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text:
\section{Introduction}
\label{sec:intro}
Automatic singing pitch correction is a commonly desired feature in karaoke. Modifying a singer's pitch track to make it sound more in tune but not unnatural is not straightforward. In some cases, the sung melody is unknown, i.e. no musical score is linked to the performance. However, even without such {\em a priori} score information, a listener with a basic level of practice in music can often detect notes that are out of tune and predict which direction the singer should shift the pitches to make them sound more in tune only based on the level of perceived musical harmony. In this paper we envision an automatic pitch correction program that behaves similarly. To the best of our knowledge, the proposed method is the first data-driven approach to correcting singing voice pitch based on the accompaniment.

A fully automatic pitch correction---``autotuning"---algorithm is difficult to define. When a singer performs a melody, we assume that they have the sequence of notes in mind that define this melody---a musical score---whether or not this is notated on paper. A score is typically represented as a simple sequence or piano roll of note-wise constant pitches. Thus, the score only contains some of the information of a performance, and nuances are left to the singer. Indeed, a vocalist's pitch track follows the general contour of the score but varies continuously due to expressive gestures such as pitch bending, vibrato, and the physicality of the voice. Pitch variation techniques themselves differ based on musical genre, local musical context, and personal style. Within one performance, different levels of sharpness or flatness from note to note (i.e. deviations in pitch above or below the musical score), make it difficult to know exactly what pitch the singer intended. The task of shifting the singer's pitch so that it follows the general contour indicated by the musical score, adjusting unintended out-of-tune pitches without preserving the intentional variations, is not obvious. The proposed data-driven approach tries to respect the nuanced variations of sung pitch while the system also actively estimates the amount of unintended pitch shift.

\section{Related Work}

\subsection{Related pitch-correction systems}
The first commercial pitch-correction technique, Antares Auto-Tune \cite{antares:2016}, is also one of the most commonly used. It measures the fundamental frequency of the input monophonic singing recording, then re-synthesizes the pitch-corrected audio signal. In Auto-Tune and in recent work on continuous score-coded pitch correction \cite{salazar2015continuous}, the vocals can either be tuned automatically or manually. In the automatic case, each vocal note is pitch shifted to the nearest note in a user-input set of pitches (scale) or to the pitch in the score if it is known. In the manual case, a recording engineer uses the plugin's interface to move each note to the desired score and precise pitch. With either approach, the default musical scale is the equal-tempered scale, in which each pitch $p$ belongs to the set of MIDI pitches $[0, 1, ..., 127]$ and its frequency in Hertz is defined as $440*2^{\frac{p-69}{12}}$. Some users prefer a finer resolution and include more than twelve pitches per octave, or use intervals of varying sizes between pitches. In all cases, the continuous parameter, frequency, is discretized to a small set of values and the performance frequency of every note is shifted after correction to be centered exactly around its corresponding discrete $p$, which may not always be the most artistically desirable musical choice. This approach maps performance pitch to the musical score, thus not directly taking into account a singer's intentional pitch variation for expressive means. To avoid flattening the singer's pitch to the note, producing a robotic sound, a user-adjustable "time-lag" parameter that corrects pitch gradually introduces a tradeoff between preservation of pitch variation and accuracy. Furthermore, while the original Auto-Tune is most suitable for music following the Western twelve-tone scale or other known scales, it is not easily adaptable to other musical-cultural contexts with different scales or more fluidly varying pitch. Our proposed model focuses on the automatic approach in the case where a score is not present, representing pitch as a continuous instead of discrete parameter.

\subsection{Musical intonation studies on center pitch}
Pitch correction systems such as those described above assign to every note a center frequency around which all pitch variations are centered. Quantitative and qualitative studies on musical intonation show that professional-level singers and instrumentalists often center their frequencies at values that deviate from the equal-tempered scale. In particular, they often sing or play sharp relative to an accompaniment. This phenomenon is described in \cite{parncutt2018psychocultural}, but it dates back to \cite{barbour1938just}'s work on string instruments as well as work from the early twentieth century \cite{schoen1926pitch, cameron1907tonal}. Furthermore, soloists often center their singing at a higher frequency than the accompaniment, possibly in order to stand out \cite{kantorski1986string} \cite{rakowski1985perception}. Devaney {\it et al.} \cite{devaney2011intonation} measure much variety in musical interval sizes both above and below the equal-tempered intervals in the context of melodic intervals---where pitches are sequential in time---and polyphonic choral music performed by professionals and semi-professionals. In particular, ascending melodic intervals tend to be large, while descending intervals tend to be small. Another relevant result in \cite{parncutt2018psychocultural} is that frequency and perceived pitch are often slightly different. In the proposed system, we do assign one center frequency to every note as the target of the learning system, but the system lets the fundamental frequency take any value instead of belonging to a scale. The choice of center frequency is trained by applying a deep learning system to a dataset of performances, where singers took musical choices that sounded good to them, thus taking perceived pitch into account.

\subsection{Style transfer techniques}
Recent work transfers features from a professional-level performance to an amateur performance of the same song after first aligning the two performances in time. Luo et al. proposed to match the pitch contour of the professional-level performance while preserving the spectral envelope and aperiodicity of the amateur performance \cite{luo2018singing}. Meanwhile, Yong and Nam proposed to match both the pitch and amplitude envelopes \cite{yong2018singing}. Our model shares in common with these approaches the fact that it uses features gathered from high-level performances \cite{wager2018intonation, damp2018}. It differs from the previous work by not mapping style from one specific performance of the same song, instead, taking a data-driven approach to learn a supervised model that adjusts the center pitch of an unseen performance while preserves the original singer's style and characteristics.

\subsection{Deep learning research in music signal processing}
Gomez {\it et al.} \cite{gomez2018deep} describe recent work on deep learning for singing processing. More generally, in both music and speech, various combinations of recurrent convolutional neural networks have been successfully adopted in audio signal processing and Music Information Retrieval (MIR) applications. \cite{boulanger2012modeling} applies RNNs coupled with restricted Bolzmann machines to polyphonic pitch transcription. In \cite{basaranmain}, a Convolutional Gated Recurrent Unit (CGRU), where the GRU \cite{chung2014empirical} structure, an adaptation of RNNs that addresses the gradient vanishing problem, estimates the main melody in polyphonic audio signals pre-processed using the Constant-Q Transform (CQT) followed by Nonnegative Matrix Factorization \cite{LeeDD2000nips}. The convolutional layer structure is based on \cite{bittner2017deep}, a model for polyphonic pitch transcription that takes as input the six-dimensional harmonic CQT (HCQT) and outputs a two-dimensional transcription of the same shape as the input CQT, but with a single channel. Another example of CGRU can be found in \cite{xu2017convolutional} for sound event detection. The authors tested their model with three different input formats, Short-Time Fourier Transform (STFT), Mel-frequency bins, and raw audio, and reported best results using the Mel-Frequency Cepstrum Coefficient (MFCC) bins. The MFCC bins give the advantage over raw audio of being pre-processed in a structurally meaningful way, and log-spaced bins in MFCC present the advantage over the STFT bins of being translation invariant in the frequency domain, which makes them suitable for models involving convolutional layers. In detection problems, frequency resolution is not as important in detection as in problems involving pitch, where a few cents make a significant difference in performance. For this reason, we choose to use the CQT---which is also log-spaced but high-dimensional--- combined with a GRU. Our problem differs from pitch transcription problems, which predict pitch presence at a resolution of a semitone, while we predict musical shifts contained within one semitone.

