Deep Multimodal Speaker Naming

Automatic speaker naming is the problem of localizing as well as identifying each speaking character in a TV/movie/live show video. This is a challenging problem mainly attributes to its multimodal nature, namely face cue alone is insufficient to achieve good performance. Previous multimodal approaches to this problem usually process the data of different modalities individually and merge them using handcrafted heuristics. Such approaches work well for simple scenes, but fail to achieve high performance for speakers with large appearance variations. In this paper, we propose a novel convolutional neural networks (CNN) based learning framework to automatically learn the fusion function of both face and audio cues. We show that without using face tracking, facial landmark localization or subtitle/transcript, our system with robust multimodal feature extraction is able to achieve state-of-the-art speaker naming performance evaluated on two diverse TV series. The dataset and implementation of our algorithm are publicly available online

A ribose-functionalized NAD+ with unexpected high activity and selectivity for protein poly-ADP-ribosylation.

Nicotinamide adenine dinucleotide (NAD+)-dependent ADP-ribosylation plays important roles in physiology and pathophysiology. It has been challenging to study this key type of enzymatic post-translational modification in particular for protein poly-ADP-ribosylation (PARylation). Here we explore chemical and chemoenzymatic synthesis of NAD+ analogues with ribose functionalized by terminal alkyne and azido groups. Our results demonstrate that azido substitution at 3'-OH of nicotinamide riboside enables enzymatic synthesis of an NAD+ analogue with high efficiency and yields. Notably, the generated 3'-azido NAD+ exhibits unexpected high activity and specificity for protein PARylation catalyzed by human poly-ADP-ribose polymerase 1 (PARP1) and PARP2. And its derived poly-ADP-ribose polymers show increased resistance to human poly(ADP-ribose) glycohydrolase-mediated degradation. These unique properties lead to enhanced labeling of protein PARylation by 3'-azido NAD+ in the cellular contexts and facilitate direct visualization and labeling of mitochondrial protein PARylation. The 3'-azido NAD+ provides an important tool for studying cellular PARylation

LFQ-Based Peptide and Protein Intensity Differential Expression Analysis

Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings

DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior

We present DiffBIR, which leverages pretrained text-to-image diffusion models for blind image restoration problem. Our framework adopts a two-stage pipeline. In the first stage, we pretrain a restoration module across diversified degradations to improve generalization capability in real-world scenarios. The second stage leverages the generative ability of latent diffusion models, to achieve realistic image restoration. Specifically, we introduce an injective modulation sub-network -- LAControlNet for finetuning, while the pre-trained Stable Diffusion is to maintain its generative ability. Finally, we introduce a controllable module that allows users to balance quality and fidelity by introducing the latent image guidance in the denoising process during inference. Extensive experiments have demonstrated its superiority over state-of-the-art approaches for both blind image super-resolution and blind face restoration tasks on synthetic and real-world datasets. The code is available at https://github.com/XPixelGroup/DiffBIR

The experiences of professional nurses working in district hospitals in the Western Cape metropole, where 72-hour assessments are conducted

Magister Curationis - MCurBackground: The integration of mental health into primary health care meant that patients were admitted into a less restrictive environment. They received treatment for mental illness in their communities, therefore, averting unnecessary hospitalisation in psychiatric hospitals. However, given that patients with mental illnesses were admitted to district hospitals as involuntary mental health care users (MHCUs), this setting was purported to be fraught with challenges for both staff and patients. Aim and objectives: The aim of this study was to explore and describe the experiences of professional nurses, working at selected district hospitals in the Western Cape metropole, where 72-hour assessments of involuntary mental health care users are conducted. The objectives of this study were to determine how the 72-hour unit functioned in the general ward, the experiences of professional nurses regarding the integration of the 72-hour assessment units in the general ward and suggested improvements. Methodology: A qualitative research approach, with a descriptive phenomenological design, was used to collect data through semi-structured interviews from eight (8) professional nurses, working in the two selected district hospitals in the Cape Town metropole area. Purposive sampling was employed to select the participants. Data were analysed using Tesch’s method of qualitative data analysis. Four themes, namely, patient management process affected the functioning of the ward, patient management challenges in rendering patient care, burden of caring on the Self, and staff and patient support to create a therapeutic environment, emerged during data analysis, which encapsulated the nurse's experience of working in 72-hour assessment units in selected district hospitals. Findings: The findings of this revealed that the district hospitals were ill prepared for the admission of involuntary mental health care users. There were challenges, in terms of resources, namely, infrastructure to create a therapeutic environment, knowledgeable and skilled staff to care for the MHCUs. The MHCUs were contained in the district hospitals for longer than was legislated, rather than receiving therapeutic interventions at psychiatric facilities. Needs were identified to improve the functioning of the 72-hour assessment units, which included education and training of personnel, Discussion: The non-therapeutic environment had a negative impact on the staff working in the 72-hour assessment units. Nursing staff were burdened with caring for patients in an environment where they, as well as the MHCUs, were stigmatised due to the diagnosis of mental illness. However, the participants internalised their own experiences, as they prioritised the MHCUs well-being. The findings supported previous studies, which revealed that the objectives of the Mental Health Care Act (No. 17 of 2002), which supported the integration of mental health into primary health care, were not realised after more than a decade of implementation. Recommendations: Given the limited scope of this thesis, replications of this study in other district hospitals are recommended, in order to ascertain whether the objectives of the MHCA (2002), regarding 72-hour assessments, have been realised. A therapeutic environment, which includes infrastructure and resources to ensure that MHCUs receive care, treatment and rehabilitation within the district hospitals, is required. The recruitment and retention of adequate, skilled permanent staff is crucial, to ensure that MHCUs receive care, treatment and rehabilitation. Finally, the training and education of all personnel (including security) working in the selected district hospitals should be mandatory, in order to address patient care and stigma related to mental illness

