Here we report that the presence of MgCO_(3) stimulates the extracellular polymeric substance (EPS) secretion of Microcystis Aeruginosa (M. Aeruginosa). This stimulation led to a significant reduction in the total con...Here we report that the presence of MgCO_(3) stimulates the extracellular polymeric substance (EPS) secretion of Microcystis Aeruginosa (M. Aeruginosa). This stimulation led to a significant reduction in the total concentration of NH_(4)^(+)‒N by more than 86%, and effective recovery of PO_(4)^(3-)‒P within three days from concentrated wastewater (WW), although the secreted EPS inhibited the conversion of MgCO_(3) to specific crystal forms (MgNH4PO4.6H2O or MgHPO4.7H2O). Moreover, with an increase in PO_(4)^(3-) concentration in WW, these crystals appeared, thus the removal of NH_(4)^(+)‒N and PO_(4)^(3-)‒P nutrients can be attributed to the combined effect of M. Aeruginosa and MgCO_(3). We used Surface-Enhanced Raman Spectroscopy (SERS) combined with X-ray Diffraction (XRD), Field Emission Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (FESEM-EDS), and X-ray Photoelectron Spectroscopy (XPS) to investigate the mechanism for competitive interactions between M. Aeruginosa and MgCO_(3) in removing NH_(4)^(+)‒N and PO_(4)^(3-)‒P. We identified that the bound EPS accumulated amorphous Mg–P–O dense particles on M. Aeruginosa, while soluble EPS, containing –COOH groups of humic-like substances decreased the pH of the solution and coordinated with Mg^(2+) ions. Therefore, both secreted bound and soluble EPS play a vital role in hindering the transformation of Mg^(2+) ions or MgCO_(3) to MgNH4PO4.6H2O or MgHPO4.7H2O crystals within WW, and they enhanced M. Aeruginosa 's ability in absorbing nutrients of NH_(4)^(+)‒N and PO_(4)^(3-)‒P. This mechanism plays a crucial role in the efficient recovery of NH_(4)^(+)‒N and PO_(4)^(3-)‒P from concentrated wastewater sources such as aerobically or anaerobically digested effluent from various sources like agriculture, livestock, and domestic wastewaters.展开更多
Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the i...Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the interfaces of verbal and emotional communications. The progress of AI is significant on the verbal level but modest in terms of the recognition of facial emotions even if this functionality is one of the oldest in humans and is omnipresent in our daily lives. Dysfunction in the ability for facial emotional expressions is present in many brain pathologies encountered by psychiatrists, neurologists, psychotherapists, mental health professionals including social workers. It cannot be objectively verified and measured due to a lack of reliable tools that are valid and consistently sensitive. Indeed, the articles in the scientific literature dealing with Visual-Facial-Emotions-Recognition (ViFaEmRe), suffer from the absence of 1) consensual and rational tools for continuous quantified measurement, 2) operational concepts. We have invented a software that can use computer-morphing attempting to respond to these two obstacles. It is identified as the Method of Analysis and Research of the Integration of Emotions (M.A.R.I.E.). Our primary goal is to use M.A.R.I.E. to understand the physiology of ViFaEmRe in normal healthy subjects by standardizing the measurements. Then, it will allow us to focus on subjects manifesting abnormalities in this ability. Our second goal is to make our contribution to the progress of AI hoping to add the dimension of recognition of facial emotional expressions. Objective: To study: 1) categorical vs dimensional aspects of recognition of ViFaEmRe, 2) universality vs idiosyncrasy, 3) immediate vs ambivalent Emotional-Decision-Making, 4) the Emotional-Fingerprint of a face and 5) creation of population references data. Methods: M.A.R.I.E. enables the rational, quantified measurement of Emotional Visual Acuity (EVA) in an individual observer and a population aged 20 to 70 years. Meanwhile, it can measure the range and intensity of expressed emotions through three Face- Tests, quantify the performance of a sample of 204 observers with hypernormal measures of cognition, “thymia” (defined elsewhere), and low levels of anxiety, and perform analysis of the six primary emotions. Results: We have individualized the following continuous parameters: 1) “Emotional-Visual- Acuity”, 2) “Visual-Emotional-Feeling”, 3) “Emotional-Quotient”, 4) “Emotional-Decision-Making”, 5) “Emotional-Decision-Making Graph” or “Individual-Gun-Trigger”, 6) “Emotional-Fingerprint” or “Key-graph”, 7) “Emotional-Fingerprint-Graph”, 8) detecting “misunderstanding” and 9) detecting “error”. This allowed us a taxonomy with coding of the face-emotion pair. Each face has specific measurements and graphics. The EVA improves from ages of 20 to 55 years, then decreases. It does not depend on the sex of the observer, nor the face studied. In addition, 1% of people endowed with normal intelligence do not recognize emotions. The categorical dimension is a variable for everyone. The range and intensity of ViFaEmRe is idiosyncratic and not universally uniform. The recognition of emotions is purely categorical for a single individual. It is dimensional for a population sample. Conclusions: Firstly, M.A.R.I.E. has made possible to bring out new concepts and new continuous measurements variables. The comparison between healthy and abnormal individuals makes it possible to take into consideration the significance of this line of study. From now on, these new functional parameters will allow us to identify and name “emotional” disorders or illnesses which can give additional dimension to behavioral disorders in all pathologies that affect the brain. Secondly, the ViFaEmRe is idiosyncratic, categorical, and a function of the identity of the observer and of the observed face. These findings stack up against Artificial Intelligence, which cannot have a globalist or regionalist algorithm that can be programmed into a robot, nor can AI compete with human abilities and judgment in this domain. *Here “Emotional disorders” refers to disorders of emotional expressions and recognition.展开更多
Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the i...Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the interfaces of verbal and emotional communications. The progress of AI is significant on the verbal level but modest in terms of the recognition of facial emotions even if this functionality is one of the oldest in humans and is omnipresent in our daily lives. Dysfunction in the ability for facial emotional expressions is present in many brain pathologies encountered by psychiatrists, neurologists, psychotherapists, mental health professionals including social workers. It cannot be objectively verified and measured due to a lack of reliable tools that are valid and consistently sensitive. Indeed, the articles in the scientific literature dealing with Visual-Facial-Emotions-Recognition (ViFaEmRe), suffer from the absence of 1) consensual and rational tools for continuous quantified measurement, 2) operational concepts. We have invented a software that can use computer-morphing attempting to respond to these two obstacles. It is identified as the Method of Analysis and Research of the Integration of Emotions (M.A.R.I.E.). Our primary goal is to use M.A.R.I.E. to understand the physiology of ViFaEmRe in normal healthy subjects by standardizing the measurements. Then, it will allow us to focus on subjects manifesting abnormalities in this ability. Our second goal is to make our contribution to the progress of AI hoping to add the dimension of recognition of facial emotional expressions. Objective: To study: 1) categorical vs dimensional aspects of recognition of ViFaEmRe, 2) universality vs idiosyncrasy, 3) immediate vs ambivalent Emotional-Decision-Making, 4) the Emotional-Fingerprint of a face and 5) creation of population references data. Methods: With M.A.R.I.E. enable a rational quantified measurement of Emotional-Visual-Acuity (EVA) of 1) a) an individual observer, b) in a population aged 20 to 70 years old, 2) measure the range and intensity of expressed emotions by 3 Face-Tests, 3) quantify the performance of a sample of 204 observers with hyper normal measures of cognition, “thymia,” (ibid. defined elsewhere) and low levels of anxiety 4) analysis of the 6 primary emotions. Results: We have individualized the following continuous parameters: 1) “Emotional-Visual-Acuity”, 2) “Visual-Emotional-Feeling”, 3) “Emotional-Quotient”, 4) “Emotional-Deci-sion-Making”, 5) “Emotional-Decision-Making Graph” or “Individual-Gun-Trigger”6) “Emotional-Fingerprint” or “Key-graph”, 7) “Emotional-Finger-print-Graph”, 8) detecting “misunderstanding” and 9) detecting “error”. This allowed us a taxonomy with coding of the face-emotion pair. Each face has specific measurements and graphics. The EVA improves from ages of 20 to 55 years, then decreases. It does not depend on the sex of the observer, nor the face studied. In