\begin{figure*}[t]
\subfigure[]{\includegraphics[height=1.93in]{cqt_comparison_1.eps}}
\subfigure[]{\includegraphics[height=1.93in]{cqt_comparison_2.eps}}
\subfigure[]{\includegraphics[height=1.93in]{cqt_comparison_3.eps}}
\subfigure[]{\includegraphics[height=1.93in]{cqt_comparison_4.eps}}
\subfigure[]{\includegraphics[height=1.93in]{cqt_comparison_5.eps}}
\subfigure[]{\includegraphics[height=1.93in]{cqt_comparison_6.eps}}
    \caption{Input data generation. The two first channels consist of the backing track and de-tuned vocals CQTs, shown in for three notes in (a) and (e). (c) has the original, in-tune vocals. The de-tuned vocals are shifted, by note, -80, 95, and -75 cents. A shift of less than a semitone is subtle but visible in the CQT. The third channel consists of the absolute value of the difference of the thresholded, binarized CQTs, shown in (f). (b) shows the binarized backing track: the same computation is applied to the vocal tracks. (d) shows the difference of binarized CQTs when the vocals were in tune. We expect that harmonics aligned with the backing track when audio is in tune will cancel out, where as misaligned harmonics will produce visible, straight lines. Some examples of this are found, for example, in the center right of (f) versus (d).}
    \label{fig:input}
\end{figure*}

\section{The Proposed Pitch Correction System}
\label{sec:model}
\subsection{Data}
We train the model with the ``Intonation" dataset \cite{wager2018intonation, damp2018}. The 4702 performances in the dataset were assembled from a larger database of Smule performances ranging from beginner to professional-level based on their tendency for good musical intonation, although they are not always perfectly in tune. It consists of 474 unique arrangements by 3556 singers. The performances are mostly of Western popular music. Given the karaoke nature of the performances, the monophonic vocal tracks are separate from the accompaniments. They were used in the ``raw" format, with no particular signal processing, e.g. denoising or filtering applied to them. We used one minute of audio from every performance for analysis, starting at 30 seconds into the recording. In order to generate training examples for the model, we use vocals that are de-tuned with various offsets with corresponding in-tune vocals. Timing and expressive gestures should be identical. We choose to generate out-of-tune singing by applying pitch shifts to in-tune singing while keeping the accompaniment fixed. After manually de-tuning the vocal tracks, we train the model to recover the de-tuning amounts. Smule artists' raw audio, including all pitch deviations, is treated as in-tune with intact expressive gestures. We use the simplifying assumption that a singer has one intended pitch for every note, and that the amount of de-tuning can only change at note boundaries, remaining otherwise constant. We deviate by only up to one semitone (100 cents) to avoid the ambiguity of choosing between semitones and larger intervals. The automatic Antares Auto-Tune has a similar scope as it centers the pitch around the nearest note. Our second simplifying assumption is that the amount of de-tuning between notes is independent.

For the proposed note-by-note data processing step, we find the note boundaries from the original singing performance, then apply the shift to the block of frames in the same note. The shifting is applied using Librosa's resampling and phase vocoder utilities \cite{mcfee2015librosa}. Every note in a melody is shifted by a constant value. We then generate 7 de-tuned versions of each song, where in every version, every note is shifted in either direction along the continuous logarithmic scale of cents by up to 100. The task of the network is to learn these shifts. In order to parse the melodies into notes for shifting, we analyzed the vocals pitch using the probabilistic Yin (pYIN) algorithm \cite{mauch2014pyin}. We could have treated frames where the pYIN analysis returned 0, meaning unvoiced or silent audio, to compute note boundaries. However, given that we had access to the musical scores, we aligned them to the pYIN analysis using Dynamic Time Warping (DTW) \cite{berndt1994using}. DTW computes a sequence of indices for both tracks that minimizes the total sum of distances between the two. We used the algorithm as described in \cite{muller2015fundamentals} and implemented in \cite{mcfee2015librosa}, setting the parameters to force most time distortions to be applied to the musical score, which consists of straight lines, instead of to the pYIN analysis. We then used the musical score to find the frame indices of note boundaries. Note that we used MIDI scores of the singing voice tracks only to detect the note boundaries, while the neural network does not take any score information as the input. We plan to eliminate this dependency on the note boundaries in future work by turning the algorithm into a frame-by-frame autotuner.

\subsection{Data format}
We convert the audio to the time-frequency domain, pre-processing the data to make its overtone frequencies evident. We compute the CQT of the vocals and accompaniment over 6 octaves with a resolution of 8 bins per semitone for a total dimension of 576. The lowest frequency is 100 Hertz. We use a frame size of 92 ms and a hop size of 11 ms. We normalize the audio by its standard deviation before computing the CQT. The vocals and accompaniment CQTs form two of the three input channels to the network. For the third channel, to contrast the difference between the first two channels, we binarize the two CQT spectrograms by setting each time-frequency bin to 0 if it has a value less than the global median of the song across all time and frequency, otherwise to 1. We then take the bitwise disagreement out of the two matrices based on the assumption that the in-tune singing voice will cancel out more harmonic peaks from the accompaniment track than the out-of-tune tracks. Figure \ref{fig:input} illustrates the data format and process of generating the bitwise disagreement.

\begin{table}[h!]
  \begin{center}
    \begin{tabular}{|c||c|c|c|c|}
    \hline
      & Conv1 & Conv2 & Conv3 & Conv4 \\
      \hline
      \#Filters/Units & 128 & 64 & 64 & 64 \\
      Filter size & (5, 5) & (5, 5) & (3, 3) & (3, 3) \\
      Stride & (1, 2) & (1, 2) & (2, 2) & (1, 1) \\
      Padding & (2, 2) & (2, 2) & (1, 1) & (1, 1) \\
      \hline
      & Conv5 & Conv6 & GRU & FC \\
      \hline
      \#Filters/Units & 8 & 1 & 64 & 1 \\
      Filter size & (48, 1) & (1, 1) & & \\
      Stride & (1, 1) & (1, 1) & & \\
      Padding & (24, 1) & (0, 0) & & \\
      \hline
    \end{tabular}
    \caption{The proposed network architecture}
    \label{tab:network}
  \end{center}
\end{table}

\subsection{Neural network structure}
A Convolutional Recurrent Neural Network (CRNN) allows the model to take context into account when predicting pitch correction, which is crucial for two reasons. First, the algorithm is expected to rely on aligning harmonics, which only occur in pitched sounds, but the vocal and backing tracks can both have unpitched or noisy sections. Second, a singer's note or melodic contour can last a second or multiple seconds and the choice of frequency at a given moment depends on the context. A recurrent structure in the model robustly keeps the memory of past activity and the pitched sounds of interest. The alignment would not always be obvious even for a musically proficient person if not for the long-term sequence analysis. We use convolutional filters to pre-process the three-dimensional data and reduce its dimensionality both in the time and frequency domains. The pre-processing makes it possible to have a single layer of GRU instead of a deeper one, which is computationally expensive and difficult to train. The GRU sequence length varies based on the number of frames in the note. The last output of the GRU is fed to a fully connected layer that predicts a single scalar output. We also keep track of the context by initializing the hidden state of every note with the previous note's final hidden state.

