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This article provides a comprehensive history of default risk for newly originated home mortgages in the USA over the past quarter century. The loan-level source data include the entire guarantee book for Fannie Mae and Freddie Mac. We track many loan characteristics and produce a summary measure of risk. Among our many results, we show that mortgage risk had already risen in the 1990s, planting seeds of the financial crisis well before the actual event. Our results also cast doubt on explanations of the crisis that focus on borrowers with low credit scores. The aggregate series are available for download at https://www.fhfa.gov/papers/wp1902.aspx.

Attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy alongside machine learning (ML) techniques is an emerging approach for the early detection of brain cancer in clinical practice. A crucial step in the acquisition of an IR spectrum is the transformation of the time domain signal from the biological sample to a frequency domain spectrum via a discrete Fourier transform. Further pre-processing of the spectrum is typically applied to reduce non-biological sample variance, and thus to improve subsequent analysis. However, the Fourier transformation is often assumed to be essential even though modelling of time domain data is common in other fields. We apply an inverse Fourier transform to frequency domain data to map these to the time domain. We use the transformed data to develop deep learning models utilising Recurrent Neural Networks (RNNs) to differentiate between brain cancer and control in a cohort of 1438 patients. The best performing model achieves a mean (cross-validated score) area under the receiver operating characteristic (ROC) curve (AUC) of 0.97 with sensitivity of 0.91 and specificity of 0.91. This is better than the optimal model trained on frequency domain data which achieves an AUC of 0.93 with sensitivity of 0.85 and specificity of 0.85. A dataset comprising 385 patient samples which were prospectively collected in the clinic is used to test a model defined with the best performing configuration and fit to the time domain. Its classification accuracy is found to be comparable to the gold-standard for this dataset demonstrating that RNNs can accurately classify disease states using spectroscopic data represented in the time domain.

Background: Early detection of cancer is vital to improve patient survival, since earlier diagnosis and treatment maximizes the opportunity to combat or control disease progression. Specifically for colorectal cancer (CRC), the average 5-survival rate after diagnosis decreases from 91% in early-stage CRC, to as low as 15% for stage IV CRC. Furthermore, the rapid detection and subsequent removal of pre-cancerous adenomas - e.g., advanced adenomas (AA) - can improve survival rates of affected patients. Current stool-based tests, such as FIT and FIT-DNA, have poor AA sensitivities of 24% and 42%, and specificities of 95% and 87%, respectively. Recent research into liquid biopsies have shown some promise, however tests based on tumor-derived biomarkers alone have limited sensitivity, especially in early-stage disease. Methods: We initially applied the Dxcover® Cancer Liquid Biopsy for use as a multi-cancer early detection (MCED) test. The test uses Fourier transform infrared (FTIR) spectroscopy and machine learning algorithms to build a classifier of the resultant spectral profiles to detect cancer, and can be fine-tuned to maximize either sensitivity or specificity depending on the requirements of specific international healthcare systems. Additionally, we have analyzed a retrospective cohort of serum samples comprising 100 CRC, 99 advanced adenomas removed by surgical resection and 97 colonoscopy screening controls. Results: The CRC classifier from the discovery MCED dataset reported an area under the receiver operating characteristic curve (AUROC) value of 0.91, with 74% sensitivity and 91% specificity when differentiating CRC and non-cancer, which surpasses the targets set by the Centers for Medicare & Medicaid Services (CMS) for coverage of CRC tests. When tuned for higher sensitivity, the model produced 97% sensitivity (49% specificity), and when tailored for greater specificity (97%) the sensitivity was 47%. In this study, we have progressed these findings to examine the ability of the technology to differentiate patients with CRC, AA and colonoscopy controls. Conclusions: Cancer treatment is more effective when given earlier and this low-cost strategy can facilitate the requisite earlier diagnosis. A rapid blood test that sensitive to AA and early-stage CRC could improve patient prognosis and ultimately reduce mortality. Citation Format: James M. Cameron, Georgios Antoniou, Paul M. Brennan, Justin J. Conn, Sharon King, Rose G. McHardy, Susan Moug, Jennifer Nobes, David S. Palmer, Alexandra Sala, Benjamin R. Smith, Judith Strachan, Craig Mowat, Matthew J. Baker. Early colorectal cancer detection with a spectroscopic liquid biopsy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6506.