Predicting outcomes in patients undergoing pancreatectomy using wearable technology and machine learning: Prospective cohort study

BACKGROUND: Pancreatic cancer is the third leading cause of cancer-related deaths, and although pancreatectomy is currently the only curative treatment, it is associated with significant morbidity. OBJECTIVE: The objective of this study was to evaluate the utility of wearable telemonitoring technologies to predict treatment outcomes using patient activity metrics and machine learning. METHODS: In this prospective, single-center, single-cohort study, patients scheduled for pancreatectomy were provided with a wearable telemonitoring device to be worn prior to surgery. Patient clinical data were collected and all patients were evaluated using the American College of Surgeons National Surgical Quality Improvement Program surgical risk calculator (ACS-NSQIP SRC). Machine learning models were developed to predict whether patients would have a textbook outcome and compared with the ACS-NSQIP SRC using area under the receiver operating characteristic (AUROC) curves. RESULTS: Between February 2019 and February 2020, 48 patients completed the study. Patient activity metrics were collected over an average of 27.8 days before surgery. Patients took an average of 4162.1 (SD 4052.6) steps per day and had an average heart rate of 75.6 (SD 14.8) beats per minute. Twenty-eight (58%) patients had a textbook outcome after pancreatectomy. The group of 20 (42%) patients who did not have a textbook outcome included 14 patients with severe complications and 11 patients requiring readmission. The ACS-NSQIP SRC had an AUROC curve of 0.6333 to predict failure to achieve a textbook outcome, while our model combining patient clinical characteristics and patient activity data achieved the highest performance with an AUROC curve of 0.7875. CONCLUSIONS: Machine learning models outperformed ACS-NSQIP SRC estimates in predicting textbook outcomes after pancreatectomy. The highest performance was observed when machine learning models incorporated patient clinical characteristics and activity metrics

Spatial-temporal diffusion model of aggregated infectious diseases based on population life characteristics: a case study of COVID-19

Outbreaks of infectious diseases pose significant threats to human life, and countries around the world need to implement more precise prevention and control measures to contain the spread of viruses. In this study, we propose a spatial-temporal diffusion model of infectious diseases under a discrete grid, based on the time series prediction of infectious diseases, to model the diffusion process of viruses in population. This model uses the estimated outbreak origin as the center of transmission, employing a tree-like structure of daily human travel to generalize the process of viral spread within the population. By incorporating diverse data, it simulates the congregation of people, thus quantifying the flow weights between grids for population movement. The model is validated with some Chinese cities with COVID-19 outbreaks, and the results show that the outbreak point estimation method could better estimate the virus transmission center of the epidemic. The estimated location of the outbreak point in Xi'an was only 0.965 km different from the actual one, and the results were more satisfactory. The spatiotemporal diffusion model for infectious diseases simulates daily newly infected areas, which effectively cover the actual patient infection zones on the same day. During the mid-stage of viral transmission, the coverage rate can increase to over 90%, compared to related research, this method has improved simulation accuracy by approximately 18%. This study can provide technical support for epidemic prevention and control, and assist decision-makers in developing more scientific and efficient epidemic prevention and control policies

Association of the Synapse-Associated Protein 97 (SAP97) Gene Polymorphism With Neurocognitive Function in Schizophrenic Patients

The SAP97 gene is located in the schizophrenia susceptibility locus 3q29, and it encodes the synaptic scaffolding protein that interacts with the N-methyl-D-aspartate (NMDA) receptor, which is presumed to be dysregulated in schizophrenia. In this study, we genotyped a single-nucleotide polymorphism (SNP) (rs3915512) in the SAP97 gene in 1114 patients with schizophrenia and 1036 healthy-matched controls in a Han Chinese population through the improved multiplex ligation detection reaction (imLDR) technique. Then, we analyzed the association between this SNP and the patients' clinical symptoms and neurocognitive function. Our results showed that there were no significant differences in the genotype and allele frequencies between the patients and the controls for the rs3915512 polymorphism. However, patients with the rs3915512 polymorphism TT genotype had higher neurocognitive function scores (list learning scores, symbol coding scores, category instances scores and controlled oral word association test scores) than the subjects with the A allele (P = 4.72 × 10−5, 0.027, 0.027, 0.013, respectively). Our data are the first to suggest that the SAP97 rs3915512 polymorphism may affect neurocognitive function in patients with schizophrenia