Miracle-dz/UniFAR-FTUs huggingface dataset:
Supported Internet-Delivered Cognitive Behavior Treatment for Adults with Severe Depressive Symptoms: A Secondary Analysis

Background Depression is a highly prevalent mental health issue that exacts significant economic, societal, personal, and interpersonal costs. Innovative internet-delivered interventions have been designed to increase accessibility to and cost-effectiveness of treatments. These treatments have mainly targeted mild to moderate levels of depression. The increased risk associated with severe depression, particularly of suicidal ideation often results in this population being excluded from research studies. As a result, the effectiveness of internet-delivered cognitive behavioral therapy (iCBT) in more severely depressed cohorts is less researched. Objective The aim of this study is to examine the effect of iCBT on symptoms of severe depression, comorbid symptoms of anxiety, and levels of work and social functioning. Methods Retrospective consent was provided by participants with elevated scores (>28 severe depression symptoms) on the Beck Depression Inventory (BDI-II) who accessed an iCBT intervention (Space from Depression) with support for up to 8 weeks. Data were collected at baseline, posttreatment, and 3-month follow-up on the primary outcome (BDI-II), and secondary outcomes (the Generalized Anxiety Disorder-7 and the Work and Social Adjustment Scale). Results A significant change was observed on all measures between pre- and postmeasurement and maintained at 3-month follow-up. Clinical improvement was observed for participants on the BDI-II from pre- to postmeasurement, and suicidal ideation also reduced from pre- to postmeasurement. Conclusions Users of Space from Depression with symptoms of severe depression were found to have decreased symptoms of depression and anxiety and increased levels of work and social functioning. The intervention also demonstrated its potential to decrease suicidal ideation. Further investigation is required to determine why some individuals improve, and others do not. iCBT may have the potential to be used as an adjunct treatment for severe depression symptoms, but participants may require further treatment if they receive iCBT as a standalone intervention. Although promising, further research incorporating control groups is needed to support the utility of Space from Depression for use in or as an adjunct to treatment for severe depression.
A Secure Three-Factor Multiserver Authentication Protocol against the Honest-But-Curious Servers

Three-factor multiserver authentication protocols become a prevalence in recent years. Among these protocols, almost all of them do not involve the registration center into the authentication process. To improve the protocol’s efficiency, a common secret key is shared among all severs, which leads to a serious weakness; i.e., we find that these protocols cannot resist the passive attack from the honest-but-curious servers. This paper takes Wang et al.’s protocol as an example, to exhibit how an honest-but-curious server attacks their protocol. To remedy this weakness, a novel three-factor multiserver authentication protocol is presented. By introducing the registration center into the authentication process, the new protocol can resist the passive attack from the honest-but-curious servers. Security analyses including formal and informal analyses are given, demonstrating the correctness and validity of the new protocol. Compared with related protocols, the new protocol possesses more secure properties and more practical functionalities than others at a relatively low computation cost and communication cost.
Zebra diel migrations reduce encounter risk with lions at night

Diel migrations (DM; back and forth diel movements along an ecological gradient) undertaken by prey to avoid predators during the day have been demonstrated in many taxa in aquatic ecosystems. In terrestrial ecosystems, prey often shift between various vegetation types whose cover determine their vulnerability (i.e., likelihood of being killed when attacked). We conceptualized that in terrestrial ecosystems, DM could also occur and that the contribution of DM and shifts in vegetation cover use in reducing predation risk should depend upon the predator behaviour and the correlation between encounter risk and vulnerability across vegetation types. We further hypothesized that when the predator distribution is predictable, terrestrial prey could have evolved DM strategies taking them away from the predator when it is active or efficient. We investigated whether plains zebras Equus quagga perform DM in Hwange National Park (Zimbabwe). There, zebras can forage in large patches of open grasslands located near waterholes where they can also easily detect predators. However, they are there at high risk of encountering their main predator, lions Panthera leo, especially at night. We found out that zebras employ a DM anti-predator strategy. Zebras forage near waterholes during the day but move away from them at sunset, when lions become active. We demonstrated that this DM, occurring over a few kilometres, dramatically reduces their night-time risk of encountering lions, which generally remain close to waterholes. Zebra changes in night-time selection for vegetation cover types reduced their risk of encountering lions much less. This may arise from a trade-off between encounter risk and vulnerability across vegetation types, with zebras favouring low vulnerability once DM has reduced encounter risk. In summary, here we (a) quantify, in a terrestrial system, the effect of a predator-induced DM on the likelihood of encountering a predator and (b) distinguish the effects of the DM on encounter risk from those related to day/night changes in selection for vegetation types. We discuss how prey partition their risk between encounter risk and habitat-driven vulnerability and why it is likely critical to understand the emergence of anti-predator behavioural strategies.
Preparation of a Defined Gluten Hydrolysate for Diagnosis and Clinical Investigations of Wheat Hypersensitivities