A rapid, low-cost, sensitive, multi-cancer early detection (MCED) test would be transformational in the diagnostics field. Earlier cancer detection can increase survival rates and quality of life of patients. An effective test must accurately identify the small proportion of patients with typically non-specific symptoms who have cancer. Such symptoms do not easily segregate by organ system, necessitating a multi-cancer approach. In this large-scale study (n = 2094 patients) we applied the Dxcover® Cancer Liquid Biopsy to differentiate cancer against non-cancer patients, as well as organ specific tests to identify cancers of the brain, breast, colorectal, kidney, lung, ovary, pancreas, and prostate. The test uses Fourier transform infrared (FTIR) spectroscopy to analyze all macromolecules in a minute volume of patient serum, and machine learning algorithms to build a classifier of the resultant spectral profiles to detect cancer. This approach can be fine-tuned to maximize either sensitivity or specificity depending on the requirements from different healthcare systems and cancer diagnostic pathways. The cancer v asymptomatic non-cancer classification detected 64% of stage I cancers when specificity was 99% (overall sensitivity 56%). When tuned for higher sensitivity, this model identified 99% of stage I cancers (while specificity was 58%). When examining cancer against all non-cancer (including symptomatic patients), the sensitivity-tuned model enabled 90% sensitivity with 61% specificity, with detection rates of 93% for stage I, 84% for stage II, 92% for stage III and 95% for stage IV. For organ specific cancer classifiers, area under the receiver operating characteristic (ROC) curve values were calculated for all cancers: brain (0.90), breast (0.75), colorectal (0.91), kidney (0.91), lung (0.91), ovarian (0.86), pancreatic (0.85) and prostate (0.86). Cancer treatment is more effective when given earlier and this low-cost strategy can facilitate the requisite earlier diagnosis.

Background Diagnostic delays impact the quality of life and survival of patients with brain tumors. Earlier and expeditious diagnoses in these patients are crucial to reduce the morbidities and mortalities associated with brain tumors. A simple, rapid blood test that can be administered easily in a primary care setting to efficiently identify symptomatic patients who are most likely to have a brain tumor would enable quicker referral to brain imaging for those who need it most. Methods Blood serum samples from 603 patients were prospectively collected and analyzed. Patients either had non-specific symptoms that could be indicative of a brain tumor on presentation to the Emergency Department, or a new brain tumor diagnosis and referral to the neurosurgical unit, NHS Lothian, Scotland. Patient blood serum samples were analyzed using the Dxcover® Brain Cancer liquid biopsy. This technology utilizes infrared spectroscopy combined with a diagnostic algorithm to predict the presence of intracranial disease. Results Our liquid biopsy approach reported an area under the receiver operating characteristic curve of 0.8. The sensitivity-tuned model achieves a 96% sensitivity with 45% specificity (NPV 99.3%) and identified 100% of glioblastoma multiforme patients. When tuned for a higher specificity, the model yields a sensitivity of 47% with 90% specificity (PPV 28.4%). Conclusions This simple, non-invasive blood test facilitates the triage and radiographic diagnosis of brain tumor patients while providing reassurance to healthy patients. Minimizing time to diagnosis would facilitate the identification of brain tumor patients at an earlier stage, enabling more effective, less morbid surgical and adjuvant care.

Although significant effort has been expended to analyze the binding of pyrene in β-cyclodextrin and γ-cyclodextrin, little has been published on the binding of this guest in β-cyclodextrin derivatives (methyl-β-cyclodextrin and 2-hydroxypropyl-β-cyclodextrin) or in mixtures of such derivatives, despite the fact that these derivatives are known to have different supramolecular properties that facilitate unique modes of complexation. Reported herein is a detailed spectroscopic investigation of the binding of pyrene in β-cyclodextrin derivatives and in binary mixtures of cyclodextrins. Py values, defined as the ratio of representative vibronic bands in the fluorescence emission of pyrene, were used to measure changes in the pyrene microenvironment in the presence of the cyclodextrin hosts, and indicated that unmodified β-cyclodextrin is unique in providing a fully hydrophobic environment for pyrene through the use of two cyclodextrins to bind a single pyrene guest. By comparison, both γ-cyclodextrin and modified β-cyclodextrin analogues bind pyrene in a less hydrophobic environment through 1:1 binding stoichiometries that allow for continued interactions between the incompletely encapsulated pyrene guest and the aqueous solvent system. Binary mixtures of cyclodextrins were also explored and reinforce the unique properties of the unmodified β-cyclodextrin host. Graphical Abstract The unique binding geometries of pyrene in beta-cyclodextrin and its derivatives leads to measurable fluorescence emission signals, whose information can be used to elucidate the highly structurally dependent binding geometries and stoichiometries.

