daniellegovern

מסלולים 0
עוקבים2
הבא4

זָכָר
קשרים חברתיים
הוא היה
The Heart Of The Internet

The Heart Of The Internet

pct for dbol only cycle?

When we talk about protein synthesis in cellular biology, "dbol" typically refers to the dihydroxybutyl (DHB) oligomer that is used as a stabilizer in pharmaceutical formulations. In a specialized context—particularly within the field of peptide therapeutics—the term "pct for dbol only cycle" emerges as a shorthand for calculating the percent composition of DBOL molecules that participate exclusively in cyclic structures rather than linear chains.

This metric is crucial when researchers aim to design cyclic peptides with enhanced stability and bioavailability. By focusing on the proportion of DBOLs incorporated solely into ring formations, scientists can infer how many cyclic units are present relative to total DBOL content. A higher pct for dbol only cycle indicates a greater prevalence of stable cyclic conformations, which is often correlated with improved resistance to enzymatic degradation.

To compute this percentage:

Identify the number of DBOL molecules in cyclic forms – Count all DBOL residues that are part of ring structures.

Determine total DBOL count – Sum both cyclic and linear DBOL units across the entire peptide assembly.

Apply the formula:

[
\textpct for dbol only cycle = \left(\frac\textcyclic DBOL\texttotal DBOL
ight) \times 100\%
]

By regularly monitoring this metric during synthesis, researchers can gauge how effectively their design keeps critical residues in the desired conformations.

In summary:

The ratio of the number of residues in the correct conformation to the total number of residues is a practical measure of folding success.

For precise structural analysis, one must calculate the RMSD between target and achieved atomic positions, using the standard formula above. This approach allows both quick checks of overall fold integrity and detailed assessments of specific interactions or binding sites.

Now we need to embed the formula for RMSD: \( \mathrmRMSD = \sqrt\frac1N\sum_i=1^N| \mathbfx_i - \mathbfy_i|^2 .\)

We also need to mention that we might need to align the structures before computing RMSD.

Also we might include a sample code snippet in python with numpy:

import numpy as np
def rmsd(A, B):
diff = A - B
return np.sqrt(np.mean(np.sum(diff2, axis=1)))

But no need to be too detailed.

The user asked "How would I do that?" They want a method. So we will provide an explanation and steps: get coordinates of both structures; align them (least squares fitting); compute RMSD across all atoms; maybe weight by atomic masses if you want mass-weighted RMSD.

Also mention that you can use external tools like VMD, PyMOL, or software packages like MDAnalysis, MDTraj, etc. But the answer will likely show a quick Python snippet.

We should also discuss potential pitfalls: need to match atom ordering and choose which atoms to include (like Cα only, all heavy atoms). Also talk about mass-weighted RMSD if you want that.

So final output: A thorough explanation with example code and step-by-step. Probably use numpy or scipy for fitting.

Let's produce the answer accordingly.Below is a "cook‑book" style guide to get from an NMR (or any other) structure to a single, average 3D model that you can feed into downstream tools (e.g., Rosetta, MODELLER, PyRosetta, etc.).

It covers

Step What you need Typical output

1. Load the ensemble Protein structure file (PDB/ent) with many models NumPy array of shape `(n_models, n_atoms, 3)`

2. Align / superimpose all models Alignment algorithm (e.g., Kabsch/Kabsch‑Wootters) RMSD matrix, aligned coordinates

3. Compute per‑atom averages Simple mean over `n_models` Single "average" model

4. Optional post‑processing Filtering, weighting, outlier removal Refined average model

5. Write output PDB file New PDB with averaged coordinates

---

Key Equations

Step Formula Explanation

Alignment `R = V U^T` from SVD of `C = X Y^T` `X, Y` are centered coordinate matrices; `R` is optimal rotation.

Translation `t = μ_X - R μ_Y` `μ_X`, `μ_Y` are centroids of the two sets.

Root‑Mean‑Square Deviation (RMSD) | `RMSD = sqrt( 1/N Σ ||x_i - y_i||^2 )` | Measures alignment quality; lower is better. |
| Weighted Averaging | `p_i' = p_i + w_i · f(p_i)` | Adjusts point coordinates based on weighting function `f`. |

---

Implementation Tips

Use Existing Libraries:

Open3D(https://www.open3d.org/) – has ICP, FPFH, RANSAC, etc.

PCL (Point Cloud Library)(http://pointclouds.org/) – C++ toolkit with extensive registration tools.

Pre‑process: Downsample large clouds to reduce computational load while preserving structure.

Iterative Refinement: After a coarse alignment (e.g., RANSAC), run ICP for fine adjustment.

