Introduction: People who inject drugs (PWID) have an elevated risk of trauma exposure, which can adversely affect health outcomes and create barriers to engaging with services. While high rates of post-traumatic stress disorder (PTSD) among PWID have been well-documented, less is known about the prevalence of complex PTSD (C-PTSD) in this cohort, or how symptom severity might relate to perceived barriers to alcohol and drug treatment.Methods: Participants (n = 102; 74.5% male) were recruited from the Melbourne Supervised Injecting Room (MSIR) in Victoria, Australia, as part of a larger study examining the health needs of MSIR attendees. Participants completed a survey that included assessment of potentially traumatic life events (Life Events Checklist; LEC), past 30-day symptoms of PTSD/C-PTSD (International Trauma Questionnaire; ITQ), and perceived barriers to drug and alcohol treatment (Barriers to Treatment Inventory; BTI).Results: Ninety-one participants (89.2%) had directly experienced at least one potentially traumatic event, with 24 participants (23.5%) endorsing events in 10 or more categories. Thirty participants (29.4%) met criteria for past 30-day PTSD; of these, 25 (83.3%) also met criteria for C-PTSD. C-PTSD symptom severity was associated with greater perceived barriers due to privacy concerns, lack of treatment availability, and time constraints, but lower barriers relating to perceived absence of a drug problem. PTSD symptom severity was associated with greater perceived barriers due to negative social support and lack of treatment availability. Females reported significantly greater trauma exposure and were more likely to meet PTSD/C-PTSD criteria than males.Discussion: Cumulative trauma exposure and current C-PTSD symptoms were common among people attending a supervised injecting facility. Potentially, these PWID may recognise their need for drug treatment or services despite greater perceived structural and interpersonal barriers to treatment. Further research is needed to identify and address the unique barriers to accessing support services in these settings.

Multispectral imaging aims at recording images in different spectral bands. This is extremely beneficial in diverse discrimination applications, for example in agriculture, recycling or healthcare. One approach for snapshot multispectral imaging, which is capable of recording multispectral videos, is by using camera arrays, where each camera records a different spectral band. Since the cameras are at different spatial positions, a registration procedure is necessary to map every camera to the same view. In this paper, we present a multispectral snapshot image registration with three novel components. First, a cross spectral disparity estimation network is introduced, which is trained on a popular stereo database using pseudo spectral data augmentation. Subsequently, this disparity estimation is used to accurately detect occlusions by warping the disparity map in a layer-wise manner. Finally, these detected occlusions are reconstructed by a learned deep guided neural network, which leverages the structure from other spectral components. It is shown that each element of this registration process as well as the final result is superior to the current state of the art. In terms of PSNR, our registration achieves an improvement of over 3 dB. At the same time, the runtime is decreased by a factor of over 3 on a CPU. Additionally, the registration is executable on a GPU, where the runtime can be decreased by a factor of 113. The source code and the data is available at https://github.com/FAU-LMS/MSIR.

Supervised injecting facilities (SIFs) are designed to reduce the harms associated with injecting drug use and improve access to health and support services for people who need them. The Supervised Injecting Room Cohort Study (SIRX) aims to provide evidence of the effects, including cost-effectiveness, of a SIF embedded within a community health service, the Melbourne Medically Supervised Injecting Room (MSIR), which has a range of integrated harm reduction, health and social support services on-site.

Microbial catabolic activity (MCA) defined as the degrading activity of microorganisms toward various organic compounds for their growth and energy is commonly used to assess soil microbial function potential. For its measure, several methods are available including multi-substrate-induced respiration (MSIR) measurement which allow to estimate functional diversity using selected carbon substrates targeting specific biochemical pathways. In this review, the techniques used to measure soil MCA are described and compared with respect to their accuracy and practical use. Particularly the efficiency of MSIR-based approaches as soil microbial function indicators was discussed by (i) showing their sensitivity to different agricultural practices including tillage, amendments, and cropping systems and (ii) by investigating their relationship with soil enzyme activities and some soil chemical properties (pH, soil organic carbon, cation exchange capacity). We highlighted the potential of these MSIR-based MCA measurements to improve microbial inoculant composition and to determine their potential effects on soil microbial functions. Finally, we have proposed ideas for improving MCA measurement notably through the use of molecular tools and stable isotope probing which can be combined with classic MSIR methods. Graphical abstract describing the interrelation between the different parts and the concepts developed in the review.

Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures. Neuroimaging data are multi-featured and non-linear by nature, and it is a natural way to organise these data as tensors prior to performing automated analyses such as discrimination of neurological disorders like Parkinson's Disease (PD) and Attention Deficit and Hyperactivity Disorder (ADHD). However, the existing approaches are often subject to performance bottlenecks (e.g., conventional feature extraction and deep learning based feature construction), as these can lose the structural information that correlates multiple data dimensions or/and demands excessive empirical and application-specific settings. This study proposes a Deep Factor Learning model on a Hilbert Basis tensor (namely, HB-DFL) to automatically derive latent low-dimensional and concise factors of tensors. This is achieved through the application of multiple Convolutional Neural Networks (CNNs) in a non-linear manner along all possible dimensions with no assumed a priori knowledge. HB-DFL leverages the Hilbert basis tensor to enhance the stability of the solution by regularizing the core tensor to allow any component in a certain domain to interact with any component in the other dimensions. The final multi-domain features are handled through another multi-branch CNN to achieve reliable classification, exemplified here using MRI discrimination as a typical case. A case study of MRI discrimination has been performed on public MRI datasets for discrimination of PD and ADHD. Results indicate that 1) HB-DFL outperforms the counterparts in terms of FIT, mSIR and stability (mSC and umSC) of factor learning; 2) HB-DFL identifies PD and ADHD with an accuracy significantly higher than state-of-the-art methods do. Overall, HB-DFL has significant potentials for neuroimaging data analysis applications with its stability of automatic construction of structural features.

Mass spectrometry imaging (MSI) is a powerful tool that can be used to simultaneously investigate the spatial distribution of different molecules in samples. However, it is difficult to comprehensively analyze complex biological systems with only a single analytical technique due to different analytical properties and application limitations. Therefore, many analytical methods have been combined to extend data interpretation, evaluate data credibility, and facilitate data mining to explore important temporal and spatial relationships in biological systems. Image registration is an initial and critical step for multimodal imaging data fusion. However, the image registration of multimodal images is not a simple task. The property difference between each data modality may include spatial resolution, image characteristics, or both. The image registrations between MSI and different imaging techniques are often achieved indirectly through histology. Many methods exist for image registration between MSI data and histological images. However, most of them are manual or semiautomatic and have their prerequisites. Here, we built MSI Registrar (MSIr), a web service for automatic registration between MSI and histology. It can help to reduce subjectivity and processing time efficiently. MSIr provides an interface for manually selecting region of interests from histological images; the user selects regions of interest to extract the corresponding spectrum indices in MSI data. In the performance evaluation, MSIr can quickly map MSI data to histological images and help pinpoint molecular components at specific locations in tissues. Most registrations were adequate and were without excessive shifts. MSIr is freely available at https://msir.cmdm.tw and https://github.com/CMDM-Lab/MSIr.

Accurate segmentation of mandibular canals in lower jaws is important in dental implantology. Medical experts manually determine the implant position and dimensions from 3D CT images to avoid damaging the mandibular nerve inside the canal. In this paper, we propose a novel dual-stage deep learning-based scheme for the automatic segmentation of the mandibular canal. In particular, we first enhance the CBCT scans by employing the novel histogram-based dynamic windowing scheme, which improves the visibility of mandibular canals. After enhancement, we designed 3D deeply supervised attention UNet architecture for localizing the Volumes Of Interest (VOIs), which contain the mandibular canals (i.e., left and right canals). Finally, we employed the Multi-Scale input Residual UNet (MSiR-UNet) architecture to segment the mandibular canals using VOIs accurately. The proposed method has been rigorously evaluated on 500 and 15 CBCT scans from our dataset and from the public dataset, respectively. The results demonstrate that our technique improves the existing performance of mandibular canal segmentation to a clinically acceptable range. Moreover, it is robust against the types of CBCT scans in terms of field of view.

The salt metathesis reactions of the yttrium methanediide iodide complex [Y(BIPM)(I)(THF)2] (BIPM = {C(PPh2NSiMe3)2}) with the group 1 silanide ligand-transfer reagents MSiR3 (M = Na, R3 = tBu2Me or tBu3; M = K, R3 = (SiMe3)3) gave the yttrium methanediide silanide complexes [Y(BIPM)(SitBu2Me)(THF)] (1), [Y(BIPM)(SitBu3)(THF)] (2), and [Y(BIPM){Si(SiMe3)3}(THF)] (3). Complexes 1-3 provide rare examples of structurally authenticated rare earth metal-silicon bonds and were characterized by single-crystal X-ray diffraction, multinuclear NMR and ATR-IR spectroscopies, and elemental analysis. Density functional theory calculations were performed on 1-3 to probe their electronic structures further, revealing predominantly ionic Y-Si bonding. The computed Y-Si bonds show lower covalency than Y═C bonds, which are in turn best represented by Y+-C- dipolar forms due to the strong σ-donor properties of the silanide ligands investigated; these observations are in accord with experimentally obtained 13C{1H} and 29Si{1H} NMR data for 1-3 and related Y(III) BIPM alkyl complexes in the literature. Preliminary reactivity studies were performed, with complex 1 treated separately with benzophenone, azobenzene, and N,N'-dicyclohexyl-carbodiimide. 29Si{1H} and 31P{1H} NMR spectra of these reaction mixtures indicated that 1,2-migratory insertion of the unsaturated substrate into the Y-Si bond is favored, while for the latter substrate, a [2 + 2]-cycloaddition reaction also occurs at the Y═C bond to afford [Y{C(PPh2NSiMe3)2[C(NCy)2]-κ4C,N,N',N'}{C(NCy)2(SitBu2Me)-κ2N,N'}] (4); these reactivity profiles complement and contrast with those of Y(III) BIPM alkyl complexes.