The purpose of this study was to determine whether total ankle arthroplasty (TAA) and ankle/hindfoot fusion patients receiving tranexamic acid (TXA) exhibit fewer wound complications.A retrospective review was conducted of 212 patients (217 feet) undergoing TAA (n = 72), ankle (n = 36), tibiotalocalcaneal (n = 20), pantalar (n = 1), or hindfoot fusion (ie, subtalar = 47, double = 33, and triple = 8) between 2015 and 2020 by a fellowship-trained foot and ankle surgeon at an academic medical center. Demographics, medical history, complications, and union status were compared between TXA (n = 101) and non-TXA (n = 116) cohorts. The mean follow-up was 1.24 years (range, 0.25 to 4.68).The TXA group had significantly less postoperative infections (5.9% versus 15.5%, P = 0.025). Within a subgroup analysis of ankle/hindfoot fusions, the TXA group exhibited significantly more Charcot neuroarthropathy (20.7% versus 5.7%, P = 0.006) and shorter follow-up duration (0.96 versus 1.30 years, P = 0.030); however, TXA was associated with shorter time to fusion (146 versus 202 days, P = 0.049) and fewer revision surgeries (8.6% versus 21.8%, P = 0.036). Subgroup analysis excluding feet with Charcot also demonstrated less postoperative infections (4.5% versus 14.4%, P = 0.020). Subgroup analysis of TAAs showed fewer cases of superficial infections (2.3% versus 27.6%, P = 0.002) and delayed wound healing (25.6% versus 48.3%, P = 0.047) in the TXA cohort.TXA use in ankle/hindfoot surgery was correlated with a reduction in superficial infections and radiographic time to union. The use of TXA in TAA correlated with fewer superficial infections and cases of delayed wound healing. Thus, in addition to other areas of orthopaedics, TXA seems to be beneficial in hindfoot and ankle surgery.All data were obtained from our institution's medical records. This study is not associated with a clinical trial.

To support the early detection and diagnosis of brain tumors we have developed a rapid, cost-effective and easy to use spectroscopic liquid biopsy based on the absorbance of infrared radiation. We have previously reported highly sensitive results of our approach which can discriminate patients with a recent brain tumor diagnosis and asymptomatic controls. Other liquid biopsy approaches (e.g., based on tumor genetic material) report a lower classification accuracy for early-stage tumors. In this manuscript we present an investigation into the link between brain tumor volume and liquid biopsy test performance. In a cohort of 177 patients (90 patients with high-grade glioma (glioblastoma (GBM) or anaplastic astrocytoma), or low-grade glioma (astrocytoma, oligo-astrocytoma and oligodendroglioma)) tumor volumes were calculated from magnetic resonance imaging (MRI) investigations and patients were split into two groups depending on MRI parameters (T1 with contrast enhancement or T2/FLAIR (fluid-attenuated inversion recovery)). Using attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy coupled with supervised learning methods and machine learning algorithms, 90 tumor patients were stratified against 87 control patients who displayed no symptomatic indications of cancer, and were classified as either glioma or non-glioma. Sensitivities, specificities and balanced accuracies were all greater than 88%, the area under the curve (AUC) was 0.98, and cancer patients with tumor volumes as small as 0.2 cm3 were correctly identified. Our spectroscopic liquid biopsy approach can identify gliomas that are both small and low-grade showing great promise for deployment of this technique for early detection and diagnosis. Citation Format: Alexandra Sala, Ashton G. Theakstone, Paul M. Brennan, Michael D. Jenkinson, Samantha J. Mills, Khaja Syed, Christopher Rinaldi, Yun Xu, Royston Goodacre, Holly J. Butler, David S. Palmer, Benjamin R. Smith, Matthew J. Baker. Rapid spectroscopic liquid biopsy for the early detection of brain tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5923.

