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opencc-zeroAug 2022View details →

PAM-altering SNP-based allele-specific CRISPR-Cas9 therapeutic strategies for Huntington's disease

Huntington's disease (HD) is caused by an expanded CAG repeat in huntingtin (HTT). Since HD is dominant, and loss of HTT leads to neurological abnormalities, safe therapeutic strategies require selective inactivation of mutant HTT. Previously, we proposed a concept of CRISPR-Cas9 using mutant-specific PAM sites generated by SNPs to selectively inactivate mutant HTT. Aiming at revealing suitable targets for clinical development, we analyzed the largest HD genotype dataset to reveal target PAM-altering SNPs (PAS) and subsequently evaluated their allele specificities. The gRNAs based on the PAM sites generated by rs2857935, rs16843804, and rs16843836 showed high levels of allele specificity in patient-derived cells. Simultaneous use of two gRNAs based on rs2857935-rs16843804 or rs2857935-rs16843836 produced selective genomic deletions in mutant HTT and prevented the transcription of mutant HTT mRNA without impacting the expression of normal counterpart or re-integration of the excised fragment elsewhere in the genome. RNAseq and off-target analysis confirmed high levels of allele specificity and the lack of recurrent off-targeting. Approximately 60% of HD subjects are eligible for mutant-specific CRISPR-Cas9 strategies of targeting one of these 3 PAS in conjunction with one non-allele-specific site, supporting high applicability of PAS-based allele-specific CRISPR approaches in the HD patient population.

opencc-zeroSep 2022View details →

Data: Sex-specific effects of capital resources on reproductive timing and success in red squirrels

Reproduction is an energetically expensive activity for both sexes. However, if males and females differ in their annual timing of reproduction, such that peak investment for one sex occurs during a more resource-limited period, there is an opportunity for sex-specific selection to act on the acquisition of energetic resources. Both male and female North American red squirrels (Tamiasciurus hudsonicus) cache conifer cones, although males typically have larger caches than females. Peak energetic investment in reproduction occurs for males during the mating season in winter and early spring (when squirrels rely almost exclusively on cached resources) and for females during lactation (which can align with fresh food availability). We provide evidence that suggests sex differences in cache size are likely driven by a stronger positive connection between cached resources and components of fitness for males than for females. Specifically, males with larger caches have greater siring success than males with smaller caches, whereas for females, only early breeding females experience a positive effect of cache size on the number of recruits produced. We also show that males sire pups and females give birth earlier in the year if they have larger caches compared with squirrels of the same sex with smaller caches. Sexual selection can thus extend beyond traits directly connected to mating behavior, and can act on traits related to acquiring resources needed to fuel reproduction that are expressed months or years in advance of breeding efforts.

opencc-zeroAug 2022View details →

Complete allele-specific silencing of the gain-of-function mutation of Huntington's disease

Dominant gain-of-function mechanism in Huntington's disease (HD) suggests selective inactivation of mutant HTT produces the biggest therapeutic benefit. Here, we developed a complete allele-specific CRISPR/Cas9 strategy to permanently silence mutant HTT through nonsense-mediated decay (NMD), capitalizing on an exonic PAM (protospacer adjacent motif)-Altering SNP (PAS). Comprehensive sequence/haplotype analysis identified PAS-generated NGG PAM sites on exons of common HTT haplotypes in HD patients, revealing a single clinically meaningful PAS-based mutant-specific NMD-CRISPR/Cas9 strategy. The alternative allele of rs363099 eliminates NGG PAM site on the most frequent normal HTT haplotype in HD, permitting mutant HTT-specific CRISPR/Cas9 therapeutics in ~20% of HD patients with European ancestry. Our rs363099-based CRISPR/Cas9 showed perfect allele specificity and good targeting efficiencies in cells derived from HD patients. Dramatically reduced mutant HTT mRNA and complete loss of mutant HTT protein indicate that our allele-specific CRISPR/Cas9 strategy completely inactivates mutant HTT through NMD. RNAseq analysis also supported high levels of on-target gene specificity because no other genes except HTT were altered in clonal lines developed through our NMD-CRISPR/Cas9 strategy. Together, our data demonstrating significant target population, selective inactivation of mutant HTT, good targeting efficiency, and lack of recurrent off-targeting establish its therapeutic value of novel rs363099-based mutant HTT-specific NMD-CRISPR/Cas9 strategy in HD.

