Dynamic altruistic cooperation within breast tumors by Muhammad Sufyan Bin Masroni, Lee, Kee Wah, Lee, Victor Kwan Min, Ng, Siok Bian, Law, Chao Teng, Poon, Kok Siong, Lee, Bernett Teck Kwong, Liu, Zhehao, Tan, Yuen Peng, Chng, Wee Ling, Tucker, Steven, Ngo, Lynette Su-Mien, Yip, George Wai Cheong, Nga, Min En, Hue, Susan Swee Shan, Putti, Thomas Choudary, Bay, Boon Huat, Lin, Qingsong, Zhou, Lihan, Hartman, Mikael, Loh, Tze Ping, Lakshmanan, Manikandan, Lee, Sook Yee, Tergaonkar, Vinay, Chua, Huiwen, Lee, Adeline Voon Hui, Yeo, Eric Yew Meng, Li, Mo-Huang, Chang, Chan Fong, Kee, Zizheng, Tan, Karen Mei-Ling, Tan, Soo Yong, Koay, Evelyn Siew-Chuan, Archetti, Marco, Leong, Sai Mun

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Tau filaments are tethered within brain extracellular vesicles in Alzheimer’s disease by Fowler, SL, Behr, TS, Turkes, E, O’Brien, DP, Cauhy, PM, Rawlinson, I, Edmonds, M, Foiani, MS, Schaler, A, Crowley, G, Bez, S, Ficulle, E, Tsefou, E, Fischer, R, Geary, B, Gaur, P, Miller, C, D’Acunzo, P, Levy, E, Duff, KE, Ryskeldi-Falcon, B

“…Here, we combined quantitative mass spectrometry, cryo-electron tomography and single-particle cryo-electron microscopy to study brain EVs from individuals with AD. We found tau filaments composed mainly of truncated tau that were enclosed within EVs enriched in endo-lysosomal proteins. …”

Hominin brain size increase has emerged from within-species encephalization by Püschel, TA, Nicholson, SL, Baker, J, Barton, RA, Venditti, C

“…Using a phylogenetic approach never applied before to paleoanthropological data, we show that relative brain size increase across ~7 My of hominin evolution arose from increases within individual species which account for an observed overall increase in relative brain size. …”

Macrophage-matrix interactions in the breast tumour microenvironment: the case of tenascin-C by Gammage, A

“…Using TNC as an exemplar perturbant, this thesis sought to understand how macrophage effector functions such as antigen presentation become enhanced or suppressed within the TME. To achieve this, single-cell RNA sequencing (scRNAseq) was performed on infiltrating myeloid cells derived from syngeneic, orthotopically-engrafted, murine breast tumours that were engineered to express high or low levels of TNC. scRNAseq revealed marked heterogeneity in the intra-tumoural myeloid compartment with 7 distinct subsets observed, 5 of which were macrophages (Mac1-5). …”

Identifying independent sub-networks of functional connectome of the brain by Chan, Jin Hao

“…By using FMRI scans, we are able to observe the Blood Oxygenation Level Dependent (BOLD) signals within the resting brain and derive patterns from it. …”
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Defining a critical period for inhibitory circuits within the somatosensory cortex by Lo, Shun Qiang, Sng, Judy C. G., Augustine, George J.

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Transcriptional brain networks and their key regulators across the human lifespan by Wehrspaun, CC

“…The dynamics of global network metrics indicated strong coordination of expression across the lifespan compared to similar variance within age groups. This work shows that the transcriptome of human post-mortem brains at least partially preserves the network structure of cell types and functionally related genes, and how it may be dissected using suitable combinations of bioinformatic algorithms.…”

Distinctive antibody responses to Mycobacterium tuberculosis in pulmonary and brain infection by Spatola, M, Nziza, N, Irvine, EB, Cizmeci, D, Jung, W, Van, LH, Nhat, LTH, Ha, VTN, Phu, NH, Nghia, HDT, Thwaites, GE, Lauffenburger, DA, Fortune, S, Thuong, NTT, Alter, G