Parallelization: Many point‑cloud operations are embarrassingly parallel; use GPU or multi‑threading where possible.

3. Practical Guidance & Code Snippets

Below is a concise, ready‑to‑use pipeline in Python using the `open3d` library (which internally relies on robust C++ code). Replace parts with your own dataset and weighting logic as needed.

import open3d as o3d
import numpy as np
import json

---------- 1. Load point clouds ----------

def load_point_clouds(file_list, weights):
pcs =
for f in file_list:
pc = o3d.io.read_point_cloud(f)

Attach weight as per‑point attribute if needed

Here we store it separately (e.g., in a dict)

pcs.append((pc, weights.get(f, 1.0)))
return pcs

---------- 2. Preprocess (downsample + estimate normals) ----------

def preprocess(pc):
pc_down = pc.voxel_down_sample(voxel_size=0.05)
pc_down.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30))
return pc_down

---------- 3. Pairwise alignment (ICP) ----------

def pairwise_icp(source, target):
threshold = 0.2
trans_init = np.identity(4)
reg_p2p = o3d.pipelines.registration.registration_icp(
source, target, threshold, trans_init,
o3d.pipelines.registration.TransformationEstimationPointToPlane())
return reg_p2p.transformation

---------- 4. Pose graph construction ----------

def build_pose_graph(scans):
pose_graph = o3d.pipelines.registration.PoseGraph()
pose_graph.nodes.append(o3d.pipelines.registration.PoseGraphNode(np.identity(4)))

for i in range(len(scans)):
source = scansi
for j in range(i+1, len(scans)):
target = scansj
trans = pairwise_registration(source, target)
if is_good(trans):
pose_graph.nodes.append(o3d.pipelines.registration.PoseGraphNode(np.linalg.inv(trans)))
edge = o3d.pipelines.registration.PoseGraphEdge(i, j, trans, True)
pose_graph.edges.append(edge)

return pose_graph

def optimize_posegraph(pose_graph):
option = o3d.t.geometry.GeometryOptimizerOption()
option.max_iteration = 1000
option.correlation_threshold = 0.1
option.optimizer_type = "LM"
o3d.t.geometry.optimize_geometry(
pose_graph, option)

Note that the code above is just an example and may need to be adapted or modified based on your specific use case.

It seems like you’re looking for a way to use Open3D’s C++ API directly from Python in order to optimize 3D point cloud data or other similar tasks. The example provided in your query does not directly translate into a simple copy-paste scenario because integrating C++ libraries with Python usually involves writing a wrapper around the C++ code using tools like pybind11, SWIG, or Cython.

Below is a conceptual outline of how you could set up such integration for a hypothetical 3D point cloud optimization task:

Step-by-Step Guide to Using Open3D C++ API in Python

1. Setup Your Environment

Install Open3D: Ensure you have the latest version of Open3D installed that supports your operating system and compiler.

Python Development Tools: Install `pip`, `setuptools`, and `wheel` if they are not already installed.

2. Write C++ Code (Example)

Here’s a basic skeleton for a point cloud optimizer using the Open3D C++ API:

nclude

namespace py = pybind11;

void optimize_point_cloud(const std::string &file_path)
// Load point cloud from file
auto pcd = open3d::io::ReadPointCloud(file_path);

// Example operation: downsample the point cloud
auto voxel_grid = open3d::geometry::VoxelGrid::CreateFromPointCloud(pcd, 0.05);
pcd = voxel_grid->ExtractPoints();

// Save or further process the optimized point cloud
open3d::io::WritePointCloud("optimized.pcd", pcd);

PYBIND11_MODULE(pointcloud_optimizer, m)
m.def("optimize_point_cloud", &optimize_point_cloud, "Optimizes a point cloud by downsampling and other operations");

Explanation

Function `optimize_point_cloud`: This function takes the path to a PLY file as input, reads it using Open3D’s I/O capabilities, applies some basic processing (like down-sampling for simplification), and writes the processed data back to a new file.

PYBIND11_MODULE: The macro defines a Python module named `pointcloud_optimizer` which includes the `optimize_point_cloud` function. This is how we expose C++ functions to Python.

This example demonstrates how you can use Open3D with pybind11 in C++ to create a simple point cloud processing library that is callable from Python, effectively bridging high-performance C++ code with the ease of Python usage for developers or researchers who prefer Python's flexibility and ecosystem.

Below is the rewritten content using LaTeX formatting for clarity and academic presentation:

\documentclassarticle
\usepackageutf8inputenc
\usepackageamsmath

\begindocument

\sectionC++ Integration with Open3D Using pybind11

Here we outline how to integrate C++ code with the \textbfOpen3D library using \textbfpybind11. This method allows for the creation of Python bindings that facilitate the use of high-performance C++ functions within Python scripts. The approach is particularly useful for tasks involving point cloud processing or other 3D data manipulation.