Table \ref{tab:network} displays the structure of the proposed network. Given that the input is a processed audio signal, its structure is different along the time and frequency axes, unlike images, which are translationally invariant. For this reason, we choose not to use max pooling, but use strides of two in the time axis in three convolutional layers. In the third layer, we also stride along the frequency axis, but perform this only once to compress without losing too much frequency information. The fifth convolutional layer has a filter of size 48 in the frequency domain, which maps to one octave in the CQT and captures frequency relationships in this larger space, as shown to be successful in \cite{bittner2017deep} and \cite{hsu2017learning}. The error function is the average Mean-Squared Error (MSE) between the pitch shift estimate and ground truth over the full sequence. We use the Adam optimizer \cite{kingma2014adam}, initialized with a learning rate of 0.00005. We do not use batch normalization because we only process one note at a time with seven differently shifted (but otherwise identical) versions. We apply gradient normalization \cite{pascanu2013difficulty} with a threshold of 100. The convolutional parameters were initialized using He \cite{he2015delving}, and the GRU hidden state of the first note of every song was initialized as a normal distribution with $\mu=0$ and $sd=0.0001$. The model was built in PyTorch \cite{paszke2017automatic}. To save processing time, after computing the random shifts and CQTs during the first epoch, we saved these to disk and shuffled the shifts at every note in following epochs.

\begin{figure}[t]
    \centering
    \includegraphics[width=0.5\textwidth]{losses_2_vs_3_ch.eps}
    \caption{Training and validation losses when input data consists of either two or three channels. The loss was computed approximately nine times per epoch, every 500 performances, each of which consists of 50.4 notes (training samples) on average.}
    \label{fig:loss}
\end{figure}

\begin{figure}[t]
    \centering
    \includegraphics[width=0.5\textwidth]{results.eps}
    \caption{The upper plot shows predicted shifts and the ground truth for a validation performance. Every straight line segment shows the single scalar prediction for one note, stretched over the duration of frames. The bottom plot shows the original pitch track, which is considered in tune, the track after de-tuning, and the result of applying autotuning.}
    \label{fig:results}
\end{figure}

\section{Experimental Results and Discussion}
\label{sec:results}
We trained on the full dataset, excluding a validation list of 508 songs, selected not to share the same backing track as the training songs. Figure \ref{fig:loss} shows the training and validation loss, measured every 500 training songs, or 25200 notes on average. Within an epoch, training loss was computed as a running average over the samples visited up to that point within the epoch, while validation loss was computed every time over the full set. The average validation loss decreases to 0.123, corresponding to 35 cents. For comparison, we trained the model with two channels, omitting the thresholded difference channel. Figure \ref{fig:loss} shows that loss did not decrease as quickly. In part, this is because loss only decreased with a learning rate 10 times smaller. Figure \ref{fig:results} compares predicted shifts and the ground truth on a validation sample and shows the result of using these shifts to autotune the de-tuned pYIN pitch tracks. In many cases, the autotuned track is close to the original, but some notes are off by a large value. To autotune the singing voice, we input the predicted corrections to a phase vocoder or professional pitch shifting program.\footnote{Sample audio results are available at \url{http://homes.sice.indiana.edu/scwager/deepautotuner.html}}

Although the proposed network learns the pitch shift of a given note of singing voice with a substantially low MSE, we have not tested it on real-world data where the out-of-tune singing voice might have different characteristics from the synthesized ones. To properly address this issue in the future, we plan to use a subjective test where users can listen to the outputs of this pitch correction algorithm and determine whether the singing sounds more in tune than before. When using an automatic approach without score, there will always be some errors where the correction is in the wrong direction. One way to address this is to combine the algorithm with user input or fall back to the Auto-Tune algorithm when necessary.

\section{Conclusion}
This experiment is the first iteration of a deep-learning model that estimates pitch correction for an out-of-tune monophonic input vocal track using the instrumental accompaniment track as reference. Our results on a CGRU indicate that spectral information the accompaniment and vocal tracks is useful for determining the amount of pitch correction required at a note level. This project is an initial prototype that we plan to develop into a model that predicts frame-by-frame corrections without relying on knowledge of note boundaries. The model can also be trained on different musical genres than Western music, especially genres with more fluidly varying pitch, when data is available. While the current model outputs the amount by which singing should be shifted in pitch, the model can easily be extended to perform autotuning, either by post-processing the voice recording or by developing the model to directly output the modified audio.

\vfill\pagebreak

\bibliographystyle{IEEEbib}

redpajama_set_name: RedPajamaArXiv


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redpajama_set_name: RedPajamaCommonCrawl


text:
Q: How do I obtain a new access token with a refresh token with Google OAuth 2.0? I am building a react-native app on expo. The app uses Google Drive APIs to create and write into Spreadsheets.
I use firebase to keep track of the spreadsheet Id and similar info in a database.
I would like to automatically ask for a new access token with the refresh token once the first has expired.
Looking at the docs I put together this HTTP call but it returns a 401 Unauthorized error...
async function getNewToken(refreshToken) {
  return await fetch('https://oauth2.googleapis.com/token', {
    method: 'post',
    headers: {
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      client_id: '<MY_CLIENT_ID>',
      client_secret: '<MY_CLIENT_SECRET>',
      refresh_token: refreshToken,
      grant_type: 'refresh_token',
    }),
  });
}