“…<br> Collectively, these data point to functionally divergent humoral responses depending on the site of infection (i.e. lungs versus brain) and demonstrate a highly compartmentalized <i>M. tuberculosis</i>-specific antibody response within the CSF in TBM. …”

Normative modeling of brain morphometry in clinical high risk for psychosis by Haas, Shalaila S., Ge, Ruiyang, Agartz, Ingrid, Amminger, G. Paul, Andreassen, Ole A., Bachman, Peter, Baeza, Inmaculada, Choi, Sunah, Colibazzi, Tiziano, Cropley, Vanessa L., de la Fuente-Sandoval, Camilo, Ebdrup, Bjørn H., Fortea, Adriana, Fusar-Poli, Paolo, Glenthøj, Birte Yding, Glenthøj, Louise Birkedal, Haut, Kristen M., Hayes, Rebecca A., Heekeren, Karsten, Hooker, Christine I., Hwang, Wu Jeong, Jahanshad, Neda, Kaess, Michael, Kasai, Kiyoto, Katagiri, Naoyuki, Kim, Minah, Kindler, Jochen, Koike, Shinsuke, Kristensen, Tina D., Kwon, Jun Soo, Lawrie, Stephen M., Lebedeva, Irina, Lee, Jimmy, Lemmers-Jansen, Imke L. J., Lin, Ashleigh, Ma, Xiaoqian, Mathalon, Daniel H., McGuire, Philip, Michel, Chantal, Mizrahi, Romina, Mizuno, Masafumi, Møller, Paul, Mora-Durán, Ricardo, Nelson, Barnaby, Nemoto, Takahiro, Nordentoft, Merete, Nordholm, Dorte, Omelchenko, Maria A., Pantelis, Christos, Pariente, Jose C., Raghava, Jayachandra M., Reyes-Madrigal, Francisco, Røssberg, Jan I., Rössler, Wulf, Salisbury, Dean F., Sasabayashi, Daiki, Schall, Ulrich, Smigielski, Lukasz, Sugranyes, Gisela, Suzuki, Michio, Takahashi, Tsutomu, Tamnes, Christian K., Theodoridou, Anastasia, Thomopoulos, Sophia I., Thompson, Paul M., Tomyshev, Alexander S., Uhlhaas, Peter J., Værnes, Tor G., van Amelsvoort, Therese A. M. J., van Erp, Theo G. M., Waltz, James A., Wenneberg, Christina, Westlye, Lars T., Wood, Stephen J., Zhou, Juan H., Hernaus, Dennis, Jalbrzikowski, Maria, Kahn, René S., Corcoran, Cheryl M., Frangou, Sophia

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A multimodal neural signature of face processing in autism within the fusiform gyrus by Floris, DL, Llera, A, Zabihi, M, Moessnang, C, Jones, EJH, Mason, L, Haartsen, R, Holz, NE, Mei, T, Elleaume, C, Vieira, BH, Pretzsch, CM, Forde, NJ, Baumeister, S, Dell’Acqua, F, Durston, S, Banaschewski, T, Ecker, C, Holt, RJ, Baron-Cohen, S, Bourgeron, T, Charman, T, Loth, E, Murphy, DGM, Beckmann, CF

“…Its neural correlates have been explored extensively across single neuroimaging modalities within key regions of the face processing network, such as the fusiform gyrus (FFG). …”

Imagining the Voltage of Neurons Distributed Across Entire Brains of Larval Zebrafish by Wang, Zeguan

“…However, due to the high imaging speed and signal-to-noise ratio requirements of GEVIs, microscopy hardware to date has only been able to image the voltage of neurons within subregions of the brain, even for small animals like the larval zebrafish. …”
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An Exploratory Study of Large-Scale Brain Networks during Gambling Using SEEG by Taylor, Christopher, Breault, Macauley Smith, Dorman, Daniel, Greene, Patrick, Sacré, Pierre, Sampson, Aaron, Niebur, Ernst, Stuphorn, Veit, González-Martínez, Jorge, Sarma, Sridevi