addition, 1% of people endowed with normal intelligence do not recognize emotions. The categorical dimension is a variable for everyone. The range and intensity of ViFaEmRe is idiosyncratic and not universally uniform. The recognition of emotions is purely categorical for a single individual. It is dimensional for a population sample. Conclusions: Firstly, M.A.R.I.E. has made possible to bring out new concepts and new continuous measurements variables. The comparison between healthy and abnormal individuals makes it possible to take into consideration the significance of this line of study. From now on, these new functional parameters will allow us to identify and name “emotional” disorders or illnesses which can give additional dimension to behavioral disorders in all pathologies that affect the brain. Secondly, the ViFaEmRe is idiosyncratic, categorical, and a function of the identity of the observer and of the observed face. These findings stack up against Artificial Intelligence, which cannot have a globalist or regionalist algorithm that can be programmed into a robot, nor can AI compete with human abilities and judgment in this domain. *Here “Emotional disorders” refers to disorders of emotional expressions and recognition.展开更多
Candidiasis, also known as candidiasis vulvovaginitis, is an infection caused by different types of Candida fungi, the most frequent being Candida albicans. The present study reports an effective strategy, which opens...Candidiasis, also known as candidiasis vulvovaginitis, is an infection caused by different types of Candida fungi, the most frequent being Candida albicans. The present study reports an effective strategy, which opens new avenues for the treatment of this public health problem. The MAC<sup>®</sup> Methodology, conventional laser light-emitting (LLLT)/LED) methods are based on the biphasic response demonstrated many times in LLLT research and as with other forms of drugs, a “drug” (irradiation parameters) and a “dose” (irradiation times) and the “Arndt-Schulz Law” is often cited as a suitable model to describe the dose-dependent effects of LLLT. This method uses photopharmaceuticals, cell markers and the use of correct parameters for each case to induce the acceleration of tissue repair. The present study shows a case of a 32-year-old patient diagnosed with recurrent candidiasis 4 years ago. Eighteen sessions were performed (every other day) using a photoactivated component (Methylene blue 1% + Clotrimazole 1%) and LED phototherapy (red, blue and violet) with emission times of 60 - 260 seconds for each applicator, according to the dose recommendations of the scar acceleration method (MAC<sup>®</sup>). At the sixth treatment session there was a noticeable decrease in the itching sensation reported by the patient. In session 11 she reported feeling a great improvement, indicating that she no longer felt itching in any area after 18 sessions. The present case demonstrates new methodologies to treat common problems in the population that have a positive impact on the quality of life. This methodology has a promising future because it is non-invasive and requires a great biological transformation for inflammatory, fungal and viral control.展开更多
Professor M.A. （Alexandrovich） Guzev is a physics graduate of theUniversity of Leningrad （now Saint Petersburg, 1984）. He began hisscientific career at the Institute of Physics, Leningrad Universityin 1984. After ...Professor M.A. （Alexandrovich） Guzev is a physics graduate of theUniversity of Leningrad （now Saint Petersburg, 1984）. He began hisscientific career at the Institute of Physics, Leningrad Universityin 1984. After commencing his Ph.D. （Physics ＆ Mathematics） in1987, he continued his work as a researcher of Pacific OceanologicalInstitute, Far Eastern Branch of Russian Academy of Sciences（FEBRAS）, Vladivostok, Russia, from 1987 to 1991.展开更多
Ma Xiaoying, Assyriology, Ph.D., 1994.12 "Women’s Social Status in Old Babylonia Reflected in Marital Property" (Supervisors: Professors Lin Zhichun, Thomas Lee, Tova Meltzer, Wu Yuhong) Wang Liying, Cla...Ma Xiaoying, Assyriology, Ph.D., 1994.12 "Women’s Social Status in Old Babylonia Reflected in Marital Property" (Supervisors: Professors Lin Zhichun, Thomas Lee, Tova Meltzer, Wu Yuhong) Wang Liying, Classics, Ph.D., 1995.6 "Sallust’s Bellum Catilinae" (Supervisors: Porfessors Wang Dunshu, P.Ruth Taylor-Briggs, F. Ahlheid, Lin Zhichun)展开更多
基金supported by Cultivating Fund Project of Hubei Hongshan Laboratory(2022hspy002).
