European Employment Survey for Chemists (ESEC3) Careers of Chemistry Graduates in Europe.

What are the country-specific differences in the education of chemistry students across the EU? How do students' preferred careers change with the level of their qualifications? Do students have sufficient information to make good career choices? Which media are now considered most effective for job searches? What do students currently consider to be a successful career? Three Employment Surveys for European Chemists (ESEC1-ESEC3) were conducted between 2013 and 2020. The web-based questionnaires were open to chemists from all countries. A total of 9747 responses were received. The reports for ESEC1 and ESEC2 are freely available.[1,2] Subsequent questionnaires have been continuously adapted to reflect the latest discussions in our societies. In particular, ESEC3 focused on students' careers. The results are reported here. Where results from earlier surveys are included, this is explicitly stated. Graduates of the last 15 years are particularly important for two reasons. They represent the largest group of ESEC participants, with 59 % of the responses, and they represent the labour market with the greatest future prospects. Their situation is of particular importance for real decisions about the chemistry labour market. For this reason, this report focuses exclusively on graduates of the last 15 years.

Employment Survey for European Chemists (ESEC3) How Diverse is Europe's Chemical Workforce?

Understanding diversity is the foundation for progress on inclusion. Diversity covers a wide range of characteristics, not just gender. Does the training meet the current needs of the workplace? What role does gender discrimination play in the chemical workforce? To what extent are partners treated equally when caring for a family member? Three Employment Surveys for European Chemists (ESEC1 - ESEC3) were conducted between 2013 and 2020. The online questionnaires were open to chemists from all countries. 9747 responses were collected for all three surveys together. The reports for ESEC1 and ESEC2 are freely available.[1,2] The questionnaires from ESEC1 to ESEC3 were continuously adapted to reflect the latest discussions in our societies, for example diversity, which was specifically addressed in ESEC3. The results are reported here. The Royal Society of Chemistry asked similar questions.[3] Graduates from the last 15 years are particularly important for two reasons. They are the largest group of ESEC participants, with 59 % of the responses, and they represent the labour market with the greatest future prospects. Their situation is of particular importance for real decisions about the chemistry labour market. For this reason, the current report focuses exclusively on graduates of the last 15 years.

Employment and Careers of European Chemists (ESEC2).

Employment conditions and career opportunities are in the focus of the new Employment Survey for European Chemists (ESEC2). Conditions and opportunities are individually analysed for all countries with a statistically significant number of responses. The results provide important clues for careers in these countries and in Europe as a whole. The importance of employer sectors varies very much between European countries. A chapter of this report is devoted to career planning of students and new graduates. This is the first general evaluation of the survey. It provides many details about the chemistry workforce in Europe and its development.

Guest Editorial: The Professional Status of European Chemists and Chemical Engineers.

Which country pays its chemists and chemical engineers the highest salaries? Where can I find a new job quickest? Which chemical sub-discipline offers most jobs? Reliable answers for these and other questions have been derived from the first European employment survey for chemists and chemical engineers, which was carried out in 2013. Here we publish the first general evaluation of the results of this survey.

Raman spectroscopic grading of astrocytoma tissues: using soft reference information.

Gliomas are the most frequent primary brain tumours. During neurosurgical treatment, locating the exact tumour border is often difficult. This study assesses grading of astrocytomas based on Raman spectroscopy for a future application in intra-surgical guidance. Our predictive classification models distinguish the surgically relevant classes "normal tissue" and "low" and "high grade astrocytoma" in Raman maps of moist bulk samples (80 patients) acquired with a fibre-optic probe. We introduce partial class memberships as a strategy to utilize borderline cases for classification. Borderline cases supply the most valuable training and test data for our application. They are (a) examples of the sought boundary and (b) the cases for which new diagnostics are needed. Besides, the number of suitable training samples increases considerably: soft logistic regression (LR) utilizes 85% more spectra and 50% more patients than linear discriminant analysis (LDA). The predictive soft LR models achieve ca. 85, 67 and 84% (normal, low and high grade) sensitivity and specificity. We discuss the different heuristics of LR and LDA in the light of borderline samples. While we focus on prediction, the spectroscopic interpretation of the predictive models agrees with previous descriptive studies. Unsaturated lipids are used to differentiate between normal and tumour tissues, while the total lipid content prominently contributes to the determination of the tumour grade. The high-wavenumber region above 2,800 cm(-1) alone did not allow successful grading. We give a proof of concept for Raman spectroscopic grading of moist astrocytoma tissues and propose to include borderline samples into classifier training and testing.