What am I getting wrong?
Thanks in advance

redpajama_set_name: RedPajamaStackExchange


text:
Justice-Involved
Indiana's green jobs growth is expected to be well above the national average in the coming years
Green jobs in The Hoosier State are projected to increase by more than 29% over the next five years
By Laura Aka
WorkingNation's Green Jobs Now series is looking at green jobs opportunities and the skills needed
to get those jobs across the country with a series of state-by-state reports. Next: Indiana.
In recent weeks, the U.S. Supreme Court determined that the Clean Air Act does not give the Environmental Protection Agency broad authority to regulate greenhouse gas emissions from power plants. Now, according to the ruling, the EPA must get specific directions from the U.S. Congress on how to regulation those emissions.
Annually, The Hoosier State is among the top ten coal-producing states in the nation and the Indiana attorney general was among the 19 who challenged the EPA's authority.
Environmental leaders in the state say the Supreme Court ruling will not stop their efforts to push the state toward alternate energy sources.
"It will not deter our work to train the next generation of climate leaders here in Indiana," says Danni Schaust, resilience implementation manager at the Environmental Resilience Institute at Indiana University. The university runs the McKinney Climate Fellows (MCF) program for undergraduate and graduate students focused on climate, sustainability, and community resilience.
Ryan Hadley, executive director, Indiana Office of Energy Development
"Our McKinney Climate Fellows will continue to move forward tirelessly with their critical work," says Schaust, "working alongside our valued public and private sector partners, tackling the impacts of a changing climate head-on."
"I think it's too early to tell what, if any, impact the ruling may have," says Ryan Hadley, executive director of the Indiana Office of Energy Development, when asked how the opinion might affect green jobs creation in his state.
He says, "Indiana will continue to see growth in green-related jobs, especially as we consider the transformation of Indiana's electric generation portfolio. Over the last decade, Indiana has grown its wind generation output from virtually zero in 2008 to 6.5% in 2020."
Hadley references a 2020 report commissioned by the Indiana Utility Regulatory Commission as part of a comprehensive energy study. At the time, the report said, "This expected expansion in alternative energy sources will generate new jobs that will partially offset the job losses associated with coal-fired plant closures, although there is some uncertainty surrounding the potential size of these impacts."
The report continued, "The state's investor-owned electricity producers expect that Indiana's combined generation capacity of wind, solar, and natural gas energy will expand by an estimated 5,850 megawatts between 2023 and 2030."
Green Jobs Growth in The Hoosier State
"Workers with green skills are also spreading across a wide range of industries – such as utilities, manufacturing, and professional services – illustrating the increasing need for green skills across Indiana's economy," according to Green Jobs Now: Indiana, a WorkingNation and Lightcast analysis of the green labor market in the state.
Findings estimate there are over 11,678 workers in Indiana's green economy. The report projects in the next five years, employment for green jobs in the state will increase by 29.2%, well above national average of 5.7%.
What's a Green Job?
We look at four different categories when we break down the opportunities in the green jobs ecosystem.
Core jobs have "a primary responsibility associated with the green economy." The data indicates a top core green job in Indiana is a wind turbine technician.
Enabled jobs have "primary responsibilities separate or tangential to the green economy" with building and general maintenance technicians identified as primary jobs.
Not to be confused with enabled jobs, enabling jobs "aren't associated with green tech per se, but they support the green economy."
The report states, "There were 3,586 green jobs openings in the state in 2021."
Green Jobs Now: Indiana finds there are 1,146,364 workers in Indiana who, with new skills, could be green workers. "These workers come from a variety of different occupations and educational backgrounds and reskilling them could build the pipeline of green workers faster than relying on new postsecondary graduates alone."
"However, doing so will require a mix of training program formats that support the reskilling and redeployment of these workers."
The most in-demand skills related to the green economy in Indiana include energy conservation, energy efficiency, and renewable energy knowledge.
The report states, "Many of these same green skills offer strong salary boosts across roles, the highest being carbon management which commands an average salary boost of over $24,000 annually."
"Others, such as carbon reduction commanding nearly $2,000 annually. This suggests that investing in training workers in these skills will have strong returns, but Indiana must ensure a green training infrastructure exists to support reskilling workers in these capabilities."
According to the data, the average green jobs salary in Indiana is $60,012.
'Fighting the good fight'
"While Indiana is not thought of by most folks as environmentally forward thinking, I know firsthand the progress that is being made every day by hardworking Hoosiers committed to tackling the climate crisis," says Schaust of the Environmental Resilience Institute.
"My main focus is on the McKinney Climate Fellows program and supporting students, businesses, and nonprofits as they adapt to and address the climate crisis in Indiana and throughout the Midwest," she explains.
Danni Schaust, resilience implementation manager, Environmental Resilience Institute (Photo: ERI)
Schaust says the program fellows are primarily students from the O'Neill School of Public and Environmental Affairs. But she notes that fellows – who typically range from sophomores to third-year Ph.D. students – have included some studying geography, health care management, and social work.
"We don't limit ourselves to any particular major because we are dealing with a global crisis. We understand and acknowledge that we need everyone in this fight. And we need everyone thinking about 'How can I, in my career, address climate change?'"
The program includes summer internships for the fellows. "They'll work for those communities, those nonprofits, and those businesses for 10 to 12 weeks over the summer," says Schaust.
"Throughout, we provide them mentorship, ongoing professional development, and once they finish the program, we actually continue to keep in touch with our alumni and help them prepare for job interviews, salary negotiation, networking, all of those really important professional skills that they may not be getting in the classroom or may not have time to really focus on in their academic studies."
Schaust says, "Our fellows receive not only professional training, but resources and access to tools that we believe make them more personally resilient to continue 'fighting the good fight.'"
She continues, "We believe that a holistic approach is necessary to training the next generation of climate professionals – trainings such as how to deal with and manage climate emotions have become an integral part of the curriculum in recent years."
"Since this program launched in 2017, we have worked with close to 200 students, getting them trained in climate careers, getting them professional opportunities to apply their skills outside of the classroom. Around 35% to 40% of our graduates now work in climate careers across the United States," says Schaust.
But she points out that many fellows are becoming more locally focused in their career pursuits. "I've seen the conversation change from a federal government pipeline to 'I want to work in a local community. I want to work here in my hometown of Lafayette, Indiana. I want to work with my local government or the nonprofits in the area to make sure we're addressing food insecurity, to make sure that the community is aware of the climate change impacts we are going to see, and that we are prepared as a community to address those.'"
Core Green Jobs and Second Chances
As a professor of political science at University of Notre Dame, Patrick Regan wasn't planning on becoming a small business owner. Life took a turn for Regan after he began teaching a class called Politics of Climate Change at Westville Correctional Facility.
"In the process of teaching in the prison, I learned that we all make mistakes and sometimes, otherwise, good people make colossally stupid mistakes," says Regan.
Patrick Regan, president and co-founder, Crossroads Solar (Photo: Crossroads Solar)
"In one class we were talking and one guy who's all tattooed up said, 'The problem is, professor, your people won't hire me.' I'm like, 'Well, I bet I could fix that.' I was old enough in the career of academia that I could take the gamble, quit my job, and see whether I could answer the question of whether we could hire them."
Regan knew he had to create an enterprise that had some lasting power. "I had to start a business that had longevity beyond me. I thought about climate change. I thought this is a no-brainer. Long after I'm burned out from this, solar will be instrumental in saving the planet. So, I decided to start Crossroads Solar, a solar panel manufacturing company."
After experimenting by building a solar panel in his own basement, Regan went to China in late 2019 to purchase an assembly line. He had even arranged for engineers from China to travel to Indiana in 2020 to help with the setup. With travel no longer feasible due to the pandemic, Regan recruited four engineering students from Notre Dame to receive virtual instruction from the Chinese engineers.
(Photo: Crossroads Solar)
In the fall of 2021, the company started panel production to scale. Currently, Crossroads Solar has 12 employees.
"Everybody who works for me is a felon and it ranges from murder to bank robbery to drug-related stuff," notes Regan. "It's allowed me to test some things about society and our ability to think about people differently. I always call this an experiment in forgiveness and reconciliation."
Regan, who is the company's president and co-founder, is not only offering second chances to people with histories of incarceration, but his concerns regarding climate change are strong. "Every one of us is going to have to stop burning fossil fuels in a way or in magnitudes that are so great, the only way we're going to replace them is some alternative form energy."
Regan says solar is probably the best candidate for replacing the use of fossil fuels. "Might not be in 10 years. It might not be in five years. But today, it's what we got. And if we don't stop burning fossil fuels today, we're going to be screwed."
However, he is quick to express his frustration over the state's decision to phase down the credits for energy delivered back to the grid that have been previously afforded to people with solar panels. Those credits were reduced for solar systems installed – as of July 1 – by customers of an investor-owned utility. "We often mask the truth in an attempt to not infuriate, intimidate, or offend anybody. And with the climate we've got to offend," says Regan.
Where Does Your Motor Oil Go?
"I would prefer to move people up," says Scott Miller, senior VP of refinery operations, Safety-Kleen in East Chicago, Indiana, about the company's own talent pipeline.
Scott Miller, senior VP of refinery operations, Safety-Kleen (Photo: Safety-Kleen)
Miller explains the business. "Every time you get your oil changed that oil gets drained out of that vehicle. It goes into a tank somewhere. There's maybe a hundred thousand of these tanks throughout North America. Two things basically happen to it – it's either burned as a fuel or it can be re-refined and made back into lubricants."
"Safety-Kleen is probably the largest collector of used motor oil as part of their collection business. We take that used motor oil – 230-plus million gallons a year – and run it through one of our refineries," he explains.