opencc-zeroAug 2022View details →

Sex-specific endocrine regulation of seasonal aggression in Siberian hamsters

Coordinating physiological and behavioural processes across the annual cycle is essential in enabling individuals to maximize fitness. While the mechanisms underlying seasonal reproduction and its associated behaviours are well-characterized, fewer studies have examined the hormonal basis of non-reproductive social behaviours (e.g., aggression) on a seasonal timescale. Our previous work suggests that the pineal hormone melatonin facilitates a 'seasonal switch' in neuroendocrine regulation of aggression in male and female Siberian hamsters (Phodopus sungorus), specifically by acting on the adrenal glands to increase the production of the androgen dehydroepiandrosterone (DHEA) during the short-day (SD) photoperiods of the non-breeding season. Here, we provide evidence that the activity of 3β-hydroxysteroid dehydrogenase/∆5-∆4 isomerase (3β-HSD), a key enzyme within the steroidogenic pathway that mediates DHEA synthesis and metabolism, varies in a sex-specific and melatonin-dependent manner. Although both male and female hamsters displayed increased aggression in response to SDs and SD-like melatonin, only males showed an increase in adrenal 3β-HSD activity. Conversely, SD and melatonin-treated females exhibited reductions in both adrenal and neural 3β-HSD activity. Collectively, these results suggest a potential role for 3β-HSD in modulating non-breeding aggression and, more broadly, demonstrate how distinct neuroendocrine mechanisms may underlie the same behavioural phenotype in males and females.

opencc-by-4.0Sep 2022View details →

Data from: A Nonadjuvanted Whole-Inactivated Pneumococcal Vaccine Induces Multiserotype Opsonophagocytic Responses Mediated by Noncapsule-Specific Antibodies

Streptococcus pneumoniae (Spn) remains a major cause of global mortality, with extensive antigenic diversity between capsular serotypes that poses an ongoing challenge for vaccine development. Widespread use of pneumococcal conjugate vaccines (PCVs) targeting Spn capsules has greatly reduced infections by vaccine-included serotypes, but has led to increased infections by non-included serotypes. To date, high cost of PCVs has also limited their usefulness in low-income regions where disease burdens are highest. To overcome these limitations, serotype-independent vaccines are being actively researched. We have developed a whole-cell gamma-irradiated Spn vaccine (termed Gamma-PN) providing serotype-independent protection. We demonstrate that Gamma-PN immunization via the clinically relevant intramuscular route induces protein-specific antibodies able to bind numerous non-vaccine encapsulated serotypes, which mediate opsonophagocytic killing and protection against lethal challenges. Gamma-PN induced comparable or superior OPKA responses to serotypes found in the licensed Prevnar13 (PCV13), and a superior response to non-included serotypes, including emergent 22F and 35B. Additionally, despite a lower observed reactogenicity, administration of Gamma-PN without adjuvant resulted in higher OPKA responses and improved protection compared to adjuvanted Gamma-PN. To our knowledge, this has never been demonstrated for a whole-inactivated Spn vaccine. Eliminating the requirement for adjuvant comes with numerous benefits for clinical applications of this vaccine, and poses interesting questions for the inclusion of adjuvant in similar vaccines in development.

opencc-zeroOct 2022View details →

Using web-sourced photographs to examine temporal patterns in sex-specific diet of a highly sexually dimorphic raptor

Traditional methods to study raptor diet are usually limited temporally, e.g., prey remains at nesting sites, and are unsuitable to examine dietary changes throughout the year. Using web-sourced photography, we explore temporal patterns in prey size and key prey species between sexes of the sexually dimorphic Eurasian Sparrowhawk (Accipiter nisus) throughout the United Kingdom. We examined 666 photographs of sparrowhawk on prey and identified the prey species involved, together with sparrowhawk sex. Changes in prey size and proportions of key prey species over time (seasonally and monthly) were explored for each sex. Prey weight was substantially higher for females than males. However, on average, prey size for both sexes declined during the summer period (May-June) being the lowest in June which is the main nestling-rearing month for both sparrowhawks and their prey. Compared to summer, Rock Doves (Columba livia) were more important prey for female sparrowhawk in winter. Whereas, for males, Eurasian Blackbirds (Turdus merula) were more important in spring compared to autumn. Web-sourced photography can overcome several limitations of previous methods used to study raptor diet including the ability to quantify diet between the sexes throughout the entire year, however, may also introduce a prey-size bias towards larger prey items.