Intra-operative optical diagnostics with vibrational spectroscopy.

Established methods for characterization of tissue and diagnostics, for example histochemistry, magnetic resonance imaging (MRI), X-ray tomography, or positron emission tomography (PET), are mostly not suitable for intra-operative use. However, there is a clear need for an intra-operative diagnostics especially to identify the borderline between normal and tumor tissue. Currently, vibrational spectroscopy techniques (both Raman and infrared) complement the standard methods for tissue diagnostics. Vibrational spectroscopy has the potential for intra-operative use, because it can provide a biochemically based profile of tissue in real time and without requiring additional contrast agents, which may perturb the tissue under investigation. In addition, no electric potential needs to be applied, and the measurements are not affected by electromagnetic fields. Currently, promising approaches include Raman fiber techniques and nonlinear Raman spectroscopy. Infrared spectroscopy is also being used to examine freshly resected tissue ex vivo in the operating theater. The immense volume of information contained in Raman and infrared spectra requires multivariate analysis to extract relevant information to distinguish different types of tissue. The promise and limitations of vibrational spectroscopy methods as intra-operative tools are surveyed in this review.

Raman spectroscopic imaging for in vivo detection of cerebral brain metastases.

We report for the first time a proof-of-concept experiment employing Raman spectroscopy to detect intracerebral tumors in vivo by brain surface mapping. Raman spectroscopy is a non-destructive biophotonic method which probes molecular vibrations. It provides a specific fingerprint of the biochemical composition and structure of tissue without using any labels. Here, the Raman system was coupled to a fiber-optic probe. Metastatic brain tumors were induced by injection of murine melanoma cells into the carotid artery of mice, which led to subcortical and cortical tumor growth within 14 days. Before data acquisition, the cortex was exposed by creating a bony window covered by a calcium fluoride window. Spectral contributions were assigned to proteins, lipids, blood, water, bone, and melanin. Based on the spectral information, Raman images enabled the localization of cortical and subcortical tumor cell aggregates with accuracy of roughly 250 µm. This study demonstrates the prospects of Raman spectroscopy as an intravital tool to detect cerebral pathologies and opens the field for biophotonic imaging of the living brain. Future investigations aim to reduce the exposure time from minutes to seconds and improve the lateral resolution.

PM-IRRAS mapping of ultrathin molecular films with high spatial resolution.

Polarization modulation infrared reflection absorption spectroscopy (PM-IRRAS) is a very sensitive technique to characterize the degree of molecular ordering in thin films on metallic surfaces. This is the first report of the coupling of a PM-IRRAS microscope to a free electron laser (FEL), a light source of highest brilliance. Some FELs emit in the infrared region and permit the mapping of molecular properties at high lateral resolution. We studied the molecular orientation of octadecanephosphonic acid (OPA) attached to a gold surface with microstructured aluminum oxide islands on the gold. The spatial resolution achieved is 12 microm which corresponds to the diffraction limit of the infrared light used in this study. This is a substantial improvement compared to previous studies using a PM-IRRA accessory together with a commercial Fourier transform infrared spectrometer, where the lateral resolution is noise-limited rather than diffraction-limited. The spectral maps reveal that OPA is preferably attached to the aluminum oxide islands via the bidentate binding mode whereas the tridentate mode is dominating in case of OPA attached to the gold areas.

Characterization of lipid extracts from brain tissue and tumors using Raman spectroscopy and mass spectrometry.

Brain tissue is characterized by high lipid content. The amount of lipids decreases, and its composition changes in the most frequent primary brain tumor, the glioma. Scope of the current paper was to extract quantitatively lipids from porcine and human brain tissue as well as from five human gliomas using a modified protocol according to Folch. The lipid extracts were studied by Raman spectroscopy with 785 nm excitation and by mass spectrometry with electron impact ionization. Porcine and human brain tissues have similar water and lipid content and show similar Raman and mass spectra. In contrast, gliomas are characterized by increased water content and decreased lipid content. Elevated phosphatidylcholine to cholesterol ratios in lipid extracts of gliomas were indicated by Raman bands of the choline group and cholesterol. Due to its higher sensitivity, mass spectrometry detected increased levels of cholesterol ester relative to cholesterol in lipid extracts of gliomas. For comparison, thin tissue sections were prepared from the glioma specimens before lipid extraction; infrared spectroscopic images were recorded and analyzed by a supervised classification model. This study demonstrates how to improve the analysis of brain tumors and to complement the diagnosis of brain pathologies using a multimodal approach.