"You've seen oil come out of the cars [looking] black, right? When we're done with it, it looks almost like water. That product is called a base oil. These base oils are blended with additives to make the motor oils that go in your car or semi-truck, or hydraulic oils, or any number of other types of lubricants in the market. So, that's a recycled product."
"We have a closed loop process that, in theory, if you buy our lubricants and you use our use motor oil collection, it just goes round and round."
Safety-Kleen has eight facilities throughout North America with the largest being the East Chicago site.
Safety-Kleen, East Chicago, Indiana (Photo: Safety-Kleen)
Miller says he's seen a change in recent years regarding environmental concern. "When I first started in this business 24 years ago, we tried to make the whole green concept. We tried to market it. We tried to sell our oil based on that. I'll be honest with you. It was very limited in people's interest. I'd say in the last four or five years, there's been a big change, obviously, with carbon awareness now. It's done a complete about face."
Based on a recent third-party study, Safety-Kleen's re-refineries annually "avoid an estimated 1.5 million metric tons of greenhouse gases – the equivalent of having 3.7 billion miles driven by gas-powered automobiles."
The study also found that the company's "base oil achieved as much as a 78% reduction in greenhouse gas emissions compared to base oil made via a traditional refining process on a per gallon basis."
Opportunities at Various Levels
Miller says there is a vast array of jobs at Safety-Kleen requiring different skill levels. "We have a lot of entry-level jobs. Typically, someone who comes in here without experience in the industry will come into the shipping and receiving department. They're unloading and loading trucks and rail cars. We also have barges here. They're sampling these trucks when they come in because any container of used oil that comes in has to be sampled to make sure that it's not hazardous waste – that it's lubricant that can be re-refined and meets all our quality specs."
"We have a rather extensive laboratory with quite a few people. We have technicians, chemists, and supervisors in there. A lot of them are degreed, but not all of them. Some of them might have a technology degree to operate the lab equipment."
"We have refinery operations which are the people who actually run the refinery. We try to get people with some experience in running chemical plants or refineries, but often we actually have to train them from the ground up."
Miller says there are also the administrative and managerial positions, as well as independent contractors including electricians, painters, welders, and pipefitters.
He adds it's important that the company allows its workers to grow. "[If] they can prove themselves in receiving, they look like they can show up for work, they're on the ball, they're eager to learn, and they can follow procedures in this environment which is very industrial – we love to move them up through the organization. We love it."
A Passion for Chemistry
When he was in high school, chemistry was not Bernard Liang's favorite subject. Then he attended college at the University of Illinois Urbana-Champaign.
Bernard Liang, laboratory supervisor, Safety-Kleen (Photo: Safety-Kleen)
Liang says, "During college, I wasn't expecting to do chemistry. When I started taking chem classes, it just suddenly made more sense than what I learned in high school. I just kept going with it and was like, 'Yeah, I'm really good at chemistry.' I found more passion."
"I really enjoyed doing the labs and going to lectures. Organic chemistry was a lot of critical thinking because you didn't have much to memorize. You just had to apply as you go."
Now a laboratory supervisor, Liang started at Safety-Kleen eight years ago as a lab chemist. He explains what his duties entailed, "It's learning how to do basic lab techniques. How to do a titration. How to pipette something. The other half is more hands-on learning where you have to learn how to prep a sample, how to calibrate, and how to fix something."
"It became really like fascinating because I had never really had that kind of thought – what we do with our waste engine oils when we get our oil changes. It's good for the environment, because we'll take it, clean it up, and send it back out. I really like that."
Advancement and Retention
Jimmy Bowers is a Safety-Kleen operator II – who began at the company in an entry-level job. "The entry-level position for the operations side is a third operator. They basically start with tank readings, sampling if needed, loading certain rail cars and trailers."
Jimmy Bowers, operator II, Safety-Kleen (Photo: Safety-Kleen)
These days, Bowers says, "I oversee the everyday operation of the refinery. I deal with the re-refining of used oil through both processes of distillation and hydrogen."
He appreciates that he has been able to move up in the company. "I have a little bit of college education, but without a full degree. I see a lot of people advancing pretty rapidly throughout the company without having those degrees and they're successful in doing so. That made me want to stick around."
He echoes Miller, saying the company helps people advance. "We do on-the-job training. You are set up with somebody with some experience. They'll walk you through the process of rounds every two hours. That's when you'll collect your numbers, check all your tanks, make sure everything's okay. Then we do some written testing to make sure you know your job. They have packets with a lot of information as far as what job duties are. You'll go through that over several months – finish a little bit of the packet, go to the next thing, finish that part of the packet. It's different sequences as to where you are in your level of training."
Bowers says, at first, he was a bit ignorant about the company's environmental impact. "[The company is] good about letting us know exactly where our product starts, where it goes, how we're green, the steps we're taking to be green, it's safe. Seeing that, not only are we taking environmental impact off the world, but also our customers are also taking environmental impact off the world.
Local Government Addressing Climate Issues
"We have a real focus on helping local governments and communities adapt to the climate crisis," says Indiana University's Schaust.
She says through the Resilience Cohort, launched in 2019, "We're doing our best to help local governments catch up, get their communities involved, and start to get folks preparing for what's coming."
Schaust explains, "We provide, through the Resilience Cohort program, ongoing training on how to do these different skills, how to really crunch this data, look at realistic projections for different climate adaptation strategies, and help them build a plan that they can then carry forward. We provide a lot of support to our local government partners to get that in-house training, but also to help them start to make the case that there is a need to bring in additional staff."
One cohort member is Huntington, located in northeast Indiana. "Being part of the Resilience Cohort has been great for us because you get a chance to see how other cities and other leaders are navigating these things, as well as being introduced to subject matter experts and trainings that you wouldn't have access to otherwise," says Mayor Richard Strick.
He is eager to give credit to some of the fellows from Indiana University. "We've had several that have come through to help with different aspects of this. With a greenhouse gas equivalent emissions survey. Now working on what's known as a climate action plan. Also an intern that's working with us on tree canopy and growth in our tree canopy."
Mayor Richard Strick, City of Huntington
Strick notes, "Here in Indiana, we're anticipating more high heat days and more intense rainfall. Storm water runoff and flooding comes from that. Then the question became, 'Okay, what are additional steps we can take to better position ourselves?'"
"When we talk about what we might call the green sector jobs or adapting to these things with climate resiliency – really, we're talking about just the next stage, the next evolution of the economy," says Strick. "When it comes to thinking about jobs, especially as they relate to this sector, I think the biggest thing is to connect it to what people are already doing."
The city's parks serve as flood plains, not just recreational spaces for residents. Strick says those spaces require planting and arboretum work. He adds the city is looking to switch to electric equipment including leaf blowers, weeders, and mowers.
"None of us like change, right? Some of us adapt to it more readily than others, but change is difficult on all of us. And we're talking about some pretty significant transitions here in the economy and the environment. Part of the challenge is walking people through those changes and transitions and connecting it to what already is here."
Strick cites offers an example with a current project. "We're preparing STEM research for high schoolers and middle schoolers tied into removal of a low head dam from the Little River. The low head dams were invented as economic development. You could run a flour mill or a power plant on a river that didn't have much drop to it. That's obsolete technology now. It can become a threat to safety, but also it prevents you from enjoying the river and having kayaks and canoes go through it."
"Eventually somebody's going to come in and they are going to demolish that dam, remove it, and haul it off. Those are jobs that are very familiar for us. It's helping folks understand that the skills they already have can be brought to bear on these challenges and solutions."
Looking Ahead to the Future
Schaust says she has seen more need in the talent pipeline, but notes, "There's still a little more demand than there is supply of jobs. I've seen huge change in the past six years, but there's still a little more students than there are full-time opportunities. That's something I I'm hoping to see continue to change."
Strick says, "The key for me as an elected official is helping folks to understand why we do the things that we do. We want to make sure that our house is an order. We want to have full transparency with folks and explain the 'why' behind these things. Get it to that level of being able to help them embrace them and value them. And if they don't value them – at least tolerate them and understand why some folks in the community might value them."
Regarding the recent SCOTUS opinion, Strick says his city is not going to lose its momentum in addressing climate issues. "We're making these investments because it's what's right for our community."
Liang says about his job at Safety-Kleen, "It made me more conscious about what I dispose of at home. I've been more careful. Is this bathroom cleaner safe to go down the drain? This cooking oil – do I really want to pour this down the sink? Where is it going to go? It's taught me to be more proactive with recycling."
He adds, "I prefer to work at a company that does take care of the waste that they produce."
Bowers says, "I'm not stuck in one position. I can get from a third [operator] to a second to a first – and maybe even a supervisor. They don't put that roadblock on you for not having a degree. To me, that's important because there are a lot of people without education that are still smart and still have the ability to do things."
Miller says, "Sustainability five years ago, nobody even talked about it. And now, we have sustainability officers in major corporations, right? So, it is a real thing. And I think it's a good thing."
Crossroads Solar has landed a big win with national distributor – Krannich Solar – which will move the panels through a sales network. Regan says, "I am maybe evangelical, but we're either going to do something about the climate or we're going to fry the planet."
WorkingNation producer Deidra White contributed to the reporting for this article.
Back to Work: Opportunities in the green sector, health care, and agriculture
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Combining coding and entrepreneurship to spark K-12 interest in tech


