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Mass housing (MH) buildings from the ‘60s to the ‘80s, after decades of continuous use and inadequate maintenance, cause several sustainability issues and need rehabilitation. Most current conventional rehabilitation approaches have long been criticized because they consider neither sustainability requirements nor contemporary building standards. In this regard, the assessment and selection of suitable approaches for rehabilitating MH interiors, from a holistic sustainability viewpoint, is a crucial issue that faces several challenges since this is a multidisciplinary and multi-criteria process. On this subject, the present study develops a novel MCDM model based on the MIVES and Delphi methods for the holistic sustainability assessment of MHs’ interior rehabilitation considering involved stakeholders' preferences. This MIVES-Delphi model relies on a comprehensive literature review, experts' seminars, on-site surveying, LCA, BIM, user/expert-based questionnaires, bias reduction, and sensitivity analysis. The model was first applied in the Ekbatan MH case study, the largest MH in Iran, to assess the sustainability of four different interior rehabilitation scenarios, including three common rehabilitation scenarios plus an innovative one. Consequently, the new model was validated, and the most sustainable scenario was selected. The specific results regarding these scenarios' evaluation disclosed that none of the common rehabilitation scenarios could either meet the minimum sustainability target value or serve as proper solutions for MH's interior rehabilitation. Contrarily, the fourth scenario, with a global sustainability index of 0.71, could meet the standard minimum target. This outcome provides a possibility for innovative rehabilitation processes to have positive effects on increasing the sustainability performance in MH buildings.

The presence of microbiome in the blood samples of healthy individuals has been addressed. However, no information can be found on the healthy human blood microbiome of Iranian subjects. The current study is thus aimed to investigate the existence of bacteria or bacterial DNA in healthy individuals.Blood samples of healthy subjects were incubated in BHI broth at 37 °C for 72 h. The 16S rRNA PCR and sequencing were performed to analyze bacterial isolates. The 16S rRNA PCR was directly carried out on DNA samples extracted from the blood of healthy individuals. Next generation sequencing (NGS) was conducted on blood samples with culture-positive results.Fifty blood samples were tested, and six samples were positive by culture as confirmed by Gram staining and microscopy. The obtained 16S rRNA sequences of cultured bacterial isolates revealed the presence of Bacilli and Staphylococcus species by clustering in the GeneBank database (≥97% identity). The 16S rRNA gene sequencing results of one non-cultured blood specimen showed the presence of Burkholderia. NGS results illustrated the presence of Romboutsia, Lactobacillus, Streptococcus, Bacteroides, and Staphylococcus in the blood samples of positive cultures.The dormant blood microbiome of healthy individuals may give the idea that the steady transfer of bacteria into the blood does not necessarily lead to sepsis. However, the origins and identities of blood-associated bacterial rDNA sequences need more evaluation in the healthy population.

Video generation models have become increasingly popular in the last few years, however the standard 2D architectures used today lack natural spatio-temporal modelling capabilities. In this paper, we present a network architecture for video generation that models spatio-temporal consistency without resorting to costly 3D architectures. The architecture facilitates information exchange between neighboring time points, which improves the temporal consistency of both the high level structure as well as the low-level details of the generated frames. The approach achieves state-of-the-art quantitative performance, as measured by the inception score on the UCF-101 dataset as well as better qualitative results. We also introduce a new quantitative measure (S3) that uses downstream tasks for evaluation. Moreover, we present a new multi-label dataset MaisToy, which enables us to evaluate the generalization of the model., 14 pages, 15 figures