Gluten is the trigger for celiac disease (CD), non-celiac gluten/wheat sensitivity (NCGS), and wheat allergy. An oral food challenge is often needed for diagnosis, but there are no standardized gluten challenge materials with known composition available. To fill this gap, two materials, commercially available gluten and a food-grade gluten hydrolysate (pepgluten), were extensively characterized. Pepgluten was prepared from gluten by incubation with a pepsin dietary supplement and acetic acid at 37 °C for 120 min. The components of pepgluten were crude protein (707 mg/g), starch (104 mg/g), water (59 mg/g), fat (47 mg/g), dietary fiber (41 mg/g) and ash (11 mg/g). The protein/peptide fraction of pepgluten (1 g) contained equivalents derived from 369 mg gliadins and 196 mg glutenins, resulting in 565 mg total gluten equivalents, 25 mg albumins/globulins, 22 mg α-amylase/trypsin inhibitors and 48 mg pepsin capsule proteins. The slightly acidic, dough-like smell and bitter taste of pepgluten could be completely camouflaged in multivitamin juice with bitter lemon, grapefruit juice, or vegetable and fruit smoothies. Thus, pepgluten met the criteria for placebo-controlled challenges (active and placebo materials are identical regarding appearance, taste, smell, and texture) and is appropriate as a standard preparation for the oral food challenge and clinical investigations to study wheat hypersensitivities.
Generative Ensembles for Robust Anomaly Detection

Deep generative models are capable of learning probability distributions over large, high-dimensional datasets such as images, video and natural language. Generative models trained on samples from $p(x)$ ought to assign low likelihoods to out-of-distribution (OoD) samples from $q(x)$, making them suitable for anomaly detection applications. We show that in practice, likelihood models are themselves susceptible to OoD errors, and even assign large likelihoods to images from other natural datasets. To mitigate these issues, we propose Generative Ensembles, a model-independent technique for OoD detection that combines density-based anomaly detection with uncertainty estimation. Our method outperforms ODIN and VIB baselines on image datasets, and achieves comparable performance to a classification model on the Kaggle Credit Fraud dataset.
Crystal structures of green fluorescent protein with the unnatural amino acid 4-nitro-L-phenylalanine.

The X-ray crystal structures of two superfolder green fluorescent protein (sfGFP) constructs containing a genetically incorporated spectroscopic reporter unnatural amino acid, 4-nitro-L-phenylalanine (pNO2F), at two unique sites in the protein have been determined. Amber codon-suppression methodology was used to site-specifically incorporate pNO2F at a solvent-accessible (Asp133) and a partially buried (Asn149) site in sfGFP. The Asp133pNO2F sfGFP construct crystallized with two molecules per asymmetric unit in space group P3221 and the crystal structure was refined to 2.05 Å resolution. Crystals of Asn149pNO2F sfGFP contained one molecule of sfGFP per asymmetric unit in space group P4122 and the structure was refined to 1.60 Å resolution. The alignment of Asp133pNO2F or Asn149pNO2F sfGFP with wild-type sfGFP resulted in small root-mean-square deviations, illustrating that these residues do not significantly alter the protein structure and supporting the use of pNO2F as an effective spectroscopic reporter of local protein structure and dynamics.
Concept Learning in Engineering based on Refinement Operator

