in the relationship between bullying victimization and suicidal ideation. We proposed an integrated emotion-response model that enhances understanding of suicidal ideation among college and offers a framework for more effective prevention strategies.