Compact Furniture Dolly

One problem faced by people during moving is how to move large pieces of furniture, i.e. sofa, mattress, table, refrigerator, etc., conveniently without damaging them. A typical small dolly is incapable of carrying such large pieces, and a bulky dolly is inappropriate for an indoor use because it can damage the furniture and the floor. Besides, in either case, the user should lift up and put the load on the dolly. People who want to change positions of their furniture regularly to get a fresh look for the house, it is cumbersome to call a moving company every time. Our team aims to design a dolly system that can load large pieces of furniture onto adjustable frames with multiple wheels so that the user can tow or push furniture without hassle. The lift function of the dolly can jack up the furniture by pushing up the bottom of the furniture. The lift system does not require much power so that most people can easily load furniture onto the system. The frames are extendable for various types of furniture. When not in use, the system can be disassembled and stowed easily

Fast non-autoregressive inverse folding with discrete diffusion

Generating protein sequences that fold into a intended 3D structure is a fundamental step in de novo protein design. De facto methods utilize autoregressive generation, but this eschews higher order interactions that could be exploited to improve inference speed. We describe a non-autoregressive alternative that performs inference using a constant number of calls resulting in a 23 times speed up without a loss in performance on the CATH benchmark. Conditioned on the 3D structure, we fine-tune ProteinMPNN to perform discrete diffusion with a purity prior over the index sampling order. Our approach gives the flexibility in trading off inference speed and accuracy by modulating the diffusion speed. Code: https://github.com/johnyang101/pmpnndiffComment: NeurIPS Machine learning for Stuctural Biology worksho

Improving Protein Optimization with Smoothed Fitness Landscapes

The ability to engineer novel proteins with higher fitness for a desired property would be revolutionary for biotechnology and medicine. Modeling the combinatorially large space of sequences is infeasible; prior methods often constrain optimization to a small mutational radius, but this drastically limits the design space. Instead of heuristics, we propose smoothing the fitness landscape to facilitate protein optimization. First, we formulate protein fitness as a graph signal then use Tikunov regularization to smooth the fitness landscape. We find optimizing in this smoothed landscape leads to improved performance across multiple methods in the GFP and AAV benchmarks. Second, we achieve state-of-the-art results utilizing discrete energy-based models and MCMC in the smoothed landscape. Our method, called Gibbs sampling with Graph-based Smoothing (GGS), demonstrates a unique ability to achieve 2.5 fold fitness improvement (with in-silico evaluation) over its training set. GGS demonstrates potential to optimize proteins in the limited data regime. Code: https://github.com/kirjner/GGSComment: ICLR 2024. Code: https://github.com/kirjner/GG

Split-spectrum amplitude-decorrelation angiography with optical coherence tomography

Amplitude decorrelation measurement is sensitive to transverse flow and immune to phase noise in comparison to Doppler and other phase-based approaches. However, the high axial resolution of OCT makes it very sensitive to the pulsatile bulk motion noise in the axial direction. To overcome this limitation, we developed split-spectrum amplitude-decorrelation angiography (SSADA) to improve the signal-to-noise ratio (SNR) of flow detection. The full OCT spectrum was split into several narrower bands. Inter-B-scan decorrelation was computed using the spectral bands separately and then averaged. The SSADA algorithm was tested on in vivo images of the human macula and optic nerve head. It significantly improved both SNR for flow detection and connectivity of microvascular network when compared to other amplitude-decorrelation algorithms.National Institutes of Health (U.S.) (Grant R01 EY013516)National Institutes of Health (U.S.) (Grant R01-EY11289-26)United States. Air Force Office of Scientific Research (FA9550-10-1-0551

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes

Induction of cell cycle arrest and apoptosis by copper complex Cu(SBCM)₂ towards oestrogen-receptor positive MCF-7 breast cancer cells

Copper complexes have the potential to be developed as targeted therapy for cancer because cancer cells take up larger amounts of copper than normal cells. Copper complex Cu(SBCM)2 has been reported to induce cell cycle arrest and apoptosis towards triple-negative breast cancer cells. Nevertheless, its effect towards other breast cancer subtypes has not been explored. Therefore, the present study was conducted to investigate the effect of Cu(SBCM)₂ towards oestrogen-receptor positive MCF-7 breast cancer cells. Growth inhibition of Cu(SBCM)₂ towards MCF-7 and human non-cancerous MCF-10A breast cells was determined by MTT assay. Morphological changes of Cu(SBCM)2-treated-MCF-7 cells were observed under an inverted microscope. Annexin V/PI apoptosis assay and cell cycle analysis were evaluated by flow cytometry. The expression of wild-type p53 protein was evaluated by Western blot analysis. The intracellular ROS levels of MCF-7 treated with Cu(SBCM)₂ were detected using DCFH-DA under a fluorescence microscope. The cells were then co-treated with Cu(SBCM)₂ and antioxidants to evaluate the involvement of ROS in the cytotoxicity of Cu(SBCM)2. Docking studies of Cu(SBCM)2 with DNA, DNA topoisomerase I, and human ribonucleotide reductase were also performed. The growth of MCF-7 cells was inhibited by Cu(SBCM)2 in a dose-dependent manner with less toxicity towards MCF-10A cells. It was found that Cu(SBCM)₂ induced G2/M cell cycle arrest and apoptosis in MCF-7 cells, possibly via a p53 pathway. Induction of intracellular ROS was not detected in MCF-7 cells. Interestingly, antioxidants enhance the cytotoxicity of Cu(SBCM)2 towards MCF-7 cells. DNA topoisomerase I may be the most likely target that accounts for the cytotoxicity of Cu(SBCM)₂

Imaging biomarker roadmap for cancer studies.

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.Development of this roadmap received support from Cancer Research UK and the Engineering and Physical Sciences Research Council (grant references A/15267, A/16463, A/16464, A/16465, A/16466 and A/18097), the EORTC Cancer Research Fund, and the Innovative Medicines Initiative Joint Undertaking (grant agreement number 115151), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies' in kind contribution