Suitability of infrared spectroscopic imaging as an intraoperative tool in cerebral glioma surgery.

Infrared spectroscopic imaging is a promising intraoperative tool which enables rapid, on-site diagnosis of brain tumors during neurosurgery. A classification model was recently developed using infrared spectroscopic images from thin tissue sections to grade malignant gliomas, the most frequent class of primary brain tumor. In this study the model was applied to 54 specimens from six patients with inhomogeneous gliomas composed of regions with different tumor density and morphology. The resection was controlled using neuronavigation which transfers the findings obtained by preoperative magnetic resonance imaging (MRI) into the operating field. For comparison, all specimens were independently evaluated by histopathology after hematoxylin and eosin staining. The infrared-derived grading agreed with histopathology and MRI findings for almost all specimens. With regard to histopathological assessment, sensitivities of 100% (22/22) and 93.1% (27/29) and specificities of 96.9% (31/32) and 88.0% (22/25) were achieved, depending on whether the classification was based on the predominant or maximal tumor grade, respectively, in the specimen. Altogether, in 98% (53/54) of all specimens the decision to continue or not continue tumor resection could have been made according to the infrared spectroscopic classification. This retrospective study clearly demonstrates that infrared spectroscopic imaging may help to define tumor margins intraoperatively and to detect high-grade tumor residues for achieving more radical tumor resection.

Quantification of brain lipids by FTIR spectroscopy and partial least squares regression.

Brain tissue is characterized by high lipid content. Its content decreases and the lipid composition changes during transformation from normal brain tissue to tumors. Therefore, the analysis of brain lipids might complement the existing diagnostic tools to determine the tumor type and tumor grade. Objective of this work is to extract lipids from gray matter and white matter of porcine brain tissue, record infrared (IR) spectra of these extracts and develop a quantification model for the main lipids based on partial least squares (PLS) regression. IR spectra of the pure lipids cholesterol, cholesterol ester, phosphatidic acid, phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, phosphatidylinositol, sphingomyelin, galactocerebroside and sulfatide were used as references. Two lipid mixtures were prepared for training and validation of the quantification model. The composition of lipid extracts that were predicted by the PLS regression of IR spectra was compared with lipid quantification by thin layer chromatography.

Hydrogel-based piezoresistive pH sensors: investigations using FT-IR attenuated total reflection spectroscopic imaging.

The strong swelling ability of the pH-responsive poly(acrylic acid)/poly(vinyl alcohol) (PAA/PVA) hydrogel makes the development of a new type of sensor possible, which combines piezoresistive-responsive elements as mechanoelectrical transducers and the phase transition behavior of hydrogels as a chemomechanical transducer. The sensor consists of a pH-responsive PAA/PVA hydrogel and a standard pressure sensor chip. However, a time-dependent sensor output voltage mirrors only the physical swelling process of the hydrogel but not the corresponding chemical reactions. Therefore, an investigation of the swelling behavior of this hydrogel is essential for the optimization of sensor design. In this work, Fourier transform infrared (FT-IR) spectroscopic imaging was used to study the swelling of the hydrogel under in situ conditions. In particular, laterally and time-resolved FT-IR images were obtained in the attenuated total reflection mode and the entire data set of more than 80,000 FT-IR spectra was evaluated by principal component analysis (PCA). The first and third principal components (PCs) indicate the swelling process. Molecular changes within the carboxyl groups were observed in the second and fourth PC and identified as key processes for the swelling behavior. It was found that time-dependent molecular changes are similar to the electrical sensor output signal. The results of the FT-IR spectroscopic images render an improved chemical sensor possible and demonstrate that in situ FT-IR imaging is a powerful method for the characterization of molecular processes within chemical-sensitive materials.