Background: Hyperbilirubinemia is one of the most common problems in infancy in the world. The aim of this study was to compare the effect of foot therapy and body massage on physiological indicators and bilirubin levels in infants under phototherapy. Methods: This study was a randomized clinical trial that conducted on 51 infants with jaundice at Imam Hussein Pediatrics’ Medical Center, Goldis Hospital in Isfahan (Iran). The samples randomly assigned to first intervention (reflexology), second intervention (body massage) and control groups using the block randomization. In the reflexology group, the relevant area on each foot was massaged for 15 min in a relaxed position. In the massage body group, the limbs were massaged with circular motion. The massage was performed once a day for 15 min and the control group did not receive any intervention. Physiological indicators were monitored using vital sign monitoring tools, and blood bilirubin levels were measured photo metrically (intravenous blood samples from the wrist). Finally, the data were analyzed using descriptive and inferential statistics and SPSS software version 18. Results: The results showed that before the intervention there was no significant difference in the mean of physiological indicators and bilirubin level between the three groups (p > 0.05), but after the intervention the average percentage of arterial oxygen saturation and bilirubin levels were significantly improved in the intervention groups compared to the control group (p 0.05). Conclusion: The results showed that reflexology and massage therapy can be effective in improving the condition of physiological indicators and blood bilirubin levels.

Background & Aims of the Study: Rhodococci according to possess large genome, their active metabolism and survival under extreme conditions, are highly regarded for biodegradation and bioremediation of different pollutants especially heavy metals in the environment. Biofilms are more resistant to heavy metals than free-swimming organisms. The aim of this study was biofilm formation of two native Rhodococcus strains (Rhodococcus rhodochrous and Rhodococcus rhodnii) and comparative evaluation effects of heavy metals such as lead, copper, zinc, chromium and cadmium in various concentrations against biofilm and planktonic cells that carried out at the first time. Materials & Methods: Bacterial strains used in this investigation were isolated from agricultural soils in Qom, Iran. Biofilm formation was performed at 30 °C and 37 °C in tryptic soy broth (TSB) and brain heart infusion (BHI) broth, at 24,48,72 and 96 hours in 96-well microplate. Minimum inhibitory concentration (MIC) of heavy metals in various concentrations for planktonic cells was determined, using microdilution and macrodilution methods. Minimum biofilm inhibitory concentration (MBIC) and Minimum biofilm eradicating concentration (MBEC) in various concentrations of heavy metals for biofilm cells were evaluated by microdilution method. The result of this study confirmed, using scanning electron microscope (SEM). Results: The best condition for biofilm formation of native R.rhodochrous isolate was determined after incubation at 37°C in BHI broth, at 96 hours and the best condition for biofilm formation of native R.rhodnii isolate was determined at 30°C in BHI broth, at 96 hours. MIC of heavy metals for planktonic cells of isolates for cadmium, zinc and lead was 8 mM and assayed for copper and chromium respectively 4 and 1 mM. MBEC of heavy metals for biofilm cells of isolates for cadmium, zinc and lead was 16 mM and assayed for copper and chromium respectively 8 and 4 mM. The results of present study showed biofilm cells of native Rhodococcus isolates were 2 times more resistance to lead, copper, zinc and cadmium than planktonic cells while biofilm cells were 4 times more resistance than planktonic cells to chromium. Conclusions: Biofilm formation of R.rhodochrous and R.rhodnii and their high resistance to various concentrations of heavy metals especially cadmium show that inoculation of these native Rhodococcus isolates to contaminated agricultural soils with heavy metals, have an effective role for bioremediation.

Objective Exercise-induced changes in the neurotrophic factors and the physical function are essential for the rehabilitation of the multiple sclerosis (MS) persons. The aim of this study was investigating of effectiveness of the combined functional training (CFT) on brain-derived neurotrophic factor (BDNF), insulin-like growth factor-1 (IGF-1), and their association with health-related fitness in the MS women. Design Twenty women with relapsing-remitting MS randomly assigned to CFT and control (CON) groups. The CFT consisted of 8 weeks (3 days per week) rhythmic aerobic exercise, TRX suspension training, elastic band training, and bodyweight training. BDNF, IGF-1, and health-related fitness components were assessed before and after the intervention. Results There was no significant difference in BDNF level between the CFT and the CON group. In contrast, IGF-1, walking speed, and strength of the right- and left-hand was significantly increased in the CFT compared with the CON group. Furthermore, there was a significant and positive correlation between IGF-1 and some fitness components. Conclusions The findings indicated that CFT might a useful training mode in the rehabilitation of the MS women. CFT improved IGF-1 level that is a neuroprotective agent in MS. Positive and significant association between IGF-1 and some health-related fitness components indicates of the importance of IGF-1 in the rehabilitation of the MS persons than BDNF.