Diagnostic delays impact the quality of life and survival of patients with brain tumors. Earlier and expeditious diagnoses in these patients are crucial to reducing the morbidities and mortalities associated with brain tumors. A simple, rapid blood test that can be administered easily in a primary care setting to efficiently identify symptomatic patients who are most likely to have a brain tumor would enable quicker referral to brain imaging for those who need it most. Blood serum samples from 603 patients were prospectively collected and analyzed. Patients either had non-specific symptoms that could be indicative of a brain tumor on presentation to the Emergency Department, or a new brain tumor diagnosis and referral to the neurosurgical unit, NHS Lothian, Scotland. Patient blood serum samples were analyzed using the Dxcover® Brain Cancer liquid biopsy. This technology utilizes infrared spectroscopy combined with a diagnostic algorithm to predict the presence of intracranial disease. Our liquid biopsy approach reported an area under the receiver operating characteristic curve of 0.8. The sensitivity-tuned model achieves a 96% sensitivity with 45% specificity (NPV 99.3%) and identified 100% of glioblastoma multiforme patients. When tuned for a higher specificity, the model yields sensitivity of 47% with 90% specificity (PPV 28.4%). This simple, non-invasive blood test facilitates the triage and radiographic diagnosis of brain tumor patients, while providing reassurance to healthy patients. Minimizing time to diagnosis would facilitate identification of brain tumor patients at an earlier stage, enabling more effective, less morbid surgical and adjuvant care. Citation Format: James M. Cameron, Paul M. Brennan, Georgios Antoniou, Holly J. Butler, Loren Christie, Justin J.A. Conn, Tom Curran, Ewan Gray, Mark G. Hegarty, Michael Jenkinson, Daniel Orringer, David S. Palmer, Alexandra Sala, Benjamin R. Smith, Matthew J. Baker. Clinical validation of a spectroscopic liquid biopsy for early detection of brain cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5922.

The development of a robust multi-cancer early detection (MCED) test would be transformational within the cancer diagnostics field, as earlier detection is vital to inhibit disease progression, improve patient prognosis and reduce mortality rates. In this large-scale spectroscopic study, we present the Dxcover® Cancer Liquid Biopsy, the world’s first infrared spectroscopy-based blood test for the early detection of multiple cancers. The full cohort (n=2094) was comprised of patients with a confirmed cancer diagnosis (n=1543), symptomatic non-cancer patients (n=460) and healthy volunteers (n=91). For the cancer versus non-cancer classification, receiver operating characteristic analysis produced a mean curve with an area under the curve (AUC) value of 0.86. When adjusted for greater sensitivity, our model achieved 90% sensitivity with 61% specificity, and yielded a sensitivity of 56% with 91% specificity when tailored for higher specificity. We also tuned for maximized sensitivity or specificity, whilst the other parameter was fixed above a minimum value of 45% for the cross-validation result, based upon performance levels of currently available clinically benchmarked tests. This resulted in a 94% sensitivity where specificity was 47%, and a 94% specificity with 48% sensitivity. The overall detection rates were 93% for stage I, 84% for stage II, 92% for stage III and 95% for stage IV for the sensitivity-tuned model. The specificity-tuned model detected 90% of non-cancer patients accurately, resulting in few false negatives (54/551). For the binary classifiers of individual cancer types against symptomatic control patients, promising AUC values were reported for all cancers: brain (0.90), breast (0.74), colorectal (0.91), kidney (0.91), lung (0.90), ovarian (0.85), pancreatic (0.81) and prostate (0.85). Importantly, the detection rates did not vary significantly according to cancer stage, suggesting the technology is very effective at detecting early-stage tumors, which is a necessity for MCED tests. Citation Format: James M. Cameron, Alexandra Sala, Georgios Antoniou, Paul M. Brennan, Justin J. A. Conn, Siobhan Connal, David S. Palmer, Benjamin R. Smith, Matthew J. Baker. Multi-cancer early detection with a spectroscopic liquid biopsy platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5920.

In recent years, the diagnosis of brain tumors has been investigated with attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy on dried human serum samples to eliminate spectral interferences of the water component, with promising results. This research evaluates ATR-FTIR on both liquid and air-dried samples to investigate "digital drying" as an alternative approach for the analysis of spectra obtained from liquid samples. Digital drying approaches, consisting of water subtraction and least-squares method, have demonstrated a greater random forest (RF) classification performance than the air-dried spectra approach when discriminating cancer vs control samples, reaching sensitivity values higher than 93.0% and specificity values higher than 83.0%. Moreover, quantum cascade laser infrared (QCL-IR) based spectroscopic imaging is utilized on liquid samples to assess the implications of a deep-penetration light source on disease classification. The RF classification of QCL-IR data has provided sensitivity and specificity amounting to 85.1% and 75.3% respectively.