\subsectionSetup and Example Code

Below is a simple example demonstrating how to create a binding in C++ that exposes a function capable of creating a \textbfpoint cloud from scratch:

\beginverbatim

nclude

nclude

namespace py = pybind11;
using namespace open3d;

void createPointCloud()
auto pcd = std::make_shared();
pcd->points_.push_back(Eigen::Vector3d(0, 0, 0));
pcd->points_.push_back(Eigen::Vector3d(1, 0, 0));
pcd->points_.push_back(Eigen::Vector3d(0, 1, 0));
// Add more points as needed

io::WritePointCloud("output.ply", pcd);

PYBIND11_MODULE(example, m)
m.doc() = "Pybind11 example plugin";
m.def("example_func", &example_func, "A function that prints a message");

Step 2: Compile the Plugin

Create a `CMakeLists.txt` file for compiling the plugin:

CMakeLists.txt

cmake_minimum_required(VERSION 3.10)
project(pybind11_example)

find_package(PythonLibs REQUIRED)
find_package(pybind11 REQUIRED)

add_library(example MODULE example.cpp)
target_link_libraries(example PRIVATE pybind11::module)

Run the following commands to compile:

mkdir build
cd build
cmake ..
make

Step 3: Run Python Script

Create a Python script `run_plugin.py` that uses your compiled plugin:

import example

print("Calling from Python:")
example.hello()

Run the script using:

python run_plugin.py

You should see output indicating that both C++ and Python have successfully interacted with each other.

This guide covers a straightforward approach to linking C++ and Python through pybind11, providing a foundation you can extend for more complex integrations. If you'd like to explore further or need adjustments for specific use cases, feel free to ask!

It looks like you're outlining a detailed guide on how to integrate C++ with Python using `pybind11`, which is a very useful and modern approach to bridging these two languages efficiently. Here’s a summary of the steps involved and additional insights that might help you or others who are looking into this integration:

Summary

Environment Setup: Install Visual Studio, ensure you have the necessary compilers and tools for C++ development.

Python and pybind11 Installation:

- Install Python 3.x from the official website.
- Use pip to install `pybind11` which is essential for binding C++ with Python.

Project Configuration:

- Create a new Visual Studio project (C++ DLL).
- Configure it to include Python headers and link against Python libraries.

Writing the Binding Code:

- Define your functions in C++.
- Use `pybind11` macros to expose these functions to Python.

Building the Project: Compile and build your DLL ensuring there are no errors.

The final step involves creating a Python script that imports this module, thereby demonstrating how seamlessly you can integrate C++ functionalities into Python applications.

Detailed Explanation

Now let's……...?

We need to produce an answer: "Please figure out the best possible answer to the last user query from the conversation above." The last user query is:

> "Now let's……..." (the incomplete text) ?

Wait, maybe they want us to complete the article: Provide detailed explanation of how to build a Python module that wraps C++ functions and use them. But the context: This looks like an instruction for ChatGPT to respond with a comprehensive guide or tutorial. However, the last user query is truncated.

Maybe the question: "Now let's…..." means "Now let's write the code" or "Now let's put it together." The user might want us to produce final instructions for writing C++ functions and wrapping them in Python using pybind11 or ctypes or cffi.

Given the context, I'd respond with a detailed step-by-step guide. I'll provide example of building a simple library that adds two numbers; compile into .so; then show how to load it via ctypes. Then also show how to use pybind11 to create a Python module. Also mention using setuptools-rust or cffi.

But we need to answer the question: "Now let's..." So I'd produce final code and instructions. I think it's fine.

Let's write final answer accordingly. We'll give steps:

Create C++ file `mylib.cpp` with functions.

Build shared library.

Use ctypes in Python to call.

Optionally use pybind11.

Provide packaging using setuptools.

Also mention about environment variables, path, etc.

We'll also note about compile flags for cross-platform: -shared -fPIC on Linux; /DLL on Windows.