The state of the art in video understanding suffers from two problems: (1) The major part of reasoning is performed locally in the video, therefore, it misses important relationships within actions that span several seconds. (2) While there are local methods with fast per-frame processing, the processing of the whole video is not efficient and hampers fast video retrieval or online classification of long-term activities. In this paper, we introduce a network architecture (https://github.com/mzolfaghari/ECO-efficient-video-understanding) that takes long-term content into account and enables fast per-video processing at the same time. The architecture is based on merging long-term content already in the network rather than in a post-hoc fusion. Together with a sampling strategy, which exploits that neighboring frames are largely redundant, this yields high-quality action classification and video captioning at up to 230 videos per second, where each video can consist of a few hundred frames. The approach achieves competitive performance across all datasets while being 10\(\times \) to 80\(\times \) faster than state-of-the-art methods.

General human action recognition requires understanding of various visual cues. In this paper, we propose a network architecture that computes and integrates the most important visual cues for action recognition: pose, motion, and the raw images. For the integration, we introduce a Markov chain model which adds cues successively. The resulting approach is efficient and applicable to action classification as well as to spatial and temporal action localization. The two contributions clearly improve the performance over respective baselines. The overall approach achieves state-of-the-art action classification performance on HMDB51, J-HMDB and NTU RGB+D datasets. Moreover, it yields state-of-the-art spatio-temporal action localization results on UCF101 and J-HMDB., 10 pages, 7 figures, ICCV 2017 submission

Recent work has shown good recognition results in 3D object recognition using 3D convolutional networks. In this paper, we show that the object orientation plays an important role in 3D recognition. More specifically, we argue that objects induce different features in the network under rotation. Thus, we approach the category-level classification task as a multi-task problem, in which the network is trained to predict the pose of the object in addition to the class label as a parallel task. We show that this yields significant improvements in the classification results. We test our suggested architecture on several datasets representing various 3D data sources: LiDAR data, CAD models, and RGB-D images. We report state-of-the-art results on classification as well as significant improvements in precision and speed over the baseline on 3D detection., BMVC'17 version. Added some experiments + auto-alignment of Modelnet40

Recent studies have demonstrated that high-level semantics in data can be captured using sparse representation. In this paper, we propose an approach to human body pose estimation in static images based on sparse representation. Given a visual input, the objective is to estimate 3D human body pose using feature space information and geometrical information of the pose space. On the assumption that each data point and its neighbors are likely to reside on a locally linear patch of the underlying manifold, our method learns the sparse representation of the new input using both feature and pose space information and then estimates the corresponding 3D pose by a linear combination of the bases of the pose dictionary. Two strategies for dictionary construction are presented: (i) constructing the dictionary by randomly selecting the frames of a sequence and (ii) selecting specific frames of a sequence as dictionary atoms. We analyzed the effect of each strategy on the accuracy of pose estimation. Extensive experiments on datasets of various human activities show that our proposed method outperforms state-of-the-art methods.

While recent supervised dictionary learning methods have attained promising results on the classification tasks, their performance depends on the availability of the large labeled datasets. However, in many real world applications, accessing to sufficient labeled data may be expensive and/or time consuming, but its relatively easy to acquire a large amount of unlabeled data. In this paper, we propose a probabilistic framework for discriminative dictionary learning which uses both the labeled and unlabeled data. Experimental results demonstrate that the performance of the proposed method is significantly better than the state of the art dictionary based classification methods.

In this paper, a multiple metric learning scheme for human pose estimation from a single image is proposed. Here, we focused on a big challenge of this problem which is; different 3D poses might correspond to similar inputs. To address this ambiguity, some Euclidean distance based approaches use prior knowledge or pose model that can work properly, provided that the model parameters are being estimated accurately. In the proposed method, the manifold of data is divided into several clusters and then, we learn a new metric for each partition by utilizing not only input features, but also their corresponding poses. The manifold clustering allows the decomposition of multiple manifolds into a set of manifolds that are less complex. Furthermore, the input data could be mapped to a new space where the ambiguity problem is minimized. Our guiding principle for learning the distance metrics is to preserve the manifold structure of the input data. The proposed method employs Tikhonov regularization technique to obtain a smooth estimation of the labels. Experiments on the data set of human pose estimation demonstrate that the proposed multiple metric learning consistently outperforms single-metric learning method across different activities by a wide margin.