Formaldehyde forms a variety of hydrated and methoxylated species when reacted with water and methanol. Vibrational spectroscopy has been deployed for both remote and in situ sensing of formaldehyde species and it can be a useful tool for process development, monitoring and control at both laboratory and industrial scale, as well as for environmental, atmospheric and space monitoring. While IR and Raman spectroscopic studies of formaldehyde species in solid, liquid or gas phases have been reported, assignments of vibrational frequencies of relevant species in previous literature have been contradictory and incomplete. In this work we report IR and Raman spectra for formaldehyde-water-methanol solutions across a wide range of formaldehyde concentrations and solvent compositions. We present an analysis of vibrational spectra of formaldehyde-water-methanol systems using a combination of experimental measurements and gas phase quantum mechanical density functional theory simulations. For the first time, we explicitly consider spectra of oligomeric mixtures of formaldehyde species in relation to spectra of specific representative hydrated and methoxylated species and we resolve some previously reported contradictions in assignments of vibrational frequencies for formaldehyde systems.

Over a third of brain tumour patients visit their general practitioner more than five times prior to diagnosis in the UK, leading to 62% of patients being diagnosed as emergency presentations. Unfortunately, symptoms are non-specific to brain tumours, and the majority of these patients complain of headaches on multiple occasions before being referred to a neurologist. As there are currently no methods in place for the early detection of brain cancer, the affected patients' average life expectancy is reduced by 20 years. These statistics indicate that the current pathway is ineffective, and there is a vast need for a rapid diagnostic test. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy is sensitive to the hallmarks of cancer, as it analyses the full range of macromolecular classes. The combination of serum spectroscopy and advanced data analysis has previously been shown to rapidly and objectively distinguish brain tumour severity. Recently, a novel high-throughput ATR accessory has been developed, which could be cost-effective to the National Health Service in the UK, and valuable for clinical translation. In this study, 765 blood serum samples have been collected from healthy controls and patients diagnosed with various types of brain cancer, contributing to one of the largest spectroscopic studies to date. Three robust machine learning techniques - random forest, partial least squares-discriminant analysis and support vector machine - have all provided promising results. The novel high-throughput technology has been validated by separating brain cancer and non-cancer with balanced accuracies of 90% which is comparable to the traditional fixed diamond crystal methodology. Furthermore, the differentiation of brain tumour type could be useful for neurologists, as some are difficult to distinguish through medical imaging alone. For example, the highly aggressive glioblastoma multiforme and primary cerebral lymphoma can appear similar on magnetic resonance imaging (MRI) scans, thus are often misdiagnosed. Here, we report the ability of infrared spectroscopy to distinguish between glioblastoma and lymphoma patients, at a sensitivity and specificity of 90.1% and 86.3%, respectively. A reliable serum diagnostic test could avoid the need for surgery and speed up time to definitive chemotherapy and radiotherapy.

Non-specific symptoms, as well as the lack of a cost-effective test to triage patients in primary care, has resulted in increased time-to-diagnosis and a poor prognosis for brain cancer patients. A rapid, cost-effective, triage test could significantly improve this patient pathway. A blood test using attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy for the detection of brain cancer, alongside machine learning technology, is advancing towards clinical translation. However, whilst the methodology is simple and does not require extensive sample preparation, the throughput of such an approach is limited. Here we describe the development of instrumentation for the analysis of serum that is able to differentiate cancer and control patients at a sensitivity and specificity of 93.2% and 92.8%. Furthermore, preliminary data from the first prospective clinical validation study of its kind are presented, demonstrating how this innovative technology can triage patients and allow rapid access to imaging., Diagnosing brain cancer is frequently difficult and requires specialist equipment. Here, the authors develop their previous attenuated total reflectance-Fourier transform infrared spectroscopy method and incoporate the use of disposable silicon wafers for diagnosing brain cancer using serum samples.

We establish necessary and sufficient conditions for the existence of a decomposition of a complete multigraph into edge-disjoint cycles of specified lengths, or into edge-disjoint cycles of specified lengths and a perfect matching.

Reported herein is a sensitive and selective array-based sensing strategy based on differential interactions with three supramolecular cyclodextrin–fluorophore sensors. Each interaction results in a distinct fluorescence modulation response, and linear discriminant analyses of these responses results in 100% successful classification of three classes of isomeric analytes and two classes of analogous analytes. Calculated limits of detection for this system are at or near literature-reported levels of concern.