Semantic interoperability has been acknowledged as a challenge for industrial engineering due to the heterogeneity of data models in the involved software tools. In this paper, we show how to learn declarative class definitions of engineering objects in the XML-based data format AutomationML (AML). Specifically, we transform AML document to the description logic OWL 2 DL and use the DL-Learner framework to learn the concepts of named classes. Moreover, we extend the ALC refinement operator in DL-Learner by exploiting the syntax specification of AML and show significant better learning performance.
Joint On-line Learning of a Zero-shot Spoken Semantic Parser and a Reinforcement Learning Dialogue Manager

Despite many recent advances for the design of dialogue systems, a true bottleneck remains the acquisition of data required to train its components. Unlike many other language processing applications, dialogue systems require interactions with users, therefore it is complex to develop them with pre-recorded data. Building on previous works, on-line learning is pursued here as a most convenient way to address the issue. Data collection, annotation and use in learning algorithms are performed in a single process. The main difficulties are then: to bootstrap an initial basic system, and to control the level of additional cost on the user side. Considering that well-performing solutions can be used directly off the shelf for speech recognition and synthesis, the study is focused on learning the spoken language understanding and dialogue management modules only. Several variants of joint learning are investigated and tested with user trials to confirm that the overall on-line learning can be obtained after only a few hundred training dialogues and can overstep an expert-based system.
Decision Science: A New Hope

Decision science is an area of enquiry that crosses many disciplines, from psychology to economics, each with their own perspective of decision-making. Traditionally, mathematicians have envisaged decision-making as a purely rational endeavor, whereas psychologists and behavioral economists have critiqued this narrative and suggested that cognitive short cuts are the real mechanisms behind how decisions are made. However, contemporary dual process theorists argue that two systems of the mind exist: system 1 (intuitive decision-making) and system 2 (rational decision-making). The current review will present a relatively new metaphor for decision-making: the unified threshold model. This model is a global approach to decision-making which allows both intuitive and rational decision-making processes to be explained in a more flexible manner than the dual process model. This review will introduce the reader to different types of threshold models (counter and diffusion), their assumptions, and their ability to explain decision-making behavior. Implications and future research will also be discussed. In summary, the aim of this review is to highlight that a rather than the unified threshold model of decision-making may give a more adequate explanation of decision-making data in comparison to previous models and theories.
Case Study of Onychomycosis Patients Treated with 1,064-nm Nd:YAG Laser

Onychomycosis is the most frequent nail disease, with an estimated prevalence of 2–8%. Current treatment strategies include the use of oral and topical antifungals, despite low cure rates following these treatments. The objectives of this study were to assess the therapeutic response of patients with onychomycosis to 1,064-nm Nd:YAG laser treatment, the clinical evaluation method available for this therapy, and the possible side effects of this treatment. Twenty patients with onychomycosis underwent laser therapy. A total of 34 nails with onychomycosis were assessed according to the Onychomycosis Severity Index (OSI). This index generates scores that classify onychomycosis as mild, moderate, or severe. The OSI was determined before treatment and after a mean follow-up period of 8 months. The comparison between the initial and the final OSI for all 34 nails treated with laser therapy showed a significant difference; however, a low association was shown between these variables. A general reduction in the area of involvement and in the OSI numerical scores was observed. These data show a trend toward improvement in onychomycosis treated with 1,064-nm Nd:YAG laser. The OSI allowed an adequate clinical assessment of the response to laser therapy. Laser treatment did not cause marked discomfort in most patients, indicating that laser is a well-tolerated procedure.
Clinical and pathologic characteristics of pauci-immune anti-myeloperoxidase antibody associated glomerulonephritis with nephrotic range proteinuria