Epitope Mapping of VZV-specific skin TRM

T cell lines were generated from skin biopsies taken from rash site and contralateral (uninvolved) site skin of persons recovered from herpes zoster at days 45, 90, and 360 from rash onset. The cell lines were screened for reactivity to all VZV open-reading frame (ORF) products using high throughput assays designed to activate and measure responses of only CD8 T cells or only CD4 T cells. Artificial antigen presenting cells expressing a single HLA class I molecule from a study subject and a single VZV ORF was used to activate CD8 T cells. Secreted IFNgamma was measured (data represent OD450) in culture supernatants using ELISA. For CD4 T cells, crude proteins made by in vitro transcription translation (IVTT) was used as antigen. T cell proliferation was measured using 3H-thymidine incorporation assay (data represent CPM). Proteins that generated positive responses were mapped to epitope containing proteins using B-LCL as antigen presenting cells. Further studies using pan-specific HLA-DR, HLA-DP, or HLA-DQ blocking antibodies were done to identify the restricting HLA locus of the CD4 T cell response. More detailed studies confirmed the restricting HLA molecule through using artificial antigen presenting cells expressing single HLA class I (for CD8 T cells) or class II (for CD4 T cells). IFNg ELISA (OD450) was used to measure T cell activation.

opencc-by-4.0Jul 2022View details →

Project files provided as supporting information to the manuscript "Fast, accurate, and system-specific variable-resolution modelling of proteins"

Project files provided as supporting information to the manuscript "Fast, accurate, and system-specific variable-resolution modelling of proteins". The dataset contains the following files: - RMSD_adk: files containing the root-mean-square deviation computed on the C-alpha atoms/beads in the atomistic and CANVAS simulations of adk (Figure 6). - RMSF_adk: files containing the root-mean-square fluctuations of the C-alpha atoms/beads in the atomistic and CANVAS simulations of adk (Figure 6). - APBS_adk: PQR file of the system and DX file of the surface potential, for both the atomistic and CANVAS systems (Figure 7). - SASA_antibody: files containing the per-residue solvent accessible surface area of the atomistic region of the ake protein, in the atomistic and CANVAS simulations (Figure 8). - RMSF_antibody: files containing the root-mean-square fluctuations of the C-alpha atoms/beads in the atomistic and CANVAS simulations of antibody (Figure 9). - APBS_antibody: PQR file of the system and DX file of the surface potential, for both the atomistic and CANVAS systems (Figure 10). - SASA_antibody: files containing the per-residue solvent accessible surface area of the hinge region of the antibody for each conformational cluster, in the atomistic and CANVAS simulations (Figure 11). - RMSIP_antibody: RMSIP between the essential subspaces computed from the atomistic and CANVAS simulations (Figure S3). - rgyr_antibody: files containing the radii of gyration of the antibody for each conformational cluster, in the atomistic and CANVAS simulations (Figure S4). - ake_AA.mp4: video of the all-atom simulation of adenylate kinase - ake_canvas.mp4: video of the canvas simulation of adenylate kinase

opencc-zeroOct 2022View details →

DeepProjection: Specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning

The efficient extraction of image data from curved tissue sheets embedded in volumetric imaging data remains a serious and unsolved problem in quantitative studies of embryogenesis. Here we present DeepProjection (DP), a trainable projection algorithm based on deep learning. This algorithm is trained on user-generated training data to locally classify the 3D stack content and rapidly and robustly predict binary masks containing the target content, e.g., tissue boundaries, while masking highly fluorescent out-of-plane artifacts. A projection of the masked 3D stack then yields background-free 2D images with undistorted fluorescence intensity values. The binary masks can further be applied to other fluorescent channels or to extract the local tissue curvature. DP is designed as a first processing step than can be followed, for example, by segmentation to track cell fate. We apply DP to follow the dynamic movements of 2D-tissue sheets during dorsal closure in Drosophila embryos and of the periderm layer in the elongating Danio embryo. DeepProjection is available as fully documented Python package.