Mycobacterium haemophilum is likely an underrecognized cause of infection among patients with deficits in their cell-mediated immunity. Despite its name, which translates to “blood loving,” it remains unclear whether hyperferritinemic patients are also at higher risk of this infection. We present a 24-year-old woman with disseminated M. haemophilum infection who has hyperferritinemia and T-cell acute lymphoblastic leukemia in remission and was initially misdiagnosed as having erythema nodosum and avascular necrosis. This report highlights the clinical and histological similarities of M. haemophilum and erythema nodosum, discusses some diagnostic peculiarities, and also reviews similar cases within the literature.

Pre-processing is an essential step in the analysis of spectral data. Mid-IR spectroscopy of biological samples is often subject to instrumental and sample specific variances which may often conceal valuable biological information. Whilst pre-processing can effectively reduce this unwanted variance, the plethora of possible processing steps has resulted in a lack of consensus in the field, often meaning that analysis outputs are not comparable. As pre-processing is specific to the sample under investigation, here we present a systematic approach for defining the optimum pre-processing protocol for biofluid ATR-FTIR spectroscopy. Using a trial-and-error based approach and a clinically relevant dataset describing control and brain cancer patients (3,897 spectra), the effects of pre-processing permutations on subsequent classification algorithms were observed, by assessing key diagnostic performance parameters, including sensitivity and specificity. It was found that optimum diagnostic performance correlated with the use of minimal binning and baseline correction, with derivative functions improving diagnostic performance most significantly. If smoothing is required, a Sovitzky-Golay approach was the preferred option in this investigation. Heavy binning appeared to reduce classification most significantly, alongside wavelet noise reduction (filter length ≥ 6), resulting in the lowest diagnostic performances of all pre-processing permutations tested.

In this article I present an analysis of Australian Aboriginal sorcery, applying concepts from the New Melanesian Ethnography. My starting-point is Keen's approach to magic among the Yolngu, which engages Strathern's concept of the dividual, but which focuses on the extension of partible aspects of the person in space and time. Building on Keen's analysis, I draw on ethnographic material from Cape York Peninsula to argue that Aboriginal sorcery might be understood not only as the extension of partible aspects of the person, but also in terms of the interplay between the internal divisions and external connections of dividual personhood, linking that interplay to the various invasive techniques understood to be employed by sorcerers. On that basis, I argue that, in the central Peninsula, sorcery beliefs are best understood as forms of ‘indigenous analysis’ (Strathern) or ‘naive critique’ (Kapferer) that simultaneously articulate and obscure the anxieties that inhere in postcolonial Aboriginal sociality.

Ferroelectric polymers, such as poly(vinylidene difluoride) (PVDF), have many potential applications in flexible electronic devices. PVDF has six experimentally observed polymorphs, three of which are ferroelectric. In this work we use density functional theory to investigate the structural properties, energetics and polarisation of the stable α-phase, its ferroelectric analogue, the δ-phase, and the β-phase, which has the best ferroelectric properties. The results from a variety of exchange and correlation functionals were compared and it was found that van der Waals (vdW) interactions have an important effect on the calculated crystal structures and energetics, with the vdW-DF functional giving the best agreement with experimental lattice parameters. The spontaneous polarisation was found to strongly correlate with the unit cell volumes, which depend on the functional used. While the relative phase energies were not strongly dependent on the functional, the cohesive energies were significantly underestimated using the PBE functional. The inclusion of vdW interactions is, therefore, important to obtain the correct lattice structures, polarisation and energetics of PVDF polymorphs.

We assessed zebra mussel (Dreissena polymorpha) distribution and density in four newly infested manmade reservoirs in the upper Neosho River Basin, Kansas, from March–November 2011. Density was estimated via monthly plankton samples to monitor veligers, visual searches to detect recruited zebra mussels, and colonization substrates to monitor settling zebra mussels. Infested impoundments upstream were likely the source of zebra mussels dispersing into downstream impoundments in the basin. Marion Reservoir had greater veliger densities downlake than in its upper region while veliger densities in the other three reservoirs were not significantly different among lake regions. Veliger and adult densities were less in the outlet downstream from Marion Reservoir than in the downlake portion of the reservoir. Differences among reservoirs could reflect differences in time since infestation as well as variability in temperature and local physicochemical factors.