Rapid and label-free classification of human glioma cells by infrared spectroscopic imaging.

The discrimination of cell types is a crucial task in cell biology. Available techniques, based on an irreversible treatment of the cells, do not allow a sensitive label-free characterization under in situ conditions. Infrared spectroscopic imaging is a new and useful tool for studying individual cells. It has established itself as a powerful method to probe the molecular composition and to indicate the biochemistry of cells. Monolayers of cultivated U343, T1115 and T508 human glioma cells were characterized using infrared spectroscopic imaging. A classification algorithm based on linear discriminant analysis was developed to distinguish different cells without labeling. The classification is based upon spectral features which mainly arise from proteins, nucleic acids, and cholesterol. An accuracy of 91% and 84% was obtained for cells of U343 and T1115, respectively. Cells of the T508 cell line exhibit some misclassifications resulting in a lower accuracy rate of 73%. As the results demonstrate, the potential of infrared spectroscopic imaging method to assess the overall molecular composition of cells in a non-destructive manner opens the possibility to characterize cells on a molecular level without labels or an irreversible treatment.

Assessing and improving the stability of chemometric models in small sample size situations.

Small sample sizes are very common in multivariate analysis. Sample sizes of 10-100 statistically independent objects (rejects from processes or loading dock analysis, or patients with a rare disease), each with hundreds of data points, cause unstable models with poor predictive quality. Model stability is assessed by comparing models that were built using slightly varying training data. Iterated k-fold cross-validation is used for this purpose. Aggregation stabilizes models. It is possible to assess the quality of the aggregated model without calculating further models. The validation and aggregation methods investigated in this study apply to regression as well as to classification. These techniques are useful for analyzing data with large numbers of variates, e.g., any spectral data like FT-IR, Raman, UV/VIS, fluorescence, AAS, and MS. FT-IR images of tumor tissue were used in this study. Some tissue types occur frequently, while some are very rare. They are classified using LDA. Initial models were severely unstable. Aggregation stabilizes the predictions. The hit rate increased from 67% to 82%.

High-impact sulfur compounds: constitutional and configurational assignment of sulfur-containing heterocycles.

To unambiguously identify their structures and to evaluate their organoleptic properties, several constitutional und configurational isomers of dialkyl-tetrathianes and dialkyl-pentathiepanes were synthesized by two different synthetic protocols, and separated by preparative gas chromatography. Raman and NMR spectroscopy were used to differentiate between the constitutional 3,6-dialkyl-1,2,4,5-tetrathiane and the 4,6-dialkyl-1,2,3,5-tetrathiane isomers. Furthermore, cis- and trans-isomers of 3,6-dialkyl-1,2,4,5-tetrathianes were distinguished by temperature-dependent NMR experiments. Static, quantum-chemical simulations of the NMR spectra for these cis- and trans-isomers were calculated in the gas layer in order to confirm our experimental assignments. In addition, the assignment of 4,7-alkyl-1,2,3,5,6-pentathiepanes were deducted from their Raman spectra. Dialkyl-tetrathianes and dialkyl-pentathiepanes are interesting components to be used in flavor applications due to their unique olfactory impact and facets.

Identification of primary tumors of brain metastases by SIMCA classification of IR spectroscopic images.

Brain metastases are secondary intracranial lesions which occur more frequently than primary brain tumors. The four most abundant types of brain metastasis originate from primary tumors of lung cancer, colorectal cancer, breast cancer and renal cell carcinoma. As metastatic cells contain the molecular information of the primary tissue cells and IR spectroscopy probes the molecular fingerprint of cells, IR spectroscopy based methods constitute a new approach to determine the origin of brain metastases. IR spectroscopic images of 4 by 4 mm2 tissue areas were recorded in transmission mode by a FTIR imaging spectrometer coupled to a focal plane array detector. Unsupervised cluster analysis revealed variances within each cryosection. Selected clusters of five IR images with known diagnoses trained a supervised classification model based on the algorithm soft independent modeling of class analogies (SIMCA). This model was applied to distinguish normal brain tissue from brain metastases and to identify the primary tumor of brain metastases in 15 independent IR images. All specimens were assigned to the correct tissue class. This proof-of-concept study demonstrates that IR spectroscopy can complement established methods such as histopathology or immunohistochemistry for diagnosis.