Abstract Background: Heavy proteinuria in antineutrophil cytoplasmic antibody (ANCA)-associated glomerulonephritis (GN) is usually considered to be associated with immune deposits in renal biopsy. Nephrotic ANCA GN without immune deposits (pauci-immune) is rare and has not been studied specially. In this study characteristics of these patients are to be investigated. Methods: Clinical and pathological characteristics from 20 kidney biopsy-proven pauci-immune anti-myeloperoxidase antibody-associated GN patients with nephrotic proteinuria were analyzed and were compared with ANCA GN patients without nephrotic proteinuria. Results: Acute kidney injury (AKI) and gross hematuria were much prevalent but extra-renal involvement was less prevalent in pauci-immune ANCA GN with nephrotic proteinuria than in pauci-immune ANCA GN without nephrotic proteinuria. No more severe hypoalbuminemia, hypercoagulability, hyperlipidemia or higher thrombosis incidence were found between two groups. Compared with patients without nephrotic proteinuria, patients with nephrotic proteinuria had more prevalent crescentic category in histopathology. Proteinuria decreased quickly after treatment but much poorer renal prognosis was found in pauci-immune ANCA GN with nephrotic proteinuria. The results of urinary albumin to total protein ratio and urinary protein electrophoresis showed pauci-immune ANCA GN with nephrotic proteinuria had obvious non-selective proteinuria. Conclusions: Pauci-immune ANCA GN with nephrotic proteinuria do not have more severe hypoalbuminemia, hypercoagulability or hyperlipidemia than patients without nephrotic proteinuria. Non-selective proteinuria might be the reason. However, pauci-immune ANCA GN with nephrotic proteinuria have more prevalent crescentic category in histopathology, higher incidence of AKI, gross hematuria and poorer renal prognosis despite of good sensitivity to therapy of proteinuria.
DHCR24 predicts poor clinicopathological features of patients with bladder cancer

Abstract To investigate the prognostic value of DHCR24 for patients with bladder cancer (BC). We used public bladder cancer microarray studies to evaluate the expression of DHCR24 between normal bladder tissues and BC cells, to investigate the relationship between the expression of DHCR24 and the clinical features of BC patients. Survival analysis was performed to investigate the correlation between DHCR24 expression and the survivals of BC patients. Gene set enrichment analysis was conducted to identify relevant mechanisms. The results showed that DHCR24 was up-regulated in BC cells compared with that in normal bladder tissues (P = .0389). Results of chi-square test suggested that BC patients in DHCR24 low expression group were proved to have better clinical characteristics (including tumor grade, disease progression, T staging, and N staging) as compared with those in DHCR24 low expression group (P < .0001, P = .002, P = .005, and P = .002, respectively). BC patients in DHCR24 low expression group were associated with better cancer-specific survival and overall survival (P < .0001 and P = .0008, respectively). DHCR24 might promote the proliferation of BC cells through several oncogenesis-associated biological processes (estrogen response, heme metabolism, P53 pathway, cholesterol homeostasis, mTORC1 signaling, peroxisome, xenobiotic metabolism, glycolysis, and protein secretion). Thus, DHCR24 might be a therapeutic target for patients with BC.
Understanding an emerging treatment population: Protocol for and baseline characteristics of a prospective cohort of people receiving treatment for pharmaceutical opioid dependence.

INTRODUCTION AND AIMS Despite large increases in pharmaceutical opioid dependence and related mortality, few studies have focused on the characteristics and treatment experiences of those with pharmaceutical opioid dependence. We describe the formation of a prospective cohort of people receiving treatment for pharmaceutical opioid dependence and describe their baseline characteristics. DESIGN AND METHODS People who had entered treatment for pharmaceutical opioid dependence (n = 108) were recruited through drug treatment services in New South Wales, Australia. We describe baseline characteristics of those that commenced pharmaceutical opioids for pain or other reasons and conducted a thematic analysis of responses regarding their treatment experience. RESULTS Mean age was 41 years (SD 11), half were male (48%). Just over half reported lifetime heroin use (57%). Oxycodone (49%) and codeine (29%) were the most common opioids reported. Most (85%) reported past-year problematic pain, 38% reported chronic pain. Half (52%) reported moderate to severe depression symptoms. Most (66%) commenced opioids for pain, and this group were older, less likely to report a previous overdose and less likely to report use of illicit drugs compared to those commencing for other reasons. Five themes related to treatment expectations: (i) stigma; (ii) the restrictive nature of treatment; (iii) knowledge; (iv) pain; and (v) positive experience with buprenorphine. DISCUSSION AND CONCLUSIONS This study describes the complexities in an important emerging treatment population of pharmaceutical opioid-dependent people. Findings highlights that addressing knowledge and perceptions around treatment may be critical to address the rising mortality associated with pharmaceutical opioid dependence.
Learning the Parameters of Deep Probabilistic Logic Programs