Isobaric specific heat of gases and vapors under standard conditions

Isobaric specific heat of gases and vapors under standard conditions Junjie Chen Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com   Gases are treated as a large collection of tiny particles subject to the laws of physics. Their properties are attributed primarily to the motion of the molecules and can be explained by the kinetic theory of gases. It is not obvious that this should be the case, and for many years a static picture of gases was instead espoused, in which the pressure, for instance, was attributed to repulsive forces between essentially stationary particles pushing on the container walls. How the kinetic-molecular picture finally came to be universally accepted is a fascinating piece of scientific history and is discussed briefly below in the section Kinetic theory of gases. Any theory of gas behaviour based on this kinetic model must also be a statistical one because of the enormous numbers of particles involved. The kinetic theory of gases is now a classical part of statistical physics and is indeed a sort of miniature display case for many of the fundamental concepts and methods of science. Such important modern concepts as distribution functions, cross sections, microscopic reversibility, and time-reversal invariance have their historical roots in kinetic theory, as does the entire atomistic view of matter. Gases and vapors, Isobaric specific heat (kilojoules per kelvin per kilogram) Acetone      1.47 Acetylene      1.69 Air      1.01 Alcohol (ethanol)       1.88 Alcohol (methanol)       1.93 Ammonia      2.19 Argon      0.520 Benzene        1.09 Blast furnace gas      1.03 Bromine      0.25 Butane      1.67 Carbon dioxide      0.844 Carbon monoxide      1.02 Carbon disulphide      0.67 Chlorine              0.48 Chloroform      0.63 Ethane      1.75 Ether (diethyl ether)       1.95 Ethylene      1.53 Helium      5.19 Hydrochloric acid      0.795 Hydrogen      14.32 Hydrogen chloride      0.8 Hydroxyl      1.76 Krypton      0.25 Methane      2.22 Natural gas      2.34 Neon      1.03 Nitric oxide      0.995 Nitrogen      1.04 Nitrogen tetroxide      4.69 Nitrous oxide      0.88 Oxygen      0.919 Propane      1.67 Propene (propylene)       1.5 Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China