text:
Rosemarie Garland-Thomson
Professor of English and Bioethics, Emory University
Photo of Helen Keller wearing a scholar's cap and gown and reading a document written in braille.
HERS Wellesley Institute for Women in Higher Education Administration, Participant
Bucknell University, Lewisburg, PA, Women's and Gender Studies Visiting Distinguished Lecturer; March (1 week residency)
Scholar-in-Residence, Greater Philadelphia Women's Studies Consortium, Philadelphia, PA (1 week residency)
Maury Green Fellow, Radcliffe Institute for Advanced Study, Harvard University (1 year residency)
Emory Halle Institute for Global Learning Faculty Ambassadorial Trip, China and Korea (June 2011) and Germany (May 2003)
Society for Disability Studies Senior Scholar Award
Scholar-in-Residence, Union Institute & University (1 week residency)
Humanities Visiting Professorship, Ohio State University; awarded but declined, (1 year residency)
Howorth Lectureship, University of Mississippi (1 week residency)
Emory U. Fox Center for Humanistic Inquiry Senior Fellowship (1 year residency)
Wood Institute Research Fellowship, College of Physicians of Philadelphia (1 week residency)
Andrew W. Mellon Research Fellowship, Massachusetts Historical Society (1 week residency)
Women's Studies Visiting Scholar, Drew University (1 week residency)
Earl and Edna Stice Feminist Scholar of Social Justice Lectureship, University of Washington (1 week residency)
Branigin Lectureship, Institute for Advanced Study, Indiana University (1 week residency)
Maryland Institute for Technology in the Humanities Resident Fellow (1 semester residency)
National Endowment for the Humanities Summer Institute "Disability Studies," Co-Director, San Francisco State University (5 week residency)
National Endowment for the Humanities Fellowship for University Teachers
National Endowment for the Humanities Summer Institute on "Death in America," Participant, Columbia University (4 week residency)
National Endowment for the Humanities Summer Institute on "Embodiment," Participant, University of California, Santa Cruz (6 week residency)
American Association of University Women Dissertation Fellowship
Andrew W. Mellon Dissertation Fellowship, Brandeis University
Women's Studies Dissertation Fellowship, Brandeis University
Modern Language Association Florence Howe Award for Feminist Scholarship
Feminist Association of Bioethics (FAB) Travel Grant
Named by the Utne Reader as one of "50 Visionaries Who Are Changing Your World"
Somatechnics Research Center Residency at Macquarie University, Sydney, Australia (1 month residency)
University of Sydney Law School, Visiting Professor, Sydney, Australia (1 week residency)
Fulbright Finalist, Distinguished Chair Program; awarded but declined
Deutscher Akademischer Austausch Dienst Research Award (3 month residency)
Deutscher Akademischer Austausch Dienst Summer Institute, "Disability Studies and Eugenics," Participant, Potsdam, Germany (1 month residency)
Bogliasco Fellowship, Liguria Study Center for the Arts and Humanities, Bogliasco, Italy (1 month residency)


Abstract: In this paper, the impact of full liberalization of world cotton markets on Sudan and some selected African countries was estimated. Last version of Agricultural Trade Policy Simulation Model (ATPSM) was ap¬plied after updating cotton data for Sudan. The results of the simulation are consistent with expectation that the liberalization of cotton markets will lead to an increase in the world market price of cotton. The higher world market prices of cotton have a positive impact on the production and trade of the selected countries. Also, a moderate gain for the cotton producers and total welfare is registered. The paper concluded that the removal of all distorted policies from cotton markets could bring a greater benefit for Sudan and African countries in terms of production and trade. Also, reforming of cotton markets in Sudan and African countries is vital to increase the gain from a liberalized, competitive world cotton market.