文摘Here we report that the presence of MgCO_(3) stimulates the extracellular polymeric substance (EPS) secretion of Microcystis Aeruginosa (M. Aeruginosa). This stimulation led to a significant reduction in the total concentration of NH_(4)^(+)‒N by more than 86%, and effective recovery of PO_(4)^(3-)‒P within three days from concentrated wastewater (WW), although the secreted EPS inhibited the conversion of MgCO_(3) to specific crystal forms (MgNH4PO4.6H2O or MgHPO4.7H2O). Moreover, with an increase in PO_(4)^(3-) concentration in WW, these crystals appeared, thus the removal of NH_(4)^(+)‒N and PO_(4)^(3-)‒P nutrients can be attributed to the combined effect of M. Aeruginosa and MgCO_(3). We used Surface-Enhanced Raman Spectroscopy (SERS) combined with X-ray Diffraction (XRD), Field Emission Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (FESEM-EDS), and X-ray Photoelectron Spectroscopy (XPS) to investigate the mechanism for competitive interactions between M. Aeruginosa and MgCO_(3) in removing NH_(4)^(+)‒N and PO_(4)^(3-)‒P. We identified that the bound EPS accumulated amorphous Mg–P–O dense particles on M. Aeruginosa, while soluble EPS, containing –COOH groups of humic-like substances decreased the pH of the solution and coordinated with Mg^(2+) ions. Therefore, both secreted bound and soluble EPS play a vital role in hindering the transformation of Mg^(2+) ions or MgCO_(3) to MgNH4PO4.6H2O or MgHPO4.7H2O crystals within WW, and they enhanced M. Aeruginosa 's ability in absorbing nutrients of NH_(4)^(+)‒N and PO_(4)^(3-)‒P. This mechanism plays a crucial role in the efficient recovery of NH_(4)^(+)‒N and PO_(4)^(3-)‒P from concentrated wastewater sources such as aerobically or anaerobically digested effluent from various sources like agriculture, livestock, and domestic wastewaters.
文摘Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the interfaces of verbal and emotional communications. The progress of AI is significant on the verbal level but modest in terms of the recognition of facial emotions even if this functionality is one of the oldest in humans and is omnipresent in our daily lives. Dysfunction in the ability for facial emotional expressions is present in many brain pathologies encountered by psychiatrists, neurologists, psychotherapists, mental health professionals including social workers. It cannot be objectively verified and measured due to a lack of reliable tools that are valid and consistently sensitive. Indeed, the articles in the scientific literature dealing with Visual-Facial-Emotions-Recognition (ViFaEmRe), suffer from the absence of 1) consensual and rational tools for continuous quantified measurement, 2) operational concepts. We have invented a software that can use computer-morphing attempting to respond to these two obstacles. It is identified as the Method of Analysis and Research of the Integration of Emotions (M.A.R.I.E.). Our primary goal is to use M.A.R.I.E. to understand the physiology of ViFaEmRe in normal healthy subjects by standardizing the measurements. Then, it will allow us to focus on subjects manifesting abnormalities in this ability. Our second goal is to make our contribution to the progress of AI hoping to add the dimension of recognition of facial emotional expressions. Objective: To study: 1) categorical vs dimensional aspects of recognition of ViFaEmRe, 2) universality vs idiosyncrasy, 3) immediate vs ambivalent Emotional-Decision-Making, 4) the Emotional-Fingerprint of a face and 5) creation of population references data. Methods: M.A.R.I.E. enables the rational, quantified measurement of Emotional Visual Acuity (EVA) in an individual observer and a population aged 20 to 70 years. Meanwhile, it can measure the range and intensity of expressed emotions through three Face- Tests, quantify the performance of a sample of 204 observers with hypernormal measures of cognition, “thymia” (defined elsewhere), and low levels of anxiety, and perform analysis of the six primary emotions. Results: We have individualized the following continuous parameters: 1) “Emotional-Visual- Acuity”, 2) “Visual-Emotional-Feeling”, 3) “Emotional-Quotient”, 