PRFFECT is a computer program to aid with spectral preprocessing and the development of classification models. Via a simple text interface, PRFFECT allows users to select wavenumber ranges, perform spectral preprocessing, carry out data partitioning (into training and testing datasets), run a Random Forest classification, compute statistical results, and identify important descriptors for the classification. The preprocessing options provided fall into four categories: binning, smoothing, normalisation, and baseline correction. The program outputs a wide-variety of useful data, including classification metrics and graphs showing the importance of individual wavenumbers to the classification models. As proof-of-concept, PRFFECT has been benchmarked on preprocessing and classification of four food analysis datasets. Sensitivities and specificities above 0.92 were obtained in all cases. The results show that different preprocessing procedures are optimal for different datasets. The PRFFECT software is available freely to the community via GitHub. Link: https://github.com/Palmer-Lab/PRFFECT .

Suppose M=m1,m2,.,mr and N=n1,n2,.,nt are arbitrary lists of positive integers. In this article, we determine necessary and sufficient conditions on M and N for the existence of a simple graph G, which admits a face 2-colorable planar embedding in which the faces of one color have boundary lengths m1,m2,.,mr and the faces of the other color have boundary lengths n1,n2,.,nt. Such a graph is said to have a planar (M;N)-biembedding. We also determine necessary and sufficient conditions on M and N for the existence of a simple graph G whose edge set can be partitioned into r cycles of lengths m1,m2,.,mr and also into t cycles of lengths n1,n2,.,nt. Such a graph is said to be (M;N)-decomposable.

In rivers of the upper Neosho basin, Kansas, USA, lowhead dams influence the downstream dispersal of zebra mussels (Dreissena polymorpha). We quantified density of planktonic zebra mussel larvae and abundance of recruited individuals at 13 sites in this river–reservoir system to examine potential effects of four lowhead dams on dispersal dynamics of this invasive species. Density and abundance declined downstream from reservoir sources but repeatedly increased at sites inundated by lowhead dams compared to free-flowing areas, with colonization extending 189 river-km. The pattern of zebra mussel dispersal in these rivers is best described by the downstream-march model, with lowhead dams acting as stepping stones. Even though these zebra mussel populations are likely not self-recruiting behind lowhead dams, reproduction at these dams could lead to recruitment downstream. Thus, in rivers with lowhead dams, control of zebra mussel metapopulations may not be accomplished solely by limiting source–sink dynamics from upstream infested lakes.

We compared zebra mussel (Dreissena polymorpha) colonization of six commonly used construction materials (PVC, concrete, steel, galvanized steel, pressure-treated wood, aluminum) plus a native unionid mussel shell, and examined effectiveness of the Intersleek® 970 Fluoropolymer Foul Release Coating system applied to the six materials in limiting colonization by this aquatic nuisance species. Coated and uncoated substrates were deployed in Marion Reservoir, Kansas, from April 2011 to August 2012. Density of colonizing zebra mussels was evaluated in July and November 2011, and durability of the substrates was monitored until August 2012. Mussels differentially utilized materials, with eight of the 13 substrates being colonized. PVC was colonized more heavily than galvanized steel and all surfaces coated with foul release fluoropolymer, which were largely mussel-free. Coating eventually showed signs of pitting, but did not delaminate from the materials to which it was applied. These results suggest that use of particular materials and coatings may aid management of some zebra mussel infestations.

Zebra mussel (Dreissena polymorpha) invasion greatly impacts aquatic systems, with economic and ecological consequences. Monitoring zebra mussel density and reproductive timing are essential for effective detection, management, and control of this invasive species. Veligers, the free-floating planktonic life stage of zebra mussels, are often monitored to estimate reproductive effort and as a first indicator of zebra mussel invasion. Although many veliger monitoring methods exist and vary greatly in cost and effort, no study has compared results yielded by different sampling techniques. We compared estimated veliger densities collected with offshore vertical plankton net tows and oblique plankton net tows taken from shore from April through October 2011 in Marion Reservoir, Kansas. Secondarily, we compared enumeration results returned by an established laboratory and a laboratory that had recently begun enumerating veligers. Using ANOVA testing, we found no significant difference between sampling methods or laboratories. We recommend using oblique plankton net tows taken from shore because of their potential to reduce monitoring costs compared to other sampling techniques by reducing equipment, time, and manpower expenses.