Isobaric specific heat of metals and metalloids under standard conditions

Isobaric specific heat of metals and metalloids under standard conditions Junjie Chen Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com   A metal is any of a class of substances characterized by high electrical and thermal conductivity as well as by malleability, ductility, and high reflectivity of light. Approximately three-quarters of all known chemical elements are metals. The most abundant varieties in the Earth's crust are aluminum, iron, calcium, sodium, potassium, and magnesium. The vast majority of metals are found in ores (mineral-bearing substances), but a few such as copper, gold, platinum, and silver frequently occur in the free state because they do not readily react with other elements. Metals are usually crystalline solids. In most cases, they have a relatively simple crystal structure distinguished by a close packing of atoms and a high degree of symmetry. Typically, the atoms of metals contain less than half the full complement of electrons in their outermost shell. Because of this characteristic, metals tend not to form compounds with each other. They do, however, combine more readily with nonmetals (for example, oxygen and sulfur), which generally have more than half the maximum number of valence electrons. Metals differ widely in their chemical reactivity. The most reactive include lithium, potassium, and radium, whereas those of low reactivity are gold, silver, palladium, and platinum. The high electrical and thermal conductivities of the simple metals (namely the non-transition metals of the periodic table) are best explained by reference to the free-electron theory. According to this concept, the individual atoms in such metals have lost their valence electrons to the entire solid, and these free electrons that give rise to conductivity move as a group throughout the solid. In the case of the more complex metals (namely the transition elements), conductivities are better explained by the band theory, which takes into account not only the presence of free electrons but also their interaction with so-called d electrons. The mechanical properties of metals, such as hardness, ability to resist repeated stressing (fatigue strength), ductility, and malleability, are often attributed to defects or imperfections in their crystal structure. The absence of a layer of atoms in its densely packed structure, for example, enables a metal to deform plastically, and prevents it from being brittle. A metalloid, in chemistry, is an imprecise term used to describe a chemical element that forms a simple substance having properties intermediate between those of a typical metal and a typical nonmetal. The term is normally applied to a group of between six and nine elements (boron, silicon, germanium, arsenic, antimony, tellurium, and possibly bismuth, polonium, astatine) found near the center of the P-block or main block of the periodic table. There is no single property which can be used to unambiguously identify an element as a metalloid. Since most metalloids tend to display semiconducting properties in at least one of their allomorphic modifications, the class might reasonably be extended to also include gray silicon (which, unlike white silicon, is a semiconductor rather than a metal) and the graphite form of carbon (which, unlike the diamond form, is a semimetal rather than an insulator). Chemically, metalloids correspond to atoms having intermediate electronegativities and an ability to display a range of both positive and negative oxidation states in their compounds. Metals and metalloids, Isobaric specific heat (kilojoules per kelvin per kilogram) Aluminum      0.91 Antimony      0.21 Barium      0.20 Beryllium      1.83 Bismuth      0.13 Cadmium      0.23 Calsium      0.63 Carbon Steel      0.49 Cast Iron      0.46 Cesium      0.24 Chromium      0.46 Cobalt      0.42 Copper      0.39 Gallium      0.37 Germanium      0.32 Gold      0.13 Hafnium      0.14 Indium      0.24 Iridium      0.13 Iron      0.45 Lanthanum      0.195 Lead      0.13 Lithium      3.57 Lutetium      0.15 Magnesium      1.05 Manganese      0.48 Mercury      0.14 Molybdenum      0.25 Nickel      0.44 Niobium      0.27 Osmium      0.13 Palladium      0.24 Platinum      0.13 Plutonium      0.13 Potassium      0.75 Rhenium      0.14 Rhodium      0.24 Rubidium      0.36 Ruthenium      0.24 Scandium      0.57 Selenium      0.32 Silicon      0.71 Silver      0.23 Sodium      1.21 Strontium      0.30 Tantalum      0.14 Thallium      0.13 Thorium      0.13 Tin      0.21 Titanium      0.54 Tungsten      0.13 Uranium      0.12 Vanadium      0.39 Yttrium      0.30 Zinc      0.39 Zirconium      0.27 Wrought Iron      0.50 Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China

Isobaric specific heat for liquid water at different temperatures

Isobaric specific heat for liquid water at different temperatures Junjie Chen Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com   Heat capacity is the ratio of heat absorbed by a material to the temperature change. It is usually expressed as calories per degree in terms of the actual amount of material being considered, most commonly a mole (the molecular weight in grams). The heat capacity in calories per gram is called specific heat. The definition of the calorie is based on the specific heat of water, defined as one calorie per degree Celsius. At sufficiently high temperatures, the heat capacity per atom tends to be the same for all elements. For metals of higher atomic weight, this approximation is already a good one at room temperature, giving rise to the law of Dulong and Petit. Temperature (degrees Celsius), Isobaric specific heat (joules per kelvin per mole), Isobaric specific heat (kilojoules per kelvin per kilogram), Isobaric specific heat (kilowatts-hour per kelvin per kilogram), Isobaric specific heat (kilocalories per kelvin per kilogram) 0.01       76.026   4.2199   0.001172      1.0079 10   75.586   4.1955   0.001165      1.0021 20   75.386   4.1844   0.001162      0.9994 25   75.336   4.1816   0.001162      0.9988 30   75.309   4.1801   0.001161      0.9984 40   75.300   4.1796   0.001161      0.9983 50   75.334   4.1815   0.001162      0.9987 60   75.399   4.1851   0.001163      0.9996 70   75.491   4.1902   0.001164      1.0008 80   75.611   4.1969   0.001166      1.0024 90   75.763   4.2053   0.001168      1.0044 100 75.950   4.2157   0.001171      1.0069 110 76.177   4.2283   0.001175      1.0099 120 76.451   4.2435   0.001179      1.0135 140 77.155   4.2826   0.001190      1.0229 160 78.107   4.3354   0.001204      1.0355 180 79.360   4.4050   0.001224      1.0521 200 80.996   4.4958   0.001249      1.0738 220 83.137   4.6146   0.001282      1.1022 240 85.971   4.7719   0.001326      1.1397 260 89.821   4.9856   0.001385      1.1908 280 95.285   5.2889   0.001469      1.2632 300 103.60   5.7504   0.001597      1.3735 320 117.78   6.5373   0.001816      1.5614 340 147.88   8.2080   0.002280      1.9604 360 270.31   15.004   0.004168      3.5836 Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China