4) “Emotional-Decision-Making”, 5) “Emotional-Decision-Making Graph” or “Individual-Gun-Trigger”, 6) “Emotional-Fingerprint” or “Key-graph”, 7) “Emotional-Fingerprint-Graph”, 8) detecting “misunderstanding” and 9) detecting “error”. This allowed us a taxonomy with coding of the face-emotion pair. Each face has specific measurements and graphics. The EVA improves from ages of 20 to 55 years, then decreases. It does not depend on the sex of the observer, nor the face studied. In addition, 1% of people endowed with normal intelligence do not recognize emotions. The categorical dimension is a variable for everyone. The range and intensity of ViFaEmRe is idiosyncratic and not universally uniform. The recognition of emotions is purely categorical for a single individual. It is dimensional for a population sample. Conclusions: Firstly, M.A.R.I.E. has made possible to bring out new concepts and new continuous measurements variables. The comparison between healthy and abnormal individuals makes it possible to take into consideration the significance of this line of study. From now on, these new functional parameters will allow us to identify and name “emotional” disorders or illnesses which can give additional dimension to behavioral disorders in all pathologies that affect the brain. Secondly, the ViFaEmRe is idiosyncratic, categorical, and a function of the identity of the observer and of the observed face. These findings stack up against Artificial Intelligence, which cannot have a globalist or regionalist algorithm that can be programmed into a robot, nor can AI compete with human abilities and judgment in this domain. *Here “Emotional disorders” refers to disorders of emotional expressions and recognition.
文摘Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the interfaces of verbal and emotional communications. The progress of AI is significant on the verbal level but modest in terms of the recognition of facial emotions even if this functionality is one of the oldest in humans and is omnipresent in our daily lives. Dysfunction in the ability for facial emotional expressions is present in many brain pathologies encountered by psychiatrists, neurologists, psychotherapists, mental health professionals including social workers. It cannot be objectively verified and measured due to a lack of reliable tools that are valid and consistently sensitive. Indeed, the articles in the scientific literature dealing with Visual-Facial-Emotions-Recognition (ViFaEmRe), suffer from the absence of 1) consensual and rational tools for continuous quantified measurement, 2) operational concepts. We have invented a software that can use computer-morphing attempting to respond to these two obstacles. It is identified as the Method of Analysis and Research of the Integration of Emotions (M.A.R.I.E.). Our primary goal is to use M.A.R.I.E. to understand the physiology of ViFaEmRe in normal healthy subjects by standardizing the measurements. Then, it will allow us to focus on subjects manifesting abnormalities in this ability. Our second goal is to make our contribution to the progress of AI hoping to add the dimension of recognition of facial emotional expressions. Objective: To study: 1) categorical vs dimensional aspects of recognition of ViFaEmRe, 2) universality vs idiosyncrasy, 3) immediate vs ambivalent Emotional-Decision-Making, 4) the Emotional-Fingerprint of a face and 5) creation of population references data. Methods: With M.A.R.I.E. enable a rational quantified measurement of Emotional-Visual-Acuity (EVA) of 1) a) an individual observer, b) in a population aged 20 to 70 years old, 2) measure the range and intensity of expressed emotions by 3 Face-Tests, 3) quantify the performance of a sample of 204 observers with hyper normal measures of cognition, “thymia,” (ibid. defined elsewhere) and low levels of anxiety 4) analysis of the 6 primary emotions. Results: We have individualized the following continuous parameters: 1) “Emotional-Visual-Acuity”, 2) “Visual-Emotional-Feeling”, 3) “Emotional-Quotient”, 4) “Emotional-Deci-sion-Making”, 5) “Emotional-Decision-Making Graph” or “Individual-Gun-Trigger”6) “Emotional-Fingerprint” or “Key-graph”, 7) “Emotional-Finger-print-Graph”, 8) detecting “misunderstanding” and 9) detecting “error”. This allowed us a taxonomy with coding of the face-emotion pair. Each face has specific measurements and graphics. The EVA improves from ages of 20 to 55 years, then decreases. It does not depend on the sex of the observer, nor the face studied. In addition, 1% of people endowed with normal intelligence do not recognize emotions. The categorical dimension is a variable for everyone. The range and intensity of ViFaEmRe is idiosyncratic and not universally uniform. The recognition of emotions is purely categorical for a single individual. It is dimensional for a population sample. Conclusions: Firstly, M.A.R.I.E. has made possible to bring out new concepts and new continuous measurements variables. The comparison between healthy and abnormal individuals makes it possible to take into consideration the significance of this line of study. From now on, these new functional parameters will allow us to identify and name “emotional” disorders or illnesses which can give additional dimension to behavioral disorders in all pathologies that affect the brain. Secondly, the ViFaEmRe is idiosyncratic, categorical, and a function of the identity of the observer and of the observed face. These findings stack up against Artificial Intelligence, which cannot have a globalist or regionalist algorithm that can be programmed into a robot, nor can AI compete with human abilities and judgment in this domain. *Here “Emotional disorders” refers to disorders of emotional expressions and recognition.
文摘Candidiasis, also known as candidiasis vulvovaginitis, is an infection caused by different types of Candida fungi, the most frequent being Candida albicans. The present study reports an effective strategy, which opens new avenues for the treatment of this public health problem. The MAC<sup>®</sup> Methodology, conventional laser light-emitting (LLLT)/LED) methods are based on the biphasic response demonstrated many times in LLLT research and as with other forms of drugs, a “drug” (irradiation parameters) and a “dose” (irradiation times) and the “Arndt-Schulz Law” is often cited as a suitable model to describe the dose-dependent effects of LLLT. This method uses photopharmaceuticals, cell markers and the use of correct parameters for each case to induce the acceleration of tissue repair. The present study shows a case of a 32-year-old patient diagnosed with recurrent candidiasis 4 years ago. Eighteen sessions were performed (every other day) using a photoactivated component (Methylene blue 1% + Clotrimazole 1%) and LED phototherapy (red, blue and violet) with emission times of 60 - 260 seconds for each applicator, according to the dose recommendations of the scar acceleration method (MAC<sup>®</sup>). At the sixth treatment session there was a noticeable decrease in the itching sensation reported by the patient. In session 11 she reported feeling a great improvement, indicating that she no longer felt itching in any area after 18 sessions. The present case demonstrates new methodologies to treat common problems in the population that have a positive impact on the quality of life. This methodology has a promising future because it is non-invasive and requires a great biological transformation for inflammatory, fungal and viral control.
文摘Professor M.A. （Alexandrovich） Guzev is a physics graduate of theUniversity of Leningrad （now Saint Petersburg, 1984）. He began hisscientific career at the Institute of Physics, Leningrad Universityin 1984. After commencing his Ph.D. （Physics ＆ Mathematics） in1987, he continued his work as a researcher of Pacific OceanologicalInstitute, Far Eastern Branch of Russian Academy of Sciences（FEBRAS）, Vladivostok, Russia, from 1987 to 1991.
文摘Ma Xiaoying, Assyriology, Ph.D., 1994.12 "Women’s Social Status in Old Babylonia Reflected in Marital Property" (Supervisors: Professors Lin Zhichun, Thomas Lee, Tova Meltzer, Wu Yuhong) Wang Liying, Classics, Ph.D., 1995.6 "Sallust’s Bellum Catilinae" (Supervisors: Porfessors Wang Dunshu, P.Ruth Taylor-Briggs, F. Ahlheid, Lin Zhichun)