Probabilistic logic programming (PLP) is a powerful tool for reasoning in relational domains with uncertainty. However, both inference and learning from examples are computationally expensive tasks. We consider a restriction of PLP called hierarchical PLP whose clauses and predicates are hierarchically organized forming a deep neural network or arithmetic circuit. Inference in this language is much cheaper than for general PLP languages. In this work we present an algorithm called Deep Parameter learning for HIerarchical probabilistic Logic programs (DPHIL) that learns hierarchical PLP parameters using gradient descent and back-propagation.
Loss of lipid phosphatase SHIP1 promotes macrophage differentiation through suppression of dendritic cell differentiation

ABSTRACT SH2-containing inositol 5ʹ-phosphatase-1 (SHIP1) deficiency in mice results in abnormal myeloid expansion, and proinflammatory conditions in the lung. However, the mechanisms involved in SHIP1-mediated regulation of myeloid differentiation remain unclear. Here we show that SHIP1 is a key regulator of early differentiation for dendritic cells (DCs). We also provide critical evidence to modify the function of SHIP1 in in vitro development of BMDCs using the recent framework of defining DCs. We found that loss of SHIP1 suppresses GM-CSF-induced formation of bone marrow-derived DC (BMDC) colonies, leading to reduced BMDC number in BM cell culture. The number of maturated BMDCs decreased in SHIP1-KO culture, due to reduction of immature BMDCs, suggesting SHIP1 is critical for lineage commitment rather than for maturation from myeloid precursors to DCs. We further showed that F4/80+/MHCIIlow BM macrophage-like cells (BMMs) were the main population of SHIP1-KO BM culture. Treatment of wild-type BM culture with 3 α-aminocholestane (3AC), a specific inhibitor for functional activity of SHIP1, caused a similar developmental defect in BMDCs as seen in SHIP1-KO cells, resulting in the absence of BMDC colony, and increased number of BMMs in BM culture. In conclusion, our results suggest that differentiation of BMDCs are markedly impaired under SHIP1 deficient condition, which causes skewed development of myeloid lineage cells manifested as pathological conditions associated with an excess of macrophage population.
Evaluating the diagnostic and prognostic value of long non-coding RNA SNHG15 in pancreatic ductal adenocarcinoma.

OBJECTIVE Long non-coding RNA SNHG15 (SNHG15) has been reported to play very important roles in the malignancy behaviors of various tumors, including pancreatic ductal adenocarcinoma (PDAC). However, its clinical significance in PDAC remains largely unclear. The aim of this study was to investigate whether the aberrant expression of SNHG15 can be used as potential prognostic and diagnostic markers of human PDAC. MATERIALS AND METHODS TaqMan Real Time-PCR was performed to investigate the expression of SNHG15 in PDAC tissues and serum samples. Receiver operator characteristic (ROC) analysis was applied to obtain the diagnostic utility of SNHG15. Association between SNHG15 levels and clinicopathological factors was analyzed. Kaplan-Meier curves and multivariate Cox proportional models were used to study the impact on clinical outcome. RESULTS SNHG15 levels were significantly up-regulated in both sera and tumors tissues from PDAC patients. ROC curve analysis revealed that SNHG15 may be a potential biomarker for differentiating PDAC tissues from normal pancreatic tissues, and the plasma levels of SNHG15 may be a potential biomarker for differentiating PDAC patients from healthy controls. Clinicopathologic analysis revealed that high SNHG15 expression was associated with tumor differentiation (p = 0.000), lymph node metastasis (p = 0.001) and tumor stage (p = 0.005). Furthermore, patients with high SNHG15 expression had a shorter overall survival compared with the low SNHG15 expression group (p = 0.003). Also, Cox multivariate analyses confirmed that SNHG15 expression was an independent prognostic factor in PDAC (p < 0.004). CONCLUSIONS Our study firstly indicated the potential value of SNHG15 as an important biomarker for the diagnosis and prognosis prediction of PDAC.