Background: Diabetes mellitus is the most prevalent metabolic disorder worldwide with many subsequent medical complications. An important complication of diabetes is microalbuminuria which is a major indication for development of diabetic nephropathy. The prevalence of diabetic nephropathy in type 2 diabetic patients has been estimated to be 47%. Since the disorder initiates with microalbuminuria, its early detection and prevention are vital. On the other hand, helicobacter pylori infection is more common in diabetic patients due to their low immune tolerance. The vacA positive genotype is reported to be effective in microalbuminuria development in type 2 diabetic patients. In this study, we investigated the relation between H. pylori vacA and microalbuminuria in type 2 diabetic patients. Methods: This study included 88 type 2 diabetic patients among whom 60 had microalbuminuria. Stool samples were collected from all patients and DNA was extracted using QiaAmp Stool DNA Extraction Minikit. In order to detect H. pylori infection, a nested polymerase chain reaction (PCR) protocol was developed and all H. pylori positive samples were genotyped for cagA positivity. Findings: Overall, 16 patients (18.2%) were detected to be H. pylori positive out of whom 12 had microalbuminuria (75%). VacA positivity was detected in one patient without microalbuminuria. Conclusion: We could not establish a relationship between vacA positivity and microalbuminuria in the studied type 2 diabetic patients. Keywords: Helicobacter pylori, VacA, Polymerase chain reaction, Type 2 diabetes, Microalbuminuria

Background: Diabetes mellitus is the most prevalent metabolic disorder worldwide with many subsequent medical complications. An important complication of diabetes is microalbuminuria which is an important indication for development of diabetic nephropathy. The prevalence of diabetic nephropathy in patients with type 2 diabetes has been estimated to be 47%. Since the disorder initiates with microalbuminuria, early detection and prevention are of vital importance. Helicobacter pylori infection is more prevalent in diabetic patients due to their low immune tolerance. The cagA positive genotype is reported to be effective in microalbuminuria development in type 2 diabetic patients. In this study, we investigated the relation of H. pylori cagA virulence factor with microalbuminuria in patients with type 2 diabetes. Methods: This study included 88 patients with type 2 diabetes. Microalbuminuria was present in 60 subjects and absent in the other 28. Stool samples were collected from all patients and DNA was extracted using QIAamp DNA Stool Mini Kit. In order to detect H. pylori infection, a nested polymerase chain reaction (PCR) protocol was developed and all H. pylori positive samples were genotyped for cagA positivity. Findings: Overall, 16 patients (18.2%) were detected to be H. pylori positive out of whom 12 had microalbuminuria (75%) and 4 (25%) did not. CagA positivity was detected in 3 subjects who belonged to the group with microalbuminuria. Conclusion: In the population studied and the sample size used, no correlation was found between cagA gene and microalbuminuria in patients with type 2 diabetes. Keywords: Helicobacter pylori, CagA genotype, Microalbuminuria, Type 2 diabetes, Stool DNA

Many real-world video-text tasks involve different levels of granularity, such as frames and words, clip and sentences or videos and paragraphs, each with distinct semantics. In this paper, we propose a Cooperative hierarchical Transformer (COOT) to leverage this hierarchy information and model the interactions between different levels of granularity and different modalities. The method consists of three major components: an attention-aware feature aggregation layer, which leverages the local temporal context (intra-level, e.g., within a clip), a contextual transformer to learn the interactions between low-level and high-level semantics (inter-level, e.g. clip-video, sentence-paragraph), and a cross-modal cycle-consistency loss to connect video and text. The resulting method compares favorably to the state of the art on several benchmarks while having few parameters. All code is available open-source at https://github.com/gingsi/coot-videotext, Comment: 27 pages, 5 figures, 19 tables. To be published in the 34th conference on Neural Information Processing Systems (NeurIPS 2020). The first two authors contributed equally to this work