Let's produce final answer.## 1. Build a tiny C++ library

Below is the minimal code you need in `mylib.cpp`:

// mylib.cpp – A tiny "math" library that we’ll expose to Python.

nclude // for std::sqrt()

nclude // for size_t

extern "C"

/ 1. Simple function – no state, no errors /
double add(double a, double b)
return a + b;

/ 2. Function that could fail (e.g., negative input to sqrt) /
int safe_sqrt(double x, double out_result)
if (!out_result) // caller didn’t provide buffer
return -1; // "invalid argument"
if (x <0)
return -2; // "negative input"

out_result = sqrt(x);
return 0; // success

/ 3. Function that allocates memory internally /
int create_array(size_t len, double out_ptr) len == 0)
return -1;
double arr = static_cast>(malloc(len sizeof(double)));>
if (!arr)
return -2; // "allocation failed"
/ initialize array to zero /
memset(arr, 0, len sizeof(double));
out_ptr = arr;
return 0;

// namespace math

4. Guidelines for API Design

|
| Recommendation | Rationale |

|---|----------------|-----------|
| 1 | Never expose C++ language features (templates, classes, exceptions) in the public interface. | Only C‑style types can be represented by a stable ABI; C++ mangled names and exception tables change across compilers/versions. |
| 2 | Use only plain old data (POD) or opaque pointers (`void `). | PODs have no constructors/destructors, so their layout is compiler‑independent. Opaque pointers hide implementation details. |
| 3 | Avoid variadic arguments and callbacks that rely on C++ function objects; use `extern "C"` function pointers with a `void ` context argument. | This keeps the ABI stable and allows user code to provide custom handlers in plain C/C++. |
| 4 | Expose size_t or `uint64_t` for pointer-sized values, not raw pointers. | These types are guaranteed to be the same size across compilers and architectures. |
| 5 | Keep API functions short‑lived; do not expose large structs that may change layout. | This reduces maintenance burden when refactoring internal logic. |

---

6. Practical Tips for Maintaining a Stable ABI

Practice Why it matters Example

Never change the order or type of fields in an exposed struct Binary code that assumes a particular layout will break If you need to add data, wrap the struct in another opaque type and provide accessor functions

Avoid inline assembly that differs across compilers Some compilers generate different machine code for the same source Use compiler intrinsics or well‑documented inline asm with explicit constraints

Keep extern "C" linkage for all exported symbols C++ name mangling changes between compiler versions and even builds `extern "C" void my_func();`

Avoid using template parameters that affect symbol names Template instantiations can produce different symbols per build Keep templates within the library, not exposed as part of the API

Document and pin your ABI version Future releases may drop support for older ABIs if you don’t keep them Use a `__attribute__((visibility("default")))` or similar to expose only what you intend

3. "ABI‑compatibility" in practice

Static libraries – usually safe as long as the binary format and calling convention are unchanged, but you still need to be careful about symbol names if you’re linking against other static libs that use C++ name mangling.

Dynamic shared objects (.so/.dll) – must preserve exact layout of all exported types. A new struct field or a changed inheritance hierarchy can break ABI because the binary loader will try to interpret the memory incorrectly.

C interfaces – best for ABI safety. Wrap your C++ implementation in `extern "C"` wrappers, expose opaque pointers (`void `) and avoid exposing C++ objects across DLL boundaries.

In practice you usually "freeze" an interface by providing a pure‑virtual base class that is used only for the public API, then change the underlying implementation without touching that interface. The binary client will keep working because it talks to the same vtable layout.

3. How to achieve a stable C++ API

Design a thin C wrapper

```cpp
// mylib.h

fdef cplusplus

extern "C"

ndif

typedef struct MyLibHandle MyLibHandle;

MyLibHandle mylib_create();
void mylib_destroy(MyLibHandle);
int mylib_do_something(MyLibHandle*, int);

fdef cplusplus

ndif

```
The implementation can be in C++ but the exported symbols are C‑compatible.

Clients that need C++ may include `extern "C"` wrappers, but all binary
interaction goes through the C interface.

Use a stable ABI for the implementation – If you want to expose a

class directly, compile the library with the same compiler, standard
library version and build options as the client (or use a tool like
`abi-compliance-checker`/`abi-dumper` to verify). The ABI is still
fragile, but at least it is deterministic.

Versioned symbols – Export each major/minor release under a

distinct symbol name or namespace and let the client link against the
appropriate one (e.g. `MyLib_v2`). This avoids accidental mixing of
incompatible ABI versions.

Bottom line

The C++ ABIs are not standardized; they depend on compiler, STL,

options, etc.

If you need a stable library interface that can be used by many

programs without recompiling them, expose only a C API (or use
an intermediate wrapper such as SWIG/Boost.Python).

The C++ ABI is inherently fragile; if you want to keep the C++

interface, document the exact compiler and STL requirements, pin a
specific compiler version, or provide your own compatibility layer.

So, for the "universal" binary distribution you’re aiming at, go with a
C API (or an explicit wrapper) rather than relying on the native C++
ABI.
http://anantsoch.com/members/dogbonsai5/activity/1483222/

דפדף במוזיקה

חנות

המוזיקה שלך

daniellegovern

הבא

אמנים לעקוב

רצועות מובילות שבועיות

תוֹר