“…However, the connectivity of large-scale brain networks during decision-making is not well understood. …”
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Opto-electrical bimodal recording of neural activity in awake head-restrained mice by Cobar, Luis Fernando, Kashef, Alireza, Bose, Krishnashish, Tashiro, Ayumu

“…Electrical and optical monitoring of neural activity is major approaches for studying brain functions. Each has its own set of advantages and disadvantages, such as the ability to determine cell types and temporal resolution. …”
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Breast cancer patient-derived whole-tumor cell culture model for efficient drug profiling and treatment response prediction by Chen, Xinsong, Sifakis, Emmanouil G., Robertson, Stephanie, Neo, Shi Yong, Jun, Seong-Hwan, Tong, Le, Tay, Apple Hui Min, Lövrot, John, Hellgren, Roxanna, Margolin, Sara, Bergh, Jonas, Foukakis, Theodoros, Lagergren, Jens, Lundqvist, Andreas, Ma, Ran, Hartman, Johan

“…Breast cancer (BC) is a complex disease comprising multiple distinct subtypes with different genetic features and pathological characteristics. …”
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Technological Innovation and Integration of Whole Brain Imaging, Olfactory Stimulation, and Correlative Microscopy in Larval Zebrafish by Swain, Corban N.

“…Towards this end, this thesis presents novel approaches and technologies for whole-brain imaging, olfactory stimulation, and correlative imaging---i.e. the utilization and registration of multiple imaging modalities within a single sample. …”
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Functional and effective connectivity study of the human brain topology using a novel unifying framework by Rashid, Muhammad Hakimi Mohd

“…There has been a rapid expansion of neuroscientific research employing brain connectivity analysis. Among these are studies unravelling the neural mechanisms of brain diseases and treatments. …”
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Multiple signatures of a disease in potential biomarker space : getting the signatures consensus and identification of novel biomarkers by Ow, Ghim Siong, Kuznetsov, Vladimir Andreevich

“…Comparison of the empirical frequency distribution (EFD) with the expected background frequency distribution (BFD) allows dichotomization of statistically novel (SN) and common (SC) gIDs within the tGSS. Results: We identify SN or SC biomarkers for tGSSs obtained from previous studies of high-grade serous ovarian cancer (HG-SOC) and breast cancer (BC). …”
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Growth hormone increases BDNF and mTOR expression in specific brain regions after photothrombotic stroke in mice by Sanchez-Bezanilla, Sonia, Beard, Daniel J., Hood, Rebecca J., Åberg, N. David, Crock, Patricia, Walker, Frederick R., Nilsson, Michael, Isgaard, Jörgen, Ong, Lin Kooi

“…Conclusion. r-hGH increases pro-BDNF within the peri-infarct area and regions that are known to experience secondary neurodegeneration after stroke. …”
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Differences between neonatal units with high and low rates of breast milk feeding for very preterm babies at discharge: a qualitative study of staff experiences by McLeish, J, Aloysius, A, Gale, C, Quigley, M, Kurinczuk, JJ, Alderdice, F

“…Conclusions: Effective support can be influenced by having a supernumerary post dedicated to infant feeding; strong leadership that champions breast milk feeding and breastfeeding within Family Integrated Care; maintaining accountability by using existing quality improvement tools and accredited standards for neonatal units; and training for the whole multi-disciplinary team that encourages and enables every member of staff to take an appropriate share of responsibility for consistently informing and assisting mothers with expressing and breastfeeding. …”

Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain-computer interface by Nagarajan, Aarthy, Robinson, Neethu, Ang, Kai Keng, Chua, Karen Sui Geok, Chew, Effie, Guan, Cuntai

“…Objective. Motor imagery (MI) brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been developed primarily for stroke rehabilitation, however, due to limited stroke data, current deep learning methods for cross-subject classification rely on healthy data. …”
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