Isochoric specific heat for liquid water at different temperatures

Isochoric specific heat for liquid water at different temperatures Junjie Chen Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com   Specific heat is the quantity of heat required to raise the temperature of one gram of a substance by one Celsius degree. The units of specific heat are usually calories or joules per gram per Celsius degree. For example, the specific heat of water is one calorie (or 4.186 joules) per gram per Celsius degree. The Scottish scientist Joseph Black, in the 18th century, noticed that equal masses of different substances needed different amounts of heat to raise them through the same temperature interval, and, from this observation, he founded the concept of specific heat. Measurements of specific heats of substances allow calculation of their atomic weights. Temperature (degrees Celsius), Isochoric specific heat (joules per kelvin per mole), Isochoric specific heat (kilojoules per kelvin per kilogram), Isochoric specific heat (kilowatts-hour per kelvin per kilogram), Isochoric specific heat (kilocalories per kelvin per kilogram) 0.01       75.981   4.2174   0.001172      1.0073 10   75.505   4.1910   0.001164      1.0010 20   74.893   4.1570   0.001155      0.9929 25   74.548   4.1379   0.001149      0.9883 30   74.181   4.1175   0.001144      0.9834 40   73.392   4.0737   0.001132      0.9730 50   72.540   4.0264   0.001118       0.9617 60   71.644   3.9767   0.001105      0.9498 70   70.716   3.9252   0.001090      0.9375 80   69.774   3.8729   0.001076      0.9250 90   68.828   3.8204   0.001061      0.9125 100 67.888   3.7682   0.001047      0.9000 110 66.960   3.7167   0.001032      0.8877 120 66.050   3.6662   0.001018      0.8757 140 64.306   3.5694   0.000992      0.8525 160 62.674   3.4788   0.000966      0.8309 180 61.163   3.3949   0.000943      0.8109 200 59.775   3.3179   0.000922      0.7925 220 58.514   3.2479   0.000902      0.7757 240 57.381   3.1850   0.000885      0.7607 260 56.392   3.1301   0.000869      0.7476 280 55.578   3.0849   0.000857      0.7368 300 55.003   3.0530   0.000848      0.7292 320 54.819   3.0428   0.000845      0.7268 340 55.455   3.0781   0.000855      0.7352 360 59.402   3.2972   0.000916      0.7875 Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China

opencc-by-4.0Sep 2017View details →

ROS-specific Huntingtin Interactions: GFP reactivation assay WT vs HD mouse striatal cells second attempt

Optimization step in the lead up to phenotypic analysis of ROS-dependent huntingtin interacting proteins.

opencc-by-4.0Dec 2016View details →

ROS-specific Huntingtin Interactions: GFP reactivation assay WT vs HD mouse striatal cells

Optimization step in the lead up to phenotypic analysis of ROS-dependent huntingtin interacting proteins.

opencc-by-4.0Sep 2017View details →

ROS-specific Huntingtin Interactions: GFP reactivation assay optimization in HEK 293 cells

Optimization step in the lead up to phenotypic analysis of ROS-dependent huntingtin interacting proteins.

ROS-specific Huntingtin Interactions: Testing inducible expression of huntingtin-specific chromobodies

Testing step in the generation of an inducible system for expressing YFP-tagged huntingtin-specific intrabodies in live cells.

ROS-specific Huntingtin Interactions: Cloning huntingtin-specific chromobodies into pTRE-3G

Cloning step in the generation of an inducible system for expressing YFP-tagged huntingtin-specific intrabodies in live cells.

ROS-specific Huntingtin Interactions: G418 kill curve in TruHD fibroblasts

Optimization step in the development of an inducible system expressing YFP-tagged huntingtin-specific intrabodies for stable transfection in TruHD fibroblasts.

ROS-specific Huntingtin Interactions: Comparing transfection methods for inducible expression of huntingtin-specific chromobody

Optimization step in the development of an inducible system expressing YFP-tagged huntingtin-specific intrabodies for stable transfection in TruHD fibroblasts.