Students in the MHS Culinary Arts pathway are mounting a fundraiser for upcoming competitions by selling traditional holiday pies for Thanksgiving.
Pumpkin or apple pies, with or without whipped cream, are available for $10 each.
Orders must be placed by Friday, Nov. 17 at 3 p.m.
Pies will be boxed and available for delivery on Tuesday evening, Nov. 21 before 7:30 p.m. or for pickup on Wednesday, Nov. 22 before noon.


text:
Q: MVC 5 Web API JSON I have the JSON Data from the below URL:
http://api.openweathermap.org/data/2.5/forecast?q=Chennai&appid=2e0bfcd75d67a0452c6890c445c51030&units=metric
My ForecastController:
public class ForecastController : Controller
{

    // GET: Forecast
    public async Task<ActionResult> FiveDayForecast(string txtCity)
    {
        HttpResponseMessage httpResponse = null;
        using (var Client = new HttpClient())
        {
            try
            {
             Client.BaseAddress = new Uri("http://api.openweathermap.org");
             httpResponse = await Client.GetAsync($"/data/2.5/forecast?
             q=Chennai&appid=2e0bfcd75d67a0452c6890c445c51030&units=metric");

                httpResponse.EnsureSuccessStatusCode();
                var stringResult = await httpResponse.Content.ReadAsStringAsync();
                ForecastModel rawWeather = new ForecastModel();
                rawWeather = JsonConvert.DeserializeObject<ForecastModel>(stringResult);
                return View(rawWeather);
            }
            catch (HttpRequestException httpRequestException)
            {
                return View($"Error getting weather from OpenWeather: {httpRequestException.Message}");
            }

        }
      }
     }

ForecastModel:

using Newtonsoft.Json;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Web;

namespace WeatherForecast.Models
{
[JsonObject]
public class ForecastModel
{
    public string cod { get; set; }
    public double message { get; set; }
    public int cnt { get; set; }
    public List<List> list { get; set; }
    public City city { get; set; }

}
public class Main
{
    public double temp { get; set; }
    public double temp_min { get; set; }
    public double temp_max { get; set; }
    public double pressure { get; set; }
    public double sea_level { get; set; }
    public double grnd_level { get; set; }
    public int humidity { get; set; }
    public double temp_kf { get; set; }
}

public class Weather
{
    public int id { get; set; }
    public string main { get; set; }
    public string description { get; set; }
    public string icon { get; set; }
}

public class Clouds
{
    public int all { get; set; }
}

public class Wind
{
    public double speed { get; set; }
    public double deg { get; set; }
}

public class Sys
{
    public string pod { get; set; }
}

public class Rain
{
    public double __invalid_name__3h { get; set; }
}

public class List
{
    public int dt { get; set; }
    public Main main { get; set; }
    public List<Weather> weather { get; set; }
    public Clouds clouds { get; set; }
    public Wind wind { get; set; }
    public Sys sys { get; set; }
    public string dt_txt { get; set; }
    public Rain rain { get; set; }
}

public class Coord
{
    public double lat { get; set; }
    public double lon { get; set; }
}

public class City
{
    public int id { get; set; }
    public string name { get; set; }
    public Coord coord { get; set; }
    public string country { get; set; }
}

}
View:
@model IEnumerable<WeatherForecast.Models.ForecastModel>

@{
ViewBag.Title = "FiveDayForecast";
}

5 day weather forecast

5 day weather forecast

@foreach (var item in @Model)
{

Today

            @**@

}

I get the following error:
The model item passed into the dictionary is of type 'WeatherForecast.Models.ForecastModel', but this dictionary requires a model item of type 'System.Collections.Generic.IEnumerable`1[WeatherForecast.Models.ForecastModel]'
I have tried many options like casting it a list and all other possibilities that i found.No luck.
 List<ForecastModel> rawWeather = new List<ForecastModel>();
 rawWeather = JsonConvert.DeserializeObject<List<ForecastModel>>
 (stringResult);

Then i get the below error:
Cannot deserialize the current JSON object (e.g. {"name":"value"}) into type 'System.Collections.Generic.List`1[WeatherForecast.Models.ForecastModel]' because the type requires a JSON array (e.g. [1,2,3]) to deserialize correctly.
To fix this error either change the JSON to a JSON array (e.g. [1,2,3]) or change the deserialized type so that it is a normal .NET type (e.g. not a primitive type like integer, not a collection type like an array or List) that can be deserialized from a JSON object. JsonObjectAttribute can also be added to the type to force it to deserialize from a JSON object.
Path 'cod', line 1, position 7.
Somebody please show some light.
Thanks a lot.


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Cholesterol is a sterol (a combination steroid and alcohol). Cholesterol is a lipid found in the cell membranes of all tissues, and it is transported in the blood plasma of all animals. Because cholesterol is synthesized by all eukaryotes, trace amounts of cholesterol are also found in membranes of plants and fungi. The name originates from the Greek chole- (bile) and stereos (solid), and the chemical suffix -ol for an alcohol, as researchers first identified cholesterol in solid form in gallstones. However, it is only in 1815 that chemist Eugene Chevreul named the compound "cholesterine".
Most of the cholesterol is synthesized by the body and some has dietary origin. Cholesterol is more abundant in tissues which either synthesize more or have more abundant densely-packed membranes, for example, the liver, spinal cord and brain. It plays a central role in many biochemical processes, such as the composition of cell membranes and the synthesis of steroid hormones. Cholesterol is insoluble in blood, but is transported in the circulatory system bound to one of the varieties of lipoprotein, spherical particles which have an exterior composed mainly of water-soluble proteins.
The main types of these water-soluble proteins, low-density lipoprotein (LDL) and high-density lipoprotein (HDL) carry cholesterol from and to the liver. According to the lipid hypothesis, abnormally high cholesterol levels (hypercholesterolemia) and abnormal proportions of LDL and HDL are associated with cardiovascular disease by promoting atheroma development in arteries (atherosclerosis). This disease process leads to myocardial infarction (heart attack), stroke and peripheral vascular disease. As high cholesterol LDL contributes to this process, it is termed "bad cholesterol", while high levels of HDL ("good cholesterol") offer a degree of protection. The balance can be redressed with exercise, a healthy diet, and sometimes high cholesterol treatment.
Low-density lipoprotein, or LDL, is known as bad cholesterol.
According to American Heart Association: When too much LDL (bad cholesterol) circulates in the blood, it can slowly build up in the inner walls of the arteries that feed the heart and brain. Together with other substances, it can form plaque, a thick, hard deposit that can narrow the arteries and make them less flexible. This condition is known as atherosclerosis. If a clot forms and blocks a narrowed artery, heart attack or stroke can result if you don't begin high cholesterol treatment.
High-density lipoprotein, or HDL, is known as good cholesterol.
According to American Heart Association: About one-fourth to one-third of blood cholesterol is carried by high-density lipoprotein (HDL). HDL cholesterol is known as good cholesterol, because high cholesterol levels of HDL seem to protect against heart attack. Low levels of HDL (less than 40 mg/dL) also increase the risk of heart disease. Medical experts think that HDL tends to carry cholesterol away from the arteries and back to the liver, where it's passed from the body. Some experts believe that HDL removes excess cholesterol from arterial plaque, slowing its buildup.
About two in three adults have a cholesterol level that is higher than recommended (high cholesterol). Having high cholesterol affects your heart and blood vessels and increases your risk of developing cardiovascular disease (CVD). High cholesterol level causes fatty deposits (known as plaques) to build up inside your blood vessels.
Some people are inclined to manufacture more cholesterol than needed, for genetic reasons. It runs in the family. In other cases, high cholesterol can be caused by a diet too heavy in the wrong kinds of fats, combined with too little exercise to burn up the calories, and being overweight.
In time, the blood vessels supplying your heart may become so narrow that they can't deliver enough oxygen to the heart muscle, particularly when you're exerting yourself. This can cause you to feel chest pain (angina). If a fatty plaque breaks off, it may cause a blood clot which can block blood flow to your heart (heart disease) or brain (stroke). This is why it's very important to know your cholesterol levels and start high cholesterol treatment if needed.
Heart disease is the number one killer of both men and women today. It's shocking to learn that this deadly disease threatens our children as well and can even begin in the womb if the mother's diet is insufficient during her pregnancy.
Gender: After menopause, a woman's LDL-cholesterol level ("bad" cholesterol) goes up, as does her risk for heart disease.
Age: Your risk increases as you get older. Men aged 45 years or older and women aged 55 years or older are at increased risk of high cholesterol.
Family history: Your risk increases if a father or brother was affected by early heart disease (before age 55) or a mother or sister was affected by early heart disease (before age 65).
Diet: The saturated fat and cholesterol in the food you eat raise total and LDL-cholesterol levels.
Weight: Being overweight can make your LDL-cholesterol level go up and your HDL level go down. You should consider any weight loss program.
Physical activity/exercise: Increased physical activity helps to lower high cholesterol levels. It also helps you lose weight.
How to Lower Cholesterol Levels?
The main goal of high cholesterol treatment is to lower high cholesterol level enough to reduce your risk of developing heart disease or having a heart attack. The higher your risk, the lower your LDL goal will be. Doctors starts high cholesterol treatment by determining your "goals" for lowering LDL based on the number of risk factors you have for heart disease.
Control any other risk factors you may have, such as high blood pressure and smoking.
Follow a low saturated fat, low cholesterol eating plan.
Begin high cholesterol treatment therapy as directed by your physician.
Therapeutic Lifestyle Changes (TLC) is a set of things you can do to help lower high cholesterol. The main part of high cholesterol treatment is The TLC Diet.
The TLC Diet is a low-saturated-fat, low-cholesterol eating plan that calls for less than 7 percent of calories from saturated fat and less than 200 mg of dietary cholesterol per day. The TLC diet recommends only enough calories to maintain a desirable weight and avoid weight gain. If your LDL is not lowered enough by reducing your saturated fat and cholesterol intakes, the amount of soluble fiber in your diet can be increased. Certain food products that contain plant stanols or plant sterols (for example, "lower high cholesterol" margarines) can also be added to the TLC diet to boost its LDL-lowering power in high cholesterol treatment.
Foods low in saturated fat include fat-free or 1 percent dairy products, lean meats, fish, skinless poultry, whole grain foods, and fruits and vegetables. Look for soft margarines (liquid or tub varieties) that are low in saturated fat and contain little or no trans fat (another type of dietary fat that can raise your cholesterol level). Limit high cholesterol foods such as liver and other organ meats, egg yolks, and full-fat dairy products. Good sources of soluble fiber include oats, certain fruits (such as oranges and pears) and vegetables (such as brussels sprouts and carrots), and dried peas and beans.
There are many alternative treatments designed for lowering cholesterol. These natural products have been proven in scientific studies to reduce cholesterol levels. For example, taking calcium supplements along with a low-fat or low-calorie diet might reduce cholesterol. For calcium to work effectively you need magnesium and plenty of vitamin D.
#1 - Hypercet, 96 points out of 100. Hypercet can help support and maintain your healthy cholesterol levels within the normal range. This formula is designed to work with and assist your normal body functions to help maintain optimum health. Hypercet is a 100% safe, non-addictive, natural herbal remedy formulated by a team of experts in the field of natural medicine.
Hypercet contains: Calcium, Magnesium, Beta 1, 3d glucans, and Chromium.
Why #1? Hypercet has been designed to safely support the body in its ability to maintain healthy levels of LDL cholesterol and HDL cholesterol, while also acting as a general cardiovascular tonic and supporting the body's natural ability to deal with harmful free radicals. Hypercet is a great combination of Calcium, Magnesium and other components to support and keep healthy cholesterol levels within the normal range.
#2 - Cholestasys, 85 points out of 100. Cholestasys is a nutritional herbal matrix formulated to supplement what you may not find in your everyday diet. As part of a healthy lifestyle, the natural nutrition in Cholestasys helps support a diet low in saturated fat and cholesterol.
Cholestasys ingredients: Niacin, Standardized (2.5%) Guggul oleoresin extract, Cayenne (fruit powder), Garlic (bulb powder), Plant Sterol Complex, Policosanol.
Cholestasys guarantee: the satisfaction guarantee is designed for a single user for 60 days use of product. Therefore, only two bottles/packages of your product can be opened to be eligible for a refund.
Why not #1? Cholestasys guarantee is only 60 days. Other than that, this cholesterol treatment product is great.
#3 - Biogetica CholesterolFormula, 74 points out of 100. Biogetica CholesterolFormula has been designed to safely support the body in its ability to maintain healthy levels of LDL cholesterol and HDL cholesterol, while also acting as a general cardiovascular tonic, supporting the body's natural ability to deal with harmful free radicals.
Biogetica CholesterolFormula Ingredients: Guggul, Red yeast rice, Rooibos.
Why not #1? Biogetica CholesterolFormula do not include Calcium, Magnesium and other components required to support and keep healthy cholesterol levels.
Even if you begin high cholesterol treatment, you will need to continue your treatment with lifestyle changes. High cholesterol treatment is most effective when combined with a low cholesterol diet and exercise program. Whenever we do any physical activity, this increases HDL cholesterol level in some people. This HDL indicates the lower risk of heart disease.
Physical activity can also help control weight, diabetes and high blood pressure. Aerobic physical activity raises your heart and breathing rates. Regular moderate intensity physical activity such as brisk walking, jogging and swimming also condition your heart and lungs.
How to Reduce Cholesterol Levels?


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This was the Ladies U-18 category. Their age should not make one take them lightly. The game they possess and their fierce focus would reduce lesser ping pong practioners (me, for instance) to tears.
You can feel the action.
Amazing Ayush! Great action shot.
I've seen such players on TV, Ayush, and marvel at their skill!
A great shot. I love the series.
i'm rootin' for her, did she win?
I'll challenge you to a game Ayush, then you're certain to win.
One of the few Olympic games that I can watch. Reaction time and speed is amazing.
I was a pretty good player in my youth, but I wouldn't take on this one now!
It's all far too fast for me. Watching the experts play, I can't often see the ball. I wonder if we are looking at a future champion?
This brings back memories of TAR season 1.
Wonderful sport [for those not inclined to tears], Ayush.


I often use firm tofu and crumble it to make "sausage" with it.
Extra Firm Tofu sold at Trader Joe's is extremely firm and provides a great texture.
I would particularly recommend this to first-time tofu eaters, as the texture is similar to that of chicken.
The cream sauce was accented with fresh thyme and sage.
Love the combination of these herbs.
They never fail to help create tasty sauce.
I like any kinds of pasta, but if I have a choice, I almost always choose long pasta.
On the other hand, my husband prefers short past.
So I usually cook both, which is really not a big deal, because I cook them in the same pot together.
We've become quite skilled at picking up our own pasta.
The pasta was served with this simple salad and sauteed zucchini (no photo).
This was written by Reiko Tanaka. Posted on Sunday, September 9, 2018, at 8:21 pm. Filed under Mushrooms, Noodles/Pasta, Tofu. Bookmark the permalink. Follow comments here with the RSS feed.	Post a comment or leave a trackback.


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11 Effects of repeated prenatal ultrasound examinations on childhood outcome up to 8 years of age: follow-up of a randomised controlled trial.
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Beitr Infusionsther. 1992; 30: 235–264.
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